src/core/NEON/kernels/NEArithmeticAdditionKernel.cpp

/*
 * Copyright (c) 2016-2019 ARM Limited.
 *
 * SPDX-License-Identifier: MIT
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to
 * deal in the Software without restriction, including without limitation the
 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
 * sell copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in all
 * copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 */
#include "arm_compute/core/NEON/kernels/NEArithmeticAdditionKernel.h"

#include "arm_compute/core/CPP/Validate.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/IAccessWindow.h"
#include "arm_compute/core/ITensor.h"
#include "arm_compute/core/NEON/NEFixedPoint.h"
#include "arm_compute/core/NEON/wrapper/wrapper.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Validate.h"

#include <algorithm>
#include <arm_neon.h>
#include <cstdint>
#include <map>
#include <string>

using namespace arm_compute;

namespace arm_compute
{
class Coordinates;
} // namespace arm_compute

namespace
{
template <typename T, bool is_sat>
void add_same(const ITensor *in1, const ITensor *in2, ITensor *out, ConvertPolicy policy, const Window &window)
{
    ARM_COMPUTE_UNUSED(policy);

    /** NEON vector tag type. */
    using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t<T, wrapper::traits::BitWidth::W128>;

    // Create input windows
    Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
    Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape());

    // Clear X Dimension on execution window as we handle manually
    Window win = window;
    win.set(Window::DimX, Window::Dimension(0, 1, 1));

    constexpr int window_step_x         = 16 / sizeof(T);
    const auto    window_start_x        = static_cast<int>(window.x().start());
    const auto    window_end_x          = static_cast<int>(window.x().end());
    const bool    is_broadcast_across_x = (input1_win.x().step() == 0) || (input2_win.x().step() == 0);

    if(is_broadcast_across_x)
    {
        const bool     is_broadcast_input_2 = input2_win.x().step() == 0;
        Window         broadcast_win        = is_broadcast_input_2 ? input2_win : input1_win;
        Window         non_broadcast_win    = !is_broadcast_input_2 ? input2_win : input1_win;
        const ITensor *broadcast_tensor     = is_broadcast_input_2 ? in2 : in1;
        const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? in2 : in1;

        // Clear X Dimension on execution window as we handle manually
        non_broadcast_win.set(Window::DimX, Window::Dimension(0, 1, 1));

        Iterator broadcast_input(broadcast_tensor, broadcast_win);
        Iterator non_broadcast_input(non_broadcast_tensor, non_broadcast_win);
        Iterator output(out, win);

        execute_window_loop(win, [&](const Coordinates & id)
        {
            const auto non_broadcast_input_ptr = reinterpret_cast<const T *>(non_broadcast_input.ptr());
            const auto output_ptr              = reinterpret_cast<T *>(output.ptr());

            const T    broadcast_value     = *reinterpret_cast<const T *>(broadcast_input.ptr());
            const auto broadcast_value_vec = wrapper::vdup_n(broadcast_value, ExactTagType{});

            // Compute S elements per iteration
            int x = window_start_x;
            for(; x <= (window_end_x - window_step_x); x += window_step_x)
            {
                const auto non_broadcast_v = wrapper::vloadq(non_broadcast_input_ptr + x);
                const auto res             = is_sat ? wrapper::vqadd(broadcast_value_vec, non_broadcast_v) : wrapper::vadd(broadcast_value_vec, non_broadcast_v);
                wrapper::vstore(output_ptr + x, res);
            }

            // Compute left-over elements
            for(; x < window_end_x; ++x)
            {
                const auto non_broadcast_v = *(non_broadcast_input_ptr + x);
                *(output_ptr + x)          = is_sat ? wrapper::add_sat(broadcast_value, non_broadcast_v) : broadcast_value + non_broadcast_v;
            }
        },
        broadcast_input, non_broadcast_input, output);
    }
    else
    {
        // Clear X Dimension on execution window as we handle manually
        input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
        input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));

        Iterator input1(in1, input1_win);
        Iterator input2(in2, input2_win);
        Iterator output(out, win);

        execute_window_loop(win, [&](const Coordinates & id)
        {
            const auto input1_ptr = reinterpret_cast<const T *>(input1.ptr());
            const auto input2_ptr = reinterpret_cast<const T *>(input2.ptr());
            const auto output_ptr = reinterpret_cast<T *>(output.ptr());

