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 *
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 * of this software and associated documentation files (the "Software"), to deal
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#include <vector>
#include <cstdint>

#include <miopen/conv/solvers.hpp>
#include <miopen/generic_search.hpp>
#include <miopen/conv/wrw_invoke_params.hpp>
#include <miopen/solver/problem_description_interpreter.hpp>
#if MIOPEN_BACKEND_HIP && MIOPEN_USE_COMPOSABLEKERNEL
#include <miopen/solver/ck_utility_common.hpp>
#include <ck/library/tensor_operation_instance/gpu/grouped_convolution_backward_weight_bilinear.hpp>
#include <ck/library/tensor_operation_instance/gpu/grouped_convolution_backward_weight_scale.hpp>
#include <ck/library/tensor_operation_instance/gpu/grouped_convolution_backward_weight.hpp>
#endif
#include <miopen/solver/implicitgemm_ck_util.hpp>
MIOPEN_DECLARE_ENV_VAR_BOOL(MIOPEN_DEBUG_3D_CONV_IMPLICIT_GEMM_HIP_WRW_XDLOPS)

namespace miopen {
namespace solver {
namespace conv {

using ProblemDescription = miopen::conv::ProblemDescription;

#if MIOPEN_BACKEND_HIP && MIOPEN_USE_COMPOSABLEKERNEL

using InLayout    = ck::tensor_layout::convolution::NDHWGC;
using WeiLayout   = ck::tensor_layout::convolution::GKZYXC;
using OutLayout   = ck::tensor_layout::convolution::NDHWGK;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using Bilinear    = ck::tensor_operation::element_wise::Bilinear;
using Scale       = ck::tensor_operation::element_wise::Scale;

static constexpr ck::index_t NumDimSpatial = 3;

template <typename DataType>
using DeviceOpGBwdWeightBilinear =
    ck::tensor_operation::device::DeviceGroupedConvBwdWeightMultipleD<NumDimSpatial,
                                                                      InLayout,
                                                                      WeiLayout,
                                                                      OutLayout,
                                                                      ck::Tuple<WeiLayout>,
                                                                      DataType,
                                                                      DataType,
                                                                      DataType,
                                                                      ck::Tuple<DataType>,
                                                                      PassThrough,
                                                                      Bilinear,
                                                                      PassThrough>;

template <typename DataType>
using DeviceOpGBwdWeightScale =
    ck::tensor_operation::device::DeviceGroupedConvBwdWeightMultipleD<NumDimSpatial,
                                                                      InLayout,
                                                                      WeiLayout,
                                                                      OutLayout,
                                                                      ck::Tuple<>,
                                                                      DataType,
                                                                      DataType,
                                                                      DataType,
                                                                      ck::Tuple<>,
                                                                      PassThrough,
                                                                      Scale,
                                                                      PassThrough>;

template <typename DataType>
using DeviceOpGBwdWeightDefault =
    ck::tensor_operation::device::DeviceGroupedConvBwdWeight<NumDimSpatial,
                                                             InLayout,
                                                             WeiLayout,
                                                             OutLayout,
                                                             DataType,
                                                             DataType,
                                                             DataType,
                                                             PassThrough,
                                                             PassThrough,
                                                             PassThrough>;

template <typename DataType>
using DeviceOpGBwdWeightBilinearPtrs =
    ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
        DeviceOpGBwdWeightBilinear<DataType>>;

template <typename DataType>
using DeviceOpGBwdWeightScalePtrs =
    ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
        DeviceOpGBwdWeightScale<DataType>>;

template <typename DataType>
using DeviceOpGBwdWeightDefaultPtrs =
    ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
        DeviceOpGBwdWeightDefault<DataType>>;

