/*
 * Copyright (c) 2017-2021, 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.
 */
#include "src/cpu/kernels/CpuGemmLowpMatrixMultiplyKernel.h"

#include "arm_compute/core/Error.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/ITensor.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Utils.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/core/Window.h"

#include "src/core/helpers/AutoConfiguration.h"
#include "src/core/helpers/WindowHelpers.h"

#include <arm_neon.h>

namespace arm_compute
{
namespace cpu
{
namespace kernels
{
namespace
{
void inline vector_matrix_multiply_u8(Iterator     &ina,
                                      Iterator     &inb,
                                      Iterator     &out,
                                      int           width_a,
                                      int           width_b,
                                      int           width_out,
                                      size_t        stride_b,
                                      const Window &window)
{
    execute_window_loop(
        window,
        [&](const Coordinates &id)
        {
            if (id.x() > width_b)
            {
                return;
            }

            // Note: Since the input are all positives, we can use uint32_t
            // Accumulators for the block 0
            uint32x4x4_t c0 = {{vdupq_n_u32(0), vdupq_n_u32(0), vdupq_n_u32(0), vdupq_n_u32(0)}};

            auto vec_a          = reinterpret_cast<const uint8_t *>(ina.ptr());
            auto matrix_b       = reinterpret_cast<const uint8_t *>(inb.ptr());
            auto vec_a_end_addr = vec_a + width_a;

            // This for loop performs 8 accumulations
            for (; vec_a <= (vec_a_end_addr - 8);)
            {
                const uint8x8_t  a00_u8 = vld1_u8(vec_a);
                const uint8x16_t b00_u8 = vld1q_u8(matrix_b + 0 * stride_b);
                const uint8x16_t b10_u8 = vld1q_u8(matrix_b + 1 * stride_b);
                const uint8x16_t b20_u8 = vld1q_u8(matrix_b + 2 * stride_b);
                const uint8x16_t b30_u8 = vld1q_u8(matrix_b + 3 * stride_b);
                const uint8x16_t b40_u8 = vld1q_u8(matrix_b + 4 * stride_b);
                const uint8x16_t b50_u8 = vld1q_u8(matrix_b + 5 * stride_b);
                const uint8x16_t b60_u8 = vld1q_u8(matrix_b + 6 * stride_b);
                const uint8x16_t b70_u8 = vld1q_u8(matrix_b + 7 * stride_b);

                // Convert a00_u8 to uint16_t and get the lower part
                const uint16x4x2_t a00_u16 = {{vget_low_u16(vmovl_u8(a00_u8)), vget_high_u16(vmovl_u8(a00_u8))}};

                const uint16x4x4_t b00_u16 = {
                    {vget_low_u16(vmovl_u8(vget_low_u8(b00_u8))), vget_high_u16(vmovl_u8(vget_low_u8(b00_u8))),
                     vget_low_u16(vmovl_u8(vget_high_u8(b00_u8))), vget_high_u16(vmovl_u8(vget_high_u8(b00_u8)))}};

                const uint16x4x4_t b10_u16 = {
                    {vget_low_u16(vmovl_u8(vget_low_u8(b10_u8))), vget_high_u16(vmovl_u8(vget_low_u8(b10_u8))),
                     vget_low_u16(vmovl_u8(vget_high_u8(b10_u8))), vget_high_u16(vmovl_u8(vget_high_u8(b10_u8)))}};

                const uint16x4x4_t b20_u16 = {
                    {vget_low_u16(vmovl_u8(vget_low_u8(b20_u8))), vget_high_u16(vmovl_u8(vget_low_u8(b20_u8))),
                     vget_low_u16(vmovl_u8(vget_high_u8(b20_u8))), vget_high_u16(vmovl_u8(vget_high_u8(b20_u8)))}};

                const uint16x4x4_t b30_u16 = {
                    {vget_low_u16(vmovl_u8(vget_low_u8(b30_u8))), vget_high_u16(vmovl_u8(vget_low_u8(b30_u8))),
                     vget_low_u16(vmovl_u8(vget_high_u8(b30_u8))), vget_high_u16(vmovl_u8(vget_high_u8(b30_u8)))}};

                const uint16x4x4_t b40_u16 = {
                    {vget_low_u16(vmovl_u8(vget_low_u8(b40_u8))), vget_high_u16(vmovl_u8(vget_low_u8(b40_u8))),
                     vget_low_u16(vmovl_u8(vget_high_u8(b40_u8))), vget_high_u16(vmovl_u8(vget_high_u8(b40_u8)))}};

                const uint16x4x4_t b50_u16 = {
                    {vget_low_u16(vmovl_u8(vget_low_u8(b50_u8))), vget_high_u16(vmovl_u8(vget_low_u8(b50_u8))),
                     vget_low_u16(vmovl_u8(vget_high_u8(b50_u8))), vget_high_u16(vmovl_u8(vget_high_u8(b50_u8)))}};

                const uint16x4x4_t b60_u16 = {
                    {vget_low_u16(vmovl_u8(vget_low_u8(b60_u8))), vget_high_u16(vmovl_u8(vget_low_u8(b60_u8))),
                     vget_low_u16(vmovl_u8(vget_high_u8(b60_u8))), vget_high_u16(vmovl_u8(vget_high_u8(b60_u8)))}};

                const uint16x4x4_t b70_u16 = {
                    {vget_low_u16(vmovl_u8(vget_low_u8(b70_u8))), vget_high_u16(vmovl_u8(vget_low_u8(b70_u8))),
                     vget_low_u16(vmovl_u8(vget_high_u8(b70_u8))), vget_high_u16(vmovl_u8(vget_high_u8(b70_u8)))}};

                // Accumulate 0:
                c0.val[0] = vmlal_lane_u16(c0.val[0], b00_u16.val[0], a00_u16.val[0], 0);
                c0.val[1] = vmlal_lane_u16(c0.val[1], b00_u16.val[1], a00_u16.val[0], 0);
                c0.val[2] = vmlal_lane_u16(c0.val[2], b00_u16.val[2], a00_u16.val[0], 0);
                c0.val[3] = vmlal_lane_u16(c0.val[3], b00_u16.val[3], a00_u16.val[0], 0);

