""" Debug: QK only (no PV) with KV-tile interleaving pipeline. Outputs P to GMEM to verify QK+softmax pipeline works. n=128, single KV tile, identity softmax. """ 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 QkOnlyTest: 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 def _setup(self, qk_mma): qk_ik = cute.size(qk_mma.shape_mnk, mode=[2]) self.qk_mma_tiler = (128, 128, qk_ik * 4) 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), 128, 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.c_smem_s = utils.sm100.make_smem_layout_epi(self.o_dtype, self.c_layout, self.epi_tile, 2) 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) self.tilePlikeFP32 = self.qk_mma_tiler[1] // Float32.width * self.o_dtype.width self.tmem_s0_offset = 0; self.tmem_p0_offset = 32 self.tmem_o0_offset = 0 # Output is at S offset for QK-only tCS = qk_mma.make_fragment_C(cute.append(qk_as, self.num_acc_stage)) self.num_tmem_alloc_cols = utils.get_num_tmem_alloc_cols([tCS], 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, 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() 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) self._setup(qk_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)) 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) 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, tma_q, mQ, tma_k, mK, tma_c, mC, self.cluster_layout_vmnk, self.q_smem_s, self.k_smem_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, tma_q, mQ, tma_k, mK, tma_c, mC, cl_vmnk, q_smem_s, k_smem_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_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) 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)) gC = cute.local_tile(mC, cute.slice_(self.qk_mma_tiler,(None,None,0)), (None,None,None)) n_kv_tiles = cute.size(gK, mode=[3]) qk_thr = qk_mma.get_slice(0) tCgQ = qk_thr.partition_A(gQ); tCgK = qk_thr.partition_B(gK) tCgC = qk_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)) tAgQ = tAgQ[(None,0,None,0)]; tBgK = tBgK[(None,0,None,0)] tCrQ = qk_mma.make_fragment_A(sQ); tCrK = qk_mma.make_fragment_B(sK) 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) tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_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) 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) # QK only, accumulate across KV tiles sh = s_prod.acquire_and_advance() qk_mma.set(tcgen05.Field.ACCUMULATE, kt != 0) 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() # Wait for softmax (identity: just signal done) softmax_done_bar.arrive_and_wait() acc_pipe.producer_commit(acc_st); acc_st.advance() acc_pipe.producer_tail(acc_st) # EPILOGUE 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)) for kt in range(n_kv_tiles): si_handle = s_cons.wait_and_advance() # Identity softmax: no-op, just signal MMA si_handle.release() softmax_done_bar.arrive() # Output S (QK result) to GMEM tCtS_base = cute.make_tensor(tmem_ptr + self.tmem_s0_offset, tCtS_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, tCtS_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) n = 128 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') c = torch.zeros(m, n, 1, dtype=torch.bfloat16, device='cuda') # (128, 128) output = S qf = q[:,:,0].float(); kf = k[:,:,0].float() ref = (qf @ kf.T) 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)) 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 = QkOnlyTest() print('Compiling...', flush=True) compiled = cute.compile(kernel, mQ, mK, mC, stream) print('Running...', flush=True) compiled(mQ, mK, 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'QK-only n={n}: 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()}') print(f' out stats: min={out.min().item():.4f} max={out.max().item():.4f}') print(f' ref stats: min={ref.min().item():.4f} max={ref.max().item():.4f}') if __name__ == '__main__': test()