submission 117026

from torch._higher_order_ops.torchbind import call_torchbind_fake
import cuda.bindings.driver as cuda

import torch
from task import input_t, output_t

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

# =============================================================================
# Kernel Configuration
# =============================================================================

# Tile sizes for M, N, K dimensions
mma_tiler_mnk = (128, 128, 256)
# Shape of the K dimension for the MMA instruction
mma_inst_shape_k = 64
# 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
# Number of threads per CUDA thread block
threads_per_cta = 128

# Optimization: Increase pipeline depth to 4 stages to hide DRAM latency
num_acc_stage = 1
num_ab_stage = 4
# Total number of columns in tmem
num_tmem_alloc_cols = 512

# =============================================================================
# Kernel Definition
# =============================================================================

@cute.kernel
def kernel(
    tiled_mma: 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,
    mC_mnl: cute.Tensor,
    a_smem_layout_staged: cute.ComposedLayout,
    b_smem_layout_staged: cute.ComposedLayout,
    sfa_smem_layout_staged: cute.Layout,
    sfb_smem_layout_staged: cute.Layout,
    num_tma_load_bytes: cutlass.Constexpr[int],
):
    """
    GPU device kernel performing the batched GEMM computation.
    """
    warp_idx = cute.arch.warp_idx()
    warp_idx = cute.arch.make_warp_uniform(warp_idx)
    
    # Setup cta/thread coordinates
    bidx, bidy, bidz = cute.arch.block_idx()
    cta_coord = (bidx, bidy, bidz)
    
    mma_tile_coord_mnl = (
        cta_coord[0] // cute.size(tiled_mma.thr_id.shape),
        cta_coord[1],
        cta_coord[2],
    )
    
    tidx, _, _ = cute.arch.thread_idx()

    # Define shared storage
    @cute.struct
    class SharedStorage:
        ab_mbar_ptr: cute.struct.MemRange[cutlass.Int64, num_ab_stage * 2]
        acc_mbar_ptr: cute.struct.MemRange[cutlass.Int64, num_acc_stage * 2]
        tmem_holding_buf: cutlass.Int32

    smem = utils.SmemAllocator()
    storage = smem.allocate(SharedStorage)
    
    # Allocate Shared Memory Buffers
    sA = smem.allocate_tensor(
        element_type=ab_dtype,
        layout=a_smem_layout_staged.outer,
        byte_alignment=128,
        swizzle=a_smem_layout_staged.inner,
    )
    sB = smem.allocate_tensor(
        element_type=ab_dtype,
        layout=b_smem_layout_staged.outer,
        byte_alignment=128,
        swizzle=b_smem_layout_staged.inner,
    )
    sSFA = smem.allocate_tensor(
        element_type=sf_dtype,
        layout=sfa_smem_layout_staged,
        byte_alignment=128,
    )
    sSFB = smem.allocate_tensor(
        element_type=sf_dtype,
        layout=sfb_smem_layout_staged,
        byte_alignment=128,
    )

    # Initialize Pipelines
    ab_pipeline_producer_group = pipeline.CooperativeGroup(pipeline.Agent.Thread)
    ab_pipeline_consumer_group = pipeline.CooperativeGroup(pipeline.Agent.Thread, 1)
    
    ab_producer, ab_consumer = pipeline.PipelineTmaUmma.create(
        barrier_storage=storage.ab_mbar_ptr.data_ptr(),
        num_stages=num_ab_stage,
        producer_group=ab_pipeline_producer_group,
        consumer_group=ab_pipeline_consumer_group,
        tx_count=num_tma_load_bytes,
    ).make_participants()
    
    acc_producer, acc_consumer = pipeline.PipelineUmmaAsync.create(
        barrier_storage=storage.acc_mbar_ptr.data_ptr(),
        num_stages=num_acc_stage,
        producer_group=ab_pipeline_producer_group,
        consumer_group=pipeline.CooperativeGroup(
            pipeline.Agent.Thread,
            threads_per_cta,
        ),
    ).make_participants()