            // Compute S elements per iteration
            int x = window_start_x;
            for(; x <= (window_end_x - window_step_x); x += window_step_x)
            {
                const auto val1 = wrapper::vloadq(input1_ptr + x);
                const auto val2 = wrapper::vloadq(input2_ptr + x);
                const auto res  = is_sat ? wrapper::vqadd(val1, val2) : wrapper::vadd(val1, val2);
                wrapper::vstore(output_ptr + x, res);
            }

            // Compute left-over elements
            for(; x < window_end_x; ++x)
            {
                const auto val1   = *(input1_ptr + x);
                const auto val2   = *(input2_ptr + x);
                *(output_ptr + x) = is_sat ? wrapper::add_sat(val1, val2) : val1 + val2;
            }
        },
        input1, input2, output);
    }
}

void add_QASYMM8_QASYMM8_QASYMM8(const ITensor *in1, const ITensor *in2, ITensor *out, ConvertPolicy policy, const Window &window)
{
    ARM_COMPUTE_UNUSED(policy);

    // Create input windows
    Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
    Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape());

    // Clear X Dimension on execution window as we handle manually
    Window win = window;
    win.set(Window::DimX, Window::Dimension(0, 1, 1));

    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());
    const bool is_broadcast_across_x = (input1_win.x().step() == 0) || (input2_win.x().step() == 0);

    const float output_scale    = out->info()->quantization_info().scale;
    const float invoutput_scale = 1.f / output_scale;
    const int   output_offset   = out->info()->quantization_info().offset;

    const float32x4_t vscale1    = vdupq_n_f32(in1->info()->quantization_info().scale);
    const float32x4_t vscale2    = vdupq_n_f32(in2->info()->quantization_info().scale);
    const float32x4_t invvscaleo = vdupq_n_f32(1.f / output_scale);
    const int32x4_t   voffset1   = vdupq_n_s32(in1->info()->quantization_info().offset);
    const int32x4_t   voffset2   = vdupq_n_s32(in2->info()->quantization_info().offset);
    const float32x4_t voffseto   = vdupq_n_f32(output_offset);

    if(is_broadcast_across_x)
    {
        const bool             is_broadcast_input_2 = input2_win.x().step() == 0;
        Window                 broadcast_win        = is_broadcast_input_2 ? input2_win : input1_win;
        Window                 non_broadcast_win    = !is_broadcast_input_2 ? input2_win : input1_win;
        const ITensor         *broadcast_tensor     = is_broadcast_input_2 ? in2 : in1;
        const ITensor         *non_broadcast_tensor = !is_broadcast_input_2 ? in2 : in1;
        const QuantizationInfo broadcast_qinfo      = broadcast_tensor->info()->quantization_info();
        const QuantizationInfo non_broadcast_qinfo  = non_broadcast_tensor->info()->quantization_info();

        // Clear X Dimension on execution window as we handle manually
        non_broadcast_win.set(Window::DimX, Window::Dimension(0, 1, 1));

        Iterator broadcast_input(broadcast_tensor, broadcast_win);
        Iterator non_broadcast_input(non_broadcast_tensor, non_broadcast_win);
        Iterator output(out, win);

        execute_window_loop(win, [&](const Coordinates & id)
        {
            const auto non_broadcast_input_ptr = reinterpret_cast<const uint8_t *>(non_broadcast_input.ptr());
            const auto output_ptr              = reinterpret_cast<uint8_t *>(output.ptr());

            const uint8_t    broadcast_value     = *reinterpret_cast<const uint8_t *>(broadcast_input.ptr());
            const uint8x16_t broadcast_value_vec = vdupq_n_u8(broadcast_value);

            const float32x4x4_t bf =
            {
                {
                    vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_low_u8(broadcast_value_vec))))), voffset2)), vscale2),
                    vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(vmovl_u8(vget_low_u8(broadcast_value_vec))))), voffset2)), vscale2),
                    vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_high_u8(broadcast_value_vec))))), voffset2)), vscale2),
                    vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(vmovl_u8(vget_high_u8(broadcast_value_vec))))), voffset2)), vscale2),
                }
            };
            const float bfs = static_cast<int32_t>(broadcast_value - broadcast_qinfo.offset) * broadcast_qinfo.scale;

