submission 169378

# submission_cluster11_newchallenge_ktile8_dispatch.py
from typing import Type, Tuple, Union

import torch

import cutlass
import cutlass.cute as cute
from cutlass.cute.nvgpu import cpasync, tcgen05
import cutlass.utils as utils
import cutlass.pipeline as pipeline
import cutlass.utils.blackwell_helpers as sm100_utils
import cutlass.utils.blockscaled_layout as blockscaled_utils
from cutlass.cute.runtime import make_ptr
from task import input_t, output_t


class Sm100BlockScaledPersistentDenseGemmKernel:
    """Persistent block-scaled GEMM kernel for SM100 (Blackwell).
    C = A x SFA x B x SFB (block-scaled NVF4 path)

    Notes:
      - A/B: Float4E2M1FN
      - SF : Float8E4M3FN
      - Acc: Float32
      - C  : Float16/BFloat16/Float32
    """

    def __init__(
        self,
        sf_vec_size: int,
        mma_tiler_mn: Tuple[int, int],
        cluster_shape_mn: Tuple[int, int],
        mma_inst_tile_k: int = 4,
    ):
        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
        # K dimension is deferred in _setup_attributes
        self.mma_tiler = (*mma_tiler_mn, 1)

        # Override K-tiling (# of MMA-Inst-K blocks per CTA tile)
        self.mma_inst_tile_k = mma_inst_tile_k

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

        self.occupancy = 1

        # Warp specialization
        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)
        )

        # Named barriers
        self.cta_sync_barrier = pipeline.NamedBarrier(
            barrier_id=1, num_threads=self.threads_per_cta
        )
        self.epilog_sync_barrier = pipeline.NamedBarrier(
            barrier_id=2, num_threads=32 * len(self.epilog_warp_id)
        )
        self.tmem_alloc_barrier = pipeline.NamedBarrier(
            barrier_id=3,
            num_threads=32 * len((self.mma_warp_id, *self.epilog_warp_id)),
        )

        self.smem_capacity = utils.get_smem_capacity_in_bytes("sm_100")
        # TMEM columns capacity (Blackwell)
        self.num_tmem_alloc_cols = 512

    def _setup_attributes(self):
        # MMA instruction shapes
        self.mma_inst_shape_mn = (self.mma_tiler[0], self.mma_tiler[1])
        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),
        )

        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,
        )

        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,
        )

        mma_inst_shape_k = cute.size(tiled_mma.shape_mnk, mode=[2])
        mma_inst_tile_k = self.mma_inst_tile_k

        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(tiled_mma.thr_id.shape),
            self.mma_tiler[1],
            self.mma_tiler[2],
        )

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

        # Multicast CTAs
        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

        # 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,
        )

        # Stage counts
        self.num_acc_stage, self.num_ab_stage, self.num_c_stage = self._compute_stages(
            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,
        )

        # SMEM layouts
        self.a_smem_layout_staged = sm100_utils.make_smem_layout_a(
            tiled_mma,
            self.mma_tiler,
            self.a_dtype,
            self.num_ab_stage,
        )
        self.b_smem_layout_staged = sm100_utils.make_smem_layout_b(
            tiled_mma,
            self.mma_tiler,
            self.b_dtype,
            self.num_ab_stage,
        )
        self.sfa_smem_layout_staged = blockscaled_utils.make_smem_layout_sfa(
            tiled_mma,
            self.mma_tiler,
            self.sf_vec_size,
            self.num_ab_stage,
        )
        self.sfb_smem_layout_staged = blockscaled_utils.make_smem_layout_sfb(
            tiled_mma,
            self.mma_tiler,
            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,
        )

    @cute.jit
    def __call__(
        self,
        a_ptr: cute.Pointer,
        b_ptr: cute.Pointer,
        sfa_ptr: cute.Pointer,
        sfb_ptr: cute.Pointer,
        c_ptr: cute.Pointer,
        layouts: cutlass.Constexpr[
            Tuple[tcgen05.OperandMajorMode, tcgen05.OperandMajorMode, utils.LayoutEnum]
        ],
        problem_mnkl: Tuple[int, int, int, int],
        max_active_clusters: cutlass.Constexpr,
        epilogue_op: cutlass.Constexpr = lambda x: x,
    ):
        # Static attrs
        self.a_dtype: Type[cutlass.Numeric] = a_ptr.value_type
        self.b_dtype: Type[cutlass.Numeric] = b_ptr.value_type
        self.sf_dtype: Type[cutlass.Numeric] = sfa_ptr.value_type
        self.c_dtype: Type[cutlass.Numeric] = c_ptr.value_type

