/*
 * Copyright (c) 2022 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/gpu/cl/kernels/ClGemmLowpMatrixMultiplyReshapedOnlyRhsMMULKernel.h"

#include "arm_compute/core/CL/CLHelpers.h"
#include "arm_compute/core/CL/ICLTensor.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"

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

#include "support/Cast.h"

namespace arm_compute
{
namespace opencl
{
namespace kernels
{
using namespace misc::shape_calculator;

namespace
{
using ElementsProcessed = Steps;

Status validate_arguments(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *dst, const GEMMKernelInfo &gemm_info,
                          const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, const ITensorInfo *bias,
                          const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts)
{
    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src0, src1, dst);
    ARM_COMPUTE_RETURN_ERROR_ON_MSG(!arm_matrix_multiply_supported(CLKernelLibrary::get().get_device()), "The extension cl_arm_matrix_multiply is not supported on the target platform");
    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src0, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED);
    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src0, src1);
    ARM_COMPUTE_RETURN_ERROR_ON_MSG(src0->num_dimensions() > 4, "The number of dimensions for the LHS matrix must be <= 4");
    ARM_COMPUTE_RETURN_ERROR_ON_MSG(src1->num_dimensions() > 3, "The number of dimensions for the RHS matrix must be <= 3");

    const GEMMRHSMatrixInfo       rhs_info     = gemm_info.rhs_info;
    const GEMMLHSMatrixInfo       lhs_info     = gemm_info.lhs_info;
    const GEMMLowpOutputStageInfo output_stage = gemm_info.output_stage;

    ARM_COMPUTE_RETURN_ERROR_ON_MSG(rhs_info.k0 != 4 || lhs_info.k0 != 4, "Only 4 is supported as value for k0");
    ARM_COMPUTE_RETURN_ERROR_ON_MSG(!(lhs_info.m0 == 1 || lhs_info.m0 == 2 || lhs_info.m0 == 4), "Only 1,2,4 are supported for m0");
    ARM_COMPUTE_RETURN_ERROR_ON_MSG(!(rhs_info.n0 == 1 || rhs_info.n0 == 4 || rhs_info.n0 == 8), "Only 1,4,8 are supported for n0");
    ARM_COMPUTE_RETURN_ERROR_ON_MSG(rhs_info.export_to_cl_image, "Export to CLImage not supported for quantized GEMM");

    const int m = gemm_info.m;
    const int n = gemm_info.n;
    const int k = gemm_info.k;

    TensorShape tensor_shape1{ src1->tensor_shape() };
    tensor_shape1.set(0, n);
    tensor_shape1.set(1, k);

    const TensorInfo tensor_info1          = src1->clone()->set_tensor_shape(tensor_shape1);
    const TensorInfo tensor_info_reshaped1 = src1->clone()->set_tensor_shape(compute_rhs_reshaped_shape(tensor_info1, rhs_info));

    ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(0) != static_cast<unsigned int>(k));
    if(gemm_info.reinterpret_input_as_3d)
    {
        ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(1) * src0->dimension(2) != static_cast<unsigned int>(m));
    }
    else
    {
        ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(1) != static_cast<unsigned int>(m));
    }
    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(src1, &tensor_info_reshaped1);

    const TensorShape expected_dst_shape = compute_mm_shape(*src0, *src1, gemm_info);
    if(dst->total_size() != 0)
    {
        const TensorInfo tensor_info_dst = dst->clone()->set_tensor_shape(expected_dst_shape);
        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, &tensor_info_dst);
        if(output_stage.type == GEMMLowpOutputStageType::NONE)
        {
            ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(dst, 1, DataType::S32);
        }
        else
        {
            ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src0, dst);
        }
    }

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

    ARM_COMPUTE_RETURN_ERROR_ON_MSG((output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN) || (output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FLOAT),
                                    "Only GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT is supported");

    // Checks performed if the dst stage needs to be fused
    if(output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT)
    {
        // If a_offset == 0, vector_sum_col can be a nullptr
        if(gemm_info.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) != expected_dst_shape[0]);
        }

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

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

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

            if(expected_dst_shape.num_dimensions() > 1)
            {
                const unsigned int dst_batch_idx = reinterpret_as_3d ? 3 : 2;