            // Compute S elements per iteration
            int x = window_start_x;
            for(; x <= (window_end_x - window_step_x); x += window_step_x)
            {
                const uint8x16_t    a = vld1q_u8(non_broadcast_input_ptr + x);
                const float32x4x4_t af =
                {
                    {
                        vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_low_u8(a))))), voffset1)), vscale1),
                        vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(vmovl_u8(vget_low_u8(a))))), voffset1)), vscale1),
                        vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_high_u8(a))))), voffset1)), vscale1),
                        vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(vmovl_u8(vget_high_u8(a))))), voffset1)), vscale1),
                    }
                };

                const int32x4x4_t rf =
                {
                    {
                        vcvtq_s32_f32(vmlaq_f32(voffseto, vaddq_f32(af.val[0], bf.val[0]), invvscaleo)),
                        vcvtq_s32_f32(vmlaq_f32(voffseto, vaddq_f32(af.val[1], bf.val[1]), invvscaleo)),
                        vcvtq_s32_f32(vmlaq_f32(voffseto, vaddq_f32(af.val[2], bf.val[2]), invvscaleo)),
                        vcvtq_s32_f32(vmlaq_f32(voffseto, vaddq_f32(af.val[3], bf.val[3]), invvscaleo)),
                    }
                };

                const uint8x8_t pa = vqmovun_s16(vcombine_s16(vqmovn_s32(rf.val[0]), vqmovn_s32(rf.val[1])));
                const uint8x8_t pb = vqmovun_s16(vcombine_s16(vqmovn_s32(rf.val[2]), vqmovn_s32(rf.val[3])));
                vst1q_u8(output_ptr + x, vcombine_u8(pa, pb));
            }

            // Compute left-over elements
            for(; x < window_end_x; ++x)
            {
                const float afs   = static_cast<int32_t>(*(non_broadcast_input_ptr + x) - non_broadcast_qinfo.offset) * non_broadcast_qinfo.scale;
                *(output_ptr + x) = std::max(0, std::min(static_cast<int32_t>((afs + bfs) * invoutput_scale + output_offset), 255));
            }
        },
        broadcast_input, non_broadcast_input, output);
    }
    else
    {
        // Clear X Dimension on execution window as we handle manually
        input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
        input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));

        const QuantizationInfo input1_qinfo = in1->info()->quantization_info();
        const QuantizationInfo input2_qinfo = in2->info()->quantization_info();

        Iterator input1(in1, input1_win);
        Iterator input2(in2, input2_win);
        Iterator output(out, win);

        execute_window_loop(win, [&](const Coordinates & id)
        {
            const auto input1_ptr = reinterpret_cast<const uint8_t *>(input1.ptr());
            const auto input2_ptr = reinterpret_cast<const uint8_t *>(input2.ptr());
            const auto output_ptr = reinterpret_cast<uint8_t *>(output.ptr());

            // Compute S elements per iteration
            int x = window_start_x;
            for(; x <= (window_end_x - window_step_x); x += window_step_x)
            {
                const uint8x16_t a = vld1q_u8(input1_ptr + x);
                const uint8x16_t b = vld1q_u8(input2_ptr + x);

                const float32x4x4_t af =
                {
                    {
                        vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_low_u8(a))))), voffset1)), vscale1),
                        vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(vmovl_u8(vget_low_u8(a))))), voffset1)), vscale1),
                        vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_high_u8(a))))), voffset1)), vscale1),
                        vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(vmovl_u8(vget_high_u8(a))))), voffset1)), vscale1),
                    }
                };

                const float32x4x4_t bf =
                {
                    {
                        vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_low_u8(b))))), voffset2)), vscale2),
                        vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(vmovl_u8(vget_low_u8(b))))), voffset2)), vscale2),
                        vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_high_u8(b))))), voffset2)), vscale2),
                        vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(vmovl_u8(vget_high_u8(b))))), voffset2)), vscale2),
                    }
                };

                const int32x4x4_t rf =
                {
                    {
                        vcvtq_s32_f32(vmlaq_f32(voffseto, vaddq_f32(af.val[0], bf.val[0]), invvscaleo)),
                        vcvtq_s32_f32(vmlaq_f32(voffseto, vaddq_f32(af.val[1], bf.val[1]), invvscaleo)),
                        vcvtq_s32_f32(vmlaq_f32(voffseto, vaddq_f32(af.val[2], bf.val[2]), invvscaleo)),
                        vcvtq_s32_f32(vmlaq_f32(voffseto, vaddq_f32(af.val[3], bf.val[3]), invvscaleo)),
                    }
                };

                const uint8x8_t pa = vqmovun_s16(vcombine_s16(vqmovn_s32(rf.val[0]), vqmovn_s32(rf.val[1])));
                const uint8x8_t pb = vqmovun_s16(vcombine_s16(vqmovn_s32(rf.val[2]), vqmovn_s32(rf.val[3])));
                vst1q_u8(output_ptr + x, vcombine_u8(pa, pb));
            }

