submission 234128

#!POPCORN leaderboard nvfp4_dual_gemm
import cutlass
import cutlass.cute as cute
from cutlass.cute.nvgpu import cpasync, tcgen05
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
from cutlass.cute.runtime import make_ptr
from typing import Tuple, Type, Union

from task import input_t, output_t


from cutlass.cute.tensor import TensorSSA
from cutlass import Float32, Float16, Int8, Int32, Int64
from cutlass.cutlass_dsl import T, dsl_user_op
from cutlass._mlir import ir
from cutlass._mlir.dialects import nvvm, arith, llvm, vector, builtin


@dsl_user_op
def swiglu_f32(
    acc_gate_vec: ir.Value, 
    acc_val_vec: ir.Value, 
    *, 
    loc=None, 
    ip=None
) -> ir.Value:
    """
    Computes SwiGLU: Res = (Gate * Sigmoid(Gate)) * Value in FP32, then packs to FP16.
    
    Fixed Register Indices:
    $0 = Output (Packed f16x2)
    $1 = Gate[0]
    $2 = Gate[1]
    $3 = Value[0]
    $4 = Value[1]
    """
    
    # 1. Determine shapes
    vec_type = ir.VectorType(acc_gate_vec.type)
    num_elements = vec_type.shape[0]
    
    num_pairs = num_elements // 2
    
    # Output: vector<N x f16>
    out_f16_type = ir.VectorType.get([num_elements], Float16.mlir_type, loc=loc)
    
    # Intermediate: vector<N/2 x i32>
    packed_vec_type = ir.VectorType.get([num_pairs], Int32.mlir_type, loc=loc)
    packed_res = llvm.mlir_undef(packed_vec_type, loc=loc, ip=ip)

    # PTX Assembly
    asm_str = """
    {
        .reg .f32 %g0, %g1, %v0, %v1;
        .reg .f32 %s0, %s1, %r0, %r1;
        .reg .f32 %c0, %c1;
        .reg .f16 %h0, %h1;
        .reg .f32 %neg_log2e, %one;
        
        mov.f32 %neg_log2e, -1.44269504;
        mov.f32 %one, 1.0;

        // Load inputs (Shifted indices start at $1)
        mov.f32 %g0, $1;
        mov.f32 %g1, $2;
        mov.f32 %v0, $3;
        mov.f32 %v1, $4;

        // --- Element 0 ---
        mul.f32 %c0, %g0, %neg_log2e;     // exp arg
        ex2.approx.ftz.f32 %c0, %c0;      // exp result
        add.f32 %c0, %c0, %one;           // 1 + exp
        rcp.approx.ftz.f32 %c0, %c0;      // sigmoid = 1 / (1+exp)
        mul.f32 %s0, %g0, %c0;            // swish = g * sigmoid
        mul.f32 %r0, %s0, %v0;            // res = swish * val

        // --- Element 1 ---
        mul.f32 %c1, %g1, %neg_log2e;
        ex2.approx.ftz.f32 %c1, %c1;
        add.f32 %c1, %c1, %one;
        rcp.approx.ftz.f32 %c1, %c1;
        mul.f32 %s1, %g1, %c1;
        mul.f32 %r1, %s1, %v1;

        // --- Pack to f16x2 ---
        cvt.rn.f16.f32 %h0, %r0;
        cvt.rn.f16.f32 %h1, %r1;
        
        // Write to Output ($0)
        mov.b32 $0, {%h0, %h1};
    }
    """

    for i in range(num_pairs):
        idx0 = arith.constant(Int32.mlir_type, i * 2, loc=loc, ip=ip)
        idx1 = arith.constant(Int32.mlir_type, i * 2 + 1, loc=loc, ip=ip)
        
        # Extract 4 input floats
        g0 = llvm.extractelement(acc_gate_vec, idx0, loc=loc, ip=ip)
        g1 = llvm.extractelement(acc_gate_vec, idx1, loc=loc, ip=ip)
        v0 = llvm.extractelement(acc_val_vec, idx0, loc=loc, ip=ip)
        v1 = llvm.extractelement(acc_val_vec, idx1, loc=loc, ip=ip)
        
        # Run Inline ASM
        packed_val = llvm.inline_asm(
            Int32.mlir_type,
            [g0, g1, v0, v1],
            asm_str,
            "=r,f,f,f,f",             # Constraints: $0=Out, $1..$4=In
            has_side_effects=False,
            is_align_stack=False,
            asm_dialect=llvm.AsmDialect.AD_ATT,
            loc=loc,
            ip=ip,
        )
        
        ins_idx = arith.constant(Int32.mlir_type, i, loc=loc, ip=ip)
        packed_res = llvm.insertelement(packed_res, packed_val, ins_idx, loc=loc, ip=ip)

