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#include <iostream>
#include "cutlass/cutlass.h"
#include "cute/tensor.hpp"
#include "cute/atom/mma_atom.hpp"
#include "cutlass/numeric_types.h"
#include "cutlass/gemm/device/gemm_universal_adapter.h"
#include "cutlass/gemm/kernel/gemm_universal.hpp"
#include "cutlass/gemm/collective/collective_builder.hpp"
#include "cutlass/epilogue/dispatch_policy.hpp"
#include "cutlass/epilogue/collective/collective_builder.hpp"
#include "cutlass/epilogue/thread/activation.h"
#include "../../../common/cutlass_unit_test.h"
#include "../gemm_testbed_3x.hpp"

using namespace cute;

// * Test list
// 1. 128x128_tnn_vs64in
// 2. 128x192_tnn_vs64in
// 3. 128x256_tnn_vs64in
// 4. 256x128_tnn_vs64in
// 5. 256x192_tnn_vs64in
// 6. 256x256_tnn_vs64in

#if defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)

// 1. 
namespace cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_f32_f32_128x128x256_0_vs64_tnn_align64_o_1sm {

    using LayoutA = cutlass::layout::RowMajor;
    using LayoutB = cutlass::layout::ColumnMajor;
    using LayoutC = cutlass::layout::ColumnMajor;
    using LayoutD = cutlass::layout::ColumnMajor;

    using ElementPairA = cutlass::mx_float4_t<cutlass::float_e2m1_t>;
    using ElementPairB = cutlass::mx_float4_t<cutlass::float_e2m1_t>;
    using ElementC = float;
    using ElementD = float;

    constexpr int kAlignmentA = 64;
    constexpr int kAlignmentB = 32;
    constexpr int kAlignmentD = 128 / cutlass::sizeof_bits<ElementD>::value;
    constexpr int kAlignmentC = cute::is_same_v<ElementC, void> ? kAlignmentD : 128 / cutlass::sizeof_bits<ElementC>::value;

    using ProblemShape = Shape<int,int,int,int>;
    using ClusterShape = cute::Shape<cute::_1, cute::_1, cute::_1>;
    using MmaTileShape = Shape<_128, _128, _256>;
    using ArchTag = cutlass::arch::Sm100;
    using OpClassTag = cutlass::arch::OpClassBlockScaledSparseTensorOp;
    using EpilogueTile = cutlass::epilogue::collective::EpilogueTileAuto;
    using EpilogueScheduleType = cutlass::epilogue::TmaWarpSpecialized1SmMxf4;
    using KernelScheduleType = cutlass::gemm::KernelSparseTmaWarpSpecialized1SmMxf4Sm100;
    using ElementAccumulator = float;
    using ElementEpilogueCompute = float;
    using ElementBias = float;
    using TileScheduler = cutlass::gemm::PersistentScheduler;

    using CollectiveEpilogue =
        typename cutlass::epilogue::collective::CollectiveBuilder<
            ArchTag,
            OpClassTag,
            MmaTileShape,
            ClusterShape,
            EpilogueTile,
            ElementAccumulator,
            ElementEpilogueCompute,
            ElementC, LayoutC, kAlignmentC,
            ElementD, LayoutD, kAlignmentD,
            EpilogueScheduleType,
            cutlass::epilogue::fusion::PerRowLinCombPerRowBiasEltAct<
                cutlass::epilogue::thread::Clamp,
                ElementD,
                ElementEpilogueCompute,
                ElementBias,
                ElementC,
                ElementEpilogueCompute>
        >::CollectiveOp;

    using StageCount = cutlass::gemm::collective::StageCountAutoCarveoutEpi<CollectiveEpilogue>;

    using CollectiveMainloop =
        typename cutlass::gemm::collective::CollectiveBuilder<
            ArchTag,
            OpClassTag,
            ElementPairA, LayoutA, kAlignmentA,
            ElementPairB, LayoutB, kAlignmentB,
            ElementAccumulator,
            MmaTileShape,
            ClusterShape,
            StageCount,
            KernelScheduleType
        >::CollectiveOp;

    using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
        cute::Shape<int,int,int,int>,
        CollectiveMainloop,
        CollectiveEpilogue,
        void>;

    using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
}

// 2.
namespace cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_f32_f32_128x192x256_0_vs64_tnn_align64_o_1sm {

    using LayoutA = cutlass::layout::RowMajor;
    using LayoutB = cutlass::layout::ColumnMajor;
    using LayoutC = cutlass::layout::ColumnMajor;
    using LayoutD = cutlass::layout::ColumnMajor;

    using ElementPairA = cutlass::mx_float4_t<cutlass::float_e2m1_t>;
    using ElementPairB = cutlass::mx_float4_t<cutlass::float_e2m1_t>;
    using ElementC = float;
    using ElementD = float;

    constexpr int kAlignmentA = 64;
    constexpr int kAlignmentB = 32;
    constexpr int kAlignmentD = 128 / cutlass::sizeof_bits<ElementD>::value;
    constexpr int kAlignmentC = cute::is_same_v<ElementC, void> ? kAlignmentD : 128 / cutlass::sizeof_bits<ElementC>::value;

    using ProblemShape = Shape<int,int,int,int>;
    using ClusterShape = cute::Shape<cute::_1, cute::_1, cute::_1>;
    using MmaTileShape = Shape<_128, _192, _256>;
    using ArchTag = cutlass::arch::Sm100;
    using OpClassTag = cutlass::arch::OpClassBlockScaledSparseTensorOp;
    using EpilogueTile = cutlass::epilogue::collective::EpilogueTileAuto;
    using EpilogueScheduleType = cutlass::epilogue::TmaWarpSpecialized1SmMxf4;
    using KernelScheduleType = cutlass::gemm::KernelSparseTmaWarpSpecialized1SmMxf4Sm100;
    using ElementAccumulator = float;
    using ElementEpilogueCompute = float;
    using ElementBias = float;
    using TileScheduler = cutlass::gemm::PersistentScheduler;

    using CollectiveEpilogue =
        typename cutlass::epilogue::collective::CollectiveBuilder<
            ArchTag,
            OpClassTag,
            MmaTileShape,
            ClusterShape,
            EpilogueTile,
            ElementAccumulator,
            ElementEpilogueCompute,
            ElementC, LayoutC, kAlignmentC,
            ElementD, LayoutD, kAlignmentD,
            EpilogueScheduleType,
            cutlass::epilogue::fusion::PerRowLinCombPerRowBiasEltAct<
                cutlass::epilogue::thread::Clamp,
                ElementD,
                ElementEpilogueCompute,
                ElementBias,
                ElementC,
                ElementEpilogueCompute>
        >::CollectiveOp;

    using StageCount = cutlass::gemm::collective::StageCountAutoCarveoutEpi<CollectiveEpilogue>;

    using CollectiveMainloop =
        typename cutlass::gemm::collective::CollectiveBuilder<
            ArchTag,
            OpClassTag,
            ElementPairA, LayoutA, kAlignmentA,
            ElementPairB, LayoutB, kAlignmentB,
            ElementAccumulator,
            MmaTileShape,
            ClusterShape,
            StageCount,
            KernelScheduleType
        >::CollectiveOp;

    using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
        cute::Shape<int,int,int,int>,
        CollectiveMainloop,
        CollectiveEpilogue,
        void>;

    using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
}

// 3.
namespace cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_f32_f32_128x256x256_0_vs64_tnn_align64_o_1sm {

