diff --git a/inc/external/acl/acl.h b/inc/external/acl/acl.h index a5194472..8d261201 100644 --- a/inc/external/acl/acl.h +++ b/inc/external/acl/acl.h @@ -25,9 +25,9 @@ extern "C" { #endif -// Current version is 1.1.0 +// Current version is 1.0.0 #define ACL_MAJOR_VERSION 1 -#define ACL_MINOR_VERSION 1 +#define ACL_MINOR_VERSION 0 #define ACL_PATCH_VERSION 0 /** diff --git a/inc/external/acl/acl_base.h b/inc/external/acl/acl_base.h index 90da8b8f..64d4bd81 100644 --- a/inc/external/acl/acl_base.h +++ b/inc/external/acl/acl_base.h @@ -150,8 +150,6 @@ typedef enum { ACL_DOUBLE = 11, ACL_BOOL = 12, ACL_STRING = 13, - ACL_COMPLEX64 = 16, - ACL_COMPLEX128 = 17 } aclDataType; typedef enum { diff --git a/inc/external/acl/acl_mdl.h b/inc/external/acl/acl_mdl.h index 522dbd38..2bf85e29 100644 --- a/inc/external/acl/acl_mdl.h +++ b/inc/external/acl/acl_mdl.h @@ -297,21 +297,9 @@ ACL_FUNC_VISIBILITY aclError aclmdlSetDatasetTensorDesc(aclmdlDataset *dataset, /** * @ingroup AscendCL - * @brief Get aclTensorDesc from aclmdlDataset - * - * @param dataset [IN] aclmdlDataset pointer; - * @param index [IN] index of tensorDesc - * - * @retval Get address of aclTensorDesc when executed successfully. - * @retval Failure return NULL - */ -ACL_FUNC_VISIBILITY aclTensorDesc *aclmdlGetDatasetTensorDesc(const aclmdlDataset *dataset, size_t index); - -/** - * @ingroup AscendCL * @brief Get the number of aclDataBuffer in aclmdlDataset * - * @param dataset [IN] aclmdlDataset pointer + * @param dataset [IN] aclmdlDataset poiter * * @retval the number of aclDataBuffer */ @@ -321,7 +309,7 @@ ACL_FUNC_VISIBILITY size_t aclmdlGetDatasetNumBuffers(const aclmdlDataset *datas * @ingroup AscendCL * @brief Get the aclDataBuffer in aclmdlDataset by index * - * @param dataset [IN] aclmdlDataset pointer + * @param dataset [IN] aclmdlDataset poiter * @param index [IN] the index of aclDataBuffer * * @retval Get successfully, return the address of aclDataBuffer diff --git a/inc/external/acl/acl_op.h b/inc/external/acl/acl_op.h index f340b6bc..d2e59bfb 100644 --- a/inc/external/acl/acl_op.h +++ b/inc/external/acl/acl_op.h @@ -137,34 +137,6 @@ ACL_FUNC_VISIBILITY aclError aclopSetAttrString(aclopAttr *attr, const char *att /** * @ingroup AscendCL - * @brief set an attribute. the type of the attribute is aclDataType - * - * @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 aclopSetAttrDataType(aclopAttr *attr, const char *attrName, aclDataType attrValue); - -/** - * @ingroup AscendCL - * @brief set an attribute. the type of the attribute is list of aclDataType - * - * @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 aclopSetAttrListDataType(aclopAttr *attr, const char *attrName, int numValues, - const aclDataType values[]); - -/** - * @ingroup AscendCL * @brief set an attribute. the type of the attribute is list of bools * * @param attr [OUT] pointer to the instance of aclopAttr diff --git a/inc/external/acl/acl_prof.h b/inc/external/acl/acl_prof.h index a93374b0..3784d8c6 100644 --- a/inc/external/acl/acl_prof.h +++ b/inc/external/acl/acl_prof.h @@ -40,20 +40,13 @@ typedef enum { ACL_AICORE_MEMORY_BANDWIDTH = 2, ACL_AICORE_L0B_AND_WIDTH = 3, ACL_AICORE_RESOURCE_CONFLICT_RATIO = 4, - ACL_AICORE_MEMORY_UB = 5, ACL_AICORE_NONE = 0xFF } aclprofAicoreMetrics; -typedef enum { - ACL_STEP_START = 0, // step start - ACL_STEP_END = 1 // step end -} aclprofStepTag; - typedef struct aclprofConfig aclprofConfig; typedef struct aclprofStopConfig aclprofStopConfig; typedef struct aclprofAicoreEvents aclprofAicoreEvents; typedef struct aclprofSubscribeConfig aclprofSubscribeConfig; -typedef struct aclprofStepInfo aclprofStepInfo; /** * @ingroup AscendCL @@ -329,36 +322,6 @@ ACL_FUNC_VISIBILITY uint64_t aclprofGetOpDuration(const void *opInfo, size_t opI */ ACL_FUNC_VISIBILITY size_t aclprofGetModelId(const void *opInfo, size_t opInfoLen, uint32_t index); -/** - * @ingroup AscendCL - * @brief - * - * @param stepInfo [IN] pointer to stepInfo data - * @param aclprofstepTag [IN] start or end flag - * @param stream [IN] steam info - * - * @retval 0 for failed - */ -ACL_FUNC_VISIBILITY aclError aclprofGetStepTimestamp(aclprofStepInfo *stepInfo, aclprofStepTag tag, aclrtStream stream); - -/** - * @ingroup AscendCL - * @brief create pointer to aclprofStepInfo data - * - * - * @retval aclprofStepInfo pointer - */ -ACL_FUNC_VISIBILITY aclprofStepInfo *aclprofCreateStepInfo(); - -/** - * @ingroup AscendCL - * @brief destroy aclprofStepInfo pointer - * - * - * @retval void - */ -ACL_FUNC_VISIBILITY void aclprofDestroyStepInfo(aclprofStepInfo *stepinfo); - #ifdef __cplusplus } #endif diff --git a/inc/external/acl/acl_rt.h b/inc/external/acl/acl_rt.h index 50dbc34d..5ee70724 100644 --- a/inc/external/acl/acl_rt.h +++ b/inc/external/acl/acl_rt.h @@ -44,12 +44,6 @@ typedef enum aclrtEventStatus { ACL_EVENT_STATUS_RESERVED = 2, } aclrtEventStatus; -typedef enum aclrtEventWaitStatus { - ACL_EVENT_WAIT_STATUS_COMPLETE = 0, - ACL_EVENT_WAIT_STATUS_NOT_READY = 1, - ACL_EVENT_WAIT_STATUS_RESERVED = 0xffff, -} aclrtEventWaitStatus; - typedef enum aclrtCallbackBlockType { ACL_CALLBACK_NO_BLOCK, ACL_CALLBACK_BLOCK, @@ -507,18 +501,6 @@ ACL_FUNC_VISIBILITY aclError aclrtQueryEvent(aclrtEvent event, aclrtEventStatus /** * @ingroup AscendCL - * @brief Queries an event's wait-status - * - * @param event [IN] event to query - * @param status [OUT] event wait-status - * - * @retval ACL_SUCCESS The function is successfully executed. - * @retval OtherValues Failure - */ -ACL_FUNC_VISIBILITY aclError aclrtQueryEventWaitStatus(aclrtEvent event, aclrtEventWaitStatus *status); - -/** - * @ingroup AscendCL * @brief Block Host Running, wait event to be complete * * @param event [IN] event to wait diff --git a/inc/external/acl/ops/acl_dvpp.h b/inc/external/acl/ops/acl_dvpp.h index 5418ebd3..8c9d036f 100644 --- a/inc/external/acl/ops/acl_dvpp.h +++ b/inc/external/acl/ops/acl_dvpp.h @@ -158,20 +158,6 @@ enum acldvppJpegFormat { ACL_JPEG_CSS_UNKNOWN = 1000 }; -enum acldvppChannelDescParamType { ACL_DVPP_CSC_MATRIX_UINT32 = 0 }; - -enum aclvdecChannelDescParamType { ACL_VDEC_CSC_MATRIX_UINT32 = 0 }; - -// Csc Matrix can be used both for acldvppChannelDescParamType and aclvdecChannelDescParamType -enum acldvppCscMatrix { - ACL_DVPP_CSC_MATRIX_BT601_WIDE = 0, - ACL_DVPP_CSC_MATRIX_BT601_NARROW, - ACL_DVPP_CSC_MATRIX_BT709_WIDE, - ACL_DVPP_CSC_MATRIX_BT709_NARROW, - ACL_DVPP_CSC_MATRIX_BT2020_WIDE, - ACL_DVPP_CSC_MATRIX_BT2020_NARROW -}; - /** * @ingroup AscendCL * @brief alloc device memory for dvpp. @@ -2574,90 +2560,7 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcBatchCropResizeMakeBorderAsync( acldvppChannelDesc *channelDesc, acldvppBatchPicDesc *srcBatchPicDescs, uint32_t *roiNums, uint32_t size, acldvppBatchPicDesc *dstBatchPicDescs, acldvppRoiConfig *cropAreas[], acldvppBorderConfig *borderCfgs[], acldvppResizeConfig *resizeConfig, aclrtStream stream); -/** - * @ingroup AscendCL - * @brief set param for dvpp channel desc - * - * @par Function - * set attribution in dvpp channelDesc for specified type - * - * @param channelDesc [OUT] the channel destruction - * @param paramType [IN] specified param type - * @param length [IN] mem length of param - * @param param [IN] pointer to param - * - * @retval ACL_SUCCESS The function is successfully executed. - * @retval OtherValues Failure - * - * @see acldvppGetChannelDescParam | acldvppCreateChannelDesc | acldvppDestroyChannelDesc - */ -ACL_FUNC_VISIBILITY aclError acldvppSetChannelDescParam(acldvppChannelDesc *channelDesc, - acldvppChannelDescParamType paramType, size_t length, - const void *param); - -/** - * @ingroup AscendCL - * @brief get param of dvpp channel desc - * - * @par Function - * get attribution value in dvpp channelDesc for specified type - * - * @param channelDesc [IN] the channel destruction - * @param paramType [IN] specified param type - * @param length [IN] mem length allocated for output param - * @param paramRetSize [OUT] mem length of output param - * @param param [OUT] pointer to output param - * - * @retval ACL_SUCCESS The function is successfully executed. - * @retval OtherValues Failure - * - * @see acldvppSetChannelDescParam | acldvppCreateChannelDesc | acldvppDestroyChannelDesc - */ -ACL_FUNC_VISIBILITY aclError acldvppGetChannelDescParam(const acldvppChannelDesc *channelDesc, - acldvppChannelDescParamType paramType, size_t length, - size_t *paramRetSize, void *param); -/** - * @ingroup AscendCL - * @brief set param for vdec channel desc - * - * @par Function - * set attribution in channelDesc for specified type - * - * @param channelDesc [OUT] the vdec channel destruction - * @param paramType [IN] specified param type - * @param length [IN] mem length of param - * @param param [IN] pointer to param - * - * @retval ACL_SUCCESS The function is successfully executed. - * @retval OtherValues Failure - * - * @see aclvdecGetChannelDescParam | aclvdecCreateChannelDesc | aclvdecDestroyChannelDesc - */ -ACL_FUNC_VISIBILITY aclError aclvdecSetChannelDescParam(aclvdecChannelDesc *channelDesc, - aclvdecChannelDescParamType paramType, size_t length, - const void *param); -/** - * @ingroup AscendCL - * @brief get param of vdec channel desc - * - * @par Function - * get attribution value in channelDesc for specified type - * - * @param channelDesc [IN] the vdec channel destruction - * @param paramType [IN] specified param type - * @param length [IN] mem length allocated for output param - * @param paramRetSize [OUT] mem length of output param - * @param param [OUT] pointer to output param - * - * @retval ACL_SUCCESS The function is successfully executed. - * @retval OtherValues Failure - * - * @see aclvdecSetChannelDescParam | aclvdecCreateChannelDesc | aclvdecDestroyChannelDesc - */ -ACL_FUNC_VISIBILITY aclError aclvdecGetChannelDescParam(const aclvdecChannelDesc *channelDesc, - aclvdecChannelDescParamType paramType, size_t length, - size_t *paramRetSize, void *param); #ifdef __cplusplus } #endif diff --git a/inc/external/ge/ge_ir_build.h b/inc/external/ge/ge_ir_build.h index 729685a9..04e059a1 100644 --- a/inc/external/ge/ge_ir_build.h +++ b/inc/external/ge/ge_ir_build.h @@ -1,18 +1,18 @@ /** - * 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. - */ +* 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. +* 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_IR_BUILD_H_ #define INC_EXTERNAL_GE_IR_BUILD_H_ diff --git a/inc/external/hccl/hccl.h b/inc/external/hccl/hccl.h index c24b5374..8261adc4 100644 --- a/inc/external/hccl/hccl.h +++ b/inc/external/hccl/hccl.h @@ -145,33 +145,6 @@ extern HcclResult HcclGetRankId(HcclComm comm, uint32_t *rank); extern HcclResult HcclBarrier(HcclComm comm, aclrtStream stream); /** - * @brief AllGather operator. - * - * @param sendBuff A pointer identifying the input data address of the operator. - * @param count An integer(u64) identifying the number of the send data. - * @param dataType The data type of the operator, must be one of the following types: int8, int32, float16, float32. - * @param destRank An integer identifying the destination rank. - * @param comm A pointer identifying the communication resource based on. - * @param stream A pointer identifying the stream information. - * @return HcclResult - */ -extern HcclResult HcclSend(void *sendBuf, uint64_t count, HcclDataType dataType, uint32_t destRank, HcclComm comm, - aclrtStream stream); -/** - * @brief AllGather operator. - * - * @param recvBuff A pointer identifying the output data address of the operator. - * @param count An integer(u64) identifying the number of the receive data. - * @param dataType The data type of the operator, must be one of the following types: int8, int32, float16, float32. - * @param srcRank An integer identifying the source rank. - * @param comm A pointer identifying the communication resource based on. - * @param stream A pointer identifying the stream information. - * @return HcclResult - */ -extern HcclResult HcclRecv(void *recvBuf, uint64_t count, HcclDataType dataType, uint32_t srcRank, HcclComm comm, - aclrtStream stream); - -/** * @brief Destroy HCCL comm * * @param comm A pointer identifying the communication resource targetting diff --git a/inc/framework/executor/ge_executor.h b/inc/framework/executor/ge_executor.h index ce7c82ac..fcca561c 100644 --- a/inc/framework/executor/ge_executor.h +++ b/inc/framework/executor/ge_executor.h @@ -50,30 +50,14 @@ class GE_FUNC_VISIBILITY GeExecutor { public: GeExecutor(); ~GeExecutor() = default; + ge::Status Initialize(); + ge::Status Finalize(); - Status Initialize(); - Status Finalize(); - - /// - /// @ingroup ge - /// @brief Initialize global execute environment. - /// @param [in] options: environment variables. - /// @return init result - /// - static Status Initialize(const std::map &options); - - /// - /// @ingroup ge - /// @brief Finalize global execute environment. - /// @return execute result - /// - static Status FinalizeEx(); - - Status UnloadModel(uint32_t modelId); + ge::Status UnloadModel(uint32_t modelId); // Get input and output descriptor - Status GetModelDescInfo(uint32_t model_id, std::vector &input_desc, std::vector &output_desc, - bool new_model_desc = false); + ge::Status GetModelDescInfo(uint32_t model_id, std::vector &input_desc, + std::vector &output_desc, bool new_model_desc = false); /// /// @ingroup ge @@ -84,7 +68,7 @@ class GE_FUNC_VISIBILITY GeExecutor { /// @param [in] batch_size: batch size entered by user in dynamic multi-batch scenario /// @return execute result /// - Status SetDynamicBatchSize(uint32_t model_id, void *dynamic_input_addr, uint64_t length, uint64_t batch_size); + ge::Status SetDynamicBatchSize(uint32_t model_id, void *dynamic_input_addr, uint64_t length, uint64_t batch_size); /// /// @ingroup ge @@ -96,8 +80,8 @@ class GE_FUNC_VISIBILITY GeExecutor { /// @param [in] image_width: image width entered by user in dynamic multi-resolution scenario /// @return execute result /// - Status SetDynamicImageSize(uint32_t model_id, void *dynamic_input_addr, uint64_t length, uint64_t image_height, - uint64_t image_width); + ge::Status SetDynamicImageSize(uint32_t model_id, void *dynamic_input_addr, uint64_t length, uint64_t image_height, + uint64_t image_width); /// /// @ingroup ge @@ -109,8 +93,8 @@ class GE_FUNC_VISIBILITY GeExecutor { /// @param [in] dynamic_dims: array of dynamic dimensions /// @return execute result /// - Status SetDynamicDims(uint32_t model_id, void *dynamic_input_addr, uint64_t length, - const std::vector &dynamic_dims); + ge::Status SetDynamicDims(uint32_t model_id, void *dynamic_input_addr, uint64_t length, + const std::vector &dynamic_dims); /// /// @ingroup ge @@ -120,8 +104,8 @@ class GE_FUNC_VISIBILITY GeExecutor { /// @param [out] cur_dynamic_dims: current dynamic dims /// @return execute result /// - Status GetCurDynamicDims(uint32_t model_id, const std::vector &dynamic_dims, - std::vector &cur_dynamic_dims); + ge::Status GetCurDynamicDims(uint32_t model_id, const std::vector &dynamic_dims, + std::vector &cur_dynamic_dims); /// /// @ingroup ge @@ -131,7 +115,8 @@ class GE_FUNC_VISIBILITY GeExecutor { /// @param [out] dynamic_type /// @return execute result /// - Status GetDynamicBatchInfo(uint32_t model_id, std::vector> &batch_info, int32_t &dynamic_type); + ge::Status GetDynamicBatchInfo(uint32_t model_id, std::vector> &batch_info, + int32_t &dynamic_type); /// /// @ingroup ge @@ -140,7 +125,7 @@ class GE_FUNC_VISIBILITY GeExecutor { /// @param [out] batch_info /// @return execute result /// - Status GetCombinedDynamicDims(uint32_t model_id, std::vector> &batch_info); + ge::Status GetCombinedDynamicDims(uint32_t model_id, std::vector> &batch_info); /// /// @ingroup ge @@ -149,9 +134,9 @@ class GE_FUNC_VISIBILITY GeExecutor { /// @param [out] user_designate_shape_order /// @return execute result /// - Status GetUserDesignateShapeOrder(uint32_t model_id, std::vector &user_designate_shape_order); + ge::Status GetUserDesignateShapeOrder(uint32_t model_id, std::vector &user_designate_shape_order); - Status GetCurShape(const uint32_t model_id, std::vector &batch_info, int32_t &dynamic_type); + ge::Status GetCurShape(const uint32_t model_id, std::vector &batch_info, int32_t &dynamic_type); /// /// @ingroup ge @@ -163,22 +148,22 @@ class GE_FUNC_VISIBILITY GeExecutor { /// @param [in] aippParms: kAippDynamicPara by user in dynamic aipp /// @return execute result /// - Status SetDynamicAippData(uint32_t model_id, void *dynamic_input_addr, uint64_t length, - const std::vector &aipp_batch_para, - const kAippDynamicPara &aippParms); + ge::Status SetDynamicAippData(uint32_t model_id, void *dynamic_input_addr, uint64_t length, + const std::vector &aippBatchPara, + const kAippDynamicPara &aippParms); - Status GetAIPPInfo(uint32_t model_id, uint32_t index, AippConfigInfo &aipp_info); + ge::Status GetAIPPInfo(uint32_t model_id, uint32_t index, AippConfigInfo &aipp_info); - Status GetOpAttr(uint32_t model_id, const std::string &op_name, const std::string &attr_name, - std::string &attr_value); + ge::Status GetOpAttr(uint32_t model_id, const std::string &op_name, const std::string &attr_name, + std::string &attr_value); - Status GetModelAttr(uint32_t model_id, std::vector &dynamic_output_shape_info); + ge::Status GetModelAttr(uint32_t model_id, std::vector &dynamic_output_shape_info); - Status GetAippType(uint32_t model_id, uint32_t index, InputAippType &type, size_t &aipp_index); + ge::Status GetAippType(uint32_t model_id, uint32_t index, InputAippType &type, size_t &aipp_index); - Status CommandHandle(const Command &command); + ge::Status CommandHandle(const ge::Command &command); - Status SetDump(const DumpConfig &dump_config); + ge::Status SetDump(const DumpConfig &dump_config); /// /// @ingroup ge @@ -188,7 +173,7 @@ class GE_FUNC_VISIBILITY GeExecutor { /// @return SUCCESS /// @return FAILED /// - Status GetMaxUsedMemory(uint32_t model_id, uint32_t &max_size); + ge::Status GetMaxUsedMemory(uint32_t model_id, uint32_t &max_size); /// /// @ingroup ge @@ -197,7 +182,7 @@ class GE_FUNC_VISIBILITY GeExecutor { /// @param [out] ModelData &model_data: Offline model memory data /// @return SUCCESS handle successfully / others handle failed /// - Status LoadDataFromFile(const std::string &path, ModelData &model_data); + ge::Status LoadDataFromFile(const std::string &path, ge::ModelData &model_data); /// /// @ingroup ge @@ -210,8 +195,8 @@ class GE_FUNC_VISIBILITY GeExecutor { /// @param [out] uint32_t &model_id: Corresponding identification after model loading /// @return SUCCESS handle successfully / others handle failed /// - Status LoadModelFromData(uint32_t &model_id, const ModelData &model_data, void *dev_ptr, size_t mem_size, - void *weight_ptr, size_t weight_size); + ge::Status LoadModelFromData(uint32_t &model_id, const ge::ModelData &model_data, void *dev_ptr, size_t mem_size, + void *weight_ptr, size_t weight_size); /// /// @ingroup ge @@ -222,8 +207,9 @@ class GE_FUNC_VISIBILITY GeExecutor { /// @param [in] output_queue_ids: input queue ids create from user. /// @return: 0 for success / others for fail /// - Status LoadModelWithQ(uint32_t &model_id, const ModelData &model_data, const std::vector &input_queue_ids, - const std::vector &output_queue_ids); + ge::Status LoadModelWithQ(uint32_t &model_id, const ge::ModelData &model_data, + const std::vector &input_queue_ids, + const std::vector &output_queue_ids); /// /// @ingroup ge @@ -235,8 +221,8 @@ class GE_FUNC_VISIBILITY GeExecutor { /// @param [out] domi::OutputData *output_data: Model output data /// @return SUCCESS handle successfully / others handle failed /// - Status ExecModel(uint32_t model_id, void *stream, const RunModelData &input_data, RunModelData &output_data, - bool async_mode = false); + ge::Status ExecModel(uint32_t model_id, void *stream, const ge::RunModelData &input_data, + ge::RunModelData &output_data, bool async_mode = false); /// /// @ingroup ge @@ -250,9 +236,9 @@ class GE_FUNC_VISIBILITY GeExecutor { /// @param [out] std::vector &output_desc: description of model output data /// @return SUCCESS handle successfully / others handle failed /// - Status ExecModel(uint32_t model_id, void *stream, const RunModelData &run_input_data, - const std::vector &input_desc, RunModelData &run_output_data, - std::vector &output_desc, bool async_mode = false); + ge::Status ExecModel(uint32_t model_id, void *stream, const ge::RunModelData &run_input_data, + const std::vector &input_desc, ge::RunModelData &run_output_data, + std::vector &output_desc, bool async_mode = false); /// /// @ingroup ge @@ -262,7 +248,7 @@ class GE_FUNC_VISIBILITY GeExecutor { /// @param [out] size_t &weight_size Weight memory space size /// @return SUCCESS handle successfully / others handle failed /// - Status GetMemAndWeightSize(const std::string &path, size_t &mem_size, size_t &weight_size); + ge::Status GetMemAndWeightSize(const std::string &path, size_t &mem_size, size_t &weight_size); /// /// @ingroup ge @@ -273,39 +259,39 @@ class GE_FUNC_VISIBILITY GeExecutor { /// @param [out] size_t &weight_size Weight memory space size /// @return SUCCESS handle successfully / others handle failed /// - Status GetMemAndWeightSize(const void *model_data, size_t model_size, size_t &mem_size, size_t &weight_size); + ge::Status GetMemAndWeightSize(const void *model_data, size_t model_size, size_t &mem_size, size_t &weight_size); - static Status LoadSingleOp(const std::string &modelName, const ModelData &modelData, void *stream, - SingleOp **single_op); + static ge::Status LoadSingleOp(const std::string &modelName, const ge::ModelData &modelData, void *stream, + SingleOp **single_op); - static Status LoadSingleOpV2(const std::string &modelName, const ModelData &modelData, void *stream, - SingleOp **single_op, const uint64_t model_id); + static ge::Status LoadSingleOpV2(const std::string &modelName, const ge::ModelData &modelData, void *stream, + SingleOp **single_op, const uint64_t model_id); - static Status ExecuteAsync(SingleOp *executor, const std::vector &inputs, - std::vector &outputs); + static ge::Status ExecuteAsync(SingleOp *executor, const std::vector &inputs, + std::vector &outputs); - static Status LoadDynamicSingleOp(const std::string &model_name, const ModelData &modelData, void *stream, - DynamicSingleOp **single_op); + static ge::Status LoadDynamicSingleOp(const std::string &model_name, const ge::ModelData &modelData, void *stream, + DynamicSingleOp **single_op); - static Status LoadDynamicSingleOpV2(const std::string &model_name, const ModelData &modelData, void *stream, - DynamicSingleOp **single_op, const uint64_t model_id); + static ge::Status LoadDynamicSingleOpV2(const std::string &model_name, const ge::ModelData &modelData, void *stream, + DynamicSingleOp **single_op, const uint64_t model_id); - static Status ExecuteAsync(DynamicSingleOp *executor, const std::vector &input_desc, - const std::vector &inputs, std::vector &output_desc, - std::vector &outputs); + static ge::Status ExecuteAsync(DynamicSingleOp *executor, const std::vector &input_desc, + const std::vector &inputs, std::vector &output_desc, + std::vector &outputs); - static Status ReleaseSingleOpResource(void *stream); + static ge::Status ReleaseSingleOpResource(void *stream); - static Status GetDeviceIdByModelId(uint32_t model_id, uint32_t &device_id); + static ge::Status GetDeviceIdByModelId(uint32_t model_id, uint32_t &device_id); - Status GetBatchInfoSize(uint32_t model_id, size_t &shape_count); - Status GetOrigInputInfo(uint32_t model_id, uint32_t index, OriginInputInfo &orig_input_info); - Status GetAllAippInputOutputDims(uint32_t model_id, uint32_t index, std::vector &input_dims, - std::vector &output_dims); - Status GetOpDescInfo(uint32_t device_id, uint32_t stream_id, uint32_t task_id, OpDescInfo &op_desc_info); + ge::Status GetBatchInfoSize(uint32_t model_id, size_t &shape_count); + ge::Status GetOrigInputInfo(uint32_t model_id, uint32_t index, OriginInputInfo &orig_input_info); + ge::Status GetAllAippInputOutputDims(uint32_t model_id, uint32_t index, std::vector &input_dims, + std::vector &output_dims); + ge::Status GetOpDescInfo(uint32_t device_id, uint32_t stream_id, uint32_t task_id, OpDescInfo &op_desc_info); private: - static std::atomic_bool is_inited_; + static bool isInit_; }; } // namespace ge diff --git a/inc/framework/ge_runtime/task_info.h b/inc/framework/ge_runtime/task_info.h index abc4783d..084e3073 100644 --- a/inc/framework/ge_runtime/task_info.h +++ b/inc/framework/ge_runtime/task_info.h @@ -343,13 +343,14 @@ class FusionEndTaskInfo : public TaskInfo { class HcclTaskInfo : public TaskInfo { public: HcclTaskInfo(const std::string &op_name, uint32_t stream_id, const std::string hccl_type, void *input_data_addr, - void *output_data_addr, int64_t workspace_size, int64_t hccl_stream_num, + void *output_data_addr, void *workspace_addr, int64_t workspace_size, int64_t hccl_stream_num, const std::vector &private_def, void *ops_kernel_store, int32_t count, int64_t root_id, int64_t op_type, int64_t data_type, const std::string &group, bool dump_flag) : TaskInfo(op_name, stream_id, TaskInfoType::HCCL, dump_flag), hccl_type_(hccl_type), input_data_addr_(input_data_addr), output_data_addr_(output_data_addr), + workspace_addr_(workspace_addr), workspace_size_(workspace_size), hccl_stream_num_(hccl_stream_num), private_def_(private_def), @@ -370,6 +371,9 @@ class HcclTaskInfo : public TaskInfo { void *output_data_addr() const { return output_data_addr_; } + void *workspace_addr() const { + return workspace_addr_; + } int64_t workspace_size() const { return workspace_size_; } @@ -402,6 +406,7 @@ class HcclTaskInfo : public TaskInfo { std::string hccl_type_; void *input_data_addr_; void *output_data_addr_; + void *workspace_addr_; int64_t workspace_size_; int64_t hccl_stream_num_; std::vector private_def_; diff --git a/third_party/fwkacllib/inc/ops/array_ops.h b/third_party/fwkacllib/inc/ops/array_ops.h index 450c893e..fd35b546 100644 --- a/third_party/fwkacllib/inc/ops/array_ops.h +++ b/third_party/fwkacllib/inc/ops/array_ops.h @@ -35,7 +35,7 @@ namespace ge { * @li values:A `Tensor`. Must have the same type as `sorted_x`. \n *@par Attributes: -*out_type:An optional `DType` from: `int32, int64`. +*@li out_type:An optional `DType` from: `int32, int64`. Defaults to `int32`. \n *@par Outputs: @@ -504,7 +504,7 @@ REG_OP(Constant) *x: A tensor. \n *@par Outputs: -*y: A copy of input tensor. \n +*y: A tensor. \n *@par Third-party framework compatibility *Compatible with the TensorFlow operator Snapshot. @@ -684,9 +684,7 @@ REG_OP(ExpandDims) *@par Inputs: *@li x: Original tensor. - -*@par Attributes: -*@li axes: List of ints indicating the dimensions to be inserted. \n +*@li axis: List of ints. \n *@par Outputs: *y: Reshape tensor with same data as input. \n @@ -757,10 +755,10 @@ REG_OP(Squeeze) *@brief Returns an integer representing the rank of input tensor. The rank of a tensor is the number of indices required to uniquely select each element of the tensor, that is, the dimension size of the tensor. \n *@par Inputs: -*x: A Tensor of type float32, float16, int8, int16, uint16, uint8, int32, int64, uint32, uint64, bool, double. \n +*x: A tensor. \n *@par Outputs: -*y: A tensor. The rank of input tensor. Type is int32. \n +*y: A tensor. The rank of input tensor. \n *@par Third-party framework compatibility *Compatible with the TensorFlow operator Rank. @@ -850,6 +848,7 @@ REG_OP(PlaceHolder) *x: A tensor. \n *@par Attributes: +*@li dtype: data type of tensor. *@li shape: tensor shape. \n *@par Outputs: @@ -868,13 +867,13 @@ REG_OP(PlaceholderWithDefault) *@brief Reads and returns the value of the input variable tensor. \n *@par Inputs: -*x: A tensor must have numeric type. \n +*x: A tensor. \n *@par Attributes: *dtype: An optional int32 or int64. The output data type. Defaults to int32. \n *@par Outputs: -*y: A tensor must have numeric type. \n +*y: A tensor. \n *@par Third-party framework compatibility *Compatible with the TensorFlow operator ReadVariableOp. @@ -1135,10 +1134,10 @@ This is an M-length vector. This is an R-length vector *@par Attributes: -*normalize: boolean (if true, edit distances are normalized by length of truth). \n +*@li normalize: boolean (if true, edit distances are normalized by length of truth). \n *@par Outputs: -*output: A dense float tensor with rank R - 1. \n +*@li output: A dense float tensor with rank R - 1. \n *@par Third-party framework compatibility * Compatible with TensorFlow EditDistance operator. @@ -1155,17 +1154,18 @@ REG_OP(EditDistance) .OP_END_FACTORY_REG(EditDistance) /** -* @brief sort the input tensor without returning the value of index. +* @brief sort_v2. * @par Inputs: -* x: An ND tensor of type float16. +* @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: -* y: An ND tensor of type float16. +* @li y: An ND tensor of type float16. * @attention Constraints: * @li Axis should select the last dim. @@ -1206,7 +1206,7 @@ REG_OP(Expand) *@Returns a tensor containing the indices of all non-zero elements of input. \n *@par Inputs: -*x: A Tensor. Must be one of the following types: float16, float32, int32, int64. +*@li x: A Tensor. Must be one of the following types: float16, float32, int32, int64. *@par Attributes: * transpose: the output tensor will be transposed if true. \n @@ -1230,15 +1230,15 @@ REG_OP(NonZero) * @par Inputs: * One inputs, including: -* x: A Tensor. Must be one of the following types: +* @li x: A Tensor. Must be one of the following types: * float16, float32, int32, int8 ,uint8. \n * @par Attributes: -* shape: A required listInt to specify the shape that the input tensor expanded to. \n +* @li shape: A required listInt to specify the shape that the input tensor expanded to. \n * @par Outputs: -* y: A Tensor. Has the same type as "x", and the shape specified by input and attr shape \n +* @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. @@ -1249,38 +1249,6 @@ REG_OP(ExpandD) .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32, DT_INT8, DT_UINT8})) .REQUIRED_ATTR(shape, ListInt) .OP_END_FACTORY_REG(ExpandD) - -/** -*@brief Finds unique elements in a 1D tensor. \n - -*@par Inputs: -*x: 1D tensor. Must be one of the following types: -* float16, float32, double, int64, int32, int16, uint16, int8 ,uint8. \n - -*@par Attributes: -*@li return_inverse: Whether to also return the indices for where elements in the original -* input ended up in the returned unique list. -*@li return_inverse: Whether to also return the counts for each unique element. - -*@par Outputs: -*@li y1: The output list of unique scalar elements. Has the same type as "x". -*@li y2: Representing the indices for where elements in the original input map to in the output. -*@li y3: Representing the number of occurrences for each unique value or tensor. \n - -* @par Third-party framework compatibility -* Compatible with the troch operator _unique2. -*/ - -REG_OP(UniqueWithCountsAndSorting) - .INPUT(x, TensorType({ DT_INT8, DT_UINT8, DT_INT16, DT_UINT16, \ - DT_INT32, DT_INT64, DT_FLOAT16, DT_FLOAT, DT_DOUBLE })) - .OUTPUT(y1, TensorType({ DT_INT8, DT_UINT8, DT_INT16, DT_UINT16, \ - DT_INT32, DT_INT64, DT_FLOAT16, DT_FLOAT, DT_DOUBLE })) - .OUTPUT(y2, TensorType({ DT_INT32, DT_INT64 })) - .OUTPUT(y3, TensorType({ DT_INT32, DT_INT64 })) - .ATTR(return_inverse, Bool, false) - .ATTR(return_counts, Bool, false) - .OP_END_FACTORY_REG(UniqueWithCountsAndSorting) } // namespace ge #endif // OPS_BUILT_IN_OP_PROTO_INC_ARRAY_OPS_H_ diff --git a/third_party/fwkacllib/inc/ops/control_flow_ops.h b/third_party/fwkacllib/inc/ops/control_flow_ops.h index cd993599..e5bd3534 100644 --- a/third_party/fwkacllib/inc/ops/control_flow_ops.h +++ b/third_party/fwkacllib/inc/ops/control_flow_ops.h @@ -96,7 +96,7 @@ REG_OP(RefMerge) * Otherwise, the data is forwarded to "output_false" . \n *@par Inputs: - *@li data: The tensor to be forwarded. + *@li data: The tensor to be forwarded. \n * Must be one of the following types: float16, float32, float64, * int8, int16, int32, int64, uint8, uint16, uint32, uint64, bool. *@li pred: A boolean scalar. The output port that will receive data . \n diff --git a/third_party/fwkacllib/inc/ops/ctc_ops.h b/third_party/fwkacllib/inc/ops/ctc_ops.h index bbc610ff..e907b828 100644 --- a/third_party/fwkacllib/inc/ops/ctc_ops.h +++ b/third_party/fwkacllib/inc/ops/ctc_ops.h @@ -74,7 +74,7 @@ REG_OP(CTCLoss) *@li sequence_length: A vector containing sequence lengths, size `(batch_size)`. \n *@par Attributes: -* merge_repeated: If True, merge repeated classes in output. \n +*@li merge_repeated: If True, merge repeated classes in output. \n *@par Outputs: *@li decoded_indices: Indices matrix, size `(total_decoded_outputs x 2)`, @@ -108,8 +108,6 @@ REG_OP(CTCGreedyDecoder) *@par Attributes: *@li merge_repeated: If True, merge repeated classes in output. \n -*@li beam_width:A scalar >= 0 (beam search beam width). -*@li top_paths:A scalar >= 0, <= beam_width (controls output size). *@par Outputs: *@li decoded_indices: A list (length: top_paths) of indices matrices. Matrix j, @@ -164,7 +162,7 @@ REG_OP(CTCBeamSearchDecoder) * Compatible with Pytorch CTCLoss operator. *@par Restrictions: -*The length of Label should in [4, 1000]. +*Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. */ REG_OP(CTCLossV2) .INPUT(log_probs, TensorType({DT_FLOAT, DT_DOUBLE})) @@ -205,7 +203,7 @@ REG_OP(CTCLossV2) * Compatible with Pytorch CTCLoss operator. *@par Restrictions: -*The limit of Label’s length is 1K. +*Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. */ REG_OP(CTCLossV2Grad) .INPUT(grad_out, TensorType({DT_FLOAT, DT_DOUBLE})) diff --git a/third_party/fwkacllib/inc/ops/data_flow_ops.h b/third_party/fwkacllib/inc/ops/data_flow_ops.h index 32454d27..6021f4e3 100644 --- a/third_party/fwkacllib/inc/ops/data_flow_ops.h +++ b/third_party/fwkacllib/inc/ops/data_flow_ops.h @@ -1201,8 +1201,6 @@ REG_OP(TensorArraySize) *@brief A queue implementation that dequeues elements in a random order. \n *@par Attributes: -*@li component_types:A list of fully-defined Tensortype objects with -the same length as shapes, or None. *@li shapes: (Optional.) A list of fully-defined TensorShape objects with the same length as dtypes, or None. *@li capacity: An integer. The upper bound on the number of elements that may @@ -1283,7 +1281,6 @@ The length of this attr must be either 0 or the same as the length of elements are not constrained, and only one element may be dequeued at a time. *@li container: An optional string. Defaults to "". If non-empty, this queue is placed in the given container. Otherwise, a default container is used. -*@li capacity:An integer. The upper bound on the number of elements that may be stored in this queue. *@li shared_name: An optional string. Defaults to "". If non-empty, this queue will be shared under the given name across multiple sessions. \n @@ -1438,7 +1435,7 @@ REG_OP(OrderedMapClear) *@par Inputs: *Including: -* resource: A Tensor of type DT_RESOURCE. +* @li resource: A Tensor of type DT_RESOURCE. *@par Outputs: *handle: A Tensor of type DT_STRING ref. \n @@ -1529,7 +1526,7 @@ REG_OP(OrderedMapPeek) *@par Inputs: *Including: -* indices: A Tensor of type DT_INT32. \n +* @li indices: A Tensor of type DT_INT32. \n *@par Attributes: *@li capacity: An optional int that is >= 0. Defaults to "0". @@ -2335,40 +2332,6 @@ REG_OP(CacheAllIndexToLocal) .OP_END_FACTORY_REG(CacheAllIndexToLocal) /** -*@brief