* 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 welfordfinalize.h
* \brief
*/
#if !defined(__ASCENDC_INCLUDE_INTERNAL_HEADERS__)
#define __ASCENDC_INCLUDE_INTERNAL_HEADERS__
#define __UNDEF_ASCENDC_INCLUDE_INTERNAL_HEADERS_WELFORDFINALIZE_H__
#endif
#ifndef LIB_NORMALIZATION_WELFORDFINALIZE_H
#define LIB_NORMALIZATION_WELFORDFINALIZE_H
#include "include/adv_api/normalization/welfordfinalize_utils.h"
#include "kernel_tensor.h"
#include "kernel_tiling/kernel_tiling.h"
#if defined(__NPU_ARCH__) && (__NPU_ARCH__ == 2002 || __NPU_ARCH__ == 2201)
#include "../../../impl/adv_api/detail/normalization/welfordfinalize/welfordfinalize_common_impl.h"
#elif defined(__NPU_ARCH__) && (__NPU_ARCH__ == 3510 || __NPU_ARCH__ == 5102)
#include "../../../impl/adv_api/detail/normalization/welfordfinalize/welfordfinalize_3510_impl.h"
#endif
namespace AscendC
{
#pragma begin_pipe(V)
#if defined(__NPU_ARCH__) && (__NPU_ARCH__ == 2002 || __NPU_ARCH__ == 2201)
* \brief Calculate the final mean and variance using the Welford algorithm.
*
* \note support data type: float
*
* \param [out] outputMean, output LocalTensor, shape is [8]
* \param [out] outputVariance, output LocalTensor, shape is [8]
* \param [in] inputMean, input LocalTensor, shape is [abLength]
* \param [in] inputVariance, input LocalTensor, shape is [abLength]
* \param [in] sharedTmpBuffer, input local temporary Tensor
* \param [in] para, para detailed information about the original data shape
*/
template <bool isReuseSource = false>
__aicore__ inline void WelfordFinalize(const LocalTensor<float>& outputMean, const LocalTensor<float>& outputVariance,
const LocalTensor<float>& inputMean, const LocalTensor<float>& inputVariance,
const LocalTensor<uint8_t>& sharedTmpBuffer, WelfordFinalizePara& para)
{
if ASCEND_IS_AIC {
return;
}
WelfordFinalizeImpl<isReuseSource>(outputMean, outputVariance, inputMean, inputVariance, sharedTmpBuffer, para);
}
* \brief Calculate the final mean and variance using the Welford algorithm.
*
* \note support data type: float
*
* \param [out] outputMean, output LocalTensor, shape is [8]
* \param [out] outputVariance, output LocalTensor, shape is [8]
* \param [in] inputMean, input LocalTensor, shape is [abLength]
* \param [in] inputVariance, input LocalTensor, shape is [abLength]
* \param [in] counts, input LocalTensor, shape is [abLength]
* \param [in] sharedTmpBuffer, input local temporary Tensor
* \param [in] para, para detailed information about the original data shape
*/
template <bool isReuseSource = false>
__aicore__ inline void WelfordFinalize(const LocalTensor<float>& outputMean, const LocalTensor<float>& outputVariance,
const LocalTensor<float>& inputMean, const LocalTensor<float>& inputVariance, const LocalTensor<int32_t>& counts,
const LocalTensor<uint8_t>& sharedTmpBuffer, WelfordFinalizePara& para)
{
if ASCEND_IS_AIC {
return;
}
WelfordFinalizeImpl<isReuseSource>(
outputMean, outputVariance, inputMean, inputVariance, counts, sharedTmpBuffer, para);
}
* \brief Calculate the final mean and variance using the Welford algorithm.
*
* \note support data type: float
*
* \param [out] outputMean, output LocalTensor, shape is [8]
* \param [out] outputVariance, output LocalTensor, shape is [8]
* \param [in] inputMean, input LocalTensor, shape is [abLength]
* \param [in] inputVariance, input LocalTensor, shape is [abLength]
* \param [in] para, para detailed information about the original data shape
*/
template <bool isReuseSource = false>
__aicore__ inline void WelfordFinalize(const LocalTensor<float>& outputMean, const LocalTensor<float>& outputVariance,
const LocalTensor<float>& inputMean, const LocalTensor<float>& inputVariance, WelfordFinalizePara& para)
{
if ASCEND_IS_AIC {
return;
}
WelfordFinalizeImpl<isReuseSource>(outputMean, outputVariance, inputMean, inputVariance, para);
}
* \brief Calculate the final mean and variance using the Welford algorithm.
*
* \note support data type: float
*
* \param [out] outputMean, output LocalTensor, shape is [8]
* \param [out] outputVariance, output LocalTensor, shape is [8]
* \param [in] inputMean, input LocalTensor, shape is [abLength]
* \param [in] inputVariance, input LocalTensor, shape is [abLength]
* \param [in] counts, input LocalTensor, shape is [abLength]
* \param [in] para, para detailed information about the original data shape
*/
template <bool isReuseSource = false>
__aicore__ inline void WelfordFinalize(const LocalTensor<float>& outputMean, const LocalTensor<float>& outputVariance,
const LocalTensor<float>& inputMean, const LocalTensor<float>& inputVariance, const LocalTensor<int32_t>& counts,
WelfordFinalizePara& para)
{
if ASCEND_IS_AIC {
return;
}
WelfordFinalizeImpl<isReuseSource>(outputMean, outputVariance, inputMean, inputVariance, counts, para);
}
#else
* \brief Calculate the final mean and variance using the Welford algorithm.
