/**
 * Copyright (c) 2024 Huawei Technologies Co., Ltd.
 * This file is a part of the CANN Open Software.
 * Licensed under CANN Open Software License Agreement Version 1.0 (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 softmax.h
 * \brief SoftMax and AdjustSoftMaxRes api of AscendC
 */
#ifndef LIB_SOFTMAX_SOFTMAX_H
#define LIB_SOFTMAX_SOFTMAX_H

#include "kernel_tensor.h"
#include "kernel_tiling/kernel_tiling.h"
#if __CCE_AICORE__ >= 200
#include "../../impl/activation/softmax/softmax_common.h"
#include "../../impl/activation/softmax/softmax_base_impl.h"
#pragma begin_pipe(V)

namespace AscendC {
/*!
 * \ingroup SoftMax
 * \brief compute process: max = rowmax(x), sum = rowsum(exp(x-max)), y = exp(x-max)/sum
 * \note support data type: half and float
 * \param [out] dst: output y
 * \param [out] sumTensor: output sum
 * \param [out] maxTensor: output max
 * \param [in] src: input x
 * \param [in] softmaxShapeInfo: input src shape
 * \param [in] tiling: input softmaxtiling
 * \param [in] isReuseSource: reserved param
 * \param [in] isBasicBlock: if src shape[m,k] satisfy the condition(m%8 == 0 && k%64 == 0), you can set true to improve
 *                           performance , but it is a reserved param when isDataFormatNZ = true
 * \param [in] isDataFormatNZ: if the data format of input src is NZ
 */
template <typename T, bool isReuseSource = false, bool isBasicBlock = false, bool isDataFormatNZ = false>
__aicore__ inline void SoftMax(const LocalTensor<T>& dst, const LocalTensor<T>& sumTensor,
    const LocalTensor<T>& maxTensor, const LocalTensor<T>& src, const SoftMaxTiling& tiling,
    const SoftMaxShapeInfo& softmaxShapeInfo = {})
{
    if ASCEND_IS_AIC {
        return;
    }
    TRACE_START(TraceId::SoftMax);
    SoftMaxImpl<T, T, isBasicBlock, isDataFormatNZ>(dst, sumTensor, maxTensor, src, tiling, softmaxShapeInfo);
    TRACE_STOP(TraceId::SoftMax);
}

/*!
 * \ingroup SoftMax
 * \brief compute process: max = rowmax(x), sum = rowsum(exp(x-max)), y = exp(x-max)/sum
 * \param [out] dst: output y with dtype of half
 * \param [out] sumTensor: output sum with dtype of float
 * \param [out] maxTensor: output max with dtype of float
 * \param [in] src: input x with dtype of half
 * \param [in] softmaxShapeInfo: input src shape
 * \param [in] tiling: input softmaxtiling
 * \param [in] isReuseSource: reserved param
 * \param [in] isBasicBlock: if src shape[m,k] satisfy the condition(m%8 == 0 && k%64 == 0), you can set true to improve
 *                           performance, but it is a reserved param when isDataFormatNZ = true
 * \param [in] isDataFormatNZ: if the data format of input src is NZ
 */
template <typename T, bool isReuseSource = false, bool isBasicBlock = false, bool isDataFormatNZ = false>
__aicore__ inline void SoftMax(const LocalTensor<half>& dst, const LocalTensor<float>& sumTensor,
    const LocalTensor<float>& maxTensor, const LocalTensor<half>& src, const SoftMaxTiling& tiling,
    const SoftMaxShapeInfo& softmaxShapeInfo = {})
{
    if ASCEND_IS_AIC {
        return;
    }
    TRACE_START(TraceId::SoftMax);
    SoftMaxImpl<half, float, isBasicBlock, isDataFormatNZ>(dst, sumTensor, maxTensor, src, tiling, softmaxShapeInfo);
    TRACE_STOP(TraceId::SoftMax);
}

/*!
 * \ingroup SoftMax
 * \brief compute process: max = rowmax(x), sum = rowsum(exp(x-max)), y = exp(x-max)/sum
 * \note support data type: half and float
 * \param [out] dst: output y
 * \param [in] src: input x
 * \param [in] softmaxShapeInfo: input src shape
 * \param [in] tiling: input softmaxtiling
 * \param [in] isReuseSource: reserved param
 * \param [in] isBasicBlock: reserved param
 */
template <typename T, bool isReuseSource = false, bool isBasicBlock = false>
__aicore__ inline void SoftMax(const LocalTensor<T>& dst, const LocalTensor<T>& src, const SoftMaxTiling& tiling,
    const SoftMaxShapeInfo& softmaxShapeInfo = {})
{
    if ASCEND_IS_AIC {
        return;
    }
    TRACE_START(TraceId::SoftMax);
    SoftMaxImpl<T, isReuseSource, isBasicBlock>(dst, src, tiling, softmaxShapeInfo);
    TRACE_STOP(TraceId::SoftMax);
}

/*!
 * \ingroup SoftMax
 * \brief compute process: max = rowmax(x), sum = rowsum(exp(x-max)), y = exp(x-max)/sum
 * \note support data type: half and float
 * \param [out] dst: output y
 * \param [in] src: input x
 * \param [in] sharedTmpBuffer: input local temporary Tensor,you can get the range by tilingfunc of
 *                               GetSoftMaxMinTmpSize/GetSoftMaxMaxTmpSize
 * \param [in] softmaxShapeInfo: input src shape
 * \param [in] tiling: input softmaxtiling
 * \param [in] isReuseSource: reserved param
 * \param [in] isBasicBlock: reserved param
 */
template <typename T, bool isReuseSource = false, bool isBasicBlock = false>
__aicore__ inline void SoftMax(const LocalTensor<T>& dst, const LocalTensor<T>& src,
    const LocalTensor<uint8_t>& sharedTmpBuffer, const SoftMaxTiling& tiling,
    const SoftMaxShapeInfo& softmaxShapeInfo = {})
{
    if ASCEND_IS_AIC {
        return;
    }
    TRACE_START(TraceId::SoftMax);
    SoftMaxImpl<T, isReuseSource, isBasicBlock>(dst, src, sharedTmpBuffer, tiling, softmaxShapeInfo);
    TRACE_STOP(TraceId::SoftMax);
}

