/** * 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 simplesoftmax.h * \brief SimpleSoftMax api of AscendC */ #ifndef LIB_SOFTMAX_SIMPLESOFTMAX_H #define LIB_SOFTMAX_SIMPLESOFTMAX_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/simple_softmax_base_impl.h" #pragma begin_pipe(V) namespace AscendC { /*! * \ingroup SimpleSoftMax * \brief compute process: y = exp(x-inmax)/insum * \note support data type: half and float * \param [out] dst: output y * \param [in] inSumTensor: input insum * \param [in] inMaxTensor: input inmax * \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 __aicore__ inline void SimpleSoftMax(const LocalTensor& dst, const LocalTensor& inSumTensor, const LocalTensor& inMaxTensor, const LocalTensor& src, const SoftMaxTiling& tiling, const SoftMaxShapeInfo& softmaxShapeInfo = {}) { if ASCEND_IS_AIC { return; } SimpleSoftMaxImpl(dst, inSumTensor, inMaxTensor, src, tiling, softmaxShapeInfo); } /*! * \ingroup SimpleSoftMax * \brief compute process: y = exp(x-inmax)/insum * \param [out] dst: output y with dtype of half * \param [in] inSumTensor: input insum with dtype of float * \param [in] inMaxTensor: input inmax 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 __aicore__ inline void SimpleSoftMax(const LocalTensor& dst, const LocalTensor& inSumTensor, const LocalTensor& inMaxTensor, const LocalTensor& src, const SoftMaxTiling& tiling, const SoftMaxShapeInfo& softmaxShapeInfo = {}) { if ASCEND_IS_AIC { return; } SimpleSoftMaxImpl(dst, inSumTensor, inMaxTensor, src, tiling, softmaxShapeInfo); } /*! * \ingroup SimpleSoftMax * \brief compute process: y = exp(x-inmax)/insum * \note support data type: half and float * \param [out] dst: output y * \param [in] inSumTensor: input insum * \param [in] inMaxTensor: input inmax * \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 __aicore__ inline void SimpleSoftMax(const LocalTensor& dst, const LocalTensor& inSumTensor, const LocalTensor& inMaxTensor, const LocalTensor& src, const LocalTensor& sharedTmpBuffer, const SoftMaxTiling& tiling, const SoftMaxShapeInfo& softmaxShapeInfo = {}) { if ASCEND_IS_AIC { return; } SimpleSoftMaxImpl(dst, inSumTensor, inMaxTensor, src, sharedTmpBuffer, tiling, softmaxShapeInfo); } /*! * \ingroup SimpleSoftMax * \brief compute process: y = exp(x-inmax)/insum * \param [out] dst: output y with dtype of half * \param [in] inSumTensor: input insum with dtype of float * \param [in] inMaxTensor: input inmax 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 __aicore__ inline void SimpleSoftMax(const LocalTensor& dst, const LocalTensor& inSumTensor, const LocalTensor& inMaxTensor, const LocalTensor& src, const LocalTensor& sharedTmpBuffer, const SoftMaxTiling& tiling, const SoftMaxShapeInfo& softmaxShapeInfo = {}) { if ASCEND_IS_AIC { return; } SimpleSoftMaxImpl(dst, inSumTensor, inMaxTensor, src, sharedTmpBuffer, tiling, softmaxShapeInfo); } } // namespace AscendC #pragma end_pipe #endif #endif // LIB_SOFTMAX_SIMPLESOFTMAX_H