src/core/NEON/kernels/arm_gemm/quantized.cpp

/*
 * 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.
 */

#include "arm_gemm.hpp"

#include <arm_neon.h>

namespace arm_gemm {

namespace {

/* Requantize a block of data, using the requantize parameters in 'qp'.
 *
 * row_bias and col_bias are assumed to be precomputed values which include
 * any externally supplied bias, plus the row/column contibution sums, plus
 * the overall constant offset (A_offset * B_offset * depth).
 *
 * Note that this function works equally well for uint8_t output: just set
 * minval/maxval appropriately and cast the output pointer.  It is caller's
 * responsibility to ensure that minval/maxval are representable in the
 * target type - the downcast to (u)int8_t is done by simply extracting the
 * LSB.
 *
 * The 'do_shift_correction' template parameter turns on the correction
 * applied to negative values being shifted right to make sure they round
 * properly - if negative values are never output (e.g. fused ReLU) this is
 * unnecessary.
 */
template<bool do_shift_correction>
void requantize_block_32_int(const ARequantizeLayer32 &qp, unsigned int width, unsigned int height,
                             const int32_t *input, unsigned int in_stride, int8_t *output, unsigned int out_stride,
                             const int32_t *row_bias, const int32_t *col_bias) {
    const int32x4_t v_mul      = vdupq_n_s32(qp.requant_mul);
    const int32x4_t v_shift    = vdupq_n_s32(qp.requant_shift);
    const int32x4_t v_minval   = vdupq_n_s32(qp.minval);
    const int32x4_t v_maxval   = vdupq_n_s32(qp.maxval);
    const int32x4_t v_c_offset = vdupq_n_s32(qp.c_offset);

    /* To make sure we have plenty of accumulators, compute two rows at a
     * time.  If the number of rows is odd, compute the bottom row twice to
     * avoid needing a duplicate codepath. */
    for (unsigned int row=0; row<height; row+=2) {
        /* Prefer to do 4 vectors (16 values) at once as this collapses
         * neatly to a single vector of output, failing that a vector at a
         * time and then the odd ones out at the end.  */
        unsigned int blocks=(width / 16);
        unsigned int regs=(width % 16) / 4;
        unsigned int odds=(width % 4);

        const int32_t *colptr = col_bias;

        const int32_t *in_ptr = input + (row * in_stride);
        int8_t *out_ptr = output + (row * out_stride);
        int32_t row_sum = row_bias[row];

        const int32_t *in_ptr1;
        int8_t *out_ptr1;
        int32_t row_sum1;

        if (row == height-1) {
            in_ptr1  = in_ptr;
            out_ptr1 = out_ptr;
            row_sum1 = row_sum;
        } else {
            in_ptr1  = in_ptr + in_stride;
            out_ptr1 = out_ptr + out_stride;
            row_sum1 = row_bias[row+1];
        }

        const int32x4_t v_row_sum  = vdupq_n_s32(row_sum);
        const int32x4_t v_row_sum1 = vdupq_n_s32(row_sum1);

        while (blocks--) {
            // Load column pointers
            int32x4_t v_col0 = vld1q_s32(colptr);
            int32x4_t v_col1 = vld1q_s32(colptr + 4);
            int32x4_t v_col2 = vld1q_s32(colptr + 8);
            int32x4_t v_col3 = vld1q_s32(colptr + 12);
            colptr += 16;

            // Load input data (row 0);
            int32x4_t v_in00 = vld1q_s32(in_ptr);
            int32x4_t v_in01 = vld1q_s32(in_ptr + 4);
            int32x4_t v_in02 = vld1q_s32(in_ptr + 8);
            int32x4_t v_in03 = vld1q_s32(in_ptr + 12);
            in_ptr += 16;

            // Load input data (row 1);
            int32x4_t v_in10 = vld1q_s32(in_ptr1);
            int32x4_t v_in11 = vld1q_s32(in_ptr1 + 4);
            int32x4_t v_in12 = vld1q_s32(in_ptr1 + 8);
            int32x4_t v_in13 = vld1q_s32(in_ptr1 + 12);
            in_ptr1 += 16;

