/*
 * Copyright (c) 2019-2022 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/gemm/ClGemmHelpers.h"

#include "arm_compute/core/CL/CLHelpers.h"
#include "arm_compute/core/CL/CLKernelLibrary.h"
#include "arm_compute/core/CL/OpenCL.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"

#include <utility>

namespace arm_compute
{
namespace opencl
{
namespace kernels
{
namespace gemm
{
std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> configure_lhs_rhs_info(unsigned int m, unsigned int n, unsigned int m0, unsigned int n0, unsigned int k0, unsigned int v0, unsigned int h0,
                                                                       bool lhs_interleave, bool rhs_interleave, bool lhs_transpose, bool rhs_transpose, bool export_to_cl_image)
{
    ARM_COMPUTE_ERROR_ON(m0 == 0 || n0 == 0);
    v0 = std::max(std::min(static_cast<int>(m / m0), static_cast<int>(v0)), static_cast<int>(1));
    h0 = std::max(std::min(static_cast<int>(n / n0), static_cast<int>(h0)), static_cast<int>(1));

    const GEMMLHSMatrixInfo lhs_info(m0, k0, v0, lhs_transpose, lhs_interleave);
    const GEMMRHSMatrixInfo rhs_info(n0, k0, h0, rhs_transpose, rhs_interleave, export_to_cl_image);

    return std::make_pair(lhs_info, rhs_info);
}

std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> select_lhs_rhs_info(std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> info_img,
                                                                    std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> info_buf,
                                                                    unsigned int n, unsigned int k, unsigned int b, DataType data_type)
{
    const TensorInfo  tensor_rhs_info(TensorShape(n, k, b), 1, data_type);
    const TensorShape shape = misc::shape_calculator::compute_rhs_reshaped_shape(tensor_rhs_info, info_img.second);
    const TensorInfo  tensor_reshaped_info(shape, 1, data_type);

    if(bool(validate_image2d_support_on_rhs(tensor_reshaped_info, info_img.second)))
    {
        return info_img;
    }
    else
    {
        return info_buf;
    }
}

void update_padding_for_cl_image(ITensorInfo *tensor)
{
    constexpr unsigned int num_floats_per_pixel = 4;

    const unsigned int stride_y_in_elements = tensor->strides_in_bytes()[1] / tensor->element_size();
    const unsigned int pixel_alignment      = get_cl_image_pitch_alignment(CLKernelLibrary::get().get_device());

    ARM_COMPUTE_ERROR_ON_MSG(pixel_alignment == 0, "Cannot retrieve cl_image pitch alignment");
    if(pixel_alignment == 0)
    {
        return;
    }

    const unsigned int row_pitch_alignment = pixel_alignment * num_floats_per_pixel;
    const unsigned int round_up_width      = ((stride_y_in_elements + row_pitch_alignment - 1) / row_pitch_alignment) * row_pitch_alignment;
    const unsigned int padding             = round_up_width - stride_y_in_elements;

    tensor->extend_padding(PaddingSize(0, tensor->padding().right + padding, 0, 0));
}

Status validate_image2d_support_on_rhs(const ITensorInfo &tensor_reshaped_info, const GEMMRHSMatrixInfo &rhs_info)
{
    if(rhs_info.export_to_cl_image)
    {
        ARM_COMPUTE_RETURN_ERROR_ON_MSG((rhs_info.n0 == 2) || (rhs_info.n0 == 3), "Export to cl_image only supported with n0 = 4, 8 or 16");
        ARM_COMPUTE_RETURN_ERROR_ON_MSG((rhs_info.k0 == 2) || (rhs_info.k0 == 3), "Export to cl_image only supported with k0 = 4, 8 or 16");
        ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_NOT_IN(&tensor_reshaped_info, DataType::F32, DataType::F16);
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(!image2d_from_buffer_supported(CLKernelLibrary::get().get_device()), "The extension cl_khr_image2d_from_buffer is not supported on the target platform");
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(get_cl_image_pitch_alignment(CLKernelLibrary::get().get_device()) == 0, "Impossible to retrieve the cl_image pitch alignment");

        // Check the width and height of the output tensor.
        // Since we cannot create a 3d image from a buffer, the third dimension is collapsed on the second dimension
        const size_t max_image_w = CLKernelLibrary::get().get_device().getInfo<CL_DEVICE_IMAGE2D_MAX_WIDTH>();
        const size_t max_image_h = CLKernelLibrary::get().get_device().getInfo<CL_DEVICE_IMAGE2D_MAX_HEIGHT>();

        ARM_COMPUTE_RETURN_ERROR_ON_MSG(tensor_reshaped_info.tensor_shape()[0] > max_image_w * 4, "Not supported width for cl_image");
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(tensor_reshaped_info.tensor_shape()[1] * tensor_reshaped_info.tensor_shape()[2] > max_image_h, "Not supported height for cl_image");
    }

    return Status{};
}

bool is_mmul_kernel_preferred(const unsigned int m, const unsigned int n, const unsigned int k, const unsigned int b,
                              const DataType data_type, unsigned int &best_m0, unsigned int &best_n0)
{
    ARM_COMPUTE_UNUSED(n, k, b, data_type);

    const unsigned int mmul_k0 = 4;
    best_m0                    = 4;
    best_n0                    = 4;

    const unsigned int ceil_to_multiple_m_m0             = ceil_to_multiple(m, best_m0);
    const unsigned int m_div_m0                          = ceil_to_multiple_m_m0 / best_m0;
    const unsigned int ceil_to_multiple_m_div_m0_mmul_k0 = ceil_to_multiple(m_div_m0, mmul_k0);
    const unsigned int gws_y                             = ceil_to_multiple_m_div_m0_mmul_k0 / mmul_k0;

    return ((k % mmul_k0) == 0) && (gws_y > 4);
}
} // namespace gemm
} // namespace kernels
} // namespace opencl
} // namespace arm_compute