submission 187091
billcarson · python · License unknown
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submission.py
curl "https://kernelindex.com/api/v1/implementations/kernelbot-nvfp4-gemm-187091?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 digestsha256:9ae160ba911ced09b69e3ae5b092e49407740c411c7227a2cded78747694f31e
license declaredunknown
license concludedunknown
authorsbillcarson
imported2026-08-15
Techniques
Extracted from the mirrored source by pattern, never inferred. Each row cites its line.
async-copy
def cp_async_bulk_remote(fp4
PyTorch reference implementation of NVFP4 block-scaled GEMM.fused-epilogue
:ivar epi_tile: Epilogue tile configuration.mbarrier
tmem_print_bar = pipeline.NamedBarrier(num-warps = 1
num_warps=1,persistent-kernel
Provides common interface for persistent tile scheduling.shared-memory
:ivar a_smem_layout_staged: Staged shared memory layout for A.tcgen05
import cutlass.cute.nvgpu.tcgen05.mma as mmatma
"cp.async.bulk.shared::cluster.shared::cta.mbarrier::complete_tx::bytes [$0], [$1], $2, [$3];",warp-specialization
producer_group: CooperativeGroup,Kernel source
submission.py4505 lines
import os
import subprocess
from dataclasses import dataclass
from pathlib import Path
from typing import Optional, Tuple, Type, Union
from weakref import ref
import cuda.bindings.driver as cuda
import cutlass
import cutlass._mlir.dialects.cute as _cute_ir
import cutlass._mlir.dialects.cute_nvgpu as _cute_nvgpu_ir
import cutlass.cute as cute
import cutlass.cute.nvgpu.tcgen05 as tcgen05
import cutlass.cute.nvgpu.tcgen05.mma as mma
import cutlass.pipeline as pipeline
import cutlass.torch as cutlass_torch
import cutlass.utils as utils
import cutlass.utils.blackwell_helpers as sm100_utils
import cutlass.utils.blockscaled_layout as blockscaled_utils
import torch
from cutlass._mlir import ir
from cutlass._mlir.dialects import llvm, nvvm
from cutlass.base_dsl import detect_gpu_arch
from cutlass.cute.nvgpu import cpasync, tcgen05
from cutlass.cute.runtime import from_dlpack, make_ptr
from cutlass.cutlass_dsl import (
BFloat16,
Boolean,
Float4E2M1FN,
Float8E4M3FN,
Float8E5M2,
Float16,
Float32,
Int8,
Int32,
Integer,
Numeric,
NumericMeta,
T,
TFloat32,
Uint8,
dsl_user_op,
extract_mlir_values,
min,
new_from_mlir_values,
)
from cutlass.pipeline import (
CooperativeGroup,
PipelineAsync,
PipelineOp,
PipelineState,
pipeline_init_arrive,
pipeline_init_wait,
)
from task import input_t, output_t
DEBUG = os.environ.get("DEBUG_KERNEL", "0") == "1"
LINE_INFO = os.environ.get("LINE_INFO", "0") == "1"
N_TILE = int(os.environ.get("N_TILE", "-1"))
COMPILE_PTX = os.environ.get("COMPILE_PTX", "1") == "1"
DEBUG_BLOCK = (0, 0, 0)
CUDA_DSA = os.environ.get("CUDA_DSA", "0") == "1"
USE_SIMPLE_SCHEDULER = os.environ.get("USE_SIMPLE_SCHEDULER", "1") == "1"
USE_TMA_STORE = os.environ.get("USE_TMA_STORE", "1") == "1"
USE_RED_WARPS = os.environ.get("USE_RED_WARPS", "0") == "1"
COMPILE_FOR_SM100 = os.environ.get("COMPILE_FOR_SM100_THISWONTBESET", "0") == "1"
CLUSTER_N = int(os.environ.get("CLUSTER_N", "1"))
MAX_KSPLITS = int(os.environ.get("MAX_KSPLITS", "2"))
cluster_shape_mn = (1, CLUSTER_N)
# Kernel configuration parameters
# Tile sizes for M, N, K dimensions
mma_tiler_mnk = (128, N_TILE, 256)
mma_tiler_sfb_mnk = (128, 128, 256)
mma_inst_shape_mnk = (128, N_TILE, 64)
mma_inst_shape_mnk_sfb = (128, 128, 64)
mma_inst_tile_k = 4
# FP4 data type for A and B
ab_dtype = cutlass.Float4E2M1FN
# FP8 data type for scale factors
sf_dtype = cutlass.Float8E4M3FN
# FP16 output type
c_dtype = cutlass.Float16
# Scale factor block size (16 elements share one scale)
sf_vec_size = 16
# Number of threads per CUDA thread block
# Stage numbers of shared memory and tmem
num_acc_stage = 1
num_ab_stage = 1
# Total number of columns in tmem
@cute.jit
def is_debug_thr() -> bool:
"""Utility function to identify debug thread (thread 0 in block 0)
:return: True if the current thread is the debug thread, False otherwise
:rtype: bool
"""
return cute.arch.thread_idx() == (0, 0, 0) and cute.arch.block_idx() == DEBUG_BLOCK
@cute.jit
def debug(*args):
"""Utility function to print debug information from the debug thread
:param args: Arguments to print
"""
if cutlass.const_expr(DEBUG):
if is_debug_thr():
cute.printf(*args)
@cute.jit
def hdebug(*args):
"""Utility function to print debug information from the debug thread
:param args: Arguments to print
"""
if cutlass.const_expr(DEBUG):
cute.printf(*args)
@cute.jit
def alwaysprint(*args):
"""Utility function to always print debug information from all threads
:param args: Arguments to print
"""
if is_debug_thr():
cute.printf(*args)
@cute.jit
def mma_debug(*args):
"""Utility function to print debug information from the debug thread
:param args: Arguments to print
"""
if cutlass.const_expr(DEBUG):
if (
cute.arch.block_idx() == DEBUG_BLOCK
and (cute.arch.thread_idx()[0] % 32) == 0
):
cute.printf(*args)
@cute.jit
def tma_debug(*args):
mma_debug(*args)
@cute.jit
def print_tmem(
tensor: cute.Tensor,
num_cols: cutlass.Constexpr[cute.Int32],
num_rows: cutlass.Constexpr[cute.Int32] = 32,
num_warps=1,
):
"""Utility function to print tensor memory contents from the debug thread
:param tensor: Tensor to print
:type tensor: cute.Tensor
"""
if cutlass.const_expr(True):
tmem_print_bar = pipeline.NamedBarrier(
barrier_id=10,
num_threads=32 * num_warps,
)
tidx = cute.arch.thread_idx()[0]
op = tcgen05.Ld32x32bOp(tcgen05.Repetition(num_cols), tcgen05.Pack.NONE)
copy_atom_t2r = cute.make_copy_atom(op, cute.Float32)
tmem_ptr = cute.recast_ptr(tensor.iterator, None, cute.Float32)
tmem = cute.make_tensor(
tmem_ptr, layout=cute.make_layout((32, num_cols), stride=(65536, 1))
)
tiled_copy_t2r = tcgen05.make_tmem_copy(copy_atom_t2r, tmem)
thr_copy_t2r = tiled_copy_t2r.get_slice(tidx)
tTR_tTmem = thr_copy_t2r.partition_S(tmem)
tTR_rTmem = cute.make_rmem_tensor((((1, num_cols), 1), 1, 1), cute.Float32)
cute.copy(thr_copy_t2r, tTR_tTmem, tTR_rTmem)
if tidx % 128 == 0:
cute.printf("=== TMEM DUMP ===")
for r in cutlass.range_constexpr(num_rows):
if (tidx % 128) == r:
cute.printf("row {}: {}", r, tTR_rTmem)
tmem_print_bar.arrive_and_wait()
if tidx % 128 == 0:
cute.printf("=== TMEM DUMP END ===\n")
# Helper function for ceiling division
def ceil_div(a, b):
return (a + b - 1) // b
def get_k_splits(k):
ksplits = ceil_div(k, 4096)
if ksplits > MAX_KSPLITS:
ksplits = MAX_KSPLITS
return ksplits
K_IDX = 0
N_IDX = 1
class PipelineTmaAsyncNoSelfSignal(pipeline.PipelineTmaAsync):
"""A subclass of PipelineTmaAsync that doesn't set is_signaling_thread to true
when tidx == cta_rank_in_cluster. This is useful for cases where we don't want
a CTA to signal itself.
"""
@staticmethod
@cute.jit
def init_empty_barrier_arrive_signal(
cta_layout_vmnk: cute.Layout,
tidx: Int32,
mcast_mode_mn: tuple[int, int] = (1, 1),
):
"""Initialize the empty barrier arrive signal, excluding self-signaling.
Same as parent class but adds condition: tidx != cta_rank_in_cluster
"""
cluster_shape_vmnk = cta_layout_vmnk.shape
cta_rank_in_cluster = cute.arch.make_warp_uniform(
cute.arch.block_idx_in_cluster()
)
tidx = tidx % 32
# Don't signal if tidx == cta_rank_in_cluster (i.e., don't self-signal)
is_signalling_thread = (tidx < cute.size(cluster_shape_vmnk)) and (
tidx != cta_rank_in_cluster
)
dst_rank = tidx % cute.size(cluster_shape_vmnk)
dst_cta_coord = cta_layout_vmnk.get_hier_coord(dst_rank)
cur_cta_coord = cta_layout_vmnk.get_hier_coord(cta_rank_in_cluster)
is_mcast_mode_m = (
dst_cta_coord[0] == cur_cta_coord[0]
and dst_cta_coord[1] == cur_cta_coord[1]
and dst_cta_coord[3] == cur_cta_coord[3]
)
is_mcast_mode_n = (
dst_cta_coord[0] == cur_cta_coord[0]
and dst_cta_coord[2] == cur_cta_coord[2]
and dst_cta_coord[3] == cur_cta_coord[3]
)
assert not (mcast_mode_mn[0] == 0 and mcast_mode_mn[1] == 0)
if mcast_mode_mn[0] == 1 and mcast_mode_mn[1] == 0:
is_signalling_thread = is_signalling_thread and is_mcast_mode_m
elif mcast_mode_mn[0] == 0 and mcast_mode_mn[1] == 1:
is_signalling_thread = is_signalling_thread and is_mcast_mode_n
elif mcast_mode_mn[0] == 1 and mcast_mode_mn[1] == 1:
is_mcast_mode_m_or_n = is_mcast_mode_m or is_mcast_mode_n
is_signalling_thread = is_signalling_thread and is_mcast_mode_m_or_n
return dst_rank, is_signalling_thread
@staticmethod
def create(
*,
num_stages: int,
producer_group: CooperativeGroup,
consumer_group: CooperativeGroup,
tx_count: int,
barrier_storage: cute.Pointer = None,
cta_layout_vmnk: Optional[cute.Layout] = None,
tidx: Optional[Int32] = None,
mcast_mode_mn: tuple[int, int] = (1, 1),
defer_sync: bool = False,
):
"""Create a new PipelineTmaAsyncNoSelfSignal instance."""
if not isinstance(barrier_storage, cute.Pointer):
raise ValueError(
f"Expected barrier_storage to be a cute.Pointer, but got {type(barrier_storage)}"
)
producer_type = PipelineOp.TmaLoad
consumer_type = PipelineOp.AsyncThread
producer = (producer_type, producer_group)
consumer = (consumer_type, consumer_group)
sync_object_full = pipeline.PipelineAsync._make_sync_object(
barrier_storage.align(min_align=8), num_stages, producer, tx_count
)
sync_object_empty = pipeline.PipelineAsync._make_sync_object(
barrier_storage.align(min_align=8) + num_stages, num_stages, consumer
)
if tidx is None:
tidx, _, _ = cute.arch.thread_idx()
if cta_layout_vmnk is None:
cta_layout_vmnk = cute.make_layout((1, 1, 1, 1))
(
dst_rank,
is_signalling_thread,
) = PipelineTmaAsyncNoSelfSignal.init_empty_barrier_arrive_signal(
cta_layout_vmnk, tidx, mcast_mode_mn
)
if cta_layout_vmnk is None or cute.size(cta_layout_vmnk) == 1:
dst_rank = None
else:
dst_rank = dst_rank
producer_mask = None
if not defer_sync:
cute.arch.mbarrier_init_fence()
if cta_layout_vmnk is None or cute.size(cta_layout_vmnk) == 1:
pipeline.agent_sync(pipeline.Agent.ThreadBlock)
else:
pipeline.agent_sync(pipeline.Agent.ThreadBlockCluster, is_relaxed=True)
return PipelineTmaAsyncNoSelfSignal(
sync_object_full,
sync_object_empty,
num_stages,
producer_mask,
dst_rank,
is_signalling_thread,
)
class WorkTileInfo:
"""A class to represent information about a work tile.
:ivar tile_idx: The index of the tile.
:type tile_idx: cute.Coord
:ivar is_valid_tile: Whether the tile is valid.
:type is_valid_tile: Boolean
"""
def __init__(self, tile_idx: cute.Coord, is_valid_tile: Boolean):
self._tile_idx = tile_idx
self._is_valid_tile = Boolean(is_valid_tile)
def __extract_mlir_values__(self) -> list[ir.Value]:
values = extract_mlir_values(self.tile_idx)
values.extend(extract_mlir_values(self.is_valid_tile))
return values
def __new_from_mlir_values__(self, values: list[ir.Value]) -> "WorkTileInfo":
assert len(values) == 4
new_tile_idx = new_from_mlir_values(self._tile_idx, values[:-1])
new_is_valid_tile = new_from_mlir_values(self._is_valid_tile, [values[-1]])
return WorkTileInfo(new_tile_idx, new_is_valid_tile)
@property
def is_valid_tile(self) -> Boolean:
"""Check latest tile returned by the scheduler is valid or not. Any scheduling
requests after all tasks completed will return an invalid tile.
:return: The validity of the tile.
:rtype: Boolean
"""
return self._is_valid_tile
@property
def tile_idx(self) -> cute.Coord:
"""
Get the index of the tile.
:return: The index of the tile.
:rtype: cute.Coord
"""
return self._tile_idx
@cute.jit
def get_mnkl_indices(
tile: WorkTileInfo,
) -> tuple[
cutlass.Constexpr[cute.Int32], cute.Int32, cute.Int32, cutlass.Constexpr[cute.Int32]
]:
tile_idx = tile.tile_idx
# tile_idx = (k_split, n_tile, 0)
return 0, tile_idx[N_IDX], tile_idx[K_IDX], 0
class TileScheduler:
"""Base class for tile schedulers.
Provides common interface for persistent tile scheduling.
"""
@dsl_user_op
def get_current_work(self, *, loc=None, ip=None) -> WorkTileInfo:
raise NotImplementedError
@dsl_user_op
def initial_work_tile_info(self, *, loc=None, ip=None) -> WorkTileInfo:
raise NotImplementedError
@dsl_user_op
def advance_to_next_work(self, *, advance_count: int = 1, loc=None, ip=None):
raise NotImplementedError
@property
def num_tiles_executed(self) -> Int32:
raise NotImplementedError
@property
def k_split_idx(self) -> Int32:
"""Returns the k-split index for this CTA."""
raise NotImplementedError
class SimpleTileSchedulerParams:
"""Simplified tile scheduler parameters for fixed M=128, L=1 case.
Grid layout: (k_splits, num_persistent_n_ctas, 1)
- grid.x = k_splits (each x handles one K-split)
- grid.y = min(N_tiles, max_clusters // k_splits)
Work tile: (k_split, n_tile) where:
- k_split = bidx (fixed for CTA lifetime)
- n_tile = bidy + wave * n_stride (advances each iteration)
"""
def __init__(
self,
k_splits: int,
n_tiles: int,
n_stride: int,
*,
loc=None,
ip=None,
):
self.k_splits = k_splits
self.n_tiles = n_tiles
self.n_stride = n_stride
self._loc = loc
def __extract_mlir_values__(self):
# k_splits, n_tiles, and n_stride are static Python ints, no MLIR values to extract
return []
def __new_from_mlir_values__(self, values):
# No MLIR values to reconstruct, just return a copy with same static values
return SimpleTileSchedulerParams(
self.k_splits,
self.n_tiles,
self.n_stride,
loc=self._loc,
)
@staticmethod
def get_grid_shape(
cluster_shape_kn: Tuple[int, int],
n_tiles: int,
max_active_clusters: int,
*,
loc=None,
ip=None,
) -> Tuple[int, int, int]:
"""Computes grid shape: (k_splits, num_persistent_n_ctas, 1)"""
# Number of N-tile CTAs we can run per wave
ksplits, cluster_n = cluster_shape_kn
max_n_ctas = max_active_clusters * cluster_n
num_persistent_n_ctas = min(n_tiles, max_n_ctas)
return (ksplits, num_persistent_n_ctas, 1)
class SimpleTileScheduler(TileScheduler):
"""Simplified persistent tile scheduler for fixed M=128, L=1 case.
Each CTA has:
- Fixed k_split = block_idx.x
- Advancing n_tile = block_idx.y + wave * grid_dim.y
"""
def __init__(
self,
params: SimpleTileSchedulerParams,
k_split: Int32,
current_n_tile: Int32,
num_tiles_executed: Int32,
):
self.params = params
self.k_split = k_split
self._current_n_tile = current_n_tile
self._num_tiles_executed = num_tiles_executed
def __extract_mlir_values__(self) -> list[ir.Value]:
values = extract_mlir_values(self.k_split)
values.extend(extract_mlir_values(self._current_n_tile))
values.extend(extract_mlir_values(self._num_tiles_executed))
return values
def __new_from_mlir_values__(self, values: list[ir.Value]) -> "SimpleTileScheduler":
assert len(values) == 3
return SimpleTileScheduler(
self.params,
new_from_mlir_values(self.k_split, [values[0]]),
new_from_mlir_values(self._current_n_tile, [values[1]]),
new_from_mlir_values(self._num_tiles_executed, [values[2]]),
)
@staticmethod
@dsl_user_op
def create(
params: SimpleTileSchedulerParams,
block_idx: Tuple[Integer, Integer, Integer],
*,
loc=None,
ip=None,
):
"""Initialize the simplified tile scheduler.
block_idx.x = k_split (fixed)
block_idx.y = initial n_tile offset
params.n_stride = how much to advance each wave (statically known)
"""
bidx, bidy, _ = block_idx
k_split = Int32(bidx)
current_n_tile = Int32(bidy)
num_tiles_executed = Int32(0)
return SimpleTileScheduler(params, k_split, current_n_tile, num_tiles_executed)
@dsl_user_op
def get_current_work(self, *, loc=None, ip=None) -> WorkTileInfo:
"""Returns current work tile: (k_split, n_tile, 0)"""
is_valid = self._current_n_tile < Int32(self.params.n_tiles)
tile_idx = (self.k_split, self._current_n_tile, Int32(0))
return WorkTileInfo(tile_idx, is_valid)
@dsl_user_op
def initial_work_tile_info(self, *, loc=None, ip=None) -> WorkTileInfo:
return self.get_current_work(loc=loc, ip=ip)
@dsl_user_op
def advance_to_next_work(self, *, advance_count: int = 1, loc=None, ip=None):
"""Advance to next N-tile: n_tile += n_stride"""
self._current_n_tile += Int32(advance_count * self.params.n_stride)
self._num_tiles_executed += Int32(1)
@property
def num_tiles_executed(self) -> Int32:
return self._num_tiles_executed
@property
def k_split_idx(self) -> Int32:
"""Returns the k-split index for this CTA (block_idx.x)."""
return self.k_split
class PersistentTileSchedulerParams:
"""A class to represent parameters for a persistent tile scheduler.