        m, n, k, l = problem_mnkl
        self.a_major_mode, self.b_major_mode, self.c_layout = layouts

        if cutlass.const_expr(self.a_dtype != self.b_dtype):
            raise TypeError(f"Type must match: {self.a_dtype} != {self.b_dtype}")

        self._setup_attributes()

        a_tensor = cute.make_tensor(
            a_ptr,
            cute.make_layout(
                (m, cute.assume(k, 32), l),
                stride=(cute.assume(k, 32), 1, cute.assume(m * k, 32)),
            ),
        )
        b_tensor = cute.make_tensor(
            b_ptr,
            cute.make_layout(
                (n, cute.assume(k, 32), l),
                stride=(cute.assume(k, 32), 1, cute.assume(n * k, 32)),
            ),
        )
        c_tensor = cute.make_tensor(
            c_ptr,
            cute.make_layout((cute.assume(m, 32), n, l), stride=(n, 1, m * n)),
        )

        # SF tensors
        sfa_layout = blockscaled_utils.tile_atom_to_shape_SF(
            a_tensor.shape, self.sf_vec_size
        )
        sfa_tensor = cute.make_tensor(sfa_ptr, sfa_layout)

        sfb_layout = blockscaled_utils.tile_atom_to_shape_SF(
            b_tensor.shape, self.sf_vec_size
        )
        sfb_tensor = cute.make_tensor(sfb_ptr, sfb_layout)

        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,
        )
        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,
        )

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

        # TMA load A
        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,
            a_tensor,
            a_smem_layout,
            self.mma_tiler,
            tiled_mma,
            self.cluster_layout_vmnk.shape,
        )

        # TMA load B
        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,
            b_tensor,
            b_smem_layout,
            self.mma_tiler,
            tiled_mma,
            self.cluster_layout_vmnk.shape,
        )

        # TMA load 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,
            sfa_tensor,
            sfa_smem_layout,
            self.mma_tiler,
            tiled_mma,
            self.cluster_layout_vmnk.shape,
            internal_type=cutlass.Int16,
        )

        # TMA load 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,
            sfb_tensor,
            sfb_smem_layout,
            self.mma_tiler_sfb,
            tiled_mma_sfb,
            self.cluster_layout_sfb_vmnk.shape,
            internal_type=cutlass.Int16,
        )

        # SFB slicing hack for N=192 (keep)
        if cutlass.const_expr(self.cta_tile_shape_mnk[1] == 192):
            x = tma_tensor_sfb.stride[0][1]
            y = cute.ceil_div(tma_tensor_sfb.shape[0][1], 4)
            new_shape = (
                (tma_tensor_sfb.shape[0][0], ((2, 2), y)),
                tma_tensor_sfb.shape[1],
                tma_tensor_sfb.shape[2],
            )
            x_times_3 = 3 * x
            new_stride = (
                (tma_tensor_sfb.stride[0][0], ((x, x), x_times_3)),
                tma_tensor_sfb.stride[1],
                tma_tensor_sfb.stride[2],
            )
            tma_tensor_sfb_new_layout = cute.make_layout(new_shape, stride=new_stride)
            tma_tensor_sfb = cute.make_tensor(
                tma_tensor_sfb.iterator, tma_tensor_sfb_new_layout
            )

        # Tx bytes
        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

        # TMA store 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(),
            c_tensor,
            epi_smem_layout,
            self.epi_tile,
        )

        # Grid
        self.tile_sched_params, grid = self._compute_grid(
            c_tensor,
            self.cta_tile_shape_mnk,
            self.cluster_shape_mn,
            max_active_clusters,
        )

        self.buffer_align_bytes = 1024

        @cute.struct
        class SharedStorage:
            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

            sC: cute.struct.Align[
                cute.struct.MemRange[
                    self.c_dtype, cute.cosize(self.c_smem_layout_staged.outer)
                ],
                self.buffer_align_bytes,
            ]
            sA: cute.struct.Align[
                cute.struct.MemRange[
                    self.a_dtype, cute.cosize(self.a_smem_layout_staged.outer)
                ],
                self.buffer_align_bytes,
            ]
            sB: cute.struct.Align[
                cute.struct.MemRange[
                    self.b_dtype, cute.cosize(self.b_smem_layout_staged.outer)
                ],
                self.buffer_align_bytes,
            ]
            sSFA: cute.struct.Align[
                cute.struct.MemRange[
                    self.sf_dtype, cute.cosize(self.sfa_smem_layout_staged)
                ],
                self.buffer_align_bytes,
            ]
            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

        self.kernel(
            tiled_mma,
            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,
            epilogue_op,
        ).launch(
            grid=grid,
            block=[self.threads_per_cta, 1, 1],
            cluster=(*self.cluster_shape_mn, 1),
            min_blocks_per_mp=1,
        )
        return