LRUCacheV2, aicore LRUCache. -*@par Inputs: -*index_list: exchange index list -*data: host data -*cache: gm cache -*tag: cache's tag -*is_last_call: if is last call write all cache to data -*@par Outputs: -*data: output data -*cache: gm cache -*tag: cache's tag -*index_offset_list: index_offset_list -*not_in_cache_index_list: output not in cache's index_list -*not_in_cache_number: scalar -*@par Attributes: -*pre_route_count: types of all outputs -*Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. -*/ -REG_OP(LRUCacheV2) - .INPUT(index_list, TensorType::BasicType()) - .INPUT(data, TensorType::BasicType()) - .INPUT(cache, TensorType::BasicType()) - .INPUT(tag, TensorType::BasicType()) - .INPUT(is_last_call, TensorType::BasicType()) - .OUTPUT(data, TensorType::BasicType()) - .OUTPUT(cache, TensorType::BasicType()) - .OUTPUT(tag, TensorType::BasicType()) - .OUTPUT(index_offset_list, TensorType::BasicType()) - .OUTPUT(not_in_cache_index_list, TensorType::BasicType()) - .OUTPUT(not_in_cache_number, TensorType::BasicType()) - .REQUIRED_ATTR(pre_route_count, Int) - .OP_END_FACTORY_REG(LRUCacheV2) - -/** *@brief DynamicGetNext, dynamic get next data *@par Inputs: *x: the iterator, all types are available diff --git a/third_party/fwkacllib/inc/ops/elewise_calculation_ops.h b/third_party/fwkacllib/inc/ops/elewise_calculation_ops.h index b4299026..f61e2939 100644 --- a/third_party/fwkacllib/inc/ops/elewise_calculation_ops.h +++ b/third_party/fwkacllib/inc/ops/elewise_calculation_ops.h @@ -624,9 +624,9 @@ REG_OP(Log1p) *@attention Constraints: *@li x2: The input data does not support 0 -*@li When NUM exceeds 2048 , the accuracy of operator cannot guarantee the +*@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 +*@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 @@ -2066,9 +2066,9 @@ REG_OP(FloorDiv) *@attention Constraints: *@li x2: The input data does not support 0 -*@li When NUM exceeds 2048 , the accuracy of operator cannot guarantee the +*@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 +*@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 @@ -2200,9 +2200,9 @@ REG_OP(Tan) *@attention Constraints: *@li x2: The input data does not support 0 -*@li When NUM exceeds 2048 , the accuracy of operator cannot guarantee the +*@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 +*@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 @@ -2467,11 +2467,11 @@ REG_OP(Eltwise) *@par Inputs: *One inputs, including: - * input_x: A tensor. Must be one of the following types: + * @li input_x: A tensor. Must be one of the following types: * float16, float32. \n *@par Outputs: - *output_y: A Tensor with the same type and shape of input_x's. \n + *y: A Tensor with the same type and shape of input_x's. \n *@par Third-party framework compatibility *Compatible with the Pytorch operator Erfinv. \n @@ -3154,13 +3154,13 @@ REG_OP(FusedMulAddNL2loss) *@brief Tests whether the input exceeds a threshold. \n *@par Inputs: -* x: A Tensor with any format. Must be one of the following types: float16, float32. \n +*@li x: A Tensor with any format. Must be one of the following types: float16, float32. \n *@par Attributes: -* threshold: A required float32. Defaults to "0.0". "x" is compared with "threshold", outputs "1" for inputs above threshold; "0" otherwise. \n +*@li threshold: A required float32. Defaults to "0.0". "x" is compared with "threshold", outputs "1" for inputs above threshold; "0" otherwise. \n *@par Outputs: -* y: A Tensor with any format. Has the same type as the input. Must be one of the following types: float16, float32. +*@li y: A Tensor with any format. Has the same type as the input. Must be one of the following types: float16, float32. *@par Third-party framework compatibility * Compatible with the Caffe operator Threshold. */ @@ -3175,7 +3175,7 @@ REG_OP(FusedMulAddNL2loss) *@brief Returns the index number corresponding to the maximum value entered. \n *@par Inputs: -*x: A tensor. Must be one of the following types: float16, float32. \n +*@li x: A tensor. Must be one of the following types: float16, float32. \n *@par Attributes: *@li axis: An optional int. Specify the axis to be cut at the input tensor. If this parameter is not provided, find the topk for each batch. Defaults to 10000 @@ -3203,11 +3203,12 @@ REG_OP(ArgMaxWithK) *@brief Multiply tensor with scale. \n *@par Inputs: -*One input, including: -*x: A Tensor. Must be one of the following types:int32,int16, float16, float32. +*Five inputs, including: +* @li x1: A Tensor. Must be one of the following types:int32,int16, float16, float32. +* @li x2: A scale. Must be float. \n *@par Outputs: -*y: A Tensor. Has the same type and shape as "x1". \n +*@li y: A Tensor. Has the same type and shape as "x1". \n *@par Third-party framework compatibility: * Compatible with the Pytorch operator muls. @@ -3222,11 +3223,12 @@ REG_OP(Muls) *@brief Fill tensor with scale. \n *@par Inputs: -*One input, including: -*x1: A Tensor. Must be one of the following types:int32,int16, float16, float32. +*Five inputs, including: +* @li x1: A Tensor. Must be one of the following types:int32,int16, float16, float32. +* @li x2: A scale. Must be float. \n *@par Outputs: -*y: A Tensor. Has the same type and shape as "x1". \n +*@li y: A Tensor. Has the same type and shape as "x1". \n *@par Third-party framework compatibility: * Compatible with the Pytorch operator fills. @@ -3376,7 +3378,7 @@ REG_OP(TensorMove) *@par Inputs: *One inputs, including: -*x: A Tensor. Must be one of the following types: float16, float32, int8, uint8, int16, uint16, int32, uint32, int64, uint64. \n +* @li x: A Tensor. Must be one of the following types: float16, float32, int8, uint8, int16, uint16, int32, uint32, int64, uint64. \n *@par Outputs: *output_x: A Tensor. Has the same type as "x". \n @@ -3395,7 +3397,7 @@ REG_OP(TensorRedirect) * multiply the result by the scalar value and add it to tensor x1 * @par Inputs: -* Four inputs, including: +* 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. @@ -3404,7 +3406,7 @@ REG_OP(TensorRedirect) * float16, float32, int32. \n * @par Outputs: -* y: A mutable Tensor. Has the same type as "x1". \n +* @li y: A mutable Tensor. Has the same type as "x1". \n * @par Third-party framework compatibility * Compatible with the Pytorch operator Addcdiv. @@ -3418,12 +3420,12 @@ REG_OP(Addcdiv) .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 +* @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: -* Four inputs, including: +* 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. @@ -3431,7 +3433,7 @@ REG_OP(Addcdiv) * @li value: A tensor which includes only one element of the same type as x1. \n * @par Outputs: -* y: A mutable output Tensor. Has the same type as "x1". \n +* @li y: A mutable output Tensor. Has the same type as "x1". \n * @par Third-party framework compatibility * Compatible with the Pytorch operator Addcmul. @@ -3453,7 +3455,7 @@ REG_OP(Addcmul) * @li alpha: A scalar tensor of type float16, float32. \n * @par Outputs: -* y: An ND tensor tensor with the same shape and type as "x1". \n +* @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. @@ -3466,6 +3468,25 @@ REG_OP(AxpyV2) .OP_END_FACTORY_REG(AxpyV2) /** +* @brief Computes the result of x1 - x2. + +* @par Inputs: +* @li x1: An ND tensor of type float16, 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: @@ -3481,7 +3502,7 @@ REG_OP(AxpyV2) * the difference between C1 and offset in "x1" and "x2". \n * @par Outputs: -* y: A Tensor of the same type as "x1", and the same shape as "x1", +* @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) @@ -3502,7 +3523,7 @@ REG_OP(StrideAdd) * @li input_y: A Tensor. the second tensor. \n * @par Outputs: -*output_z: A Tensor. Bool type, compare result of the two inputs. \n +* @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 @@ -3514,21 +3535,21 @@ REG_OP(TensorEqual) .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 + * @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: + * + * @par inputs * one input including: - * x: dynamic input A Tensor. Must be one of the following types: float32, float16, double, int32, int64 - * - * @par Outputs: + * @li x: dynamic input A Tensor. Must be one of the following types: float32, float16, double, int32, int64 + * + * @par output * one output including: - * y:A Tensor of the same type as x - * + * @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})) + .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) @@ -3613,16 +3634,16 @@ REG_OP(DataCompare) *which Hardmax will be performed.The output tensor has the same shape and contains the Hardmax values of the *corresponding input. * -*@par Inputs: +*@par inputs *one input including: -*x: input A Tensor.Must be one of the following types:float32,float16 +*@li x: input A Tensor.Must be one of the following types:float32,float16 * *@par Attributes: -*axis:A required int attribute that decides which dimension will be used to cal the hard_max +*@li axis:A required int attribute that decides which dimension will be used to cal the hard_max * -*@par Outputs: +*@par output: *one output including: -*y:A Tensor of the same type as x +*@li y:A Tensor of the same type as x * */ REG_OP(HardMax) @@ -3640,7 +3661,7 @@ REG_OP(HardMax) * @li input_y: A Tensor. the second tensor must be 1d. \n * @par Outputs: -* output: A Tensor. Result of the two inputs, must be 1d. \n +* @li output: A Tensor. Result of the two inputs, must be 1d. \n * @par Third-party framework compatibility * Compatible with the Pytorch dot operator. \n @@ -3650,7 +3671,7 @@ REG_OP(Dot) .INPUT(input_y, TensorType({DT_FLOAT, DT_FLOAT16, DT_UINT8, DT_INT8, DT_INT32})) .OUTPUT(output, TensorType({DT_FLOAT, DT_FLOAT16, DT_UINT8, DT_INT8, DT_INT32})) .OP_END_FACTORY_REG(Dot) - + /** *@brief Returns a new tensor with boolean elements representing \n *if each element of input is “close” to the corresponding element of other \n @@ -3698,7 +3719,7 @@ REG_OP(IsClose) * *@attention Constraints: *@li indices: only support int32,and shape same to "updates" -*@li The value range of "dimension" is [-dims, dims - 1]. "dims" is the dimension length of "x". +*@li The value range of "dimension" is [-dims, dims - 1]. "dims" is the dimension length of "x". *@li y:A Tensor, the type and shape is same to "var" \n *@par Third-party framework compatibility @@ -3733,7 +3754,7 @@ REG_OP(ArgMaxGrad) *@attention Constraints: *@li indices: only support int32,and shape same to "updates" -*@li The value range of "dimension" is [-dims, dims - 1]. "dims" is the dimension length of "x". +*@li The value range of "dimension" is [-dims, dims - 1]. "dims" is the dimension length of "x". *@li y:A Tensor, the type and shape is same to "var" \n *@par Third-party framework compatibility @@ -3784,15 +3805,15 @@ REG_OP(AddMatMatElements) *@par Inputs: *Two inputs, including: -* @li input_x1: A tensor. Must be the following types: float32. -* @li input_x2: A tensor. Must of the following types: float32. \n +* @li input_x1: A tensor. Must be the following types: +* float32. \n -* @par Attributes: -* @li dim:The type is Int and the default value is 1. -* @li eps:The type is Float and the default value is 1e-8. \n +*@par Inputs: +*@li input_x2: A tensor. Must of the following types: +* float32. \n *@par Outputs: -* output_y: A Tensor with the same type of input_x's. \n +*@li output_y: A Tensor with the same type of input_x's. \n *@par Third-party framework compatibility *Compatible with the Pytorch operator CosineSimilarity. \n @@ -3805,45 +3826,6 @@ REG_OP(CosineSimilarity) .ATTR(eps, Float, 1e-8) .OP_END_FACTORY_REG(CosineSimilarity) -/** -*@brief count adam result. \n - -*@par Inputs: -*eleven inputs, including: -* @li var: A Tensor. Support float16/float32.\n -* @li m: A Tensor. Datatype and shape are same as exp_avg.\n -* @li v: A Tensor. Datatype and shape are same as exp_avg.\n -* @li lr: A Tensor. Datatype is same as exp_avg. Shape (1, ).\n -* @li beta1: A Tensor. Datatype is same as exp_avg. Shape (1, ).\n -* @li beta2: A Tensor. Datatype is same as exp_avg. Shape (1, ).\n -* @li epsilon: A Tensor. Datatype is same as exp_avg. Shape (1, ).\n -* @li grad: A Tensor. Datatype and shape are same as exp_avg.\n -* @li max_grad_norm: A Tensor. Datatype is same as exp_avg. Shape (1, ).\n -* @li global_grad_norm: A Tensor. Datatype is same as exp_avg. Shape (1, ).\n -* @li weight_decay: A Tensor. Datatype is same as exp_avg. Shape (1, ).\n - -*@par Outputs: -*three inputs, including: -* @li var: A Tensor. Datatype and shape are same as exp_avg.\n -* @li m: A Tensor. Datatype and shape are same as exp_avg.\n -* @li v: A Tensor. Datatype and shape are same as exp_avg.\n -*/ -REG_OP(ApplyAdamV2) - .INPUT(var, TensorType({ DT_FLOAT, DT_FLOAT16 })) - .INPUT(m, TensorType({ DT_FLOAT, DT_FLOAT16 })) - .INPUT(v, TensorType({ DT_FLOAT, DT_FLOAT16 })) - .INPUT(lr, TensorType({ DT_FLOAT, DT_FLOAT16 })) - .INPUT(beta1, TensorType({ DT_FLOAT, DT_FLOAT16 })) - .INPUT(beta2, TensorType({ DT_FLOAT, DT_FLOAT16 })) - .INPUT(epsilon, TensorType({ DT_FLOAT, DT_FLOAT16 })) - .INPUT(grad, TensorType({ DT_FLOAT, DT_FLOAT16 })) - .INPUT(max_grad_norm, TensorType({ DT_FLOAT, DT_FLOAT16 })) - .INPUT(global_grad_norm, TensorType({ DT_FLOAT, DT_FLOAT16 })) - .INPUT(weight_decay, TensorType({ DT_FLOAT, DT_FLOAT16 })) - .OUTPUT(var, TensorType({ DT_FLOAT, DT_FLOAT16 })) - .OUTPUT(m, TensorType({ DT_FLOAT, DT_FLOAT16 })) - .OUTPUT(v, TensorType({ DT_FLOAT, DT_FLOAT16 })) - .OP_END_FACTORY_REG(ApplyAdamV2) } // namespace ge #endif // OPS_BUILT_IN_OP_PROTO_INC_ELEWISE_CALCULATION_OPS_H_ diff --git a/third_party/fwkacllib/inc/ops/functional_ops.h b/third_party/fwkacllib/inc/ops/functional_ops.h index 7cfe39c4..b09ac058 100644 --- a/third_party/fwkacllib/inc/ops/functional_ops.h +++ b/third_party/fwkacllib/inc/ops/functional_ops.h @@ -163,6 +163,9 @@ REG_OP(Case) * if it is not a scalar, non-empty means True and empty means False. *@li body: A subgraph takes 'input' and returns a another list of tensors . \n + *@par Attributes: + *parallel_iterations: An optional int, default as 10 . \n + *@par Outputs: *output: The output tensors returned by "body". Has the same type as "input" . \n diff --git a/third_party/fwkacllib/inc/ops/image_ops.h b/third_party/fwkacllib/inc/ops/image_ops.h index 2327e76e..6909345a 100644 --- a/third_party/fwkacllib/inc/ops/image_ops.h +++ b/third_party/fwkacllib/inc/ops/image_ops.h @@ -28,7 +28,7 @@ namespace ge { *@brief Decode the frame(s) of a GIF-encoded image to a uint8 tensor . \n *@par Inputs: -*contents:A Tensor of type string. 0-D. The GIF-encoded image. \n +*@li contents:A Tensor of type string. 0-D. The GIF-encoded image. \n *@par Outputs: *image:A Tensor of type uint8. \n @@ -128,8 +128,8 @@ crops from the input image tensor and resizes them using bilinear sampling or nearest neighbor sampling to a common output size specified by crop_size . \n *@par Inputs: -*Input x must be a 4-D tensor. Inputs include: -*@li x:A Tensor. Must be one of the following types:uint8, uint16, int8, +*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]. The format must be NHWC. *@li boxes: A Tensor of type float. A 2-D tensor of shape [num_boxes, 4]. @@ -266,9 +266,8 @@ depth] containing the original image size. Both image_height and image_width need to be positive . \n *@par Attributes: -*@li method: A string specifying the interpolation method. Only 'bilinear' is -supported for now . -*@li T: output of type \n +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]. The format @@ -586,11 +585,9 @@ REG_OP(ResizeNearestNeighborV2GradD) channels], The image tensor that was resized . \n *@par Attributes: -*@li align_corners: An optional bool. Defaults to False. If true, the centers of +*align_corners: An optional bool. Defaults to False. If true, the centers of the 4 corner pixels of the input and grad tensors are aligned. Defaults to -false . -*@li half_pixel_centers: indicates if the offset coordinates are normalized. Defaults -to false . \n +false . \n *@par Outputs: *y: A Tensor. Has the same type as original_image . \n @@ -620,10 +617,9 @@ REG_OP(ResizeBilinearV2Grad) size for the images . \n *@par Attributes: -* @li align_corners: If true, the centers of the 4 corner pixels of the input and +*align_corners: If true, the centers of the 4 corner pixels of the input and output tensors are aligned, preserving the values at the corner pixels. -Defaults to false . -* @li half_pixel_centers: An optional bool. Defaults to False . \n +Defaults to false . \n *@par Outputs: *y: 4-D with shape [batch, new_height, new_width, channels] . \n @@ -688,9 +684,6 @@ be non-negative. In the case of 0, the cropped area does not need to overlap any of the bounding boxes supplied . *@li aspect_ratio_range: The cropped area of the image must have an aspect ratio = width / height within this range. -*@li area_range: An optional list of `floats`. Defaults to `[0.05, 1]`. The -cropped area of the image must contain a fraction of the supplied image -within this range. *@li max_attempts: Number of attempts at generating a cropped region of the image of the specified constraints. After max_attempts failures, return the entire image. @@ -747,9 +740,6 @@ generator is seeded by the given seed. Otherwise, it is seeded by a random seed. *@li seed2: A second seed to avoid seed collision. *@li aspect_ratio_range: The cropped area of the image must have an aspect ratio = width / height within this range. -*@li area_range: An optional list of `floats`. Defaults to `[0.05, 1]`. The -cropped area of the image must contain a fraction of the supplied image -within this range. *@li max_attempts: Number of attempts at generating a cropped region of the image of the specified constraints. After max_attempts failures, return the entire image. @@ -797,10 +787,9 @@ REG_OP(SampleDistortedBoundingBoxExt2) The new size for the images . \n *@par Attributes: -*@li align_corners: If true, the centers of the 4 corner pixels of the input and +*align_corners: If true, the centers of the 4 corner pixels of the input and output tensors are aligned, preserving the values at the corner pixels. Defaults to false . \n -*@li half_pixel_centers: An optional bool. Defaults to False . \n *@par Outputs: *y: 4-D with shape [batch, new_height, new_width, channels] . \n @@ -1010,6 +999,10 @@ deciding whether boxes overlap too. *@li score_threshold: A 0-D float tensor representing the threshold for deciding when to remove boxes based on score . \n +*@par Attributes: +*pad_to_max_output_size: If true, the output selected_indices is padded +to be of length max_output_size. Defaults to false . \n + *@par Outputs: *selected_indices: A 1-D integer tensor of shape [M] representing the selected indices from the boxes tensor, where M <= max_output_size . \n @@ -1101,8 +1094,8 @@ REG_OP(EncodePng) *contents: 0-D. PNG-decoded image . *@par Attributes: -*@li channels: graph channels \n -*@li dtype: type of image +*channels: graph channels \n +*dtype: type of image *@par Outputs: *image: is a 3-D uint8 or uint16 Tensor of shape [height, width, channels] @@ -1123,10 +1116,10 @@ REG_OP(DecodePng) *@brief Bmp-decode an image. \n *@par Inputs: -*contents: A Tensor of type string. 0-D. The BMP-encoded image. \n +*@li contents: A Tensor of type string. 0-D. The BMP-encoded image. \n *@par Attributes: -*channels: Decode the desired number of color channels of the image. \n +*@li channels: Decode the desired number of color channels of the image. \n *@par Outputs: *image: A Tensor dtype of uint8. @@ -1260,7 +1253,6 @@ REG_OP(KeepRatioResizeBilinear) No default value. *@li align_corners: An optional bool. If "true", the centers of the corner pixels of the input and output tensors are aligned. Defaults to "false" . \n -*@li half_pixel_centers: An optional bool. Defaults to False . \n *@par Outputs: *y: A Tensor with the same type and format as input "images" . \n @@ -1389,7 +1381,6 @@ REG_OP(NonMaxSuppressionV5) *@li scale: A `Tensor` of type `float32`. *@li translation: A `Tensor` of type `float32` . \n -*@par Attributes: *@li kernel_type: type is string, default lanczos3 *@li antialias: type is bool, default true \n @@ -1420,7 +1411,6 @@ REG_OP(ScaleAndTranslate) *@li scale: A `Tensor` of type `float32`. *@li translation: A `Tensor` of type `float32` . \n -*@par Attributes: *@li kernel_type: type is string, default lanczos3 *@li antialias: type is bool, default true @@ -1470,10 +1460,9 @@ if they fall beyond [0, 1]. If false, do not do clipping and output the box coordinates as it is. If not specified, defaults to true . \n *@par Outputs: -*@li nmsed_boxes:type is float -*@li nmsed_scores:type is float -*@li nmsed_classes:type is float -*@li valid_detections:type is INT32 \n +*nmsed_boxes:type is float +*nmsed_scores:type is float +*nmsed_classes:type is float \n *@par Third-party framework compatibility * Compatible with tensorflow CombinedNonMaxSuppression operator. @@ -1519,9 +1508,6 @@ REG_OP(IMGWarp) *@par Outputs: *map_img: A Tensor after resize. \n - -*@par Restrictions: -*Warning:THIS FUNCTION IS EXPERIMENTAL. Please do not use. */ REG_OP(Remap) .INPUT(img, TensorType({DT_UINT8, DT_FLOAT16, DT_FLOAT32})) @@ -1538,7 +1524,7 @@ and 4 mean input[(h_top, w_left), (h_top, w_right), (h_bottom, w_left), (h_bott *@li warp_index: the resize offset A 4-D float tensor of shape `[n, 2, h, w]`, 2 means (x, y) for resize point. *@par Outputs: -*warp_img: A Tensor after ResizeBilinear, A 4-D tensor of shape `[n, c, h, w]`. \n +*remap_img: A Tensor after ResizeBilinear, A 4-D tensor of shape `[n, c, h, w]`. \n */ REG_OP(IMGWarpResize) .INPUT(img, TensorType({DT_FLOAT32})) @@ -1573,39 +1559,6 @@ REG_OP(SpatialTransformerD) .OP_END_FACTORY_REG(SpatialTransformerD) /** -*@brief Function spatial transformer . \n - -*@par Inputs: -*@li x: A Tensor dtype of float16, float32, double, uint8, int8, uint16, int16, int32, uint32, uint64, int64. -*@li theta: A Tensor dtype of float16, float32, double, uint8, int8, uint16, int16, int32, uint32, uint64, int64, - auxiliary coefficients . \n - -*@par Attributes: -*@li output_size: A tuple output size. -*@li default_theta: A tuple default theta -*@li use_default_theta: List use default theta - -*@par Outputs: -*y: A Tensor dtype of float16, float32, double, uint8, int8, uint16, int16, int32, uint32, uint64, int64, - should be same shape and type as x. - -*@par Restrictions: -*Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. -*/ -REG_OP(SpatialTransformer) - .INPUT(x, TensorType({DT_FLOAT,DT_FLOAT16,DT_DOUBLE,DT_UINT8,DT_INT8,DT_UINT16, - DT_INT16,DT_INT32,DT_UINT32,DT_UINT64,DT_INT64})) - .OPTIONAL_INPUT(theta, TensorType({DT_FLOAT,DT_FLOAT16,DT_DOUBLE,DT_UINT8,DT_INT8, - DT_UINT16,DT_INT16,DT_INT32,DT_UINT32,DT_UINT64,DT_INT64})) - .OUTPUT(y, TensorType({DT_FLOAT,DT_FLOAT16,DT_DOUBLE,DT_UINT8,DT_INT8,DT_UINT16, - DT_INT16,DT_INT32,DT_UINT32,DT_UINT64,DT_INT64})) - .ATTR(output_size, ListInt, {-1, -1}) - .ATTR(default_theta, ListFloat, {}) - .ATTR(align_corners, Bool, false) - .ATTR(use_default_theta, ListInt, {}) - .OP_END_FACTORY_REG(SpatialTransformer) - -/** * @brief Resize the input tensor. \n currently, only support resize image tensor using nearest neighbor and linear interpolation. @@ -1670,7 +1623,7 @@ REG_OP(Resize) *@brief Function parse image from string to int. \n *@par Inputs: -* contents: A Tensor of type string. 0-D. The JPEG-encoded image. \n +*@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. @@ -1715,7 +1668,7 @@ REG_OP(DenseImageWarp) *@par Inputs: *One inputs, including: -* x: A tensor. Must be one of the following types: +* @li x: A tensor. Must be one of the following types: * float16, float32. \n *@par Attributes: @@ -1760,7 +1713,7 @@ REG_OP(ResizeD) *@par Inputs: *One inputs, including: -* grads: A tensor. Must be one of the following types: +* @li grads: A tensor. Must be one of the following types: * float16, float32. \n *@par Attributes: @@ -1809,8 +1762,8 @@ REG_OP(ResizeGradD) *@li flow: 4-D Tensor with shape `[batch, height, width, 2]`. \n *@par Outputs: -*@li grad_image: Returns 4-D with the same shape and dtype as `image`. -*@li grad_flow: Returns 4-D with the same shape and dtype as `flow`. \n +*grad_image: Returns 4-D with the same shape and dtype as `image`. +*grad_flow: Returns 4-D with the same shape and dtype as `flow`. \n */ REG_OP(DenseImageWarpGrad) .INPUT(grad, TensorType({DT_FLOAT, DT_FLOAT16})) @@ -1864,12 +1817,12 @@ REG_OP(GridSampler2D) *@li assist: Assist matrix, a 4-D tensor of type float16. *@par Attributes: -*align_corners: An optional bool. If "true", the centers of the corner +*@li align_corners: An optional bool. If "true", the centers of the corner pixels of the input and output tensors are aligned. Defaults to "false" . *@par Outputs: -*@li diff: Returns 4-D Tensor with the same shape and dtype as `grid`. -*@li position: Returns 4-D Tensor with the same shape as `grid`. +*diff: Returns 4-D Tensor with the same shape and dtype as `grid`. +*position: Returns 4-D Tensor with the same shape as `grid`. */ REG_OP(GridUnnormal) .INPUT(grid, TensorType({DT_FLOAT16, DT_FLOAT})) @@ -1887,13 +1840,10 @@ REG_OP(GridUnnormal) *@li position: 4-D Tensor with shape `[batch, output_height, output_width, 2]`. *@par Attributes: -*padding_mode: An optional string specifying the pad method. Only 'zeros' is supported for now . +*@li padding_mode: An optional string specifying the pad method. Only 'zeros' is supported for now . *@par Outputs: *y: Returns 4-D Tensor with the same dtype as `x`. - -*@par Restrictions: -*Warning:THIS FUNCTION IS EXPERIMENTAL. Please do not use. */ REG_OP(ImageUnfold) .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT})) @@ -1986,204 +1936,5 @@ REG_OP(GridSampler3DGrad) .ATTR(align_corners, Bool, false) .OP_END_FACTORY_REG(GridSampler3DGrad) -/** -*@brief Upsample the 3-D data with the nearest neighbor ​interpolation algorithm. \n - -*@par Inputs: -*One inputs, including: -*x: A 5-D input tensor [N, C, D, H, W]. Must be one of the following types: -* float16, float32, float64. \n - -*@par Attributes: -*@li output_size: An optional listInt. Defaults to none. - contain 3 elements: output_depth, output_height, output_width. The number of elements of 'output_size' - should be the same as the rank of input 'x'. Only one of 'scales' and 'output_size' can be specified. \n -*@li scales: An optional listFloat. Defaults to none. - The scale array along each dimension, contain 3 elements: scale_depth, scale_height, scale_width. - The number of elements of 'scales' should be the same as the rank of input 'x'. One of 'scales' and - 'output_size' MUST be specified and it is an error if both are specified. \n - -*@par Outputs: -*y: A 5-D tensor. Has the same type as input x, shape depends on x and output_size/scales. \n - -*@par Restrictions: -*Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. \n -*/ - -REG_OP(UpsampleNearest3d) - .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) - .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) - .ATTR(output_size, ListInt, {}) - .ATTR(scales, ListFloat, {}) - .OP_END_FACTORY_REG(UpsampleNearest3d) - -/** -*@brief Upsample the 3-D data with the trilinear ​interpolation algorithm. \n - -*@par Inputs: -*One inputs, including: -*x: A 5-D input tensor [N, C, D, H, W]. Must be one of the following types: -* float16, float32, float64. \n - -*@par Attributes: -*@li output_size: An optional listInt. Defaults to none. - contain 3 elements: output_depth, output_height, output_width. The number of elements of 'output_size' should - be the same as the rank of input 'x'. Only one of 'scales' and 'output_size' can be specified. \n -*@li scales: An optional listFloat. Defaults to none. - The scale array along each dimension, contain 3 elements: scale_depth, scale_height, scale_width. - The number of elements of 'scales' should be the same as the rank of input 'x'. - One of 'scales' and 'output_size' MUST be specified and it is an error if both are specified. \n -*@li align_corners: An optional bool. Defaults to false. - If true, the input and output tensors are aligned by the center points of their corner pixels, preserving the - values at the corner pixels. If false, the input and output tensors are aligned by the corner points of their - corner pixels, and the interpolation use edge value padding for out of boundary values. \n - -*@par Outputs: -*y: A 5-D tensor. Has the same type as input x, shape depends on x and output_size/scales. \n - -*@par Restrictions: -*Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. \n -*/ - -REG_OP(UpsampleTrilinear3d) - .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) - .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) - .ATTR(output_size, ListInt, {}) - .ATTR(scales, ListFloat, {}) - .ATTR(align_corners, Bool, false) - .OP_END_FACTORY_REG(UpsampleTrilinear3d) - -/** -*@brief Upsample the 3-D gradient data with the nearest neighbor ​interpolation algorithm. \n - -*@par Inputs: -*One inputs, including: -*grad_output: A 5-D input tensor [N, C, D, H, W]. Must be one of the following types: -* float16, float32, float64. \n - -*@par Attributes: -*@li input_size: An required listInt. - contain 5 elements: [min_batch, channels, depth, height, width]. Must: - input_size[0] == grad_output_tensor_size[0] - input_size[1] == grad_output_tensor_size[1]. \n -*@li output_size: An optional listInt. Defaults to none. - contain 3 elements: depth, height, width. The number of elements of 'output_size' should - be the same as the rank of input 'grad_output'. Only one of 'scales' and 'output_size' can be specified. Must: - grad_output_tensor_size[2] == floor(input_size[2] * scales[0]) == output_size[0] - grad_output_tensor_size[3] == floor(input_size[3] * scales[1]) == output_size[1] - grad_output_tensor_size[4] == floor(input_size[4] * scales[2]) == output_size[2]. \n -*@li scales: An optional listFloat. Defaults to none. - The scale array along each dimension, contain 3 elements: scale_depth, scale_height, scale_width. - The number of elements of 'scales' should be the same as the rank of input 'grad_output'. - One of 'scales' and 'output_size' MUST be specified and it is an error if both are specified. \n - -*@par Outputs: -*y: A 5-D tensor. Has the same type as input grad_output, shape depends on Attributes:input_size. \n - -*@par Restrictions: -*Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. -*/ - -REG_OP(UpsampleNearest3dGrad) - .INPUT(grad_output, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) - .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) - .REQUIRED_ATTR(input_size, ListInt) - .ATTR(output_size, ListInt, {}) - .ATTR(scales, ListFloat, {}) - .OP_END_FACTORY_REG(UpsampleNearest3dGrad) - -/** -*@brief Upsample the 3-D gradient data trilinear ​interpolation algorithm. \n - -*@par Inputs: -*One inputs, including: -*grad_output: A 5-D input tensor [N, C, D, H, W]. Must be one of the following types: -* float16, float32, float64. \n - -*@par Attributes: -*@li input_size: An required listInt. - contain 5 elements: [min_batch, channels, depth, height, width]. Must: - input_size[0] == grad_output_tensor_size[0] - input_size[1] == grad_output_tensor_size[1]. \n -*@li output_size: An optional listInt. Defaults to none. - contain 3 elements: depth, height, width. The number of elements of 'output_size' should - be the same as the rank of input 'grad_output'. Only one of 'scales' and 'output_size' can be specified. Must: - grad_output_tensor_size[2] == floor(input_size[2] * scales[0]) == output_size[0] - grad_output_tensor_size[3] == floor(input_size[3] * scales[1]) == output_size[1] - grad_output_tensor_size[4] == floor(input_size[4] * scales[2]) == output_size[2]. \n -*@li scales: An optional listFloat. Defaults to none. - The scale array along each dimension, contain 3 elements: scale_depth, scale_height, scale_width. - The number of elements of 'scales' should be the same as the rank of input 'grad_output'. - One of 'scales' and 'output_size' MUST be specified and it is an error if both are specified. \n - -*@par Outputs: -*y: A Tensor with shape depends on intput_size and output_size/scales. Must be one of the following - types: float16, float32, float64. \n - -*@par Restrictions: -*Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. -*/ - -REG_OP(UpsampleTrilinear3dGrad) - .INPUT(grad_output, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) - .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) - .REQUIRED_ATTR(input_size, ListInt) - .ATTR(output_size, ListInt, {}) - .ATTR(scales, ListFloat, {}) - .ATTR(align_corners, Bool, false) - .OP_END_FACTORY_REG(UpsampleTrilinear3dGrad) - - -/** -*@brief Upsample the 1-D data with the nearest neighbor ​interpolation algorithm. \n - -*@par Inputs: -*x: A 1-D input tensor [N, C, W]. Must be one of the following types: -* float16, float32, float64. \n - -*@par Attributes: -*@li output_size: An required listInt contains output_width. -*@li scales: An optional listFloat contains scale_width. Defaults to be zero. \n - -*@par Outputs: -*y: A 3-D tensor. Has the same type as input x, shape depends on x and output_size/scales. \n - -*@par Restrictions: -*Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. \n -*/ - -REG_OP(UpsampleNearest1d) - .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) - .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) - .REQUIRED_ATTR(output_size, ListInt) - .ATTR(scales, ListFloat, {}) - .OP_END_FACTORY_REG(UpsampleNearest1d) - -/** -*@brief Upsample the 1-D gradient data with the nearest neighbor ​interpolation algorithm. \n - -*@par Inputs: -*grad_output: A 3-D input tensor [N, C, W]. Must be one of the following types: -* float16, float32, float64. \n - -*@par Attributes: -*@li output_size: An required listInt contains output_width. -*@li scales: An optional listFloat contains scale_width. Defaults to be zero. -*@li input_size: An required listInt contains output_width. \n - -*@par Outputs: -*y: A 3-D tensor. Has the same type as input grad_output, shape depends on Attributes:input_size. \n - -*@par Restrictions: -*Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. \n -*/ - -REG_OP(UpsampleNearest1dGrad) - .INPUT(grad_output, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) - .