Stage C: integrate example3 multi-tile fixes into unit test
- Combined K+V barrier (one acquire per kt, kvh.count == kt) - O rescale for kt > 0 (online softmax O correction) - final_o_bar sync (MMA signals before producer_tail) - s_k as constructor param (compile-time for V layout) - kv_tx_bytes covers both K and V transfers - Test covers n=128, 256, 512, 1024
This commit is contained in:
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tests/fmha_v3_stage_c_example3.py
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tests/fmha_v3_stage_c_example3.py
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"""
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FMHA v3 Stage-C Multi-Tile (combined K+V barrier).
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Replaces the interleaved K-then-V acquires with a single acquire per kt that
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loads K and V onto the SAME barrier slot. tx_count is sized for K+V together.
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With one acquire per tile, the pipeline `count` returned by acquire_and_advance
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goes 0, 1, 2, ... and matches the KV tile index directly — no interleaving
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problem, and no need for Python ints or integer-division gymnastics in the
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TMA coordinate. kvh.count stays a first-class pipeline state value, which is
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the form cute.copy accepts.
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Changes vs the single-tile file:
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1. s_k MUST equal actual n. v_fmha layout uses s_k as the V sequence dim.
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2. kv pipeline carries combined K+V per stage:
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- tx_count = K_bytes + V_bytes
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- producer: one acquire per kt, K and V copies share kvh.barrier
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- consumer: one wait per kt, kvh.index used for both sK and sV reads
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- release happens after PV (no separate K-early-release path)
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Bonus: this also fixes the unused-SMEM-slot quirk where the original kernel
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only ever used sK[0] and sV[1] because of the interleaved count.
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3. O rescale between KV tiles re-enabled (gated on kt > 0). Lives in softmax
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body BEFORE softmax_done_bar.arrive(), so MMA's PV[kt] reads a rescaled O.
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4. Explicit MMA→softmax sync before the final normalize.
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final_o_bar is a NamedBarrier with 32 MMA + 128 softmax threads. MMA
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.arrive() between acc_pipe.producer_commit and producer_tail; softmax
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.arrive_and_wait() before reading O. Without this, softmax can race MMA's PV[N-1] and divide a
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partially-accumulated O by row_sum. The single-tile test masked the race
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because the timing happened to work.
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Alternative if combined-barrier ever bites: keep the interleaved pipeline and
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index GMEM by `kh.count // 2` / `vh.count // 2`. Requires CuTeDSL to support
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Int32 floor-division in a TMA coordinate. Not used here.
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"""
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import torch, cutlass, cutlass.cute as cute, cutlass.utils as utils, cutlass.pipeline as pipeline
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from cutlass.cute.nvgpu import cpasync, tcgen05
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from cutlass import Float32, BFloat16, Int32, Boolean, const_expr
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from cutlass.utils import LayoutEnum
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from cutlass.utils.tmem_allocator import find_tmem_tensor_col_offset
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import cuda.bindings.driver as cuda
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import cutlass.torch as ct
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import math
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HEAD_DIM = 64
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class FmhaV3StageCMulti:
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def __init__(self, s_k=128, scale_softmax=None):
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# s_k MUST equal actual sequence length n.
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self.s_k = s_k
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self.acc_dtype = Float32; self.qk_acc_dtype = Float32
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self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
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self.use_2cta_instrs = False; self.epilog_sync_bar_id = 1
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self.cluster_shape_mn = (1, 1); self.cta_group = tcgen05.CtaGroup.ONE
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self.epilogue_warp_id = (0,1,2,3); self.mma_warp_id = 4; self.tma_warp_id = 5
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self.threads_per_cta = 192; self.num_c_stage = 2
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self.kv_stage = 2; self.q_stage = 1; self.num_c_stage = 2
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self.scale_softmax = scale_softmax if scale_softmax is not None else 1.0 / math.sqrt(HEAD_DIM)
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self.scale_softmax_log2 = self.scale_softmax * math.log2(math.e)
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def _setup(self, qk_mma, pv_mma):
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qk_ik = cute.size(qk_mma.shape_mnk, mode=[2])
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self.qk_mma_tiler = (128, 128, qk_ik * 4)
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pv_ik = cute.size(pv_mma.shape_mnk, mode=[2])
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self.pv_mma_tiler = (128, HEAD_DIM, pv_ik * (128 // pv_ik))
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self.mma_tiler = self.qk_mma_tiler
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self.cluster_layout_vmnk = cute.tiled_divide(cute.make_layout((1,1,1)), (qk_mma.thr_id.shape,))
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self.cta_tile_shape_mnk = (self.qk_mma_tiler[0]//cute.size(qk_mma.thr_id.shape), HEAD_DIM, self.qk_mma_tiler[2])
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self.c_layout = LayoutEnum.ROW_MAJOR
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self.epi_tile = utils.sm100.compute_epilogue_tile_shape(self.cta_tile_shape_mnk, False, self.c_layout, self.o_dtype)
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self.num_ab_stage = 1; self.num_acc_stage = 1
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self.q_smem_s = utils.sm100.make_smem_layout_a(qk_mma, self.qk_mma_tiler, self.q_dtype, self.q_stage)
