* 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 groupnorm.h
* \brief
*/
#ifndef LIB_NORMALIZATION_GROUPNORM_H
#define LIB_NORMALIZATION_GROUPNORM_H
#if defined(__NPU_ARCH__) && (__NPU_ARCH__ == 2201 || __NPU_ARCH__ == 2002 || __NPU_ARCH__ == 3101 || __NPU_ARCH__ == 5102)
#include "kernel_tensor.h"
#if defined(__NPU_ARCH__) && (__NPU_ARCH__ == 2201 || __NPU_ARCH__ == 2002)
#include "../../../impl/adv_api/detail/normalization/groupnorm/groupnorm_common_impl.h"
#elif defined(__NPU_ARCH__) && (__NPU_ARCH__ == 3101 || __NPU_ARCH__ == 5102)
#include "../../../impl/adv_api/detail/normalization/groupnorm/groupnorm_c310_impl.h"
#endif
#include "kernel_tiling/kernel_tiling.h"
namespace AscendC {
#pragma begin_pipe(V)
* \brief Applies Group Normalization over a mini-batch of inputs as described in the paper Group Normalization.
*
* \note support data type: half and float
*
* \param [out] output, output LocalTensor, shape is [n, C, H, W]
* \param [out] outputMean, output LocalTensor, shape is [n, groupNum]
* \param [out] outputVariance, output LocalTensor, shape is [n, groupNum]
* \param [in] inputX, input LocalTensor, shape is [n, C, H, W]
* \param [in] gamma, input LocalTensor, shape is [C]
* \param [in] beta, input LocalTensor, shape is [C]
* \param [in] sharedTmpBuffer, input local temporary Tensor
* \param [in] epsilon, weighting factor
* \param [in] tiling, groupnormtiling
*/
template <typename T, bool isReuseSource = false>
__aicore__ inline void GroupNorm(const LocalTensor<T>& output, const LocalTensor<T>& outputMean,
const LocalTensor<T>& outputVariance, const LocalTensor<T>& inputX, const LocalTensor<T>& gamma,
const LocalTensor<T>& beta, const LocalTensor<uint8_t>& sharedTmpBuffer, const T epsilon, GroupNormTiling& tiling)
{
GroupNormImpl<T, isReuseSource>(output, outputMean, outputVariance, inputX, gamma, beta, sharedTmpBuffer, epsilon,
tiling);
}
* \brief Applies Group Normalization over a mini-batch of inputs as described in the paper Group Normalization.
*
* \note support data type: half and float
*
* \param [out] output, output LocalTensor, shape is [n, C, H, W]
* \param [out] outputMean, output LocalTensor, shape is [n, groupNum]
* \param [out] outputVariance, output LocalTensor, shape is [n, groupNum]
* \param [in] inputX, input LocalTensor, shape is [n, C, H, W]
* \param [in] gamma, input LocalTensor, shape is [C]
* \param [in] beta, input LocalTensor, shape is [C]
* \param [in] epsilon, weighting factor
* \param [in] tiling, groupnormtiling
*/
template <typename T, bool isReuseSource = false>
__aicore__ inline void GroupNorm(const LocalTensor<T>& output, const LocalTensor<T>& outputMean,
const LocalTensor<T>& outputVariance, const LocalTensor<T>& inputX, const LocalTensor<T>& gamma,
const LocalTensor<T>& beta, const T epsilon, GroupNormTiling& tiling)
{
GroupNormImpl<T, isReuseSource>(output, outputMean, outputVariance, inputX, gamma, beta, epsilon, tiling);
}
#pragma end_pipe
}
#endif
#endif
