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/*
* Copyright (c) 2019-2022 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_CPU_GEMMLOWP_OFFSETCONTRIBUTION_OUTPUTSTAGE_KERNEL_H
#define ARM_COMPUTE_CPU_GEMMLOWP_OFFSETCONTRIBUTION_OUTPUTSTAGE_KERNEL_H
#include "arm_compute/core/KernelDescriptors.h"
#include "src/core/common/Macros.h"
#include "src/cpu/ICpuKernel.h"
namespace arm_compute
{
namespace cpu
{
namespace kernels
{
/** Kernel used to add the offset contribution and perform the output stage after @ref CpuGemmLowpMatrixMultiplyKernel.
*
* The computation is performed in-place
*
* This kernel takes a final int32 accumulator value (the output of @ref CpuGemmLowpMatrixMultiplyKernel),
* and adds to it the offset contribution of matrix A and matrix B in-place.
*
* The output stage can perform either QuantizeDownInt32ToUint8Scale or QuantizeDownInt32ToUint8ScaleByFixedPoint for Uint8.
* The output stage can perform either QuantizeDownInt32ToInt8Scale or QuantizeDownInt32ToInt8ScaleByFixedPoint for Int8.
*
* For QuantizeDownInt32ToUint8Scale/QuantizeDownInt32ToInt8Scale the final result is:
*
* ((mm_result'[i][k] + result_offset) * result_mult_int) >> result_shift
*
* For QuantizeDownInt32ToUint8ScaleByFixedPoint/QuantizeDownInt32ToInt8ScaleByFixedPoint the final result is:
*
* (FixedPointMul(mm_result'[i][k], result_fixedpoint_multiplier) >> result_shift) + result_offset_after_shift
*
* where FixedPointMul(x, y) is the nearest integer to the following
* mathematical expression, evaluated without overflow or intermediate rounding:
*
* (x * y) / 2^31
*
* and mm_result'[i][k] = mm_result[i][k] +
* (vector_sum_col[k] * a_offset) +
* (vector_sum_row[i] * b_offset) +
* (a_offset * b_offset * k)
*/
class CpuGemmLowpOffsetContributionOutputStageKernel : public ICpuKernel<CpuGemmLowpOffsetContributionOutputStageKernel>
{
public:
/** Default constructor */
CpuGemmLowpOffsetContributionOutputStageKernel() = default;
ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE(CpuGemmLowpOffsetContributionOutputStageKernel);
/** Initialise the kernel inputs and output.
*
* @param[in] mm_result Input tensor info containing the result of @ref CpuGemmLowpMatrixMultiplyKernel. Data type supported: S32
* @param[in] vector_sum_col Input row-vector tensor info of sums of all the entries in each column of matrix B.
* Can be a 1D or 2D tensor, in case of 2D, y dim is the batch dimension
* Note: vector_sum_col can be a nullptr in case a_offset = 0. Data type supported: same as @p mm_result
* @param[in] vector_sum_row Input row-vector tensor info of sums of all the entries in each row of matrix A.
* Can be a 1D or 2D tensor, in case of 2D, y dim is the batch dimension
* @param[in] bias Biases tensor info. Only shared biases supported and it can be a nullptr if the addition of biases is not required.
* Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p mm_result.
* @param[out] dst Output tensor info containing the final quantized result. Data type supported: QASYMM8/QASYMM8_SIGNED
* @param[in] k Number of matrix A columns or Matrix B rows
* @param[in] a_offset Offset to be added to each element of the matrix A.
* @param[in] b_offset Offset to be added to each element of the matrix B.
* @param[in] output_stage GEMMLowp output stage info, providing the type of quantization and the necessary parameters.
*/
void configure(const ITensorInfo *mm_result,
const ITensorInfo *vector_sum_col,
const ITensorInfo *vector_sum_row,
const ITensorInfo *bias,
ITensorInfo *dst,
int32_t k,
int32_t a_offset,
int32_t b_offset,
GEMMLowpOutputStageInfo output_stage);
/** Static function to check if given info will lead to a valid configuration
*
* Similar to CpuGemmLowpOffsetContributionOutputStageKernel::configure()
*
* @return a status
*/
static Status validate(const ITensorInfo *mm_result,
const ITensorInfo *vector_sum_col,
const ITensorInfo *vector_sum_row,
const ITensorInfo *bias,
const ITensorInfo *dst,
int32_t a_offset,
int32_t b_offset,
GEMMLowpOutputStageInfo output_stage);
// Inherited methods overridden:
void run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) override;
const char *name() const override;
private:
/** Function to use for the particular tensors passed to configure() */
int32_t _a_offset{0};
int32_t _b_offset{0};
int32_t _k_offset{0};
bool _is_vector_sum_col_batched{true};
GEMMLowpOutputStageInfo _output_stage{GEMMLowpOutputStageInfo()};
};
} // namespace kernels
} // namespace cpu
} // namespace arm_compute
#endif /* ARM_COMPUTE_CPU_GEMMLOWP_OFFSETCONTRIBUTION_OUTPUTSTAGE_KERNEL_H */
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