/*
 * Copyright (c) 2019-2021, 2023 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/CpuGemmLowpOffsetContributionOutputStageKernel.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 "src/core/NEON/NEAsymm.h"
#include "src/core/NEON/wrapper/wrapper.h"

#include <arm_neon.h>

namespace arm_compute
{
namespace cpu
{
namespace kernels
{
namespace
{
inline int32x4x4_t load_results_input(const Iterator &mm_result_it, int32_t x)
{
    return {{vld1q_s32(reinterpret_cast<const int32_t *>(mm_result_it.ptr()) + x + 0),
             vld1q_s32(reinterpret_cast<const int32_t *>(mm_result_it.ptr()) + x + 4),
             vld1q_s32(reinterpret_cast<const int32_t *>(mm_result_it.ptr()) + x + 8),
             vld1q_s32(reinterpret_cast<const int32_t *>(mm_result_it.ptr()) + x + 12)}};
}

inline int32x4x4_t load(const int32_t *ptr, int32_t x)
{
    return {{vld1q_s32(ptr + x + 0), vld1q_s32(ptr + x + 4), vld1q_s32(ptr + x + 8), vld1q_s32(ptr + x + 12)}};
}

inline int32x4x4_t add_s32(int32x4x4_t a, int32x4_t b)
{
    return {{vaddq_s32(a.val[0], b), vaddq_s32(a.val[1], b), vaddq_s32(a.val[2], b), vaddq_s32(a.val[3], b)}};
}

inline int32x4x4_t add_s32(int32x4x4_t a, int32x4x4_t b)
{
    return {{vaddq_s32(a.val[0], b.val[0]), vaddq_s32(a.val[1], b.val[1]), vaddq_s32(a.val[2], b.val[2]),
             vaddq_s32(a.val[3], b.val[3])}};
}

inline int32x4x4_t mul_s32(int32x4x4_t &a, int32_t mul_scalar)
{
    return {{vmulq_n_s32(a.val[0], mul_scalar), vmulq_n_s32(a.val[1], mul_scalar), vmulq_n_s32(a.val[2], mul_scalar),
             vmulq_n_s32(a.val[3], mul_scalar)}};
}

inline int32x4x4_t mul_s32(int32x4x4_t &a, const int32_t *multilpier)
{
    return {{vmulq_s32(a.val[0], vld1q_s32(multilpier)), vmulq_s32(a.val[1], vld1q_s32(multilpier + 4)),
             vmulq_s32(a.val[2], vld1q_s32(multilpier + 8)), vmulq_s32(a.val[3], vld1q_s32(multilpier + 12))}};
}

inline int32x4x4_t get_a_offset(const int32_t *vector_sum_col_ptr, int32_t a_offset, int32_t x)
{
    int32x4x4_t a_offset_term_s32 = load(vector_sum_col_ptr, x);

    a_offset_term_s32.val[0] = vmulq_n_s32(a_offset_term_s32.val[0], a_offset);
    a_offset_term_s32.val[1] = vmulq_n_s32(a_offset_term_s32.val[1], a_offset);
    a_offset_term_s32.val[2] = vmulq_n_s32(a_offset_term_s32.val[2], a_offset);
    a_offset_term_s32.val[3] = vmulq_n_s32(a_offset_term_s32.val[3], a_offset);
    return a_offset_term_s32;
}

inline int32x4_t get_b_offset(const int32_t *vector_sum_row_ptr, int32_t b_offset)
{
    int32x4_t b_offset_term_s32 = vld1q_dup_s32(vector_sum_row_ptr);
    b_offset_term_s32           = vmulq_n_s32(b_offset_term_s32, b_offset);
    return b_offset_term_s32;
}

inline int32x4x4_t get_k_offset(int32_t k_offset)
{
    return {{vdupq_n_s32(k_offset), vdupq_n_s32(k_offset), vdupq_n_s32(k_offset), vdupq_n_s32(k_offset)}};
}

inline uint8x16_t finalize_quantization_floating_point(
    int32x4x4_t &in_s32, int32x4_t result_shift_s32, uint8x16_t min_u8, uint8x16_t max_u8, bool is_bounded_relu)
{
    const static int32x4_t zero_s32 = vdupq_n_s32(0);

    // Shift final result (negative value shift right)
    in_s32.val[0] = vshlq_s32(in_s32.val[0], result_shift_s32);
    in_s32.val[1] = vshlq_s32(in_s32.val[1], result_shift_s32);
    in_s32.val[2] = vshlq_s32(in_s32.val[2], result_shift_s32);
    in_s32.val[3] = vshlq_s32(in_s32.val[3], result_shift_s32);

    // Saturate negative values
    in_s32.val[0] = vmaxq_s32(in_s32.val[0], zero_s32);
    in_s32.val[1] = vmaxq_s32(in_s32.val[1], zero_s32);
    in_s32.val[2] = vmaxq_s32(in_s32.val[2], zero_s32);
    in_s32.val[3] = vmaxq_s32(in_s32.val[3], zero_s32);

    // Convert S32 to S16
    const int16x8x2_t in_s16 = {{vcombine_s16(vqmovn_s32(in_s32.val[0]), vqmovn_s32(in_s32.val[1])),
                                 vcombine_s16(vqmovn_s32(in_s32.val[2]), vqmovn_s32(in_s32.val[3]))}};