            // Add on row bias and column bias
            v_in00 = vaddq_s32(v_in00, v_row_sum);
            v_in01 = vaddq_s32(v_in01, v_row_sum);
            v_in02 = vaddq_s32(v_in02, v_row_sum);
            v_in03 = vaddq_s32(v_in03, v_row_sum);

            v_in10 = vaddq_s32(v_in10, v_row_sum1);
            v_in11 = vaddq_s32(v_in11, v_row_sum1);
            v_in12 = vaddq_s32(v_in12, v_row_sum1);
            v_in13 = vaddq_s32(v_in13, v_row_sum1);

            v_in00 = vaddq_s32(v_in00, v_col0);
            v_in01 = vaddq_s32(v_in01, v_col1);
            v_in02 = vaddq_s32(v_in02, v_col2);
            v_in03 = vaddq_s32(v_in03, v_col3);

            v_in10 = vaddq_s32(v_in10, v_col0);
            v_in11 = vaddq_s32(v_in11, v_col1);
            v_in12 = vaddq_s32(v_in12, v_col2);
            v_in13 = vaddq_s32(v_in13, v_col3);

            // Quantize - start with multiply
            v_in00 = vqrdmulhq_s32(v_in00, v_mul);
            v_in01 = vqrdmulhq_s32(v_in01, v_mul);
            v_in02 = vqrdmulhq_s32(v_in02, v_mul);
            v_in03 = vqrdmulhq_s32(v_in03, v_mul);

            v_in10 = vqrdmulhq_s32(v_in10, v_mul);
            v_in11 = vqrdmulhq_s32(v_in11, v_mul);
            v_in12 = vqrdmulhq_s32(v_in12, v_mul);
            v_in13 = vqrdmulhq_s32(v_in13, v_mul);

            // Compute and add on corrective offset
            if (do_shift_correction) {
                int32x4_t v_temp00 = vandq_s32(v_in00, v_shift);
                int32x4_t v_temp01 = vandq_s32(v_in01, v_shift);
                int32x4_t v_temp02 = vandq_s32(v_in02, v_shift);
                int32x4_t v_temp03 = vandq_s32(v_in03, v_shift);

                int32x4_t v_temp10 = vandq_s32(v_in10, v_shift);
                int32x4_t v_temp11 = vandq_s32(v_in11, v_shift);
                int32x4_t v_temp12 = vandq_s32(v_in12, v_shift);
                int32x4_t v_temp13 = vandq_s32(v_in13, v_shift);

                v_temp00 = vshrq_n_s32(v_temp00, 31);
                v_temp01 = vshrq_n_s32(v_temp01, 31);
                v_temp02 = vshrq_n_s32(v_temp02, 31);
                v_temp03 = vshrq_n_s32(v_temp03, 31);

                v_temp10 = vshrq_n_s32(v_temp10, 31);
                v_temp11 = vshrq_n_s32(v_temp11, 31);
                v_temp12 = vshrq_n_s32(v_temp12, 31);
                v_temp13 = vshrq_n_s32(v_temp13, 31);

                v_in00 = vqaddq_s32(v_in00, v_temp00);
                v_in01 = vqaddq_s32(v_in01, v_temp01);
                v_in02 = vqaddq_s32(v_in02, v_temp02);
                v_in03 = vqaddq_s32(v_in03, v_temp03);

                v_in10 = vqaddq_s32(v_in10, v_temp10);
                v_in11 = vqaddq_s32(v_in11, v_temp11);
                v_in12 = vqaddq_s32(v_in12, v_temp12);
                v_in13 = vqaddq_s32(v_in13, v_temp13);
            }

            v_in00 = vrshlq_s32(v_in00, v_shift);
            v_in01 = vrshlq_s32(v_in01, v_shift);
            v_in02 = vrshlq_s32(v_in02, v_shift);
            v_in03 = vrshlq_s32(v_in03, v_shift);

            v_in10 = vrshlq_s32(v_in10, v_shift);
            v_in11 = vrshlq_s32(v_in11, v_shift);
            v_in12 = vrshlq_s32(v_in12, v_shift);
            v_in13 = vrshlq_s32(v_in13, v_shift);

            v_in00 = vaddq_s32(v_in00, v_c_offset);
            v_in01 = vaddq_s32(v_in01, v_c_offset);
            v_in02 = vaddq_s32(v_in02, v_c_offset);
            v_in03 = vaddq_s32(v_in03, v_c_offset);