namespace {

template <typename DataType>
struct CKArgs
{
    CKArgs(const ProblemDescription& problem)
    {
        G               = ProblemInterpreter::GetGroupCountG(problem);
        N               = ProblemInterpreter::GetBatchN(problem);
        K1              = ProblemInterpreter::GetOutputChannelK(problem);
        C1              = ProblemInterpreter::GetInputChannelC(problem);
        C               = C1 / G; // Number of input Channel per group
        K               = K1 / G; // Number of output Channel per group
        Hi              = ProblemInterpreter::GetInputHeightHi(problem);
        Wi              = ProblemInterpreter::GetInputWidthWi(problem);
        Ho              = ProblemInterpreter::GetOutputHeightHo(problem);
        Wo              = ProblemInterpreter::GetOutputWidthWo(problem);
        Y               = ProblemInterpreter::GetFilterHeightY(problem);
        X               = ProblemInterpreter::GetFilterWidthX(problem);
        Di              = ProblemInterpreter::GetInputDepthDi(problem);
        Do              = ProblemInterpreter::GetOutputDepthDo(problem);
        Z               = ProblemInterpreter::GetFilterDepthZ(problem);
        data_type       = ProblemInterpreter::GetOutputDataType(problem);
        alpha_beta_case = ProblemInterpreter::GetAlphaBetaCase(problem);

        in_lengths  = {G, N, C, Di, Hi, Wi};
        out_lengths = {G, N, K, Do, Ho, Wo};
        wei_lengths = {G, K, C, Z, Y, X};

        // CK strides are in GNCDHW order
        if(problem.IsLayoutNHWC())
        {
            // first entry reserved for G's stride
            auto copy_strides = [](const auto& src, auto& dst) {
                assert(dst.size() == (src.size() + 1));
                std::copy(src.begin(), src.end(), dst.begin() + 1);
            };
            copy_strides(problem.GetIn().GetStrides(), in_strides);
            copy_strides(problem.GetOut().GetStrides(), out_strides);
            copy_strides(problem.GetWeights().GetStrides(), wei_strides);

            // On a backward pass, problem.GetIn() means y(or out),
            // and problem.GetOut means x(or in)
            /// \todo remove this when we stop swapping in and out tensors/descriptors
            std::swap(in_strides, out_strides);

            // Now compute G's stride
            in_strides[0]  = C;
            out_strides[0] = K;
            wei_strides[0] = K * wei_strides[1];
        }
        else
        {
            assert(problem.IsLayoutDefault()); // already checked in IsApplicable
            // for default layout, we produce packed strides for NHWC layout
            // because we transpose to NHWC layout before calling CK kernel
            in_strides  = {C, Di * Hi * Wi * G * C, 1, Hi * Wi * G * C, Wi * G * C, G * C};
            out_strides = {K, Do * Ho * Wo * G * K, 1, Ho * Wo * G * K, Wo * G * K, G * K};
            wei_strides = {K * Z * Y * X * C, Z * Y * X * C, 1, Y * X * C, X * C, C};
        }

        filter_strides   = {ProblemInterpreter::GetAdjustedConvolutionStrideD(problem),
                          ProblemInterpreter::GetAdjustedConvolutionStrideH(problem),
                          ProblemInterpreter::GetAdjustedConvolutionStrideW(problem)};
        filter_dilations = {ProblemInterpreter::GetAdjustedConvolutionDilationD(problem),
                            ProblemInterpreter::GetAdjustedConvolutionDilationH(problem),
                            ProblemInterpreter::GetAdjustedConvolutionDilationW(problem)};
        lPadding         = {ProblemInterpreter::GetInputLeftPadD(problem),
                    ProblemInterpreter::GetInputLeftPadH(problem),
                    ProblemInterpreter::GetInputLeftPadW(problem)};
        rPadding         = {ProblemInterpreter::GetAdjustedInputRightPadD(problem),
                    ProblemInterpreter::GetAdjustedInputRightPadH(problem),
                    ProblemInterpreter::GetAdjustedInputRightPadW(problem)};
    }
    CKArgs(const CKArgs&) = default;
    CKArgs(CKArgs&&)      = default;
    CKArgs& operator=(const CKArgs&) = default;

