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 *
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#include <unordered_map>
#include "miopen/solver.hpp"
#include "miopen/gcn_asm_utils.hpp"

namespace miopen {
namespace solver {

bool ConvAsm5x10u2v2b1::IsApplicable(const ConvolutionContext& params) const
{
    if(!params.use_asm_kernels)
        return false;
    if(!params.Is2d())
        return false;
    if(params.rmv != rocm_meta_version::AMDHSA_1_0)
        return false;

    const std::string name = params.GetStream().GetDeviceName();
    const bool device_is_gfx8_9_no_xnack =
        (name == "gfx800" || name == "gfx802" || name == "gfx803" || name == "gfx804" ||
         name == "gfx900" || name == "gfx904" || name == "gfx906" || name == "gfx908");
    if(!device_is_gfx8_9_no_xnack)
    {
        return false;
    }
    if(!params.direction.IsBackwardData())
    {
        return false;
    }
    assert(params.weights_layout.length() == 0); // _weights_layout is not supported yet.

    // Min image + padding shall be not smaller than filter matrix.
    const int min_out_width  = 138;
    const int min_out_height = 16;
    // These two found experimentally.
    const int max_out_width  = 8192 - 1;
    const int max_out_height = 131077 - 1;

    // clang-format off
    return params.pad_w == 0                     // -q   pad_w   fixed
        && params.pad_h == 0                     // -p   pad_h   fixed
        && params.kernel_stride_w == 2             // -v   inp_v   fixed
        && params.kernel_stride_h == 2             // -u   inp_u   fixed
        && params.kernel_size_w == 10            // -x   wei_w   fixed
        && params.kernel_size_h == 5             // -y   wei_h   fixed
        && params.kernel_dilation_w == 1
        && params.kernel_dilation_h == 1
        && params.n_outputs % 16 == 0            // -c   wei_c   no upper limit
        && params.n_inputs >= 16                 // -k   wei_k   no upper limit
        && params.out_width >= min_out_width     // -W   inp_w
        && params.out_width <= max_out_width
        && params.out_height >= min_out_height   // -H   inp_h
        && params.out_height <= max_out_height
        && params.IsFp32()
        && params.group_counts == 1
        && params.out_layout == "NCHW";          // hardcoded
        // && (isForwardDirection() ? _weights_layout == "KCHW" : _weights_layout == "CKHW" )
    // clang-format on
}

ConvSolution ConvAsm5x10u2v2b1::GetSolution(const ConvolutionContext& params) const
{
    ConvSolution result;
    std::ostringstream options;
    GenerateClangDefsym(options, "inp_h", params.out_height);
    GenerateClangDefsym(options, "inp_w", params.out_width);
    GenerateClangDefsym(options, "wei_c", params.n_outputs);
    GenerateClangDefsym(options, "wei_k", params.n_inputs);
    GenerateClangDefsym(options, "ROCM_METADATA_VERSION", 4);

    KernelInfo constr_params;
    constr_params.comp_options = options.str();

    constr_params.l_wk.push_back(64);
    constr_params.l_wk.push_back(8);
    constr_params.l_wk.push_back(1);

    // global-work = [align(out_w,64), (align(out_h,4)/4)*align(wei_c/2,8), batch_n]
    constr_params.g_wk.push_back(AlignUp(params.in_width, 64));
    constr_params.g_wk.push_back(AlignUp(params.in_height, 4) / 4 *
                                 AlignUp(params.n_outputs / 2, 8));
    constr_params.g_wk.push_back(params.batch_sz);

    constr_params.kernel_file = "conv5x10u2v2b1.s";
    constr_params.kernel_name = "conv5x10u2v2b1";

    result.construction_params.push_back(constr_params);
    return result;
}
} // namespace solver
} // namespace miopen