This class is designed to manage and compute the layout of clusters and tiles
in a batched gemm problem.
:ivar cluster_shape_mn: Shape of the cluster in (m, n) dimensions (K dimension cta count must be 1).
:type cluster_shape_mn: tuple
:ivar problem_layout_ncluster_mnl: Layout of the problem in terms of
number of clusters in (m, n, l) dimensions.
:type problem_layout_ncluster_mnl: cute.Layout
"""
def __init__(
self,
problem_shape_ntile_mnl: cute.Shape,
cluster_shape_mnk: cute.Shape,
swizzle_size: int = 1,
raster_along_m: bool = True,
*,
loc=None,
ip=None,
):
"""
Initializes the PersistentTileSchedulerParams with the given parameters.
:param problem_shape_ntile_mnl: The shape of the problem in terms of
number of CTA (Cooperative Thread Array) in (m, n, l) dimensions.
:type problem_shape_ntile_mnl: cute.Shape
:param cluster_shape_mnk: The shape of the cluster in (m, n) dimensions.
:type cluster_shape_mnk: cute.Shape
:param swizzle_size: Swizzling size in the unit of cluster. 1 means no swizzle
:type swizzle_size: int
:param raster_along_m: Rasterization order of clusters. Only used when swizzle_size > 1.
True means along M, false means along N.
:type raster_along_m: bool
:raises ValueError: If cluster_shape_k is not 1.
"""
# if cluster_shape_mnk[2] != 1:
# raise ValueError(f"unsupported cluster_shape_k {cluster_shape_mnk[2]}")
if swizzle_size < 1:
raise ValueError(f"expect swizzle_size >= 1, but get {swizzle_size}")
self.problem_shape_ntile_mnl = problem_shape_ntile_mnl
# cluster_shape_mnk is kept for reconstruction
self._cluster_shape_mnk = cluster_shape_mnk
self.cluster_shape_mn = cluster_shape_mnk[:2]
self.swizzle_size = swizzle_size
self._raster_along_m = raster_along_m
self._loc = loc
# By default, we follow m major (col-major) raster order, so make a col-major layout
self.problem_layout_ncluster_mnl = cute.make_layout(
cute.ceil_div(
self.problem_shape_ntile_mnl, cluster_shape_mnk[:2], loc=loc, ip=ip
),
loc=loc,
ip=ip,
)
if swizzle_size > 1:
problem_shape_ncluster_mnl = cute.round_up(
self.problem_layout_ncluster_mnl.shape,
(1, swizzle_size, 1) if raster_along_m else (swizzle_size, 1, 1),
)
if raster_along_m:
self.problem_layout_ncluster_mnl = cute.make_layout(
(
problem_shape_ncluster_mnl[0],
(swizzle_size, problem_shape_ncluster_mnl[1] // swizzle_size),
problem_shape_ncluster_mnl[2],
),
stride=(
swizzle_size,
(1, swizzle_size * problem_shape_ncluster_mnl[0]),
problem_shape_ncluster_mnl[0] * problem_shape_ncluster_mnl[1],
),
loc=loc,
ip=ip,
)
else:
self.problem_layout_ncluster_mnl = cute.make_layout(
(
(swizzle_size, problem_shape_ncluster_mnl[0] // swizzle_size),
problem_shape_ncluster_mnl[1],
problem_shape_ncluster_mnl[2],
),
stride=(
(1, swizzle_size * problem_shape_ncluster_mnl[1]),
swizzle_size,
problem_shape_ncluster_mnl[0] * problem_shape_ncluster_mnl[1],
),
loc=loc,
ip=ip,
)
def __extract_mlir_values__(self):
values, self._values_pos = [], []
for obj in [
self.problem_shape_ntile_mnl,
self._cluster_shape_mnk,
self.swizzle_size,
self._raster_along_m,
]:
obj_values = extract_mlir_values(obj)
values += obj_values
self._values_pos.append(len(obj_values))
return values
def __new_from_mlir_values__(self, values):
obj_list = []
for obj, n_items in zip(
[
self.problem_shape_ntile_mnl,
self._cluster_shape_mnk,
self.swizzle_size,
self._raster_along_m,
],
self._values_pos,
):
obj_list.append(new_from_mlir_values(obj, values[:n_items]))
values = values[n_items:]
return PersistentTileSchedulerParams(*(tuple(obj_list)), loc=self._loc)
@dsl_user_op
def get_grid_shape(
self, max_active_clusters: Int32, *, loc=None, ip=None
) -> Tuple[Integer, Integer, Integer]:
"""
Computes the grid shape based on the maximum active clusters allowed.
:param max_active_clusters: The maximum number of active clusters that
can run in one wave.
:type max_active_clusters: Int32
:return: A tuple containing the grid shape in (m, n, persistent_clusters).
- m: self.cluster_shape_m.
- n: self.cluster_shape_n.
- persistent_clusters: Number of persistent clusters that can run.
"""
# Total ctas in problem size
num_ctas_mnl = tuple(
cute.size(x) * y
for x, y in zip(
self.problem_layout_ncluster_mnl.shape, self.cluster_shape_mn
)
) + (self.problem_layout_ncluster_mnl.shape[2],)
num_ctas_in_problem = cute.size(num_ctas_mnl, loc=loc, ip=ip)
num_ctas_per_cluster = cute.size(self.cluster_shape_mn, loc=loc, ip=ip)
# Total ctas that can run in one wave
num_ctas_per_wave = max_active_clusters * num_ctas_per_cluster
num_persistent_ctas = min(num_ctas_in_problem, num_ctas_per_wave)
num_persistent_clusters = num_persistent_ctas // num_ctas_per_cluster
return (*self.cluster_shape_mn, num_persistent_clusters)
class StaticPersistentTileScheduler(TileScheduler):
"""A scheduler for static persistent tile execution in CUTLASS/CuTe kernels.
:ivar params: Tile schedule related params, including cluster shape and problem_layout_ncluster_mnl
:type params: PersistentTileSchedulerParams
:ivar num_persistent_clusters: Number of persistent clusters that can be launched
:type num_persistent_clusters: Int32
:ivar cta_id_in_cluster: ID of the CTA within its cluster
:type cta_id_in_cluster: cute.Coord
:ivar _num_tiles_executed: Counter for executed tiles
:type _num_tiles_executed: Int32
:ivar _current_work_linear_idx: Current cluster index
:type _current_work_linear_idx: Int32
"""
def __init__(
self,
params: PersistentTileSchedulerParams,
num_persistent_clusters: Int32,
current_work_linear_idx: Int32,
cta_id_in_cluster: cute.Coord,
num_tiles_executed: Int32,
):
"""
Initializes the StaticPersistentTileScheduler with the given parameters.
:param params: Tile schedule related params, including cluster shape and problem_layout_ncluster_mnl.
:type params: PersistentTileSchedulerParams
:param num_persistent_clusters: Number of persistent clusters that can be launched.
:type num_persistent_clusters: Int32
:param current_work_linear_idx: Current cluster index.
:type current_work_linear_idx: Int32
:param cta_id_in_cluster: ID of the CTA within its cluster.
:type cta_id_in_cluster: cute.Coord
:param num_tiles_executed: Counter for executed tiles.
:type num_tiles_executed: Int32
"""
self.params = params
self.num_persistent_clusters = num_persistent_clusters
self._current_work_linear_idx = current_work_linear_idx
self.cta_id_in_cluster = cta_id_in_cluster
self._num_tiles_executed = num_tiles_executed
def __extract_mlir_values__(self) -> list[ir.Value]:
values = extract_mlir_values(self.num_persistent_clusters)
values.extend(extract_mlir_values(self._current_work_linear_idx))
values.extend(extract_mlir_values(self.cta_id_in_cluster))
values.extend(extract_mlir_values(self._num_tiles_executed))
return values
def __new_from_mlir_values__(
self, values: list[ir.Value]
) -> "StaticPersistentTileScheduler":
assert len(values) == 6
new_num_persistent_clusters = new_from_mlir_values(
self.num_persistent_clusters, [values[0]]
)
new_current_work_linear_idx = new_from_mlir_values(
self._current_work_linear_idx, [values[1]]
)
new_cta_id_in_cluster = new_from_mlir_values(
self.cta_id_in_cluster, values[2:5]
)
new_num_tiles_executed = new_from_mlir_values(
self._num_tiles_executed, [values[5]]
)
return StaticPersistentTileScheduler(
self.params,
new_num_persistent_clusters,
new_current_work_linear_idx,
new_cta_id_in_cluster,
new_num_tiles_executed,
)
@staticmethod
@dsl_user_op
def create(
params: PersistentTileSchedulerParams,
block_idx: Tuple[Integer, Integer, Integer],
grid_dim: Tuple[Integer, Integer, Integer],
*,
loc=None,
ip=None,
):
"""Initialize the static persistent tile scheduler.
:param params: Parameters for the persistent
tile scheduler.
:type params: PersistentTileSchedulerParams
:param block_idx: The 3d block index in the format (bidx, bidy, bidz).
:type block_idx: Tuple[Integer, Integer, Integer]
:param grid_dim: The 3d grid dimensions for kernel launch.
:type grid_dim: Tuple[Integer, Integer, Integer]
:return: A StaticPersistentTileScheduler object.
:rtype: StaticPersistentTileScheduler
"""
# Calculate the number of persistent clusters by dividing the total grid size
# by the number of CTAs per cluster
num_persistent_clusters = cute.size(grid_dim, loc=loc, ip=ip) // cute.size(
params.cluster_shape_mn, loc=loc, ip=ip
)
bidx, bidy, bidz = block_idx
# Initialize workload index equals to the cluster index in the grid
current_work_linear_idx = Int32(bidz)
# CTA id in the cluster
cta_id_in_cluster = (
Int32(bidx % params.cluster_shape_mn[0]),
Int32(bidy % params.cluster_shape_mn[1]),
Int32(0),
)
# Initialize number of tiles executed to zero
num_tiles_executed = Int32(0)
return StaticPersistentTileScheduler(
params,
num_persistent_clusters,
current_work_linear_idx,
cta_id_in_cluster,
num_tiles_executed,
)
# called by host
@staticmethod
def get_grid_shape(
params: PersistentTileSchedulerParams,
max_active_clusters: Int32,
*,
loc=None,
ip=None,
) -> Tuple[Integer, Integer, Integer]:
"""Calculates the grid shape to be launched on GPU using problem shape,
threadblock shape, and active cluster size.
:param params: Parameters for grid shape calculation.
:type params: PersistentTileSchedulerParams
:param max_active_clusters: Maximum active clusters allowed.
:type max_active_clusters: Int32
:return: The calculated 3d grid shape.
:rtype: Tuple[Integer, Integer, Integer]
"""
return params.get_grid_shape(max_active_clusters, loc=loc, ip=ip)
# private method
def _get_current_work_for_linear_idx(
self, current_work_linear_idx: Int32, *, loc=None, ip=None
) -> WorkTileInfo:
"""Compute current tile coord given current_work_linear_idx and cta_id_in_cluster.
:param current_work_linear_idx: The linear index of the current work.
:type current_work_linear_idx: Int32
:return: An object containing information about the current tile coordinates
and validity status.
:rtype: WorkTileInfo
"""
is_valid = current_work_linear_idx < cute.size(
self.params.problem_layout_ncluster_mnl, loc=loc, ip=ip
)
if self.params.swizzle_size == 1:
cur_cluster_coord = self.params.problem_layout_ncluster_mnl.get_hier_coord(
current_work_linear_idx, loc=loc, ip=ip
)
else:
cur_cluster_coord = self.params.problem_layout_ncluster_mnl.get_flat_coord(
current_work_linear_idx, loc=loc, ip=ip
)
# cur_tile_coord is a tuple of i32 values
cur_tile_coord = tuple(
Int32(x) * Int32(z) + Int32(y)
for x, y, z in zip(
cur_cluster_coord,
self.cta_id_in_cluster,
(*self.params.cluster_shape_mn, Int32(1)),
)
)
return WorkTileInfo(cur_tile_coord, is_valid)
@dsl_user_op
def get_current_work(self, *, loc=None, ip=None) -> WorkTileInfo:
return self._get_current_work_for_linear_idx(
self._current_work_linear_idx, loc=loc, ip=ip
)
@dsl_user_op
def initial_work_tile_info(self, *, loc=None, ip=None) -> WorkTileInfo:
return self.get_current_work(loc=loc, ip=ip)
@dsl_user_op
def advance_to_next_work(self, *, advance_count: int = 1, loc=None, ip=None):
self._current_work_linear_idx += Int32(advance_count) * Int32(
self.num_persistent_clusters
)
self._num_tiles_executed += Int32(1)
@property
def num_tiles_executed(self) -> Int32:
return self._num_tiles_executed
@property
def k_split_idx(self) -> Int32:
"""Returns the k-split index for this CTA (cta_id_in_cluster[0] when using cluster_shape_kn)."""
return self.cta_id_in_cluster[0]
# Type alias for scheduler params - either SimpleTileSchedulerParams or PersistentTileSchedulerParams
TileSchedulerParams = Union[SimpleTileSchedulerParams, PersistentTileSchedulerParams]
class KernelArgs:
"""A class to bundle all kernel arguments and implement the DynamicExpression protocol.
This class wraps all the arguments passed to the GEMM kernel, making it easier to
manage and pass around as a single object. It implements the DynamicExpression
protocol for JIT function argument generation.
All fields are handled via the DynamicExpression protocol (__extract_mlir_values__
and __new_from_mlir_values__).
Dynamic fields (extracted/created from MLIR values):
:ivar tiled_mma_64: Optional tiled MMA operation for N=64 tile (None when N_TILE != 64).
:ivar tiled_mma_128: The tiled MMA operation for N=128 tile (always used for scale factor B).
:ivar tma_atom_a: TMA copy atom for tensor A.
:ivar mA_mkl: Tensor A in (M, K, L) layout.
:ivar tma_atom_b: TMA copy atom for tensor B.
:ivar mB_nkl: Tensor B in (N, K, L) layout.
:ivar tma_atom_sfa: TMA copy atom for scale factor A.
:ivar mSFA_mkl: Scale factor tensor for A in (M, K, L) layout.
:ivar tma_atom_sfb: TMA copy atom for scale factor B.
:ivar mSFB_nkl: Scale factor tensor for B in (N, K, L) layout.
:ivar tma_atom_c: Optional TMA copy atom for tensor C.
:ivar mC_mnl: Tensor C in (M, N, L) layout.
:ivar cluster_layout_vmnk: Cluster layout in (V, M, N, K) dimensions.
:ivar cluster_layout_sfb_vmnk: Cluster layout for SFB in (V, M, N, K) dimensions.
:ivar cluster_layout_red_vmnk: Cluster layout for reduction in (V, M, N, K) dimensions.
:ivar a_smem_layout_staged: Staged shared memory layout for A.
:ivar b_smem_layout_staged: Staged shared memory layout for B.
:ivar sfa_smem_layout_staged: Staged shared memory layout for scale factor A.
:ivar sfb_smem_layout_staged: Staged shared memory layout for scale factor B.
:ivar c_smem_layout_staged: Optional staged shared memory layout for C.
:ivar epi_tile: Epilogue tile configuration.
:ivar tile_sched_params: Tile scheduler parameters.
"""
def __init__(
self,
tiled_mma_64: Optional[cute.TiledMma],
tiled_mma_128: 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: Optional[cute.CopyAtom],
mC_mnl: cute.Tensor,
cluster_layout_vmnk: cute.Layout,
cluster_layout_sfb_vmnk: cute.Layout,
cluster_layout_red_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: Optional[Union[cute.Layout, cute.ComposedLayout]],
epi_tile: cute.Tile,
tile_sched_params: TileSchedulerParams,
):
self.tiled_mma_64 = tiled_mma_64
self.tiled_mma_128 = tiled_mma_128
self.tma_atom_a = tma_atom_a
self.mA_mkl = mA_mkl
self.tma_atom_b = tma_atom_b
self.mB_nkl = mB_nkl
self.tma_atom_sfa = tma_atom_sfa
self.mSFA_mkl = mSFA_mkl
self.tma_atom_sfb = tma_atom_sfb
self.mSFB_nkl = mSFB_nkl
self.tma_atom_c = tma_atom_c
self.mC_mnl = mC_mnl
self.cluster_layout_vmnk = cluster_layout_vmnk
self.cluster_layout_sfb_vmnk = cluster_layout_sfb_vmnk
self.cluster_layout_red_vmnk = cluster_layout_red_vmnk
self.a_smem_layout_staged = a_smem_layout_staged
self.b_smem_layout_staged = b_smem_layout_staged
self.sfa_smem_layout_staged = sfa_smem_layout_staged
self.sfb_smem_layout_staged = sfb_smem_layout_staged
self.c_smem_layout_staged = c_smem_layout_staged
self.epi_tile = epi_tile
self.tile_sched_params = tile_sched_params
def __extract_mlir_values__(self) -> list[ir.Value]:
"""Extract MLIR values from all dynamic arguments.
:return: A flattened list of MLIR values from all dynamic arguments.
:rtype: list[ir.Value]
"""
values = []
# Extract values from TiledMma (dynamic arguments)
if self.tiled_mma_64 is not None:
values.extend(extract_mlir_values(self.tiled_mma_64))
values.extend(extract_mlir_values(self.tiled_mma_128))
# Extract values from CopyAtoms (dynamic arguments)
values.extend(extract_mlir_values(self.tma_atom_a))
values.extend(extract_mlir_values(self.tma_atom_b))
values.extend(extract_mlir_values(self.tma_atom_sfa))
values.extend(extract_mlir_values(self.tma_atom_sfb))
if self.tma_atom_c is not None:
values.extend(extract_mlir_values(self.tma_atom_c))
# Extract values from tensors (dynamic arguments)
values.extend(extract_mlir_values(self.mA_mkl))
values.extend(extract_mlir_values(self.mB_nkl))
values.extend(extract_mlir_values(self.mSFA_mkl))
values.extend(extract_mlir_values(self.mSFB_nkl))
values.extend(extract_mlir_values(self.mC_mnl))
# Extract values from cluster layouts
values.extend(extract_mlir_values(self.cluster_layout_vmnk))
values.extend(extract_mlir_values(self.cluster_layout_sfb_vmnk))
values.extend(extract_mlir_values(self.cluster_layout_red_vmnk))
# Extract values from smem layouts
values.extend(extract_mlir_values(self.a_smem_layout_staged))
values.extend(extract_mlir_values(self.b_smem_layout_staged))
values.extend(extract_mlir_values(self.sfa_smem_layout_staged))
values.extend(extract_mlir_values(self.sfb_smem_layout_staged))
if self.c_smem_layout_staged is not None:
values.extend(extract_mlir_values(self.c_smem_layout_staged))
# Extract values from epi_tile and tile_sched_params
values.extend(extract_mlir_values(self.epi_tile))
values.extend(extract_mlir_values(self.tile_sched_params))
return values
def __new_from_mlir_values__(self, values: list[ir.Value]) -> "KernelArgs":
"""Create a new KernelArgs instance from MLIR values.
