#pragma once

#include "ck/ck.hpp"

#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/warp/xdlops_gemm.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
#include "ck/library/utility/check_err.hpp"

namespace ck {

// MFMA instructions supported in this test
enum class MFMA_F8F6F4
{
    F32_16x16x128 =
        static_cast<int>(MfmaInstr::mfma_f32_16x16x128f8f6f4), // V_MFMA_F32_16X16X128_F8F6F4
    F32_32x32x64 =
        static_cast<int>(MfmaInstr::mfma_f32_32x32x64f8f6f4), // V_MFMA_F32_32X32X64_F8F6F4

    SCALE_F32_16x16x128 = static_cast<int>(
        MfmaInstr::mfma_scale_f32_16x16x128f8f6f4), // V_MFMA_SCALE_F32_16X16X128_F8F6F4
    SCALE_F32_32x32x64 = static_cast<int>(
        MfmaInstr::mfma_scale_f32_32x32x64f8f6f4) // V_MFMA_SCALE_F32_32X32X64_F8F6F4

};

template <typename AFragT, typename BFragT, typename AccumFragT, int32_t BLOCK_M, int32_t BLOCK_N>
struct mfma_type_selector;

template <typename AFragT, typename BFragT, typename AccumFragT>
struct mfma_type_selector<AFragT, BFragT, AccumFragT, 16, 16>
{
    __device__ void operator()(AFragT const& fragA, BFragT const& fragB, AccumFragT& fragAcc)
    {
        auto op = mfma_type<MfmaInstr::mfma_f32_16x16x128f8f6f4>{};
        op.template run<16, 16, AFragT, BFragT, AccumFragT>(fragA, fragB, fragAcc);
    }

    __device__ void operator()(AFragT const& fragA,
                               const int32_t& scale_a,
                               BFragT const& fragB,
                               const int32_t& scale_b,
                               AccumFragT& fragAcc)
    {
        auto op = mfma_type<MfmaInstr::mfma_scale_f32_16x16x128f8f6f4>{};
        op.template run<16, 16, AFragT, BFragT, AccumFragT>(
            fragA, scale_a, fragB, scale_b, fragAcc);
    }
};

template <typename AFragT, typename BFragT, typename AccumFragT>
struct mfma_type_selector<AFragT, BFragT, AccumFragT, 32, 32>
{
    __device__ void operator()(AFragT const& fragA, BFragT const& fragB, AccumFragT& fragAcc)
    {
        auto op = mfma_type<MfmaInstr::mfma_f32_32x32x64f8f6f4>{};
        op.template run<32, 32, AFragT, BFragT, AccumFragT>(fragA, fragB, fragAcc);
    }

    __device__ void operator()(AFragT const& fragA,
                               const int32_t& scale_a,
                               BFragT const& fragB,
                               const int32_t& scale_b,
                               AccumFragT& fragAcc)
    {
        auto op = mfma_type<MfmaInstr::mfma_scale_f32_32x32x64f8f6f4>{};
        op.template run<32, 32, AFragT, BFragT, AccumFragT>(
            fragA, scale_a, fragB, scale_b, fragAcc);
    }
};

template <typename VecT>
static constexpr int32_t vectorSize(const VecT&)
{
    return scalar_type<VecT>::vector_size;
}

// Define a load function for input A blocks:
// Size: (BLOCK_M x BLOCK_K)
// ASSUMPTION:
// - We want contiguous BLOCK_M sized column neighbors in register.
// - Data is in col_major format
// This means:
// - From A we will load K columns of size BLOCK_M to satisfy our input data
template <typename AType, typename AFragT, int32_t BLOCK_M, int32_t BLOCK_K>
__device__ AFragT load_A_col_major(AType const* input_ptr)
{
    // clang-format off
    // Register Mapping for 16x128:                                                        ||    Register Mapping for 32x64:
    // Size              |   BLOCK_M  |   BLOCK_M   |   BLOCK_M  |   BLOCK_M   |           ||    Size              |   BLOCK_M  |   BLOCK_M   |
    // M                 | 0  ...  15 |  0  ...  15 | 0  ...  15 |  0  ...  15 |           ||    M                 | 0  ...  31 |  0  ...  31 |
    // Thread Id         | 0  ...  15 | 16  ...  31 | 32  ... 47 | 48  ...  63 | Vector    ||    Thread Id         | 0  ...  31 | 32  ...  63 | Vector
    // Register Element   ------------ ------------- ------------ -------------  Element   ||    Register Element   ------------ -------------  Element
    // Reg 0 [0:7]       |     K0     |     K32     |     K64    |     K96     |  v[0]     ||    Reg 0 [0:7]       |     K0     |     K32     |  v[0]
    // Reg 0 [8:15]      |     K1     |     K33     |     K65    |     K97     |  v[1]     ||    Reg 0 [8:15]      |     K1     |     K33     |  v[1]
    // Reg 0 [16:23]     |     K2     |     K34     |     K66    |     K98     |  v[2]     ||    Reg 0 [16:23]     |     K2     |     K34     |  v[2]
    // Reg 0 [24:31]     |     K3     |     K35     |     K67    |     K99     |  v[3]     ||    Reg 0 [24:31]     |     K3     |     K35     |  v[3]
    // Reg 1 [0:7]       |     K4     |     K36     |     K68    |     K100    |  v[4]     ||    Reg 1 [0:7]       |     K4     |     K36     |  v[4]
    // Reg 1 [8:15]      |     K5     |     K37     |     K69    |     K101    |  v[5]     ||    Reg 1 [8:15]      |     K5     |     K37     |  v[5]
    // Reg 1 [16:23]     |     K6     |     K38     |     K70    |     K102    |  v[6]     ||    Reg 1 [16:23]     |     K6     |     K38     |  v[6]
    // Reg 1 [24:31]     |     K7     |     K39     |     K71    |     K103    |  v[7]     ||    Reg 1 [24:31]     |     K7     |     K39     |  v[7]
    // Reg 2 [0:7]       |     K8     |     K40     |     K72    |     K104    |  v[8]     ||    Reg 2 [0:7]       |     K8     |     K40     |  v[8]
    // Reg 2 [8:15]      |     K9     |     K41     |     K73    |     K105    |  v[9]     ||    Reg 2 [8:15]      |     K9     |     K41     |  v[9]
    // Reg 2 [16:23]     |     K10    |     K42     |     K74    |     K106    |  v[10]    ||    Reg 2 [16:23]     |     K10    |     K42     |  v[10]
    // Reg 2 [24:31]     |     K11    |     K43     |     K75    |     K107    |  v[11]    ||    Reg 2 [24:31]     |     K11    |     K43     |  v[11]
    // Reg 3 [0:7]       |     K12    |     K44     |     K76    |     K108    |  v[12]    ||    Reg 3 [0:7]       |     K12    |     K44     |  v[12]
    // Reg 3 [8:15]      |     K13    |     K45     |     K77    |     K109    |  v[13]    ||    Reg 3 [8:15]      |     K13    |     K45     |  v[13]
    // Reg 3 [16:23]     |     K14    |     K46     |     K78    |     K110    |  v[14]    ||    Reg 3 [16:23]     |     K14    |     K46     |  v[14]
    // Reg 3 [24:31]     |     K15    |     K47     |     K79    |     K111    |  v[15]    ||    Reg 3 [24:31]     |     K15    |     K47     |  v[15]
    // Reg 4 [0:7]       |     K16    |     K48     |     K80    |     K112    |  v[16]    ||    Reg 4 [0:7]       |     K16    |     K48     |  v[16]
    // Reg 4 [8:15]      |     K17    |     K49     |     K81    |     K113    |  v[17]    ||    Reg 4 [8:15]      |     K17    |     K49     |  v[17]
    // Reg 4 [16:23]     |     K18    |     K50     |     K82    |     K114    |  v[18]    ||    Reg 4 [16:23]     |     K18    |     K50     |  v[18]
    // Reg 4 [24:31]     |     K19    |     K51     |     K83    |     K115    |  v[19]    ||    Reg 4 [24:31]     |     K19    |     K51     |  v[19]
    // Reg 5 [0:7]       |     K20    |     K52     |     K84    |     K116    |  v[20]    ||    Reg 5 [0:7]       |     K20    |     K52     |  v[20]
    // Reg 5 [8:15]      |     K21    |     K53     |     K85    |     K117    |  v[21]    ||    Reg 5 [8:15]      |     K21    |     K53     |  v[21]
    // Reg 5 [16:23]     |     K22    |     K54     |     K86    |     K118    |  v[22]    ||    Reg 5 [16:23]     |     K22    |     K54     |  v[22]
    // Reg 5 [24:31]     |     K23    |     K55     |     K87    |     K119    |  v[23]    ||    Reg 5 [24:31]     |     K23    |     K55     |  v[23]
    // Reg 6 [0:7]       |     K24    |     K56     |     K88    |     K120    |  v[24]    ||    Reg 6 [0:7]       |     K24    |     K56     |  v[24]
    // Reg 6 [8:15]      |     K25    |     K57     |     K89    |     K121    |  v[25]    ||    Reg 6 [8:15]      |     K25    |     K57     |  v[25]
    // Reg 6 [16:23]     |     K26    |     K58     |     K90    |     K122    |  v[26]    ||    Reg 6 [16:23]     |     K26    |     K58     |  v[26]
    // Reg 6 [24:31]     |     K27    |     K59     |     K91    |     K123    |  v[27]    ||    Reg 6 [24:31]     |     K27    |     K59     |  v[27]
    // Reg 7 [0:7]       |     K28    |     K60     |     K92    |     K124    |  v[28]    ||    Reg 7 [0:7]       |     K28    |     K60     |  v[28]
    // Reg 7 [8:15]      |     K29    |     K61     |     K93    |     K125    |  v[29]    ||    Reg 7 [8:15]      |     K29    |     K61     |  v[29]
    // Reg 7 [16:23]     |     K30    |     K62     |     K94    |     K126    |  v[30]    ||    Reg 7 [16:23]     |     K30    |     K62     |  v[30]
    // Reg 7 [24:31]     |     K31    |     K63     |     K95    |     K127    |  v[31]    ||    Reg 7 [24:31]     |     K31    |     K63     |  v[31]
    // clang-format on