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) - .REQUIRED_ATTR(input_size, ListInt) - .REQUIRED_ATTR(output_size, ListInt) - .ATTR(scales, ListFloat, {}) - .OP_END_FACTORY_REG(UpsampleNearest1dGrad) } // namespace ge #endif // OPS_BUILT_IN_OP_PROTO_INC_IMAGE_OPS_H_ diff --git a/third_party/fwkacllib/inc/ops/linalg_ops.h b/third_party/fwkacllib/inc/ops/linalg_ops.h index f6cc8694..69c77bf6 100644 --- a/third_party/fwkacllib/inc/ops/linalg_ops.h +++ b/third_party/fwkacllib/inc/ops/linalg_ops.h @@ -347,9 +347,6 @@ REG_OP(SelfAdjointEig) .OP_END_FACTORY_REG(SelfAdjointEig) /** -*@par Restrictions: -*Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. - *@brief Computes the sign and the log of the absolute value of the determinant of one or more square matrices . \n @@ -385,10 +382,9 @@ REG_OP(Slogdet) *x:Tensor of shape [..., M, N]. Let P be the minimum of M and N . \n *@par Attributes: -*@li compute_uv:If True then left and right singular vectors will be computed and +*compute_uv:If True then left and right singular vectors will be computed and returned in u and v, respectively. Otherwise, only the singular values will -be computed, which can be significantly faster . -*@li full_matrices:the param effect u,v. \n +be computed, which can be significantly faster . \n *@par Outputs: *@li sigma:Singular values. Shape is [..., P]. The values are sorted in @@ -431,9 +427,6 @@ denotes the lower triangular factor `L` with unit diagonal. *@li p: upper triangular part denotes the upper triangular factor `U`.Permutation of the rows encoded as a list of indices in `0..M-1`. Shape is `[..., M]` . \n -*@par Attributes: -*output_idx_type: An optional DType from: int32, int64. - *@par Third-party framework compatibility * Compatible with TensorFlow Lu operator. */ @@ -474,12 +467,6 @@ left-hand side . \n *@par Outputs: y: Tensor of shape `[..., M, K]` containing the solutions \n -*@par Attributes: -*partial_pivoting: Whether to perform partial pivoting. `True` by default. -Partial pivoting makes the procedure more stable, but slower. Partial -pivoting is unnecessary in some cases, including diagonally dominant and -symmetric positive definite matrices - *@par Third-party framework compatibility * Compatible with TensorFlow TridiagonalSolve operator. */ diff --git a/third_party/fwkacllib/inc/ops/list_ops.h b/third_party/fwkacllib/inc/ops/list_ops.h index 0aa94e73..a1b622e9 100644 --- a/third_party/fwkacllib/inc/ops/list_ops.h +++ b/third_party/fwkacllib/inc/ops/list_ops.h @@ -35,10 +35,10 @@ namespace ge { *@li max_num_elements: The maximum number of elements. \n *@par Attributes: -*element_dtype: The type of elements in the list. \n +*@li element_dtype: The type of elements in the list. \n *@par Outputs: -*handle: An empty tensor list . \n +*@li handle: An empty tensor list . \n *@par Third-party framework compatibility. *Compatible with tensorflow EmptyTensorList operator. @@ -59,10 +59,10 @@ and the other elements of the given list in `input_handle`. \n *@li tensor: The tensor to put on the list. \n *@par Attributes: -*element_dtype: The type of elements in the list. \n +*@li element_dtype: The type of elements in the list. \n *@par Outputs: -*output_handle:A list with the elements of old list followed by tensor. \n +*@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. @@ -86,7 +86,7 @@ list with all but that element. \n *@li element_shape: A shape compatible with that of elements in the list. \n *@par Attributes: -*element_dtype: The type of elements in the list. \n +*@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. @@ -110,10 +110,10 @@ REG_OP(TensorListPopBack) *@brief The number of tensors in the input tensor list. \n *@par Inputs: -*input_handle: The input list. \n +*@li input_handle: The input list. \n *@par Outputs: -*length:The number of tensors in the list. \n +*@li length:The number of tensors in the list. \n *@par Third-party framework compatibility. *Compatible with tensorflow TensorListLength operator. @@ -127,13 +127,13 @@ REG_OP(TensorListLength) *@brief The shape of elements in the input tensor list. \n *@par Inputs: -*input_handle: The input list. \n +*@li input_handle: The input list. \n *@par Attributes: -*shape_type: The type of shape in the list. \n +*@li shape_type: The type of shape in the list. \n *@par Outputs: -*element_shape:A shape compatible with that of elements in the list. \n +*@li element_shape:A shape compatible with that of elements in the list. \n *@par Third-party framework compatibility. *Compatible with tensorflow TensorListElementShape operator. @@ -156,7 +156,7 @@ REG_OP(TensorListElementShape) *@li shape_type: The type of shape in the list. \n *@par Outputs: -*handle: An output tensor list . \n +*@li handle: An output tensor list . \n *@par Third-party framework compatibility. *Compatible with tensorflow TensorListReserve operator. @@ -178,10 +178,10 @@ REG_OP(TensorListReserve) *@li element_shape: A shape compatible with that of elements in the list. \n *@par Attributes: -*element_dtype: The type of elements in the list. \n +*@li element_dtype: The type of elements in the list. \n *@par Outputs: -*item: An output tensor value of index position . \n +*@li item: An output tensor value of index position . \n *@par Third-party framework compatibility. *Compatible with tensorflow TensorListGetItem operator. @@ -206,10 +206,10 @@ REG_OP(TensorListGetItem) *@li item: The element to be assigned to that position. \n *@par Attributes: -*element_dtype: The type of elements in the list. \n +*@li element_dtype: The type of elements in the list. \n *@par Outputs: -*output_handle: An output tensor list . \n +*@li output_handle: An output tensor list . \n *@par Third-party framework compatibility. *Compatible with tensorflow TensorListSetItem operator. @@ -233,10 +233,10 @@ REG_OP(TensorListSetItem) *@li tensor: The tensor push into tensor list. \n *@par Attributes: -*element_dtype: The type of elements in the list. \n +*@li element_dtype: The type of elements in the list. \n *@par Outputs: -*output_handles: The output tensor lists. \n +*@li output_handles: The output tensor lists. \n *@par Third-party framework compatibility. *Compatible with tensorflow TensorListPushBackBatch operator. @@ -263,7 +263,7 @@ REG_OP(TensorListPushBackBatch) *@li num_elements: The number of elements in the list. \n *@par Outputs: -*tensor: The tensor of list. \n +*@li tensor: The tensor of list. \n *@par Third-party framework compatibility. *Compatible with tensorflow TensorListStack operator. @@ -293,7 +293,7 @@ the leading dim of input_handle.element_shape or the element_shape input arg is not already set. \n *@par Attributes: -*element_dtype: The type of elements in the list. \n +*@li element_dtype: The type of elements in the list. \n *@par Outputs: *@li tensor: The concated result. @@ -324,10 +324,10 @@ REG_OP(TensorListConcatV2) *@li lengths: Vector of sizes of the 0th dimension of tensors in the list. \n *@par Attributes: -*element_dtype: The type of elements in the list. \n +*@li element_dtype: The type of elements in the list. \n *@par Outputs: -*output_handle: The list. \n +*@li output_handle: The list. \n *@par Third-party framework compatibility. *Compatible with tensorflow TensorListSplit operator. @@ -351,10 +351,10 @@ REG_OP(TensorListSplit) *@li element_shape: The shape of elements in the list. \n *@par Attributes: -*element_dtype: The type of elements in the list. \n +*@li element_dtype: The type of elements in the list. \n *@par Outputs: -*output_handle: An output tensor list . \n +*@li output_handle: An output tensor list . \n *@par Third-party framework compatibility. *Compatible with tensorflow TensorListFromTensor operator. @@ -377,7 +377,7 @@ REG_OP(TensorListFromTensor) *@li size: size of the output list. \n *@par Outputs: -*output_handle: The output tensor list. \n +*@li output_handle: The output tensor list. \n *@par Third-party framework compatibility. *Compatible with tensorflow TensorListResize operator. @@ -397,10 +397,10 @@ REG_OP(TensorListResize) *@li element_shape: The shape of elements in the list. \n *@par Attributes: -*element_dtype: The type of elements in the list. \n +*@li element_dtype: The type of elements in the list. \n *@par Outputs: -*values: The tensor. \n +*@li values: The tensor. \n *@par Third-party framework compatibility. *Compatible with tensorflow TensorListGather operator. @@ -429,10 +429,10 @@ the largest index in indices. If -1, the list is just large enough to include the largest index in indices. \n *@par Attributes: -*element_dtype: The type of elements in the list. \n +*@li element_dtype: The type of elements in the list. \n *@par Outputs: -*output_handle: The TensorList. \n +*@li output_handle: The TensorList. \n *@par Third-party framework compatibility. *Compatible with tensorflow TensorListScatterV2 operator. @@ -458,10 +458,10 @@ REG_OP(TensorListScatterV2) *@li indices: The indices used to index into the list. \n *@par Attributes: -*element_dtype: The type of elements in the list. \n +*@li element_dtype: The type of elements in the list. \n *@par Outputs: -*output_handle: The TensorList. \n +*@li output_handle: The TensorList. \n *@par Third-party framework compatibility. *Compatible with tensorflow TensorListScatterIntoExistingList operator. @@ -485,10 +485,10 @@ REG_OP(TensorListScatterIntoExistingList) *@li input_b: The input tensor list B. \n *@par Attributes: -*element_dtype: The type of elements in the list. \n +*@li element_dtype: The type of elements in the list. \n *@par Outputs: -*output: The output list. \n +*@li output: The output list. \n *@par Third-party framework compatibility. *Compatible with tensorflow TensorListConcatLists operator. diff --git a/third_party/fwkacllib/inc/ops/lookup_ops.h b/third_party/fwkacllib/inc/ops/lookup_ops.h index b1fc254f..5d928e5a 100644 --- a/third_party/fwkacllib/inc/ops/lookup_ops.h +++ b/third_party/fwkacllib/inc/ops/lookup_ops.h @@ -77,8 +77,8 @@ REG_OP(LookupTableInsert) *handle: A Tensor of type resource. Handle to the table . \n *@par Attributes: -*@li Tkeys: A DType of keys. -*@li Tvalues: A DType of values. +*@li Tkeys: A DType. +*@li Tvalues: A DType . \n *@par Outputs: *@li keys: A Tensor of type Tkeys. diff --git a/third_party/fwkacllib/inc/ops/math_ops.h b/third_party/fwkacllib/inc/ops/math_ops.h index 6eb418d8..319bcf70 100644 --- a/third_party/fwkacllib/inc/ops/math_ops.h +++ b/third_party/fwkacllib/inc/ops/math_ops.h @@ -227,10 +227,10 @@ REG_OP(Bucketize) *@par Inputs: *One inputs, including: -*input_x: A tensor. Must be one of the following types: float16, float32, int8, uint8, int32. \n +* @li input_x: A tensor. Must be one of the following types: float16, float32, int8, uint8, int32. \n *@par Outputs: -*output_y: A tensor with the same type and shape of input_x \n +*y: A tensor with the same type and shape of input_x \n *@par Third-party framework compatibility *Compatible with the Pytorch operator Trunc. \n @@ -298,7 +298,7 @@ REG_OP(SparseSegmentMean) *@par Inputs: *The input grad must have be type float or double. Inputs include: -*@li x: A Tensor. Must be one of the following types: float, double. +*@li grad: A Tensor. Must be one of the following types: float, double. gradient propagated to the SparseSegmentMean op. *@li indices: A Tensor. Must be one of the following types: int32, int64. indices passed to the corresponding SparseSegmentMean op. @@ -365,7 +365,6 @@ REG_OP(InitData) component of an element of this dataset. *@li output_shapes: A nested structure of TensorShape objects corresponding to each component of an element of this dataset. -*@li output_num:output of nums. *@li channel_name: A string. Default "" . \n *@par Outputs: @@ -539,11 +538,11 @@ REG_OP(NextAfter) *@par Inputs: *One inputs, including: -* input_x: A tensor. Must be one of the following types: +* @li input_x: A tensor. Must be one of the following types: * float16, float32. \n *@par Attributes: -*p: An optional float.Defaults to 2. \n +*@li p: An optional float.Defaults to 2. \n *@par Outputs: *y: A Tensor with the same type and shape of input_x's. \n @@ -561,10 +560,10 @@ REG_OP(Pdist) *@brief Compute element-wise finiteness, return a boolean tensor. *@par Inputs: - *x:A Tensor of type float16, float32, double. + *x:A Tensor. *@par Outputs: - *y:A Tensor. Returns which elements of x are finite + *y:A Tensor. Has the same shape as x. *@par Third-party framework compatibility. *Compatible with tensorflow IsFinite operator. @@ -578,10 +577,10 @@ REG_OP(IsFinite) *@brief Compute element-wise infiniteness, return a boolean tensor. *@par Inputs: - *x:A Tensor of type float16, float32, double. + *x:A Tensor. *@par Outputs: - *y:A Tensor. Has the same shape as x. Returns which elements of x are isinf. + *y:A Tensor. Has the same shape as x. *@par Third-party framework compatibility. *Compatible with tensorflow IsInf operator. @@ -595,11 +594,7 @@ REG_OP(IsInf) *@brief Computes the complex absolute value of a tensor. *@par Inputs: - *x: x of complex numbers, this operation returns a tensor of type - float or double that is the absolute value of each element in x . - -* @par Attributes: -* Tout: representing the output of type. + *x:A Tensor. *@par Outputs: *y:A tensor of type `float` or `double` that is the absolute value of each element in `x`. @@ -617,10 +612,10 @@ REG_OP(ComplexAbs) *@brief Returns which elements of x are NaN. *@par Inputs: - *x:A Tensor of type float16, float32, double. + *x:A Tensor. *@par Outputs: - *y:A Tensor. Has the same shape as x. Returns which elements of x are isnan + *y:A Tensor. Has the same shape as x. *@par Third-party framework compatibility. *Compatible with tensorflow IsNan operator. @@ -634,10 +629,7 @@ REG_OP(IsNan) *@brief Returns the real part of a complex number. *@par Inputs: - *input:A Tensor. Must have numeric type. - - *@par Attributes: - *Tout: Type of outputs. \n + *input:A Tensor. *@par Outputs: *output:A Tensor. Has the same shape as input. @@ -678,8 +670,7 @@ REG_OP(Conj) *@li weight: A Tensor dtype of float32 . \n *@par Attributes: -*@li reduction: An optional attribute. Defaults to "mean" . -*@li ignore_index:An optional attribute.Defaults to -100 . \n +*reduction: An optional attribute. Defaults to "mean" . \n *@par Outputs: *@li y: A Tensor dtype of float32. @@ -709,8 +700,7 @@ REG_OP(NLLLoss) *@li total_weight:A Tensor dtype of float32 . \n *@par Attributes: -*@li reduction: An optional attribute. Defaults to "mean" . -*@li ignore_index:An optional attribute.Defaults to -100 . \n +*reduction: An optional attribute. Defaults to "mean" . \n *@par Outputs: *x_grad: A Tensor. Must be the following type: float32 . \n @@ -730,24 +720,24 @@ REG_OP(NLLLossGrad) .OP_END_FACTORY_REG(NLLLossGrad) /** -*@brief IFMR(Input Feature Map Reconstruction). \n +*@brief The ifmr . \n *@par Inputs: -*@li data: A Tensor of feature map. -*@li data_min: A Tensor of min value of feature map. -*@li data_max: A Tensor of max value of feature map. -*@li cumsum: A Tensor of cumsum bin of data . \n +*@li data:A Tensor of feature map +*@li data_min:A Tensor of min value of feature map. +*@li data_max:A Tensor of max value of feature map. +*@li cumsum:A Tensor of cumsum bin of data . \n *@par Attributes: -*@li min_percentile: min init percentile. -*@li max_percentile: max init percentile. -*@li search_range: search range. -*@li search_step: step size of searching. -*@li with_offset: whether using offset . \n +*min_percentile: min init percentile. +*max_percentile: max init percentile. +*search_range: search range. +*search_step: step size of searching. +*with_offset: whether using offset . \n *@par Outputs: -*@li scale: optimal scale. -*@li offset: optimal offset . \n +*scale: optimal scale. +*offset: optimal offset . \n *@par Third-party framework compatibility *Compatible with mindspore @@ -768,16 +758,16 @@ REG_OP(IFMR) .OP_END_FACTORY_REG(IFMR) /** -*@brief Weights Adaptive Range Quantization. \n +*@brief weights adaptive range quantization. \n *@par Inputs: -*@li w: A Tensor of weights. \n -*@li w_min: A Tensor of weights reduce_min. \n -*@li w_max: A Tensor of weights reduce_max. \n +*@li w:A Tensor of weights. \n +*@li w_min:A Tensor of weights reduce_min. \n +*@li w_max:A Tensor of weights reduce_max. \n *@par Attributes: -*@li num_bits: the bits num used for quantize. -*@li offset_flag: whether using offset. \n +*num_bits: the bits num used for quantize. +*offset_flag: whether using offset. \n *@par Outputs: *y: fake quantized weights. \n @@ -799,22 +789,22 @@ REG_OP(WtsARQ) .OP_END_FACTORY_REG(WtsARQ) /** -*@brief Activations Universal Linear Quantization. \n +*@brief The acts_ulq. \n *@par Inputs: -*@li x: A Tensor of feature map. -*@li clamp _min: A Tensor of min clamp value of feature map. -*@li clamp _max: A Tensor of max clamp value of feature map. +*@li x:A Tensor of feature map +*@li clamp _min:A Tensor of min clamp value of feature map. +*@li clamp _max:A Tensor of max clamp value of feature map. *@par Attributes: -*@li fixed_min: fix min to zero. -*@li num_bits: quant bits. \n +*fixed_min: fix min to zero. +*num_bits: quant bits. \n *@par Outputs: -*@li y: output fake quant feature map. -*@li clamp_min_mask: where x > clamp_min. -*@li clamp_min_mask: where x < clamp_max. -*@li x_clamped_loss: clamp loss. \n +*y: output fake quant feature map. +*clamp_min_mask: where x > clamp_min +*clamp_min_mask: where x < clamp_max +*x_clamped_loss: clamp loss. \n *@par Third-party framework compatibility *Compatible with mindspore @@ -836,12 +826,12 @@ REG_OP(ActsULQ) .OP_END_FACTORY_REG(ActsULQ) /** -*@brief The gradient of Activations Universal Linear Quantization. \n +*@brief The acts_ulq_input_grad. \n *@par Inputs: -*@li y_grad: A Tensor of gradient. -*@li clamp_min_mask: A Tensor of boolean mask indicating whether an additional one is needed'. -*@li clamp_max_mask: A Tensor of boolean mask indicating whether an additional one is needed'. +*@li y_grad: A Tensor of gradient +*@li clamp_min_mask: A Tensor of boolean mask indicating whether an additional one is needed' +*@li clamp_max_mask: A Tensor of boolean mask indicating whether an additional one is needed' *@par Outputs: *x_grapd: The gradient of inpust. \n @@ -861,10 +851,10 @@ REG_OP(ActsULQInputGrad) .OP_END_FACTORY_REG(ActsULQInputGrad) /** -*@brief The gradient of Activations Universal Linear Quantization clamp max. \n +*@brief The act_ulq_clamp_max_grad. \n *@par Inputs: -*@li y_grad: A Tensor of gradient. +*@li y_grad: A Tensor of gradient *@li clamp_max_mask: A Tensor of boolean mask indicating whether an additional one is needed. *@li x_clamped_loss: A Tensor of gradient. \n @@ -886,10 +876,10 @@ REG_OP(ActULQClampMaxGrad) .OP_END_FACTORY_REG(ActULQClampMaxGrad) /** -*@brief The gradient of Activations Universal Linear Quantization clamp min. \n +*@brief The act_ulq_clamp_min_grad. \n *@par Inputs: -*@li y_grad: A Tensor of gradient. +*@li y_grad: A Tensor of gradient *@li clamp_min_mask: A Tensor of boolean mask indicating whether an additional one is needed. *@li x_clamped_loss: A Tensor of gradient. \n @@ -914,7 +904,7 @@ REG_OP(ActULQClampMinGrad) * @brief Computes Lp norm. * @par Inputs: -* x: An ND tensor of type float16, float32. \n +* @li x: An ND tensor of type float16, float32. \n * * @par Attributes: * @li p: Int, "inf" or "-inf", default value is 2. @@ -923,7 +913,7 @@ REG_OP(ActULQClampMinGrad) * @li epsilon: Float, default is 1e-12. \n * @par Outputs: -* y: An ND tensor of type float16, float32. The shape of y is depending +* @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 @@ -942,13 +932,11 @@ REG_OP(LpNorm) * @brief get complex. * @par Inputs: -* @li real: An ND tensor of type float32 double, representing the real part of a complex number. -* @li imag: An ND tensor of type float32 double, representing the imaginary part of a complex number. \n +* @li real: An ND tensor of type float32. double +* @li imag: An ND tensor of type float32. double \n * -* @par Attributes: -* Tout: representing the output of type. * @par Outputs: -* out: An ND tensor of type complex64, complex128 \n +* @li out: An ND tensor of type complex64, complex128 \n */ REG_OP(Complex) .INPUT(real, TensorType({DT_FLOAT, DT_DOUBLE})) @@ -961,13 +949,10 @@ REG_OP(Complex) * @brief deal complex. * @par Inputs: -* input: An ND tensor of type complex64, complex128 \n - -* @par Attributes: -* Tout: representing the output of type. - +* @li input: An ND tensor of type complex64, complex128 \n +* * @par Outputs: -* output: An ND tensor of type float32. double \n +* @li output: An ND tensor of type float32. double \n */ REG_OP(Imag) .INPUT(input, TensorType({DT_COMPLEX64, DT_COMPLEX128})) @@ -1003,7 +988,7 @@ REG_OP(Angle) * float16, float32. \n *@par Attributes: -* reduction: Specifies the reduction to apply to the output: +* @li reduction: Specifies the reduction to apply to the output: * 'none' | 'mean' | 'sum'. Default: 'mean'. \n *@par Outputs: diff --git a/third_party/fwkacllib/inc/ops/matrix_calculation_ops.h b/third_party/fwkacllib/inc/ops/matrix_calculation_ops.h index 81c6a29e..b317be37 100644 --- a/third_party/fwkacllib/inc/ops/matrix_calculation_ops.h +++ b/third_party/fwkacllib/inc/ops/matrix_calculation_ops.h @@ -61,28 +61,21 @@ REG_OP(MatMul) *@brief Multiplies matrix "a" by matrix "b", producing "a * b" . \n *@par Inputs: -*Four inputs, including: -* @li x1: A matrix Tensor. 2D. Must be one of the following types: float32, - float16, int32, int8. Has format [ND, NHWC, FRACTAL_NZ]. -* @li x2: A matrix Tensor. 2D. Must be one of the following types: float32, - float16, int32, int8. Has format [ND, NHWC, FRACTAL_NZ]. -* @li bias: A 1D Tensor. Must be one of the following types: float32, - float16, int32. Has format [ND, NHWC]. -* @li offset_w: A Optional 1D Tensor for quantized inference. Type is int8. - Reserved. \n +*Two inputs, including: +* @li x1: A matrix Tensor. 2D. Must be one of the following types: float16, +* float32, int32. Has format [ND, NHWC, FRACTAL_NZ]. +* @li x2: A matrix Tensor. 2D. Must be one of the following types: float16, +* float32, int32. Has format [ND, NHWC, FRACTAL_NZ]. +* @li bias: A 1D Tensor. Must be one of the following types: float16, +* float32, int32. Has format [ND, NHWC] . \n *@par Attributes: -* @li transpose_x1: A bool. If True, changes the shape of "x1" from [K, M] to - [M, K]. -* @li transpose_x2: A bool. If True, changes the shape of "x2" from [N, K] to -[K, N]. -* @li offset_x: An optional integer for quantized MatMulV2. -* The negative offset added to the input x1 for int8 type. Ensure offset_x - within the effective range of int8 [-128, 127]. Defaults to "0". \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: float32, - float16, int32. Has format [ND, NHWC, FRACTAL_NZ]. \n +*y: The result matrix Tensor. 2D. Must be one of the following types: float16, +* float32, int32. Has format [ND, NHWC, FRACTAL_NZ] . \n *@par Third-party framework compatibility * Compatible with the TensorFlow operator BatchMatmul. @@ -102,27 +95,19 @@ REG_OP(MatMulV2) *@brief Multiplies matrix "a" by matrix "b", producing "a * b" . \n *@par Inputs: -*Five inputs, including: +*Two inputs, including: * @li x1: A matrix Tensor. 2D. Must be one of the following types: int8. * @li x2: A matrix Tensor. 2D. Must be one of the following types: int8. * @li compress_index: A compress index matrix of type int8. -* @li bias: An optional Tensor. 1D. Must be one of the following types: int32, - float16. -* @li offset_w: An optional matrix Tensor. 2D. Must be one of the following - types: int8. \n +* @li bias: A 1D Tensor. Must be one of the following types: int32, float16. *@par Attributes: -*@li transpose_x1: A bool. If True, changes the shape of "x1" from [K, M] to - [M, K]. -*@li transpose_x2: A bool. If True, changes the shape of "x2" from [N, K] to - [K, N]. -*@li offset_x: An optional integer for quantized MatMulV2Compress. -*The negative offset added to the input x1 for int8 type. Ensure offset_x - within the effective range of int8 [-128, 127]. Defaults to "0". \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: int32, -* float16. \n +*y: The result matrix Tensor. 2D. Must be one of the following types: float16, +* int32. \n */ REG_OP(MatMulV2Compress) @@ -503,13 +488,13 @@ REG_OP(ScatterElements) *@par Inputs: * Three inputs, including: -*@li var: An ND Tensor . +*@li var: An ND Tensor . \n *Must be one of the following types: float16, float32, int32, int8, uint8 *@li indices: An ND Tensor of type int32 or int64 -*@li updates: An Tensor. format:NCHW, NHWC . +*@li updates: An Tensor. format:NCHW, NHWC . \n *Must be one of the following types: float16, float32, int32, int8, uint8 @@ -532,61 +517,6 @@ REG_OP(ScatterAdd) .OP_END_FACTORY_REG(ScatterAdd) /** -*@brief Use a scalar to modify the tensor. \n - -*@par Inputs: -*inputs, including: -*@li index: An ND Tensor . \n - -*Must be one of the following types: float16, float32, int32, int8, uint8 - -*@par Attributes: -* dim : the axis along which to index . -* value : the source element(s) to scatter . \n - -*@par Outputs: -*y: A Tensor. Has the same type and format as input "index" . \n - -*@par Third-party framework compatibility -* Compatible with the Pytorch operator ScatterScalar. -*/ -REG_OP(ScatterScalar) - .INPUT(index, 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(dim, Int) - .REQUIRED_ATTR(value, Float) - .OP_END_FACTORY_REG(ScatterScalar) - -/** -*@brief Use a tensor to modify the tensor . \n - -*@par Inputs: -* Two inputs, including: -*@li index: An ND Tensor . \n - -*Must be one of the following types: float16, float32, int32, int8, uint8 - -*@li src: An ND Tensor . \n - -*Must be one of the following types: float16, float32, int32, int8, uint8 - -*@par Attributes: -* dim : the axis along which to index . \n - -*@par Outputs: -*y: A Tensor. Has the same type and format as input "index" . \n - -*@par Third-party framework compatibility -* Compatible with the Pytorch operator ScatterTensor. -*/ -REG_OP(ScatterTensor) - .INPUT(index, TensorType({DT_FLOAT16,DT_FLOAT,DT_INT32,DT_INT8,DT_UINT8})) - .INPUT(src, 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(dim, Int) - .OP_END_FACTORY_REG(ScatterTensor) - -/** *@brief Divides a variable reference by sparse updates . \n *@par Inputs: @@ -600,7 +530,7 @@ REG_OP(ScatterTensor) *Must be one of the following types: float16, float, int32, int8, uint8 *@par Attributes: -*use_locking: An optional bool. Defaults to "False". If "True", +*@li use_locking: An optional bool. Defaults to "False". If "True", * the operation will be protected by a lock . \n *@par Outputs: @@ -822,12 +752,10 @@ REG_OP(DiagPart) *@par Attributes: *@li num_output: Reserved. -*@li transpose: A bool, specifying weight whether to transpose input w, either "true" or "false". Defaults to "false". +*@li transpose: A bool, specifying weight whether to transpose, either "true" or "false". Defaults to "false". *@li axis: Optional. A int, 1 or 2, specifying which dimension the input "K" starts from. Defaults to 1. * The product of the subsequent dimensions starting form first dimension or the second dimension is "K". -*@li offset_x: An optional integer for quantized FullyConnection. -*The negative offset added to the input image for int8 type. Ensure offset_x within the -*effective range of int8 [-128, 127]. Defaults to "0". \n +*@li offset_x: Reserved . \n *@par Outputs: *y: The result tensor of type float16, int32, float32 . \n @@ -851,34 +779,27 @@ REG_OP(FullyConnection) .OP_END_FACTORY_REG(FullyConnection) /** -*@brief Also known as a "fully-connected-compress" layer, computes an inner -product with a set of learned weights, and (optionally) adds biases . \n +*@brief Also known as a "fully-connected-compress" layer, computes an inner product with a set of learned weights, and (optionally) adds biases . \n *@par Inputs: -* Five inputs, including: +* Four inputs, including: *@li x: A Tensor of type uint8, int8. -*@li w: A weight matrix of type int8. -*@li compress_index: A compress index matrix of type int8. -*@li b: A Tensor of type int32. -*@li offset_w: A Tensor of type int8. +*@li w: A weight matrix of type int8, int8. +*@li w: A compress index matrix of type int8, int8. +*@li b: A Tensor of type float16, int32, int32. +*@li offset_w: A Tensor of type int8.i *@par Attributes: -*@li num_output: A int, specifying the number of outputs. -*@li transpose: A bool, specifying whether to transpose input w, either "true" - or "false". Defaults to "false". -*@li axis: Optional. A int, 1 or 2, specifying which dimension the input "K" -starts from. Defaults to "1". -* The product of the subsequent dimensions starting form first dimension or the -second dimension is "K". -*@li offset_x: An optional integer for quantized FullyConnectionCompress. -*The negative offset added to the input image for int8 type. Ensure offset_x -within the effective range of int8 [-128, 127]. Defaults to "0". \n +*@li num_output: Reserved. +*@li transpose: A bool, specifying whether to transpose, either "true" or "false". Defaults to "false". +*@li axis: Reserved. +*@li offset_x: Reserved . \n *@par Outputs: -*y: The result tensor of type int32. \n +*y: The result tensor of type int32 . \n *@par Third-party framework compatibility -* Compatible with the Caffe operator InnerProduct. \n +* Compatible with the Caffe operator InnerProduct . \n *@par Quantization supported or not * Yes @@ -1004,13 +925,13 @@ REG_OP(ScatterMin) *@par Inputs: * Three inputs, including: -*@li var: An ND Tensor . +*@li var: An ND Tensor . \n *Must be one of the following types: float16, float, int32, int8, uint8 *@li indices: An NCHW, NHWC, or ND Tensor . \n *Must be one of the following types: int32 or int64 -*@li updates: An NCHW, NHWC, or ND Tensor . +*@li updates: An NCHW, NHWC, or ND Tensor . \n *Must be one of the following types: float16, float, int32, int8, uint8 @@ -1037,13 +958,13 @@ REG_OP(ScatterMax) *@par Inputs: * Three inputs, including: -*@li var: An ND Tensor . +*@li var: An ND Tensor . \n *Must be one of the following types: float16, float, int32, int8, uint8 *@li indices: An ND Tensor . \n *Must be one of the following types: int32 or int64 -*@li updates: An ND Tensor . +*@li updates: An ND Tensor . \n *Must be one of the following types: float16, float, int32, int8, uint8 @@ -1192,46 +1113,14 @@ REG_OP(IndexAdd) .OP_END_FACTORY_REG(IndexAdd) /** -* @brief According to the index number of indexes, replace the value -*corresponding to X1 with the value in x2. - -* @par Inputs: -* Three inputs, including: -* @li x1: A Tensor. Must be one of the following types: -* float16, float32, int32, int8, uint8. -* @li x2: A Tensor of the same type as "x1". -* @li indices: A Tensor of the indices, type should be int32. - -* @par Attributes: -* @li accumulate: Does it support self accumulation.Defaults to 0. - -* @par Outputs: -* @li y: A Tensor. Same as input "x1". - -* @par Third-party framework compatibility -* Compatible with the Pytorch operator index_put. - -* @par Restrictions: -* Warning:THIS FUNCTION IS EXPERIMENTAL. Please do not use. -*/ -REG_OP(IndexPut) - .INPUT(x1, TensorType({DT_INT64, DT_INT32, DT_INT8, DT_UINT8, DT_FLOAT32, DT_FLOAT16})) - .INPUT(x2, TensorType({DT_INT64, DT_INT32, DT_INT8, DT_UINT8, DT_FLOAT32, DT_FLOAT16})) - .INPUT(indices, TensorType({DT_INT64, DT_INT32})) - .OUTPUT(y, TensorType({DT_INT64, DT_INT32, DT_INT8, DT_UINT8, DT_FLOAT32, DT_FLOAT16})) - .ATTR(accumulate, Int, 0) - .OP_END_FACTORY_REG(IndexPut) - -/** *@brief: Returns the upper triangular part of a matrix (2-D tensor) or batch of matrices input \n *@par Inputs: -*x: A Tensor. Must be one of the following types: -*float16, float32, double, int32, uint8, int16, int8, complex64, int64, -*qint8, quint8, qint32, uint16, complex128, uint32, uint64. \n - -*@par Attributes: -*diagonal: An optional attribute indicates the diagonal to consider. \n +* Two inputs, including: +*@li x: A Tensor. Must be one of the following types: +* float16, float32, double, int32, uint8, int16, int8, complex64, int64, +* qint8, quint8, qint32, uint16, complex128, uint32, uint64. +*@li diagonal:(int, optional) – the diagonal to consider。\n *@par Outputs: *y: A Tensor. Has the same type as "x" . \n @@ -1249,12 +1138,11 @@ REG_OP(Triu) *@brief: Returns the upper triangular part of a matrix (2-D tensor) or batch of matrices input \n *@par Inputs: -*x: A Tensor. Must be one of the following types: -*float16, float32, double, int32, uint8, int16, int8, complex64, int64, -*qint8, quint8, qint32, uint16, complex128, uint32, uint64. \n - -*@par Attributes: -*diagonal: An optional attribute indicates the diagonal to consider. \n +* Two inputs, including: +*@li x: A Tensor. Must be one of the following types: +* float16, float32, double, int32, uint8, int16, int8, complex64, int64, +* qint8, quint8, qint32, uint16, complex128, uint32, uint64. +*@li diagonal:(int, optional) – the diagonal to consider。\n *@par Outputs: *y: A Tensor. Has the same type as "x" . \n @@ -1325,30 +1213,6 @@ REG_OP(Eye) .ATTR(dtype, Int, 0) .OP_END_FACTORY_REG(Eye) -/** -*@brief: Fill diagonal of at least 2 dimension tensors with value . \n - -*@par Inputs: -*x: A Tensor. Must be one of the following types: -* float32, int32, int64 . \n - -*@par Outputs: -*y: A Tensor. Has the same type as "x" . \n - -*@par Attributes: -*fill_value:The value to fill in -*wrap: An optional bool. Defaults to "False". If "True", Use recursive fill. \n - -*@par Third-party framework compatibility -* Compatible with the Pytorch operator FillDiagonal. -*/ -REG_OP(FillDiagonal) - .INPUT(x, TensorType({DT_FLOAT, DT_INT32, DT_INT64})) - .OUTPUT(y, TensorType({DT_FLOAT, DT_INT32, DT_INT64})) - .REQUIRED_ATTR(fill_value, Float) - .ATTR(wrap, Bool, false) - .OP_END_FACTORY_REG(FillDiagonal) - } // namespace ge #endif // OPS_BUILT_IN_OP_PROTO_INC_MATRIX_CALCULATION_OPS_H_ diff --git a/third_party/fwkacllib/inc/ops/nn_calculation_ops.h b/third_party/fwkacllib/inc/ops/nn_calculation_ops.h index a55cebe2..98473c65 100644 --- a/third_party/fwkacllib/inc/ops/nn_calculation_ops.h +++ b/third_party/fwkacllib/inc/ops/nn_calculation_ops.h @@ -195,7 +195,7 @@ REG_OP(DepthwiseConv2DBackpropInput) .INPUT(input_size, TensorType({DT_INT32, DT_INT64})) .INPUT(filter, TensorType({DT_FLOAT16})) .INPUT(out_backprop, TensorType({DT_FLOAT16})) - .OUTPUT(input_grad, TensorType({DT_FLOAT16, DT_FLOAT32})) + .OUTPUT(input_grad, TensorType({DT_FLOAT16})) .REQUIRED_ATTR(strides, ListInt) .ATTR(dilations, ListInt, {1, 1, 1, 1}) .REQUIRED_ATTR(pads, ListInt) @@ -255,7 +255,7 @@ REG_OP(DepthwiseConv2DBackpropInput) REG_OP(DepthwiseConv2DBackpropInputD) .INPUT(filter, TensorType({DT_FLOAT16})) .INPUT(out_backprop, TensorType({DT_FLOAT16})) - .OUTPUT(input_grad, TensorType({DT_FLOAT16, DT_FLOAT32})) + .OUTPUT(input_grad, TensorType({DT_FLOAT16})) .REQUIRED_ATTR(input_size, ListInt) .REQUIRED_ATTR(strides, ListInt) .ATTR(dilations, ListInt, {1, 1, 1, 1}) @@ -367,19 +367,19 @@ REG_OP(BiasAddGrad) * Gradients with respect to the output of the convolution. *\n *\n - * The following are the supported data types and data formats:\n - *\n - | Tensor | out_bckprop | filter | y\n - ------------|-------------|---------|--------\n - | Data Type | float16 | float16 | float16\n - | |-------------|---------|--------\n - | | float32 | float32 | float32\n - | |-------------|---------|--------\n - | | float64 | float64 | float64\n - ------------|-------------|---------|--------\n - | Format | NCHW | NCHW | NCHW\n - | | NHWC | HWCN | NHWC\n - *\n + * 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 @@ -398,37 +398,36 @@ REG_OP(BiasAddGrad) * "NHWC". Specify the data format of the input and output data. *\n *\n - * The following value range restrictions must be met:\n - *\n - | Name | Field | Scope\n - -------------------|----------|--------------\n - | input_size | H | [1, 200000]\n - | | W | [1, 4096]\n - -------------------|----------|--------------\n - | Filter | H | [1, 255]\n - | | W | [1, 255]\n - -------------------|----------|--------------\n - | out_backprop | H*strideH| [1, 200000]\n - | | W*strideW| [1, 4096]\n - -------------------|----------|--------------\n - | y(fmap) | H | [1, 200000]\n - | | W | [1, 4096]\n - -------------------|----------|--------------\n - | Stride | H | [1, 63]\n - | | W | [1, 63]\n - -------------------|----------|--------------\n - | Padding | Top | [0, 255]\n - | | Bottom | [0, 255]\n - | | Left | [0, 255]\n - | | Right | [0, 255]\n - -------------------|----------|--------------\n - | Dilation | H | [1, 255]\n - | | W | [1, 255]\n - *\n + * The following value range restrictions must be met: +*@verbatim + | Name | Field | Scope + -------------------|----------|-------------- + | input_size | H | [1, 4096] + | | W | [1, 4096] + -------------------|----------|-------------- + | Filter | H | [1, 255] + | | W | [1, 255] + -------------------|----------|-------------- + | out_backprop | H*strideH| [1, 4096] + | | W*strideW| [1, 4096] + -------------------|----------|-------------- + | y(fmap) | H | [1, 4096] + | | W | [1, 4096] + -------------------|----------|-------------- + | Stride | H | [1, 63] + | | W | [1, 63] + -------------------|----------|-------------- + | Padding | Top | [0, 255] + | | Bottom | [0, 255] + | | Left | [0, 255] + | | Right | [0, 255] + -------------------|----------|-------------- + | Dilation | H | [1, 255] + | | W | [1, 255] +@endverbatim * In Ascend910, fmap or out_backprop's H and W not support 1 when * fmap_h + pad_top + pad_bottom != (filter_height - 1) * dilation_h + 1 - * and filter_width > fmap_width * If filter_h = 1 and filter_w = 1, out_backprop_w * stride_h * stride_w < 4096 *\n * @@ -497,7 +496,7 @@ REG_OP(Conv2DBackpropInput) REG_OP(Conv2DBackpropInputD) .INPUT(filter, TensorType({DT_FLOAT16, DT_INT8})) .INPUT(out_backprop, TensorType({DT_FLOAT16, DT_INT8})) - .OUTPUT(y, TensorType({DT_FLOAT16, DT_INT32, DT_FLOAT32})) + .OUTPUT(y, TensorType({DT_FLOAT16, DT_INT32})) .REQUIRED_ATTR(input_size, ListInt) .REQUIRED_ATTR(strides, ListInt) .REQUIRED_ATTR(pads, ListInt) @@ -509,7 +508,7 @@ REG_OP(Conv2DBackpropInputD) /** *@brief Computes the Deconvolution with respect to the input. *@par Inputs: - * Two required inputs: + * Three inputs: * @li x: A Tensor of type float16 or int8. 