    template <typename ConvPtr>
    auto MakeArgPtr(const ConvPtr& conv_ptr,
                    ConstData_t x,
                    Data_t dw,
                    ConstData_t dy,
                    float alpha,
                    float beta) const
    {
        using DeviceP = std::remove_pointer_t<decltype(conv_ptr.get())>;
        if constexpr(std::is_same_v<DeviceP, DeviceOpGBwdWeightBilinear<DataType>>)
        {
            return MakeBilinearArgPtr(conv_ptr, x, dw, dy, alpha, beta);
        }
        else if constexpr(std::is_same_v<DeviceP, DeviceOpGBwdWeightScale<DataType>>)
        {
            (void)beta;
            return MakeScaleArgPtr(conv_ptr, x, dw, dy, alpha);
        }
        else
        {
            (void)alpha;
            (void)beta;
            static_assert(std::is_same_v<DeviceP, DeviceOpGBwdWeightDefault<DataType>>,
                          "Default should be wrw pass through");
            return MakeDefaultArgPtr(conv_ptr, x, dw, dy);
        }
    }
    template <typename ConvPtr>
    auto MakeBilinearArgPtr(const ConvPtr& conv_ptr,
                            ConstData_t x,
                            Data_t dw,
                            ConstData_t dy,
                            float alpha,
                            float beta) const
    {
        return conv_ptr->MakeArgumentPointer(x,
                                             dw,
                                             dy,
                                             {dw},
                                             in_lengths,
                                             in_strides,
                                             wei_lengths,
                                             wei_strides,
                                             out_lengths,
                                             out_strides,
                                             {wei_lengths},
                                             {wei_strides},
                                             filter_strides,
                                             filter_dilations,
                                             lPadding,
                                             rPadding,
                                             PassThrough{},
                                             Bilinear{alpha, beta},
                                             PassThrough{},
                                             split_k);
    }

    template <typename ConvPtr>
    auto MakeScaleArgPtr(
        const ConvPtr& conv_ptr, ConstData_t x, Data_t dw, ConstData_t dy, float alpha) const
    {
        return conv_ptr->MakeArgumentPointer(x,
                                             dw,
                                             dy,
                                             {},
                                             in_lengths,
                                             in_strides,
                                             wei_lengths,
                                             wei_strides,
                                             out_lengths,
                                             out_strides,
                                             {},
                                             {},
                                             filter_strides,
                                             filter_dilations,
                                             lPadding,
                                             rPadding,
                                             PassThrough{},
                                             Scale{alpha},
                                             PassThrough{},
                                             split_k);
    }

    template <typename ConvPtr>
    auto MakeDefaultArgPtr(const ConvPtr& conv_ptr, ConstData_t x, Data_t dw, ConstData_t dy) const
    {
        return conv_ptr->MakeArgumentPointer(x,
                                             dw,
                                             dy,
                                             in_lengths,
                                             in_strides,
                                             wei_lengths,
                                             wei_strides,
                                             out_lengths,
                                             out_strides,
                                             filter_strides,
                                             filter_dilations,
                                             lPadding,
                                             rPadding,
                                             PassThrough{},
                                             PassThrough{},
                                             PassThrough{},
                                             split_k);
    }

    template <typename ConvPtr>
    auto MakeArgPtr(const ConvPtr& conv_ptr,
                    const ConvWrwTensors& tensors,
                    float alpha,
                    float beta) const
    {
        return MakeArgPtr(conv_ptr, tensors.x, tensors.dw, tensors.dy, alpha, beta);
    }