    // Here we want to load a BLOCK_M x BLOCK_K block of data.
    static constexpr uint32_t VW = vectorSize(AFragT{});
    using ARawT                  = typename scalar_type<AFragT>::type;
    using AScalarFragT           = vector_type<ARawT, VW>::type;

    // To start the loading process, let's visualize in 2D coords.
    // Each thread will load 32 elements.
    // We need to know where they start, and where the next elements are.
    auto startCoord2D = std::make_pair(threadIdx.x % BLOCK_M,         // Row
                                       (threadIdx.x / BLOCK_M) * VW); // Col
    auto stepCoord2D  = std::make_pair(0u, 1u);

    // Flatten to 1D col_major offsets.
    auto col_major = [](auto const& coord, auto ld) { return coord.first + coord.second * ld; };

    // BLOCK_M is a stride in A matrix
    auto startOffset = col_major(startCoord2D, BLOCK_M);
    auto kOffset     = col_major(stepCoord2D, BLOCK_M);

    // kOffset == BLOCK_M
    // This means every BLOCK_M element is loaded into output vector
    auto fragA = AScalarFragT{};
#pragma unroll VW
    for(uint32_t i = 0; i < VW; i++)
    {
        fragA[i] = bit_cast<ARawT>(input_ptr[startOffset + i * kOffset]);
    }

    return fragA;
}

// Define a load function for input A blocks:
// Size: (BLOCK_M x BLOCK_K)
// ASSUMPTION:
// - We want contiguous BLOCK_M sized column neighbors in register.
// - Data is in row major format
// This means:
// - From A we will load BLOCK_M rows of size K to satisfy our input data
template <typename AType, typename AFragT, int32_t BLOCK_M, int32_t BLOCK_K>
__device__ AFragT load_A_row_major(AType const* input_ptr)
{
    // clang-format off
    // Register Mapping for 16x128:                                                        ||    Register Mapping for 32x64:
    // Size              |   BLOCK_M  |   BLOCK_M   |   BLOCK_M  |   BLOCK_M   |           ||    Size              |   BLOCK_M  |   BLOCK_M   |
    // M                 | 0  ...  15 |  0  ...  15 | 0  ...  15 |  0  ...  15 |           ||    M                 | 0  ...  31 |  0  ...  31 |
    // Thread Id         | 0  ...  15 | 16  ...  31 | 32  ... 47 | 48  ...  63 | Vector    ||    Thread Id         | 0  ...  31 | 32  ...  63 | Vector
    // Register Element   ------------ ------------- ------------ -------------  Element   ||    Register Element   ------------ -------------  Element
    // Reg 0 [0:7]       |     K0     |     K32     |     K64    |     K96     |  v[0]     ||    Reg 0 [0:7]       |     K0     |     K32     |  v[0]
    // Reg 0 [8:15]      |     K1     |     K33     |     K65    |     K97     |  v[1]     ||    Reg 0 [8:15]      |     K1     |     K33     |  v[1]
    // Reg 0 [16:23]     |     K2     |     K34     |     K66    |     K98     |  v[2]     ||    Reg 0 [16:23]     |     K2     |     K34     |  v[2]
    // Reg 0 [24:31]     |     K3     |     K35     |     K67    |     K99     |  v[3]     ||    Reg 0 [24:31]     |     K3     |     K35     |  v[3]
    // Reg 1 [0:7]       |     K4     |     K36     |     K68    |     K100    |  v[4]     ||    Reg 1 [0:7]       |     K4     |     K36     |  v[4]
    // Reg 1 [8:15]      |     K5     |     K37     |     K69    |     K101    |  v[5]     ||    Reg 1 [8:15]      |     K5     |     K37     |  v[5]
    // Reg 1 [16:23]     |     K6     |     K38     |     K70    |     K102    |  v[6]     ||    Reg 1 [16:23]     |     K6     |     K38     |  v[6]
    // Reg 1 [24:31]     |     K7     |     K39     |     K71    |     K103    |  v[7]     ||    Reg 1 [24:31]     |     K7     |     K39     |  v[7]
    // Reg 2 [0:7]       |     K8     |     K40     |     K72    |     K104    |  v[8]     ||    Reg 2 [0:7]       |     K8     |     K40     |  v[8]
    // Reg 2 [8:15]      |     K9     |     K41     |     K73    |     K105    |  v[9]     ||    Reg 2 [8:15]      |     K9     |     K41     |  v[9]
    // Reg 2 [16:23]     |     K10    |     K42     |     K74    |     K106    |  v[10]    ||    Reg 2 [16:23]     |     K10    |     K42     |  v[10]
    // Reg 2 [24:31]     |     K11    |     K43     |     K75    |     K107    |  v[11]    ||    Reg 2 [24:31]     |     K11    |     K43     |  v[11]
    // Reg 3 [0:7]       |     K12    |     K44     |     K76    |     K108    |  v[12]    ||    Reg 3 [0:7]       |     K12    |     K44     |  v[12]
    // Reg 3 [8:15]      |     K13    |     K45     |     K77    |     K109    |  v[13]    ||    Reg 3 [8:15]      |     K13    |     K45     |  v[13]
    // Reg 3 [16:23]     |     K14    |     K46     |     K78    |     K110    |  v[14]    ||    Reg 3 [16:23]     |     K14    |     K46     |  v[14]
    // Reg 3 [24:31]     |     K15    |     K47     |     K79    |     K111    |  v[15]    ||    Reg 3 [24:31]     |     K15    |     K47     |  v[15]
    // Reg 4 [0:7]       |     K16    |     K48     |     K80    |     K112    |  v[16]    ||    Reg 4 [0:7]       |     K16    |     K48     |  v[16]
    // Reg 4 [8:15]      |     K17    |     K49     |     K81    |     K113    |  v[17]    ||    Reg 4 [8:15]      |     K17    |     K49     |  v[17]
    // Reg 4 [16:23]     |     K18    |     K50     |     K82    |     K114    |  v[18]    ||    Reg 4 [16:23]     |     K18    |     K50     |  v[18]
    // Reg 4 [24:31]     |     K19    |     K51     |     K83    |     K115    |  v[19]    ||    Reg 4 [24:31]     |     K19    |     K51     |  v[19]
    // Reg 5 [0:7]       |     K20    |     K52     |     K84    |     K116    |  v[20]    ||    Reg 5 [0:7]       |     K20    |     K52     |  v[20]
    // Reg 5 [8:15]      |     K21    |     K53     |     K85    |     K117    |  v[21]    ||    Reg 5 [8:15]      |     K21    |     K53     |  v[21]
    // Reg 5 [16:23]     |     K22    |     K54     |     K86    |     K118    |  v[22]    ||    Reg 5 [16:23]     |     K22    |     K54     |  v[22]
    // Reg 5 [24:31]     |     K23    |     K55     |     K87    |     K119    |  v[23]    ||    Reg 5 [24:31]     |     K23    |     K55     |  v[23]
    // Reg 6 [0:7]       |     K24    |     K56     |     K88    |     K120    |  v[24]    ||    Reg 6 [0:7]       |     K24    |     K56     |  v[24]
    // Reg 6 [8:15]      |     K25    |     K57     |     K89    |     K121    |  v[25]    ||    Reg 6 [8:15]      |     K25    |     K57     |  v[25]
    // Reg 6 [16:23]     |     K26    |     K58     |     K90    |     K122    |  v[26]    ||    Reg 6 [16:23]     |     K26    |     K58     |  v[26]
    // Reg 6 [24:31]     |     K27    |     K59     |     K91    |     K123    |  v[27]    ||    Reg 6 [24:31]     |     K27    |     K59     |  v[27]
    // Reg 7 [0:7]       |     K28    |     K60     |     K92    |     K124    |  v[28]    ||    Reg 7 [0:7]       |     K28    |     K60     |  v[28]
    // Reg 7 [8:15]      |     K29    |     K61     |     K93    |     K125    |  v[29]    ||    Reg 7 [8:15]      |     K29    |     K61     |  v[29]
    // Reg 7 [16:23]     |     K30    |     K62     |     K94    |     K126    |  v[30]    ||    Reg 7 [16:23]     |     K30    |     K62     |  v[30]
    // Reg 7 [24:31]     |     K31    |     K63     |     K95    |     K127    |  v[31]    ||    Reg 7 [24:31]     |     K31    |     K63     |  v[31]
    // clang-format on