4D with shape * [batch, out_channels, out_height, out_width]. Gradients with respect * to the output of the convolution. @@ -521,16 +520,16 @@ REG_OP(Conv2DBackpropInputD) * Type is int8. Reserved.\n *\n *\n - * The following are the supported data types and data formats:\n - *\n - | Tensor | x | filter | bias | y\n - ------------|---------|---------|---------|--------\n - | Data Type | float16 | float16 | float16 | float16\n - | |---------|---------|---------|--------\n - | | int8 | int8 | int32 | int32\n - ------------|---------|---------|---------|--------\n - | Format | NCHW | NCHW | ND | NCHW\n - *\n + * 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 * @@ -551,35 +550,35 @@ REG_OP(Conv2DBackpropInputD) * within the effective range of int8 [-128, 127]. Defaults to "0". *\n *\n - * The following value range restrictions must be met:\n - *\n - | Name | Field | Scope\n - -------------------|----------|--------------\n - | x (out_backprop) | H*strideH| [1, 200000]\n - | | W*strideW| [1, 4096]\n - -------------------|----------|--------------\n - | Filter | H | [1, 255]\n - | | W | [1, 255]\n - -------------------|----------|--------------\n - | y (fmap) | H | [1, 200000]\n - | | W | [1, 4096]\n - -------------------|----------|--------------\n - | Stride | H | [1, 63]\n - | | W | [1, 63]\n - -------------------|----------|--------------\n - | Padding | Top | [0, 255]\n - | | Bottom | [0, 255]\n - | | Left | [0, 255]\n - | | Right | [0, 255]\n - -------------------|----------|--------------\n - | Dilation | H | [1, 255]\n - | | W | [1, 255]\n - -------------------|----------|--------------\n - | Offset_x | | [-128, 127]\n - *\n + * The following value range restrictions must be met: +*@verbatim + | Name | Field | Scope + -------------------|----------|-------------- + | x (out_backprop) | H*strideH| [1, 4096] + | | W*strideW| [1, 4096] + -------------------|----------|-------------- + | Filter | H | [1, 255] + | | W | [1, 255] + -------------------|----------|-------------- + | y (fmap) | H | [1, 4096] + | | W | [1, 4096] + -------------------|----------|-------------- + | Stride | H | [1, 63] + | | W | [1, 63] + -------------------|----------|-------------- + | Padding | Top | [0, 255] + | | Bottom | [0, 255] + | | Left | [0, 255] + | | Right | [0, 255] + -------------------|----------|-------------- + | Dilation | H | [1, 255] + | | W | [1, 255] + -------------------|----------|-------------- + | Offset_x | | [-128, 127] + +@endverbatim * In Ascend910, fmap or out_backprop's H and W not support 1 when * fmap_h + pad_top + pad_bottom != (filter_height - 1) * dilation_h + 1 - * and filter_width > fmap_width * If filter_h = 1 and filter_w = 1, out_backprop_w * stride_h * stride_w < 4096 *\n * @@ -629,19 +628,19 @@ REG_OP(Deconvolution) * convolution. *\n *\n - * The following are the supported data types and data formats:\n - *\n - | Tensor | x | out_backprop | y\n - ------------|---------|--------------|---------\n - | Data Type | float16 | float16 | float16\n - | |---------|--------------|---------\n - | | float32 | float32 | float32\n - | |---------|--------------|---------\n - | | float64 | float64 | float64\n - |-----------|---------|--------------|---------\n - | Format | NCHW | NCHW | NCHW\n - | | NHWC | NHWC | HWCN\n - *\n + * 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 @@ -659,34 +658,39 @@ REG_OP(Deconvolution) * @li data_format: An optional string from: "NHWC", "NCHW". Defaults to * "NHWC". Specify the data format of the input and output data. *\n +*\n +* The following value range restrictions must be met: +*@verbatim + | Name | Field | Scope + -------------------|----------|-------------- + | x(fmap) | H | [1, 4096] + | | W | [1, 4096] + -------------------|----------|-------------- + | Filter Size | H | [1, 255] + | | W | [1, 255] + -------------------|----------|-------------- + | out_backprop | H | [1, 4096] + | | W | [1, 4096] + -------------------|----------|-------------- + | y | H | [1, 4096] + | | W | [1, 4096] + -------------------|----------|-------------- + | Stride | H | [1, 63] + | | W | [1, 63] + -------------------|----------|-------------- + | Padding | Top | [0, 255] + | | Bottom | [0, 255] + | | Left | [0, 255] + | | Right | [0, 255] + -------------------|----------|-------------- + | Dilation | H | [1, 255] + | | W | [1, 255] + +@endverbatim + * In Ascend910, out_backprop's H and W not support 1 when + * fmap_h + pad_top + pad_bottom != (filter_height - 1) * dilation_h + 1 *\n - * The following value range restrictions must be met:\n - *\n - | Name | Field | Scope\n - -------------------|----------|--------------\n - | x(fmap) | H | [1, 200000]\n - | | W | [1, 4096]\n - -------------------|----------|--------------\n - | Filter Size | H | [1, 255]\n - | | W | [1, 255]\n - -------------------|----------|--------------\n - | out_backprop | H | [1, 200000]\n - | | W | [1, 4096]\n - -------------------|----------|--------------\n - | y | H | [1, 200000]\n - | | W | [1, 4096]\n - -------------------|----------|--------------\n - | Stride | H | [1, 63]\n - | | W | [1, 63]\n - -------------------|----------|--------------\n - | Padding | Top | [0, 255]\n - | | Bottom | [0, 255]\n - | | Left | [0, 255]\n - | | Right | [0, 255]\n - -------------------|----------|--------------\n - | Dilation | H | [1, 255]\n - | | W | [1, 255]\n - *\n + * *@par Outputs: * y: A Tensor. Has the same type as x, has the same format as filter_size. *\n @@ -776,16 +780,16 @@ REG_OP(Conv2DBackpropFilterD) *\n *\n * The following are the supported data types and data formats: -*\n -*\n -| Tensor | x | filter | bias | y |\n -| :-------: | :-----: | :-----: | :-----: | :-----: |\n -| Data Type | float16 | float16 | float16 | float16 |\n -| | float32 | float32 | float32 | float32 |\n -| | int8 | int8 | int32 | int32 |\n -| Format | NCHW | NCHW | ND | NCHW |\n -| | NHWC | HWCN | | NHWC |\n -*\n +*@verbatim + | Tensor | x | filter | bias | y + ------------|---------|---------|---------|-------- + | Data Type | float16 | float16 | float16 | float16 + | | float32 | float32 | float32 | float32 + | | int8 | int8 | int32 | int32 + ------------|---------|---------|---------|-------- + | Format | NCHW | NCHW | ND | NCHW + | | NHWC | HWCN | | NHWC +@endverbatim * For float32 type, the actual calculation on the chip is based on * float16. *\n @@ -809,30 +813,35 @@ REG_OP(Conv2DBackpropFilterD) *\n *\n * The following value range restrictions must be met: -*\n -*\n -| Name | Field | Scope |\n -| :--------------: | :------: | :---------: |\n -| Input Image Size | H | [1, 100000] |\n -| | W | [1, 4096] |\n -| Filter Size | H | [1, 255] |\n -| | W | [1, 255] |\n -| Stride | H | [1, 63] |\n -| | W | [1, 63] |\n -| Padding | Top | [0, 255] |\n -| | Bottom | [0, 255] |\n -| | Left | [0, 255] |\n -| | Right | [0, 255] |\n -| Dilation | H | [1, 255] |\n -| | W | [1, 255] |\n -| Offset_x | - | [-128, 127] |\n -*\n +*@verbatim + | Name | Field | Scope + -------------------|----------|-------------- + | Input Image Size | H | [1, 100000] + | | W | [1, 4096] + -------------------|----------|-------------- + | Filter Size | H | [1, 255] + | | W | [1, 255] + -------------------|----------|-------------- + | Stride | H | [1, 63] + | | W | [1, 63] + -------------------|----------|-------------- + | Padding | Top | [0, 255] + | | Bottom | [0, 255] + | | Left | [0, 255] + | | Right | [0, 255] + -------------------|----------|-------------- + | Dilation | H | [1, 255] + | | W | [1, 255] + -------------------|----------|-------------- + | Offset_x | | [-128, 127] + +@endverbatim * The W dimension of the input image supports cases exceeding 4096, but it may * cause compilation errors. *\n * *@par Outputs: -* y: A 4D Tensor of output feature map. Has the same type as "x". With the +*@li y: A 4D Tensor of output feature map. Has the same type as "x". With the * format "NHWC", the data is stored in the order of: [batch, out_height, * out_width, out_channels]. *\n @@ -947,15 +956,16 @@ REG_OP(Conv2DCompress) *\n *\n * The following are the supported data types and data formats: -*\n -*\n -| Tensor | x | filter | offsets | bias | y |\n -| :-------: | :-----: | :-----: | :-----: | :-----: | :-----: |\n -| Data Type | float16 | float16 | float16 | float16 | float16 |\n -| | float32 | float32 | float32 | float32 | float32 |\n -| Format | NCHW | NCHW | NCHW | ND | NCHW |\n -| | NHWC | HWCN | NCHW | | NHWC |\n -*\n +*@verbatim + | Tensor | x | filter | offsets | bias | y + ------------|---------|---------|---------|----------|-------- + | Data Type | float16 | float16 | float16 | float16 | float16 + | |---------|---------|---------|----------|-------- + | | float32 | float32 | float32 | float32 | float32 + ------------|---------|---------|---------|----------|-------- + | Format | NCHW | NCHW | NCHW | ND | NCHW + | | NHWC | HWCN | NHWC | | NHWC +@endverbatim * For float32 type, the actual convolution calculation part on the chip is * based on float16. *\n @@ -982,18 +992,19 @@ REG_OP(Conv2DCompress) *\n *\n * The following value range restrictions must be met: -*\n -*\n -| Name | Field | Scope |\n -| :--------------: | :------: | :-------------------------: |\n -| Input Image Size | H | [1, 100000 / filter_height] |\n -| | W | [1, 4096 / filter_width] |\n -| Filter Size | H | [1, 63] |\n -| | W | [1, 63] |\n +*@verbatim + | Name | Field | Scope + --------------------|--------|---------------------------- + | Input Image Size | H | [1, 100000 / filter_height] + | | W | [1, 4096 / filter_width] + --------------------|--------|---------------------------- + | Filter Size | H | [1, 63] + | | W | [1, 63] +@endverbatim *\n * *@par Outputs: -* y: A 4D Tensor of output feature map. Has the same type as "x". With the +*@li y: A 4D Tensor of output feature map. Has the same type as "x". With the * format "NHWC", the data is stored in the order of: [batch, out_height, * out_width, out_channels]. *\n @@ -1031,38 +1042,41 @@ REG_OP(DeformableConv2D) /** *@brief Computes a 3D convolution given 5D "x" and "filter" tensors. - -*@par Inputs: + *@par Inputs: * @li x: A 5D tensor. Must be one of the following types: float16, * (Currently does not support int8). The format of x is NCDHW or NDHWC. * @li filter: A 5D tensor of the same type as "x". * (Currently does not support int8). - * The format is NCDHW, NDHWC or DHWCN. - * @li bias: Optional. An 1D tensor of the same type as "x". - * @li offset_w: Optional. An 1D tensor for quantized deconvolution. Reserved. \n + * The format is NCDHW, NDHWC or DHWCN . \n -*@par Attributes: - * @li strides: Required. A list of 5 integers. Specifies the stride of the sliding window +*@par Optional input: + * @li bias: An optional 1D tensor of the same type as "x". + * @li offset_w: An optional 1D tensor for quantized deconvolution. Reserved . \n + +*@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". - * @li pads: Required. A list of 6 integers. + * @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 dilations: Optional. A list of 5 integers. Specifies the dilation factor for each - * dimension of "x". - * @li groups: Optional. Number of blocked connections from input channels to output + * tail, top, bottom, left and right . \n + +*@par Attributes: + * @li groups: Number of blocked connections from input channels to output * channels. - * @li data_format: Optional. An string from: "NDHWC", "NCDHW". + * @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". * The N, C and D dimensions must be 1. Has the same format as "x". - * @li offset_x: Optional. An int. Input offset, used for quantized inference. - * Defaults to 0. Reserved. \n + * @li offset_x: An optional int. Input offset, used for quantized inference. + * Defaults to 0. Reserved . \n *@par Outputs: - * y: A Tensor. Has the same type and data format as "x". \n + *y: A Tensor. Has the same type and data format as "x". \n *@attention Constraints: - * The image size after padding is greater than the filter size. \n + *The image size after padding is greater than the filter size . \n *@par Third-party framework compatibility * @li Compatible with the TensorFlow operator conv3d. @@ -1071,9 +1085,9 @@ REG_OP(DeformableConv2D) REG_OP(Conv3D) .INPUT(x, TensorType({DT_FLOAT16})) .INPUT(filter, TensorType({DT_FLOAT16})) - .OPTIONAL_INPUT(bias, TensorType({DT_FLOAT16, DT_FLOAT32})) + .OPTIONAL_INPUT(bias, TensorType({DT_FLOAT16})) .OPTIONAL_INPUT(offset_w, TensorType({DT_INT8})) - .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT32})) + .OUTPUT(y, TensorType({DT_FLOAT16})) .REQUIRED_ATTR(strides, ListInt) .REQUIRED_ATTR(pads, ListInt) .ATTR(dilations, ListInt, {1, 1, 1, 1, 1}) @@ -1085,8 +1099,8 @@ REG_OP(Conv3D) /** *@brief Computes the gradients of convolution 3d with respect to the input. - *@par Inputs: + * Three inputs: * @li input_size: A Tensor of type int32, int64. An integer vector representing * the shape of input, where input is a 5-D tensor * [batch, depth, height, width, channels] or @@ -1096,25 +1110,28 @@ REG_OP(Conv3D) * @li out_backprop: A Tensor. Must have the same type as filter. * 5-D with shape [batch, depth, out_height, out_width, out_channels] * or [batch, out_channels, depth, out_height, out_width]. Gradients with - * respect to the output of the convolution. \n + * respect to the output of the convolution . \n -*@par Attributes: - * @li strides: Required. A list of 5 integers. Specifies the stride of the sliding window +*@par Required Attributes: + * @li strides: A list of 5 integers. Specifies the stride of the sliding window * for each dimension of "out_backprop". * The N and C dimensions must be 1. Has the same format as "out_backprop". - * @li pads: Required. A list of 6 integers. + * @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 dilations: Optional. A tuple/list of 5 integers, The dilation factor for each + * tail, top, bottom, left and right . \n + +*@par Attributes: + * Three attributes: + * @li groups: Number of blocked connections from input channels to output + * channels. + * @li data_format: An optional string from: "NDHWC", "NCDHW". + * Defaults to "NDHWC". Specify the data format of the input and output data. + * @li dilations: A tuple/list of 5 integers, The dilation factor for each * dimension of the input. * The N, C and D dimensions must be 1. Has the same format as "out_backprop". - * @li groups: Optional. Number of blocked connections from input channels to output - * channels. - * @li data_format: Optional. An string from: "NDHWC", "NCDHW". - * Defaults to "NDHWC". Specify the data format of the input and output data. \n *@par Outputs: - * y: A Tensor. Has the same type as filter,and has same format as "input_size". \n + * 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 @@ -1133,44 +1150,45 @@ REG_OP(Conv3DBackpropInput) /** *@brief Computes the gradients of convolution 3d with respect to the input. - *@par Inputs: + * Two inputs: * @li filter: A Tensor whose type is float16. The format of filter is NCDHW, * NDHWC or DHWCN. * @li out_backprop: A Tensor. Must have the same type as filter. The format is - * NDHWC or NCDHW. \n + * NDHWC or NCDHW. \n -*@par Attributes: - * @li input_size: Required. A tuple/list of type int32, int64. An integer vector - * representing the shape of input, where input is a 5-D tensor - * [batch, depth, height, width, channels] or - * [batch, channels, depth, height, width]. - * @li strides: Required. A list of 5 integers. Specifies the stride of the sliding window +*@par Required Attributes: + * @li strides: A list of 5 integers. Specifies the stride of the sliding window * for each dimension of "out_backprop". * The N and C dimensions must be 1. Has the same format as "out_backprop". - * @li pads: Required. A list of 6 integers. Supports only padding along the D, H and W + * @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 dilations: Optional. A tuple/list of 5 integers, The dilation factor for each + * @li input_size: A tuple/list of type int32, int64. An integer vector + * representing the shape of input, where input is a 5-D tensor + * [batch, depth, height, width, channels] or + * [batch, channels, depth, height, width] . \n + +*@par Attributes: + * Three attributes: + * @li groups: Number of blocked connections from input channels to output + * channels. + * @li data_format: An optional string from: "NDHWC", "NCDHW". + * Defaults to "NDHWC". Specify the data format of the input and output data. + * @li dilations: A tuple/list of 5 integers, The dilation factor for each * dimension of input. * The N, C and D dimensions must be 1. Has the same format as "out_backprop". - * @li groups: Optional. Number of blocked connections from input channels to output - * channels. - * @li data_format: Optional. An string from: "NDHWC", "NCDHW". - * Defaults to "NDHWC". Specify the data format of the input and output data. \n - *@par Outputs: - * y: A Tensor. Has the same type and data format as "out_backprop". \n - + * 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. \n + * Compatible with Tensorflow's conv3d_backprop_input *@par Restrictions: - * Warning: THIS FUNCTION IS DEPRECATED. Please use Conv3DBackpropInput instead. +* Warning: THIS FUNCTION IS DEPRECATED. Please use Conv3DBackpropInput instead. */ REG_OP(Conv3DBackpropInputD) .INPUT(filter, TensorType({DT_FLOAT16})) .INPUT(out_backprop, TensorType({DT_FLOAT16})) - .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT32})) + .OUTPUT(y, TensorType({DT_FLOAT16})) .REQUIRED_ATTR(input_size, ListInt) .REQUIRED_ATTR(strides, ListInt) .REQUIRED_ATTR(pads, ListInt) @@ -1224,8 +1242,8 @@ REG_OP(LSTM) /** *@brief Computes the gradients of convolution3D with respect to the filter - *@par Inputs: + * Three inputs: * @li x: A Tensor. Must be one of the following types: float16, float32. * Currently does not support double. * 5-D with shape [batch, in_depth, in_height, in_width, in_channels] @@ -1240,23 +1258,26 @@ REG_OP(LSTM) * or [batch, out_channels, out_depth, out_height, out_width]. * Gradients with respect to the output of the convolution. \n -*@par Attributes: - * @li strides: Required. A tuple/list of 5 integers. Specifies the stride of the sliding +*@par Required Attributes: + * @li strides: A tuple/list of 5 integers. Specifies the stride of the sliding * window for each dimension of "x". The N and C dimensions must be 1. * Has the same format as "x". - * @li pads: Required. A tuple/list of 6 integers, [front, back, top, bottom, left, right] - * pads on feature map. - * @li dilations: Optional. A tuple/list of 5 integers, The dilation factor for each + * @li pads: A tuple/list of 6 integers, [front, back, top, bottom, left, right] + * pads on feature map . \n + +*@par Attributes: + * Three attributes: + * @li dilations: A tuple/list of 5 integers, The dilation factor for each * dimension of input. * The N, C and D dimensions must be 1. Has the same format as "x". - * @li groups: Optional. Number of blocked connections from input channels to output + * @li groups: Number of blocked connections from input channels to output * channels. - * @li data_format: Optional. An string from: "NDHWC", "NCDHW". - * Defaults to "NDHWC". Specify the data format of the input and output data. \n + * @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" and the format is NDHWC, NCDHW or DHWCN. \n - + * 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 */ @@ -1274,8 +1295,8 @@ REG_OP(Conv3DBackpropFilter) /** *@brief Computes the gradients of convolution with respect to the filter. - *@par Inputs: + * Two inputs: * @li x: A Tensor of type float16. * 5-D with shape [batch, in_depth, in_height, in_width, in_channels] * or [batch, in_channels, in_depth, in_height, in_width]. @@ -1284,34 +1305,37 @@ REG_OP(Conv3DBackpropFilter) * or [batch, out_channels, out_depth, out_height, out_width]. * Gradients with respect to the output of the convolution. \n -*@par Attributes: - * @li filter_size: Required. A tuple/list of type integers. An integer vector +*@par Required Attributes: + * @li filter_size: A tuple/list of type integers. An integer vector * representing the tensor shape of filter, where filter is a 5-D tensor * [filter_depth, filter_height, filter_width, in_channels, out_channels], * [out_channels, filter_depth, filter_height, filter_width, in_channels] * or [out_channels, in_channels, filter_depth, filter_height, filter_width]. - * @li strides: Required. A tuple/list of 5 integers. Specifies the stride of the sliding + * @li strides: A tuple/list of 5 integers. Specifies the stride of the sliding * window for each dimension of "x". * The N and C dimensions must be 1. Has the same format as "x". - * @li pads: Required. A tuple/list of 6 integers, [front, back, top, bottom, left, right] - * pads on feature map. - * @li dilations: Optional. A tuple/list of 5 integers, The dilation factor for each + * @li pads: A tuple/list of 6 integers, [front, back, top, bottom, left, right] + * pads on feature map. \n + +*@par Attributes: + * Three attributes: + * @li dilations: A tuple/list of 5 integers, The dilation factor for each * dimension of input. * The N, C and D dimensions must be 1. Has the same format as "x". - * @li groups: Optional. Number of blocked connections from input channels to output + * @li groups: Number of blocked connections from input channels to output * channels. - * @li data_format: Optional. An optional string from: "NDHWC", "NCDHW". - * Defaults to "NDHWC". Specify the data format of the input and output data. \n + * @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 of type float32 and the format is NDHWC, NCDHW or DHWCN. \n - + * y: A Tensor of type float32 and the format is NDHWC, NCDHW or DHWCN. *@par Third-party framework compatibility - * Compatible with Tensorflow's conv3d_backprop_filter. \n - + * Compatible with Tensorflow's conv3d_backprop_filter *@par Restrictions: - * Warning: THIS FUNCTION IS DEPRECATED. Please use Conv3DBackpropFilter instead. +* Warning: THIS FUNCTION IS DEPRECATED. Please use Conv3DBackpropFilter instead. */ + + REG_OP(Conv3DBackpropFilterD) .INPUT(x, TensorType({DT_FLOAT16})) .INPUT(out_backprop, TensorType({DT_FLOAT16})) @@ -1326,32 +1350,37 @@ REG_OP(Conv3DBackpropFilterD) /** *@brief Computes the transpose of convolution 3d with respect to the input. - *@par Inputs: + * Three inputs: * @li input_size: A Tensor of type int32. An integer vector representing the * shape of input. * @li x: A Tensor of type float16, currently does not support int8. The format * is NDHWC or NCDHW. * @li filter: A Tensor of type float16, currently does not support int8. * The format is NDHWC, NCDHW or DHWCN. - * @li bias: Optional. An optional 1D tensor of the same type as "x". Reserved. - * @li offset_w: Optional. An optional 1D tensor for quantized deconvolution. Reserved. \n -*@par Attributes: - * @li strides: Required. A tuple/list of 5 integers. Specifies the stride of the sliding +*@par Optional input: + * Two optional inputs + * @li bias: An optional 1D tensor of the same type as "x". Reserved. + * @li offset_w: An optional 1D tensor for quantized deconvolution. Reserved . \n + +*@par Required Attributes: + * @li strides: A tuple/list of 5 integers. Specifies the stride of the sliding * window for each dimension of "x". * The N and C dimensions must be 1. Has the same format as "x". - * @li pads: Required. A tuple/list of 6 integers. - * @li dilations: Optional. A tuple/list of 5 integers, + * @li pads: A tuple/list of 6 integers + +*@par Attributes: + * Five attributes: + * @li groups: Number of blocked connections from input channels to output + * channels. + * @li dilations: A tuple/list of 5 integers, * The dilation factor for each dimension of input. * The N, C and D dimensions must be 1. Has the same format as "x". - * @li groups: Optional. Number of blocked connections from input channels to output - * channels. - * @li data_format: Optional. An string from: "NDHWC", "NCDHW". + * @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: Optional. The size will be added in the output shape. - * @li offset_x: Optional. Input offset_x value. Reserved. \n - + * @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". */ @@ -1359,9 +1388,9 @@ REG_OP(Conv3DTranspose) .INPUT(input_size, TensorType({DT_INT32, DT_INT64})) .INPUT(x, TensorType({DT_FLOAT16})) .INPUT(filter, TensorType({DT_FLOAT16})) - .OPTIONAL_INPUT(bias, TensorType({DT_FLOAT16, DT_FLOAT32})) + .OPTIONAL_INPUT(bias, TensorType({DT_FLOAT16})) .OPTIONAL_INPUT(offset_w, TensorType({DT_INT8})) - .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT32})) + .OUTPUT(y, TensorType({DT_FLOAT16})) .REQUIRED_ATTR(strides, ListInt) .REQUIRED_ATTR(pads, ListInt) .ATTR(dilations, ListInt, {1, 1, 1, 1, 1}) @@ -1373,44 +1402,46 @@ REG_OP(Conv3DTranspose) /** *@brief Computes the transpose of convolution 3d with respect to the input. - *@par Inputs: * @li x: A Tensor of type float16, currently does not support int8. * The format is NDHWC or NCDHW. * @li filter: A Tensor of type float16, currently does not support int8. * The format is NDHWC, NCDHW or DHWCN. - * @li bias: Optional. An 1D tensor of the same type as "x". Reserved. - * @li offset_w: Optional. An 1D tensor for quantized deconvolution. Reserved. \n -*@par Attributes: - * @li input_size: Required. A tuple/list of type int32. - * An integer vector representing the shape of input. - * @li strides: Required. A tuple/list of 5 integers. +*@par Optional inputs: + * @li bias: An optional 1D tensor of the same type as "x". Reserved. + * @li offset_w: An optional 1D tensor for quantized deconvolution. Reserved . \n + +*@par Required Attributes: + * @li input_size: A tuple/list of type int32. + * An integer vector representing the shape of input + * @li strides: A tuple/list of 5 integers. * Specifies the stride of the sliding window for each dimension of "x". * The N and C dimensions must be 1. Has the same format as "x". - * @li pads: Required. A tuple/list of 6 integers. - * @li dilations: Optional. A tuple/list of 5 integers, The dilation factor for each + * @li pads: A tuple/list of 6 integers . \n + +*@par Attributes: + * Five attributes: + * @li dilations: A tuple/list of 5 integers, The dilation factor for each * dimension of input. * The N, C and D dimensions must be 1. Has the same format as "x". - * @li groups: Optional. Number of blocked connections from input channels to output + * @li groups: Number of blocked connections from input channels to output * channels. - * @li data_format: Optional. An optional string from: "NDHWC", "NCDHW". + * @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: Optional. The size will be added in the output shape. - * @li offset_x: Optional. Input offset_x value. Reserved. \n - + * @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". \n - + * y: A Tensor. Has the same type and format as "x". *@par Restrictions: - * Warning: THIS FUNCTION IS DEPRECATED. Please use Conv3DTranspose instead. +* Warning: THIS FUNCTION IS DEPRECATED. Please use Conv3DTranspose instead. */ REG_OP(Conv3DTransposeD) .INPUT(x, TensorType({DT_FLOAT16})) .INPUT(filter, TensorType({DT_FLOAT16})) - .OPTIONAL_INPUT(bias, TensorType({DT_FLOAT16, DT_FLOAT32})) + .OPTIONAL_INPUT(bias, TensorType({DT_FLOAT16})) .OPTIONAL_INPUT(offset_w, TensorType({DT_INT8})) - .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT32})) + .OUTPUT(y, TensorType({DT_FLOAT16})) .REQUIRED_ATTR(input_size, ListInt) .REQUIRED_ATTR(strides, ListInt) .REQUIRED_ATTR(pads, ListInt) @@ -1438,17 +1469,17 @@ REG_OP(Conv3DTransposeD) * @li offset_w: An optional 1D tensor for quantized inference. Reserved. *\n *\n - * The following are the supported data types and data formats:\n - *\n - | Tensor | x | filter | bias | y\n - ------------|---------|---------|---------|--------\n - | Data Type | float16 | float16 | float16 | float16\n - | |---------|---------|---------|--------\n - | | int8 | int8 | int32 | int32\n - ------------|---------|---------|---------|--------\n - | Format | NCHW | NCHW | ND | NCHW\n - | | NHWC | HWCN | | NHWC\n - *\n + * 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 * @@ -1473,38 +1504,38 @@ REG_OP(Conv3DTransposeD) * within the effective range of int8 [-128, 127]. Defaults to "0". *\n *\n - * The following value range restrictions must be met:\n - *\n - | Name | Field | Scope\n - -------------------|----------|--------------\n - | input_size | H | [1, 200000]\n - | | W | [1, 4096]\n - -------------------|----------|--------------\n - | x (out_backprop) | H*strideH| [1, 200000]\n - | | W*strideW| [1, 4096]\n - -------------------|----------|--------------\n - | filter | H | [1, 255]\n - | | W | [1, 255]\n - -------------------|----------|--------------\n - | y (fmap) | H | [1, 200000]\n - | | W | [1, 4096]\n - -------------------|----------|--------------\n - | Stride | H | [1, 63]\n - | | W | [1, 63]\n - -------------------|----------|--------------\n - | Padding | Top | [0, 255]\n - | | Bottom | [0, 255]\n - | | Left | [0, 255]\n - | | Right | [0, 255]\n - -------------------|----------|--------------\n - | Dilation | H | [1, 255]\n - | | W | [1, 255]\n - -------------------|----------|--------------\n - | Offset_x | | [-128, 127]\n - *\n + * The following value range restrictions must be met: +*@verbatim + | Name | Field | Scope + -------------------|----------|-------------- + | input_size | H | [1, 4096] + | | W | [1, 4096] + -------------------|----------|-------------- + | x (out_backprop) | H*strideH| [1, 4096] + | | W*strideW| [1, 4096] + -------------------|----------|-------------- + | filter | H | [1, 255] + | | W | [1, 255] + -------------------|----------|-------------- + | y (fmap) | H | [1, 4096] + | | W | [1, 4096] + -------------------|----------|-------------- + | Stride | H | [1, 63] + | | W | [1, 63] + -------------------|----------|-------------- + | Padding | Top | [0, 255] + | | Bottom | [0, 255] + | | Left | [0, 255] + | | Right | [0, 255] + -------------------|----------|-------------- + | Dilation | H | [1, 255] + | | W | [1, 255] + -------------------|----------|-------------- + | Offset_x | | [-128, 127] + +@endverbatim * In Ascend910, fmap or out_backprop's H and W not support 1 when * fmap_h + pad_top + pad_bottom != (filter_height - 1) * dilation_h + 1 - * and filter_width > fmap_width * If filter_h = 1 and filter_w = 1, out_backprop_w * stride_h * stride_w < 4096 *\n * @@ -1526,9 +1557,9 @@ REG_OP(Conv2DTranspose) .INPUT(input_size, TensorType({DT_INT32, DT_INT64})) .INPUT(x, TensorType({DT_FLOAT16, DT_INT8})) .INPUT(filter, TensorType({DT_FLOAT16, DT_INT8})) - .OPTIONAL_INPUT(bias, TensorType({DT_FLOAT16, DT_INT32, DT_FLOAT32})) + .OPTIONAL_INPUT(bias, TensorType({DT_FLOAT16, DT_INT32})) .OPTIONAL_INPUT(offset_w, TensorType({DT_INT8})) - .OUTPUT(y, TensorType({DT_FLOAT16, DT_INT32, DT_FLOAT32})) + .OUTPUT(y, TensorType({DT_FLOAT16, DT_INT32})) .REQUIRED_ATTR(strides, ListInt) .REQUIRED_ATTR(pads, ListInt) .ATTR(dilations, ListInt, {1, 1, 1, 1}) @@ -1573,9 +1604,9 @@ REG_OP(Conv2DTranspose) REG_OP(Conv2DTransposeD) .INPUT(x, TensorType({DT_FLOAT16, DT_INT8})) .INPUT(filter, TensorType({DT_FLOAT16, DT_INT8})) - .OPTIONAL_INPUT(bias, TensorType({DT_FLOAT16, DT_INT32, DT_FLOAT32})) + .OPTIONAL_INPUT(bias, TensorType({DT_FLOAT16, DT_INT32})) .OPTIONAL_INPUT(offset_w, TensorType({DT_INT8})) - .OUTPUT(y, TensorType({DT_FLOAT16, DT_INT32, DT_FLOAT32})) + .OUTPUT(y, TensorType({DT_FLOAT16, DT_INT32})) .REQUIRED_ATTR(input_size, ListInt) .REQUIRED_ATTR(strides, ListInt) .REQUIRED_ATTR(pads, ListInt) @@ -1592,12 +1623,14 @@ REG_OP(Conv2DTransposeD) * Two inputs: * @li x: A Tensor of type float16,float32 * @li offsets: A Tensor of type float16,float32.Deformation offset parameter. -*@par Attributes: +*@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: + * Four attributes: * @li dilations: A tuple/list of 4 integers, The dilation factor for each dimension * of input. Defaults to [1, 1, 1, 1] * @li data_format: An optional string from: "NCHW", "NHWC". Defaults to "NCHW". Specify the data format of the input x. @@ -1626,20 +1659,22 @@ REG_OP(DeformableOffsets) * @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 Attributes: +*@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: - * @li grad_x: A Tensor of type float16, float32. Gradients with respect to input_x - * @li grad_offsets: A Tensor of type float16, float32. Gradients with respect to input_offsets + * 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})) @@ -1660,9 +1695,11 @@ REG_OP(DeformableOffsetsGrad) *@brief Computes the deformed dilation output with the expected input *@par Inputs: * One inputs: - * x: A Tensor of type int8, float16, float32 -*@par Attributes: + * @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: diff --git a/third_party/fwkacllib/inc/ops/nn_detect_ops.h b/third_party/fwkacllib/inc/ops/nn_detect_ops.h index bd14df77..5fa40ad6 100644 --- a/third_party/fwkacllib/inc/ops/nn_detect_ops.h +++ b/third_party/fwkacllib/inc/ops/nn_detect_ops.h @@ -153,42 +153,6 @@ REG_OP(Iou) .OP_END_FACTORY_REG(Iou) /** -*@brief First calculate the minimum closure area of the two boxes, IoU, -* the proportion of the closed area that does not belong to the two boxes in the closure area, -* and finally subtract this proportion from IoU to get GIoU . \n - -*@par Inputs: -* Two inputs, including: -*@li bboxes: Bounding boxes, a 2D Tensor of type float16 or float32 with -* shape (N, 4). "N" indicates the number of bounding boxes, and the value -* "4" refers to [x1, y1, x2, y2] or [x, y, w, h]. -*@li gtboxes: Ground-truth boxes, a 2D Tensor of type float16 or float32 -* with shape (M, 4). "M" indicates the