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nvfp4-megamoe-kernel/tests/archive/unit_test_fmha_v3_proper.py
biondizzle c395b279d2 Clean up tests: archive superseded files, keep only essential unit tests 
Kept in tests/unit/:
- test_fmha_v3.py (stages A+B)
- test_fmha_v3_diag.py (identity softmax, n=128+256)
- test_fmha_v3_stage_c.py (real softmax, n=128 cos 0.999998)
- layertest.py + cudagraph_test.py (required for every change)
- infrastructure: cache, custom_op, cutedsl, router, fp4, fused, interleave

Archived: 19 superseded unit tests + 10 root-level scratch files
Root level: only fmha_v3_stage_c_example7.py remains (now in unit/)
2026-05-22 20:25:27 +00:00

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"""
FMHA v3 Proper: 11-warp with correction warp group + epilogue warp.
Warp layout: softmax(0-3), correction(4-7), MMA(8), TMA(9), epilogue(10)
"""
import math, torch, cutlass, cutlass.cute as cute, cutlass.utils as utils, cutlass.pipeline as pipeline
from cutlass.cute.nvgpu import cpasync, tcgen05
from cutlass import Float32, BFloat16, Int32, Boolean, const_expr
from cutlass.utils import LayoutEnum
from cutlass.utils.tmem_allocator import find_tmem_tensor_col_offset
import cuda.bindings.driver as cuda, cutlass.torch as ct
HEAD_DIM = 64
class FmhaV3Proper:
def __init__(self):
self.qk_acc_dtype = Float32; self.pv_acc_dtype = Float32
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
self.use_2cta_instrs = False; self.cluster_shape_mn = (1, 1)
self.cta_group = tcgen05.CtaGroup.ONE
self.softmax_warp_ids = (0,1,2,3)
self.correction_warp_ids = (4,5,6,7)
self.mma_warp_id = 8; self.tma_warp_id = 9; self.epilogue_warp_id = 10
self.threads_per_cta = 352
self.q_stage = 1; self.kv_stage = 2; self.num_acc_stage = 1
self.mma_softmax_stage = 1; self.softmax_corr_stage = 1
self.mma_corr_stage = 2; self.epi_stage = 2; self.num_c_stage = 2
self.scale_softmax_log2 = Float32(1.0 / math.sqrt(HEAD_DIM) * math.log2(math.e))
def _setup(self, qk_mma, pv_mma):
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, 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), 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.num_ab_stage = 1
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)
self.c_smem_s = utils.sm100.make_smem_layout_epi(self.o_dtype, self.c_layout, self.epi_tile, 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])
tOtO = pv_thr.make_fragment_C(pv_as)
self.tmem_s0_offset = 0; self.tmem_p0_offset = 32; self.tmem_vec0_offset = 0
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
o_after = max(self.qk_mma_tiler[1], self.tmem_p0_offset + p_cols_fp32)
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
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))
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) * 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_fmha = cute.make_tensor(v.iterator, cute.make_layout((HEAD_DIM, 128, 1), stride=(1, HEAD_DIM, HEAD_DIM * 128)))
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_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, cute.nvgpu.OperandMajorMode.K, self.v_major, self.pv_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_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_tmem_s, c_smem_s, epi_tile):
warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx())
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)
@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]
mma_si_bar: cute.struct.MemRange[cutlass.Int64, self.mma_softmax_stage*2]
si_corr_bar: cute.struct.MemRange[cutlass.Int64, self.softmax_corr_stage*2]
mma_corr_bar: cute.struct.MemRange[cutlass.Int64, self.mma_corr_stage*2]
corr_epi_bar: cute.struct.MemRange[cutlass.Int64, self.epi_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()
