From bfc1518046548b45c19571429d491209efd1b6c6 Mon Sep 17 00:00:00 2001 From: biondizzle Date: Fri, 22 May 2026 09:42:39 +0000 Subject: [PATCH] FMHA Stage-C2: production 12-warp pipeline with correction warps MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Softmax warps (0-3): S→softmax→P, vec=[old_max,new_max]→TMEM - Correction warps (4-7): O rescale in TMEM, final normalize by row_sum - MMA warp (8): QK→S, PV→O with pipeline chaining - TMA warp (9): Q/K/V load - Epilogue warp (10): O TMEM→GMEM via epilogue_tma_store - Empty warp (11): tmem dealloc mbar init - Pipeline: mma_s→softmax→s_corr→correction→corr_epi→epilogue + mma_corr→correction - Supports multi-tile KV with online O rescale - Follows CUTLASS FMHA correction_rescale pattern exactly --- tests/unit/test_fmha_v3_stage_c2.py | 458 ++++++++++++++++++++++++++++ 1 file changed, 458 insertions(+) create mode 100644 tests/unit/test_fmha_v3_stage_c2.py diff --git a/tests/unit/test_fmha_v3_stage_c2.py b/tests/unit/test_fmha_v3_stage_c2.py new file mode 100644 index 00000000..5908cfcd --- /dev/null +++ b/tests/unit/test_fmha_v3_stage_c2.py @@ -0,0 +1,458 @@ +""" +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. +""" +import 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 +import cutlass.torch as ct +import math + +HEAD_DIM = 64 + +class FmhaV3StageC2: + def __init__(self, s_k=128, scale_softmax=None): + self.s_k = s_k + self.acc_dtype = Float32; 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.epilog_sync_bar_id = 1 + self.cluster_shape_mn = (1, 1); self.cta_group = tcgen05.CtaGroup.ONE + # 12-warp layout + 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.empty_warp_id = 11 + self.threads_per_cta = 32 * 12 + # Pipeline stages + self.mma_softmax_stage = 1; self.softmax_corr_stage = 1 + self.mma_corr_stage = 2; self.epi_stage = 2 + # TMA stages + self.kv_stage = 2; self.q_stage = 1; self.num_c_stage = 2 + # Softmax + 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): + 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.num_acc_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_vec0_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 + 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)) + 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() + # FMHA-style V: reconstruct as (HEAD_DIM, s_k, 1) MN-major + 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_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_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 + @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_s_bar: cute.struct.MemRange[cutlass.Int64, self.mma_softmax_stage * 2] + s_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] + tmem_dealloc: cutlass.Int64; holding: cutlass.Int32 + + smem = utils.SmemAllocator(); st = smem.allocate(SS) + def cg(n): return pipeline.CooperativeGroup(pipeline.Agent.Thread, n) + qp, qc = pipeline.PipelineTmaUmma.create(barrier_storage=st.q_bar.data_ptr(), num_stages=self.q_stage, producer_group=cg(1), consumer_group=cg(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=cg(1), consumer_group=cg(1), tx_count=self.kv_tx_bytes, cta_layout_vmnk=cl_vmnk, defer_sync=True).make_participants() + # MMA → Softmax: S ready + mma_s_prod, mma_s_cons = pipeline.PipelineUmmaAsync.create(barrier_storage=st.mma_s_bar.data_ptr(), num_stages=self.mma_softmax_stage, producer_group=cg(1), consumer_group=cg(32 * len(self.softmax_warp_ids)), cta_layout_vmnk=cl_vmnk, defer_sync=True).make_participants() + # Softmax → Correction: vec ready + s_corr_prod, s_corr_cons = pipeline.PipelineAsync.create(barrier_storage=st.s_corr_bar.data_ptr(), num_stages=self.softmax_corr_stage, producer_group=cg(32 * len(self.softmax_warp_ids)), consumer_group=cg(32 * len(self.correction_warp_ids))).make_participants() + # MMA → Correction: O ready + 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=cg(1), consumer_group=cg(32 * len(self.correction_warp_ids)), cta_layout_vmnk=cl_vmnk, defer_sync=True).make_participants() + # Correction → Epilogue: O in SMEM ready + corr_epi_prod, corr_epi_cons = pipeline.PipelineAsync.create(barrier_storage=st.corr_epi_bar.data_ptr(), num_stages=self.epi_stage, producer_group=cg(32 * len(self.correction_warp_ids)), consumer_group=cg(32)).make_participants() + # TMEM alloc barrier: softmax + correction + MMA + tmem_bar = pipeline.NamedBarrier(barrier_id=2, num_threads=32 * len((*self.softmax_warp_ids, *self.correction_warp_ids, self.mma_warp_id))) + 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) + if warp_idx == self.empty_warp_id: + cute.arch.mbarrier_init(st.tmem_dealloc, 32 * len((*self.softmax_warp_ids, *self.correction_warp_ids))) + cute.arch.mbarrier_init_fence() + 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) + tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_as, 1)) + tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_as, 1)) + pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk) + + # ==================== TMA WARP (9) ==================== + 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 WARP (8) ==================== + 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() + for kt in range(n_kv_tiles): + # QK -> S + kh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1) + sh = mma_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) + qk_mma.set(tcgen05.Field.ACCUMULATE, True) + cute.arch.fence_view_async_tmem_store(); sh.commit(); kh.release() + # PV -> O (softmax consumes S and produces P between these two) + vh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1) + oh = mma_corr_prod.acquire_and_advance() + 