    # 3. Bitcast to vector<N x f16>
    res_f16 = llvm.bitcast(out_f16_type, packed_res, loc=loc, ip=ip)
    
    return res_f16

#mma_tiler_mnk= (128, 64, 256)  
#mma_inst_shape_k = 64
ab_dtype = cutlass.Float4E2M1FN  
sf_dtype = cutlass.Float8E4M3FN  
c_dtype = cutlass.Float16  
sf_vec_size = 16  
threads_per_cta = 128  
OCCUPANCY = 1

_TMA_CACHE_EVICT_NORMAL = 0x1000000000000000
_TMA_CACHE_EVICT_FIRST = 0x12F0000000000000
_TMA_CACHE_EVICT_LAST = 0x14F0000000000000

class Sm100BlockScaledPersistentDenseGemmKernel:
    def __init__(
        self,
        sf_vec_size: int,
        mma_tiler_mn: Tuple[int, int],
        cluster_shape_mn: Tuple[int, int],
        occupancy: int = 1,
    ):
        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)
        self.cta_group = (
            tcgen05.CtaGroup.TWO if self.use_2cta_instrs else tcgen05.CtaGroup.ONE
        )
        self.occupancy = int(occupancy)
        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)
        )
        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)),
        )
        self.smem_capacity = utils.get_smem_capacity_in_bytes("sm_100")
        self.num_tmem_alloc_cols = 512
    def _setup_attributes(self):
        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 = 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(tiled_mma.thr_id.shape),
            self.mma_tiler[1],
            self.mma_tiler[2],
        )
        self.cta_tile_shape_mnk_sfb = (
            self.mma_tiler_sfb[0] // cute.size(tiled_mma.thr_id.shape),
            self.mma_tiler_sfb[1],
            self.mma_tiler_sfb[2],
        )
        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,),
        )
        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
        self.epi_tile = sm100_utils.compute_epilogue_tile_shape(
            self.cta_tile_shape_mnk,
            self.use_2cta_instrs,
            self.c_layout,
            self.c_dtype,
        )
        self.epi_tile_n = cute.size(self.epi_tile[1])
        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,
        )
        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,
        )
        self.overlapping_accum = self.num_acc_stage == 1
        sf_atom_mn = 32
        self.num_sfa_tmem_cols = (self.cta_tile_shape_mnk[0] // sf_atom_mn) * mma_inst_tile_k
        self.num_sfb_tmem_cols = (self.cta_tile_shape_mnk_sfb[1] // sf_atom_mn) * mma_inst_tile_k
        self.num_sf_tmem_cols = self.num_sfa_tmem_cols + self.num_sfb_tmem_cols
        self.num_accumulator_tmem_cols = self.cta_tile_shape_mnk[1] * self.num_acc_stage if not self.overlapping_accum else self.cta_tile_shape_mnk[1] * 2 - self.num_sf_tmem_cols
        self.iter_acc_early_release_in_epilogue = self.num_sf_tmem_cols // self.epi_tile_n
    @cute.jit
    def __call__(
        self,
        a_ptr: cute.Pointer,
        b1_ptr: cute.Pointer,
        b2_ptr: cute.Pointer,
        sfa_ptr: cute.Pointer,
        sfb1_ptr: cute.Pointer,
        sfb2_ptr: cute.Pointer,
        c_ptr: cute.Pointer,
        problem_size: tuple,
        max_active_clusters: cutlass.Constexpr,
        epilogue_op: cutlass.Constexpr = lambda x: x
        * (1.0 / (1.0 + cute.math.exp(-x, fastmath=True))),
    ):
        m, n, k, l = problem_size
        sf_k = k // self.sf_vec_size
        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)),
            ),
        )
        b1_tensor = cute.make_tensor(
            b1_ptr,
            cute.make_layout(
                (n, cute.assume(k, 32), l),
                stride=(cute.assume(k, 32), 1, cute.assume(n * k, 32)),
            ),
        )

        b2_tensor = cute.make_tensor(
            b2_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)),
        )
        sfa_tensor = cute.make_tensor(
            sfa_ptr,
            cute.make_layout((m, sf_k, l), stride=(sf_k, 1, m * sf_k)),
        )
        sfb1_tensor = cute.make_tensor(
            sfb1_ptr,
            cute.make_layout((n, sf_k, l), stride=(sf_k, 1, n * sf_k)),
        )

        sfb2_tensor = cute.make_tensor(
            sfb2_ptr,
            cute.make_layout((n, sf_k, l), stride=(sf_k, 1, n * sf_k)),
        )

