submission 141700

# Adapted from https://github.com/NVIDIA/cutlass/blob/main/examples/python/CuTeDSL/blackwell/dense_blockscaled_gemm_persistent.py
# Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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"""
compared to v0:
1. remove the work tile loop for m == 128
2. k_tile_cnt constant

1: 10.951μs
k: 16384; l: 1; m: 128; n: 7168; seed: 1111
 ⏱ 19.1 ± 0.02 µs
 ⚡ 19.0 µs 🐌 19.8 µs

k: 7168; l: 1; m: 128; n: 4096; seed: 1111
 ⏱ 10.4 ± 0.01 µs
 ⚡ 10.4 µs 🐌 10.5 µs

k: 2048; l: 1; m: 128; n: 7168; seed: 1111
 ⏱ 6.59 ± 0.007 µs
 ⚡ 6.49 µs 🐌 6.70 µs

1 + 2: 10.917μs
k: 16384; l: 1; m: 128; n: 7168; seed: 1111
 ⏱ 19.1 ± 0.02 µs
 ⚡ 18.9 µs 🐌 19.7 µs

k: 7168; l: 1; m: 128; n: 4096; seed: 1111
 ⏱ 10.4 ± 0.00 µs
 ⚡ 10.4 µs 🐌 10.4 µs

k: 2048; l: 1; m: 128; n: 7168; seed: 1111
 ⏱ 6.54 ± 0.006 µs
 ⚡ 6.45 µs 🐌 6.70 µs
"""

from typing import Tuple, Type, Union

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

"""
This example provides an experimental implementation of the SM100 batched dense blockscaled GEMM kernel, please note that the APIs and implementation details related to this kernel may change in future releases.

A high-performance persistent batched dense blockscaled GEMM example for the NVIDIA Blackwell SM100 architecture
using CUTE DSL.
- Matrix A is MxKxL, L is batch dimension, A can be row-major("K") or column-major("M") for MXF8 input type and can only be row-major("K") for MXF4/NVF4 input type
- Matrix B is NxKxL, L is batch dimension, B can be row-major("N") or column-major("K") for MXF8 input type and can only be row-major("K") for MXF4/NVF4 input type
- Matrix C is MxNxL, L is batch dimension, C can be row-major("N") or column-major("M")
- Matrix SFA layout is filled internally according to A shape and BlockScaledBasicChunk, which has M×ceil_div(K, sf_vec_size)×L elements respectively
- Matrix SFB layout is filled internally according to B shape and BlockScaledBasicChunk, which has N×ceil_div(K, sf_vec_size)×L elements respectively

This GEMM kernel supports the following features:
    - Utilizes Tensor Memory Access (TMA) for efficient memory operations
    - Utilizes Blackwell's tcgen05.mma for matrix multiply-accumulate (MMA) operations (including 2cta mma instructions)
    - Implements TMA multicast with cluster to reduce L2 memory traffic
    - Support persistent tile scheduling to better overlap memory load/store with mma between tiles
    - Support warp specialization to avoid explicit pipelining between mainloop load and mma

This GEMM works as follows:
1. DMA warp: Load A and B matrices from global memory (GMEM) to shared memory (SMEM) using TMA operations.
2. MMA warp:
    - Load scale factor A/B from shared memory (SMEM) to tensor memory (TMEM) using tcgen05.cp instruction.
    - Perform matrix multiply-accumulate (MMA) operations using tcgen05.mma instruction.
3. EPILOGUE warp:
    - Load completed accumulator from tensor memory (TMEM) to registers (RMEM) using tcgen05.ld.
    - Type convert C matrix to output type.
    - Optionally store C matrix from registers (RMEM) to shared memory (SMEM) to global memory (GMEM) with TMA operations,
      or directly store C matrix from registers (RMEM) to global memory (GMEM) without TMA operations.
    - Optionally accept an elementwise lambda function epilogue_op to apply to the output tensor:
      e.g., relu can set epilogue_op = lambda x: cute.where(x > 0, x, cute.full_like(x, 0))

SM100 tcgen05.mma.kind.block_scale instructions operate as follows:
- Read matrix A from SMEM
- Read matrix B from SMEM
- Read scalefactor A from TMEM
- Read scalefactor B from TMEM
- Write accumulator to TMEM
The accumulator in TMEM must then be loaded to registers before writing back to GMEM.

Input arguments to this example is shown below:

.. code-block:: bash

    python examples/blackwell/dense_blockscaled_gemm_persistent.py             \
      --ab_dtype Float4E2M1FN --sf_dtype Float8E8M0FNU --sf_vec_size 16        \
      --c_dtype Float16                                                        \
      --mma_tiler_mn 256,128 --cluster_shape_mn 2,1                            \
      --mnkl 8192,8192,1024,1

To collect performance with NCU profiler:

.. code-block:: bash

    ncu python examples/blackwell/dense_blockscaled_gemm_persistent.py         \
      --ab_dtype Float4E2M1FN --sf_dtype Float8E8M0FNU --sf_vec_size 16        \
      --c_dtype Float16                                                        \
      --mma_tiler_mn 256,128 --cluster_shape_mn 2,1                            \
      --mnkl 8192,8192,1024,1                                                  \
      --warmup_iterations 1 --iterations 10 --skip_ref_check


Constraints:
* Supported input data types: mxf8, mxf4, nvf4
  see detailed valid dtype combinations in below Sm100BlockScaledPersistentDenseGemmKernel class documentation
* A/B tensor must have the same data type, mixed data type is not supported (e.g., mxf8 x mxf4)
* Mma tiler M must be 128 or 256(use_2cta_instrs)
* Mma tiler N must be 64/128/192/256
* Cluster shape M/N must be positive and power of 2, total cluster size <= 16
* Cluster shape M must be multiple of 2 if Mma tiler M is 256(use_2cta_instrs)
* The contiguous dimension of A/B/C tensors must be at least 16 bytes aligned,
  i.e, number of elements is a multiple of 16 and 32 for Float8 and Float4, respectively.
"""


class Sm100BlockScaledPersistentDenseGemmKernel:
    """This class implements batched matrix multiplication (C = A x SFA x B x SFB) with support for various data types
    and architectural features specific to Blackwell GPUs with persistent tile scheduling and warp specialization.