number of ground truth boxes, and -* the value "4" refers to [x1, y1, x2, y2] or [x, y, w, h] . \n - -*@par Attributes: -*@li trans: An optional bool, true for 'xywh', false for 'xyxy'. -*@li is_cross: An optional bool, control whether the output shape is [M, N] or [1, N] -*@li mode: Computation mode, a character string with the value range of [iou, iof] . \n - -*@par Outputs: -* overlap: A 2D Tensor of type float16 or float32 with shape [M, N] or [1, N], -* specifying the IoU or IoF ratio . \n - -*@attention Constraints: -* Only computation of float16 data is supported. To avoid overflow, the input -* length and width are scaled by 0.2 internally. -*/ -REG_OP(GIoU) - .INPUT(bboxes, TensorType({DT_FLOAT16, DT_FLOAT})) - .INPUT(gtboxes, TensorType({DT_FLOAT16, DT_FLOAT})) - .OUTPUT(overlap, TensorType({DT_FLOAT16, DT_FLOAT})) - .ATTR(trans, Bool, false) - .ATTR(is_cross, Bool, true) - .ATTR(mode, String, "iou") - .OP_END_FACTORY_REG(GIoU) - -/** *@brief Performs the backpropagation of ROIAlign for training scenarios . \n *@par Inputs: @@ -453,7 +417,7 @@ REG_OP(PSROIPooling) *@brief Returns detection result . \n *@par Inputs: -* Five inputs, including: +* Four inputs, including: *@li rois: An NCHW tensor of type floa16 or float32, output from operator proposal_d at the preceding layer, used as the input of operator FSRDetectionOutput. *@li bbox_delta: An NCHWC0 tensor of type floa16 or float32, specifying the prediction offset, used to update the coordinates [x1, y1, x2, y2] of each ROI. *@li score: An NCHWC0 tensor of type floa16 or float32, specifying the probability of each class. Class 0 is the background class. @@ -495,7 +459,7 @@ REG_OP(FSRDetectionOutput) *@brief Returns detection result . \n *@par Inputs: -* Three inputs, including: +* Four inputs, including: *@li bbox_delta: An ND tensor of type floa16 or float32, specifying the box loc predictions, used as the input of operator SSDDetectionOutput. *@li score: An ND tensor of type floa16 or float32, specifying the box confidences data, used as the input of operator SSDDetectionOutput. *@li anchors: An ND tensor of type floa16 or float32, output from operator PriorBoxD, used as the input of operator SSDDetectionOutput. @@ -510,6 +474,7 @@ REG_OP(FSRDetectionOutput) *@li code_type: An optional int32, specify the code type. Defaults to 1(only supports 2). The corner is 1, center_size is 2, corner_size is 3 *@li keep_top_k: An optional int32, specify the topk value after nms. Defaults to -1 *@li confidence_threshold: An optional float32, specify the topk filter threshold. Only consider detections with confidence greater than the threshold +*@li kernel_name: An optional string, specifying the operator name. Defaults to "ssd_detection_output". *@par Outputs: *@li out_boxnum: A tensor of type int32, specifying the number of output boxes. *@li y: A tensor of type float16 or float32 with shape [batch,keep_top_k, 8], describing the information of each output box. @@ -1024,26 +989,26 @@ REG_OP(SPP) * feature map . \n *@attention Constraints: -* For the feature map input: -*@li If pooled_h = pooled_w = 2, the feature map size must not exceed 50. -*@li If pooled_h = pooled_w = 3, the feature map size must not exceed 60. -*@li If pooled_h = pooled_w = 4, the feature map size must not exceed 70. -*@li If pooled_h = pooled_w = 5, the feature map size must not exceed 70. -*@li If pooled_h = pooled_w = 6, the feature map size must not exceed 80. -*@li If pooled_h = pooled_w = 7, the feature map size must not exceed 80. -*@li If pooled_h = pooled_w = 8, the feature map size must not exceed 80. -*@li If pooled_h = pooled_w = 9, the feature map size must not exceed 70. -*@li If pooled_h = pooled_w = 10, the feature map size must not exceed 70. -*@li If pooled_h = pooled_w = 11, the feature map size must not exceed 70. -*@li If pooled_h = pooled_w = 12, the feature map size must not exceed 70. -*@li If pooled_h = pooled_w = 13, the feature map size must not exceed 70. -*@li If pooled_h = pooled_w = 14, the feature map size must not exceed 70. -*@li If pooled_h = pooled_w = 15, the feature map size must not exceed 70. -*@li If pooled_h = pooled_w = 16, the feature map size must not exceed 70. -*@li If pooled_h = pooled_w = 17, the feature map size must not exceed 50. -*@li If pooled_h = pooled_w = 18, the feature map size must not exceed 40. -*@li If pooled_h = pooled_w = 19, the feature map size must not exceed 40. -*@li If pooled_h = pooled_w = 20, the feature map size must not exceed 40. +*@li For the feature map input: +(1) If pooled_h = pooled_w = 2, the feature map size must not exceed 50. +(2) If pooled_h = pooled_w = 3, the feature map size must not exceed 60. +(3) If pooled_h = pooled_w = 4, the feature map size must not exceed 70. +(4) If pooled_h = pooled_w = 5, the feature map size must not exceed 70. +(5) If pooled_h = pooled_w = 6, the feature map size must not exceed 80. +(6) If pooled_h = pooled_w = 7, the feature map size must not exceed 80. +(7) If pooled_h = pooled_w = 8, the feature map size must not exceed 80. +(8) If pooled_h = pooled_w = 9, the feature map size must not exceed 70. +(9) If pooled_h = pooled_w = 10, the feature map size must not exceed 70. +(10) If pooled_h = pooled_w = 11, the feature map size must not exceed 70. +(11) If pooled_h = pooled_w = 12, the feature map size must not exceed 70. +(12) If pooled_h = pooled_w = 13, the feature map size must not exceed 70. +(13) If pooled_h = pooled_w = 14, the feature map size must not exceed 70. +(14) If pooled_h = pooled_w = 15, the feature map size must not exceed 70. +(15) If pooled_h = pooled_w = 16, the feature map size must not exceed 70. +(16) If pooled_h = pooled_w = 17, the feature map size must not exceed 50. +(17) If pooled_h = pooled_w = 18, the feature map size must not exceed 40. +(18) If pooled_h = pooled_w = 19, the feature map size must not exceed 40. +(19) If pooled_h = pooled_w = 20, the feature map size must not exceed 40. *@par Third-party framework compatibility * It is a custom operator. It has no corresponding operator in Caffe. */ @@ -1257,7 +1222,9 @@ REG_OP(RpnProposalsD) * @li box_filter: bool, mark of box_filter. Defaults to "true" * @li core_max_num: int, max number of core. Defaults to "8" *@par Outputs: -*sorted_box: A Tensor. Must be float16. N-D with shape [N, 1]. +* @li sorted_rois: A Tensor. Must be float16. N-D with shape [N, 4]. +* @li sorted_scores: A Tensor. Must be float16. N-D with shape [N, 1]. +* @li sorted_classes: A Tensor. Must be float16. N-D with shape [N, 1]. */ REG_OP(RpnProposalPostProcessing) .INPUT(sorted_proposal, TensorType({DT_FLOAT16})) @@ -1415,7 +1382,7 @@ REG_OP(BatchMultiClassNonMaxSuppression) * @li shape_hw: A 1D Tensor of type int32 . \n * @par Attributes: -* reversed_box: An optional bool, specifying the last two dims is "4,num" or +* @li reversed_box: An optional bool, specifying the last two dims is "4,num" or * "num,4", "true" for "4,num", "false" for "num,4". Defaults to "false" . \n * @par Outputs: @@ -1462,9 +1429,9 @@ REG_OP(NormalizeBBox) * @li anchors: A Tensor. Must be int32. * *@par Attributes: -* @li scales: optional, listfloat. +* @li scales: optional, listfloat, . * @li decode_clip: optional, float, threahold of decode process. -* @li reversed_boxes: optional, bool. +* @li reversed_boxes: optional, bool,. * *@par Outputs: * y: A Tensor. Must have the same type as box_predictions. @@ -1479,16 +1446,16 @@ REG_OP(DecodeBboxV2) .OP_END_FACTORY_REG(DecodeBboxV2) /** -*@brief sort the input tensor and return the value of index. +*@brief Computes sort function. * *@par Inputs: *Inputs include: -* x: A Tensor. Dtype support: float16, float, int16, int8, +* x: A Tensor. Dtype support: flaot16, flaot, int16, int8, uint8, int32, int64. - +* *@par Attributes: -* @li axis: An optional attribute indicates the sorting axis. -* @li descending: An optional attribute indicates desending sort or not. +* @li axis: optional, int. +* @li descending: optional,bool. * *@par Outputs: * @li y1: A Tensor. Must have the same type as x. @@ -1601,18 +1568,16 @@ deciding when to remove boxes based on score . \n the last dim representing (batch_id,class_id,index_id) . \n *@par Attributes: -*@li center_point_box:Integer indicate the format of the box data. +*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 -*@li max_boxes_size: An optional attribute integer representing the real maximum -*number of boxes to be selected by non max suppression . \n *@par Outputs: -*selected_indices: A 2-D integer tensor of shape [M] representing the +*@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: @@ -1638,7 +1603,7 @@ REG_OP(NonMaxSuppressionV7) *@brief Obtains the ROI feature matrix from the feature map list. It is a customized fused operator for mmdetection. \n *@par Inputs: -* Two inputs, including: +* Three inputs, including: *@li features: A 5HD Tensor list of type float32 or float16. *@li rois: ROI position. A 2D Tensor of float32 or float16 with shape (N, 5). "N" indicates the number of ROIs, * the value "5" indicates the indexes of images where the ROIs are located, "x0", "y0", "x1", and "y1". @@ -1795,7 +1760,7 @@ REG_OP(AnchorResponseFlags) * "N" indicates the number of ROIs. \n *@par Attributes: -*performance_mode: select performance mode, "high_precision" or "high_performance". +*@li performance_mode: select performance mode, "high_precision" or "high_performance". * select "high_precision" when input type is float32, the output tensor precision * will be smaller than 0.0001, select "high_performance" when input type is float32, * the ops will be best performance, but precision will be only smaller than 0.005. @@ -1830,12 +1795,12 @@ REG_OP(YoloBoxesEncode) *@li num_gts: A Tensor. Support int32. real k. shape (1, ) *@par Attributes: -*@li pos_iou_thr: float. IOU threshold for positive bboxes. -*@li min_pos_iou: float. minimum iou for a bbox to be considered as a positive bbox -*@li gt_max_assign_all: bool. whether to assign all bboxes with the same highest overlap with some gt to that gt. +*@li output_dim: float. IOU threshold for positive bboxes. +*@li group_size: float. minimum iou for a bbox to be considered as a positive bbox +*@li spatial_scale: bool. whether to assign all bboxes with the same highest overlap with some gt to that gt. *@par Outputs: -* assigned_gt_inds_pos: A Tensor. Support float16/float32. shape (n, ). +*@li assigned_gt_inds_pos: A Tensor. Support float16/float32. shape (n, ). */ REG_OP(GridAssignPositive) .INPUT(assigned_gt_inds, TensorType({ DT_FLOAT, DT_FLOAT16 })) @@ -1851,40 +1816,6 @@ REG_OP(GridAssignPositive) .REQUIRED_ATTR(min_pos_iou, Float) .REQUIRED_ATTR(gt_max_assign_all, Bool) .OP_END_FACTORY_REG(GridAssignPositive) - -/** -*@brief GIoUGrad . \n - -*@par Inputs: -*@li dy : data of grad increment, a 1D Tensor of type float16 or float32 with -* shape (N,). -*@li bboxes: Bounding boxes, a 2D Tensor of type float16 or float32 with -* shape (4, N). "N" indicates the number of bounding boxes, and the value -* "4" refers to [x1, y1, x2, y2] or [x, y, w, h]. -*@li gtboxes: Ground-truth boxes, a 2D Tensor of type float16 or float32 -* with shape (4, M). "M" indicates the number of ground truth boxes, and -* the value "4" refers to [x1, y1, x2, y2] or [x, y, w, h] . \n - -*@par Attributes: -*@li trans: An optional attr, true for 'xywh', false for 'xyxy', only support true now. -*@li is_cross: An optional attr, if false M equals N, only support false now. -*@li mode: An optional attr, a character string with the value range of ['iou', 'iof'], -* only support 'iou' now. \n - -*@par Outputs: -*@li dbboxes: A 2D Tensor of type float16 or float32 with shape [4, N]. -*@li dgtboxes: A 2D Tensor of type float16 or float32 with shape [4, M]. -*/ -REG_OP(GIoUGrad) - .INPUT(dy, TensorType({DT_FLOAT16, DT_FLOAT})) - .INPUT(bboxes, TensorType({DT_FLOAT16, DT_FLOAT})) - .INPUT(gtboxes, TensorType({DT_FLOAT16, DT_FLOAT})) - .OUTPUT(dbboxes, TensorType({DT_FLOAT16, DT_FLOAT})) - .OUTPUT(dgtboxes, TensorType({DT_FLOAT16, DT_FLOAT})) - .ATTR(trans, Bool, false) - .ATTR(is_cross, Bool, true) - .ATTR(mode, String, "iou") - .OP_END_FACTORY_REG(GIoUGrad) } // namespace ge #endif // OPS_BUILT_IN_OP_PROTO_INC_NN_DETECT_OPS_H_ diff --git a/third_party/fwkacllib/inc/ops/nn_norm_ops.h b/third_party/fwkacllib/inc/ops/nn_norm_ops.h index 9ce7abfd..b44c0780 100644 --- a/third_party/fwkacllib/inc/ops/nn_norm_ops.h +++ b/third_party/fwkacllib/inc/ops/nn_norm_ops.h @@ -54,16 +54,15 @@ REG_OP(LogSoftmaxGrad) *@par Inputs: *Two inputs, including: * @li features: A Tensor. Must be one of the following types: half, float32, double. -*A "batch_size * num_classes" matrix. +* A "batch_size * num_classes" matrix. * @li labels: A Tensor. Must be one of the following types: 'int32', 'int64'. -*batch_size vector with values in [0, num_classes). -*This is the label for the given minibatch entry. \n +* batch_size vector with values in [0, num_classes). +* This is the label for the given minibatch entry. *@par Outputs: -*@li loss: A Tensor for per example loss (a "batch_size" vector). Has the same type as "features". -*@li backprop: A Tensor for the backpropagated gradients (a batch_size * num_classes matrix). -Has the same type as "features" . \n +*loss: A Tensor for per example loss (a "batch_size" vector). Has the same type as "features". +*backprop: A Tensor for the backpropagated gradients (a batch_size * num_classes matrix). Has the same type as "features" . \n *@par Third-party framework compatibility *Compatible with the TensorFlow operator SparseSoftmaxCrossEntropyWithLogits. @@ -85,8 +84,8 @@ REG_OP(SparseSoftmaxCrossEntropyWithLogits) * @li labels: A Tensor of the same type as "features". A "batch_size * num_classes" matrix . \n *@par Outputs: -* @li loss: A Tensor for per example loss (a "batch_size" vector). Has the same type as "features". -* @li backprop: A Tensor for the backpropagated gradients (a batch_size * num_classes matrix). Has the same type as "features" . \n +*loss: A Tensor for per example loss (a "batch_size" vector). Has the same type as "features". +*backprop: A Tensor for the backpropagated gradients (a batch_size * num_classes matrix). Has the same type as "features" . \n *@par Third-party framework compatibility *Compatible with the TensorFlow operator SoftmaxCrossEntropyWithLogits. @@ -128,13 +127,12 @@ REG_OP(SoftmaxGrad) *@brief Computes the sigmoid cross entropy loss of "predict" and "target" . \n *@par Inputs: -* Three inputs, including: +* Two 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 . -*@li dout:A multi-dimensional Tensor of float16 or float32,specifying the gradient transferred from the upper layer. \n +*@li target: A multi-dimensional Tensor of type float16 or float32, specifying the target value . \n *@par Outputs: -*gradient: 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 the scenario where "reduction" is set to "none"of PyTorch operator SigmoidCrossEntropyWithLogitsGrad. @@ -150,12 +148,13 @@ REG_OP(SigmoidCrossEntropyWithLogitsGrad) *@brief Performs the backpropagation of SigmoidCrossEntropyWithLogits for training scenarios . \n *@par Inputs: -* Two inputs, including: +* Three inputs, including: *@li predict: A multi-dimensional Tensor of type float16 or float32, specifying the predictive value. -*@li target: A multi-dimensional Tensor of type float16 or float32, specifying the target value. \n +*@li target: A multi-dimensional Tensor of type float16 or float32, specifying the target value. +*@li dout: A multi-dimensional Tensor of float16 or float32, specifying the gradient transferred from the upper layer . \n *@par Outputs: -*loss: Return loss. Has the same dimensions and type as "predict" . \n +*gradient: Return gradient. Has the same dimensions and type as "predict" . \n *@par Third-party framework compatibility * Compatible with the scenario where "reduction" is set to "none"of PyTorch operator SigmoidCrossEntropyWithLogits. @@ -573,7 +572,7 @@ REG_OP(LayerNorm) *@par Inputs: *One input, including: -* x: A Tensor. Must be one of the following types: float16, float32 . \n +* @li x: A Tensor. Must be one of the following types: float16, float32 . \n *@par Attributes: * @li p: Specify L_p norm, the type is float. @@ -582,7 +581,7 @@ REG_OP(LayerNorm) *@par Outputs: *One outputs, including: -* y: shape and dtype of output, should be same shape and type as input. +* @li y: shape and dtype of output, should be same shape and type as input. */ REG_OP(Renorm) .INPUT(x, TensorType::BasicType()) @@ -812,7 +811,7 @@ REG_OP(LayerNormBetaGammaBackpropV2) * shape of "keep_prob" should be (1,) or [1,]. * Has the same type as "x" . \n -*@par Outputs: +*@par Output: *y: A mutable Tensor. Has the same type as "x". */ REG_OP(DropOutDoMask) @@ -840,7 +839,7 @@ REG_OP(DropOutDoMask) * shape of "keep_prob" should be (1,) or [1,]. * Has the same type as "x" . \n -*@par Outputs: +*@par Output: *y: A mutable Tensor. Has the same type as "x". *@par Restrictions: *Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. @@ -1011,7 +1010,7 @@ REG_OP(LRNGrad) *@li grads: A Tensor. Has the same type as acts. *@par Attributes: - *blank_label: An optional attribute. Defaults to 0. + *@li blank_label: An optional attribute. Defaults to 0. *@par Third-party framework compatibility * Compatible with TensorFlow RNNTLoss operator. @@ -1199,11 +1198,13 @@ REG_OP(INInferV2D) * @li epsilon: An attribute of type Float. \n * @par Outputs: -* Three outputs, including: +*Three outputs, including: * @li y: A Tensor. Has the same type as "x". \n * @li mean: A Tensor. Has the same type as "x". \n * @li variance: A Tensor. Has the same type as "x". \n +* @par Third-party framework compatibility +* Can be used by onnx InstanceNormalization */ REG_OP(InstanceNorm) .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT})) @@ -1217,22 +1218,24 @@ REG_OP(InstanceNorm) .OP_END_FACTORY_REG(InstanceNorm) /** -* @brief InstanceNormGrad operator interface implementation. +*@brief InstanceNormGrad operator interface implementation. -* @par Inputs: -* Five inputs, including: +*@par Inputs: +*Five inputs, including: * @li dy: A Tensor. Must be one of the following types: float16, float32. * @li x: A Tensor. Must be one of the following types: float16, float32. * @li variance: A Tensor. Must be one of the following types: float16, float32. * @li mean: A Tensor. Must be one of the following types: float16, float32. * @li gamma: A Tensor. Must be one of the following types: float16, float32 . \n -* @par Outputs: -* Three outputs, including: +*@par Outputs: +*Three outputs, including: * @li pd_x: A Tensor. Must be one of the following types: float16, float32. * @li pd_gamma: A Tensor. Must be one of the following types: float16, float32. * @li pd_beta: A Tensor. Must be one of the following types: float16, float32. +*@par Restrictions: +*Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. */ REG_OP(InstanceNormGrad) .INPUT(dy, TensorType({DT_FLOAT, DT_FLOAT16})) @@ -1246,6 +1249,58 @@ REG_OP(InstanceNormGrad) .OP_END_FACTORY_REG(InstanceNormGrad) /** +*@brief InstanceNormXBackprop operator interface implementation. + +*@par Inputs: +*Five inputs, including: +* @li dy: A Tensor. Must be one of the following types: float16, float32. +* @li x: A Tensor. Must be one of the following types: float16, float32. +* @li variance: A Tensor. Must be one of the following types: float16, float32. +* @li mean: A Tensor. Must be one of the following types: float16, float32. +* @li gamma: A Tensor. Must be one of the following types: float16, float32 . \n + +*@par Outputs: +*Two outputs, including: +* @li pd_x: A Tensor. Must be one of the following types: float16, float32. +* @li res_for_gamma: A Tensor. Must be one of the following types: float32. + +*@par Restrictions: +*Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. +*/ +REG_OP(InstanceNormXBackprop) + .INPUT(dy, TensorType({DT_FLOAT, DT_FLOAT16})) + .INPUT(x, TensorType({DT_FLOAT, DT_FLOAT16})) + .INPUT(variance, TensorType({DT_FLOAT, DT_FLOAT16})) + .INPUT(mean, TensorType({DT_FLOAT, DT_FLOAT16})) + .INPUT(gamma, TensorType({DT_FLOAT, DT_FLOAT16})) + .OUTPUT(pd_x, TensorType({DT_FLOAT, DT_FLOAT16})) + .OUTPUT(res_for_gamma, TensorType({DT_FLOAT})) + .OP_END_FACTORY_REG(InstanceNormXBackprop) + +/** +*@brief InstanceNormBetaGammaBackprop operator interface implementation. + +*@par Inputs: +*Two inputs, including: +* @li dy: A Tensor. Must be one of the following types: float16, float32. +* @li res_for_gamma: A Tensor. Must be one of the following types: float32.\n + +*@par Outputs: +*Two outputs, including: +* @li pd_gamma: A Tensor. Must be one of the following types: float16, float32. +* @li pd_beta: A Tensor. Must be one of the following types: float16, float32. + +*@par Restrictions: +*Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. +*/ +REG_OP(InstanceNormBetaGammaBackprop) + .INPUT(dy, TensorType({DT_FLOAT, DT_FLOAT16})) + .INPUT(res_for_gamma, TensorType({DT_FLOAT})) + .OUTPUT(pd_gamma, TensorType({DT_FLOAT, DT_FLOAT16})) + .OUTPUT(pd_beta, TensorType({DT_FLOAT, DT_FLOAT16})) + .OP_END_FACTORY_REG(InstanceNormBetaGammaBackprop) + +/** * @brief Computes Kl_div_loss_grad or Kl_div_loss_backward. \n * @par Inputs: @@ -1285,10 +1340,10 @@ REG_OP(KlDivLossGrad) * @li label: A Tensor. Has the same type as "grads". Required. \n * @par Attributes: -* reduction: An optional attribute of type String. Defaults to "mean". \n +* @li reduction: An optional attribute of type String. Defaults to "mean". \n * @par Outputs: -* y: A Tensor. Has the same type as "x". \n +* @li y: A Tensor. Has the same type as "x". \n * @par Third-party framework compatibility * Compatible with the Pytorch operator L1LossGrad. @@ -1313,7 +1368,7 @@ REG_OP(L1LossGrad) * @li reduction: An optional string.Defaults to "mean". \n * @par Outputs: -* y: An ND tensor tensor with the same shape and type as "predict". \n +* @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. @@ -1335,10 +1390,10 @@ REG_OP(LpLoss) * @li dout: An ND tensor of type float16, float32. \n * @par Attributes: -* reduction: An optional string.Defaults to "mean". \n +* @li reduction: An optional string.Defaults to "mean". \n * @par Outputs: -* y: An ND tensor tensor with the same shape and type as "predict". \n +* @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. @@ -1359,10 +1414,10 @@ REG_OP(MseLossGrad) * @li label: An ND Tensor of dtype float16 or float32.\n * * @par Attributes: -* reduction:An optional str from sum, none, mean, Defaults to "mean".\n +* @li reduction:An optional str from sum, none, mean, Defaults to "mean".\n * * @par Outputs: -* y: when reduction=sum/mean, y is scale. when reduction=none, y has +* @li y: when reduction=sum/mean, y is scale. when reduction=none, y has * same type and shape as "predict".\n */ REG_OP(MseLoss) @@ -1390,7 +1445,7 @@ REG_OP(MseLoss) * Must be one of the following: "none", "mean", "sum". \n * @par Outputs: -* gradient: A Tensor. Has the same type as "predict". \n +* @li gradient: A Tensor. Has the same type as "predict". \n * @par Third-party framework compatibility * Compatible with the Pytorch operator SmoothL1LossBackward. @@ -1425,7 +1480,7 @@ REG_OP(SmoothL1LossGradV2) * the output,'sum': the output will be summed. Default: 'mean'. \n * @par Outputs: -* loss: Indicates the loss between the predictive value and target value. +* @li loss: Indicates the loss between the predictive value and target value. * Has the same dimensions as "predict". \n * @par Third-party framework compatibility @@ -1443,12 +1498,12 @@ REG_OP(SmoothL1LossV2) * @brief Computes Centralization. result = x - mean(x, axes) * @par Inputs: -* x: An ND tensor of type float16, float32. +* @li x: An ND tensor of type float16, float32. * @par Attributes: -* axes: The dimensions to reduce. Must be one of the following types: int, list, tuple, NoneType. +* @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: -* y: A Tensor. Has the same type as "x". \n +* @li y: A Tensor. Has the same type as "x". \n * @par Third-party framework compatibility * custom operator \n @@ -1466,7 +1521,7 @@ REG_OP(Centralization) *@par Inputs: *One inputs, including: -* x: A tensor . Must be one of the following types: +* @li x: A tensor . Must be one of the following types: * float16, float32, int32, uint32, int8, uint8. \n *@par Attributes: @@ -1491,14 +1546,14 @@ REG_OP(Roll) logistic loss between input_x and input_y (containing 1 or -1). \n *@par Inputs: - *Tow inputs, including: + *One inputs, including: * @li input_x: A tensor. Must be one of the following types: * float16, float32. \n * @li input_y: A tensor. Must be one of the following types: * float16, float32. \n *@par Attributes: - *reduction: An optional string.Defaults to "mean". \n + *@li lambd: An optional string.Defaults to "mean". \n *@par Outputs: *output_z: while reduction == "none", A Tensor with the same type and shape of input_x's. \n @@ -1525,10 +1580,10 @@ REG_OP(SoftMarginLoss) * @li pos_weight: An optional ND tensor of type float16, float32. \n * @par Attributes: -* reduction: An optional string.Defaults to "mean". \n +* @li reduction: An optional string.Defaults to "mean". \n * @par Outputs: -* gradient: An ND tensor tensor with the same shape and type as "predict". \n +* @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. @@ -1548,14 +1603,24 @@ REG_OP(SigmoidCrossEntropyWithLogitsGradV2) * @par Inputs: * Two inputs, including: - * @li input_x: A tensor. Must be one of the following types: float16, float32. - * @li target: A tensor. Must be one of the following types: float16, float32. \n + * @li input_x: A tensor. Must be one of the following types: + * float16, float32. \n + * + * @par Inputs: + * @li target: A tensor. Must be one of the following types: + * float16, float32. \n * @par Attributes: * four Attributes, including: - * @li log_input: An optional bool. Defaults to "True" - * @li full: An optional bool. Defaults to "False" - * @li eps: An optional float. Defaults to "1e-8" + * @li log_input: An optional bool. Defaults to "True" \n + * + * @par Attributes: + * @li full: An optional bool. Defaults to "False" \n + * + * @par Attributes: + * @li eps: An optional float. Defaults to "1e-8" \n + * + * @par Attributes: * @li reduction: An optional string. Defaults to "mean" \n * @par Outputs: @@ -1576,14 +1641,14 @@ REG_OP(PoissonNllLoss) /** *@brief rnn_gen_mask * @par Inputs: - * seq_length: A ND Tensor of type int32. Recoed the current length of each batch.\n + * @li seq_length: A ND Tensor of type int32. Recoed the current length of each batch.\n * * @par Attributes: * @li num_step: A required int.\n * @li hidden_size: A required int. \n * * - * @par Ouputs: + * @par Output: * y: A mutable Tensor of type float16, with the shape of [num_step, batch_size, hidden_size]. \n * */ @@ -1601,16 +1666,18 @@ REG_OP(RnnGenMask) * @par Inputs: * Two inputs, including: * @li x: A tensor. Must be one of the following types: -* float16, float32. +* float16, float32. \n +* +* @par Inputs: * @li target: A tensor. Must be the following types: * int32. \n * @par Attributes: -* reduction: An optional string. Defaults to "mean" \n +* @li reduction: An optional string. Defaults to "mean" \n * @par Outputs: -* @li y: A Tensor has same element type as input x. \n -* @li is_target: A Tensor has same element type as input target. \n +* y: A Tensor has same element type as input x. \n +* is_target: A Tensor has same element type as input target. \n * @par Third-party framework compatibility * Compatible with the Pytorch operator MultiLabelMarginLoss. \n diff --git a/third_party/fwkacllib/inc/ops/nn_ops.h b/third_party/fwkacllib/inc/ops/nn_ops.h index 5b1a4dd0..49fd02fa 100644 --- a/third_party/fwkacllib/inc/ops/nn_ops.h +++ b/third_party/fwkacllib/inc/ops/nn_ops.h @@ -106,16 +106,16 @@ REG_OP(FusedBatchNormV2) .OP_END_FACTORY_REG(FusedBatchNormV2) /** - * @brief Large amount of data sort.First operator of TopK. + * @brief: Large amount of data sort.First operator of TopK. * @par Inputs: * two input, including: * @li input_data: A Tensor. Data to be sorted. Support float16 * @li input_index: A Tensor. Range(0, 2048). Datatype and format is same as input_data. * @par Attributes: - * k_num: Int.Number to be sorted. + * @li k_num: Int.Number to be sorted. * @par Outputs: - * One output, including: - * output_proposal: A Tensor. Datatype and format is same as input_data. Proposal sorted for each channel. + * 1 output, including: + * @li output_proposal: A Tensor. Datatype and format is same as input_data. Proposal sorted for each channel. */ REG_OP(SegmentSort) .INPUT(input_data, TensorType({DT_FLOAT16})) @@ -127,13 +127,13 @@ REG_OP(SegmentSort) /** * @brief: Large amount of data sort.Second operator of TopK. * @par Inputs: - * One input, including: - * input_proposal: A Tensor. Proposal sorted for each channel. Support float16 + * two input, including: + * @li input_proposal: A Tensor. Proposal sorted for each channel. Support float16 * @par Attributes: - * k_num: Int.Number to be sorted. + * @li k_num: Int.Number to be sorted. * @par Outputs: - * One output, including: - * output_proposal: A Tensor. Datatype and format is same as input_data. Proposal sorted for each channel. + * 1 output, including: + * @li output_proposal: A Tensor. Datatype and format is same as input_data. Proposal sorted for each channel. */ REG_OP(MultiMerge) .INPUT(input_proposal, TensorType({DT_FLOAT16})) @@ -142,14 +142,14 @@ REG_OP(MultiMerge) .OP_END_FACTORY_REG(MultiMerge) /** - * @brief Large amount of data sort.Third operator of TopK. + * @brief: Large amount of data sort.Third operator of TopK. * @par Inputs: - * One input, including: - * input_proposal: A Tensor. Proposal sorted for each channel. Support float16 + * two input, including: + * @li input_proposal: A Tensor. Proposal sorted for each channel. Support float16 * @par Attributes: - * k_num: Int.Number to be sorted. + * @li k_num: Int.Number to be sorted. * @par Outputs: - * Two output, including: + * 2 output, including: * @li output_data: A Tensor. Datatype and format is same as input_data. Data sorted. * @li output_index: A Tensor. int32. Data index. */ diff --git a/third_party/fwkacllib/inc/ops/nn_pooling_ops.h b/third_party/fwkacllib/inc/ops/nn_pooling_ops.h index 72363d18..80a21333 100644 --- a/third_party/fwkacllib/inc/ops/nn_pooling_ops.h +++ b/third_party/fwkacllib/inc/ops/nn_pooling_ops.h @@ -29,7 +29,7 @@ namespace ge { /** *@brief Performs pooling on the input. *@par Inputs: -* x: An NCHW tensor of type float16, float32, int8. +*@li x: An NCHW tensor of type float16, float32, int8. *@par Attributes: *@li mode: An optional int32, specifying the pooling algorithm, either "0" (max pooling) or "1" (avg pooling). Defaults to "0". *@li global_pooling: An optional bool. Defaults to "false". @@ -50,7 +50,6 @@ namespace ge { *dilation[2]: An optional int32, specifying the left dilation. Defaults to "1". *dilation[3]: An optional int32, specifying the right dilation. Defaults to "1". *@li ceil_mode: An optional int32, either "0" (ceil mode) or "1" (floor mode). Defaults to "0". -*@li data_format: An optional string, Specify the data format of the input and output data. With the default format "NCHW". *@par Outputs: *y: An NCHW tensor of type float16, float32, int32. *@attention Constraints: @@ -205,7 +204,7 @@ REG_OP(AvgPool3D) *y: The average pooled output tensor . \n *@attention Constraints: -*"ksize" is in the range [1, 255]. "strides" is in the range [1, 63] +*@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. @@ -282,10 +281,10 @@ REG_OP(AvgPool3DGrad) * @li data_format: A string, format of input data . \n * @par Outputs: -* output: The average pooled output tensor . \n +* @output: The average pooled output tensor . \n * @attention Constraints: -* "ksize" is in the range [1, 255]. "strides" is in the range [1, 63] +* @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. @@ -432,47 +431,6 @@ REG_OP(MaxPool3D) .OP_END_FACTORY_REG(MaxPool3D) /** -* @brief Performs max pooling3d on both max values and indices. -* -* @par Inputs: -* One input: -* x: An 6D tensor. Supported type: float16. Format as NDC1HWC0. -* @par Attributes: -* @li ksize: A required list of int32 values, -* specifying the size of the window for each dimension of the input tensor. -* No default value. -* @li strides: A required list of int32 values, -* specifying the stride of the sliding window for each dimension of -* the input tensor. No default value. -* @li pads: A required 3*2-dimension-list of int32 values. -* specifying the pad of three dimension of input, implement with 0. -* @li dilation: dilation of kernel. default value is {1,1,1,1,1}. -* @li ceil_mode: default value is false. -* @li data_format: the format of torch input, default value is "NCDHW". -* @li argmax_type: the function of this field is to determine the type of -* output argmax, "bitmask" is the default value, the argmax will return -* a img2col bitmask. "index_int32" and "index_int64" represent the torch -* output indices. -* @par Outputs: -* y: An 6D tensor. the maxpool3d output(max value), format as NDoC1HoWoC0. -* @par Outputs: -* argmax: A 5D uint16 tensor. the indice output. -* format as NC1HWC0, actually it represent N, Do, C1*ksize, Ho*Wo//16, 16. -*/ -REG_OP(MaxPool3DWithArgmax) - .INPUT(x, TensorType::RealNumberType()) - .OUTPUT(y, TensorType::RealNumberType()) - .OUTPUT(argmax, TensorType::IndexNumberType()) - .REQUIRED_ATTR(ksize, ListInt) - .REQUIRED_ATTR(strides, ListInt) - .REQUIRED_ATTR(pads, ListInt) - .ATTR(dilation, ListInt, {1, 1, 1, 1, 1}) - .ATTR(ceil_mode, Bool, false) - .ATTR(data_format, String, "NCDHW") - .ATTR(argmax_type, String, "bitmask") - .OP_END_FACTORY_REG(MaxPool3DWithArgmax) - -/** *@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. @@ -564,7 +522,8 @@ REG_OP(MaxPool3DGradGrad) * y: A mutable tensor. Has the same shape and type as "x1" . \n * @attention Constraints: -* @li ksize is limited by buffer with full tiling. +* @li Computing gradients of global pooling is not supported, which means +* "ksize < x1". * @li "ksize" is in the range [1, 255]. "strides" is in the range [1, 63] * @par Third-party framework compatibility @@ -609,7 +568,7 @@ REG_OP(MaxPoolGrad) * @li Other dimensions of ksize and strides is 1 . \n * @par Outputs: -* y: Has the same type and format as input "x1" . \n +* @li y: Has the same type and format as input "x1" . \n * @par Third-party framework compatibility * @li Compatible with the TensorFlow operator MaxPoolGradGrad. @@ -629,7 +588,7 @@ REG_OP(MaxPoolGradGrad) *@brief Performs max_pool_ext2 on the input . \n *@par Inputs: -* Three inputs: +* Two inputs: *@li x: An NC1HWC0 Tensor of type float16. *@li strides: A required type of int32 values, specifying the stride of the sliding window for each dimension of the input tensor. No default value. *@li ksize: A required type of int32 values, specifying the size of the window for each dimension of the input tensor. No default value. @@ -676,8 +635,7 @@ REG_OP(MaxPoolV2) *@li strides: A required list of int8, int16, int32, or int64 values, * specifying the stride of the sliding window for each dimension of * the input tensor. No default value. -*@li padding: A required string. No default value . -*@li Targmax:An optional int with default value 7 . \n +*@li padding: A required string. No default value . \n *@par Outputs: *@li y: A