1 /* 2 * Copyright (c) 2019-2022 Arm Limited. 3 * 4 * SPDX-License-Identifier: MIT 5 * 6 * Permission is hereby granted, free of charge, to any person obtaining a copy 7 * of this software and associated documentation files (the "Software"), to 8 * deal in the Software without restriction, including without limitation the 9 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or 10 * sell copies of the Software, and to permit persons to whom the Software is 11 * furnished to do so, subject to the following conditions: 12 * 13 * The above copyright notice and this permission notice shall be included in all 14 * copies or substantial portions of the Software. 15 * 16 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 17 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 18 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 19 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 20 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 21 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 22 * SOFTWARE. 23 */ 24 #ifndef ARM_COMPUTE_CPU_GEMMLOWP_OFFSETCONTRIBUTION_OUTPUTSTAGE_KERNEL_H 25 #define ARM_COMPUTE_CPU_GEMMLOWP_OFFSETCONTRIBUTION_OUTPUTSTAGE_KERNEL_H 26 27 #include "arm_compute/core/KernelDescriptors.h" 28 #include "src/core/common/Macros.h" 29 #include "src/cpu/ICpuKernel.h" 30 31 namespace arm_compute 32 { 33 namespace cpu 34 { 35 namespace kernels 36 { 37 /** Kernel used to add the offset contribution and perform the output stage after @ref CpuGemmLowpMatrixMultiplyKernel. 38 * 39 * The computation is performed in-place 40 * 41 * This kernel takes a final int32 accumulator value (the output of @ref CpuGemmLowpMatrixMultiplyKernel), 42 * and adds to it the offset contribution of matrix A and matrix B in-place. 43 * 44 * The output stage can perform either QuantizeDownInt32ToUint8Scale or QuantizeDownInt32ToUint8ScaleByFixedPoint for Uint8. 45 * The output stage can perform either QuantizeDownInt32ToInt8Scale or QuantizeDownInt32ToInt8ScaleByFixedPoint for Int8. 46 * 47 * For QuantizeDownInt32ToUint8Scale/QuantizeDownInt32ToInt8Scale the final result is: 48 * 49 * ((mm_result'[i][k] + result_offset) * result_mult_int) >> result_shift 50 * 51 * For QuantizeDownInt32ToUint8ScaleByFixedPoint/QuantizeDownInt32ToInt8ScaleByFixedPoint the final result is: 52 * 53 * (FixedPointMul(mm_result'[i][k], result_fixedpoint_multiplier) >> result_shift) + result_offset_after_shift 54 * 55 * where FixedPointMul(x, y) is the nearest integer to the following 56 * mathematical expression, evaluated without overflow or intermediate rounding: 57 * 58 * (x * y) / 2^31 59 * 60 * and mm_result'[i][k] = mm_result[i][k] + 61 * (vector_sum_col[k] * a_offset) + 62 * (vector_sum_row[i] * b_offset) + 63 * (a_offset * b_offset * k) 64 */ 65 66 class CpuGemmLowpOffsetContributionOutputStageKernel : public ICpuKernel<CpuGemmLowpOffsetContributionOutputStageKernel> 67 { 68 public: 69 /** Default constructor */ 70 CpuGemmLowpOffsetContributionOutputStageKernel() = default; 71 ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE(CpuGemmLowpOffsetContributionOutputStageKernel); 72 /** Initialise the kernel inputs and output. 73 * 74 * @param[in] mm_result Input tensor info containing the result of @ref CpuGemmLowpMatrixMultiplyKernel. Data type supported: S32 75 * @param[in] vector_sum_col Input row-vector tensor info of sums of all the entries in each column of matrix B. 76 * Can be a 1D or 2D tensor, in case of 2D, y dim is the batch dimension 77 * Note: vector_sum_col can be a nullptr in case a_offset = 0. Data type supported: same as @p mm_result 78 * @param[in] vector_sum_row Input row-vector tensor info of sums of all the entries in each row of matrix A. 79 * Can be a 1D or 2D tensor, in case of 2D, y dim is the batch dimension 80 * @param[in] bias Biases tensor info. Only shared biases supported and it can be a nullptr if the addition of biases is not required. 81 * Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p mm_result. 82 * @param[out] dst Output tensor info containing the final quantized result. Data type supported: QASYMM8/QASYMM8_SIGNED 83 * @param[in] k Number of matrix A columns or Matrix B rows 84 * @param[in] a_offset Offset to be added to each element of the matrix A. 85 * @param[in] b_offset Offset to be added to each element of the matrix B. 86 * @param[in] output_stage GEMMLowp output stage info, providing the type of quantization and the necessary parameters. 87 */ 88 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, 89 int32_t b_offset, 90 GEMMLowpOutputStageInfo output_stage); 91 /** Static function to check if given info will lead to a valid configuration 92 * 93 * Similar to CpuGemmLowpOffsetContributionOutputStageKernel::configure() 94 * 95 * @return a status 96 */ 97 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, 98 int32_t b_offset, 99 GEMMLowpOutputStageInfo output_stage); 100 101 // Inherited methods overridden: 102 void run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) override; 103 const char *name() const override; 104 105 private: 106 /** Function to use for the particular tensors passed to configure() */ 107 int32_t _a_offset{ 0 }; 108 int32_t _b_offset{ 0 }; 109 int32_t _k_offset{ 0 }; 110 bool _is_vector_sum_col_batched{ true }; 111 GEMMLowpOutputStageInfo _output_stage{ GEMMLowpOutputStageInfo() }; 112 }; 113 } // namespace kernels 114 } // namespace cpu 115 } // namespace arm_compute 116 #endif /* ARM_COMPUTE_CPU_GEMMLOWP_OFFSETCONTRIBUTION_OUTPUTSTAGE_KERNEL_H */ 117