    template <typename ConvPtr>
    bool IsSupportedBy(const ConvPtr& conv_ptr) const
    {
        auto arg_ptr = MakeArgPtr(conv_ptr, nullptr, nullptr, nullptr, 1.0f, 0.0f);
        if(CKWrwRequireWorkspace(G, C, K, data_type, alpha_beta_case))
        {
            // Creat dummy workspace to pass the ck IsSupportedArgument check.
            int dummy_var = 1;
            conv_ptr->SetWorkSpacePointer(arg_ptr.get(), &dummy_var);
        }
        return conv_ptr->IsSupportedArgument(arg_ptr.get());
    }

    int G;
    int N;
    int K;
    int C;
    int C1;
    int K1;
    int Hi;
    int Wi;
    int Di;
    int Ho;
    int Wo;
    int Do;
    int Y;
    int X;
    int Z;
    miopenAlphaBetaCase_t alpha_beta_case;
    miopenDataType_t data_type;
    ck::index_t split_k = 2;
    std::array<ck::index_t, 6> in_lengths;
    std::array<ck::index_t, 6> in_strides;
    std::array<ck::index_t, 6> out_lengths;
    std::array<ck::index_t, 6> out_strides;
    std::array<ck::index_t, 6> wei_lengths;
    std::array<ck::index_t, 6> wei_strides;
    std::array<ck::index_t, 3> filter_strides;
    std::array<ck::index_t, 3> filter_dilations;
    std::array<ck::index_t, 3> lPadding;
    std::array<ck::index_t, 3> rPadding;
};
} // namespace

template <typename DataType>
void PerformanceConfigHipImplicitGemm3DGroupWrwXdlops::Init(const ProblemDescription& problem)
{
    switch(problem.GetAlphaBetaCase())
    {
    case BILINEAR:
        valid_kernels =
            FillValidKernelsIDs<DeviceOpGBwdWeightBilinearPtrs<DataType>, CKArgs<DataType>>(
                problem);
        break;
    case SCALE:
        valid_kernels =
            FillValidKernelsIDs<DeviceOpGBwdWeightScalePtrs<DataType>, CKArgs<DataType>>(problem);
        break;
    default:
        valid_kernels =
            FillValidKernelsIDs<DeviceOpGBwdWeightDefaultPtrs<DataType>, CKArgs<DataType>>(problem);
        break;
    }
    index     = 0;
    kernel_id = valid_kernels[index];
}

template <typename DataType>
bool PerformanceConfigHipImplicitGemm3DGroupWrwXdlops::CheckIsSupportCKArgs(
    const ProblemDescription& problem) const
{
    switch(problem.GetAlphaBetaCase())
    {
    case BILINEAR:
        return IsCKArgsSupported<DeviceOpGBwdWeightBilinearPtrs<DataType>, CKArgs<DataType>>(
            problem, kernel_id);
    case SCALE:
        return IsCKArgsSupported<DeviceOpGBwdWeightScalePtrs<DataType>, CKArgs<DataType>>(
            problem, kernel_id);
    default:
        return IsCKArgsSupported<DeviceOpGBwdWeightDefaultPtrs<DataType>, CKArgs<DataType>>(
            problem, kernel_id);
    }
}

template <typename DataType>
bool ConvHipImplicitGemm3DGroupWrwXdlops::CheckCKApplicability(
    const ProblemDescription& problem) const
{
    switch(problem.GetAlphaBetaCase())
    {
    case BILINEAR:
        return IsCKApplicable<DeviceOpGBwdWeightBilinearPtrs<DataType>, CKArgs<DataType>>(problem);
    case SCALE:
        return IsCKApplicable<DeviceOpGBwdWeightScalePtrs<DataType>, CKArgs<DataType>>(problem);
    default:
        return IsCKApplicable<DeviceOpGBwdWeightDefaultPtrs<DataType>, CKArgs<DataType>>(problem);
    }
}
#endif

void PerformanceConfigHipImplicitGemm3DGroupWrwXdlops::HeuristicInit(
    [[maybe_unused]] const ProblemDescription& problem)
{
    index     = 0;
    kernel_id = "";