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self.k_smem_s = utils.sm100.make_smem_layout_b(qk_mma, self.qk_mma_tiler, self.q_dtype, self.kv_stage)
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self.v_smem_s = utils.sm100.make_smem_layout_b(pv_mma, self.pv_mma_tiler, self.q_dtype, self.kv_stage)
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self.c_smem_s = utils.sm100.make_smem_layout_epi(self.o_dtype, self.c_layout, self.epi_tile, 2)
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self.p_tmem_s = utils.sm100.make_smem_layout_a(pv_mma, self.pv_mma_tiler, self.q_dtype, 1)
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qk_thr = qk_mma.get_slice(0); qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
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tStS = qk_thr.make_fragment_C(qk_as)
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pv_thr = pv_mma.get_slice(0); pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
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tOtO = pv_thr.make_fragment_C(pv_as)
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self.tmem_s0_offset = 0; self.tmem_p0_offset = 32
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p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
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p_end = self.tmem_p0_offset + p_cols_fp32
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s_cols = self.qk_mma_tiler[1]
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o_after = max(s_cols, p_end)
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self.tmem_o0_offset = ((o_after + 31) // 32) * 32
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o_cols = find_tmem_tensor_col_offset(tOtO)
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total = self.tmem_o0_offset + o_cols
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self.num_tmem_alloc_cols = 1
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while self.num_tmem_alloc_cols < total:
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self.num_tmem_alloc_cols *= 2
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cta = cute.size(qk_mma.thr_id.shape)
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q_s = cute.slice_(self.q_smem_s,(None,None,None,0))
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k_s = cute.slice_(self.k_smem_s,(None,None,None,0))
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v_s = cute.slice_(self.v_smem_s,(None,None,None,0))
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self.q_tx_bytes = cute.size_in_bytes(self.q_dtype, q_s) * cta
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# Combined barrier: tx_count covers BOTH K and V transfers per acquire.
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self.kv_tx_bytes = (cute.size_in_bytes(self.q_dtype, k_s) +
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cute.size_in_bytes(self.q_dtype, v_s)) * cta
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@cute.jit
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def __call__(self, q, k, v, c, stream):
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self.q_dtype = q.element_type; self.o_dtype = c.element_type; self.c_dtype = self.o_dtype
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self.a_major = LayoutEnum.from_tensor(q).mma_major_mode()
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self.b_major = LayoutEnum.from_tensor(k).mma_major_mode()
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v_fmha = cute.make_tensor(
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v.iterator,
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cute.make_layout(
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(HEAD_DIM, self.s_k, 1),
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stride=(1, HEAD_DIM, HEAD_DIM * self.s_k),
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),
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)
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self.v_major = LayoutEnum.from_tensor(v_fmha).mma_major_mode()
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self.c_layout = LayoutEnum.from_tensor(c)
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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)
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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)
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self._setup(qk_mma, pv_mma)
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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))
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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)
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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)
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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)
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epi_s = cute.select(self.c_smem_s,mode=[0,1])
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tma_c,mC = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(),c,epi_s,self.epi_tile)
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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)
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@cute.kernel
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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):
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warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx())
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tidx,_,_ = cute.arch.thread_idx()
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if warp_idx == self.tma_warp_id:
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cpasync.prefetch_descriptor(tma_q); cpasync.prefetch_descriptor(tma_k); cpasync.prefetch_descriptor(tma_v); cpasync.prefetch_descriptor(tma_c)
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@cute.struct
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class SS:
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q_bar: cute.struct.MemRange[cutlass.Int64, self.q_stage*2]
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kv_bar: cute.struct.MemRange[cutlass.Int64, self.kv_stage*2]
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s_bar: cute.struct.MemRange[cutlass.Int64, 2]
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acc_bar: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage*2]
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tmem_dealloc: cutlass.Int64; holding: cutlass.Int32
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smem = utils.SmemAllocator(); st = smem.allocate(SS)
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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()
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# Combined K+V pipeline: each stage carries BOTH K and V loaded together.
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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()
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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()
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softmax_done_bar = pipeline.NamedBarrier(barrier_id=3, num_threads=32 + 32*len(self.epilogue_warp_id))
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# Final-O sync: MMA arrives between producer_commit and producer_tail;
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# softmax arrives_and_waits before reading O for the final normalize.