                // Accumulate 1:
                c0.val[0] = vmlal_lane_u16(c0.val[0], b10_u16.val[0], a00_u16.val[0], 1);
                c0.val[1] = vmlal_lane_u16(c0.val[1], b10_u16.val[1], a00_u16.val[0], 1);
                c0.val[2] = vmlal_lane_u16(c0.val[2], b10_u16.val[2], a00_u16.val[0], 1);
                c0.val[3] = vmlal_lane_u16(c0.val[3], b10_u16.val[3], a00_u16.val[0], 1);

                // Accumulate 2:
                c0.val[0] = vmlal_lane_u16(c0.val[0], b20_u16.val[0], a00_u16.val[0], 2);
                c0.val[1] = vmlal_lane_u16(c0.val[1], b20_u16.val[1], a00_u16.val[0], 2);
                c0.val[2] = vmlal_lane_u16(c0.val[2], b20_u16.val[2], a00_u16.val[0], 2);
                c0.val[3] = vmlal_lane_u16(c0.val[3], b20_u16.val[3], a00_u16.val[0], 2);

                // Accumulate 3:
                c0.val[0] = vmlal_lane_u16(c0.val[0], b30_u16.val[0], a00_u16.val[0], 3);
                c0.val[1] = vmlal_lane_u16(c0.val[1], b30_u16.val[1], a00_u16.val[0], 3);
                c0.val[2] = vmlal_lane_u16(c0.val[2], b30_u16.val[2], a00_u16.val[0], 3);
                c0.val[3] = vmlal_lane_u16(c0.val[3], b30_u16.val[3], a00_u16.val[0], 3);

                // Accumulate 4:
                c0.val[0] = vmlal_lane_u16(c0.val[0], b40_u16.val[0], a00_u16.val[1], 0);
                c0.val[1] = vmlal_lane_u16(c0.val[1], b40_u16.val[1], a00_u16.val[1], 0);
                c0.val[2] = vmlal_lane_u16(c0.val[2], b40_u16.val[2], a00_u16.val[1], 0);
                c0.val[3] = vmlal_lane_u16(c0.val[3], b40_u16.val[3], a00_u16.val[1], 0);

                // Accumulate 5:
                c0.val[0] = vmlal_lane_u16(c0.val[0], b50_u16.val[0], a00_u16.val[1], 1);
                c0.val[1] = vmlal_lane_u16(c0.val[1], b50_u16.val[1], a00_u16.val[1], 1);
                c0.val[2] = vmlal_lane_u16(c0.val[2], b50_u16.val[2], a00_u16.val[1], 1);
                c0.val[3] = vmlal_lane_u16(c0.val[3], b50_u16.val[3], a00_u16.val[1], 1);

                // Accumulate 6:
                c0.val[0] = vmlal_lane_u16(c0.val[0], b60_u16.val[0], a00_u16.val[1], 2);
                c0.val[1] = vmlal_lane_u16(c0.val[1], b60_u16.val[1], a00_u16.val[1], 2);
                c0.val[2] = vmlal_lane_u16(c0.val[2], b60_u16.val[2], a00_u16.val[1], 2);
                c0.val[3] = vmlal_lane_u16(c0.val[3], b60_u16.val[3], a00_u16.val[1], 2);

                // Accumulate 7:
                c0.val[0] = vmlal_lane_u16(c0.val[0], b70_u16.val[0], a00_u16.val[1], 3);
                c0.val[1] = vmlal_lane_u16(c0.val[1], b70_u16.val[1], a00_u16.val[1], 3);
                c0.val[2] = vmlal_lane_u16(c0.val[2], b70_u16.val[2], a00_u16.val[1], 3);
                c0.val[3] = vmlal_lane_u16(c0.val[3], b70_u16.val[3], a00_u16.val[1], 3);

                vec_a += 8;
                matrix_b += 8 * stride_b;
            }

            // This for loop performs the left-over accumulations
            for (; vec_a < vec_a_end_addr;)
            {
                const uint8x8_t  a00_u8 = vld1_dup_u8(vec_a);
                const uint8x16_t b00_u8 = vld1q_u8(matrix_b);

                const uint16x4x4_t b00_u16 = {
                    {vget_low_u16(vmovl_u8(vget_low_u8(b00_u8))), vget_high_u16(vmovl_u8(vget_low_u8(b00_u8))),
                     vget_low_u16(vmovl_u8(vget_high_u8(b00_u8))), vget_high_u16(vmovl_u8(vget_high_u8(b00_u8)))}};

                // Convert a00_u8 to uint16_t and get the lower part
                const uint16x4_t a00_u16 = vget_low_u16(vmovl_u8(a00_u8));

                // Accumulate 0:
                c0.val[0] = vmlal_lane_u16(c0.val[0], b00_u16.val[0], a00_u16, 0);
                c0.val[1] = vmlal_lane_u16(c0.val[1], b00_u16.val[1], a00_u16, 0);
                c0.val[2] = vmlal_lane_u16(c0.val[2], b00_u16.val[2], a00_u16, 0);
                c0.val[3] = vmlal_lane_u16(c0.val[3], b00_u16.val[3], a00_u16, 0);

                vec_a += 1;
                matrix_b += stride_b;
            }

            auto vec_out = reinterpret_cast<int32_t *>(out.ptr());
            if (id.x() < (width_out - 16))
            {
                vst1q_s32(vec_out + 0, vreinterpretq_s32_u32(c0.val[0]));
                vst1q_s32(vec_out + 4, vreinterpretq_s32_u32(c0.val[1]));
                vst1q_s32(vec_out + 8, vreinterpretq_s32_u32(c0.val[2]));
                vst1q_s32(vec_out + 12, vreinterpretq_s32_u32(c0.val[3]));
            }
            else
            {
                auto left_over = width_out - id.x();
                for (auto k = 0; k < 4 && left_over; ++k)
                {
                    for (auto j = 0; j < 4 && left_over; ++j, --left_over)
                    {
                        *(vec_out + k * 4 + j) = c0.val[k][j];
                    }
                }
            }
        },
        ina, inb, out);
}

void inline vector_matrix_multiply_s8(Iterator     &ina,
                                      Iterator     &inb,
                                      Iterator     &out,
                                      int           width_a,
                                      int           width_b,
                                      int           width_out,
                                      size_t        stride_b,
                                      const Window &window)
{
    execute_window_loop(
        window,
        [&](const Coordinates &id)
        {
            if (id.x() > width_b)
            {
                return;
            }

