submission 486666

from typing import NamedTuple, Tuple, Type, Union

import cutlass
import cutlass.cute as cute
import cutlass.pipeline as pipeline
import cutlass.utils as utils
import cutlass.utils.blackwell_helpers as sm100_utils
import cutlass.utils.blockscaled_layout as blockscaled_utils
import torch
from cutlass.cute.nvgpu import cpasync, tcgen05
from cutlass.cute.runtime import make_ptr
from cutlass.pipeline import pipeline_init_arrive, pipeline_init_wait


class Sm100GroupedBlockScaledGemmKernel:
    def __init__(
        self,
        sf_vec_size: int,
        mma_tiler_mn: Tuple[int, int],
        cluster_shape_mn: Tuple[int, int],
    ):
        self.acc_dtype = cutlass.Float32
        self.sf_vec_size = sf_vec_size
        self.use_2cta_instrs = mma_tiler_mn[0] == 256
        self.cluster_shape_mn = cluster_shape_mn
        self.mma_tiler = (*mma_tiler_mn, 1)  # K dimension is deferred in _setup_attributes

        self.cta_group = tcgen05.CtaGroup.TWO if self.use_2cta_instrs else tcgen05.CtaGroup.ONE

        self.tensormap_update_mode = utils.TensorMapUpdateMode.SMEM

        self.occupancy = 1

        # warp ids
        self.epilog_warp_id = (0, 1, 2, 3)
        self.mma_warp_id = 4
        self.tma_warp_id = 5
        self.threads_per_cta = 32 * len((self.mma_warp_id, self.tma_warp_id, *self.epilog_warp_id))

        # Set barrier for epilogue sync and tmem ptr sync
        self.epilog_sync_barrier = pipeline.NamedBarrier(
            barrier_id=1,
            num_threads=32 * len(self.epilog_warp_id),
        )
        self.tmem_alloc_barrier = pipeline.NamedBarrier(
            barrier_id=2,
            num_threads=32 * len((self.mma_warp_id, *self.epilog_warp_id)),
        )

        # Barrier used by MMA/TMA warps to signal A/B tensormap initialization completion
        self.tensormap_ab_init_barrier = pipeline.NamedBarrier(
            barrier_id=3,
            num_threads=64,
        )
        self.smem_capacity = utils.get_smem_capacity_in_bytes("sm_100")
        SM100_TMEM_CAPACITY_COLUMNS = 512
        self.num_tmem_alloc_cols = SM100_TMEM_CAPACITY_COLUMNS

    # Set up configurations that dependent on gemm inputs.
    def _setup_attributes(self):
        # Compute mma instruction shapes
        # (MMA_Tile_Shape_M, MMA_Tile_Shape_N, MMA_Inst_Shape_K)
        self.mma_inst_shape_mn = (
            self.mma_tiler[0],
            self.mma_tiler[1],
        )
        # (CTA_Tile_Shape_M, Round_Up(MMA_Tile_Shape_N, 128), MMA_Inst_Shape_K)
        self.mma_inst_shape_mn_sfb = (
            self.mma_inst_shape_mn[0] // (2 if self.use_2cta_instrs else 1),
            cute.round_up(self.mma_inst_shape_mn[1], 128),
        )

        self.tiled_mma = sm100_utils.make_blockscaled_trivial_tiled_mma(
            self.a_dtype,
            self.a_major_mode,
            self.b_major_mode,
            self.sf_dtype,
            self.sf_vec_size,
            self.cta_group,
            self.mma_inst_shape_mn,
        )

        self.tiled_mma_sfb = sm100_utils.make_blockscaled_trivial_tiled_mma(
            self.a_dtype,
            self.a_major_mode,
            self.b_major_mode,
            self.sf_dtype,
            self.sf_vec_size,
            cute.nvgpu.tcgen05.CtaGroup.ONE,
            self.mma_inst_shape_mn_sfb,
        )

        # Compute mma/cluster/tile shapes
        mma_inst_shape_k = cute.size(self.tiled_mma.shape_mnk, mode=[2])
        mma_inst_tile_k = 4
        self.mma_tiler = (
            self.mma_inst_shape_mn[0],
            self.mma_inst_shape_mn[1],
            mma_inst_shape_k * mma_inst_tile_k,
        )
        self.mma_tiler_sfb = (
            self.mma_inst_shape_mn_sfb[0],
            self.mma_inst_shape_mn_sfb[1],
            mma_inst_shape_k * mma_inst_tile_k,
        )
        self.cta_tile_shape_mnk = (
            self.mma_tiler[0] // cute.size(self.tiled_mma.thr_id.shape),
            self.mma_tiler[1],
            self.mma_tiler[2],
        )
        self.cluster_tile_shape_mnk = tuple(
            x * y for x, y in zip(self.cta_tile_shape_mnk, (*self.cluster_shape_mn, 1))
        )

        # Compute cluster layout
        self.cluster_layout_vmnk = cute.tiled_divide(
            cute.make_layout((*self.cluster_shape_mn, 1)),
            (self.tiled_mma.thr_id.shape,),
        )
        self.cluster_layout_sfb_vmnk = cute.tiled_divide(
            cute.make_layout((*self.cluster_shape_mn, 1)),
            (self.tiled_mma_sfb.thr_id.shape,),
        )

        # Compute number of multicast CTAs for A/B
        self.num_mcast_ctas_a = cute.size(self.cluster_layout_vmnk.shape[2])
        self.num_mcast_ctas_b = cute.size(self.cluster_layout_vmnk.shape[1])
        self.num_mcast_ctas_sfb = cute.size(self.cluster_layout_sfb_vmnk.shape[1])
        self.is_a_mcast = self.num_mcast_ctas_a > 1
        self.is_b_mcast = self.num_mcast_ctas_b > 1
        self.is_sfb_mcast = self.num_mcast_ctas_sfb > 1

        # Compute epilogue subtile
        self.epi_tile = sm100_utils.compute_epilogue_tile_shape(
            self.cta_tile_shape_mnk,
            self.use_2cta_instrs,
            self.c_layout,
            self.c_dtype,
        )

        # Setup A/B/C stage count in shared memory and ACC stage count in tensor memory
        self.num_acc_stage, self.num_ab_stage, self.num_c_stage = self._compute_stages(
            self.tiled_mma,
            self.mma_tiler,
            self.a_dtype,
            self.b_dtype,
            self.epi_tile,
            self.c_dtype,
            self.c_layout,
            self.sf_dtype,
            self.sf_vec_size,
            self.smem_capacity,
            self.occupancy,
        )

        # Compute A/B/SFA/SFB/C shared memory layout
        self.a_smem_layout_staged = sm100_utils.make_smem_layout_a(
            self.tiled_mma,
            self.mma_tiler,
            self.a_dtype,
            self.num_ab_stage,
        )
        self.b_smem_layout_staged = sm100_utils.make_smem_layout_b(
            self.tiled_mma,
            self.mma_tiler,
            self.b_dtype,
            self.num_ab_stage,
        )
        self.sfa_smem_layout_staged = blockscaled_utils.make_smem_layout_sfa(
            self.tiled_mma,
            self.mma_tiler,
            self.sf_vec_size,
            self.num_ab_stage,
        )
        self.sfb_smem_layout_staged = blockscaled_utils.make_smem_layout_sfb(
            self.tiled_mma,
            self.mma_tiler,  # NOTE: sfb fix
            self.sf_vec_size,
            self.num_ab_stage,
        )
        self.c_smem_layout_staged = sm100_utils.make_smem_layout_epi(
            self.c_dtype,
            self.c_layout,
            self.epi_tile,
            self.num_c_stage,
        )

        mbar_smem_bytes = self._get_mbar_smem_bytes(
            num_acc_stage=self.num_acc_stage,
            num_ab_stage=self.num_ab_stage,
            num_c_stage=self.num_c_stage,
        )

        # Use utils.TensorMapUpdateMode.SMEM by default
        tensormap_smem_bytes = (
            Sm100GroupedBlockScaledGemmKernel.bytes_per_tensormap
            * Sm100GroupedBlockScaledGemmKernel.num_tensormaps
        )
        if (
            mbar_smem_bytes
            + tensormap_smem_bytes
            + Sm100GroupedBlockScaledGemmKernel.tensor_memory_management_bytes
            > self.reserved_smem_bytes
        ):
            raise ValueError(
                f"smem consumption for mbar and tensormap {mbar_smem_bytes + tensormap_smem_bytes} exceeds the "
                f"reserved smem bytes {self.reserved_smem_bytes}"
            )

    @cute.jit
    def __call__(
        self,
        initial_a: cute.Tensor,
        initial_b: cute.Tensor,
        initial_c: cute.Tensor,
        initial_sfa: cute.Tensor,
        initial_sfb: cute.Tensor,
        group_count: cutlass.Constexpr[int],
        problem_shape_mnkl: cute.Tensor,
        tensor_address_abc: cute.Tensor,
        tensor_address_sfasfb: cute.Tensor,
        total_num_clusters: cutlass.Constexpr[int],
        tensormap_cute_tensor: cute.Tensor,
        max_active_clusters: cutlass.Constexpr[int],
    ):
        self.a_dtype = initial_a.element_type
        self.b_dtype = initial_b.element_type
        self.sf_dtype = initial_sfa.element_type
        self.c_dtype = initial_c.element_type
        self.a_major_mode = utils.LayoutEnum.from_tensor(initial_a).mma_major_mode()
        self.b_major_mode = utils.LayoutEnum.from_tensor(initial_b).mma_major_mode()
        self.c_layout = utils.LayoutEnum.from_tensor(initial_c)

        self._setup_attributes()

        sfa_layout = blockscaled_utils.tile_atom_to_shape_SF(initial_a.shape, self.sf_vec_size)
        initial_sfa = cute.make_tensor(initial_sfa.iterator, sfa_layout)

        sfb_layout = blockscaled_utils.tile_atom_to_shape_SF(initial_b.shape, self.sf_vec_size)
        initial_sfb = cute.make_tensor(initial_sfb.iterator, sfb_layout)

        tiled_mma = self.tiled_mma
        atom_thr_size = cute.size(tiled_mma.thr_id.shape)

        a_op = sm100_utils.cluster_shape_to_tma_atom_A(self.cluster_shape_mn, tiled_mma.thr_id)
        a_smem_layout = cute.slice_(self.a_smem_layout_staged, (None, None, None, 0))
        tma_atom_a, tma_tensor_a = cute.nvgpu.make_tiled_tma_atom_A(
            a_op,
            initial_a,
            a_smem_layout,
            self.mma_tiler,
            tiled_mma,
            self.cluster_layout_vmnk.shape,
        )

        b_op = sm100_utils.cluster_shape_to_tma_atom_B(self.cluster_shape_mn, tiled_mma.thr_id)
        b_smem_layout = cute.slice_(self.b_smem_layout_staged, (None, None, None, 0))
        tma_atom_b, tma_tensor_b = cute.nvgpu.make_tiled_tma_atom_B(
            b_op,
            initial_b,
            b_smem_layout,
            self.mma_tiler,
            tiled_mma,
            self.cluster_layout_vmnk.shape,
        )