:param values: List of MLIR values to reconstruct dynamic arguments from.
:type values: list[ir.Value]
:return: A new KernelArgs instance with reconstructed dynamic arguments.
:rtype: KernelArgs
"""
idx = 0
# Reconstruct TiledMma objects
new_tiled_mma_64 = None
if self.tiled_mma_64 is not None:
tiled_mma_64_values = values[
idx : idx + len(extract_mlir_values(self.tiled_mma_64))
]
idx += len(tiled_mma_64_values)
new_tiled_mma_64 = new_from_mlir_values(
self.tiled_mma_64, tiled_mma_64_values
)
tiled_mma_128_values = values[
idx : idx + len(extract_mlir_values(self.tiled_mma_128))
]
idx += len(tiled_mma_128_values)
new_tiled_mma_128 = new_from_mlir_values(
self.tiled_mma_128, tiled_mma_128_values
)
# Reconstruct CopyAtom objects
tma_atom_a_values = values[
idx : idx + len(extract_mlir_values(self.tma_atom_a))
]
idx += len(tma_atom_a_values)
new_tma_atom_a = new_from_mlir_values(self.tma_atom_a, tma_atom_a_values)
tma_atom_b_values = values[
idx : idx + len(extract_mlir_values(self.tma_atom_b))
]
idx += len(tma_atom_b_values)
new_tma_atom_b = new_from_mlir_values(self.tma_atom_b, tma_atom_b_values)
tma_atom_sfa_values = values[
idx : idx + len(extract_mlir_values(self.tma_atom_sfa))
]
idx += len(tma_atom_sfa_values)
new_tma_atom_sfa = new_from_mlir_values(self.tma_atom_sfa, tma_atom_sfa_values)
tma_atom_sfb_values = values[
idx : idx + len(extract_mlir_values(self.tma_atom_sfb))
]
idx += len(tma_atom_sfb_values)
new_tma_atom_sfb = new_from_mlir_values(self.tma_atom_sfb, tma_atom_sfb_values)
new_tma_atom_c = None
if self.tma_atom_c is not None:
tma_atom_c_values = values[
idx : idx + len(extract_mlir_values(self.tma_atom_c))
]
idx += len(tma_atom_c_values)
new_tma_atom_c = new_from_mlir_values(self.tma_atom_c, tma_atom_c_values)
# Reconstruct Tensor objects
mA_values = values[idx : idx + len(extract_mlir_values(self.mA_mkl))]
idx += len(mA_values)
new_mA_mkl = new_from_mlir_values(self.mA_mkl, mA_values)
mB_values = values[idx : idx + len(extract_mlir_values(self.mB_nkl))]
idx += len(mB_values)
new_mB_nkl = new_from_mlir_values(self.mB_nkl, mB_values)
mSFA_values = values[idx : idx + len(extract_mlir_values(self.mSFA_mkl))]
idx += len(mSFA_values)
new_mSFA_mkl = new_from_mlir_values(self.mSFA_mkl, mSFA_values)
mSFB_values = values[idx : idx + len(extract_mlir_values(self.mSFB_nkl))]
idx += len(mSFB_values)
new_mSFB_nkl = new_from_mlir_values(self.mSFB_nkl, mSFB_values)
mC_values = values[idx : idx + len(extract_mlir_values(self.mC_mnl))]
idx += len(mC_values)
new_mC_mnl = new_from_mlir_values(self.mC_mnl, mC_values)
# Reconstruct cluster layouts
cluster_layout_vmnk_values = values[
idx : idx + len(extract_mlir_values(self.cluster_layout_vmnk))
]
idx += len(cluster_layout_vmnk_values)
new_cluster_layout_vmnk = new_from_mlir_values(
self.cluster_layout_vmnk, cluster_layout_vmnk_values
)
cluster_layout_sfb_vmnk_values = values[
idx : idx + len(extract_mlir_values(self.cluster_layout_sfb_vmnk))
]
idx += len(cluster_layout_sfb_vmnk_values)
new_cluster_layout_sfb_vmnk = new_from_mlir_values(
self.cluster_layout_sfb_vmnk, cluster_layout_sfb_vmnk_values
)
cluster_layout_red_vmnk_values = values[
idx : idx + len(extract_mlir_values(self.cluster_layout_red_vmnk))
]
idx += len(cluster_layout_red_vmnk_values)
new_cluster_layout_red_vmnk = new_from_mlir_values(
self.cluster_layout_red_vmnk, cluster_layout_red_vmnk_values
)
# Reconstruct smem layouts
a_smem_layout_staged_values = values[
idx : idx + len(extract_mlir_values(self.a_smem_layout_staged))
]
idx += len(a_smem_layout_staged_values)
new_a_smem_layout_staged = new_from_mlir_values(
self.a_smem_layout_staged, a_smem_layout_staged_values
)
b_smem_layout_staged_values = values[
idx : idx + len(extract_mlir_values(self.b_smem_layout_staged))
]
idx += len(b_smem_layout_staged_values)
new_b_smem_layout_staged = new_from_mlir_values(
self.b_smem_layout_staged, b_smem_layout_staged_values
)
sfa_smem_layout_staged_values = values[
idx : idx + len(extract_mlir_values(self.sfa_smem_layout_staged))
]
idx += len(sfa_smem_layout_staged_values)
new_sfa_smem_layout_staged = new_from_mlir_values(
self.sfa_smem_layout_staged, sfa_smem_layout_staged_values
)
sfb_smem_layout_staged_values = values[
idx : idx + len(extract_mlir_values(self.sfb_smem_layout_staged))
]
idx += len(sfb_smem_layout_staged_values)
new_sfb_smem_layout_staged = new_from_mlir_values(
self.sfb_smem_layout_staged, sfb_smem_layout_staged_values
)
new_c_smem_layout_staged = None
if self.c_smem_layout_staged is not None:
c_smem_layout_staged_values = values[
idx : idx + len(extract_mlir_values(self.c_smem_layout_staged))
]
idx += len(c_smem_layout_staged_values)
new_c_smem_layout_staged = new_from_mlir_values(
self.c_smem_layout_staged, c_smem_layout_staged_values
)
# Reconstruct epi_tile and tile_sched_params
epi_tile_values = values[idx : idx + len(extract_mlir_values(self.epi_tile))]
idx += len(epi_tile_values)
new_epi_tile = new_from_mlir_values(self.epi_tile, epi_tile_values)
tile_sched_params_values = values[
idx : idx + len(extract_mlir_values(self.tile_sched_params))
]
idx += len(tile_sched_params_values)
new_tile_sched_params = new_from_mlir_values(
self.tile_sched_params, tile_sched_params_values
)
return KernelArgs(
tiled_mma_64=new_tiled_mma_64,
tiled_mma_128=new_tiled_mma_128,
tma_atom_a=new_tma_atom_a,
mA_mkl=new_mA_mkl,
tma_atom_b=new_tma_atom_b,
mB_nkl=new_mB_nkl,
tma_atom_sfa=new_tma_atom_sfa,
mSFA_mkl=new_mSFA_mkl,
tma_atom_sfb=new_tma_atom_sfb,
mSFB_nkl=new_mSFB_nkl,
tma_atom_c=new_tma_atom_c,
mC_mnl=new_mC_mnl,
cluster_layout_vmnk=new_cluster_layout_vmnk,
cluster_layout_sfb_vmnk=new_cluster_layout_sfb_vmnk,
cluster_layout_red_vmnk=new_cluster_layout_red_vmnk,
a_smem_layout_staged=new_a_smem_layout_staged,
b_smem_layout_staged=new_b_smem_layout_staged,
sfa_smem_layout_staged=new_sfa_smem_layout_staged,
sfb_smem_layout_staged=new_sfb_smem_layout_staged,
c_smem_layout_staged=new_c_smem_layout_staged,
epi_tile=new_epi_tile,
tile_sched_params=new_tile_sched_params,
)
def create_tile_scheduler(
tile_sched_params: TileSchedulerParams,
block_idx: Tuple[Integer, Integer, Integer],
grid_dim: Tuple[Integer, Integer, Integer],
) -> TileScheduler:
"""Factory function to create the appropriate tile scheduler based on USE_SIMPLE_SCHEDULER flag."""
if cutlass.const_expr(USE_SIMPLE_SCHEDULER):
return SimpleTileScheduler.create(tile_sched_params, block_idx)
else:
return StaticPersistentTileScheduler.create(
tile_sched_params, block_idx, grid_dim
)
@dataclass(frozen=True)
class PipelineKSplitTmaUmma(pipeline.PipelineTmaUmma):
"""
PipelineTmaUmma is used for TMA producers and UMMA consumers (e.g. Blackwell mainloops).
"""
@staticmethod
def create(
*,
num_stages: int,
producer_group: CooperativeGroup,
consumer_group: CooperativeGroup,
tx_count: int,
barrier_storage: cute.Pointer = None,
cta_layout_vmnk: Optional[cute.Layout] = None,
mcast_mode_mn: tuple[int, int] = (1, 1),
defer_sync: bool = False,
):
"""Creates and initializes a new PipelineTmaUmma instance.
:param num_stages: Number of buffer stages for this pipeline
:type num_stages: int
:param producer_group: CooperativeGroup for the producer agent
:type producer_group: CooperativeGroup
:param consumer_group: CooperativeGroup for the consumer agent
:type consumer_group: CooperativeGroup
:param tx_count: Number of bytes expected to be written to the transaction barrier for one stage
:type tx_count: int
:param barrier_storage: Pointer to the shared memory address for this pipeline's mbarriers
:type barrier_storage: cute.Pointer, optional
:param cta_layout_vmnk: Layout of the cluster shape
:type cta_layout_vmnk: cute.Layout, optional
:param mcast_mode_mn: Tuple specifying multicast modes for m and n dimensions (each 0 or 1)
:type mcast_mode_mn: tuple[int, int], optional
:raises ValueError: If barrier_storage is not a cute.Pointer instance
:return: A new PipelineTmaUmma instance configured with the provided parameters
:rtype: PipelineTmaUmma
"""
if not isinstance(barrier_storage, cute.Pointer):
raise ValueError(
f"Expected barrier_storage to be a cute.Pointer, but got {type(barrier_storage)}"
)
producer_type = PipelineOp.TmaLoad
consumer_type = PipelineOp.TCGen05Mma
producer = (producer_type, producer_group)
consumer = (consumer_type, consumer_group)
sync_object_full = PipelineAsync._make_sync_object(
barrier_storage.align(min_align=8), num_stages, producer, tx_count
)
sync_object_empty = PipelineAsync._make_sync_object(
barrier_storage.align(min_align=8) + num_stages, num_stages, consumer
)
is_leader_cta = True
producer_mask = pipeline.PipelineTmaUmma._compute_mcast_arrival_mask(
cta_layout_vmnk, mcast_mode_mn
)
consumer_mask = producer_mask
return pipeline.PipelineTmaUmma(
sync_object_full,
sync_object_empty,
num_stages,
producer_mask,
consumer_mask,
is_leader_cta,
cute.nvgpu.tcgen05.CtaGroup.ONE,
)
# borrowed from official flash attention repo
@dsl_user_op
def set_block_rank(
smem_ptr: cute.Pointer, peer_cta_rank_in_cluster: Int32, *, loc=None, ip=None
) -> Int32:
"""Map the given smem pointer to the address at another CTA rank in the cluster."""
smem_ptr_i32 = smem_ptr.toint(loc=loc, ip=ip).ir_value()
return Int32(
llvm.inline_asm(
T.i32(),
[smem_ptr_i32, peer_cta_rank_in_cluster.ir_value()],
"mapa.shared::cluster.u32 $0, $1, $2;",
"=r,r,r",
has_side_effects=False,
is_align_stack=False,
asm_dialect=llvm.AsmDialect.AD_ATT,
)
)
@dsl_user_op
def store_shared_remote_fp32x4(
a: Float32,
b: Float32,
c: Float32,
d: Float32,
smem_ptr: cute.Pointer,
mbar_ptr: cute.Pointer,
peer_cta_rank_in_cluster: Int32,
*,
loc=None,
ip=None,
) -> None:
remote_smem_ptr_i32 = set_block_rank(
smem_ptr, peer_cta_rank_in_cluster, loc=loc, ip=ip
).ir_value()
remote_mbar_ptr_i32 = set_block_rank(
mbar_ptr, peer_cta_rank_in_cluster, loc=loc, ip=ip
).ir_value()
llvm.inline_asm(
None,
[
remote_smem_ptr_i32,
remote_mbar_ptr_i32,
Float32(a).ir_value(loc=loc, ip=ip),
Float32(b).ir_value(loc=loc, ip=ip),
Float32(c).ir_value(loc=loc, ip=ip),
Float32(d).ir_value(loc=loc, ip=ip),
],
"{\n\t"
".reg .v4 .f32 abcd;\n\t"
"mov.f32 abcd.x, $2;\n\t"
"mov.f32 abcd.y, $3;\n\t"
"mov.f32 abcd.z, $4;\n\t"
"mov.f32 abcd.w, $5;\n\t"
"st.async.shared::cluster.mbarrier::complete_tx::bytes.v4.f32 [$0], abcd, [$1];\n\t"
"}\n",
"r,r,f,f,f,f",
has_side_effects=True,
is_align_stack=False,
asm_dialect=llvm.AsmDialect.AD_ATT,
)
@dsl_user_op
def cp_async_bulk_remote(
dst_smem_ptr: cute.Pointer,
src_smem_ptr: cute.Pointer,
mbar_ptr: cute.Pointer,
cp_bytes: cutlass.Int32,
peer_cta_rank_in_cluster: Int32,
*,
loc=None,
ip=None,
) -> None:
src_smem_ptr_i32 = src_smem_ptr.toint(loc=loc, ip=ip).ir_value()
remote_smem_ptr_i32 = set_block_rank(
dst_smem_ptr, peer_cta_rank_in_cluster, loc=loc, ip=ip
).ir_value()
remote_mbar_ptr_i32 = set_block_rank(
mbar_ptr, peer_cta_rank_in_cluster, loc=loc, ip=ip
).ir_value()
llvm.inline_asm(
None,
[
remote_smem_ptr_i32,
src_smem_ptr_i32,
cute.Int32(cp_bytes).ir_value(loc=loc, ip=ip),
remote_mbar_ptr_i32,
],
"cp.async.bulk.shared::cluster.shared::cta.mbarrier::complete_tx::bytes [$0], [$1], $2, [$3];",
"r,r,r,r",
has_side_effects=True,
is_align_stack=False,
asm_dialect=llvm.AsmDialect.AD_ATT,
)
@dsl_user_op
def st_global_na_8xf32(
gmem_ptr: cute.Pointer,
vals: cute.Tensor,
*,
loc=None,
ip=None,
) -> None:
gmem_ptr_int = gmem_ptr.toint(loc=loc, ip=ip).ir_value(loc=loc, ip=ip)
v0, v1, v2, v3, v4, v5, v6, v7 = (
vals[0].ir_value(loc=loc, ip=ip),
vals[1].ir_value(loc=loc, ip=ip),
vals[2].ir_value(loc=loc, ip=ip),
vals[3].ir_value(loc=loc, ip=ip),
vals[4].ir_value(loc=loc, ip=ip),
vals[5].ir_value(loc=loc, ip=ip),
vals[6].ir_value(loc=loc, ip=ip),
vals[7].ir_value(loc=loc, ip=ip),
)
llvm.inline_asm(
T.i32(),
[gmem_ptr_int, v0, v1, v2, v3, v4, v5, v6, v7],
"st.global.L1::no_allocate.v8.f32 [$0], {$1, $2, $3, $4, $5, $6, $7, $8};",
"l,r,r,r,r,r,r,r,r",
has_side_effects=True,
is_align_stack=False,
asm_dialect=llvm.AsmDialect.AD_ATT,
loc=loc,
ip=ip,
)
@cute.jit
def st_global_na_8xf32_tensor(src: cute.Tensor, dst: cute.Tensor):
assert cute.size(src.shape) == cute.size(dst.shape)
def flatten(tensor: cute.Tensor) -> cute.Tensor:
flat = cute.recast_tensor(
cute.coalesce(tensor, target_profile=(0)), cute.Float32
)
return cute.flat_divide(flat, (8,))
src_f32 = flatten(src)
dst_f32 = flatten(dst)
for i in range(cute.size(src_f32, mode=[1])):
st_global_na_8xf32(dst_f32[(None, i)].iterator, src_f32[(None, i)])
class FP4GEMMKernel:
def __init__(
self,
K: int,
mma_tiler: tuple[int, int, int],
):
self._set_constants(mma_tiler)
self.ksplits, self.ksplits_tile_cnt = self._get_k_splits(K)
self.use_red_warps = USE_RED_WARPS and self.ksplits > 1
self.dealloc_warps = None
# Set specialized warp ids
# When USE_RED_WARPS is enabled, add dedicated reduction warps (0-3)
# and increment all other warp ids by 4
if cutlass.const_expr(self.use_red_warps):
self.red_warp_id = (0, 1, 2, 3)
self.epilog_warp_id = (4, 5, 6, 7)
self.mma_warp_id = 8
self.tma_warp_id = 9
self.dealloc_warps = self.red_warp_id + self.epilog_warp_id
else:
self.red_warp_id = () # No dedicated reduction warps
self.epilog_warp_id = (0, 1, 2, 3)
self.mma_warp_id = 4
self.tma_warp_id = 5
self.dealloc_warps = self.epilog_warp_id
self.threads_per_cta = self.threads_per_warp * len(
(
*self.red_warp_id,
self.mma_warp_id,
self.tma_warp_id,
*self.epilog_warp_id,
)
)
self.tmem_alloc_warp = self.epilog_warp_id[2]
if cutlass.const_expr(self.use_red_warps):
self.tmem_alloc_warp = self.red_warp_id[2]
# Set barrier id for epilogue sync and tmem ptr sync
self.epilog_sync_barrier = pipeline.NamedBarrier(
barrier_id=1,
num_threads=self.threads_per_warp * len(self.dealloc_warps),
# * len(self.epilog_warp_id),
)
self.tmem_alloc_barrier = pipeline.NamedBarrier(
barrier_id=2,
num_threads=self.threads_per_warp
* len((self.mma_warp_id, *self.red_warp_id, *self.epilog_warp_id)),
)
def _set_constants(self, mma_tiler: tuple[int, int, int]):
self.acc_dtype = cutlass.Float32
self.ab_dtype = ab_dtype
self.sf_vec_size = sf_vec_size
self.sf_dtype = sf_dtype
self.c_dtype = c_dtype
# only used for reductions.
# TODO: refactor, it's currently confusing
self.d_dtype = c_dtype
self.cluster_shape_mn = cluster_shape_mn
self.mma_tiler = mma_tiler
self.mma_tiler_sfb = mma_tiler_sfb_mnk
self.major_mode = tcgen05.OperandMajorMode.K
self.cd_majorness = utils.LayoutEnum.ROW_MAJOR
self.cta_group = tcgen05.CtaGroup.ONE
self.threads_per_warp = 32
self.occupancy = 1
self.buffer_align_bytes = 1024
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
@cute.jit
def is_work_tile_valid(self, work_tile: WorkTileInfo) -> Boolean:
"""Check if the work tile is valid.