            // Accumulators for the block 0
            int32x4x4_t c0 = {{vdupq_n_s32(0), vdupq_n_s32(0), vdupq_n_s32(0), vdupq_n_s32(0)}};

            auto vec_a          = reinterpret_cast<const int8_t *>(ina.ptr());
            auto matrix_b       = reinterpret_cast<const int8_t *>(inb.ptr());
            auto vec_a_end_addr = vec_a + width_a;

            // This for loop performs 8 accumulations
            for (; vec_a <= (vec_a_end_addr - 8);)
            {
                const int8x8_t  a00_s8 = vld1_s8(vec_a);
                const int8x16_t b00_s8 = vld1q_s8(matrix_b + 0 * stride_b);
                const int8x16_t b10_s8 = vld1q_s8(matrix_b + 1 * stride_b);
                const int8x16_t b20_s8 = vld1q_s8(matrix_b + 2 * stride_b);
                const int8x16_t b30_s8 = vld1q_s8(matrix_b + 3 * stride_b);
                const int8x16_t b40_s8 = vld1q_s8(matrix_b + 4 * stride_b);
                const int8x16_t b50_s8 = vld1q_s8(matrix_b + 5 * stride_b);
                const int8x16_t b60_s8 = vld1q_s8(matrix_b + 6 * stride_b);
                const int8x16_t b70_s8 = vld1q_s8(matrix_b + 7 * stride_b);

                // Convert a00_s8 to int16_t and get the lower part
                const int16x4x2_t a00_s16 = {{vget_low_s16(vmovl_s8(a00_s8)), vget_high_s16(vmovl_s8(a00_s8))}};

                const int16x4x4_t b00_s16 = {
                    {vget_low_s16(vmovl_s8(vget_low_s8(b00_s8))), vget_high_s16(vmovl_s8(vget_low_s8(b00_s8))),
                     vget_low_s16(vmovl_s8(vget_high_s8(b00_s8))), vget_high_s16(vmovl_s8(vget_high_s8(b00_s8)))}};

                const int16x4x4_t b10_s16 = {
                    {vget_low_s16(vmovl_s8(vget_low_s8(b10_s8))), vget_high_s16(vmovl_s8(vget_low_s8(b10_s8))),
                     vget_low_s16(vmovl_s8(vget_high_s8(b10_s8))), vget_high_s16(vmovl_s8(vget_high_s8(b10_s8)))}};

                const int16x4x4_t b20_s16 = {
                    {vget_low_s16(vmovl_s8(vget_low_s8(b20_s8))), vget_high_s16(vmovl_s8(vget_low_s8(b20_s8))),
                     vget_low_s16(vmovl_s8(vget_high_s8(b20_s8))), vget_high_s16(vmovl_s8(vget_high_s8(b20_s8)))}};

                const int16x4x4_t b30_s16 = {
                    {vget_low_s16(vmovl_s8(vget_low_s8(b30_s8))), vget_high_s16(vmovl_s8(vget_low_s8(b30_s8))),
                     vget_low_s16(vmovl_s8(vget_high_s8(b30_s8))), vget_high_s16(vmovl_s8(vget_high_s8(b30_s8)))}};

                const int16x4x4_t b40_s16 = {
                    {vget_low_s16(vmovl_s8(vget_low_s8(b40_s8))), vget_high_s16(vmovl_s8(vget_low_s8(b40_s8))),
                     vget_low_s16(vmovl_s8(vget_high_s8(b40_s8))), vget_high_s16(vmovl_s8(vget_high_s8(b40_s8)))}};

                const int16x4x4_t b50_s16 = {
                    {vget_low_s16(vmovl_s8(vget_low_s8(b50_s8))), vget_high_s16(vmovl_s8(vget_low_s8(b50_s8))),
                     vget_low_s16(vmovl_s8(vget_high_s8(b50_s8))), vget_high_s16(vmovl_s8(vget_high_s8(b50_s8)))}};

                const int16x4x4_t b60_s16 = {
                    {vget_low_s16(vmovl_s8(vget_low_s8(b60_s8))), vget_high_s16(vmovl_s8(vget_low_s8(b60_s8))),
                     vget_low_s16(vmovl_s8(vget_high_s8(b60_s8))), vget_high_s16(vmovl_s8(vget_high_s8(b60_s8)))}};

                const int16x4x4_t b70_s16 = {
                    {vget_low_s16(vmovl_s8(vget_low_s8(b70_s8))), vget_high_s16(vmovl_s8(vget_low_s8(b70_s8))),
                     vget_low_s16(vmovl_s8(vget_high_s8(b70_s8))), vget_high_s16(vmovl_s8(vget_high_s8(b70_s8)))}};

                // Accumulate 0:
                c0.val[0] = vmlal_lane_s16(c0.val[0], b00_s16.val[0], a00_s16.val[0], 0);
                c0.val[1] = vmlal_lane_s16(c0.val[1], b00_s16.val[1], a00_s16.val[0], 0);
                c0.val[2] = vmlal_lane_s16(c0.val[2], b00_s16.val[2], a00_s16.val[0], 0);
                c0.val[3] = vmlal_lane_s16(c0.val[3], b00_s16.val[3], a00_s16.val[0], 0);