                TensorShape vector_sum_row_shape = vector_sum_row->tensor_shape();
                vector_sum_row_shape.collapse_from(1);
                TensorShape collapsed_dst_shape(expected_dst_shape);
                collapsed_dst_shape.collapse_from(dst_batch_idx);

                ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_row_shape[1] != collapsed_dst_shape[dst_batch_idx],
                                                "vector_sum_row must have the same number of batches of dst tensor");

                if(gemm_info.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");
                }
            }
        }

        if(dst->total_size() != 0)
        {
            ARM_COMPUTE_RETURN_ERROR_ON(output_stage.output_data_type != dst->data_type());
        }
        ARM_COMPUTE_RETURN_ERROR_ON(output_stage.gemmlowp_min_bound > output_stage.gemmlowp_max_bound);

        if(output_multipliers != nullptr && output_shifts != nullptr)
        {
            ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output_multipliers, 1, DataType::S32);
            ARM_COMPUTE_RETURN_ERROR_ON(output_multipliers->num_dimensions() > 1);
            ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output_shifts, 1, DataType::S32);
            ARM_COMPUTE_RETURN_ERROR_ON(output_shifts->num_dimensions() > 1);
            if(output_stage.is_quantized_per_channel)
            {
                ARM_COMPUTE_RETURN_ERROR_ON(expected_dst_shape[0] != output_shifts->dimension(0));
                ARM_COMPUTE_RETURN_ERROR_ON(expected_dst_shape[0] != output_multipliers->dimension(0));
            }
        }
    }
    return Status{};
}

std::pair<Status, Window> validate_and_configure_window(const ITensorInfo *src0, const ITensorInfo *src1, ITensorInfo *dst, const GEMMKernelInfo &gemm_info,
                                                        ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, ITensorInfo *bias,
                                                        ITensorInfo *output_multipliers, ITensorInfo *output_shifts, ElementsProcessed &num_elements_processed)
{
    const GEMMLowpOutputStageInfo output_stage = gemm_info.output_stage;

    unsigned int &num_elems_processed_per_iteration_x = num_elements_processed[0];
    unsigned int &num_elems_processed_per_iteration_y = num_elements_processed[1];
    bool          reinterpret_output_as_3d            = (gemm_info.depth_output_gemm3d != 0);

    Window win{};
    bool   window_changed = false;

    constexpr unsigned int mmul_n0 = 4;
    constexpr unsigned int mmul_m0 = 4;
    constexpr unsigned int mmul_k0 = 16;

    reinterpret_output_as_3d = false;
    // dst tensor auto initialization if not yet initialized
    const TensorShape expected_dst_shape = compute_mm_shape(*src0, *src1, gemm_info);
    if(output_stage.type != GEMMLowpOutputStageType::NONE)
    {
        auto_init_if_empty(*dst, src0->clone()->set_tensor_shape(expected_dst_shape).set_data_type(output_stage.output_data_type));
    }
    else
    {
        auto_init_if_empty(*dst, src0->clone()->set_tensor_shape(expected_dst_shape).set_data_type(DataType::S32));
    }

    TensorInfo tmp_info(*dst);

    if(reinterpret_output_as_3d)
    {
        // Since the dst tensor has to be reinterpreted as 3D and the execute window is based on a 2D GEMM,
        // the window needs to be constructed on the 2D collapsed version of the tensor
        TensorShape tmp_shape(dst->tensor_shape());
        tmp_shape.collapse(2U, 1U);
        tmp_info.set_tensor_shape(tmp_shape);
    }

    // Configure kernel window
    num_elems_processed_per_iteration_x = 1;
    num_elems_processed_per_iteration_y = 1;

    win = calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));

    if(output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT)
    {
        if(gemm_info.a_offset != 0)
        {
            AccessWindowHorizontal vector_sum_col_access(vector_sum_col, 0, num_elems_processed_per_iteration_x);
            window_changed = window_changed || update_window_and_padding(win, vector_sum_col_access);
        }
        // No access window needed for vector_sum_row
        ARM_COMPUTE_UNUSED(vector_sum_row);

        if(bias != nullptr)
        {
            AccessWindowHorizontal bias_access(bias, 0, num_elems_processed_per_iteration_x);
            window_changed = window_changed || update_window_and_padding(win, bias_access);
        }

        if(output_multipliers != nullptr && output_stage.is_quantized_per_channel)
        {
            AccessWindowHorizontal output_multipliers_access(output_multipliers, 0, num_elems_processed_per_iteration_x);
            AccessWindowHorizontal output_shifts_access(output_shifts, 0, num_elems_processed_per_iteration_x);
            window_changed = window_changed || update_window_and_padding(win, output_multipliers_access, output_shifts_access);
        }
    }