    :param sf_vec_size: Scalefactor vector size.
    :type sf_vec_size: int
    :param mma_tiler_mn: Shape of the Matrix Multiply-Accumulate (MMA) tile (M,N)
    :type mma_tiler_mn: Tuple[int, int]
    :param cluster_shape_mn: Cluster dimensions (M,N) for parallel processing
    :type cluster_shape_mn: Tuple[int, int]

    :note: In current version, A and B tensor must have the same data type
        - i.e., Float8E4M3FN for A and Float8E5M2 for B is not supported

    :note: Supported combinations of A/B data types, SF data typs and SF vector size:
        - MXF8: A/B: Float8E5M2/Float8E4M3FN + SF: Float8E8M0FNU + sf_vec_size: 32
        - MXF4: A/B: Float4E2M1FN + SF: Float8E8M0FNU + sf_vec_size: 32
        - NVF4: A/B: Float4E2M1FN + SF: Float8E8M0FNU/Float8E4M3FN + sf_vec_size: 16

    :note: Supported accumulator data types:
        - Float32

    :note: Supported C data types:
        - Float32
        - Float16/BFloat16
        - Float8E4M3FN/Float8E5M2
    :note: Constraints:
        - MMA tiler M must be 128 or 256 (use_2cta_instrs)
        - MMA tiler N must be 64/128/192/256
        - Cluster shape M must be multiple of 2 if Mma tiler M is 256
        - Cluster shape M/N must be positive and power of 2, total cluster size <= 16
        - Also, Cluster shape M/N must be <= 4 for scale factor multicasts due to limited size of scale factors

    Example:
        >>> gemm = Sm100BlockScaledPersistentDenseGemmKernel(
        ...     sf_vec_size=16,
        ...     mma_tiler_mn=(256, 128),
        ...     cluster_shape_mn=(2, 1)
        ... )
        >>> gemm(a_tensor, b_tensor, sfa_tensor, sfb_tensor, c_tensor, max_active_clusters)
    """

    def __init__(
        self,
        sf_vec_size: int,
        mma_tiler_mn: Tuple[int, int],
        cluster_shape_mn: Tuple[int, int],
    ):
        """Initializes the configuration for a Blackwell dense GEMM kernel.

        This configuration includes several key aspects:

        1.  MMA Instruction Settings (tcgen05):
            - acc_dtype: Data types for MMA accumulator, always set to Float32
            - sf_vec_size: Scalefactor A/B vector size.
            - mma_tiler_mn: The (M, N) shape of the MMA instruction tiler.

        2.  Cluster Shape:
            - cluster_shape_mn: The (ClusterM, ClusterN) shape of the CTA cluster.

        :param sf_vec_size: Scalefactor vector size.
        :type sf_vec_size: int
        :param mma_tiler_mn: Tuple (M, N) shape of the MMA instruction.
        :type mma_tiler_mn: Tuple[int, int]
        :param cluster_shape_mn: Tuple (ClusterM, ClusterN) shape of the cluster.
        :type cluster_shape_mn: Tuple[int, int]
        """

        self.acc_dtype = cutlass.Float32
        self.sf_vec_size = sf_vec_size
        self.use_2cta_instrs = False
        self.cluster_shape_mn = cluster_shape_mn
        # K dimension is deferred in _setup_attributes
        self.mma_tiler = (*mma_tiler_mn, 1)

        self.cta_group = tcgen05.CtaGroup.ONE

        self.occupancy = 1
        # Set specialized warp ids
        self.epilog_warp_id = (
            0,
            1,
            2,
            3,
        )
        self.mma_warp_id = 4
        self.tma_warp_id = 5
        self.threads_per_cta = 32 * len(
            (self.mma_warp_id, self.tma_warp_id, *self.epilog_warp_id)
        )
        # Set barrier id for cta sync, epilogue sync and tmem ptr sync
        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")
        SM100_TMEM_CAPACITY_COLUMNS = 512
        self.num_tmem_alloc_cols = SM100_TMEM_CAPACITY_COLUMNS

    def _setup_attributes(self):
        """Set up configurations that are dependent on GEMM inputs

        This method configures various attributes based on the input tensor properties
        (data types, leading dimensions) and kernel settings:
        - Configuring tiled MMA
        - Computing MMA/cluster/tile shapes
        - Computing cluster layout
        - Computing multicast CTAs for A/B/SFA/SFB
        - Computing epilogue subtile
        - Setting up A/B/SFA/SFB/C stage counts in shared memory
        - Computing A/B/SFA/SFB/C shared memory layout
        """
        # Compute mma instruction shapes
        # (MMA_Tile_Shape_M, MMA_Tile_Shape_N, MMA_Inst_Shape_K)
        self.mma_inst_shape_mn = (
            self.mma_tiler[0],
            self.mma_tiler[1],
        )
        # (CTA_Tile_Shape_M, Round_Up(MMA_Tile_Shape_N, 128), MMA_Inst_Shape_K)
        self.mma_inst_shape_mn_sfb = (
            self.mma_inst_shape_mn[0],
            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,
        )