            // Compute left-over elements
            for(; x < window_end_x; ++x)
            {
                const float afs   = static_cast<int32_t>((*(input1_ptr + x)) - input1_qinfo.offset) * input1_qinfo.scale;
                const float bfs   = static_cast<int32_t>((*(input2_ptr + x)) - input2_qinfo.offset) * input2_qinfo.scale;
                *(output_ptr + x) = std::max(0, std::min(static_cast<int32_t>((afs + bfs) * invoutput_scale + output_offset), 255));
            }
        },
        input1, input2, output);
    }
}

void add_S16_U8_S16(const ITensor *in1, const ITensor *in2, ITensor *out, ConvertPolicy policy, const Window &window)
{
    // Create input windows
    Window win        = window;
    Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
    Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape());

    // Clear X Dimension on execution window as we handle manually
    win.set(Window::DimX, Window::Dimension(0, 1, 1));
    input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
    input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));

    Iterator input1(in1, input1_win);
    Iterator input2(in2, input2_win);
    Iterator output(out, win);

    const int  window_step_x  = 8;
    const auto window_start_x = static_cast<int>(window.x().start());
    const auto window_end_x   = static_cast<int>(window.x().end());

    execute_window_loop(win, [&](const Coordinates & id)
    {
        const auto input1_ptr = reinterpret_cast<const int16_t *>(input1.ptr());
        const auto input2_ptr = reinterpret_cast<const uint8_t *>(input2.ptr());
        const auto output_ptr = reinterpret_cast<int16_t *>(output.ptr());

        if(policy == ConvertPolicy::WRAP)
        {
            // Compute S elements per iteration
            int x = window_start_x;
            for(; x <= (window_end_x - window_step_x); x += window_step_x)
            {
                const auto vin1 = wrapper::vloadq(input1_ptr + x);
                const auto vin2 = vreinterpretq_s16_u16(wrapper::vmovl(wrapper::vload(input2_ptr + x)));
                wrapper::vstore(output_ptr + x, wrapper::vadd(vin1, vin2));
            }

            // Compute left-over elements
            for(; x < window_end_x; ++x)
            {
                *(output_ptr + x) = *(input1_ptr + x) + static_cast<int16_t>(*(input2_ptr + x));
            }
        }
        else
        {
            // Compute S elements per iteration
            int x = window_start_x;
            for(; x <= (window_end_x - window_step_x); x += window_step_x)
            {
                const auto vin1 = wrapper::vloadq(input1_ptr + x);
                const auto vin2 = vreinterpretq_s16_u16(wrapper::vmovl(wrapper::vload(input2_ptr + x)));
                wrapper::vstore(output_ptr + x, wrapper::vqadd(vin1, vin2));
            }

            // Compute left-over elements
            for(; x < window_end_x; ++x)
            {
                *(output_ptr + x) = wrapper::add_sat(*(input1_ptr + x), static_cast<int16_t>(*(input2_ptr + x)));
            }
        }
    },
    input1, input2, output);
}

inline void add_U8_S16_S16(const ITensor *input1, const ITensor *input2, ITensor *output, ConvertPolicy policy, const Window &window)
{
    // Simply swap the two input buffers:
    add_S16_U8_S16(input2, input1, output, policy, window);
}

void add_U8_U8_S16(const ITensor *in1, const ITensor *in2, ITensor *out, ConvertPolicy policy, const Window &window)
{
    // Create input windows
    Window win        = window;
    Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
    Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape());

    // Clear X Dimension on execution window as we handle manually
    win.set(Window::DimX, Window::Dimension(0, 1, 1));
    input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
    input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));