Tensor. Has the same type and format as input "x". @@ -687,7 +645,7 @@ REG_OP(MaxPoolV2) * ksize[1] * ksize[2] <= 255. *@li "stride is a list that has length 4: strides[0] = 1 or strides[3] = 1, * strides[1] <= 63, strides[0] >= 1, strides[2] <= 63, strides[2] >= 1. -*@li "padding" is either "SAME" or "VALID" . +*@li "padding" is either "SAME" or "VALID" . \n *@par Third-party framework compatibility * Compatible with the TensorFlow operator MaxPoolWithArgmax. @@ -752,15 +710,14 @@ REG_OP(MaxPoolGradWithArgmax) *@brief Performs transform mask to argmax . \n *@par Inputs: -* Two inputs: -*@li x: An NC1HWC0 Tensor of type float16. -*@li mask: An NC1HWC0 Tensor of type uint16 . \n +* Two input: +*x: An NC1HWC0 Tensor of type float16. +*mask: An NC1HWC0 Tensor of type uint16 . \n *@par Attributes: *@li ksize: A required list of int8, int16, int32, or int64 values, specifying the size of the window for each dimension of the input tensor. No default value. *@li strides: A required list of int8, int16, int32, or int64 values, specifying the stride of the sliding window for each dimension of the input tensor. No default value. -*@li padding: A required string. No default value . -*@li originshape:A required list of int8, int16, int32, or int64 values, No default value. \n +*@li padding: A required string. No default value . \n *@par Outputs: *argmax: An NC1HWC0 Tensor of type int32 . \n @@ -797,7 +754,7 @@ REG_OP(Mask2Argmax) * @li strides: A required list, specifying the stride of the sliding window. * @li padding: A required string, window sliding mode. Either SAME or VALID. * @par Outputs: -* y:Result tensor. Supported type: float, double, int32, +* @li y:Result tensor. Supported type: float, double, int32, * uint8, int16, int8, int64, uint16, half, uint32, uint64 * @attention Constraints: @@ -810,7 +767,7 @@ REG_OP(Mask2Argmax) * (shape_max_pool[2] * shape_max_pool[3] + 31) // 16, 16), else failed . \n * @par Third-party framework compatibility -* Compatible with the TensorFlow operator MaxPoolGradGradWithArgmax. +* @li Compatible with the TensorFlow operator MaxPoolGradGradWithArgmax. */ REG_OP(MaxPoolGradGradWithArgmax) .INPUT(x, TensorType::RealNumberType()) @@ -974,11 +931,11 @@ REG_OP(AvgPoolV2GradD) .OP_END_FACTORY_REG(AvgPoolV2GradD) /** -*@brief upsample the layer, similar to the nearest-neighbor difference scaling algorithm. +*@brief :upsample the layer *@par Inputs: * one input, including: -* x: A tensor of type float16 or float32. +*@li x: A tensor of type float16 or float32. *@par Attributes: *@li scale: A optional float32, scale factor of x. Defaults to "1.0". *@li stride_h: An optional int32, broadcast the axis of h. Defaults to "2". @@ -1462,7 +1419,7 @@ REG_OP(MaxPoolV3) * the floor function will be used. Default False \n * @par Outputs: -* out_grad: A mutable tensor. Has the same shape and type as "x1" . \n +* y: A mutable tensor. Has the same shape and type as "x1" . \n * @attention Constraints: * @li Computing gradients of global pooling is not supported, which means @@ -1490,8 +1447,8 @@ REG_OP(MaxPoolV3Grad) *@brief Performs Dilation2D on the input . \n *@par Inputs: -*@li x: A tensor of shape is 4d, format is support NHWC. -*@li filter: A tensor of shape is 3d, the type is same with x, and the c dimension is same with x. \n +*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. @@ -1523,9 +1480,9 @@ REG_OP(Dilation2D) *@brief Performs Dilation2DBackpropFilter on the input. \n *@par Inputs: -*@li x: A tensor of shape is 4d, format is support NHWC. -*@li filter: A tensor of shape is 3d, the type is same with x, and the c dimension is same with x. -*@li out_backprop: Has the same type and format as input x and the c dimension is same with x. \n +*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. +*out_backprop: Has the same type and format as input 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 dimension are 1. @@ -1562,9 +1519,9 @@ REG_OP(Dilation2DBackpropFilter) *@brief Performs Dilation2DBackpropInput on the input. \n *@par Inputs: -*@li x: A tensor of shape is 4d, format is support NHWC. -*@li filter: A tensor of shape is 3d, the type is same with x, and the c dimension is same with x. -*@li out_backprop: Has the same type and format as input x and the c dimension is same with x. \n +*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. +*out_backprop: Has the same type and format as input 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 dimension are 1. diff --git a/third_party/fwkacllib/inc/ops/nn_training_ops.h b/third_party/fwkacllib/inc/ops/nn_training_ops.h index 9dd502cd..75e91aee 100644 --- a/third_party/fwkacllib/inc/ops/nn_training_ops.h +++ b/third_party/fwkacllib/inc/ops/nn_training_ops.h @@ -289,8 +289,7 @@ REG_OP(SparseApplyAdagradV2D) * Should be from a Variable(). *@li lr: A scalar. Has the same type as "var". *@li grad: A tensor for the gradient. Has the same type as "var". -*@li momentum: Momentum. Must be a scalar. - +* *@par Attributes: *@li use_nesterov: An optional bool. Defaults to "False". * If "True", the tensor passed to compute grad will be @@ -702,7 +701,7 @@ REG_OP(ApplyPowerSignD) /** *@brief Updates "var" as FOBOS algorithm with fixed learning rate. * prox_v = var - alpha * delta -* var = sign(prox_v)/(1+alpha * l2) * max{|prox_v|-alpha * l1,0} +* var = sign(prox_v)/(1+alpha*l2) * max{|prox_v|-alpha*l1,0} * *@attention Constraints: * the input tensors must have the same shape. @@ -2129,12 +2128,10 @@ REG_OP(FusedMulApplyMomentumExtern) * otherwise the behavior is undefined, but may exhibit less contention. * *@par Outputs: -* @li var: A mutable tensor. Has the same type as input "var". -* @li accum: A mutable tensor. Has the same type as input "accum". +* var: A mutable tensor. Has the same type as input "var". * *@attention Constraints: -* @li var: A mutable tensor. Has the same type as input "var". -* @li accum: A mutable tensor. Has the same type as input "accum". +* The input tensors must have the same shape. * *@par Third-party framework compatibility * Compatible with the TensorFlow operator ResourceApplyKerasMomentum. diff --git a/third_party/fwkacllib/inc/ops/nonlinear_fuc_ops.h b/third_party/fwkacllib/inc/ops/nonlinear_fuc_ops.h index 01ff77cb..ca1c24eb 100644 --- a/third_party/fwkacllib/inc/ops/nonlinear_fuc_ops.h +++ b/third_party/fwkacllib/inc/ops/nonlinear_fuc_ops.h @@ -28,8 +28,8 @@ namespace ge { *@brief Computes the for the gelu of "x" . \n *@par Inputs: -*One input, including: -*x: A Tensor. Must be one of the following types: float16, float32 +*Two inputs, including: +* @li x: A Tensor. Must be one of the following types: float16, float32 *@par Outputs: *y: A Tensor. Has the same type as "x". @@ -66,8 +66,8 @@ REG_OP(GeluGrad) *@brief Computes the for the fast_gelu of "x" . \n *@par Inputs: -*One input, including: -*x: A Tensor. Must be one of the following types: float16, float32 +*Two inputs, including: +* @li x: A Tensor. Must be one of the following types: float16, float32 *@par Outputs: *y: A Tensor. Has the same type as "x". @@ -83,7 +83,7 @@ REG_OP(FastGelu) *@brief Computes the gradient for the fast_gelu of "x" . \n *@par Inputs: -*Two inputs, including: +*Three inputs, including: * @li dy: A Tensor. Must be one of the following types: float16, float32 * @li x: A Tensor of the same type as "dy" . \n @@ -169,7 +169,7 @@ REG_OP(Relu) * x: A Tensor of type RealNumberType . \n * @par Outputs: -* y: A Tensor with the same type as x . \n +* y: A Tensor of type RealNumberType . \n * @par Third-party framework compatibility * Compatible with the TensorFlow operator Relu6. @@ -209,12 +209,8 @@ REG_OP(Relu6D) * backprops = gradients * (features > 0) * (features < 6) . \n * @par Inputs: -* @li gradients: A Tensor of type RealNumberType. The backpropagated - gradients to the corresponding Relu6 operation. -* @li features: A Tensor with the same type as gradients.he features passed - as input to the corresponding Relu6 operation, or its output; - using either one produces the same result. \n - +* @li features: A Tensor of type RealNumberType. +* @li gradients: A Tensor of type RealNumberType . \n * @par Outputs: * backprops: A Tensor of type RealNumberType . \n @@ -232,7 +228,7 @@ REG_OP(Relu6Grad) *Applies the element-wise function: * Computes the backward for the elu: if x>0, 1; otherwise elu() + alpha . *@par Inputs: -*Two inputs, including: +*One inputs, including: * @li grads: A tensor. Must be one of the following types: * float16, float32. * @li activations: A tensor. Must be one of the following types: @@ -242,7 +238,7 @@ REG_OP(Relu6Grad) *y: A Tensor with the same type and shape of grads's. * *@par Attributes: -*alpha: scalar parameter, default value = 1.0 +*@li alpha: scalar parameter, default value = 1.0 */ REG_OP(EluGradV2) .INPUT(grads, TensorType({DT_FLOAT, DT_FLOAT16})) @@ -543,9 +539,13 @@ REG_OP(Elu) *x: A float16, float32, for the input data type . \n *@par Attributes: -*@li alpha1: A float32. Defines at which negative value the ELU saturates. Defaults to "1.0" . -*@li alpha2: A float32. Defines at which negative value the ELU saturates. Defaults to "1.0" . -*@li alpha3: A float32. Defines at which positive value the ELU saturates. Defaults to "1.0" . \n +*alpha1: A float32. Defines at which negative value the ELU saturates. Defaults to "1.0" . \n + +*@par Attributes: +*alpha2: A float32. Defines at which negative value the ELU saturates. Defaults to "1.0" . \n + +*@par Attributes: +*alpha3: A float32. Defines at which positive value the ELU saturates. Defaults to "1.0" . \n *@par Outputs: *y: A float16, float32, for the normalized result . \n @@ -706,8 +706,8 @@ REG_OP(Mish) * @li x: A Tensor. Must be one of the following types: float16, float32 * @li tanhx: A Tensor. shape, datatype and format is same as x * @par Outputs: - * One output, including: - * x_grad: A Tensor. shape, datatype and format is same as x + * 1 output, including: + * @li x_grad: A Tensor. shape, datatype and format is same as x */ REG_OP(MishGrad) @@ -721,20 +721,20 @@ REG_OP(MishGrad) * @brief pytorch hardtanh_backward operator. * * @par Inputs: - * Two inputs, including: + * 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: - * Two attributes, including: + * 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: - * One output, including: - * y, hardtanh_backward output tensor, datatype and format is same as + * 1 output, including: + * @li y, hardtanh_backward output tensor, datatype and format is same as * input result. \n * @attention Constraints: @@ -756,7 +756,7 @@ REG_OP(HardtanhGrad) * @par Inputs: * One inputs, including: -* x: A mutable Tensor. Must be one of the following types: +* @li x: A mutable Tensor. Must be one of the following types: * float16, float32. \n * @par Attributes: @@ -765,7 +765,7 @@ REG_OP(HardtanhGrad) * @li threshold: An optional float. Defaults to "20.0" \n * @par Outputs: -* y: A mutable Tensor. Has the same type as "x" \n +* @li y: A mutable Tensor. Has the same type as "x" \n * @par Third-party framework compatibility * Compatible with the Pytorch operator Softplus. @@ -792,7 +792,7 @@ REG_OP(SoftplusV2) * @li threshold: An optional float. Defaults to "20.0" \n * @par Outputs: -* output_backprops: A mutable Tensor. Has the same type as "input_gradients" \n +* @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. @@ -809,16 +809,13 @@ REG_OP(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: + * @par inputs * one input including: - * x: input A Tensor. Must be one of the following types: float32, float16 + * @li x: input A Tensor. Must be one of the following types: float32, float16 * - * @par Attributes: - * alpha: An optional float. Defaults to 1.0. \n - - * @par Outputs: + * @par output * one output including: - * y:A Tensor of the same type as x + * @li y:A Tensor of the same type as x * */ REG_OP(ThresholdedRelu) @@ -832,14 +829,14 @@ REG_OP(ThresholdedRelu) * @par Inputs: * One inputs, including: -* input_x: A tensor. Must be one of the following types: +* @li input_x: A tensor. Must be one of the following types: * float16, float32. \n * @par Attributes: -* lambd: An optional float. Defaults to 0.5. \n +* @li lambd: An optional float. Defaults to 0.5. \n * @par Outputs: -* output_y: A Tensor with the same dtype and shape of input_x's. \n +* 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 @@ -866,7 +863,7 @@ REG_OP(HardShrink) *backprops: A Tensor with the same type and shape of features's. \n * *@par Attributes: -*lambd: An optional float.Defaults to 0.5. \n +*@li lambd: An optional float.Defaults to 0.5. \n * *@par Third-party framework compatibility *Compatible with the Pytorch operator Hardshrink_backward. \n @@ -883,7 +880,7 @@ REG_OP(HardShrink) * @par Inputs: * One inputs, including: -* input_x: A tensor. Must be one of the following types: +* @li input_x: A tensor. Must be one of the following types: * float16, float32, int32. \n * @par Attributes: @@ -908,11 +905,11 @@ REG_OP(HardSigmoid) * @par Inputs: * One inputs, including: -* input_x: A tensor. Must be one of the following types: +* @li input_x: A tensor. Must be one of the following types: * float16, float32. \n * @par Attributes: -* lambd: An optional float. Defaults to 0.5. \n +* @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 @@ -936,7 +933,7 @@ REG_OP(SoftShrink) * @li input_x: A tensor of the same dtype as "input_grad". \n * @par Attributes: -* lambd: An optional float. Defaults to 0.5. \n +* @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 @@ -979,12 +976,12 @@ REG_OP(LogSigmoidGrad) *@par Inputs: *One inputs, including: -* x: A tensor. Must be one of the following types: +* @li x: A tensor. Must be one of the following types: * float16, float32. \n *@par Outputs: *One outputs, including: -* y: A tensor with the same type and shape of x's. \n +* @li y: A tensor with the same type and shape of x's. \n *@par Third-party framework compatibility *Compatible with the Pytorch operator LogSigmoid. \n @@ -1006,7 +1003,7 @@ REG_OP(LogSigmoid) *@par Outputs: *One outputs, including: -* y: A tensor with the same type and shape of x's. \n +* @li y: A tensor with the same type and shape of x's. \n * @par Attributes: * @li alpha: An optional float. Defaults to 0.16666666. \n diff --git a/third_party/fwkacllib/inc/ops/pad_ops.h b/third_party/fwkacllib/inc/ops/pad_ops.h index 9d0e7a62..6854c866 100644 --- a/third_party/fwkacllib/inc/ops/pad_ops.h +++ b/third_party/fwkacllib/inc/ops/pad_ops.h @@ -33,8 +33,8 @@ namespace ge { *@li value: A 0D scalar. Specifies the value to fill the returned tensor. * Must be one of the following types: -* float16, float32, double, int32, uint8, int16, int8, complex64, int64, bool, -* qint8, quint8, qint32, qint16, quint16, uint16, complex128, uint32, uint64, . +* float16, float32, double, int32, uint8, int16, int8, complex64, int64, +* qint8, quint8, qint32, uint16, complex128, uint32, uint64. * *@par Outputs: * y: A tensor. Has the same type as "value". @@ -46,14 +46,8 @@ namespace ge { */ REG_OP(Fill) .INPUT(dims, TensorType::IndexNumberType()) - .INPUT(value, TensorType({DT_FLOAT, DT_DOUBLE, DT_INT32, DT_UINT8, DT_INT16, - DT_INT8, DT_COMPLEX64, DT_INT64, DT_BOOL, DT_QINT8, - DT_QUINT8, DT_QINT32, DT_QINT16, DT_QUINT16, DT_UINT16, - DT_COMPLEX128, DT_FLOAT16, DT_UINT32, DT_UINT64})) - .OUTPUT(y, TensorType({DT_FLOAT, DT_DOUBLE, DT_INT32, DT_UINT8, DT_INT16, - DT_INT8, DT_COMPLEX64, DT_INT64, DT_BOOL, DT_QINT8, - DT_QUINT8, DT_QINT32, DT_QINT16, DT_QUINT16, DT_UINT16, - DT_COMPLEX128, DT_FLOAT16, DT_UINT32, DT_UINT64})) + .INPUT(value, TensorType::BasicType()) + .OUTPUT(y, TensorType::BasicType()) .OP_END_FACTORY_REG(Fill) /** @@ -219,11 +213,11 @@ REG_OP(PadV2) *@brief Pads a tensor . \n *@par Inputs: -*@li x: A Tensor. Must be one of the following types: float16, float32, int32 . \n -*@li constant_values: A Tensor. Must have the same type as input. +*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: -*paddings: A required Attribute. +*paddings: An optional "vector>". Defaults to "{}". * For each dimension D of input, paddings[D, 0] indicates how many * values to add before the contents of tensor in that dimension, * and paddings[D, 1] indicates how many values to add after the @@ -467,7 +461,7 @@ REG_OP(FillV2) * @li dims: An required listInt to specify the shape that the value to fill. * @par Outputs: -* y: A Tensor. Has the shape specify by attr shape, and full of the value specify by attr value. +* @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. diff --git a/third_party/fwkacllib/inc/ops/parsing_ops.h b/third_party/fwkacllib/inc/ops/parsing_ops.h index e578997c..b625180a 100644 --- a/third_party/fwkacllib/inc/ops/parsing_ops.h +++ b/third_party/fwkacllib/inc/ops/parsing_ops.h @@ -54,26 +54,27 @@ REG_OP(StringToNumber) /** *@brief Convert serialized tensorflow.TensorProto prototype to Tensor. *@brief Parse an Example prototype. -*@par Inputs: -*@li serialized: A Tensor of type string. -*@li dense_defaults: DYNAMIC INPUT Tensor type as string, float, int64. \n +*@par Input: +*serialized: A Tensor of type string. +*dense_defaults: DYNAMIC INPUT Tensor type as string, float, int64. \n *@par Attributes: -*@li num_sparse: type int num of inputs sparse_indices , sparse_values, sparse_shapes -*@li sparse_keys: ListString -*@li sparse_types: types of sparse_values -*@li dense_keys: ListString -*@li Tdense: output of dense_defaults type -*@li dense_shapes: output of dense_defaults shape \n +*num_sparse: type int num of inputs sparse_indices , sparse_values, sparse_shapes +*out_type: output type +*sparse_keys: ListString +*sparse_types: types of sparse_values +*dense_keys: ListString +*dense_shapes: output of dense_defaults shape +*dense_types: output of dense_defaults type \n *@par Outputs: -*@li sparse_indices: A Tensor of type string. -*@li sparse_values: Has the same type as sparse_types. -*@li sparse_shapes: A Tensor of type int64 -*@li dense_values: Has the same type as dense_defaults. +*sparse_indices: A Tensor of type string. +*sparse_values: Has the same type as sparse_types. +*sparse_shapes: A Tensor of type int64 +*dense_values: Has the same type as dense_defaults. *Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. -*/ +**/ REG_OP(ParseSingleExample) .INPUT(serialized, TensorType({DT_STRING})) .DYNAMIC_INPUT(dense_defaults, TensorType({DT_STRING,DT_FLOAT,DT_INT64})) @@ -91,16 +92,16 @@ REG_OP(ParseSingleExample) /** *@brief Decodes raw file into tensor . \n -*@par Inputs: +*@par Input: *bytes: A Tensor of type string. *@par Attributes: -*@li little_endian: bool ture -*@li out_type: output type +*little_endian: bool ture +*out_type: output type *@par Outputs: *Output: A Tensor -*/ +**/ REG_OP(DecodeRaw) .INPUT(bytes, TensorType({DT_STRING})) .OUTPUT(output, TensorType({DT_BOOL,DT_FLOAT16,DT_DOUBLE,DT_FLOAT, @@ -146,20 +147,18 @@ REG_OP(ParseTensor) *@par Inputs: *Inputs include: -*@li records: Each string is a record/row in the csv and all records should have the +*records: Each string is a record/row in the csv and all records should have the *same format. \n -*@li record_defaults: One tensor per column of the input record, with either a +*record_defaults: One tensor per column of the input record, with either a *scalar default value for that column or an empty vector if the column is *required. \n *@par Attributes: -*@li OUT_TYPE: The numeric type to interpret each string in string_tensor as . \n -*@li field_delim: char delimiter to separate fields in a record. \n -*@li use_quote_delim: If false, treats double quotation marks as regular characters +*OUT_TYPE: The numeric type to interpret each string in string_tensor as . \n +*field_delim: char delimiter to separate fields in a record. \n +*use_quote_delim: If false, treats double quotation marks as regular characters *inside of the string fields (ignoring RFC 4180, Section 2, Bullet 5). \n -*@li na_value: Additional string to recognize as NA/NaN. \n -*@li select_cols: Optional sorted list of column indices to select. If specified, -only this subset of columns will be parsed and returned. +*na_value: Additional string to recognize as NA/NaN. \n *@par Outputs: *output: A Tensor. Has the same type as x . \n @@ -187,25 +186,25 @@ REG_OP(DecodeCSV) /** *@brief Convert serialized tensorflow.TensorProto prototype to Tensor. *@brief Parse an Example prototype. -*@par Inputs: -*@li serialized: A Tensor of type string. \n -*@li name:A Tensor of type string. \n -*@li sparse_keys: Dynamic input tensor of string. \n -*@li dense_keys: Dynamic input tensor of string \n -*@li dense_defaults: Dynamic input tensor type as string, float, int64. \n +*@par Input: +*serialized: A Tensor of type string. \n +*name:A Tensor of type string. \n +*sparse_keys: Dynamic input tensor of string. \n +*dense_keys: Dynamic input tensor of string \n +*dense_defaults: Dynamic input tensor type as string, float, int64. \n *@par Attributes: -*@li Nsparse: Number of sparse_keys, sparse_indices and sparse_shapes \n -*@li Ndense: Number of dense_keys \n -*@li sparse_types: types of sparse_values \n -*@li Tdense: Type of dense_defaults dense_defaults and dense_values \n -*@li dense_shapes: output of dense_defaults shape \n +*Nsparse: Number of sparse_keys, sparse_indices and sparse_shapes \n +*Ndense: Number of dense_keys \n +*sparse_types: types of sparse_values \n +*Tdense: Type of dense_defaults dense_defaults and dense_values \n +*dense_shapes: output of dense_defaults shape \n *@par Outputs: -*@li sparse_indices: A Tensor of type string. \n -*@li sparse_values: Has the same type as sparse_types. \n -*@li sparse_shapes: A Tensor of type int64 \n -*@li dense_values: Has the same type as dense_defaults. \n +*sparse_indices: A Tensor of type string. \n +*sparse_values: Has the same type as sparse_types. \n +*sparse_shapes: A Tensor of type int64 \n +*dense_values: Has the same type as dense_defaults. \n *@par Third-party framework compatibility \n *@li compatible with tensorflow StringToNumber operator. \n */ @@ -229,37 +228,37 @@ REG_OP(ParseExample) /** *@brief Transforms a scalar brain.SequenceExample proto (as strings) into typed *tensors. -*@par Inputs: -*@li serialized: A Tensor of type string. \n -*@li feature_list_dense_missing_assumed_empty:A Tensor of type string. \n -*@li context_sparse_keys: Dynamic input tensor of string. \n -*@li context_dense_keys: Dynamic input tensor of string \n -*@li feature_list_sparse_keys: Dynamic input tensor of string \n -*@li feature_list_dense_keys: Dynamic input tensor of string \n -*@li context_dense_defaults: Dynamic input tensor of string, float, int64 \n -*@li debug_name: A Tensor of type string. \n +*@par Input: +*serialized: A Tensor of type string. \n +*feature_list_dense_missing_assumed_empty:A Tensor of type string. \n +*context_sparse_keys: Dynamic input tensor of string. \n +*context_dense_keys: Dynamic input tensor of string \n +*feature_list_sparse_keys: Dynamic input tensor of string \n +*feature_list_dense_keys: Dynamic input tensor of string \n +*context_dense_defaults: Dynamic input tensor of string, float, int64 \n +*debug_name: A Tensor of type string. \n *@par Attributes: -*@li Ncontext_sparse: Number of context_sparse_keys, context_sparse_indices and context_sparse_shapes \n -*@li Ncontext_dense: Number of context_dense_keys \n -*@li Nfeature_list_sparse: Number of feature_list_sparse_keys \n -*@li Nfeature_list_dense: Number of feature_list_dense_keys \n -*@li context_sparse_types: Types of context_sparse_values \n -*@li Tcontext_dense: Number of dense_keys \n -*@li feature_list_dense_types: Types of feature_list_dense_values \n -*@li context_dense_shapes: Shape of context_dense \n -*@li feature_list_sparse_types: Type of feature_list_sparse_values \n -*@li feature_list_dense_shapes: Shape of feature_list_dense \n +*Ncontext_sparse: Number of context_sparse_keys, context_sparse_indices and context_sparse_shapes \n +*Ncontext_dense: Number of context_dense_keys \n +*Nfeature_list_sparse: Number of feature_list_sparse_keys \n +*Nfeature_list_dense: Number of feature_list_dense_keys \n +*context_sparse_types: Types of context_sparse_values \n +*Tcontext_dense: Number of dense_keys \n +*feature_list_dense_types: Types of feature_list_dense_values \n +*context_dense_shapes: Shape of context_dense \n +*feature_list_sparse_types: Type of feature_list_sparse_values \n +*feature_list_dense_shapes: Shape of feature_list_dense \n *@par Outputs: -*@li context_sparse_indices: Dynamic output tensor of type int64. \n -*@li context_sparse_values: Dynamic output tensor of type string, float, int64. \n -*@li context_sparse_shapes: Dynamic output tensor of type int64 \n -*@li context_dense_values: Dynamic output tensor of type string, float, int64. \n -*@li feature_list_sparse_indices: Dynamic output tensor of type int64. \n -*@li feature_list_sparse_values: Dynamic output tensor of type string, float, int64. \n -*@li feature_list_sparse_shapes: Dynamic output tensor of type int64 \n -*@li feature_list_dense_values: Dynamic output tensor of type string, float, int64. \n +*context_sparse_indices: Dynamic output tensor of type int64. \n +*context_sparse_values: Dynamic output tensor of type string, float, int64. \n +*context_sparse_shapes: Dynamic output tensor of type int64 \n +*context_dense_values: Dynamic output tensor of type string, float, int64. \n +*feature_list_sparse_indices: Dynamic output tensor of type int64. \n +*feature_list_sparse_values: Dynamic output tensor of type string, float, int64. \n +*feature_list_sparse_shapes: Dynamic output tensor of type int64 \n +*feature_list_dense_values: Dynamic output tensor of type string, float, int64. \n *@par Third-party framework compatibility \n *@li compatible with tensorflow StringToNumber operator. \n */ diff --git a/third_party/fwkacllib/inc/ops/quantize_ops.h b/third_party/fwkacllib/inc/ops/quantize_ops.h index 0636833c..69d5e67e 100644 --- a/third_party/fwkacllib/inc/ops/quantize_ops.h +++ b/third_party/fwkacllib/inc/ops/quantize_ops.h @@ -63,11 +63,10 @@ REG_OP(Dequantize) /** *@brief Quantizes the input . \n *@par Inputs: -*@li x: shape and dtype of input_x. \n -*@li scales: shape and dtype of input_scales. \n -*@li zero_points: shape and dtype of input_zero_points \n +*x: shape and dtype of input_x. \n +*scales: shape and dtype of input_scales. \n +*zero_points: shape and dtype of input_zero_points \n *@par Attributes: -*@li dtype: required, type. *@li axis: the processed dim. \n *@par Outputs: *y: shape and dtype of output_y, should be same shape as input, dtype is same as the quantified type . \n @@ -92,8 +91,7 @@ REG_OP(Quantize) *@li offset: A required float16, specifying the offset. *@li sqrt_mode: A optional bool, specifying whether to perform square root on "scale", either "True" or "False". Defaults to "False". *@li round_mode: An optional string, specifying the float16 to int8 cast type. -* The value range is [Round, Floor, Ceil, Truncate]. Defaults to "Round" . -*@li dst_type: A optional int32, specifying the output data type. Defaults to "DT_INT8" . \n +* The value range is [Round, Floor, Ceiling, Truncate]. Defaults to "Round" . \n *@par Outputs: *y: The quantized output tensor of type int8 and with format NC1HWC0 . \n diff --git a/third_party/fwkacllib/inc/ops/ragged_array_ops.h b/third_party/fwkacllib/inc/ops/ragged_array_ops.h index 5af2dd74..20484623 100644 --- a/third_party/fwkacllib/inc/ops/ragged_array_ops.h +++ b/third_party/fwkacllib/inc/ops/ragged_array_ops.h @@ -37,18 +37,13 @@ namespace ge { *deprecated name. *@li indices: Indices in the outermost dimension of `params` of the values that should be *gathered. - -*@par Attributes: -*@li PARAMS_RAGGED_RANK:The ragged rank of the params_nested_splits. -*@li Tsplits:A type of output_nested_splits. *@li OUTPUT_RAGGED_RANK: The ragged rank of the output RaggedTensor. `output_nested_splits` will contain *this number of `row_splits` tensors. This value should equal *`indices.shape.ndims + params.ragged_rank - 1` . \n *@par Outputs: -*@li output_nested_splits:A Returns The `nested_row_splits` tensors that define the row-partitioning for the -*returned RaggedTensor.The `flat_values` for the returned RaggedTensor . -*@li output_dense_values:The `flat_values` for the returned RaggedTensor. \n +*y:A Returns The `nested_row_splits` tensors that define the row-partitioning for the +*returned RaggedTensor.The `flat_values` for the returned RaggedTensor . \n *@par Third-party framework compatibility * Compatible with tensorflow RaggedGather operator. diff --git a/third_party/fwkacllib/inc/ops/ragged_conversion_ops.h b/third_party/fwkacllib/inc/ops/ragged_conversion_ops.h index ceaa64e4..020e3da4 100644 --- a/third_party/fwkacllib/inc/ops/ragged_conversion_ops.h +++ b/third_party/fwkacllib/inc/ops/ragged_conversion_ops.h @@ -61,6 +61,7 @@ REG_OP(RaggedTensorToSparse) *@brief Create a dense tensor from a ragged tensor, possibly altering its shape . \n *@par Inputs: +*Six inputs, including: *@li shape:A `Tensor`. Must be one of the following types: `int64`, `int32`. *@li values:A 1D tensor representing the values of the ragged tensor. *@li default_value:A `Tensor`. Must have the same type as `values`. @@ -77,7 +78,7 @@ The types of the row partition tensors. At present, these can be: is preceeded by "FIRST_DIM_SIZE" . \n *@par Outputs: -*result: A `Tensor`. Has the same type as `values`. +*@li result: A `Tensor`. Has the same type as `values`. */ REG_OP(RaggedTensorToTensor) .INPUT(shape, TensorType({DT_INT32, DT_INT64})) diff --git a/third_party/fwkacllib/inc/ops/ragged_math_ops.h b/third_party/fwkacllib/inc/ops/ragged_math_ops.h index 4376437f..258b0ca1 100644 --- a/third_party/fwkacllib/inc/ops/ragged_math_ops.h +++ b/third_party/fwkacllib/inc/ops/ragged_math_ops.h @@ -35,11 +35,7 @@ namespace ge { *@li deltas: The deltas of each range . \n *@par Outputs: -*@li rt_dense_values:The `flat_values` for the returned `RaggedTensor`. -*@li rt_nested_splits:The `row_splits` for the returned `RaggedTensor`. \n - -*@par Attributes: -*Tsplits:A type of rt_nested_splits. +*y:A Returns The `row_splits` for the returned `RaggedTensor`.The `flat_values` for the returned `RaggedTensor` . \n *@attention Constraints: *The input tensors `starts`, `limits`, and `deltas` may be scalars or vectors. diff --git a/third_party/fwkacllib/inc/ops/random_ops.h b/third_party/fwkacllib/inc/ops/random_ops.h index 66f9b65f..b65a68f1 100644 --- a/third_party/fwkacllib/inc/ops/random_ops.h +++ b/third_party/fwkacllib/inc/ops/random_ops.h @@ -148,32 +148,6 @@ REG_OP(RandomGamma) .OP_END_FACTORY_REG(RandomGamma) /** -*@brief Returns the random permutation of integers from 0 to n-1. \n - -*@par Attributes: -*@li n: An required int. -*@li dtype: An optional str. Defaults to int64 . -*@li layout: An optional int. Defaults to 0 . \n - -*@par Outputs: -*out: A required Tensor. Must be one of the following types: - float16, float32, float32, int8, uint8, int16, int32, int64. \n - -*@attention Constraints: -*The implementation for Randperm on Ascend uses AICPU, with bad performance. - -*@par Third-party framework compatibility -*@li compatible with Pytorch Randperm operator. -*/ -REG_OP(Randperm) - .OUTPUT(out, TensorType({DT_INT64, DT_INT32, DT_INT16, - DT_UINT8, DT_INT8, DT_FLOAT16, DT_FLOAT32, DT_DOUBLE})) - .REQUIRED_ATTR(n, Int) - .ATTR(layout, Int, 0) - .ATTR(dtype, Type, DT_INT64) - .OP_END_FACTORY_REG(Randperm) - -/** *@brief Outputs random values from the Poisson distribution(s) described by rate . \n *@par Inputs: @@ -183,12 +157,11 @@ REG_OP(Randperm) *@par Attributes: *@li dtype: An optional type from: half, float32, float64, int32, int64. Defaults to int64. -*@li seed: An optional int. Defaults to 0. If either seed or seed2 are set to be non-zero, -the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed. -*@li seed2: An optional int. Defaults to 0 . A second seed to avoid seed collision. \n +*@li seed: An optional int. Defaults to 0. +*@li seed2: An optional int. Defaults to 0 . \n *@par Outputs: -*y: A Tensor of type dtype float16, float, double, int32, int64. \n +*y: A Tensor of type dtype . \n *@attention Constraints: *The implementation for RandomPoisson on Ascend uses AICPU, with bad performance. @@ -215,13 +188,11 @@ REG_OP(RandomPoisson) *x: A Tensor. The tensor to be shuffled . \n *@par Attributes: -*@li seed: An optional int. Defaults to 0. If either seed or seed2 are set to be non-zero, -the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed. -*@li seed2: An optional int. Defaults to 0 . A second seed to avoid seed collision. \n +*@li seed: An optional int. Defaults to 0. +*@li seed2: An optional int. Defaults to 0 . \n *@par Outputs: -*y: A Tensor. Has the same type as x . A Tensor of type float16, float, -*double, int32, int64, int16, uint16, int8, uint8, int32,int64. \n +*y: A Tensor. Has the same type as x . \n *@attention Constraints: *The implementation for RandomShuffle on Ascend uses AICPU, with bad performance. @@ -249,12 +220,11 @@ REG_OP(RandomShuffle) *@par Attributes: *@li dtype: A type from: half, float16, float32, float64. The type of the output. -*@li seed: An optional int. Defaults to 0. If either seed or seed2 are set to be non-zero, -the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed. -*@li seed2: An optional int. Defaults to 0 . A second seed to avoid seed collision. \n +*@li seed: An optional int. Defaults to 0. +*@li seed2: An optional int. Defaults to 0 . \n *@par Outputs: -*y: A Tensor of type float32, float16, double. \n +*y: A Tensor of type dtype . \n *@attention Constraints: *The implementation for RandomStandardNormal on Ascend uses AICPU, with bad performance. @@ -271,28 +241,6 @@ REG_OP(RandomStandardNormal) .OP_END_FACTORY_REG(RandomStandardNormal) /** -*@brief Output random value from separate normal distribution. \n - -*@par Inputs: -*Inputs include: -*mean: The mean is a tensor with the mean of each output element’s normal distribution . -*std: The std is a tensor with the standard deviation of each output element’s normal distribution. \n -*@par Outputs: -*y: A Tensor of type dtype . \n - -*@attention Constraints: -*The implementation for Normal on Ascend uses AICPU, with bad performance. - -*@par Third-party framework compatibility -*@li compatible with Pytorch Normal operator. -*/ -REG_OP(Normal) - .INPUT(mean, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) - .INPUT(std, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) - .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) - .OP_END_FACTORY_REG(Normal) - -/** *@brief Outputs random integers from a uniform distribution . \n *@par Inputs: @@ -302,9 +250,8 @@ REG_OP(Normal) * @li max: A Tensor. Must have the same type as minval. 