        # Compute mma/cluster/tile shapes
        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],
        )

        # Compute 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,),
        )

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

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

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

        # Compute A/B/SFA/SFB/C shared memory layout
        self.a_smem_layout_staged = sm100_utils.make_smem_layout_a(
            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,
        problem_size: tuple,
        max_active_clusters: cutlass.Constexpr,
        k_tile_cnt: cutlass.Constexpr,
    ):
        """Execute the GEMM operation in steps:
        - Setup static attributes before smem/grid/tma computation
        - Setup TMA load/store atoms and tensors
        - Compute grid size with regard to hardware constraints
        - Define shared storage for kernel
        - Launch the kernel synchronously

        :param a_tensor: Input tensor A
        :type a_tensor: cute.Tensor
        :param b_tensor: Input tensor B
        :type b_tensor: cute.Tensor
        :param sfa_tensor: Scale factor tensor A
        :type sfa_tensor: cute.Tensor
        :param sfb_tensor: Scale factor tensor B
        :type sfb_tensor: cute.Tensor
        :param c_tensor: Output tensor C
        :type c_tensor: cute.Tensor
        :param max_active_clusters: Maximum number of active clusters
        :type max_active_clusters: cutlass.Constexpr
        :param k_tile_cnt: The number of k tiles
        :type k_tile_cnt: cutlass.Constexpr
        :raises TypeError: If input data types are incompatible with the MMA instruction.
        """
        m, n, k, l = problem_size
        # Setup attributes that depend on gemm inputs
        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))
        )
        # Setup sfa/sfb tensor by filling A/B tensor to scale factor atom layout
        # ((Atom_M, Rest_M),(Atom_K, Rest_K),RestL)
        sfa_layout = blockscaled_utils.tile_atom_to_shape_SF(
            a_tensor.shape, sf_vec_size
        )
        sfa_tensor = cute.make_tensor(sfa_ptr, sfa_layout)

        # ((Atom_N, Rest_N),(Atom_K, Rest_K),RestL)
        sfb_layout = blockscaled_utils.tile_atom_to_shape_SF(
            b_tensor.shape, sf_vec_size
        )
        sfb_tensor = cute.make_tensor(sfb_ptr, sfb_layout)

        # Setup static attributes before smem/grid/tma computation
        self.a_dtype: Type[cutlass.Numeric] = a_tensor.element_type
        self.b_dtype: Type[cutlass.Numeric] = b_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(b_tensor).mma_major_mode()
        self.c_layout = utils.LayoutEnum.from_tensor(c_tensor)


        # Setup attributes that dependent on gemm inputs
        self._setup_attributes()

        # Setup sfa/sfb tensor by filling A/B tensor to scale factor atom layout
        # ((Atom_M, Rest_M),(Atom_K, Rest_K),RestL)
        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)

        # ((Atom_N, Rest_N),(Atom_K, Rest_K),RestL)
        sfb_layout = blockscaled_utils.tile_atom_to_shape_SF(
            b_tensor.shape, self.sf_vec_size
        )
        sfb_tensor = cute.make_tensor(sfb_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)

        # Setup TMA load for 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,
        )

        # Setup TMA load for 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,
        )

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

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

        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],
            )
            # Use right multiplication for ScaledBasis (3 * x instead of x * 3)
            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
            )

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

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

        # Compute grid size
        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

        # Define shared storage for kernel
        @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
            # (EPI_TILE_M, EPI_TILE_N, STAGE)
            sC: cute.struct.Align[
                cute.struct.MemRange[
                    self.c_dtype,
                    cute.cosize(self.c_smem_layout_staged.outer),
                ],
                self.buffer_align_bytes,
            ]
            # (MMA, MMA_M, MMA_K, STAGE)
            sA: cute.struct.Align[
                cute.struct.MemRange[
                    self.a_dtype, cute.cosize(self.a_smem_layout_staged.outer)
                ],
                self.buffer_align_bytes,
            ]
            # (MMA, MMA_N, MMA_K, STAGE)
            sB: cute.struct.Align[
                cute.struct.MemRange[
                    self.b_dtype, cute.cosize(self.b_smem_layout_staged.outer)
                ],
                self.buffer_align_bytes,
            ]
            # (MMA, MMA_M, MMA_K, STAGE)
            sSFA: cute.struct.Align[
                cute.struct.MemRange[
                    self.sf_dtype, cute.cosize(self.sfa_smem_layout_staged)
                ],
                self.buffer_align_bytes,
            ]
            # (MMA, MMA_N, MMA_K, STAGE)
            sSFB: cute.struct.Align[
                cute.struct.MemRange[
                    self.sf_dtype, cute.cosize(self.sfb_smem_layout_staged)
                ],
                self.buffer_align_bytes,
            ]

        self.shared_storage = SharedStorage

        # Launch the kernel synchronously
        self.kernel(
            tiled_mma,
            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,
            k_tile_cnt,
        ).launch(
            grid=grid,
            block=[self.threads_per_cta, 1, 1],
            cluster=(*self.cluster_shape_mn, 1),
        )
        return

