submission 408700
leymore4172 · python · License unknown
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submission.py
curl "https://kernelindex.com/api/v1/implementations/kernelbot-nvfp4-group-gemm-408700?include=source"interfacepython
Compatibility
measured onNVIDIA B200
declared hardwareNVIDIA B200
architecturessm_100
dtypesfp8_e4m3, nvfp4
Benchmark evidence
1 measurement across 1 GPU, fastest first.
Operation / workload
Hardware
Latency
Rank
Observed
Reported · How evidence levels are derived →
Source and license
sourceavailable
revision digestsha256:fe23769874ae59bf80567f610516361af887fd037b62da522bda60b6f1d4aa64
license declaredunknown
license concludedunknown
authorsleymore4172
imported2026-08-15
Techniques
Extracted from the mirrored source by pattern, never inferred. Each row cites its line.
fused-epilogue
- Computing epilogue subtilembarrier
self.epilog_sync_barrier = pipeline.NamedBarrier(persistent-kernel
warp-specialized persistent kernel.shared-memory
self.smem_capacity = utils.get_smem_capacity_in_bytes("sm_100")tcgen05
tcgen05.CtaGroup.TWO if self.use_2cta_instrs else tcgen05.CtaGroup.ONEwarp-specialization
ab_pipeline_producer_group = pipeline.CooperativeGroup(pipeline.Agent.Thread)Kernel source
submission.py2743 lines
import argparse
import functools
from typing import List, Type, Tuple, Union
from inspect import isclass
import torch
import cuda.bindings.driver as cuda
import cutlass
import cutlass.cute as cute
from cutlass.cute.nvgpu import cpasync, tcgen05
import cutlass.torch as cutlass_torch
import cutlass.utils as utils
import cutlass.pipeline as pipeline
from cutlass.pipeline import pipeline_init_arrive, pipeline_init_wait
import cutlass.utils.blackwell_helpers as sm100_utils
import cutlass.utils.blockscaled_layout as blockscaled_utils
from cutlass.cute.runtime import from_dlpack
class Sm100GroupedBlockScaledGemmKernel:
"""This example demonstrates an implementation of grouped blockscaled GEMM using a TMA plus Blackwell SM100 TensorCore
warp-specialized persistent kernel.
: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 tensors 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 128/256
- 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
- Cluster shape M/N must be <= 4 for scale factor multicasts due to limited size of scale factors
"""
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 grouped blockscaled GEMM kernel.
Besides configurations for dense persistent blockscaled GEMM, there is an extra config specific to grouped blockscaled GEMM:
: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 = mma_tiler_mn[0] == 256
self.cluster_shape_mn = cluster_shape_mn
# K dimension is deferred in _setup_attributes
self.mma_tiler = (*mma_tiler_mn, 1)
self.cta_group = (
tcgen05.CtaGroup.TWO if self.use_2cta_instrs else tcgen05.CtaGroup.ONE
)
self.tensormap_update_mode = utils.TensorMapUpdateMode.SMEM
self.occupancy = 1
# 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 for epilogue sync and tmem ptr sync
self.epilog_sync_barrier = pipeline.NamedBarrier(
barrier_id=1,
num_threads=32 * len(self.epilog_warp_id),
)
self.tmem_alloc_barrier = pipeline.NamedBarrier(
barrier_id=2,
num_threads=32 * len((self.mma_warp_id, *self.epilog_warp_id)),
)
# Barrier used by MMA/TMA warps to signal A/B tensormap initialization completion
self.tensormap_ab_init_barrier = pipeline.NamedBarrier(
barrier_id=3,
num_threads=64,
)
self.smem_capacity = utils.get_smem_capacity_in_bytes("sm_100")
SM100_TMEM_CAPACITY_COLUMNS = 512
self.num_tmem_alloc_cols = SM100_TMEM_CAPACITY_COLUMNS
# Set up configurations that dependent on gemm inputs.
def _setup_attributes(self):
"""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
- Checking reserved smem bytes size capacity for mbar, tensor memory management and tensormap updates utilization
"""
# Compute mma instruction shapes
# (MMA_Tile_Shape_M, MMA_Tile_Shape_N, MMA_Inst_Shape_K)
self.mma_inst_shape_mn = (
self.mma_tiler[0],
self.mma_tiler[1],
)
# (CTA_Tile_Shape_M, Round_Up(MMA_Tile_Shape_N, 128), MMA_Inst_Shape_K)
self.mma_inst_shape_mn_sfb = (
self.mma_inst_shape_mn[0] // (2 if self.use_2cta_instrs else 1),
cute.round_up(self.mma_inst_shape_mn[1], 128),
)
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],
)
self.cluster_tile_shape_mnk = tuple(
x * y for x, y in zip(self.cta_tile_shape_mnk, (*self.cluster_shape_mn, 1))
)
# Compute cluster layout
self.cluster_layout_vmnk = cute.tiled_divide(
cute.make_layout((*self.cluster_shape_mn, 1)),
(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])
self.is_a_mcast = self.num_mcast_ctas_a > 1
self.is_b_mcast = self.num_mcast_ctas_b > 1
self.is_sfb_mcast = self.num_mcast_ctas_sfb > 1
# Compute epilogue subtile
self.epi_tile = sm100_utils.compute_epilogue_tile_shape(
self.cta_tile_shape_mnk,
self.use_2cta_instrs,
self.c_layout,
self.c_dtype,
)
# Setup A/B/C stage count in shared memory and ACC stage count in tensor memory
self.num_acc_stage, self.num_ab_stage, self.num_c_stage = self._compute_stages(
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,
)
mbar_smem_bytes = self._get_mbar_smem_bytes(
num_acc_stage=self.num_acc_stage,
num_ab_stage=self.num_ab_stage,
num_c_stage=self.num_c_stage,
)
# Use utils.TensorMapUpdateMode.SMEM by default
tensormap_smem_bytes = (
Sm100GroupedBlockScaledGemmKernel.bytes_per_tensormap
* Sm100GroupedBlockScaledGemmKernel.num_tensormaps
)
if (
mbar_smem_bytes
+ tensormap_smem_bytes
+ Sm100GroupedBlockScaledGemmKernel.tensor_memory_management_bytes
> self.reserved_smem_bytes
):
raise ValueError(
f"smem consumption for mbar and tensormap {mbar_smem_bytes + tensormap_smem_bytes} exceeds the "
f"reserved smem bytes {self.reserved_smem_bytes}"
)
@cute.jit
def __call__(
self,
initial_a: cute.Tensor,
initial_b: cute.Tensor,
initial_c: cute.Tensor,
initial_sfa: cute.Tensor,
initial_sfb: cute.Tensor,
group_count: cutlass.Constexpr[int],
problem_shape_mnkl: cute.Tensor,
strides_abc: cute.Tensor,
tensor_address_abc: cute.Tensor,
tensor_address_sfasfb: cute.Tensor,
total_num_clusters: cutlass.Constexpr[int],
tensormap_cute_tensor: cute.Tensor,
max_active_clusters: cutlass.Constexpr[int],
):
"""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
For grouped GEMM, tensor shapes, tensor strides, and tensor address are all provided
by different tensors in global memory. The "initial" tensors only carry data type and
majorness information.
:param initial_a: Initial tensor A, used for data type and majorness information.
:type initial_a: cute.Tensor
:param initial_b: Initial tensor B, used for data type and majorness information.
:type initial_b: cute.Tensor
:param initial_c: Initial tensor C, used for data type and majorness information.
:type initial_c: cute.Tensor
:param initial_sfa: Initial tensor SFA, used for data type and majorness information.
:type initial_sfa: cute.Tensor
:param initial_sfb: Initial tensor SFB, used for data type and majorness information.
:type initial_sfb: cute.Tensor
:param group_count: The number of GEMM groups.
:type group_count: cutlass.Constexpr[int]
:param problem_shape_mnkl: Tensor containing the (M, N, K, L) shape for each group.
:type problem_shape_mnkl: cute.Tensor
:param strides_abc: Tensor containing the strides for A, B, and C for each group.
:type strides_abc: cute.Tensor
:param tensor_address_abc: Tensor containing the base addresses for A, B, and C for each group.
:type tensor_address_abc: cute.Tensor
:param tensor_address_sfasfb: Tensor containing the base addresses for SFA and SFB for each group.
:type tensor_address_sfasfb: cute.Tensor
:param total_num_clusters: Total number of clusters needed for all groups.
:type total_num_clusters: cutlass.Constexpr[int]
:param tensormap_cute_tensor: Tensor for storing tensormaps.
:type tensormap_cute_tensor: cute.Tensor
:param max_active_clusters: Maximum number of active clusters.
:type max_active_clusters: cutlass.Constexpr[int]
:raises TypeError: If A and B data types do not match.
"""
self.a_dtype = initial_a.element_type
self.b_dtype = initial_b.element_type
self.sf_dtype = initial_sfa.element_type
self.c_dtype = initial_c.element_type
self.a_major_mode = utils.LayoutEnum.from_tensor(initial_a).mma_major_mode()
self.b_major_mode = utils.LayoutEnum.from_tensor(initial_b).mma_major_mode()
self.c_layout = utils.LayoutEnum.from_tensor(initial_c)
if cutlass.const_expr(self.a_dtype != self.b_dtype):
raise TypeError(f"Type mismatch: {self.a_dtype} != {self.b_dtype}")
# 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(
initial_a.shape, self.sf_vec_size
)
initial_sfa = cute.make_tensor(initial_sfa.iterator, sfa_layout)
# ((Atom_N, Rest_N),(Atom_K, Rest_K),RestL)
sfb_layout = blockscaled_utils.tile_atom_to_shape_SF(
initial_b.shape, self.sf_vec_size
)
initial_sfb = cute.make_tensor(initial_sfb.iterator, sfb_layout)
tiled_mma = 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,
initial_a,
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,
initial_b,
b_smem_layout,
self.mma_tiler,
tiled_mma,
self.cluster_layout_vmnk.shape,
)
# Setup TMA load for SFA
sfa_op = sm100_utils.cluster_shape_to_tma_atom_A(
self.cluster_shape_mn, tiled_mma.thr_id
)
sfa_smem_layout = cute.slice_(
self.sfa_smem_layout_staged, (None, None, None, 0)
)
tma_atom_sfa, tma_tensor_sfa = cute.nvgpu.make_tiled_tma_atom_A(
sfa_op,
initial_sfa,
sfa_smem_layout,
self.mma_tiler,
tiled_mma,
self.cluster_layout_vmnk.shape,
internal_type=cutlass.Int16,
)
# Setup TMA load for SFB
sfb_op = sm100_utils.cluster_shape_to_tma_atom_SFB(
self.cluster_shape_mn, tiled_mma.thr_id
)
sfb_smem_layout = cute.slice_(
self.sfb_smem_layout_staged, (None, None, None, 0)
)
tma_atom_sfb, tma_tensor_sfb = cute.nvgpu.make_tiled_tma_atom_B(
sfb_op,
initial_sfb,
sfb_smem_layout,
self.mma_tiler_sfb,
tiled_mma_sfb,
self.cluster_layout_sfb_vmnk.shape,
internal_type=cutlass.Int16,
)
a_copy_size = cute.size_in_bytes(self.a_dtype, a_smem_layout)
b_copy_size = cute.size_in_bytes(self.b_dtype, b_smem_layout)
sfa_copy_size = cute.size_in_bytes(self.sf_dtype, sfa_smem_layout)
sfb_copy_size = cute.size_in_bytes(self.sf_dtype, sfb_smem_layout)
self.num_tma_load_bytes = (
a_copy_size + b_copy_size + sfa_copy_size + sfb_copy_size
) * atom_thr_size
# Setup TMA store for C
epi_smem_layout = cute.slice_(self.c_smem_layout_staged, (None, None, 0))
tma_atom_c, tma_tensor_c = cpasync.make_tiled_tma_atom(
cpasync.CopyBulkTensorTileS2GOp(),
initial_c,
epi_smem_layout,
self.epi_tile,
)
# Compute grid size
self.tile_sched_params, grid = self._compute_grid(
total_num_clusters, self.cluster_shape_mn, max_active_clusters
)
self.buffer_align_bytes = 1024
self.size_tensormap_in_i64 = (
Sm100GroupedBlockScaledGemmKernel.num_tensormaps
* Sm100GroupedBlockScaledGemmKernel.bytes_per_tensormap
// 8
)
# Define shared storage for kernel
@cute.struct
class SharedStorage:
tensormap_buffer: cute.struct.MemRange[
cutlass.Int64, self.size_tensormap_in_i64
]
ab_full_mbar_ptr: cute.struct.MemRange[cutlass.Int64, self.num_ab_stage]
ab_empty_mbar_ptr: cute.struct.MemRange[cutlass.Int64, self.num_ab_stage]
acc_full_mbar_ptr: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage]
acc_empty_mbar_ptr: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage]
tmem_dealloc_mbar_ptr: cutlass.Int64
tmem_holding_buf: cutlass.Int32
# (EPI_TILE_M, EPI_TILE_N, STAGE)
sC: cute.struct.Align[
cute.struct.MemRange[
self.c_dtype,
cute.cosize(self.c_smem_layout_staged.outer),
],
self.buffer_align_bytes,
]
# (MMA, MMA_M, MMA_K, STAGE)
sA: cute.struct.Align[
cute.struct.MemRange[
self.a_dtype, cute.cosize(self.a_smem_layout_staged.outer)
],
self.buffer_align_bytes,
]
# (MMA, MMA_N, MMA_K, STAGE)
sB: cute.struct.Align[
cute.struct.MemRange[
self.b_dtype, cute.cosize(self.b_smem_layout_staged.outer)
],
self.buffer_align_bytes,
]
# (MMA, MMA_M, MMA_K, STAGE)
sSFA: cute.struct.Align[
cute.struct.MemRange[
self.sf_dtype, cute.cosize(self.sfa_smem_layout_staged)
],
self.buffer_align_bytes,
]
# (MMA, MMA_N, MMA_K, STAGE)
sSFB: cute.struct.Align[
cute.struct.MemRange[
self.sf_dtype, cute.cosize(self.sfb_smem_layout_staged)
],
self.buffer_align_bytes,
]
self.shared_storage = SharedStorage
# Launch the kernel synchronously
self.kernel(
tiled_mma,
tiled_mma_sfb,
tma_atom_a,
tma_tensor_a,
tma_atom_b,
tma_tensor_b,
tma_atom_sfa,
tma_tensor_sfa,
tma_atom_sfb,
tma_tensor_sfb,
tma_atom_c,
tma_tensor_c,
self.cluster_layout_vmnk,
self.cluster_layout_sfb_vmnk,
self.a_smem_layout_staged,
self.b_smem_layout_staged,
self.sfa_smem_layout_staged,
self.sfb_smem_layout_staged,
self.c_smem_layout_staged,
self.epi_tile,
self.tile_sched_params,
group_count,
problem_shape_mnkl,
strides_abc,
tensor_address_abc,
tensor_address_sfasfb,
tensormap_cute_tensor,
).launch(
grid=grid,
block=[self.threads_per_cta, 1, 1],
cluster=(*self.cluster_shape_mn, 1),
smem=self.shared_storage.size_in_bytes(),
min_blocks_per_mp=1,
)
return
# GPU device kernel
@cute.kernel
def kernel(
self,
tiled_mma: cute.TiledMma,
tiled_mma_sfb: cute.TiledMma,
tma_atom_a: cute.CopyAtom,
mA_mkl: cute.Tensor,
tma_atom_b: cute.CopyAtom,
mB_nkl: cute.Tensor,
tma_atom_sfa: cute.CopyAtom,
mSFA_mkl: cute.Tensor,
tma_atom_sfb: cute.CopyAtom,
mSFB_nkl: cute.Tensor,
tma_atom_c: cute.CopyAtom,
mC_mnl: cute.Tensor,
cluster_layout_vmnk: cute.Layout,
cluster_layout_sfb_vmnk: cute.Layout,
a_smem_layout_staged: cute.ComposedLayout,
b_smem_layout_staged: cute.ComposedLayout,
sfa_smem_layout_staged: cute.Layout,
sfb_smem_layout_staged: cute.Layout,
c_smem_layout_staged: Union[cute.Layout, cute.ComposedLayout],
epi_tile: cute.Tile,
tile_sched_params: utils.PersistentTileSchedulerParams,
group_count: cutlass.Constexpr,
problem_sizes_mnkl: cute.Tensor,
strides_abc: cute.Tensor,
ptrs_abc: cute.Tensor,
ptrs_sfasfb: cute.Tensor,
tensormaps: cute.Tensor,
):
"""
GPU device kernel performing the grouped GEMM computation.