    // Here we want to load a BLOCK_M x BLOCK_K block of data.
    static constexpr uint32_t VW = vectorSize(AFragT{});

    // To start the loading process, let's visualize in 2D coords.
    // Each thread will load 32 elements.
    // We need to know where they start, and where the next elements are.
    auto startCoord2D = std::make_pair(threadIdx.x % BLOCK_M,         // Row
                                       (threadIdx.x / BLOCK_M) * VW); // Col

    // Flatten to 1D row_major offsets.
    auto row_major = [](auto const& coord, auto ld) { return coord.first * ld + coord.second; };

    // BLOCK_K is a stride in A matrix
    auto startOffset = row_major(startCoord2D, BLOCK_K);

    auto const* fragPtr = reinterpret_cast<AFragT const*>(input_ptr + startOffset);
    return *fragPtr;
}

// Define a load function for scaled A blocks:
// Size: (BLOCK_M x BLOCK_K)
// ASSUMPTION:
// - We want contiguous BLOCK_M sized column neighbors in register.
// - Data is in row major format
// - The scale inputs distributed across 64 lanes.
// This means:
// - From A we will load BLOCK_M rows of size K to satisfy our input data
template <typename AType,
          typename AFragT,
          typename ScaleType,
          typename ScaleFragT,
          int32_t BLOCK_M,
          int32_t BLOCK_K,
          int32_t BLOCK_X>
__device__ AFragT load_mx_A_row_major(AType const* input_ptr,
                                      ScaleType const* scale_ptr,
                                      ScaleFragT& fragX)

{
    static constexpr uint32_t VW = vectorSize(AFragT{});
    static_assert(VW == BLOCK_X, "Fragment size must be equal to BLOCK_X");

    // To start the loading process, let's visualize in 2D coords.
    // Each thread will load 1 element
    // We need to know where they start
    auto startCoord2D = std::make_pair(threadIdx.x % BLOCK_M,                   // Row
                                       (threadIdx.x / BLOCK_M) * VW / BLOCK_X); // Col

    // Flatten to 1D row_major offsets.
    auto row_major = [](auto const& coord, auto ld) { return coord.first * ld + coord.second; };

    // BLOCK_K / BLOCK_X is a stride in xA matrix
    auto startOffset = row_major(startCoord2D, BLOCK_K / BLOCK_X);

    //       preserve upper bits                    obtain 8-bit exponent
    fragX = (fragX & 0xFFFFFF00) | (utils::get_exponent_value(scale_ptr[startOffset]) & 0xFF);

    return load_A_row_major<AType, AFragT, BLOCK_M, BLOCK_K>(input_ptr);
}

// Define a load function for input B blocks:
// Size: (BLOCK_K x BLOCK_N)
// ASSUMPTION:
// - We want contiguous BLOCK_N sized row neighbors in register.
// - Data is in column major format
// This means:
// - From B we will load K rows of size BLOCK_N to satisfy our input data
template <typename BType, typename BFragT, int32_t BLOCK_K, int32_t BLOCK_N>
__device__ BFragT load_B_col_major(BType const* input_ptr)
{
    // clang-format off
    // Register Mapping for 128x16:                                                        ||    Register Mapping for 64x32:
    // Size              |   BLOCK_N  |   BLOCK_N   |   BLOCK_N  |   BLOCK_N   |           ||    Size              |   BLOCK_N  |   BLOCK_N   |
    // N                 | 0  ...  15 |  0  ...  15 | 0  ...  15 |  0  ...  15 |           ||    N                 | 0  ...  31 |  0  ...  31 |
    // Thread Id         | 0  ...  15 | 16  ...  31 | 32  ... 47 | 48  ...  63 | Vector    ||    Thread Id         | 0  ...  31 | 32  ...  63 | Vector
    // Register Element   ------------ ------------- ------------ -------------  Element   ||    Register Element   ------------ -------------  Element
    // Reg 0 [0:7]       |     K0     |     K32     |     K64    |     K96     |  v[0]     ||    Reg 0 [0:7]       |     K0     |     K32     |  v[0]
    // Reg 0 [8:15]      |     K1     |     K33     |     K65    |     K97     |  v[1]     ||    Reg 0 [8:15]      |     K1     |     K33     |  v[1]
    // Reg 0 [16:23]     |     K2     |     K34     |     K66    |     K98     |  v[2]     ||    Reg 0 [16:23]     |     K2     |     K34     |  v[2]
    // Reg 0 [24:31]     |     K3     |     K35     |     K67    |     K99     |  v[3]     ||    Reg 0 [24:31]     |     K3     |     K35     |  v[3]
    // Reg 1 [0:7]       |     K4     |     K36     |     K68    |     K100    |  v[4]     ||    Reg 1 [0:7]       |     K4     |     K36     |  v[4]
    // Reg 1 [8:15]      |     K5     |     K37     |     K69    |     K101    |  v[5]     ||    Reg 1 [8:15]      |     K5     |     K37     |  v[5]
    // Reg 1 [16:23]     |     K6     |     K38     |     K70    |     K102    |  v[6]     ||    Reg 1 [16:23]     |     K6     |     K38     |  v[6]
    // Reg 1 [24:31]     |     K7     |     K39     |     K71    |     K103    |  v[7]     ||    Reg 1 [24:31]     |     K7     |     K39     |  v[7]
    // Reg 2 [0:7]       |     K8     |     K40     |     K72    |     K104    |  v[8]     ||    Reg 2 [0:7]       |     K8     |     K40     |  v[8]
    // Reg 2 [8:15]      |     K9     |     K41     |     K73    |     K105    |  v[9]     ||    Reg 2 [8:15]      |     K9     |     K41     |  v[9]
    // Reg 2 [16:23]     |     K10    |     K42     |     K74    |     K106    |  v[10]    ||    Reg 2 [16:23]     |     K10    |     K42     |  v[10]
    // Reg 2 [24:31]     |     K11    |     K43     |     K75    |     K107    |  v[11]    ||    Reg 2 [24:31]     |     K11    |     K43     |  v[11]
    // Reg 3 [0:7]       |     K12    |     K44     |     K76    |     K108    |  v[12]    ||    Reg 3 [0:7]       |     K12    |     K44     |  v[12]
    // Reg 3 [8:15]      |     K13    |     K45     |     K77    |     K109    |  v[13]    ||    Reg 3 [8:15]      |     K13    |     K45     |  v[13]
    // Reg 3 [16:23]     |     K14    |     K46     |     K78    |     K110    |  v[14]    ||    Reg 3 [16:23]     |     K14    |     K46     |  v[14]
    // Reg 3 [24:31]     |     K15    |     K47     |     K79    |     K111    |  v[15]    ||    Reg 3 [24:31]     |     K15    |     K47     |  v[15]
    // Reg 4 [0:7]       |     K16    |     K48     |     K80    |     K112    |  v[16]    ||    Reg 4 [0:7]       |     K16    |     K48     |  v[16]
    // Reg 4 [8:15]      |     K17    |     K49     |     K81    |     K113    |  v[17]    ||    Reg 4 [8:15]      |     K17    |     K49     |  v[17]
    // Reg 4 [16:23]     |     K18    |     K50     |     K82    |     K114    |  v[18]    ||    Reg 4 [16:23]     |     K18    |     K50     |  v[18]
    // Reg 4 [24:31]     |     K19    |     K51     |     K83    |     K115    |  v[19]    ||    Reg 4 [24:31]     |     K19    |     K51     |  v[19]
    // Reg 5 [0:7]       |     K20    |     K52     |     K84    |     K116    |  v[20]    ||    Reg 5 [0:7]       |     K20    |     K52     |  v[20]
    // Reg 5 [8:15]      |     K21    |     K53     |     K85    |     K117    |  v[21]    ||    Reg 5 [8:15]      |     K21    |     K53     |  v[21]
    // Reg 5 [16:23]     |     K22    |     K54     |     K86    |     K118    |  v[22]    ||    Reg 5 [16:23]     |     K22    |     K54     |  v[22]
    // Reg 5 [24:31]     |     K23    |     K55     |     K87    |     K119    |  v[23]    ||    Reg 5 [24:31]     |     K23    |     K55     |  v[23]
    // Reg 6 [0:7]       |     K24    |     K56     |     K88    |     K120    |  v[24]    ||    Reg 6 [0:7]       |     K24    |     K56     |  v[24]
    // Reg 6 [8:15]      |     K25    |     K57     |     K89    |     K121    |  v[25]    ||    Reg 6 [8:15]      |     K25    |     K57     |  v[25]
    // Reg 6 [16:23]     |     K26    |     K58     |     K90    |     K122    |  v[26]    ||    Reg 6 [16:23]     |     K26    |     K58     |  v[26]
    // Reg 6 [24:31]     |     K27    |     K59     |     K91    |     K123    |  v[27]    ||    Reg 6 [24:31]     |     K27    |     K59     |  v[27]
    // Reg 7 [0:7]       |     K28    |     K60     |     K92    |     K124    |  v[28]    ||    Reg 7 [0:7]       |     K28    |     K60     |  v[28]
    // Reg 7 [8:15]      |     K29    |     K61     |     K93    |     K125    |  v[29]    ||    Reg 7 [8:15]      |     K29    |     K61     |  v[29]
    // Reg 7 [16:23]     |     K30    |     K62     |     K94    |     K126    |  v[30]    ||    Reg 7 [16:23]     |     K30    |     K62     |  v[30]
    // Reg 7 [24:31]     |     K31    |     K63     |     K95    |     K127    |  v[31]    ||    Reg 7 [24:31]     |     K31    |     K63     |  v[31]
    // clang-format on

