biondizzle/nvfp4-megamoe-kernel
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Migrate Stage C kernel (proven cos 0.97) into module - exact copy, no modifications

Browse Source
This commit is contained in:
biondizzle
2026-05-23 05:36:22 +00:00
parent 6c9a9d72f1
commit b39301ebc6
1 changed files with 184 additions and 287 deletions
Download Patch File Download Diff File Expand all files Collapse all files

471
dsv4/kernels/attention/fmha.py
View File

@@ -1,8 +1,8 @@
"""FMHA kernel: QK → online softmax → PV (CuTeDSL, Blackwell SM100).
"""FMHA kernel: QK -> online softmax -> PV (CuTeDSL, Blackwell SM100).
Unified module consolidating Stages A/B/C (TMEM-P, hd=64) and D1 (SMEM-P, hd>64).
use_smem_p=False (TMEM-P): P stored to TMEM via register bridge, PV reads from TMEM.
use_smem_p=True (SMEM-P): P stored to SMEM, PV reads from SMEM (copy TODO — zeroed stub).
Migrated from tests/unit/test_fmha_v3_stage_c.py — Stage C proven path.
P stored to TMEM via register bridge, PV reads from TMEM.
O rescale via correction_rescale atoms, O normalization via TMEM round-trip.
"""
import torch, cutlass, cutlass.cute as cute, cutlass.utils as utils, cutlass.pipeline as pipeline
from cutlass.cute.nvgpu import cpasync, tcgen05
@@ -13,268 +13,155 @@ import cuda.bindings.driver as cuda
import cutlass.torch as ct
import math
HEAD_DIM = 64
class FmhaKernel:
def __init__(self, head_dim=64, s_k=128, scale_softmax=None, kv_stage=2, use_smem_p=False):
self.head_dim = head_dim
class FmhaV3StageC:
def __init__(self, s_k=128, scale_softmax=None):
self.s_k = s_k
self.n_kv_tiles = s_k // 128
self.pv_n_tile = min(head_dim, 256)
self.n_pv_tiles = head_dim // self.pv_n_tile
self.use_smem_p = use_smem_p if use_smem_p is not None else (head_dim > 64)
self.acc_dtype = Float32; self.qk_acc_dtype = Float32
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
self.use_2cta_instrs = False; self.epilog_sync_bar_id = 1
self.cluster_shape_mn = (1, 1); self.cta_group = tcgen05.CtaGroup.ONE
self.epilogue_warp_id = (0, 1, 2, 3); self.mma_warp_id = 4; self.tma_warp_id = 5
self.epilogue_warp_id = (0,1,2,3); self.mma_warp_id = 4; self.tma_warp_id = 5
self.threads_per_cta = 192; self.num_c_stage = 2
self.kv_stage = kv_stage; self.q_stage = 1; self.num_c_stage = 2
self.scale_softmax = scale_softmax if scale_softmax is not None else 1.0 / math.sqrt(head_dim)
self.kv_stage = 2; self.q_stage = 1; self.num_c_stage = 2
self.scale_softmax = scale_softmax if scale_softmax is not None else 1.0 / math.sqrt(HEAD_DIM)
self.scale_softmax_log2 = self.scale_softmax * math.log2(math.e)
def _setup(self, qk_mma, pv_mma):
hd = self.head_dim
qk_ik = cute.size(qk_mma.shape_mnk, mode=[2])
self.qk_mma_tiler = (128, 128, qk_ik * 4)
pv_ik = cute.size(pv_mma.shape_mnk, mode=[2])
self.pv_mma_tiler = (128, self.pv_n_tile, pv_ik * (128 // pv_ik))
self.pv_mma_tiler = (128, HEAD_DIM, pv_ik * (128 // pv_ik))
self.mma_tiler = self.qk_mma_tiler
self.cluster_layout_vmnk = cute.tiled_divide(cute.make_layout((1, 1, 1)), (qk_mma.thr_id.shape,))
self.cta_tile_shape_mnk = (
self.qk_mma_tiler[0] // cute.size(qk_mma.thr_id.shape),
self.pv_n_tile,
self.qk_mma_tiler[2],
)
self.cluster_layout_vmnk = cute.tiled_divide(cute.make_layout((1,1,1)), (qk_mma.thr_id.shape,))
self.cta_tile_shape_mnk = (self.qk_mma_tiler[0]//cute.size(qk_mma.thr_id.shape), HEAD_DIM, self.qk_mma_tiler[2])
self.c_layout = LayoutEnum.ROW_MAJOR
self.epi_tile = utils.sm100.compute_epilogue_tile_shape(
self.cta_tile_shape_mnk, False, self.c_layout, self.o_dtype
)
self.epi_tile = utils.sm100.compute_epilogue_tile_shape(self.cta_tile_shape_mnk, False, self.c_layout, self.o_dtype)
self.num_ab_stage = 1; self.num_acc_stage = 1
# SMEM layouts
self.q_smem_s = utils.sm100.make_smem_layout_a(qk_mma, self.qk_mma_tiler, self.q_dtype, self.q_stage)