"""
warp_idx = cute.arch.warp_idx()
warp_idx = cute.arch.make_warp_uniform(warp_idx)
if warp_idx == self.tma_warp_id:
cute.nvgpu.cpasync.prefetch_descriptor(tma_atom_a)
cute.nvgpu.cpasync.prefetch_descriptor(tma_atom_b)
cute.nvgpu.cpasync.prefetch_descriptor(tma_atom_sfa)
cute.nvgpu.cpasync.prefetch_descriptor(tma_atom_sfb)
cute.nvgpu.cpasync.prefetch_descriptor(tma_atom_c)
use_2cta_instrs = cute.size(tiled_mma.thr_id.shape) == 2
#
# Setup cta/thread coordinates
#
# Coords inside cluster
bidx, bidy, bidz = cute.arch.block_idx()
mma_tile_coord_v = bidx % cute.size(tiled_mma.thr_id.shape)
is_leader_cta = mma_tile_coord_v == 0
cta_rank_in_cluster = cute.arch.make_warp_uniform(
cute.arch.block_idx_in_cluster()
)
block_in_cluster_coord_vmnk = cluster_layout_vmnk.get_flat_coord(
cta_rank_in_cluster
)
block_in_cluster_coord_sfb_vmnk = cluster_layout_sfb_vmnk.get_flat_coord(
cta_rank_in_cluster
)
# coord inside cta
tidx, _, _ = cute.arch.thread_idx()
#
# Alloc and init: tensormap buffer, a+b full/empty, accumulator full/empty, tensor memory dealloc barrier
#
smem = utils.SmemAllocator()
storage = smem.allocate(self.shared_storage)
tensormap_smem_ptr = storage.tensormap_buffer.data_ptr()
tensormap_a_smem_ptr = tensormap_smem_ptr
tensormap_b_smem_ptr = (
tensormap_a_smem_ptr
+ Sm100GroupedBlockScaledGemmKernel.bytes_per_tensormap // 8
)
tensormap_sfa_smem_ptr = (
tensormap_b_smem_ptr
+ Sm100GroupedBlockScaledGemmKernel.bytes_per_tensormap // 8
)
tensormap_sfb_smem_ptr = (
tensormap_sfa_smem_ptr
+ Sm100GroupedBlockScaledGemmKernel.bytes_per_tensormap // 8
)
tensormap_c_smem_ptr = (
tensormap_sfb_smem_ptr
+ Sm100GroupedBlockScaledGemmKernel.bytes_per_tensormap // 8
)
tmem_dealloc_mbar_ptr = storage.tmem_dealloc_mbar_ptr
tmem_holding_buf = storage.tmem_holding_buf
# Initialize mainloop ab_pipeline (barrier) and states
ab_pipeline_producer_group = pipeline.CooperativeGroup(pipeline.Agent.Thread)
num_tma_producer = self.num_mcast_ctas_a + self.num_mcast_ctas_b - 1
ab_pipeline_consumer_group = pipeline.CooperativeGroup(
pipeline.Agent.Thread, num_tma_producer
)
ab_pipeline = pipeline.PipelineTmaUmma.create(
barrier_storage=storage.ab_full_mbar_ptr.data_ptr(),
num_stages=self.num_ab_stage,
producer_group=ab_pipeline_producer_group,
consumer_group=ab_pipeline_consumer_group,
tx_count=self.num_tma_load_bytes,
cta_layout_vmnk=cluster_layout_vmnk,
)
# Initialize acc_pipeline (barrier) and states
acc_pipeline_producer_group = pipeline.CooperativeGroup(pipeline.Agent.Thread)
num_acc_consumer_threads = len(self.epilog_warp_id) * (
2 if use_2cta_instrs else 1
)
acc_pipeline_consumer_group = pipeline.CooperativeGroup(
pipeline.Agent.Thread, num_acc_consumer_threads
)
acc_pipeline = pipeline.PipelineUmmaAsync.create(
barrier_storage=storage.acc_full_mbar_ptr.data_ptr(),
num_stages=self.num_acc_stage,
producer_group=acc_pipeline_producer_group,
consumer_group=acc_pipeline_consumer_group,
cta_layout_vmnk=cluster_layout_vmnk,
)
# Tensor memory dealloc barrier init
if use_2cta_instrs:
if warp_idx == self.tma_warp_id:
num_tmem_dealloc_threads = 32
with cute.arch.elect_one():
cute.arch.mbarrier_init(
tmem_dealloc_mbar_ptr, num_tmem_dealloc_threads
)
# Cluster arrive after barrier init
pipeline_init_arrive(cluster_shape_mn=self.cluster_shape_mn, is_relaxed=True)
#
# Setup smem tensor A/B/SFA/SFB/C
#
sC = storage.sC.get_tensor(
c_smem_layout_staged.outer, swizzle=c_smem_layout_staged.inner
)
# (MMA, MMA_M, MMA_K, STAGE)
sA = storage.sA.get_tensor(
a_smem_layout_staged.outer, swizzle=a_smem_layout_staged.inner
)
# (MMA, MMA_N, MMA_K, STAGE)
sB = storage.sB.get_tensor(
b_smem_layout_staged.outer, swizzle=b_smem_layout_staged.inner
)
# (MMA, MMA_M, MMA_K, STAGE)
sSFA = storage.sSFA.get_tensor(sfa_smem_layout_staged)
# (MMA, MMA_N, MMA_K, STAGE)
sSFB = storage.sSFB.get_tensor(sfb_smem_layout_staged)
#
# Compute multicast mask for A/B/SFA/SFB buffer full
#
a_full_mcast_mask = None
b_full_mcast_mask = None
sfa_full_mcast_mask = None
sfb_full_mcast_mask = None
if cutlass.const_expr(self.is_a_mcast or self.is_b_mcast or use_2cta_instrs):
a_full_mcast_mask = cpasync.create_tma_multicast_mask(
cluster_layout_vmnk, block_in_cluster_coord_vmnk, mcast_mode=2
)
b_full_mcast_mask = cpasync.create_tma_multicast_mask(
cluster_layout_vmnk, block_in_cluster_coord_vmnk, mcast_mode=1
)
sfa_full_mcast_mask = cpasync.create_tma_multicast_mask(
cluster_layout_vmnk, block_in_cluster_coord_vmnk, mcast_mode=2
)
sfb_full_mcast_mask = cpasync.create_tma_multicast_mask(
cluster_layout_sfb_vmnk, block_in_cluster_coord_sfb_vmnk, mcast_mode=1
)
#
# Local_tile partition global tensors
#
# (bM, bK, RestM, RestK, RestL)
gA_mkl = cute.local_tile(
mA_mkl, cute.slice_(self.mma_tiler, (None, 0, None)), (None, None, None)
)
# (bN, bK, RestN, RestK, RestL)
gB_nkl = cute.local_tile(
mB_nkl, cute.slice_(self.mma_tiler, (0, None, None)), (None, None, None)
)
# (bM, bK, RestM, RestK, RestL)
gSFA_mkl = cute.local_tile(
mSFA_mkl, cute.slice_(self.mma_tiler, (None, 0, None)), (None, None, None)
)
# (bN, bK, RestN, RestK, RestL)
gSFB_nkl = cute.local_tile(
mSFB_nkl, cute.slice_(self.mma_tiler, (0, None, None)), (None, None, None)
)
# (bM, bN, RestM, RestN, RestL)
gC_mnl = cute.local_tile(
mC_mnl, cute.slice_(self.mma_tiler, (None, None, 0)), (None, None, None)
)
#
# Partition global tensor for TiledMMA_A/B/C
#
thr_mma = tiled_mma.get_slice(mma_tile_coord_v)
thr_mma_sfb = tiled_mma_sfb.get_slice(mma_tile_coord_v)
# (MMA, MMA_M, MMA_K, RestM, RestK, RestL)
tCgA = thr_mma.partition_A(gA_mkl)
# (MMA, MMA_N, MMA_K, RestN, RestK, RestL)
tCgB = thr_mma.partition_B(gB_nkl)
# (MMA, MMA_M, MMA_K, RestM, RestK, RestL)
tCgSFA = thr_mma.partition_A(gSFA_mkl)
# (MMA, MMA_N, MMA_K, RestN, RestK, RestL)
tCgSFB = thr_mma_sfb.partition_B(gSFB_nkl)
# (MMA, MMA_M, MMA_N, RestM, RestN, RestL)
tCgC = thr_mma.partition_C(gC_mnl)
#
# Partition global/shared tensor for TMA load A/B
#
# TMA load A partition_S/D
a_cta_layout = cute.make_layout(
cute.slice_(cluster_layout_vmnk, (0, 0, None, 0)).shape
)
# ((atom_v, rest_v), STAGE)
# ((atom_v, rest_v), RestM, RestK, RestL)
tAsA, tAgA = cpasync.tma_partition(
tma_atom_a,
block_in_cluster_coord_vmnk[2],
a_cta_layout,
cute.group_modes(sA, 0, 3),
cute.group_modes(tCgA, 0, 3),
)
# TMA load B partition_S/D
b_cta_layout = cute.make_layout(
cute.slice_(cluster_layout_vmnk, (0, None, 0, 0)).shape
)
# ((atom_v, rest_v), STAGE)
# ((atom_v, rest_v), RestN, RestK, RestL)
tBsB, tBgB = cpasync.tma_partition(
tma_atom_b,
block_in_cluster_coord_vmnk[1],
b_cta_layout,
cute.group_modes(sB, 0, 3),
cute.group_modes(tCgB, 0, 3),
)
# TMA Load SFA partition_S/D
sfa_cta_layout = a_cta_layout
# ((atom_v, rest_v), STAGE)
# ((atom_v, rest_v), RestM, RestK, RestL)
tAsSFA, tAgSFA = cute.nvgpu.cpasync.tma_partition(
tma_atom_sfa,
block_in_cluster_coord_vmnk[2],
sfa_cta_layout,
cute.group_modes(sSFA, 0, 3),
cute.group_modes(tCgSFA, 0, 3),
)
tAsSFA = cute.filter_zeros(tAsSFA)
tAgSFA = cute.filter_zeros(tAgSFA)
# TMA Load SFB partition_S/D
sfb_cta_layout = cute.make_layout(
cute.slice_(cluster_layout_sfb_vmnk, (0, None, 0, 0)).shape
)
# ((atom_v, rest_v), STAGE)
# ((atom_v, rest_v), RestN, RestK, RestL)
tBsSFB, tBgSFB = cute.nvgpu.cpasync.tma_partition(
tma_atom_sfb,
block_in_cluster_coord_sfb_vmnk[1],
sfb_cta_layout,
cute.group_modes(sSFB, 0, 3),
cute.group_modes(tCgSFB, 0, 3),
)
tBsSFB = cute.filter_zeros(tBsSFB)
tBgSFB = cute.filter_zeros(tBgSFB)
#
# Partition shared/tensor memory tensor for TiledMMA_A/B/C
#
# (MMA, MMA_M, MMA_K, STAGE)
tCrA = tiled_mma.make_fragment_A(sA)
# (MMA, MMA_N, MMA_K, STAGE)
tCrB = tiled_mma.make_fragment_B(sB)
# (MMA, MMA_M, MMA_N)
acc_shape = tiled_mma.partition_shape_C(self.mma_tiler[:2])
# (MMA, MMA_M, MMA_N, STAGE)
tCtAcc_fake = tiled_mma.make_fragment_C(
cute.append(acc_shape, self.num_acc_stage)
)
#
# Cluster wait before tensor memory alloc
#
pipeline_init_wait(cluster_shape_mn=self.cluster_shape_mn)
#
# Get tensormap buffer address
#
grid_dim = cute.arch.grid_dim()
tensormap_workspace_idx = (
bidz * grid_dim[1] * grid_dim[0] + bidy * grid_dim[0] + bidx
)
tensormap_manager = utils.TensorMapManager(
utils.TensorMapUpdateMode.SMEM,
Sm100GroupedBlockScaledGemmKernel.bytes_per_tensormap,
)
tensormap_a_gmem_ptr = tensormap_manager.get_tensormap_ptr(
tensormaps[(tensormap_workspace_idx, 0, None)].iterator
)
tensormap_b_gmem_ptr = tensormap_manager.get_tensormap_ptr(
tensormaps[(tensormap_workspace_idx, 1, None)].iterator
)
tensormap_sfa_gmem_ptr = tensormap_manager.get_tensormap_ptr(
tensormaps[(tensormap_workspace_idx, 2, None)].iterator
)
tensormap_sfb_gmem_ptr = tensormap_manager.get_tensormap_ptr(
tensormaps[(tensormap_workspace_idx, 3, None)].iterator
)
tensormap_c_gmem_ptr = tensormap_manager.get_tensormap_ptr(
tensormaps[(tensormap_workspace_idx, 4, None)].iterator
)
#
# Specialized TMA load warp
#
if warp_idx == self.tma_warp_id:
#
# Persistent tile scheduling loop
#
tile_sched = utils.StaticPersistentTileScheduler.create(
tile_sched_params, cute.arch.block_idx(), grid_dim
)
# grouped gemm tile scheduler helper will compute the group index for the tile we're working on
group_gemm_ts_helper = utils.GroupedGemmTileSchedulerHelper(
group_count,
tile_sched_params,
self.cluster_tile_shape_mnk,
utils.create_initial_search_state(),
)
tensormap_init_done = cutlass.Boolean(False)
# group index of last tile
last_group_idx = cutlass.Int32(-1)
# Track N,K dimensions to skip redundant B/SFB updates
last_n = cutlass.Int32(-1)
last_k = cutlass.Int32(-1)
work_tile = tile_sched.initial_work_tile_info()
ab_producer_state = pipeline.make_pipeline_state(
pipeline.PipelineUserType.Producer, self.num_ab_stage
)
while work_tile.is_valid_tile:
cur_tile_coord = work_tile.tile_idx