    # GPU device kernel
    @cute.kernel
    def kernel(
        self,
        tiled_mma: cute.TiledMma,
        tiled_mma_sfb: cute.TiledMma,
        tma_atom_a: cute.CopyAtom,
        mA_mkl: cute.Tensor,
        tma_atom_b: cute.CopyAtom,
        mB_nkl: cute.Tensor,
        tma_atom_sfa: cute.CopyAtom,
        mSFA_mkl: cute.Tensor,
        tma_atom_sfb: cute.CopyAtom,
        mSFB_nkl: cute.Tensor,
        tma_atom_c: cute.CopyAtom,
        mC_mnl: cute.Tensor,
        cluster_layout_vmnk: cute.Layout,
        cluster_layout_sfb_vmnk: cute.Layout,
        a_smem_layout_staged: cute.ComposedLayout,
        b_smem_layout_staged: cute.ComposedLayout,
        sfa_smem_layout_staged: cute.Layout,
        sfb_smem_layout_staged: cute.Layout,
        c_smem_layout_staged: Union[cute.Layout, cute.ComposedLayout],
        epi_tile: cute.Tile,
        tile_sched_params: utils.PersistentTileSchedulerParams,
        k_tile_cnt: cutlass.Constexpr,
    ):
        """
        GPU device kernel performing the Persistent batched GEMM computation.
        """
        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

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

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

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

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

        # Tensor memory dealloc barrier init
        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=self.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()

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

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

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

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

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

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

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

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

        #
        # Specialized TMA load warp
        #
        if warp_idx == self.tma_warp_id:
            #
            # Persistent tile scheduling loop
            #
            tile_sched = utils.StaticPersistentTileScheduler.create(
                tile_sched_params, cute.arch.block_idx(), 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
            )

            # Get tile coord from tile scheduler
            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],
            )

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

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

            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
            # ((atom_v, rest_v), RestK)
            tBgSFB_slice = tBgSFB[(None, slice_n, None, mma_tile_coord_mnl[2])]

            # Peek (try_wait) AB buffer empty for k_tile = prefetch_k_tile_cnt
            ab_producer_state.reset_count()
            peek_ab_empty_status = cutlass.Boolean(1)
            if ab_producer_state.count < k_tile_cnt:
                peek_ab_empty_status = ab_pipeline.producer_try_acquire(
                    ab_producer_state
                )
            #
            # Tma load loop
            #
            for _ in cutlass.range(k_tile_cnt, unroll=1):
                # Conditionally wait for AB buffer empty
                ab_pipeline.producer_acquire(ab_producer_state, peek_ab_empty_status)

                # TMA load A/B/SFA/SFB
                cute.copy(
                    tma_atom_a,
                    tAgA_slice[(None, ab_producer_state.count)],
                    tAsA[(None, ab_producer_state.index)],
                    tma_bar_ptr=ab_pipeline.producer_get_barrier(ab_producer_state),
                )
                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),
                )
                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),
                )
                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),
                )

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

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

        #
        # Specialized MMA warp
        #
        if warp_idx == self.mma_warp_id:
            #
            # Bar sync for retrieve tensor memory ptr from shared mem
            #
            tmem.wait_for_alloc()

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

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

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

            #
            # Persistent tile scheduling loop
            #
            tile_sched = utils.StaticPersistentTileScheduler.create(
                tile_sched_params, cute.arch.block_idx(), 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
            )

            # Get tile coord from tile scheduler
            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],
            )

            # Set tensor memory buffer for current tile
            # (MMA, MMA_M, MMA_N)
            tCtAcc = tCtAcc_base[(None, None, None, acc_producer_state.index)]

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

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

            tCtSFB_mma = tCtSFB
            if cutlass.const_expr(self.cta_tile_shape_mnk[1] == 64):
                # Move in increments of 64 columns of SFB
                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 the ACCUMULATE field for each tile
            #
            tiled_mma.set(tcgen05.Field.ACCUMULATE, False)

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

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

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

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

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

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

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

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

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

            #
            # Wait for accumulator buffer empty
            #
            acc_pipeline.producer_tail(acc_producer_state)
        #
        # Specialized epilogue warps
        #
        if warp_idx < self.mma_warp_id:
            #
            # Alloc tensor memory buffer
            #
            tmem.allocate(self.num_tmem_alloc_cols)

            #
            # Bar sync for retrieve tensor memory ptr from shared memory
            #
            tmem.wait_for_alloc()

            #
            # Retrieving tensor memory ptr and make accumulator tensor
            #
            acc_tmem_ptr = tmem.retrieve_ptr(self.acc_dtype)
            # (MMA, MMA_M, MMA_N, STAGE)
            tCtAcc_base = cute.make_tensor(acc_tmem_ptr, tCtAcc_fake.layout)

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

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

            #
            # Persistent tile scheduling loop
            #
            tile_sched = utils.StaticPersistentTileScheduler.create(
                tile_sched_params, cute.arch.block_idx(), 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
            )

            # Threads/warps participating in tma store pipeline
            c_producer_group = pipeline.CooperativeGroup(
                pipeline.Agent.Thread,
                32 * len(self.epilog_warp_id),
            )
            c_pipeline = pipeline.PipelineTmaStore.create(
                num_stages=self.num_c_stage,
                producer_group=c_producer_group,
            )

            # Get tile coord from tile scheduler
            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],
            )

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

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

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

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

            #
            # Store accumulator to global memory in subtiles
            #
            subtile_cnt = cute.size(tTR_tAcc.shape, mode=[3])
            for subtile_idx in cutlass.range(subtile_cnt):
                #
                # Load accumulator from tensor memory buffer to register
                #
                tTR_tAcc_mn = tTR_tAcc[(None, None, None, subtile_idx)]
                cute.copy(tiled_copy_t2r, tTR_tAcc_mn, tTR_rAcc)