    // Convert S16 to U8
    uint8x16_t out_u8 = vcombine_u8(vqmovun_s16(in_s16.val[0]), vqmovun_s16(in_s16.val[1]));

    if (is_bounded_relu)
    {
        out_u8 = vmaxq_u8(out_u8, min_u8);
        out_u8 = vminq_u8(out_u8, max_u8);
    }

    return out_u8;
}

inline int8x16_t finalize_quantization_floating_point(
    int32x4x4_t &in_s32, int32x4_t result_shift_s32, int8x16_t min_s8, int8x16_t max_s8, bool is_bounded_relu)
{
    const static int32x4_t zero_s32 = vdupq_n_s32(0);

    // Shift final result (negative value shift right)
    in_s32.val[0] = vshlq_s32(in_s32.val[0], result_shift_s32);
    in_s32.val[1] = vshlq_s32(in_s32.val[1], result_shift_s32);
    in_s32.val[2] = vshlq_s32(in_s32.val[2], result_shift_s32);
    in_s32.val[3] = vshlq_s32(in_s32.val[3], result_shift_s32);

    // Saturate negative values
    in_s32.val[0] = vmaxq_s32(in_s32.val[0], zero_s32);
    in_s32.val[1] = vmaxq_s32(in_s32.val[1], zero_s32);
    in_s32.val[2] = vmaxq_s32(in_s32.val[2], zero_s32);
    in_s32.val[3] = vmaxq_s32(in_s32.val[3], zero_s32);

    // Convert S32 to S16
    const int16x8x2_t in_s16 = {{vcombine_s16(vqmovn_s32(in_s32.val[0]), vqmovn_s32(in_s32.val[1])),
                                 vcombine_s16(vqmovn_s32(in_s32.val[2]), vqmovn_s32(in_s32.val[3]))}};

    // Convert S16 to S8
    int8x16_t out_s8 = vcombine_s8(vqmovn_s16(in_s16.val[0]), vqmovn_s16(in_s16.val[1]));

    if (is_bounded_relu)
    {
        out_s8 = vmaxq_s8(out_s8, min_s8);
        out_s8 = vminq_s8(out_s8, max_s8);
    }

    return out_s8;
}

inline int8x16_t finalize_quantization_floating_point(
    int32x4x4_t &in_s32, int32x4x4_t result_shift_s32, int8x16_t min_s8, int8x16_t max_s8, bool is_bounded_relu)
{
    const static int32x4_t zero_s32 = vdupq_n_s32(0);

    // Shift final result (negative value shift right)
    in_s32.val[0] = vshlq_s32(in_s32.val[0], vnegq_s32(result_shift_s32.val[0]));
    in_s32.val[1] = vshlq_s32(in_s32.val[1], vnegq_s32(result_shift_s32.val[1]));
    in_s32.val[2] = vshlq_s32(in_s32.val[2], vnegq_s32(result_shift_s32.val[2]));
    in_s32.val[3] = vshlq_s32(in_s32.val[3], vnegq_s32(result_shift_s32.val[3]));

    // Saturate negative values
    in_s32.val[0] = vmaxq_s32(in_s32.val[0], zero_s32);
    in_s32.val[1] = vmaxq_s32(in_s32.val[1], zero_s32);
    in_s32.val[2] = vmaxq_s32(in_s32.val[2], zero_s32);
    in_s32.val[3] = vmaxq_s32(in_s32.val[3], zero_s32);

    // Convert S32 to S16
    const int16x8x2_t in_s16 = {{vcombine_s16(vqmovn_s32(in_s32.val[0]), vqmovn_s32(in_s32.val[1])),
                                 vcombine_s16(vqmovn_s32(in_s32.val[2]), vqmovn_s32(in_s32.val[3]))}};

    // Convert S16 to S8
    int8x16_t out_s8 = vcombine_s8(vqmovn_s16(in_s16.val[0]), vqmovn_s16(in_s16.val[1]));

    if (is_bounded_relu)
    {
        out_s8 = vmaxq_s8(out_s8, min_s8);
        out_s8 = vminq_s8(out_s8, max_s8);
    }

    return out_s8;
}

template <typename T>
struct VectorTyper
{
    using stype = T;
    using vtype = typename wrapper::traits::neon_bitvector_t<T, wrapper::traits::BitWidth::W128>;
};

inline Window get_win_vector_sum(const Window &window)
{
    Window win_vector_sum(window);
    win_vector_sum.set(Window::DimY, Window::Dimension(0, 0, 0));
    win_vector_sum.set(Window::DimZ, Window::Dimension(0, 0, 0));
    return win_vector_sum;
}

inline Iterator get_vector_sum_col_it(const Window &window, const ITensor *vector_sum_col)
{
    Iterator vector_sum_col_it(vector_sum_col, get_win_vector_sum(window));
    return vector_sum_col_it;
}

inline Iterator get_vector_sum_row_it(const Window &window, const ITensor *vector_sum_row)
{
    Window win_vector_sum_row = get_win_vector_sum(window);
    win_vector_sum_row.set(Window::DimX, Window::Dimension(0, 0, 0));
    Iterator vector_sum_row_it(vector_sum_row, win_vector_sum_row);
    return vector_sum_row_it;
}

inline Iterator get_bias_it(const Window &window, const ITensor *bias)
{
    Window win_bias(window);
    win_bias.set(Window::DimY, Window::Dimension(0, 1, 1));
    win_bias.set(Window::DimZ, Window::Dimension(0, 1, 1));
    Iterator bias_it(bias, win_bias);
    return bias_it;
}