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final_o_bar = pipeline.NamedBarrier(barrier_id=4, num_threads=32 + 32*len(self.epilogue_warp_id))
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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)
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tmem_bar = pipeline.NamedBarrier(barrier_id=2,num_threads=32*len((self.mma_warp_id,*self.epilogue_warp_id)))
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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)
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pipeline.pipeline_init_arrive(cluster_shape_mn=cl_vmnk,is_relaxed=True)
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sQ = smem.allocate_tensor(element_type=self.q_dtype,layout=q_smem_s.outer,byte_alignment=128,swizzle=q_smem_s.inner)
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sK = smem.allocate_tensor(element_type=self.q_dtype,layout=k_smem_s.outer,byte_alignment=128,swizzle=k_smem_s.inner)
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sV = smem.allocate_tensor(element_type=self.q_dtype,layout=v_smem_s.outer,byte_alignment=128,swizzle=v_smem_s.inner)
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sC = smem.allocate_tensor(element_type=self.o_dtype,layout=c_smem_s.outer,byte_alignment=128,swizzle=c_smem_s.inner)
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gQ = cute.local_tile(mQ,cute.slice_(self.qk_mma_tiler,(None,0,None)),(None,None,None))
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gK = cute.local_tile(mK,cute.slice_(self.qk_mma_tiler,(0,None,None)),(None,None,None))
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gV = cute.local_tile(mV,cute.slice_(self.pv_mma_tiler,(0,None,None)),(None,None,None))
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gC = cute.local_tile(mC,cute.slice_(self.pv_mma_tiler,(None,None,0)),(None,None,None))
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n_kv_tiles = cute.size(gK, mode=[3])
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qk_thr = qk_mma.get_slice(0); pv_thr = pv_mma.get_slice(0)
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tCgQ = qk_thr.partition_A(gQ); tCgK = qk_thr.partition_B(gK)
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tCgV = pv_thr.partition_B(gV); tCgC = pv_thr.partition_C(gC)
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a_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,0,None,0)).shape)
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tAsQ,tAgQ = cpasync.tma_partition(tma_q,0,a_lay,cute.group_modes(sQ,0,3),cute.group_modes(tCgQ,0,3))
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b_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,None,0,0)).shape)
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tBsK,tBgK = cpasync.tma_partition(tma_k,0,b_lay,cute.group_modes(sK,0,3),cute.group_modes(tCgK,0,3))
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tVsV,tVgV = cpasync.tma_partition(tma_v,0,b_lay,cute.group_modes(sV,0,3),cute.group_modes(tCgV,0,3))
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tAgQ = tAgQ[(None,0,None,0)]; tBgK = tBgK[(None,0,None,0)]; tVgV = tVgV[(None,0,None,0)]
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tCrQ = qk_mma.make_fragment_A(sQ); tCrK = qk_mma.make_fragment_B(sK)
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tCrV = pv_mma.make_fragment_B(sV)
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qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
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tStS = qk_thr.make_fragment_C(qk_as)
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tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
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pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
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tOtO = pv_thr.make_fragment_C(pv_as)
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tOtO0 = cute.make_tensor(tOtO.iterator + self.tmem_o0_offset, tOtO.layout)
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tP = cute.make_tensor(tStS.iterator, p_tmem_s.outer)
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tOrP_base = pv_thr.make_fragment_A(tP)
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tOrP = tOrP_base[(None,None,None,0)]
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tOrP0 = cute.make_tensor(
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tOrP.iterator + self.qk_acc_dtype.width // self.q_dtype.width * self.tmem_p0_offset,
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tOrP.layout)
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tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_as, self.num_acc_stage))
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tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_as, self.num_acc_stage))
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pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
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# ===== TMA LOAD warp =====
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# One acquire per kt; K and V both target kvh.barrier. kvh.count == kt.
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if warp_idx == self.tma_warp_id:
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qp.reset(); qh = qp.acquire_and_advance()
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cute.copy(tma_q, tAgQ[(None, qh.count)], tAsQ[(None, qh.index)], tma_bar_ptr=qh.barrier)
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qp.tail()
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kvp.reset(); pk = kvp.try_acquire()
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for kt in cutlass.range(n_kv_tiles, unroll=1):
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kvh = kvp.acquire_and_advance(pk)
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# Both transfers decrement the same barrier's tx_count.
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# kvh.count is a pipeline-state Int32 (the form cute.copy accepts).
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cute.copy(tma_k, tBgK[(None, kvh.count)], tBsK[(None, kvh.index)], tma_bar_ptr=kvh.barrier)
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cute.copy(tma_v, tVgV[(None, kvh.count)], tVsV[(None, kvh.index)], tma_bar_ptr=kvh.barrier)
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pk = cutlass.Boolean(1)
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kvp.tail()
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# ===== MMA warp =====
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# One wait per kt; same slot index used for both K (QK) and V (PV).
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# Release happens AFTER PV — combined slot stays held across QK+PV.
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if warp_idx == self.mma_warp_id:
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tmem.wait_for_alloc()
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qc.reset(); qh = qc.wait_and_advance(); qh.release()
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kvc.reset(); pk = kvc.try_wait()
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acc_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
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acc_pipe.producer_acquire(acc_st)
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for kt in range(n_kv_tiles):
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kvh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
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sh = s_prod.acquire_and_advance()
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qk_mma.set(tcgen05.Field.ACCUMULATE, False)
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for kb in cutlass.range(cute.size(tCrQ, mode=[2]), unroll_full=True):
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cute.gemm(qk_mma, tStS0, tCrQ[(None,None,kb,0)], tCrK[(None,None,kb,kvh.index)], tStS0)
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qk_mma.set(tcgen05.Field.ACCUMULATE, True)
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cute.arch.fence_view_async_tmem_store()
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sh.commit()
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softmax_done_bar.arrive_and_wait()
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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,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()