                // Accumulate 1:
                c0.val[0] = vmlal_lane_s16(c0.val[0], b10_s16.val[0], a00_s16.val[0], 1);
                c0.val[1] = vmlal_lane_s16(c0.val[1], b10_s16.val[1], a00_s16.val[0], 1);
                c0.val[2] = vmlal_lane_s16(c0.val[2], b10_s16.val[2], a00_s16.val[0], 1);
                c0.val[3] = vmlal_lane_s16(c0.val[3], b10_s16.val[3], a00_s16.val[0], 1);

                // Accumulate 2:
                c0.val[0] = vmlal_lane_s16(c0.val[0], b20_s16.val[0], a00_s16.val[0], 2);
                c0.val[1] = vmlal_lane_s16(c0.val[1], b20_s16.val[1], a00_s16.val[0], 2);
                c0.val[2] = vmlal_lane_s16(c0.val[2], b20_s16.val[2], a00_s16.val[0], 2);
                c0.val[3] = vmlal_lane_s16(c0.val[3], b20_s16.val[3], a00_s16.val[0], 2);

                // Accumulate 3:
                c0.val[0] = vmlal_lane_s16(c0.val[0], b30_s16.val[0], a00_s16.val[0], 3);
                c0.val[1] = vmlal_lane_s16(c0.val[1], b30_s16.val[1], a00_s16.val[0], 3);
                c0.val[2] = vmlal_lane_s16(c0.val[2], b30_s16.val[2], a00_s16.val[0], 3);
                c0.val[3] = vmlal_lane_s16(c0.val[3], b30_s16.val[3], a00_s16.val[0], 3);

                // Accumulate 4:
                c0.val[0] = vmlal_lane_s16(c0.val[0], b40_s16.val[0], a00_s16.val[1], 0);
                c0.val[1] = vmlal_lane_s16(c0.val[1], b40_s16.val[1], a00_s16.val[1], 0);
                c0.val[2] = vmlal_lane_s16(c0.val[2], b40_s16.val[2], a00_s16.val[1], 0);
                c0.val[3] = vmlal_lane_s16(c0.val[3], b40_s16.val[3], a00_s16.val[1], 0);

                // Accumulate 5:
                c0.val[0] = vmlal_lane_s16(c0.val[0], b50_s16.val[0], a00_s16.val[1], 1);
                c0.val[1] = vmlal_lane_s16(c0.val[1], b50_s16.val[1], a00_s16.val[1], 1);
                c0.val[2] = vmlal_lane_s16(c0.val[2], b50_s16.val[2], a00_s16.val[1], 1);
                c0.val[3] = vmlal_lane_s16(c0.val[3], b50_s16.val[3], a00_s16.val[1], 1);

                // Accumulate 6:
                c0.val[0] = vmlal_lane_s16(c0.val[0], b60_s16.val[0], a00_s16.val[1], 2);
                c0.val[1] = vmlal_lane_s16(c0.val[1], b60_s16.val[1], a00_s16.val[1], 2);
                c0.val[2] = vmlal_lane_s16(c0.val[2], b60_s16.val[2], a00_s16.val[1], 2);
                c0.val[3] = vmlal_lane_s16(c0.val[3], b60_s16.val[3], a00_s16.val[1], 2);

                // Accumulate 7:
                c0.val[0] = vmlal_lane_s16(c0.val[0], b70_s16.val[0], a00_s16.val[1], 3);
                c0.val[1] = vmlal_lane_s16(c0.val[1], b70_s16.val[1], a00_s16.val[1], 3);
                c0.val[2] = vmlal_lane_s16(c0.val[2], b70_s16.val[2], a00_s16.val[1], 3);
                c0.val[3] = vmlal_lane_s16(c0.val[3], b70_s16.val[3], a00_s16.val[1], 3);

                vec_a += 8;
                matrix_b += 8 * stride_b;
            }

            // This for loop performs the left-over accumulations
            for (; vec_a < vec_a_end_addr;)
            {
                const int8x8_t  a00_s8 = vld1_dup_s8(vec_a);
                const int8x16_t b00_s8 = vld1q_s8(matrix_b);

                const int16x4x4_t b00_s16 = {
                    {vget_low_s16(vmovl_s8(vget_low_s8(b00_s8))), vget_high_s16(vmovl_s8(vget_low_s8(b00_s8))),
                     vget_low_s16(vmovl_s8(vget_high_s8(b00_s8))), vget_high_s16(vmovl_s8(vget_high_s8(b00_s8)))}};

                // Convert a00_s8 to uint16_t and get the lower part
                const int16x4_t a00_s16 = vget_low_s16(vmovl_s8(a00_s8));

                // Accumulate 0:
                c0.val[0] = vmlal_lane_s16(c0.val[0], b00_s16.val[0], a00_s16, 0);
                c0.val[1] = vmlal_lane_s16(c0.val[1], b00_s16.val[1], a00_s16, 0);
                c0.val[2] = vmlal_lane_s16(c0.val[2], b00_s16.val[2], a00_s16, 0);
                c0.val[3] = vmlal_lane_s16(c0.val[3], b00_s16.val[3], a00_s16, 0);

                vec_a += 1;
                matrix_b += stride_b;
            }

            auto vec_out = reinterpret_cast<int32_t *>(out.ptr());
            if (id.x() < (width_out - 16))
            {
                vst1q_s32(vec_out + 0, c0.val[0]);
                vst1q_s32(vec_out + 4, c0.val[1]);
                vst1q_s32(vec_out + 8, c0.val[2]);
                vst1q_s32(vec_out + 12, c0.val[3]);
            }
            else
            {
                auto left_over = width_out - id.x();
                for (auto k = 0; k < 4 && left_over; ++k)
                {
                    for (auto j = 0; j < 4 && left_over; ++j, --left_over)
                    {
                        *(vec_out + k * 4 + j) = c0.val[k][j];
                    }
                }
            }
        },
        ina, inb, out);
}

void inline matrix_multiply_u8(
    Iterator &ina, Iterator &inb, Iterator &out, int width_b, const TensorInfo &out_info, const Window &window)
{
    const auto   width_out  = static_cast<int>(out_info.dimension(0));
    const auto   height_out = static_cast<int>(out_info.dimension(1));
    const size_t out_stride = out_info.strides_in_bytes()[1] / out_info.element_size();
    execute_window_loop(
        window,
        [&](const Coordinates &id)
        {
            const uint8_t *mtx_a0 = ina.ptr();
            const uint8_t *mtx_b0 = inb.ptr();