template <typename VT>
inline void run_offset_contribution_output_stage_window(const int32_t     *vector_sum_col_ptr,
                                                        const int32_t     *vector_sum_row_ptr,
                                                        const int32_t     *bias_ptr,
                                                        Iterator           mm_result_it,
                                                        Iterator           out_it,
                                                        const int32x4_t    result_offset_s32,
                                                        const int32x4_t    result_shift_s32,
                                                        typename VT::vtype min_vec,
                                                        typename VT::vtype max_vec,
                                                        int32_t            a_offset,
                                                        int32_t            b_offset,
                                                        int32_t            k_offset,
                                                        int32_t            multiplier,
                                                        int32_t            shift,
                                                        int32_t            offset,
                                                        int32_t            min_bound,
                                                        int32_t            max_bound,
                                                        int                window_step_x,
                                                        int                window_start_x,
                                                        int                window_end_x,
                                                        bool               has_a_offset,
                                                        bool               has_b_offset,
                                                        bool               has_bias,
                                                        bool               is_bounded_relu,
                                                        bool               is_fixed_point)
{
    int32x4x4_t offset_term_s32 = {0, 0, 0, 0};
    if (!is_fixed_point)
    {
        // Combine quantization offset with other offsets.
        offset_term_s32 = add_s32(offset_term_s32, result_offset_s32);
    }
    if (has_a_offset && has_b_offset)
    {
        offset_term_s32 = add_s32(offset_term_s32, get_k_offset(k_offset));
    }
    if (has_b_offset)
    {
        offset_term_s32 = add_s32(offset_term_s32, get_b_offset(vector_sum_row_ptr, b_offset));
    }

    int x = window_start_x;
    for (; x <= (window_end_x - window_step_x); x += window_step_x)
    {
        int32x4x4_t in_s32 = load_results_input(mm_result_it, x);

        if (has_a_offset)
        {
            in_s32 = add_s32(in_s32, get_a_offset(vector_sum_col_ptr, a_offset, x));
        }
        if (has_bias)
        {
            in_s32 = add_s32(in_s32, load(bias_ptr, x));
        }
        if (!is_fixed_point || has_b_offset)
        {
            in_s32 = add_s32(in_s32, offset_term_s32);
        }
        if (!is_fixed_point)
        {
            in_s32 = mul_s32(in_s32, multiplier);
        }

        if (is_fixed_point)
        {
            wrapper::vstore(
                reinterpret_cast<typename VT::stype *>(out_it.ptr() + x),
                finalize_quantization(in_s32, multiplier, shift, result_offset_s32, min_vec, max_vec, is_bounded_relu));
        }
        else
        {
            wrapper::vstore(
                reinterpret_cast<typename VT::stype *>(out_it.ptr() + x),
                finalize_quantization_floating_point(in_s32, result_shift_s32, min_vec, max_vec, is_bounded_relu));
        }
    }
    // Compute left-over elements
    for (; x < window_end_x; ++x)
    {
        int32_t in_value =
            *(reinterpret_cast<const int32_t *>(mm_result_it.ptr()) + x) + wrapper::vgetlane(offset_term_s32.val[0], 0);

        if (has_a_offset)
        {
            in_value += (*(vector_sum_col_ptr + x) * a_offset);
        }
        if (has_bias)
        {
            in_value += *(bias_ptr + x);
        }

        if (is_fixed_point)
        {
            // Finalize and store the result
            *reinterpret_cast<typename VT::stype *>(out_it.ptr() + x) =
                finalize_quantization(in_value, multiplier, shift, offset, static_cast<typename VT::stype>(min_bound),
                                      static_cast<typename VT::stype>(max_bound), is_bounded_relu);
        }
        else
        {
            // Finalize quantization
            in_value = (in_value * multiplier) >> shift;

            // Bound and store the result
            if (is_bounded_relu)
            {
                in_value = static_cast<typename VT::stype>(
                    std::max<int32_t>(min_bound, std::min<int32_t>(max_bound, in_value)));
            }
            *reinterpret_cast<typename VT::stype *>(out_it.ptr() + x) =
                static_cast<typename VT::stype>(std::max<int32_t>(
                    static_cast<int32_t>(std::numeric_limits<typename VT::stype>::lowest()),
                    std::min<int32_t>(static_cast<int32_t>(std::numeric_limits<typename VT::stype>::max()), in_value)));
        }
    }
}

inline void run_offset_contribution_output_stage_window_symm(const int32_t  *vector_sum_col_ptr,
                                                             const int32_t  *bias_ptr,
                                                             Iterator        mm_result_it,
                                                             Iterator        out_it,
                                                             const int32_t  *result_multipliers,
                                                             const int32_t  *result_shifts,
                                                             const int32x4_t result_offset,
                                                             int8x16_t       min_s8,
                                                             int8x16_t       max_s8,
                                                             int32_t         a_offset,
                                                             int32_t         offset,
                                                             int32_t         min_bound,
                                                             int32_t         max_bound,
                                                             int             window_step_x,
                                                             int             window_start_x,
                                                             int             window_end_x,
                                                             bool            has_a_offset,
                                                             bool            has_bias,
                                                             bool            is_bounded_relu,
                                                             bool            is_fixed_point)
{
    int32x4x4_t offset_term_s32 = {0, 0, 0, 0};
    if (!is_fixed_point)
    {
        // Combine quantization offset with other offsets.
        offset_term_s32 = add_s32(offset_term_s32, result_offset);
    }

    int x = window_start_x;
    for (; x <= (window_end_x - window_step_x); x += window_step_x)
    {
        int32x4x4_t in_s32 = load_results_input(mm_result_it, x);