|
||||
# Signal softmax FIRST so it can run normalize + epilogue. Then
|
||||
# wait for the epilogue's consumer-release in producer_tail.
|
||||
# Reverse order deadlocks: producer_tail blocks waiting for
|
||||
# consumer release; softmax blocks at final_o_bar waiting for
|
||||
# MMA arrive; the epilogue (which does the release) is gated
|
||||
# behind softmax's final_o_bar wait. Cycle.
|
||||
final_o_bar.arrive()
|
||||
acc_pipe.producer_tail(acc_st)
|
||||
|
||||
# ===== SOFTMAX + 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
|
||||
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 store
|
||||
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)
|
||||
|
||||
# O rescale / normalize path
|
||||
cO = cute.make_identity_tensor((self.pv_mma_tiler[0], self.pv_mma_tiler[1]))
|
||||
tOcO = pv_thr.partition_C(cO)
|
||||
corr_tile_size = 16
|
||||
tOtO_i_layout = cute.composition(tOtO.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(tOtO.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)
|
||||
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_load_o = tiled_tmem_load_o.get_slice(sfw_idx)
|
||||
thr_store_o = tiled_tmem_store_o.get_slice(sfw_idx)
|
||||
tTMEM_LOAD_OtO = thr_load_o.partition_S(tOtO_i)
|
||||
tTMEM_LOAD_OcO = thr_load_o.partition_D(tOcO_i)
|
||||
tTMEM_STORE_OtO = thr_store_o.partition_D(tOtO_i)
|
||||
|
||||
o_col_tiles = self.pv_mma_tiler[1] // corr_tile_size
|
||||
|
||||
row_max = -Float32.inf
|
||||
row_sum = Float32(0.0)
|
||||
scale_log2 = Float32(self.scale_softmax_log2)
|
||||
|
||||
for kt in range(n_kv_tiles):
|
||||
si_handle = s_cons.wait_and_advance()
|
||||
|
||||
# Load S[kt]
|
||||
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()
|
||||
|
||||
# Pass 1: update row_max
|
||||
old_row_max = row_max
|
||||
frg_cnt = 4
|
||||
frg_tile = cute.size(tTMEM_LOADrS) // frg_cnt
|
||||
tTMEM_LOADrS_frg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(frg_tile))
|
||||
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)
|
||||
|
||||
row_max_safe = row_max
|
||||
if row_max == -cutlass.Float32.inf:
|
||||
row_max_safe = Float32(0.0)
|
||||
|
||||
# acc_scale used for both row_sum rescale and O rescale.
|
||||
acc_scale_ = scale_log2 * (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
|
||||
|
||||
# Pass 2: P = exp2((S - new_max) * log2), accumulate row_sum,
|
||||
# store BF16 P through the FP32-backed register bridge.
|
||||
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_scale = (Float32(0.0) - row_max_safe) * scale_log2
|
||||
|
||||
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_scale
|
||||
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()
|
||||
|
||||
# O rescale for kt > 0. Reads O written by MMA's PV[kt-1];
|
||||
# visibility is provided by s_cons.wait_and_advance above
|
||||
# (acquires on MMA's S[kt] commit, which orders PV[kt-1] before).
|
||||
if kt > 0:
|
||||
for i in range(o_col_tiles):
|
||||
tTMEM_LOAD_O_i = cute.make_tensor(
|
||||
tTMEM_LOAD_OtO.iterator + i * corr_tile_size,
|
||||
tTMEM_LOAD_OtO.layout,
|
||||
)
|
||||
tTMEM_STORE_O_i = cute.make_tensor(
|
||||
tTMEM_STORE_OtO.iterator + i * corr_tile_size,
|
||||
tTMEM_STORE_OtO.layout,
|
||||
)
|
||||
tTMrO = cute.make_rmem_tensor(tTMEM_LOAD_OcO.shape, self.acc_dtype)
|
||||
cute.copy(tiled_tmem_load_o, tTMEM_LOAD_O_i, tTMrO)
|
||||
cute.arch.fence_view_async_tmem_load()
|
||||
for k in cutlass.range(cute.size(tTMrO), vectorize=True):
|
||||
tTMrO[k] = tTMrO[k] * acc_scale
|
||||
cute.copy(tiled_tmem_store_o, tTMrO, tTMEM_STORE_O_i)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
si_handle.release()
|
||||
softmax_done_bar.arrive()
|
||||
|
||||
# Wait for MMA's last PV to commit before reading O for normalize.
|
||||
# Without this barrier softmax can race MMA's PV[N-1].
|
||||
final_o_bar.arrive_and_wait()
|
||||
|
||||
# Final O = O / row_sum
|
||||
inv_row_sum = Float32(1.0) / row_sum
|
||||
for i in range(o_col_tiles):
|
||||
tTMEM_LOAD_O_i = cute.make_tensor(
|
||||
tTMEM_LOAD_OtO.iterator + i * corr_tile_size,
|
||||
tTMEM_LOAD_OtO.layout,
|
||||
)
|
||||
tTMEM_STORE_O_i = cute.make_tensor(
|
||||
tTMEM_STORE_OtO.iterator + i * corr_tile_size,
|
||||
tTMEM_STORE_OtO.layout,
|
||||
)
|
||||
tTMrO = cute.make_rmem_tensor(tTMEM_LOAD_OcO.shape, self.acc_dtype)
|
||||
cute.copy(tiled_tmem_load_o, tTMEM_LOAD_O_i, tTMrO)
|
||||
cute.arch.fence_view_async_tmem_load()
|
||||
for k in cutlass.range(cute.size(tTMrO), vectorize=True):
|
||||
tTMrO[k] = tTMrO[k] * inv_row_sum
|
||||
cute.copy(tiled_tmem_store_o, tTMrO, tTMEM_STORE_O_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)
|
||||
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(self, tidx, warp_idx, tma_c, tCtO_base, sC, tCgC, epi_tile, 0, const_expr(lambda x: x), (0,0,0), acc_cons_st, acc_pipe, c_pipe)
|
||||
c_pipe.producer_tail()
|
||||
tmem.relinquish_alloc_permit()
|
||||
tmem.free(tmem_ptr)
|
||||
|
||||
|
||||
def test():
|
||||
torch.manual_seed(42)
|
||||
for n in [128, 256, 512, 1024]:
|
||||
torch.manual_seed(42)
|
||||
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()
|
||||
scale = 1.0 / math.sqrt(hd)
|
||||
attn = qf @ kf.T * scale
|
||||
attn = torch.softmax(attn, dim=-1)
|
||||
ref = attn @ 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)