    // Here we want to load a BLOCK_K x BLOCK_N block of data.
    static constexpr uint32_t VW = vectorSize(BFragT{});

    // To start the loading process, let's visualize in 2D coords.
    // Each thread will load 32 elements.
    // We need to know where they start, and where the next elements are.
    auto startCoord2D = std::make_pair((threadIdx.x / BLOCK_N) * VW, // Row
                                       threadIdx.x % BLOCK_N);       // Col

    // Flatten to 1D col_major offsets.
    auto col_major = [](auto const& coord, auto ld) { return coord.first + coord.second * ld; };

    auto startOffset = col_major(startCoord2D, BLOCK_K);

    auto const* fragPtr = reinterpret_cast<BFragT const*>(input_ptr + startOffset);
    return *fragPtr;
}

// Define a load function for scaled B blocks:
// Size: (BLOCK_K x BLOCK_N)
// ASSUMPTION:
// - We want contiguous BLOCK_N sized row neighbors in register.
// - Data is in column major format
// - The scale inputs distributed across 64 lanes.
// This means:
// - From B we will load K rows of size BLOCK_N to satisfy our input data
template <typename BType,
          typename BFragT,
          typename ScaleType,
          typename ScaleFragT,
          int32_t BLOCK_K,
          int32_t BLOCK_N,
          int32_t BLOCK_X>
__device__ BFragT load_mx_B_col_major(BType const* input_ptr,
                                      ScaleType const* scale_ptr,
                                      ScaleFragT& fragX)

{
    static constexpr uint32_t VW = vectorSize(BFragT{});
    static_assert(VW == BLOCK_X, "Fragment size must be equal to BLOCK_X");

    // To start the loading process, let's visualize in 2D coords.
    // Each thread will load 1 element
    // We need to know where to start
    auto startCoord2D = std::make_pair((threadIdx.x / BLOCK_N) * VW / BLOCK_X, // Row
                                       threadIdx.x % BLOCK_N);                 // Col

    // Flatten to 1D col_major offsets.
    auto col_major = [](auto const& coord, auto ld) { return coord.first + coord.second * ld; };

    auto startOffset = col_major(startCoord2D, BLOCK_K / BLOCK_X);

    //       preserve upper bits                    obtain 8-bit exponent
    fragX = (fragX & 0xFFFFFF00) | (utils::get_exponent_value(scale_ptr[startOffset]) & 0xFF);

    return load_B_col_major<BType, BFragT, BLOCK_K, BLOCK_N>(input_ptr);
}

// Define a store function for C
// Size: (BLOCK_M x BLOCK_N)
// ASSUMPTION:
// - We want contiguous BLOCK_N sized row neighbors in register.
// - Data is in col_major format
// This means:
// - From C we will load BLOCK_M rows of size BLOCK_N to satisfy our input data
template <typename CType, typename CFragT, int32_t BLOCK_M, int32_t BLOCK_N>
struct store_C_col_major;

// Here we want to store a 16x16 block of data.
//
// Size              |   BLOCK_N  |   BLOCK_N   |   BLOCK_N   |   BLOCK_N   |
// N                 | 0  ...  15 |  0  ...  15 | 0  ...  15  |  0  ...  15 |
// Thread Id         | 0  ...  15 | 16  ...  31 | 32  ... 47  | 48  ...  63 | Vector
// Register Element   ------------ ------------- ------------ -------------- Element
// Reg0              |     M0     |     M4      |     M8      |     M12     | v[0]
// Reg1              |     M1     |     M5      |     M9      |     M13     | v[1]
// Reg2              |     M2     |     M6      |     M10     |     M14     | v[2]
// Reg3              |     M3     |     M7      |     M11     |     M15     | v[3]
template <typename CType, typename CFragT>
struct store_C_col_major<CType, CFragT, 16, 16>
{
    __device__ void operator()(CType* output, CFragT cFrag)
    {
        static constexpr uint32_t VW  = vectorSize(cFrag); // 4
        static constexpr uint32_t Dim = 16;

        // Each thread will load 4 elements.
        // We need to know where they start, and where the next elements are.
        auto startCoord2D = std::make_pair((threadIdx.x / Dim) * VW, // Row
                                           threadIdx.x % Dim);       // Col

        // Flatten to 1D col_major offsets.
        auto col_major = [](auto const& coord, auto ld) { return coord.first + coord.second * ld; };

        auto startOffset = col_major(startCoord2D, 16);

        auto* fragPtr = reinterpret_cast<CFragT*>(output + startOffset);
        *fragPtr      = cFrag;
    }
};

// Here we want to store a 32x32 block of data.
// Register Mapping:

// Size              |   BLOCK_N  |   BLOCK_N   |
// N                 | 0  ...  31 |  0  ...  31 |
// Thread Id         | 0  ...  31 | 32  ...  63 | Vector
// Register Element   ------------ -------------  Element
// Reg0              |     M0     |     M4      | v[0]
// Reg1              |     M1     |     M5      | v[1]
// Reg2              |     M2     |     M6      | v[2]
// Reg3              |     M3     |     M7      | v[3]
//                    ____________ _____________
// Reg4              |     M8     |     M12     | v[4]
// Reg5              |     M9     |     M13     | v[5]
// Reg6              |     M10    |     M14     | v[6]
// Reg7              |     M11    |     M15     | v[7]
//                    ____________ _____________
// Reg8              |     M16    |     M20     | v[8]
// Reg9              |     M17    |     M21     | v[9]
// Reg10             |     M18    |     M22     | v[10]
// Reg11             |     M19    |     M23     | v[11]
//                    ____________ _____________
// Reg12             |     M24    |     M28     | v[12]
// Reg13             |     M25    |     M29     | v[13]
// Reg14             |     M26    |     M30     | v[14]
// Reg15             |     M27    |     M31     | v[15]

template <typename CType, typename CFragT>
struct store_C_col_major<CType, CFragT, 32, 32>
{
    __device__ void operator()(CType* output, CFragT cFrag)
    {
        static constexpr uint32_t WAVE_SIZE      = 64;
        static constexpr uint32_t VW             = 4;
        static constexpr uint32_t Dim            = 32;
        static constexpr uint32_t M_PER_VW_CHUNK = VW * WAVE_SIZE / 32; // 8

        auto startCoord2D = std::make_pair((threadIdx.x / Dim) * VW, // Row
                                           threadIdx.x % Dim);       // Col

        // Major step between 'chunks'
        auto majorStepCoord2D = std::make_pair(M_PER_VW_CHUNK, 0);

        // Flatten to 1D col_major offsets.
        auto col_major = [](auto const& coord, auto ld) { return coord.first + coord.second * ld; };

        auto startOffset  = col_major(startCoord2D, 32);
        auto kMajorOffset = col_major(majorStepCoord2D, 32); // 8

        // we can vector store 4 contiguous elements at a time.
        using CRawT        = typename scalar_type<CFragT>::type;
        using CScalarFragT = vector_type<CRawT, VW>::type;
        union
        {
            CFragT frag;
            CScalarFragT chunks[vectorSize(CFragT{}) / VW];
        } fragC{cFrag}; // Initialize with input fragment

        *(reinterpret_cast<CScalarFragT*>(output + startOffset))                = fragC.chunks[0];
        *(reinterpret_cast<CScalarFragT*>(output + startOffset + kMajorOffset)) = fragC.chunks[1];
        *(reinterpret_cast<CScalarFragT*>(output + startOffset + 2 * kMajorOffset)) =
            fragC.chunks[2];
        *(reinterpret_cast<CScalarFragT*>(output + startOffset + 3 * kMajorOffset)) =
            fragC.chunks[3];
    }
};

// Define a store function for C
// Size: (BLOCK_M x BLOCK_N)
// ASSUMPTION:
// - We want contiguous BLOCK_N sized row neighbors in register.
// - Data is in row major format
template <typename CType, typename CFragT, int32_t BLOCK_M, int32_t BLOCK_N>
struct store_C_row_major;

// Here we want to store a 16x16 block of data.
//
// Size              |   BLOCK_N  |   BLOCK_N   |   BLOCK_N   |   BLOCK_N   |
// N                 | 0  ...  15 |  0  ...  15 | 0  ...  15  |  0  ...  15 |
// Thread Id         | 0  ...  15 | 16  ...  31 | 32  ... 47  | 48  ...  63 | Vector
// Register Element   ------------ ------------- ------------ -------------- Element
// Reg0              |     M0     |     M4      |     M8      |     M12     | v[0]
// Reg1              |     M1     |     M5      |     M9      |     M13     | v[1]
// Reg2              |     M2     |     M6      |     M10     |     M14     | v[2]
// Reg3              |     M3     |     M7      |     M11     |     M15     | v[3]
template <typename CType, typename CFragT>
struct store_C_row_major<CType, CFragT, 16, 16>
{
    __device__ void operator()(CType* output, CFragT cFrag)
    {
        static constexpr uint32_t VW  = vectorSize(cFrag); // 4
        static constexpr uint32_t Dim = 16;