    # Local Tile Partitioning
    gA_mkl = cute.local_tile(mA_mkl, cute.slice_(mma_tiler_mnk, (None, 0, None)), (None, None, None))
    gB_nkl = cute.local_tile(mB_nkl, cute.slice_(mma_tiler_mnk, (0, None, None)), (None, None, None))
    gSFA_mkl = cute.local_tile(mSFA_mkl, cute.slice_(mma_tiler_mnk, (None, 0, None)), (None, None, None))
    gSFB_nkl = cute.local_tile(mSFB_nkl, cute.slice_(mma_tiler_mnk, (0, None, None)), (None, None, None))
    gC_mnl = cute.local_tile(mC_mnl, cute.slice_(mma_tiler_mnk, (None, None, 0)), (None, None, None))
    
    k_tile_cnt = cute.size(gA_mkl, mode=[3])

    # Thread Partitioning
    thr_mma = tiled_mma.get_slice(0)
    tCgA = thr_mma.partition_A(gA_mkl)
    tCgB = thr_mma.partition_B(gB_nkl)
    tCgSFA = thr_mma.partition_A(gSFA_mkl)
    tCgSFB = thr_mma.partition_B(gSFB_nkl)
    tCgC = thr_mma.partition_C(gC_mnl)

    # TMA Partitioning
    tAsA, tAgA = cpasync.tma_partition(tma_atom_a, 0, cute.make_layout(1), cute.group_modes(sA, 0, 3), cute.group_modes(tCgA, 0, 3))
    tBsB, tBgB = cpasync.tma_partition(tma_atom_b, 0, cute.make_layout(1), cute.group_modes(sB, 0, 3), cute.group_modes(tCgB, 0, 3))
    tAsSFA, tAgSFA = cpasync.tma_partition(tma_atom_sfa, 0, cute.make_layout(1), cute.group_modes(sSFA, 0, 3), cute.group_modes(tCgSFA, 0, 3))
    tBsSFB, tBgSFB = cpasync.tma_partition(tma_atom_sfb, 0, cute.make_layout(1), cute.group_modes(sSFB, 0, 3), cute.group_modes(tCgSFB, 0, 3))

    tAsSFA = cute.filter_zeros(tAsSFA); tAgSFA = cute.filter_zeros(tAgSFA)
    tBsSFB = cute.filter_zeros(tBsSFB); tBgSFB = cute.filter_zeros(tBgSFB)

    # Fragments
    tCrA = tiled_mma.make_fragment_A(sA)
    tCrB = tiled_mma.make_fragment_B(sB)
    acc_shape = tiled_mma.partition_shape_C(mma_tiler_mnk[:2])
    tCtAcc_fake = tiled_mma.make_fragment_C(acc_shape)

    # TMEM Allocation
    tmem_alloc_barrier = pipeline.NamedBarrier(barrier_id=1, num_threads=threads_per_cta)
    tmem = utils.TmemAllocator(storage.tmem_holding_buf, barrier_for_retrieve=tmem_alloc_barrier)
    tmem.allocate(num_tmem_alloc_cols)
    tmem.wait_for_alloc()
    acc_tmem_ptr = tmem.retrieve_ptr(cutlass.Float32)
    tCtAcc = cute.make_tensor(acc_tmem_ptr, tCtAcc_fake.layout)

    # TMEM Layouts for Scales
    sfa_tmem_ptr = cute.recast_ptr(acc_tmem_ptr + tcgen05.find_tmem_tensor_col_offset(tCtAcc), dtype=sf_dtype)
    tCtSFA_layout = blockscaled_utils.make_tmem_layout_sfa(tiled_mma, mma_tiler_mnk, sf_vec_size, cute.slice_(sfa_smem_layout_staged, (None, None, None, 0)))
    tCtSFA = cute.make_tensor(sfa_tmem_ptr, tCtSFA_layout)

    sfb_tmem_ptr = cute.recast_ptr(acc_tmem_ptr + tcgen05.find_tmem_tensor_col_offset(tCtAcc) + tcgen05.find_tmem_tensor_col_offset(tCtSFA), dtype=sf_dtype)
    tCtSFB_layout = blockscaled_utils.make_tmem_layout_sfb(tiled_mma, mma_tiler_mnk, sf_vec_size, cute.slice_(sfb_smem_layout_staged, (None, None, None, 0)))
    tCtSFB = cute.make_tensor(sfb_tmem_ptr, tCtSFB_layout)