|
||||
|
||||
# Each n requires its own compiled kernel (s_k is compile-time).
|
||||
kernel = FmhaV3StageCMulti(s_k=n)
|
||||
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} '
|
||||
f'o0={kernel.tmem_o0_offset} alloc={kernel.num_tmem_alloc_cols} '
|
||||
f'kv_tx_bytes={kernel.kv_tx_bytes}', 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_abs = (out - ref).abs().max().item()
|
||||
n_tiles = n // 128
|
||||
print(f'FMHA Stage-C Multi n={n} ({n_tiles} kv tiles): '
|
||||
f'cos {cos:.6f} max_abs {max_abs:.4f} '
|
||||
f'{"PASS" if cos >= 0.99 else "FAIL"}')
|
||||
if cos < 0.99:
|
||||
print(f' out[0,:4]={out[0,:4].tolist()}')
|
||||
print(f' ref[0,:4]={ref[0,:4].tolist()}')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test()
|
||||
@@ -1,8 +1,26 @@
|
||||
"""
|
||||
FMHA v3 Stage-C: Real softmax + O normalization.
|
||||
Builds on the 12w identity-softmax test by replacing identity softmax with
|
||||
online softmax (row_max, exp2 scaling, P store) and adding O normalization
|
||||
by row_sum before the epilogue writes to GMEM.
|
||||
FMHA v3 Stage-C: Real online softmax with full multi-tile support.
|
||||
|
||||
Integrated from:
|
||||
- test_fmha_v3.py (Stage A+B: QK→identity softmax→PV)
|
||||
- test_fmha_v3_stage_c_full.py (Stage C: single-tile real softmax)
|
||||
- fmha_v3_stage_c_example3.py (Multi-tile: combined K+V barrier, O rescale, final_o_bar)
|
||||
|
||||
Architecture (6-warp, 192 threads):
|
||||
Warps 0-3: Softmax + Epilogue (row_max, exp2, P store, O rescale, O normalize, TMA store)
|
||||
Warp 4: MMA (QK GEMM, PV GEMM)
|
||||
Warp 5: TMA (Q, K, V load)
|
||||
|
||||
Multi-tile key changes vs single-tile:
|
||||
1. Combined K+V barrier: one acquire_and_advance per kt, both K and V share kvh.barrier.
|
||||
kvh.count == kt naturally — no interleaving, no Python int in TMA coordinates.
|
||||
2. kv_tx_bytes covers BOTH K and V transfers per stage.
|
||||
3. V FMHA layout uses s_k as the sequence dimension (compile-time, not hardcoded 128).
|
||||
4. MMA: same slot index (kvh.index) for both K (QK) and V (PV). Release after PV.
|
||||
5. O rescale for kt > 0: exp2((old_max - new_max) * scale_log2) on O in TMEM.
|
||||
6. final_o_bar: MMA arrives between producer_commit and producer_tail;
|
||||
softmax arrives_and_wait before reading O for final normalize.
|
||||
7. s_k is a constructor parameter — each sequence length requires its own compiled kernel.
|
||||
"""
|
||||
import torch, cutlass, cutlass.cute as cute, cutlass.utils as utils, cutlass.pipeline as pipeline
|
||||
from cutlass.cute.nvgpu import cpasync, tcgen05
|
||||
@@ -15,8 +33,10 @@ import math
|
||||
|
||||
HEAD_DIM = 64
|
||||
|
||||
|
||||
class FmhaV3StageC:
|
||||
def __init__(self, s_k=128, scale_softmax=None):
|
||||
# s_k MUST equal actual sequence length n (compile-time constant for V layout).
|
||||
self.s_k = s_k
|
||||
self.acc_dtype = Float32; self.qk_acc_dtype = Float32
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
@@ -49,24 +69,24 @@ class FmhaV3StageC:
|
||||
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
|
||||
# P occupies [tmem_p0_offset, tmem_p0_offset + p_cols_fp32)
|
||||
# S occupies [0, qk_mma_tiler[1]) = [0, 128)
|
||||
# O must NOT overlap P. Place O after max(S end, P end), aligned to 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 # 32 + 64 = 96
|
||||
s_cols = self.qk_mma_tiler[1] # 128
|
||||
o_after = max(s_cols, p_end) # 128
|
||||
self.tmem_o0_offset = ((o_after + 31) // 32) * 32 # align to 32 = 128
|
||||
o_cols = find_tmem_tensor_col_offset(tOtO) # footprint of O
|
||||
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
|
||||
# Must be multiple of 32 AND power of 2
|
||||
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))