        # Setup TMA load for SFA
        sfa_op = sm100_utils.cluster_shape_to_tma_atom_A(self.cluster_shape_mn, tiled_mma.thr_id)
        sfa_smem_layout = cute.slice_(self.sfa_smem_layout_staged, (None, None, None, 0))
        tma_atom_sfa, tma_tensor_sfa = cute.nvgpu.make_tiled_tma_atom_A(
            sfa_op,
            initial_sfa,
            sfa_smem_layout,
            self.mma_tiler,
            tiled_mma,
            self.cluster_layout_vmnk.shape,
            internal_type=cutlass.Int16,
        )

        # Setup TMA load for SFB
        sfb_op = sm100_utils.cluster_shape_to_tma_atom_SFB(self.cluster_shape_mn, tiled_mma.thr_id)
        sfb_smem_layout = cute.slice_(self.sfb_smem_layout_staged, (None, None, None, 0))
        tma_atom_sfb, tma_tensor_sfb = cute.nvgpu.make_tiled_tma_atom_B(
            sfb_op,
            initial_sfb,
            sfb_smem_layout,
            self.mma_tiler_sfb,
            self.tiled_mma_sfb,
            self.cluster_layout_sfb_vmnk.shape,
            internal_type=cutlass.Int16,
        )

        a_copy_size = cute.size_in_bytes(self.a_dtype, a_smem_layout)
        b_copy_size = cute.size_in_bytes(self.b_dtype, b_smem_layout)
        sfa_copy_size = cute.size_in_bytes(self.sf_dtype, sfa_smem_layout)
        sfb_copy_size = cute.size_in_bytes(self.sf_dtype, sfb_smem_layout)
        self.num_tma_load_bytes = (
            a_copy_size + b_copy_size + sfa_copy_size + sfb_copy_size
        ) * atom_thr_size

        # Setup TMA store for C
        epi_smem_layout = cute.slice_(self.c_smem_layout_staged, (None, None, 0))
        tma_atom_c, tma_tensor_c = cpasync.make_tiled_tma_atom(
            cpasync.CopyBulkTensorTileS2GOp(),
            initial_c,
            epi_smem_layout,
            self.epi_tile,
        )

        # Compute grid size
        self.tile_sched_params, grid = self._compute_grid(
            total_num_clusters, self.cluster_shape_mn, max_active_clusters
        )

        self.buffer_align_bytes = 1024
        self.size_tensormap_in_i64 = (
            Sm100GroupedBlockScaledGemmKernel.num_tensormaps
            * Sm100GroupedBlockScaledGemmKernel.bytes_per_tensormap
            // 8
        )

        # Define shared storage for kernel
        @cute.struct
        class SharedStorage:
            tensormap_buffer: cute.struct.MemRange[cutlass.Int64, self.size_tensormap_in_i64]
            ab_full_mbar_ptr: cute.struct.MemRange[cutlass.Int64, self.num_ab_stage]
            ab_empty_mbar_ptr: cute.struct.MemRange[cutlass.Int64, self.num_ab_stage]
            acc_full_mbar_ptr: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage]
            acc_empty_mbar_ptr: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage]
            tmem_dealloc_mbar_ptr: cutlass.Int64
            tmem_holding_buf: cutlass.Int32
            # (EPI_TILE_M, EPI_TILE_N, STAGE)
            sC: cute.struct.Align[
                cute.struct.MemRange[
                    self.c_dtype,
                    cute.cosize(self.c_smem_layout_staged.outer),
                ],
                self.buffer_align_bytes,
            ]
            # (MMA, MMA_M, MMA_K, STAGE)
            sA: cute.struct.Align[
                cute.struct.MemRange[self.a_dtype, cute.cosize(self.a_smem_layout_staged.outer)],
                self.buffer_align_bytes,
            ]
            # (MMA, MMA_N, MMA_K, STAGE)
            sB: cute.struct.Align[
                cute.struct.MemRange[self.b_dtype, cute.cosize(self.b_smem_layout_staged.outer)],
                self.buffer_align_bytes,
            ]
            # (MMA, MMA_M, MMA_K, STAGE)
            sSFA: cute.struct.Align[
                cute.struct.MemRange[self.sf_dtype, cute.cosize(self.sfa_smem_layout_staged)],
                self.buffer_align_bytes,
            ]
            # (MMA, MMA_N, MMA_K, STAGE)
            sSFB: cute.struct.Align[
                cute.struct.MemRange[self.sf_dtype, cute.cosize(self.sfb_smem_layout_staged)],
                self.buffer_align_bytes,
            ]

        self.shared_storage = SharedStorage

        # Launch the kernel synchronously
        self.kernel(
            tiled_mma,
            self.tiled_mma_sfb,
            tma_atom_a,
            tma_tensor_a,
            tma_atom_b,
            tma_tensor_b,
            tma_atom_sfa,
            tma_tensor_sfa,
            tma_atom_sfb,
            tma_tensor_sfb,
            tma_atom_c,
            tma_tensor_c,
            self.cluster_layout_vmnk,
            self.cluster_layout_sfb_vmnk,
            self.a_smem_layout_staged,
            self.b_smem_layout_staged,
            self.sfa_smem_layout_staged,
            self.sfb_smem_layout_staged,
            self.c_smem_layout_staged,
            self.epi_tile,
            self.tile_sched_params,
            group_count,
            problem_shape_mnkl,
            tensor_address_abc,
            tensor_address_sfasfb,
            tensormap_cute_tensor,
        ).launch(
            grid=grid,
            block=[self.threads_per_cta, 1, 1],
            cluster=(*self.cluster_shape_mn, 1),
            smem=self.shared_storage.size_in_bytes(),
            min_blocks_per_mp=1,
        )
        return

    #  GPU device kernel
    @cute.kernel
    def kernel(
        self,
        tiled_mma: cute.TiledMma,
        tiled_mma_sfb: cute.TiledMma,
        tma_atom_a: cute.CopyAtom,
        mA_mkl: cute.Tensor,
        tma_atom_b: cute.CopyAtom,
        mB_nkl: cute.Tensor,
        tma_atom_sfa: cute.CopyAtom,
        mSFA_mkl: cute.Tensor,
        tma_atom_sfb: cute.CopyAtom,
        mSFB_nkl: cute.Tensor,
        tma_atom_c: cute.CopyAtom,
        mC_mnl: cute.Tensor,
        cluster_layout_vmnk: cute.Layout,
        cluster_layout_sfb_vmnk: cute.Layout,
        a_smem_layout_staged: cute.ComposedLayout,
        b_smem_layout_staged: cute.ComposedLayout,
        sfa_smem_layout_staged: cute.Layout,
        sfb_smem_layout_staged: cute.Layout,
        c_smem_layout_staged: Union[cute.Layout, cute.ComposedLayout],
        epi_tile: cute.Tile,
        tile_sched_params: utils.PersistentTileSchedulerParams,
        group_count: cutlass.Constexpr,
        problem_sizes_mnkl: cute.Tensor,
        ptrs_abc: cute.Tensor,
        ptrs_sfasfb: cute.Tensor,
        tensormaps: cute.Tensor,
    ):
        warp_idx = cute.arch.warp_idx()
        warp_idx = cute.arch.make_warp_uniform(warp_idx)
        if warp_idx == self.tma_warp_id:
            cute.nvgpu.cpasync.prefetch_descriptor(tma_atom_a)
            cute.nvgpu.cpasync.prefetch_descriptor(tma_atom_b)
            cute.nvgpu.cpasync.prefetch_descriptor(tma_atom_sfa)
            cute.nvgpu.cpasync.prefetch_descriptor(tma_atom_sfb)
            cute.nvgpu.cpasync.prefetch_descriptor(tma_atom_c)

        use_2cta_instrs = cute.size(tiled_mma.thr_id.shape) == 2

        #
        # Setup cta/thread coordinates
        #
        # Coords inside cluster
        bidx, bidy, bidz = cute.arch.block_idx()
        mma_tile_coord_v = bidx % cute.size(tiled_mma.thr_id.shape)
        is_leader_cta = mma_tile_coord_v == 0
        cta_rank_in_cluster = cute.arch.make_warp_uniform(cute.arch.block_idx_in_cluster())
        block_in_cluster_coord_vmnk = cluster_layout_vmnk.get_flat_coord(cta_rank_in_cluster)
        block_in_cluster_coord_sfb_vmnk = cluster_layout_sfb_vmnk.get_flat_coord(
            cta_rank_in_cluster
        )
        # coord inside cta
        tidx, _, _ = cute.arch.thread_idx()