        a_tensor.mark_compact_shape_dynamic(mode=1, stride_order=(2, 0, 1), divisibility=2)
        b1_tensor.mark_compact_shape_dynamic(mode=1, stride_order=(2, 0, 1), divisibility=2)
        b2_tensor.mark_compact_shape_dynamic(mode=1, stride_order=(2, 0, 1), divisibility=2)
        c_tensor.mark_compact_shape_dynamic(mode=1, stride_order=(2, 0, 1), divisibility=1)
        self.a_dtype: Type[cutlass.Numeric] = a_tensor.element_type
        self.b_dtype: Type[cutlass.Numeric] = b1_tensor.element_type
        self.sf_dtype: Type[cutlass.Numeric] = sfa_tensor.element_type
        self.c_dtype: Type[cutlass.Numeric] = c_tensor.element_type
        self.a_major_mode = utils.LayoutEnum.from_tensor(a_tensor).mma_major_mode()
        self.b_major_mode = utils.LayoutEnum.from_tensor(b1_tensor).mma_major_mode()
        self.c_layout = utils.LayoutEnum.from_tensor(c_tensor)
        self._setup_attributes()

        sfa_layout = blockscaled_utils.tile_atom_to_shape_SF(
            a_tensor.shape, self.sf_vec_size
        )
        sfa_tensor = cute.make_tensor(sfa_tensor.iterator, sfa_layout)
        sfb_layout = blockscaled_utils.tile_atom_to_shape_SF(
            b1_tensor.shape, self.sf_vec_size
        )
        sfb1_tensor = cute.make_tensor(sfb1_tensor.iterator, sfb_layout)
        sfb2_tensor = cute.make_tensor(sfb2_tensor.iterator, 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)
        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,
        )
        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_b1, tma_tensor_b1 = cute.nvgpu.make_tiled_tma_atom_B(
            b_op,
            b1_tensor,
            b_smem_layout,
            self.mma_tiler,
            tiled_mma,
            self.cluster_layout_vmnk.shape,
        )

        tma_atom_b2, tma_tensor_b2 = cute.nvgpu.make_tiled_tma_atom_B(
            b_op,
            b2_tensor,
            b_smem_layout,
            self.mma_tiler,
            tiled_mma,
            self.cluster_layout_vmnk.shape,
        )

        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,
        )
        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_sfb1, tma_tensor_sfb1 = cute.nvgpu.make_tiled_tma_atom_B(
            sfb_op,
            sfb1_tensor,
            sfb_smem_layout,
            self.mma_tiler_sfb,
            tiled_mma_sfb,
            self.cluster_layout_sfb_vmnk.shape,
            internal_type=cutlass.Int16,
        )

        tma_atom_sfb2, tma_tensor_sfb2 = cute.nvgpu.make_tiled_tma_atom_B(
            sfb_op,
            sfb2_tensor,
            sfb_smem_layout,
            self.mma_tiler_sfb,
            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*2) + sfa_copy_size + (sfb_copy_size*2)
        ) * atom_thr_size
        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_m = (m // self.cta_tile_shape_mnk[0]) * cute.size(tiled_mma.thr_id.shape)
        grid_n = n // self.cta_tile_shape_mnk[1]
        grid = (grid_m, grid_n, l)

        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,
            ]
            sB1: cute.struct.Align[
                cute.struct.MemRange[
                    self.b_dtype, cute.cosize(self.b_smem_layout_staged.outer)
                ],
                self.buffer_align_bytes,
            ]

            sB2: 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,
            ]
            sSFB1: cute.struct.Align[
                cute.struct.MemRange[
                    self.sf_dtype, cute.cosize(self.sfb_smem_layout_staged)
                ],
                self.buffer_align_bytes,
            ]
            sSFB2: 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_b1,
            tma_tensor_b1,
            tma_atom_b2,
            tma_tensor_b2,
            tma_atom_sfa,
            tma_tensor_sfa,
            tma_atom_sfb1,
            tma_tensor_sfb1,
            tma_atom_sfb2,
            tma_tensor_sfb2,
            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,
            epilogue_op,
        ).launch(
            grid=grid,
            block=[self.threads_per_cta, 1, 1],
            cluster=(*self.cluster_shape_mn, 1),
            min_blocks_per_mp=self.occupancy,
        )
        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_b1: cute.CopyAtom,
        mB1_nkl: cute.Tensor,
        tma_atom_b2: cute.CopyAtom,
        mB2_nkl: cute.Tensor,
        tma_atom_sfa: cute.CopyAtom,
        mSFA_mkl: cute.Tensor,
        tma_atom_sfb1: cute.CopyAtom,
        mSFB1_nkl: cute.Tensor,
        tma_atom_sfb2: cute.CopyAtom,
        mSFB2_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,
        epilogue_op: cutlass.Constexpr,
    ):
        warp_idx = cute.arch.warp_idx()
        warp_idx = cute.arch.make_warp_uniform(warp_idx)
        if warp_idx == self.tma_warp_id:
            cpasync.prefetch_descriptor(tma_atom_a)
            cpasync.prefetch_descriptor(tma_atom_b1)
            cpasync.prefetch_descriptor(tma_atom_b2)
            cpasync.prefetch_descriptor(tma_atom_sfa)
            cpasync.prefetch_descriptor(tma_atom_sfb1)
            cpasync.prefetch_descriptor(tma_atom_sfb2)
            cpasync.prefetch_descriptor(tma_atom_c)

