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/*
* Copyright (c) 2019 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_NEGEMMLOWPOFFSETCONTRIBUTIONOUTPUTSTAGEKERNEL_H
#define ARM_COMPUTE_NEGEMMLOWPOFFSETCONTRIBUTIONOUTPUTSTAGEKERNEL_H
#include "arm_compute/core/NEON/INEKernel.h"
namespace arm_compute
{
class ITensor;
/** NEON kernel used to add the offset contribution and perform the output stage after @ref NEGEMMLowpMatrixMultiplyKernel.
*
* The computation is performed in-place
*
* This kernel takes a final int32 accumulator value (the output of @ref NEGEMMLowpMatrixMultiplyKernel),
* 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 NEGEMMLowpOffsetContributionOutputStageKernel : public INEKernel
{
public:
const char *name() const override
{
return "NEGEMMLowpOffsetContributionOutputStageKernel";
}
/** Constructor */
NEGEMMLowpOffsetContributionOutputStageKernel();
/** Prevent instances of this class from being copied (As this class contains pointers)*/
NEGEMMLowpOffsetContributionOutputStageKernel(const NEGEMMLowpOffsetContributionOutputStageKernel &) = delete;
/** Prevent instances of this class from being copied (As this class contains pointers)*/
NEGEMMLowpOffsetContributionOutputStageKernel &operator=(const NEGEMMLowpOffsetContributionOutputStageKernel &) = delete;
/** Allow instances of this class to be moved */
NEGEMMLowpOffsetContributionOutputStageKernel(NEGEMMLowpOffsetContributionOutputStageKernel &&) = default;
/** Allow instances of this class to be moved */
NEGEMMLowpOffsetContributionOutputStageKernel &operator=(NEGEMMLowpOffsetContributionOutputStageKernel &&) = default;
/** Initialise the kernel's input and output.
*
* @param[in] mm_result Input tensor containing the result of @ref NEGEMMLowpMatrixMultiplyKernel. Data type supported: S32
* @param[in] vector_sum_col Input row-vector of sums of all the entries in each column of matrix B.
* 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 of sums of all the entries in each row of matrix A.
* @param[in] bias Biases tensor. 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] output Output tensor 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 ITensor *mm_result, const ITensor *vector_sum_col, const ITensor *vector_sum_row, const ITensor *bias, ITensor *output, 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 of @ref NEGEMMLowpOffsetContributionOutputStageKernel
*
* @param[in] mm_result Input tensor info containing the result of @ref NEGEMMLowpMatrixMultiplyKernel. Data type supported: S32
* @param[in] vector_sum_col Tensor info for the input row-vector of sums of all the entries in each column of matrix B.
* 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 Tensor info for the input row-vector of sums of all the entries in each row of matrix A.
* Note: vector_sum_row can be a nullptr in case b_offset = 0. Data type supported: same as @p mm_result
* @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[in] output Output tensor info containing the final quantized result. Data type supported: QASYMM8/QASYMM8_SIGNED
* @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.
*
* @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 *output, int32_t a_offset,
int32_t b_offset,
GEMMLowpOutputStageInfo output_stage);
// Inherited methods overridden:
void run(const Window &window, const ThreadInfo &info) override;
using NEGEMMLowpOffsetContributionOutputStageFunction = std::function<void(const Window, const ITensor *, const ITensor *, const ITensor *, const ITensor *,
ITensor *, int32_t, int32_t, int32_t, bool, GEMMLowpOutputStageInfo)>;
private:
/** Function to use for the particular tensors passed to configure() */
NEGEMMLowpOffsetContributionOutputStageFunction _function;
const ITensor *_vector_sum_col;
const ITensor *_vector_sum_row;
const ITensor *_bias;
const ITensor *_mm_result;
ITensor *_output;
int32_t _a_offset;
int32_t _b_offset;
int32_t _k_offset;
bool _slide_vector_sum_col;
GEMMLowpOutputStageInfo _output_stage;
};
} // namespace arm_compute
#endif /* ARM_COMPUTE_NEGEMMLOWPOFFSETCONTRIBUTIONOUTPUTSTAGEKERNEL_H */
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