|
||||
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) * cta
|
||||
# Combined barrier: tx_count covers BOTH K and V transfers per acquire.
|
||||
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):
|
||||
@@ -74,6 +94,7 @@ class FmhaV3StageC:
|
||||
self.a_major = LayoutEnum.from_tensor(q).mma_major_mode()
|
||||
self.b_major = LayoutEnum.from_tensor(k).mma_major_mode()
|
||||
# FMHA-style V: reconstruct as (HEAD_DIM, s_k, 1) MN-major
|
||||
# s_k is compile-time — must match actual sequence length n.
|
||||
v_fmha = cute.make_tensor(
|
||||
v.iterator,
|
||||
cute.make_layout(
|
||||
@@ -111,9 +132,17 @@ class FmhaV3StageC:
|
||||
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()
|
||||
# Combined K+V pipeline: each stage carries BOTH K and V loaded together.
|
||||
# One acquire per kt → kvh.count == kt (pipeline state value, accepted by cute.copy).
|
||||
# No interleaving problem. No Python int in TMA coordinates.
|
||||
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 sync: MMA arrives between producer_commit and producer_tail;
|
||||
# softmax arrives_and_waits before reading O for the final normalize.
|
||||
# This prevents softmax from racing MMA's PV[N-1] and dividing a
|
||||
# partially-accumulated O by row_sum.
|
||||
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)
|
||||
@@ -162,22 +191,25 @@ class FmhaV3StageC:
|
||||
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
|
||||
# ===== TMA LOAD warp =====
|
||||
# One acquire per kt; K and V both target kvh.barrier. kvh.count == kt.
|
||||
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)
|
||||
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)
|
||||
for kt in cutlass.range(n_kv_tiles, unroll=1):
|
||||
kvh = kvp.acquire_and_advance(pk)
|
||||
# Both transfers decrement the same barrier's tx_count.
|
||||
# kvh.count is a pipeline-state Int32 (the form cute.copy accepts).
|
||||
cute.copy(tma_k, tBgK[(None, kvh.count)], tBsK[(None, kvh.index)], tma_bar_ptr=kvh.barrier)
|
||||
cute.copy(tma_v, tVgV[(None, kvh.count)], tVsV[(None, kvh.index)], tma_bar_ptr=kvh.barrier)
|
||||
pk = cutlass.Boolean(1)
|
||||
kvp.tail()
|
||||
|
||||
# MMA
|
||||
# ===== MMA warp =====
|
||||
# One wait per kt; same slot index used for both K (QK) and V (PV).
|
||||
# Release happens AFTER PV — combined slot stays held across QK+PV.
|
||||
if warp_idx == self.mma_warp_id:
|
||||
tmem.wait_for_alloc()
|
||||
qc.reset(); qh = qc.wait_and_advance(); qh.release()
|
||||
@@ -185,46 +217,48 @@ class FmhaV3StageC:
|
||||
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)
|
||||
kvh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
sh = s_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)
|
||||
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)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
sh.commit(); kh.release()
|
||||
sh.commit()
|
||||
softmax_done_bar.arrive_and_wait()
|
||||
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)
|
||||
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()
|
||||
vh.release()
|
||||
kvh.release()
|
||||
acc_pipe.producer_commit(acc_st); acc_st.advance()
