""" FMHA v3 + Stage C: QK -> online softmax -> PV with KV-tile interleaving. Stage C: row_max, exp2, O rescale, row_sum, final normalization. FMHA pattern P store preserved from Stage B. """ import math 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 HEAD_DIM = 64 class FmhaV3Softmax: def __init__(self): self.acc_dtype = Float32; self.qk_acc_dtype = Float32 self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16 self.use_2cta_instrs = False; self.epilog_sync_bar_id = 1 self.cluster_shape_mn = (1, 1); self.cta_group = tcgen05.CtaGroup.ONE self.epilogue_warp_id = (0,1,2,3); self.mma_warp_id = 4; self.tma_warp_id = 5 self.threads_per_cta = 192; self.num_c_stage = 2 self.kv_stage = 2; self.q_stage = 1; self.num_c_stage = 2 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_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 self.tmem_vec_offset = 0 # Reuse S region for per-row inv_row_sum vector # align to 32 = 128 self.tmem_vec_offset = 0 # Reuse S region (free after softmax loop) 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 self.scale_softmax_log2 = Float32(1.0 / math.sqrt(HEAD_DIM) * math.log2(math.e)) @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() # # s_k hardcoded # BROKEN in @cute.jit # FMHA-style V: reconstruct as (HEAD_DIM, s_k, 1) MN-major 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.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 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] s_bar: cute.struct.MemRange[cutlass.Int64, 2] acc_bar: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage*2] tmem_dealloc: cutlass.Int64; holding: cutlass.Int32 smem = utils.SmemAllocator(); st = smem.allocate(SS) qp,qc = pipeline.PipelineTmaUmma.create(barrier_storage=st.q_bar.data_ptr(),num_stages=self.q_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.q_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants() kvp,kvc = pipeline.PipelineTmaUmma.create(barrier_storage=st.kv_bar.data_ptr(),num_stages=self.kv_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.kv_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants() s_prod,s_cons = pipeline.PipelineUmmaAsync.create(barrier_storage=st.s_bar.data_ptr(),num_stages=1,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32*len(self.epilogue_warp_id))).make_participants() softmax_done_bar = pipeline.NamedBarrier(barrier_id=3, num_threads=32 + 32*len(self.epilogue_warp_id)) pv_done_bar = pipeline.NamedBarrier(barrier_id=4, num_threads=32 + 32*len(self.epilogue_warp_id)) acc_pipe = pipeline.PipelineUmmaAsync.create(barrier_storage=st.acc_bar.data_ptr(),num_stages=self.num_acc_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,len(self.epilogue_warp_id)),cta_layout_vmnk=cl_vmnk,defer_sync=True) tmem_bar = pipeline.NamedBarrier(barrier_id=2,num_threads=32*len((self.mma_warp_id,*self.epilogue_warp_id))) tmem = utils.TmemAllocator(st.holding.ptr,barrier_for_retrieve=tmem_bar,allocator_warp_id=self.epilogue_warp_id[0],is_two_cta=cute.size(qk_mma.thr_id.shape)==2,two_cta_tmem_dealloc_mbar_ptr=st.tmem_dealloc.ptr) pipeline.pipeline_init_arrive(cluster_shape_mn=cl_vmnk,is_relaxed=True) 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) # --- PV read view (for MMA only, NOT for softmax store) --- 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, self.num_acc_stage)) 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 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 = 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() 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) pv_mma.set(tcgen05.Field.ACCUMULATE, True) cute.arch.fence_view_async_tmem_store() vh.release() pv_done_bar.arrive() acc_pipe.producer_commit(acc_st); acc_st.advance() acc_pipe.producer_tail(acc_st) # ===================== EPILOGUE WARPS (STAGE C: ONLINE SOFTMAX) ===================== 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) --- 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 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) 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) # --- Vector TMEM (per-row row_sum storage, FMHA pattern) --- # composition(tStS.layout, (128, 2)) = 2 FP32 columns per logical row # vec[0] = row_sum (final, after loop), vec[1] = unused # Reuses S TMEM region (offset 0), free after softmax loop writes tStS_vec_layout = cute.composition(tStS.layout, cute.make_layout((128, 2))) tStS_vec = cute.make_tensor(tStS.iterator + self.tmem_vec_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) 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(sfw_idx) tTMEM_LOAD_VECtS = thr_tmem_load_vec.partition_S(tStS_vec) tTMEM_LOAD_VECcS = thr_tmem_load_vec.partition_D(tScS_vec) # --- C6: O TMEM load/store for