    using LayoutA = cutlass::layout::RowMajor;
    using LayoutB = cutlass::layout::ColumnMajor;
    using LayoutC = cutlass::layout::ColumnMajor;
    using LayoutD = cutlass::layout::ColumnMajor;

    using ElementPairA = cutlass::mx_float4_t<cutlass::float_e2m1_t>;
    using ElementPairB = cutlass::mx_float4_t<cutlass::float_e2m1_t>;
    using ElementC = float;
    using ElementD = float;

    constexpr int kAlignmentA = 64;
    constexpr int kAlignmentB = 32;
    constexpr int kAlignmentD = 128 / cutlass::sizeof_bits<ElementD>::value;
    constexpr int kAlignmentC = cute::is_same_v<ElementC, void> ? kAlignmentD : 128 / cutlass::sizeof_bits<ElementC>::value;

    using ProblemShape = Shape<int,int,int,int>;
    using ClusterShape = cute::Shape<cute::_1, cute::_1, cute::_1>;
    using MmaTileShape = Shape<_128, _256, _256>;
    using ArchTag = cutlass::arch::Sm100;
    using OpClassTag = cutlass::arch::OpClassBlockScaledSparseTensorOp;
    using EpilogueTile = cutlass::epilogue::collective::EpilogueTileAuto;
    using EpilogueScheduleType = cutlass::epilogue::TmaWarpSpecialized1SmMxf4;
    using KernelScheduleType = cutlass::gemm::KernelSparseTmaWarpSpecialized1SmMxf4Sm100;
    using ElementAccumulator = float;
    using ElementEpilogueCompute = float;
    using ElementBias = float;
    using TileScheduler = cutlass::gemm::PersistentScheduler;

    using CollectiveEpilogue =
        typename cutlass::epilogue::collective::CollectiveBuilder<
            ArchTag,
            OpClassTag,
            MmaTileShape,
            ClusterShape,
            EpilogueTile,
            ElementAccumulator,
            ElementEpilogueCompute,
            ElementC, LayoutC, kAlignmentC,
            ElementD, LayoutD, kAlignmentD,
            EpilogueScheduleType,
            cutlass::epilogue::fusion::PerRowLinCombPerRowBiasEltAct<
                cutlass::epilogue::thread::Clamp,
                ElementD,
                ElementEpilogueCompute,
                ElementBias,
                ElementC,
                ElementEpilogueCompute>
        >::CollectiveOp;

    using StageCount = cutlass::gemm::collective::StageCountAutoCarveoutEpi<CollectiveEpilogue>;

    using CollectiveMainloop =
        typename cutlass::gemm::collective::CollectiveBuilder<
            ArchTag,
            OpClassTag,
            ElementPairA, LayoutA, kAlignmentA,
            ElementPairB, LayoutB, kAlignmentB,
            ElementAccumulator,
            MmaTileShape,
            ClusterShape,
            StageCount,
            KernelScheduleType
        >::CollectiveOp;

    using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
        cute::Shape<int,int,int,int>,
        CollectiveMainloop,
        CollectiveEpilogue,
        void>;

    using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
}

// 3.2
namespace cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_f32_f32_128x256x512_0_vs64_tnn_align64_o_1sm {

    using LayoutA = cutlass::layout::RowMajor;
    using LayoutB = cutlass::layout::ColumnMajor;
    using LayoutC = cutlass::layout::ColumnMajor;
    using LayoutD = cutlass::layout::ColumnMajor;

    using ElementPairA = cutlass::mx_float4_t<cutlass::float_e2m1_t>;
    using ElementPairB = cutlass::mx_float4_t<cutlass::float_e2m1_t>;
    using ElementC = float;
    using ElementD = float;

    constexpr int kAlignmentA = 64;
    constexpr int kAlignmentB = 32;
    constexpr int kAlignmentD = 128 / cutlass::sizeof_bits<ElementD>::value;
    constexpr int kAlignmentC = cute::is_same_v<ElementC, void> ? kAlignmentD : 128 / cutlass::sizeof_bits<ElementC>::value;

    using ProblemShape = Shape<int,int,int,int>;
    using ClusterShape = cute::Shape<cute::_1, cute::_1, cute::_1>;
    using MmaTileShape = Shape<_128, _256, _512>;
    using ArchTag = cutlass::arch::Sm100;
    using OpClassTag = cutlass::arch::OpClassBlockScaledSparseTensorOp;
    using EpilogueTile = cutlass::epilogue::collective::EpilogueTileAuto;
    using EpilogueScheduleType = cutlass::epilogue::TmaWarpSpecialized1SmMxf4;
    using KernelScheduleType = cutlass::gemm::KernelSparseTmaWarpSpecialized1SmMxf4Sm100;
    using ElementAccumulator = float;
    using ElementEpilogueCompute = float;
    using ElementBias = float;
    using TileScheduler = cutlass::gemm::PersistentScheduler;

    using CollectiveEpilogue =
        typename cutlass::epilogue::collective::CollectiveBuilder<
            ArchTag,
            OpClassTag,
            MmaTileShape,
            ClusterShape,
            EpilogueTile,
            ElementAccumulator,
            ElementEpilogueCompute,
            ElementC, LayoutC, kAlignmentC,
            ElementD, LayoutD, kAlignmentD,
            EpilogueScheduleType,
            cutlass::epilogue::fusion::PerRowLinCombPerRowBiasEltAct<
                cutlass::epilogue::thread::Clamp,
                ElementD,
                ElementEpilogueCompute,
                ElementBias,
                ElementC,
                ElementEpilogueCompute>
        >::CollectiveOp;

    using StageCount = cutlass::gemm::collective::StageCountAutoCarveoutEpi<CollectiveEpilogue>;

    using CollectiveMainloop =
        typename cutlass::gemm::collective::CollectiveBuilder<
            ArchTag,
            OpClassTag,
            ElementPairA, LayoutA, kAlignmentA,
            ElementPairB, LayoutB, kAlignmentB,
            ElementAccumulator,
            MmaTileShape,
            ClusterShape,
            StageCount,
            KernelScheduleType
        >::CollectiveOp;

    using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
        cute::Shape<int,int,int,int>,
        CollectiveMainloop,
        CollectiveEpilogue,
        void>;

    using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
}

// 4.
namespace cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_f32_f32_256x128x256_0_vs64_tnn_align64_o_2sm {

    using LayoutA = cutlass::layout::RowMajor;
    using LayoutB = cutlass::layout::ColumnMajor;
    using LayoutC = cutlass::layout::ColumnMajor;
    using LayoutD = cutlass::layout::ColumnMajor;

    using ElementPairA = cutlass::mx_float4_t<cutlass::float_e2m1_t>;
    using ElementPairB = cutlass::mx_float4_t<cutlass::float_e2m1_t>;
    using ElementC = float;
    using ElementD = float;

    constexpr int kAlignmentA = 64;
    constexpr int kAlignmentB = 32;
    constexpr int kAlignmentD = 128 / cutlass::sizeof_bits<ElementD>::value;
    constexpr int kAlignmentC = cute::is_same_v<ElementC, void> ? kAlignmentD : 128 / cutlass::sizeof_bits<ElementC>::value;

    using ProblemShape = Shape<int,int,int,int>;
    using ClusterShape = cute::Shape<cute::_2, cute::_1, cute::_1>;
    using MmaTileShape = Shape<_256, _128, _256>;
    using ArchTag = cutlass::arch::Sm100;
    using OpClassTag = cutlass::arch::OpClassBlockScaledSparseTensorOp;
    using EpilogueTile = cutlass::epilogue::collective::EpilogueTileAuto;
    using EpilogueScheduleType = cutlass::epilogue::TmaWarpSpecialized2SmMxf4;
    using KernelScheduleType = cutlass::gemm::KernelSparseTmaWarpSpecialized2SmMxf4Sm100;
    using ElementAccumulator = float;
    using ElementEpilogueCompute = float;
    using ElementBias = float;
    using TileScheduler = cutlass::gemm::PersistentScheduler;