:param work_tile: The work tile to check.
:return: True if the work tile is valid, False otherwise.
"""
# return work_tile.tile_idx[1] == 0
# return work_tile.tile_idx[1] < 10
# # return work_tile.tile_idx[N_IDX] == cute.arch.block_idx()[1]
return work_tile.is_valid_tile
@cute.jit
def __call__(
self,
a_tensor: cute.Tensor,
b_tensor: cute.Tensor,
sfa_tensor: cute.Tensor,
sfb_tensor: cute.Tensor,
c_tensor: cute.Tensor,
max_active_clusters: cutlass.Constexpr,
):
hdebug("\n===================== Input Tensors =====================")
hdebug("a_tensor layout: {}", a_tensor.layout)
hdebug("b_tensor layout: {}", b_tensor.layout)
hdebug("sfa_tensor layout: {}", sfa_tensor.layout)
hdebug("sfb_tensor layout: {}", sfb_tensor.layout)
hdebug("c_tensor layout: {}", c_tensor.layout)
hdebug("==========================================================\n\n")
# Create tiled_mma_128 (always needed for scale factor B)
tiled_mma_128 = sm100_utils.make_blockscaled_trivial_tiled_mma(
self.ab_dtype,
self.major_mode,
self.major_mode,
self.sf_dtype,
self.sf_vec_size,
self.cta_group,
self.mma_tiler_sfb[:2], # Always 128x128
)
# Create tiled_mma_64 only when N_TILE == 64
tiled_mma_64 = sm100_utils.make_blockscaled_trivial_tiled_mma(
self.ab_dtype,
self.major_mode,
self.major_mode,
self.sf_dtype,
self.sf_vec_size,
self.cta_group,
self.mma_tiler[:2],
)
tiled_mma = None
if cutlass.const_expr(self.mma_tiler[1] == 64):
tiled_mma = tiled_mma_64
else:
tiled_mma = tiled_mma_128
# tiled_mma_sfb always uses tiled_mma_128
tiled_mma_sfb = tiled_mma_128
self.use_tma_store = USE_TMA_STORE
assert cute.is_static(self.ksplits_tile_cnt)
self.cluster_shape_kn = (self.ksplits, self.cluster_shape_mn[1])
# When ksplits > 1, we need sC for reduction but don't use TMA store
# When ksplits == 1, both flags follow USE_TMA_STORE env var
if cutlass.const_expr(self.ksplits > 1):
self.use_tma_store = False
self.initialize_sC = True
else:
# ksplits == 1: both follow USE_TMA_STORE
self.initialize_sC = self.use_tma_store
# 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,),
)
self.cluster_layout_red_vmnk = cute.tiled_divide(
cute.make_layout((*self.cluster_shape_kn, 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 = 1
self.is_a_mcast = self.num_mcast_ctas_a > 1
# N_tiles should be based on mma_tiler[1], not SFB tiles (which are always 128)
# When N_TILE=64, there are 2x as many MMA tiles as SFB tiles
# b_tensor shape is (N, K, L), so N is at mode 0
N = cute.size(b_tensor, mode=[0])
N_tiles = N // self.mma_tiler[1]
# Compute epilogue subtile
self.epi_tile = sm100_utils.compute_epilogue_tile_shape(
self.mma_tiler,
False,
self.cd_majorness,
self.c_dtype,
)
if cutlass.const_expr(self.ksplits > 1):
self.epi_tile: cute.Tile = (
self.mma_tiler[0],
self.mma_tiler[1] // self.ksplits,
)
self.c_dtype = cute.Float32
self.epi_tile_n = cute.size(self.epi_tile[1])
# 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.ab_dtype,
self.ab_dtype,
self.epi_tile,
self.c_dtype,
self.cd_majorness,
self.sf_dtype,
self.sf_vec_size,
self.smem_capacity,
self.initialize_sC,
)
self.r2s_tiler = (16,)
self.r2s_tiles = 1
self.num_red_stages = 0
self.red_recv_bytes_per_tile = 0
self.red_send_bytes_per_tile = 0
if cutlass.const_expr(self.ksplits > 1):
self.r2s_tiles = self.epi_tile_n // self.r2s_tiler[0]
self.num_red_stages = self.r2s_tiles
self.red_send_bytes_per_tile = (
cute.size_in_bytes(
self.c_dtype,
cute.make_layout(self.epi_tile),
)
// self.r2s_tiles
)
self.red_recv_bytes_per_tile = self.red_send_bytes_per_tile * (
self.ksplits - 1
)
self.red_send_bytes_per_warp_tile = self.red_send_bytes_per_tile // len(
self.epilog_warp_id
)
self.red_recv_bytes_per_warp_tile = self.red_recv_bytes_per_tile // len(
self.epilog_warp_id
)
# 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.ab_dtype,
self.num_ab_stage,
)
self.b_smem_layout_staged = sm100_utils.make_smem_layout_b(
tiled_mma,
self.mma_tiler,
self.ab_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,
)
# Only create c_smem_layout_staged if initialize_sC is true
self.c_smem_layout_staged = None
if cutlass.const_expr(self.initialize_sC):
if cutlass.const_expr(self.ksplits > 1):
elems_per_warp_copy = 128
self.c_smem_layout_staged = cute.make_ordered_layout(
(
cute.size(self.epi_tile) // elems_per_warp_copy,
elems_per_warp_copy,
self.ksplits,
),
order=(1, 0, 2),
)
else:
self.c_smem_layout_staged = sm100_utils.make_smem_layout_epi(
self.c_dtype,
self.cd_majorness,
self.epi_tile,
self.num_c_stage,
)
hdebug("\n===================== SMEM Layouts (staged) =====================")
hdebug("a_smem_layout_staged: {}", self.a_smem_layout_staged)
hdebug("b_smem_layout_staged: {}", self.b_smem_layout_staged)
hdebug("sfa_smem_layout_staged: {}", self.sfa_smem_layout_staged)
hdebug("sfb_smem_layout_staged: {}", self.sfb_smem_layout_staged)
hdebug("c_smem_layout_staged: {}", self.c_smem_layout_staged)
hdebug("use_tma_store: {}", self.use_tma_store)
hdebug("initialize_sC: {}", self.initialize_sC)
hdebug("==================================================================\n\n")
# Compute number of TMEM columns for SFA/SFB/Accumulator
sf_atom_mn = 32
self.num_sfa_tmem_cols = (self.mma_tiler[0] // sf_atom_mn) * mma_inst_tile_k
self.num_sfb_tmem_cols = (self.mma_tiler_sfb[1] // sf_atom_mn) * mma_inst_tile_k
self.num_sf_tmem_cols = self.num_sfa_tmem_cols + self.num_sfb_tmem_cols
self.num_accumulator_tmem_cols = self.mma_tiler[1] * self.num_acc_stage
# Setup sfa/sfb tensor by filling A/B tensor to scale factor atom layout
# ((Atom_M, Rest_M),(Atom_K, Rest_K),RestL)
sfa_layout = blockscaled_utils.tile_atom_to_shape_SF(
a_tensor.shape, self.sf_vec_size
)
sfa_tensor = cute.make_tensor(sfa_tensor.iterator, sfa_layout)
# ((Atom_N, Rest_N),(Atom_K, Rest_K),RestL)
sfb_layout = blockscaled_utils.tile_atom_to_shape_SF(
b_tensor.shape, self.sf_vec_size
)
sfb_tensor = cute.make_tensor(sfb_tensor.iterator, sfb_layout)
atom_thr_size = cute.size(tiled_mma.thr_id.shape)
# Setup TMA load for A
a_op = sm100_utils.cluster_shape_to_tma_atom_A(
self.cluster_shape_mn, tiled_mma.thr_id
)
a_smem_layout = cute.slice_(self.a_smem_layout_staged, (None, None, None, 0))
tma_atom_a, tma_tensor_a = cute.nvgpu.make_tiled_tma_atom_A(
a_op,
a_tensor,
a_smem_layout,
self.mma_tiler,
tiled_mma,
self.cluster_layout_vmnk.shape,
)
# Setup TMA load for B
b_op = sm100_utils.cluster_shape_to_tma_atom_B(
self.cluster_shape_mn, tiled_mma.thr_id
)
b_smem_layout = cute.slice_(self.b_smem_layout_staged, (None, None, None, 0))
tma_atom_b, tma_tensor_b = cute.nvgpu.make_tiled_tma_atom_B(
b_op,
b_tensor,
b_smem_layout,
self.mma_tiler,
tiled_mma,
self.cluster_layout_vmnk.shape,
)
# Setup TMA load for SFA
sfa_op = sm100_utils.cluster_shape_to_tma_atom_A(
self.cluster_shape_mn, tiled_mma.thr_id
)
sfa_smem_layout = cute.slice_(
self.sfa_smem_layout_staged, (None, None, None, 0)
)
tma_atom_sfa, tma_tensor_sfa = cute.nvgpu.make_tiled_tma_atom_A(
sfa_op,
sfa_tensor,
sfa_smem_layout,
self.mma_tiler,
tiled_mma,
self.cluster_layout_vmnk.shape,
internal_type=cutlass.Int16,
)
# Setup TMA load for SFB
sfb_op = sm100_utils.cluster_shape_to_tma_atom_SFB(
self.cluster_shape_mn, tiled_mma.thr_id
)
sfb_smem_layout = cute.slice_(
self.sfb_smem_layout_staged, (None, None, None, 0)
)
tma_atom_sfb, tma_tensor_sfb = cute.nvgpu.make_tiled_tma_atom_B(
sfb_op,
sfb_tensor,
sfb_smem_layout,
self.mma_tiler_sfb,
tiled_mma_sfb,
self.cluster_layout_sfb_vmnk.shape,
internal_type=cutlass.Int16,
)
a_copy_size = cute.size_in_bytes(self.ab_dtype, a_smem_layout)
b_copy_size = cute.size_in_bytes(self.ab_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
hdebug("\n===================== TMA Atoms and Tensors =====================")
hdebug(f"\ntma_atom_a:\n{tma_atom_a}\n")
hdebug("tma_tensor_a layout: {}", tma_tensor_a.layout)
hdebug("a_copy_size: {}", a_copy_size)
hdebug(f"\ntma_atom_b:\n{tma_atom_b}\n")
hdebug("tma_tensor_b layout: {}", tma_tensor_b.layout)
hdebug("b_copy_size: {}", b_copy_size)
hdebug(f"\ntma_atom_sfa:\n{tma_atom_sfa}\n")
hdebug("tma_tensor_sfa layout: {}", tma_tensor_sfa.layout)
hdebug("sfa_copy_size: {}", sfa_copy_size)
hdebug(f"\ntma_atom_sfb:\n{tma_atom_sfb}\n")
hdebug("tma_tensor_sfb layout: {}", tma_tensor_sfb.layout)
hdebug("sfb_copy_size: {}", sfb_copy_size)
hdebug("num_tma_load_bytes: {}", self.num_tma_load_bytes)
hdebug("==================================================================\n\n")
# Setup TMA store for C
tma_atom_c = None
tma_tensor_c = None
if cutlass.const_expr(self.use_tma_store):
epi_smem_layout = cute.slice_(self.c_smem_layout_staged, (None, None, 0))
c_copy_op = cpasync.CopyBulkTensorTileS2GOp()
tma_atom_c, tma_tensor_c = cpasync.make_tiled_tma_atom(
c_copy_op,
c_tensor,
epi_smem_layout,
self.epi_tile,
)
hdebug("\n===================== TMA Store (C) =====================")
hdebug(f"\ntma_atom_c:\n{tma_atom_c}\n")
hdebug("tma_tensor_c layout: {}", tma_tensor_c.layout)
hdebug("N_tiles: {}", N_tiles)
hdebug("==========================================================\n\n")
# Compute grid size
self.tile_sched_params, grid = self._compute_grid(
N_tiles,
self.cluster_shape_kn,
max_active_clusters,
)
# Calculate max_tiles: maximum number of work tiles any CTA will process
# max_tiles = ceil(N_tiles / n_stride) where n_stride = grid[1]
n_stride = grid[1]
self.max_tiles = (N_tiles + n_stride - 1) // n_stride
hdebug("\n===================== Kernel Shared Storage =====================")
hdebug("num_ab_stage: {}", self.num_ab_stage)
hdebug("num_acc_stage: {}", self.num_acc_stage)
hdebug("num_red_stages: {}", self.num_red_stages)
hdebug("num_r2s_tiles: {}", self.r2s_tiles)
hdebug("red_recv_bytes_per_tile: {}", self.red_recv_bytes_per_tile)
hdebug("red_send_bytes_per_tile: {}", self.red_send_bytes_per_tile)
hdebug("==================================================================\n\n")
# Define shared storage for kernel
@cute.struct
class SharedStorage:
ab_full_mbar_ptr: cute.struct.MemRange[cutlass.Int64, self.num_ab_stage]
ab_empty_mbar_ptr: cute.struct.MemRange[cutlass.Int64, self.num_ab_stage]
acc_full_mbar_ptr: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage]
acc_empty_mbar_ptr: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage]
red_full_mbar_ptr: cute.struct.MemRange[cutlass.Int64, self.num_red_stages]
red_empty_mbar_ptr: cute.struct.MemRange[cutlass.Int64, self.num_red_stages]
tmem_dealloc_mbar_ptr: cutlass.Int64
tmem_holding_buf: cutlass.Int32
# (MMA, MMA_M, MMA_K, STAGE)
sA: cute.struct.Align[
cute.struct.MemRange[
self.ab_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.ab_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
# Create kernel args bundle
args = KernelArgs(
tiled_mma_64=tiled_mma_64,
tiled_mma_128=tiled_mma_128,
tma_atom_a=tma_atom_a,
mA_mkl=tma_tensor_a,
tma_atom_b=tma_atom_b,
mB_nkl=tma_tensor_b,
tma_atom_sfa=tma_atom_sfa,
mSFA_mkl=tma_tensor_sfa,
tma_atom_sfb=tma_atom_sfb,
mSFB_nkl=tma_tensor_sfb,
tma_atom_c=tma_atom_c,
mC_mnl=tma_tensor_c if self.use_tma_store else c_tensor,
cluster_layout_vmnk=self.cluster_layout_vmnk,
cluster_layout_sfb_vmnk=self.cluster_layout_sfb_vmnk,
cluster_layout_red_vmnk=self.cluster_layout_red_vmnk,
a_smem_layout_staged=self.a_smem_layout_staged,
b_smem_layout_staged=self.b_smem_layout_staged,
sfa_smem_layout_staged=self.sfa_smem_layout_staged,
sfb_smem_layout_staged=self.sfb_smem_layout_staged,
c_smem_layout_staged=self.c_smem_layout_staged,
epi_tile=self.epi_tile,
tile_sched_params=self.tile_sched_params,
)
# Launch the kernel synchronously
self.kernel(args).launch(
grid=grid,
block=[self.threads_per_cta, 1, 1],
cluster=(*self.cluster_shape_kn, 1),
min_blocks_per_mp=1,
)
# GPU device kernel wrapper that takes KernelArgs
@cute.kernel
def kernel(self, args: KernelArgs):
"""GPU device kernel wrapper that unpacks KernelArgs and calls _kernel.
:param args: Bundled kernel arguments
:type args: KernelArgs
"""
# Select tiled_mma based on N_TILE: use tiled_mma_64 if available, else tiled_mma_128
tiled_mma = args.tiled_mma_128
if cutlass.const_expr(self.mma_tiler[1] == 64):
tiled_mma = args.tiled_mma_64
# tiled_mma_sfb always uses tiled_mma_128
tiled_mma_sfb = args.tiled_mma_128
self._kernel(
tiled_mma,
tiled_mma_sfb,
args.tma_atom_a,
args.mA_mkl,
args.tma_atom_b,
args.mB_nkl,
args.tma_atom_sfa,
args.mSFA_mkl,
args.tma_atom_sfb,
args.mSFB_nkl,
args.tma_atom_c,
args.mC_mnl,
args.cluster_layout_vmnk,
args.cluster_layout_sfb_vmnk,
args.cluster_layout_red_vmnk,
args.a_smem_layout_staged,
args.b_smem_layout_staged,
args.sfa_smem_layout_staged,
args.sfb_smem_layout_staged,
args.c_smem_layout_staged,
args.epi_tile,
args.tile_sched_params,
)
# GPU device kernel
@cute.jit
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: Optional[cute.CopyAtom],
mC_mnl: cute.Tensor,
cluster_layout_vmnk: cute.Layout,
cluster_layout_sfb_vmnk: cute.Layout,
cluster_layout_red_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: Optional[Union[cute.Layout, cute.ComposedLayout]],
epi_tile: cute.Tile,
tile_sched_params: TileSchedulerParams,
):
# assert cute.is_static(tiled_mma), "tiled_mma must be static"
# assert cute.is_static(tiled_mma_sfb), "tiled_mma_sfb must be static"
# assert cute.is_static(tma_atom_a), "tma_atom_a must be static"
assert cute.is_static(mA_mkl.layout), "mA_mkl must be static"
# assert cute.is_static(tma_atom_b), "tma_atom_b must be static"
assert cute.is_static(mB_nkl.layout), "mB_nkl must be static"
# assert cute.is_static(tma_atom_sfa), "tma_atom_sfa must be static"
assert cute.is_static(mSFA_mkl.layout), "mSFA_mkl must be static"
# assert cute.is_static(tma_atom_sfb), "tma_atom_sfb must be static"
assert cute.is_static(mSFB_nkl.layout), "mSFB_nkl must be static"
# assert cute.is_static(tma_atom_c), "tma_atom_c must be static"
assert cute.is_static(mC_mnl.layout), "mC_mnl must be static"
assert cute.is_static(cluster_layout_vmnk), "cluster_layout_vmnk must be static"
assert cute.is_static(cluster_layout_sfb_vmnk), (
"cluster_layout_sfb_vmnk must be static"
)
assert cute.is_static(cluster_layout_red_vmnk), (
"cluster_layout_red_vmnk must be static"
)
assert cute.is_static(a_smem_layout_staged), (
"a_smem_layout_staged must be static"
)
assert cute.is_static(b_smem_layout_staged), (
"b_smem_layout_staged must be static"
)
assert cute.is_static(sfa_smem_layout_staged), (
"sfa_smem_layout_staged must be static"
)
assert cute.is_static(sfb_smem_layout_staged), (
"sfb_smem_layout_staged must be static"
)
assert cute.is_static(c_smem_layout_staged), (
"c_smem_layout_staged must be static"
)
assert cute.is_static(epi_tile), "epi_tile must be static"
assert (
cute.is_static(tile_sched_params.k_splits)
and cute.is_static(tile_sched_params.n_tiles)
and cute.is_static(tile_sched_params.n_stride)
), "tile_sched_params must be static"
"""
GPU device kernel performing the Persistent batched GEMM computation.