            // Note: Since the input are all positives, we can use uint32_t
            // Accumulators for the block 0
            uint32x4x4_t c0 = {{vdupq_n_u32(0), vdupq_n_u32(0), vdupq_n_u32(0), vdupq_n_u32(0)}};

            // Accumulators for the block 1
            uint32x4x4_t c1 = {{vdupq_n_u32(0), vdupq_n_u32(0), vdupq_n_u32(0), vdupq_n_u32(0)}};

            // Accumulators for the block 2
            uint32x4x4_t c2 = {{vdupq_n_u32(0), vdupq_n_u32(0), vdupq_n_u32(0), vdupq_n_u32(0)}};

            // Accumulators for the block 3
            uint32x4x4_t c3 = {{vdupq_n_u32(0), vdupq_n_u32(0), vdupq_n_u32(0), vdupq_n_u32(0)}};

            for (int k = 0; k < width_b; k += 16, mtx_a0 += 4, mtx_b0 += 16)
            {
                const uint8x8_t  a00_u8 = vld1_u8(mtx_a0);
                const uint8x16_t b00_u8 = vld1q_u8(mtx_b0);

                // Convert a00_u8 to uint16_t and get the lower part
                const uint16x4_t a00_u16 = vget_low_u16(vmovl_u8(a00_u8));

                // Convert b00_s8 to uint16_t
                const uint16x4x4_t b00_u16 = {
                    {vget_low_u16(vmovl_u8(vget_low_u8(b00_u8))), vget_high_u16(vmovl_u8(vget_low_u8(b00_u8))),
                     vget_low_u16(vmovl_u8(vget_high_u8(b00_u8))), vget_high_u16(vmovl_u8(vget_high_u8(b00_u8)))}};

                // 4x4 block 0
                c0.val[0] = vmlal_lane_u16(c0.val[0], b00_u16.val[0], a00_u16, 0);
                c0.val[1] = vmlal_lane_u16(c0.val[1], b00_u16.val[1], a00_u16, 0);
                c0.val[2] = vmlal_lane_u16(c0.val[2], b00_u16.val[2], a00_u16, 0);
                c0.val[3] = vmlal_lane_u16(c0.val[3], b00_u16.val[3], a00_u16, 0);

                // 4x4 block 1
                c1.val[0] = vmlal_lane_u16(c1.val[0], b00_u16.val[0], a00_u16, 1);
                c1.val[1] = vmlal_lane_u16(c1.val[1], b00_u16.val[1], a00_u16, 1);
                c1.val[2] = vmlal_lane_u16(c1.val[2], b00_u16.val[2], a00_u16, 1);
                c1.val[3] = vmlal_lane_u16(c1.val[3], b00_u16.val[3], a00_u16, 1);

                // 4x4 block 2
                c2.val[0] = vmlal_lane_u16(c2.val[0], b00_u16.val[0], a00_u16, 2);
                c2.val[1] = vmlal_lane_u16(c2.val[1], b00_u16.val[1], a00_u16, 2);
                c2.val[2] = vmlal_lane_u16(c2.val[2], b00_u16.val[2], a00_u16, 2);
                c2.val[3] = vmlal_lane_u16(c2.val[3], b00_u16.val[3], a00_u16, 2);

                // 4x4 block 3
                c3.val[0] = vmlal_lane_u16(c3.val[0], b00_u16.val[0], a00_u16, 3);
                c3.val[1] = vmlal_lane_u16(c3.val[1], b00_u16.val[1], a00_u16, 3);
                c3.val[2] = vmlal_lane_u16(c3.val[2], b00_u16.val[2], a00_u16, 3);
                c3.val[3] = vmlal_lane_u16(c3.val[3], b00_u16.val[3], a00_u16, 3);
            }

            auto mtx_out = reinterpret_cast<int32_t *>(out.ptr());