    using CollectiveEpilogue =
        typename cutlass::epilogue::collective::CollectiveBuilder<
            ArchTag,
            OpClassTag,
            MmaTileShape,
            ClusterShape,
            EpilogueTile,
            ElementAccumulator,
            ElementEpilogueCompute,
            ElementC, LayoutC, kAlignmentC,
            ElementD, LayoutD, kAlignmentD,
            EpilogueScheduleType,
            cutlass::epilogue::fusion::PerRowLinCombPerRowBiasEltAct<
                cutlass::epilogue::thread::Clamp,
                ElementD,
                ElementEpilogueCompute,
                ElementBias,
                ElementC,
                ElementEpilogueCompute>
        >::CollectiveOp;

    using StageCount = cutlass::gemm::collective::StageCountAutoCarveoutEpi<CollectiveEpilogue>;

    using CollectiveMainloop =
        typename cutlass::gemm::collective::CollectiveBuilder<
            ArchTag,
            OpClassTag,
            ElementPairA, LayoutA, kAlignmentA,
            ElementPairB, LayoutB, kAlignmentB,
            ElementAccumulator,
            MmaTileShape,
            ClusterShape,
            StageCount,
            KernelScheduleType
        >::CollectiveOp;

    using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
        cute::Shape<int,int,int,int>,
        CollectiveMainloop,
        CollectiveEpilogue,
        void>;

    using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
}

// 5.
namespace cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_f32_f32_256x192x256_0_vs64_tnn_align64_o_2sm {

    using LayoutA = cutlass::layout::RowMajor;
    using LayoutB = cutlass::layout::ColumnMajor;
    using LayoutC = cutlass::layout::ColumnMajor;
    using LayoutD = cutlass::layout::ColumnMajor;

    using ElementPairA = cutlass::mx_float4_t<cutlass::float_e2m1_t>;
    using ElementPairB = cutlass::mx_float4_t<cutlass::float_e2m1_t>;
    using ElementC = float;
    using ElementD = float;

    constexpr int kAlignmentA = 64;
    constexpr int kAlignmentB = 32;
    constexpr int kAlignmentD = 128 / cutlass::sizeof_bits<ElementD>::value;
    constexpr int kAlignmentC = cute::is_same_v<ElementC, void> ? kAlignmentD : 128 / cutlass::sizeof_bits<ElementC>::value;

    using ProblemShape = Shape<int,int,int,int>;
    using ClusterShape = cute::Shape<cute::_2, cute::_1, cute::_1>;
    using MmaTileShape = Shape<_256, _192, _256>;
    using ArchTag = cutlass::arch::Sm100;
    using OpClassTag = cutlass::arch::OpClassBlockScaledSparseTensorOp;
    using EpilogueTile = cutlass::epilogue::collective::EpilogueTileAuto;
    using EpilogueScheduleType = cutlass::epilogue::TmaWarpSpecialized2SmMxf4;
    using KernelScheduleType = cutlass::gemm::KernelSparseTmaWarpSpecialized2SmMxf4Sm100;
    using ElementAccumulator = float;
    using ElementEpilogueCompute = float;
    using ElementBias = float;
    using TileScheduler = cutlass::gemm::PersistentScheduler;

    using CollectiveEpilogue =
        typename cutlass::epilogue::collective::CollectiveBuilder<
            ArchTag,
            OpClassTag,
            MmaTileShape,
            ClusterShape,
            EpilogueTile,
            ElementAccumulator,
            ElementEpilogueCompute,
            ElementC, LayoutC, kAlignmentC,
            ElementD, LayoutD, kAlignmentD,
            EpilogueScheduleType,
            cutlass::epilogue::fusion::PerRowLinCombPerRowBiasEltAct<
                cutlass::epilogue::thread::Clamp,
                ElementD,
                ElementEpilogueCompute,
                ElementBias,
                ElementC,
                ElementEpilogueCompute>
        >::CollectiveOp;

    using StageCount = cutlass::gemm::collective::StageCountAutoCarveoutEpi<CollectiveEpilogue>;

    using CollectiveMainloop =
        typename cutlass::gemm::collective::CollectiveBuilder<
            ArchTag,
            OpClassTag,
            ElementPairA, LayoutA, kAlignmentA,
            ElementPairB, LayoutB, kAlignmentB,
            ElementAccumulator,
            MmaTileShape,
            ClusterShape,
            StageCount,
            KernelScheduleType
        >::CollectiveOp;

    using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
        cute::Shape<int,int,int,int>,
        CollectiveMainloop,
        CollectiveEpilogue,
        void>;

    using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
}

// 6.
namespace cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_f32_f32_256x256x256_0_vs64_tnn_align64_o_2sm {

    using LayoutA = cutlass::layout::RowMajor;
    using LayoutB = cutlass::layout::ColumnMajor;
    using LayoutC = cutlass::layout::ColumnMajor;
    using LayoutD = cutlass::layout::ColumnMajor;

    using ElementPairA = cutlass::mx_float4_t<cutlass::float_e2m1_t>;
    using ElementPairB = cutlass::mx_float4_t<cutlass::float_e2m1_t>;
    using ElementC = float;
    using ElementD = float;

    constexpr int kAlignmentA = 64;
    constexpr int kAlignmentB = 32;
    constexpr int kAlignmentD = 128 / cutlass::sizeof_bits<ElementD>::value;
    constexpr int kAlignmentC = cute::is_same_v<ElementC, void> ? kAlignmentD : 128 / cutlass::sizeof_bits<ElementC>::value;

    using ProblemShape = Shape<int,int,int,int>;
    using ClusterShape = cute::Shape<cute::_2, cute::_1, cute::_1>;
    using MmaTileShape = Shape<_256, _256, _256>;
    using ArchTag = cutlass::arch::Sm100;
    using OpClassTag = cutlass::arch::OpClassBlockScaledSparseTensorOp;
    using EpilogueTile = cutlass::epilogue::collective::EpilogueTileAuto;
    using EpilogueScheduleType = cutlass::epilogue::TmaWarpSpecialized2SmMxf4;
    using KernelScheduleType = cutlass::gemm::KernelSparseTmaWarpSpecialized2SmMxf4Sm100;
    using ElementAccumulator = float;
    using ElementEpilogueCompute = float;
    using ElementBias = float;
    using TileScheduler = cutlass::gemm::PersistentScheduler;

    using CollectiveEpilogue =
        typename cutlass::epilogue::collective::CollectiveBuilder<
            ArchTag,
            OpClassTag,
            MmaTileShape,
            ClusterShape,
            EpilogueTile,
            ElementAccumulator,
            ElementEpilogueCompute,
            ElementC, LayoutC, kAlignmentC,
            ElementD, LayoutD, kAlignmentD,
            EpilogueScheduleType,
            cutlass::epilogue::fusion::PerRowLinCombPerRowBiasEltAct<
                cutlass::epilogue::thread::Clamp,
                ElementD,
                ElementEpilogueCompute,
                ElementBias,
                ElementC,
                ElementEpilogueCompute>
        >::CollectiveOp;

    using StageCount = cutlass::gemm::collective::StageCountAutoCarveoutEpi<CollectiveEpilogue>;