"""
warp_idx = cute.arch.warp_idx()
warp_idx = cute.arch.make_warp_uniform(warp_idx)
#
# Prefetch tma desc
#
if warp_idx == self.tma_warp_id:
cpasync.prefetch_descriptor(tma_atom_a)
cpasync.prefetch_descriptor(tma_atom_b)
cpasync.prefetch_descriptor(tma_atom_sfa)
cpasync.prefetch_descriptor(tma_atom_sfb)
if cutlass.const_expr(self.ksplits == 1 and self.use_tma_store):
cpasync.prefetch_descriptor(tma_atom_c)
# if cutlass.const_expr(self.ksplits == 1):
# if warp_idx == self.epilog_warp_id[3]:
# cpasync.prefetch_descriptor(tma_atom_c)
#
# Setup cta/thread coordinates
#
# Coords inside cluster
cta_rank_in_cluster = cute.arch.make_warp_uniform(
cute.arch.block_idx_in_cluster()
)
block_in_cluster_coord_vmnk = cluster_layout_red_vmnk.get_flat_coord(
cta_rank_in_cluster
)
# Coord inside cta
tidx, _, _ = cute.arch.thread_idx()
#
# Alloc and init: a+b full/empty, accumulator full/empty, tensor memory dealloc barrier
#
smem = utils.SmemAllocator()
storage = smem.allocate(self.shared_storage)
# Initialize mainloop ab_pipeline (barrier) and states
ab_pipeline_producer_group = pipeline.CooperativeGroup(pipeline.Agent.Thread)
num_tma_producer = self.num_mcast_ctas_a + self.num_mcast_ctas_b - 1
ab_pipeline_consumer_group = pipeline.CooperativeGroup(
pipeline.Agent.Thread, num_tma_producer
)
ab_pipeline = PipelineKSplitTmaUmma.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_red_vmnk,
mcast_mode_mn=(1, 0),
defer_sync=True,
)
# Initialize acc_pipeline (barrier) and states
acc_pipeline_producer_group = pipeline.CooperativeGroup(pipeline.Agent.Thread)
num_acc_consumer_threads = len((*self.epilog_warp_id, *self.red_warp_id))
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,
defer_sync=True,
)
# if is_debug_thr():
# cute.printf(
# "acc_pipline\n\tProducer mcast mask: 0x%x\n\tConsumer mcast mask: 0x%x\n\tcta_group: {}",
# acc_pipeline.producer_mask,
# acc_pipeline.consumer_mask,
# )
# print(acc_pipeline.cta_group)
# return
red_pipeline = None
if cutlass.const_expr(self.ksplits > 1):
red_pipeline_producer_group = pipeline.CooperativeGroup(
pipeline.Agent.Thread
)
red_cons_warps = len(self.epilog_warp_id)
if cutlass.const_expr(self.use_red_warps):
red_cons_warps = len(self.red_warp_id)
red_pipeline_consumer_group = pipeline.CooperativeGroup(
pipeline.Agent.Thread,
(self.ksplits - 1) * red_cons_warps,
)
red_pipeline = PipelineTmaAsyncNoSelfSignal.create(
num_stages=self.num_red_stages,
producer_group=red_pipeline_producer_group,
consumer_group=red_pipeline_consumer_group,
tx_count=self.red_recv_bytes_per_tile,
barrier_storage=storage.red_full_mbar_ptr.data_ptr(),
cta_layout_vmnk=cluster_layout_red_vmnk,
tidx=tidx,
# Multicast along N dimension (mode 2) so that CTAs with the same
# cluster N index communicate: ranks 0↔1 and ranks 2↔3 exchange data
# cluster_layout_red_vmnk has shape (v, ksplits, N, k) = (1, 2, 2, 1)
mcast_mode_mn=(0, 1),
defer_sync=True,
)
# Tensor memory dealloc barrier init
tmem = utils.TmemAllocator(
storage.tmem_holding_buf,
barrier_for_retrieve=self.tmem_alloc_barrier,
allocator_warp_id=self.tmem_alloc_warp,
)
# Cluster arrive after barrier init
pipeline_init_arrive(cluster_shape_mn=self.cluster_shape_kn, is_relaxed=True)
#
# Compute multicast mask for A/B/SFA/SFB buffer full
#
a_full_mcast_mask = None
sfa_full_mcast_mask = None
sfb_full_mcast_mask = None
if cutlass.const_expr(self.is_a_mcast):
a_full_mcast_mask = cpasync.create_tma_multicast_mask(
cluster_layout_red_vmnk, block_in_cluster_coord_vmnk, mcast_mode=2
)
sfa_full_mcast_mask = cpasync.create_tma_multicast_mask(
cluster_layout_red_vmnk, block_in_cluster_coord_vmnk, mcast_mode=2
)
#
# Setup smem tensor A/B/SFA/SFB/C
#
# (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)
# Allocate sC dynamically only when initialize_sC is true
sC = None
if cutlass.const_expr(self.initialize_sC):
if cutlass.const_expr(self.ksplits > 1):
sC = smem.allocate_tensor(
element_type=self.c_dtype,
layout=c_smem_layout_staged,
byte_alignment=self.buffer_align_bytes,
)
else:
# (EPI_TILE_M, EPI_TILE_N, STAGE)
sC = smem.allocate_tensor(
element_type=self.c_dtype,
layout=c_smem_layout_staged.outer,
byte_alignment=self.buffer_align_bytes,
swizzle=c_smem_layout_staged.inner,
)
debug("\n===================== SMEM Tensors =====================")
debug("sA layout: {}", sA.layout)
debug("sB layout: {}", sB.layout)
debug("sSFA layout: {}", sSFA.layout)
debug("sSFB layout: {}", sSFB.layout)
if cutlass.const_expr(self.initialize_sC):
debug("sC layout: {}", sC.layout)
debug("========================================================\n\n")
#
# Local_tile partition global tensors
#
# (bM, bK, RestM, RestK, RestL)
gA_mkl = cute.local_tile(
mA_mkl, cute.slice_(self.mma_tiler, (None, 0, None)), (None, None, None)
)
# (bN, bK, RestN, RestK, RestL)
gB_nkl = cute.local_tile(
mB_nkl, cute.slice_(self.mma_tiler, (0, None, None)), (None, None, None)
)
# (bM, bK, RestM, RestK, RestL)
gSFA_mkl = cute.local_tile(
mSFA_mkl, cute.slice_(self.mma_tiler, (None, 0, None)), (None, None, None)
)
# (bN, bK, RestN, RestK, RestL)
gSFB_nkl = cute.local_tile(
mSFB_nkl,
cute.slice_(self.mma_tiler_sfb, (0, None, None)),
(None, None, None),
)
# (bM, bN, RestM, RestN, RestL)
gC_mnl = cute.local_tile(
mC_mnl, cute.slice_(self.mma_tiler, (None, None, 0)), (None, None, None)
)
debug(
"\n===================== Global Tensors (local_tile) ====================="
)
debug("gA_mkl layout: {}", gA_mkl.layout)
debug("gB_nkl layout: {}", gB_nkl.layout)
debug("gSFA_mkl layout: {}", gSFA_mkl.layout)
debug("gSFB_nkl layout: {}", gSFB_nkl.layout)
debug("gC_mnl layout: {}", gC_mnl.layout)
debug(
"======================================================================\n\n"
)
#
# Partition global tensor for TiledMMA_A/B/C
#
thr_mma = tiled_mma.get_slice(0)
thr_mma_sfb = tiled_mma_sfb.get_slice(0)
# (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)
debug("\n===================== TiledMMA Partitions =====================")
debug("tCgA layout: {}", tCgA.layout)
debug("tCgB layout: {}", tCgB.layout)
debug("tCgSFA layout: {}", tCgSFA.layout)
debug("tCgSFB layout: {}", tCgSFB.layout)
debug("tCgC layout: {}", tCgC.layout)
debug(f"\ntiled_mma:\n{tiled_mma}\n")
debug(f"\ntiled_mma_sfb:\n{tiled_mma_sfb}\n")
debug("===============================================================\n\n")
#
# 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)
# Note: For B, there's no M clustering (cluster_shape_mn[0]=1), so all CTAs
# independently load their own B tiles. Use coord 0 since b_cta_layout has size 1.
tBsB, tBgB = cpasync.tma_partition(
tma_atom_b,
0,
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)
# Note: For SFB, there's no M clustering (cluster_shape_mn[0]=1), so all CTAs
# independently load their own SFB tiles. Use coord 0 since sfb_cta_layout has size 1.
tBsSFB, tBgSFB = cute.nvgpu.cpasync.tma_partition(
tma_atom_sfb,
0,
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)
debug("\n===================== TMA Partitions =====================")
debug("tAsA layout: {}", tAsA.layout)
debug("tAgA layout: {}", tAgA.layout)
debug("tBsB layout: {}", tBsB.layout)
debug("tBgB layout: {}", tBgB.layout)
debug("tAsSFA layout: {}", tAsSFA.layout)
debug("tAgSFA layout: {}", tAgSFA.layout)
debug("tBsSFB layout: {}", tBsSFB.layout)
debug("tBgSFB layout: {}", tBgSFB.layout)
debug("==========================================================\n\n")
#
# 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)
# not an actual tensor, just used for making layouts
tCtAcc_fake = tiled_mma.make_fragment_C(
cute.append(acc_shape, self.num_acc_stage)
)
debug("\n===================== MMA Fragments =====================")
debug("tCrA layout: {}", tCrA.layout)
debug("tCrB layout: {}", tCrB.layout)
debug("acc_shape: {}", acc_shape)
debug("tCtAcc_fake layout: {}", tCtAcc_fake.layout)
debug("========================================================\n\n")
#
# Cluster wait before tensor memory alloc
#
pipeline_init_wait(cluster_shape_mn=self.cluster_shape_kn)
#
# Specialized TMA load warp
#
if warp_idx == self.tma_warp_id:
self.load_warp(
tiled_mma,
tile_sched_params,
ab_pipeline,
tma_atom_a,
tma_atom_b,
tma_atom_sfa,
tma_atom_sfb,
tAgA,
tBgB,
tAgSFA,
tBgSFB,
tAsA,
tBsB,
tAsSFA,
tBsSFB,
a_full_mcast_mask,
sfa_full_mcast_mask,
sfb_full_mcast_mask,
)
#
# Specialized MMA warp
#
if warp_idx == self.mma_warp_id:
self.mma_warp(
tiled_mma,
tile_sched_params,
tmem,
tCtAcc_fake,
sfa_smem_layout_staged,
sfb_smem_layout_staged,
sSFA,
sSFB,
tCrA,
tCrB,
ab_pipeline,
acc_pipeline,
)
#
# Dedicated reduction warps
if cutlass.const_expr(self.use_red_warps):
if warp_idx <= self.red_warp_id[-1]:
self.red_warp(
warp_idx,
tidx,
tiled_mma,
tile_sched_params,
tmem,
tCtAcc_fake,
tCgC,
epi_tile,
sC,
red_pipeline,
acc_pipeline,
)
#
# Specialized epilogue warps
#
if cutlass.const_expr(self.use_red_warps):
if (
warp_idx <= self.epilog_warp_id[-1]
and warp_idx >= self.epilog_warp_id[0]
):
# Use send-only version when reduction is handled by separate red_warp
self.epi_warp_ksplit_send_only(
warp_idx,
tidx,
tiled_mma,
tile_sched_params,
tmem,
tCtAcc_fake,
tCgC,
epi_tile,
sC,
red_pipeline,
acc_pipeline,
)
elif warp_idx < self.mma_warp_id:
if cutlass.const_expr(self.ksplits == 1):
if cutlass.const_expr(self.use_tma_store):
self.epi_warp_tma_store(
warp_idx,
tidx,
tiled_mma,
tile_sched_params,
tmem,
tCtAcc_fake,
tCgC,
epi_tile,
sC,
tma_atom_c,
acc_pipeline,
)
else:
self.epi_warp_no_tma_store(
warp_idx,
tidx,
tiled_mma,
tile_sched_params,
tmem,
tCtAcc_fake,
tCgC,
epi_tile,
acc_pipeline,
)
else:
self.epi_warp_ksplit(
warp_idx,
tidx,
tiled_mma,
tile_sched_params,
tmem,
tCtAcc_fake,
tCgC,
epi_tile,
sC,
red_pipeline,
acc_pipeline,
)
@cute.jit
def load_warp(
self,
tiled_mma: cute.TiledMma,
tile_sched_params: TileSchedulerParams,
ab_pipeline: pipeline.PipelineTmaUmma,
tma_atom_a: cute.CopyAtom,
tma_atom_b: cute.CopyAtom,
tma_atom_sfa: cute.CopyAtom,
tma_atom_sfb: cute.CopyAtom,
tAgA: cute.Tensor,
tBgB: cute.Tensor,
tAgSFA: cute.Tensor,
tBgSFB: cute.Tensor,
tAsA: cute.Tensor,
tBsB: cute.Tensor,
tAsSFA: cute.Tensor,
tBsSFB: cute.Tensor,
a_full_mcast_mask,
sfa_full_mcast_mask,
sfb_full_mcast_mask,
):
"""
Specialized TMA load warp for loading A/B/SFA/SFB tiles.
"""
#
# Persistent tile scheduling loop
#
tile_sched = create_tile_scheduler(
tile_sched_params, cute.arch.block_idx(), cute.arch.grid_dim()
)
work_tile = tile_sched.initial_work_tile_info()
ab_producer_state = pipeline.make_pipeline_state(
pipeline.PipelineUserType.Producer, self.num_ab_stage
)
tAgA = self.tma_gmem_ksplit_partition(tAgA)
tBgB = self.tma_gmem_ksplit_partition(tBgB)
tAgSFA = self.tma_gmem_ksplit_partition(tAgSFA)
tBgSFB = self.tma_gmem_ksplit_partition(tBgSFB)
tma_debug(
"\n===================== Load Warp KSPLIT Tensors =====================\n"
"tAgA:\n\t{}\n\t{}\n"
"tBgB:\n\t{}\n\t{}\n"
"tAgSFA:\n\t{}\n\t{}\n"
"tBgSFB:\n\t{}\n\t{}\n"
"====================================================================\n\n",
tAgA.layout,
tAgA.iterator,
tBgB.layout,
tBgB.iterator,
tAgSFA.layout,
tAgSFA.iterator,
tBgSFB.layout,
tBgSFB.iterator,
)
while self.is_work_tile_valid(work_tile):
# Get tile coord from tile scheduler
m_tile, n_tile, ksplit_tile, l_tile = get_mnkl_indices(work_tile)
tma_debug(
"Load warp: block_idx=({},{},{}), m_tile={}, n_tile={}, ksplit_tile={}\n",
cute.arch.block_idx()[0],
cute.arch.block_idx()[1],
cute.arch.block_idx()[2],
m_tile,
n_tile,
ksplit_tile,
)
#
# Slice to per mma tile index
#
# ((atom_v, rest_v), RestK)
tAgA_slice = tAgA[(None, m_tile, (None, ksplit_tile), l_tile)]
# ((atom_v, rest_v), RestK)
tBgB_slice = tBgB[(None, n_tile, (None, ksplit_tile), l_tile)]
# ((atom_v, rest_v), RestK)
tAgSFA_slice = tAgSFA[(None, m_tile, (None, ksplit_tile), l_tile)]
slice_n = n_tile
if cutlass.const_expr(self.mma_tiler[1] == 64):
slice_n = n_tile // 2
# ((atom_v, rest_v), RestK)
tBgSFB_slice = tBgSFB[(None, slice_n, (None, ksplit_tile), l_tile)]
# 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 < self.ksplits_tile_cnt:
peek_ab_empty_status = ab_pipeline.producer_try_acquire(
ab_producer_state
)
#
# Tma load loop
#
for k_tile in cutlass.range(0, self.ksplits_tile_cnt, 1, unroll=1):
# Conditionally wait for AB buffer empty
ab_pipeline.producer_acquire(ab_producer_state, peek_ab_empty_status)
smem_stage = ab_producer_state.index
tma_mbar_ptr = ab_pipeline.producer_get_barrier(ab_producer_state)
# TMA load A/B/SFA/SFB
cute.copy(
tma_atom_a,
tAgA_slice[(None, ab_producer_state.count)],
tAsA[(None, smem_stage)],
tma_bar_ptr=tma_mbar_ptr,
mcast_mask=a_full_mcast_mask,
)
cute.copy(
tma_atom_b,
tBgB_slice[(None, ab_producer_state.count)],
tBsB[(None, smem_stage)],
tma_bar_ptr=tma_mbar_ptr,
)
cute.copy(
tma_atom_sfa,
tAgSFA_slice[(None, ab_producer_state.count)],
tAsSFA[(None, smem_stage)],
tma_bar_ptr=tma_mbar_ptr,
mcast_mask=sfa_full_mcast_mask,
)
cute.copy(
tma_atom_sfb,
tBgSFB_slice[(None, ab_producer_state.count)],
tBsSFB[(None, smem_stage)],
tma_bar_ptr=tma_mbar_ptr,
mcast_mask=sfb_full_mcast_mask,
)
# 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 < self.ksplits_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()
#
# Wait A/B buffer empty
#
ab_pipeline.producer_tail(ab_producer_state)
@cute.jit
def mma_warp(
self,
tiled_mma: cute.TiledMma,
tile_sched_params: TileSchedulerParams,
tmem: utils.TmemAllocator,
tCtAcc_fake: cute.Tensor,
sfa_smem_layout_staged: cute.Layout,
sfb_smem_layout_staged: cute.Layout,
sSFA: cute.Tensor,
sSFB: cute.Tensor,
tCrA: cute.Tensor,
tCrB: cute.Tensor,
ab_pipeline: pipeline.PipelineTmaUmma,
acc_pipeline: pipeline.PipelineUmmaAsync,
):
"""
Specialized MMA warp for performing matrix multiply-accumulate operations.