    @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,
        epilogue_op: cutlass.Constexpr,
    ):
        warp_idx = cute.arch.warp_idx()
        warp_idx = cute.arch.make_warp_uniform(warp_idx)

        # Prefetch TMA desc
        if warp_idx == self.tma_warp_id:
            cpasync.prefetch_descriptor(tma_atom_a)
            cpasync.prefetch_descriptor(tma_atom_b)
            cpasync.prefetch_descriptor(tma_atom_sfa)
            cpasync.prefetch_descriptor(tma_atom_sfb)
            cpasync.prefetch_descriptor(tma_atom_c)

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

        # Block/cluster coords
        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
        )
        tidx = cute.arch.thread_idx()[0]

        # Allocate smem storage
        smem = utils.SmemAllocator()
        storage = smem.allocate(self.shared_storage)

        # AB pipeline
        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,
        )

        # ACC pipeline
        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,
        )

        # TMEM allocator
        tmem = utils.TmemAllocator(
            storage.tmem_holding_buf,
            barrier_for_retrieve=self.tmem_alloc_barrier,
            allocator_warp_id=self.epilog_warp_id[0],
            is_two_cta=use_2cta_instrs,
            two_cta_tmem_dealloc_mbar_ptr=storage.tmem_dealloc_mbar_ptr,
        )

        # Cluster arrive after barrier init
        if cute.size(self.cluster_shape_mn) > 1:
            cute.arch.cluster_arrive_relaxed()

        # SMEM tensors
        sC = storage.sC.get_tensor(
            c_smem_layout_staged.outer, swizzle=c_smem_layout_staged.inner
        )
        sA = storage.sA.get_tensor(
            a_smem_layout_staged.outer, swizzle=a_smem_layout_staged.inner
        )
        sB = storage.sB.get_tensor(
            b_smem_layout_staged.outer, swizzle=b_smem_layout_staged.inner
        )
        sSFA = storage.sSFA.get_tensor(sfa_smem_layout_staged)
        sSFB = storage.sSFB.get_tensor(sfb_smem_layout_staged)

        # Multicast masks
        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 tiles
        gA_mkl = cute.local_tile(
            mA_mkl, cute.slice_(self.mma_tiler, (None, 0, None)), (None, None, None)
        )
        gB_nkl = cute.local_tile(
            mB_nkl, cute.slice_(self.mma_tiler, (0, None, None)), (None, None, None)
        )
        gSFA_mkl = cute.local_tile(
            mSFA_mkl, cute.slice_(self.mma_tiler, (None, 0, None)), (None, None, None)
        )
        gSFB_nkl = cute.local_tile(
            mSFB_nkl,
            cute.slice_(self.mma_tiler_sfb, (0, None, None)),
            (None, None, None),
        )
        gC_mnl = cute.local_tile(
            mC_mnl, cute.slice_(self.mma_tiler, (None, None, 0)), (None, None, None)
        )
        k_tile_cnt = cute.size(gA_mkl, mode=[3])

        # MMA partitions
        thr_mma = tiled_mma.get_slice(mma_tile_coord_v)
        thr_mma_sfb = tiled_mma_sfb.get_slice(mma_tile_coord_v)
        tCgA = thr_mma.partition_A(gA_mkl)
        tCgB = thr_mma.partition_B(gB_nkl)
        tCgSFA = thr_mma.partition_A(gSFA_mkl)
        tCgSFB = thr_mma_sfb.partition_B(gSFB_nkl)
        tCgC = thr_mma.partition_C(gC_mnl)