#if MIOPEN_BACKEND_HIP && MIOPEN_USE_COMPOSABLEKERNEL
    switch(problem.GetInDataType())
    {
    case miopenHalf: Init<ck::half_t>(problem); break;
    case miopenFloat: Init<float>(problem); break;
    case miopenInt8: Init<int8_t>(problem); break;
    case miopenBFloat16: Init<ck::bhalf_t>(problem); break;
    case miopenInt64:
    case miopenInt32:
    case miopenFloat8:
    case miopenBFloat8:
    case miopenDouble: break;
    }
#endif
}

bool PerformanceConfigHipImplicitGemm3DGroupWrwXdlops::SetNextValue(
    const ProblemDescription& problem)
{
    if(valid_kernels.empty())
    {
        HeuristicInit(problem);
        assert(!valid_kernels.empty());
        return true;
    }
    if((index + 1) < valid_kernels.size())
    {
        ++index;
        kernel_id = valid_kernels[index];
        return true;
    }
    else
        return false;
}

bool PerformanceConfigHipImplicitGemm3DGroupWrwXdlops::IsValidValue() const
{
    return index < valid_kernels.size();
}

bool PerformanceConfigHipImplicitGemm3DGroupWrwXdlops::IsValid(
    [[maybe_unused]] const ProblemDescription& problem) const
{
#if MIOPEN_BACKEND_HIP && MIOPEN_USE_COMPOSABLEKERNEL
    switch(problem.GetInDataType())
    {
    case miopenHalf: return CheckIsSupportCKArgs<ck::half_t>(problem);
    case miopenFloat: return CheckIsSupportCKArgs<float>(problem);
    case miopenInt8: return CheckIsSupportCKArgs<int8_t>(problem);
    case miopenBFloat16: return CheckIsSupportCKArgs<ck::bhalf_t>(problem);
    case miopenInt64:
    case miopenInt32:
    case miopenFloat8:
    case miopenBFloat8:
    case miopenDouble: break;
    }
#endif
    return false;
}

bool PerformanceConfigHipImplicitGemm3DGroupWrwXdlops::operator==(
    const PerformanceConfigHipImplicitGemm3DGroupWrwXdlops& other) const
{
    return kernel_id == other.kernel_id;
}

PerformanceConfigHipImplicitGemm3DGroupWrwXdlops
ConvHipImplicitGemm3DGroupWrwXdlops::GetDefaultPerformanceConfig(
    const ExecutionContext&, const ProblemDescription& problem) const
{
    PerformanceConfigHipImplicitGemm3DGroupWrwXdlops pp;
    pp.HeuristicInit(problem);
    return pp;
}

bool ConvHipImplicitGemm3DGroupWrwXdlops::IsValidPerformanceConfig(
    const ExecutionContext&,
    const ProblemDescription& problem,
    const PerformanceConfigHipImplicitGemm3DGroupWrwXdlops& config) const
{
    return config.IsValid(problem);
}

size_t
ConvHipImplicitGemm3DGroupWrwXdlops::GetWorkspaceSize(const ExecutionContext&,
                                                      const ProblemDescription& problem) const
{
    return GetWorkspaceSizeLayoutTransformConv(problem);
}

PerformanceConfigHipImplicitGemm3DGroupWrwXdlops
ConvHipImplicitGemm3DGroupWrwXdlops::Search(const ExecutionContext& ctx,
                                            const ProblemDescription& problem,
                                            const AnyInvokeParams& invoke_ctx) const
{
    return GenericSearch(*this, ctx, problem, invoke_ctx);
}