rescale (correction_rescale pattern) --- 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) o_tmem_load_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.qk_acc_dtype) o_tmem_store_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.qk_acc_dtype) 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_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(sfw_idx) o_thr_store = o_tiled_tmem_store.get_slice(sfw_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 # --- C2: Per-thread row state (persist across KV tiles) --- row_max = -cutlass.Float32.inf row_sum = cutlass.Float32(0.0) # --- C3: QK scale = 1/sqrt(HEAD_DIM) * log2(e) for exp2 --- scale = self.scale_softmax_log2 # ============================================================= # Per-KV-tile online softmax loop # ============================================================= for kt in range(n_kv_tiles): si_handle = s_cons.wait_and_advance() # Load S from TMEM (FP32, QK C-fragment layout) tTMEM_LOADrS = cute.make_rmem_tensor(tTMEM_LOADcS.shape, self.qk_acc_dtype) cute.copy(tiled_tmem_load, tTMEM_LOADtS, tTMEM_LOADrS) # --- C4: Compute tile_max via .reduce(MAX) --- 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) # --- C5: Compute rescale factor --- acc_scale = cute.math.exp2(scale * (old_row_max - row_max_safe), fastmath=True) # --- C6: Rescale O in TMEM (load O, multiply by acc_scale, store O) --- # acc_scale belongs to QK row (N//4), but O rows are in PV partition (N). # Store acc_scale to vector by QK row, read by PV row. if kt > 0: pv_done_bar.arrive_and_wait() # Store acc_scale to vector indexed by QK logical row qk_row_c6 = tTMEM_LOADcS[0][0] thr_vs_c6 = tiled_tmem_store_vec.get_slice(qk_row_c6) tVStore_c6 = thr_vs_c6.partition_D(tStS_vec) tVStoreSrc_c6 = thr_vs_c6.partition_S(tScS_vec) tVStoreRmem_c6 = cute.make_rmem_tensor(tVStoreSrc_c6.shape, self.qk_acc_dtype) tVStoreRmem_c6[0] = acc_scale cute.copy(tiled_tmem_store_vec, tVStoreRmem_c6, tVStore_c6) cute.arch.fence_view_async_tmem_store() # Read acc_scale from vector indexed by PV logical row pv_row_c6 = tTMEM_LOADcO[0][0] thr_vl_c6 = tiled_tmem_load_vec.get_slice(pv_row_c6) tVLoad_c6 = thr_vl_c6.partition_S(tStS_vec) tVLoadDst_c6 = thr_vl_c6.partition_D(tScS_vec) tVLoadRmem_c6 = cute.make_rmem_tensor(tVLoadDst_c6.shape, self.qk_acc_dtype) cute.copy(tiled_tmem_load_vec, tVLoad_c6, tVLoadRmem_c6) cute.arch.fence_view_async_tmem_load() acc_scale_pv = tVLoadRmem_c6[0] tTMrO = cute.make_rmem_tensor((tTMEM_LOADcO.shape, o_col_tiles), self.qk_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] * acc_scale_pv cute.copy(o_tiled_tmem_store, tTMrO_i, tTMEM_STOREtO_i) cute.arch.fence_view_async_tmem_store() # Rescale row_sum row_sum = row_sum * acc_scale # --- C7: Compute P = exp2((S - row_max_safe) * scale) --- minus_row_max_scale = (cutlass.Float32(0.0) - row_max_safe) * scale # Register bridge (FMHA pattern: FP32 backing + BF16 view) rP_words = cute.make_rmem_tensor(tTMEM_STOREcP.shape, self.qk_acc_dtype) rP_bf16 = cute.make_tensor(cute.recast_ptr(rP_words.iterator, dtype=self.q_dtype), tTMEM_LOADrS.layout) 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)) # Scale S, compute exp2, store through register bridge 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)) # Store P to TMEM cute.copy(tiled_tmem_store, rP_words, tTMEM_STOREtP) cute.arch.fence_view_async_tmem_store() si_handle.release() softmax_done_bar.arrive() # --- C8: Row sum accumulation (CUTLASS FMHA packed f32x2 pattern) --- # P values still in tTMEM_LOADrS registers. # 4 accumulators for 4 reduction_unroll columns. 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) tile_sum = local_row_sum_0[0] + local_row_sum_0[1] row_sum = row_sum + tile_sum # --- C9: Final normalization via O TMEM rescale --- pv_done_bar.arrive_and_wait() # Use QK-accumulated row_sum directly (DEBUG: check if row mapping matches PV) inv_row_sum = cutlass.Float32(1.0) / row_sum # Normalize O in TMEM using PV-correct inv_row_sum tTMrO_final = cute.make_rmem_tensor((tTMEM_LOADcO.shape, o_col_tiles), self.qk_acc_dtype) for i in range(o_col_tiles): tTMrO_i_ = tTMrO_final[None, i] tTMrO_i_layout = cute.composition(tTMrO_i_.layout, cute.make_layout(tTMrO_final.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() # Now O in TMEM is normalized. Use standard epilogue_tma_store with identity. 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(): 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 = FmhaV3Softmax() 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_vec_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 softmax n={n}: cosine {cos:.6f} max_err {max_err:.6f} {'PASS' if cos >= 0.999 else 'FAIL'}", flush=True) if __name__ == "__main__": test()