"""
#
# Bar sync for retrieve tensor memory ptr from shared mem
#
tmem.wait_for_alloc()
#
# Retrieving tensor memory ptr and make accumulator/SFA/SFB tensor
#
acc_tmem_ptr = tmem.retrieve_ptr(self.acc_dtype)
# Make accumulator tmem tensor
# (MMA, MMA_M, MMA_N, STAGE)
tCtAcc_base = cute.make_tensor(acc_tmem_ptr, tCtAcc_fake.layout)
# Make SFA tmem tensor
sfa_tmem_ptr = cute.recast_ptr(
acc_tmem_ptr + self.num_accumulator_tmem_cols,
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 + self.num_accumulator_tmem_cols + self.num_sfa_tmem_cols,
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.mma_sf_s2t_copy(sSFA, tCtSFA)
(
tiled_copy_s2t_sfb,
tCsSFB_compact_s2t,
tCtSFB_compact_s2t,
) = self.mma_sf_s2t_copy(sSFB, tCtSFB)
# mma_debug(
# f"\n===================== MMA Warp TMEM Tensors =====================\n"
# f"tCtAcc_base:\n\t{tCtAcc_base.layout}\n\t{tCtAcc_base.iterator}\n"
# f"tCtSFA:\n\t{tCtSFA.layout}\n\t{tCtSFA.iterator}\n"
# f"tCtSFB:\n\t{tCtSFB.layout}\n\t{tCtSFB.iterator}\n"
# f"================================================================\n\n"
# )
# mma_debug(
# f"\n===================== S2T Copy Partitions =====================\n"
# f"tCsSFA_compact_s2t:\n\t{tCsSFA_compact_s2t.layout}\n\t{tCsSFA_compact_s2t.iterator}\n"
# f"tCtSFA_compact_s2t:\n\t{tCtSFA_compact_s2t.layout}\n\t{tCtSFA_compact_s2t.iterator}\n"
# f"tCsSFB_compact_s2t:\n\t{tCsSFB_compact_s2t.layout}\n\t{tCsSFB_compact_s2t.iterator}\n"
# f"tCtSFB_compact_s2t:\n\t{tCtSFB_compact_s2t.layout}\n\t{tCtSFB_compact_s2t.iterator}\n"
# f"\ntiled_copy_s2t_sfa:\n{tiled_copy_s2t_sfa}\n"
# f"\ntiled_copy_s2t_sfb:\n{tiled_copy_s2t_sfb}\n"
# f"===============================================================\n\n"
# )
#
# Persistent tile scheduling loop
#
tile_sched = create_tile_scheduler(
tile_sched_params, cute.arch.block_idx(), cute.arch.grid_dim()
)
work_tile = tile_sched.initial_work_tile_info()
ab_consumer_state = pipeline.make_pipeline_state(
pipeline.PipelineUserType.Consumer, self.num_ab_stage
)
acc_producer_state = pipeline.make_pipeline_state(
pipeline.PipelineUserType.Producer, self.num_acc_stage
)
while self.is_work_tile_valid(work_tile):
# Get tile coord from tile scheduler
mnkl_indices = get_mnkl_indices(work_tile)
mma_tile_coord_mnl = (
mnkl_indices[0],
mnkl_indices[1],
mnkl_indices[3],
)
# Set tensor memory buffer for current tile
# (MMA, MMA_M, MMA_N)
tCtAcc = tCtAcc_base[(None, None, None, acc_producer_state.index)]
# Peek (try_wait) AB buffer full for k_tile = 0
ab_consumer_state.reset_count()
peek_ab_full_status = cutlass.Boolean(1)
if ab_consumer_state.count < self.ksplits_tile_cnt:
peek_ab_full_status = ab_pipeline.consumer_try_wait(ab_consumer_state)
#
# Wait for accumulator buffer empty
#
acc_pipeline.producer_acquire(acc_producer_state)
tCtSFB_mma = tCtSFB
if cutlass.const_expr(self.mma_tiler[1] == 64):
# Move in increments of 64 columns of SFB
offset = cutlass.Int32((mma_tile_coord_mnl[1] % 2) * 2)
shifted_ptr = cute.recast_ptr(
acc_tmem_ptr
+ self.num_accumulator_tmem_cols
+ self.num_sfa_tmem_cols
+ offset,
dtype=self.sf_dtype,
)
tCtSFB_mma = cute.make_tensor(shifted_ptr, tCtSFB_layout)
#
# Reset the ACCUMULATE field for each tile
#
tiled_mma.set(tcgen05.Field.ACCUMULATE, False)
#
# Mma mainloop
#
for k_tile in cutlass.range(self.ksplits_tile_cnt):
smem_stage = ab_consumer_state.index
# 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,
smem_stage,
)
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])
# assert num_kblocks == mma_inst_tile_k
for kblock_idx in cutlass.range(num_kblocks, unroll_full=True):
kblock_coord = (
None,
None,
kblock_idx,
smem_stage,
)
# Set SFA/SFB tensor to tiled_mma
sf_kblock_coord = (None, None, kblock_idx)
# this updates the smem descriptor for sfa and sfb
tiled_mma.set(
tcgen05.Field.SFA,
tCtSFA[sf_kblock_coord].iterator,
)
tiled_mma.set(
tcgen05.Field.SFB,
tCtSFB_mma[sf_kblock_coord].iterator,
)
cute.gemm(
tiled_mma,
tCtAcc,
tCrA[kblock_coord],
tCrB[kblock_coord],
tCtAcc,
)
# Enable accumulate on tCtAcc after first kblock
tiled_mma.set(tcgen05.Field.ACCUMULATE, True)
# Async arrive AB buffer empty
ab_pipeline.consumer_release(ab_consumer_state)
# Peek (try_wait) AB buffer full for k_tile = k_tile + 1
ab_consumer_state.advance()
peek_ab_full_status = cutlass.Boolean(1)
if ab_consumer_state.count < self.ksplits_tile_cnt:
peek_ab_full_status = ab_pipeline.consumer_try_wait(
ab_consumer_state
)
#
# Async arrive accumulator buffer full
#
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)
def mma_sf_s2t_copy(
self,
sSF: cute.Tensor,
tSF: cute.Tensor,
) -> 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,
) -> Tuple[cute.TiledCopy, cute.Tensor, cute.Tensor]:
# Make tiledCopy for tensor memory load
copy_atom_t2r = sm100_utils.get_tmem_load_op(
self.mma_tiler,
self.cd_majorness,
self.c_dtype,
self.acc_dtype,
epi_tile,
False,
)
# (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]:
copy_atom_r2s = sm100_utils.get_smem_store_op(
self.cd_majorness, 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]:
# (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
def tma_gmem_ksplit_partition(self, gT: cute.Tensor) -> cute.Tensor:
return cute.logical_divide(gT, (None, None, self.ksplits_tile_cnt, None))
def _get_k_splits(self, K: int):
ksplits = get_k_splits(K)
ksplit_tiles = K // (self.mma_tiler[2] * ksplits)
return ksplits, ksplit_tiles
@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,
initialize_sC: bool,
) -> Tuple[int, int, int]:
# ACC stages
num_acc_stage = 2
# C stages: 2 if initializing sC, 0 otherwise
num_c_stage = 2 if initialize_sC else 0
# 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
)
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
if initialize_sC:
c_smem_layout_staged_one = sm100_utils.make_smem_layout_epi(
c_dtype,
c_layout,
epi_tile,
1,
)
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 - (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
- ab_bytes_per_stage * num_ab_stage
- (mbar_helpers_bytes + c_bytes)
) // c_bytes_per_stage
else:
# No C smem needed for non-TMA store
num_ab_stage = (smem_capacity - mbar_helpers_bytes) // ab_bytes_per_stage
return num_acc_stage, num_ab_stage, num_c_stage
@staticmethod
def _compute_grid(
N_tiles: cutlass.Constexpr[cute.Int32],
cluster_shape_kn: Tuple[int, int],
max_active_clusters: cutlass.Constexpr,
) -> Tuple[TileSchedulerParams, Tuple[int, int, int]]:
ksplits = cluster_shape_kn[0]
if USE_SIMPLE_SCHEDULER:
grid = SimpleTileSchedulerParams.get_grid_shape(
cluster_shape_kn, N_tiles, max_active_clusters
)
n_stride = grid[1] # num_persistent_n_ctas
tile_sched_params = SimpleTileSchedulerParams(ksplits, N_tiles, n_stride)
else:
# For static scheduler with k-splits:
# - Interpret "M" dimension as k_split, "N" dimension as n_tile
# - problem_shape_ntile_mnl = (ksplits, N_tiles, 1)
# - cluster_shape_mnk uses cluster_shape_kn (k_cluster, n_cluster, 1)
# where cluster_shape_kn[0] = k_splits, cluster_shape_kn[1] = n_cluster
# - cluster_shape_mn is a "fake" shape only used for TMA multicasting
problem_shape_ntile_mnl = (ksplits, N_tiles, 1)
# Use cluster_shape_kn for the actual clustering
cluster_shape_mnk = (cluster_shape_kn[0], cluster_shape_kn[1], 1)
tile_sched_params = PersistentTileSchedulerParams(
problem_shape_ntile_mnl,
cluster_shape_mnk,
)
grid = StaticPersistentTileScheduler.get_grid_shape(
tile_sched_params, max_active_clusters
)
hdebug("\n===================== Grid Configuration =====================")
hdebug("N_tiles: {}", N_tiles)
hdebug("k_splits: {}", ksplits)
hdebug("cluster_shape_kn: {}", cluster_shape_kn)
hdebug("grid: (k_splits={}, n_persistent={}, 1)", grid[0], grid[1])
hdebug("==============================================================\n\n")
# cute.printf("\n===================== Grid Configuration =====================")
# cute.printf("N_tiles: {}", N_tiles)
# cute.printf("k_splits: {}", ksplits)
# cute.printf("cluster_shape_kn: {}", cluster_shape_kn)
# cute.printf("grid: (k_splits={}, n_persistent={}, 1)", grid[0], grid[1])
# cute.printf(
# "==============================================================\n\n"
# )
return tile_sched_params, grid
@cute.jit
def epi_warp_ksplit(
self,
warp_idx: cutlass.Int32,
tidx: cutlass.Int32,
tiled_mma: cute.TiledMma,
tile_sched_params: TileSchedulerParams,
tmem: utils.TmemAllocator,
tCtAcc_fake: cute.Tensor,
tCgC: cute.Tensor,
epi_tile: cute.Tile,
sAccRed: cute.Tensor,
red_pipeline: pipeline.PipelineTmaAsync,
acc_pipeline: pipeline.PipelineUmmaAsync,
):
"""
Specialized epilogue warps for storing results to global memory with k-split reduction.
Each CTA handles a portion of the N-tile and receives partials from peer CTAs
via st.async.shared::cluster, sums in FP32, then stores directly to global memory.
"""
#
# Alloc tensor memory buffer
#
tmem.allocate(self.num_tmem_alloc_cols)
#
# Bar sync for retrieve tensor memory ptr from shared memory
#
tmem.wait_for_alloc()
#
# Retrieving tensor memory ptr and make accumulator tensor
#
acc_tmem_ptr = tmem.retrieve_ptr(self.acc_dtype)
# (MMA, MMA_M, MMA_N, STAGE)
tCtAcc_base = cute.make_tensor(acc_tmem_ptr, tCtAcc_fake.layout)
# cute.arch.warpgroup_reg_alloc(256)
#
# 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,
)
thr_copy_t2r = tiled_copy_t2r.get_slice(tidx)
tTR_rC = cute.make_rmem_tensor(tTR_rAcc.shape, cute.Float32)
# tiled_copy_r2s, tRS_rC_send, tRS_sC = self.epilog_smem_copy_and_partition(
# tiled_copy_t2r, tTR_rC, epi_tidx, sAccRed
# )
# thr_copy_r2s = tiled_copy_r2s.get_slice(tidx)
copy_acc_red_atom = cute.make_copy_atom(
cute.nvgpu.CopyUniversalOp(), self.acc_dtype
)
r2s_copy_atom = cute.make_copy_atom(
cute.nvgpu.CopyUniversalOp(),
self.acc_dtype,
num_bits_per_copy=128,
)
thr_layout = cute.make_layout((4, 32), stride=(32, 1))
val_layout = cute.make_layout((self.r2s_tiler[0] // 4, 4), stride=(4, 1))
tiled_copy_r2s = cute.make_tiled_copy_tv(r2s_copy_atom, thr_layout, val_layout)
thr_copy_r2s = tiled_copy_r2s.get_slice(tidx)
# (Cpy, r2sTile, _, KSPLIT)
tRS_sC = thr_copy_r2s.partition_D(sAccRed)
tRS_rC = tiled_copy_r2s.retile(tTR_rC)
tRS_rC_send = cute.logical_divide(tRS_rC, self.r2s_tiler)
# ((32),2,1,1):((1),32,0,0)
tSR_rC_local = cute.make_rmem_tensor_like(tRS_rC_send, dtype=self.acc_dtype)
tSR_rC_recv = cute.make_rmem_tensor_like(tSR_rC_local, self.acc_dtype)
tDrD = cute.make_rmem_tensor_like(tSR_rC_local, self.d_dtype)
#
# Persistent tile scheduling loop
#
tile_sched = create_tile_scheduler(
tile_sched_params, cute.arch.block_idx(), cute.arch.grid_dim()
)
work_tile = tile_sched.initial_work_tile_info()
my_ksplit_idx = tile_sched.k_split_idx
# (KSPLIT_TILE_M, KSPLIT_TILE_N, KSPLIT_M, KSPLIT_N, RestM, RestN, RestL)
gC_ksplit = cute.flat_divide(
tCgC[((None, None), 0, 0, None, None, None)], epi_tile
)
gC_ksplit = gC_ksplit[(None, None, None, my_ksplit_idx, None, None, None)]
# (CPY, CPY_M, CPY_N, KSPLIT_M, RestM, RestN, RestL)
bRG_gC = thr_copy_t2r.partition_D(gC_ksplit)
bRG_gC = cute.logical_divide(bRG_gC, self.r2s_tiler)
# red_op_tile = (8,)
# tRed_rAcc_layout = cute.logical_divide(cute.coalesce(tSR_rC_local), red_op_tile)
# tRed_rAcc = cute.make_rmem_tensor_like(tRed_rAcc_layout, dtype=self.acc_dtype)
# tRed_rAcc_recv = cute.make_rmem_tensor_like(
# tRed_rAcc_layout, dtype=self.acc_dtype
# )
# tRed_rD = cute.make_rmem_tensor_like(tRed_rAcc_layout, dtype=self.d_dtype)
debug(
"\n===================== Epilogue Warp Tensors =====================\n"
"gC_ksplit:\n\t(KSPLIT_TILE_M, KSPLIT_TILE_N, KSPLIT_M, KSPLIT_N, RestM, RestN, RestL)\n\t{}\n\t{}\n"
"bRG_gC:\n\t(CPY, CPY_M, CPY_N, KSPLIT_M, RestM, RestN, RestL)\n\t{}\n\t{}\n"
"sAccRed:\n\t{}\n\t{}\n"
"tCtAcc_base:\n\t{}\n\t{}\n"
"tTR_tAcc_base:\n\t{}\n\t{}\n"
"tTR_rAcc:\n\t{}\n\t{}\n"
"tTR_rC:\n\t{}\n\t{}\n"
"tRS_rC_send:\n\t{}\n\t{}\n"
"tRS_sC:\n\t{}\n\t{}\n"
"tSR_rC_local:\n\t{}\n\t{}\n"
"tSR_rC_recv:\n\t{}\n\t{}\n"
"tDrD:\n\t{}\n\t{}\n"
"epi_tile:\n{}\n"
f"\ntiled_copy_t2r:\n{tiled_copy_t2r}\n"
f"\nthr_copy_t2r:\n{thr_copy_t2r}\n"
f"\ntiled_copy_r2s:\n{tiled_copy_r2s}\n"
f"\nthr_copy_r2s:\n{thr_copy_r2s}\n"
"================================================================\n\n",
gC_ksplit.layout,
gC_ksplit.iterator,
bRG_gC.layout,
bRG_gC.iterator,
sAccRed.layout,
sAccRed.iterator,
tCtAcc_base.layout,
tCtAcc_base.iterator,
tTR_tAcc_base.layout,
tTR_tAcc_base.iterator,
tTR_rAcc.layout,
tTR_rAcc.iterator,
tTR_rC.layout,
tTR_rC.iterator,
tRS_rC_send.layout,
tRS_rC_send.iterator,
tRS_sC.layout,
tRS_sC.iterator,
tSR_rC_local.layout,
tSR_rC_local.iterator,
tSR_rC_recv.layout,
tSR_rC_recv.iterator,
tDrD.layout,
tDrD.iterator,
epi_tile,
)
acc_consumer_state = pipeline.make_pipeline_state(
pipeline.PipelineUserType.Consumer, self.num_acc_stage
)
red_producer_state = pipeline.make_pipeline_state(
pipeline.PipelineUserType.Producer, self.num_red_stages
)
red_consumer_state = pipeline.make_pipeline_state(
pipeline.PipelineUserType.Consumer, self.num_red_stages
)
cluster_n_base_rank = cute.arch.block_in_cluster_idx()[1] * self.ksplits
# Testing to see if smem is swizzled
# lane_idx = cute.arch.lane_idx()
# # tRS_rC_send.store(tiled_copy_r2s.retile(tTR_rAcc).load())
# for i in cutlass.range_constexpr(cute.size(tRS_rC_send)):
# tRS_rC_send[i] = cute.Float32(lane_idx * 100 + i)
# tRS_sC_send = tRS_sC[(None, None, None, my_ksplit_idx)]
while self.is_work_tile_valid(work_tile):
# Get tile coord from tile scheduler
mnkl_indices = get_mnkl_indices(work_tile)
bRG_gC_slice = bRG_gC[(None, 0, 0, 0, 0, mnkl_indices[1], mnkl_indices[3])]
acc_stage = acc_consumer_state.index
# 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_stage)]
# Wait for peer remotes to finish reading partials
if warp_idx == self.epilog_warp_id[2]:
pre_arrive_red_producer_state = red_producer_state.clone()
for r2s_tile in cutlass.range_constexpr(self.r2s_tiles):
if cutlass.const_expr(self.max_tiles == 1):
red_pipeline.producer_acquire(
pre_arrive_red_producer_state,
try_acquire_token=cute.Boolean(True),
)
else:
red_pipeline.producer_acquire(pre_arrive_red_producer_state)
pre_arrive_red_producer_state.advance()
#
# Wait for accumulator buffer full
#
acc_pipeline.consumer_wait(acc_consumer_state)
# ksplit_peer is the CTA rank for remote communication
ksplit_peer = ((my_ksplit_idx + 1) % self.ksplits) + cluster_n_base_rank
# peer_ksplit_idx is the k-split index of the peer (for local tensor indexing)
peer_ksplit_idx = (my_ksplit_idx + 1) % self.ksplits
tTR_tAcc = cute.group_modes(tTR_tAcc, 3, cute.rank(tTR_tAcc))
# Index shared memory by k-split index (0 or 1), not CTA rank
tRS_sC_send = tRS_sC[(None, None, None, my_ksplit_idx)]
tSR_sC_recv = tRS_sC[(None, None, None, peer_ksplit_idx)]
# Each ksplit peer CTA copies
# 1. to local smem[my_ksplit_idx]
# 2. from local smem[my_ksplit_idx] -> dsmem[my_ksplit_idx]
# To support more than 2 k-splits, we'll need buffer space in smem for each k split
# - or we can use red.shared::cluster
# Index tensor memory by k-split index, not CTA rank
tTR_tAcc_mn = tTR_tAcc[(None, None, None, peer_ksplit_idx)]
cute.copy(tiled_copy_t2r, tTR_tAcc_mn, tTR_rAcc)
tRS_rC_send.store(tiled_copy_r2s.retile(tTR_rAcc).load())
for r2s_tile in cutlass.range_constexpr(self.r2s_tiles):
r2s_coord = ((None, r2s_tile), 0, None)
tRS_sC_send_slice = tRS_sC_send[(None, r2s_tile, None)]
cute.copy(
tiled_copy_r2s,
tRS_rC_send[r2s_coord],
dst=tRS_sC_send_slice,
)
# Fence and barrier to make sure shared memory store is visible to TMA store
cute.arch.fence_proxy(
cute.arch.ProxyKind.async_shared,
space=cute.arch.SharedSpace.shared_cta,
)
with cute.arch.elect_one():
cp_async_bulk_remote(
tRS_sC_send_slice.iterator,
tRS_sC_send_slice.iterator,
red_pipeline.sync_object_full.get_barrier(
red_producer_state.index
),
self.red_send_bytes_per_warp_tile,
ksplit_peer,
)
red_producer_state.advance()
cute.copy(
tiled_copy_t2r, tTR_tAcc[(None, None, None, my_ksplit_idx)], tTR_rAcc
)
acc_vec = tiled_copy_r2s.retile(tTR_rAcc).load()
tSR_rC_local.store(acc_vec)
for r2s_tile in cutlass.range_constexpr(self.r2s_tiles):
red_pipeline.consumer_wait(red_consumer_state)
r2s_coord = ((None, r2s_tile), 0, None)
cute.copy(
copy_acc_red_atom,
tSR_sC_recv[(None, r2s_tile, None)],
tSR_rC_recv[r2s_coord],
)
tSR_rC_local_tile = tSR_rC_local[r2s_coord]
tSR_rC_recv_tile = tSR_rC_recv[r2s_coord]
tSR_rC_red_accum = tSR_rC_local_tile.load()
tSR_rC_red_recv = tSR_rC_recv_tile.load()
for pidx in cutlass.range_constexpr(0, cute.size(tSR_rC_local_tile), 2):
accum_pair = (tSR_rC_red_accum[pidx], tSR_rC_red_accum[pidx + 1])
recv_pair = (tSR_rC_red_recv[pidx], tSR_rC_red_recv[pidx + 1])
result = cute.arch.add_packed_f32x2(
accum_pair, recv_pair, rnd=nvvm.RoundingModeKind.RN
)
tSR_rC_local_tile[pidx], tSR_rC_local_tile[pidx + 1] = result
tDrD_tile = tDrD[r2s_coord]
tDrD_tile.store(tSR_rC_local_tile.load().to(self.d_dtype))
# cute.autovec_copy(tDrD_tile, bRG_gC_slice[((None, r2s_tile),)])
st_global_na_8xf32_tensor(tDrD_tile, bRG_gC_slice[((None, r2s_tile),)])