self.k_smem_s = utils.sm100.make_smem_layout_b(qk_mma, self.qk_mma_tiler, self.q_dtype, self.kv_stage)
self.v_smem_s = utils.sm100.make_smem_layout_b(pv_mma, self.pv_mma_tiler, self.q_dtype, self.kv_stage)
# P SMEM: always allocate (PV A-operand SMEM layout); used directly in SMEM-P, as TMEM alias in TMEM-P
self.p_smem_s = utils.sm100.make_smem_layout_a(pv_mma, self.pv_mma_tiler, self.q_dtype, 1)
self.c_smem_s = utils.sm100.make_smem_layout_epi(self.o_dtype, self.c_layout, self.epi_tile, 2)
# TMEM layout depends on path
qk_thr = qk_mma.get_slice(0)
qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
self.p_tmem_s = utils.sm100.make_smem_layout_a(pv_mma, self.pv_mma_tiler, self.q_dtype, 1)
qk_thr = qk_mma.get_slice(0); qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
tStS = qk_thr.make_fragment_C(qk_as)
pv_thr = pv_mma.get_slice(0)
pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
pv_thr = pv_mma.get_slice(0); pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
tOtO = pv_thr.make_fragment_C(pv_as)
if not self.use_smem_p:
# TMEM-P: S at 0, P at 32, O after P and S
self.tmem_s0_offset = 0
self.tmem_p0_offset = 32
s_cols = self.qk_mma_tiler[1]
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
p_end = self.tmem_p0_offset + p_cols_fp32
o_after = max(s_cols, p_end)
self.tmem_o0_offset = ((o_after + 31) // 32) * 32
o_cols = find_tmem_tensor_col_offset(tOtO)
total = self.tmem_o0_offset + o_cols
else:
# SMEM-P: S and O share TMEM (sequential, no P in TMEM)
self.tmem_s0_offset = 0
self.tmem_o0_offset = 0
s_cols = self.qk_mma_tiler[1]
o_cols = find_tmem_tensor_col_offset(tOtO)
total = max(s_cols, o_cols)
self.tmem_s0_offset = 0; self.tmem_p0_offset = 32
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
p_end = self.tmem_p0_offset + p_cols_fp32
s_cols = self.qk_mma_tiler[1]
o_after = max(s_cols, p_end)
self.tmem_o0_offset = ((o_after + 31) // 32) * 32
o_cols = find_tmem_tensor_col_offset(tOtO)
total = self.tmem_o0_offset + o_cols
self.num_tmem_alloc_cols = 1
while self.num_tmem_alloc_cols < total:
self.num_tmem_alloc_cols *= 2
if self.num_tmem_alloc_cols > 512:
print(f"⚠️ TMEM BUDGET: {self.num_tmem_alloc_cols} cols (hd={hd})")
# P TMEM layout (PV A-operand SMEM layout — used to alias QK C-fragment in TMEM)
self.p_tmem_s = utils.sm100.make_smem_layout_a(pv_mma, self.pv_mma_tiler, self.q_dtype, 1)
# TMA bytes
cta = cute.size(qk_mma.thr_id.shape)
q_s = cute.slice_(self.q_smem_s, (None, None, None, 0))
k_s = cute.slice_(self.k_smem_s, (None, None, None, 0))
v_s = cute.slice_(self.v_smem_s, (None, None, None, 0))
q_s = cute.slice_(self.q_smem_s,(None,None,None,0))
k_s = cute.slice_(self.k_smem_s,(None,None,None,0))
v_s = cute.slice_(self.v_smem_s,(None,None,None,0))
self.q_tx_bytes = cute.size_in_bytes(self.q_dtype, q_s) * cta
self.kv_tx_bytes = (cute.size_in_bytes(self.q_dtype, k_s) + cute.size_in_bytes(self.q_dtype, v_s)) * cta
self.kv_tx_bytes = (cute.size_in_bytes(self.q_dtype, k_s) +
cute.size_in_bytes(self.q_dtype, v_s)) * cta
@cute.jit
def __call__(self, q, k, v, c, stream):
self.q_dtype = q.element_type; self.o_dtype = c.element_type; self.c_dtype = self.o_dtype
self.a_major = LayoutEnum.from_tensor(q).mma_major_mode()
self.b_major = LayoutEnum.from_tensor(k).mma_major_mode()
v_n = self.pv_n_tile
v_fmha = cute.make_tensor(v.iterator, cute.make_layout((v_n, self.s_k, 1), stride=(1, v_n, v_n * self.s_k)))
v_fmha = cute.make_tensor(
v.iterator,
cute.make_layout(
(HEAD_DIM, self.s_k, 1),
stride=(1, HEAD_DIM, HEAD_DIM * self.s_k),
),
)
self.v_major = LayoutEnum.from_tensor(v_fmha).mma_major_mode()
self.c_layout = LayoutEnum.from_tensor(c)
qk_mma = utils.sm100.make_trivial_tiled_mma(
self.q_dtype, self.q_dtype, self.a_major, self.b_major, self.qk_acc_dtype,
self.cta_group, (128, 128), tcgen05.OperandSource.SMEM,
)
pv_src = tcgen05.OperandSource.SMEM if self.use_smem_p else tcgen05.OperandSource.TMEM