        use_2cta_instrs = cute.size(tiled_mma.thr_id.shape) == 2
        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()
        smem = utils.SmemAllocator()
        storage = smem.allocate(self.shared_storage)
        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
        )
        try:
            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,
                defer_sync=True,
            )
        except TypeError:
            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_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
        )
        try:
            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,
                defer_sync=True,
            )
        except TypeError:
            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 = 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,
        )
        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
        )
        sB1 = storage.sB1.get_tensor(
            b_smem_layout_staged.outer, swizzle=b_smem_layout_staged.inner
        )
        sB2 = storage.sB2.get_tensor(
            b_smem_layout_staged.outer, swizzle=b_smem_layout_staged.inner
        )
        sSFA = storage.sSFA.get_tensor(sfa_smem_layout_staged)
        sSFB1 = storage.sSFB1.get_tensor(sfb_smem_layout_staged)
        sSFB2 = storage.sSFB2.get_tensor(sfb_smem_layout_staged)
        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
            )
        gA_mkl = cute.local_tile(
            mA_mkl, cute.slice_(self.mma_tiler, (None, 0, None)), (None, None, None)
        )
        gB1_nkl = cute.local_tile(
            mB1_nkl, cute.slice_(self.mma_tiler, (0, None, None)), (None, None, None)
        )
        gB2_nkl = cute.local_tile(
            mB2_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)
        )
        gSFB1_nkl = cute.local_tile(
            mSFB1_nkl,
            cute.slice_(self.mma_tiler_sfb, (0, None, None)),
            (None, None, None),
        )
        gSFB2_nkl = cute.local_tile(
            mSFB2_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])
        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)
        tCgB1 = thr_mma.partition_B(gB1_nkl)
        tCgB2 = thr_mma.partition_B(gB2_nkl)
        tCgSFA = thr_mma.partition_A(gSFA_mkl)
        tCgSFB1 = thr_mma_sfb.partition_B(gSFB1_nkl)
        tCgSFB2 = thr_mma_sfb.partition_B(gSFB2_nkl)
        tCgC = thr_mma.partition_C(gC_mnl)
        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
        )
        tBsB1, tBgB1 = cpasync.tma_partition(
            tma_atom_b1,
            block_in_cluster_coord_vmnk[1],
            b_cta_layout,
            cute.group_modes(sB1, 0, 3),
            cute.group_modes(tCgB1, 0, 3),
        )
        tBsB2, tBgB2 = cpasync.tma_partition(
            tma_atom_b2,
            block_in_cluster_coord_vmnk[1],
            b_cta_layout,
            cute.group_modes(sB2, 0, 3),
            cute.group_modes(tCgB2, 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
        )
        tBsSFB1, tBgSFB1 = cute.nvgpu.cpasync.tma_partition(
            tma_atom_sfb1,
            block_in_cluster_coord_sfb_vmnk[1],
            sfb_cta_layout,
            cute.group_modes(sSFB1, 0, 3),
            cute.group_modes(tCgSFB1, 0, 3),
        )
        tBsSFB2, tBgSFB2 = cute.nvgpu.cpasync.tma_partition(
            tma_atom_sfb2,
            block_in_cluster_coord_sfb_vmnk[1],
            sfb_cta_layout,
            cute.group_modes(sSFB2, 0, 3),
            cute.group_modes(tCgSFB2, 0, 3),
        )
        tBgSFB1 = cute.filter_zeros(tBgSFB1)
        tBgSFB2 = cute.filter_zeros(tBgSFB2)
        tBsSFB1 = cute.filter_zeros(tBsSFB1)
        tBsSFB2 = cute.filter_zeros(tBsSFB2)