# Release the reduction pipeline buffers
# select threads are predesignated to release. no need to guard with elect_one.
if cutlass.const_expr(self.max_tiles > 1):
red_pipeline.consumer_release(red_consumer_state)
red_consumer_state.advance()
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()
#
# Dealloc the tensor memory buffer
#
# tmem.relinquish_alloc_permit()
if warp_idx == self.epilog_warp_id[2]:
self.epilog_sync_barrier.arrive_and_wait()
tmem.free(acc_tmem_ptr)
# Finalize red_pipeline
if cutlass.const_expr(self.max_tiles > 1):
red_pipeline.producer_tail(red_producer_state)
else:
self.epilog_sync_barrier.arrive()
@cute.jit
def epi_warp_tma_store(
self,
warp_idx: cutlass.Int32,
tidx: cutlass.Int32,
tiled_mma: cute.TiledMma,
tile_sched_params: TileSchedulerParams,
tmem: utils.TmemAllocator,
tCtAcc_fake: cute.Tensor,
tCgC: cute.Tensor,
epi_tile: cute.Tile,
sC: cute.Tensor,
tma_atom_c: cute.CopyAtom,
acc_pipeline: pipeline.PipelineUmmaAsync,
):
"""
Specialized epilogue warps for storing results to global memory.
"""
#
# Alloc tensor memory buffer
#
tmem.allocate(self.num_tmem_alloc_cols)
#
# Bar sync for retrieve tensor memory ptr from shared memory
#
tmem.wait_for_alloc()
#
# Retrieving tensor memory ptr and make accumulator tensor
#
acc_tmem_ptr = tmem.retrieve_ptr(self.acc_dtype)
# (MMA, MMA_M, MMA_N, STAGE)
tCtAcc_base = cute.make_tensor(acc_tmem_ptr, tCtAcc_fake.layout)
#
# Partition for epilogue
#
epi_tidx = tidx
(
tiled_copy_t2r,
tTR_tAcc_base,
tTR_rAcc,
) = self.epilog_tmem_copy_and_partition(
epi_tidx,
tCtAcc_base,
tCgC,
epi_tile,
)
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
)
debug(
f"\n===================== Epilogue Warp Tensors =====================\n"
f"tCtAcc_base:\n\t{tCtAcc_base.layout}\n\t{tCtAcc_base.iterator}\n"
f"tTR_tAcc_base:\n\t{tTR_tAcc_base.layout}\n\t{tTR_tAcc_base.iterator}\n"
f"tTR_rAcc:\n\t{tTR_rAcc.layout}\n\t{tTR_rAcc.iterator}\n"
f"tTR_rC:\n\t{tTR_rC.layout}\n\t{tTR_rC.iterator}\n"
f"tRS_rC:\n\t{tRS_rC.layout}\n\t{tRS_rC.iterator}\n"
f"tRS_sC:\n\t{tRS_sC.layout}\n\t{tRS_sC.iterator}\n"
f"bSG_sC:\n\t{bSG_sC.layout}\n\t{bSG_sC.iterator}\n"
f"bSG_gC_partitioned:\n\t{bSG_gC_partitioned.layout}\n\t{bSG_gC_partitioned.iterator}\n"
f"\ntiled_copy_t2r:\n{tiled_copy_t2r}\n"
f"\ntiled_copy_r2s:\n{tiled_copy_r2s}\n"
f"================================================================\n\n"
)
#
# Persistent tile scheduling loop
#
tile_sched = create_tile_scheduler(
tile_sched_params, cute.arch.block_idx(), cute.arch.grid_dim()
)
work_tile = tile_sched.initial_work_tile_info()
acc_consumer_state = pipeline.make_pipeline_state(
pipeline.PipelineUserType.Consumer, self.num_acc_stage
)
# Threads/warps participating in tma store pipeline
c_producer_group = pipeline.CooperativeGroup(
pipeline.Agent.Thread,
self.threads_per_warp * len(self.epilog_warp_id),
)
c_pipeline = pipeline.PipelineTmaStore.create(
num_stages=self.num_c_stage,
producer_group=c_producer_group,
)
while self.is_work_tile_valid(work_tile):
# Get tile coord from tile scheduler
mnkl_indices = get_mnkl_indices(work_tile)
mma_tile_coord_mnl = (
mnkl_indices[0],
mnkl_indices[1],
mnkl_indices[3],
)
#
# Slice to per mma tile index
#
# ((ATOM_V, REST_V), EPI_M, EPI_N)
bSG_gC = bSG_gC_partitioned[
(
None,
None,
None,
*mma_tile_coord_mnl,
)
]
# Set tensor memory buffer for current tile
# (T2R, T2R_M, T2R_N, EPI_M, EPI_M)
tTR_tAcc = tTR_tAcc_base[
(None, None, None, None, None, acc_consumer_state.index)
]
#
# Wait for accumulator buffer full
#
acc_pipeline.consumer_wait(acc_consumer_state)
tTR_tAcc = cute.group_modes(tTR_tAcc, 3, cute.rank(tTR_tAcc))
bSG_gC = cute.group_modes(bSG_gC, 1, cute.rank(bSG_gC))
#
# Store accumulator to global memory in subtiles
#
subtile_cnt = cute.size(tTR_tAcc.shape, mode=[3])
num_prev_subtiles = tile_sched.num_tiles_executed * subtile_cnt
for subtile_idx in cutlass.range(subtile_cnt):
# Load accumulator from tensor memory buffer to register
#
tTR_tAcc_mn = tTR_tAcc[(None, None, None, subtile_idx)]
cute.copy(tiled_copy_t2r, tTR_tAcc_mn, tTR_rAcc)
#
# Convert to C type
#
acc_vec = tiled_copy_r2s.retile(tTR_rAcc).load().to(self.c_dtype)
tRS_rC.store(acc_vec)
#
# Store C to shared memory
#
c_buffer = (num_prev_subtiles + subtile_idx) % self.num_c_stage
cute.copy(
tiled_copy_r2s,
tRS_rC,
dst=tRS_sC[(None, None, None, c_buffer)],
)
# Fence and barrier to make sure shared memory store is visible to TMA store
cute.arch.fence_proxy(
cute.arch.ProxyKind.async_shared,
space=cute.arch.SharedSpace.shared_cta,
)
self.epilog_sync_barrier.arrive_and_wait()
#
# TMA store C to global memory
#
if warp_idx == self.epilog_warp_id[2]:
cute.copy(
tma_atom_c,
bSG_sC[(None, c_buffer)],
bSG_gC[(None, subtile_idx)],
)
# Fence and barrier to make sure shared memory store is visible to TMA store
c_pipeline.producer_commit()
c_pipeline.producer_acquire()
self.epilog_sync_barrier.arrive_and_wait()
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()
#
# Dealloc the tensor memory buffer
#
self.epilog_sync_barrier.arrive_and_wait()
tmem.free(acc_tmem_ptr)
#
# Wait for C store complete
#
c_pipeline.producer_tail()
@cute.jit
def epi_warp_no_tma_store(
self,
warp_idx: cutlass.Int32,
tidx: cutlass.Int32,
tiled_mma: cute.TiledMma,
tile_sched_params: TileSchedulerParams,
tmem: utils.TmemAllocator,
tCtAcc_fake: cute.Tensor,
tCgC: cute.Tensor,
epi_tile: cute.Tile,
acc_pipeline: pipeline.PipelineUmmaAsync,
):
"""
Specialized epilogue warps for storing results to global memory without TMA.
Uses SIMT copy to store directly from registers to global memory.
"""
#
# Alloc tensor memory buffer
#
tmem.allocate(self.num_tmem_alloc_cols)
#
# Bar sync for retrieve tensor memory ptr from shared memory
#
tmem.wait_for_alloc()
#
# Retrieving tensor memory ptr and make accumulator tensor
#
acc_tmem_ptr = tmem.retrieve_ptr(self.acc_dtype)
# (MMA, MMA_M, MMA_N, STAGE)
tCtAcc_base = cute.make_tensor(acc_tmem_ptr, tCtAcc_fake.layout)
#
# Partition for epilogue
#
epi_tidx = tidx
(
tiled_copy_t2r,
tTR_tAcc_base,
tTR_rAcc,
) = self.epilog_tmem_copy_and_partition(
epi_tidx,
tCtAcc_base,
tCgC,
epi_tile,
)
# Partition global memory for non-TMA store
gC_epi = cute.flat_divide(
tCgC[((None, None), 0, 0, None, None, None)], epi_tile
)
# (T2R, T2R_M, T2R_N, EPI_M, EPI_N, RestM, RestN, RestL)
thr_copy_t2r = tiled_copy_t2r.get_slice(epi_tidx)
tTR_gC_partitioned = thr_copy_t2r.partition_D(gC_epi)
# (T2R, T2R_M, T2R_N)
tTR_rC = cute.make_rmem_tensor(
tTR_gC_partitioned[(None, None, None, 0, 0, 0, 0, 0)].shape, self.c_dtype
)
simt_atom = cute.make_copy_atom(cute.nvgpu.CopyUniversalOp(), self.c_dtype)
debug(
f"\n===================== Epilogue (No TMA) Warp Tensors =====================\n"
f"tCtAcc_base:\n\t{tCtAcc_base.layout}\n\t{tCtAcc_base.iterator}\n"
f"tTR_tAcc_base:\n\t{tTR_tAcc_base.layout}\n\t{tTR_tAcc_base.iterator}\n"
f"tTR_rAcc:\n\t{tTR_rAcc.layout}\n\t{tTR_rAcc.iterator}\n"
f"tTR_rC:\n\t{tTR_rC.layout}\n\t{tTR_rC.iterator}\n"
f"tTR_gC_partitioned:\n\t{tTR_gC_partitioned.layout}\n\t{tTR_gC_partitioned.iterator}\n"
f"\ntiled_copy_t2r:\n{tiled_copy_t2r}\n"
f"===================================================================\n\n"
)
#
# Persistent tile scheduling loop
#
tile_sched = create_tile_scheduler(
tile_sched_params, cute.arch.block_idx(), cute.arch.grid_dim()
)
work_tile = tile_sched.initial_work_tile_info()
acc_consumer_state = pipeline.make_pipeline_state(
pipeline.PipelineUserType.Consumer, self.num_acc_stage
)
while self.is_work_tile_valid(work_tile):
# Get tile coord from tile scheduler
mnkl_indices = get_mnkl_indices(work_tile)
mma_tile_coord_mnl = (
mnkl_indices[0],
mnkl_indices[1],
mnkl_indices[3],
)
#
# Slice to per mma tile index
#
# (T2R, T2R_M, T2R_N, EPI_M, EPI_N)
tTR_gC = tTR_gC_partitioned[
(None, None, None, None, None, *mma_tile_coord_mnl)
]
# Set tensor memory buffer for current tile
# (T2R, T2R_M, T2R_N, EPI_M, EPI_N)
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))
tTR_gC = cute.group_modes(tTR_gC, 3, cute.rank(tTR_gC))
#
# Store accumulator to global memory in subtiles
#
subtile_cnt = cute.size(tTR_tAcc.shape, mode=[3])
for subtile_idx in cutlass.range(subtile_cnt):
#
# Load accumulator from tensor memory buffer to register
#
tTR_tAcc_mn = tTR_tAcc[(None, None, None, subtile_idx)]
cute.copy(tiled_copy_t2r, tTR_tAcc_mn, tTR_rAcc)
#
# Convert to C type
#
acc_vec = tTR_rAcc.load()
acc_vec = acc_vec.to(self.c_dtype)
tTR_rC.store(acc_vec)
#
# Store C to global memory using SIMT copy
#
cute.copy(simt_atom, tTR_rC, tTR_gC[(None, None, None, subtile_idx)])
#
# 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()
#
# Dealloc the tensor memory buffer
#
# Synchronize all epilogue warps before TMEM dealloc
self.epilog_sync_barrier.arrive_and_wait()
tmem.free(acc_tmem_ptr)
@cute.jit
def epi_warp_ksplit_send_only(
self,
warp_idx: cutlass.Int32,
tidx: cutlass.Int32,
tiled_mma: cute.TiledMma,
tile_sched_params: TileSchedulerParams,
tmem: utils.TmemAllocator,
tCtAcc_fake: cute.Tensor,
tCgC: cute.Tensor,
epi_tile: cute.Tile,
sAccRed: cute.Tensor,
red_pipeline: pipeline.PipelineTmaAsync,
acc_pipeline: pipeline.PipelineUmmaAsync,
):
"""
Specialized epilogue warps for sending partials to peer CTAs only.
This version only handles the producer side of k-split reduction:
copying from TMEM to SMEM and sending to remote CTAs via st.async.shared::cluster.
Used when USE_RED_WARPS is enabled and reduction is handled by separate red_warp.
"""
#
# Bar sync for retrieve tensor memory ptr from shared memory
#
tmem.wait_for_alloc()
#
# Retrieving tensor memory ptr and make accumulator tensor
#
acc_tmem_ptr = tmem.retrieve_ptr(self.acc_dtype)
# (MMA, MMA_M, MMA_N, STAGE)
tCtAcc_base = cute.make_tensor(acc_tmem_ptr, tCtAcc_fake.layout)
#
# Partition for epilogue
#
epi_tidx = tidx % (len(self.red_warp_id) * self.threads_per_warp)
(
tiled_copy_t2r,
tTR_tAcc_base,
tTR_rAcc,
) = self.epilog_tmem_copy_and_partition(
epi_tidx,
tCtAcc_base,
tCgC,
epi_tile,
)
tTR_rC = cute.make_rmem_tensor(tTR_rAcc.shape, cute.Float32)
tiled_copy_r2s, tRS_rC_send, tRS_sC = self.epilog_smem_copy_and_partition(
tiled_copy_t2r, tTR_rC, epi_tidx, sAccRed
)
r2s_tiler = (32,)
tRS_rC_send = cute.logical_divide(tRS_rC_send, r2s_tiler)
tRS_sC = cute.logical_divide(tRS_sC, r2s_tiler)
#
# Persistent tile scheduling loop
#
tile_sched = create_tile_scheduler(
tile_sched_params, cute.arch.block_idx(), cute.arch.grid_dim()
)
work_tile = tile_sched.initial_work_tile_info()
my_ksplit_idx = tile_sched.k_split_idx
acc_consumer_state = pipeline.make_pipeline_state(
pipeline.PipelineUserType.Consumer, self.num_acc_stage
)
red_producer_state = pipeline.make_pipeline_state(
pipeline.PipelineUserType.Producer, self.num_acc_stage
)
cluster_n_base_rank = cute.arch.block_in_cluster_idx()[1] * self.ksplits
while self.is_work_tile_valid(work_tile):
acc_stage = acc_consumer_state.index
# 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_stage)]
# This does two things
# 1. Wait for peer remotes to finish reading partials
# 2. Arrives and increments transaction amount.
if warp_idx == self.epilog_warp_id[2]:
red_pipeline.producer_acquire(red_producer_state)
#
# Wait for accumulator buffer full
#
acc_pipeline.consumer_wait(acc_consumer_state)
# ksplit_peer is the CTA rank for remote communication
ksplit_peer = ((my_ksplit_idx + 1) % self.ksplits) + cluster_n_base_rank
# peer_ksplit_idx is the k-split index of the peer (for local tensor indexing)
peer_ksplit_idx = (my_ksplit_idx + 1) % self.ksplits
tTR_tAcc = cute.group_modes(tTR_tAcc, 3, cute.rank(tTR_tAcc))
# Index shared memory by k-split index (0 or 1), not CTA rank
tRS_sC_send = tRS_sC[(None, None, None, my_ksplit_idx)]
# Index tensor memory by k-split index, not CTA rank
tTR_tAcc_mn = tTR_tAcc[(None, None, None, peer_ksplit_idx)]
cute.copy(tiled_copy_t2r, tTR_tAcc_mn, tTR_rAcc)
tRS_rC_send.store(tiled_copy_r2s.retile(tTR_rAcc).load())
r2s_tiles = cute.size(cute.get(tRS_sC.layout, [0, 1]))
for r2s_tile in cutlass.range_constexpr(r2s_tiles):
tRS_sC_send_slice = tRS_sC_send[((None, r2s_tile), None, None)]
cute.copy(
tiled_copy_r2s,
tRS_rC_send[((None, r2s_tile), None, None)],
dst=tRS_sC_send_slice,
)
# Fence and barrier to make sure shared memory store is visible to TMA store
cute.arch.fence_proxy(
cute.arch.ProxyKind.async_shared,
space=cute.arch.SharedSpace.shared_cta,
)
with cute.arch.elect_one():
cp_async_bulk_remote(
tRS_sC_send_slice.iterator,
tRS_sC_send_slice.iterator,
red_pipeline.sync_object_full.get_barrier(
red_producer_state.index
),
self.red_send_bytes_per_warp_tile // r2s_tiles,
ksplit_peer,
)
red_producer_state.advance()
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()
#
# Dealloc the tensor memory buffer
#
self.epilog_sync_barrier.arrive_and_wait()
if warp_idx == self.epilog_warp_id[2]:
red_pipeline.producer_tail(red_producer_state)
# red_pipeline.producer_tail(red_producer_state)
@cute.jit
def red_warp(
self,
warp_idx: cutlass.Int32,
tidx: cutlass.Int32,
tiled_mma: cute.TiledMma,
tile_sched_params: TileSchedulerParams,
tmem: utils.TmemAllocator,
tCtAcc_fake: cute.Tensor,
tCgC: cute.Tensor,
epi_tile: cute.Tile,
sAccRed: cute.Tensor,
red_pipeline: pipeline.PipelineTmaAsync,
acc_pipeline: pipeline.PipelineUmmaAsync,
):
"""
Specialized reduction warps for k-split reduction.
