/* * Copyright (c) 2019-2022, 2024 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 ACL_SRC_CPU_KERNELS_CPUGEMMLOWPOFFSETCONTRIBUTIONOUTPUTSTAGEKERNEL_H #define ACL_SRC_CPU_KERNELS_CPUGEMMLOWPOFFSETCONTRIBUTIONOUTPUTSTAGEKERNEL_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 { 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); /** Set the a offset * Warning: if a_offset is non-zero then vector_sum_col must be set in run_op. * Run configure or validate again if you aren't sure * * @param[in] a_offset Offset to be added to each element of the matrix A. */ void set_a_offset(int32_t a_offset); /** Set the b offset * Warning: if b_offset is non-zero then vector_sum_col must be set in run_op. * Run configure or validate again if you aren't sure * * @param[in] b_offset Offset to be added to each element of the matrix B. */ void set_b_offset(int32_t b_offset); // 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{0}; // Number of columns of A or rows of B, used in last offset term bool _is_vector_sum_col_batched{true}; GEMMLowpOutputStageInfo _output_stage{GEMMLowpOutputStageInfo()}; }; } // namespace kernels } // namespace cpu } // namespace arm_compute #endif // ACL_SRC_CPU_KERNELS_CPUGEMMLOWPOFFSETCONTRIBUTIONOUTPUTSTAGEKERNEL_H