            v_in10 = vaddq_s32(v_in10, v_c_offset);
            v_in11 = vaddq_s32(v_in11, v_c_offset);
            v_in12 = vaddq_s32(v_in12, v_c_offset);
            v_in13 = vaddq_s32(v_in13, v_c_offset);

            v_in00 = vmaxq_s32(v_in00, v_minval);
            v_in01 = vmaxq_s32(v_in01, v_minval);
            v_in02 = vmaxq_s32(v_in02, v_minval);
            v_in03 = vmaxq_s32(v_in03, v_minval);

            v_in10 = vmaxq_s32(v_in10, v_minval);
            v_in11 = vmaxq_s32(v_in11, v_minval);
            v_in12 = vmaxq_s32(v_in12, v_minval);
            v_in13 = vmaxq_s32(v_in13, v_minval);

            v_in00 = vminq_s32(v_in00, v_maxval);
            v_in01 = vminq_s32(v_in01, v_maxval);
            v_in02 = vminq_s32(v_in02, v_maxval);
            v_in03 = vminq_s32(v_in03, v_maxval);

            v_in10 = vminq_s32(v_in10, v_maxval);
            v_in11 = vminq_s32(v_in11, v_maxval);
            v_in12 = vminq_s32(v_in12, v_maxval);
            v_in13 = vminq_s32(v_in13, v_maxval);

            int16x8_t v_uz00 = vuzp1q_s16(vreinterpretq_s16_s32(v_in00), vreinterpretq_s16_s32(v_in01));
            int16x8_t v_uz01 = vuzp1q_s16(vreinterpretq_s16_s32(v_in02), vreinterpretq_s16_s32(v_in03));

            int16x8_t v_uz10 = vuzp1q_s16(vreinterpretq_s16_s32(v_in10), vreinterpretq_s16_s32(v_in11));
            int16x8_t v_uz11 = vuzp1q_s16(vreinterpretq_s16_s32(v_in12), vreinterpretq_s16_s32(v_in13));

            int8x16_t v_uz0 = vuzp1q_s8(vreinterpretq_s8_s16(v_uz00), vreinterpretq_s8_s16(v_uz01));
            int8x16_t v_uz1 = vuzp1q_s8(vreinterpretq_s8_s16(v_uz10), vreinterpretq_s8_s16(v_uz11));

            vst1q_s8(out_ptr, v_uz0);
            out_ptr += 16;
            vst1q_s8(out_ptr1, v_uz1);
            out_ptr1 += 16;
        }

        while (regs--) {
            // Load column pointers
            int32x4_t v_col0 = vld1q_s32(colptr);
            colptr += 4;

            // Load input data (row 0);
            int32x4_t v_in00 = vld1q_s32(in_ptr);
            in_ptr += 4;

            // Load input data (row 1);
            int32x4_t v_in10 = vld1q_s32(in_ptr1);
            in_ptr1 += 4;

            // Add on row sum and bias constant
            v_in00 = vaddq_s32(v_in00, v_row_sum);

            v_in10 = vaddq_s32(v_in10, v_row_sum1);

            // Subtract col sum * a_offset
            v_in00 = vaddq_s32(v_in00, v_col0);

            v_in10 = vaddq_s32(v_in10, v_col0);

            // Quantize - start with multiply
            v_in00 = vqrdmulhq_s32(v_in00, v_mul);

            v_in10 = vqrdmulhq_s32(v_in10, v_mul);

            // Compute and add on corrective offset
            if (do_shift_correction) {
                int32x4_t v_temp00 = vandq_s32(v_in00, v_shift);

                int32x4_t v_temp10 = vandq_s32(v_in10, v_shift);

                v_temp00 = vshrq_n_s32(v_temp00, 31);

                v_temp10 = vshrq_n_s32(v_temp10, 31);

                v_in00 = vqaddq_s32(v_in00, v_temp00);

                v_in10 = vqaddq_s32(v_in10, v_temp10);
            }

            v_in00 = vrshlq_s32(v_in00, v_shift);

            v_in10 = vrshlq_s32(v_in10, v_shift);

            v_in00 = vaddq_s32(v_in00, v_c_offset);

            v_in10 = vaddq_s32(v_in10, v_c_offset);

            v_in00 = vmaxq_s32(v_in00, v_minval);