        if (has_a_offset)
        {
            in_s32 = add_s32(in_s32, get_a_offset(vector_sum_col_ptr, a_offset, x));
        }
        if (has_bias)
        {
            in_s32 = add_s32(in_s32, load(bias_ptr, x));
        }
        if (!is_fixed_point)
        {
            in_s32 = add_s32(in_s32, offset_term_s32);
            in_s32 = mul_s32(in_s32, result_multipliers + x);
        }

        if (is_fixed_point)
        {
            vst1q_s8(reinterpret_cast<int8_t *>(out_it.ptr() + x),
                     finalize_quantization_symm(in_s32, load(result_multipliers, x), load(result_shifts, x),
                                                result_offset, min_s8, max_s8, is_bounded_relu));
        }
        else
        {
            vst1q_s8(
                reinterpret_cast<int8_t *>(out_it.ptr() + x),
                finalize_quantization_floating_point(in_s32, load(result_shifts, x), min_s8, max_s8, is_bounded_relu));
        }
    }
    // Compute left-over elements
    for (; x < window_end_x; ++x)
    {
        int32_t in_value =
            *(reinterpret_cast<const int32_t *>(mm_result_it.ptr()) + x) + wrapper::vgetlane(offset_term_s32.val[0], 0);

        if (has_a_offset)
        {
            in_value += (*(vector_sum_col_ptr + x) * a_offset);
        }
        if (has_bias)
        {
            in_value += *(bias_ptr + x);
        }

        if (is_fixed_point)
        {
            // Finalize and store the result
            *(out_it.ptr() + x) =
                finalize_quantization(in_value, result_multipliers[x], result_shifts[x], offset,
                                      static_cast<int8_t>(min_bound), static_cast<int8_t>(max_bound), is_bounded_relu);
        }
        else
        {
            // Finalize quantization
            in_value = (in_value * result_multipliers[x]) >> (-result_shifts[x]);

            // Bound and store the result
            if (is_bounded_relu)
            {
                in_value = static_cast<int8_t>(std::max<int32_t>(min_bound, std::min<int32_t>(max_bound, in_value)));
            }
            *(out_it.ptr() + x) = static_cast<int8_t>(std::max<int32_t>(-128, std::min<int32_t>(127, in_value)));
        }
    }
}

template <typename T>
void run_offset_contribution_output_stage(const Window           &window,
                                          const ITensor          *mm_result,
                                          const ITensor          *vector_sum_col,
                                          const ITensor          *vector_sum_row,
                                          const ITensor          *bias,
                                          ITensor                *output,
                                          int32_t                 a_offset,
                                          int32_t                 b_offset,
                                          int32_t                 k_offset,
                                          bool                    is_vector_sum_col_batched,
                                          GEMMLowpOutputStageInfo output_stage,
                                          bool                    is_gemm3d,
                                          bool                    is_bounded_relu,
                                          bool                    is_fixed_point)
{
    //  Semantics of XYZW Explained for each tensor
    //
    //  | Tensor            |    XYZW when is_gemm3d == false       |    XYZW when is_gemm3d == true                    |
    // -------------------------------------------------------------------------------------------------------------------
    //  | mm_result         |  x -> width,  y -> height, z -> batch |  x -> width, y -> height, z -> depth, w -> batch  |
    //  | collapsed window  |  x -> width,  y -> height, z -> batch |  x -> width, y -> height, z -> depth * batch      |
    //  | vector_sum_row    |  x -> height, y -> batch              |  x -> height * depth, y -> batch                  |
    //  | Vector_sum_col    |  x -> width,  y -> batch              |  x -> width, y -> batch                           |

    using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t<T, wrapper::traits::BitWidth::W128>;
    using Typer        = VectorTyper<T>;

    const int height_input = is_gemm3d ? mm_result->info()->dimension(1) : 0;
    const int depth_input  = is_gemm3d ? mm_result->info()->dimension(2) : 1;

    const int32_t multiplier = output_stage.gemmlowp_multiplier;
    const int32_t shift      = output_stage.gemmlowp_shift;
    const int32_t offset     = output_stage.gemmlowp_offset;
    const int32_t min_bound  = output_stage.gemmlowp_min_bound;
    const int32_t max_bound  = output_stage.gemmlowp_max_bound;

    const int32x4_t result_offset_s32 = vdupq_n_s32(offset);
    const int32x4_t result_shift_s32  = vdupq_n_s32(is_fixed_point ? shift : -shift);
    const auto      min_vec           = wrapper::vdup_n(static_cast<T>(min_bound), ExactTagType{});
    const auto      max_vec           = wrapper::vdup_n(static_cast<T>(max_bound), ExactTagType{});

    const int  window_step_x  = 16;
    const auto window_start_x = static_cast<int>(window.x().start());
    const auto window_end_x   = static_cast<int>(window.x().end());

    Window win(window);
    win.set(Window::DimX, Window::Dimension(0, 1, 1));

    Window collapsed_window = win.collapse_if_possible(win, Window::DimZ);

    Iterator mm_result_it(mm_result, win);
    Iterator out_it(output, win);

    if ((a_offset != 0) && (b_offset != 0))
    {
        ARM_COMPUTE_ERROR_ON_NULLPTR(vector_sum_col);
        ARM_COMPUTE_ERROR_ON_NULLPTR(vector_sum_row);