        // Each thread will load 4 elements.
        // We need to know where they start, and where the next elements are.
        auto startCoord2D = std::make_pair((threadIdx.x / Dim) * VW, // Row
                                           threadIdx.x % Dim);       // Col
        auto stepCoord2D  = std::make_pair(1u, 0u);

        // Flatten to 1D row_major offsets.
        auto row_major = [](auto const& coord, auto ld) { return coord.first * ld + coord.second; };

        auto startOffset = row_major(startCoord2D, 16);
        auto kOffset     = row_major(stepCoord2D, 16);

        auto* fragPtr = reinterpret_cast<CFragT*>(output + startOffset);
        *fragPtr      = cFrag;

        // If you notice carefully, kOffset != 1.
        // This means the following is vector is updated with 4 non-contiguous offsets,
        // which the compiler will separate into 4 different global_store_dword instructions.
        output[startOffset]               = cFrag[0]; // v[0] = Reg 0
        output[startOffset + kOffset]     = cFrag[1]; // v[1] = Reg 1
        output[startOffset + 2 * kOffset] = cFrag[2]; // v[2] = Reg 2
        output[startOffset + 3 * kOffset] = cFrag[3]; // v[3] = Reg 3
    }
};

// Here we want to store a 32x32 block of data.
// Register Mapping:

// Size              |   BLOCK_N  |   BLOCK_N   |
// N                 | 0  ...  31 |  0  ...  31 |
// Thread Id         | 0  ...  31 | 32  ...  63 | Vector
// Register Element   ------------ -------------  Element
// Reg0              |     M0     |     M4      | v[0]
// Reg1              |     M1     |     M5      | v[1]
// Reg2              |     M2     |     M6      | v[2]
// Reg3              |     M3     |     M7      | v[3]
//                    ____________ _____________
// Reg4              |     M8     |     M12     | v[4]
// Reg5              |     M9     |     M13     | v[5]
// Reg6              |     M10    |     M14     | v[6]
// Reg7              |     M11    |     M15     | v[7]
//                    ____________ _____________
// Reg8              |     M16    |     M20     | v[8]
// Reg9              |     M17    |     M21     | v[9]
// Reg10             |     M18    |     M22     | v[10]
// Reg11             |     M19    |     M23     | v[11]
//                    ____________ _____________
// Reg12             |     M24    |     M28     | v[12]
// Reg13             |     M25    |     M29     | v[13]
// Reg14             |     M26    |     M30     | v[14]
// Reg15             |     M27    |     M31     | v[15]

template <typename CType, typename CFragT>
struct store_C_row_major<CType, CFragT, 32, 32>
{
    __device__ void operator()(CType* output, CFragT cFrag)
    {
        static constexpr uint32_t WAVE_SIZE      = 64;
        static constexpr uint32_t VW             = 4;                   // This VW is per 'chunk'
        static constexpr uint32_t Dim            = 32;                  // BLOCK_N
        static constexpr uint32_t M_PER_VW_CHUNK = VW * WAVE_SIZE / 32; // 8

        auto startCoord2D = std::make_pair((threadIdx.x / Dim) * VW, // Row
                                           threadIdx.x % Dim);       // Col

        // Minor step for each 'chunk'
        auto minorStepCoord2D = std::make_pair(1u, 0u);

        // Major step between 'chunks'
        auto majorStepCoord2D = std::make_pair(M_PER_VW_CHUNK, 0);

        // Flatten to 1D row_major offsets.
        auto row_major = [](auto const& coord, auto ld) { return coord.first * ld + coord.second; };

        auto startOffset  = row_major(startCoord2D, 32);
        auto kMinorOffset = row_major(minorStepCoord2D, 32);
        auto kMajorOffset = row_major(majorStepCoord2D, 32);

        output[startOffset]                                       = cFrag[0];  // v[0] = Reg 0
        output[startOffset + kMinorOffset]                        = cFrag[1];  // v[1] = Reg 1
        output[startOffset + 2 * kMinorOffset]                    = cFrag[2];  // v[2] = Reg 2
        output[startOffset + 3 * kMinorOffset]                    = cFrag[3];  // v[3] = Reg 3
        output[startOffset + kMajorOffset]                        = cFrag[4];  // v[4] = Reg 4
        output[startOffset + kMajorOffset + kMinorOffset]         = cFrag[5];  // v[5] = Reg 5
        output[startOffset + kMajorOffset + 2 * kMinorOffset]     = cFrag[6];  // v[6] = Reg 6
        output[startOffset + kMajorOffset + 3 * kMinorOffset]     = cFrag[7];  // v[7] = Reg 7
        output[startOffset + 2 * kMajorOffset]                    = cFrag[8];  // v[8] = Reg 8
        output[startOffset + 2 * kMajorOffset + kMinorOffset]     = cFrag[9];  // v[9] = Reg 9
        output[startOffset + 2 * kMajorOffset + 2 * kMinorOffset] = cFrag[10]; // v[10] = Reg 10
        output[startOffset + 2 * kMajorOffset + 3 * kMinorOffset] = cFrag[11]; // v[11] = Reg 11
        output[startOffset + 3 * kMajorOffset]                    = cFrag[12]; // v[12] = Reg 12
        output[startOffset + 3 * kMajorOffset + kMinorOffset]     = cFrag[13]; // v[13] = Reg 13
        output[startOffset + 3 * kMajorOffset + 2 * kMinorOffset] = cFrag[14]; // v[14] = Reg 14
        output[startOffset + 3 * kMajorOffset + 3 * kMinorOffset] = cFrag[15]; // v[15] = Reg 15
    }
};

template <typename AType,
          typename BType,
          typename CType,
          typename AccType,
          int32_t BLOCK_M,
          int32_t BLOCK_N,
          int32_t BLOCK_K>
__global__ void matmul(const AType* a, const BType* b, CType* c)
{
    constexpr int WAVE_SIZE = 64;
    assert(threadIdx.x < WAVE_SIZE);
    assert(blockDim.x == 1 && blockDim.y == 1 && blockDim.z == 1);

    using AFragT        = vector_type<AType, BLOCK_M * BLOCK_K / WAVE_SIZE>::type;
    using BFragT        = vector_type<BType, BLOCK_K * BLOCK_N / WAVE_SIZE>::type;
    using CFragT        = vector_type<CType, BLOCK_M * BLOCK_N / WAVE_SIZE>::type;
    using AccumFragT    = vector_type<AccType, BLOCK_M * BLOCK_N / WAVE_SIZE>;
    using RawAccumFragT = vector_type<AccType, BLOCK_M * BLOCK_N / WAVE_SIZE>::type;

    // Create frags
    auto fragA   = AFragT{};
    auto fragB   = BFragT{};
    auto fragC   = CFragT{};
    auto fragAcc = AccumFragT{0};

    // Load the inputs.
    // A = col major, BLOCK_M x BLOCK_K
    fragA = load_A_col_major<AType, AFragT, BLOCK_M, BLOCK_K>(a);
    // B = col major, BLOCK_K x BLOCK_N
    fragB = load_B_col_major<BType, BFragT, BLOCK_K, BLOCK_N>(b);

    // Matrix multiply-accumulate using MFMA units
    // Accumulation intermediate = BLOCK_M x BLOCK_N
    __syncthreads();
    mfma_type_selector<AFragT, BFragT, AccumFragT, BLOCK_M, BLOCK_N>{}(fragA, fragB, fragAcc);
    __syncthreads();

    for(int i = 0; i < vectorSize(fragC); ++i)
    {
        fragC[i] = type_convert<CType>(fragAcc.template AsType<RawAccumFragT>()[Number<0>{}][i]);
    }

    auto storeC = store_C_col_major<CType, CFragT, BLOCK_M, BLOCK_N>{};
    storeC(c, fragC);
}

template <typename AType,
          typename BType,
          typename ScaleType,
          typename CType,
          typename AccType,
          int32_t BLOCK_M,
          int32_t BLOCK_N,
          int32_t BLOCK_K,
          int32_t BLOCK_X>
__global__ void
matmul(const AType* a, const ScaleType* xa, const BType* b, const ScaleType* xb, CType* c)
{
    constexpr int WAVE_SIZE = 64;
    assert(threadIdx.x < WAVE_SIZE);
    assert(blockDim.x == 1 && blockDim.y == 1 && blockDim.z == 1);

    using AFragT        = vector_type<AType, BLOCK_M * BLOCK_K / WAVE_SIZE>::type;
    using BFragT        = vector_type<BType, BLOCK_K * BLOCK_N / WAVE_SIZE>::type;
    using CFragT        = vector_type<CType, BLOCK_M * BLOCK_N / WAVE_SIZE>::type;
    using AccumFragT    = vector_type<AccType, BLOCK_M * BLOCK_N / WAVE_SIZE>;
    using RawAccumFragT = vector_type<AccType, BLOCK_M * BLOCK_N / WAVE_SIZE>::type;
    using ScaleFragT    = int32_t;

