* 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 apply_gradient_descent_proto.h
* \brief
*/
#ifndef OPS_OPTIM_APPLY_GRADIENT_DESCENT_GRAPH_PLUGIN_PROTO_H_
#define OPS_OPTIM_APPLY_GRADIENT_DESCENT_GRAPH_PLUGIN_PROTO_H_
#include "graph/operator_reg.h"
namespace ge {
*@brief Updates "var" by subtracting 'alpha' * 'delta' from it.
* var -= delta * alpha
*
*@attention Constraints:
* the input tensors must have the same shape.
*
*@par Inputs:
*@li var: A mutable tensor. Should be from a Variable().
*@li alpha: A scalar. Has the same type as "var".
*@li delta: A tensor for the change. Has the same type as "var".
*
*@par Attributes:
* use_locking: An optional bool. Defaults to "False".
* If "True", updating of the "var" tensors is protected
* by a lock; otherwise the behavior is undefined, but may exhibit less
* contention.
*
*@par Outputs:
* var: A mutable tensor. Has the same type as input "var".
*
*@par Third-party framework compatibility
*Compatible with the TensorFlow operator ApplyGradientDescent.
*
*/
REG_OP(ApplyGradientDescent)
.INPUT(var, TensorType::NumberType())
.INPUT(alpha, TensorType::NumberType())
.INPUT(delta, TensorType::NumberType())
.OUTPUT(var, TensorType::NumberType())
.ATTR(use_locking, Bool, false)
.OP_END_FACTORY_REG(ApplyGradientDescent)
}
#endif
