""" Debug: Verify softmax store actually writes non-zero P to TMEM. Test: QK only + identity softmax + read back S region (should be partially overwritten by P). If S region is modified after softmax, the store is working. """ 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_col_offset import cuda.bindings.driver as cuda import cutlass.torch as ct HEAD_DIM = 64 class SoftmaxStoreDebug: 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 self.tmem_o0_offset = find_tmem_tensor_col_offset(tOtO) tCS = qk_mma.make_fragment_C(cute.append(qk_as, self.num_acc_stage)) tCO = pv_mma.make_fragment_C(cute.append(pv_as, self.num_acc_stage)) self.num_tmem_alloc_cols = utils.get_num_tmem_alloc_cols([tCS, tCO], arch="sm_100") 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 a_major = LayoutEnum.from_tensor(q).mma_major_mode() b_major = LayoutEnum.from_tensor(k).mma_major_mode() v_major = LayoutEnum.from_tensor(v).mma_major_mode() qk_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, a_major, 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, 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,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)) 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 (MMA only) 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 — same as v3 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_async_view_tmem_store() vh.release() acc_pipe.producer_commit(acc_st); acc_st.advance() acc_pipe.producer_tail(acc_st) # EPILOGUE — debug: dump S+P region to GMEM instead of doing PV 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 + P store (same as v3) tmem_load_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype) tiled_tmem_load = tcgen05.make_tmem_copy(tmem_load_atom, tStS0) thr_load = tiled_tmem_load.get_slice(sfw_idx) tTMEM_LOADtS = thr_load.partition_S(tStS0) cS = cute.make_identity_tensor((self.qk_mma_tiler[0], self.qk_mma_tiler[1])) tScS = qk_thr.partition_C(cS) tTMEM_LOADcS = thr_load.partition_D(tScS) p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width tStP_layout = cute.composition(tStS.layout, cute.make_layout((self.pv_mma_tiler[0], p_cols_fp32))) tStP0 = cute.make_tensor(tStS.iterator + self.tmem_p0_offset, tStP_layout) tmem_store_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype) tiled_tmem_store = tcgen05.make_tmem_copy(tmem_store_atom, tStP0) thr_store = tiled_tmem_store.get_slice(sfw_idx) tTMEM_STOREtP = thr_store.partition_D(tStP0) tScP_layout = cute.composition(tScS.layout, cute.make_layout((self.pv_mma_tiler[0], p_cols_fp32))) tScP = cute.make_tensor(tScS.iterator, tScP_layout) tTMEM_STOREcP = thr_store.partition_S(tScP) for kt in range(n_kv_tiles): si_handle = s_cons.wait_and_advance() tTMEM_LOADrS = cute.make_rmem_tensor(tTMEM_LOADcS.shape, self.qk_acc_dtype) cute.copy(tiled_tmem_load, tTMEM_LOADtS, tTMEM_LOADrS) rP_words = cute.make_rmem_tensor(tTMEM_STOREcP.shape, self.qk_acc_dtype) rP_bf16 = cute.make_tensor(cute.recast_ptr(rP_words.iterator, dtype=self.q_dtype), tTMEM_LOADrS.layout) frg_cnt = 4; frg_tile = cute.size(tTMEM_LOADrS) // frg_cnt tTMEM_LOADrS_frg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(frg_tile)) rP_bf16_frg = cute.logical_divide(rP_bf16, cute.make_layout(frg_tile)) for j in range(frg_cnt): 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() softmax_done_bar.arrive() # Now read back the P region directly # Read from tStP0 (the same region we wrote to) tmem_read_P_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype) tiled_tmem_read_P = tcgen05.make_tmem_copy(tmem_read_P_atom, tStP0) thr_read_P = tiled_tmem_read_P.get_slice(sfw_idx) tTMEM_READtP = thr_read_P.partition_S(tStP0) rP_read = cute.make_rmem_tensor(thr_read_P.partition_D(tScP).shape, self.qk_acc_dtype) cute.copy(tiled_tmem_read_P, tTMEM_READtP, rP_read) # Print the first few values if sfw_idx == 0: print(f"DEBUG P readback: size={cute.size(rP_read)} val0={float(rP_read.load()[0])}") # Epilogue as before 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)