    using CollectiveMainloop =
        typename cutlass::gemm::collective::CollectiveBuilder<
            ArchTag,
            OpClassTag,
            ElementPairA, LayoutA, kAlignmentA,
            ElementPairB, LayoutB, kAlignmentB,
            ElementAccumulator,
            MmaTileShape,
            ClusterShape,
            StageCount,
            KernelScheduleType
        >::CollectiveOp;

    using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
        cute::Shape<int,int,int,int>,
        CollectiveMainloop,
        CollectiveEpilogue,
        void>;

    using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
}

// 6.
namespace cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_f32_f32_256x256x512_0_vs64_tnn_align64_o_2sm {

    using LayoutA = cutlass::layout::RowMajor;
    using LayoutB = cutlass::layout::ColumnMajor;
    using LayoutC = cutlass::layout::ColumnMajor;
    using LayoutD = cutlass::layout::ColumnMajor;

    using ElementPairA = cutlass::mx_float4_t<cutlass::float_e2m1_t>;
    using ElementPairB = cutlass::mx_float4_t<cutlass::float_e2m1_t>;
    using ElementC = float;
    using ElementD = float;

    constexpr int kAlignmentA = 64;
    constexpr int kAlignmentB = 32;
    constexpr int kAlignmentD = 128 / cutlass::sizeof_bits<ElementD>::value;
    constexpr int kAlignmentC = cute::is_same_v<ElementC, void> ? kAlignmentD : 128 / cutlass::sizeof_bits<ElementC>::value;

    using ProblemShape = Shape<int,int,int,int>;
    using ClusterShape = cute::Shape<cute::_2, cute::_1, cute::_1>;
    using MmaTileShape = Shape<_256, _256, _512>;
    using ArchTag = cutlass::arch::Sm100;
    using OpClassTag = cutlass::arch::OpClassBlockScaledSparseTensorOp;
    using EpilogueTile = cutlass::epilogue::collective::EpilogueTileAuto;
    using EpilogueScheduleType = cutlass::epilogue::TmaWarpSpecialized2SmMxf4;
    using KernelScheduleType = cutlass::gemm::KernelSparseTmaWarpSpecialized2SmMxf4Sm100;
    using ElementAccumulator = float;
    using ElementEpilogueCompute = float;
    using ElementBias = float;
    using TileScheduler = cutlass::gemm::PersistentScheduler;

    using CollectiveEpilogue =
        typename cutlass::epilogue::collective::CollectiveBuilder<
            ArchTag,
            OpClassTag,
            MmaTileShape,
            ClusterShape,
            EpilogueTile,
            ElementAccumulator,
            ElementEpilogueCompute,
            ElementC, LayoutC, kAlignmentC,
            ElementD, LayoutD, kAlignmentD,
            EpilogueScheduleType,
            cutlass::epilogue::fusion::PerRowLinCombPerRowBiasEltAct<
                cutlass::epilogue::thread::Clamp,
                ElementD,
                ElementEpilogueCompute,
                ElementBias,
                ElementC,
                ElementEpilogueCompute>
        >::CollectiveOp;

    using StageCount = cutlass::gemm::collective::StageCountAutoCarveoutEpi<CollectiveEpilogue>;

    using CollectiveMainloop =
        typename cutlass::gemm::collective::CollectiveBuilder<
            ArchTag,
            OpClassTag,
            ElementPairA, LayoutA, kAlignmentA,
            ElementPairB, LayoutB, kAlignmentB,
            ElementAccumulator,
            MmaTileShape,
            ClusterShape,
            StageCount,
            KernelScheduleType
        >::CollectiveOp;

    using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
        cute::Shape<int,int,int,int>,
        CollectiveMainloop,
        CollectiveEpilogue,
        void>;

    using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
}

// 1.
TEST(cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_f32_f32_128x128x256_0_vs64_tnn_align64_o_1sm, functional) {
  namespace gemm = cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_f32_f32_128x128x256_0_vs64_tnn_align64_o_1sm;
  EXPECT_TRUE(test::gemm::device::TestSmall<gemm::Gemm>(
    1, 1,
    test::gemm::device::CheckEquality::RELATIVE,
    test::gemm::device::ScalarLoc::ON_DEVICE,
    test::gemm::device::VectorScale::ENABLED,
    {256, 3840}));
}

// 2.
TEST(cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_f32_f32_128x192x256_0_vs64_tnn_align64_o_1sm, functional) {
  namespace gemm = cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_f32_f32_128x192x256_0_vs64_tnn_align64_o_1sm;
  EXPECT_TRUE(test::gemm::device::TestSmall<gemm::Gemm>(
    1, 1,
    test::gemm::device::CheckEquality::RELATIVE,
    test::gemm::device::ScalarLoc::ON_DEVICE,
    test::gemm::device::VectorScale::ENABLED,
    {256, 3840}));
}

// 3.
TEST(cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_f32_f32_128x256x256_0_vs64_tnn_align64_o_1sm, functional) {
  namespace gemm = cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_f32_f32_128x256x256_0_vs64_tnn_align64_o_1sm;
  EXPECT_TRUE(test::gemm::device::TestSmall<gemm::Gemm>(
    1, 1,
    test::gemm::device::CheckEquality::RELATIVE,
    test::gemm::device::ScalarLoc::ON_DEVICE,
    test::gemm::device::VectorScale::ENABLED,
    {256, 3840}));
}

// 3.2
TEST(cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_f32_f32_128x256x512_0_vs64_tnn_align64_o_1sm, functional) {
  namespace gemm = cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_f32_f32_128x256x512_0_vs64_tnn_align64_o_1sm;
  EXPECT_TRUE(test::gemm::device::TestSmall<gemm::Gemm>(
    1, 1,
    test::gemm::device::CheckEquality::RELATIVE,
    test::gemm::device::ScalarLoc::ON_DEVICE,
    test::gemm::device::VectorScale::ENABLED,
    {512, 3840}));
}

// 4.
TEST(cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_f32_f32_256x128x256_0_vs64_tnn_align64_o_2sm, functional) {
  namespace gemm = cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_f32_f32_256x128x256_0_vs64_tnn_align64_o_2sm;
  EXPECT_TRUE(test::gemm::device::TestSmall<gemm::Gemm>(
    1, 1,
    test::gemm::device::CheckEquality::RELATIVE,
    test::gemm::device::ScalarLoc::ON_DEVICE,
    test::gemm::device::VectorScale::ENABLED,
    {256, 3840}));
}

// 5.
TEST(cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_f32_f32_256x192x256_0_vs64_tnn_align64_o_2sm, functional) {
  namespace gemm = cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_f32_f32_256x192x256_0_vs64_tnn_align64_o_2sm;
  EXPECT_TRUE(test::gemm::device::TestSmall<gemm::Gemm>(
    1, 1,
    test::gemm::device::CheckEquality::RELATIVE,
    test::gemm::device::ScalarLoc::ON_DEVICE,
    test::gemm::device::VectorScale::ENABLED,
    {256, 3840}));
}

// 6.
TEST(cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_f32_f32_256x256x256_0_vs64_tnn_align64_o_2sm, functional) {
  namespace gemm = cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_f32_f32_256x256x256_0_vs64_tnn_align64_o_2sm;
  EXPECT_TRUE(test::gemm::device::TestSmall<gemm::Gemm>(
    1, 1,
    test::gemm::device::CheckEquality::RELATIVE,
    test::gemm::device::ScalarLoc::ON_DEVICE,
    test::gemm::device::VectorScale::ENABLED,
    {256, 3840}));
}