0-D . \n *@par Attributes: -*@li seed: An optional int. Defaults to 0. If either seed or seed2 are set to be non-zero, -the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed. -*@li seed2: An optional int. Defaults to 0 . A second seed to avoid seed collision. \n +*@li seed: An optional int. Defaults to 0. +*@li seed2: An optional int. Defaults to 0 . \n *@par Outputs: *y: A Tensor. Has the same type as min . \n @@ -333,9 +280,8 @@ REG_OP(RandomUniformInt) *@par Attributes: *@li dtype: A type from: half, float16, float32, float64. The type of the output. -*@li seed: An optional int. Defaults to 0. If either seed or seed2 are set to be non-zero, -the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed. -*@li seed2: An optional int. Defaults to 0 . A second seed to avoid seed collision. \n +*@li seed: An optional int. Defaults to 0. +*@li seed2: An optional int. Defaults to 0 . \n *@par Outputs: *y: A Tensor of type dtype . \n @@ -362,14 +308,11 @@ REG_OP(RandomUniform) *shape: A Tensor. Must be one of the following types: int32, int64 . \n *@par Attributes: -*@li seed: An optional int. Defaults to 0.If either `seed` or `seed2` -are set to be non-zero, the random number generator is seeded by the given -seed. Otherwise, it is seeded by a random seed. -*@li seed2: An optional int. Defaults to 0 . A second seed to avoid seed collision. \n +*@li seed: An optional int. Defaults to 0. +*@li seed2: An optional int. Defaults to 0 . \n *@par Outputs: -*y: A Tensor of types: float16, float32, double . A tensor of the specified shape -filled with random truncated normal values. \n +*size: A Tensor of types: float16, float32, double . \n *@attention Constraints: *The implementation for TruncatedNormal on Ascend uses AICPU, with bad performance. @@ -562,15 +505,15 @@ REG_OP(RandomChoiceWithMask) *@par Inputs: *Inputs including: -* x: A required Tensor. Must be one of the following types: - float16, float32, int8, uint8, int16, uint16, int32, uint32, int64, uint64 . \n +* @li x: A required Tensor. Must be one of the following types: + float16, float32, int8, uint8, int16, uint16, int32, uint32, int64, uint64 . \n *@par Attributes: -* group: A required int32, specifying the number of groups to split the channel dimension into. Defaults to "1" . \n +*@li group: A required int32, specifying the number of groups to split the channel dimension into. Defaults to "1" . \n *@par Outputs: -* y: A required Tensor. Has same type and shape as "x". Must be one of the following types: - float16, float32, int8, uint8, int16, uint16, int32, uint32, int64, uint64 . \n +*y: A required Tensor. Has same type and shape as "x". Must be one of the following types: + float16, float32, int8, uint8, int16, uint16, int32, uint32, int64, uint64 . \n *@attention Constraints: *@li "group" must be greater than 0 and must evenly divide the channel dimension size. @@ -641,50 +584,6 @@ REG_OP(DropoutV2) .OUTPUT(seed, TensorType({ DT_FLOAT })) .REQUIRED_ATTR(p, Float) .OP_END_FACTORY_REG(DropoutV2) - -/** -* @brief The Bernoulli distribution with probability . \n - -* @par Inputs: -* @li x: A ND Tensor. Must be one of the following data types: - int8, uint8, int16, int32, int64, bool, float32, float64 . -* @li p: A ND Tensor. The probability of an element to be zeroed. - Must be one of the following data types: float32, float64. \n - -* @par Attributes: -* seed: An Integer, the seed of the random generator. Default value -1 - to use current timestamp, otherwise it should be a positive integer. - -* @par Outputs: -* y: A tensor with the same shape and type as "x". -*/ - -REG_OP(Bernoulli) - .INPUT(x, TensorType({ DT_INT8, DT_UINT8, DT_INT16, DT_INT32, DT_INT64, DT_BOOL, DT_FLOAT, DT_DOUBLE})) - .INPUT(p, TensorType({ DT_FLOAT, DT_DOUBLE })) - .OUTPUT(y, TensorType({ DT_INT8, DT_UINT8, DT_INT16, DT_INT32, DT_INT64, DT_BOOL, DT_FLOAT, DT_DOUBLE})) - .ATTR(seed, Int, -1) - .OP_END_FACTORY_REG(Bernoulli) - -/** - * @brief: Fill the input tensor with values drawn from the uniform distribution U(from, to). \n - - * @par Inputs: - * x: A Tensor. Must be one of the following types: float16, float, double. \n - - * @par Attributes: - * @li from: The lower bound of the uniform. Defaults: 0.0 - * @li to: The upper bound of the uniform. Defaults: 1.0 \n - - * @par Outputs: - * y: A Tensor has the same type as x. \n - */ -REG_OP(Uniform) - .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) - .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) - .ATTR(from, Float, 0.0) - .ATTR(to, Float, 1.0) - .OP_END_FACTORY_REG(Uniform) } // namespace ge #endif // OPS_BUILT_IN_OP_PROTO_INC_RANDOM_OPS_H_ diff --git a/third_party/fwkacllib/inc/ops/reduce_ops.h b/third_party/fwkacllib/inc/ops/reduce_ops.h index 1578ba59..97c7b8e1 100644 --- a/third_party/fwkacllib/inc/ops/reduce_ops.h +++ b/third_party/fwkacllib/inc/ops/reduce_ops.h @@ -576,7 +576,7 @@ REG_OP(ReduceAll) *@li axis: A mutable Tensor. The dimensions to reduce . \n *@par Attributes: -*keep_dims: A bool. If true, retains reduced dimensions with length 1. Defaults to "False" . \n +*@li keep_dims: A bool. If true, retains reduced dimensions with length 1. Defaults to "False" . \n *@par Outputs: *y: A Tensor. Has the same type and format as input "x" . \n @@ -967,9 +967,9 @@ REG_OP(EuclideanNormD) Defaults to "0.00001" . \n *@par Outputs: -*@li y: A Tensor of type float16 or float32 for the normalized "x". -*@li batch_mean: A Tensor of type float32 for the result mean. -*@li batch_ variance: A Tensor of type float32 for the result variance . \n +*y: A Tensor of type float16 or float32 for the normalized "x". +*batch_mean: A Tensor of type float32 for the result mean. +*batch_ variance: A Tensor of type float32 for the result variance . \n *@attention Constraints: *For Ascend 310, the result accuracy fails to reach 0.001 due to the square root instruction. @@ -987,7 +987,7 @@ REG_OP(INInferV2) .OP_END_FACTORY_REG(INInferV2) /** -*@brief Performs reduce instance normalization. \n +*@brief Performs reduced instance normalization . \n *@par Inputs: *x: A Tensor of type float16 or float32. \n @@ -1008,31 +1008,32 @@ REG_OP(INTrainingReduceV2) /** -*@brief Performs update instance normalization. \n +*@brief Performs update instance normalization . \n *@par Inputs: -* Seven inputs, including: +* Seven inputs, including: (NC1HWC0supported) *@li x: A Tensor of type float16 or float32. *@li sum: A Tensor of type float32 for the output of operator INTrainingReduceV2. *@li square_sum: A Tensor of type float32 for the output of operator INTrainingReduceV2. *@li gamma: A Tensor of type float32, for the scaling gamma. *@li beta: A Tensor of type float32, for the scaling beta. *@li mean: A Tensor of type float32, for the updated mean. -*@li variance: A Tensor of type float32, for the updated variance. \n +*@li variance: A Tensor of type float32, for the updated variance . \n *@par Attributes: *@li momentum: A required float32, specifying the momentum to update mean and var. -*@li epsilon: A required float32, specifying the small value added to variance to avoid dividing by zero. \n +*@li epsilon: A required float32, specifying the small value added to variance to avoid dividing by zero . \n *@par Outputs: * Three outputs *@li y: A Tensor of type float16 or float32, for normalized "x". *@li batch_mean: A Tensor of type float32, for the updated mean. -*@li batch_variance: A Tensor of type float32, for the updated variance. \n +*@li batch_variance: A Tensor of type float32, for the updated variance . \n *@attention Constraints: -* This operator is a InstanceNorm fusion operator for updating the moving averages for training. +*@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. */ REG_OP(INTrainingUpdateV2) .INPUT(x, TensorType({DT_FLOAT16,DT_FLOAT})) @@ -1051,80 +1052,6 @@ REG_OP(INTrainingUpdateV2) /** -*@brief Performs the backpropagation of InstanceNorm. \n - -*@par Inputs: -* Seven inputs, including: -*@li dy: A Tensor of type float16 or float32. -*@li x: A Tensor of type float16 or float32. -*@li variance: A Tensor of type float32, for the variance of "x". -*@li mean: A Tensor of type float32, for the mean of "x". -*@li res_gamma: A Tensor of type float32. -*@li res_beta: A Tensor of type float32. -*@li gamma: A Tensor of type float32. \n - -*@par Outputs: -*pd_x: A Tensor of type float16 or float32, for the offset of "x". \n - -*@attention Constraints: -* The preceding layer of this operator must be INTrainingUpdateGrad. \n -*/ -REG_OP(INTrainingReduceGrad) - .INPUT(dy, TensorType({DT_FLOAT16,DT_FLOAT})) - .INPUT(x, TensorType({DT_FLOAT16,DT_FLOAT})) - .INPUT(variance, TensorType({DT_FLOAT})) - .INPUT(mean, TensorType({DT_FLOAT})) - .INPUT(res_gamma, TensorType({DT_FLOAT})) - .INPUT(res_beta, TensorType({DT_FLOAT})) - .INPUT(gamma, TensorType({DT_FLOAT})) - .OUTPUT(pd_x, TensorType({DT_FLOAT16,DT_FLOAT})) - .OP_END_FACTORY_REG(INTrainingReduceGrad) - -/** -*@brief Performs the backpropagation of InstanceNorm. \n - -*@par Inputs: -* Four inputs, including: -*@li dy: A Tensor of type float16 or float32, for the gradient. -*@li x: A Tensor of type float16 or float32. -*@li variance: A Tensor of type float32, for the variance of "x". -*@li mean: A Tensor of type float32, for the mean of "x". \n - -*@par Outputs: -*@li res_gamma: A Tensor of type float32. -*@li res_beta: A Tensor of type float32. \n - -*/ -REG_OP(INTrainingUpdateGrad) - .INPUT(dy, TensorType({DT_FLOAT16,DT_FLOAT})) - .INPUT(x, TensorType({DT_FLOAT16,DT_FLOAT})) - .INPUT(variance, TensorType({DT_FLOAT})) - .INPUT(mean, TensorType({DT_FLOAT})) - .OUTPUT(res_gamma, TensorType({DT_FLOAT})) - .OUTPUT(res_beta, TensorType({DT_FLOAT})) - .OP_END_FACTORY_REG(INTrainingUpdateGrad) - -/** -*@brief Performs the backpropagation of InstanceNorm. \n - -*@par Inputs: -* Two inputs, including: -*@li res_gamma: A Tensor of type float32. -*@li res_beta: A Tensor of type float32. \n - -*@par Outputs: -*@li pd_gamma: A Tensor of type float32. -*@li pd_beta: A Tensor of type float32. \n - -*/ -REG_OP(INTrainingUpdateGradGammaBeta) - .INPUT(res_gamma, TensorType({DT_FLOAT})) - .INPUT(res_beta, TensorType({DT_FLOAT})) - .OUTPUT(pd_gamma, TensorType({DT_FLOAT})) - .OUTPUT(pd_beta, TensorType({DT_FLOAT})) - .OP_END_FACTORY_REG(INTrainingUpdateGradGammaBeta) - -/** *@brief Performs reduced group normalization . \n *@par Inputs: @@ -1136,7 +1063,7 @@ REG_OP(INTrainingUpdateGradGammaBeta) *@par Attributes: -*num_groups: Int, specifying the num of groups. required, same to GNTrainingUpdate . \n +*@li num_groups: Int, specifying the num of groups. required, same to GNTrainingUpdate . \n *@attention Constraints: * This operator is a GroupNorm fusion operator for updating the moving averages for training. @@ -1154,7 +1081,7 @@ REG_OP(GNTrainingReduce) *@brief Performs update group normalization . \n *@par Inputs: -* Seven inputs, including: (NCHW NHWC supported) +* Eight inputs, including: (NCHW NHWC supported) *@li x: A Tensor of type float16 or float32. *@li sum: A 5D Tensor of type float32, shape is [N, G, 1, 1, 1] for NCHW, [N, 1, 1, G, 1] for NHWC @@ -1218,8 +1145,8 @@ include: *@li keep_dims:A bool, An optional bool. Defaults to False. If True, retain reduced dimensions with length 1.. *@li separator:string. -*@par Outputs: -*output:A Tensor of type string. +*@par output: +*@li output::A Tensor of type string.. */ REG_OP(ReduceJoin) .INPUT(input, TensorType({DT_STRING})) @@ -1233,7 +1160,7 @@ REG_OP(ReduceJoin) * @brief Calculates the standard deviation and average value of Tensors. * @par Inputs: -* x: A Tensor. Must be one of the following types: +* @li x: A Tensor. Must be one of the following types: * float16, float32. \n * @par Attributes: diff --git a/third_party/fwkacllib/inc/ops/resource_variable_ops.h b/third_party/fwkacllib/inc/ops/resource_variable_ops.h index 156f2f34..74ac83f8 100644 --- a/third_party/fwkacllib/inc/ops/resource_variable_ops.h +++ b/third_party/fwkacllib/inc/ops/resource_variable_ops.h @@ -33,12 +33,10 @@ namespace ge { *y:A Tensor of type resource. \n *@par Attributes: -* @li container: optional, string. the container this -variable is placed in. -* @li shared_name: optional, string.the name by which - this variable is referred to. -* @li dtype: required, type. the output of type. -* @li shape: optional, ListInt. the output of shape. \n +* @li container: optional, string. +* @li shared_name: optional, string. +* @li dtype: required, type. +* @li shape: optional, ListInt. \n *@see VarHandleOp. */ @@ -55,11 +53,11 @@ REG_OP(VarHandleOp) *@brief Assigns a new value to a variable. \n *@par Inputs: -*@li resource:Handle to the resource in which to store the variable. -*@li value:The value to set the new tensor to use. \n +*resource:Handle to the resource in which to store the variable. +*value:The value to set the new tensor to use. \n *@par Attributes: -* dtype: required, type. \n +* @li dtype: required, type. \n *@see AssignVariableOp. */ @@ -75,11 +73,11 @@ REG_OP(AssignVariableOp) *@brief Adds a value to the current value of a variable. \n *@par Inputs: -*@li resource:Handle to the resource in which to store the variable. -*@li value:The value by which the variable will be incremented. \n +*resource:Handle to the resource in which to store the variable. +*value:The value by which the variable will be incremented. \n *@par Attributes: -* dtype: required, type. \n +* @li dtype: required, type. \n *@see AssignAddVariableOp. */ @@ -95,11 +93,11 @@ REG_OP(AssignAddVariableOp) *@brief Subtracts a value to the current value of a variable. \n *@par Inputs: -*@li resource:Handle to the resource in which to store the variable. -*@li value:The value by which the variable will be incremented. \n +*resource:Handle to the resource in which to store the variable. +*value:The value by which the variable will be incremented. \n *@par Attributes: -* dtype: required, type. \n +* @li dtype: required, type. \n *@see AssignSubVariableOp. */ diff --git a/third_party/fwkacllib/inc/ops/rnn.h b/third_party/fwkacllib/inc/ops/rnn.h index 20828a89..80546860 100644 --- a/third_party/fwkacllib/inc/ops/rnn.h +++ b/third_party/fwkacllib/inc/ops/rnn.h @@ -127,7 +127,9 @@ REG_OP(DynamicLSTM) *@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 false. +*@li activation:An string identifying the type of activation function in the op. Default to "tanh". Only tanh is currently supported. *@li forget_bias:An float identifying the forget bias in the op. Default to 0. +*@li is_training:An bool identifying is training in the op. Default to true. *@par Outputs: *eight outputs: \n @@ -489,6 +491,7 @@ REG_OP(DynamicLSTMV2) *ten inputs: \n *@li w:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. *@li init_c:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. +*@li h:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. *@li c:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. *@li dy:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. *@li dh:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. @@ -501,11 +504,10 @@ REG_OP(DynamicLSTMV2) *@par Outputs: -*four outputs: \n +*eight outputs: \n *@li dx:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. *@li dh_prev:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. *@li dc_prev:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. -*@li dgate:A 4D Tensor. Must be one of the following types: float16. The format must be FRACTAL_NZ. */ REG_OP(LSTMInputGrad) .INPUT(w, TensorType({DT_FLOAT16, DT_FLOAT})) @@ -569,13 +571,13 @@ REG_OP(DynamicLSTMGradCell) .INPUT(f, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(o, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(tanhct, TensorType({DT_FLOAT16, DT_FLOAT})) - .INPUT(t_state, TensorType({DT_INT32, DT_INT32})) .INPUT(mask, TensorType({DT_FLOAT16, DT_FLOAT})) + .INPUT(t_state, TensorType({DT_INT32, DT_INT32})) .OUTPUT(dgate, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(dct_1, TensorType({DT_FLOAT16, DT_FLOAT})) - .ATTR(forget_bias, Float, 1.0) - .ATTR(activation, String, "tanh") - .ATTR(direction, String, "UNIDIRECTIONAL") + .ATTR(forget_bias, Float, 1) + .ATTR(activation, String, "") + .ATTR(direction, String, "Forward") .ATTR(gate_order, String, "ijfo") .OP_END_FACTORY_REG(DynamicLSTMGradCell) @@ -1068,7 +1070,7 @@ REG_OP(GRUV2HiddenGradCell) * If "False", "grad_weight" will not be scale by word_frequency. \n * @par Outputs: -* y: A mutable output Tensor of new word grad has the same type as "grads". \n +* @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. @@ -1220,7 +1222,7 @@ REG_OP(CommonGRU) * is equivalent to the size of indices. This matches the CSR format.. \n * @par Outputs: -* y: A mutable output Tensor of new word grad has the same type as "grads". \n +* @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 EmbeddingBag. diff --git a/third_party/fwkacllib/inc/ops/rpn_ops.h b/third_party/fwkacllib/inc/ops/rpn_ops.h index 850b3e5a..089af326 100644 --- a/third_party/fwkacllib/inc/ops/rpn_ops.h +++ b/third_party/fwkacllib/inc/ops/rpn_ops.h @@ -28,12 +28,12 @@ namespace ge { * iou_threshold with higher scoring box according to their * intersection-over-union (IoU) . \n -* @par Inputs: -* box_scores: 2-D tensor with shape of [N, 8], including proposal boxes and +*@par Input: +* @li box_scores: 2-D tensor with shape of [N, 8], including proposal boxes and * corresponding confidence scores . \n * @par Attributes: -* iou_threshold: An optional float. The threshold for deciding whether boxes +* @li iou_threshold: An optional float. The threshold for deciding whether boxes * overlap too much with respect to IOU . \n * @par Outputs: diff --git a/third_party/fwkacllib/inc/ops/sdca_ops.h b/third_party/fwkacllib/inc/ops/sdca_ops.h index 601b360b..34c6a268 100644 --- a/third_party/fwkacllib/inc/ops/sdca_ops.h +++ b/third_party/fwkacllib/inc/ops/sdca_ops.h @@ -45,13 +45,7 @@ namespace ge { *corresponding weights in sparse_weights. This field maybe omitted for the dense approach.It's a dynamic input. *@li sparse_weights: a list of vectors where each value is the weight associated with a sparse feature group. *@li dense_weights: a list of vectors where the values are the weights associated with a dense feature group.It's a dynamic input. -*@li example_state_data: a list of vectors containing the example state data. \n - -*@par Attributes: -*@li adaptive: the type is bool default false. -*@li num_sparse_features:The num of sparse. -*@li num_sparse_features_with_values: The num of sparse_feature_values -*@li num_dense_features:The num of dense. +*@li example_state_data: a list of vectors containing the example state data. *@li loss_type: Type of the primal loss. Currently SdcaSolver supports logistic, squared and hinge losses. *@li l1: Symmetric l1 regularization strength. *@li l2: Symmetric l2 regularization strength. @@ -59,10 +53,10 @@ namespace ge { *@li num_inner_iterations: Number of iterations per mini-batch . \n *@par Outputs: -*@li out_example_state_data: A Returns a list of vectors containing the updated example state +*y: A Returns a list of vectors containing the updated example state *data.a list of vectors where each value is the delta -*@li out_delta_sparse_weights:weights associated with a sparse feature group.a list of vectors where the values are the delta -*@li out_delta_dense_weights:weights associated with a dense feature group . \n +*weights associated with a sparse feature group.a list of vectors where the values are the delta +*weights associated with a dense feature group . \n *@par Third-party framework compatibility * Compatible with tensorflow SdcaOptimizerV2 operator. diff --git a/third_party/fwkacllib/inc/ops/selection_ops.h b/third_party/fwkacllib/inc/ops/selection_ops.h index 43f72ef3..1c26e033 100644 --- a/third_party/fwkacllib/inc/ops/selection_ops.h +++ b/third_party/fwkacllib/inc/ops/selection_ops.h @@ -258,7 +258,7 @@ REG_OP(GatherV2D) REG_OP(GatherElements) .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32, DT_INT64})) - .INPUT(index, TensorType({DT_INT32, DT_INT64})) + .INPUT(index, TensorType({DT_INT64})) .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32, DT_INT64})) .ATTR(dim, Int, 0) .OP_END_FACTORY_REG(GatherElements) @@ -508,7 +508,7 @@ REG_OP(UnsortedSegmentSum) *@par Inputs: *One inputs, including: -* assist: A tensor. Must be one of the following types: +* @li assist: A tensor. Must be one of the following types: * float16, float32. \n * @par Attributes: @@ -970,11 +970,10 @@ REG_OP(TopKV2) * for matrices) . \n * @par Attributes: -* @li sorted: Defaults to true. +* @li sorted: An optional bool. Defaults to true. * If true, the resulting "k" elements will be sorted by the values in descending * order. -* @li largest:If true the resulting `k` elements will be sorted by the values in descending order. -* @li dim:0-D. Number of top elements to look for along the last dimension (along each row for matrices). \n +* @li T: Indicator of indices type . \n * @par Outputs: * @li values: A Tensor, specifying the sorted data. Has the same type as @@ -983,7 +982,7 @@ REG_OP(TopKV2) * @see TopK() * @par Third-party framework compatibility -* Compatible with the TensorFlow operator TopKV2. +* @li Compatible with the TensorFlow operator TopKV2. */ REG_OP(TopK) .INPUT(x, TensorType::RealNumberType()) @@ -1086,6 +1085,7 @@ REG_OP(InTopKD) * @brief Says whether the targets are in the top "k" predictions . \n * @par Inputs: +* Two inputs, including: * @li x1: A 2D Tensor of type float32. A "batch_size * classes" tensor. * @li x2: A 1D Tensor of type IndexNumberType. A batch_size tensor of class ids. * @li k: A 1D Tensor of the same type as "x2". @@ -1618,12 +1618,12 @@ REG_OP(UnsortedSegmentMinD) * y: A Tensor of type RealNumberType . \n * @attention Constraints: -* segment_ids must be non-negative tensor. +* @li segment_ids must be non-negative tensor. * @see UnsortedSegmentSum(), UnsortedSegmentProd(), * @par Third-party framework compatibility -* Compatible with the TensorFlow operator UnsortedSegmentMax. +* @li Compatible with the TensorFlow operator UnsortedSegmentMax. */ REG_OP(UnsortedSegmentMax) .INPUT(x, TensorType::RealNumberType()) @@ -1875,15 +1875,15 @@ REG_OP(Crop) *@par Inputs: *One inputs, including: -* x: A tensor . Must be one of the following types: +* @li x: A tensor . Must be one of the following types: * float16, float32, int32, uint32, int8, uint8. \n *@par Attributes: -* axis: Axis along which to cummin. \n +* @li axis: Axis along which to cummin. \n *@par Outputs: -* @li y: A Tensor with the same type and shape of x's. -* @li indices: A Tensor with the int32 type and the same shape of x's. \n +* y: A Tensor with the same type and shape of x's. \n +* indices: A Tensor with the int32 type and the same shape of x's. \n *@par Third-party framework compatibility *Compatible with the Pytorch operator Cummin. \n @@ -1968,14 +1968,17 @@ REG_OP(WriteSelect) .OP_END_FACTORY_REG(WriteSelect) /** -*@brief Read data by stride. +*@brief Read data by stride . \n *@par Inputs: -*x: A Tensor. Must be one of the following types: float16, int8. \n +*One input: +*x: A Tensor. Must be one of the following types: float16, int8 . \n *@par Attributes: -*@li axis: A required int32, specifying the index of axis to read by stride. \n -*@li stride: A required int32, specifying the value of reading stride. \n +*@li axis: A required int32, specifying the index of axis to read by stride . \n + +*@par Attributes: +*@li stride: A required int32, specifying the value of reading stride . \n *@par Outputs: *y: A Tensor of the same type as "x". @@ -1988,14 +1991,16 @@ REG_OP(StridedRead) .OP_END_FACTORY_REG(StridedRead) /** -*@brief Write data by stride. +*@brief: Write data by stride . \n *@par Inputs: -*x: A Tensor. Must be one of the following types: float16, int8. \n +*x: A Tensor. Must be one of the following types: float16, int8 . \n + +*@par Attributes: +*@li axis: A required int32, specifying the index of axis to write by stride . \n *@par Attributes: -*@li axis: A required int32, specifying the index of axis to write by stride. \n -*@li stride: A required int32, specifying the value of writing stride. \n +*@li stride: A required int32, specifying the value of writing stride . \n *@par Outputs: *y: A Tensor. Has the same type as "x". @@ -2071,10 +2076,10 @@ REG_OP(CumulativeLogsumexpD) * @li updates: A Tensor of the same type as "var". \n * @par Attributes: -* axis: An required int to specify the axis to perform indices add. \n +* @li axis: An required int to specify the axis to perform indices add. \n * @par Outputs: -* var: A Tensor. Same as input "var". +* @li var: A Tensor. Same as input "var". * @par Third-party framework compatibility * Compatible with the Pytorch operator index_add_. @@ -2099,7 +2104,7 @@ REG_OP(InplaceIndexAdd) * @li value: A Tensor of dtype float16 or float32 or int64 or int32 or int8. * @par Outputs: -* y: A tensor. Must be one of the following dtypes: +* @li y: A tensor. Must be one of the following dtypes: * float16, float32, int64, int32, int8. */ REG_OP(MaskedFill) @@ -2118,7 +2123,7 @@ REG_OP(MaskedFill) * @li mask: A Tensor of dtype is bool. \n * @par Outputs: -* y: A tensor with the same type as x. \n +* @li y: A tensor with the same type as x. \n * @par Third-party framework compatibility * Compatible with the Numpy operator select. @@ -2129,50 +2134,13 @@ REG_OP(MaskedSelectV2) .INPUT(mask, TensorType({DT_BOOL})) .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT})) .OP_END_FACTORY_REG(MaskedSelectV2) - -/** -* @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 or float64 or int64 or int32 or int16 or int8 or uint8. -* @li mask: A Tensor of dtype is bool. \n - -* @par Outputs: -* @li y: A tensor with the same type as x. \n - -*/ -REG_OP(MaskedSelect) - .INPUT(x, TensorType({DT_FLOAT, DT_FLOAT16, DT_DOUBLE, DT_UINT8, DT_INT8, DT_INT16, DT_INT32, DT_INT64})) - .INPUT(mask, TensorType({DT_BOOL})) - .OUTPUT(y, TensorType({DT_FLOAT, DT_FLOAT16, DT_DOUBLE, DT_UINT8, DT_INT8, DT_INT16, DT_INT32, DT_INT64})) - .OP_END_FACTORY_REG(MaskedSelect) - -/** -* @brief update 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 float64 or int64 or int32 or int16 or int8 or uint8. -* @li mask: A Tensor of dtype is bool. -* @li updates: A tensor with the same type as x. \n - -* @par Outputs: -* @li y: A tensor with the same type as x. \n -*/ -REG_OP(MaskedScatter) - .INPUT(x, TensorType({DT_FLOAT, DT_FLOAT16, DT_DOUBLE, DT_UINT8, DT_INT8, DT_INT16, DT_INT32, DT_INT64})) - .INPUT(mask, TensorType({DT_BOOL})) - .INPUT(updates, TensorType({DT_FLOAT, DT_FLOAT16, DT_DOUBLE, DT_UINT8, DT_INT8, DT_INT16, DT_INT32, DT_INT64})) - .OUTPUT(y, TensorType({DT_FLOAT, DT_FLOAT16, DT_DOUBLE, DT_UINT8, DT_INT8, DT_INT16, DT_INT32, DT_INT64})) - .OP_END_FACTORY_REG(MaskedScatter) /** * @brief Slice a tensor at its last dim, e.x. a[..., begin:end:stride]. \n * @par Inputs: * One inputs, including: -* x: A Tensor. Must be one of the following types: float16, float32, int16, int32. +* @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 @@ -2180,7 +2148,7 @@ REG_OP(MaskedScatter) * @li stride: An attribute of type Int, stride of slice. \n * @par Outputs: -* y: A Tensor. Has the same type as "x". \n +* @li y: A Tensor. Has the same type as "x". \n * @par Third-party framework compatibility * No compatibility @@ -2194,36 +2162,39 @@ REG_OP(SliceLastDim) .OP_END_FACTORY_REG(SliceLastDim) /** -* @brief Extracts a strided slice of a tensor. Roughly speaking, this op -* extracts a slice of size (end-begin)/stride from the given input tensor. -* Starting at the location specified by begin the slice continues by +* @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: -* Five inputs, including: -* @li x: A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, -* complex64, int64, qint8, quint8, qint32, qint16, quint16, uint16, -* complex128, float16, uint32, uint64, complex64, complex128. +* 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. \n +* +* @li strides: A Tensor of type int32 or int64, for the increment. * * @par Attributes: -* @li begin_mask: A Tensor of type int32. -* A bitmask where a bit "i" being "1" means to ignore the begin +* @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. +* @li end_mask: A Tensor of type int32. \n * Analogous to "begin_mask". -* @li ellipsis_mask: A Tensor of type int32. -* A bitmask where bit "i" being "1" means the "i"th position +* @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. -* A bitmask where bit "i" being "1" means the "i"th +* @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. -* A bitmask where bit "i" implies that the "i"th -* specification should shrink the dimensionality. \n +* @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". @@ -2260,7 +2231,7 @@ REG_OP(StridedSliceV2) * float16, float32, int32. \n * @par Attributes: -* dim: A required int. Used to select the dimension of this tensor. \n +* @li dim: A required int. Used to select the dimension of this tensor. \n *@par Outputs: *y: A Tensor with the same type and shape of input_x's. \n @@ -2336,34 +2307,6 @@ REG_OP(MaskedFillRange) .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT8, DT_INT32})) .REQUIRED_ATTR(axis, Int) .OP_END_FACTORY_REG(MaskedFillRange) - -/** -* @brief After a set of sorted data and a new set of data are re-sorted, get the first k data. \n -* -* @par Inputs: -* Six inputs, including: -* @li topk_pq_distance: A sorted Tensor, Will be updated after calculation. Must be one of the following types: float32, float16. -* @li topk_pq_index: A Tensor of type int32, index corresponding to topk_pq_distance. -* @li topk_pq_ivf: A Tensor of type int32 , the bucket number corresponding to topk_pq_distance. -* @li pq_distance: A Tensor of type float32 or float16, the new data set will be reordered with topk_pq_distance and updated to topk_pq_distance. -* @li pq_index: A Tensor of type int32, index corresponding to pq_distance. -* @li pq_ivf: A scalar of type int32 , the bucket number corresponding to pq_distance. \n -* -* @par Attributes: -* @li order: A string, indicates the sorting method of topk_pq_distance. \n -* -* @par Restrictions: -* Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. -*/ -REG_OP(InplaceTopKDistance) - .INPUT(topk_pq_distance, TensorType({DT_FLOAT16, DT_FLOAT})) - .INPUT(topk_pq_index, TensorType({DT_INT32})) - .INPUT(topk_pq_ivf, TensorType({DT_INT32})) - .INPUT(pq_distance, TensorType({DT_FLOAT16, DT_FLOAT})) - .INPUT(pq_index, TensorType({DT_INT32})) - .INPUT(pq_ivf, TensorType({DT_INT32})) - .ATTR(order, String, "asc") - .OP_END_FACTORY_REG(InplaceTopKDistance) } // namespace ge #endif // OPS_BUILT_IN_OP_PROTO_INC_SELECTION_OPS_H_ diff --git a/third_party/fwkacllib/inc/ops/sparse_ops.h b/third_party/fwkacllib/inc/ops/sparse_ops.h index 8eb7b521..a1fc9ee6 100644 --- a/third_party/fwkacllib/inc/ops/sparse_ops.h +++ b/third_party/fwkacllib/inc/ops/sparse_ops.h @@ -281,9 +281,9 @@ REG_OP(SparseSliceGrad) * @li size: A 1D Tensor of type int64. The size of the slice . \n *@par Outputs: -*@li y_indices: A Tensor of type int64. -*@li y_values: A Tensor. Has the same type as "values". -*@li y_shape: A Tensor of type int64 . \n +*y_indices: A Tensor of type int64. +*y_values: A Tensor. Has the same type as "values". +*y_values: A Tensor of type int64 . \n *@par Third-party framework compatibility * Compatible with the TensorFlow operator SparseSlice. @@ -313,8 +313,8 @@ REG_OP(SparseSlice) * @li sum_indices: A 2D Tensor of type int64. The indices of the sum SparseTensor, with size [nnz(sum), ndims] . \n *@par Outputs: -*@li x1_val_grad: A Tensor. Has the same type as "backprop_val_grad". -*@li x2_val_grad: A Tensor. Has the same type as "backprop_val_grad" . \n +*x1_val_grad: A Tensor. Has the same type as "backprop_val_grad". +*x2_val_grad: A Tensor. Has the same type as "backprop_val_grad" . \n *@par Third-party framework compatibility * Compatible with the TensorFlow operator SparseAddGrad. @@ -363,7 +363,7 @@ REG_OP(SparseFillEmptyRowsGrad) *@par Inputs: * @li x1_indices: A 2D Tensor of type int32 or int64. -*The indices of the matrix "SparseTensor", with size [nnz, 2]. +* @li The indices of the matrix "SparseTensor", with size [nnz, 2]. * @li x1_values: A 1D Tensor. The values of the SparseTensor, with size [nnz]. * @li x1_shape: A 1D Tensor of type int64. The shape of the SparseTensor, with size [2]. * @li x2: A dense matrix Tensor of the same type as "x1_values". 2D . \n @@ -373,9 +373,9 @@ REG_OP(SparseFillEmptyRowsGrad) *@par Attributes: *@li adjoint_a: An optional bool. Defaults to "False".Use the adjoint of A in the matrix multiply. -*If A is complex, this is transpose(conj(A)). Otherwise it is transpose(A). +*@li If A is complex, this is transpose(conj(A)). Otherwise it is transpose(A). *@li adjoint_b: An optional bool. Defaults to "False".Use the adjoint of B in the matrix multiply. -*If B is complex, this is transpose(conj(B)). Otherwise it is transpose(B) . \n +*@li If B is complex, this is transpose(conj(B)). Otherwise it is transpose(B) . \n *@par Third-party framework compatibility * Compatible with the TensorFlow operator SparseTensorDenseMatMul. @@ -400,13 +400,9 @@ REG_OP(SparseTensorDenseMatMul) * @li indices: A 0D, 1D, or 2D Tensor of type int32 or int64. * @li output_shape: A 1D Tensor of the same type as "sparse_indices". The shape of the dense output tensor. * @li values: A 1D Tensor. Values corresponding to each row of "sparse_indices", -or a scalar value to be used for all sparse indices. +* @li or a scalar value to be used for all sparse indices. * @li default_value: A Tensor of the same type as "sparse_values" . \n -*@par Attributes: -*validate_indices: If true, indices are checked to make sure they are sorted in -lexicographic order and that there are no repeats. \n - *@par Outputs: *y: A Tensor. Has the same type as "values" . \n @@ -431,6 +427,7 @@ REG_OP(SparseToDense) *Concatenation is with respect to the dense versions of these sparse tensors . \n *@par Inputs: +*3 or 5 inputs,contains: * @li indices:A list of at least 2 `Tensor` objects with type `int64`.2-D. *Indices of each input `SparseTensor`.It's a dynamic input. * @li values:A list with the same length as `indices` of `Tensor` objects with the same type. @@ -703,6 +700,7 @@ REG_OP(SparseReduceMaxSparse) *@brief Computes the sum of elements across dimensions of a SparseTensor . \n *@par Inputs: +*4 or 5 inputs, including: * @li x_indices: A 2D Tensor of type int64. *"N x R" matrix with the indices of non-empty values in a *SparseTensor, possibly not in canonical ordering. @@ -713,11 +711,13 @@ REG_OP(SparseReduceMaxSparse) *A length-"K" vector containing the reduction axes . \n *@par Attributes: -*keep_dims: An optional bool. Defaults to "False". +* keep_dims: An optional bool. Defaults to "False". *If true, retains reduced dimensions with length 1 . \n *@par Outputs: -*y: A Tensor. Has the same type as "x_values". \n +* @li y_indices: A Tensor of type int64. +* @li y_values: A Tensor. Has the same type as "input_values". +* @li y_shape: A Tensor of type int64 . \n *@par Third-party framework compatibility * Compatible with the TensorFlow operator SparseReduceSum. @@ -818,6 +818,7 @@ REG_OP(SparseSplit) *@brief Generates sparse cross from a list of sparse and dense tensors . \n *@par Inputs: +*8 or 10 inputs, including: * @li indices: A list of 2D Tensor objects of type int64. * Indices of each input SparseTensor.It's a dynamic input. * @li values: A list of 1D Tensor objects of type int64 or string. @@ -898,8 +899,9 @@ REG_OP(AddManySparseToTensorsMap) *@brief Reads SparseTensors from a "SparseTensorsMap" and concatenate them . \n *@par Inputs: +*2 or 4 inputs, including: * handles: A 1D Tensor of type int64. -*The "N" serialized SparseTensor objects . \n +* The "N" serialized SparseTensor objects . \n *@par Attributes: * @li dtype: A tf.DType. The "dtype" of the SparseTensor objects stored in the "SparseTensorsMap". @@ -909,9 +911,9 @@ REG_OP(AddManySparseToTensorsMap) *The shared name for the "SparseTensorsMap" read by this op . \n *@par Outputs: -* @li indices: A Tensor of type int64.2-D. The `indices` of the minibatch `SparseTensor`. -* @li values: A Tensor of type "dtype". 