    Iterator input1(in1, input1_win);
    Iterator input2(in2, input2_win);
    Iterator output(out, win);

    const int  window_step_x  = 8;
    const auto window_start_x = static_cast<int>(window.x().start());
    const auto window_end_x   = static_cast<int>(window.x().end());

    execute_window_loop(win, [&](const Coordinates & id)
    {
        const auto input1_ptr = reinterpret_cast<const uint8_t *>(input1.ptr());
        const auto input2_ptr = reinterpret_cast<const uint8_t *>(input2.ptr());
        const auto output_ptr = reinterpret_cast<int16_t *>(output.ptr());

        if(policy == ConvertPolicy::WRAP)
        {
            // Compute S elements per iteration
            int x = window_start_x;
            for(; x <= (window_end_x - window_step_x); x += window_step_x)
            {
                const auto vin1 = vreinterpretq_s16_u16(wrapper::vmovl(wrapper::vload(input1_ptr + x)));
                const auto vin2 = vreinterpretq_s16_u16(wrapper::vmovl(wrapper::vload(input2_ptr + x)));
                wrapper::vstore(output_ptr + x, wrapper::vadd(vin1, vin2));
            }

            // Compute left-over elements
            for(; x < window_end_x; ++x)
            {
                *(output_ptr + x) = static_cast<int16_t>(*(input1_ptr + x)) + static_cast<int16_t>(*(input2_ptr + x));
            }
        }
        else
        {
            // Compute S elements per iteration
            int x = window_start_x;
            for(; x <= (window_end_x - window_step_x); x += window_step_x)
            {
                const auto vin1 = vreinterpretq_s16_u16(wrapper::vmovl(wrapper::vload(input1_ptr + x)));
                const auto vin2 = vreinterpretq_s16_u16(wrapper::vmovl(wrapper::vload(input2_ptr + x)));
                wrapper::vstore(output_ptr + x, wrapper::vqadd(vin1, vin2));
            }

            // Compute left-over elements
            for(; x < window_end_x; ++x)
            {
                *(output_ptr + x) = wrapper::add_sat(static_cast<int16_t>(*(input1_ptr + x)),
                                                     static_cast<int16_t>(*(input2_ptr + x)));
            }
        }
    },
    input1, input2, output);
}

Status validate_arguments(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output, ConvertPolicy policy)
{
    ARM_COMPUTE_UNUSED(policy);

    ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(&input1);
    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input1, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::F16, DataType::F32);
    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input2, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::F16, DataType::F32);

    const TensorShape out_shape = TensorShape::broadcast_shape(input1.tensor_shape(), input2.tensor_shape());

    ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible");
    ARM_COMPUTE_RETURN_ERROR_ON_MSG((input1.tensor_shape().x() != input2.tensor_shape().x()) && ((input1.data_type() != input2.data_type()) || (input1.data_type() != output.data_type())
                                                                                                 || (input2.data_type() != output.data_type())),
                                    "Broadcasting across width is supported on configurations where all tensors have the same data type");

    // Validate in case of configured output
    if(output.total_size() > 0)
    {
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(
            !(input1.data_type() == DataType::U8 && input2.data_type() == DataType::U8 && output.data_type() == DataType::U8)
            && !(input1.data_type() == DataType::U8 && input2.data_type() == DataType::U8 && output.data_type() == DataType::S16)
            && !(input1.data_type() == DataType::U8 && input2.data_type() == DataType::S16 && output.data_type() == DataType::S16)
            && !(input1.data_type() == DataType::S16 && input2.data_type() == DataType::U8 && output.data_type() == DataType::S16)
            && !(input1.data_type() == DataType::S16 && input2.data_type() == DataType::S16 && output.data_type() == DataType::S16)
            && !(input1.data_type() == DataType::F32 && input2.data_type() == DataType::F32 && output.data_type() == DataType::F32)
            && !(input1.data_type() == DataType::F16 && input2.data_type() == DataType::F16 && output.data_type() == DataType::F16)
            && !(input1.data_type() == DataType::QASYMM8 && input2.data_type() == DataType::QASYMM8 && output.data_type() == DataType::QASYMM8),
            "You called addition with the wrong image formats");

        ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, output.tensor_shape(), 0),
                                        "Wrong shape for output");
    }

    return Status{};
}

std::pair<Status, Window> validate_and_configure_window(ITensorInfo &input1, ITensorInfo &input2, ITensorInfo &output)
{
    const std::pair<TensorShape, ValidRegion> broadcast_pair = ITensorInfo::broadcast_shape_and_valid_region(input1, input2);
    const TensorShape &out_shape    = broadcast_pair.first;
    const ValidRegion &valid_region = broadcast_pair.second;