        #
        # Alloc and init: tensormap buffer, a+b full/empty, accumulator full/empty, tensor memory dealloc barrier
        #
        smem = utils.SmemAllocator()
        storage = smem.allocate(self.shared_storage)

        tensormap_smem_ptr = storage.tensormap_buffer.data_ptr()
        tensormap_a_smem_ptr = tensormap_smem_ptr
        tensormap_b_smem_ptr = (
            tensormap_a_smem_ptr + Sm100GroupedBlockScaledGemmKernel.bytes_per_tensormap // 8
        )
        tensormap_sfa_smem_ptr = (
            tensormap_b_smem_ptr + Sm100GroupedBlockScaledGemmKernel.bytes_per_tensormap // 8
        )
        tensormap_sfb_smem_ptr = (
            tensormap_sfa_smem_ptr + Sm100GroupedBlockScaledGemmKernel.bytes_per_tensormap // 8
        )
        tensormap_c_smem_ptr = (
            tensormap_sfb_smem_ptr + Sm100GroupedBlockScaledGemmKernel.bytes_per_tensormap // 8
        )

        tmem_dealloc_mbar_ptr = storage.tmem_dealloc_mbar_ptr
        tmem_holding_buf = storage.tmem_holding_buf

        # Initialize mainloop ab_pipeline (barrier) and states
        ab_pipeline_producer_group = pipeline.CooperativeGroup(pipeline.Agent.Thread)
        num_tma_producer = self.num_mcast_ctas_a + self.num_mcast_ctas_b - 1
        ab_pipeline_consumer_group = pipeline.CooperativeGroup(
            pipeline.Agent.Thread, num_tma_producer
        )
        ab_pipeline = pipeline.PipelineTmaUmma.create(
            barrier_storage=storage.ab_full_mbar_ptr.data_ptr(),
            num_stages=self.num_ab_stage,
            producer_group=ab_pipeline_producer_group,
            consumer_group=ab_pipeline_consumer_group,
            tx_count=self.num_tma_load_bytes,
            cta_layout_vmnk=cluster_layout_vmnk,
        )

        # Initialize acc_pipeline (barrier) and states
        acc_pipeline_producer_group = pipeline.CooperativeGroup(pipeline.Agent.Thread)
        num_acc_consumer_threads = len(self.epilog_warp_id) * (2 if use_2cta_instrs else 1)
        acc_pipeline_consumer_group = pipeline.CooperativeGroup(
            pipeline.Agent.Thread, num_acc_consumer_threads
        )
        acc_pipeline = pipeline.PipelineUmmaAsync.create(
            barrier_storage=storage.acc_full_mbar_ptr.data_ptr(),
            num_stages=self.num_acc_stage,
            producer_group=acc_pipeline_producer_group,
            consumer_group=acc_pipeline_consumer_group,
            cta_layout_vmnk=cluster_layout_vmnk,
        )

        # Tensor memory dealloc barrier init
        if use_2cta_instrs:
            if warp_idx == self.tma_warp_id:
                num_tmem_dealloc_threads = 32
                with cute.arch.elect_one():
                    cute.arch.mbarrier_init(tmem_dealloc_mbar_ptr, num_tmem_dealloc_threads)

        # Cluster arrive after barrier init
        pipeline_init_arrive(cluster_shape_mn=self.cluster_shape_mn, is_relaxed=True)

        #
        # Setup smem tensor A/B/SFA/SFB/C
        #
        sC = storage.sC.get_tensor(c_smem_layout_staged.outer, swizzle=c_smem_layout_staged.inner)
        # (MMA, MMA_M, MMA_K, STAGE)
        sA = storage.sA.get_tensor(a_smem_layout_staged.outer, swizzle=a_smem_layout_staged.inner)
        # (MMA, MMA_N, MMA_K, STAGE)
        sB = storage.sB.get_tensor(b_smem_layout_staged.outer, swizzle=b_smem_layout_staged.inner)
        # (MMA, MMA_M, MMA_K, STAGE)
        sSFA = storage.sSFA.get_tensor(sfa_smem_layout_staged)
        # (MMA, MMA_N, MMA_K, STAGE)
        sSFB = storage.sSFB.get_tensor(sfb_smem_layout_staged)

        #
        # Compute multicast mask for A/B/SFA/SFB buffer full
        #
        a_full_mcast_mask = None
        b_full_mcast_mask = None
        sfa_full_mcast_mask = None
        sfb_full_mcast_mask = None
        if cutlass.const_expr(self.is_a_mcast or self.is_b_mcast or use_2cta_instrs):
            a_full_mcast_mask = cpasync.create_tma_multicast_mask(
                cluster_layout_vmnk, block_in_cluster_coord_vmnk, mcast_mode=2
            )
            b_full_mcast_mask = cpasync.create_tma_multicast_mask(
                cluster_layout_vmnk, block_in_cluster_coord_vmnk, mcast_mode=1
            )
            sfa_full_mcast_mask = cpasync.create_tma_multicast_mask(
                cluster_layout_vmnk, block_in_cluster_coord_vmnk, mcast_mode=2
            )
            sfb_full_mcast_mask = cpasync.create_tma_multicast_mask(
                cluster_layout_sfb_vmnk, block_in_cluster_coord_sfb_vmnk, mcast_mode=1
            )

        #
        # Local_tile partition global tensors
        #
        # (bM, bK, RestM, RestK, RestL)
        gA_mkl = cute.local_tile(
            mA_mkl, cute.slice_(self.mma_tiler, (None, 0, None)), (None, None, None)
        )
        # (bN, bK, RestN, RestK, RestL)
        gB_nkl = cute.local_tile(
            mB_nkl, cute.slice_(self.mma_tiler, (0, None, None)), (None, None, None)
        )
        # (bM, bK, RestM, RestK, RestL)
        gSFA_mkl = cute.local_tile(
            mSFA_mkl, cute.slice_(self.mma_tiler, (None, 0, None)), (None, None, None)
        )
        # (bN, bK, RestN, RestK, RestL)
        gSFB_nkl = cute.local_tile(
            mSFB_nkl,
            cute.slice_(self.mma_tiler_sfb, (0, None, None)),
            (None, None, None),
        )
        # (bM, bN, RestM, RestN, RestL)
        gC_mnl = cute.local_tile(
            mC_mnl, cute.slice_(self.mma_tiler, (None, None, 0)), (None, None, None)
        )

        #
        # Partition global tensor for TiledMMA_A/B/C
        #
        thr_mma = tiled_mma.get_slice(mma_tile_coord_v)
        thr_mma_sfb = tiled_mma_sfb.get_slice(mma_tile_coord_v)
        # (MMA, MMA_M, MMA_K, RestM, RestK, RestL)
        tCgA = thr_mma.partition_A(gA_mkl)
        # (MMA, MMA_N, MMA_K, RestN, RestK, RestL)
        tCgB = thr_mma.partition_B(gB_nkl)
        # (MMA, MMA_M, MMA_K, RestM, RestK, RestL)
        tCgSFA = thr_mma.partition_A(gSFA_mkl)
        # (MMA, MMA_N, MMA_K, RestN, RestK, RestL)
        tCgSFB = thr_mma_sfb.partition_B(gSFB_nkl)
        # (MMA, MMA_M, MMA_N, RestM, RestN, RestL)
        tCgC = thr_mma.partition_C(gC_mnl)

        #
        # Partition global/shared tensor for TMA load A/B
        #
        # TMA load A partition_S/D
        a_cta_layout = cute.make_layout(cute.slice_(cluster_layout_vmnk, (0, 0, None, 0)).shape)
        # ((atom_v, rest_v), STAGE)
        # ((atom_v, rest_v), RestM, RestK, RestL)
        tAsA, tAgA = cpasync.tma_partition(
            tma_atom_a,
            block_in_cluster_coord_vmnk[2],
            a_cta_layout,
            cute.group_modes(sA, 0, 3),
            cute.group_modes(tCgA, 0, 3),
        )
        # TMA load B partition_S/D
        b_cta_layout = cute.make_layout(cute.slice_(cluster_layout_vmnk, (0, None, 0, 0)).shape)
        # ((atom_v, rest_v), STAGE)
        # ((atom_v, rest_v), RestN, RestK, RestL)
        tBsB, tBgB = cpasync.tma_partition(
            tma_atom_b,
            block_in_cluster_coord_vmnk[1],
            b_cta_layout,
            cute.group_modes(sB, 0, 3),
            cute.group_modes(tCgB, 0, 3),
        )

        #  TMA Load SFA partition_S/D
        sfa_cta_layout = a_cta_layout
        # ((atom_v, rest_v), STAGE)
        # ((atom_v, rest_v), RestM, RestK, RestL)
        tAsSFA, tAgSFA = cute.nvgpu.cpasync.tma_partition(
            tma_atom_sfa,
            block_in_cluster_coord_vmnk[2],
            sfa_cta_layout,
            cute.group_modes(sSFA, 0, 3),
            cute.group_modes(tCgSFA, 0, 3),
        )
        tAsSFA = cute.filter_zeros(tAsSFA)
        tAgSFA = cute.filter_zeros(tAgSFA)

        # TMA Load SFB partition_S/D
        sfb_cta_layout = cute.make_layout(
            cute.slice_(cluster_layout_sfb_vmnk, (0, None, 0, 0)).shape
        )
        # ((atom_v, rest_v), STAGE)
        # ((atom_v, rest_v), RestN, RestK, RestL)
        tBsSFB, tBgSFB = cute.nvgpu.cpasync.tma_partition(
            tma_atom_sfb,
            block_in_cluster_coord_sfb_vmnk[1],
            sfb_cta_layout,
            cute.group_modes(sSFB, 0, 3),
            cute.group_modes(tCgSFB, 0, 3),
        )
        tBsSFB = cute.filter_zeros(tBsSFB)
        tBgSFB = cute.filter_zeros(tBgSFB)