            v_in10 = vmaxq_s32(v_in10, v_minval);

            v_in00 = vminq_s32(v_in00, v_maxval);

            v_in10 = vminq_s32(v_in10, v_maxval);

            int16x8_t v_uz00 = vuzp1q_s16(vreinterpretq_s16_s32(v_in00), vreinterpretq_s16_s32(v_in10));

            int8x16_t v_uz0 = vuzp1q_s8(vreinterpretq_s8_s16(v_uz00), vreinterpretq_s8_s16(v_uz00));

            vst1q_lane_s32(reinterpret_cast<int32_t *>(out_ptr), vreinterpretq_s32_s8(v_uz0), 0);
            out_ptr += 4;
            vst1q_lane_s32(reinterpret_cast<int32_t *>(out_ptr1), vreinterpretq_s32_s8(v_uz0), 1);
            out_ptr1 += 4;
        }

        if (odds) {
            int32x4_t v_col0 = vdupq_n_s32(0);
            int32x4_t v_in00 = vdupq_n_s32(0);
            int32x4_t v_in10 = vdupq_n_s32(0);

            do {
                v_col0 = vld1q_lane_s32(colptr, v_col0, 0);
                v_in00 = vld1q_lane_s32(in_ptr, v_in00, 0);
                v_in10 = vld1q_lane_s32(in_ptr1, v_in10, 0);
                if (odds == 1) { break; }

                v_col0 = vld1q_lane_s32(colptr + 1, v_col0, 1);
                v_in00 = vld1q_lane_s32(in_ptr + 1, v_in00, 1);
                v_in10 = vld1q_lane_s32(in_ptr1 + 1, v_in10, 1);
                if (odds == 2) { break; }

                v_col0 = vld1q_lane_s32(colptr + 2, v_col0, 2);
                v_in00 = vld1q_lane_s32(in_ptr + 2, v_in00, 2);
                v_in10 = vld1q_lane_s32(in_ptr1 + 2, v_in10, 2);
            } while (0);

            // Add on row sum and bias constant
            v_in00 = vaddq_s32(v_in00, v_row_sum);

            v_in10 = vaddq_s32(v_in10, v_row_sum1);

            // Subtract col sum * a_offset
            v_in00 = vaddq_s32(v_in00, v_col0);

            v_in10 = vaddq_s32(v_in10, v_col0);

            // Quantize - start with multiply
            v_in00 = vqrdmulhq_s32(v_in00, v_mul);

            v_in10 = vqrdmulhq_s32(v_in10, v_mul);

            // Compute and add on corrective offset
            if (do_shift_correction) {
                int32x4_t v_temp00 = vandq_s32(v_in00, v_shift);

                int32x4_t v_temp10 = vandq_s32(v_in10, v_shift);

                v_temp00 = vshrq_n_s32(v_temp00, 31);

                v_temp10 = vshrq_n_s32(v_temp10, 31);

                v_in00 = vqaddq_s32(v_in00, v_temp00);

                v_in10 = vqaddq_s32(v_in10, v_temp10);
            }

            v_in00 = vrshlq_s32(v_in00, v_shift);

            v_in10 = vrshlq_s32(v_in10, v_shift);

            v_in00 = vaddq_s32(v_in00, v_c_offset);

            v_in10 = vaddq_s32(v_in10, v_c_offset);

            v_in00 = vmaxq_s32(v_in00, v_minval);

            v_in10 = vmaxq_s32(v_in10, v_minval);

            v_in00 = vminq_s32(v_in00, v_maxval);

            v_in10 = vminq_s32(v_in10, v_maxval);

            do {
                vst1q_lane_s8(out_ptr, vreinterpretq_s8_s32(v_in00), 0);
                vst1q_lane_s8(out_ptr1, vreinterpretq_s8_s32(v_in10), 0);

                if (odds==1) { break; }

                vst1q_lane_s8(out_ptr + 1, vreinterpretq_s8_s32(v_in00), 4);
                vst1q_lane_s8(out_ptr1 + 1, vreinterpretq_s8_s32(v_in10), 4);

                if (odds==2) { break; }

                vst1q_lane_s8(out_ptr + 2, vreinterpretq_s8_s32(v_in00), 8);
                vst1q_lane_s8(out_ptr1 + 2, vreinterpretq_s8_s32(v_in10), 8);
            } while(0);
        }
    }
}