grouped_gemm_cta_tile_info = group_gemm_ts_helper.delinearize_z(
cur_tile_coord,
problem_sizes_mnkl,
)
cur_k_tile_cnt = grouped_gemm_cta_tile_info.cta_tile_count_k
cur_group_idx = grouped_gemm_cta_tile_info.group_idx
cur_n = grouped_gemm_cta_tile_info.problem_shape_n
cur_k = grouped_gemm_cta_tile_info.problem_shape_k
is_group_changed = cur_group_idx != last_group_idx
# B and SFB only need update when N or K dimensions change
is_b_shape_changed = (cur_n != last_n) | (cur_k != last_k)
# skip tensormap update if we're working on the same group
if is_group_changed:
real_tensor_a = self.make_tensor_abc_for_tensormap_update(
cur_group_idx,
self.a_dtype,
(
grouped_gemm_cta_tile_info.problem_shape_m,
grouped_gemm_cta_tile_info.problem_shape_n,
grouped_gemm_cta_tile_info.problem_shape_k,
),
strides_abc,
ptrs_abc,
0, # 0 for tensor A
)
real_tensor_b = self.make_tensor_abc_for_tensormap_update(
cur_group_idx,
self.b_dtype,
(
grouped_gemm_cta_tile_info.problem_shape_m,
grouped_gemm_cta_tile_info.problem_shape_n,
grouped_gemm_cta_tile_info.problem_shape_k,
),
strides_abc,
ptrs_abc,
1, # 1 for tensor B
)
real_tensor_sfa = self.make_tensor_sfasfb_for_tensormap_update(
cur_group_idx,
self.sf_dtype,
(
grouped_gemm_cta_tile_info.problem_shape_m,
grouped_gemm_cta_tile_info.problem_shape_n,
grouped_gemm_cta_tile_info.problem_shape_k,
),
ptrs_sfasfb,
0, # 0 for tensor SFA
)
real_tensor_sfb = self.make_tensor_sfasfb_for_tensormap_update(
cur_group_idx,
self.sf_dtype,
(
grouped_gemm_cta_tile_info.problem_shape_m,
grouped_gemm_cta_tile_info.problem_shape_n,
grouped_gemm_cta_tile_info.problem_shape_k,
),
ptrs_sfasfb,
1, # 1 for tensor SFB
)
if tensormap_init_done == False:
# wait tensormap initialization complete
self.tensormap_ab_init_barrier.arrive_and_wait()
tensormap_init_done = True
tensormap_manager.update_tensormap(
(
real_tensor_a,
real_tensor_b,
real_tensor_sfa,
real_tensor_sfb,
),
(tma_atom_a, tma_atom_b, tma_atom_sfa, tma_atom_sfb),
(
tensormap_a_gmem_ptr,
tensormap_b_gmem_ptr,
tensormap_sfa_gmem_ptr,
tensormap_sfb_gmem_ptr,
),
self.tma_warp_id,
(
tensormap_a_smem_ptr,
tensormap_b_smem_ptr,
tensormap_sfa_smem_ptr,
tensormap_sfb_smem_ptr,
),
)
mma_tile_coord_mnl = (
grouped_gemm_cta_tile_info.cta_tile_idx_m
// cute.size(tiled_mma.thr_id.shape),
grouped_gemm_cta_tile_info.cta_tile_idx_n,
0,
)
#
# Slice to per mma tile index
#
# ((atom_v, rest_v), RestK)
tAgA_slice = tAgA[
(None, mma_tile_coord_mnl[0], None, mma_tile_coord_mnl[2])
]
# ((atom_v, rest_v), RestK)
tBgB_slice = tBgB[
(None, mma_tile_coord_mnl[1], None, mma_tile_coord_mnl[2])
]
# ((atom_v, rest_v), RestK)
tAgSFA_slice = tAgSFA[
(None, mma_tile_coord_mnl[0], None, mma_tile_coord_mnl[2])
]
# ((atom_v, rest_v), RestK)
tBgSFB_slice = tBgSFB[
(None, mma_tile_coord_mnl[1], None, mma_tile_coord_mnl[2])
]
# Peek (try_wait) AB buffer empty for k_tile = prefetch_k_tile_cnt
ab_producer_state.reset_count()
peek_ab_empty_status = cutlass.Boolean(1)
if ab_producer_state.count < cur_k_tile_cnt:
peek_ab_empty_status = ab_pipeline.producer_try_acquire(
ab_producer_state
)
if is_group_changed:
tensormap_manager.fence_tensormap_update(tensormap_a_gmem_ptr)
tensormap_manager.fence_tensormap_update(tensormap_b_gmem_ptr)
tensormap_manager.fence_tensormap_update(tensormap_sfa_gmem_ptr)
tensormap_manager.fence_tensormap_update(tensormap_sfb_gmem_ptr)
#
# Tma load loop
#
for k_tile in cutlass.range(0, cur_k_tile_cnt, 1, unroll=1):
# Conditionally wait for AB buffer empty
ab_pipeline.producer_acquire(
ab_producer_state, peek_ab_empty_status
)
# TMA load A/B/SFA/SFB
cute.copy(
tma_atom_a,
tAgA_slice[(None, ab_producer_state.count)],
tAsA[(None, ab_producer_state.index)],
tma_bar_ptr=ab_pipeline.producer_get_barrier(ab_producer_state),
mcast_mask=a_full_mcast_mask,
tma_desc_ptr=tensormap_manager.get_tensormap_ptr(
tensormap_a_gmem_ptr,
cute.AddressSpace.generic,
),
)
cute.copy(
tma_atom_b,
tBgB_slice[(None, ab_producer_state.count)],
tBsB[(None, ab_producer_state.index)],
tma_bar_ptr=ab_pipeline.producer_get_barrier(ab_producer_state),
mcast_mask=b_full_mcast_mask,
tma_desc_ptr=tensormap_manager.get_tensormap_ptr(
tensormap_b_gmem_ptr,
cute.AddressSpace.generic,
),
)
cute.copy(
tma_atom_sfa,
tAgSFA_slice[(None, ab_producer_state.count)],
tAsSFA[(None, ab_producer_state.index)],
tma_bar_ptr=ab_pipeline.producer_get_barrier(ab_producer_state),
mcast_mask=sfa_full_mcast_mask,
tma_desc_ptr=tensormap_manager.get_tensormap_ptr(
tensormap_sfa_gmem_ptr,
cute.AddressSpace.generic,
),
)
cute.copy(
tma_atom_sfb,
tBgSFB_slice[(None, ab_producer_state.count)],
tBsSFB[(None, ab_producer_state.index)],
tma_bar_ptr=ab_pipeline.producer_get_barrier(ab_producer_state),
mcast_mask=sfb_full_mcast_mask,
tma_desc_ptr=tensormap_manager.get_tensormap_ptr(
tensormap_sfb_gmem_ptr,
cute.AddressSpace.generic,
),
)
# Peek (try_wait) AB buffer empty for k_tile = prefetch_k_tile_cnt + k_tile + 1
ab_producer_state.advance()
peek_ab_empty_status = cutlass.Boolean(1)
if ab_producer_state.count < cur_k_tile_cnt:
peek_ab_empty_status = ab_pipeline.producer_try_acquire(
ab_producer_state
)
#
# Advance to next tile
#
tile_sched.advance_to_next_work()
work_tile = tile_sched.get_current_work()
last_group_idx = cur_group_idx
#
# Wait A/B buffer empty
#
ab_pipeline.producer_tail(ab_producer_state)
#
# Specialized MMA warp
#
if warp_idx == self.mma_warp_id:
#
# Initialize tensormaps for A, B, SFA and SFB
#
tensormap_manager.init_tensormap_from_atom(
tma_atom_a, tensormap_a_smem_ptr, self.mma_warp_id
)
tensormap_manager.init_tensormap_from_atom(
tma_atom_b, tensormap_b_smem_ptr, self.mma_warp_id
)
tensormap_manager.init_tensormap_from_atom(
tma_atom_sfa, tensormap_sfa_smem_ptr, self.mma_warp_id
)
tensormap_manager.init_tensormap_from_atom(
tma_atom_sfb, tensormap_sfb_smem_ptr, self.mma_warp_id
)
# indicate tensormap initialization has finished
self.tensormap_ab_init_barrier.arrive_and_wait()
#
# Bar sync for retrieve tensor memory ptr from shared mem
#
self.tmem_alloc_barrier.arrive_and_wait()
#
# Retrieving tensor memory ptr and make accumulator/SFA/SFB tensor
#
# Make accumulator tmem tensor
acc_tmem_ptr = cute.arch.retrieve_tmem_ptr(
self.acc_dtype,
alignment=16,
ptr_to_buffer_holding_addr=tmem_holding_buf,
)
# (MMA, MMA_M, MMA_N, STAGE)
tCtAcc_base = cute.make_tensor(acc_tmem_ptr, tCtAcc_fake.layout)
# Make SFA tmem tensor
sfa_tmem_ptr = cute.recast_ptr(
acc_tmem_ptr + tcgen05.find_tmem_tensor_col_offset(tCtAcc_base),
dtype=self.sf_dtype,
)
# (MMA, MMA_M, MMA_K)
tCtSFA_layout = blockscaled_utils.make_tmem_layout_sfa(
tiled_mma,
self.mma_tiler,
self.sf_vec_size,
cute.slice_(sfa_smem_layout_staged, (None, None, None, 0)),
)
tCtSFA = cute.make_tensor(sfa_tmem_ptr, tCtSFA_layout)
# Make SFB tmem tensor
sfb_tmem_ptr = cute.recast_ptr(
acc_tmem_ptr
+ tcgen05.find_tmem_tensor_col_offset(tCtAcc_base)
+ tcgen05.find_tmem_tensor_col_offset(tCtSFA),
dtype=self.sf_dtype,
)
# (MMA, MMA_N, MMA_K)
tCtSFB_layout = blockscaled_utils.make_tmem_layout_sfb(
tiled_mma,
self.mma_tiler,
self.sf_vec_size,
cute.slice_(sfb_smem_layout_staged, (None, None, None, 0)),
)
tCtSFB = cute.make_tensor(sfb_tmem_ptr, tCtSFB_layout)
#
# Partition for S2T copy of SFA/SFB
#
tiled_copy_s2t_sfa, tCsSFA_compact_s2t, tCtSFA_compact_s2t = (
self.mainloop_s2t_copy_and_partition(sSFA, tCtSFA)
)
tiled_copy_s2t_sfb, tCsSFB_compact_s2t, tCtSFB_compact_s2t = (
self.mainloop_s2t_copy_and_partition(sSFB, tCtSFB)
)
#
# Persistent tile scheduling loop
#
tile_sched = utils.StaticPersistentTileScheduler.create(
tile_sched_params, cute.arch.block_idx(), grid_dim
)
# grouped gemm tile scheduler helper will compute the group index for the tile we're working on
group_gemm_ts_helper = utils.GroupedGemmTileSchedulerHelper(
group_count,
tile_sched_params,
self.cluster_tile_shape_mnk,
utils.create_initial_search_state(),
)
work_tile = tile_sched.initial_work_tile_info()
ab_consumer_state = pipeline.make_pipeline_state(
pipeline.PipelineUserType.Consumer, self.num_ab_stage
)
acc_producer_state = pipeline.make_pipeline_state(
pipeline.PipelineUserType.Producer, self.num_acc_stage
)
while work_tile.is_valid_tile:
cur_tile_coord = work_tile.tile_idx
# MMA warp is only interested in number of tiles along K dimension
(
cur_k_tile_cnt,
cur_group_idx,
) = group_gemm_ts_helper.search_cluster_tile_count_k(
cur_tile_coord,
problem_sizes_mnkl,
)
# (MMA, MMA_M, MMA_N)
tCtAcc = tCtAcc_base[(None, None, None, acc_producer_state.index)]
# Peek (try_wait) AB buffer full for k_tile = 0
ab_consumer_state.reset_count()
peek_ab_full_status = cutlass.Boolean(1)
if ab_consumer_state.count < cur_k_tile_cnt and is_leader_cta:
peek_ab_full_status = ab_pipeline.consumer_try_wait(
ab_consumer_state
)
#
# Wait for accumulator buffer empty
#
if is_leader_cta:
acc_pipeline.producer_acquire(acc_producer_state)
#
# Reset the ACCUMULATE field for each tile
#
tiled_mma.set(tcgen05.Field.ACCUMULATE, False)
#
# Mma mainloop
#
for k_tile in range(cur_k_tile_cnt):
if is_leader_cta:
# Conditionally wait for AB buffer full
ab_pipeline.consumer_wait(
ab_consumer_state, peek_ab_full_status
)
# Copy SFA/SFB from smem to tmem
s2t_stage_coord = (
None,
None,
None,
None,
ab_consumer_state.index,
)
tCsSFA_compact_s2t_staged = tCsSFA_compact_s2t[s2t_stage_coord]
tCsSFB_compact_s2t_staged = tCsSFB_compact_s2t[s2t_stage_coord]
cute.copy(
tiled_copy_s2t_sfa,
tCsSFA_compact_s2t_staged,
tCtSFA_compact_s2t,
)
cute.copy(
tiled_copy_s2t_sfb,
tCsSFB_compact_s2t_staged,
tCtSFB_compact_s2t,
)
# tCtAcc += tCrA * tCrSFA * tCrB * tCrSFB
num_kblocks = cute.size(tCrA, mode=[2])
for kblock_idx in cutlass.range(num_kblocks, unroll_full=True):
kblock_coord = (
None,
None,
kblock_idx,
ab_consumer_state.index,
)
# Set SFA/SFB tensor to tiled_mma
sf_kblock_coord = (None, None, kblock_idx)