        tCrA = tiled_mma.make_fragment_A(sA)
        tCrB1 = tiled_mma.make_fragment_B(sB1)
        tCrB2 = tiled_mma.make_fragment_B(sB2)
        acc_shape = tiled_mma.partition_shape_C(self.mma_tiler[:2])
        if cutlass.const_expr(self.overlapping_accum):
            num_acc_stage_overlapped = 2
            tCtAcc_fake = tiled_mma.make_fragment_C(
                cute.append(acc_shape, num_acc_stage_overlapped)
            )
            tCtAcc_fake = cute.make_tensor(
                tCtAcc_fake.iterator,
                cute.make_layout(
                    tCtAcc_fake.shape,
                    stride = (
                        tCtAcc_fake.stride[0],
                        tCtAcc_fake.stride[1],
                        tCtAcc_fake.stride[2],
                        (256 - self.num_sf_tmem_cols) * tCtAcc_fake.stride[0][1]
                    ) 
                )
            )
        else:
            tCtAcc_fake = tiled_mma.make_fragment_C(
                cute.append(acc_shape, self.num_acc_stage)
            )
        if warp_idx == self.tma_warp_id:
            ab_producer_state = pipeline.make_pipeline_state(
                pipeline.PipelineUserType.Producer, self.num_ab_stage
            )
            mma_tile_coord_mnl = (
                bidx // cute.size(tiled_mma.thr_id.shape),
                bidy,
                bidz,
            )
            tAgA_slice = tAgA[(None, mma_tile_coord_mnl[0], None, mma_tile_coord_mnl[2])]
            tBgB1_slice = tBgB1[(None, mma_tile_coord_mnl[1], None, mma_tile_coord_mnl[2])]
            tBgB2_slice = tBgB2[(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
            tBgSFB1_slice = tBgSFB1[(None, slice_n, None, mma_tile_coord_mnl[2])]
            tBgSFB2_slice = tBgSFB2[(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,
                    cache_policy=cutlass.Int64(cutlass.Int64(_TMA_CACHE_EVICT_FIRST).ir_value()),
                )
                cute.copy(
                    tma_atom_b1,
                    tBgB1_slice[(None, ab_producer_state.count)],
                    tBsB1[(None, ab_producer_state.index)],
                    tma_bar_ptr=ab_pipeline.producer_get_barrier(ab_producer_state),
                    mcast_mask=b_full_mcast_mask,
                    cache_policy=cutlass.Int64(cutlass.Int64(_TMA_CACHE_EVICT_FIRST).ir_value()),
                )
                cute.copy(
                    tma_atom_b2,
                    tBgB2_slice[(None, ab_producer_state.count)],
                    tBsB2[(None, ab_producer_state.index)],
                    tma_bar_ptr=ab_pipeline.producer_get_barrier(ab_producer_state),
                    mcast_mask=b_full_mcast_mask,
                    cache_policy=cutlass.Int64(cutlass.Int64(_TMA_CACHE_EVICT_FIRST).ir_value()),
                )
                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,
                    cache_policy=cutlass.Int64(cutlass.Int64(_TMA_CACHE_EVICT_FIRST).ir_value()),
                )
                cute.copy(
                    tma_atom_sfb1,
                    tBgSFB1_slice[(None, ab_producer_state.count)],
                    tBsSFB1[(None, ab_producer_state.index)],
                    tma_bar_ptr=ab_pipeline.producer_get_barrier(ab_producer_state),
                    mcast_mask=sfb_full_mcast_mask,
                    cache_policy=cutlass.Int64(cutlass.Int64(_TMA_CACHE_EVICT_FIRST).ir_value()),
                )
                cute.copy(
                    tma_atom_sfb2,
                    tBgSFB2_slice[(None, ab_producer_state.count)],
                    tBsSFB2[(None, ab_producer_state.index)],
                    tma_bar_ptr=ab_pipeline.producer_get_barrier(ab_producer_state),
                    mcast_mask=sfb_full_mcast_mask,
                    cache_policy=cutlass.Int64(cutlass.Int64(_TMA_CACHE_EVICT_FIRST).ir_value()),
                )
                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)
            ab_pipeline.producer_tail(ab_producer_state)

        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 + self.num_accumulator_tmem_cols,
                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)
            #sus, might need to make a pointer with other b
            sfb1_tmem_ptr = cute.recast_ptr(
                acc_tmem_ptr + self.num_accumulator_tmem_cols + self.num_sfa_tmem_cols,
                dtype=self.sf_dtype,
            )
            sfb2_tmem_ptr = cute.recast_ptr(
                acc_tmem_ptr + self.num_accumulator_tmem_cols + self.num_sfa_tmem_cols + self.num_sfb_tmem_cols,
                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)),
            )
            tCtSFB1 = cute.make_tensor(sfb1_tmem_ptr, tCtSFB_layout)
            tCtSFB2 = cute.make_tensor(sfb2_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_sfb1,
                tCsSFB1_compact_s2t,
                tCtSFB1_compact_s2t,
            ) = self.mainloop_s2t_copy_and_partition(sSFB1, tCtSFB1)
            (
                tiled_copy_s2t_sfb2,
                tCsSFB2_compact_s2t,
                tCtSFB2_compact_s2t,
            ) = self.mainloop_s2t_copy_and_partition(sSFB2, tCtSFB2)