} // anonymous namespace

template<typename Tin, typename Tout>
void requantize_block_32(const ARequantizeLayer32 &qp, unsigned int width, unsigned int height,
                         const Tin *input, unsigned int in_stride, Tout *output, unsigned int out_stride,
                         const int32_t *row_bias, const int32_t *col_bias) {
    if (qp.minval >= qp.c_offset) {
        requantize_block_32_int<false>(qp, width, height, reinterpret_cast<const int32_t *>(input), in_stride,
                         reinterpret_cast<int8_t *>(output), out_stride, row_bias, col_bias);
    } else {
        requantize_block_32_int<true>(qp, width, height, reinterpret_cast<const int32_t *>(input), in_stride,
                         reinterpret_cast<int8_t *>(output), out_stride, row_bias, col_bias);
    }
}

template void requantize_block_32(const ARequantizeLayer32 &qp, unsigned int width, unsigned int height,
                         const int32_t *input, unsigned int in_stride, int8_t *output, unsigned int out_stride,
                         const int32_t *row_bias, const int32_t *col_bias);

template void requantize_block_32(const ARequantizeLayer32 &qp, unsigned int width, unsigned int height,
                         const uint32_t *input, unsigned int in_stride, uint8_t *output, unsigned int out_stride,
                         const int32_t *row_bias, const int32_t *col_bias);

/*
 * Routine (and helpers) to compute row sums needed for offset correction.
 *
 * This is often needed for a lot of short rows (e.g.  Syrax 5 - 6400 rows
 * of length 27), therefore it's important not to sacrifice performance on
 * odd length rows.
 *
 * To minimize performance loss in these cases, this routine will overread
 * by up to 7 bytes.
 *
 * This is handled via "mask" and "mask mode" parameters to the inner
 * routines; mask mode == 1 indicates that are between 1 and 8 bytes
 * (inclusive) needed at the end; in these cases we always read 8 bytes.
 * mask mode == 2 indicates that there are between 9 and 15 bytes needed at
 * the end, and in this case we always read 16 bytes.  In both cases the
 * 'mask' vector is set up so that the read value can be masked off to clear
 * the overread lanes.  This is handled by 'accumulate_masked_8' and
 * 'accumulate_masked_16' above.
 *
 * This routine is templated on the type to be accumulated, because the
 * innermost instruction used needs to be of the correct signedness.
 * However, beyond this point we always use signed values in both cases.
 * The instructions that need to be different are therefore wrapped in
 * helper functions below.
 */

namespace {
    struct row_sum_helpers {
        const ARequantizeLayer32 &qp;

        /* Load a full 16 byte vector, pairwise accumulate into 'sum' with uadalp or sadalp */
        template<typename T>
        inline int16x8_t accumulate_16(const T *ptr, int16x8_t sum);

        /* Load a full 16 byte vector, but mask before accumulation (see above). */
        template<typename T>
        inline int16x8_t accumulate_masked_16(const T *ptr, int16x8_t sum, uint64x2_t mask);

        /* Load 8 bytes and mask before accumulation. */
        template<typename T>
        inline int16x8_t accumulate_masked_8(const T *ptr, int16x8_t sum, uint64x2_t mask);

        /* This function does the actual work for up to 4 rows at a time.
         * It's pulled out so we can template on the row count to generate
         * the 4 different cases.  4 rows are computed at a time as this
         * reduces to a single vector write.  */
        template<unsigned int rows, typename T>
        void compute_some_rows(unsigned int blocks, const T *input, unsigned int in_stride, int32_t *row_bias, unsigned int mask_mode, uint64x2_t mask, int32x4_t offset_mul) {
            int16x8_t sums[rows];
            int32x4_t finalsums[rows];

            for (unsigned int i=0; i<rows; i++) {
                sums[i] = vdupq_n_s16(0);
            }

            for (unsigned int i=0; i<blocks; i++) {
                for (unsigned int r=0; r<rows; r++) {
                    sums[r] = accumulate_16(input + (r * in_stride) + (i * 16), sums[r]);
                }
            }