            if (id.y() < height_out && id.x() < (width_out - 16))
            {
                vst1q_s32(mtx_out + 0 * out_stride + 0, vreinterpretq_s32_u32(c0.val[0]));
                vst1q_s32(mtx_out + 0 * out_stride + 4, vreinterpretq_s32_u32(c0.val[1]));
                vst1q_s32(mtx_out + 0 * out_stride + 8, vreinterpretq_s32_u32(c0.val[2]));
                vst1q_s32(mtx_out + 0 * out_stride + 12, vreinterpretq_s32_u32(c0.val[3]));
                if (id.y() + 1 < height_out)
                {
                    vst1q_s32(mtx_out + 1 * out_stride + 0, vreinterpretq_s32_u32(c1.val[0]));
                    vst1q_s32(mtx_out + 1 * out_stride + 4, vreinterpretq_s32_u32(c1.val[1]));
                    vst1q_s32(mtx_out + 1 * out_stride + 8, vreinterpretq_s32_u32(c1.val[2]));
                    vst1q_s32(mtx_out + 1 * out_stride + 12, vreinterpretq_s32_u32(c1.val[3]));
                    if (id.y() + 2 < height_out)
                    {
                        vst1q_s32(mtx_out + 2 * out_stride + 0, vreinterpretq_s32_u32(c2.val[0]));
                        vst1q_s32(mtx_out + 2 * out_stride + 4, vreinterpretq_s32_u32(c2.val[1]));
                        vst1q_s32(mtx_out + 2 * out_stride + 8, vreinterpretq_s32_u32(c2.val[2]));
                        vst1q_s32(mtx_out + 2 * out_stride + 12, vreinterpretq_s32_u32(c2.val[3]));
                        if (id.y() + 3 < height_out)
                        {
                            vst1q_s32(mtx_out + 3 * out_stride + 0, vreinterpretq_s32_u32(c3.val[0]));
                            vst1q_s32(mtx_out + 3 * out_stride + 4, vreinterpretq_s32_u32(c3.val[1]));
                            vst1q_s32(mtx_out + 3 * out_stride + 8, vreinterpretq_s32_u32(c3.val[2]));
                            vst1q_s32(mtx_out + 3 * out_stride + 12, vreinterpretq_s32_u32(c3.val[3]));
                        }
                    }
                }
            }
            else
            {
                const auto left_over_value = width_out - id.x();
                auto       left_over       = left_over_value;
                for (auto k = 0; k < 4 && left_over; ++k)
                {
                    for (auto j = 0; j < 4 && left_over; ++j, --left_over)
                    {
                        *(mtx_out + k * 4 + j) = c0.val[k][j];
                    }
                }
                if (id.y() + 1 < height_out)
                {
                    left_over = left_over_value;
                    for (auto k = 0; k < 4 && left_over; ++k)
                    {
                        for (auto j = 0; j < 4 && left_over; ++j, --left_over)
                        {
                            *(mtx_out + out_stride + k * 4 + j) = c1.val[k][j];
                        }
                    }
                    if (id.y() + 2 < height_out)
                    {
                        left_over = left_over_value;
                        for (auto k = 0; k < 4 && left_over; ++k)
                        {
                            for (auto j = 0; j < 4 && left_over; ++j, --left_over)
                            {
                                *(mtx_out + out_stride * 2 + k * 4 + j) = c2.val[k][j];
                            }
                        }
                        if (id.y() + 3 < height_out)
                        {
                            left_over = left_over_value;
                            for (auto k = 0; k < 4 && left_over; ++k)
                            {
                                for (auto j = 0; j < 4 && left_over; ++j, --left_over)
                                {
                                    *(mtx_out + out_stride * 3 + k * 4 + j) = c3.val[k][j];
                                }
                            }
                        }
                    }
                }
            }
        },
        ina, inb, out);
}

void inline matrix_multiply_s8(
    Iterator &ina, Iterator &inb, Iterator &out, int width_b, const TensorInfo &out_info, const Window &window)
{
    const auto   width_out  = static_cast<int>(out_info.dimension(0));
    const auto   height_out = static_cast<int>(out_info.dimension(1));
    const size_t out_stride = out_info.strides_in_bytes()[1] / out_info.element_size();
    // The implementation assumes that the matrix A and Matrix B have been reshaped respectively with CpuGemmInterleave4x4 and CpuGemmTranspose1xW
    // The reshaping of the matrices helps to have a cache friendly implementation and helps to avoid the data re-arrangements needed for computing 16x4 elements per iteration
    // All the values needed for computing a single 4x4 block will be read from consecutive memory positions
    execute_window_loop(
        window,
        [&](const Coordinates &id)
        {
            auto *mtx_a0 = reinterpret_cast<const int8_t *>(ina.ptr());
            auto *mtx_b0 = reinterpret_cast<const int8_t *>(inb.ptr());

            // Note: Since the input are all positives, we can use uint32_t
            // Accumulators for the block 0
            int32x4x4_t c0 = {{vdupq_n_s32(0), vdupq_n_s32(0), vdupq_n_s32(0), vdupq_n_s32(0)}};

            // Accumulators for the block 1
            int32x4x4_t c1 = {{vdupq_n_s32(0), vdupq_n_s32(0), vdupq_n_s32(0), vdupq_n_s32(0)}};

            // Accumulators for the block 2
            int32x4x4_t c2 = {{vdupq_n_s32(0), vdupq_n_s32(0), vdupq_n_s32(0), vdupq_n_s32(0)}};

            // Accumulators for the block 3
            int32x4x4_t c3 = {{vdupq_n_s32(0), vdupq_n_s32(0), vdupq_n_s32(0), vdupq_n_s32(0)}};

            for (int k = 0; k < width_b; k += 16, mtx_a0 += 4, mtx_b0 += 16)
            {
                const int8x8_t  a00_s8 = vld1_s8(mtx_a0);
                const int8x16_t b00_s8 = vld1q_s8(mtx_b0);

                // Convert a00_s8 to uint16_t and get the lower part
                const int16x4_t a00_s16 = vget_low_s16(vmovl_s8(a00_s8));

                // Convert b00_s8 to int16_t
                const int16x4x4_t b00_s16 = {
                    {vget_low_s16(vmovl_s8(vget_low_s8(b00_s8))), vget_high_s16(vmovl_s8(vget_low_s8(b00_s8))),
                     vget_low_s16(vmovl_s8(vget_high_s8(b00_s8))), vget_high_s16(vmovl_s8(vget_high_s8(b00_s8)))}};

                // 4x4 block 0
                c0.val[0] = vmlal_lane_s16(c0.val[0], b00_s16.val[0], a00_s16, 0);
                c0.val[1] = vmlal_lane_s16(c0.val[1], b00_s16.val[1], a00_s16, 0);
                c0.val[2] = vmlal_lane_s16(c0.val[2], b00_s16.val[2], a00_s16, 0);
                c0.val[3] = vmlal_lane_s16(c0.val[3], b00_s16.val[3], a00_s16, 0);

                // 4x4 block 1
                c1.val[0] = vmlal_lane_s16(c1.val[0], b00_s16.val[0], a00_s16, 1);
                c1.val[1] = vmlal_lane_s16(c1.val[1], b00_s16.val[1], a00_s16, 1);
                c1.val[2] = vmlal_lane_s16(c1.val[2], b00_s16.val[2], a00_s16, 1);
                c1.val[3] = vmlal_lane_s16(c1.val[3], b00_s16.val[3], a00_s16, 1);