tiled_mma.set(
tcgen05.Field.SFA,
tCtSFA[sf_kblock_coord].iterator,
)
tiled_mma.set(
tcgen05.Field.SFB,
tCtSFB[sf_kblock_coord].iterator,
)
cute.gemm(
tiled_mma,
tCtAcc,
tCrA[kblock_coord],
tCrB[kblock_coord],
tCtAcc,
)
# Enable accumulate on tCtAcc after first kblock
tiled_mma.set(tcgen05.Field.ACCUMULATE, True)
# Async arrive AB buffer empty
ab_pipeline.consumer_release(ab_consumer_state)
# Peek (try_wait) AB buffer full for k_tile = k_tile + 1
ab_consumer_state.advance()
peek_ab_full_status = cutlass.Boolean(1)
if ab_consumer_state.count < cur_k_tile_cnt:
if is_leader_cta:
peek_ab_full_status = ab_pipeline.consumer_try_wait(
ab_consumer_state
)
#
# Async arrive accumulator buffer full
#
if is_leader_cta:
acc_pipeline.producer_commit(acc_producer_state)
acc_producer_state.advance()
#
# Advance to next tile
#
tile_sched.advance_to_next_work()
work_tile = tile_sched.get_current_work()
#
# Wait for accumulator buffer empty
#
acc_pipeline.producer_tail(acc_producer_state)
#
# Specialized epilogue warps
#
if warp_idx < self.mma_warp_id:
# initialize tensorap for C
tensormap_manager.init_tensormap_from_atom(
tma_atom_c,
tensormap_c_smem_ptr,
self.epilog_warp_id[0],
)
#
# Alloc tensor memory buffer
#
if warp_idx == self.epilog_warp_id[0]:
cute.arch.alloc_tmem(
self.num_tmem_alloc_cols,
tmem_holding_buf,
is_two_cta=use_2cta_instrs,
)
#
# Bar sync for retrieve tensor memory ptr from shared memory
#
self.tmem_alloc_barrier.arrive_and_wait()
#
# Retrieving tensor memory ptr and make accumulator tensor
#
acc_tmem_ptr = cute.arch.retrieve_tmem_ptr(
self.acc_dtype,
alignment=16,
ptr_to_buffer_holding_addr=tmem_holding_buf,
)
# (MMA, MMA_M, MMA_N, STAGE)
tCtAcc_base = cute.make_tensor(acc_tmem_ptr, tCtAcc_fake.layout)
### Start from here
#
# Partition for epilogue
#
epi_tidx = tidx
tiled_copy_t2r, tTR_tAcc_base, tTR_rAcc = (
self.epilog_tmem_copy_and_partition(
epi_tidx, tCtAcc_base, tCgC, epi_tile, use_2cta_instrs
)
)
tTR_rC = cute.make_rmem_tensor(tTR_rAcc.shape, self.c_dtype)
tiled_copy_r2s, tRS_rC, tRS_sC = self.epilog_smem_copy_and_partition(
tiled_copy_t2r, tTR_rC, epi_tidx, sC
)
tma_atom_c, bSG_sC, bSG_gC_partitioned = (
self.epilog_gmem_copy_and_partition(
epi_tidx, tma_atom_c, tCgC, epi_tile, sC
)
)
#
# Persistent tile scheduling loop
#
tile_sched = utils.StaticPersistentTileScheduler.create(
tile_sched_params, cute.arch.block_idx(), grid_dim
)
# grouped gemm tile scheduler helper will compute the group index for the tile we're working on
group_gemm_ts_helper = utils.GroupedGemmTileSchedulerHelper(
group_count,
tile_sched_params,
self.cluster_tile_shape_mnk,
utils.create_initial_search_state(),
)
work_tile = tile_sched.initial_work_tile_info()
acc_consumer_state = pipeline.make_pipeline_state(
pipeline.PipelineUserType.Consumer, self.num_acc_stage
)
# Threads/warps participating in tma store pipeline
c_producer_group = pipeline.CooperativeGroup(
pipeline.Agent.Thread,
32 * len(self.epilog_warp_id),
)
c_pipeline = pipeline.PipelineTmaStore.create(
num_stages=self.num_c_stage,
producer_group=c_producer_group,
)
# group index to start searching
last_group_idx = cutlass.Int32(-1)
while work_tile.is_valid_tile:
cur_tile_coord = work_tile.tile_idx
grouped_gemm_cta_tile_info = group_gemm_ts_helper.delinearize_z(
cur_tile_coord,
problem_sizes_mnkl,
)
cur_group_idx = grouped_gemm_cta_tile_info.group_idx
is_group_changed = cur_group_idx != last_group_idx
if is_group_changed:
# construct tensor c based on real shape, stride information
real_tensor_c = self.make_tensor_abc_for_tensormap_update(
cur_group_idx,
self.c_dtype,
(
grouped_gemm_cta_tile_info.problem_shape_m,
grouped_gemm_cta_tile_info.problem_shape_n,
grouped_gemm_cta_tile_info.problem_shape_k,
),
strides_abc,
ptrs_abc,
2, # 2 for tensor C
)
tensormap_manager.update_tensormap(
((real_tensor_c),),
((tma_atom_c),),
((tensormap_c_gmem_ptr),),
self.epilog_warp_id[0],
(tensormap_c_smem_ptr,),
)
mma_tile_coord_mnl = (
grouped_gemm_cta_tile_info.cta_tile_idx_m
// cute.size(tiled_mma.thr_id.shape),
grouped_gemm_cta_tile_info.cta_tile_idx_n,
0,
)
cur_k_tile_cnt = grouped_gemm_cta_tile_info.cta_tile_count_k
#
# Slice to per mma tile index
#
# ((ATOM_V, REST_V), EPI_M, EPI_N)
bSG_gC = bSG_gC_partitioned[
(
None,
None,
None,
*mma_tile_coord_mnl,
)
]
# Set tensor memory buffer for current tile
# (T2R, T2R_M, T2R_N, EPI_M, EPI_M)
tTR_tAcc = tTR_tAcc_base[
(None, None, None, None, None, acc_consumer_state.index)
]
#
# Wait for accumulator buffer full
#
acc_pipeline.consumer_wait(acc_consumer_state)
tTR_tAcc = cute.group_modes(tTR_tAcc, 3, cute.rank(tTR_tAcc))
bSG_gC = cute.group_modes(bSG_gC, 1, cute.rank(bSG_gC))
if is_group_changed:
if warp_idx == self.epilog_warp_id[0]:
tensormap_manager.fence_tensormap_update(tensormap_c_gmem_ptr)
#
# Store accumulator to global memory in subtiles
#
subtile_cnt = cute.size(tTR_tAcc.shape, mode=[3])
num_prev_subtiles = tile_sched.num_tiles_executed * subtile_cnt
for subtile_idx in range(subtile_cnt):
#
# Load accumulator from tensor memory buffer to register
#
tTR_tAcc_mn = tTR_tAcc[(None, None, None, subtile_idx)]
cute.copy(tiled_copy_t2r, tTR_tAcc_mn, tTR_rAcc)
#
# Convert to C type
#
acc_vec = tiled_copy_r2s.retile(tTR_rAcc).load()
tRS_rC.store(acc_vec.to(self.c_dtype))
#
# Store C to shared memory
#
c_buffer = (num_prev_subtiles + subtile_idx) % self.num_c_stage
cute.copy(
tiled_copy_r2s,
tRS_rC,
tRS_sC[(None, None, None, c_buffer)],
)
# Fence and barrier to make sure shared memory store is visible to TMA store
cute.arch.fence_proxy("async.shared", space="cta")
self.epilog_sync_barrier.arrive_and_wait()
#
# TMA store C to global memory
#
if warp_idx == self.epilog_warp_id[0]:
cute.copy(
tma_atom_c,
bSG_sC[(None, c_buffer)],
bSG_gC[(None, subtile_idx)],
tma_desc_ptr=tensormap_manager.get_tensormap_ptr(
tensormap_c_gmem_ptr,
cute.AddressSpace.generic,
),
)
# 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()
#
# Advance to next tile
#
tile_sched.advance_to_next_work()
work_tile = tile_sched.get_current_work()
last_group_idx = cur_group_idx
#
# Dealloc the tensor memory buffer
#
if warp_idx == self.epilog_warp_id[0]:
cute.arch.relinquish_tmem_alloc_permit(is_two_cta=use_2cta_instrs)
self.epilog_sync_barrier.arrive_and_wait()
if warp_idx == self.epilog_warp_id[0]:
if use_2cta_instrs:
cute.arch.mbarrier_arrive(
tmem_dealloc_mbar_ptr, cta_rank_in_cluster ^ 1
)
cute.arch.mbarrier_wait(tmem_dealloc_mbar_ptr, 0)
cute.arch.dealloc_tmem(
acc_tmem_ptr, self.num_tmem_alloc_cols, is_two_cta=use_2cta_instrs
)
#
# Wait for C store complete
#
c_pipeline.producer_tail()
@cute.jit
def make_tensor_abc_for_tensormap_update(
self,
group_idx: cutlass.Int32,
dtype: Type[cutlass.Numeric],
problem_shape_mnk: tuple[cutlass.Int32, cutlass.Int32, cutlass.Int32],
strides_abc: cute.Tensor,
tensor_address_abc: cute.Tensor,
tensor_index: int,
):
"""Extract stride and tensor address for a given group and construct a global tensor for A, B or C.
This function is used within the kernel to dynamically create a CUTE tensor
representing A, B, or C for the current group being processed, using the
group-specific address, shape, and stride information.
:param group_idx: The index of the current group within the grouped GEMM.
:type group_idx: cutlass.Int32
:param dtype: The data type of the tensor elements (e.g., cutlass.Float16).
:type dtype: Type[cutlass.Numeric]
:param problem_shape_mnk: The (M, N, K) problem shape for the current group.
:type problem_shape_mnk: tuple[cutlass.Int32, cutlass.Int32, cutlass.Int32]
:param strides_abc: Tensor containing strides for A, B, C for all groups. Layout: (group_count, 3, 2).
:type strides_abc: cute.Tensor
:param tensor_address_abc: Tensor containing global memory addresses for A, B, C for all groups. Layout: (group_count, 3).
:type tensor_address_abc: cute.Tensor
:param tensor_index: Specifies which tensor to create: 0 for A, 1 for B, 2 for C.
:type tensor_index: int
:return: A CUTE tensor representing the requested global memory tensor (A, B, or C) for the specified group.
:rtype: cute.Tensor
:raises TypeError: If the provided dtype is not a subclass of cutlass.Numeric.
"""
ptr_i64 = tensor_address_abc[(group_idx, tensor_index)]
if cutlass.const_expr(
not isclass(dtype) or not issubclass(dtype, cutlass.Numeric)
):
raise TypeError(
f"dtype must be a type of cutlass.Numeric, got {type(dtype)}"
)
tensor_gmem_ptr = cute.make_ptr(
dtype, ptr_i64, cute.AddressSpace.gmem, assumed_align=16
)
strides_tensor_gmem = strides_abc[(group_idx, tensor_index, None)]
strides_tensor_reg = cute.make_rmem_tensor(
cute.make_layout(2),
strides_abc.element_type,
)
cute.autovec_copy(strides_tensor_gmem, strides_tensor_reg)
stride_mn = strides_tensor_reg[0]
stride_k = strides_tensor_reg[1]
c1 = cutlass.Int32(1)
c0 = cutlass.Int32(0)
if cutlass.const_expr(tensor_index == 0): # tensor A
m = problem_shape_mnk[0]
k = problem_shape_mnk[2]
return cute.make_tensor(
tensor_gmem_ptr,
cute.make_layout((m, k, c1), stride=(stride_mn, stride_k, c0)),
)
elif cutlass.const_expr(tensor_index == 1): # tensor B
n = problem_shape_mnk[1]
k = problem_shape_mnk[2]
return cute.make_tensor(
tensor_gmem_ptr,
cute.make_layout((n, k, c1), stride=(stride_mn, stride_k, c0)),
)
else: # tensor C
m = problem_shape_mnk[0]
n = problem_shape_mnk[1]
return cute.make_tensor(
tensor_gmem_ptr,
cute.make_layout((m, n, c1), stride=(stride_mn, stride_k, c0)),
)
@cute.jit
def make_tensor_sfasfb_for_tensormap_update(
self,
group_idx: cutlass.Int32,
dtype: Type[cutlass.Numeric],
problem_shape_mnk: tuple[cutlass.Int32, cutlass.Int32, cutlass.Int32],
tensor_address_sfasfb: cute.Tensor,
tensor_index: int,
):
"""Extract tensor address for a given group and construct a global tensor for SFA or SFB.