|
||||
# Signal softmax FIRST so it can run normalize + epilogue. Then
|
||||
# wait for the epilogue's consumer-release in producer_tail.
|
||||
# Reverse order deadlocks: producer_tail blocks waiting for
|
||||
# consumer release; softmax blocks at final_o_bar waiting for
|
||||
# MMA arrive; the epilogue (which does the release) is gated
|
||||
# behind softmax's final_o_bar wait. Cycle.
|
||||
final_o_bar.arrive()
|
||||
acc_pipe.producer_tail(acc_st)
|
||||
|
||||
# SOFTMAX + EPILOGUE (warps 0-3)
|
||||
# Step 1: Real online softmax: load S, compute row_max, exp2 scale, store P
|
||||
# Step 2: After all KV tiles, normalize O in TMEM by row_sum
|
||||
# Step 3: Epilogue writes normalized O from TMEM to GMEM
|
||||
# ===== SOFTMAX + 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 (QK C-fragment layout) ---
|
||||
# S load (QK C-fragment layout)
|
||||
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 store (QK C-fragment layout composition, FMHA pattern) ---
|
||||
# P store (QK C-fragment layout composition, FMHA pattern)
|
||||
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)
|
||||
@@ -236,8 +270,7 @@ class FmhaV3StageC:
|
||||
tScP = cute.make_tensor(tScS.iterator, tScP_layout)
|
||||
tTMEM_STOREcP = thr_store.partition_S(tScP)
|
||||
|
||||
# --- Online softmax loop ---
|
||||
# --- O rescale + normalize setup (before softmax loop) ---
|
||||
# O rescale / normalize path
|
||||
cO = cute.make_identity_tensor((self.pv_mma_tiler[0], self.pv_mma_tiler[1]))
|
||||
tOcO = pv_thr.partition_C(cO)
|
||||
corr_tile_size = 16
|
||||
@@ -255,18 +288,21 @@ class FmhaV3StageC:
|
||||
tTMEM_LOAD_OcO = thr_load_o.partition_D(tOcO_i)
|
||||
tTMEM_STORE_OtO = thr_store_o.partition_D(tOtO_i)
|
||||
|
||||
row_max = -Float32.inf; row_sum = Float32(0.0)
|
||||
o_col_tiles = self.pv_mma_tiler[1] // corr_tile_size
|
||||
|
||||
row_max = -Float32.inf
|
||||
row_sum = Float32(0.0)
|
||||
scale_log2 = Float32(self.scale_softmax_log2)
|
||||
|
||||
for kt in range(n_kv_tiles):
|
||||
si_handle = s_cons.wait_and_advance()
|
||||
|
||||
# Load S from TMEM
|
||||
# Load S[kt]
|
||||
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()
|
||||
|
||||
# Compute row_max from S (element-wise, before exp2)
|
||||
# Pass 1: update row_max
|
||||
old_row_max = row_max
|
||||
frg_cnt = 4
|
||||
frg_tile = cute.size(tTMEM_LOADrS) // frg_cnt
|
||||
@@ -276,15 +312,18 @@ class FmhaV3StageC:
|
||||
row_max = cute.arch.fmax(row_max, tTMEM_LOADrS_frg[k, j] * scale_log2)
|
||||
|
||||
row_max_safe = row_max
|
||||
if row_max == -cutlass.Float32.inf: row_max_safe = Float32(0.0)
|
||||
if row_max == -cutlass.Float32.inf:
|
||||
row_max_safe = Float32(0.0)
|
||||
|
||||
# Scale existing row_sum
|
||||
# acc_scale for both row_sum rescale and O rescale.
|
||||
acc_scale_ = scale_log2 * (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)
|
||||
if old_row_max == -cutlass.Float32.inf:
|
||||
acc_scale = Float32(0.0)
|
||||
row_sum *= acc_scale
|
||||
|
||||
# Second pass: compute P and accumulate row_sum
|
||||
# Pass 2: P = exp2((S - new_max) * log2), accumulate row_sum,
|
||||
# store BF16 P through the FP32-backed register bridge.
|
||||
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_scale = (Float32(0.0) - row_max_safe) * scale_log2
|
||||
@@ -301,38 +340,54 @@ class FmhaV3StageC:
|
||||
cute.copy(tiled_tmem_store, rP_words, tTMEM_STOREtP)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# O rescale: if kt > 0, rescale O in TMEM by exp2((old_max - new_max) * scale_log2)
|
||||
# TEMPORARILY DISABLED FOR DEBUGGING
|
||||
# if kt > 0:
|
||||
# corr_scale = cute.math.exp2(old_row_max - row_max_safe, fastmath=True)
|
||||
# o_col_tiles = self.pv_mma_tiler[1] // corr_tile_size
|
||||
# for i in range(o_col_tiles):
|
||||
# tTMEM_LOAD_O_i = cute.make_tensor(tTMEM_LOAD_OtO.iterator + i * corr_tile_size, tTMEM_LOAD_OtO.layout)
|
||||
# tTMEM_STORE_O_i = cute.make_tensor(tTMEM_STORE_OtO.iterator + i * corr_tile_size, tTMEM_STORE_OtO.layout)
|
||||
# tTMrO = cute.make_rmem_tensor(tTMEM_LOAD_OcO.shape, self.acc_dtype)
|
||||
# cute.copy(tiled_tmem_load_o, tTMEM_LOAD_O_i, tTMrO)
|
||||
# for k in cutlass.range(cute.size(tTMrO), vectorize=True):
|
||||
# tTMrO[k] = tTMrO[k] * corr_scale
|
||||
# cute.copy(tiled_tmem_store_o, tTMrO, tTMEM_STORE_O_i)
|
||||
# cute.arch.fence_view_async_tmem_store()
|
||||
# O rescale for kt > 0. Reads O written by MMA's PV[kt-1];
|
||||
# visibility is provided by s_cons.wait_and_advance above
|
||||
# (acquires on MMA's S[kt] commit, which orders PV[kt-1] before).
|
||||
if kt > 0:
|
||||
for i in range(o_col_tiles):
|
||||
tTMEM_LOAD_O_i = cute.make_tensor(
|
||||
tTMEM_LOAD_OtO.iterator + i * corr_tile_size,
|
||||
tTMEM_LOAD_OtO.layout,
|
||||
)
|
||||
tTMEM_STORE_O_i = cute.make_tensor(
|
||||
tTMEM_STORE_OtO.iterator + i * corr_tile_size,
|
||||
tTMEM_STORE_OtO.layout,
|
||||
)
|
||||
tTMrO = cute.make_rmem_tensor(tTMEM_LOAD_OcO.shape, self.acc_dtype)
|
||||
cute.copy(tiled_tmem_load_o, tTMEM_LOAD_O_i, tTMrO)
|
||||
cute.arch.fence_view_async_tmem_load()
|
||||
for k in cutlass.range(cute.size(tTMrO), vectorize=True):
|
||||
tTMrO[k] = tTMrO[k] * acc_scale
|
||||
cute.copy(tiled_tmem_store_o, tTMrO, tTMEM_STORE_O_i)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
si_handle.release()
|
||||
softmax_done_bar.arrive()