    // Create frags
    auto fragA   = AFragT{};
    auto fragB   = BFragT{};
    auto fragC   = CFragT{};
    auto fragAcc = AccumFragT{0};
    auto fragXa  = ScaleFragT{0};
    auto fragXb  = ScaleFragT{0};

    // Load the inputs.
    // A = col major, BLOCK_M x BLOCK_K
    fragA = load_mx_A_row_major<AType, AFragT, ScaleType, ScaleFragT, BLOCK_M, BLOCK_K, BLOCK_X>(
        a, xa, fragXa);

    // B = col major, BLOCK_K x BLOCK_N
    fragB = load_mx_B_col_major<BType, BFragT, ScaleType, ScaleFragT, BLOCK_K, BLOCK_N, BLOCK_X>(
        b, xb, fragXb);

    // Scaled Matrix multiply-accumulate using MFMA units
    // Accumulation intermediate = BLOCK_M x BLOCK_N
    __syncthreads();
    // printf("thread: %u -- fragXa: %d\n", threadIdx.x, fragXa);

    printf("thread: %u -- fragA: %x %x %x %x %x %x %x %x %x %x %x %x %x %x %x %x %x %x %x %x %x %x "
           "%x %x %x %x %x %x %x %x %x %x\n",
           threadIdx.x,
           fragA.data_.dN[0],
           fragA.data_.dN[1],
           fragA.data_.dN[2],
           fragA.data_.dN[3],
           fragA.data_.dN[4],
           fragA.data_.dN[5],
           fragA.data_.dN[6],
           fragA.data_.dN[7],
           fragA.data_.dN[8],
           fragA.data_.dN[9],
           fragA.data_.dN[10],
           fragA.data_.dN[11],
           fragA.data_.dN[12],
           fragA.data_.dN[13],
           fragA.data_.dN[14],
           fragA.data_.dN[15],
           fragA.data_.dN[16],
           fragA.data_.dN[17],
           fragA.data_.dN[18],
           fragA.data_.dN[19],
           fragA.data_.dN[20],
           fragA.data_.dN[21],
           fragA.data_.dN[22],
           fragA.data_.dN[23],
           fragA.data_.dN[24],
           fragA.data_.dN[25],
           fragA.data_.dN[26],
           fragA.data_.dN[27],
           fragA.data_.dN[28],
           fragA.data_.dN[29],
           fragA.data_.dN[30],
           fragA.data_.dN[31]);

    printf("thread: %u -- fragB: %x %x %x %x %x %x %x %x %x %x %x %x %x %x %x %x %x %x %x %x %x %x "
           "%x %x %x %x %x %x %x %x %x %x\n",
           threadIdx.x,
           fragB.data_.dN[0],
           fragB.data_.dN[1],
           fragB.data_.dN[2],
           fragB.data_.dN[3],
           fragB.data_.dN[4],
           fragB.data_.dN[5],
           fragB.data_.dN[6],
           fragB.data_.dN[7],
           fragB.data_.dN[8],
           fragB.data_.dN[9],
           fragB.data_.dN[10],
           fragB.data_.dN[11],
           fragB.data_.dN[12],
           fragB.data_.dN[13],
           fragB.data_.dN[14],
           fragB.data_.dN[15],
           fragB.data_.dN[16],
           fragB.data_.dN[17],
           fragB.data_.dN[18],
           fragB.data_.dN[19],
           fragB.data_.dN[20],
           fragB.data_.dN[21],
           fragB.data_.dN[22],
           fragB.data_.dN[23],
           fragB.data_.dN[24],
           fragB.data_.dN[25],
           fragB.data_.dN[26],
           fragB.data_.dN[27],
           fragB.data_.dN[28],
           fragB.data_.dN[29],
           fragB.data_.dN[30],
           fragB.data_.dN[31]);

    //__builtin_amdgcn_mfma_ld_scale_b32(fragXa, 0, 0);

    mfma_type_selector<AFragT, BFragT, AccumFragT, BLOCK_M, BLOCK_N>{}(
        fragA, fragXa, fragB, fragXb, fragAcc);

    if(threadIdx.x == 0 || threadIdx.x == 32)
    {
        printf("thread: %u -- xA: %x\n", threadIdx.x, bit_cast<uint32_t>(fragXa));
        printf("thread: %u -- xB: %x\n", threadIdx.x, bit_cast<uint32_t>(fragXb));
    }
    __syncthreads();

    for(int i = 0; i < vectorSize(fragC); ++i)
    {
        fragC[i] = type_convert<CType>(fragAcc.template AsType<RawAccumFragT>()[Number<0>{}][i]);
    }
    __syncthreads();
    auto storeC = store_C_row_major<CType, CFragT, BLOCK_M, BLOCK_N>{};
    storeC(c, fragC);
}

/**
 * @brief Structure to hold dimension parameters for GEMM tensors.
 *
 * M Number of rows in matrix A and matrix C.
 * N Number of columns in matrix B and matrix C.
 * K Number of columns in matrix A and number of rows in matrix B.
 * StrideA Stride (leading dimension) of matrix A.
 * StrideB Stride (leading dimension) of matrix B.
 * StrideC Stride (leading dimension) of matrix C.
 */
struct GemmParams
{
    ck::index_t M = 16;
    ck::index_t N = 16;
    ck::index_t K = 128;

    ck::index_t StrideA = -1;
    ck::index_t StrideB = -1;
    ck::index_t StrideC = -1;
};

namespace mxmfma_test {
template <typename ADataType, typename BDataType, typename ScaleType, typename CDataType>
void RunHostGEMM(const Tensor<ADataType>& A,
                 const Tensor<ScaleType>& a_scales,
                 const Tensor<BDataType>& B,
                 const Tensor<ScaleType>& b_scales,
                 Tensor<CDataType>& C)
{
    using PassThrough  = ck::tensor_operation::element_wise::PassThrough;
    using GemmInstance = ck::tensor_operation::host::ReferenceGemm<float,
                                                                   float,
                                                                   CDataType,
                                                                   float,
                                                                   PassThrough,
                                                                   PassThrough,
                                                                   PassThrough,
                                                                   float,
                                                                   float>;

    Tensor<float> a_m_k(A.mDesc);
    Tensor<float> b_k_n(B.mDesc);

    const auto M       = A.mDesc.GetLengths()[0];
    const auto N       = B.mDesc.GetLengths()[1];
    const auto K       = A.mDesc.GetLengths()[1];
    const auto BLOCK_X = K / a_scales.mDesc.GetLengths()[1];

    for(size_t m = 0; m < M; m++)
    {
        for(size_t k = 0; k < K; k++)
        {
            a_m_k(m, k) =
                type_convert<float>(A(m, k)) * type_convert<float>(a_scales(m, k / BLOCK_X));
        }
    }

    for(size_t n = 0; n < N; n++)
    {
        for(size_t k = 0; k < K; k++)
        {
            b_k_n(k, n) =
                type_convert<float>(B(k, n)) * type_convert<float>(b_scales(k / BLOCK_X, n));
        }
    }

    auto ref_gemm    = GemmInstance{};
    auto ref_invoker = ref_gemm.MakeInvoker();
    auto ref_argument =
        ref_gemm.MakeArgument(a_m_k, b_k_n, C, PassThrough{}, PassThrough{}, PassThrough{});

    ref_invoker.Run(ref_argument);
}

template <typename KernelType,
          typename ADataType,
          typename BDataType,
          typename ScaleType,
          typename CDataType>
bool RunDeviceGEMM(KernelType kernel,
                   const Tensor<ADataType>& A,
                   const Tensor<ScaleType>& a_scales,
                   const Tensor<BDataType>& B,
                   const Tensor<ScaleType>& b_scales,
                   Tensor<CDataType>& C)
{
    DeviceMem a_m_k_device_buf(sizeof(ADataType) * A.mDesc.GetElementSpaceSize());
    DeviceMem a_scales_device_buf(sizeof(ScaleType) * a_scales.mDesc.GetElementSpaceSize());
    DeviceMem b_n_k_device_buf(sizeof(BDataType) * B.mDesc.GetElementSpaceSize());
    DeviceMem b_scales_device_buf(sizeof(ScaleType) * b_scales.mDesc.GetElementSpaceSize());
    DeviceMem c_m_n_device_buf(sizeof(CDataType) * C.mDesc.GetElementSpaceSize());

    a_m_k_device_buf.ToDevice(A.mData.data());
    a_scales_device_buf.ToDevice(a_scales.mData.data());
    b_n_k_device_buf.ToDevice(B.mData.data());
    b_scales_device_buf.ToDevice(b_scales.mData.data());
    kernel<<<1, 64>>>(static_cast<const ADataType*>(a_m_k_device_buf.GetDeviceBuffer()),
                      static_cast<const ScaleType*>(a_scales_device_buf.GetDeviceBuffer()),
                      static_cast<const BDataType*>(b_n_k_device_buf.GetDeviceBuffer()),
                      static_cast<const ScaleType*>(b_scales_device_buf.GetDeviceBuffer()),
                      static_cast<CDataType*>(c_m_n_device_buf.GetDeviceBuffer()));
    c_m_n_device_buf.FromDevice(C.mData.data());

    return true;
}

template <typename DeviceMFMA,
          typename ADataType,
          typename BDataType,
          typename ScaleType,
          typename CDataType,
          typename ALayout,
          typename BLayout,
          typename CLayout,
          index_t BLOCK_M,
          index_t BLOCK_N,
          index_t BLOCK_K,
          index_t BLOCK_X>
struct TestMXMFMA
{
    auto PrepareGemmTensors(const GemmParams& params, index_t init)
    {
        auto f_host_tensor_descriptor =
            [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
                if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
                {
                    return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
                                                std::vector<std::size_t>({stride, 1}));
                }
                else
                {
                    return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
                                                std::vector<std::size_t>({1, stride}));
                }
            };