    // Collapse along the Z direction
    // This collapse needs to be here in order to tune the Z dimension of LWS
    const unsigned int dimension_to_collapse = std::min(static_cast<unsigned int>(dst->num_dimensions()), 2u);
    Window             collapsed             = win.collapse(win, dimension_to_collapse);

    // Reconfigure window size, one arm_matrix_multiply kernel needs 16 threads to finish.
    Window::Dimension x_dimension = collapsed.x();
    Window::Dimension y_dimension = collapsed.y();

    // Make M and N multiple of M0 and N0 respectively
    const unsigned int ceil_to_multiple_n_n0 = ceil_to_multiple(x_dimension.end(), gemm_info.rhs_info.n0);
    const unsigned int ceil_to_multiple_m_m0 = ceil_to_multiple(y_dimension.end(), gemm_info.lhs_info.m0);

    // Divide M and N by M0 and N0 respectively
    const unsigned int n_div_n0 = ceil_to_multiple_n_n0 / gemm_info.rhs_info.n0;
    const unsigned int m_div_m0 = ceil_to_multiple_m_m0 / gemm_info.lhs_info.m0;

    // Make n_div_n0 and m_div_m0 multiple of mmul_n0 and mmul_k0 respectively
    const unsigned int ceil_to_multiple_n_div_n0_mmul_n0 = ceil_to_multiple(n_div_n0, mmul_n0);
    const unsigned int ceil_to_multiple_m_div_m0_mmul_m0 = ceil_to_multiple(m_div_m0, mmul_k0);

    // Ensure x_dimension is multiple of MMUL block size (mmul_n0 * mmul_m0)
    x_dimension.set_end(ceil_to_multiple_n_div_n0_mmul_n0 * mmul_n0);
    y_dimension.set_end(ceil_to_multiple_m_div_m0_mmul_m0 / mmul_m0);

    collapsed.set(Window::DimX, x_dimension);
    collapsed.set(Window::DimY, y_dimension);

    Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
    return std::make_pair(err, collapsed);
}
} // namespace

ClGemmLowpMatrixMultiplyReshapedOnlyRhsMMULKernel::ClGemmLowpMatrixMultiplyReshapedOnlyRhsMMULKernel()
{
    _type = CLKernelType::GEMM;
}

void ClGemmLowpMatrixMultiplyReshapedOnlyRhsMMULKernel::configure(const CLCompileContext &compile_context, const ITensorInfo *src0, const ITensorInfo *src1, ITensorInfo *dst,
                                                                  const GEMMKernelInfo &gemm_info,
                                                                  ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, ITensorInfo *bias,
                                                                  ITensorInfo *output_multipliers, ITensorInfo *output_shifts)
{
    ARM_COMPUTE_ERROR_ON_NULLPTR(src0, src1, dst);
    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src0, src1, dst, gemm_info, vector_sum_col, vector_sum_row, bias, output_multipliers, output_shifts));

    auto                          padding_info = get_padding_info({ src0, src1, dst, vector_sum_row });
    const GEMMRHSMatrixInfo       rhs_info     = gemm_info.rhs_info;
    const GEMMLHSMatrixInfo       lhs_info     = gemm_info.lhs_info;
    const GEMMLowpOutputStageInfo output_stage = gemm_info.output_stage;
    const int32_t                 a_offset     = gemm_info.a_offset;
    const int32_t                 b_offset     = gemm_info.b_offset;
    constexpr int                 mmul_m0      = 4;
    constexpr int                 mmul_n0      = 4;
    constexpr int                 mmul_k0      = 16;