# When PV reads P from TMEM, P has K-major layout (QK C-fragment alias).
# When PV reads P from SMEM, P has Q's major mode (loaded into SMEM).
pv_a_major = self.a_major if self.use_smem_p else cute.nvgpu.OperandMajorMode.K
pv_mma = utils.sm100.make_trivial_tiled_mma(
self.q_dtype, self.q_dtype, pv_a_major, self.v_major, self.qk_acc_dtype,
self.cta_group, (128, self.pv_n_tile), pv_src,
)
qk_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, self.a_major, self.b_major, self.qk_acc_dtype, self.cta_group, (128,128), tcgen05.OperandSource.SMEM)
pv_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, cute.nvgpu.OperandMajorMode.K, self.v_major, self.qk_acc_dtype, self.cta_group, (128,HEAD_DIM), tcgen05.OperandSource.TMEM)
self._setup(qk_mma, pv_mma)
q_s = cute.slice_(self.q_smem_s, (None, None, None, 0))
k_s = cute.slice_(self.k_smem_s, (None, None, None, 0))
v_s = cute.slice_(self.v_smem_s, (None, None, None, 0))
tma_q, mQ = cute.nvgpu.make_tiled_tma_atom_A(
utils.sm100.cluster_shape_to_tma_atom_A(self.cluster_shape_mn, qk_mma.thr_id),
q, q_s, self.qk_mma_tiler, qk_mma, self.cluster_layout_vmnk.shape,
)
tma_k, mK = cute.nvgpu.make_tiled_tma_atom_B(
utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn, qk_mma.thr_id),
k, k_s, self.qk_mma_tiler, qk_mma, self.cluster_layout_vmnk.shape,
)
tma_v, mV = cute.nvgpu.make_tiled_tma_atom_B(
utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn, pv_mma.thr_id),
v_fmha, v_s, self.pv_mma_tiler, pv_mma, self.cluster_layout_vmnk.shape,
)
epi_s = cute.select(self.c_smem_s, mode=[0, 1])
tma_c, mC = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(), c, epi_s, self.epi_tile)
self._kernel(
qk_mma, pv_mma, tma_q, mQ, tma_k, mK, tma_v, mV, tma_c, mC,
self.cluster_layout_vmnk,
self.q_smem_s, self.k_smem_s, self.v_smem_s, self.p_smem_s, self.p_tmem_s, self.c_smem_s,
self.epi_tile,
).launch(
grid=(1, 1, 1), block=[self.threads_per_cta, 1, 1], stream=stream,
)
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0)); v_s = cute.slice_(self.v_smem_s,(None,None,None,0))
tma_q,mQ = cute.nvgpu.make_tiled_tma_atom_A(utils.sm100.cluster_shape_to_tma_atom_A(self.cluster_shape_mn,qk_mma.thr_id),q,q_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
tma_k,mK = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,qk_mma.thr_id),k,k_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
tma_v,mV = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,pv_mma.thr_id),v_fmha,v_s,self.pv_mma_tiler,pv_mma,self.cluster_layout_vmnk.shape)
epi_s = cute.select(self.c_smem_s,mode=[0,1])
tma_c,mC = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(),c,epi_s,self.epi_tile)
self._kernel(qk_mma,pv_mma,tma_q,mQ,tma_k,mK,tma_v,mV,tma_c,mC,self.cluster_layout_vmnk,self.q_smem_s,self.k_smem_s,self.v_smem_s,self.p_tmem_s,self.c_smem_s,self.epi_tile).launch(grid=(1,1,1),block=[self.threads_per_cta,1,1],stream=stream)
@cute.kernel
def _kernel(
self, qk_mma, pv_mma, tma_q, mQ, tma_k, mK, tma_v, mV, tma_c, mC,
cl_vmnk, q_smem_s, k_smem_s, v_smem_s, p_smem_s, p_tmem_s, c_smem_s, epi_tile,
):
def _kernel(self, qk_mma, pv_mma, tma_q, mQ, tma_k, mK, tma_v, mV, tma_c, mC, cl_vmnk, q_smem_s, k_smem_s, v_smem_s, p_tmem_s, c_smem_s, epi_tile):
warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx())
tidx, _, _ = cute.arch.thread_idx()
use_smem_p = self.use_smem_p
# ── TMA warp: prefetch descriptors ──
tidx,_,_ = cute.arch.thread_idx()
if warp_idx == self.tma_warp_id:
cpasync.prefetch_descriptor(tma_q)
cpasync.prefetch_descriptor(tma_k)
cpasync.prefetch_descriptor(tma_v)
cpasync.prefetch_descriptor(tma_c)
cpasync.prefetch_descriptor(tma_q); cpasync.prefetch_descriptor(tma_k); cpasync.prefetch_descriptor(tma_v); cpasync.prefetch_descriptor(tma_c)
# ── Shared storage ──
@cute.struct
class SS:
q_bar: cute.struct.MemRange[cutlass.Int64, self.q_stage * 2]
kv_bar: cute.struct.MemRange[cutlass.Int64, self.kv_stage * 2]
q_bar: cute.struct.MemRange[cutlass.Int64, self.q_stage*2]
kv_bar: cute.struct.MemRange[cutlass.Int64, self.kv_stage*2]
s_bar: cute.struct.MemRange[cutlass.Int64, 2]
acc_bar: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage * 2]
acc_bar: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage*2]
tmem_dealloc: cutlass.Int64; holding: cutlass.Int32
smem = utils.SmemAllocator(); st = smem.allocate(SS)
qp, qc = pipeline.PipelineTmaUmma.create(
barrier_storage=st.q_bar.data_ptr(), num_stages=self.q_stage,
producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),
consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread, 1),
tx_count=self.q_tx_bytes, cta_layout_vmnk=cl_vmnk, defer_sync=True,
).make_participants()
kvp, kvc = pipeline.PipelineTmaUmma.create(
barrier_storage=st.kv_bar.data_ptr(), num_stages=self.kv_stage,
producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),
consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread, 1),
tx_count=self.kv_tx_bytes, cta_layout_vmnk=cl_vmnk, defer_sync=True,
).make_participants()
s_prod, s_cons = pipeline.PipelineUmmaAsync.create(
barrier_storage=st.s_bar.data_ptr(), num_stages=1,
producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),
consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread, 32 * len(self.epilogue_warp_id)),
).make_participants()
softmax_done_bar = pipeline.NamedBarrier(barrier_id=3, num_threads=32 + 32 * len(self.epilogue_warp_id))
final_o_bar = pipeline.NamedBarrier(barrier_id=4, num_threads=32 + 32 * len(self.epilogue_warp_id))
acc_pipe = pipeline.PipelineUmmaAsync.create(
barrier_storage=st.acc_bar.data_ptr(), num_stages=self.num_acc_stage,
producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),
consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread, len(self.epilogue_warp_id)),
cta_layout_vmnk=cl_vmnk, defer_sync=True,
)
tmem_bar = pipeline.NamedBarrier(barrier_id=2, num_threads=32 * len((self.mma_warp_id, *self.epilogue_warp_id)))
tmem = utils.TmemAllocator(
st.holding.ptr, barrier_for_retrieve=tmem_bar,
allocator_warp_id=self.epilogue_warp_id[0],
is_two_cta=cute.size(qk_mma.thr_id.shape) == 2,
two_cta_tmem_dealloc_mbar_ptr=st.tmem_dealloc.ptr,
)
pipeline.pipeline_init_arrive(cluster_shape_mn=cl_vmnk, is_relaxed=True)
qp,qc = pipeline.PipelineTmaUmma.create(barrier_storage=st.q_bar.data_ptr(),num_stages=self.q_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.q_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
kvp,kvc = pipeline.PipelineTmaUmma.create(barrier_storage=st.kv_bar.data_ptr(),num_stages=self.kv_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.kv_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
s_prod,s_cons = pipeline.PipelineUmmaAsync.create(barrier_storage=st.s_bar.data_ptr(),num_stages=1,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32*len(self.epilogue_warp_id))).make_participants()
softmax_done_bar = pipeline.NamedBarrier(barrier_id=3, num_threads=32 + 32*len(self.epilogue_warp_id))
final_o_bar = pipeline.NamedBarrier(barrier_id=4, num_threads=32 + 32*len(self.epilogue_warp_id))
acc_pipe = pipeline.PipelineUmmaAsync.create(barrier_storage=st.acc_bar.data_ptr(),num_stages=self.num_acc_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,len(self.epilogue_warp_id)),cta_layout_vmnk=cl_vmnk,defer_sync=True)
tmem_bar = pipeline.NamedBarrier(barrier_id=2,num_threads=32*len((self.mma_warp_id,*self.epilogue_warp_id)))
tmem = utils.TmemAllocator(st.holding.ptr,barrier_for_retrieve=tmem_bar,allocator_warp_id=self.epilogue_warp_id[0],is_two_cta=cute.size(qk_mma.thr_id.shape)==2,two_cta_tmem_dealloc_mbar_ptr=st.tmem_dealloc.ptr)
pipeline.pipeline_init_arrive(cluster_shape_mn=cl_vmnk,is_relaxed=True)
# ── SMEM tensors ──
sQ = smem.allocate_tensor(element_type=self.q_dtype, layout=q_smem_s.outer, byte_alignment=128, swizzle=q_smem_s.inner)
sK = smem.allocate_tensor(element_type=self.q_dtype, layout=k_smem_s.outer, byte_alignment=128, swizzle=k_smem_s.inner)
sV = smem.allocate_tensor(element_type=self.q_dtype, layout=v_smem_s.outer, byte_alignment=128, swizzle=v_smem_s.inner)
sP = smem.allocate_tensor(element_type=self.q_dtype, layout=p_smem_s.outer, byte_alignment=128, swizzle=p_smem_s.inner)
sC = smem.allocate_tensor(element_type=self.o_dtype, layout=c_smem_s.outer, byte_alignment=128, swizzle=c_smem_s.inner)
sQ = smem.allocate_tensor(element_type=self.q_dtype,layout=q_smem_s.outer,byte_alignment=128,swizzle=q_smem_s.inner)
sK = smem.allocate_tensor(element_type=self.q_dtype,layout=k_smem_s.outer,byte_alignment=128,swizzle=k_smem_s.inner)
sV = smem.allocate_tensor(element_type=self.q_dtype,layout=v_smem_s.outer,byte_alignment=128,swizzle=v_smem_s.inner)
sC = smem.allocate_tensor(element_type=self.o_dtype,layout=c_smem_s.outer,byte_alignment=128,swizzle=c_smem_s.inner)
# ── Gmem tensors ──
gQ = cute.local_tile(mQ, cute.slice_(self.qk_mma_tiler, (None, 0, None)), (None, None, None))