        Iterator vector_sum_col_it = get_vector_sum_col_it(collapsed_window, vector_sum_col);
        Iterator vector_sum_row_it = get_vector_sum_row_it(collapsed_window, vector_sum_row);

        const size_t sum_row_stride_y = vector_sum_row->info()->strides_in_bytes().y();

        // Offset in case vector_sum_col is batched in y dimension
        const int vector_sum_col_stride_batch =
            is_vector_sum_col_batched ? vector_sum_col->info()->strides_in_bytes().y() : 0;

        if (bias != nullptr)
        {
            Iterator bias_it = get_bias_it(collapsed_window, bias);
            execute_window_loop(
                collapsed_window,
                [&](const Coordinates &id)
                {
                    const int  batch_id           = id.z() / depth_input;
                    const auto vector_sum_col_ptr = reinterpret_cast<const int32_t *>(
                        vector_sum_col_it.ptr() + batch_id * vector_sum_col_stride_batch);
                    const auto vector_sum_row_ptr =
                        reinterpret_cast<const int32_t *>(vector_sum_row_it.ptr() + batch_id * sum_row_stride_y) +
                        id.y() + (id.z() % depth_input) * height_input;
                    run_offset_contribution_output_stage_window<Typer>(
                        vector_sum_col_ptr, vector_sum_row_ptr, reinterpret_cast<const int32_t *>(bias_it.ptr()),
                        mm_result_it, out_it, result_offset_s32, result_shift_s32, min_vec, max_vec, a_offset, b_offset,
                        k_offset, multiplier, shift, offset, min_bound, max_bound, window_step_x, window_start_x,
                        window_end_x, true, true, true, is_bounded_relu, is_fixed_point);
                },
                vector_sum_col_it, vector_sum_row_it, bias_it, mm_result_it, out_it);
        }
        else
        {
            execute_window_loop(
                collapsed_window,
                [&](const Coordinates &id)
                {
                    const int  batch_id           = id.z() / depth_input;
                    const auto vector_sum_col_ptr = reinterpret_cast<const int32_t *>(
                        vector_sum_col_it.ptr() + batch_id * vector_sum_col_stride_batch);
                    const auto vector_sum_row_ptr =
                        reinterpret_cast<const int32_t *>(vector_sum_row_it.ptr() + batch_id * sum_row_stride_y) +
                        id.y() + (id.z() % depth_input) * height_input;
                    run_offset_contribution_output_stage_window<Typer>(
                        vector_sum_col_ptr, vector_sum_row_ptr, nullptr, mm_result_it, out_it, result_offset_s32,
                        result_shift_s32, min_vec, max_vec, a_offset, b_offset, k_offset, multiplier, shift, offset,
                        min_bound, max_bound, window_step_x, window_start_x, window_end_x, true, true, false,
                        is_bounded_relu, is_fixed_point);
                },
                vector_sum_col_it, vector_sum_row_it, mm_result_it, out_it);
        }
    }
    else if ((a_offset == 0) && (b_offset != 0))
    {
        ARM_COMPUTE_ERROR_ON_NULLPTR(vector_sum_row);

        Iterator vector_sum_row_it = get_vector_sum_row_it(collapsed_window, vector_sum_row);

        const size_t sum_row_stride_y = vector_sum_row->info()->strides_in_bytes().y();

        if (bias != nullptr)
        {
            Iterator bias_it = get_bias_it(collapsed_window, bias);
            execute_window_loop(
                collapsed_window,
                [&](const Coordinates &id)
                {
                    const int  batch_id = id.z() / depth_input;
                    const auto vector_sum_row_ptr =
                        reinterpret_cast<const int32_t *>(vector_sum_row_it.ptr() + batch_id * sum_row_stride_y) +
                        id.y() + (id.z() % depth_input) * height_input;
                    run_offset_contribution_output_stage_window<Typer>(
                        nullptr, vector_sum_row_ptr, reinterpret_cast<const int32_t *>(bias_it.ptr()), mm_result_it,
                        out_it, result_offset_s32, result_shift_s32, min_vec, max_vec, a_offset, b_offset, k_offset,
                        multiplier, shift, offset, min_bound, max_bound, window_step_x, window_start_x, window_end_x,
                        false, true, true, is_bounded_relu, is_fixed_point);
                },
                vector_sum_row_it, bias_it, mm_result_it, out_it);
        }
        else
        {
            execute_window_loop(
                collapsed_window,
                [&](const Coordinates &id)
                {
                    const int  batch_id = id.z() / depth_input;
                    const auto vector_sum_row_ptr =
                        reinterpret_cast<const int32_t *>(vector_sum_row_it.ptr() + batch_id * sum_row_stride_y) +
                        id.y() + (id.z() % depth_input) * height_input;
                    run_offset_contribution_output_stage_window<Typer>(
                        nullptr, vector_sum_row_ptr, nullptr, mm_result_it, out_it, result_offset_s32, result_shift_s32,
                        min_vec, max_vec, a_offset, b_offset, k_offset, multiplier, shift, offset, min_bound, max_bound,
                        window_step_x, window_start_x, window_end_x, false, true, false, is_bounded_relu,
                        is_fixed_point);
                },
                vector_sum_row_it, mm_result_it, out_it);
        }
    }
    else if ((a_offset != 0) && (b_offset == 0))
    {
        ARM_COMPUTE_ERROR_ON_NULLPTR(vector_sum_col);

        Iterator vector_sum_col_it = get_vector_sum_col_it(collapsed_window, vector_sum_col);