/*!
 * \ingroup SoftMax
 * \brief compute process: max = rowmax(x), sum = rowsum(exp(x-max)), y = exp(x-max)/sum
 * \note support data type: half and float
 * \param [out] dst: output y
 * \param [out] sumTensor: output sum
 * \param [out] maxTensor: output max
 * \param [in] src: input x
 * \param [in] sharedTmpBuffer: input local temporary Tensor,you can get the range by tilingfunc of
 *                              GetSoftMaxMinTmpSize/GetSoftMaxMaxTmpSize
 * \param [in] softmaxShapeInfo: input src shape
 * \param [in] tiling: input softmaxtiling
 * \param [in] isReuseSource: reserved param
 * \param [in] isBasicBlock: if src shape[m,k] satisfy the condition(m%8 == 0 && k%64 == 0), you can set true to improve
 *                           performance, but it is a reserved param when isDataFormatNZ = true
 * \param [in] isDataFormatNZ: if the data format of input src is NZ
 */
template <typename T, bool isReuseSource = false, bool isBasicBlock = false, bool isDataFormatNZ = false>
__aicore__ inline void SoftMax(const LocalTensor<T>& dst, const LocalTensor<T>& sumTensor,
    const LocalTensor<T>& maxTensor, const LocalTensor<T>& src, const LocalTensor<uint8_t>& sharedTmpBuffer,
    const SoftMaxTiling& tiling, const SoftMaxShapeInfo& softmaxShapeInfo = {})
{
    if ASCEND_IS_AIC {
        return;
    }
    TRACE_START(TraceId::SoftMax);
    SoftMaxImpl<T, T, isBasicBlock, isDataFormatNZ>(dst, sumTensor, maxTensor, src, sharedTmpBuffer, tiling,
        softmaxShapeInfo);
    TRACE_STOP(TraceId::SoftMax);
}

/*!
 * \ingroup SoftMax
 * \brief compute process: max = rowmax(x), sum = rowsum(exp(x-max)), y = exp(x-max)/sum
 * \param [out] dst: output y with dtype of half
 * \param [out] sumTensor: output sum with dtype of float
 * \param [out] maxTensor: output max with dtype of float
 * \param [in] src: input x with dtype of half
 * \param [in] sharedTmpBuffer: input local temporary Tensor,you can get the range by tilingfunc of
 *                              GetSoftMaxMinTmpSize/GetSoftMaxMaxTmpSize
 * \param [in] softmaxShapeInfo: input src shape
 * \param [in] tiling: input softmaxtiling
 * \param [in] isReuseSource: reserved param
 * \param [in] isBasicBlock: if src shape[m,k] satisfy the condition(m%8 == 0 && k%64 == 0), you can set true to improve
 *                           performance, but it is a reserved param when isDataFormatNZ = true
 * \param [in] isDataFormatNZ: if the data format of input src is NZ
 */
template <typename T, bool isReuseSource = false, bool isBasicBlock = false, bool isDataFormatNZ = false>
__aicore__ inline void SoftMax(const LocalTensor<half>& dst, const LocalTensor<float>& sumTensor,
    const LocalTensor<float>& maxTensor, const LocalTensor<half>& src, const LocalTensor<uint8_t>& sharedTmpBuffer,
    const SoftMaxTiling& tiling, const SoftMaxShapeInfo& softmaxShapeInfo = {})
{
    if ASCEND_IS_AIC {
        return;
    }
    TRACE_START(TraceId::SoftMax);
    SoftMaxImpl<half, float, isBasicBlock, isDataFormatNZ>(dst, sumTensor, maxTensor, src, sharedTmpBuffer, tiling,
        softmaxShapeInfo);
    TRACE_STOP(TraceId::SoftMax);
}

/*!
 * \ingroup AdjustSoftMaxRes
 * \brief check whether inmax result has from value, if exist, reset the softmax result as to value
 * \note support data type: half and float
 * \param [out/in] softMaxRes: input need Check src LocalTensor
 * \param [in] maxTensor: softmax rowmax value of last axis
 * \param [in] from: is the value need check in maxTensor
 * \param [in] to: is the value need reset in softMaxRes
 * \param [in] softmaxShapeInfo: input src shape
 * \param [in] isDataFormatNZ: if the data format of input src is NZ
 * \return if true means inmax result has the from value
 */
template <typename T1, typename T2, bool isDataFormatNZ = false>
__aicore__ inline bool AdjustSoftMaxRes(const LocalTensor<T1>& softMaxRes, const LocalTensor<T2>& maxTensor,
    const uint32_t from, const T1 to, const SoftMaxShapeInfo& softmaxShapeInfo)
{
    if ASCEND_IS_AIC {
        return false;
    }
    return AdjustSoftMaxResImpl<T1, T2, isDataFormatNZ>(softMaxRes, maxTensor, from, to, softmaxShapeInfo);
}
} // namespace AscendC

#pragma end_pipe
#endif
#endif // LIB_SOFTMAX_SOFTMAX_H