* 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 mean.h
* \brief
*/
#ifndef LIB_REDUCE_MEAN_H
#define LIB_REDUCE_MEAN_H
#include "kernel_tensor.h"
#include "kernel_basic_intf.h"
#include "include/adv_api/reduce/mean_utils.h"
#if defined(__NPU_ARCH__) && (__NPU_ARCH__ == 3101 || __NPU_ARCH__ == 5102)
#include "../../../impl/adv_api/detail/reduce/mean/mean_c310_impl.h"
#else
#include "../../../impl/adv_api/detail/reduce/mean/mean_common_impl.h"
#endif
#if ASCENDC_CPU_DEBUG
#include "kernel_log.h"
#include <type_traits>
#include <stdio.h>
#endif
namespace AscendC {
#pragma begin_pipe(V)
* \brief This function calculates the average based on the orientation of the last axis.
* For details about the interface description, see
* https://pytorch.org/docs/stable/generated/torch.mean.html
*
* \note support data type: half and float
*
* \param [out] dstTensor, output LocalTensor
* \param [in] srcTensor, input LocalTensor
* \param [in] sharedTmpBuffer, input local temporary Tensor
* \param [in] meanParams, shape information of srcTensor
*/
template <typename T, typename accType = T, bool isReuseSource = false, bool isBasicBlock = false,
int32_t reduceDim = -1>
__aicore__ inline void Mean(const LocalTensor<T>& dstTensor, const LocalTensor<T>& srcTensor,
const LocalTensor<uint8_t>& sharedTmpBuffer, const MeanParams& meanParams)
{
if ASCEND_IS_AIC
{
return;
}
MeanImpl<T, accType, isReuseSource, isBasicBlock, reduceDim>(dstTensor, srcTensor, sharedTmpBuffer, meanParams);
}
* \brief This function calculates the average based on the orientation of the last axis.
* For details about the interface description, see
* https://pytorch.org/docs/stable/generated/torch.mean.html
*
* \note support data type: half and float
*
* \param [out] dstTensor, output LocalTensor
* \param [in] srcTensor, input LocalTensor
* \param [in] meanParams, shape information of srcTensor
*/
template <typename T, typename accType = T, bool isReuseSource = false, bool isBasicBlock = false,
int32_t reduceDim = -1>
__aicore__ inline void Mean(
const LocalTensor<T>& dstTensor, const LocalTensor<T>& srcTensor, const MeanParams& meanParams)
{
if ASCEND_IS_AIC
{
return;
}
LocalTensor<uint8_t> sharedTmpBuffer;
bool ans = PopStackBuffer<uint8_t, TPosition::LCM>(sharedTmpBuffer);
ASCENDC_ASSERT((ans), { KERNEL_LOG(KERNEL_ERROR, "PopStackBuffer Error!"); });
Mean<T, accType, isReuseSource, isBasicBlock, reduceDim>(dstTensor, srcTensor, sharedTmpBuffer, meanParams);
}
#pragma end_pipe
}
#endif