These warps wait for remote partials via st.async.shared::cluster,
sum in FP32, then store directly to global memory.
Only used when USE_RED_WARPS is enabled and ksplits > 1.
"""
#
# Alloc tensor memory buffer
#
tmem.allocate(self.num_tmem_alloc_cols)
#
# Bar sync for retrieve tensor memory ptr from shared memory
#
tmem.wait_for_alloc()
#
# Retrieving tensor memory ptr and make accumulator tensor
#
acc_tmem_ptr = tmem.retrieve_ptr(self.acc_dtype)
# (MMA, MMA_M, MMA_N, STAGE)
tCtAcc_base = cute.make_tensor(acc_tmem_ptr, tCtAcc_fake.layout)
#
# Partition for epilogue
#
epi_tidx = tidx
(
tiled_copy_t2r,
tTR_tAcc_base,
tTR_rAcc,
) = self.epilog_tmem_copy_and_partition(
epi_tidx,
tCtAcc_base,
tCgC,
epi_tile,
)
thr_copy_t2r = tiled_copy_t2r.get_slice(tidx)
tTR_rC = cute.make_rmem_tensor(tTR_rAcc.shape, cute.Float32)
tiled_copy_r2s, tRS_rC_send, tRS_sC = self.epilog_smem_copy_and_partition(
tiled_copy_t2r, tTR_rC, epi_tidx, sAccRed
)
r2s_tiler = (32,)
tRS_rC_send = cute.logical_divide(tRS_rC_send, r2s_tiler)
tRS_sC = cute.logical_divide(tRS_sC, r2s_tiler)
#
# Persistent tile scheduling loop
#
tile_sched = create_tile_scheduler(
tile_sched_params, cute.arch.block_idx(), cute.arch.grid_dim()
)
work_tile = tile_sched.initial_work_tile_info()
my_ksplit_idx = tile_sched.k_split_idx
# (KSPLIT_TILE_M, KSPLIT_TILE_N, KSPLIT_M, KSPLIT_N, RestM, RestN, RestL)
gC_ksplit = cute.flat_divide(
tCgC[((None, None), 0, 0, None, None, None)], epi_tile
)
gC_ksplit = gC_ksplit[(None, None, None, my_ksplit_idx, None, None, None)]
# (CPY, CPY_M, CPY_N, KSPLIT_M, RestM, RestN, RestL)
bRG_gC = thr_copy_t2r.partition_D(gC_ksplit)
# ((32),2,1,1):((1),32,0,0)
tSR_rC_local = cute.make_rmem_tensor_like(tRS_rC_send, dtype=self.acc_dtype)
tSR_rC_recv = cute.make_rmem_tensor_like(tSR_rC_local, self.acc_dtype)
tDrD = cute.make_rmem_tensor_like(tSR_rC_local, self.d_dtype)
acc_consumer_state = pipeline.make_pipeline_state(
pipeline.PipelineUserType.Consumer, self.num_acc_stage
)
red_consumer_state = pipeline.make_pipeline_state(
pipeline.PipelineUserType.Consumer, self.num_acc_stage
)
copy_acc_red_atom = cute.make_copy_atom(
cute.nvgpu.CopyUniversalOp(), self.acc_dtype
)
# peer_ksplit_idx is the k-split index of the peer (for local tensor indexing)
peer_ksplit_idx = (my_ksplit_idx + 1) % self.ksplits
while self.is_work_tile_valid(work_tile):
# Get tile coord from tile scheduler
mnkl_indices = get_mnkl_indices(work_tile)
bRG_gC_slice = bRG_gC[(None, 0, 0, 0, 0, mnkl_indices[1], mnkl_indices[3])]
acc_stage = acc_consumer_state.index
# 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_stage)]
tTR_tAcc = cute.group_modes(tTR_tAcc, 3, cute.rank(tTR_tAcc))
# Index shared memory by k-split index (0 or 1), not CTA rank
tSR_sC_recv = tRS_sC[(None, None, None, peer_ksplit_idx)]
#
# Wait for accumulator buffer full before reading from TMEM
#
acc_pipeline.consumer_wait(acc_consumer_state)
cute.copy(
tiled_copy_t2r, tTR_tAcc[(None, None, None, my_ksplit_idx)], tTR_rAcc
)
acc_vec = tiled_copy_r2s.retile(tTR_rAcc).load()
tSR_rC_local.store(acc_vec)
red_pipeline.consumer_wait(red_consumer_state)
cute.copy(copy_acc_red_atom, tSR_sC_recv, tSR_rC_recv)
tSR_rC_red_accum = tSR_rC_local.load()
tSR_rC_red_recv = tSR_rC_recv.load()
for pidx in cutlass.range_constexpr(0, cute.size(tSR_rC_local), 2):
accum_pair = (tSR_rC_red_accum[pidx], tSR_rC_red_accum[pidx + 1])
recv_pair = (tSR_rC_red_recv[pidx], tSR_rC_red_recv[pidx + 1])
result = cute.arch.add_packed_f32x2(
accum_pair, recv_pair, rnd=nvvm.RoundingModeKind.RN
)
tSR_rC_local[pidx], tSR_rC_local[pidx + 1] = result
tDrD.store(tSR_rC_local.load().to(self.d_dtype))
cute.autovec_copy(tDrD, bRG_gC_slice)
# Release the reduction pipeline buffers
# select threads are predesignated to release. no need to guard with elect_one.
red_pipeline.consumer_release(red_consumer_state)
with cute.arch.elect_one():
acc_pipeline.consumer_release(acc_consumer_state)
acc_consumer_state.advance()
red_consumer_state.advance()
#
# Advance to next tile
#
tile_sched.advance_to_next_work()
work_tile = tile_sched.get_current_work()
tmem.relinquish_alloc_permit()
self.epilog_sync_barrier.arrive_and_wait()
tmem.free(acc_tmem_ptr)
@cute.jit
def gemm_fp4_host(
a_ptr: cute.Pointer,
b_ptr: cute.Pointer,
sfa_ptr: cute.Pointer,
sfb_ptr: cute.Pointer,
c_ptr: cute.Pointer,
n: cutlass.Constexpr[cute.Int32],
k: cutlass.Constexpr[cute.Int32],
max_active_clusters: cutlass.Constexpr[cute.Int32],
):
m = 128
l = 1
# Setup attributes that depend on gemm inputs
a_tensor = cute.make_tensor(
a_ptr,
cute.make_layout(
(m, k, l),
stride=(k, 1, m * k),
),
)
b_tensor = cute.make_tensor(
b_ptr,
cute.make_layout(
(n, k, l),
stride=(k, 1, n * k),
),
)
c_tensor = cute.make_tensor(
c_ptr, cute.make_layout((m, n, l), stride=(n, 1, m * n))
)
# Setup sfa/sfb tensor by filling A/B tensor to scale factor atom layout
# ((Atom_M, Rest_M),(Atom_K, Rest_K),RestL)
sfa_layout = blockscaled_utils.tile_atom_to_shape_SF(a_tensor.shape, sf_vec_size)
sfa_tensor = cute.make_tensor(sfa_ptr, sfa_layout)
# ((Atom_N, Rest_N),(Atom_K, Rest_K),RestL)
sfb_layout = blockscaled_utils.tile_atom_to_shape_SF(b_tensor.shape, sf_vec_size)
sfb_tensor = cute.make_tensor(sfb_ptr, sfb_layout)
mma_tiler = None
if cutlass.const_expr(N_TILE != -1):
mma_tiler = (128, N_TILE, 256)
elif cutlass.const_expr(k == 16384):
mma_tiler = (128, 128, 256)
else:
mma_tiler = (128, 64, 256)
FP4GEMMKernel(k, mma_tiler)(
a_tensor,
b_tensor,
sfa_tensor,
sfb_tensor,
c_tensor,
max_active_clusters,
)
# Global cache for compiled kernel
KERNEL_CACHE = {}
def to_blocked(input_matrix):
rows, cols = input_matrix.shape
# Please ensure rows and cols are multiples of 128 and 4 respectively
n_row_blocks = ceil_div(rows, 128)
n_col_blocks = ceil_div(cols, 4)
padded = input_matrix
blocks = padded.view(n_row_blocks, 128, n_col_blocks, 4).permute(0, 2, 1, 3)
rearranged = blocks.reshape(-1, 4, 32, 4).transpose(1, 2).reshape(-1, 32, 16)
return rearranged.flatten()
def ref_kernel(
data: input_t,
) -> output_t:
"""
PyTorch reference implementation of NVFP4 block-scaled GEMM.
"""
a_ref, b_ref, sfa_ref_cpu, sfb_ref_cpu, _, _, c_ref = data
# Get dimensions from MxNxL layout
_, _, l = c_ref.shape
# Call torch._scaled_mm to compute the GEMM result
for l_idx in range(l):
# Convert the scale factor tensor to blocked format
scale_a = to_blocked(sfa_ref_cpu[:, :, l_idx])
scale_b = to_blocked(sfb_ref_cpu[:, :, l_idx])
# (m, k) @ (n, k).T -> (m, n)
res = torch._scaled_mm(
a_ref[:, :, l_idx],
b_ref[:, :, l_idx].transpose(0, 1),
scale_a.cuda(),
scale_b.cuda(),
bias=None,
out_dtype=torch.float16,
)
c_ref[:, :, l_idx] = res
return c_ref
C_ALIGNMENT = 32
# This function is used to compile the kernel once and cache it and then allow users to
# run the kernel multiple times to get more accurate timing results.
def _compile_kernel(n: cutlass.Constexpr[cute.Int32], k: cutlass.Constexpr[cute.Int32]):
"""
Compile the kernel once and cache it.
This should be called before any timing measurements.
Returns:
The compiled kernel function
"""
if (n, k) in KERNEL_CACHE:
return KERNEL_CACHE[(n, k)]
# Create CuTe pointers for A/B/C/SFA/SFB via torch tensor data pointer
a_ptr = make_ptr(ab_dtype, 0, cute.AddressSpace.gmem, assumed_align=16)
b_ptr = make_ptr(ab_dtype, 0, cute.AddressSpace.gmem, assumed_align=16)
c_ptr = make_ptr(c_dtype, 0, cute.AddressSpace.gmem, assumed_align=C_ALIGNMENT)
sfa_ptr = make_ptr(sf_dtype, 0, cute.AddressSpace.gmem, assumed_align=32)
sfb_ptr = make_ptr(sf_dtype, 0, cute.AddressSpace.gmem, assumed_align=32)
cutlass.cuda.initialize_cuda_context()
k_splits = get_k_splits(k)
cluster_shape_kn = (k_splits, cluster_shape_mn[1])
import socket
# TODO: remove
if COMPILE_FOR_SM100 and socket.gethostname() == "thor":
max_active_clusters = 64
else:
max_active_clusters = cutlass.utils.HardwareInfo().get_max_active_clusters(
cluster_shape_kn[0] * cluster_shape_kn[1]
)
args = (
gemm_fp4_host,
a_ptr,
b_ptr,
sfa_ptr,
sfb_ptr,
c_ptr,
n,
k,
max_active_clusters,
)
if LINE_INFO:
if CUDA_DSA:
KERNEL_CACHE[(n, k)] = cute.compile[
cute.KeepCUBIN(),
cute.KeepPTX(),
cute.GenerateLineInfo(True),
cute.EnableAssertions(),
](*args)
else:
KERNEL_CACHE[(n, k)] = cute.compile[
cute.KeepCUBIN(),
cute.KeepPTX(),
cute.GenerateLineInfo(True),
](*args)
else:
KERNEL_CACHE[(n, k)] = cute.compile[cute.KeepCUBIN(), cute.KeepPTX()](*args)
import socket
hostname = socket.gethostname()
if COMPILE_PTX and hostname == "thor":
from cute_utils import disasm
disasm(KERNEL_CACHE[(n, k)])
return KERNEL_CACHE[(n, k)]
BENCH_DIMS = [
(7168, 16384),
(4096, 7168),
(7168, 2048),
]
def compile_kernel():
"""
Pre-compile kernels for all test case dimensions.
This ensures compilation time is not included in benchmark results.
"""
# All (n, k) combinations from task.yml tests/benchmarks
for n, k in BENCH_DIMS:
_compile_kernel(n, k)
def custom_kernel(data: input_t) -> output_t:
"""
Execute the block-scaled GEMM kernel.
This is the main entry point called by the evaluation framework.
It converts PyTorch tensors to CuTe tensors, launches the kernel,
and returns the result.
Args:
data: Tuple of (a, b, sfa_ref, sfb_ref, sfa_permuted, sfb_permuted, c) PyTorch tensors
a: [m, k, l] - Input matrix in float4e2m1fn
b: [n, k, l] - Input vector in float4e2m1fn
sfa_ref: [m, k, l] - Scale factors in float8_e4m3fn, used by reference implementation
sfb_ref: [n, k, l] - Scale factors in float8_e4m3fn, used by reference implementation
sfa_permuted: [32, 4, rest_m, 4, rest_k, l] - Scale factors in float8_e4m3fn
sfb_permuted: [32, 4, rest_n, 4, rest_k, l] - Scale factors in float8_e4m3fn
c: [m, n, l] - Output vector in float16
Returns:
Output tensor c with computed results
"""
a, b, _, _, sfa_permuted, sfb_permuted, c = data
# Get dimensions from MxKxL layout
m, k, l = a.shape
n, _, _ = b.shape
# Torch use e2m1_x2 data type, thus k is halved
k = k * 2
if (n, k) not in BENCH_DIMS or m != 128 or l != 1:
return ref_kernel(data)
# Ensure kernel is compiled (will use cached version if available)
# To avoid the compilation overhead, we compile the kernel once and cache it.
compiled_func = _compile_kernel(n, k)
# Create CuTe pointers for A/B/C/SFA/SFB via torch tensor data pointer
a_ptr = make_ptr(ab_dtype, a.data_ptr(), cute.AddressSpace.gmem, assumed_align=16)
b_ptr = make_ptr(ab_dtype, b.data_ptr(), cute.AddressSpace.gmem, assumed_align=16)
c_ptr = make_ptr(
c_dtype, c.data_ptr(), cute.AddressSpace.gmem, assumed_align=C_ALIGNMENT
)
sfa_ptr = make_ptr(
sf_dtype, sfa_permuted.data_ptr(), cute.AddressSpace.gmem, assumed_align=32
)
sfb_ptr = make_ptr(
sf_dtype, sfb_permuted.data_ptr(), cute.AddressSpace.gmem, assumed_align=32
)
# Execute the compiled kernel
compiled_func(
a_ptr,
b_ptr,
sfa_ptr,
sfb_ptr,
c_ptr,
)
return c
scrolls · 4505 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 186844.
⋯ 1910 unchanged linesmax_active_clusters,)+ # Calculate max_tiles: maximum number of work tiles any CTA will process+ # max_tiles = ceil(N_tiles / n_stride) where n_stride = grid[1]+ n_stride = grid[1]+ self.max_tiles = (N_tiles + n_stride - 1) // n_stride+hdebug("\n===================== Kernel Shared Storage =====================")hdebug("num_ab_stage: {}", self.num_ab_stage)hdebug("num_acc_stage: {}", self.num_acc_stage)⋯ 1508 unchanged linesif warp_idx == self.epilog_warp_id[2]:pre_arrive_red_producer_state = red_producer_state.clone()for r2s_tile in cutlass.range_constexpr(self.r2s_tiles):- red_pipeline.producer_acquire(pre_arrive_red_producer_state)+ if cutlass.const_expr(self.max_tiles == 1):+ red_pipeline.producer_acquire(+ pre_arrive_red_producer_state,+ try_acquire_token=cute.Boolean(True),+ )+ else:+ red_pipeline.producer_acquire(pre_arrive_red_producer_state)pre_arrive_red_producer_state.advance()## Wait for accumulator buffer full⋯ 86 unchanged lines# Release the reduction pipeline buffers# select threads are predesignated to release. no need to guard with elect_one.- red_pipeline.consumer_release(red_consumer_state)+ if cutlass.const_expr(self.max_tiles > 1):+ red_pipeline.consumer_release(red_consumer_state)red_consumer_state.advance()with cute.arch.elect_one():⋯ 15 unchanged linesself.epilog_sync_barrier.arrive_and_wait()tmem.free(acc_tmem_ptr)# Finalize red_pipeline- red_pipeline.producer_tail(red_producer_state)+ if cutlass.const_expr(self.max_tiles > 1):+ red_pipeline.producer_tail(red_producer_state)else:self.epilog_sync_barrier.arrive()
scrolls · 47 diff lines total
Best evidence level for this revision: reported
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