// 6.2
TEST(cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_f32_f32_256x256x512_0_vs64_tnn_align64_o_2sm, functional) {
  namespace gemm = cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_f32_f32_256x256x512_0_vs64_tnn_align64_o_2sm;
  EXPECT_TRUE(test::gemm::device::TestSmall<gemm::Gemm>(
    1, 1,
    test::gemm::device::CheckEquality::RELATIVE,
    test::gemm::device::ScalarLoc::ON_DEVICE,
    test::gemm::device::VectorScale::ENABLED,
    {512, 3840}));
}


// 1. 
namespace cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_void_f32_128x128x256_0_vs64_tnn_align64_o_1sm {

    using LayoutA = cutlass::layout::RowMajor;
    using LayoutB = cutlass::layout::ColumnMajor;
    using LayoutC = cutlass::layout::ColumnMajor;
    using LayoutD = cutlass::layout::ColumnMajor;

    using ElementPairA = cutlass::mx_float4_t<cutlass::float_e2m1_t>;
    using ElementPairB = cutlass::mx_float4_t<cutlass::float_e2m1_t>;
    using ElementC = void;
    using ElementD = float;

    constexpr int kAlignmentA = 64;
    constexpr int kAlignmentB = 32;
    constexpr int kAlignmentD = 128 / cutlass::sizeof_bits<ElementD>::value;
    constexpr int kAlignmentC = cute::is_same_v<ElementC, void> ? kAlignmentD : 128 / cutlass::sizeof_bits<ElementC>::value;

    using ProblemShape = Shape<int,int,int,int>;
    using ClusterShape = cute::Shape<cute::_1, cute::_1, cute::_1>;
    using MmaTileShape = Shape<_128, _128, _256>;
    using ArchTag = cutlass::arch::Sm100;
    using OpClassTag = cutlass::arch::OpClassBlockScaledSparseTensorOp;
    using EpilogueTile = cutlass::epilogue::collective::EpilogueTileAuto;
    using EpilogueScheduleType = cutlass::epilogue::TmaWarpSpecialized1SmMxf4;
    using KernelScheduleType = cutlass::gemm::KernelSparseTmaWarpSpecialized1SmMxf4Sm100;
    using ElementAccumulator = float;
    using ElementEpilogueCompute = float;
    using ElementBias = float;
    using TileScheduler = cutlass::gemm::PersistentScheduler;

    using CollectiveEpilogue =
        typename cutlass::epilogue::collective::CollectiveBuilder<
            ArchTag,
            OpClassTag,
            MmaTileShape,
            ClusterShape,
            EpilogueTile,
            ElementAccumulator,
            ElementEpilogueCompute,
            ElementC, LayoutC, kAlignmentC,
            ElementD, LayoutD, kAlignmentD,
            EpilogueScheduleType,
            cutlass::epilogue::fusion::PerRowLinCombPerRowBiasEltAct<
                cutlass::epilogue::thread::Clamp,
                ElementD,
                ElementEpilogueCompute,
                ElementBias,
                ElementC,
                ElementEpilogueCompute>
        >::CollectiveOp;

    using StageCount = cutlass::gemm::collective::StageCountAutoCarveoutEpi<CollectiveEpilogue>;

    using CollectiveMainloop =
        typename cutlass::gemm::collective::CollectiveBuilder<
            ArchTag,
            OpClassTag,
            ElementPairA, LayoutA, kAlignmentA,
            ElementPairB, LayoutB, kAlignmentB,
            ElementAccumulator,
            MmaTileShape,
            ClusterShape,
            StageCount,
            KernelScheduleType
        >::CollectiveOp;

    using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
        cute::Shape<int,int,int,int>,
        CollectiveMainloop,
        CollectiveEpilogue,
        void>;

    using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
}

// 2.
namespace cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_void_f32_128x192x256_0_vs64_tnn_align64_o_1sm {

    using LayoutA = cutlass::layout::RowMajor;
    using LayoutB = cutlass::layout::ColumnMajor;
    using LayoutC = cutlass::layout::ColumnMajor;
    using LayoutD = cutlass::layout::ColumnMajor;

    using ElementPairA = cutlass::mx_float4_t<cutlass::float_e2m1_t>;
    using ElementPairB = cutlass::mx_float4_t<cutlass::float_e2m1_t>;
    using ElementC = void;
    using ElementD = float;

    constexpr int kAlignmentA = 64;
    constexpr int kAlignmentB = 32;
    constexpr int kAlignmentD = 128 / cutlass::sizeof_bits<ElementD>::value;
    constexpr int kAlignmentC = cute::is_same_v<ElementC, void> ? kAlignmentD : 128 / cutlass::sizeof_bits<ElementC>::value;

    using ProblemShape = Shape<int,int,int,int>;
    using ClusterShape = cute::Shape<cute::_1, cute::_1, cute::_1>;
    using MmaTileShape = Shape<_128, _192, _256>;
    using ArchTag = cutlass::arch::Sm100;
    using OpClassTag = cutlass::arch::OpClassBlockScaledSparseTensorOp;
    using EpilogueTile = cutlass::epilogue::collective::EpilogueTileAuto;
    using EpilogueScheduleType = cutlass::epilogue::TmaWarpSpecialized1SmMxf4;
    using KernelScheduleType = cutlass::gemm::KernelSparseTmaWarpSpecialized1SmMxf4Sm100;
    using ElementAccumulator = float;
    using ElementEpilogueCompute = float;
    using ElementBias = float;
    using TileScheduler = cutlass::gemm::PersistentScheduler;

    using CollectiveEpilogue =
        typename cutlass::epilogue::collective::CollectiveBuilder<
            ArchTag,
            OpClassTag,
            MmaTileShape,
            ClusterShape,
            EpilogueTile,
            ElementAccumulator,
            ElementEpilogueCompute,
            ElementC, LayoutC, kAlignmentC,
            ElementD, LayoutD, kAlignmentD,
            EpilogueScheduleType,
            cutlass::epilogue::fusion::PerRowLinCombPerRowBiasEltAct<
                cutlass::epilogue::thread::Clamp,
                ElementD,
                ElementEpilogueCompute,
                ElementBias,
                ElementC,
                ElementEpilogueCompute>
        >::CollectiveOp;

    using StageCount = cutlass::gemm::collective::StageCountAutoCarveoutEpi<CollectiveEpilogue>;

    using CollectiveMainloop =
        typename cutlass::gemm::collective::CollectiveBuilder<
            ArchTag,
            OpClassTag,
            ElementPairA, LayoutA, kAlignmentA,
            ElementPairB, LayoutB, kAlignmentB,
            ElementAccumulator,
            MmaTileShape,
            ClusterShape,
            StageCount,
            KernelScheduleType
        >::CollectiveOp;

    using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
        cute::Shape<int,int,int,int>,
        CollectiveMainloop,
        CollectiveEpilogue,
        void>;

    using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
}

// 3.
namespace cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_void_f32_128x256x256_0_vs64_tnn_align64_o_1sm {

    using LayoutA = cutlass::layout::RowMajor;
    using LayoutB = cutlass::layout::ColumnMajor;
    using LayoutC = cutlass::layout::ColumnMajor;
    using LayoutD = cutlass::layout::ColumnMajor;

    using ElementPairA = cutlass::mx_float4_t<cutlass::float_e2m1_t>;
    using ElementPairB = cutlass::mx_float4_t<cutlass::float_e2m1_t>;
    using ElementC = void;
    using ElementD = float;

    constexpr int kAlignmentA = 64;
    constexpr int kAlignmentB = 32;
    constexpr int kAlignmentD = 128 / cutlass::sizeof_bits<ElementD>::value;
    constexpr int kAlignmentC = cute::is_same_v<ElementC, void> ? kAlignmentD : 128 / cutlass::sizeof_bits<ElementC>::value;