        // Offset in case vector_sum_col is batched in y dimension
        const int vector_sum_col_stride_batch =
            is_vector_sum_col_batched ? vector_sum_col->info()->strides_in_bytes().y() : 0;

        if (bias != nullptr)
        {
            Iterator bias_it = get_bias_it(collapsed_window, bias);
            execute_window_loop(
                collapsed_window,
                [&](const Coordinates &id)
                {
                    const int  batch_id           = id.z() / depth_input;
                    const auto vector_sum_col_ptr = reinterpret_cast<const int32_t *>(
                        vector_sum_col_it.ptr() + batch_id * vector_sum_col_stride_batch);
                    run_offset_contribution_output_stage_window<Typer>(
                        vector_sum_col_ptr, nullptr, reinterpret_cast<const int32_t *>(bias_it.ptr()), mm_result_it,
                        out_it, result_offset_s32, result_shift_s32, min_vec, max_vec, a_offset, b_offset, k_offset,
                        multiplier, shift, offset, min_bound, max_bound, window_step_x, window_start_x, window_end_x,
                        true, false, true, is_bounded_relu, is_fixed_point);
                },
                vector_sum_col_it, bias_it, mm_result_it, out_it);
        }
        else
        {
            execute_window_loop(
                collapsed_window,
                [&](const Coordinates &id)
                {
                    const int  batch_id           = id.z() / depth_input;
                    const auto vector_sum_col_ptr = reinterpret_cast<const int32_t *>(
                        vector_sum_col_it.ptr() + batch_id * vector_sum_col_stride_batch);
                    run_offset_contribution_output_stage_window<Typer>(
                        vector_sum_col_ptr, nullptr, nullptr, mm_result_it, out_it, result_offset_s32, result_shift_s32,
                        min_vec, max_vec, a_offset, b_offset, k_offset, multiplier, shift, offset, min_bound, max_bound,
                        window_step_x, window_start_x, window_end_x, true, false, false, is_bounded_relu,
                        is_fixed_point);
                },
                vector_sum_col_it, mm_result_it, out_it);
        }
    }
    else
    {
        if (bias != nullptr)
        {
            Iterator bias_it = get_bias_it(collapsed_window, bias);
            execute_window_loop(
                collapsed_window,
                [&](const Coordinates &)
                {
                    run_offset_contribution_output_stage_window<Typer>(
                        nullptr, nullptr, reinterpret_cast<const int32_t *>(bias_it.ptr()), mm_result_it, out_it,
                        result_offset_s32, result_shift_s32, min_vec, max_vec, a_offset, b_offset, k_offset, multiplier,
                        shift, offset, min_bound, max_bound, window_step_x, window_start_x, window_end_x, false, false,
                        true, is_bounded_relu, is_fixed_point);
                },
                bias_it, mm_result_it, out_it);
        }
        else
        {
            execute_window_loop(
                collapsed_window,
                [&](const Coordinates &)
                {
                    run_offset_contribution_output_stage_window<Typer>(
                        nullptr, nullptr, nullptr, mm_result_it, out_it, result_offset_s32, result_shift_s32, min_vec,
                        max_vec, a_offset, b_offset, k_offset, multiplier, shift, offset, min_bound, max_bound,
                        window_step_x, window_start_x, window_end_x, false, false, false, is_bounded_relu,
                        is_fixed_point);
                },
                mm_result_it, out_it);
        }
        return;
    }
}

void run_offset_contribution_output_stage_symm(const Window           &window,
                                               const ITensor          *mm_result,
                                               const ITensor          *vector_sum_col,
                                               const ITensor          *vector_sum_row,
                                               const ITensor          *bias,
                                               ITensor                *output,
                                               int32_t                 a_offset,
                                               int32_t                 b_offset,
                                               int32_t                 k_offset,
                                               bool                    is_vector_sum_col_batched,
                                               GEMMLowpOutputStageInfo output_stage,
                                               bool                    is_gemm3d,
                                               bool                    is_bounded_relu,
                                               bool                    is_fixed_point)
{
    ARM_COMPUTE_UNUSED(vector_sum_row, b_offset, k_offset);

    const int depth_input = is_gemm3d ? mm_result->info()->dimension(2) : 1;

    const int32_t offset    = output_stage.gemmlowp_offset;
    const int32_t min_bound = output_stage.gemmlowp_min_bound;
    const int32_t max_bound = output_stage.gemmlowp_max_bound;

    const int32_t  *result_multipliers = output_stage.gemmlowp_multipliers.data();
    const int32_t  *result_shifts      = output_stage.gemmlowp_shifts.data();
    const int32x4_t result_offset_s32  = vdupq_n_s32(offset);
    const int8x16_t min_s8             = vdupq_n_s8(static_cast<int8_t>(min_bound));
    const int8x16_t max_s8             = vdupq_n_s8(static_cast<int8_t>(max_bound));

    const int  window_step_x  = 16;
    const auto window_start_x = static_cast<int>(window.x().start());
    const auto window_end_x   = static_cast<int>(window.x().end());

    Window win(window);
    win.set(Window::DimX, Window::Dimension(0, 1, 1));

    Window collapsed_window = win.collapse_if_possible(win, Window::DimZ);

    Iterator mm_result_it(mm_result, win);
    Iterator out_it(output, win);

    if (a_offset != 0)
    {
        ARM_COMPUTE_ERROR_ON_NULLPTR(vector_sum_col);

        Iterator vector_sum_col_it = get_vector_sum_col_it(collapsed_window, vector_sum_col);