*
* \note support data type: float
*
* \param [out] outputMean, output LocalTensor, shape is [8]
* \param [out] outputVariance, output LocalTensor, shape is [8]
* \param [in] inputMean, input LocalTensor, shape is [abLength]
* \param [in] inputVariance, input LocalTensor, shape is [abLength]
* \param [in] sharedTmpBuffer, input local temporary Tensor
* \param [in] para, para detailed information about the original data shape
*/
template <bool isReuseSource = false, const WelfordFinalizeConfig& config = WFFINALIZE_DEFAULT_CFG>
__aicore__ inline void WelfordFinalize(const LocalTensor<float>& outputMean, const LocalTensor<float>& outputVariance,
const LocalTensor<float>& inputMean, const LocalTensor<float>& inputVariance,
const LocalTensor<uint8_t>& sharedTmpBuffer, WelfordFinalizePara& para)
{
if ASCEND_IS_AIC {
return;
}
#if defined(__NPU_ARCH__) && (__NPU_ARCH__ == 3510 || __NPU_ARCH__ == 5102)
WelfordFinalizeImpl<isReuseSource, config>(
outputMean, outputVariance, inputMean, inputVariance, sharedTmpBuffer, para);
#endif
}
* \brief Calculate the final mean and variance using the Welford algorithm.
*
* \note support data type: float
*
* \param [out] outputMean, output LocalTensor, shape is [8]
* \param [out] outputVariance, output LocalTensor, shape is [8]
* \param [in] inputMean, input LocalTensor, shape is [abLength]
* \param [in] inputVariance, input LocalTensor, shape is [abLength]
* \param [in] counts, input LocalTensor, shape is [abLength]
* \param [in] sharedTmpBuffer, input local temporary Tensor
* \param [in] para, para detailed information about the original data shape
*/
template <bool isReuseSource = false, const WelfordFinalizeConfig& config = WFFINALIZE_DEFAULT_CFG>
__aicore__ inline void WelfordFinalize(const LocalTensor<float>& outputMean, const LocalTensor<float>& outputVariance,
const LocalTensor<float>& inputMean, const LocalTensor<float>& inputVariance, const LocalTensor<int32_t>& counts,
const LocalTensor<uint8_t>& sharedTmpBuffer, WelfordFinalizePara& para)
{
if ASCEND_IS_AIC {
return;
}
#if defined(__NPU_ARCH__) && (__NPU_ARCH__ == 3510 || __NPU_ARCH__ == 5102)
WelfordFinalizeImpl<isReuseSource, config>(
outputMean, outputVariance, inputMean, inputVariance, counts, sharedTmpBuffer, para);
#endif
}
* \brief Calculate the final mean and variance using the Welford algorithm.
*
* \note support data type: float
*
* \param [out] outputMean, output LocalTensor, shape is [8]
* \param [out] outputVariance, output LocalTensor, shape is [8]
* \param [in] inputMean, input LocalTensor, shape is [abLength]
* \param [in] inputVariance, input LocalTensor, shape is [abLength]
* \param [in] para, para detailed information about the original data shape
*/
template <bool isReuseSource = false, const WelfordFinalizeConfig& config = WFFINALIZE_DEFAULT_CFG>
__aicore__ inline void WelfordFinalize(const LocalTensor<float>& outputMean, const LocalTensor<float>& outputVariance,
const LocalTensor<float>& inputMean, const LocalTensor<float>& inputVariance, WelfordFinalizePara& para)
{
if ASCEND_IS_AIC {
return;
}
#if defined(__NPU_ARCH__) && (__NPU_ARCH__ == 3510 || __NPU_ARCH__ == 5102)
WelfordFinalizeImpl<isReuseSource, config>(outputMean, outputVariance, inputMean, inputVariance, para);
#endif
}
* \brief Calculate the final mean and variance using the Welford algorithm.
*
* \note support data type: float
*
* \param [out] outputMean, output LocalTensor, shape is [8]
* \param [out] outputVariance, output LocalTensor, shape is [8]
* \param [in] inputMean, input LocalTensor, shape is [abLength]
* \param [in] inputVariance, input LocalTensor, shape is [abLength]
* \param [in] counts, input LocalTensor, shape is [abLength]
* \param [in] para, para detailed information about the original data shape
*/
template <bool isReuseSource = false, const WelfordFinalizeConfig& config = WFFINALIZE_DEFAULT_CFG>
__aicore__ inline void WelfordFinalize(const LocalTensor<float>& outputMean, const LocalTensor<float>& outputVariance,
const LocalTensor<float>& inputMean, const LocalTensor<float>& inputVariance, const LocalTensor<int32_t>& counts,
WelfordFinalizePara& para)
{
if ASCEND_IS_AIC {
return;
}
#if defined(__NPU_ARCH__) && (__NPU_ARCH__ == 3510 || __NPU_ARCH__ == 5102)
WelfordFinalizeImpl<isReuseSource, config>(outputMean, outputVariance, inputMean, inputVariance, counts, para);
#endif
}
#endif
#pragma end_pipe
}
#endif
#if defined(__UNDEF_ASCENDC_INCLUDE_INTERNAL_HEADERS_WELFORDFINALIZE_H__)
#undef __ASCENDC_INCLUDE_INTERNAL_HEADERS__
#undef __UNDEF_ASCENDC_INCLUDE_INTERNAL_HEADERS_WELFORDFINALIZE_H__
#endif