        # TMA partitions
        a_cta_layout = cute.make_layout(
            cute.slice_(cluster_layout_vmnk, (0, 0, None, 0)).shape
        )
        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),
        )

        b_cta_layout = cute.make_layout(
            cute.slice_(cluster_layout_vmnk, (0, None, 0, 0)).shape
        )
        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),
        )

        sfa_cta_layout = a_cta_layout
        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)

        sfb_cta_layout = cute.make_layout(
            cute.slice_(cluster_layout_sfb_vmnk, (0, None, 0, 0)).shape
        )
        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)

        # Fragments
        tCrA = tiled_mma.make_fragment_A(sA)
        tCrB = tiled_mma.make_fragment_B(sB)
        acc_shape = tiled_mma.partition_shape_C(self.mma_tiler[:2])
        tCtAcc_fake = tiled_mma.make_fragment_C(
            cute.append(acc_shape, self.num_acc_stage)
        )

        # Cluster wait before TMEM alloc
        if cute.size(self.cluster_shape_mn) > 1:
            cute.arch.cluster_wait()
        else:
            self.cta_sync_barrier.arrive_and_wait()

        # ------------------ TMA warp ------------------
        if warp_idx == self.tma_warp_id:
            tile_sched = utils.StaticPersistentTileScheduler.create(
                tile_sched_params, cute.arch.block_idx(), cute.arch.grid_dim()
            )
            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
                mma_tile_coord_mnl = (
                    cur_tile_coord[0] // cute.size(tiled_mma.thr_id.shape),
                    cur_tile_coord[1],
                    cur_tile_coord[2],
                )

                tAgA_slice = tAgA[
                    (None, mma_tile_coord_mnl[0], None, mma_tile_coord_mnl[2])
                ]
                tBgB_slice = tBgB[
                    (None, mma_tile_coord_mnl[1], None, mma_tile_coord_mnl[2])
                ]
                tAgSFA_slice = tAgSFA[
                    (None, mma_tile_coord_mnl[0], None, mma_tile_coord_mnl[2])
                ]

                slice_n = mma_tile_coord_mnl[1]
                if cutlass.const_expr(self.cta_tile_shape_mnk[1] == 64):
                    slice_n = mma_tile_coord_mnl[1] // 2
                tBgSFB_slice = tBgSFB[(None, slice_n, None, mma_tile_coord_mnl[2])]

                ab_producer_state.reset_count()
                peek_ab_empty_status = cutlass.Boolean(1)
                if ab_producer_state.count < k_tile_cnt:
                    peek_ab_empty_status = ab_pipeline.producer_try_acquire(
                        ab_producer_state
                    )

                for k_tile in cutlass.range(0, k_tile_cnt, 1, unroll=1):
                    ab_pipeline.producer_acquire(ab_producer_state, peek_ab_empty_status)

                    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,
                    )
                    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,
                    )
                    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,
                    )
                    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,
                    )

                    ab_producer_state.advance()
                    peek_ab_empty_status = cutlass.Boolean(1)
                    if ab_producer_state.count < k_tile_cnt:
                        peek_ab_empty_status = ab_pipeline.producer_try_acquire(
                            ab_producer_state
                        )

                tile_sched.advance_to_next_work()
                work_tile = tile_sched.get_current_work()

            ab_pipeline.producer_tail(ab_producer_state)

        # ------------------ MMA warp ------------------
        if warp_idx == self.mma_warp_id:
            tmem.wait_for_alloc()

            acc_tmem_ptr = tmem.retrieve_ptr(self.acc_dtype)
            tCtAcc_base = cute.make_tensor(acc_tmem_ptr, tCtAcc_fake.layout)

            sfa_tmem_ptr = cute.recast_ptr(
                acc_tmem_ptr + tcgen05.find_tmem_tensor_col_offset(tCtAcc_base),
                dtype=self.sf_dtype,
            )
            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)

            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,
            )
            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)

            (
                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)

            tile_sched = utils.StaticPersistentTileScheduler.create(
                tile_sched_params, cute.arch.block_idx(), cute.arch.grid_dim()
            )
            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_tile_coord_mnl = (
                    cur_tile_coord[0] // cute.size(tiled_mma.thr_id.shape),
                    cur_tile_coord[1],
                    cur_tile_coord[2],
                )

                tCtAcc = tCtAcc_base[(None, None, None, acc_producer_state.index)]

                ab_consumer_state.reset_count()
                peek_ab_full_status = cutlass.Boolean(1)
                if ab_consumer_state.count < k_tile_cnt and is_leader_cta:
                    peek_ab_full_status = ab_pipeline.consumer_try_wait(
                        ab_consumer_state
                    )

                if is_leader_cta:
                    acc_pipeline.producer_acquire(acc_producer_state)

