/* * 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; 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 */