        #
        # Partition shared/tensor memory tensor for TiledMMA_A/B/C
        #
        # (MMA, MMA_M, MMA_K, STAGE)
        tCrA = tiled_mma.make_fragment_A(sA)
        # (MMA, MMA_N, MMA_K, STAGE)
        tCrB = tiled_mma.make_fragment_B(sB)
        # (MMA, MMA_M, MMA_N)
        acc_shape = tiled_mma.partition_shape_C(self.mma_tiler[:2])
        # (MMA, MMA_M, MMA_N, STAGE)
        tCtAcc_fake = tiled_mma.make_fragment_C(cute.append(acc_shape, self.num_acc_stage))

        #
        # Cluster wait before tensor memory alloc
        #
        pipeline_init_wait(cluster_shape_mn=self.cluster_shape_mn)

        #
        # Get tensormap buffer address
        #
        grid_dim = cute.arch.grid_dim()
        tensormap_workspace_idx = bidz * grid_dim[1] * grid_dim[0] + bidy * grid_dim[0] + bidx

        tensormap_manager = utils.TensorMapManager(
            utils.TensorMapUpdateMode.SMEM,
            Sm100GroupedBlockScaledGemmKernel.bytes_per_tensormap,
        )
        tensormap_a_gmem_ptr = tensormap_manager.get_tensormap_ptr(
            tensormaps[(tensormap_workspace_idx, 0, None)].iterator
        )
        tensormap_b_gmem_ptr = tensormap_manager.get_tensormap_ptr(
            tensormaps[(tensormap_workspace_idx, 1, None)].iterator
        )
        tensormap_sfa_gmem_ptr = tensormap_manager.get_tensormap_ptr(
            tensormaps[(tensormap_workspace_idx, 2, None)].iterator
        )
        tensormap_sfb_gmem_ptr = tensormap_manager.get_tensormap_ptr(
            tensormaps[(tensormap_workspace_idx, 3, None)].iterator
        )
        tensormap_c_gmem_ptr = tensormap_manager.get_tensormap_ptr(
            tensormaps[(tensormap_workspace_idx, 4, None)].iterator
        )

        #
        # Specialized TMA load warp
        #
        if warp_idx == self.tma_warp_id:
            #
            # Persistent tile scheduling loop
            #
            tile_sched = utils.StaticPersistentTileScheduler.create(
                tile_sched_params, cute.arch.block_idx(), grid_dim
            )
            # grouped gemm tile scheduler helper will compute the group index for the tile we're working on
            group_gemm_ts_helper = utils.GroupedGemmTileSchedulerHelper(
                group_count,
                tile_sched_params,
                self.cluster_tile_shape_mnk,
                utils.create_initial_search_state(),
            )
            tensormap_init_done = cutlass.Boolean(False)
            # group index of last tile
            last_group_idx = cutlass.Int32(-1)

            work_tile = tile_sched.initial_work_tile_info()

            ab_producer_state = pipeline.make_pipeline_state(
                pipeline.PipelineUserType.Producer, self.num_ab_stage
            )

            while work_tile.is_valid_tile:
                cur_tile_coord = work_tile.tile_idx
                grouped_gemm_cta_tile_info = group_gemm_ts_helper.delinearize_z(
                    cur_tile_coord,
                    problem_sizes_mnkl,
                )
                cur_k_tile_cnt = grouped_gemm_cta_tile_info.cta_tile_count_k
                cur_group_idx = grouped_gemm_cta_tile_info.group_idx
                is_group_changed = cur_group_idx != last_group_idx
                # skip tensormap update if we're working on the same group
                if is_group_changed:
                    problem_shape_mnk = (
                        grouped_gemm_cta_tile_info.problem_shape_m,
                        grouped_gemm_cta_tile_info.problem_shape_n,
                        grouped_gemm_cta_tile_info.problem_shape_k,
                    )
                    real_tensor_a = self.make_tensor_abc_for_tensormap_update(
                        cur_group_idx,
                        self.a_dtype,
                        problem_shape_mnk,
                        ptrs_abc,
                        0,  # 0 for tensor A
                    )
                    real_tensor_b = self.make_tensor_abc_for_tensormap_update(
                        cur_group_idx,
                        self.b_dtype,
                        problem_shape_mnk,
                        ptrs_abc,
                        1,  # 1 for tensor B
                    )
                    real_tensor_sfa = self.make_tensor_sfasfb_for_tensormap_update(
                        cur_group_idx,
                        self.sf_dtype,
                        problem_shape_mnk,
                        ptrs_sfasfb,
                        0,  # 0 for tensor SFA
                    )
                    real_tensor_sfb = self.make_tensor_sfasfb_for_tensormap_update(
                        cur_group_idx,
                        self.sf_dtype,
                        problem_shape_mnk,
                        ptrs_sfasfb,
                        1,  # 1 for tensor SFB
                    )
                    if not tensormap_init_done:
                        # wait tensormap initialization complete
                        self.tensormap_ab_init_barrier.arrive_and_wait()
                        tensormap_init_done = True

                    tensormap_manager.update_tensormap(
                        (
                            real_tensor_a,
                            real_tensor_b,
                            real_tensor_sfa,
                            real_tensor_sfb,
                        ),
                        (tma_atom_a, tma_atom_b, tma_atom_sfa, tma_atom_sfb),
                        (
                            tensormap_a_gmem_ptr,
                            tensormap_b_gmem_ptr,
                            tensormap_sfa_gmem_ptr,
                            tensormap_sfb_gmem_ptr,
                        ),
                        self.tma_warp_id,
                        (
                            tensormap_a_smem_ptr,
                            tensormap_b_smem_ptr,
                            tensormap_sfa_smem_ptr,
                            tensormap_sfb_smem_ptr,
                        ),
                    )

                mma_tile_coord_mnl = (
                    grouped_gemm_cta_tile_info.cta_tile_idx_m // cute.size(tiled_mma.thr_id.shape),
                    grouped_gemm_cta_tile_info.cta_tile_idx_n,
                    0,
                )

                #
                # Slice to per mma tile index
                #
                # ((atom_v, rest_v), RestK)
                tAgA_slice = tAgA[(None, mma_tile_coord_mnl[0], None, mma_tile_coord_mnl[2])]
                # ((atom_v, rest_v), RestK)
                tBgB_slice = tBgB[(None, mma_tile_coord_mnl[1], None, mma_tile_coord_mnl[2])]

                # ((atom_v, rest_v), RestK)
                tAgSFA_slice = tAgSFA[(None, mma_tile_coord_mnl[0], None, mma_tile_coord_mnl[2])]
                # ((atom_v, rest_v), RestK)
                tBgSFB_slice = tBgSFB[(None, mma_tile_coord_mnl[1], None, mma_tile_coord_mnl[2])]

                # Peek (try_wait) AB buffer empty for k_tile = prefetch_k_tile_cnt
                ab_producer_state.reset_count()
                peek_ab_empty_status = cutlass.Boolean(1)
                if ab_producer_state.count < cur_k_tile_cnt:
                    peek_ab_empty_status = ab_pipeline.producer_try_acquire(ab_producer_state)

                if is_group_changed:
                    tensormap_manager.fence_tensormap_update(tensormap_a_gmem_ptr)
                    tensormap_manager.fence_tensormap_update(tensormap_b_gmem_ptr)
                    tensormap_manager.fence_tensormap_update(tensormap_sfa_gmem_ptr)
                    tensormap_manager.fence_tensormap_update(tensormap_sfb_gmem_ptr)
                #
                # Tma load loop
                #
                for k_tile in cutlass.range(0, cur_k_tile_cnt, 1, unroll=1):
                    # Conditionally wait for AB buffer empty
                    ab_pipeline.producer_acquire(ab_producer_state, peek_ab_empty_status)

                    # TMA load A/B/SFA/SFB
                    cute.copy(
                        tma_atom_a,
                        tAgA_slice[(None, ab_producer_state.count)],
                        tAsA[(None, ab_producer_state.index)],
                        tma_bar_ptr=ab_pipeline.producer_get_barrier(ab_producer_state),
                        mcast_mask=a_full_mcast_mask,
                        tma_desc_ptr=tensormap_manager.get_tensormap_ptr(
                            tensormap_a_gmem_ptr,
                            cute.AddressSpace.generic,
                        ),
                    )
                    cute.copy(
                        tma_atom_b,
                        tBgB_slice[(None, ab_producer_state.count)],
                        tBsB[(None, ab_producer_state.index)],
                        tma_bar_ptr=ab_pipeline.producer_get_barrier(ab_producer_state),
                        mcast_mask=b_full_mcast_mask,
                        tma_desc_ptr=tensormap_manager.get_tensormap_ptr(
                            tensormap_b_gmem_ptr,
                            cute.AddressSpace.generic,
                        ),
                    )
                    cute.copy(
                        tma_atom_sfa,
                        tAgSFA_slice[(None, ab_producer_state.count)],
                        tAsSFA[(None, ab_producer_state.index)],
                        tma_bar_ptr=ab_pipeline.producer_get_barrier(ab_producer_state),
                        mcast_mask=sfa_full_mcast_mask,
                        tma_desc_ptr=tensormap_manager.get_tensormap_ptr(
                            tensormap_sfa_gmem_ptr,
                            cute.AddressSpace.generic,
                        ),
                    )
                    cute.copy(
                        tma_atom_sfb,
                        tBgSFB_slice[(None, ab_producer_state.count)],
                        tBsSFB[(None, ab_producer_state.index)],
                        tma_bar_ptr=ab_pipeline.producer_get_barrier(ab_producer_state),
                        mcast_mask=sfb_full_mcast_mask,
                        tma_desc_ptr=tensormap_manager.get_tensormap_ptr(
                            tensormap_sfb_gmem_ptr,
                            cute.AddressSpace.generic,
                        ),
                    )