This function is used within the kernel to dynamically create a CUTE tensor
representing SFA or SFB for the current group being processed, using the
group-specific address, shape information.
:param group_idx: The index of the current group within the grouped GEMM.
:type group_idx: cutlass.Int32
:param dtype: The data type of the tensor elements (e.g., cutlass.Float16).
:type dtype: Type[cutlass.Numeric]
:param problem_shape_mnk: The (M, N, K) problem shape for the current group.
:type problem_shape_mnk: tuple[cutlass.Int32, cutlass.Int32, cutlass.Int32]
:param tensor_address_sfasfb: Tensor containing global memory addresses for SFA, SFB for all groups. Layout: (group_count, 2).
:type tensor_address_sfasfb: cute.Tensor
:param tensor_index: Specifies which tensor to create: 0 for SFA, 1 for SFB.
:type tensor_index: int
:return: A CUTE tensor representing the requested global memory tensor (SFA, SFB) for the specified group.
:rtype: cute.Tensor
:raises TypeError: If the provided dtype is not a subclass of cutlass.Numeric.
"""
ptr_i64 = tensor_address_sfasfb[(group_idx, tensor_index)]
if cutlass.const_expr(
not isclass(dtype) or not issubclass(dtype, cutlass.Numeric)
):
raise TypeError(
f"dtype must be a type of cutlass.Numeric, got {type(dtype)}"
)
tensor_gmem_ptr = cute.make_ptr(
dtype, ptr_i64, cute.AddressSpace.gmem, assumed_align=16
)
c1 = cutlass.Int32(1)
if cutlass.const_expr(tensor_index == 0): # tensor SFA
m = problem_shape_mnk[0]
k = problem_shape_mnk[2]
sfa_layout = blockscaled_utils.tile_atom_to_shape_SF(
(m, k, c1), self.sf_vec_size
)
return cute.make_tensor(
tensor_gmem_ptr,
sfa_layout,
)
else: # tensor SFB
n = problem_shape_mnk[1]
k = problem_shape_mnk[2]
sfb_layout = blockscaled_utils.tile_atom_to_shape_SF(
(n, k, c1), self.sf_vec_size
)
return cute.make_tensor(
tensor_gmem_ptr,
sfb_layout,
)
def mainloop_s2t_copy_and_partition(
self,
sSF: cute.Tensor,
tSF: cute.Tensor,
) -> Tuple[cute.TiledCopy, cute.Tensor, cute.Tensor]:
"""
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,
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(
total_num_clusters: int,
cluster_shape_mn: tuple[int, int],
max_active_clusters: cutlass.Constexpr[int],
) -> tuple[utils.PersistentTileSchedulerParams, tuple[int, int, int]]:
"""Compute tile scheduler parameters and grid shape for grouped GEMM operations.
:param total_num_clusters: Total number of clusters to process across all groups.
:type total_num_clusters: 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[int]
: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, ...]]
"""
# Create problem shape with M, N dimensions from cluster shape
# and L dimension representing the total number of clusters.
problem_shape_ntile_mnl = (
cluster_shape_mn[0],
cluster_shape_mn[1],
cutlass.Int32(total_num_clusters),
)
tile_sched_params = utils.PersistentTileSchedulerParams(
problem_shape_ntile_mnl, (*cluster_shape_mn, 1)
)
grid = utils.StaticPersistentTileScheduler.get_grid_shape(
tile_sched_params, max_active_clusters
)
return tile_sched_params, grid
@staticmethod
def _get_mbar_smem_bytes(**kwargs_stages: int) -> int:
"""Calculate shared memory consumption for memory barriers based on provided stages.
Each stage requires 2 barriers, and each barrier consumes 8 bytes of shared memory.
The total consumption is the sum across all provided stages. This function calculates the total
shared memory needed for these barriers.
:param kwargs_stages: Variable keyword arguments where each key is a stage name
(e.g., num_acc_stage, num_ab_stage) and each value is the
number of stages of that type.
:type kwargs_stages: int
:return: Total shared memory bytes required for all memory barriers.
:rtype: int
"""
num_barriers_per_stage = 2
num_bytes_per_barrier = 8
mbar_smem_consumption = sum(
[
num_barriers_per_stage * num_bytes_per_barrier * stage
for stage in kwargs_stages.values()
]
)
return mbar_smem_consumption
@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 [128, 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(
problem_sizes_mnkl: List[Tuple[int, int, int, 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 problem_sizes_mnkl: The problem shape for each group
:type problem_sizes_mnkl: List[Tuple[int, int, int, 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
for m, n, k, l in problem_sizes_mnkl:
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],
problem_sizes_mnkl: List[Tuple[int, int, int, 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 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 Sm100GroupedBlockScaledGemmKernel.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 Sm100GroupedBlockScaledGemmKernel.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 Sm100GroupedBlockScaledGemmKernel.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 Sm100GroupedBlockScaledGemmKernel.is_valid_tensor_alignment(
problem_sizes_mnkl, ab_dtype, c_dtype, a_major, b_major, c_major
):
can_implement = False
return can_implement
# Size of smem we reserved for mbarrier, tensor memory management and tensormap update
reserved_smem_bytes = 1024
bytes_per_tensormap = 128
num_tensormaps = 5
# size of smem used for tensor memory management
tensor_memory_management_bytes = 12
# Create tensor and return the pointer, tensor, and stride
def create_tensor_and_stride(
l: int,
mode0: int,
mode1: int,
is_mode0_major: bool,
dtype: type[cutlass.Numeric],
is_dynamic_layout: bool = True,
) -> tuple[int, torch.Tensor, cute.Tensor, torch.Tensor, tuple[int, int]]:
"""Create GPU tensor from either a new or existing CPU tensor.
:param torch_tensor_cpu: Optional existing CPU tensor to reuse. If None, creates a new one.
:type torch_tensor_cpu: torch.Tensor, optional
"""
# Create new CPU tensor
torch_tensor_cpu = cutlass_torch.matrix(
l,
mode0,
mode1,
is_mode0_major,
cutlass.Float32,
)
# Create GPU tensor from CPU tensor (new or existing)
cute_tensor, torch_tensor = cutlass_torch.cute_tensor_like(
torch_tensor_cpu, dtype, is_dynamic_layout, assumed_align=16
)
# Mark tensor with element divisibility for 16B alignment
cute_tensor.mark_compact_shape_dynamic(
mode=0 if is_mode0_major else 1,
stride_order=(2, 1, 0) if is_mode0_major else (2, 0, 1),
divisibility=32 if dtype == cutlass.Float4E2M1FN else 16,
)
# omit stride for L mode as it is always 1
stride = (1, mode0) if is_mode0_major else (mode1, 1)
return (
torch_tensor.data_ptr(),
torch_tensor,
cute_tensor,
torch_tensor_cpu,
stride,
)
def create_tensors_abc_for_all_groups(
problem_sizes_mnkl: List[tuple[int, int, int, int]],
ab_dtype: Type[cutlass.Numeric],
c_dtype: Type[cutlass.Numeric],
a_major: str,
b_major: str,
c_major: str,
) -> tuple[
List[List[int]],
List[List[torch.Tensor]],
List[tuple],
List[List[tuple]],
List[List[torch.Tensor]],
]:
ref_torch_fp32_tensors_abc = []
torch_tensors_abc = []
cute_tensors_abc = []
strides_abc = []
ptrs_abc = []
# Iterate through all groups and create tensors for each group
for group_idx, (m, n, k, l) in enumerate(problem_sizes_mnkl):
# Create tensors A, B, C
(
ptr_a,
torch_tensor_a,
cute_tensor_a,
ref_torch_fp32_tensor_a,
stride_mk_a,
) = create_tensor_and_stride(l, m, k, a_major == "m", ab_dtype)
(
ptr_b,
torch_tensor_b,
cute_tensor_b,
ref_torch_fp32_tensor_b,
stride_nk_b,
) = create_tensor_and_stride(l, n, k, b_major == "n", ab_dtype)
(
ptr_c,
torch_tensor_c,
cute_tensor_c,
ref_torch_fp32_tensor_c,
stride_mn_c,
) = create_tensor_and_stride(l, m, n, c_major == "m", c_dtype)
ref_torch_fp32_tensors_abc.append(
[ref_torch_fp32_tensor_a, ref_torch_fp32_tensor_b, ref_torch_fp32_tensor_c]
)
ptrs_abc.append([ptr_a, ptr_b, ptr_c])
torch_tensors_abc.append([torch_tensor_a, torch_tensor_b, torch_tensor_c])
strides_abc.append([stride_mk_a, stride_nk_b, stride_mn_c])
cute_tensors_abc.append(
(
cute_tensor_a,
cute_tensor_b,
cute_tensor_c,
)
)
return (
ptrs_abc,
torch_tensors_abc,
cute_tensors_abc,
strides_abc,
ref_torch_fp32_tensors_abc,
)
@cute.jit
def cvt_sf_MKL_to_M32x4xrm_K4xrk_L(
sf_ref_tensor: cute.Tensor,
sf_mma_tensor: cute.Tensor,
):
"""Convert scale factor tensor from MKL layout to mma specification M(32x4xrest_m)xK(4xrest_k)xL layout"""
# sf_mma_tensor has flatten shape (32, 4, rest_m, 4, rest_k, l)
# group to ((32, 4, rest_m), (4, rest_k), l)
sf_mma_tensor = cute.group_modes(sf_mma_tensor, 0, 3)
sf_mma_tensor = cute.group_modes(sf_mma_tensor, 1, 3)
for i in cutlass.range(cute.size(sf_ref_tensor)):
mkl_coord = sf_ref_tensor.layout.get_hier_coord(i)
sf_mma_tensor[mkl_coord] = sf_ref_tensor[mkl_coord]
# Create scale factor tensor SFA/SFB
def create_scale_factor_tensor(l, mn, k, sf_vec_size, dtype):
def ceil_div(a, b):
return (a + b - 1) // b
sf_k = ceil_div(k, sf_vec_size)
ref_shape = (l, mn, sf_k)
atom_m = (32, 4)
atom_k = 4
mma_shape = (
l,
ceil_div(mn, atom_m[0] * atom_m[1]),
ceil_div(sf_k, atom_k),
atom_m[0],
atom_m[1],
atom_k,
)
ref_permute_order = (1, 2, 0)
mma_permute_order = (3, 4, 1, 5, 2, 0)
# Create f32 ref torch tensor (cpu)
ref_f32_torch_tensor_cpu = cutlass_torch.create_and_permute_torch_tensor(
ref_shape,
torch.float32,
permute_order=ref_permute_order,
init_type=cutlass_torch.TensorInitType.RANDOM,
init_config=cutlass_torch.RandomInitConfig(
min_val=1,
max_val=3,
),
)
# Create f32 cute torch tensor (cpu)
cute_f32_torch_tensor_cpu = cutlass_torch.create_and_permute_torch_tensor(
mma_shape,
torch.float32,
permute_order=mma_permute_order,
init_type=cutlass_torch.TensorInitType.RANDOM,
init_config=cutlass_torch.RandomInitConfig(
min_val=0,
max_val=1,
),
)
# convert ref f32 tensor to cute f32 tensor
cvt_sf_MKL_to_M32x4xrm_K4xrk_L(
from_dlpack(ref_f32_torch_tensor_cpu),
from_dlpack(cute_f32_torch_tensor_cpu),
)
cute_f32_torch_tensor = cute_f32_torch_tensor_cpu.cuda()
# reshape makes memory contiguous
ref_f32_torch_tensor_cpu = (
ref_f32_torch_tensor_cpu.permute(2, 0, 1)
.unsqueeze(-1)
.expand(l, mn, sf_k, sf_vec_size)
.reshape(l, mn, sf_k * sf_vec_size)
.permute(*ref_permute_order)
)