        // Offset in case vector_sum_col is batched in y dimension
        const int vector_sum_col_stride_batch =
            is_vector_sum_col_batched ? vector_sum_col->info()->strides_in_bytes().y() : 0;

        if (bias != nullptr)
        {
            Iterator bias_it = get_bias_it(collapsed_window, bias);
            execute_window_loop(
                collapsed_window,
                [&](const Coordinates &id)
                {
                    const int  batch_id           = id.z() / depth_input;
                    const auto vector_sum_col_ptr = reinterpret_cast<const int32_t *>(
                        vector_sum_col_it.ptr() + batch_id * vector_sum_col_stride_batch);
                    run_offset_contribution_output_stage_window_symm(
                        vector_sum_col_ptr, reinterpret_cast<const int32_t *>(bias_it.ptr()), mm_result_it, out_it,
                        result_multipliers, result_shifts, result_offset_s32, min_s8, max_s8, a_offset, offset,
                        min_bound, max_bound, window_step_x, window_start_x, window_end_x, true, true, is_bounded_relu,
                        is_fixed_point);
                },
                vector_sum_col_it, bias_it, mm_result_it, out_it);
        }
        else
        {
            execute_window_loop(
                collapsed_window,
                [&](const Coordinates &id)
                {
                    const int  batch_id           = id.z() / depth_input;
                    const auto vector_sum_col_ptr = reinterpret_cast<const int32_t *>(
                        vector_sum_col_it.ptr() + batch_id * vector_sum_col_stride_batch);
                    run_offset_contribution_output_stage_window_symm(
                        vector_sum_col_ptr, nullptr, mm_result_it, out_it, result_multipliers, result_shifts,
                        result_offset_s32, min_s8, max_s8, a_offset, offset, min_bound, max_bound, window_step_x,
                        window_start_x, window_end_x, true, false, is_bounded_relu, is_fixed_point);
                },
                vector_sum_col_it, mm_result_it, out_it);
        }
    }
    else
    {
        if (bias != nullptr)
        {
            Iterator bias_it = get_bias_it(collapsed_window, bias);
            execute_window_loop(
                collapsed_window,
                [&](const Coordinates &)
                {
                    run_offset_contribution_output_stage_window_symm(
                        nullptr, reinterpret_cast<const int32_t *>(bias_it.ptr()), mm_result_it, out_it,
                        result_multipliers, result_shifts, result_offset_s32, min_s8, max_s8, a_offset, offset,
                        min_bound, max_bound, window_step_x, window_start_x, window_end_x, false, true, is_bounded_relu,
                        is_fixed_point);
                },
                bias_it, mm_result_it, out_it);
        }
        else
        {
            execute_window_loop(
                collapsed_window,
                [&](const Coordinates &)
                {
                    run_offset_contribution_output_stage_window_symm(
                        nullptr, nullptr, mm_result_it, out_it, result_multipliers, result_shifts, result_offset_s32,
                        min_s8, max_s8, a_offset, offset, min_bound, max_bound, window_step_x, window_start_x,
                        window_end_x, false, false, is_bounded_relu, is_fixed_point);
                },
                mm_result_it, out_it);
        }
        return;
    }
}

Status validate_arguments(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)
{
    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(mm_result, 1, DataType::S32);
    if (output->data_type() != DataType::QASYMM8)
    {
        ARM_COMPUTE_RETURN_ERROR_ON(mm_result->dimension(0) > 1 && output_stage.gemmlowp_multipliers.size() > 1 &&
                                    b_offset != 0);
    }
    ARM_COMPUTE_RETURN_ERROR_ON(output_stage.gemmlowp_min_bound > output_stage.gemmlowp_max_bound);
    ARM_COMPUTE_RETURN_ERROR_ON(output_stage.type != GEMMLowpOutputStageType::QUANTIZE_DOWN &&
                                output_stage.type != GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT);

    if (bias != nullptr)
    {
        ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::S32);
        ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1);
        ARM_COMPUTE_RETURN_ERROR_ON(mm_result->dimension(0) != bias->dimension(0));
    }

    // If a_offset == 0, vector_sum_col can be a nullptr
    if (a_offset != 0)
    {
        ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(vector_sum_col, 1, DataType::S32);
        ARM_COMPUTE_RETURN_ERROR_ON(vector_sum_col->dimension(0) != mm_result->dimension(0));
        ARM_COMPUTE_RETURN_ERROR_ON(vector_sum_col->num_dimensions() > 2);
    }

    // If b_offset == 0, vector_sum_row can be a nullptr
    if (b_offset != 0)
    {
        ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(vector_sum_row, 1, DataType::S32);

        // Check if input is a 3D reinterpretation
        const bool reinterpret_as_3d =
            mm_result->num_dimensions() > 1 && mm_result->tensor_shape().y() != vector_sum_row->tensor_shape().x();

        // Validate input
        ARM_COMPUTE_RETURN_ERROR_ON(reinterpret_as_3d && vector_sum_row->dimension(0) !=
                                                             (mm_result->dimension(1) * mm_result->dimension(2)));
        ARM_COMPUTE_RETURN_ERROR_ON(!reinterpret_as_3d && vector_sum_row->dimension(0) != mm_result->dimension(1));

        TensorShape output_shape = output->tensor_shape();
        if (output_shape.num_dimensions() > 1)
        {
            const unsigned int output_batch_idx = reinterpret_as_3d ? 3 : 2;

            TensorShape vector_sum_row_shape = vector_sum_row->tensor_shape();
            vector_sum_row_shape.collapse_from(1);
            output_shape.collapse_from(output_batch_idx);

            ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_row_shape[1] != output_shape[output_batch_idx],
                                            "mm_result tensor must have the same number of batches of output tensor");

            if (a_offset != 0)
            {
                TensorShape vector_sum_col_shape = vector_sum_col->tensor_shape();
                vector_sum_col_shape.collapse_from(1);

                ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_col_shape[1] != 1 &&
                                                    vector_sum_col_shape[1] != vector_sum_row_shape[1],
                                                "vector_sum_col tensor must have the same number of batches of "
                                                "vector_sum_row_shape or the number of batches must be set to 1");
            }
        }

        // Check Tensor Rank of vector_sum_row
        ARM_COMPUTE_RETURN_ERROR_ON(vector_sum_row->num_dimensions() > 3);
    }

    if (output->total_size() != 0)
    {
        ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED);
        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(mm_result, output);
    }

    return Status{};
}
} // namespace

void CpuGemmLowpOffsetContributionOutputStageKernel::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)
{
    ARM_COMPUTE_UNUSED(vector_sum_row, bias);
    // Perform validate step
    ARM_COMPUTE_ERROR_ON_NULLPTR(mm_result, dst);
    ARM_COMPUTE_ERROR_THROW_ON(
        validate_arguments(mm_result, vector_sum_col, vector_sum_row, bias, dst, a_offset, b_offset, output_stage));

    _a_offset     = a_offset;
    _b_offset     = b_offset;
    _k_offset     = a_offset * b_offset * k;
    _output_stage = output_stage;

    // If a_offset == 0, vector_sum_col can be a nullptr
    if (a_offset != 0)
    {
        // Check if vector_sum_col_shape should be slidden or not
        // Don't slide vector_sum_col_shape along the y dimension if vector_sum_col_shape has just 1 dimension and vector_sum_row_shape more than 1
        // This scenario can happen when the the matrix multiplication is used to perform a convolution operation
        _is_vector_sum_col_batched = vector_sum_col->tensor_shape().num_dimensions() > 1;
    }

    // Output auto inizialitation if not yet initialized
    auto_init_if_empty(*dst, mm_result->clone()->set_data_type(DataType::QASYMM8));

    // Configure kernel window
    Window win = calculate_max_window(*mm_result, Steps());

    // Note: This kernel performs 16 elements per iteration.
    // However, since we use a left-over for loop, we cannot have any read or write out of memory
    // For this reason num_elems_processed_per_iteration is 1 and so update_window_and_padding() can be skipped
    ICpuKernel::configure(win);
}

Status CpuGemmLowpOffsetContributionOutputStageKernel::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)
{
    ARM_COMPUTE_ERROR_ON_NULLPTR(mm_result, output);
    ARM_COMPUTE_RETURN_ON_ERROR(
        validate_arguments(mm_result, vector_sum_col, vector_sum_row, bias, output, a_offset, b_offset, output_stage));
    return Status{};
}

void CpuGemmLowpOffsetContributionOutputStageKernel::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 mm_result      = tensors.get_const_tensor(TensorType::ACL_SRC_0);
    auto vector_sum_col = tensors.get_const_tensor(TensorType::ACL_SRC_1);
    auto vector_sum_row = tensors.get_const_tensor(TensorType::ACL_SRC_2);
    auto bias           = tensors.get_const_tensor(TensorType::ACL_SRC_3);
    auto dst            = tensors.get_tensor(TensorType::ACL_DST);

    PixelValue type_min{};
    PixelValue type_max{};
    std::tie(type_min, type_max) = get_min_max(dst->info()->data_type());
    int32_t type_min_int         = type_min.get<int32_t>();
    int32_t type_max_int         = type_max.get<int32_t>();

    const bool reinterpret_as_3d = vector_sum_row != nullptr && mm_result->info()->num_dimensions() > 1 &&
                                   mm_result->info()->tensor_shape().y() != vector_sum_row->info()->tensor_shape().x();

    const bool is_bounded_relu =
        !(_output_stage.gemmlowp_min_bound <= type_min_int && _output_stage.gemmlowp_max_bound >= type_max_int);

    // Check if we need to perform fixed point requantization
    const bool is_fixed_point = _output_stage.type != GEMMLowpOutputStageType::QUANTIZE_DOWN;

    // Check if symmetric per-channel execution
    const bool is_signed = dst->info()->data_type() == DataType::QASYMM8_SIGNED;

    // Check if symmetric per-channel execution
    const bool is_symm = _output_stage.is_quantized_per_channel;

    if (is_symm)
    {
        run_offset_contribution_output_stage_symm(window, mm_result, vector_sum_col, vector_sum_row, bias, dst,
                                                  _a_offset, _b_offset, _k_offset, _is_vector_sum_col_batched,
                                                  _output_stage, reinterpret_as_3d, is_bounded_relu, is_fixed_point);
    }
    else
    {
        if (is_signed)
        {
            run_offset_contribution_output_stage<int8_t>(
                window, mm_result, vector_sum_col, vector_sum_row, bias, dst, _a_offset, _b_offset, _k_offset,
                _is_vector_sum_col_batched, _output_stage, reinterpret_as_3d, is_bounded_relu, is_fixed_point);
        }
        else
        {
            run_offset_contribution_output_stage<uint8_t>(
                window, mm_result, vector_sum_col, vector_sum_row, bias, dst, _a_offset, _b_offset, _k_offset,
                _is_vector_sum_col_batched, _output_stage, reinterpret_as_3d, is_bounded_relu, is_fixed_point);
        }
    }
}

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