                    # Peek (try_wait) AB buffer empty for k_tile = prefetch_k_tile_cnt + k_tile + 1
                    ab_producer_state.advance()
                    peek_ab_empty_status = cutlass.Boolean(1)
                    if ab_producer_state.count < cur_k_tile_cnt:
                        peek_ab_empty_status = ab_pipeline.producer_try_acquire(ab_producer_state)

                #
                # Advance to next tile
                #
                tile_sched.advance_to_next_work()
                work_tile = tile_sched.get_current_work()
                last_group_idx = cur_group_idx

            #
            # Wait A/B buffer empty
            #
            ab_pipeline.producer_tail(ab_producer_state)

        #
        # Specialized MMA warp
        #
        if warp_idx == self.mma_warp_id:
            #
            # Initialize tensormaps for A, B, SFA and SFB
            #
            tensormap_manager.init_tensormap_from_atom(
                tma_atom_a, tensormap_a_smem_ptr, self.mma_warp_id
            )
            tensormap_manager.init_tensormap_from_atom(
                tma_atom_b, tensormap_b_smem_ptr, self.mma_warp_id
            )
            tensormap_manager.init_tensormap_from_atom(
                tma_atom_sfa, tensormap_sfa_smem_ptr, self.mma_warp_id
            )
            tensormap_manager.init_tensormap_from_atom(
                tma_atom_sfb, tensormap_sfb_smem_ptr, self.mma_warp_id
            )
            # indicate tensormap initialization has finished
            self.tensormap_ab_init_barrier.arrive_and_wait()

            #
            # Bar sync for retrieve tensor memory ptr from shared mem
            #
            self.tmem_alloc_barrier.arrive_and_wait()

            #
            # Retrieving tensor memory ptr and make accumulator/SFA/SFB tensor
            #
            # Make accumulator tmem tensor
            acc_tmem_ptr = cute.arch.retrieve_tmem_ptr(
                self.acc_dtype,
                alignment=16,
                ptr_to_buffer_holding_addr=tmem_holding_buf,
            )
            # (MMA, MMA_M, MMA_N, STAGE)
            tCtAcc_base = cute.make_tensor(acc_tmem_ptr, tCtAcc_fake.layout)

            # Make SFA tmem tensor
            sfa_tmem_ptr = cute.recast_ptr(
                acc_tmem_ptr + tcgen05.find_tmem_tensor_col_offset(tCtAcc_base),
                dtype=self.sf_dtype,
            )
            # (MMA, MMA_M, MMA_K)
            tCtSFA_layout = blockscaled_utils.make_tmem_layout_sfa(
                tiled_mma,
                self.mma_tiler,
                self.sf_vec_size,
                cute.slice_(sfa_smem_layout_staged, (None, None, None, 0)),
            )
            tCtSFA = cute.make_tensor(sfa_tmem_ptr, tCtSFA_layout)

            # Make SFB tmem tensor
            sfb_tmem_ptr = cute.recast_ptr(
                acc_tmem_ptr
                + tcgen05.find_tmem_tensor_col_offset(tCtAcc_base)
                + tcgen05.find_tmem_tensor_col_offset(tCtSFA),
                dtype=self.sf_dtype,
            )
            # (MMA, MMA_N, MMA_K)
            tCtSFB_layout = blockscaled_utils.make_tmem_layout_sfb(
                tiled_mma,
                self.mma_tiler,
                self.sf_vec_size,
                cute.slice_(sfb_smem_layout_staged, (None, None, None, 0)),
            )
            tCtSFB = cute.make_tensor(sfb_tmem_ptr, tCtSFB_layout)
            #
            # Partition for S2T copy of SFA/SFB
            #
            tiled_copy_s2t_sfa, tCsSFA_compact_s2t, tCtSFA_compact_s2t = (
                self.mainloop_s2t_copy_and_partition(sSFA, tCtSFA)
            )
            tiled_copy_s2t_sfb, tCsSFB_compact_s2t, tCtSFB_compact_s2t = (
                self.mainloop_s2t_copy_and_partition(sSFB, tCtSFB)
            )

            #
            # Persistent tile scheduling loop
            #
            tile_sched = utils.StaticPersistentTileScheduler.create(
                tile_sched_params, cute.arch.block_idx(), grid_dim
            )
            # grouped gemm tile scheduler helper will compute the group index for the tile we're working on
            group_gemm_ts_helper = utils.GroupedGemmTileSchedulerHelper(
                group_count,
                tile_sched_params,
                self.cluster_tile_shape_mnk,
                utils.create_initial_search_state(),
            )

            work_tile = tile_sched.initial_work_tile_info()
            ab_consumer_state = pipeline.make_pipeline_state(
                pipeline.PipelineUserType.Consumer, self.num_ab_stage
            )
            acc_producer_state = pipeline.make_pipeline_state(
                pipeline.PipelineUserType.Producer, self.num_acc_stage
            )
            while work_tile.is_valid_tile:
                cur_tile_coord = work_tile.tile_idx
                # MMA warp is only interested in number of tiles along K dimension
                (
                    cur_k_tile_cnt,
                    cur_group_idx,
                ) = group_gemm_ts_helper.search_cluster_tile_count_k(
                    cur_tile_coord,
                    problem_sizes_mnkl,
                )

                # (MMA, MMA_M, MMA_N)
                tCtAcc = tCtAcc_base[(None, None, None, acc_producer_state.index)]

                # Peek (try_wait) AB buffer full for k_tile = 0
                ab_consumer_state.reset_count()
                peek_ab_full_status = cutlass.Boolean(1)
                if ab_consumer_state.count < cur_k_tile_cnt and is_leader_cta:
                    peek_ab_full_status = ab_pipeline.consumer_try_wait(ab_consumer_state)

                #
                # Wait for accumulator buffer empty
                #
                if is_leader_cta:
                    acc_pipeline.producer_acquire(acc_producer_state)

                #
                # Reset the ACCUMULATE field for each tile
                #
                tiled_mma.set(tcgen05.Field.ACCUMULATE, False)

                #
                # Mma mainloop
                #
                for k_tile in range(cur_k_tile_cnt):
                    if is_leader_cta:
                        # Conditionally wait for AB buffer full
                        ab_pipeline.consumer_wait(ab_consumer_state, peek_ab_full_status)

                        #  Copy SFA/SFB from smem to tmem
                        s2t_stage_coord = (
                            None,
                            None,
                            None,
                            None,
                            ab_consumer_state.index,
                        )
                        tCsSFA_compact_s2t_staged = tCsSFA_compact_s2t[s2t_stage_coord]
                        tCsSFB_compact_s2t_staged = tCsSFB_compact_s2t[s2t_stage_coord]
                        cute.copy(
                            tiled_copy_s2t_sfa,
                            tCsSFA_compact_s2t_staged,
                            tCtSFA_compact_s2t,
                        )
                        cute.copy(
                            tiled_copy_s2t_sfb,
                            tCsSFB_compact_s2t_staged,
                            tCtSFB_compact_s2t,
                        )

                        # tCtAcc += tCrA * tCrSFA * tCrB * tCrSFB
                        num_kblocks = cute.size(tCrA, mode=[2])
                        for kblock_idx in cutlass.range(num_kblocks, unroll_full=True):
                            kblock_coord = (
                                None,
                                None,
                                kblock_idx,
                                ab_consumer_state.index,
                            )

                            # Set SFA/SFB tensor to tiled_mma
                            sf_kblock_coord = (None, None, kblock_idx)
                            tiled_mma.set(
                                tcgen05.Field.SFA,
                                tCtSFA[sf_kblock_coord].iterator,
                            )
                            tiled_mma.set(
                                tcgen05.Field.SFB,
                                tCtSFB[sf_kblock_coord].iterator,
                            )

                            cute.gemm(
                                tiled_mma,
                                tCtAcc,
                                tCrA[kblock_coord],
                                tCrB[kblock_coord],
                                tCtAcc,
                            )

                            # Enable accumulate on tCtAcc after first kblock
                            tiled_mma.set(tcgen05.Field.ACCUMULATE, True)

                        # Async arrive AB buffer empty
                        ab_pipeline.consumer_release(ab_consumer_state)

                    # Peek (try_wait) AB buffer full for k_tile = k_tile + 1
                    ab_consumer_state.advance()
                    peek_ab_full_status = cutlass.Boolean(1)
                    if ab_consumer_state.count < cur_k_tile_cnt:
                        if is_leader_cta:
                            peek_ab_full_status = ab_pipeline.consumer_try_wait(ab_consumer_state)

                #
                # Async arrive accumulator buffer full
                #
                if is_leader_cta:
                    acc_pipeline.producer_commit(acc_producer_state)
                acc_producer_state.advance()

                #
                # Advance to next tile
                #
                tile_sched.advance_to_next_work()
                work_tile = tile_sched.get_current_work()

            #
            # Wait for accumulator buffer empty
            #
            acc_pipeline.producer_tail(acc_producer_state)

        #
        # Specialized epilogue warps
        #
        if warp_idx < self.mma_warp_id:
            # initialize tensorap for C
            tensormap_manager.init_tensormap_from_atom(
                tma_atom_c,
                tensormap_c_smem_ptr,
                self.epilog_warp_id[0],
            )
            #
            # Alloc tensor memory buffer
            #
            if warp_idx == self.epilog_warp_id[0]:
                cute.arch.alloc_tmem(
                    self.num_tmem_alloc_cols,
                    tmem_holding_buf,
                    is_two_cta=use_2cta_instrs,
                )

            #
            # Bar sync for retrieve tensor memory ptr from shared memory
            #
            self.tmem_alloc_barrier.arrive_and_wait()

            #
            # Retrieving tensor memory ptr and make accumulator tensor
            #
            acc_tmem_ptr = cute.arch.retrieve_tmem_ptr(
                self.acc_dtype,
                alignment=16,
                ptr_to_buffer_holding_addr=tmem_holding_buf,
            )
            # (MMA, MMA_M, MMA_N, STAGE)
            tCtAcc_base = cute.make_tensor(acc_tmem_ptr, tCtAcc_fake.layout)