            /* Handle the final masked read if needed. */
            if (mask_mode > 0) {
                for (unsigned int r=0; r<rows; r++) {
                    if (mask_mode == 1) {
                        sums[r] = accumulate_masked_8(input + (r * in_stride) + (blocks * 16), sums[r], mask);
                    } else {
                        sums[r] = accumulate_masked_16(input + (r * in_stride) + (blocks * 16), sums[r], mask);
                    }
                }
            }

            for (unsigned int i=0; i<rows; i++) {
                finalsums[i] = vpaddlq_s16(sums[i]);
            }

            int32x4_t t0, t1;
            int32x2_t t2;

            /* Result writeback - need to write back one value per row
             * processed.  Multiply all the final totals by -b_offset so
             * that the terms can simply be added in the requantize code.
             * */
            switch (rows) {
                case 1:
                    /* If we only have one output, just use ADDV.  Multiply
                     * the offset into all four components separately so it
                     * can stay in the SIMD register file.  */
                    t0 = vmulq_s32(finalsums[0], offset_mul);
                    *row_bias = vaddvq_s32(t0);
                    break;

                case 2:
                    /* For two outputs, two rounds of pairwise adds will
                     * generate the result in a 2-vector we can store in one
                     * go.  */
                    t0 = vpaddq_s32(finalsums[0], finalsums[1]);
                    t0 = vpaddq_s32(t0, t0);
                    t2 = vmul_s32(vget_low_s32(t0), vget_low_s32(offset_mul));
                    vst1_s32(row_bias, t2);
                    break;

                case 3:
                    /* Three rows - need to store the low two words plus the odd value from lane 2 */
                    t0 = vpaddq_s32(finalsums[0], finalsums[1]);
                    t1 = vpaddq_s32(finalsums[2], finalsums[2]);

                    t0 = vpaddq_s32(t0, t1);
                    t0 = vmulq_s32(t0, offset_mul);

                    vst1_s32(row_bias, vget_low_s32(t0));
                    row_bias[2] = vgetq_lane_s32(t0, 2);
                    break;

                case 4:
                    /* Four rows (most common case) - reduce to a single
                     * vector with pairwise adds.  */
                    t0 = vpaddq_s32(finalsums[0], finalsums[1]);
                    t1 = vpaddq_s32(finalsums[2], finalsums[3]);

                    t0 = vpaddq_s32(t0, t1);
                    t0 = vmulq_s32(t0, offset_mul);

                    vst1q_s32(row_bias, t0);
                    break;
                default:
                    break;
            }
        }

        row_sum_helpers(const ARequantizeLayer32 &qp) : qp(qp) { }
    };

    template<>
    int16x8_t row_sum_helpers::accumulate_16(const uint8_t *ptr, int16x8_t sum) {
        return vreinterpretq_s16_u16(vpadalq_u8(vreinterpretq_u16_s16(sum), vld1q_u8(ptr)));
    }

    template<>
    int16x8_t row_sum_helpers::accumulate_16(const int8_t *ptr, int16x8_t sum) {
        return vpadalq_s8(sum, vld1q_s8(ptr));
    }

    template<>
    int16x8_t row_sum_helpers::accumulate_masked_16(const int8_t *ptr, int16x8_t sum, uint64x2_t mask) {
        int8x16_t v = vandq_s8(vld1q_s8(ptr), vreinterpretq_s8_u64(mask));
        return vpadalq_s8(sum, v);
    }

    template<>
    int16x8_t row_sum_helpers::accumulate_masked_16(const uint8_t *ptr, int16x8_t sum, uint64x2_t mask) {
        uint8x16_t v = vandq_u8(vld1q_u8(ptr), vreinterpretq_u8_u64(mask));
        return vreinterpretq_s16_u16(vpadalq_u8(vreinterpretq_u16_s16(sum), v));
    }

    template<>
    int16x8_t row_sum_helpers::accumulate_masked_8(const int8_t *ptr, int16x8_t sum, uint64x2_t mask) {
        int8x16_t v = vcombine_s8(vld1_s8(ptr), vdup_n_s8(0));
        v = vreinterpretq_s8_u64(vandq_u64(mask, vreinterpretq_u64_s8(v)));
        return vpadalq_s8(sum, v);
    }

