* Copyright (c) 2025 Huawei Technologies Co., Ltd.
* This program is free software, you can redistribute it and/or modify it under the terms and conditions of
* CANN Open Software License Agreement Version 2.0 (the "License").
* Please refer to the License for details. You may not use this file except in compliance with the License.
* THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED,
* INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.
* See LICENSE in the root of the software repository for the full text of the License.
*/
* \file axpy_v2_proto.h
* \brief
*/
#ifndef OPS_OP_PROTO_INC_AXPY_V2_H_
#define OPS_OP_PROTO_INC_AXPY_V2_H_
#include "graph/operator_reg.h"
#include "graph/types.h"
namespace ge {
* @brief Computes the result of x2 * alpha + x1.
* @par Inputs:
* @li x1: An ND tensor of type float16, bfloat16, float32, int32, int64, uint8, int8, bool.
* @li x2: An ND tensor of type float16, bfloat16, float32, int32, int64, uint8, int8, bool.
* The shapes of "x1", "x2" must comply with the broadcast rule.
* @li alpha: A scalar tensor of type float16, bfloat16, float32, int32, int64, uint8, int8, bool. Shape must be [1]. \n
* @par Outputs:
* y: An ND tensor with type is after 'x1', 'x2' and 'alpha' type promotion,
* whose shape is generated after 'x1', 'x2' broadcast opratioan. \n
* @par Third-party framework compatibility
* Compatible with the PyTorch operator Axpy.
*/
REG_OP(AxpyV2)
.INPUT(x1, "T1")
.INPUT(x2, "T2")
.INPUT(alpha, "T3")
.OUTPUT(y, "T4")
.DATATYPE(T1, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32, DT_BF16, DT_INT64, DT_UINT8, DT_INT8, DT_BOOL}))
.DATATYPE(T2, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32, DT_BF16, DT_INT64, DT_UINT8, DT_INT8, DT_BOOL}))
.DATATYPE(T3, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32, DT_BF16, DT_INT64, DT_UINT8, DT_INT8, DT_BOOL}))
.DATATYPE(T4, Promote({"T1", "T2", "T3"}))
.OP_END_FACTORY_REG(AxpyV2)
}
#endif