    using ProblemShape = Shape<int,int,int,int>;
    using ClusterShape = cute::Shape<cute::_1, cute::_1, cute::_1>;
    using MmaTileShape = Shape<_128, _256, _256>;
    using ArchTag = cutlass::arch::Sm100;
    using OpClassTag = cutlass::arch::OpClassBlockScaledSparseTensorOp;
    using EpilogueTile = cutlass::epilogue::collective::EpilogueTileAuto;
    using EpilogueScheduleType = cutlass::epilogue::TmaWarpSpecialized1SmMxf4;
    using KernelScheduleType = cutlass::gemm::KernelSparseTmaWarpSpecialized1SmMxf4Sm100;
    using ElementAccumulator = float;
    using ElementEpilogueCompute = float;
    using ElementBias = float;
    using TileScheduler = cutlass::gemm::PersistentScheduler;

    using CollectiveEpilogue =
        typename cutlass::epilogue::collective::CollectiveBuilder<
            ArchTag,
            OpClassTag,
            MmaTileShape,
            ClusterShape,
            EpilogueTile,
            ElementAccumulator,
            ElementEpilogueCompute,
            ElementC, LayoutC, kAlignmentC,
            ElementD, LayoutD, kAlignmentD,
            EpilogueScheduleType,
            cutlass::epilogue::fusion::PerRowLinCombPerRowBiasEltAct<
                cutlass::epilogue::thread::Clamp,
                ElementD,
                ElementEpilogueCompute,
                ElementBias,
                ElementC,
                ElementEpilogueCompute>
        >::CollectiveOp;

    using StageCount = cutlass::gemm::collective::StageCountAutoCarveoutEpi<CollectiveEpilogue>;

    using CollectiveMainloop =
        typename cutlass::gemm::collective::CollectiveBuilder<
            ArchTag,
            OpClassTag,
            ElementPairA, LayoutA, kAlignmentA,
            ElementPairB, LayoutB, kAlignmentB,
            ElementAccumulator,
            MmaTileShape,
            ClusterShape,
            StageCount,
            KernelScheduleType
        >::CollectiveOp;

    using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
        cute::Shape<int,int,int,int>,
        CollectiveMainloop,
        CollectiveEpilogue,
        void>;

    using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
}

// 3.2
namespace cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_void_f32_128x256x512_0_vs64_tnn_align64_o_1sm {

    using LayoutA = cutlass::layout::RowMajor;
    using LayoutB = cutlass::layout::ColumnMajor;
    using LayoutC = cutlass::layout::ColumnMajor;
    using LayoutD = cutlass::layout::ColumnMajor;

    using ElementPairA = cutlass::mx_float4_t<cutlass::float_e2m1_t>;
    using ElementPairB = cutlass::mx_float4_t<cutlass::float_e2m1_t>;
    using ElementC = void;
    using ElementD = float;

    constexpr int kAlignmentA = 64;
    constexpr int kAlignmentB = 32;
    constexpr int kAlignmentD = 128 / cutlass::sizeof_bits<ElementD>::value;
    constexpr int kAlignmentC = cute::is_same_v<ElementC, void> ? kAlignmentD : 128 / cutlass::sizeof_bits<ElementC>::value;

    using ProblemShape = Shape<int,int,int,int>;
    using ClusterShape = cute::Shape<cute::_1, cute::_1, cute::_1>;
    using MmaTileShape = Shape<_128, _256, _512>;
    using ArchTag = cutlass::arch::Sm100;
    using OpClassTag = cutlass::arch::OpClassBlockScaledSparseTensorOp;
    using EpilogueTile = cutlass::epilogue::collective::EpilogueTileAuto;
    using EpilogueScheduleType = cutlass::epilogue::TmaWarpSpecialized1SmMxf4;
    using KernelScheduleType = cutlass::gemm::KernelSparseTmaWarpSpecialized1SmMxf4Sm100;
    using ElementAccumulator = float;
    using ElementEpilogueCompute = float;
    using ElementBias = float;
    using TileScheduler = cutlass::gemm::PersistentScheduler;

    using CollectiveEpilogue =
        typename cutlass::epilogue::collective::CollectiveBuilder<
            ArchTag,
            OpClassTag,
            MmaTileShape,
            ClusterShape,
            EpilogueTile,
            ElementAccumulator,
            ElementEpilogueCompute,
            ElementC, LayoutC, kAlignmentC,
            ElementD, LayoutD, kAlignmentD,
            EpilogueScheduleType,
            cutlass::epilogue::fusion::PerRowLinCombPerRowBiasEltAct<
                cutlass::epilogue::thread::Clamp,
                ElementD,
                ElementEpilogueCompute,
                ElementBias,
                ElementC,
                ElementEpilogueCompute>
        >::CollectiveOp;

    using StageCount = cutlass::gemm::collective::StageCountAutoCarveoutEpi<CollectiveEpilogue>;

    using CollectiveMainloop =
        typename cutlass::gemm::collective::CollectiveBuilder<
            ArchTag,
            OpClassTag,
            ElementPairA, LayoutA, kAlignmentA,
            ElementPairB, LayoutB, kAlignmentB,
            ElementAccumulator,
            MmaTileShape,
            ClusterShape,
            StageCount,
            KernelScheduleType
        >::CollectiveOp;

    using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
        cute::Shape<int,int,int,int>,
        CollectiveMainloop,
        CollectiveEpilogue,
        void>;

    using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
}

// 4.
namespace cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_void_f32_256x128x256_0_vs64_tnn_align64_o_2sm {

    using LayoutA = cutlass::layout::RowMajor;
    using LayoutB = cutlass::layout::ColumnMajor;
    using LayoutC = cutlass::layout::ColumnMajor;
    using LayoutD = cutlass::layout::ColumnMajor;

    using ElementPairA = cutlass::mx_float4_t<cutlass::float_e2m1_t>;
    using ElementPairB = cutlass::mx_float4_t<cutlass::float_e2m1_t>;
    using ElementC = void;
    using ElementD = float;

    constexpr int kAlignmentA = 64;
    constexpr int kAlignmentB = 32;
    constexpr int kAlignmentD = 128 / cutlass::sizeof_bits<ElementD>::value;
    constexpr int kAlignmentC = cute::is_same_v<ElementC, void> ? kAlignmentD : 128 / cutlass::sizeof_bits<ElementC>::value;

    using ProblemShape = Shape<int,int,int,int>;
    using ClusterShape = cute::Shape<cute::_2, cute::_1, cute::_1>;
    using MmaTileShape = Shape<_256, _128, _256>;
    using ArchTag = cutlass::arch::Sm100;
    using OpClassTag = cutlass::arch::OpClassBlockScaledSparseTensorOp;
    using EpilogueTile = cutlass::epilogue::collective::EpilogueTileAuto;
    using EpilogueScheduleType = cutlass::epilogue::TmaWarpSpecialized2SmMxf4;
    using KernelScheduleType = cutlass::gemm::KernelSparseTmaWarpSpecialized2SmMxf4Sm100;
    using ElementAccumulator = float;
    using ElementEpilogueCompute = float;
    using ElementBias = float;
    using TileScheduler = cutlass::gemm::PersistentScheduler;