            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
            )
            mma_tile_coord_mnl = (
                bidx // cute.size(tiled_mma.thr_id.shape),
                bidy,
                bidz,
            )
            if cutlass.const_expr(self.overlapping_accum):
                acc_stage_index = acc_producer_state.phase ^ 1
            else:
                acc_stage_index = acc_producer_state.index
            tCtAcc1 = tCtAcc_base[(None, None, None, acc_stage_index)]
            tCtAcc2 = tCtAcc_base[(None, None, None, acc_stage_index + 1)]
            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)
            tCtSFB1_mma = tCtSFB1
            tCtSFB2_mma = tCtSFB2
            if cutlass.const_expr(self.cta_tile_shape_mnk[1] == 192):
                 pass 
            elif cutlass.const_expr(self.cta_tile_shape_mnk[1] == 64):
                offset = cutlass.Int32((mma_tile_coord_mnl[1] % 2) * 2)

                # Adjust SFB1
                shifted_ptr1 = cute.recast_ptr(
                    acc_tmem_ptr 
                    + self.num_accumulator_tmem_cols
                    + self.num_sfa_tmem_cols
                    + offset,
                    dtype=self.sf_dtype,
                )
                tCtSFB1_mma = cute.make_tensor(shifted_ptr1, tCtSFB_layout)

                # Adjust SFB2
                shifted_ptr2 = cute.recast_ptr(
                    acc_tmem_ptr 
                    + self.num_accumulator_tmem_cols
                    + self.num_sfa_tmem_cols
                    + self.num_sfb_tmem_cols # Add stride to get to SFB2
                    + offset,                # Add the alternating offset
                    dtype=self.sf_dtype,
                )
                tCtSFB2_mma = cute.make_tensor(shifted_ptr2, tCtSFB_layout)

            tiled_mma.set(tcgen05.Field.ACCUMULATE, False)
            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]
                    tCsSFB1_compact_s2t_staged = tCsSFB1_compact_s2t[s2t_stage_coord]
                    tCsSFB2_compact_s2t_staged = tCsSFB2_compact_s2t[s2t_stage_coord]
                    cute.copy(
                        tiled_copy_s2t_sfa,
                        tCsSFA_compact_s2t_staged,
                        tCtSFA_compact_s2t,
                    )
                    cute.copy(
                        tiled_copy_s2t_sfb1,
                        tCsSFB1_compact_s2t_staged,
                        tCtSFB1_compact_s2t,
                    )
                    cute.copy(
                        tiled_copy_s2t_sfb2,
                        tCsSFB2_compact_s2t_staged,
                        tCtSFB2_compact_s2t,
                    )
                    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,
                        )
                        sf_kblock_coord = (None, None, kblock_idx)
                        tiled_mma.set(
                            tcgen05.Field.SFA,
                            tCtSFA[sf_kblock_coord].iterator,
                        )
                        tiled_mma.set(
                            tcgen05.Field.SFB,
                            tCtSFB1_mma[sf_kblock_coord].iterator,
                        )
                        cute.gemm(
                            tiled_mma,
                            tCtAcc1,
                            tCrA[kblock_coord],
                            tCrB1[kblock_coord],
                            tCtAcc1,
                        )
                        tiled_mma.set(
                            tcgen05.Field.SFB,
                            tCtSFB2_mma[sf_kblock_coord].iterator,
                        )
                        cute.gemm(
                            tiled_mma,
                            tCtAcc2,
                            tCrA[kblock_coord],
                            tCrB2[kblock_coord],
                            tCtAcc2,
                        )
                        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()
            acc_pipeline.producer_tail(acc_producer_state)

        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_tAcc1_base,
                tTR_rAcc1,
            ) = self.epilog_tmem_copy_and_partition(
                epi_tidx, tCtAcc_base, tCgC, epi_tile, use_2cta_instrs
            )
            (
                tiled_copy_t2r,
                tTR_tAcc2_base,
                tTR_rAcc2,
            ) = self.epilog_tmem_copy_and_partition(
                epi_tidx, tCtAcc_base, tCgC, epi_tile, use_2cta_instrs
            )