                // 4x4 block 2
                c2.val[0] = vmlal_lane_s16(c2.val[0], b00_s16.val[0], a00_s16, 2);
                c2.val[1] = vmlal_lane_s16(c2.val[1], b00_s16.val[1], a00_s16, 2);
                c2.val[2] = vmlal_lane_s16(c2.val[2], b00_s16.val[2], a00_s16, 2);
                c2.val[3] = vmlal_lane_s16(c2.val[3], b00_s16.val[3], a00_s16, 2);

                // 4x4 block 3
                c3.val[0] = vmlal_lane_s16(c3.val[0], b00_s16.val[0], a00_s16, 3);
                c3.val[1] = vmlal_lane_s16(c3.val[1], b00_s16.val[1], a00_s16, 3);
                c3.val[2] = vmlal_lane_s16(c3.val[2], b00_s16.val[2], a00_s16, 3);
                c3.val[3] = vmlal_lane_s16(c3.val[3], b00_s16.val[3], a00_s16, 3);
            }
            auto mtx_out = reinterpret_cast<int32_t *>(out.ptr());
            if (id.y() < height_out && id.x() < (width_out - 16))
            {
                vst1q_s32(mtx_out + 0 * out_stride + 0, c0.val[0]);
                vst1q_s32(mtx_out + 0 * out_stride + 4, c0.val[1]);
                vst1q_s32(mtx_out + 0 * out_stride + 8, c0.val[2]);
                vst1q_s32(mtx_out + 0 * out_stride + 12, c0.val[3]);
                if (id.y() + 1 < height_out)
                {
                    vst1q_s32(mtx_out + 1 * out_stride + 0, c1.val[0]);
                    vst1q_s32(mtx_out + 1 * out_stride + 4, c1.val[1]);
                    vst1q_s32(mtx_out + 1 * out_stride + 8, c1.val[2]);
                    vst1q_s32(mtx_out + 1 * out_stride + 12, c1.val[3]);
                    if (id.y() + 2 < height_out)
                    {
                        vst1q_s32(mtx_out + 2 * out_stride + 0, c2.val[0]);
                        vst1q_s32(mtx_out + 2 * out_stride + 4, c2.val[1]);
                        vst1q_s32(mtx_out + 2 * out_stride + 8, c2.val[2]);
                        vst1q_s32(mtx_out + 2 * out_stride + 12, c2.val[3]);
                        if (id.y() + 3 < height_out)
                        {
                            vst1q_s32(mtx_out + 3 * out_stride + 0, c3.val[0]);
                            vst1q_s32(mtx_out + 3 * out_stride + 4, c3.val[1]);
                            vst1q_s32(mtx_out + 3 * out_stride + 8, c3.val[2]);
                            vst1q_s32(mtx_out + 3 * out_stride + 12, c3.val[3]);
                        }
                    }
                }
            }
            else if (id.y() < height_out)
            {
                const auto left_over_value = width_out - id.x();
                auto       left_over       = left_over_value;
                for (auto k = 0; k < 4 && left_over; ++k)
                {
                    for (auto j = 0; j < 4 && left_over; ++j, --left_over)
                    {
                        *(mtx_out + k * 4 + j) = c0.val[k][j];
                    }
                }
                if (id.y() + 1 < height_out)
                {
                    left_over = left_over_value;
                    for (auto k = 0; k < 4 && left_over; ++k)
                    {
                        for (auto j = 0; j < 4 && left_over; ++j, --left_over)
                        {
                            *(mtx_out + out_stride + k * 4 + j) = c1.val[k][j];
                        }
                    }
                    if (id.y() + 2 < height_out)
                    {
                        left_over = left_over_value;
                        for (auto k = 0; k < 4 && left_over; ++k)
                        {
                            for (auto j = 0; j < 4 && left_over; ++j, --left_over)
                            {
                                *(mtx_out + out_stride * 2 + k * 4 + j) = c2.val[k][j];
                            }
                        }
                        if (id.y() + 3 < height_out)
                        {
                            left_over = left_over_value;
                            for (auto k = 0; k < 4 && left_over; ++k)
                            {
                                for (auto j = 0; j < 4 && left_over; ++j, --left_over)
                                {
                                    *(mtx_out + out_stride * 3 + k * 4 + j) = c3.val[k][j];
                                }
                            }
                        }
                    }
                }
            }
        },
        ina, inb, out);
}

Status validate_arguments(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *dst)
{
    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src0, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED,
                                                         DataType::S8, DataType::U8);
    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src1, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED,
                                                         DataType::QSYMM8, DataType::QSYMM8_PER_CHANNEL, DataType::S8,
                                                         DataType::U8);
    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(dst, 1, DataType::S32);

    ARM_COMPUTE_RETURN_ERROR_ON_MSG(src0->data_type() == DataType::QASYMM8_SIGNED &&
                                        src1->data_type() == DataType::QASYMM8,
                                    "QASYMM8_SIGNED input with QASYMM8 weights not supported");

    TensorShape in0_shape = src0->tensor_shape();
    TensorShape in1_shape = src1->tensor_shape();
    TensorShape out_shape = dst->tensor_shape();

    // Check vector-by-matrix case
    if (out_shape[1] == 1)
    {
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(in0_shape[0] != in1_shape[1],
                                        "The number of input0's columns must be equal to input1's rows");
    }
    else
    {
        in0_shape.collapse(2);
        in1_shape.collapse(2);
        out_shape.collapse(2);

        ARM_COMPUTE_RETURN_ERROR_ON_MSG(in0_shape[2] != out_shape[2],
                                        "Output tensor must have the same number of batches of input0 tensor");
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(
            in1_shape[2] != 1 && in0_shape[2] != in1_shape[2],
            "Input1 tensor must have the same number of batches of input0 or the number of batches must be set to 1");
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(in1_shape[0] % 16, "Input1's width must be a multiple of 16");
    }

    return Status{};
}
} // namespace

void CpuGemmLowpMatrixMultiplyKernel::configure(const ITensorInfo *src0, const ITensorInfo *src1, ITensorInfo *dst)
{
    ARM_COMPUTE_UNUSED(src0);
    ARM_COMPUTE_ERROR_ON_NULLPTR(src0, src1, dst);
    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src0, src1, dst));