    using CollectiveEpilogue =
        typename cutlass::epilogue::collective::CollectiveBuilder<
            ArchTag,
            OpClassTag,
            MmaTileShape,
            ClusterShape,
            EpilogueTile,
            ElementAccumulator,
            ElementEpilogueCompute,
            ElementC, LayoutC, kAlignmentC,
            ElementD, LayoutD, kAlignmentD,
            EpilogueScheduleType,
            cutlass::epilogue::fusion::PerRowLinCombPerRowBiasEltAct<
                cutlass::epilogue::thread::Clamp,
                ElementD,
                ElementEpilogueCompute,
                ElementBias,
                ElementC,
                ElementEpilogueCompute>
        >::CollectiveOp;

    using StageCount = cutlass::gemm::collective::StageCountAutoCarveoutEpi<CollectiveEpilogue>;

    using CollectiveMainloop =
        typename cutlass::gemm::collective::CollectiveBuilder<
            ArchTag,
            OpClassTag,
            ElementPairA, LayoutA, kAlignmentA,
            ElementPairB, LayoutB, kAlignmentB,
            ElementAccumulator,
            MmaTileShape,
            ClusterShape,
            StageCount,
            KernelScheduleType
        >::CollectiveOp;

    using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
        cute::Shape<int,int,int,int>,
        CollectiveMainloop,
        CollectiveEpilogue,
        void>;

    using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
}

// 5.
namespace cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_void_f32_256x192x256_0_vs64_tnn_align64_o_2sm {

    using LayoutA = cutlass::layout::RowMajor;
    using LayoutB = cutlass::layout::ColumnMajor;
    using LayoutC = cutlass::layout::ColumnMajor;
    using LayoutD = cutlass::layout::ColumnMajor;

    using ElementPairA = cutlass::mx_float4_t<cutlass::float_e2m1_t>;
    using ElementPairB = cutlass::mx_float4_t<cutlass::float_e2m1_t>;
    using ElementC = void;
    using ElementD = float;

    constexpr int kAlignmentA = 64;
    constexpr int kAlignmentB = 32;
    constexpr int kAlignmentD = 128 / cutlass::sizeof_bits<ElementD>::value;
    constexpr int kAlignmentC = cute::is_same_v<ElementC, void> ? kAlignmentD : 128 / cutlass::sizeof_bits<ElementC>::value;

    using ProblemShape = Shape<int,int,int,int>;
    using ClusterShape = cute::Shape<cute::_2, cute::_1, cute::_1>;
    using MmaTileShape = Shape<_256, _192, _256>;
    using ArchTag = cutlass::arch::Sm100;
    using OpClassTag = cutlass::arch::OpClassBlockScaledSparseTensorOp;
    using EpilogueTile = cutlass::epilogue::collective::EpilogueTileAuto;
    using EpilogueScheduleType = cutlass::epilogue::TmaWarpSpecialized2SmMxf4;
    using KernelScheduleType = cutlass::gemm::KernelSparseTmaWarpSpecialized2SmMxf4Sm100;
    using ElementAccumulator = float;
    using ElementEpilogueCompute = float;
    using ElementBias = float;
    using TileScheduler = cutlass::gemm::PersistentScheduler;

    using CollectiveEpilogue =
        typename cutlass::epilogue::collective::CollectiveBuilder<
            ArchTag,
            OpClassTag,
            MmaTileShape,
            ClusterShape,
            EpilogueTile,
            ElementAccumulator,
            ElementEpilogueCompute,
            ElementC, LayoutC, kAlignmentC,
            ElementD, LayoutD, kAlignmentD,
            EpilogueScheduleType,
            cutlass::epilogue::fusion::PerRowLinCombPerRowBiasEltAct<
                cutlass::epilogue::thread::Clamp,
                ElementD,
                ElementEpilogueCompute,
                ElementBias,
                ElementC,
                ElementEpilogueCompute>
        >::CollectiveOp;

    using StageCount = cutlass::gemm::collective::StageCountAutoCarveoutEpi<CollectiveEpilogue>;

    using CollectiveMainloop =
        typename cutlass::gemm::collective::CollectiveBuilder<
            ArchTag,
            OpClassTag,
            ElementPairA, LayoutA, kAlignmentA,
            ElementPairB, LayoutB, kAlignmentB,
            ElementAccumulator,
            MmaTileShape,
            ClusterShape,
            StageCount,
            KernelScheduleType
        >::CollectiveOp;

    using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
        cute::Shape<int,int,int,int>,
        CollectiveMainloop,
        CollectiveEpilogue,
        void>;

    using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
}

// 6.
namespace cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_void_f32_256x256x256_0_vs64_tnn_align64_o_2sm {

    using LayoutA = cutlass::layout::RowMajor;
    using LayoutB = cutlass::layout::ColumnMajor;
    using LayoutC = cutlass::layout::ColumnMajor;
    using LayoutD = cutlass::layout::ColumnMajor;

    using ElementPairA = cutlass::mx_float4_t<cutlass::float_e2m1_t>;
    using ElementPairB = cutlass::mx_float4_t<cutlass::float_e2m1_t>;
    using ElementC = void;
    using ElementD = float;

    constexpr int kAlignmentA = 64;
    constexpr int kAlignmentB = 32;
    constexpr int kAlignmentD = 128 / cutlass::sizeof_bits<ElementD>::value;
    constexpr int kAlignmentC = cute::is_same_v<ElementC, void> ? kAlignmentD : 128 / cutlass::sizeof_bits<ElementC>::value;

    using ProblemShape = Shape<int,int,int,int>;
    using ClusterShape = cute::Shape<cute::_2, cute::_1, cute::_1>;
    using MmaTileShape = Shape<_256, _256, _256>;
    using ArchTag = cutlass::arch::Sm100;
    using OpClassTag = cutlass::arch::OpClassBlockScaledSparseTensorOp;
    using EpilogueTile = cutlass::epilogue::collective::EpilogueTileAuto;
    using EpilogueScheduleType = cutlass::epilogue::TmaWarpSpecialized2SmMxf4;
    using KernelScheduleType = cutlass::gemm::KernelSparseTmaWarpSpecialized2SmMxf4Sm100;
    using ElementAccumulator = float;
    using ElementEpilogueCompute = float;
    using ElementBias = float;
    using TileScheduler = cutlass::gemm::PersistentScheduler;

    using CollectiveEpilogue =
        typename cutlass::epilogue::collective::CollectiveBuilder<
            ArchTag,
            OpClassTag,
            MmaTileShape,
            ClusterShape,
            EpilogueTile,
            ElementAccumulator,
            ElementEpilogueCompute,
            ElementC, LayoutC, kAlignmentC,
            ElementD, LayoutD, kAlignmentD,
            EpilogueScheduleType,
            cutlass::epilogue::fusion::PerRowLinCombPerRowBiasEltAct<
                cutlass::epilogue::thread::Clamp,
                ElementD,
                ElementEpilogueCompute,
                ElementBias,
                ElementC,
                ElementEpilogueCompute>
        >::CollectiveOp;

    using StageCount = cutlass::gemm::collective::StageCountAutoCarveoutEpi<CollectiveEpilogue>;

    using CollectiveMainloop =
        typename cutlass::gemm::collective::CollectiveBuilder<
            ArchTag,
            OpClassTag,
            ElementPairA, LayoutA, kAlignmentA,
            ElementPairB, LayoutB, kAlignmentB,
            ElementAccumulator,
            MmaTileShape,
            ClusterShape,
            StageCount,
            KernelScheduleType
        >::CollectiveOp;

    using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
        cute::Shape<int,int,int,int>,
        CollectiveMainloop,
        CollectiveEpilogue,
        void>;

    using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
}

// 6.
namespace cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_void_f32_256x256x512_0_vs64_tnn_align64_o_2sm {