                # TMEM pointer offset hacks (keep)
                tCtSFB_mma = tCtSFB
                if cutlass.const_expr(self.cta_tile_shape_mnk[1] == 192):
                    offset = (
                        cutlass.Int32(2)
                        if mma_tile_coord_mnl[1] % 2 == 1
                        else cutlass.Int32(0)
                    )
                    shifted_ptr = cute.recast_ptr(
                        acc_tmem_ptr
                        + tcgen05.find_tmem_tensor_col_offset(tCtAcc_base)
                        + tcgen05.find_tmem_tensor_col_offset(tCtSFA)
                        + offset,
                        dtype=self.sf_dtype,
                    )
                    tCtSFB_mma = cute.make_tensor(shifted_ptr, tCtSFB_layout)
                elif cutlass.const_expr(self.cta_tile_shape_mnk[1] == 64):
                    offset = cutlass.Int32((mma_tile_coord_mnl[1] % 2) * 2)
                    shifted_ptr = cute.recast_ptr(
                        acc_tmem_ptr
                        + tcgen05.find_tmem_tensor_col_offset(tCtAcc_base)
                        + tcgen05.find_tmem_tensor_col_offset(tCtSFA)
                        + offset,
                        dtype=self.sf_dtype,
                    )
                    tCtSFB_mma = cute.make_tensor(shifted_ptr, tCtSFB_layout)

                # Reset ACCUMULATE each output tile
                tiled_mma.set(tcgen05.Field.ACCUMULATE, False)

                # Patch B: hoist num_kblocks
                num_kblocks = cute.size(tCrA, mode=[2])

                for k_tile in range(k_tile_cnt):
                    if is_leader_cta:
                        ab_pipeline.consumer_wait(
                            ab_consumer_state, peek_ab_full_status
                        )

                        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,
                        )

                        for kblock_idx in cutlass.range(num_kblocks, unroll_full=True):
                            kblock_coord = (
                                None,
                                None,
                                kblock_idx,
                                ab_consumer_state.index,
                            )
                            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_mma[sf_kblock_coord].iterator,
                            )

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

                            # Patch C: set ACCUMULATE=True exactly once per output tile
                            if k_tile == 0:
                                if kblock_idx == 0:
                                    tiled_mma.set(tcgen05.Field.ACCUMULATE, True)

                        ab_pipeline.consumer_release(ab_consumer_state)

                    ab_consumer_state.advance()
                    peek_ab_full_status = cutlass.Boolean(1)
                    if ab_consumer_state.count < k_tile_cnt:
                        if is_leader_cta:
                            peek_ab_full_status = ab_pipeline.consumer_try_wait(
                                ab_consumer_state
                            )

                if is_leader_cta:
                    acc_pipeline.producer_commit(acc_producer_state)
                acc_producer_state.advance()

                tile_sched.advance_to_next_work()
                work_tile = tile_sched.get_current_work()

            acc_pipeline.producer_tail(acc_producer_state)

        # ------------------ Epilogue warps ------------------
        if warp_idx < self.mma_warp_id:
            tmem.allocate(self.num_tmem_alloc_cols)
            tmem.wait_for_alloc()

            acc_tmem_ptr = tmem.retrieve_ptr(self.acc_dtype)
            tCtAcc_base = cute.make_tensor(acc_tmem_ptr, tCtAcc_fake.layout)

            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
            )

            tile_sched = utils.StaticPersistentTileScheduler.create(
                tile_sched_params, cute.arch.block_idx(), cute.arch.grid_dim()
            )
            work_tile = tile_sched.initial_work_tile_info()

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

            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,
            )

            while work_tile.is_valid_tile:
                cur_tile_coord = work_tile.tile_idx
                mma_tile_coord_mnl = (
                    cur_tile_coord[0] // cute.size(tiled_mma.thr_id.shape),
                    cur_tile_coord[1],
                    cur_tile_coord[2],
                )

                bSG_gC = bSG_gC_partitioned[
                    (None, None, None, *mma_tile_coord_mnl)
                ]
                tTR_tAcc = tTR_tAcc_base[
                    (None, None, None, None, None, acc_consumer_state.index)
                ]