    template<>
    int16x8_t row_sum_helpers::accumulate_masked_8(const uint8_t *ptr, int16x8_t sum, uint64x2_t mask) {
        uint8x16_t v = vcombine_u8(vld1_u8(ptr), vdup_n_u8(0));
        v = vreinterpretq_u8_u64(vandq_u64(mask, vreinterpretq_u64_u8(v)));
        return vreinterpretq_s16_u16(vpadalq_u8(vreinterpretq_u16_s16(sum), v));
    }
}

template<typename T>
void compute_row_sums(const ARequantizeLayer32 &qp, unsigned int width, unsigned int height,
                      const T *input, unsigned int in_stride, int32_t *row_bias) {
    row_sum_helpers thehelpers(qp);

    const int32x4_t offset_mul = vdupq_n_s32(-qp.b_offset);

    /* Work out how many full vectors of 16 bytes we will read, and how many
     * odd bytes at the end */
    unsigned int blocks = (width / 16);
    const unsigned int odds = width % 16;

    /* Generate a mask to use on the last iteration, if necessary. */
    uint64x2_t mask;
    unsigned int mask_mode = 0;

    if (odds > 0 && odds <= 8) {
        /* 1-8 odds: mask in the low lane, 0 in the top */
        uint64_t maskval = (~0ULL) >> (8 * (8-odds));

        mask = vsetq_lane_u64(maskval, vdupq_n_u64(0), 0);

        mask_mode = 1;
    } else if (odds > 8) {
        /* 9-15 odds: mask in the top lane, all 1s in the bottom. */
        uint64_t maskval = (~0ULL) >> (8 * (16-odds));

        mask = vsetq_lane_u64(maskval, vdupq_n_u64(~0ULL), 1);

        mask_mode = 2;
    }

    for (unsigned int row=0; row<height; row+=4) {
        switch(height-row) {
            default:
            case 4:
                thehelpers.compute_some_rows<4>(blocks, input + (row * in_stride), in_stride, row_bias + row, mask_mode, mask, offset_mul);
                break;
            case 3:
                thehelpers.compute_some_rows<3>(blocks, input + (row * in_stride), in_stride, row_bias + row, mask_mode, mask, offset_mul);
                break;
            case 2:
                thehelpers.compute_some_rows<2>(blocks, input + (row * in_stride), in_stride, row_bias + row, mask_mode, mask, offset_mul);
                break;
            case 1:
                thehelpers.compute_some_rows<1>(blocks, input + (row * in_stride), in_stride, row_bias + row, mask_mode, mask, offset_mul);
                break;
        }
    }
}

/* Instantiate the two versions for uint8_t and int8_t. */
template void compute_row_sums(const ARequantizeLayer32 &, unsigned int, unsigned int, const int8_t *, unsigned int, int32_t *);
template void compute_row_sums(const ARequantizeLayer32 &, unsigned int, unsigned int, const uint8_t *, unsigned int, int32_t *);

template<unsigned int active_rows, typename T>
inline void add_block(const T *input, unsigned int in_stride, int32_t *output);

template<unsigned int active_rows>
inline void add_block(const uint8_t *input, unsigned int in_stride, int32_t *output) {
    uint8x16_t inputs[4];

    for (unsigned int i=0; i<4; i++) {
        if (i < active_rows) {
            inputs[i] = vld1q_u8(input + i * in_stride);
        } else {
            inputs[i] = vdupq_n_u8(0);
        }
    }

    int16x8_t sums_16b[4];

    // Two adds for the low pairs
    sums_16b[0]=vreinterpretq_s16_u16(vaddl_u8(vget_low_u8(inputs[0]), vget_low_u8(inputs[1])));
    sums_16b[1]=vreinterpretq_s16_u16(vaddl_u8(vget_low_u8(inputs[2]), vget_low_u8(inputs[3])));
    // Two adds for the high pairs
    sums_16b[2]=vreinterpretq_s16_u16(vaddl_high_u8(inputs[0], inputs[1]));
    sums_16b[3]=vreinterpretq_s16_u16(vaddl_high_u8(inputs[2], inputs[3]));

    int32x4_t sums_32b[4];

    sums_32b[0]=vaddl_s16(vget_low_s16(sums_16b[0]), vget_low_s16(sums_16b[1]));
    sums_32b[1]=vaddl_high_s16(sums_16b[0], sums_16b[1]);
    sums_32b[2]=vaddl_s16(vget_low_s16(sums_16b[2]), vget_low_s16(sums_16b[3]));
    sums_32b[3]=vaddl_high_s16(sums_16b[2], sums_16b[3]);