# prune to mkl for reference check.
ref_f32_torch_tensor_cpu = ref_f32_torch_tensor_cpu[:, :k, :]
# Create dtype cute torch tensor (cpu)
cute_tensor, cute_torch_tensor = cutlass_torch.cute_tensor_like(
cute_f32_torch_tensor_cpu,
dtype,
is_dynamic_layout=True,
assumed_align=16,
)
# Convert f32 cute tensor to dtype cute tensor
cute_tensor = cutlass_torch.convert_cute_tensor(
cute_f32_torch_tensor,
cute_tensor,
dtype,
is_dynamic_layout=True,
)
# get pointer of the tensor
ptr = cute_torch_tensor.data_ptr()
return ref_f32_torch_tensor_cpu, ptr, cute_tensor, cute_torch_tensor
def create_tensors_sfasfb_for_all_groups(
problem_sizes_mnkl: List[tuple[int, int, int, int]],
sf_dtype: Type[cutlass.Numeric],
sf_vec_size: int,
) -> tuple[
List[List[int]],
List[List[torch.Tensor]],
List[tuple],
List[List[torch.Tensor]],
]:
ptrs_sfasfb = []
torch_tensors_sfasfb = []
cute_tensors_sfasfb = []
refs_sfasfb = []
# Iterate through all groups and create tensors for each group
for group_idx, (m, n, k, l) in enumerate(problem_sizes_mnkl):
sfa_ref, ptr_sfa, sfa_tensor, sfa_torch = create_scale_factor_tensor(
l, m, k, sf_vec_size, sf_dtype
)
sfb_ref, ptr_sfb, sfb_tensor, sfb_torch = create_scale_factor_tensor(
l, n, k, sf_vec_size, sf_dtype
)
ptrs_sfasfb.append([ptr_sfa, ptr_sfb])
torch_tensors_sfasfb.append([sfa_torch, sfb_torch])
cute_tensors_sfasfb.append(
(
sfa_tensor,
sfb_tensor,
)
)
refs_sfasfb.append([sfa_ref, sfb_ref])
return (
ptrs_sfasfb,
torch_tensors_sfasfb,
cute_tensors_sfasfb,
refs_sfasfb,
)
def run(
num_groups: int,
problem_sizes_mnkl: List[Tuple[int, int, int, int]],
ab_dtype: Type[cutlass.Numeric],
sf_dtype: Type[cutlass.Numeric],
sf_vec_size: int,
c_dtype: Type[cutlass.Numeric],
a_major: str,
b_major: str,
c_major: str,
mma_tiler_mn: Tuple[int, int],
cluster_shape_mn: Tuple[int, int],
tolerance: float = 1e-01,
warmup_iterations: int = 0,
iterations: int = 1,
skip_ref_check: bool = False,
use_cold_l2: bool = False,
**kwargs,
):
pass # run() not needed for benchmark
# ============================================================================
# Standalone C++ Wrapper (no external CUTLASS imports needed)
# ============================================================================
from task import input_t, output_t
import torch.utils.cpp_extension
# C++ extension for fast pointer extraction
_cpp_code = '''
#include <torch/extension.h>
#include <vector>
void fill_ptrs(
std::vector<torch::Tensor> A,
std::vector<torch::Tensor> B,
std::vector<torch::Tensor> C,
std::vector<torch::Tensor> SFA,
std::vector<torch::Tensor> SFB,
torch::Tensor abc,
torch::Tensor sf
) {
int n = A.size();
int64_t* pa = abc.data_ptr<int64_t>();
int64_t* ps = sf.data_ptr<int64_t>();
for (int i = 0; i < n; i++) {
pa[i*3+0] = (int64_t)A[i].data_ptr();
pa[i*3+1] = (int64_t)B[i].data_ptr();
pa[i*3+2] = (int64_t)C[i].data_ptr();
ps[i*2+0] = (int64_t)SFA[i].data_ptr();
ps[i*2+1] = (int64_t)SFB[i].data_ptr();
}
}
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.def("fill_ptrs", &fill_ptrs, "Fill pointer tensors");
}
'''
_ext = torch.utils.cpp_extension.load_inline(
name='gg_standalone_ext',
cpp_sources=[_cpp_code],
verbose=False,
extra_cflags=['-O3']
)
# ============================================================================
# Kernel cache and wrapper
# ============================================================================
_base = None
_compiled = {}
_cache = {}
def _init_base():
global _base
if _base: return _base
ab, sf, c = cutlass.Float4E2M1FN, cutlass.Float8E4M3FN, cutlass.Float16
_base = {
"abc": [create_tensor_and_stride(1,64,64,False,ab)[2],
create_tensor_and_stride(1,64,64,False,ab)[2],
create_tensor_and_stride(1,16,16,False,c)[2]],
"sf": [create_tensor_and_stride(1,16,16,False,sf)[2],
create_tensor_and_stride(1,16,16,False,sf)[2]],
"tm": cutlass_torch.cute_tensor_like(
torch.zeros((cutlass.utils.HardwareInfo().get_max_active_clusters(1),5,16),
dtype=torch.int64,device="cuda"), cutlass.Int64, is_dynamic_layout=False)[0],
"mx": cutlass.utils.HardwareInfo().get_max_active_clusters(1),
}
return _base
def _get_kernel(ng, tc, ps):
key = (ng, tc, tuple(tuple(p) for p in ps))
if key in _compiled: return _compiled[key]
b = _init_base()
st = [[[k,1],[k,1],[n,1]] for m,n,k,_ in ps]
td,_ = cutlass_torch.cute_tensor_like(torch.tensor(ps,dtype=torch.int32,device="cuda"),
cutlass.Int32,is_dynamic_layout=False,assumed_align=16)
ts,_ = cutlass_torch.cute_tensor_like(torch.tensor(st,dtype=torch.int32,device="cuda"),
cutlass.Int32,is_dynamic_layout=False,assumed_align=16)
abc_cpu = torch.zeros((ng,3), dtype=torch.int64, pin_memory=True)
sf_cpu = torch.zeros((ng,2), dtype=torch.int64, pin_memory=True)
ta,ta_ = cutlass_torch.cute_tensor_like(torch.zeros((ng,3),dtype=torch.int64,device="cuda"),
cutlass.Int64,is_dynamic_layout=False,assumed_align=16)
tf,tf_ = cutlass_torch.cute_tensor_like(torch.zeros((ng,2),dtype=torch.int64,device="cuda"),
cutlass.Int64,is_dynamic_layout=False,assumed_align=16)
kern = cute.compile(Sm100GroupedBlockScaledGemmKernel(16,(128,128),(1,1)),
b["abc"][0],b["abc"][1],b["abc"][2],b["sf"][0],b["sf"][1],
ng,td,ts,ta,tf,tc,b["tm"],b["mx"],options="--opt-level 2")
_compiled[key] = {"k":kern,"td":td,"ts":ts,"ta":ta,"tf":tf,"ta_":ta_,"tf_":tf_,
"abc":abc_cpu,"sf":sf_cpu}
return _compiled[key]
def custom_kernel(data: input_t) -> output_t:
global _cache
at, _, sr, ps = data
ng = len(ps)
key = (ng, tuple(id(t) for g in at for t in g), tuple(id(t) for g in sr for t in g))
if key in _cache:
_cache[key]()
return [at[i][2] for i in range(ng)]
tc = sum(((m+127)//128)*((n+127)//128) for m,n,_,_ in ps)
r = _get_kernel(ng, tc, ps)
b = _init_base()
A = [at[i][0] for i in range(ng)]
B = [at[i][1] for i in range(ng)]
C = [at[i][2] for i in range(ng)]
SFA = [sr[i][0] for i in range(ng)]
SFB = [sr[i][1] for i in range(ng)]
_ext.fill_ptrs(A, B, C, SFA, SFB, r["abc"], r["sf"])
r["ta_"].copy_(r["abc"], non_blocking=True)
r["tf_"].copy_(r["sf"], non_blocking=True)
def call():
r["k"](b["abc"][0],b["abc"][1],b["abc"][2],b["sf"][0],b["sf"][1],
r["td"],r["ts"],r["ta"],r["tf"],b["tm"])
_cache[key] = call
call()
return C
scrolls · 2743 lines total
Source code from GPU Mode and the KernelBot dataset · June 9 Researcher Reciprocity License v1.0
Changes from previous submission
Against this author's previous submission submission 408447.
- # Copyright (c) 2025 - 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.- # SPDX-License-Identifier: BSD-3-Clause-- # Redistribution and use in source and binary forms, with or without- # modification, are permitted provided that the following conditions are met:-- # 1. Redistributions of source code must retain the above copyright notice, this- # list of conditions and the following disclaimer.-- # 2. Redistributions in binary form must reproduce the above copyright notice,- # this list of conditions and the following disclaimer in the documentation- # and/or other materials provided with the distribution.-- # 3. Neither the name of the copyright holder nor the names of its- # contributors may be used to endorse or promote products derived from- # this software without specific prior written permission.-- # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"- # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE- # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE- # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE- # FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL- # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR- # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER- # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,- # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE- # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.-import argparseimport functoolsfrom typing import List, Type, Tuple, Union⋯ 13 unchanged linesimport cutlass.utils.blockscaled_layout as blockscaled_utilsfrom cutlass.cute.runtime import from_dlpack- """- This example provides an experimental implementation of the SM100 grouped blockscaled GEMM kernel, please note that the APIs and implementation details related to this kernel may change in future releases.- A grouped blockscaled GEMM example for the NVIDIA Blackwell SM100 architecture using CUTE DSL-- This example demonstrates an implementation of grouped blockscaled GEMM using a TMA plus Blackwell SM100 TensorCore- warp-specialized persistent kernel.- The grouped GEMM workload computes a batch of GEMM operations with distinct problem sizes. Pointers to matrices- in global memory are passed to the kernel in an array (also held in global memory). Similarly, problem shapes and- strides are also stored in arrays in GMEM.-- This differs from "Batched Array" GEMM since the size of each GEMM problem in the grouped GEMM concept may be distinct.-- To run this example:-- .. code-block:: bash-- python examples/blackwell/grouped_blockscaled_gemm.py \- --ab_dtype Float4E2M1FN --sf_dtype Float8E8M0FNU --sf_vec_size 16 \- --c_dtype Float16 \- --mma_tiler_mn 128,128 --cluster_shape_mn 1,1 \- --problem_sizes_mnkl "(8192,1280,32,1),(32,384,1536,1),(640,1280,32,1),(640,160,32,1)" \- --num_groups 4-- The above example command makes 4 groups of different m, n, k sizes. The Blackwell tcgen05 MMA tile shape- is specified as (128, 64) and the cluster shape is (1,1). The input, mma accumulator and output data type- are set as fp16, fp32 and fp16, respectively.-- To collect performance with NCU profiler:-- .. code-block:: bash-- ncu python examples/blackwell/grouped_blockscaled_gemm.py \- --ab_dtype Float4E2M1FN --sf_dtype Float8E8M0FNU --sf_vec_size 16 \- --c_dtype Float16 \- --mma_tiler_mn 128,128 --cluster_shape_mn 1,1 \- --problem_sizes_mnkl "(8192,1280,32,1),(32,384,1536,1),(640,1280,32,1),(640,160,32,1)" \- --num_groups 4- --warmup_iterations 1 --iterations 10 --skip_ref_check-- Constraints:- * Supported input data types: mxf8, mxf4, nvf4- see detailed valid dtype combinations in below Sm100GroupedBlockScaledGemmKernel class documentation- * A/B tensors 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 128 or 256- * Cluster shape M/N must be positive and power of 2, total cluster size <= 16- * Cluster shape M/N must be <= 4 for scale factor multicasts due to limited size of scale factors- * Cluster shape M must be multiple of 2 if Mma tiler M is 256(use_2cta_instrs)- * The l mode(aka, batch size) for each group must be 1.- * The majorness for A, B and C must be the same across all groups.- * The contiguous dimension of A/B/C tensors in each group 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 Sm100GroupedBlockScaledGemmKernel:"""This example demonstrates an implementation of grouped blockscaled GEMM using a TMA plus Blackwell SM100 TensorCorewarp-specialized persistent kernel.⋯ 33 unchanged linessf_vec_size: int,mma_tiler_mn: Tuple[int, int],cluster_shape_mn: Tuple[int, int],- override_ab_stage: int = None,- override_acc_stage: int = None,):"""Initializes the configuration for a Blackwell grouped blockscaled GEMM kernel.⋯ 5 unchanged lines: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]- :param override_ab_stage: Override number of A/B pipeline stages (None = auto).- :param override_acc_stage: Override number of ACC pipeline stages (None = auto)."""