            ### Start from here
            #
            # Partition for epilogue
            #
            epi_tidx = tidx
            tiled_copy_t2r, tTR_tAcc_base, tTR_rAcc = self.epilog_tmem_copy_and_partition(
                epi_tidx, tCtAcc_base, tCgC, epi_tile, use_2cta_instrs
            )

            tTR_rC = cute.make_rmem_tensor(tTR_rAcc.shape, self.c_dtype)
            tiled_copy_r2s, tRS_rC, tRS_sC = self.epilog_smem_copy_and_partition(
                tiled_copy_t2r, tTR_rC, epi_tidx, sC
            )
            tma_atom_c, bSG_sC, bSG_gC_partitioned = self.epilog_gmem_copy_and_partition(
                epi_tidx, tma_atom_c, tCgC, epi_tile, sC
            )

            #
            # Persistent tile scheduling loop
            #
            tile_sched = utils.StaticPersistentTileScheduler.create(
                tile_sched_params, cute.arch.block_idx(), grid_dim
            )
            # grouped gemm tile scheduler helper will compute the group index for the tile we're working on
            group_gemm_ts_helper = utils.GroupedGemmTileSchedulerHelper(
                group_count,
                tile_sched_params,
                self.cluster_tile_shape_mnk,
                utils.create_initial_search_state(),
            )

            work_tile = tile_sched.initial_work_tile_info()

            acc_consumer_state = pipeline.make_pipeline_state(
                pipeline.PipelineUserType.Consumer, self.num_acc_stage
            )

            # Threads/warps participating in tma store pipeline
            c_producer_group = pipeline.CooperativeGroup(
                pipeline.Agent.Thread,
                32 * len(self.epilog_warp_id),
            )
            c_pipeline = pipeline.PipelineTmaStore.create(
                num_stages=self.num_c_stage,
                producer_group=c_producer_group,
            )
            # group index to start searching
            last_group_idx = cutlass.Int32(-1)

            while work_tile.is_valid_tile:
                cur_tile_coord = work_tile.tile_idx
                grouped_gemm_cta_tile_info = group_gemm_ts_helper.delinearize_z(
                    cur_tile_coord,
                    problem_sizes_mnkl,
                )
                cur_group_idx = grouped_gemm_cta_tile_info.group_idx
                is_group_changed = cur_group_idx != last_group_idx

                if is_group_changed:
                    # construct tensor c based on real shape, stride information
                    real_tensor_c = self.make_tensor_abc_for_tensormap_update(
                        cur_group_idx,
                        self.c_dtype,
                        (
                            grouped_gemm_cta_tile_info.problem_shape_m,
                            grouped_gemm_cta_tile_info.problem_shape_n,
                            grouped_gemm_cta_tile_info.problem_shape_k,
                        ),
                        ptrs_abc,
                        2,  # 2 for tensor C
                    )
                    tensormap_manager.update_tensormap(
                        ((real_tensor_c),),
                        ((tma_atom_c),),
                        ((tensormap_c_gmem_ptr),),
                        self.epilog_warp_id[0],
                        (tensormap_c_smem_ptr,),
                    )

                mma_tile_coord_mnl = (
                    grouped_gemm_cta_tile_info.cta_tile_idx_m // cute.size(tiled_mma.thr_id.shape),
                    grouped_gemm_cta_tile_info.cta_tile_idx_n,
                    0,
                )
                cur_k_tile_cnt = grouped_gemm_cta_tile_info.cta_tile_count_k

                #
                # Slice to per mma tile index
                #
                # ((ATOM_V, REST_V), EPI_M, EPI_N)
                bSG_gC = bSG_gC_partitioned[
                    (
                        None,
                        None,
                        None,
                        *mma_tile_coord_mnl,
                    )
                ]

                # Set tensor memory buffer for current tile
                # (T2R, T2R_M, T2R_N, EPI_M, EPI_M)
                tTR_tAcc = tTR_tAcc_base[(None, None, None, None, None, acc_consumer_state.index)]

                #
                # Wait for accumulator buffer full
                #
                acc_pipeline.consumer_wait(acc_consumer_state)

                tTR_tAcc = cute.group_modes(tTR_tAcc, 3, cute.rank(tTR_tAcc))
                bSG_gC = cute.group_modes(bSG_gC, 1, cute.rank(bSG_gC))

                if is_group_changed:
                    if warp_idx == self.epilog_warp_id[0]:
                        tensormap_manager.fence_tensormap_update(tensormap_c_gmem_ptr)

                #
                # Store accumulator to global memory in subtiles
                #
                subtile_cnt = cute.size(tTR_tAcc.shape, mode=[3])
                num_prev_subtiles = tile_sched.num_tiles_executed * subtile_cnt
                for subtile_idx in range(subtile_cnt):
                    #
                    # Acquire C buffer before use (skip first iteration - pipeline starts empty)
                    #
                    if subtile_idx > 0:
                        if warp_idx == self.epilog_warp_id[0]:
                            c_pipeline.producer_acquire()

                    #
                    # Load accumulator from tensor memory buffer to register
                    #
                    tTR_tAcc_mn = tTR_tAcc[(None, None, None, subtile_idx)]
                    cute.copy(tiled_copy_t2r, tTR_tAcc_mn, tTR_rAcc)

                    #
                    # Convert to C type
                    #
                    acc_vec = tiled_copy_r2s.retile(tTR_rAcc).load()
                    tRS_rC.store(acc_vec.to(self.c_dtype))

                    #
                    # Store C to shared memory
                    #
                    c_buffer = (num_prev_subtiles + subtile_idx) % self.num_c_stage
                    cute.copy(
                        tiled_copy_r2s,
                        tRS_rC,
                        tRS_sC[(None, None, None, c_buffer)],
                    )
                    # Fence and barrier to make sure shared memory store is visible to TMA store
                    cute.arch.fence_proxy(
                        cute.arch.ProxyKind.async_shared,
                        space=cute.arch.SharedSpace.shared_cta,
                    )
                    self.epilog_sync_barrier.arrive_and_wait()

                    #
                    # TMA store C to global memory
                    #
                    if warp_idx == self.epilog_warp_id[0]:
                        cute.copy(
                            tma_atom_c,
                            bSG_sC[(None, c_buffer)],
                            bSG_gC[(None, subtile_idx)],
                            tma_desc_ptr=tensormap_manager.get_tensormap_ptr(
                                tensormap_c_gmem_ptr,
                                cute.AddressSpace.generic,
                            ),
                        )
                        c_pipeline.producer_commit()
                    self.epilog_sync_barrier.arrive_and_wait()
                #
                # Async arrive accumulator buffer empty
                #
                with cute.arch.elect_one():
                    acc_pipeline.consumer_release(acc_consumer_state)
                acc_consumer_state.advance()

                #
                # Advance to next tile
                #
                tile_sched.advance_to_next_work()
                work_tile = tile_sched.get_current_work()
                last_group_idx = cur_group_idx

            #
            # Dealloc the tensor memory buffer
            #
            if warp_idx == self.epilog_warp_id[0]:
                cute.arch.relinquish_tmem_alloc_permit(is_two_cta=use_2cta_instrs)
            self.epilog_sync_barrier.arrive_and_wait()
            if warp_idx == self.epilog_warp_id[0]:
                if use_2cta_instrs:
                    cute.arch.mbarrier_arrive(tmem_dealloc_mbar_ptr, cta_rank_in_cluster ^ 1)
                    cute.arch.mbarrier_wait(tmem_dealloc_mbar_ptr, 0)
                cute.arch.dealloc_tmem(
                    acc_tmem_ptr, self.num_tmem_alloc_cols, is_two_cta=use_2cta_instrs
                )
            #
            # Wait for C store complete
            #
            c_pipeline.producer_tail()

    @cute.jit
    def make_tensor_abc_for_tensormap_update(
        self,
        group_idx: cutlass.Int32,
        dtype: Type[cutlass.Numeric],
        problem_shape_mnk: tuple[cutlass.Int32, cutlass.Int32, cutlass.Int32],
        tensor_address_abc: cute.Tensor,
        tensor_index: int,
    ):
        ptr_i64 = tensor_address_abc[(group_idx, tensor_index)]
        tensor_gmem_ptr = cute.make_ptr(dtype, ptr_i64, cute.AddressSpace.gmem, assumed_align=16)

        c1 = cutlass.Int32(1)
        m = problem_shape_mnk[0]
        n = problem_shape_mnk[1]
        k = problem_shape_mnk[2]

        if cutlass.const_expr(tensor_index == 0):  # tensor A
            return cute.make_tensor(
                tensor_gmem_ptr,
                cute.make_layout(
                    (m, cute.assume(k, 32), c1),
                    stride=(
                        cute.assume(k, 32),
                        1,
                        cute.assume(m * k, 32),
                    ),
                ),
            )
        elif cutlass.const_expr(tensor_index == 1):  # tensor B
            return cute.make_tensor(
                tensor_gmem_ptr,
                cute.make_layout(
                    (n, cute.assume(k, 32), c1),
                    stride=(
                        cute.assume(k, 32),
                        1,
                        cute.assume(n * k, 32),
                    ),
                ),
            )
        else:  # tensor C
            return cute.make_tensor(
                tensor_gmem_ptr,
                cute.make_layout(
                    (m, cute.assume(n, 32), c1),
                    stride=(
                        cute.assume(n, 32),
                        1,
                        cute.assume(m * n, 32),
                    ),
                ),
            )