            tTR_rC = cute.make_rmem_tensor(tTR_rAcc1.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
            )
            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,
            )
            mma_tile_coord_mnl = (
                bidx // cute.size(tiled_mma.thr_id.shape),
                bidy,
                bidz,
            )
            bSG_gC = bSG_gC_partitioned[
                (
                    None,
                    None,
                    None,
                    *mma_tile_coord_mnl,
                )
            ]
            if cutlass.const_expr(self.overlapping_accum):
                acc_stage_index = acc_consumer_state.phase
                reverse_subtile = cutlass.Boolean(True) if acc_stage_index == 0 else cutlass.Boolean(False)
            else:
                acc_stage_index = acc_consumer_state.index
            tTR_tAcc1 = tTR_tAcc1_base[
                (None, None, None, None, None, acc_stage_index)
            ]
            tTR_tAcc2 = tTR_tAcc2_base[
                (None, None, None, None, None, acc_stage_index+1)
            ]
            acc_pipeline.consumer_wait(acc_consumer_state)
            tTR_tAcc1 = cute.group_modes(tTR_tAcc1, 3, cute.rank(tTR_tAcc1))
            tTR_tAcc2 = cute.group_modes(tTR_tAcc2, 3, cute.rank(tTR_tAcc2))
            bSG_gC = cute.group_modes(bSG_gC, 1, cute.rank(bSG_gC))
            subtile_cnt = cute.size(tTR_tAcc1.shape, mode=[3])
            num_prev_subtiles = cutlass.Int32(0)
            for subtile_idx in cutlass.range(subtile_cnt):
                real_subtile_idx = subtile_idx
                if cutlass.const_expr(self.overlapping_accum):
                    if reverse_subtile:
                        real_subtile_idx = self.cta_tile_shape_mnk[1] // self.epi_tile_n - 1 - subtile_idx
                tTR_tAcc_mn_1 = tTR_tAcc1[(None, None, None, real_subtile_idx)]
                tTR_tAcc_mn_2 = tTR_tAcc2[(None, None, None, real_subtile_idx)]
                cute.copy(tiled_copy_t2r, tTR_tAcc_mn_1, tTR_rAcc1)
                cute.copy(tiled_copy_t2r, tTR_tAcc_mn_2, tTR_rAcc2)
                if cutlass.const_expr(self.overlapping_accum):
                    if subtile_idx == self.iter_acc_early_release_in_epilogue:
                        cute.arch.fence_view_async_tmem_load()
                        with cute.arch.elect_one():
                            acc_pipeline.consumer_release(acc_consumer_state)
                        acc_consumer_state.advance()

                acc1_vec = tTR_rAcc1.load()
                acc2_vec = tTR_rAcc2.load()
                acc2_vec = swiglu_f32(acc1_vec, acc2_vec)
                acc2_vec = TensorSSA(acc2_vec, tRS_rC.layout, cutlass.Float16)

                tRS_rC.store(acc2_vec)
                c_buffer = (num_prev_subtiles + real_subtile_idx) % self.num_c_stage
                cute.copy(
                    tiled_copy_r2s,
                    tRS_rC,
                    tRS_sC[(None, None, None, c_buffer)],
                )
                cute.arch.fence_view_async_shared()
                self.epilog_sync_barrier.arrive_and_wait()
                if warp_idx == self.epilog_warp_id[0]:
                    try:
                        cute.copy(
                            tma_atom_c,
                            bSG_sC[(None, c_buffer)],
                            bSG_gC[(None, real_subtile_idx)],
                            cache_policy=cutlass.Int64(cutlass.Int64(_TMA_CACHE_EVICT_FIRST).ir_value()),
                        )
                    except TypeError:
                        cute.copy(
                            tma_atom_c,
                            bSG_sC[(None, c_buffer)],
                            bSG_gC[(None, real_subtile_idx)],
                        )
                    c_pipeline.producer_commit()
                    c_pipeline.producer_acquire()
                self.epilog_sync_barrier.arrive_and_wait()
            if cutlass.const_expr(not self.overlapping_accum):
                with cute.arch.elect_one():
                    acc_pipeline.consumer_release(acc_consumer_state)
                acc_consumer_state.advance()
            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]:
        num_acc_stage = 1 if mma_tiler_mnk[1] == 256 else 2
        num_c_stage = 1
        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) * 2
            + cute.size_in_bytes(sf_dtype, sfa_smem_layout_staged_one)
            + cute.size_in_bytes(sf_dtype, sfb_smem_layout_staged_one) * 2
        )
        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


#-----------------------------------------------------------

_CACHE_SMALL, _CACHE_LARGE = None, None
def compile_kernel():
    global _CACHE_SMALL, _CACHE_LARGE
    
    if _CACHE_SMALL is not None:
        return _CACHE_SMALL, _CACHE_LARGE
    
    _max_active_clusters = 4096

    gemm_small = Sm100BlockScaledPersistentDenseGemmKernel(
        sf_vec_size, (128, 64), cluster_shape_mn=(1, 1), occupancy=OCCUPANCY
    )
    gemm_large = Sm100BlockScaledPersistentDenseGemmKernel(
        sf_vec_size, (128, 128), cluster_shape_mn=(1, 1), occupancy=OCCUPANCY
    )

    a_ptr = make_ptr(cutlass.Float4E2M1FN, 0, cute.AddressSpace.gmem, assumed_align=128)

