"""Minimal hd=512 test: ONLY QK GEMM, no softmax, no PV. Goal: isolate whether the compilation hang is from QK or softmax/PV.""" import torch, math, time, cutlass import cutlass.cute as cute import cutlass.torch as ct import cuda.bindings.driver as cuda from cutlass import BFloat16, Float32 from cutlass.cute.nvgpu import cpasync, tcgen05 from cutlass.utils import LayoutEnum import cutlass.utils as utils import cutlass.pipeline as pipeline from cutlass import const_expr class QkOnly512: def __init__(self): self.head_dim = 512 self.k_tile = 256 self.n_k_sub_tiles = 2 self.kv_stage = 1 self.q_stage = 1 self.q_dtype = BFloat16 self.qk_acc_dtype = Float32 self.cta_group = tcgen05.CtaGroup.ONE self.cluster_shape_mn = (1, 1) self.qk_mma_tiler = (128, 128, self.k_tile) self.threads_per_cta = 192 self.mma_warp_id = 4 self.tma_warp_id = 5 self.epilogue_warp_id = (0,1,2,3) def _setup(self, qk_mma): 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) 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, s_out, stream): self.a_major = LayoutEnum.from_tensor(q).mma_major_mode() self.b_major = LayoutEnum.from_tensor(k).mma_major_mode() 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_shape_mn) 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_shape_mn) self._kernel(qk_mma, tma_q, mQ, tma_k, mK, s_out).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, s_out): 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) @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] 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=cute.tiled_divide(cute.make_layout((1,1,1)), (qk_mma.thr_id.shape,)),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=cute.tiled_divide(cute.make_layout((1,1,1)), (qk_mma.thr_id.shape,)),defer_sync=True).make_participants() 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) pipeline.pipeline_init_arrive(cluster_shape_mn=cute.tiled_divide(cute.make_layout((1,1,1)), (qk_mma.thr_id.shape,)),is_relaxed=True) sQ = smem.allocate_tensor(element_type=self.q_dtype,layout=self.q_smem_s.outer,byte_alignment=128,swizzle=self.q_smem_s.inner) sK = smem.allocate_tensor(element_type=self.q_dtype,layout=self.k_smem_s.outer,byte_alignment=128,swizzle=self.k_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)) qk_thr = qk_mma.get_slice(0) tCgQ = qk_thr.partition_A(gQ); tCgK = qk_thr.partition_B(gK) a_lay = cute.make_layout((1,)) b_lay = cute.make_layout((1,)) tAsQ,tAgQ = cpasync.tma_partition(tma_q,0,a_lay,cute.group_modes(sQ,0,3),cute.group_modes(tCgQ,0,3)) 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, tStS.layout) pipeline.pipeline_init_wait(cluster_shape_mn=cute.tiled_divide(cute.make_layout((1,1,1)), (qk_mma.thr_id.shape,))) # ===== TMA LOAD warp ===== if warp_idx == self.tma_warp_id: qp.reset() kvp.reset() # k_sub=0 qh0 = qp.acquire_and_advance() cute.copy(tma_q, tAgQ[(None, cutlass.Int32(0))], tAsQ[(None, qh0.index)], tma_bar_ptr=qh0.barrier) kvh0 = kvp.acquire_and_advance() cute.copy(tma_k, tBgK[(None, cutlass.Int32(0))], tBsK[(None, kvh0.index)], tma_bar_ptr=kvh0.barrier) # k_sub=1 qh1 = qp.acquire_and_advance() cute.copy(tma_q, tAgQ[(None, cutlass.Int32(1))], tAsQ[(None, qh1.index)], tma_bar_ptr=qh1.barrier) kvh1 = kvp.acquire_and_advance() cute.copy(tma_k, tBgK[(None, cutlass.Int32(1))], tBsK[(None, kvh1.index)], tma_bar_ptr=kvh1.barrier) qp.tail() kvp.tail() # ===== MMA warp ===== if warp_idx == self.mma_warp_id: tmem.wait_for_alloc() # k_sub=0 qh0 = qc.wait_and_advance(); qh0.release() kvh0 = kvc.wait_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,kvh0.index)], tStS0) qk_mma.set(tcgen05.Field.ACCUMULATE, True) kvh0.release() # k_sub=1 qh1 = qc.wait_and_advance(); qh1.release() kvh1 = kvc.wait_and_advance() 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,kvh1.index)], tStS0) qk_mma.set(tcgen05.Field.ACCUMULATE, True) kvh1.release() cute.arch.fence_view_async_tmem_store() # Epilogue warps just allocate/free TMEM if warp_idx < self.mma_warp_id: tmem.allocate(64) tmem.wait_for_alloc() tmem.relinquish_alloc_permit() tmem.free(tmem.retrieve_ptr(self.qk_acc_dtype)) def test(): torch.manual_seed(42) hd, n, m = 512, 128, 128 q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device='cuda') k = torch.randn(n, hd, 1, dtype=torch.bfloat16, device='cuda') s_out = torch.zeros(1, dtype=torch.float32, device='cuda') # dummy 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)) mS = ct.from_dlpack(s_out).mark_layout_dynamic(leading_dim=ct.get_leading_dim(s_out)) stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream) kernel = QkOnly512() print('Compiling QK-only hd=512...', flush=True) t0 = time.time() compiled = cute.compile(kernel, mQ, mK, mS, stream) t1 = time.time() print(f'Compilation took {t1-t0:.1f}s', flush=True) compiled(mQ, mK, mS, stream) torch.cuda.synchronize() print('QK-only hd=512: SUCCESS') if __name__ == '__main__': test()