    # Scale Copy Setup (Smem -> Tmem)
    copy_atom_s2t = cute.make_copy_atom(tcgen05.Cp4x32x128bOp(tcgen05.CtaGroup.ONE), sf_dtype)
    
    tCsSFA_compact = cute.filter_zeros(sSFA)
    tCtSFA_compact = cute.filter_zeros(tCtSFA)
    tiled_copy_s2t_sfa = tcgen05.make_s2t_copy(copy_atom_s2t, tCtSFA_compact)
    thr_copy_s2t_sfa = tiled_copy_s2t_sfa.get_slice(0)
    tCsSFA_compact_s2t_ = thr_copy_s2t_sfa.partition_S(tCsSFA_compact)
    tCsSFA_compact_s2t = tcgen05.get_s2t_smem_desc_tensor(tiled_copy_s2t_sfa, tCsSFA_compact_s2t_)
    tCtSFA_compact_s2t = thr_copy_s2t_sfa.partition_D(tCtSFA_compact)

    tCsSFB_compact = cute.filter_zeros(sSFB)
    tCtSFB_compact = cute.filter_zeros(tCtSFB)
    tiled_copy_s2t_sfb = tcgen05.make_s2t_copy(copy_atom_s2t, tCtSFB_compact)
    thr_copy_s2t_sfb = tiled_copy_s2t_sfb.get_slice(0)
    tCsSFB_compact_s2t_ = thr_copy_s2t_sfb.partition_S(tCsSFB_compact)
    tCsSFB_compact_s2t = tcgen05.get_s2t_smem_desc_tensor(tiled_copy_s2t_sfb, tCsSFB_compact_s2t_)
    tCtSFB_compact_s2t = thr_copy_s2t_sfb.partition_D(tCtSFB_compact)

    # Slice to per-MMA tile
    tAgA = tAgA[(None, mma_tile_coord_mnl[0], None, mma_tile_coord_mnl[2])]
    tBgB = tBgB[(None, mma_tile_coord_mnl[1], None, mma_tile_coord_mnl[2])]
    tAgSFA = tAgSFA[(None, mma_tile_coord_mnl[0], None, mma_tile_coord_mnl[2])]
    tBgSFB = tBgSFB[(None, mma_tile_coord_mnl[1], None, mma_tile_coord_mnl[2])]

    # Main Loop (Software Pipelined)
    if warp_idx == 0:
        acc_empty = acc_producer.acquire_and_advance()
        tiled_mma.set(tcgen05.Field.ACCUMULATE, False)

        # Prologue: Issue loads for first N-1 stages
        prologue_stages = min(num_ab_stage - 1, k_tile_cnt)
        
        for k_prologue in range(prologue_stages):
            prod_token = ab_producer.acquire_and_advance()
            cute.copy(tma_atom_a, tAgA[(None, k_prologue)], tAsA[(None, prod_token.index)], tma_bar_ptr=prod_token.barrier)
            cute.copy(tma_atom_b, tBgB[(None, k_prologue)], tBsB[(None, prod_token.index)], tma_bar_ptr=prod_token.barrier)
            cute.copy(tma_atom_sfa, tAgSFA[(None, k_prologue)], tAsSFA[(None, prod_token.index)], tma_bar_ptr=prod_token.barrier)
            cute.copy(tma_atom_sfb, tBgSFB[(None, k_prologue)], tBsSFB[(None, prod_token.index)], tma_bar_ptr=prod_token.barrier)

        # Steady State Loop
        for k_tile in range(k_tile_cnt):
            # Issue Next Stage
            next_load_k = k_tile + prologue_stages
            if next_load_k < k_tile_cnt:
                prod_token = ab_producer.acquire_and_advance()
                cute.copy(tma_atom_a, tAgA[(None, next_load_k)], tAsA[(None, prod_token.index)], tma_bar_ptr=prod_token.barrier)
                cute.copy(tma_atom_b, tBgB[(None, next_load_k)], tBsB[(None, prod_token.index)], tma_bar_ptr=prod_token.barrier)
                cute.copy(tma_atom_sfa, tAgSFA[(None, next_load_k)], tAsSFA[(None, prod_token.index)], tma_bar_ptr=prod_token.barrier)
                cute.copy(tma_atom_sfb, tBgSFB[(None, next_load_k)], tBsSFB[(None, prod_token.index)], tma_bar_ptr=prod_token.barrier)