    @cute.jit
    def make_tensor_sfasfb_for_tensormap_update(
        self,
        group_idx: cutlass.Int32,
        dtype: Type[cutlass.Numeric],
        problem_shape_mnk: tuple[cutlass.Int32, cutlass.Int32, cutlass.Int32],
        tensor_address_sfasfb: cute.Tensor,
        tensor_index: int,
    ):
        ptr_i64 = tensor_address_sfasfb[(group_idx, tensor_index)]
        from inspect import isclass

        if cutlass.const_expr(not isclass(dtype) or not issubclass(dtype, cutlass.Numeric)):
            raise TypeError(f"dtype must be a type of cutlass.Numeric, got {type(dtype)}")
        tensor_gmem_ptr = cute.make_ptr(dtype, ptr_i64, cute.AddressSpace.gmem, assumed_align=16)

        c1 = cutlass.Int32(1)
        if cutlass.const_expr(tensor_index == 0):  # tensor SFA
            m = problem_shape_mnk[0]
            k = problem_shape_mnk[2]
            sfa_layout = blockscaled_utils.tile_atom_to_shape_SF((m, k, c1), self.sf_vec_size)
            return cute.make_tensor(
                tensor_gmem_ptr,
                sfa_layout,
            )
        else:  # tensor SFB
            n = problem_shape_mnk[1]
            k = problem_shape_mnk[2]
            sfb_layout = blockscaled_utils.tile_atom_to_shape_SF((n, k, c1), self.sf_vec_size)
            return cute.make_tensor(
                tensor_gmem_ptr,
                sfb_layout,
            )

    def mainloop_s2t_copy_and_partition(
        self,
        sSF: cute.Tensor,
        tSF: cute.Tensor,
    ) -> Tuple[cute.TiledCopy, cute.Tensor, cute.Tensor]:
        # (MMA, MMA_MN, MMA_K, STAGE)
        tCsSF_compact = cute.filter_zeros(sSF)
        # (MMA, MMA_MN, MMA_K)
        tCtSF_compact = cute.filter_zeros(tSF)

        # Make S2T CopyAtom and tiledCopy
        copy_atom_s2t = cute.make_copy_atom(
            tcgen05.Cp4x32x128bOp(self.cta_group),
            self.sf_dtype,
        )
        tiled_copy_s2t = tcgen05.make_s2t_copy(copy_atom_s2t, tCtSF_compact)
        thr_copy_s2t = tiled_copy_s2t.get_slice(0)

        # ((ATOM_V, REST_V), Rest_Tiler, MMA_MN, MMA_K, STAGE)
        tCsSF_compact_s2t_ = thr_copy_s2t.partition_S(tCsSF_compact)
        # ((ATOM_V, REST_V), Rest_Tiler, MMA_MN, MMA_K, STAGE)
        tCsSF_compact_s2t = tcgen05.get_s2t_smem_desc_tensor(tiled_copy_s2t, tCsSF_compact_s2t_)
        # ((ATOM_V, REST_V), Rest_Tiler, MMA_MN, MMA_K)
        tCtSF_compact_s2t = thr_copy_s2t.partition_D(tCtSF_compact)

        return tiled_copy_s2t, tCsSF_compact_s2t, tCtSF_compact_s2t

    def epilog_tmem_copy_and_partition(
        self,
        tidx: cutlass.Int32,
        tAcc: cute.Tensor,
        gC_mnl: cute.Tensor,
        epi_tile: cute.Tile,
        use_2cta_instrs: Union[cutlass.Boolean, bool],
    ) -> Tuple[cute.TiledCopy, cute.Tensor, cute.Tensor]:
        # Make tiledCopy for tensor memory load
        copy_atom_t2r = sm100_utils.get_tmem_load_op(
            self.cta_tile_shape_mnk,
            self.c_layout,
            self.c_dtype,
            self.acc_dtype,
            epi_tile,
            use_2cta_instrs,
        )
        # (EPI_TILE_M, EPI_TILE_N, EPI_M, EPI_N, STAGE)
        tAcc_epi = cute.flat_divide(
            tAcc[((None, None), 0, 0, None)],
            epi_tile,
        )
        # (EPI_TILE_M, EPI_TILE_N)
        tiled_copy_t2r = tcgen05.make_tmem_copy(copy_atom_t2r, tAcc_epi[(None, None, 0, 0, 0)])

        thr_copy_t2r = tiled_copy_t2r.get_slice(tidx)
        # (T2R, T2R_M, T2R_N, EPI_M, EPI_M, STAGE)
        tTR_tAcc = thr_copy_t2r.partition_S(tAcc_epi)

        # (EPI_TILE_M, EPI_TILE_N, EPI_M, EPI_N, RestM, RestN, RestL)
        gC_mnl_epi = cute.flat_divide(gC_mnl[((None, None), 0, 0, None, None, None)], epi_tile)
        # (T2R, T2R_M, T2R_N, EPI_M, EPI_N, RestM, RestN, RestL)
        tTR_gC = thr_copy_t2r.partition_D(gC_mnl_epi)
        # (T2R, T2R_M, T2R_N)
        tTR_rAcc = cute.make_rmem_tensor(
            tTR_gC[(None, None, None, 0, 0, 0, 0, 0)].shape, self.acc_dtype
        )
        return tiled_copy_t2r, tTR_tAcc, tTR_rAcc

    def epilog_smem_copy_and_partition(
        self,
        tiled_copy_t2r: cute.TiledCopy,
        tTR_rC: cute.Tensor,
        tidx: cutlass.Int32,
        sC: cute.Tensor,
    ) -> Tuple[cute.TiledCopy, cute.Tensor, cute.Tensor]:
        copy_atom_r2s = sm100_utils.get_smem_store_op(
            self.c_layout, self.c_dtype, self.acc_dtype, tiled_copy_t2r
        )
        tiled_copy_r2s = cute.make_tiled_copy_D(copy_atom_r2s, tiled_copy_t2r)
        # (R2S, R2S_M, R2S_N, PIPE_D)
        thr_copy_r2s = tiled_copy_r2s.get_slice(tidx)
        tRS_sC = thr_copy_r2s.partition_D(sC)
        # (R2S, R2S_M, R2S_N)
        tRS_rC = tiled_copy_r2s.retile(tTR_rC)
        return tiled_copy_r2s, tRS_rC, tRS_sC

    def epilog_gmem_copy_and_partition(
        self,
        tidx: cutlass.Int32,
        atom: Union[cute.CopyAtom, cute.TiledCopy],
        gC_mnl: cute.Tensor,
        epi_tile: cute.Tile,
        sC: cute.Tensor,
    ) -> Tuple[cute.CopyAtom, cute.Tensor, cute.Tensor]:
        # (EPI_TILE_M, EPI_TILE_N, EPI_M, EPI_N, RestM, RestN, RestL)
        gC_epi = cute.flat_divide(gC_mnl[((None, None), 0, 0, None, None, None)], epi_tile)

        tma_atom_c = atom
        sC_for_tma_partition = cute.group_modes(sC, 0, 2)
        gC_for_tma_partition = cute.group_modes(gC_epi, 0, 2)
        # ((ATOM_V, REST_V), EPI_M, EPI_N)
        # ((ATOM_V, REST_V), EPI_M, EPI_N, RestM, RestN, RestL)
        bSG_sC, bSG_gC = cpasync.tma_partition(
            tma_atom_c,
            0,
            cute.make_layout(1),
            sC_for_tma_partition,
            gC_for_tma_partition,
        )
        return tma_atom_c, bSG_sC, bSG_gC

    @staticmethod
    def _compute_stages(
        tiled_mma: cute.TiledMma,
        mma_tiler_mnk: Tuple[int, int, int],
        a_dtype: Type[cutlass.Numeric],
        b_dtype: Type[cutlass.Numeric],
        epi_tile: cute.Tile,
        c_dtype: Type[cutlass.Numeric],
        c_layout: utils.LayoutEnum,
        sf_dtype: Type[cutlass.Numeric],
        sf_vec_size: int,
        smem_capacity: int,
        occupancy: int,
    ) -> Tuple[int, int, int]:
        # ACC stages
        num_acc_stage = 1 if mma_tiler_mnk[1] == 256 else 2

        # Default C stages
        num_c_stage = 2

        # Calculate smem layout and size for one stage of A, B, SFA, SFB and C
        a_smem_layout_stage_one = sm100_utils.make_smem_layout_a(
            tiled_mma,
            mma_tiler_mnk,
            a_dtype,
            1,  # a tmp 1 stage is provided
        )
        b_smem_layout_staged_one = sm100_utils.make_smem_layout_b(
            tiled_mma,
            mma_tiler_mnk,
            b_dtype,
            1,  # a tmp 1 stage is provided
        )
        sfa_smem_layout_staged_one = blockscaled_utils.make_smem_layout_sfa(
            tiled_mma,
            mma_tiler_mnk,
            sf_vec_size,
            1,  # a tmp 1 stage is provided
        )
        sfb_smem_layout_staged_one = blockscaled_utils.make_smem_layout_sfb(
            tiled_mma,
            mma_tiler_mnk,
            sf_vec_size,
            1,  # a tmp 1 stage is provided
        )

        c_smem_layout_staged_one = sm100_utils.make_smem_layout_epi(
            c_dtype,
            c_layout,
            epi_tile,
            1,
        )

        ab_bytes_per_stage = (
            cute.size_in_bytes(a_dtype, a_smem_layout_stage_one)
            + cute.size_in_bytes(b_dtype, b_smem_layout_staged_one)
            + cute.size_in_bytes(sf_dtype, sfa_smem_layout_staged_one)
            + cute.size_in_bytes(sf_dtype, sfb_smem_layout_staged_one)
        )
        mbar_helpers_bytes = 1024
        c_bytes_per_stage = cute.size_in_bytes(c_dtype, c_smem_layout_staged_one)
        c_bytes = c_bytes_per_stage * num_c_stage

        num_ab_stage = (
            smem_capacity // occupancy - (mbar_helpers_bytes + c_bytes)
        ) // ab_bytes_per_stage

        num_c_stage += (
            smem_capacity
            - occupancy * ab_bytes_per_stage * num_ab_stage
            - occupancy * (mbar_helpers_bytes + c_bytes)
        ) // (occupancy * c_bytes_per_stage)