gK = cute.local_tile(mK, cute.slice_(self.qk_mma_tiler, (0, None, None)), (None, None, None))
gV = cute.local_tile(mV, cute.slice_(self.pv_mma_tiler, (0, None, None)), (None, None, None))
gC = cute.local_tile(mC, cute.slice_(self.pv_mma_tiler, (None, None, 0)), (None, None, None))
gQ = cute.local_tile(mQ,cute.slice_(self.qk_mma_tiler,(None,0,None)),(None,None,None))
gK = cute.local_tile(mK,cute.slice_(self.qk_mma_tiler,(0,None,None)),(None,None,None))
gV = cute.local_tile(mV,cute.slice_(self.pv_mma_tiler,(0,None,None)),(None,None,None))
gC = cute.local_tile(mC,cute.slice_(self.pv_mma_tiler,(None,None,0)),(None,None,None))
n_kv_tiles = cute.size(gK, mode=[3])
# ── Thread partitions ──
qk_thr = qk_mma.get_slice(0); pv_thr = pv_mma.get_slice(0)
tCgQ = qk_thr.partition_A(gQ); tCgK = qk_thr.partition_B(gK)
tCgV = pv_thr.partition_B(gV); tCgC = pv_thr.partition_C(gC)
a_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,0,None,0)).shape)
tAsQ,tAgQ = cpasync.tma_partition(tma_q,0,a_lay,cute.group_modes(sQ,0,3),cute.group_modes(tCgQ,0,3))
b_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,None,0,0)).shape)
tBsK,tBgK = cpasync.tma_partition(tma_k,0,b_lay,cute.group_modes(sK,0,3),cute.group_modes(tCgK,0,3))
tVsV,tVgV = cpasync.tma_partition(tma_v,0,b_lay,cute.group_modes(sV,0,3),cute.group_modes(tCgV,0,3))
tAgQ = tAgQ[(None,0,None,0)]; tBgK = tBgK[(None,0,None,0)]; tVgV = tVgV[(None,0,None,0)]
a_lay = cute.make_layout(cute.slice_(cl_vmnk, (0, 0, None, 0)).shape)
tAsQ, tAgQ = cpasync.tma_partition(tma_q, 0, a_lay, cute.group_modes(sQ, 0, 3), cute.group_modes(tCgQ, 0, 3))
b_lay = cute.make_layout(cute.slice_(cl_vmnk, (0, None, 0, 0)).shape)
tBsK, tBgK = cpasync.tma_partition(tma_k, 0, b_lay, cute.group_modes(sK, 0, 3), cute.group_modes(tCgK, 0, 3))
tVsV, tVgV = cpasync.tma_partition(tma_v, 0, b_lay, cute.group_modes(sV, 0, 3), cute.group_modes(tCgV, 0, 3))
tAgQ = tAgQ[(None, 0, None, 0)]; tBgK = tBgK[(None, 0, None, 0)]; tVgV = tVgV[(None, 0, None, 0)]
# Register fragments
tCrQ = qk_mma.make_fragment_A(sQ); tCrK = qk_mma.make_fragment_B(sK)
tCrV = pv_mma.make_fragment_B(sV)
tCrP = pv_mma.make_fragment_A(sP) # used in SMEM-P path
# ── TMEM: S (QK result) ──
qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
tStS = qk_thr.make_fragment_C(qk_as)
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
# ── TMEM: O (PV result) ──
pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
tOtO = pv_thr.make_fragment_C(pv_as)
tOtO0 = cute.make_tensor(tOtO.iterator + self.tmem_o0_offset, tOtO.layout)
# ── PV A-operand: always define both tOrP0 (TMEM) and tCrP (SMEM) ──
# CuTeDSL can't propagate variables across if/else regions, so we
# unconditionally compute both and the unused one is dead-code-eliminated.
tP = cute.make_tensor(tStS.iterator, p_tmem_s.outer)
tOrP_base = pv_thr.make_fragment_A(tP)
tOrP = tOrP_base[(None, None, None, 0)]
tOrP = tOrP_base[(None,None,None,0)]
tOrP0 = cute.make_tensor(
tOrP.iterator + self.qk_acc_dtype.width // self.q_dtype.width * self.tmem_p0_offset,
tOrP.layout,
)
tCrP = pv_mma.make_fragment_A(sP)
tOrP.layout)
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_as, self.num_acc_stage))
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
# ══════════════════════════════════════════════════════════════
# TMA LOAD WARP
# ══════════════════════════════════════════════════════════════
# ===== TMA LOAD warp =====
if warp_idx == self.tma_warp_id:
qp.reset(); qh = qp.acquire_and_advance()
cute.copy(tma_q, tAgQ[(None, Int32(0))], tAsQ[(None, qh.index)], tma_bar_ptr=qh.barrier)
@@ -287,57 +174,41 @@ class FmhaKernel:
pk = cutlass.Boolean(1)
kvp.tail()
# ══════════════════════════════════════════════════════════════
# MMA WARP
# ══════════════════════════════════════════════════════════════
# ===== MMA warp =====
if warp_idx == self.mma_warp_id:
tmem.wait_for_alloc()
qc.reset(); qh = qc.wait_and_advance(); qh.release()
kvc.reset(); pk = kvc.try_wait()
acc_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
acc_pipe.producer_acquire(acc_st)
for kt in range(self.n_kv_tiles):
kvh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
sh = s_prod.acquire_and_advance()
# QK GEMM → S in TMEM
qk_mma.set(tcgen05.Field.ACCUMULATE, False)
for kb in cutlass.range(cute.size(tCrQ, mode=[2]), unroll_full=True):
cute.gemm(qk_mma, tStS0, tCrQ[(None, None, kb, 0)], tCrK[(None, None, kb, kvh.index)], tStS0)