mma_si_prod,mma_si_cons = pipeline.PipelineUmmaAsync.create(barrier_storage=st.mma_si_bar.data_ptr(),num_stages=self.mma_softmax_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32*len(self.softmax_warp_ids))).make_participants()
si_corr_prod,si_corr_cons = pipeline.PipelineAsync.create(barrier_storage=st.si_corr_bar.data_ptr(),num_stages=self.softmax_corr_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32*len(self.softmax_warp_ids)),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32*len(self.correction_warp_ids))).make_participants()
mma_corr_prod,mma_corr_cons = pipeline.PipelineUmmaAsync.create(barrier_storage=st.mma_corr_bar.data_ptr(),num_stages=self.mma_corr_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32*len(self.correction_warp_ids))).make_participants()
corr_epi_prod,corr_epi_cons = pipeline.PipelineAsync.create(barrier_storage=st.corr_epi_bar.data_ptr(),num_stages=self.epi_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32*len(self.correction_warp_ids)),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32)).make_participants()
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.correction_warp_ids)),cta_layout_vmnk=cl_vmnk,defer_sync=True)
tmem_bar = pipeline.NamedBarrier(barrier_id=2,num_threads=32*len((self.mma_warp_id,*self.softmax_warp_ids)))
tmem = utils.TmemAllocator(st.holding.ptr,barrier_for_retrieve=tmem_bar,allocator_warp_id=self.softmax_warp_ids[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)
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)
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])
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)]
tCrQ = qk_mma.make_fragment_A(sQ); tCrK = qk_mma.make_fragment_B(sK); tCrV = pv_mma.make_fragment_B(sV)
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)
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)
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)]
tOrP0 = cute.make_tensor(tOrP.iterator + self.qk_acc_dtype.width // self.q_dtype.width * self.tmem_p0_offset, 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
if warp_idx == self.tma_warp_id:
qp.reset(); qh = qp.acquire_and_advance()
cute.copy(tma_q,tAgQ[(None,qh.count)],tAsQ[(None,qh.index)],tma_bar_ptr=qh.barrier); qp.tail()
kvp.reset(); pk = kvp.try_acquire()
for kt in cutlass.range(n_kv_tiles,unroll=1):
kh = kvp.acquire_and_advance(pk); cute.copy(tma_k,tBgK[(None,kh.count)],tBsK[(None,kh.index)],tma_bar_ptr=kh.barrier); pk = cutlass.Boolean(1)
vh = kvp.acquire_and_advance(pk); cute.copy(tma_v,tVgV[(None,vh.count)],tVsV[(None,vh.index)],tma_bar_ptr=vh.barrier); pk = cutlass.Boolean(1)
kvp.tail()
# MMA
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(n_kv_tiles):
kh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
sh = mma_si_prod.acquire_and_advance()
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,kh.index)], tStS0)
qk_mma.set(tcgen05.Field.ACCUMULATE, True)
cute.arch.fence_view_async_tmem_store(); sh.commit(); kh.release()
if kt > 0:
o_handle = mma_corr_cons.wait_and_advance(); o_handle.release()
sh2 = mma_si_prod.acquire_and_advance()
vh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
pv_mma.set(tcgen05.Field.ACCUMULATE, kt != 0)
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,vh.index)], tOtO0)
pv_mma.set(tcgen05.Field.ACCUMULATE, True)
cute.arch.fence_view_async_tmem_store(); vh.release()
o_prod_h = mma_corr_prod.acquire_and_advance(); o_prod_h.commit()
o_handle = mma_corr_cons.wait_and_advance(); o_handle.release()
acc_pipe.producer_commit(acc_st); acc_st.advance(); acc_pipe.producer_tail(acc_st)
# SOFTMAX (warps 0-3)
if warp_idx < len(self.softmax_warp_ids):
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.softmax_warp_ids))
scale = self.scale_softmax_log2