|
||||
|
||||
# --- Final O normalization by row_sum ---
|
||||
# Wait for MMA's last PV to commit before reading O for normalize.
|
||||
# Without this barrier softmax can race MMA's PV[N-1].
|
||||
final_o_bar.arrive_and_wait()
|
||||
|
||||
# Final O = O / row_sum
|
||||
inv_row_sum = Float32(1.0) / row_sum
|
||||
o_col_tiles = self.pv_mma_tiler[1] // corr_tile_size
|
||||
for i in range(o_col_tiles):
|
||||
tTMEM_LOAD_O_i = cute.make_tensor(tTMEM_LOAD_OtO.iterator + i * corr_tile_size, tTMEM_LOAD_OtO.layout)
|
||||
tTMEM_STORE_O_i = cute.make_tensor(tTMEM_STORE_OtO.iterator + i * corr_tile_size, tTMEM_STORE_OtO.layout)
|
||||
tTMEM_LOAD_O_i = cute.make_tensor(
|
||||
tTMEM_LOAD_OtO.iterator + i * corr_tile_size,
|
||||
tTMEM_LOAD_OtO.layout,
|
||||
)
|
||||
tTMEM_STORE_O_i = cute.make_tensor(
|
||||
tTMEM_STORE_OtO.iterator + i * corr_tile_size,
|
||||
tTMEM_STORE_OtO.layout,
|
||||
)
|
||||
tTMrO = cute.make_rmem_tensor(tTMEM_LOAD_OcO.shape, self.acc_dtype)
|
||||
cute.copy(tiled_tmem_load_o, tTMEM_LOAD_O_i, tTMrO)
|
||||
cute.arch.fence_view_async_tmem_load()
|
||||
for k in cutlass.range(cute.size(tTMrO), vectorize=True):
|
||||
tTMrO[k] = tTMrO[k] * inv_row_sum
|
||||
cute.copy(tiled_tmem_store_o, tTMrO, tTMEM_STORE_O_i)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# --- Epilogue: write O from TMEM to GMEM ---
|
||||
# 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)
|
||||
c_grp = pipeline.CooperativeGroup(pipeline.Agent.Thread, 32 * len(self.epilogue_warp_id))
|
||||
@@ -345,39 +400,52 @@ class FmhaV3StageC:
|
||||
|
||||
def test():
|
||||
torch.manual_seed(42)
|
||||
# Test single tile (n=128) and multi-tile (n=256)
|
||||
for n in [128, 256]:
|
||||
for seed in [42]:
|
||||
torch.manual_seed(seed)
|
||||
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()
|
||||
scale = 1.0 / math.sqrt(hd)
|
||||
attn = qf @ kf.T * scale
|
||||
attn = torch.softmax(attn, dim=-1)
|
||||
ref = attn @ 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 = FmhaV3StageC(s_k=128)
|
||||
if seed == 42:
|
||||
print(f'seed={seed}: Compiling...', flush=True)
|
||||
compiled = cute.compile(kernel, mQ, mK, mV, mC, stream)
|
||||
if seed == 42:
|
||||
print(f'tmem_offsets: s0={kernel.tmem_s0_offset} p0={kernel.tmem_p0_offset} o0={kernel.tmem_o0_offset} alloc={kernel.num_tmem_alloc_cols}', 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()
|
||||
print(f'FMHA Stage-C n={n} seed={seed}: cosine {cos:.6f} {"PASS" if cos >= 0.99 else "FAIL"}')
|
||||
if cos < 0.99:
|
||||
print(f' out[0,:4]={out[0,:4].tolist()} ref[0,:4]={ref[0,:4].tolist()}')
|
||||
for n in [128, 256, 512, 1024]:
|
||||
torch.manual_seed(42)
|
||||
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')
|
||||
|
||||
# PyTorch reference
|
||||
qf = q[:, :, 0].float()
|
||||
kf = k[:, :, 0].float()
|
||||
scale = 1.0 / math.sqrt(hd)
|
||||
attn = qf @ kf.T * scale
|
||||
attn = torch.softmax(attn, dim=-1)
|
||||
ref = attn @ 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)
|
||||
|
||||
# Each n requires its own compiled kernel (s_k is compile-time).
|
||||
kernel = FmhaV3StageC(s_k=n)
|
||||
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} '
|
||||
f'o0={kernel.tmem_o0_offset} alloc={kernel.num_tmem_alloc_cols} '
|
||||
f'kv_tx_bytes={kernel.kv_tx_bytes}', 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_abs = (out - ref).abs().max().item()
|
||||
n_tiles = n // 128
|
||||
print(f'FMHA Stage-C n={n} ({n_tiles} kv tiles): '
|
||||
f'cos {cos:.6f} max_abs {max_abs:.4f} '
|
||||
f'{"PASS" if cos >= 0.99 else "FAIL"}')
|
||||
if cos < 0.99:
|
||||
print(f' out[0,:4]={out[0,:4].tolist()}')
|
||||
print(f' ref[0,:4]={ref[0,:4].tolist()}')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test()
|
||||
|
||||
Reference in New Issue
Block a user