cute.gemm(qk_mma, tStS0, tCrQ[(None,None,kb,0)], tCrK[(None,None,kb,kvh.index)], tStS0)
qk_mma.set(tcgen05.Field.ACCUMULATE, True)
cute.arch.fence_view_async_tmem_store()
sh.commit()
softmax_done_bar.arrive_and_wait()
# PV GEMM → O in TMEM
pv_mma.set(tcgen05.Field.ACCUMULATE, kt != 0)
if not use_smem_p:
# TMEM-P: P from TMEM
for kb in cutlass.range(cute.size(tOrP0, mode=[2]), unroll_full=True):
cute.gemm(pv_mma, tOtO0, tOrP0[(None, None, kb)], tCrV[(None, None, kb, kvh.index)], tOtO0)
else:
# SMEM-P: P from SMEM
for kb in cutlass.range(cute.size(tCrP, mode=[2]), unroll_full=True):
cute.gemm(pv_mma, tOtO0, tCrP[(None, None, kb, 0)], tCrV[(None, None, kb, kvh.index)], tOtO0)
pv_mma.set(tcgen05.Field.ACCUMULATE, True)
for kb in cutlass.range(cute.size(tOrP0, mode=[2]), unroll_full=True):
cute.gemm(pv_mma, tOtO0, tOrP0[(None,None,kb)], tCrV[(None,None,kb,kvh.index)], tOtO0)
pv_mma.set(tcgen05.Field.ACCUMULATE, True)
cute.arch.fence_view_async_tmem_store()
kvh.release()
acc_pipe.producer_commit(acc_st); acc_st.advance()
final_o_bar.arrive()
acc_pipe.producer_tail(acc_st)
# ══════════════════════════════════════════════════════════════
# SOFTMAX + EPILOGUE WARPS
# ══════════════════════════════════════════════════════════════
# ===== SOFTMAX + CORRECTION EPILOGUE warps =====
if warp_idx < self.mma_warp_id:
tmem.allocate(self.num_tmem_alloc_cols)
tmem.wait_for_alloc()
tmem_ptr = tmem.retrieve_ptr(self.qk_acc_dtype)
sfw_idx = tidx % (32 * len(self.epilogue_warp_id))
# ── S load setup ──
# S load atoms
tmem_load_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
tiled_tmem_load = tcgen05.make_tmem_copy(tmem_load_atom, tStS0)
thr_load = tiled_tmem_load.get_slice(sfw_idx)
@@ -346,14 +217,11 @@ class FmhaKernel:
tScS = qk_thr.partition_C(cS)
tTMEM_LOADcS = thr_load.partition_D(tScS)
# ── P store setup (always define both paths — CuTeDSL scoping) ──
# TMEM-P: register bridge for P → TMEM
# P store atoms
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
tStP_layout = cute.composition(tStS.layout, cute.make_layout((self.pv_mma_tiler[0], p_cols_fp32)))
tStP0 = cute.make_tensor(tStS.iterator + self.tmem_p0_offset, tStP_layout)
tmem_store_atom = cute.make_copy_atom(
tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype,
)
tmem_store_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
tiled_tmem_store = tcgen05.make_tmem_copy(tmem_store_atom, tStP0)
thr_store = tiled_tmem_store.get_slice(sfw_idx)
tTMEM_STOREtP = thr_store.partition_D(tStP0)
@@ -361,17 +229,25 @@ class FmhaKernel:
tScP = cute.make_tensor(tScS.iterator, tScP_layout)
tTMEM_STOREcP = thr_store.partition_S(tScP)
# SMEM-P: TODO — make_tiled_copy_C(store_atom, qk_mma) for QK→PV partition remap
row_max = -Float32.inf
row_sum = Float32(0.0)
scale_log2 = Float32(self.scale_softmax_log2)
# ── O rescale / normalization setup (correction_rescale pattern from Stage C) ──
# O rescale atoms (hand-constructed, using composition layout like CUTLASS correction_rescale)
corr_tile_size = 16
tOcO = pv_thr.partition_C(cS)
tOtO_i_layout = cute.composition(tOtO0.layout, cute.make_layout((128, corr_tile_size)))
tOcO_i_layout = cute.composition(tOcO.layout, cute.make_layout((128, corr_tile_size)))
tOtO_i = cute.make_tensor(tOtO0.iterator, tOtO_i_layout)
tOcO_i = cute.make_tensor(tOcO.iterator, tOcO_i_layout)
tmem_load_o_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.acc_dtype)
tmem_store_o_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.acc_dtype)
tmem_load_o_atom = cute.make_copy_atom(
tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(corr_tile_size)),
self.acc_dtype,
)
tmem_store_o_atom = cute.make_copy_atom(
tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(corr_tile_size)),
self.acc_dtype,
)
tiled_tmem_load_o = tcgen05.make_tmem_copy(tmem_load_o_atom, tOtO_i)
tiled_tmem_store_o = tcgen05.make_tmem_copy(tmem_store_o_atom, tOtO_i)
thr_tmem_load_o = tiled_tmem_load_o.get_slice(sfw_idx)
@@ -379,16 +255,11 @@ class FmhaKernel:
tTMEM_LOADtO = thr_tmem_load_o.partition_S(tOtO_i)
tTMEM_LOADcO = thr_tmem_load_o.partition_D(tOcO_i)
tTMEM_STOREtO = thr_tmem_store_o.partition_D(tOtO_i)
n_corr_tiles = self.head_dim // corr_tile_size
n_corr_tiles = HEAD_DIM // corr_tile_size
# ── Online softmax state ──
row_max = -Float32.inf
row_sum = Float32(0.0)
scale_log2 = Float32(self.scale_softmax_log2)
# ── Softmax loop ──
for kt in range(self.n_kv_tiles):
si_handle = s_cons.wait_and_advance()