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)
tTMEM_LOADtS = thr_load.partition_S(tStS0)
cS = cute.make_identity_tensor((self.qk_mma_tiler[0], self.qk_mma_tiler[1]))
tScS = qk_thr.partition_C(cS); tTMEM_LOADcS = thr_load.partition_D(tScS)
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)
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)
tScP_layout = cute.composition(tScS.layout, cute.make_layout((self.pv_mma_tiler[0], p_cols_fp32)))
tScP = cute.make_tensor(tScS.iterator, tScP_layout); tTMEM_STOREcP = thr_store.partition_S(tScP)
tStS_vec_layout = cute.composition(tStS.layout, cute.make_layout((128, 2)))
tStS_vec = cute.make_tensor(tStS.iterator + self.tmem_vec0_offset, tStS_vec_layout)
tScS_vec_layout = cute.composition(tScS.layout, cute.make_layout((128, 2)))
tScS_vec = cute.make_tensor(tScS.iterator, tScS_vec_layout)
tmem_store_vec_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(2)), self.qk_acc_dtype)
tiled_tmem_store_vec = tcgen05.make_tmem_copy(tmem_store_vec_atom, tStS_vec)
thr_tmem_store_vec = tiled_tmem_store_vec.get_slice(sfw_idx)
tTMEM_STORE_VECtS = thr_tmem_store_vec.partition_D(tStS_vec)
tTMEM_STORE_VECcS = thr_tmem_store_vec.partition_S(tScS_vec)
row_max = -cutlass.Float32.inf; row_sum = cutlass.Float32(0.0)
for kt in range(n_kv_tiles):
si_handle = mma_si_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)
old_row_max = row_max
row_max = tTMEM_LOADrS.load().reduce(cute.ReductionOp.MAX, row_max, 0)
row_max_safe = row_max
if row_max == -cutlass.Float32.inf: row_max_safe = cutlass.Float32(0.0)
vec_handle = si_corr_prod.acquire_and_advance()
tTMEM_STORE_VECrS = cute.make_rmem_tensor(tTMEM_STORE_VECcS.shape, self.qk_acc_dtype)
tTMEM_STORE_VECrS[0] = old_row_max; tTMEM_STORE_VECrS[1] = row_max_safe
cute.copy(tiled_tmem_store_vec, tTMEM_STORE_VECrS, tTMEM_STORE_VECtS)
cute.arch.fence_view_async_tmem_store(); vec_handle.commit()
minus_row_max_scale = (cutlass.Float32(0.0) - row_max_safe) * 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)
frg_cnt = 4; frg_tile = cute.size(tTMEM_LOADrS) // frg_cnt
tTMEM_LOADrS_frg = cute.logical_divide(tTMEM_LOADrS, 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 cutlass.range(cute.size(tTMEM_LOADrS_frg, mode=[0]), vectorize=True):
tTMEM_LOADrS_frg[k, j] = tTMEM_LOADrS_frg[k, j] * scale + minus_row_max_scale
tTMEM_LOADrS_frg[k, j] = cute.math.exp2(tTMEM_LOADrS_frg[k, j], fastmath=True)
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(); si_handle.release()
acc_scale = cute.math.exp2(scale * (old_row_max - row_max_safe), fastmath=True)
row_sum = row_sum * acc_scale
local_row_sum_0 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
local_row_sum_1 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
local_row_sum_2 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
local_row_sum_3 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
reduction_unroll = 4; rfrg_tile = cute.size(tTMEM_LOADrS) // reduction_unroll
tTMEM_LOADrS_rfrg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(rfrg_tile))
for j in cutlass.range_constexpr(0, cute.size(tTMEM_LOADrS_rfrg, mode=[0]), 2):
local_row_sum_0 = cute.arch.add_packed_f32x2(local_row_sum_0, (tTMEM_LOADrS_rfrg[j, 0], tTMEM_LOADrS_rfrg[j+1, 0]))
local_row_sum_1 = cute.arch.add_packed_f32x2(local_row_sum_1, (tTMEM_LOADrS_rfrg[j, 1], tTMEM_LOADrS_rfrg[j+1, 1]))
local_row_sum_2 = cute.arch.add_packed_f32x2(local_row_sum_2, (tTMEM_LOADrS_rfrg[j, 2], tTMEM_LOADrS_rfrg[j+1, 2]))
local_row_sum_3 = cute.arch.add_packed_f32x2(local_row_sum_3, (tTMEM_LOADrS_rfrg[j, 3], tTMEM_LOADrS_rfrg[j+1, 3]))
local_row_sum_0 = cute.arch.add_packed_f32x2(local_row_sum_0, local_row_sum_1)
local_row_sum_2 = cute.arch.add_packed_f32x2(local_row_sum_2, local_row_sum_3)