    for (unsigned int i=0; i<4; i++) {
        vst1q_s32(output + 4*i, vaddq_s32(sums_32b[i], vld1q_s32(output + 4*i)));
    }
}

template<unsigned int active_rows>
inline void add_block(const int8_t *input, unsigned int in_stride, int32_t *output) {
    int8x16_t inputs[4];

    for (unsigned int i=0; i<4; i++) {
        if (i < active_rows) {
            inputs[i] = vld1q_s8(input + i * in_stride);
        } else {
            inputs[i] = vdupq_n_s8(0);
        }
    }

    int16x8_t sums_16b[4];

    // Two adds for the low pairs
    sums_16b[0]=vaddl_s8(vget_low_s8(inputs[0]), vget_low_s8(inputs[1]));
    sums_16b[1]=vaddl_s8(vget_low_s8(inputs[2]), vget_low_s8(inputs[3]));
    // Two adds for the high pairs
    sums_16b[2]=vaddl_high_s8(inputs[0], inputs[1]);
    sums_16b[3]=vaddl_high_s8(inputs[2], inputs[3]);

    int32x4_t sums_32b[4];

    sums_32b[0]=vaddl_s16(vget_low_s16(sums_16b[0]), vget_low_s16(sums_16b[1]));
    sums_32b[1]=vaddl_high_s16(sums_16b[0], sums_16b[1]);
    sums_32b[2]=vaddl_s16(vget_low_s16(sums_16b[2]), vget_low_s16(sums_16b[3]));
    sums_32b[3]=vaddl_high_s16(sums_16b[2], sums_16b[3]);

    for (unsigned int i=0; i<4; i++) {
        vst1q_s32(output + 4*i, vaddq_s32(sums_32b[i], vld1q_s32(output + 4*i)));
    }
}


/* "first_col" parameter is used to offset the read into the qp.bias array,
 * in cases where we are not computing the first columns of the output (i.e.
 * in multithreaded cases where we divide columns across threads) */
template<typename T>
void compute_col_sums(const ARequantizeLayer32 &qp, unsigned int width, unsigned int height, const T *input, unsigned int in_stride, int32_t *col_bias, unsigned int depth, unsigned int first_col) {
    memset(reinterpret_cast<void *>(col_bias), 0, width * sizeof(int32_t));

    for (unsigned int row=0; row<height; row+=4) {
        unsigned int numrows=std::min(height-row, 4u);

        for (unsigned int col=0; col<width; col+=16) {
            unsigned int numcols=std::min(width-col, 16u);

            if (numcols==16) {
                switch(numrows) {
                    case 1:
                        add_block<1>(input + row * in_stride + col, in_stride, col_bias + col);
                        break;

                    case 2:
                        add_block<2>(input + row * in_stride + col, in_stride, col_bias + col);
                        break;

                    case 3:
                        add_block<3>(input + row * in_stride + col, in_stride, col_bias + col);
                        break;

                    case 4:
                        add_block<4>(input + row * in_stride + col, in_stride, col_bias + col);
                        break;
                    default:
                        break;
                }
            } else {
                for (; col<width; col++) {
                    int32_t sum=0;
                    for (unsigned int r=0; r<numrows; r++) {
                        sum += input[(row + r)*in_stride + col];
                    }
                    col_bias[col] += sum;
                }
            }
        }
    }

    for (unsigned int col=0; col<width; col++) {
        int32_t result = col_bias[col];

        result = (qp.a_offset * qp.b_offset * depth) - (result * qp.a_offset);

        if (qp.bias != nullptr) {
            result += qp.bias[col + first_col];
        }

        col_bias[col] = result;
    }
}

template void compute_col_sums(const ARequantizeLayer32 &qp, unsigned int width, unsigned int height, const int8_t *input, unsigned int in_stride, int32_t *col_bias, unsigned int depth, unsigned int first_col);
template void compute_col_sums(const ARequantizeLayer32 &qp, unsigned int width, unsigned int height, const uint8_t *input, unsigned int in_stride, int32_t *col_bias, unsigned int depth, unsigned int first_col);

} // namespace arm_gemm