    using LayoutA = cutlass::layout::RowMajor;
    using LayoutB = cutlass::layout::ColumnMajor;
    using LayoutC = cutlass::layout::ColumnMajor;
    using LayoutD = cutlass::layout::ColumnMajor;

    using ElementPairA = cutlass::mx_float4_t<cutlass::float_e2m1_t>;
    using ElementPairB = cutlass::mx_float4_t<cutlass::float_e2m1_t>;
    using ElementC = void;
    using ElementD = float;

    constexpr int kAlignmentA = 64;
    constexpr int kAlignmentB = 32;
    constexpr int kAlignmentD = 128 / cutlass::sizeof_bits<ElementD>::value;
    constexpr int kAlignmentC = cute::is_same_v<ElementC, void> ? kAlignmentD : 128 / cutlass::sizeof_bits<ElementC>::value;

    using ProblemShape = Shape<int,int,int,int>;
    using ClusterShape = cute::Shape<cute::_2, cute::_1, cute::_1>;
    using MmaTileShape = Shape<_256, _256, _512>;
    using ArchTag = cutlass::arch::Sm100;
    using OpClassTag = cutlass::arch::OpClassBlockScaledSparseTensorOp;
    using EpilogueTile = cutlass::epilogue::collective::EpilogueTileAuto;
    using EpilogueScheduleType = cutlass::epilogue::TmaWarpSpecialized2SmMxf4;
    using KernelScheduleType = cutlass::gemm::KernelSparseTmaWarpSpecialized2SmMxf4Sm100;
    using ElementAccumulator = float;
    using ElementEpilogueCompute = float;
    using ElementBias = float;
    using TileScheduler = cutlass::gemm::PersistentScheduler;

    using CollectiveEpilogue =
        typename cutlass::epilogue::collective::CollectiveBuilder<
            ArchTag,
            OpClassTag,
            MmaTileShape,
            ClusterShape,
            EpilogueTile,
            ElementAccumulator,
            ElementEpilogueCompute,
            ElementC, LayoutC, kAlignmentC,
            ElementD, LayoutD, kAlignmentD,
            EpilogueScheduleType,
            cutlass::epilogue::fusion::PerRowLinCombPerRowBiasEltAct<
                cutlass::epilogue::thread::Clamp,
                ElementD,
                ElementEpilogueCompute,
                ElementBias,
                ElementC,
                ElementEpilogueCompute>
        >::CollectiveOp;

    using StageCount = cutlass::gemm::collective::StageCountAutoCarveoutEpi<CollectiveEpilogue>;

    using CollectiveMainloop =
        typename cutlass::gemm::collective::CollectiveBuilder<
            ArchTag,
            OpClassTag,
            ElementPairA, LayoutA, kAlignmentA,
            ElementPairB, LayoutB, kAlignmentB,
            ElementAccumulator,
            MmaTileShape,
            ClusterShape,
            StageCount,
            KernelScheduleType
        >::CollectiveOp;

    using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
        cute::Shape<int,int,int,int>,
        CollectiveMainloop,
        CollectiveEpilogue,
        void>;

    using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
}

// 1.
TEST(cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_void_f32_128x128x256_0_vs64_tnn_align64_o_1sm, functional) {
  namespace gemm = cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_void_f32_128x128x256_0_vs64_tnn_align64_o_1sm;
  EXPECT_TRUE(test::gemm::device::TestSmall<gemm::Gemm>(
    1, 0,
    test::gemm::device::CheckEquality::RELATIVE,
    test::gemm::device::ScalarLoc::ON_DEVICE,
    test::gemm::device::VectorScale::ENABLED,
    {256, 3840}));
}

// 2.
TEST(cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_void_f32_128x192x256_0_vs64_tnn_align64_o_1sm, functional) {
  namespace gemm = cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_void_f32_128x192x256_0_vs64_tnn_align64_o_1sm;
  EXPECT_TRUE(test::gemm::device::TestSmall<gemm::Gemm>(
    1, 0,
    test::gemm::device::CheckEquality::RELATIVE,
    test::gemm::device::ScalarLoc::ON_DEVICE,
    test::gemm::device::VectorScale::ENABLED,
    {256, 3840}));
}

// 3.
TEST(cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_void_f32_128x256x256_0_vs64_tnn_align64_o_1sm, functional) {
  namespace gemm = cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_void_f32_128x256x256_0_vs64_tnn_align64_o_1sm;
  EXPECT_TRUE(test::gemm::device::TestSmall<gemm::Gemm>(
    1, 0,
    test::gemm::device::CheckEquality::RELATIVE,
    test::gemm::device::ScalarLoc::ON_DEVICE,
    test::gemm::device::VectorScale::ENABLED,
    {256, 3840}));
}

// 3.2
TEST(cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_void_f32_128x256x512_0_vs64_tnn_align64_o_1sm, functional) {
  namespace gemm = cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_void_f32_128x256x512_0_vs64_tnn_align64_o_1sm;
  EXPECT_TRUE(test::gemm::device::TestSmall<gemm::Gemm>(
    1, 0,
    test::gemm::device::CheckEquality::RELATIVE,
    test::gemm::device::ScalarLoc::ON_DEVICE,
    test::gemm::device::VectorScale::ENABLED,
    {512, 3840}));
}

// 4.
TEST(cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_void_f32_256x128x256_0_vs64_tnn_align64_o_2sm, functional) {
  namespace gemm = cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_void_f32_256x128x256_0_vs64_tnn_align64_o_2sm;
  EXPECT_TRUE(test::gemm::device::TestSmall<gemm::Gemm>(
    1, 0,
    test::gemm::device::CheckEquality::RELATIVE,
    test::gemm::device::ScalarLoc::ON_DEVICE,
    test::gemm::device::VectorScale::ENABLED,
    {256, 3840}));
}

// 5.
TEST(cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_void_f32_256x192x256_0_vs64_tnn_align64_o_2sm, functional) {
  namespace gemm = cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_void_f32_256x192x256_0_vs64_tnn_align64_o_2sm;
  EXPECT_TRUE(test::gemm::device::TestSmall<gemm::Gemm>(
    1, 0,
    test::gemm::device::CheckEquality::RELATIVE,
    test::gemm::device::ScalarLoc::ON_DEVICE,
    test::gemm::device::VectorScale::ENABLED,
    {256, 3840}));
}

// 6.
TEST(cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_void_f32_256x256x256_0_vs64_tnn_align64_o_2sm, functional) {
  namespace gemm = cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_void_f32_256x256x256_0_vs64_tnn_align64_o_2sm;
  EXPECT_TRUE(test::gemm::device::TestSmall<gemm::Gemm>(
    1, 0,
    test::gemm::device::CheckEquality::RELATIVE,
    test::gemm::device::ScalarLoc::ON_DEVICE,
    test::gemm::device::VectorScale::ENABLED,
    {256, 3840}));
}

// 6.2
TEST(cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_void_f32_256x256x512_0_vs64_tnn_align64_o_2sm, functional) {
  namespace gemm = cutlass3x_sm100_bssptensorop_bsspgemm_ue8m0xe2m1_ue8m0xe2m1_f32_void_f32_256x256x512_0_vs64_tnn_align64_o_2sm;
  EXPECT_TRUE(test::gemm::device::TestSmall<gemm::Gemm>(
    1, 0,
    test::gemm::device::CheckEquality::RELATIVE,
    test::gemm::device::ScalarLoc::ON_DEVICE,
    test::gemm::device::VectorScale::ENABLED,
    {512, 3840}));
}

#endif