local_row_sum_0 = cute.arch.add_packed_f32x2(local_row_sum_0, local_row_sum_2)
row_sum = row_sum + local_row_sum_0[0] + local_row_sum_0[1]
# Final vector: (row_sum, row_max)
vec_handle = si_corr_prod.acquire_and_advance()
tTMEM_STORE_VECrS = cute.make_rmem_tensor(tTMEM_STORE_VECcS.shape, self.qk_acc_dtype)
tTMEM_STORE_VECrS[0] = row_sum; tTMEM_STORE_VECrS[1] = row_max
cute.copy(tiled_tmem_store_vec, tTMEM_STORE_VECrS, tTMEM_STORE_VECtS)
cute.arch.fence_view_async_tmem_store(); vec_handle.commit()
si_handle = mma_si_cons.wait_and_advance(); si_corr_prod.acquire(); si_handle.release()
tmem.relinquish_alloc_permit()
# CORRECTION (warps 4-7)
if warp_idx >= len(self.softmax_warp_ids) and warp_idx < len(self.softmax_warp_ids) + len(self.correction_warp_ids):
corr_idx = tidx % (32 * len(self.correction_warp_ids))
scale = self.scale_softmax_log2
# Create tScS from common-scope qk_thr (same as softmax section)
cS_corr = cute.make_identity_tensor((self.qk_mma_tiler[0], self.qk_mma_tiler[1]))
tScS = qk_thr.partition_C(cS_corr)
tStS_vec_layout = cute.composition(tStS.layout, cute.make_layout((128, 2)))
tStS_vec = cute.make_tensor(tStS.iterator + self.tmem_vec0_offset, tStS_vec_layout)
tScS_vec_layout = cute.composition(tScS.layout, cute.make_layout((128, 2)))
tScS_vec = cute.make_tensor(tScS.iterator, tScS_vec_layout)
tmem_load_vec_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(2)), self.qk_acc_dtype)
tiled_tmem_load_vec = tcgen05.make_tmem_copy(tmem_load_vec_atom, tStS_vec)
thr_tmem_load_vec = tiled_tmem_load_vec.get_slice(corr_idx)
tTMEM_LOAD_VECtS = thr_tmem_load_vec.partition_S(tStS_vec)
tTMEM_LOAD_VECcS = thr_tmem_load_vec.partition_D(tScS_vec)
corr_tile_size = 16
cO = cute.make_identity_tensor((self.pv_mma_tiler[0], self.pv_mma_tiler[1]))
tOcO = pv_thr.partition_C(cO)
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)
o_tmem_load_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.pv_acc_dtype)
o_tmem_store_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.pv_acc_dtype)
o_tiled_tmem_load = tcgen05.make_tmem_copy(o_tmem_load_atom, tOtO_i)
o_tiled_tmem_store = tcgen05.make_tmem_copy(o_tmem_store_atom, tOtO_i)
o_thr_load = o_tiled_tmem_load.get_slice(corr_idx)
o_thr_store = o_tiled_tmem_store.get_slice(corr_idx)
tTMEM_LOADtO = o_thr_load.partition_S(tOtO_i)
tTMEM_LOADcO = o_thr_load.partition_D(tOcO_i)
tTMEM_STOREtO = o_thr_store.partition_D(tOtO_i)
o_col_tiles = self.pv_mma_tiler[1] // corr_tile_size
# Ignore first vec (no rescale for first PV)
vec_handle = si_corr_cons.wait_and_advance()
vec_handle.release()
for kt in range(n_kv_tiles):
if kt > 0:
# Wait for vector (old_max, new_max) from softmax
vec_handle = si_corr_cons.wait_and_advance()
tTMEM_LOAD_VECrS = cute.make_rmem_tensor(tTMEM_LOAD_VECcS.shape, self.qk_acc_dtype)
cute.copy(tiled_tmem_load_vec, tTMEM_LOAD_VECtS, tTMEM_LOAD_VECrS)
corr_scale_ = scale * (tTMEM_LOAD_VECrS[0] - tTMEM_LOAD_VECrS[1])
corr_scale = cute.math.exp2(corr_scale_, fastmath=True)
# Wait for O from MMA
o_handle = mma_corr_cons.wait_and_advance()
# correction_rescale: O *= corr_scale in TMEM
tTMrO = cute.make_rmem_tensor((tTMEM_LOADcO.shape, o_col_tiles), self.pv_acc_dtype)
for i in range(o_col_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(o_tiled_tmem_load, tTMEM_LOADtO_i, tTMrO_i)
for j in cutlass.range(cute.size(tTMrO_i), vectorize=True):
tTMrO_i[j] = tTMrO_i[j] * corr_scale
cute.copy(o_tiled_tmem_store, tTMrO_i, tTMEM_STOREtO_i)
cute.arch.fence_view_async_tmem_store()
vec_handle.release()
o_handle.release()
# --- correction_epilog: final normalize O /= row_sum ---
# Wait for final vector (row_sum, row_max) from softmax
vec_handle = si_corr_cons.wait_and_advance()
tTMEM_LOAD_VECrS = cute.make_rmem_tensor(tTMEM_LOAD_VECcS.shape, self.qk_acc_dtype)