                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))

                subtile_cnt = cute.size(tTR_tAcc.shape, mode=[3])
                num_prev_subtiles = tile_sched.num_tiles_executed * subtile_cnt

                for subtile_idx in cutlass.range(subtile_cnt):
                    tTR_tAcc_mn = tTR_tAcc[(None, None, None, subtile_idx)]
                    cute.copy(tiled_copy_t2r, tTR_tAcc_mn, tTR_rAcc)

                    acc_vec = tiled_copy_r2s.retile(tTR_rAcc).load()
                    acc_vec = epilogue_op(acc_vec.to(self.c_dtype))
                    tRS_rC.store(acc_vec)

                    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)],
                    )
                    cute.arch.fence_proxy(
                        cute.arch.ProxyKind.async_shared,
                        space=cute.arch.SharedSpace.shared_cta,
                    )
                    self.epilog_sync_barrier.arrive_and_wait()

                    if warp_idx == self.epilog_warp_id[0]:
                        cute.copy(
                            tma_atom_c,
                            bSG_sC[(None, c_buffer)],
                            bSG_gC[(None, subtile_idx)],
                        )
                        c_pipeline.producer_commit()
                        c_pipeline.producer_acquire()
                    self.epilog_sync_barrier.arrive_and_wait()

                with cute.arch.elect_one():
                    acc_pipeline.consumer_release(acc_consumer_state)
                acc_consumer_state.advance()

                tile_sched.advance_to_next_work()
                work_tile = tile_sched.get_current_work()

            tmem.relinquish_alloc_permit()
            self.epilog_sync_barrier.arrive_and_wait()
            tmem.free(acc_tmem_ptr)
            c_pipeline.producer_tail()

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

        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)

        tCsSF_compact_s2t_ = thr_copy_s2t.partition_S(tCsSF_compact)
        tCsSF_compact_s2t = tcgen05.get_s2t_smem_desc_tensor(
            tiled_copy_s2t, tCsSF_compact_s2t_
        )
        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]:
        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,
        )
        tAcc_epi = cute.flat_divide(
            tAcc[((None, None), 0, 0, None)],
            epi_tile,
        )
        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)
        tTR_tAcc = thr_copy_t2r.partition_S(tAcc_epi)

        gC_mnl_epi = cute.flat_divide(
            gC_mnl[((None, None), 0, 0, None, None, None)], epi_tile
        )
        tTR_gC = thr_copy_t2r.partition_D(gC_mnl_epi)

        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)
        thr_copy_r2s = tiled_copy_r2s.get_slice(tidx)
        tRS_sC = thr_copy_r2s.partition_D(sC)
        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]:
        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)
        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 (restore original heuristic: N==256 -> 1, else 2)
        num_acc_stage = 1 if mma_tiler_mnk[1] == 256 else 2

        num_c_stage = 2

        a_smem_layout_stage_one = sm100_utils.make_smem_layout_a(
            tiled_mma, mma_tiler_mnk, a_dtype, 1
        )
        b_smem_layout_staged_one = sm100_utils.make_smem_layout_b(
            tiled_mma, mma_tiler_mnk, b_dtype, 1
        )
        sfa_smem_layout_staged_one = blockscaled_utils.make_smem_layout_sfa(
            tiled_mma, mma_tiler_mnk, sf_vec_size, 1
        )
        sfb_smem_layout_staged_one = blockscaled_utils.make_smem_layout_sfb(
            tiled_mma, mma_tiler_mnk, sf_vec_size, 1
        )
        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(
        c: cute.Tensor,
        cta_tile_shape_mnk: Tuple[int, int, int],
        cluster_shape_mn: Tuple[int, int],
        max_active_clusters: cutlass.Constexpr,
    ) -> Tuple[utils.PersistentTileSchedulerParams, Tuple[int, int, int]]:
        c_shape = cute.slice_(cta_tile_shape_mnk, (None, None, 0))
        gc = cute.zipped_divide(c, tiler=c_shape)
        num_ctas_mnl = gc[(0, (None, None, None))].shape
        cluster_shape_mnl = (*cluster_shape_mn, 1)

        tile_sched_params = utils.PersistentTileSchedulerParams(
            num_ctas_mnl, cluster_shape_mnl
        )
        grid = utils.StaticPersistentTileScheduler.get_grid_shape(
            tile_sched_params, max_active_clusters
        )
        return tile_sched_params, grid