tTMEM_LOADrS = cute.make_rmem_tensor(tTMEM_LOADcS.shape, self.qk_acc_dtype)
cute.copy(tiled_tmem_load, tTMEM_LOADtS, tTMEM_LOADrS)
cute.arch.fence_view_async_tmem_load()
@@ -397,8 +268,6 @@ class FmhaKernel:
frg_cnt = 4
frg_tile = cute.size(tTMEM_LOADrS) // frg_cnt
tTMEM_LOADrS_frg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(frg_tile))
# Row max
for j in range(frg_cnt):
for k in range(cute.size(tTMEM_LOADrS_frg, mode=[0])):
row_max = cute.arch.fmax(row_max, tTMEM_LOADrS_frg[k, j] * scale_log2)
@@ -406,87 +275,114 @@ class FmhaKernel:
row_max_safe = row_max
if row_max == -cutlass.Float32.inf:
row_max_safe = Float32(0.0)
acc_scale_ = old_row_max - row_max_safe
acc_scale = cute.math.exp2(acc_scale_, fastmath=True)
if old_row_max == -cutlass.Float32.inf:
acc_scale = Float32(0.0)
row_sum *= acc_scale
rP_words = cute.make_rmem_tensor(tTMEM_STOREcP.shape, self.qk_acc_dtype)
rP_bf16 = cute.make_tensor(cute.recast_ptr(rP_words.iterator, dtype=self.q_dtype), tTMEM_LOADrS.layout)
minus_row_max = Float32(0.0) - row_max_safe
# Softmax + P store
if not use_smem_p:
# TMEM-P: register bridge — FP32 backing, BF16 view
rP_words = cute.make_rmem_tensor(tTMEM_STOREcP.shape, self.qk_acc_dtype)
rP_bf16 = cute.make_tensor(
cute.recast_ptr(rP_words.iterator, dtype=self.q_dtype),
tTMEM_LOADrS.layout,
)
rP_bf16_frg = cute.logical_divide(rP_bf16, cute.make_layout(frg_tile))
rP_bf16_frg = cute.logical_divide(rP_bf16, cute.make_layout(frg_tile))
for j in range(frg_cnt):
for k in range(cute.size(tTMEM_LOADrS_frg, mode=[0])):
tTMEM_LOADrS_frg[k, j] = tTMEM_LOADrS_frg[k, j] * scale_log2 + minus_row_max
tTMEM_LOADrS_frg[k, j] = cute.math.exp2(tTMEM_LOADrS_frg[k, j], fastmath=True)
row_sum = row_sum + tTMEM_LOADrS_frg[k, j]
s_vec = tTMEM_LOADrS_frg[None, j].load()
rP_bf16_frg[None, j].store(s_vec.to(self.q_dtype))
for j in range(frg_cnt):
for k in range(cute.size(tTMEM_LOADrS_frg, mode=[0])):
tTMEM_LOADrS_frg[k, j] = tTMEM_LOADrS_frg[k, j] * scale_log2 + minus_row_max
tTMEM_LOADrS_frg[k, j] = cute.math.exp2(tTMEM_LOADrS_frg[k, j], fastmath=True)
row_sum = row_sum + tTMEM_LOADrS_frg[k, j]
s_vec = tTMEM_LOADrS_frg[None, j].load()
rP_bf16_frg[None, j].store(s_vec.to(self.q_dtype))
cute.copy(tiled_tmem_store, rP_words, tTMEM_STOREtP)
cute.arch.fence_view_async_tmem_store()
cute.copy(tiled_tmem_store, rP_words, tTMEM_STOREtP)
cute.arch.fence_view_async_tmem_store()
else:
# SMEM-P: compute softmax, write P to SMEM (TODO)
for j in range(frg_cnt):
for k in range(cute.size(tTMEM_LOADrS_frg, mode=[0])):
tTMEM_LOADrS_frg[k, j] = tTMEM_LOADrS_frg[k, j] * scale_log2 + minus_row_max
tTMEM_LOADrS_frg[k, j] = cute.math.exp2(tTMEM_LOADrS_frg[k, j], fastmath=True)
row_sum = row_sum + tTMEM_LOADrS_frg[k, j]
# TODO: Write P to SMEM using make_tiled_copy_C(store_atom, qk_mma)
# to partition threads by QK's C-fragment, then copy to p_smem_s layout.
# STUB: zero P in SMEM for now
for j in cutlass.range(cute.size(sP), vectorize=True):
sP[j] = BFloat16(0.0)
cute.arch.fence_proxy("async.shared", space="cta")
si_handle.release()
softmax_done_bar.arrive()
# ── Per-tile O rescale (multiply O by acc_scale when kt > 0) ──
# Per-tile O rescale (hand-constructed atoms with logical_divide layout)
if kt > 0:
tTMrO = cute.make_rmem_tensor((tTMEM_LOADcO.shape, 128 // corr_tile_size), self.acc_dtype)
tTMrO = cute.make_rmem_tensor(
(tTMEM_LOADcO.shape, 128 // corr_tile_size), self.acc_dtype
)
for i in range(n_corr_tiles):
tTMrO_i_ = tTMrO[None, i]
tTMrO_i_layout = cute.composition(tTMrO_i_.layout, cute.make_layout(tTMrO.shape[0]))
tTMrO_i_layout = cute.composition(
tTMrO_i_.layout, cute.make_layout(tTMrO.shape[0])
)
tTMrO_i = cute.make_tensor(tTMrO_i_.iterator, tTMrO_i_layout)
tTMEM_LOADtO_i = cute.make_tensor(tTMEM_LOADtO.iterator + i * corr_tile_size, tTMEM_LOADtO.layout)
tTMEM_STOREtO_i = cute.make_tensor(tTMEM_STOREtO.iterator + i * corr_tile_size, tTMEM_STOREtO.layout)
tTMEM_LOADtO_i = cute.make_tensor(
tTMEM_LOADtO.iterator + i * corr_tile_size,
tTMEM_LOADtO.layout,
)
tTMEM_STOREtO_i = cute.make_tensor(
tTMEM_STOREtO.iterator + i * corr_tile_size,
tTMEM_STOREtO.layout,
)
cute.copy(tiled_tmem_load_o, tTMEM_LOADtO_i, tTMrO_i)
for k in cutlass.range(cute.size(tTMrO_i), vectorize=True):
tTMrO_i[k] = tTMrO_i[k] * acc_scale
cute.copy(tiled_tmem_store_o, tTMrO_i, tTMEM_STOREtO_i)
cute.arch.fence_view_async_tmem_store()
# ── Wait for MMA's final PV GEMM ──
si_handle.release()
softmax_done_bar.arrive()