    _m = gemm_info.m;
    _n = gemm_info.n;
    _k = gemm_info.k;

    ElementsProcessed num_elements_processed{};

    // Configure kernel window
    auto win_config = validate_and_configure_window(src0, src1, dst, gemm_info, vector_sum_col, vector_sum_row, bias, output_multipliers, output_shifts, num_elements_processed);
    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
    ICLKernel::configure_internal(win_config.second);

    const unsigned int m0_leftover = _m % lhs_info.m0;
    const unsigned int n0_leftover = _n % rhs_info.n0;

    // Create build options
    CLBuildOptions build_opts;
    build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(src0->data_type()));
    build_opts.add_option("-DVEC_TYPE=" + get_cl_type_from_data_type(src0->data_type()) + "4");
    build_opts.add_option("-DACC_DATA_TYPE=int");
    build_opts.add_option("-DOUT_DATA_TYPE=" + get_cl_type_from_data_type(dst->data_type()));
    build_opts.add_option("-DM0=" + support::cpp11::to_string(lhs_info.m0));
    build_opts.add_option("-DN0=" + support::cpp11::to_string(rhs_info.n0));
    build_opts.add_option("-DK0=" + support::cpp11::to_string(rhs_info.k0));
    build_opts.add_option("-DM0_LEFTOVER=" + support::cpp11::to_string(m0_leftover));
    build_opts.add_option("-DN0_LEFTOVER=" + support::cpp11::to_string(n0_leftover));
    build_opts.add_option("-DMMUL_M0=" + support::cpp11::to_string(mmul_m0));
    build_opts.add_option("-DMMUL_N0=" + support::cpp11::to_string(mmul_n0));
    build_opts.add_option("-DMMUL_K0=" + support::cpp11::to_string(mmul_k0));
    build_opts.add_option("-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(gemm_info.activation_info.activation())));
    build_opts.add_option("-DA_VAL=" + float_to_string_with_full_precision(gemm_info.activation_info.a()));
    build_opts.add_option("-DB_VAL=" + float_to_string_with_full_precision(gemm_info.activation_info.b()));

    std::string kernel_name("gemmlowp_mm_reshaped_only_rhs_mmul");

    if(output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT)
    {
        build_opts.add_option("-DFUSED_OUTPUT_STAGE_FIXED_POINT");
        _fuse_output_stage = true;
        // If a_offset == 0, vector_sum_col can be a nullptr
        if(a_offset != 0 && vector_sum_col != nullptr)
        {
            build_opts.add_option("-DA_OFFSET=" + support::cpp11::to_string(a_offset));
            build_opts.add_option_if(vector_sum_col->tensor_shape().num_dimensions() > 1, "-DSUM_COL_HAS_BATCHES");
        }
        // If b_offset == 0, vector_sum_row can be a nullptr
        build_opts.add_option_if(b_offset != 0, "-DB_OFFSET=" + support::cpp11::to_string(b_offset));
        build_opts.add_option("-DK_OFFSET=" + support::cpp11::to_string(a_offset * b_offset * src0->dimension(0)));
        build_opts.add_option_if(bias != nullptr, "-DADD_BIAS");
        build_opts.add_option_if(gemm_info.broadcast_bias == true, "-DBROADCAST_BIAS");
        build_opts.add_option("-DRESULT_OFFSET=" + support::cpp11::to_string(output_stage.gemmlowp_offset));
        build_opts.add_option("-DRESULT_MULTIPLIER=" + support::cpp11::to_string(output_stage.gemmlowp_multipliers[0]));
        build_opts.add_option("-DRESULT_SHIFT=" + support::cpp11::to_string(output_stage.gemmlowp_shifts[0]));

        const int min = output_stage.gemmlowp_min_bound;
        const int max = output_stage.gemmlowp_max_bound;

        PixelValue min_val{};
        PixelValue max_val{};
        std::tie(min_val, max_val) = get_min_max(dst->data_type());
        build_opts.add_option_if(min != min_val.get<int32_t>(), "-DMIN_BOUND=" + support::cpp11::to_string(min));
        build_opts.add_option_if(max != max_val.get<int32_t>(), "-DMAX_BOUND=" + support::cpp11::to_string(max));
    }