    @staticmethod
    def is_valid_dtypes_and_scale_factor_vec_size(
        ab_dtype: Type[cutlass.Numeric],
        sf_dtype: Type[cutlass.Numeric],
        sf_vec_size: int,
        c_dtype: Type[cutlass.Numeric],
    ) -> bool:
        is_valid = True
        if ab_dtype not in {cutlass.Float4E2M1FN}:
            is_valid = False
        if sf_vec_size not in {16}:
            is_valid = False
        if sf_dtype not in {cutlass.Float8E4M3FN}:
            is_valid = False
        if c_dtype not in {cutlass.Float32, cutlass.Float16, cutlass.BFloat16}:
            is_valid = False
        return is_valid

    @staticmethod
    def is_valid_layouts(
        ab_dtype: Type[cutlass.Numeric],
        c_dtype: Type[cutlass.Numeric],
        a_major: str,
        b_major: str,
        c_major: str,
    ) -> bool:
        is_valid = True
        if ab_dtype is cutlass.Float4E2M1FN and not (a_major == "k" and b_major == "k"):
            is_valid = False
        if ab_dtype is cutlass.Float4E2M1FN and c_major == "m":
            is_valid = False
        return is_valid

    @staticmethod
    def is_valid_mma_tiler_and_cluster_shape(
        mma_tiler_mn: Tuple[int, int],
        cluster_shape_mn: Tuple[int, int],
    ) -> bool:
        is_valid = True
        if mma_tiler_mn[0] not in [128, 256]:
            is_valid = False
        if mma_tiler_mn[1] not in [64, 128, 192, 256]:
            is_valid = False
        if cluster_shape_mn[0] % (2 if mma_tiler_mn[0] == 256 else 1) != 0:
            is_valid = False
        is_power_of_2 = lambda x: x > 0 and (x & (x - 1)) == 0
        if (
            cluster_shape_mn[0] * cluster_shape_mn[1] > 16
            or cluster_shape_mn[0] <= 0
            or cluster_shape_mn[1] <= 0
            or cluster_shape_mn[0] > 4
            or cluster_shape_mn[1] > 4
            or not is_power_of_2(cluster_shape_mn[0])
            or not is_power_of_2(cluster_shape_mn[1])
        ):
            is_valid = False
        return is_valid

    @staticmethod
    def is_valid_tensor_alignment(
        m: int,
        n: int,
        k: int,
        l: int,
        ab_dtype: Type[cutlass.Numeric],
        c_dtype: Type[cutlass.Numeric],
        a_major: str,
        b_major: str,
        c_major: str,
    ) -> bool:
        is_valid = True

        def check_contigous_16B_alignment(dtype, is_mode0_major, tensor_shape):
            major_mode_idx = 0 if is_mode0_major else 1
            num_major_elements = tensor_shape[major_mode_idx]
            num_contiguous_elements = 16 * 8 // dtype.width
            return num_major_elements % num_contiguous_elements == 0

        if (
            not check_contigous_16B_alignment(ab_dtype, a_major == "m", (m, k, l))
            or not check_contigous_16B_alignment(ab_dtype, b_major == "n", (n, k, l))
            or not check_contigous_16B_alignment(c_dtype, c_major == "m", (m, n, l))
        ):
            is_valid = False
        return is_valid

    @staticmethod
    def can_implement(
        ab_dtype: Type[cutlass.Numeric],
        sf_dtype: Type[cutlass.Numeric],
        sf_vec_size: int,
        c_dtype: Type[cutlass.Numeric],
        mma_tiler_mn: Tuple[int, int],
        cluster_shape_mn: Tuple[int, int],
        m: int,
        n: int,
        k: int,
        l: int,
        a_major: str,
        b_major: str,
        c_major: str,
    ) -> bool:
        can_implement = True
        if not Sm100BlockScaledPersistentDenseGemmKernel.is_valid_dtypes_and_scale_factor_vec_size(
            ab_dtype, sf_dtype, sf_vec_size, c_dtype
        ):
            can_implement = False
        if not Sm100BlockScaledPersistentDenseGemmKernel.is_valid_layouts(
            ab_dtype, c_dtype, a_major, b_major, c_major
        ):
            can_implement = False
        if not Sm100BlockScaledPersistentDenseGemmKernel.is_valid_mma_tiler_and_cluster_shape(
            mma_tiler_mn, cluster_shape_mn
        ):
            can_implement = False
        if not Sm100BlockScaledPersistentDenseGemmKernel.is_valid_tensor_alignment(
            m, n, k, l, ab_dtype, c_dtype, a_major, b_major, c_major
        ):
            can_implement = False
        return can_implement