    # Create CuTe pointers for A/B/C/SFA/SFB via torch tensor data pointer
    a_ptr = make_ptr(
        ab_dtype, 0, cute.AddressSpace.gmem, assumed_align=16
    )
    b1_ptr = make_ptr(
        ab_dtype, 0, cute.AddressSpace.gmem, assumed_align=16
    )
    b2_ptr = make_ptr(
        ab_dtype, 0, cute.AddressSpace.gmem, assumed_align=16
    )
    c_ptr = make_ptr(
        c_dtype, 0, cute.AddressSpace.gmem, assumed_align=16
    )
    sfa_ptr = make_ptr(
        sf_dtype, 0, cute.AddressSpace.gmem, assumed_align=32
    )
    sfb1_ptr = make_ptr(
        sf_dtype, 0, cute.AddressSpace.gmem, assumed_align=32
    )
    sfb2_ptr = make_ptr(
        sf_dtype, 0, cute.AddressSpace.gmem, assumed_align=32
    )

    _CACHE_SMALL = cute.compile(gemm_small, a_ptr,
                                        b1_ptr,
                                        b2_ptr, 
                                        sfa_ptr, 
                                        sfb1_ptr, 
                                        sfb2_ptr, 
                                        c_ptr, 
                                        (0, 0, 0, 0),
                                        _max_active_clusters,
                                        options="--opt-level 2 --gpu-arch sm_100a")


    _CACHE_LARGE = cute.compile(gemm_large, a_ptr,
                                        b1_ptr,
                                        b2_ptr, 
                                        sfa_ptr, 
                                        sfb1_ptr, 
                                        sfb2_ptr, 
                                        c_ptr, 
                                        (0, 0, 0, 0),
                                        _max_active_clusters,
                                        options="--opt-level 2 --gpu-arch sm_100a")
    return _CACHE_SMALL, _CACHE_LARGE


def custom_kernel(data: input_t) -> output_t:
    """
    Execute the block-scaled dual GEMM kernel with silu activation,
    C = silu(A @ B1) * (A @ B2).
    
    This is the main entry point called by the evaluation framework.
    It converts PyTorch tensors to CuTe tensors, launches the kernel,
    and returns the result.
    
    Args:
        data: Tuple of (a, b1, b2, sfa_cpu, sfb1_cpu, sfb2_cpu, c) PyTorch tensors
            a: [m, k, l] - Input matrix in float4e2m1fn 
            b1: [n, k, l] - Input matrix in float4e2m1fn 
            b2: [n, k, l] - Input matrix in float4e2m1fn 
            sfa_cpu: [m, k, l] - Scale factors in float8_e4m3fn, used by reference implementation
            sfb1_cpu: [n, k, l] - Scale factors in float8_e4m3fn, used by reference implementation
            sfb2_cpu: [n, k, l] - Scale factors in float8_e4m3fn, used by reference implementation
            sfa_permuted: [32, 4, rest_m, 4, rest_k, l] - Scale factors in float8_e4m3fn
            sfb1_permuted: [32, 4, rest_n, 4, rest_k, l] - Scale factors in float8_e4m3fn
            sfb2_permuted: [32, 4, rest_n, 4, rest_k, l] - Scale factors in float8_e4m3fn
            c: [m, n, l] - Output vector in float16
    
    Returns:
        Output tensor c with computed results
    """
    a, b1, b2, _, _, _, sfa_permuted, sfb1_permuted, sfb2_permuted, c = data
    
    _, k, _ = a.shape
    m, n, l = c.shape
    k = k * 2 

    small, large = compile_kernel()
    if m <= 256:
        compiled_func = small
    else:
        compiled_func = large

    a_ptr = make_ptr(
        ab_dtype, a.data_ptr(), cute.AddressSpace.gmem, assumed_align=16
    )
    b1_ptr = make_ptr(
        ab_dtype, b1.data_ptr(), cute.AddressSpace.gmem, assumed_align=16
    )
    b2_ptr = make_ptr(
        ab_dtype, b2.data_ptr(), cute.AddressSpace.gmem, assumed_align=16
    )
    c_ptr = make_ptr(
        c_dtype, c.data_ptr(), cute.AddressSpace.gmem, assumed_align=16
    )
    sfa_ptr = make_ptr(
        sf_dtype, sfa_permuted.data_ptr(), cute.AddressSpace.gmem, assumed_align=32
    )
    sfb1_ptr = make_ptr(
        sf_dtype, sfb1_permuted.data_ptr(), cute.AddressSpace.gmem, assumed_align=32
    )
    sfb2_ptr = make_ptr(
        sf_dtype, sfb2_permuted.data_ptr(), cute.AddressSpace.gmem, assumed_align=32
    )

    compiled_func(a_ptr, b1_ptr, b2_ptr, sfa_ptr, sfb1_ptr, sfb2_ptr, c_ptr, (m, n, k, l))

    return c