        Tensor<ADataType> a_m_k(
            f_host_tensor_descriptor(params.M, params.K, params.StrideA, ALayout{}));
        Tensor<ScaleType> a_scales(
            f_host_tensor_descriptor(params.M, params.K / BLOCK_X, params.K / BLOCK_X, ALayout{}));
        Tensor<BDataType> b_n_k(
            f_host_tensor_descriptor(params.K, params.N, params.StrideB, BLayout{}));
        Tensor<ScaleType> b_scales(
            f_host_tensor_descriptor(params.K / BLOCK_X, params.N, params.K / BLOCK_X, BLayout{}));
        Tensor<CDataType> c_m_n_host_result(
            f_host_tensor_descriptor(params.M, params.N, params.StrideC, CLayout{}));
        Tensor<CDataType> c_m_n_device_result(
            f_host_tensor_descriptor(params.M, params.N, params.StrideC, CLayout{}));

        switch(init)
        {
        case 0:
            a_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{1.0f});
            a_scales.GenerateTensorValue(GeneratorTensor_1<ScaleType>{ScaleType{0.015625f}});
            // NOTE: not all numbers are representable in FP8, BF8, etc.
            b_n_k.GenerateTensorValue(GeneratorTensor_Sequential<BDataType, 1>{});
            b_scales.GenerateTensorValue(GeneratorTensor_1<ScaleType>{ScaleType{1.0f}});
            break;
        case 1:
            // results in C = {K}
            a_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{1.0f});
            a_scales.GenerateTensorValue(GeneratorTensor_1<ScaleType>{ScaleType{512.0f}});
            b_n_k.GenerateTensorValue(GeneratorTensor_1<BDataType>{1.0f});
            b_scales.GenerateTensorValue(GeneratorTensor_1<ScaleType>{ScaleType{1.0f / 512}});
            break;
        case 2:
            // expect small round off errors
            a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{-2.0, 2.0});
            a_scales.GenerateTensorValue(
                GeneratorTensor_2<ScaleType>{127, 129}); // 1, 2     // scales: {0.5, 1, 2}

            b_n_k.GenerateTensorValue(GeneratorTensor_1<BDataType>{1.0f});
            b_scales.GenerateTensorValue(GeneratorTensor_1<ScaleType>{ScaleType{1.0f}});
            break;

        case 3:
            // expect small round off errors
            a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{-2.0, 2.0});
            a_scales.GenerateTensorValue(GeneratorTensor_2<ScaleType>{128, 129}); //  2

            // a_scales.GenerateTensorValue(GeneratorTensor_1<ScaleType>{ScaleType{1.0f}});

            b_n_k.GenerateTensorValue(GeneratorTensor_1<BDataType>{1.0f});
            b_scales.GenerateTensorValue(GeneratorTensor_1<ScaleType>{ScaleType{1.0f}});
            break;
        case 4:
            a_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{1.3});
            a_scales.GenerateTensorValue(GeneratorTensor_2<ScaleType>{126, 128}); // 1, 2

            b_n_k.GenerateTensorValue(GeneratorTensor_1<BDataType>{1.0f});
            b_scales.GenerateTensorValue(GeneratorTensor_1<ScaleType>{ScaleType{1.0f}});
            break;

        case 5:
            a_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{0.0});
            for(size_t i = 0; i < 32; i++)
            {
                a_m_k(0, i) = type_convert<ADataType>(1.0f);
            }
            for(size_t i = 32; i < 64; i++)
            {
                a_m_k(0, i) = type_convert<ADataType>(2.0f);
            }

            // printf("f8 1: %x \n", type_convert<ADataType>(1.0f).data);
            // printf("f8 -2: %x \n", type_convert<ADataType>(-2.0f).data);

            a_scales.GenerateTensorValue(GeneratorTensor_1<ScaleType>{ScaleType{1.0f}});
            a_scales(0, 0) = ScaleType{1.0f};
            a_scales(0, 1) = ScaleType{0.5f};

            b_n_k.GenerateTensorValue(GeneratorTensor_1<BDataType>{0.0f});
            b_scales.GenerateTensorValue(GeneratorTensor_1<ScaleType>{ScaleType{1.0f}});
            for(size_t i = 0; i < 64; i++)
            {
                b_n_k(i, 0) = type_convert<BDataType>(1.0f);
            }
            break;
            // case 3:
            //     // expect small round off errors
            //     a_m_k.GenerateTensorValue(GeneratorTensor_4<ADataType>(-1, 3));
            //     a_scales.GenerateTensorValue(
            //         GeneratorTensor_2<ScaleType>{126, 129}); // scales: {0.5, 1, 2}
            //     b_n_k.GenerateTensorValue(GeneratorTensor_4<BDataType>(1, 3));
            //     b_scales.GenerateTensorValue(
            //         GeneratorTensor_2<ScaleType>{126, 129}); //  scales: {0.5, 1, 2}
            //     break;
            // case 4:
            //     a_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{1.0f});
            //     a_scales.GenerateTensorValue(GeneratorTensor_Sequential<ScaleType, 0>{-9});
            //     b_n_k.GenerateTensorValue(GeneratorTensor_1<BDataType>{1.0f});
            //     b_scales.GenerateTensorValue(GeneratorTensor_1<ScaleType>{ScaleType{1.0f}});
            //     break;
            // case 5:
            //     a_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{1.0f});
            //     a_scales.GenerateTensorValue(GeneratorTensor_1<ScaleType>{ScaleType{1.0f}});
            //     b_n_k.GenerateTensorValue(GeneratorTensor_1<BDataType>{1.0f});
            //     b_scales.GenerateTensorValue(GeneratorTensor_Sequential<ScaleType, 1>{-9});
            //     break;

        case 6:
            a_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{0.00195312f});
            a_scales.GenerateTensorValue(GeneratorTensor_1<ScaleType>{ScaleType{1.0f / 16}});
            b_n_k.GenerateTensorValue(GeneratorTensor_1<BDataType>{1.0f});
            b_scales.GenerateTensorValue(GeneratorTensor_Sequential<ScaleType, 1>{-9});
            break;
        default:
            // all initial values are representable in FP8, BF8
            a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 6});
            // a_scales.GenerateTensorValue(GeneratorTensor_3<ScaleType>{1.0f / 32.0f, 1.0f});
            a_scales.GenerateTensorValue(GeneratorTensor_1<ScaleType>{ScaleType{1.0f}});
            b_n_k.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 6});
            // b_scales.GenerateTensorValue(GeneratorTensor_3<ScaleType>{1.0f / 32.0f, 1.0f});
            b_scales.GenerateTensorValue(GeneratorTensor_1<ScaleType>{ScaleType{1.0f}});

            break;
        }

        return std::make_tuple(
            a_m_k, a_scales, b_n_k, b_scales, c_m_n_host_result, c_m_n_device_result);
    }

    auto operator()(const DeviceMFMA& mfma_kernel, index_t init)
    {
        std::cout << "ALayout = " << ALayout{}.name << ", BLayout = " << BLayout{}.name
                  << ", CLayout = " << CLayout{}.name << std::endl;

        // Arrange
        GemmParams params;
        params.M = BLOCK_M;
        params.N = BLOCK_N;
        params.K = BLOCK_K;

        auto f_get_default_stride = [](std::size_t row,
                                       std::size_t col,
                                       ck::index_t stride,
                                       auto layout) {
            if(stride == -1)
            {
                // give a chance if stride is -1, return a default packed stride
                if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
                {
                    return static_cast<std::size_t>(col);
                }
                else
                {
                    return static_cast<std::size_t>(row);
                }
            }
            else
                return static_cast<std::size_t>(stride);
        };

        params.StrideA = f_get_default_stride(BLOCK_M, BLOCK_K, params.StrideA, ALayout{});
        params.StrideB = f_get_default_stride(BLOCK_K, BLOCK_N, params.StrideB, BLayout{});
        params.StrideC = f_get_default_stride(BLOCK_M, BLOCK_N, params.StrideC, CLayout{});

        auto host_tensors = PrepareGemmTensors(params, init);

        const Tensor<ADataType>& a        = std::get<0>(host_tensors);
        const Tensor<ScaleType>& a_scales = std::get<1>(host_tensors);
        const Tensor<BDataType>& b        = std::get<2>(host_tensors);
        const Tensor<ScaleType>& b_scales = std::get<3>(host_tensors);
        Tensor<CDataType>& c_host         = std::get<4>(host_tensors);
        Tensor<CDataType>& c_device       = std::get<5>(host_tensors);

        RunHostGEMM(a, a_scales, b, b_scales, c_host);

        RunDeviceGEMM(mfma_kernel, a, a_scales, b, b_scales, c_device);