            # Wait for Current Stage
            cons_token = ab_consumer.wait_and_advance()

            # Copy Scales SMEM -> TMEM
            s2t_stage_coord = (None, None, None, None, cons_token.index)
            cute.copy(tiled_copy_s2t_sfa, tCsSFA_compact_s2t[s2t_stage_coord], tCtSFA_compact_s2t)
            cute.copy(tiled_copy_s2t_sfb, tCsSFB_compact_s2t[s2t_stage_coord], tCtSFB_compact_s2t)

            # GEMM Math
            num_kblocks = cute.size(tCrA, mode=[2])
            for kblock_idx in cutlass.range(num_kblocks, unroll_full=True):
                kblock_coord = (None, None, kblock_idx, cons_token.index)
                sf_coord = (None, None, kblock_idx)
                
                tiled_mma.set(tcgen05.Field.SFA, tCtSFA[sf_coord].iterator)
                tiled_mma.set(tcgen05.Field.SFB, tCtSFB[sf_coord].iterator)

                cute.gemm(tiled_mma, tCtAcc, tCrA[kblock_coord], tCrB[kblock_coord], tCtAcc)
                tiled_mma.set(tcgen05.Field.ACCUMULATE, True)

            cons_token.release()
            
        acc_empty.commit()

    # Epilogue
    op = tcgen05.Ld32x32bOp(tcgen05.Repetition.x128, tcgen05.Pack.NONE)
    copy_atom_t2r = cute.make_copy_atom(op, cutlass.Float32)
    tiled_copy_t2r = tcgen05.make_tmem_copy(copy_atom_t2r, tCtAcc)
    thr_copy_t2r = tiled_copy_t2r.get_slice(tidx)
    
    tTR_tAcc = thr_copy_t2r.partition_S(tCtAcc)
    tTR_gC = thr_copy_t2r.partition_D(tCgC)
    tTR_rAcc = cute.make_rmem_tensor(tTR_gC[None, None, None, None, 0, 0, 0].shape, cutlass.Float32)
    tTR_rC = cute.make_rmem_tensor(tTR_gC[None, None, None, None, 0, 0, 0].shape, c_dtype)
    
    simt_atom = cute.make_copy_atom(cute.nvgpu.CopyUniversalOp(), c_dtype)
    tTR_gC = tTR_gC[(None, None, None, None, *mma_tile_coord_mnl)]

    acc_full = acc_consumer.wait_and_advance()

    cute.copy(tiled_copy_t2r, tTR_tAcc, tTR_rAcc)
    acc_vec = tTR_rAcc.load().to(c_dtype)
    tTR_rC.store(acc_vec)
    cute.copy(simt_atom, tTR_rC, tTR_gC)

    acc_full.release()

    cute.arch.barrier()
    tmem.free(acc_tmem_ptr)
    return

# =============================================================================
# JIT Compilation
# =============================================================================

@cute.jit
def my_kernel(
    a_ptr: cute.Pointer,
    b_ptr: cute.Pointer,
    sfa_ptr: cute.Pointer,
    sfb_ptr: cute.Pointer,
    c_ptr: cute.Pointer,
    problem_size: tuple,
):
    m, n, k, l = problem_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))))
    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)))
    
    sfa_layout = blockscaled_utils.tile_atom_to_shape_SF(a_tensor.shape, sf_vec_size)
    sfa_tensor = cute.make_tensor(sfa_ptr, sfa_layout)
    sfb_layout = blockscaled_utils.tile_atom_to_shape_SF(b_tensor.shape, sf_vec_size)
    sfb_tensor = cute.make_tensor(sfb_ptr, sfb_layout)

    mma_op = tcgen05.MmaMXF4NVF4Op(
        sf_dtype,
        (mma_tiler_mnk[0], mma_tiler_mnk[1], mma_inst_shape_k),
        tcgen05.CtaGroup.ONE,
        tcgen05.OperandSource.SMEM,
    )
    tiled_mma = cute.make_tiled_mma(mma_op)