        return num_acc_stage, num_ab_stage, num_c_stage

    @staticmethod
    def _compute_grid(
        total_num_clusters: int,
        cluster_shape_mn: tuple[int, int],
        max_active_clusters: cutlass.Constexpr[int],
    ) -> tuple[utils.PersistentTileSchedulerParams, tuple[int, int, int]]:
        # Create problem shape with M, N dimensions from cluster shape
        # and L dimension representing the total number of clusters.
        problem_shape_ntile_mnl = (
            cluster_shape_mn[0],
            cluster_shape_mn[1],
            cutlass.Int32(total_num_clusters),
        )

        tile_sched_params = utils.PersistentTileSchedulerParams(
            problem_shape_ntile_mnl, (*cluster_shape_mn, 1)
        )

        grid = utils.StaticPersistentTileScheduler.get_grid_shape(
            tile_sched_params, max_active_clusters
        )

        return tile_sched_params, grid

    @staticmethod
    def _get_mbar_smem_bytes(**kwargs_stages: int) -> int:
        num_barriers_per_stage = 2
        num_bytes_per_barrier = 8
        mbar_smem_consumption = sum(
            [
                num_barriers_per_stage * num_bytes_per_barrier * stage
                for stage in kwargs_stages.values()
            ]
        )
        return mbar_smem_consumption

    reserved_smem_bytes = 1024
    bytes_per_tensormap = 128
    num_tensormaps = 5
    tensor_memory_management_bytes = 12


_KERNEL_OBJ: Sm100GroupedBlockScaledGemmKernel | None = None

SF_VEC_SIZE = 16
AB_DTYPE = cutlass.Float4E2M1FN
SF_DTYPE = cutlass.Float8E4M3FN
C_TYPE = cutlass.Float16


@cute.jit
def my_kernel_grouped(
    # Metadata pointers (device)
    ptr_problem_shape_mnkl: cute.Pointer,  # int32
    ptr_tensor_address_abc: cute.Pointer,  # int64
    ptr_tensor_address_sfasfb: cute.Pointer,  # int64
    ptr_tensormaps: cute.Pointer,  # int64
    # Compile-time constants (pass plain python ints)
    total_num_clusters: cutlass.Constexpr[int],
    max_active_clusters: cutlass.Constexpr[int],
    group_count: cutlass.Constexpr[int],
):
    # problem_shape_mnkl: (G,4)
    problem_shape_mnkl = cute.make_tensor(
        ptr_problem_shape_mnkl,
        cute.make_layout((group_count, 4), stride=(4, 1)),
    )

    # ptrs: (G,3) and (G,2)
    tensor_address_abc = cute.make_tensor(
        ptr_tensor_address_abc,
        cute.make_layout((group_count, 3), stride=(3, 1)),
    )
    tensor_address_sfasfb = cute.make_tensor(
        ptr_tensor_address_sfasfb,
        cute.make_layout((group_count, 2), stride=(2, 1)),
    )

    # tensormaps: (workspace, 5, 16)
    tensormaps = cute.make_tensor(
        ptr_tensormaps,
        cute.make_layout((total_num_clusters, 5, 16), stride=(5 * 16, 16, 1)),
    )

    fake_m = cutlass.Int32(128)
    fake_n = cutlass.Int32(128)
    fake_k = cutlass.Int32(256)
    fake_l = cutlass.Int32(1)

    initial_a = cute.make_tensor(
        cute.make_ptr(AB_DTYPE, 0, cute.AddressSpace.gmem, assumed_align=16),
        cute.make_layout((fake_m, fake_k, fake_l), stride=(fake_k, 1, fake_m * fake_k)),
    )
    initial_b = cute.make_tensor(
        cute.make_ptr(AB_DTYPE, 0, cute.AddressSpace.gmem, assumed_align=16),
        cute.make_layout((fake_n, fake_k, fake_l), stride=(fake_k, 1, fake_n * fake_k)),
    )
    initial_c = cute.make_tensor(
        cute.make_ptr(C_TYPE, 0, cute.AddressSpace.gmem, assumed_align=16),
        cute.make_layout((fake_m, fake_n, fake_l), stride=(fake_n, 1, fake_m * fake_n)),
    )
    initial_sfa = cute.make_tensor(
        cute.make_ptr(SF_DTYPE, 0, cute.AddressSpace.gmem, assumed_align=16),
        cute.make_layout(
            (fake_m, cutlass.Int32(8), fake_l),
            stride=(cutlass.Int32(8), 1, fake_m * 8),
        ),
    )
    initial_sfb = cute.make_tensor(
        cute.make_ptr(SF_DTYPE, 0, cute.AddressSpace.gmem, assumed_align=16),
        cute.make_layout(
            (fake_n, cutlass.Int32(8), fake_l),
            stride=(cutlass.Int32(8), 1, fake_n * 8),
        ),
    )

    kernel_obj = _KERNEL_OBJ

    kernel_obj(
        initial_a,
        initial_b,
        initial_c,
        initial_sfa,
        initial_sfb,
        group_count,
        problem_shape_mnkl,
        tensor_address_abc,
        tensor_address_sfasfb,
        total_num_clusters,
        tensormaps,
        max_active_clusters,
    )
    return


_COMPILED_KERNEL_CACHE = {}


def compile_grouped_kernel(
    sf_vec_size: int,
    group_count: int,
    total_num_clusters: int,
    mma_tiler_mn: Tuple[int, int],
    cluster_shape_mn: Tuple[int, int],
    max_active_clusters: int,
):
    global _KERNEL_OBJ, _COMPILED_KERNEL_CACHE

    cache_key = (
        sf_vec_size,
        mma_tiler_mn,
        cluster_shape_mn,
        max_active_clusters,
        group_count,
        total_num_clusters,
    )
    if cache_key in _COMPILED_KERNEL_CACHE:
        return _COMPILED_KERNEL_CACHE[cache_key]

    _KERNEL_OBJ = Sm100GroupedBlockScaledGemmKernel(
        sf_vec_size=sf_vec_size,
        mma_tiler_mn=mma_tiler_mn,
        cluster_shape_mn=cluster_shape_mn,
    )

    ptr_problem_shape_mnkl = make_ptr(cutlass.Int32, 0, cute.AddressSpace.gmem, assumed_align=16)
    ptr_tensor_address_abc = make_ptr(cutlass.Int64, 0, cute.AddressSpace.gmem, assumed_align=16)
    ptr_tensor_address_sfasfb = make_ptr(cutlass.Int64, 0, cute.AddressSpace.gmem, assumed_align=16)
    ptr_tensormaps = make_ptr(cutlass.Int64, 0, cute.AddressSpace.gmem, assumed_align=16)

    compiled = cute.compile(
        my_kernel_grouped,
        ptr_problem_shape_mnkl,
        ptr_tensor_address_abc,
        ptr_tensor_address_sfasfb,
        ptr_tensormaps,
        total_num_clusters,
        max_active_clusters,
        group_count,
    )
    _COMPILED_KERNEL_CACHE[cache_key] = compiled
    return compiled


def is_aligned(problem_sizes, tile_m, tile_n):
    for m, n, _, _ in problem_sizes:
        if (m % tile_m) != 0 or (n % tile_n) != 0:
            return False
    return True


class KernelConfig(NamedTuple):
    mma_tiler_mn: Tuple[int, int]
    cluster_shape_mn: Tuple[int, int]
    max_active_clusters: int


fast_cfg = KernelConfig(
    mma_tiler_mn=(256, 512),
    cluster_shape_mn=(2, 2),
    max_active_clusters=64,
)

safe_cfg = KernelConfig(
    mma_tiler_mn=(128, 256),
    cluster_shape_mn=(1, 1),
    max_active_clusters=64,
)


def ceil_div(a: int, b: int) -> int:
    return (a + b - 1) // b


def custom_kernel(data):
    abc_tensors, _, sfasfb_tensors, problem_sizes = data

    group_count = len(problem_sizes)

    problem_shape_mnkl = torch.tensor(problem_sizes, dtype=torch.int32, device="cuda")

    cfg = fast_cfg if is_aligned(problem_sizes, 256, 256) else safe_cfg

    abc_ptrs = []
    sfs_ptrs = []
    for (a, b, c), (sfa, sfb) in zip(abc_tensors, sfasfb_tensors):
        abc_ptrs.append((a.data_ptr(), b.data_ptr(), c.data_ptr()))
        sfs_ptrs.append((sfa.data_ptr(), sfb.data_ptr()))

    tensor_addr_abc = torch.tensor(abc_ptrs, dtype=torch.int64, device="cuda")
    tensor_addr_sfasfb = torch.tensor(sfs_ptrs, dtype=torch.int64, device="cuda")

    tile_m, tile_n = cfg.mma_tiler_mn
    total_num_clusters = sum(
        ceil_div(m, tile_m) * ceil_div(n, tile_n) for m, n, _, _ in problem_sizes
    )

    tensormaps = torch.empty((total_num_clusters, 5, 16), dtype=torch.int64, device="cuda")

    compiled = compile_grouped_kernel(
        SF_VEC_SIZE,
        group_count,
        total_num_clusters,
        *cfg,
    )

    p_problem = make_ptr(cutlass.Int32, problem_shape_mnkl.data_ptr(), cute.AddressSpace.gmem, 16)
    p_abc = make_ptr(cutlass.Int64, tensor_addr_abc.data_ptr(), cute.AddressSpace.gmem, 16)
    p_sfs = make_ptr(cutlass.Int64, tensor_addr_sfasfb.data_ptr(), cute.AddressSpace.gmem, 16)
    p_tm = make_ptr(cutlass.Int64, tensormaps.data_ptr(), cute.AddressSpace.gmem, 16)

    compiled(p_problem, p_abc, p_sfs, p_tm)

    return [abc_tensors[i][2] for i in range(group_count)]