    TensorShape in1_shape = src1->tensor_shape();
    in1_shape.collapse(2);

    _slide_matrix_b = in1_shape[2] != 1;

    constexpr unsigned int num_elems_processed_per_iteration_x = 16;
    constexpr unsigned int num_elems_processed_per_iteration_y = 4;

    Window win;
    // Check if the output tensor is a vector. If so,the kernel runs the vector-matrix multiplication
    if ((dst->dimension(1) == 1))
    {
        // Configure kernel window
        win = calculate_max_window(*dst, Steps(num_elems_processed_per_iteration_x));
    }
    else
    {
        win =
            calculate_max_window(*dst, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
    }

    ICpuKernel::configure(win);
}

Status
CpuGemmLowpMatrixMultiplyKernel::validate(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *dst)
{
    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src0, src1, dst));
    return Status{};
}

void CpuGemmLowpMatrixMultiplyKernel::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info)
{
    ARM_COMPUTE_UNUSED(info);
    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICpuKernel::window(), window);

    auto src0 = tensors.get_const_tensor(TensorType::ACL_SRC_0);
    auto src1 = tensors.get_const_tensor(TensorType::ACL_SRC_1);
    auto dst  = tensors.get_tensor(TensorType::ACL_DST);

    // Check if the output tensor is a vector. If so,the kernel runs the vector-matrix multiplication path
    if ((dst->info()->dimension(1) == 1))
    {
        const auto width_matrix_a = static_cast<int>(src0->info()->dimension(0));
        const auto width_matrix_b = static_cast<int>(src1->info()->dimension(0));
        const auto width_out      = static_cast<int>(dst->info()->dimension(0));
        const auto in_b_stride =
            static_cast<int>(src1->info()->strides_in_bytes()[1] / data_size_from_type(src1->info()->data_type()));

        // The implementation computes 16 elements per iteration
        const int window_start_x = 16 * info.thread_id;
        const int window_step_x  = 16 * info.num_threads;
        // Make sure (window_end_x - window_start_x) is a multiple of window_step_x
        const int window_end_x = ceil_to_multiple(width_matrix_b - window_start_x, window_step_x) + window_start_x;

        Window win_out(window);
        win_out.set(Window::DimX, Window::Dimension(window_start_x, window_end_x, window_step_x));
        win_out.set(Window::DimY, Window::Dimension(0, 1, 1));

        Window win_a(window);
        win_a.set(Window::DimX, Window::Dimension(0, 0, 0));
        win_a.set(Window::DimY, Window::Dimension(0, 0, 0));

        Window win_b;
        // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2
        // This scenario can happen when the the matrix multiplication is used to perform a convolution operation
        if (src1->info()->num_dimensions() >= 3)
        {
            win_b = window;
        }
        win_b.set(Window::DimX, Window::Dimension(window_start_x, window_end_x, window_step_x));
        win_b.set(Window::DimY, Window::Dimension(0, 1, 1));

        Iterator ina(src0, win_a);
        Iterator inb(src1, win_b);
        Iterator out(dst, win_out);

        switch (src0->info()->data_type())
        {
            case DataType::S8:
            case DataType::QASYMM8_SIGNED:
            {
                vector_matrix_multiply_s8(ina, inb, out, width_matrix_a, width_matrix_b, width_out, in_b_stride,
                                          window);
                break;
            }
            case DataType::U8:
            case DataType::QASYMM8:
            {
                vector_matrix_multiply_u8(ina, inb, out, width_matrix_a, width_matrix_b, width_out, in_b_stride,
                                          window);
                break;
            }
            default:
            {
                ARM_COMPUTE_ERROR("Not supported");
                break;
            }
        }
    }
    else
    {
        const size_t in_b_stride = src1->info()->strides_in_bytes()[1];
        const int    width_b     = src1->info()->dimension(0);

        // Set step_x and step_y for matrix A. Scale by a factor of 4 the Y range as the input interleaved matrix A has 4 times less the rows of the output matrix
        Window win_a(window);
        win_a.set(Window::DimX, Window::Dimension(0, 0, 0));
        win_a.set(Window::DimY, Window::Dimension(window.y().start() / 4, window.y().end() / 4, 1));

        // Set step_x and step_y for matrix B. Scale by a factor of 16 the X range as the input transposed matrix A has 16 times less the columns of the output matrix
        Window win_b;
        // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2
        // This scenario can happen when the the matrix multiplication is used to perform a convolution operation
        if (_slide_matrix_b)
        {
            win_b = window;
        }
        win_b.set(Window::DimX, Window::Dimension(window.x().start() / 16, window.x().end() / 16, in_b_stride));
        win_b.set(Window::DimY, Window::Dimension(0, 0, 0));

        // The step x and step y for the output matrix has been already set using in configure()
        Iterator ina(src0, win_a);
        Iterator inb(src1, win_b);
        Iterator out(dst, window);

        switch (src0->info()->data_type())
        {
            case DataType::S8:
            case DataType::QASYMM8_SIGNED:
            {
                matrix_multiply_s8(ina, inb, out, width_b, *dst->info(), window);
                break;
            }
            case DataType::U8:
            case DataType::QASYMM8:
            {
                matrix_multiply_u8(ina, inb, out, width_b, *dst->info(), window);
                break;
            }
            default:
            {
                ARM_COMPUTE_ERROR("Not supported");
                break;
            }
        }
    }
}

const char *CpuGemmLowpMatrixMultiplyKernel::name() const
{
    return "CpuGemmLowpMatrixMultiplyKernel";
}
} // namespace kernels
} // namespace cpu
} // namespace arm_compute
