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/*
* Copyright (c) 2017-2020 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#ifndef ARM_COMPUTE_NEGEMMLOWPMATRIXMULTIPLYKERNEL_H
#define ARM_COMPUTE_NEGEMMLOWPMATRIXMULTIPLYKERNEL_H
#include "src/core/NEON/INEKernel.h"
namespace arm_compute
{
class ITensor;
/** NEON kernel to multiply matrices
*
* @note @ref NEGEMMLowpMatrixMultiplyKernel low precision matrix product kernel
* This kernel performs the following computation:
*
* -# Convert a values from int8 to int32
* -# Convert b values from int8 to int32
* -# Compute the int32 matrix product of the resulting a * b and store the result as int32
*
*/
class NEGEMMLowpMatrixMultiplyKernel : public INEKernel
{
public:
const char *name() const override
{
return "NEGEMMLowpMatrixMultiplyKernel";
}
/** Constructor */
NEGEMMLowpMatrixMultiplyKernel();
/** Prevent instances of this class from being copied (As this class contains pointers)*/
NEGEMMLowpMatrixMultiplyKernel(const NEGEMMLowpMatrixMultiplyKernel &) = delete;
/** Prevent instances of this class from being copied (As this class contains pointers)*/
NEGEMMLowpMatrixMultiplyKernel &operator=(const NEGEMMLowpMatrixMultiplyKernel &) = delete;
/** Allow instances of this class to be moved */
NEGEMMLowpMatrixMultiplyKernel(NEGEMMLowpMatrixMultiplyKernel &&) = default;
/** Allow instances of this class to be moved */
NEGEMMLowpMatrixMultiplyKernel &operator=(NEGEMMLowpMatrixMultiplyKernel &&) = default;
/** Default destructor */
~NEGEMMLowpMatrixMultiplyKernel() = default;
/** Initialise the kernel's input and output.
*
* The input matrices @p input0 and @p input1 must be the output of the kernels: @ref NEGEMMInterleave4x4Kernel and @ref NEGEMMTranspose1xWKernel. These two
* kernels change the layout of the original matrices to be more cache-friendly.
*
* @param[in] input0 Input tensor containing the interleaved Matrix A. Data type supported: U8/QASYMM8/S8/QASYMM8_SIGNED
* @param[in] input1 Input tensor containing the transposed1xW Matrix B. Data type supported: U8/QASYMM8/S8/QASYMM8_SIGNED/QSYMM8/QSYMM8_PER_CHANNEL
* @param[out] output Output tensor to store the result of matrix multiplication. Data type supported: S32
*/
void configure(const ITensor *input0, const ITensor *input1, ITensor *output);
/** Static function to check if given info will lead to a valid configuration of @ref NEGEMMLowpMatrixMultiplyKernel
*
* @param[in] input0 Input tensor info containing the interleaved Matrix A. Data type supported: U8/QASYMM8/S8/QASYMM8_SIGNED
* @param[in] input1 Input tensor info containing the transposed Matrix B. Data type supported: U8/QASYMM8/S8/QASYMM8_SIGNED/QSYMM8/QSYMM8_PER_CHANNEL
* @param[in] output Output tensor info to store the result of matrix multiplication. Data type supported: S32
*
* @return a status
*/
static Status validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output);
// Inherited methods overridden:
void run(const Window &window, const ThreadInfo &info) override;
private:
const ITensor *_input0;
const ITensor *_input1;
ITensor *_output;
bool _slide_matrix_b;
};
} // namespace arm_compute
#endif /*ARM_COMPUTE_NEGEMMLOWPMATRIXMULTIPLYKERNEL_H*/