* 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 antiquantize.h
* \brief
*/
#ifndef LIB_QUANTIZATION_ANTIQUANTIZE_H
#define LIB_QUANTIZATION_ANTIQUANTIZE_H
#include "kernel_basic_intf.h"
#include "kernel_tensor.h"
#include "../../../impl/adv_api/detail/quantization/antiquantize/antiquantize_impl.h"
namespace AscendC {
#pragma begin_pipe(V)
#if defined(__NPU_ARCH__) && (__NPU_ARCH__ == 3101 || __NPU_ARCH__ == 5102)
* \ingroup AntiQuantize
* \brief For AntiQuantizeParams {m, n, groupSize}, m means src tensor has m rows, each row has n num;
* Calculation process depends on AntiQuantize policy. For example, AntiQuantize PER_TENSOR is specified as below:
* dstTensor[i][j] = round((src[i][j] + offset) * scale)
* For more information, please refer to document.
* \tparam AntiQuantizeConfig: {policy, hasOffset, kDim} indicate AntiQuantize config parameters.
* \tparam DstT: dstTensor data type.
* \tparam SrcT: srcTensor data type.
* \tparam ScaleT: scale data type, which can be scalar or LocalTensor depending on policy.
* \tparam OffsetT: offset data type, which can be scalar or LocalTensor depending on policy.
* \param [out] dstTensor: Output localTensor.
* \param [in] srcTensor: Input src localTensor.
* \param [in] scale: Input scale.
* \param [in] offset: input offset.
* \param [in] sharedTmpBuffer： extra temporary shared space used for intermediate values among calculation process,
* whose required space size should refer to corresponding tiling API, which is defined at
* ascend_dequant_tiling.h. Generally, the more space you allocate, the better performance you will achieve,
* and the performance reaches peak when buffer size is maximum(calculated by tiling function). Moreover, it
* is not guaranteed that the shared space will be cleared after usage, the data could be anything.
* \param [in] params: AntiQuantizeParams with m, n, groupSize to describe the calculation process like above.
*/
template <const AntiQuantizeConfig& config, typename DstT, typename SrcT, typename ScaleT, typename OffsetT>
__aicore__ inline void AntiQuantize(const LocalTensor<DstT>& dstTensor, const LocalTensor<SrcT>& srcTensor,
const ScaleT& scale, const OffsetT& offset, const LocalTensor<uint8_t>& sharedTmpBuffer,
const AntiQuantizeParams& params)
{
AntiQuantizeImpl<config>(dstTensor, srcTensor, scale, offset, sharedTmpBuffer, params);
}
* \ingroup AntiQuantize
* \brief For AntiQuantizeParams {m, n, groupSize}, m means src tensor has m rows, each row has n num;
* Calculation process depends on AntiQuantize policy. For example, AntiQuantize PER_TENSOR is specified as below:
* dstTensor[i][j] = round((src[i][j] + offset) * scale)
* For more information, please refer to document.
* \tparam AntiQuantizeConfig: {policy, hasOffset, kDim} indicate AntiQuantize config parameters.
* \tparam DstT: dstTensor data type.
* \tparam SrcT: srcTensor data type.
* \tparam ScaleT: scale data type, which can be scalar or LocalTensor depending on policy.
* \tparam OffsetT: offset data type, which can be scalar or LocalTensor depending on policy.
* \param [out] dstTensor: Output localTensor.
* \param [in] srcTensor: Input src localTensor.
* \param [in] scale: Input scale.
* \param [in] offset: input offset.
* \param [in] params: AntiQuantizeParams with m, n, groupSize to describe the calculation process like above.
*/
template <const AntiQuantizeConfig& config, typename DstT, typename SrcT, typename ScaleT, typename OffsetT>
__aicore__ inline void AntiQuantize(const LocalTensor<DstT>& dstTensor, const LocalTensor<SrcT>& srcTensor,
const ScaleT& scale, const OffsetT& offset, const AntiQuantizeParams& params)
{
AntiQuantizeImpl<config>(dstTensor, srcTensor, scale, offset, params);
}
#endif
#pragma end_pipe
}
#endif