# ----------------- Hackathon wrapper -----------------

# Data types
ab_dtype = cutlass.Float4E2M1FN
sf_dtype = cutlass.Float8E4M3FN
c_dtype  = cutlass.Float16

# Default configuration (baseline)
_SF_VEC_SIZE = 16
_MMA_TILER_MN = (128, 64)
_CLUSTER_SHAPE_MN = (1, 1)

# Compile options
_COMPILE_OPT_LEVEL = 3

# Single supported K-tiling (IMPORTANT: avoid useless compile-fail + fallback noise)
_GEMM = Sm100BlockScaledPersistentDenseGemmKernel(
    sf_vec_size=_SF_VEC_SIZE,
    mma_tiler_mn=_MMA_TILER_MN,
    cluster_shape_mn=_CLUSTER_SHAPE_MN,
    mma_inst_tile_k=4,
)

# Compile cache keyed by problem_size (m,n,k,l)
_compiled = {}
_max_active_clusters = {}


def _get_max_active_clusters(cluster_shape_mn):
    cluster_size = cluster_shape_mn[0] * cluster_shape_mn[1]
    if cluster_size in _max_active_clusters:
        return _max_active_clusters[cluster_size]
    hw = utils.HardwareInfo()
    val = hw.get_max_active_clusters(cluster_size)
    _max_active_clusters[cluster_size] = val
    return val


def compile_kernel(problem_size):
    """Compile and cache a kernel specialized for (m,n,k,l).

    NOTE:
      - We intentionally DO NOT try experimental ktile variants here.
      - Some ktile variants fail to compile due to SFA TMA layout/vmap mismatch,
        producing noisy stderr and wasting compile time.
    """
    ps = tuple(problem_size)
    if ps in _compiled:
        return _compiled[ps]

    # Dummy pointers for compilation
    a_ptr = make_ptr(ab_dtype, 0, cute.AddressSpace.gmem, assumed_align=16)
    b_ptr = make_ptr(ab_dtype, 0, cute.AddressSpace.gmem, assumed_align=16)
    sfa_ptr = make_ptr(sf_dtype, 0, cute.AddressSpace.gmem, assumed_align=16)
    sfb_ptr = make_ptr(sf_dtype, 0, cute.AddressSpace.gmem, assumed_align=16)
    c_ptr = make_ptr(c_dtype, 0, cute.AddressSpace.gmem, assumed_align=16)

    layouts = (
        tcgen05.OperandMajorMode.K,
        tcgen05.OperandMajorMode.K,
        utils.LayoutEnum.ROW_MAJOR,
    )

    max_active_clusters = _get_max_active_clusters(_CLUSTER_SHAPE_MN)

    compiled = cute.compile(
        _GEMM,
        a_ptr, b_ptr, sfa_ptr, sfb_ptr, c_ptr,
        layouts,
        ps,
        max_active_clusters,
        options=f"--opt-level {_COMPILE_OPT_LEVEL}",
    )

    _compiled[ps] = compiled
    return compiled


def custom_kernel(data: input_t) -> output_t:
    a, b, _, _, sfa_permuted, sfb_permuted, c = data

    m, k_packed, l = a.shape
    n, _, _ = b.shape

    # Torch packs 2 FP4 values into one byte (e2m1_x2), so logical K doubles
    k = k_packed * 2

    problem_size = (m, n, k, l)
    compiled = compile_kernel(problem_size)

    a_ptr = make_ptr(ab_dtype, a.data_ptr(), cute.AddressSpace.gmem, assumed_align=16)
    b_ptr = make_ptr(ab_dtype, b.data_ptr(), cute.AddressSpace.gmem, assumed_align=16)
    sfa_ptr = make_ptr(sf_dtype, sfa_permuted.data_ptr(), cute.AddressSpace.gmem, assumed_align=16)
    sfb_ptr = make_ptr(sf_dtype, sfb_permuted.data_ptr(), cute.AddressSpace.gmem, assumed_align=16)
    c_ptr = make_ptr(c_dtype, c.data_ptr(), cute.AddressSpace.gmem, assumed_align=16)

    # The compiled callable uses the default CUDA execution queue.
    compiled(a_ptr, b_ptr, sfa_ptr, sfb_ptr, c_ptr, problem_size)
    return c