bool ConvHipImplicitGemm3DGroupWrwXdlops::IsApplicable(
    [[maybe_unused]] const ExecutionContext& ctx,
    [[maybe_unused]] const ProblemDescription& problem) const
{
#if MIOPEN_BACKEND_HIP && MIOPEN_USE_COMPOSABLEKERNEL
    if(env::disabled(MIOPEN_DEBUG_3D_CONV_IMPLICIT_GEMM_HIP_WRW_XDLOPS))
        return false;
    if(env::enabled(MIOPEN_DEBUG_CONVOLUTION_DETERMINISTIC))
        return false;
    if(!problem.AllTensorsDimsFitIntoInt())
        return false;
    if(problem.HasMixedDataTypes())
        return false;
    if(!problem.IsDirectionBackwardWrW())
        return false;
    if(!problem.Is3d())
        return false;
    if(!(problem.IsLayoutNHWC() || problem.IsLayoutDefault()))
        return false;
    // needed because layout transpose kernel does not support non-packed tensors
    if(problem.IsLayoutDefault() && problem.HasNonPackedTensors())
        return false;
    if(!ck_utility::is_ck_whitelist(ctx.GetStream().GetDeviceName()))
        return false;
    switch(problem.GetInDataType())
    {
    case miopenHalf: return CheckCKApplicability<ck::half_t>(problem);
    case miopenFloat: return CheckCKApplicability<float>(problem);
    case miopenInt8: return CheckCKApplicability<int8_t>(problem);
    case miopenBFloat16: return CheckCKApplicability<ck::bhalf_t>(problem);
    case miopenInt64:
    case miopenInt32:
    case miopenFloat8:
    case miopenBFloat8:
    case miopenDouble: break;
    }
#endif
    return false;
}

ConvSolution ConvHipImplicitGemm3DGroupWrwXdlops::GetSolution(
    [[maybe_unused]] const ExecutionContext& ctx,
    [[maybe_unused]] const ProblemDescription& problem,
    [[maybe_unused]] const PerformanceConfigHipImplicitGemm3DGroupWrwXdlops& config) const
{
#if MIOPEN_BACKEND_HIP && MIOPEN_USE_COMPOSABLEKERNEL
    return MakeSolutionGroupConvImplicitGemmXdlops(
        problem,
        [&](auto data_type_val) {
            using T = decltype(data_type_val);
            switch(problem.GetAlphaBetaCase())
            {
            case BILINEAR:
                return InitInvokerFactoryWrwNCHW<3,
                                                 DeviceOpGBwdWeightBilinearPtrs<T>,
                                                 CKArgs<T>,
                                                 miopen::conv::WrWInvokeParams>(
                    ctx, problem, config.kernel_id);
            case SCALE:
                return InitInvokerFactoryWrwNCHW<3,
                                                 DeviceOpGBwdWeightScalePtrs<T>,
                                                 CKArgs<T>,
                                                 miopen::conv::WrWInvokeParams>(
                    ctx, problem, config.kernel_id);
            default:
                return InitInvokerFactoryWrwNCHW<3,
                                                 DeviceOpGBwdWeightDefaultPtrs<T>,
                                                 CKArgs<T>,
                                                 miopen::conv::WrWInvokeParams>(
                    ctx, problem, config.kernel_id);
            }
        },
        [&](auto data_type_val) {
            using T = decltype(data_type_val);
            switch(problem.GetAlphaBetaCase())
            {
            case BILINEAR:
                return InitInvokerFactoryNHWC<DeviceOpGBwdWeightBilinearPtrs<T>,
                                              CKArgs<T>,
                                              miopen::conv::WrWInvokeParams>(
                    ctx, problem, config.kernel_id);
            case SCALE:
                return InitInvokerFactoryNHWC<DeviceOpGBwdWeightScalePtrs<T>,
                                              CKArgs<T>,
                                              miopen::conv::WrWInvokeParams>(
                    ctx, problem, config.kernel_id);
            default:
                return InitInvokerFactoryNHWC<DeviceOpGBwdWeightDefaultPtrs<T>,
                                              CKArgs<T>,
                                              miopen::conv::WrWInvokeParams>(
                    ctx, problem, config.kernel_id);
            }
        });

#else
    return {};
#endif
}

} // namespace conv
} // namespace solver
} // namespace miopen