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

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

                #
                # TMA store C to global memory
                #
                if warp_idx == self.epilog_warp_id[0]:
                    cute.copy(
                        tma_atom_c,
                        bSG_sC[(None, c_buffer)],
                        bSG_gC[(None, subtile_idx)],
                    )
                    # Fence and barrier to make sure shared memory store is visible to TMA store
                    c_pipeline.producer_commit()
                    c_pipeline.producer_acquire()
                self.epilog_sync_barrier.arrive_and_wait()

            #
            # Async arrive accumulator buffer empty
            #
            with cute.arch.elect_one():
                acc_pipeline.consumer_release(acc_consumer_state)
            acc_consumer_state.advance()

            #
            # Dealloc the tensor memory buffer
            #
            tmem.relinquish_alloc_permit()
            self.epilog_sync_barrier.arrive_and_wait()
            tmem.free(acc_tmem_ptr)
            #
            # Wait for C store complete
            #
            c_pipeline.producer_tail()

    def mainloop_s2t_copy_and_partition(
        self,
        sSF: cute.Tensor,
        tSF: cute.Tensor,
    ) -> Tuple[cute.TiledCopy, cute.Tensor, cute.Tensor]:
        """
        Make tiledCopy for smem to tmem load for scale factor tensor, then use it to partition smem memory (source) and tensor memory (destination).

        :param sSF: The scale factor tensor in smem
        :type sSF: cute.Tensor
        :param tSF: The scale factor tensor in tmem
        :type tSF: cute.Tensor

        :return: A tuple containing (tiled_copy_s2t, tCsSF_compact_s2t, tCtSF_compact_s2t) where:
            - tiled_copy_s2t: The tiled copy operation for smem to tmem load for scale factor tensor(s2t)
            - tCsSF_compact_s2t: The partitioned scale factor tensor in smem
            - tSF_compact_s2t: The partitioned scale factor tensor in tmem
        :rtype: Tuple[cute.TiledCopy, cute.Tensor, cute.Tensor]
        """
        # (MMA, MMA_MN, MMA_K, STAGE)
        tCsSF_compact = cute.filter_zeros(sSF)
        # (MMA, MMA_MN, MMA_K)
        tCtSF_compact = cute.filter_zeros(tSF)

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

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

        return tiled_copy_s2t, tCsSF_compact_s2t, tCtSF_compact_s2t

    def epilog_tmem_copy_and_partition(
        self,
        tidx: cutlass.Int32,
        tAcc: cute.Tensor,
        gC_mnl: cute.Tensor,
        epi_tile: cute.Tile,
        # use_2cta_instrs: Union[cutlass.Boolean, bool],
    ) -> Tuple[cute.TiledCopy, cute.Tensor, cute.Tensor]:
        """
        Make tiledCopy for tensor memory load, then use it to partition tensor memory (source) and register array (destination).

        :param tidx: The thread index in epilogue warp groups
        :type tidx: cutlass.Int32
        :param tAcc: The accumulator tensor to be copied and partitioned
        :type tAcc: cute.Tensor
        :param gC_mnl: The global tensor C
        :type gC_mnl: cute.Tensor
        :param epi_tile: The epilogue tiler
        :type epi_tile: cute.Tile
        :param use_2cta_instrs: Whether use_2cta_instrs is enabled
        :type use_2cta_instrs: bool

        :return: A tuple containing (tiled_copy_t2r, tTR_tAcc, tTR_rAcc) where:
            - tiled_copy_t2r: The tiled copy operation for tmem to register copy(t2r)
            - tTR_tAcc: The partitioned accumulator tensor
            - tTR_rAcc: The accumulated tensor in register used to hold t2r results
        :rtype: Tuple[cute.TiledCopy, cute.Tensor, cute.Tensor]
        """
        # Make tiledCopy for tensor memory load
        copy_atom_t2r = sm100_utils.get_tmem_load_op(
            self.cta_tile_shape_mnk,
            self.c_layout,
            self.c_dtype,
            self.acc_dtype,
            epi_tile,
            self.use_2cta_instrs,
        )
        # (EPI_TILE_M, EPI_TILE_N, EPI_M, EPI_N, STAGE)
        tAcc_epi = cute.flat_divide(
            tAcc[((None, None), 0, 0, None)],
            epi_tile,
        )
        # (EPI_TILE_M, EPI_TILE_N)
        tiled_copy_t2r = tcgen05.make_tmem_copy(
            copy_atom_t2r, tAcc_epi[(None, None, 0, 0, 0)]
        )

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

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

    def epilog_smem_copy_and_partition(
        self,
        tiled_copy_t2r: cute.TiledCopy,
        tTR_rC: cute.Tensor,
        tidx: cutlass.Int32,
        sC: cute.Tensor,
    ) -> Tuple[cute.TiledCopy, cute.Tensor, cute.Tensor]:
        """
        Make tiledCopy for shared memory store, then use it to partition register array (source) and shared memory (destination).

        :param tiled_copy_t2r: The tiled copy operation for tmem to register copy(t2r)
        :type tiled_copy_t2r: cute.TiledCopy
        :param tTR_rC: The partitioned accumulator tensor
        :type tTR_rC: cute.Tensor
        :param tidx: The thread index in epilogue warp groups
        :type tidx: cutlass.Int32
        :param sC: The shared memory tensor to be copied and partitioned
        :type sC: cute.Tensor
        :type sepi: cute.Tensor