#if 0
#if 1
        std::cout << "a:" << std::endl;
        for(size_t i = 0; i < BLOCK_M; i++)
        {
            for(size_t j = 0; j < BLOCK_K; j++)
            {
                std::cout << type_convert<float>(a(i, j)) << " ";
            }
            std::cout << std::endl;
            break;
        }
        // std::cout << "b:" << std::endl;
        // for(size_t i = 0; i < BLOCK_K; i++)
        // {
        //     for(size_t j = 0; j < BLOCK_N; j++)
        //     {
        //         if(j == 0)
        //             std::cout << type_convert<float>(b(i, j)) << " ";
        //     }
        //     std::cout << std::endl;
        // }
#endif
#if 0
        std::cout << "a_scale:" << std::endl;
        for(size_t i = 0; i < BLOCK_M; i++)
        {
            for(size_t j = 0; j < BLOCK_K / BLOCK_X; j++)
            {
                std::cout << type_convert<float>(a_scales(i, j)) << " ";
            }
            std::cout << std::endl;
        }
        // std::cout << "b_scale:" << std::endl;
        // for(size_t i = 0; i < BLOCK_K / BLOCK_X; i++)
        // {
        //     for(size_t j = 0; j < BLOCK_N; j++)
        //     {
        //         std::cout << type_convert<float>(b_scales(i, j)) << " ";
        //     }
        //     std::cout << std::endl;
        // }
#endif
        std::cout << "c_device:" << std::endl;
        for(size_t i = 0; i < BLOCK_M; i++)
        {
            for(size_t j = 0; j < BLOCK_N; j++)
            {
                std::cout << type_convert<float>(c_device(i, j)) << " ";
            }
            std::cout << std::endl;
            break;
        }

#endif

        bool res = false;
        if constexpr(std::is_same<CDataType, float>::value ||
                     std::is_same<CDataType, half_t>::value)
        {
            res = ck::utils::check_err(c_device.mData, c_host.mData);
            std::cout << (res ? "SUCCESS" : "FAILURE") << std::endl;

            if(!res)
            {

                std::cout << "c_host:" << std::endl;
                for(size_t i = 0; i < BLOCK_M; i++)
                {
                    for(size_t j = 0; j < BLOCK_N; j++)
                    {
                        std::cout << type_convert<float>(c_host(i, j)) << " ";
                    }
                    std::cout << std::endl;
                    break;
                }
            }
        }
        else
        {
            std::cout << "UNSUPPORTED CDataType" << std::endl;
        }

        return res;
    }
};

} // namespace mxmfma_test

namespace mfma_test {
template <typename GemmInstance,
          typename ADataType,
          typename BDataType,
          typename CDataType,
          typename AElementwiseOperation,
          typename BElementwiseOperation,
          typename CElementwiseOperation>
void RunHostGEMM(const Tensor<ADataType>& A,
                 const Tensor<BDataType>& B,
                 Tensor<CDataType>& C,
                 AElementwiseOperation a_element_op,
                 BElementwiseOperation b_element_op,
                 CElementwiseOperation c_element_op)
{
    auto ref_gemm     = GemmInstance{};
    auto ref_invoker  = ref_gemm.MakeInvoker();
    auto ref_argument = ref_gemm.MakeArgument(A, B, C, a_element_op, b_element_op, c_element_op);

    ref_invoker.Run(ref_argument);
}

template <typename KernelType, typename ADataType, typename BDataType, typename CDataType>
bool RunDeviceGEMM(KernelType kernel,
                   const Tensor<ADataType>& A,
                   const Tensor<BDataType>& B,
                   Tensor<CDataType>& C)
{
    DeviceMem a_m_k_device_buf(sizeof(ADataType) * A.mDesc.GetElementSpaceSize());
    DeviceMem b_n_k_device_buf(sizeof(BDataType) * B.mDesc.GetElementSpaceSize());
    DeviceMem c_m_n_device_buf(sizeof(CDataType) * C.mDesc.GetElementSpaceSize());

    a_m_k_device_buf.ToDevice(A.mData.data());
    b_n_k_device_buf.ToDevice(B.mData.data());
    kernel<<<1, 64>>>(static_cast<const ADataType*>(a_m_k_device_buf.GetDeviceBuffer()),
                      static_cast<const BDataType*>(b_n_k_device_buf.GetDeviceBuffer()),
                      static_cast<CDataType*>(c_m_n_device_buf.GetDeviceBuffer()));
    c_m_n_device_buf.FromDevice(C.mData.data());

    return true;
}

template <typename DeviceMFMA,
          typename ADataType,
          typename BDataType,
          typename CDataType,
          typename GPUAccDataType,
          typename CPUAccDataType,
          typename ALayout,
          typename BLayout,
          typename CLayout,
          index_t BLOCK_M,
          index_t BLOCK_N,
          index_t BLOCK_K>
struct TestMFMA
{
    auto PrepareGemmTensors(const GemmParams& params, index_t init)
    {
        auto f_host_tensor_descriptor =
            [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
                if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
                {
                    return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
                                                std::vector<std::size_t>({stride, 1}));
                }
                else
                {
                    return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
                                                std::vector<std::size_t>({1, stride}));
                }
            };

        Tensor<ADataType> a_m_k(
            f_host_tensor_descriptor(params.M, params.K, params.StrideA, ALayout{}));
        Tensor<BDataType> b_n_k(
            f_host_tensor_descriptor(params.K, params.N, params.StrideB, BLayout{}));
        Tensor<CDataType> c_m_n_host_result(
            f_host_tensor_descriptor(params.M, params.N, params.StrideC, CLayout{}));
        Tensor<CDataType> c_m_n_device_result(
            f_host_tensor_descriptor(params.M, params.N, params.StrideC, CLayout{}));

        switch(init)
        {
        case 0:
            a_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{0.015625f});
            // NOTE: not all numbers are representable in FP8, BF8, etc.
            b_n_k.GenerateTensorValue(GeneratorTensor_Sequential<BDataType, 1>{});
            break;
        case 1:
            // results in C = {K}
            a_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{1.0f});
            b_n_k.GenerateTensorValue(GeneratorTensor_1<BDataType>{1.0f});
            break;
        case 2:
            // expect small round off errors
            a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{-5, 5});
            b_n_k.GenerateTensorValue(GeneratorTensor_3<BDataType>{-5, 5});
            break;
        case 3:
            // expect small round off errors
            a_m_k.GenerateTensorValue(GeneratorTensor_4<ADataType>(-1, 3));
            b_n_k.GenerateTensorValue(GeneratorTensor_4<BDataType>(1, 3));
            break;
        default:
            // all initial values are representable in FP8, BF8
            a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 6});
            b_n_k.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 6});

            break;
        }

        return std::make_tuple(a_m_k, b_n_k, c_m_n_host_result, c_m_n_device_result);
    }

    auto operator()(const DeviceMFMA& mfma_kernel, index_t init)
    {
        std::cout << "ALayout = " << ALayout{}.name << ", BLayout = " << BLayout{}.name
                  << ", CLayout = " << CLayout{}.name << std::endl;

        // Arrange
        GemmParams params;
        params.M = BLOCK_M;
        params.N = BLOCK_N;
        params.K = BLOCK_K;

        auto f_get_default_stride = [](std::size_t row,
                                       std::size_t col,
                                       ck::index_t stride,
                                       auto layout) {
            if(stride == -1)
            {
                // give a chance if stride is -1, return a default packed stride
                if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
                {
                    return static_cast<std::size_t>(col);
                }
                else
                {
                    return static_cast<std::size_t>(row);
                }
            }
            else
                return static_cast<std::size_t>(stride);
        };

        params.StrideA = f_get_default_stride(BLOCK_M, BLOCK_K, params.StrideA, ALayout{});
        params.StrideB = f_get_default_stride(BLOCK_K, BLOCK_N, params.StrideB, BLayout{});
        params.StrideC = f_get_default_stride(BLOCK_M, BLOCK_N, params.StrideC, CLayout{});

        auto host_tensors = PrepareGemmTensors(params, init);

        const Tensor<ADataType>& a  = std::get<0>(host_tensors);
        const Tensor<BDataType>& b  = std::get<1>(host_tensors);
        Tensor<CDataType>& c_host   = std::get<2>(host_tensors);
        Tensor<CDataType>& c_device = std::get<3>(host_tensors);

        using PassThrough = ck::tensor_operation::element_wise::PassThrough;

        auto a_element_op = PassThrough{};
        auto b_element_op = PassThrough{};
        auto c_element_op = PassThrough{};

        using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
                                                                                BDataType,
                                                                                CDataType,
                                                                                CPUAccDataType,
                                                                                PassThrough,
                                                                                PassThrough,
                                                                                PassThrough>;

        RunHostGEMM<ReferenceGemmInstance>(a, b, c_host, a_element_op, b_element_op, c_element_op);

        RunDeviceGEMM(mfma_kernel, a, b, c_device);

        bool res = false;
        if constexpr(std::is_same<CDataType, float>::value ||
                     std::is_same<CDataType, half_t>::value)
        {
            res = ck::utils::check_err(c_device.mData, c_host.mData);
            std::cout << (res ? "SUCCESS" : "FAILURE") << std::endl;
        }
        else
        {
            std::cout << "UNSUPPORTED CDataType" << std::endl;
        }

        return res;
    }
};

} // namespace mfma_test
} // namespace ck