    // Auto initialize output if not initialized
    {
        set_shape_if_empty(output, out_shape);

        if(input1.data_type() == DataType::S16 || input2.data_type() == DataType::S16)
        {
            set_format_if_unknown(output, Format::S16);
        }
        else if(input1.data_type() == DataType::F16 && input2.data_type() == DataType::F16)
        {
            set_format_if_unknown(output, Format::F16);
        }
        else if(input1.data_type() == DataType::F32 || input2.data_type() == DataType::F32)
        {
            set_format_if_unknown(output, Format::F32);
        }
        else if(input1.data_type() == DataType::QASYMM8 || input2.data_type() == DataType::QASYMM8)
        {
            set_data_type_if_unknown(output, DataType::QASYMM8);
        }
    }

    Window win = calculate_max_window(valid_region, Steps());

    // NEArithmeticAdditionKernel doesn't need padding so update_window_and_padding() can be skipped
    Coordinates coord;
    coord.set_num_dimensions(output.num_dimensions());
    output.set_valid_region(valid_region);

    return std::make_pair(Status{}, win);
    ;
}
} // namespace

NEArithmeticAdditionKernel::NEArithmeticAdditionKernel()
    : _func(nullptr), _input1(nullptr), _input2(nullptr), _output(nullptr), _policy()
{
}

void NEArithmeticAdditionKernel::configure(const ITensor *input1, const ITensor *input2, ITensor *output, ConvertPolicy policy)
{
    ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output);
    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(*input1->info(), *input2->info(), *output->info(), policy));

    // Configure kernel window
    auto win_config = validate_and_configure_window(*input1->info(), *input2->info(), *output->info());
    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);

    static std::map<std::string, AddFunction *> map_function =
    {
        { "add_wrap_QASYMM8_QASYMM8_QASYMM8", &add_QASYMM8_QASYMM8_QASYMM8 },
        { "add_saturate_QASYMM8_QASYMM8_QASYMM8", &add_QASYMM8_QASYMM8_QASYMM8 },
        { "add_wrap_U8_U8_U8", &add_same<uint8_t, false> },
        { "add_saturate_U8_U8_U8", &add_same<uint8_t, true> },
        { "add_wrap_S16_U8_S16", &add_S16_U8_S16 },
        { "add_saturate_S16_U8_S16", &add_S16_U8_S16 },
        { "add_wrap_U8_S16_S16", &add_U8_S16_S16 },
        { "add_saturate_U8_S16_S16", &add_U8_S16_S16 },
        { "add_wrap_U8_U8_S16", &add_U8_U8_S16 },
        { "add_saturate_U8_U8_S16", &add_U8_U8_S16 },
        { "add_wrap_S16_S16_S16", &add_same<int16_t, false> },
        { "add_saturate_S16_S16_S16", &add_same<int16_t, true> },
        { "add_wrap_F32_F32_F32", &add_same<float, false> },
        { "add_saturate_F32_F32_F32", &add_same<float, false> },
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
        { "add_wrap_F16_F16_F16", &add_same<float16_t, false> },
        { "add_saturate_F16_F16_F16", &add_same<float16_t, false> },
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
    };

    _input1 = input1;
    _input2 = input2;
    _output = output;
    _policy = policy;

    std::string function_to_call("add_");
    function_to_call += policy == ConvertPolicy::WRAP ? "wrap_" : "saturate_";
    function_to_call += string_from_data_type(input1->info()->data_type()) + "_";
    function_to_call += string_from_data_type(input2->info()->data_type()) + "_";
    function_to_call += string_from_data_type(output->info()->data_type());

    auto it = map_function.find(function_to_call);

    if(it != map_function.end())
    {
        _func = it->second;
    }

    INEKernel::configure(win_config.second);
}

Status NEArithmeticAdditionKernel::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, ConvertPolicy policy)
{
    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output);

    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(*input1, *input2, *output, policy));
    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(*input1->clone(), *input2->clone(), *output->clone()).first);

    return Status{};
}

void NEArithmeticAdditionKernel::run(const Window &window, const ThreadInfo &info)
{
    ARM_COMPUTE_UNUSED(info);
    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
    ARM_COMPUTE_ERROR_ON(_func == nullptr);

    (*_func)(_input1, _input2, _output, _policy, window);
}