        :return: A tuple containing (tiled_copy_r2s, tRS_rC, tRS_sC) where:
            - tiled_copy_r2s: The tiled copy operation for register to smem copy(r2s)
            - tRS_rC: The partitioned tensor C (register source)
            - tRS_sC: The partitioned tensor C (smem destination)
        :rtype: Tuple[cute.TiledCopy, cute.Tensor, cute.Tensor]
        """
        copy_atom_r2s = sm100_utils.get_smem_store_op(
            self.c_layout, self.c_dtype, self.acc_dtype, tiled_copy_t2r
        )
        tiled_copy_r2s = cute.make_tiled_copy_D(copy_atom_r2s, tiled_copy_t2r)
        # (R2S, R2S_M, R2S_N, PIPE_D)
        thr_copy_r2s = tiled_copy_r2s.get_slice(tidx)
        tRS_sC = thr_copy_r2s.partition_D(sC)
        # (R2S, R2S_M, R2S_N)
        tRS_rC = tiled_copy_r2s.retile(tTR_rC)
        return tiled_copy_r2s, tRS_rC, tRS_sC

    def epilog_gmem_copy_and_partition(
        self,
        tidx: cutlass.Int32,
        atom: Union[cute.CopyAtom, cute.TiledCopy],
        gC_mnl: cute.Tensor,
        epi_tile: cute.Tile,
        sC: cute.Tensor,
    ) -> Tuple[cute.CopyAtom, cute.Tensor, cute.Tensor]:
        """Make tiledCopy for global memory store, then use it to:
        partition shared memory (source) and global memory (destination) for TMA store version.

        :param tidx: The thread index in epilogue warp groups
        :type tidx: cutlass.Int32
        :param atom: The copy_atom_c to be used for TMA store version, or tiled_copy_t2r for none TMA store version
        :type atom: cute.CopyAtom or cute.TiledCopy
        :param gC_mnl: The global tensor C
        :type gC_mnl: cute.Tensor
        :param epi_tile: The epilogue tiler
        :type epi_tile: cute.Tile
        :param sC: The shared memory tensor to be copied and partitioned
        :type sC: cute.Tensor

        :return: A tuple containing (tma_atom_c, bSG_sC, bSG_gC) where:
            - tma_atom_c: The TMA copy atom
            - bSG_sC: The partitioned shared memory tensor C
            - bSG_gC: The partitioned global tensor C
        :rtype: Tuple[cute.CopyAtom, cute.Tensor, cute.Tensor]
        """
        # (EPI_TILE_M, EPI_TILE_N, EPI_M, EPI_N, RestM, RestN, RestL)
        gC_epi = cute.flat_divide(
            gC_mnl[((None, None), 0, 0, None, None, None)], epi_tile
        )

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

    @staticmethod
    def _compute_stages(
        tiled_mma: cute.TiledMma,
        mma_tiler_mnk: Tuple[int, int, int],
        a_dtype: Type[cutlass.Numeric],
        b_dtype: Type[cutlass.Numeric],
        epi_tile: cute.Tile,
        c_dtype: Type[cutlass.Numeric],
        c_layout: utils.LayoutEnum,
        sf_dtype: Type[cutlass.Numeric],
        sf_vec_size: int,
        smem_capacity: int,
        occupancy: int,
    ) -> Tuple[int, int, int]:
        """Computes the number of stages for A/B/C operands based on heuristics.

        :param tiled_mma: The tiled MMA object defining the core computation.
        :type tiled_mma: cute.TiledMma
        :param mma_tiler_mnk: The shape (M, N, K) of the MMA tiler.
        :type mma_tiler_mnk: tuple[int, int, int]
        :param a_dtype: Data type of operand A.
        :type a_dtype: type[cutlass.Numeric]
        :param b_dtype: Data type of operand B.
        :type b_dtype: type[cutlass.Numeric]
        :param epi_tile: The epilogue tile shape.
        :type epi_tile: cute.Tile
        :param c_dtype: Data type of operand C (output).
        :type c_dtype: type[cutlass.Numeric]
        :param c_layout: Layout enum of operand C.
        :type c_layout: utils.LayoutEnum
        :param sf_dtype: Data type of Scale factor.
        :type sf_dtype: type[cutlass.Numeric]
        :param sf_vec_size: Scale factor vector size.
        :type sf_vec_size: int
        :param smem_capacity: Total available shared memory capacity in bytes.
        :type smem_capacity: int
        :param occupancy: Target number of CTAs per SM (occupancy).
        :type occupancy: int

        :return: A tuple containing the computed number of stages for:
                 (ACC stages, A/B operand stages, C stages)
        :rtype: tuple[int, int, int]
        """
        # ACC stages
        num_acc_stage = 1 if mma_tiler_mnk[1] == 256 else 2

        # Default C stages
        num_c_stage = 2

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

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

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

        # Calculate A/B/SFA/SFB stages:
        # Start with total smem per CTA (capacity / occupancy)
        # Subtract reserved bytes and initial C stages bytes
        # Divide remaining by bytes needed per A/B/SFA/SFB stage
        num_ab_stage = (
            smem_capacity // occupancy - (mbar_helpers_bytes + c_bytes)
        ) // ab_bytes_per_stage

        # Refine epilogue stages:
        # Calculate remaining smem after allocating for A/B/SFA/SFB stages and reserved bytes
        # Add remaining unused smem to epilogue
        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]]:
        """Use persistent tile scheduler to compute the grid size for the output tensor C.