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(); oh.commit(); vh.release() + mma_s_prod.tail(); mma_corr_prod.tail() + cute.arch.relinquish_tmem_alloc_permit() + cute.arch.mbarrier_wait(st.tmem_dealloc, 0) + tmem_ptr = cute.arch.retrieve_tmem_ptr(self.qk_acc_dtype, alignment=16, ptr_to_buffer_holding_addr=st.holding) + cute.arch.dealloc_tmem(tmem_ptr, Int32(self.num_tmem_alloc_cols)) + + # ==================== 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)) + + # S load setup + 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 setup (QK C-fragment composition) + 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))) + tTMEM_STOREcP = thr_store.partition_S(cute.make_tensor(tScS.iterator, tScP_layout)) + + # Vec store setup ([old_max, new_max] per iteration, [row_sum, row_max] at end) + 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) + 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_store_vec = tiled_tmem_store_vec.get_slice(sfw_idx) + tTMEM_STORE_VECtS = thr_store_vec.partition_D(tStS_vec) + tScS_vec_layout = cute.composition(tScS.layout, cute.make_layout((128, 2))) + tScS_vec = cute.make_tensor(tScS.iterator, tScS_vec_layout) + tTMEM_STORE_VECcS = thr_store_vec.partition_S(tScS_vec) + + row_max = -Float32.inf; row_sum = Float32(0.0) + vec_handle = s_corr_prod.acquire_and_advance() + scale_log2 = Float32(self.scale_softmax_log2) + + for kt in range(n_kv_tiles): + si_handle = mma_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() + + # Row max (element-wise fmax) + 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) + + # Vec = [old_max, new_max] for correction + 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() + + # Scale row_sum and compute P + 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 + 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() + si_handle.release() + vec_handle = s_corr_prod.acquire_and_advance() + + # Final vec = [row_sum, row_max] + 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() + s_corr_prod.acquire() # balance final pipe step + s_corr_prod.tail() + cute.arch.mbarrier_arrive(st.tmem_dealloc) + 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): + tmem.wait_for_alloc() + corr_idx = tidx % (32 * len(self.correction_warp_ids)) + # Vec load setup + 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) + 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_load_vec = tiled_tmem_load_vec.get_slice(corr_idx) + tTMEM_LOAD_VECtS = thr_load_vec.partition_S(tStS_vec) + tScS_vec = cute.make_tensor(tScS.iterator, cute.composition(tScS.layout, cute.make_layout((128, 2)))) + tTMEM_LOAD_VECcS = thr_load_vec.partition_D(tScS_vec) + # O rescale setup (matching CUTLASS correction_rescale) + 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(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.pv_acc_dtype) + tmem_store_o_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.pv_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(corr_idx) + thr_store_o = tiled_tmem_store_o.get_slice(corr_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) + scale_log2 = Float32(self.scale_softmax_log2) + + # Correction rescale loop: for each KV tile (except first), rescale O + first_vec = s_corr_cons.wait_and_advance(); first_vec.release() + for kt in range(n_kv_tiles - 1): + vec = s_corr_cons.wait_and_advance() + # Read vec = [old_max, new_max] + 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() + old_max = tTMEM_LOAD_VECrS[0]; new_max = tTMEM_LOAD_VECrS[1] + corr_scale = cute.math.exp2(scale_log2 * (old_max - new_max), fastmath=True) + # Wait for O from MMA, rescale O in TMEM + o_handle = mma_corr_cons.wait_and_advance() + 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.pv_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_handle.release(); vec.release() + + # Final: read [row_sum, row_max], normalize O, write to SMEM via epilogue_tma_store + final_vec = s_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() + row_sum = tTMEM_LOAD_VECrS[0]; row_max = tTMEM_LOAD_VECrS[1] + final_vec.release() + + final_o = mma_corr_cons.wait_and_advance() + epi_handle = corr_epi_prod.acquire_and_advance() + + # Normalize O in TMEM by 1/row_sum + inv_row_sum = Float32(1.0) / row_sum + for i in range(self.pv_mma_tiler[1] // corr_tile_size): + 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.pv_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] * inv_row_sum + cute.copy(tiled_tmem_store_o, tTMrO, tTMEM_STORE_O_i) + cute.arch.fence_view_async_tmem_store() + final_o.release() + epi_handle.commit() + cute.arch.mbarrier_arrive(st.tmem_dealloc) + + # ==================== EPILOGUE WARP (10) ==================== + if warp_idx == self.epilogue_warp_id: + tmem.wait_for_alloc() + tmem.allocate(self.num_tmem_alloc_cols) + tmem_ptr = tmem.retrieve_ptr(self.qk_acc_dtype) + # Wait for correction to finish normalizing O + epi_handle = corr_epi_cons.wait_and_advance() + # Use epilogue_tma_store to write O from TMEM to GMEM + tCtO_base = cute.make_tensor(tmem_ptr + self.tmem_o0_offset, tCtO_fake.layout) + acc_cons_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Consumer, 1) + c_grp = pipeline.CooperativeGroup(pipeline.Agent.Thread, 32) + c_pipe = pipeline.PipelineTmaStore.create(num_stages=self.num_c_stage, producer_group=c_grp) + acc_pipe = 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=c_grp, cta_layout_vmnk=cl_vmnk, defer_sync=True) + 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() + epi_handle.release() + tmem.relinquish_alloc_permit() + tmem.free(tmem_ptr) +def test(): + torch.manual_seed(42) + for n in [128]: + for seed in [42, 123, 999]: + 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 = FmhaV3StageC2() + 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()}') + +if __name__ == '__main__': + test()