    // A macro guard to compile ONLY the kernel of interest
    build_opts.add_option("-D" + upper_string(kernel_name));

    // Create kernel
    _kernel = create_kernel(compile_context, kernel_name, build_opts.options());

    // Set config_id for enabling LWS tuning
    _config_id = kernel_name;
    _config_id += "_";
    _config_id += (bias != nullptr ? "add_bias_" : "");
    _config_id += (gemm_info.broadcast_bias ? "broadcast_bias_" : "");
    _config_id += (gemm_info.activation_info.enabled() ? "fused_activation_" : "");
    _config_id += lower_string(string_from_data_type(src0->data_type()));
    _config_id += "_";
    _config_id += support::cpp11::to_string(_m);
    _config_id += "_";
    _config_id += support::cpp11::to_string(_n);
    _config_id += "_";
    _config_id += support::cpp11::to_string(_k);
    _config_id += "_";
    _config_id += support::cpp11::to_string(lhs_info.m0);
    _config_id += "_";
    _config_id += support::cpp11::to_string(rhs_info.n0);

    ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info));
}

Status ClGemmLowpMatrixMultiplyReshapedOnlyRhsMMULKernel::validate(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *dst, const GEMMKernelInfo &gemm_info,
                                                                   const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, const ITensorInfo *bias,
                                                                   const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts)
{
    ElementsProcessed num_elements_processed{};
    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src0, src1, dst, gemm_info, vector_sum_col, vector_sum_row, bias, output_multipliers, output_shifts));
    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(src0->clone().get(),
                                                              src1->clone().get(),
                                                              dst->clone().get(),
                                                              gemm_info,
                                                              vector_sum_col != nullptr ? vector_sum_col->clone().get() : nullptr,
                                                              vector_sum_row != nullptr ? vector_sum_row->clone().get() : nullptr,
                                                              bias != nullptr ? bias->clone().get() : nullptr,
                                                              output_multipliers != nullptr ? output_multipliers->clone().get() : nullptr,
                                                              output_shifts != nullptr ? output_shifts->clone().get() : nullptr,
                                                              num_elements_processed)
                                .first);

    return Status{};
}

void ClGemmLowpMatrixMultiplyReshapedOnlyRhsMMULKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue)
{
    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);

    const auto src0           = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0));
    const auto src1           = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1));
    const auto src2           = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_2));
    const auto vector_sum_col = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_VEC_COL_SUM));
    const auto vector_sum_row = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_VEC_ROW_SUM));
    auto       dst            = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));

    ARM_COMPUTE_ERROR_ON_NULLPTR(src0, src1, dst);

    if(src1->info()->num_dimensions() < 3)
    {
        // The stride_z for matrix B must be zero if we do not slice
        ARM_COMPUTE_ERROR_ON(src1->info()->strides_in_bytes()[3] != 0);
    }

    cl::Image2D src1_image2d;

    Window slice = window.first_slice_window_3D();

    do
    {
        unsigned int idx = 0;

        add_3d_tensor_nhw_argument(idx, src0);
        add_3d_tensor_nhw_argument(idx, src1);

        // Bias buffer (_add_bias == true)
        if(src2 != nullptr)
        {
            add_3d_tensor_nhw_argument(idx, src2);
        }
        // dst buffer
        add_3d_tensor_nhw_argument(idx, dst);

        // Pass m, n and k at runtime as signed ints, to ensure results of any subtraction they could be operand in, would still be signed.
        _kernel.setArg<cl_int>(idx++, _m);
        _kernel.setArg<cl_int>(idx++, _n);
        _kernel.setArg<cl_int>(idx++, _k);

        if(_fuse_output_stage)
        {
            if(vector_sum_col != nullptr)
            {
                add_3d_tensor_nhw_argument(idx, vector_sum_col);
            }
            if(vector_sum_row != nullptr)
            {
                add_3d_tensor_nhw_argument(idx, vector_sum_row);
            }
        }

        enqueue(queue, *this, slice, cl::NDRange(32, 2), false);
    }
    while(window.slide_window_slice_3D(slice));
}
} // namespace kernels
} // namespace opencl
} // namespace arm_compute