        :param c: The output tensor C
        :type c: cute.Tensor
        :param cta_tile_shape_mnk: The shape (M, N, K) of the CTA tile.
        :type cta_tile_shape_mnk: tuple[int, int, int]
        :param cluster_shape_mn: Shape of each cluster in M, N dimensions.
        :type cluster_shape_mn: tuple[int, int]
        :param max_active_clusters: Maximum number of active clusters.
        :type max_active_clusters: cutlass.Constexpr

        :return: A tuple containing:
            - tile_sched_params: Parameters for the persistent tile scheduler.
            - grid: Grid shape for kernel launch.
        :rtype: 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:
        """
        Check if the dtypes and sf_vec_size are valid combinations

        :param ab_dtype: The data type of the A and B operands
        :type ab_dtype: Type[cutlass.Numeric]
        :param sf_dtype: The data type of the scale factor
        :type sf_dtype: Type[cutlass.Numeric]
        :param sf_vec_size: The vector size of the scale factor
        :type sf_vec_size: int
        :param c_dtype: The data type of the output tensor
        :type c_dtype: Type[cutlass.Numeric]

        :return: True if the dtypes and sf_vec_size are valid, False otherwise
        :rtype: bool
        """
        is_valid = True

        # Check valid ab_dtype
        if ab_dtype not in {
            cutlass.Float4E2M1FN,
            cutlass.Float8E5M2,
            cutlass.Float8E4M3FN,
        }:
            is_valid = False

        # Check valid sf_vec_size
        if sf_vec_size not in {16, 32}:
            is_valid = False

        # Check valid sf_dtype
        if sf_dtype not in {cutlass.Float8E8M0FNU, cutlass.Float8E4M3FN}:
            is_valid = False

        # Check valid sf_dtype and sf_vec_size combinations
        if sf_dtype == cutlass.Float8E4M3FN and sf_vec_size == 32:
            is_valid = False
        if ab_dtype in {cutlass.Float8E5M2, cutlass.Float8E4M3FN} and sf_vec_size == 16:
            is_valid = False

        # Check valid c_dtype
        if c_dtype not in {
            cutlass.Float32,
            cutlass.Float16,
            cutlass.BFloat16,
            cutlass.Float8E5M2,
            cutlass.Float8E4M3FN,
        }:
            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:
        """
        Check if layouts and dtypes are valid combinations

        :param ab_dtype: The data type of the A and B operands
        :type ab_dtype: Type[cutlass.Numeric]
        :param c_dtype: The data type of the output tensor
        :type c_dtype: Type[cutlass.Numeric]
        :param a_major: The major dimension of the A tensor
        :type a_major: str
        :param b_major: The major dimension of the B tensor
        :type b_major: str
        :param c_major: The major dimension of the C tensor
        :type c_major: str

        :return: True if the layouts are valid, False otherwise
        :rtype: bool
        """
        is_valid = True

        if ab_dtype is cutlass.Float4E2M1FN and not (a_major == "k" and b_major == "k"):
            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:
        """
        Check if the mma tiler and cluster shape are valid

        :param mma_tiler_mn: The (M, N) shape of the MMA instruction tiler
        :type mma_tiler_mn: Tuple[int, int]
        :param cluster_shape_mn: The (ClusterM, ClusterN) shape of the CTA cluster
        :type cluster_shape_mn: Tuple[int, int]

        :return: True if the mma tiler and cluster shape are valid, False otherwise
        :rtype: bool
        """
        is_valid = True
        # Skip invalid mma tile shape
        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
        # Skip illegal cluster shape
        if cluster_shape_mn[0] % (2 if mma_tiler_mn[0] == 256 else 1) != 0:
            is_valid = False
        # Skip invalid cluster shape
        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
            # Special cluster shape check for scale factor multicasts.
            # Due to limited size of scale factors, we can't multicast among more than 4 CTAs.
            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:
        """
        Check if the tensor alignment is valid

        :param m: The number of rows in the A tensor
        :type m: int
        :param n: The number of columns in the B tensor
        :type n: int
        :param k: The number of columns in the A tensor
        :type k: int
        :param l: The number of columns in the C tensor
        :type l: int
        :param ab_dtype: The data type of the A and B operands
        :type ab_dtype: Type[cutlass.Numeric]
        :param c_dtype: The data type of the output tensor
        :type c_dtype: Type[cutlass.Numeric]
        :param a_major: The major axis of the A tensor
        :type a_major: str
        :param b_major: The major axis of the B tensor
        :type b_major: str
        :param c_major: The major axis of the C tensor
        :type c_major: str

        :return: True if the problem shape is valid, False otherwise
        :rtype: 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:
        """
        Check if the gemm can be implemented

        :param ab_dtype: The data type of the A and B operands
        :type ab_dtype: Type[cutlass.Numeric]
        :param sf_dtype: The data type of the scale factor tensor
        :type sf_dtype: Type[cutlass.Numeric]
        :param sf_vec_size: The vector size
        :type sf_vec_size: int
        :param c_dtype: The data type of the output tensor
        :type c_dtype: Type[cutlass.Numeric]
        :param mma_tiler_mn: The (M, N) shape of the MMA instruction tiler
        :type mma_tiler_mn: Tuple[int, int]
        :param cluster_shape_mn: The (ClusterM, ClusterN) shape of the CTA cluster
        :type cluster_shape_mn: Tuple[int, int]
        :param m: The number of rows in the A tensor
        :type m: int
        :param n: The number of columns in the B tensor
        :type n: int
        :param k: The number of columns in the A tensor
        :type k: int
        :param l: The number of columns in the C tensor
        :type l: int
        :param a_major: The major axis of the A tensor
        :type a_major: str
        :param b_major: The major axis of the B tensor
        :type b_major: str
        :param c_major: The major axis of the C tensor
        :type c_major: str