cute.copy(tiled_tmem_load_vec, tTMEM_LOAD_VECtS, tTMEM_LOAD_VECrS)
cute.arch.fence_view_async_tmem_load()
vec_handle.release()
inv_row_sum = cutlass.Float32(1.0) / tTMEM_LOAD_VECrS[0]
# Wait for final O from MMA
o_handle = mma_corr_cons.wait_and_advance()
epi_handle = corr_epi_prod.acquire_and_advance()
# Final normalize O in TMEM
tTMrO = cute.make_rmem_tensor((tTMEM_LOADcO.shape, o_col_tiles), self.pv_acc_dtype)
for i in range(o_col_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(o_tiled_tmem_load, 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(o_tiled_tmem_store, tTMrO_i, tTMEM_STOREtO_i)
cute.arch.fence_view_async_tmem_store()
o_handle.release()
epi_handle.commit()
# --- EPILOGUE WARP (warp 10) - TMA store O ---
# After correction normalizes O in TMEM, the epilogue reads O from TMEM,
# writes to SMEM, then TMA stores from SMEM to GMEM.
# For now, the softmax warps (which have tmem_ptr) handle the TMA store
# after correction signals completion. This matches our working 6-warp code's
# epilogue_tma_store pattern.
# The epilogue warp (warp 10) just waits for the signal and does TMA store.
# Since it doesn't have tmem_ptr, we need a different approach.
# Simplest: let the softmax warps also do the TMA store after correction
# signals O is ready. But softmax warps already exited...
#
# Alternative: the epilogue warp uses acc_pipe + epilogue_tma_store
# which reads from TMEM directly.
# For initial test: skip epilogue TMA store, just verify correction works.
# Then add TMA store via a separate mechanism.
#
# Actually, looking at our working 6-warp code, the epilogue_tma_store
# reads from tCtO_base which is a TMEM tensor at tmem_ptr + offset.
# The epilogue warp doesn't have tmem_ptr. BUT it can create the same
# tensor if it knows the address. The MMA warp has it from alloc_tmem.
#
# For the initial version, let the softmax warps do TMA store
# (they have tmem_ptr) after waiting for correction to finish.
# This is a temporary simplification.
if warp_idx == self.epilogue_warp_id:
epi_handle = corr_epi_cons.wait_and_advance()
epi_handle.release()
def test():
import math
torch.manual_seed(42)
for n in [128, 256, 384]:
m, hd = 128, HEAD_DIM
q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device="cuda")
k = torch.randn(n, hd, 1, dtype=torch.bfloat16, device="cuda")
v = torch.randn(n, hd, dtype=torch.bfloat16, device="cuda")
v_kernel = v.unsqueeze(-1)
c = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device="cuda")
qf = q[:,:,0].float(); kf = k[:,:,0].float()
attn = qf @ kf.T / math.sqrt(hd)
ref = torch.softmax(attn, dim=-1) @ v.float()
mQ = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
mK = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
mV = ct.from_dlpack(v_kernel).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_kernel))
mC = ct.from_dlpack(c).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c))
stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
kernel = FmhaV3Proper()
print(f"n={n}: Compiling...", flush=True)
compiled = cute.compile(kernel, mQ, mK, mV, mC, stream)
print(f"n={n}: tmem: s0={kernel.tmem_s0_offset} p0={kernel.tmem_p0_offset} o0={kernel.tmem_o0_offset} vec={kernel.tmem_vec0_offset} alloc={kernel.num_tmem_alloc_cols}", flush=True)
print(f"n={n}: Running...", flush=True)
compiled(mQ, mK, mV, mC, stream)
torch.cuda.synchronize()
out = c[:,:,0].float()
cos = torch.nn.functional.cosine_similarity(out.flatten().unsqueeze(0), ref.flatten().unsqueeze(0)).item()
max_err = (out - ref).abs().max().item()
print(f"FMHA proper n={n}: cosine {cos:.6f} max_err {max_err:.6f} {'PASS' if cos >= 0.999 else 'FAIL'}", flush=True)
if __name__ == "__main__":
test()
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