* 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 batchnorm.h
* \brief
*/
#ifndef LIB_NORMALIZATION_BATCHNORM_H
#define LIB_NORMALIZATION_BATCHNORM_H
#include "kernel_tensor.h"
#include "kernel_basic_intf.h"
#include "kernel_pop_stack_buffer.h"
#include "kernel_tiling/kernel_tiling.h"
#if defined(__NPU_ARCH__) && (__NPU_ARCH__ == 2002 || __NPU_ARCH__ == 2201)
#include "../../../impl/adv_api/detail/normalization/batchnorm/batchnorm_common_impl.h"
#elif defined(__NPU_ARCH__) && __NPU_ARCH__ == 3101
#include "../../../impl/adv_api/detail/normalization/batchnorm/batchnorm_c310_impl.h"
#endif
#if defined(__NPU_ARCH__) && (__NPU_ARCH__ == 2201 || __NPU_ARCH__ == 2002 || __NPU_ARCH__ == 3101)
namespace AscendC {
#pragma begin_pipe(V)
* BatchNorm *
* ************************************************************************************************** */
* @ingroup BatchNorm
* @brief compute output = gamm * (x - outputMean) * rsqrt(outputVariance + epsilon) + beta
* @brief compute outputMean = sum(x) / batch
* @brief compute outputVariance = sqrt(x - outputMean) / batch
* @param [out] output output LocalTensor
* @param [out] outputMean output LocalTensor
* @param [out] outputVariance output LocalTensor
* @param [in] inputX input LocalTensor
* @param [in] gamm input LocalTensor
* @param [in] beta input LocalTensor
* @param [in] sharedTmpBuffer input local temporary Tensor
* @param [in] epsilon
* @param [in] tiling batchnormtiling
*/
template <typename T, bool isReuseSource = false, bool isBasicBlock = false>
__aicore__ inline void BatchNorm(const LocalTensor<T>& output, const LocalTensor<T>& outputMean,
const LocalTensor<T>& outputVariance, const LocalTensor<T>& inputX, const LocalTensor<T>& gamm,
const LocalTensor<T>& beta, const LocalTensor<uint8_t>& sharedTmpBuffer, const T epsilon, BatchNormTiling& tiling)
{
if ASCEND_IS_AIC {
return;
}
BatchNormAPI::BatchNormImpl<T, isReuseSource, isBasicBlock>(output, outputMean, outputVariance, inputX, gamm, beta,
sharedTmpBuffer, epsilon, tiling);
}
template <typename T, bool isReuseSource = false, bool isBasicBlock = false>
__aicore__ inline void BatchNorm(const LocalTensor<T>& output, const LocalTensor<T>& outputMean,
const LocalTensor<T>& outputVariance, const LocalTensor<T>& inputX, const LocalTensor<T>& gamm,
const LocalTensor<T>& beta, const T epsilon, BatchNormTiling& tiling)
{
if ASCEND_IS_AIC {
return;
}
BatchNormAPI::BatchNormImpl<T, isReuseSource, isBasicBlock>(output, outputMean, outputVariance, inputX, gamm, beta,
epsilon, tiling);
}
#pragma end_pipe
}
#endif
#endif