1-D. The `values` of the minibatch `SparseTensor`. -* @li shape: A Tensor of type int64 . 1-D. The `shape` of the minibatch `SparseTensor`. \n +* @li indices: A Tensor of type int64. +* @li values: A Tensor of type "dtype". +* @li shape: A Tensor of type int64 . \n *@par Third-party framework compatibility * Compatible with the TensorFlow operator TakeManySparseFromTensorsMap. @@ -987,7 +989,8 @@ REG_OP(SerializeManySparse) *@brief Deserializes SparseTensor objects . \n *@par Inputs: -*serialized_sparse: A Tensor. The serialized SparseTensor objects. +*Two inputs, including: +* serialized_sparse: A Tensor. The serialized SparseTensor objects. *The last dimension must have 3 columns . \n *@par Attributes: diff --git a/third_party/fwkacllib/inc/ops/spectral_ops.h b/third_party/fwkacllib/inc/ops/spectral_ops.h index ab9e1dec..34ccb398 100644 --- a/third_party/fwkacllib/inc/ops/spectral_ops.h +++ b/third_party/fwkacllib/inc/ops/spectral_ops.h @@ -31,10 +31,10 @@ namespace ge { inner-most dimension of `x`. \n *@par Inputs: -*x: A Tensor. Must be the following types: complex64, complex128. \n +*@li x: A Tensor. Must be the following types: complex64, complex128. \n *@par Outputs: -*y: A complex tensor of the same rank as `x`. \n +*@li y: A complex tensor of the same rank as `x`. \n *@par Third-party framework compatibility * Compatible with TensorFlow IFFT operator. @@ -52,7 +52,7 @@ REG_OP(IFFT) *@li fft_length: An int32 tensor of shape [1]. The FFT length . \n *@par Outputs: -*y: A complex64 tensor of the same rank as `input`. The inner-most +*@li y: A complex64 tensor of the same rank as `input`. The inner-most dimension of `input` is replaced with the `fft_length / 2 + 1` unique frequency components of its 1D Fourier transform . \n @@ -73,7 +73,7 @@ REG_OP(RFFT) *@li fft_length: An int32 tensor of shape [1]. The FFT length. \n *@par Outputs: -* y: A float32 tensor of the same rank as `input`. The inner-most +*@li y: A float32 tensor of the same rank as `input`. The inner-most dimension of `input` is replaced with the `fft_length` samples of its inverse 1D Fourier transform. \n @@ -91,10 +91,10 @@ REG_OP(IRFFT) *@brief 2D fast Fourier transform. \n *@par Inputs: -*x: A complex64 tensor. +*@li x: A complex64 tensor. *@par Outputs: -*y: A complex64 tensor of the same shape as `input`. The inner-most 2 +*@li y: A complex64 tensor of the same shape as `input`. The inner-most 2 dimensions of `input` are replaced with their 2D Fourier transform. \n *@par Third-party framework compatibility @@ -110,10 +110,10 @@ REG_OP(FFT2D) innermost dimension of the input. \n *@par Inputs: -*x: A Tensor. Must be the following types: complex64, complex128. \n +*@li x: A Tensor. Must be the following types: complex64, complex128. \n *@par Outputs: -*y: A complex tensor with the same shape as input. The innermost dimension +*@li y: A complex tensor with the same shape as input. The innermost dimension of the input is replaced by its 1-dimensional Fourier transform. \n *@par Third-party framework compatibility @@ -129,10 +129,10 @@ REG_OP(FFT) innermost dimension of the input. \n *@par Inputs: -*x: A Tensor. Must be the following types: complex64, complex128. \n +*@li x: A Tensor. Must be the following types: complex64, complex128. \n *@par Outputs: -*y: A complex tensor with the same shape as input. The innermost dimension +*@li y: A complex tensor with the same shape as input. The innermost dimension of the input is replaced by its inverse two-dimensional Fourier transform. \n *@par Third-party framework compatibility diff --git a/third_party/fwkacllib/inc/ops/split_combination_ops.h b/third_party/fwkacllib/inc/ops/split_combination_ops.h index 98d4d111..fe25a46f 100644 --- a/third_party/fwkacllib/inc/ops/split_combination_ops.h +++ b/third_party/fwkacllib/inc/ops/split_combination_ops.h @@ -161,11 +161,14 @@ REG_OP(SplitVD) /** *@brief Concatenates a list of N tensors along the first dimension. *@par Inputs: -* One input, including: -* values: A list of Tensors. Must be one of the following types: int8, int16, int32, +* Two inputs, including: +* @li values: A list of Tensors. Must be one of the following types: int8, int16, int32, * int64, uint8, uint16, uint32, uint64, float16, float32. * Tensors to be concatenated. All must have size 1 in the first dimension and same shape. -* It's a dynamic input. \n +* It's a dynamic input. +* @li shape: A Tensor of the same type as "x". +* The final shape of the result. Should be equal to the shapes of any input +* but with the number of input values in the first dimension . \n *@par Attributes: * @li shape: A required list of ints. diff --git a/third_party/fwkacllib/inc/ops/state_ops.h b/third_party/fwkacllib/inc/ops/state_ops.h index d1ec00b5..3c8e32b6 100644 --- a/third_party/fwkacllib/inc/ops/state_ops.h +++ b/third_party/fwkacllib/inc/ops/state_ops.h @@ -104,7 +104,7 @@ REG_OP(DestroyTemporaryVariable) *@brief Checks whether a tensor has been initialized. Outputs boolean scalar indicating whether the tensor has been initialized . \n *@par Inputs: -*x: A Tensor of type float16, float32, double, bool, int8, uint8, uint16, int16, int32, uint32, uint64, int64. +*x: A tensor . \n *@par Outputs: *y: A tensor, indicating whether "x" has been initialized . \n diff --git a/third_party/fwkacllib/inc/ops/stateful_random_ops.h b/third_party/fwkacllib/inc/ops/stateful_random_ops.h index f4eb763c..c2f65c6a 100644 --- a/third_party/fwkacllib/inc/ops/stateful_random_ops.h +++ b/third_party/fwkacllib/inc/ops/stateful_random_ops.h @@ -32,10 +32,7 @@ namespace ge { *@par Inputs: *This op may use some OS-provided source of non-determinism (e.g. an RNG), *so each execution will give different results. Inputs included: -*shape: The shape of the output tensor . \n - -*@par Attributes: -*dtype: required, type. \n +*@li shape: The shape of the output tensor . \n *@par Outputs: *y:A Returns Non-deterministic integer values with specified shape . \n @@ -57,10 +54,13 @@ REG_OP(NonDeterministicInts) *counter is an unspecified implementation detail . \n *@par Inputs: -*@li x: The handle of the resource variable that stores the state of the RNG. +*@li resource: The handle of the resource variable that stores the state of the RNG. *@li algorithm: The RNG algorithm. *@li delta: The amount of advancement . \n +*@par Outputs: +*y:A Returns the created operation . \n + *@par Third-party framework compatibility * Compatible with tensorflow RngSkip operator. */ @@ -81,16 +81,11 @@ power of two. The bias is small for values of `maxval - minval` significantly smaller than the range of the output (either `2^32` or `2^64`) . \n *@par Inputs: -*@li x: The handle of the resource variable that stores the state of the RNG. +*@li resource: The handle of the resource variable that stores the state of the RNG. *@li algorithm: The RNG algorithm. *@li shape: The shape of the output tensor. -*@li counts: A 0/1-D Tensor or Python value. The counts of the binomial -distribution. Must be broadcastable with the leftmost dimension defined by `shape`. -*@li probs: A 0/1-D Tensor or Python value. The probability of success for the -binomial distribution. Must be broadcastable with the leftmost dimension defined by `shape`.\n - -*@par Attributes: -*dtype: required, type. \n +*@li minval: Minimum value (inclusive, scalar). +*@li maxval: Maximum value (exclusive, scalar) . \n *@par Outputs: *y:A Returns Random values with specified shape . \n @@ -114,7 +109,7 @@ REG_OP(StatefulRandomBinomial) *The generated values will have mean 0 and standard deviation 1 . \n *@par Inputs: -*@li x: The handle of the resource variable that stores the state of the RNG. +*@li resource: The handle of the resource variable that stores the state of the RNG. *@li algorithm: The RNG algorithm. *@li shape: The shape of the output tensor . \n @@ -139,7 +134,7 @@ REG_OP(StatefulStandardNormalV2) *deviations from the mean are dropped and re-picked . \n *@par Inputs: -*@li x: The handle of the resource variable that stores the state of the RNG. +*@li resource: The handle of the resource variable that stores the state of the RNG. *@li algorithm: The RNG algorithm. *@li shape: The shape of the output tensor . \n @@ -163,7 +158,7 @@ The generated values follow a uniform distribution in the range `[0, 1)`. The lower bound 0 is included in the range, while the upper bound 1 is excluded. *@par Inputs: -*@li x: The handle of the resource variable that stores the state of the RNG. +*@li resource: The handle of the resource variable that stores the state of the RNG. *@li algorithm: The RNG algorithm. *@li shape: The shape of the output tensor . \n @@ -186,7 +181,7 @@ REG_OP(StatefulUniform) The generated values are uniform integers covering the whole range of `dtype` . \n *@par Inputs: -*@li x: The handle of the resource variable that stores the state of the RNG. +*@li resource: The handle of the resource variable that stores the state of the RNG. *@li algorithm: The RNG algorithm. *@li shape: The shape of the output tensor . \n @@ -214,7 +209,7 @@ power of two. The bias is small for values of `maxval - minval` significantly smaller than the range of the output (either `2^32` or `2^64`) . \n *@par Inputs: -*@li x: The handle of the resource variable that stores the state of the RNG. +*@li resource: The handle of the resource variable that stores the state of the RNG. *@li algorithm: The RNG algorithm. *@li shape: The shape of the output tensor. *@li minval: Minimum value (inclusive, scalar). diff --git a/third_party/fwkacllib/inc/ops/string_ops.h b/third_party/fwkacllib/inc/ops/string_ops.h index a78d63a1..f9cc2549 100644 --- a/third_party/fwkacllib/inc/ops/string_ops.h +++ b/third_party/fwkacllib/inc/ops/string_ops.h @@ -295,7 +295,7 @@ REG_OP(StringSplit) *@par Inputs: include: -*input:A Tensor of type string. The text to be processed. \n +*@li input:A Tensor of type string. The text to be processed. \n *@par Attributes: *@li pattern:A string. The regular expression to match the input. @@ -303,8 +303,8 @@ include: *@li replace_global:An optional bool. Defaults to True. If True, the replacement is global, otherwise the replacement is done only on the first match. -*@par Outputs: -*output::A Tensor of type string. +*@par output: +*@li output::A Tensor of type string. */ REG_OP(StaticRegexReplace) .INPUT(input, TensorType({DT_STRING})) @@ -322,13 +322,13 @@ REG_OP(StaticRegexReplace) *@par Inputs: include: -*input:A Tensor of type string. The text to be processed. \n +*@li input:A Tensor of type string. The text to be processed. \n *@par Attributes: -*pattern:A string. The regular expression to match the input. +*@li pattern:A string. The regular expression to match the input. -*@par Outputs: -*output::A bool tensor with the same shape as `input`. +*@par output: +*@li output::A bool tensor with the same shape as `input`. */ REG_OP(StaticRegexFullMatch) .INPUT(input, TensorType({DT_STRING})) @@ -347,10 +347,10 @@ include: *@li num_segments:A Tensor. Must be one of the following types: int32, int64. A scalar. *@par Attributes: -*separator:An optional string. Defaults to "". The separator to use when joining. +*@li separator:An optional string. Defaults to "". The separator to use when joining. -*@par Outputs: -*output::A Tensor of type string.. +*@par output: +*@li output::A Tensor of type string.. */ REG_OP(UnsortedSegmentJoin) .INPUT(input, TensorType({DT_STRING})) @@ -366,13 +366,13 @@ REG_OP(UnsortedSegmentJoin) *@par Inputs: include: -*input:A Tensor of type string. The text to be processed. +*@li input:A Tensor of type string. The text to be processed. *@par Attributes: -*encoding:An optional string. Defaults to "". +*@li encoding:An optional string. Defaults to "". -*@par Outputs: -*output::A Tensor of type string.. +*@par output: +*@li output::A Tensor of type string.. */ REG_OP(StringLower) .INPUT(input, TensorType({DT_STRING})) @@ -386,13 +386,13 @@ REG_OP(StringLower) *@par Inputs: include: -*input:A Tensor of type string. The text to be processed. +*@li input:A Tensor of type string. The text to be processed. *@par Attributes: -*encoding:An optional string. Defaults to "". +*@li encoding:An optional string. Defaults to "". -*@par Outputs: -*output::A Tensor of type string.. +*@par output: +*@li output::A Tensor of type string.. */ REG_OP(StringUpper) .INPUT(input, TensorType({DT_STRING})) @@ -901,10 +901,10 @@ REG_OP(DecodeBase64) *@brief StringNormalization performs string operations for basic cleaning . \n *@par Inputs: -*input: only accepts [C] or [1, C] UTF-8 strings tensor . \n +*@li input: only accepts [C] or [1, C] UTF-8 strings tensor . \n *@par Outputs: -*output: UTF-8 strings tensor after cleaning . \n +*@li output: UTF-8 strings tensor after cleaning . \n *@par Attributes: *@li stopwords : list of strings (default is empty). @@ -919,13 +919,13 @@ case-sensitive. Default is false. *string enum that cases output to be lowercased/uppercases/unchanged. Valid values are "LOWER", "UPPER", "NONE". Default is "NONE". -*@li locale : string (default is "C"). +*@li local : string (default is "en_US"). *Environment dependent string that denotes the locale according to which output -strings needs to be upper/lowercased.Default C or platform specific equivalent -as decided by the implementation. \n +strings needs to be upper/lowercased.Default en_US or platform specific equivalent +as decided by the implementation . \n *@attention Constraints: -*input can be either a 1-D or 2-D tensor, the shape of 2-D tensor must be [1, C]. +*@li input can be either a 1-D or 2-D tensor, the shape of 2-D tensor must be [1, C]. */ REG_OP(StringNormalizer) .INPUT(input, TensorType({DT_STRING})) @@ -933,7 +933,7 @@ REG_OP(StringNormalizer) .ATTR(stopwords, ListString, {}) .ATTR(is_case_sensitive, Bool, false) .ATTR(case_change_action, String, "NONE") - .ATTR(locale, String, "C") + .ATTR(local, String, "en_US") .OP_END_FACTORY_REG(StringNormalizer) } // namespace ge diff --git a/third_party/fwkacllib/inc/ops/transformation_ops.h b/third_party/fwkacllib/inc/ops/transformation_ops.h index f403fe12..4a46e35f 100644 --- a/third_party/fwkacllib/inc/ops/transformation_ops.h +++ b/third_party/fwkacllib/inc/ops/transformation_ops.h @@ -29,15 +29,15 @@ namespace ge { *@par Inputs: *The input handle must have the resource type. Inputs include: -*x:A list of Tensor objects. One or more tensors from which +*@li x:A list of Tensor objects. One or more tensors from which the enqueued tensors should be taken . \n *@par Outputs: -*y:A list of Tensor objects. One or more tensors from which +*@li y:A list of Tensor objects. One or more tensors from which the enqueued tensors should be taken . \n *@par Attributes: -*type: An optional ge::DataType. It refers to the target data type of outputs . \n +*@li type: An optional ge::DataType. It refers to the target data type of outputs . \n *@par Third-party framework compatibility *Compatible with tensorflow QueueIsClosed operator. @@ -723,12 +723,11 @@ REG_OP(CompressFcOp) *@brief Performs Col2im for each batch entry. \n *@par Inputs: -*@li x: The Col Tensor. 4-D, shape: `(n, c, kernel_h*kernel_w, ho*wo)`. -where ho/wo is do = (output_d + 2*padding_d - dilation_d*(kernel_d - 1) - 1)//stride_d + 1. -*@li output_size: The img shape Tensor. 1-D, shape:`(2)`, value: (output_h, output_w). \n +*@li input_x: The Col Tensor. 5-D, shape: `(n, c1, kernel_h*kernel_w, ho*wo, c0)`. +where ho/wo is do = (output_d + 2*padding_d - dilation_d*(kernel_d - 1) - 1)//stride_d + 1 \n *@par Outputs: -*y: The img Tensor. 4-D, shape: `(n, c, output_h, output_w)`. \n +*@li output_y: The img Tensor. 5-D, shape: `(n, c1, output_h, output_w, c0)`. \n *@par Attributes: *@li kernel_shape: ListInt, value: `(kernel_h, kernel_w)`, the shape of kernel in convolution. @@ -910,7 +909,7 @@ output shape would be [max(ngram_indexes) + 1]. If input shape is [N, C], this o *@li either pool_strings or pool_int64s attributes must be present but not both. */ -REG_OP(TfIdfVectorizer) +REG_OP(TfidVectorizer) .INPUT(input, TensorType({DT_INT32, DT_INT64, DT_STRING})) .OUTPUT(output, TensorType({DT_FLOAT})) .REQUIRED_ATTR(max_gram_length, Int) @@ -922,7 +921,7 @@ REG_OP(TfIdfVectorizer) .ATTR(pool_int64s, ListInt, {}) .ATTR(pool_strings, ListString, {}) .ATTR(weights, ListFloat, {}) - .OP_END_FACTORY_REG(TfIdfVectorizer) + .OP_END_FACTORY_REG(TfidVectorizer) } // namespace ge #endif // OPS_BUILT_IN_OP_PROTO_INC_TRANSFORMATION_OPS_H_ diff --git a/third_party/fwkacllib/inc/runtime/base.h b/third_party/fwkacllib/inc/runtime/base.h index 70e42dc9..7fc1cdea 100644 --- a/third_party/fwkacllib/inc/runtime/base.h +++ b/third_party/fwkacllib/inc/runtime/base.h @@ -20,7 +20,7 @@ #include #include "toolchain/prof_callback.h" -#if defined(__cplusplus) +#if defined(__cplusplus) && !defined(COMPILE_OMG_PACKAGE) extern "C" { #endif @@ -357,7 +357,7 @@ RTS_API rtError_t rtLabelCreateExV2(rtLabel_t *label, rtModel_t model, rtStream_ */ RTS_API rtError_t rtGetTaskIdAndStreamID(uint32_t *taskId, uint32_t *streamId); -#if defined(__cplusplus) +#if defined(__cplusplus) && !defined(COMPILE_OMG_PACKAGE) } #endif diff --git a/third_party/fwkacllib/inc/runtime/config.h b/third_party/fwkacllib/inc/runtime/config.h index 76836e7b..a244c793 100644 --- a/third_party/fwkacllib/inc/runtime/config.h +++ b/third_party/fwkacllib/inc/runtime/config.h @@ -19,7 +19,7 @@ #include "base.h" -#if defined(__cplusplus) +#if defined(__cplusplus) && !defined(COMPILE_OMG_PACKAGE) extern "C" { #endif @@ -43,7 +43,6 @@ typedef enum tagRtChipType { CHIP_LHISI, CHIP_DC, CHIP_CLOUD_V2, - CHIP_NO_DEVICE, CHIP_END, } rtChipType_t; @@ -54,11 +53,11 @@ typedef enum tagRtAicpuScheType { } rtAicpuScheType; typedef enum tagRtDeviceCapabilityType { - RT_SCHEDULE_SOFTWARE = 0, // Software Schedule - RT_SCHEDULE_SOFTWARE_OPT, - RT_SCHEDULE_HARDWARE, // HWTS Schedule - RT_AICPU_BLOCKING_OP_NOT_SUPPORT, - RT_AICPU_BLOCKING_OP_SUPPORT, // 1910/1980/1951 ts support AICPU blocking operation + RT_SCHEDULE_SOFTWARE = 0, // SoftWare Schedule + RT_SCHEDULE_SOFTWARE_OPT, + RT_SCHEDULE_HARDWARE, // HWTS Schedule + RT_AICPU_BLOCKING_OP_NOT_SUPPORT, + RT_AICPU_BLOCKING_OP_SUPPORT, // 1910/1980/1951 ts support AICPU blocking operation } rtDeviceCapabilityType; typedef enum tagRtVersion { @@ -236,7 +235,7 @@ RTS_API rtError_t rtSetOpWaitTimeOut(uint32_t timeout); */ RTS_API rtError_t rtSetOpExecuteTimeOut(uint32_t timeout); -#if defined(__cplusplus) +#if defined(__cplusplus) && !defined(COMPILE_OMG_PACKAGE) } #endif diff --git a/third_party/fwkacllib/inc/runtime/context.h b/third_party/fwkacllib/inc/runtime/context.h index c597a657..e95d4c89 100644 --- a/third_party/fwkacllib/inc/runtime/context.h +++ b/third_party/fwkacllib/inc/runtime/context.h @@ -19,7 +19,7 @@ #include "base.h" -#if defined(__cplusplus) +#if defined(__cplusplus) && !defined(COMPILE_OMG_PACKAGE) extern "C" { #endif @@ -157,7 +157,7 @@ RTS_API rtError_t rtGetGroupCount(uint32_t *count); */ RTS_API rtError_t rtSetCtxINFMode(bool mode); -#if defined(__cplusplus) +#if defined(__cplusplus) && !defined(COMPILE_OMG_PACKAGE) } #endif diff --git a/third_party/fwkacllib/inc/runtime/dev.h b/third_party/fwkacllib/inc/runtime/dev.h index 4a9a5817..18d837eb 100644 --- a/third_party/fwkacllib/inc/runtime/dev.h +++ b/third_party/fwkacllib/inc/runtime/dev.h @@ -19,7 +19,7 @@ #include "base.h" -#if defined(__cplusplus) +#if defined(__cplusplus) && !defined(COMPILE_OMG_PACKAGE) extern "C" { #endif @@ -80,15 +80,15 @@ typedef enum tagMemoryInfo { } rtMemoryInfo_t; typedef enum tagRtDeviceModuleType { - RT_MODULE_TYPE_SYSTEM = 0, /**< system info*/ - RT_MODULE_TYPE_AICPU, /** < aicpu info*/ - RT_MODULE_TYPE_CCPU, /**< ccpu_info*/ - RT_MODULE_TYPE_DCPU, /**< dcpu info*/ - RT_MODULE_TYPE_AICORE, /**< AI CORE info*/ - RT_MODULE_TYPE_TSCPU, /**< tscpu info*/ - RT_MODULE_TYPE_PCIE, /**< PCIE info*/ - RT_MODULE_TYPE_VECTOR_CORE, /**< VECTOR CORE info*/ -} rtDeviceModuleType_t; + RT_MODULE_TYPE_SYSTEM = 0, + RT_MODULE_TYPE_AICPU, + RT_MODULE_TYPE_CCPU, + RT_MODULE_TYPE_DCPU, + RT_MODULE_TYPE_AICORE, + RT_MODULE_TYPE_TSCPU, + RT_MODULE_TYPE_PCIE, + RT_MODULE_TYPE_VECTOR_CORE +} tagRtDeviceModuleType_t; /** * @ingroup dvrt_dev @@ -380,7 +380,7 @@ RTS_API rtError_t rtSetDeviceWithoutTsd(int32_t device); */ RTS_API rtError_t rtDeviceResetWithoutTsd(int32_t device); -#if defined(__cplusplus) +#if defined(__cplusplus) && !defined(COMPILE_OMG_PACKAGE) } #endif diff --git a/third_party/fwkacllib/inc/runtime/dvfsprofile.h b/third_party/fwkacllib/inc/runtime/dvfsprofile.h index 33e2f4c1..6e451695 100644 --- a/third_party/fwkacllib/inc/runtime/dvfsprofile.h +++ b/third_party/fwkacllib/inc/runtime/dvfsprofile.h @@ -19,7 +19,7 @@ #include "base.h" -#if defined(__cplusplus) +#if defined(__cplusplus) && !defined(COMPILE_OMG_PACKAGE) extern "C" { #endif @@ -56,7 +56,7 @@ RTS_API rtError_t rtUnsetDvfsProfile(); */ RTS_API rtError_t rtGetDvfsProfile(DvfsProfileMode *pmode); -#if defined(__cplusplus) +#if defined(__cplusplus) && !defined(COMPILE_OMG_PACKAGE) } #endif diff --git a/third_party/fwkacllib/inc/runtime/event.h b/third_party/fwkacllib/inc/runtime/event.h index 81b635c3..1cd1a198 100644 --- a/third_party/fwkacllib/inc/runtime/event.h +++ b/third_party/fwkacllib/inc/runtime/event.h @@ -19,7 +19,7 @@ #include "base.h" -#if defined(__cplusplus) +#if defined(__cplusplus) && !defined(COMPILE_OMG_PACKAGE) extern "C" { #endif @@ -41,6 +41,16 @@ typedef enum rtEventWaitStatus { #define RT_EVENT_DDSYNC 0x04U #define RT_EVENT_TIME_LINE 0x08U +#define RT_EVENT_DDSYNC_NS 0x01U +#define RT_EVENT_STREAM_MARK 0x02U +#define RT_EVENT_DDSYNC 0x04U +#define RT_EVENT_TIME_LINE 0x08U + +#define RT_EVENT_DDSYNC_NS 0x01U +#define RT_EVENT_STREAM_MARK 0x02U +#define RT_EVENT_DDSYNC 0x04U +#define RT_EVENT_TIME_LINE 0x08U + /** * @ingroup dvrt_event * @brief create event instance @@ -272,7 +282,7 @@ RTS_API rtError_t rtNotifyGetAddrOffset(rtNotify_t notify, uint64_t *devAddrOffs */ RTS_API rtError_t rtSetIpcNotifyPid(const char *name, int32_t pid[], int num); -#if defined(__cplusplus) +#if defined(__cplusplus) && !defined(COMPILE_OMG_PACKAGE) } #endif diff --git a/third_party/fwkacllib/inc/runtime/kernel.h b/third_party/fwkacllib/inc/runtime/kernel.h index c1b9bd6d..9b0221c7 100644 --- a/third_party/fwkacllib/inc/runtime/kernel.h +++ b/third_party/fwkacllib/inc/runtime/kernel.h @@ -20,7 +20,7 @@ #include "base.h" #include "stream.h" -#if defined(__cplusplus) +#if defined(__cplusplus) && !defined(COMPILE_OMG_PACKAGE) extern "C" { #endif @@ -647,7 +647,7 @@ RTS_API rtError_t rtStartMDCProfiler(void **addr, uint32_t length); */ RTS_API rtError_t rtStopMDCProfiler(void *addr); -#if defined(__cplusplus) +#if defined(__cplusplus) && !defined(COMPILE_OMG_PACKAGE) } #endif diff --git a/third_party/fwkacllib/inc/runtime/mem.h b/third_party/fwkacllib/inc/runtime/mem.h index b049e762..bace4bc6 100644 --- a/third_party/fwkacllib/inc/runtime/mem.h +++ b/third_party/fwkacllib/inc/runtime/mem.h @@ -24,7 +24,7 @@ #include "config.h" #include "stream.h" -#if defined(__cplusplus) +#if defined(__cplusplus) && !defined(COMPILE_OMG_PACKAGE) extern "C" { #endif @@ -547,7 +547,7 @@ RTS_API rtError_t rtSetIpcMemPid(const char *name, int32_t pid[], int num); */ RTS_API rtError_t rtRDMADBSend(uint32_t dbIndex, uint64_t dbInfo, rtStream_t stream); -#if defined(__cplusplus) +#if defined(__cplusplus) && !defined(COMPILE_OMG_PACKAGE) } #endif diff --git a/third_party/fwkacllib/inc/runtime/rt_ffts.h b/third_party/fwkacllib/inc/runtime/rt_ffts.h old mode 100644 new mode 100755 index f2809218..720da7cd --- a/third_party/fwkacllib/inc/runtime/rt_ffts.h +++ b/third_party/fwkacllib/inc/runtime/rt_ffts.h @@ -1,5 +1,5 @@ /* - * Copyright (c) Huawei Technologies Co., Ltd. 2021. All rights reserved. + * Copyright (c) Huawei Technologies Co. , Ltd. 2021. All rights reserved. * Description: ffts interface */ @@ -8,7 +8,7 @@ #include "base.h" -#if defined(__cplusplus) +#if defined(__cplusplus) && !defined(COMPILE_OMG_PACKAGE) extern "C" { #endif @@ -19,8 +19,8 @@ extern "C" { #define RT_FFTS_MANUAL_SRC_DEPEND_TBL_LEN 32U typedef enum tagFftsType { - RT_FFTS_TYPE_AUTO_THREAD = 2, // ffts auto thread mode, same as ffts define - RT_FFTS_TYPE_MANUAL_THREAD = 3, // ffts manual thread mode, same as ffts define + RT_FFTS_TYPE_AUTO_THREAD = 2, // ffts auto thread mode, same as ffts define + RT_FFTS_TYPE_MANUAL_THREAD = 3, // ffts manual thread mode, same as ffts define } rtFftsType_t; typedef enum tagFftsSubTaskType { @@ -37,7 +37,7 @@ typedef enum tagFftsSubTaskType { } rtFftsSubTaskType_t; typedef struct tagManualThreadDmuInfo { - uint64_t dataAddr; // device mem + uint64_t dataAddr; // device mem uint16_t numOuter; uint16_t numInner; uint32_t strideOuter; @@ -50,43 +50,44 @@ typedef struct tagManualThreadDependency { } rtManualThreadDependency_t; typedef struct tagManualThreadAicAivInfo { - uint64_t taskParamAddr; // device mem + uint64_t taskParamAddr; // device mem uint16_t taskParamOffset; // when satMode=1 and FP16 computation with none INF inputs overflows/underflows, results will be +/-INF of FP16 - // when satMode=0 and FP16 computation with none INF inputs overflows/underflows, - // results will be saturated to +/-MAX of FP16 + // when satMode=0 and FP16 computation with none INF inputs overflows/underflows + // results will be saturated to +/- MAX of FP16 uint8_t satMode; - uint8_t scheduleMode; // 0:normal mode, 1:batch mode, 2:sync mode 3:reserved - uint8_t iCachePrefetchCnt; // units is 2K - uint8_t prefetchEnableBitmap; // 8 bit bitmap 1 0 1 0 - uint8_t prefetchOnceBitmap; // 8 bit bitmap 1 0 1 0 - uint16_t prefetchOnceDmuNum; // prefetch_once_dmu_descriptor_index in ffts - // num： thread0_prefetch_dmu_descriptor_index – prefetch_once_dmu_descriptor_index - uint16_t threadPrefetchDmuIdx[RT_FFTS_MAX_MANUAL_THREAD_NUM]; // max valid is threadDim + uint8_t scheduleMode; // 0:normal mode, 1:batch mode, 2:sync mode, 3: reserved + uint8_t iCachePrefetchCnt; // units is 2K + uint8_t prefetchEnableBitmap; // 8 bit bitmap 1 0 1 0 + uint8_t prefetchOnceBitmap; // 8 bit bitmap 1 0 1 0 + uint16_t prefetchOnceDmuNum; // prefetch_once_dmu_descriptor_index in ffts + // num: thread0_prefetch_dmu_descriptor_index - prefetch_once_dmu_descriptor_index + uint16_t threadPrefetchDmuIdx[RT_FFTS_MAX_MANUAL_THREAD_NUM]; // max valid is threadDim uint16_t threadBlkDim[RT_FFTS_MAX_MANUAL_THREAD_NUM]; const char *threadTaskFuncStub[RT_FFTS_MAX_MANUAL_THREAD_NUM]; - rtManualThreadDmuInfo_t *prefetchList; // dmu desc 0-64k, length is the last threadPrefetchDmuIdx[threadDim-1] + rtManualThreadDmuInfo_t *prefetchList; // dmu desc 0-64k, length is the last threadPrefetchDmuIdx[threadDim - 1] rtManualThreadDependency_t srcDepTbl[RT_FFTS_MAX_TICKET_CACHE_PER_SUBTASK]; } rtManualThreadAicAivInfo_t; typedef struct tagAutoThreadPrefetch { - uint64_t dataAddr; // device mem + uint64_t dataAddr; // device mem uint32_t dataAddrOffset; uint32_t nonTailDataLen; uint32_t tailDataLen; } rtAutoThreadPrefetch_t; typedef struct tagAutoThreadAicAivInfo { - uint64_t taskParamAddr; // device mem + uint64_t taskParamAddr; // device mem uint16_t taskParamOffset; // when satMode=1 and FP16 computation with none INF inputs overflows/underflows, results will be +/-INF of FP16 - // when satMode=0 and FP16 computation with none INF inputs overflows/underflows, results will be saturated to +/-MAX of FP16 + // when satMode=0 and FP16 computation with none INF inputs overflows/underflows + // results will be saturated to +/- MAX of FP16 uint8_t satMode; - uint8_t scheduleMode; // 0:normal mode, 1:batch mode, 2:sync mode 3:reserved - uint8_t iCachePrefetchCnt; // units is 2K - uint8_t prefetchEnableBitmap; // 8 bit bitmap - uint8_t prefetchOnceBitmap; // 8 bit bitmap + uint8_t scheduleMode; // 0:normal mode, 1:batch mode, 2:sync mode, 3: reserved + uint8_t iCachePrefetchCnt; // units is 2K + uint8_t prefetchEnableBitmap; // 8 bit bitmap + uint8_t prefetchOnceBitmap; // 8 bit bitmap uint16_t tailBlkDim; uint16_t nonTailBlkDim; @@ -94,13 +95,13 @@ typedef struct tagAutoThreadAicAivInfo { const char *nonTailTaskFuncStub; const char *tailTaskFuncStub; - // for prefetch, valid num is prefetchEnableBitmap bit count. - // if prefetchEnableBitmap='00010011', need prefetch number is 3, srcPrefetch is only 0, 1, 2 is valid + // for prefetch, valid num is prefetchEnableBitmap bit count + // if prefetchEnableBitmap = '00010011', need prefetch number is 3, srcPrefetch is only 0, 1, 2 is valid rtAutoThreadPrefetch_t srcPrefetch[RT_FFTS_MAX_TICKET_CACHE_PER_SUBTASK]; } rtAutoThreadAicAivInfo_t; typedef struct tagAutoThreadCacheInfo { - uint64_t dataAddr; // device mem + uint64_t dataAddr; // device mem uint32_t dataAddrOffset; uint32_t nonTailDataLen; uint32_t tailDataLen; @@ -108,7 +109,7 @@ typedef struct tagAutoThreadCacheInfo { } rtAutoThreadCacheInfo_t; typedef struct tagManualThreadCacheInfo { - rtManualThreadDmuInfo_t *dmuList; // 0-64k + rtManualThreadDmuInfo_t *dmuList; // 0-64k uint16_t dmuNum; uint16_t sliceDmuIdx[RT_FFTS_MAX_MANUAL_THREAD_NUM]; uint16_t ticketCacheRefCntTbl[RT_FFTS_MAX_MANUAL_THREAD_NUM]; @@ -151,11 +152,11 @@ typedef struct tagFftsSubTaskInfo { } rtFftsSubTaskInfo_t; typedef struct tagFftsDescInfo { - uint8_t tm; // thread subtask kickstart mode, 0:order, 1:disorder - uint8_t di; // discard invalidate - uint8_t dw; // discard write back - uint8_t df; // discard flush - uint8_t dataSplitUnit; // split source or ticket cache by 2^dataSplitUnit MB + uint8_t tm; // thread subtask kickstart mode, 0:order, 1:disorder + uint8_t di; // discard invalidate + uint8_t dw; // discard write back + uint8_t df; // discard flush + uint8_t dataSplitUnit; // split source or ticket cache by 2~dataSplitUnit MB uint8_t prefetchOstNum; uint8_t cacheMaintainOstNum; uint8_t aicPrefetchUpper; @@ -165,20 +166,20 @@ typedef struct tagFftsDescInfo { } rtFftsDescInfo_t; typedef struct tagFftsTaskInfo { - rtFftsType_t fftsType; + rtFftsType_t fftsType; uint16_t subTaskNum; uint16_t tickCacheNum; rtFftsDescInfo_t fftsDesc; // sub task desc, real num is subTaskNum rtFftsSubTaskInfo_t subTask[RT_FFTS_MAX_SUB_TASK_NUM]; - // ticket cache, real number is tickCacheNum. + // ticket cache, real number is ticketCacheNum rtTicketCache_t ticketCache[RT_FFTS_MAX_TICKET_CACHE_NUM]; } rtFftsTaskInfo_t; RTS_API rtError_t rtFftsTaskLaunch(rtFftsTaskInfo_t *fftsTaskInfo, rtStream_t stream); -#if defined(__cplusplus) +#if defined(__cplusplus) && !defined(COMPILE_OMG_PACKAGE) } #endif -#endif // __CCE_RUNTIME_FFTS_H +#endif //__CCE_RUNTIME_FFTS_H diff --git a/third_party/fwkacllib/inc/runtime/rt_model.h b/third_party/fwkacllib/inc/runtime/rt_model.h index d4af72c5..a7618b45 100644 --- a/third_party/fwkacllib/inc/runtime/rt_model.h +++ b/third_party/fwkacllib/inc/runtime/rt_model.h @@ -19,7 +19,7 @@ #include "base.h" -#if defined(__cplusplus) +#if defined(__cplusplus) && !defined(COMPILE_OMG_PACKAGE) extern "C" { #endif @@ -490,7 +490,7 @@ RTS_API rtError_t rtDebugRegister(rtModel_t model, uint32_t flag, const void *ad */ RTS_API rtError_t rtDebugUnRegister(rtModel_t model); -#if defined(__cplusplus) +#if defined(__cplusplus) && !defined(COMPILE_OMG_PACKAGE) } #endif diff --git a/third_party/fwkacllib/inc/runtime/rt_stars.h b/third_party/fwkacllib/inc/runtime/rt_stars.h index 016c352a..188656b1 100644 --- a/third_party/fwkacllib/inc/runtime/rt_stars.h +++ b/third_party/fwkacllib/inc/runtime/rt_stars.h @@ -8,7 +8,7 @@ #include "base.h" -#if defined(__cplusplus) +#if defined(__cplusplus) && !defined(COMPILE_OMG_PACKAGE) extern "C" { #endif @@ -23,7 +23,6 @@ extern "C" { */ RTS_API rtError_t rtStarsTaskLaunch(const void *taskSqe, uint32_t sqeLen, rtStream_t stream); - /** * @ingroup rt_stars * @brief create cdq instance. @@ -77,11 +76,10 @@ RTS_API rtError_t rtCdqEnQueue(const char *queName, uint32_t cdqeIndex, void *da * @param [in] stream launch task on the stream * @return RT_ERROR_NONE for ok, others failed */ -RTS_API rtError_t rtCdqEnQueuePtrMode(const char *queName, uint32_t cdqeIndex, const void *ptrAddr, +RTS_API rtError_t rtCdqEnQueuePtrMode(const char *queName, uint32_t cdqeIndex, const void *prtAddr, rtStream_t stream); -#if defined(__cplusplus) - +#if defined(__cplusplus) && !defined(COMPILE_OMG_PACKAGE) } #endif #endif // __CCE_RUNTIME_STARS_H diff --git a/third_party/fwkacllib/inc/runtime/stream.h b/third_party/fwkacllib/inc/runtime/stream.h index 3a078e99..f9981514 100644 --- a/third_party/fwkacllib/inc/runtime/stream.h +++ b/third_party/fwkacllib/inc/runtime/stream.h @@ -20,7 +20,7 @@ #include "base.h" #include "event.h" -#if defined(__cplusplus) +#if defined(__cplusplus) && !defined(COMPILE_OMG_PACKAGE) extern "C" { #endif @@ -211,7 +211,7 @@ RTS_API rtError_t rtDebugRegisterForStream(rtStream_t stream, uint32_t flag, con */ RTS_API rtError_t rtDebugUnRegisterForStream(rtStream_t stream); -#if defined(__cplusplus) +#if defined(__cplusplus) && !defined(COMPILE_OMG_PACKAGE) } #endif diff --git a/third_party/fwkacllib/inc/toolchain/prof_acl_api.h b/third_party/fwkacllib/inc/toolchain/prof_acl_api.h index 11e259b9..07b32149 100644 --- a/third_party/fwkacllib/inc/toolchain/prof_acl_api.h +++ b/third_party/fwkacllib/inc/toolchain/prof_acl_api.h @@ -84,7 +84,6 @@ #endif #include -#include namespace Msprofiler { namespace Api { @@ -106,37 +105,6 @@ extern "C" { MSVP_PROF_API uint64_t ProfGetOpExecutionTime(const void *data, uint32_t len, uint32_t index); -typedef uint32_t Status; -typedef struct aclprofSubscribeConfig aclprofSubscribeConfig1; -/// -/// @ingroup AscendCL -/// @brief subscribe profiling data of graph -/// @param [in] graphId: the graph id subscribed -/// @param [in] profSubscribeConfig: pointer to config of model subscribe -/// @return Status result of function -/// -Status aclgrphProfGraphSubscribe(const uint32_t graphId, - const aclprofSubscribeConfig1 *profSubscribeConfig); - -/// -/// @ingroup AscendCL -/// @brief unsubscribe profiling data of graph -/// @param [in] graphId: the graph id subscribed -/// @return Status result of function -/// -Status aclgrphProfGraphUnSubscribe(const uint32_t graphId); - -/** - * @ingroup AscendCL - * @brief get graph id from subscription data - * - * @param opInfo [IN] pointer to subscription data - * @param opInfoLen [IN] memory size of subscription data - * - * @retval graph id of subscription data - * @retval 0 for failed - */ -size_t aclprofGetGraphId(const void *opInfo, size_t opInfoLen, uint32_t index); #ifdef __cplusplus } #endif diff --git a/third_party/fwkacllib/inc/toolchain/prof_callback.h b/third_party/fwkacllib/inc/toolchain/prof_callback.h index 36b55216..5073cfb1 100644 --- a/third_party/fwkacllib/inc/toolchain/prof_callback.h +++ b/third_party/fwkacllib/inc/toolchain/prof_callback.h @@ -55,17 +55,6 @@ struct ReporterData { }; /** - * @name HashData - * @brief struct of data to hash - */ -struct HashData { - int deviceId; // the index of device - size_t dataLen; // the length of data - unsigned char *data; // the data content - uint64_t hashId; // the id of hashed data -}; - -/** * @name MsprofReporterModuleId * @brief module id of data to report */ @@ -86,7 +75,6 @@ enum MsprofReporterCallbackType { MSPROF_REPORTER_INIT, // init reporter MSPROF_REPORTER_UNINIT, // uninit reporter MSPROF_REPORTER_DATA_MAX_LEN, // data max length for calling report callback - MSPROF_REPORTER_HASH // hash data to id }; /**