    # Use default cluster layout (1,1,1) to ensure compilation stability
    cluster_layout_vmnk = cute.tiled_divide(
        cute.make_layout((1, 1, 1)),
        (tiled_mma.thr_id.shape,),
    )

    a_smem_layout_staged = sm100_utils.make_smem_layout_a(tiled_mma, mma_tiler_mnk, ab_dtype, num_ab_stage)
    b_smem_layout_staged = sm100_utils.make_smem_layout_b(tiled_mma, mma_tiler_mnk, ab_dtype, num_ab_stage)
    sfa_smem_layout_staged = blockscaled_utils.make_smem_layout_sfa(tiled_mma, mma_tiler_mnk, sf_vec_size, num_ab_stage)
    sfb_smem_layout_staged = blockscaled_utils.make_smem_layout_sfb(tiled_mma, mma_tiler_mnk, sf_vec_size, num_ab_stage)

    atom_thr_size = cute.size(tiled_mma.thr_id.shape)
    
    tma_atom_a, tma_tensor_a = cute.nvgpu.make_tiled_tma_atom_A(
        cpasync.CopyBulkTensorTileG2SOp(tcgen05.CtaGroup.ONE),
        a_tensor, cute.slice_(a_smem_layout_staged, (None, None, None, 0)),
        mma_tiler_mnk, tiled_mma, cluster_layout_vmnk.shape
    )
    tma_atom_b, tma_tensor_b = cute.nvgpu.make_tiled_tma_atom_B(
        cpasync.CopyBulkTensorTileG2SOp(tcgen05.CtaGroup.ONE),
        b_tensor, cute.slice_(b_smem_layout_staged, (None, None, None, 0)),
        mma_tiler_mnk, tiled_mma, cluster_layout_vmnk.shape
    )
    tma_atom_sfa, tma_tensor_sfa = cute.nvgpu.make_tiled_tma_atom_A(
        cpasync.CopyBulkTensorTileG2SOp(tcgen05.CtaGroup.ONE),
        sfa_tensor, cute.slice_(sfa_smem_layout_staged, (None, None, None, 0)),
        mma_tiler_mnk, tiled_mma, cluster_layout_vmnk.shape, internal_type=cutlass.Int16
    )
    tma_atom_sfb, tma_tensor_sfb = cute.nvgpu.make_tiled_tma_atom_B(
        cpasync.CopyBulkTensorTileG2SOp(tcgen05.CtaGroup.ONE),
        sfb_tensor, cute.slice_(sfb_smem_layout_staged, (None, None, None, 0)),
        mma_tiler_mnk, tiled_mma, cluster_layout_vmnk.shape, internal_type=cutlass.Int16
    )

    num_tma_load_bytes = (
        cute.size_in_bytes(ab_dtype, cute.slice_(a_smem_layout_staged, (None, None, None, 0))) +
        cute.size_in_bytes(ab_dtype, cute.slice_(b_smem_layout_staged, (None, None, None, 0))) +
        cute.size_in_bytes(sf_dtype, cute.slice_(sfa_smem_layout_staged, (None, None, None, 0))) +
        cute.size_in_bytes(sf_dtype, cute.slice_(sfb_smem_layout_staged, (None, None, None, 0)))
    ) * atom_thr_size

    grid = (
        cute.ceil_div(c_tensor.shape[0], mma_tiler_mnk[0]),
        cute.ceil_div(c_tensor.shape[1], mma_tiler_mnk[1]),
        c_tensor.shape[2],
    )

    kernel(
        tiled_mma, tma_atom_a, tma_tensor_a, tma_atom_b, tma_tensor_b,
        tma_atom_sfa, tma_tensor_sfa, tma_atom_sfb, tma_tensor_sfb, c_tensor,
        a_smem_layout_staged, b_smem_layout_staged,
        sfa_smem_layout_staged, sfb_smem_layout_staged, num_tma_load_bytes,
    ).launch(grid=grid, block=[threads_per_cta, 1, 1], cluster=(1, 1, 1))
    return

# =============================================================================
# Python Entry Points
# =============================================================================

_compiled_kernel_cache = None

def compile_kernel():
    global _compiled_kernel_cache
    if _compiled_kernel_cache is not None: return _compiled_kernel_cache

    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)

    _compiled_kernel_cache = cute.compile(my_kernel, a_ptr, b_ptr, sfa_ptr, sfb_ptr, c_ptr, (0, 0, 0, 0))
    return _compiled_kernel_cache

def custom_kernel(data: input_t) -> output_t:
    a, b, _, _, sfa_permuted, sfb_permuted, c = data
    compiled_func = compile_kernel()
    
    m, k, l = a.shape; n = b.shape[0]; k = k * 2 
    
    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)

    compiled_func(a_ptr, b_ptr, sfa_ptr, sfb_ptr, c_ptr, (m, n, k, l))
    return c