- self._override_ab_stage = override_ab_stage- self._override_acc_stage = override_acc_stageself.acc_dtype = cutlass.Float32self.sf_vec_size = sf_vec_sizeself.use_2cta_instrs = mma_tiler_mn[0] == 256⋯ 134 unchanged lines)# Setup A/B/C stage count in shared memory and ACC stage count in tensor memory- computed_acc, computed_ab, computed_c = self._compute_stages(+ self.num_acc_stage, self.num_ab_stage, self.num_c_stage = self._compute_stages(tiled_mma,self.mma_tiler,self.a_dtype,⋯ 6 unchanged linesself.smem_capacity,self.occupancy,)- # Allow override of stage counts (for tuning)- self.num_acc_stage = getattr(self, '_override_acc_stage', None) or computed_acc- self.num_ab_stage = getattr(self, '_override_ab_stage', None) or computed_ab- self.num_c_stage = computed_c# Compute A/B/SFA/SFB/C shared memory layoutself.a_smem_layout_staged = sm100_utils.make_smem_layout_a(⋯ 675 unchanged linestensormap_init_done = cutlass.Boolean(False)# group index of last tilelast_group_idx = cutlass.Int32(-1)+ # Track N,K dimensions to skip redundant B/SFB updates+ last_n = cutlass.Int32(-1)+ last_k = cutlass.Int32(-1)work_tile = tile_sched.initial_work_tile_info()⋯ 9 unchanged lines)cur_k_tile_cnt = grouped_gemm_cta_tile_info.cta_tile_count_kcur_group_idx = grouped_gemm_cta_tile_info.group_idx+ cur_n = grouped_gemm_cta_tile_info.problem_shape_n+ cur_k = grouped_gemm_cta_tile_info.problem_shape_kis_group_changed = cur_group_idx != last_group_idx+ # B and SFB only need update when N or K dimensions change+ is_b_shape_changed = (cur_n != last_n) | (cur_k != last_k)# skip tensormap update if we're working on the same groupif is_group_changed:real_tensor_a = self.make_tensor_abc_for_tensormap_update(⋯ 1679 unchanged linesuse_cold_l2: bool = False,**kwargs,):- """Run SM100 grouped blockscaledGEMM example with specified configurations.+ pass # run() not needed for benchmark- :param use_cold_l2: Whether to use circular buffer strategy to ensure cold L2 cache, defaults to False- :type use_cold_l2: bool, optional- :return: Execution time of the GEMM kernel in microseconds- :rtype: float- """- print("Running Blackwell Grouped GEMM test with:")- print(f"{num_groups} groups")- for i, (m, n, k, l) in enumerate(problem_sizes_mnkl):- print(f"Group {i}: {m}x{n}x{k}x{l}")- print(f"AB dtype: {ab_dtype}, SF dtype: {sf_dtype}, SF Vec size: {sf_vec_size}")- print(f"C dtype: {c_dtype}")- print(f"Matrix majors - A: {a_major}, B: {b_major}, C: {c_major}")- print(f"Mma Tiler (M, N): {mma_tiler_mn}, Cluster Shape (M, N): {cluster_shape_mn}")- print(f"Tolerance: {tolerance}")- print(f"Warmup iterations: {warmup_iterations}")- print(f"Iterations: {iterations}")- print(f"Skip reference checking: {skip_ref_check}")- print(f"Use cold L2: {'True' if use_cold_l2 else 'False'}")- # Skip unsupported testcase- if not Sm100GroupedBlockScaledGemmKernel.can_implement(- ab_dtype,- sf_dtype,- sf_vec_size,- c_dtype,- mma_tiler_mn,- cluster_shape_mn,- problem_sizes_mnkl,- a_major,- b_major,- c_major,- ):- raise TypeError(- f"Unsupported testcase {ab_dtype}, {sf_dtype}, {sf_vec_size}, {c_dtype}, {mma_tiler_mn}, {cluster_shape_mn}, {problem_sizes_mnkl}, {a_major}, {b_major}, {c_major}"- )+ # ============================================================================+ # Standalone C++ Wrapper (no external CUTLASS imports needed)+ # ============================================================================+ from task import input_t, output_t+ import torch.utils.cpp_extension- if not torch.cuda.is_available():- raise RuntimeError("GPU is required to run this example!")+ # C++ extension for fast pointer extraction+ _cpp_code = '''+ #include <torch/extension.h>+ #include <vector>- torch.manual_seed(2025)+ void fill_ptrs(+ std::vector<torch::Tensor> A,+ std::vector<torch::Tensor> B,+ std::vector<torch::Tensor> C,+ std::vector<torch::Tensor> SFA,+ std::vector<torch::Tensor> SFB,+ torch::Tensor abc,+ torch::Tensor sf+ ) {+ int n = A.size();+ int64_t* pa = abc.data_ptr<int64_t>();+ int64_t* ps = sf.data_ptr<int64_t>();+ for (int i = 0; i < n; i++) {+ pa[i*3+0] = (int64_t)A[i].data_ptr();+ pa[i*3+1] = (int64_t)B[i].data_ptr();+ pa[i*3+2] = (int64_t)C[i].data_ptr();+ ps[i*2+0] = (int64_t)SFA[i].data_ptr();+ ps[i*2+1] = (int64_t)SFB[i].data_ptr();+ }+ }- # Create tensors A, B, C for all groups- (- ptrs_abc,- torch_tensors_abc,- cute_tensors_abc,- strides_abc,- ref_f32_torch_tensors_abc,- ) = create_tensors_abc_for_all_groups(- problem_sizes_mnkl,- ab_dtype,- c_dtype,- a_major,- b_major,- c_major,- )- # Create tensors SFA, SFB for all groups- (- ptrs_sfasfb,- torch_tensors_sfasfb,- cute_tensors_sfasfb,- refs_f32_torch_tensors_sfasfb,- ) = create_tensors_sfasfb_for_all_groups(- problem_sizes_mnkl,- sf_dtype,- sf_vec_size,- )+ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {+ m.def("fill_ptrs", &fill_ptrs, "Fill pointer tensors");+ }+ '''- # Choose A, B, C, SFA, SFB with the smallest size to create initial tensormaps- key_size_a = lambda item: item[1][0] * item[1][2]- key_size_b = lambda item: item[1][1] * item[1][2]- key_size_c = lambda item: item[1][0] * item[1][1]- # Find the indices of the groups with the smallest tensor sizes- min_a_idx, _ = min(enumerate(problem_sizes_mnkl), key=key_size_a)- min_b_idx, _ = min(enumerate(problem_sizes_mnkl), key=key_size_b)- min_c_idx, _ = min(enumerate(problem_sizes_mnkl), key=key_size_c)- initial_cute_tensors_abc = [- cute_tensors_abc[min_a_idx][0], # A with smallest (m, k)- cute_tensors_abc[min_b_idx][1], # B with smallest (n, k)- cute_tensors_abc[min_c_idx][2], # C with smallest (m, n)- ]- initial_cute_tensors_sfasfb = [- cute_tensors_sfasfb[min_a_idx][0], # SFA with smallest (m, k)'s group- cute_tensors_sfasfb[min_b_idx][1], # SFB with smallest (n, k)'s group- ]+ _ext = torch.utils.cpp_extension.load_inline(+ name='gg_standalone_ext',+ cpp_sources=[_cpp_code],+ verbose=False,+ extra_cflags=['-O3']+ )- hardware_info = cutlass.utils.HardwareInfo()- sm_count = hardware_info.get_max_active_clusters(1)- max_active_clusters = hardware_info.get_max_active_clusters(- cluster_shape_mn[0] * cluster_shape_mn[1]- )- # Prepare tensormap buffer for each SM- num_tensormap_buffers = sm_count- tensormap_shape = (- num_tensormap_buffers,- Sm100GroupedBlockScaledGemmKernel.num_tensormaps,- Sm100GroupedBlockScaledGemmKernel.bytes_per_tensormap // 8,- )- tensor_of_tensormap, tensor_of_tensormap_torch = cutlass_torch.cute_tensor_like(- torch.empty(tensormap_shape, dtype=torch.int64),- cutlass.Int64,- is_dynamic_layout=False,- )-- grouped_blockscaled_gemm = Sm100GroupedBlockScaledGemmKernel(- sf_vec_size,- mma_tiler_mn,- cluster_shape_mn,- override_ab_stage=kwargs.get('override_ab_stage'),- override_acc_stage=kwargs.get('override_acc_stage'),- )-- # layout (num_groups, 4):(4, 1)- (- tensor_of_dim_size_mnkl,- tensor_of_dim_size_mnkl_torch,- ) = cutlass_torch.cute_tensor_like(- torch.tensor(problem_sizes_mnkl, dtype=torch.int32),- cutlass.Int32,- is_dynamic_layout=False,- assumed_align=16,- )-- # layout (num_groups, 3, 2):(6, 2, 1)- tensor_of_strides_abc, tensor_of_strides_abc_torch = cutlass_torch.cute_tensor_like(- torch.tensor(strides_abc, dtype=torch.int32),- cutlass.Int32,- is_dynamic_layout=False,- assumed_align=16,- )-- # layout (num_groups,3):(3, 1)- tensor_of_ptrs_abc, tensor_of_ptrs_abc_torch = cutlass_torch.cute_tensor_like(- torch.tensor(ptrs_abc, dtype=torch.int64),- cutlass.Int64,- is_dynamic_layout=False,- assumed_align=16,- )-- # layout (num_groups,2):(2, 1)- tensor_of_ptrs_sfasfb, tensor_of_ptrs_sfasfb_torch = cutlass_torch.cute_tensor_like(- torch.tensor(ptrs_sfasfb, dtype=torch.int64),- cutlass.Int64,- is_dynamic_layout=False,- assumed_align=16,- )-- # Compute total number of cluster tiles we need to compute for given grouped GEMM problem- def compute_total_num_clusters(- problem_sizes_mnkl: List[tuple[int, int, int, int]],- cluster_tile_shape_mn: tuple[int, int],- ) -> int:- total_num_clusters = 0- for m, n, _, _ in problem_sizes_mnkl:- num_clusters_mn = tuple(- (x + y - 1) // y for x, y in zip((m, n), cluster_tile_shape_mn)- )- total_num_clusters += functools.reduce(lambda x, y: x * y, num_clusters_mn)- return total_num_clusters-- # Compute cluster tile shape- def compute_cluster_tile_shape(- mma_tiler_mn: tuple[int, int],- cluster_shape_mn: tuple[int, int],- ) -> tuple[int, int]:- cta_tile_shape_mn = [128, mma_tiler_mn[1]]- return tuple(x * y for x, y in zip(cta_tile_shape_mn, cluster_shape_mn))-- cluster_tile_shape_mn = compute_cluster_tile_shape(mma_tiler_mn, cluster_shape_mn)- total_num_clusters = compute_total_num_clusters(- problem_sizes_mnkl, cluster_tile_shape_mn- )-- # Compile grouped GEMM kernel- compiled_grouped_gemm = cute.compile(- grouped_blockscaled_gemm,- initial_cute_tensors_abc[0],- initial_cute_tensors_abc[1],- initial_cute_tensors_abc[2],- initial_cute_tensors_sfasfb[0],- initial_cute_tensors_sfasfb[1],- num_groups,- tensor_of_dim_size_mnkl,- tensor_of_strides_abc,- tensor_of_ptrs_abc,- tensor_of_ptrs_sfasfb,- total_num_clusters,- tensor_of_tensormap,- max_active_clusters,- options=f"--opt-level 3",- )-- # reference check- if not skip_ref_check:- compiled_grouped_gemm(- initial_cute_tensors_abc[0],- initial_cute_tensors_abc[1],- initial_cute_tensors_abc[2],- initial_cute_tensors_sfasfb[0],- initial_cute_tensors_sfasfb[1],- tensor_of_dim_size_mnkl,- tensor_of_strides_abc,- tensor_of_ptrs_abc,- tensor_of_ptrs_sfasfb,- tensor_of_tensormap,- )- print("Verifying results...")-- for i, (- (a_ref, b_ref, c_ref),- (sfa_ref, sfb_ref),- (a_tensor, b_tensor, c_tensor),- (m, n, k, l),- ) in enumerate(- zip(- ref_f32_torch_tensors_abc,- refs_f32_torch_tensors_sfasfb,- cute_tensors_abc,- problem_sizes_mnkl,- )- ):- ref_res_a = torch.einsum("mkl,mkl->mkl", a_ref, sfa_ref)- ref_res_b = torch.einsum("nkl,nkl->nkl", b_ref, sfb_ref)- ref = torch.einsum("mkl,nkl->mnl", ref_res_a, ref_res_b)-- print(f"checking group {i}")- c_ref_device = c_ref.cuda()-- cute.testing.convert(- c_tensor,- from_dlpack(c_ref_device, assumed_align=16).mark_layout_dynamic(- leading_dim=(1 if c_major == "n" else 0)- ),- )-- c_ref = c_ref_device.cpu()-- if c_dtype in (cutlass.Float32, cutlass.Float16, cutlass.BFloat16):- torch.testing.assert_close(c_ref, ref, atol=tolerance, rtol=1e-02)- elif c_dtype in (cutlass.Float8E5M2, cutlass.Float8E4M3FN):- # Convert ref : f32 -> f8 -> f32- ref_f8_ = torch.empty(- *(l, m, n), dtype=torch.uint8, device="cuda"- ).permute(1, 2, 0)- ref_f8 = from_dlpack(ref_f8_, assumed_align=16).mark_layout_dynamic(- leading_dim=1- )- ref_f8.element_type = c_dtype- ref_device = ref.permute(2, 0, 1).contiguous().permute(1, 2, 0).cuda()- ref_tensor = from_dlpack(- ref_device, assumed_align=16- ).mark_layout_dynamic(leading_dim=1)- cute.testing.convert(ref_tensor, ref_f8)- cute.testing.convert(ref_f8, ref_tensor)- ref = ref_device.cpu()- torch.testing.assert_close(c_ref, ref, atol=tolerance, rtol=1e-02)- def generate_tensors():- (- ptrs_abc_workspace,- torch_tensors_abc_workspace,- cute_tensors_abc_workspace,- strides_abc_workspace,- _,- ) = create_tensors_abc_for_all_groups(- problem_sizes_mnkl,- ab_dtype,- c_dtype,- a_major,- b_major,- c_major,- )-- (- ptrs_sfasfb_workspace,- torch_tensors_sfasfb_workspace,- cute_tensors_sfasfb_workspace,- _,- ) = create_tensors_sfasfb_for_all_groups(- problem_sizes_mnkl,- sf_dtype,- sf_vec_size,- )-- initial_cute_tensors_abc_workspace = [- cute_tensors_abc_workspace[min_a_idx][0], # A with smallest (m, k)- cute_tensors_abc_workspace[min_b_idx][1], # B with smallest (n, k)- cute_tensors_abc_workspace[min_c_idx][2], # C with smallest (m, n)- ]-- initial_cute_tensors_sfasfb_workspace = [- cute_tensors_sfasfb_workspace[min_a_idx][- 0- ], # SFA with smallest (m, k)'s group- cute_tensors_sfasfb_workspace[min_b_idx][- 1- ], # SFB with smallest (n, k)'s group- ]-- # Create new tensors for this workspace- tensor_of_strides_abc_workspace, _ = cutlass_torch.cute_tensor_like(- torch.tensor(strides_abc_workspace, dtype=torch.int32),- cutlass.Int32,- is_dynamic_layout=False,- assumed_align=16,- )-- tensor_of_ptrs_abc_workspace, _ = cutlass_torch.cute_tensor_like(- torch.tensor(ptrs_abc_workspace, dtype=torch.int64),- cutlass.Int64,- is_dynamic_layout=False,- assumed_align=16,- )-- tensor_of_ptrs_sfasfb_workspace, _ = cutlass_torch.cute_tensor_like(- torch.tensor(ptrs_sfasfb_workspace, dtype=torch.int64),- cutlass.Int64,- is_dynamic_layout=False,- assumed_align=16,- )-- tensormap_workspace, _ = cutlass_torch.cute_tensor_like(- torch.empty(tensormap_shape, dtype=torch.int64),- cutlass.Int64,- is_dynamic_layout=False,- )-- return cute.testing.JitArguments(- initial_cute_tensors_abc_workspace[0],- initial_cute_tensors_abc_workspace[1],- initial_cute_tensors_abc_workspace[2],- initial_cute_tensors_sfasfb_workspace[0],- initial_cute_tensors_sfasfb_workspace[1],- tensor_of_dim_size_mnkl,- tensor_of_strides_abc_workspace,- tensor_of_ptrs_abc_workspace,- tensor_of_ptrs_sfasfb_workspace,- tensormap_workspace,- )-- workspace_count = 1- if use_cold_l2:- one_workspace_bytes = (- sum(- [- sum(- [- torch_tensor.numel() * torch_tensor.element_size()- for torch_tensor in group_tensors- ]- )- for group_tensors in torch_tensors_abc + torch_tensors_sfasfb- ]- )- +- # Add size of strides tensor- tensor_of_strides_abc_torch.numel()- * tensor_of_strides_abc_torch.element_size()- +- # Add size of ptrs tensor A, B, C- tensor_of_ptrs_abc_torch.numel() * tensor_of_ptrs_abc_torch.element_size()- +- # Add size of ptrs tensor SFA, SFB- tensor_of_ptrs_sfasfb_torch.numel()- * tensor_of_ptrs_sfasfb_torch.element_size()- +- # Add size of tensormap tensor- tensor_of_tensormap_torch.numel() * tensor_of_tensormap_torch.element_size()- )- workspace_count = cute.testing.get_workspace_count(- one_workspace_bytes, warmup_iterations, iterations- )-- exec_time = cute.testing.benchmark(- compiled_grouped_gemm,- workspace_generator=generate_tensors,- workspace_count=workspace_count,- warmup_iterations=warmup_iterations,- iterations=iterations,- )-- return exec_time # Return execution time in microseconds--# ============================================================================- # custom_kernel for eval framework compatibility+ # Kernel cache and wrapper# ============================================================================- try:- from task import input_t, output_t- except ImportError:- from typing import Tuple, List- input_t = Tuple[- List[Tuple[torch.Tensor, torch.Tensor, torch.Tensor]],- List[Tuple[torch.Tensor, torch.Tensor]],- List[Tuple[torch.Tensor, torch.Tensor]],- List[Tuple[int, int, int, int]]- ]- output_t = List[torch.Tensor]-- # Global state for caching_base = None+ _compiled = {}_cache = {}- _ptr_cache = {}- _last_call = None+ def _init_base():+ global _base+ if _base: return _base+ ab, sf, c = cutlass.Float4E2M1FN, cutlass.Float8E4M3FN, cutlass.Float16+ _base = {+ "abc": [create_tensor_and_stride(1,64,64,False,ab)[2],+ create_tensor_and_stride(1,64,64,False,ab)[2],+ create_tensor_and_stride(1,16,16,False,c)[2]],+ "sf": [create_tensor_and_stride(1,16,16,False,sf)[2],+ create_tensor_and_stride(1,16,16,False,sf)[2]],+ "tm": cutlass_torch.cute_tensor_like(+ torch.zeros((cutlass.utils.HardwareInfo().get_max_active_clusters(1),5,16),+ dtype=torch.int64,device="cuda"), cutlass.Int64, is_dynamic_layout=False)[0],+ "mx": cutlass.utils.HardwareInfo().get_max_active_clusters(1),+ }+ return _base- def custom_kernel(data: input_t) -> output_t:- """Execute grouped GEMM kernel with eval framework's input format."""- global _base, _cache, _ptr_cache, _last_call- at, _, sr, ps = data- ng = len(ps)+ def _get_kernel(ng, tc, ps):+ key = (ng, tc, tuple(tuple(p) for p in ps))+ if key in _compiled: return _compiled[key]- # Single-entry cache for repeated identical calls- if _last_call is not None:- last_ng, last_at, last_sr, cached = _last_call- if last_ng == ng and last_at is at and last_sr is sr:- cached["fn"]()- return cached["outs"]+ b = _init_base()+ st = [[[k,1],[k,1],[n,1]] for m,n,k,_ in ps]- # Use tensor ids for fast cache lookup- id_key = (ng, tuple(id(a) for a, b_, c in at for x in (a, b_, c)),- tuple(id(s) for sa, sb in sr for s in (sa, sb)))+ td,_ = cutlass_torch.cute_tensor_like(torch.tensor(ps,dtype=torch.int32,device="cuda"),+ cutlass.Int32,is_dynamic_layout=False,assumed_align=16)+ ts,_ = cutlass_torch.cute_tensor_like(torch.tensor(st,dtype=torch.int32,device="cuda"),+ cutlass.Int32,is_dynamic_layout=False,assumed_align=16)- if id_key in _ptr_cache:- r, b, outs, fn = _ptr_cache[id_key]- fn()- return outs+ abc_cpu = torch.zeros((ng,3), dtype=torch.int64, pin_memory=True)+ sf_cpu = torch.zeros((ng,2), dtype=torch.int64, pin_memory=True)+ ta,ta_ = cutlass_torch.cute_tensor_like(torch.zeros((ng,3),dtype=torch.int64,device="cuda"),+ cutlass.Int64,is_dynamic_layout=False,assumed_align=16)+ tf,tf_ = cutlass_torch.cute_tensor_like(torch.zeros((ng,2),dtype=torch.int64,device="cuda"),+ cutlass.Int64,is_dynamic_layout=False,assumed_align=16)- # Initialize base resources- if _base is None:- ab = cutlass.Float4E2M1FN- sf = cutlass.Float8E4M3FN- c = cutlass.Float16- abc = [- create_tensor_and_stride(1, 64, 64, False, ab)[2],- create_tensor_and_stride(1, 64, 64, False, ab)[2],- create_tensor_and_stride(1, 16, 16, False, c)[2],- ]- sft = [- create_tensor_and_stride(1, 16, 16, False, sf)[2],- create_tensor_and_stride(1, 16, 16, False, sf)[2],- ]- hw = cutlass.utils.HardwareInfo()- sm = hw.get_max_active_clusters(1)- tm = torch.zeros((sm, 5, 16), dtype=torch.int64, device="cuda")- tmap, _ = cutlass_torch.cute_tensor_like(tm, cutlass.Int64, is_dynamic_layout=False)- _base = {"abc": abc, "sf": sft, "tmap": tmap, "max": sm}+ kern = cute.compile(Sm100GroupedBlockScaledGemmKernel(16,(128,128),(1,1)),+ b["abc"][0],b["abc"][1],b["abc"][2],b["sf"][0],b["sf"][1],+ ng,td,ts,ta,tf,tc,b["tm"],b["mx"],options="--opt-level 2")- b = _base- # For cluster (1, 1): cluster_tile = (128, 128)- tc = sum(((m + 127) // 128) * ((n + 127) // 128) for m, n, _, _ in ps)- key = (ng, tc, tuple(tuple(x) for x in ps))+ _compiled[key] = {"k":kern,"td":td,"ts":ts,"ta":ta,"tf":tf,"ta_":ta_,"tf_":tf_,+ "abc":abc_cpu,"sf":sf_cpu}+ return _compiled[key]- # Extract pointers- ap = [[a.data_ptr(), b_.data_ptr(), c.data_ptr()] for (a, b_, c) in at]- sp = [[sa.data_ptr(), sb.data_ptr()] for (sa, sb) in sr]- st = [[[k, 1], [k, 1], [n, 1]] for m, n, k, l in ps]+ def custom_kernel(data: input_t) -> output_t:+ global _cache+ at, _, sr, ps = data+ ng = len(ps)- if key not in _cache:- d = torch.tensor(ps, dtype=torch.int32, device="cuda")- s = torch.tensor(st, dtype=torch.int32, device="cuda")- pa = torch.tensor(ap, dtype=torch.int64, device="cuda")- pf = torch.tensor(sp, dtype=torch.int64, device="cuda")+ key = (ng, tuple(id(t) for g in at for t in g), tuple(id(t) for g in sr for t in g))+ if key in _cache:+ _cache[key]()+ return [at[i][2] for i in range(ng)]- td, td_torch = cutlass_torch.cute_tensor_like(d, cutlass.Int32, is_dynamic_layout=False, assumed_align=16)- ts, ts_torch = cutlass_torch.cute_tensor_like(s, cutlass.Int32, is_dynamic_layout=False, assumed_align=16)- ta, ta_torch = cutlass_torch.cute_tensor_like(pa, cutlass.Int64, is_dynamic_layout=False, assumed_align=16)- tf, tf_torch = cutlass_torch.cute_tensor_like(pf, cutlass.Int64, is_dynamic_layout=False, assumed_align=16)+ tc = sum(((m+127)//128)*((n+127)//128) for m,n,_,_ in ps)+ r = _get_kernel(ng, tc, ps)+ b = _init_base()- # Use cluster (1, 1) like cudeepy- g = Sm100GroupedBlockScaledGemmKernel(16, (128, 128), (1, 1))- k = cute.compile(g, b["abc"][0], b["abc"][1], b["abc"][2], b["sf"][0], b["sf"][1],- ng, td, ts, ta, tf, tc, b["tmap"], b["max"], options="--opt-level 3")+ A = [at[i][0] for i in range(ng)]+ B = [at[i][1] for i in range(ng)]+ C = [at[i][2] for i in range(ng)]+ SFA = [sr[i][0] for i in range(ng)]+ SFB = [sr[i][1] for i in range(ng)]- _cache[key] = {"k": k, "td": td, "ts": ts, "ta": ta, "tf": tf,- "ta_torch": ta_torch, "tf_torch": tf_torch, "ts_torch": ts_torch}+ _ext.fill_ptrs(A, B, C, SFA, SFB, r["abc"], r["sf"])+ r["ta_"].copy_(r["abc"], non_blocking=True)+ r["tf_"].copy_(r["sf"], non_blocking=True)- r = _cache[key]+ def call():+ r["k"](b["abc"][0],b["abc"][1],b["abc"][2],b["sf"][0],b["sf"][1],+ r["td"],r["ts"],r["ta"],r["tf"],b["tm"])- # Update pointers- r["ta_torch"].copy_(torch.tensor(ap, dtype=torch.int64, device="cuda"))- r["tf_torch"].copy_(torch.tensor(sp, dtype=torch.int64, device="cuda"))- r["ts_torch"].copy_(torch.tensor(st, dtype=torch.int32, device="cuda"))-- outs = [at[i][2] for i in range(ng)]-- kernel = r["k"]- abc0, abc1, abc2 = b["abc"]- sf0, sf1 = b["sf"]- td, ts, ta, tf, tmap = r["td"], r["ts"], r["ta"], r["tf"], b["tmap"]-- def call_kernel():- kernel(abc0, abc1, abc2, sf0, sf1, td, ts, ta, tf, tmap)-- _ptr_cache[id_key] = (r, b, outs, call_kernel)- _last_call = (ng, at, sr, {"fn": call_kernel, "outs": outs})-- call_kernel()- return outs--- if __name__ == "__main__":-- def parse_comma_separated_ints(s: str) -> tuple[int, ...]:- try:- return tuple(int(x.strip()) for x in s.split(","))- except ValueError:- raise argparse.ArgumentTypeError(- "Invalid format. Expected comma-separated integers."- )-- def parse_comma_separated_tuples(s: str) -> List[tuple[int, ...]]:- if s.strip().startswith("("):- # Split on ),( to separate tuples- tuples = s.strip("()").split("),(")- result = []- tuple_len = None-- for t in tuples:- # Parse individual tuple- nums = [int(x.strip()) for x in t.split(",")]-- # Validate tuple length consistency- if tuple_len is None:- tuple_len = len(nums)- elif len(nums) != tuple_len:- raise argparse.ArgumentTypeError(- "All tuples must have the same length"- )-- result.append(tuple(nums))- return result-- raise argparse.ArgumentTypeError(- "Invalid format. Expected comma-separated integers or list of tuples"- )-- parser = argparse.ArgumentParser(- description="Example of Grouped GEMM on Blackwell."- )- parser.add_argument(- "--num_groups",- type=int,- default=2,- help="Number of groups",- )- parser.add_argument(- "--problem_sizes_mnkl",- type=parse_comma_separated_tuples,- default=((128, 128, 128, 1), (128, 128, 128, 1)),- help="a tuple of problem sizes for each group (comma-separated tuples)",- )- parser.add_argument(- "--mma_tiler_mn",- type=parse_comma_separated_ints,- default=(256, 128), # Optimized: (256,128) for 2-CTA mode- help="Mma tile shape (comma-separated)",- )- parser.add_argument(- "--cluster_shape_mn",- type=parse_comma_separated_ints,- default=(2, 1), # Optimized: (2,1) with 2-CTA mode gives best performance- help="Cluster shape (comma-separated)",- )- parser.add_argument("--ab_dtype", type=cutlass.dtype, default=cutlass.Float4E2M1FN)- parser.add_argument("--sf_dtype", type=cutlass.dtype, default=cutlass.Float8E8M0FNU)- parser.add_argument("--sf_vec_size", type=int, default=16)- parser.add_argument("--c_dtype", type=cutlass.dtype, default=cutlass.Float16)- parser.add_argument("--a_major", choices=["k", "m"], type=str, default="k")- parser.add_argument("--b_major", choices=["k", "n"], type=str, default="k")- parser.add_argument("--c_major", choices=["n", "m"], type=str, default="n")- parser.add_argument(- "--tolerance", type=float, default=1e-01, help="Tolerance for validation"- )- parser.add_argument(- "--warmup_iterations", type=int, default=0, help="Warmup iterations"- )- parser.add_argument(- "--iterations",- type=int,- default=1,- help="Number of iterations to run the kernel",- )- parser.add_argument(- "--skip_ref_check", action="store_true", help="Skip reference checking"- )- parser.add_argument(- "--use_cold_l2",- action="store_true",- default=False,- help="Use circular buffer tensor sets to ensure L2 cold cache",- )- parser.add_argument(- "--override_ab_stage", type=int, default=None,- help="Override number of A/B pipeline stages (default: auto)",- )- parser.add_argument(- "--override_acc_stage", type=int, default=None,- help="Override number of ACC pipeline stages (default: auto)",- )-- args = parser.parse_args()-- if (- len(args.problem_sizes_mnkl) != 0- and len(args.problem_sizes_mnkl) != args.num_groups- ):- parser.error("--problem_sizes_mnkl must contain exactly num_groups tuples")-- # l mode must be 1 for all groups- for _, _, _, l in args.problem_sizes_mnkl:- if l != 1:- parser.error("l must be 1 for all groups")-- if len(args.mma_tiler_mn) != 2:- parser.error("--mma_tiler_mn must contain exactly 2 values")-- if len(args.cluster_shape_mn) != 2:- parser.error("--cluster_shape_mn must contain exactly 2 values")-- run(- args.num_groups,- args.problem_sizes_mnkl,- args.ab_dtype,- args.sf_dtype,- args.sf_vec_size,- args.c_dtype,- args.a_major,- args.b_major,- args.c_major,- args.mma_tiler_mn,- args.cluster_shape_mn,- args.tolerance,- args.warmup_iterations,- args.iterations,- args.skip_ref_check,- args.use_cold_l2,- override_ab_stage=args.override_ab_stage,- override_acc_stage=args.override_acc_stage,- )- print("PASS")+ _cache[key] = call+ call()+ return C
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