        :return: True if the gemm can be implemented, False otherwise
        :rtype: bool
        """
        can_implement = True
        # Skip unsupported types
        if not Sm100BlockScaledPersistentDenseGemmKernel.is_valid_dtypes_and_scale_factor_vec_size(
            ab_dtype, sf_dtype, sf_vec_size, c_dtype
        ):
            can_implement = False
        # Skip unsupported layouts
        if not Sm100BlockScaledPersistentDenseGemmKernel.is_valid_layouts(
            ab_dtype, c_dtype, a_major, b_major, c_major
        ):
            can_implement = False
        # Skip invalid mma tile shape and cluster shape
        if not Sm100BlockScaledPersistentDenseGemmKernel.is_valid_mma_tiler_and_cluster_shape(
            mma_tiler_mn, cluster_shape_mn
        ):
            can_implement = False
        # Skip illegal problem shape for load/store alignment
        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


# Global cache for compiled kernel
_compiled_kernel_cache = {}

# FP4 data type for A and B
ab_dtype = cutlass.Float4E2M1FN
# FP8 data type for scale factors
sf_dtype = cutlass.Float8E4M3FN
# FP16 output type
c_dtype = cutlass.Float16
# Scale factor block size (16 elements share one scale)
sf_vec_size = 16

k_tile_cnts = {16384: 64, 7168: 28, 2048: 8}


# This function is used to compile the kernel once and cache it and then allow users to
# run the kernel multiple times to get more accurate timing results.
def compile_kernel(problem_size):
    """
    Compile the kernel once and cache it.
    This should be called before any timing measurements.

    Returns:
        The compiled kernel function
    """
    global _compiled_kernel_cache
    k = problem_size[2]
    if k in _compiled_kernel_cache:
        return _compiled_kernel_cache[k]

    # 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)
    b_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)
    sfb_ptr = make_ptr(sf_dtype, 0, cute.AddressSpace.gmem, assumed_align=32)

    mma_tiler_mn = (128, 64)
    # valid: 1, 2, 4
    cluster_shape_mn = (1, 1)
    # Configure gemm kernel
    gemm = Sm100BlockScaledPersistentDenseGemmKernel(
        sf_vec_size,
        mma_tiler_mn,
        cluster_shape_mn,
    )

    # Compute max active clusters on current device
    hardware_info = cutlass.utils.HardwareInfo()
    max_active_clusters = hardware_info.get_max_active_clusters(
        cluster_shape_mn[0] * cluster_shape_mn[1]
    )

    # Compile gemm kernel
    _compiled_kernel_cache[k] = cute.compile(
        gemm,
        a_ptr,
        b_ptr,
        sfa_ptr,
        sfb_ptr,
        c_ptr,
        problem_size,
        max_active_clusters,
        k_tile_cnts[k],
    )
    return _compiled_kernel_cache[k]


def custom_kernel(data: input_t) -> output_t:
    """
    Execute the block-scaled GEMM kernel.

    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, b, sfa_ref, sfb_ref, sfa_permuted, sfb_permuted, c) PyTorch tensors
            a: [m, k, l] - Input matrix in float4e2m1fn
            b: [n, k, l] - Input vector in float4e2m1fn
            sfa_ref: [m, k, l] - Scale factors in float8_e4m3fn, used by reference implementation
            sfb_ref: [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
            sfb_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, b, _, _, sfa_permuted, sfb_permuted, c = data

    # Get dimensions from MxKxL layout
    m, k, l = a.shape
    n, _, _ = b.shape
    # Torch use e2m1_x2 data type, thus k is halved
    k = k * 2

    if m > 128 or k not in k_tile_cnts:
        return torch_kernel(data)

    # Ensure kernel is compiled (will use cached version if available)
    # To avoid the compilation overhead, we compile the kernel once and cache it.
    problem_size = (m, n, k, l)
    compiled_func = compile_kernel(problem_size)

    # Create CuTe pointers for A/B/C/SFA/SFB via torch tensor data pointer
    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)
    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
    )
    sfb_ptr = make_ptr(
        sf_dtype, sfb_permuted.data_ptr(), cute.AddressSpace.gmem, assumed_align=32
    )

    # Execute the compiled kernel
    compiled_func(a_ptr, b_ptr, sfa_ptr, sfb_ptr, c_ptr, problem_size)

    return c


# Helper function for ceiling division
def ceil_div(a, b):
    return (a + b - 1) // b


# Helper function to convert scale factor tensor to blocked format
def to_blocked(input_matrix):
    rows, cols = input_matrix.shape

    # Please ensure rows and cols are multiples of 128 and 4 respectively
    n_row_blocks = ceil_div(rows, 128)
    n_col_blocks = ceil_div(cols, 4)

    padded = input_matrix
    blocks = padded.view(n_row_blocks, 128, n_col_blocks, 4).permute(0, 2, 1, 3)
    rearranged = blocks.reshape(-1, 4, 32, 4).transpose(1, 2).reshape(-1, 32, 16)

    return rearranged.flatten()


def torch_kernel(data: input_t) -> output_t:
    a_ref, b_ref, sfa_ref_cpu, sfb_ref_cpu, _, _, c_ref = data
    # Get dimensions from MxNxL layout
    _, _, l = c_ref.shape

    # Call torch._scaled_mm to compute the GEMM result
    for l_idx in range(l):
        # Convert the scale factor tensor to blocked format
        scale_a = to_blocked(sfa_ref_cpu[:, :, l_idx])
        scale_b = to_blocked(sfb_ref_cpu[:, :, l_idx])
        # (m, k) @ (n, k).T -> (m, n)
        res = torch._scaled_mm(
            a_ref[:, :, l_idx],
            b_ref[:, :, l_idx].transpose(0, 1),
            scale_a.cuda(),
            scale_b.cuda(),
            bias=None,
            out_dtype=torch.float16,
        )
        c_ref[:, :, l_idx] = res
    return c_ref