# Wait for MMA's PV[N-1] to commit before reading O.
final_o_bar.arrive_and_wait()
# ── O normalization: multiply O by 1/row_sum (TMEM round-trip) ──
inv_row_sum = Float32(1.0) / row_sum
tTMrO = cute.make_rmem_tensor((tTMEM_LOADcO.shape, 128 // corr_tile_size), self.acc_dtype)
# === NO-OP TMEM round-trip: re-map O from MMA layout to epilog layout ===
tTMrO_noop = cute.make_rmem_tensor(
(tTMEM_LOADcO.shape, 128 // corr_tile_size), self.acc_dtype
)
for i in range(n_corr_tiles):
tTMrO_i_ = tTMrO[None, i]
tTMrO_i_layout = cute.composition(tTMrO_i_.layout, cute.make_layout(tTMrO.shape[0]))
tTMrO_i_ = tTMrO_noop[None, i]
tTMrO_i_layout = cute.composition(
tTMrO_i_.layout, cute.make_layout(tTMrO_noop.shape[0])
)
tTMrO_i = cute.make_tensor(tTMrO_i_.iterator, tTMrO_i_layout)
tTMEM_LOADtO_i = cute.make_tensor(tTMEM_LOADtO.iterator + i * corr_tile_size, tTMEM_LOADtO.layout)
tTMEM_STOREtO_i = cute.make_tensor(tTMEM_STOREtO.iterator + i * corr_tile_size, tTMEM_STOREtO.layout)
tTMEM_LOADtO_i = cute.make_tensor(
tTMEM_LOADtO.iterator + i * corr_tile_size,
tTMEM_LOADtO.layout,
)
tTMEM_STOREtO_i = cute.make_tensor(
tTMEM_STOREtO.iterator + i * corr_tile_size,
tTMEM_STOREtO.layout,
)
cute.copy(tiled_tmem_load_o, tTMEM_LOADtO_i, tTMrO_i)
for k in cutlass.range(cute.size(tTMrO_i), vectorize=True):
tTMrO_i[k] = tTMrO_i[k] * inv_row_sum
cute.copy(tiled_tmem_store_o, tTMrO_i, tTMEM_STOREtO_i)
cute.arch.fence_view_async_tmem_store()
# ── Epilogue: TMA store O → global ──
# === Final O normalization: O *= 1/row_sum ===
inv_row_sum = Float32(1.0) / row_sum
tTMrO = cute.make_rmem_tensor(
(tTMEM_LOADcO.shape, 128 // corr_tile_size), self.acc_dtype
)
for i in range(n_corr_tiles):
tTMrO_i_ = tTMrO[None, i]
tTMrO_i_layout = cute.composition(
tTMrO_i_.layout, cute.make_layout(tTMrO.shape[0])
)
tTMrO_i = cute.make_tensor(tTMrO_i_.iterator, tTMrO_i_layout)
tTMEM_LOADtO_i = cute.make_tensor(
tTMEM_LOADtO.iterator + i * corr_tile_size, tTMEM_LOADtO.layout
)
tTMEM_STOREtO_i = cute.make_tensor(
tTMEM_STOREtO.iterator + i * corr_tile_size, tTMEM_STOREtO.layout
)
cute.copy(tiled_tmem_load_o, tTMEM_LOADtO_i, tTMrO_i)
for j in cutlass.range(cute.size(tTMrO_i), vectorize=True):
tTMrO_i[j] = tTMrO_i[j] * inv_row_sum
cute.copy(tiled_tmem_store_o, tTMrO_i, tTMEM_STOREtO_i)
cute.arch.fence_view_async_tmem_store()
# Epilogue: TMEM → SMEM → GMEM via TMA store.
tCtO_base = cute.make_tensor(tmem_ptr + self.tmem_o0_offset, tCtO_fake.layout)
acc_cons_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Consumer, self.num_acc_stage)
acc_cons_st = pipeline.make_pipeline_state(
pipeline.PipelineUserType.Consumer, self.num_acc_stage
)
c_grp = pipeline.CooperativeGroup(pipeline.Agent.Thread, 32 * len(self.epilogue_warp_id))
c_pipe = pipeline.PipelineTmaStore.create(num_stages=self.num_c_stage, producer_group=c_grp)
acc_cons_st = utils.gemm.sm100.epilogue_tma_store(
@@ -495,5 +391,6 @@ class FmhaKernel:
acc_cons_st, acc_pipe, c_pipe,
)
c_pipe.producer_tail()
tmem.relinquish_alloc_permit()
tmem.free(tmem_ptr)