From 64b0ded71937a9b614cf7630fc1de292ee2e1fa1 Mon Sep 17 00:00:00 2001 From: biondizzle Date: Thu, 21 May 2026 23:26:50 +0000 Subject: [PATCH] Clean up: remove debug/temp files and dangling test kernels --- tests/unit/test_fmha_v3_correction.py | 460 -------------------------- tests/unit/test_fmha_v3_tenwarp.py | 278 ---------------- 2 files changed, 738 deletions(-) delete mode 100644 tests/unit/test_fmha_v3_correction.py delete mode 100644 tests/unit/test_fmha_v3_tenwarp.py diff --git a/tests/unit/test_fmha_v3_correction.py b/tests/unit/test_fmha_v3_correction.py deleted file mode 100644 index 4661bcf2..00000000 --- a/tests/unit/test_fmha_v3_correction.py +++ /dev/null @@ -1,460 +0,0 @@ -""" -FMHA v3 Stage C: Production architecture with correction warp group. -4 softmax warps (0-3), 4 correction warps (4-7), 1 MMA warp (8), 1 TMA warp (9). -10 warps = 320 threads. - -Softmax warps: QK→softmax, write P, write row metadata to TMEM vector. -Correction warps: read row metadata, rescale O (C6), final normalize (C9). -MMA warp: QK GEMM, PV GEMM. -TMA warp: load Q, K, V. -""" -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 FmhaV3Correction: - def __init__(self, s_k: int = 128): - 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 - self.softmax_warp_ids = (0, 1, 2, 3) - self.correction_warp_ids = (4, 5, 6, 7) - self.epilogue_warp_id = (4,5,6,7) - self.mma_warp_id = 8 - self.tma_warp_id = 9 - self.threads_per_warp = 32 - self.threads_per_cta = 320 - self.num_c_stage = 2 - self.kv_stage = 2; self.q_stage = 1 - self.tmem_s0_offset = 0 - self.tmem_p0_offset = 32 - # Vector at dedicated offset (after O) - no aliasing with S/P - self.tmem_vec0_offset = None # computed in _setup after tmem_o0_offset - self.scale_softmax_log2 = Float32(1.0 / math.sqrt(HEAD_DIM) * math.log2(math.e)) - self.scale_softmax = Float32(1.0 / math.sqrt(HEAD_DIM)) - - 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) - 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 - o_after = max(self.qk_mma_tiler[1], p_end) - self.tmem_o0_offset = ((o_after + 31) // 32) * 32 - o_cols = find_tmem_tensor_col_offset(tOtO) - total = self.tmem_o0_offset + o_cols - # Vector region: 2 FP32 cols per row, align to 32 - self.tmem_vec0_offset = ((total + 31) // 32) * 32 - vec_alloc = self.tmem_vec0_offset + 32 - self.num_tmem_alloc_cols = 1 - while self.num_tmem_alloc_cols < vec_alloc: 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() - 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 - 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() - is_softmax = warp_idx < 4 - is_correction = warp_idx >= 4 and warp_idx < 8 - is_mma = warp_idx == 8 - is_tma = warp_idx == 9 - - if is_tma: - 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*4)).make_participants() - softmax_done_bar = pipeline.NamedBarrier(barrier_id=3, num_threads=32*4 + 32*1) - pv_done_bar = pipeline.NamedBarrier(barrier_id=4, num_threads=32*1 + 32*4) - corr_done_bar = pipeline.NamedBarrier(barrier_id=6, num_threads=32*4 + 32*1) - 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,4),cta_layout_vmnk=cl_vmnk,defer_sync=True) - tmem_bar = pipeline.NamedBarrier(barrier_id=2,num_threads=32*(8+1)) - tmem = utils.TmemAllocator(st.holding.ptr,barrier_for_retrieve=tmem_bar,allocator_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) - 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) - 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 WARP =============== - if is_tma: - 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 =============== - if is_mma: - 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() - if kt > 0: - corr_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) - - # =============== SOFTMAX WARPS (0-3) =============== - if is_softmax: - tmem.allocate(self.num_tmem_alloc_cols) - tmem.wait_for_alloc() - tmem_ptr = tmem.retrieve_ptr(self.qk_acc_dtype) - sfw_idx = tidx % (32 * 4) - - # QK S load - 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 - 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 store (row_max and row_sum via QK partition) - 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) - 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) - - row_max = -cutlass.Float32.inf - row_sum = cutlass.Float32(0.0) - scale = self.scale_softmax_log2 - - 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) - - 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) - acc_scale = cute.math.exp2(scale * (old_row_max - row_max_safe), fastmath=True) - row_sum = row_sum * acc_scale - - # Write old_max and new_max to vector for correction warps - 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() - - # Compute P - minus_row_max_scale = (cutlass.Float32(0.0) - row_max_safe) * 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) - 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): - 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)) - cute.copy(tiled_tmem_store, rP_words, tTMEM_STOREtP) - cute.arch.fence_view_async_tmem_store() - si_handle.release() - softmax_done_bar.arrive() - - # Row sum - 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 - - # Write final row_sum and row_max to vector - tTMEM_STORE_VECrS_final = cute.make_rmem_tensor(tTMEM_STORE_VECcS.shape, self.qk_acc_dtype) - tTMEM_STORE_VECrS_final[0] = row_sum - tTMEM_STORE_VECrS_final[1] = row_max - cute.copy(tiled_tmem_store_vec, tTMEM_STORE_VECrS_final, tTMEM_STORE_VECtS) - cute.arch.fence_view_async_tmem_store() - # Do NOT free TMEM here - correction warps still need it - # tmem.relinquish_alloc_permit() - # tmem.free(tmem_ptr) - - # =============== CORRECTION WARPS (4-7) =============== - if is_correction: - tmem.wait_for_alloc() - tmem_ptr = tmem.retrieve_ptr(self.pv_acc_dtype) - corr_idx = tidx % (32 * 4) - - # Vector load (QK partition - same layout as softmax writes) - cS = cute.make_identity_tensor((self.qk_mma_tiler[0], self.qk_mma_tiler[1])) - tScS = qk_thr.partition_C(cS) - 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) - tScS_vec_layout = cute.composition(tScS.layout, cute.make_layout((128, 2))) - tScS_vec = cute.make_tensor(tScS.iterator, tScS_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_tmem_load_vec = tiled_tmem_load_vec.get_slice(corr_idx) - tTMEM_LOAD_VECtS = thr_tmem_load_vec.partition_S(tStS_vec) - tTMEM_LOAD_VECcS = thr_tmem_load_vec.partition_D(tScS_vec) - - # O TMEM load/store via PV partition - 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(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_tmem_load_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.pv_acc_dtype) - o_tmem_store_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.pv_acc_dtype) - 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(corr_idx) - o_thr_store = o_tiled_tmem_store.get_slice(corr_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 - - scale = self.scale_softmax_log2 - - for kt in range(n_kv_tiles): - pv_done_bar.arrive_and_wait() - - # Read vector: old_max and new_max for this correction thread's QK row - 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_acc_scale = cute.math.exp2(scale * (old_max - new_max), fastmath=True) - - # C6: Rescale O - if kt > 0: - tTMrO = cute.make_rmem_tensor((tTMEM_LOADcO.shape, o_col_tiles), self.pv_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] * corr_acc_scale - cute.copy(o_tiled_tmem_store, tTMrO_i, tTMEM_STOREtO_i) - cute.arch.fence_view_async_tmem_store() - corr_done_bar.arrive() - - # C9: Final normalization - # Read vector: final row_sum - tTMEM_LOAD_VECrS_final = cute.make_rmem_tensor(tTMEM_LOAD_VECcS.shape, self.qk_acc_dtype) - cute.copy(tiled_tmem_load_vec, tTMEM_LOAD_VECtS, tTMEM_LOAD_VECrS_final) - cute.arch.fence_view_async_tmem_load() - row_sum_corr = tTMEM_LOAD_VECrS_final[0] - inv_row_sum = cutlass.Float32(1.0) / row_sum_corr - - # Normalize O - tTMrO_final = cute.make_rmem_tensor((tTMEM_LOADcO.shape, o_col_tiles), self.pv_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() - - # Epilogue: TMEM → 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, self.num_acc_stage) - c_grp = pipeline.CooperativeGroup(pipeline.Agent.Thread, 32 * 4) - 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() - - -def test(): - import math - torch.manual_seed(42) - -def test(): - import math - torch.manual_seed(42) - for n in [128]: # TODO: multi-tile needs proper C6 ordering - 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 = FmhaV3Correction() - print("n=%d: Compiling..." % n, flush=True) - compiled = cute.compile(kernel, mQ, mK, mV, mC, stream) - print("n=%d: Running..." % n, 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() - status = "PASS" if cos >= 0.999 else "FAIL" - print("FMHA correction n=%d: cosine %.6f max_err %.6f %s" % (n, cos, max_err, status), flush=True) - -if __name__ == "__main__": - test() diff --git a/tests/unit/test_fmha_v3_tenwarp.py b/tests/unit/test_fmha_v3_tenwarp.py deleted file mode 100644 index fb6e9de6..00000000 --- a/tests/unit/test_fmha_v3_tenwarp.py +++ /dev/null @@ -1,278 +0,0 @@ -""" -FMHA v3 10-warp: Same logic as 6-warp but with 4 idle epilogue warps (4-7). -Testing: does adding 4 idle warps to a 320-thread CTA break anything? -softmax warps 0-3 (same as 6-warp's epilogue warps 0-3) -MMA warp 8 (was 4), TMA warp 9 (was 5) -Idle warps 4-7 (just pass, no TMEM/barrier/pipeline interaction) -""" -import math, 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 FmhaV3TenWarp: - def __init__(self, s_k: int = 128): - self.s_k = s_k - 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 # Same as 6-warp! Idle warps are 6-9 - self.threads_per_cta = 320; 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_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 - o_after = max(self.qk_mma_tiler[1], p_end) - self.tmem_o0_offset = ((o_after + 31) // 32) * 32 - o_cols = find_tmem_tensor_col_offset(tOtO) - total = self.tmem_o0_offset + o_cols - 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() - 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)) - 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,1),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) - 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 - 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() - acc_pipe.producer_commit(acc_st); acc_st.advance() - acc_pipe.producer_tail(acc_st) - - # Softmax + Epilogue (warps 0-3, same as 6-warp) - if warp_idx < self.mma_warp_id and warp_idx < 4: - 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)) - 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) - scale = self.scale_softmax_log2 - - 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): - 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 - 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)) - cute.copy(tiled_tmem_store, rP_words, tTMEM_STOREtP) - cute.arch.fence_view_async_tmem_store() - si_handle.release() - softmax_done_bar.arrive() - - # Epilogue - 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) - - # Idle warps 4-7: absolutely no TMEM/barrier/pipeline interaction - # (warp_idx >= 4 and warp_idx < 8 - these do nothing) - - -def test(): - import math - torch.manual_seed(42) - for n in [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") - 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 = FmhaV3TenWarp() - print("n=%d: Compiling..." % n, flush=True) - compiled = cute.compile(kernel, mQ, mK, mV, mC, stream) - print("n=%d: Running..." % n, 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() - status = "PASS" if cos >= 0.999 else "FAIL" - print("TenWarp n=%d: cosine %.6f max_err %.6f %s" % (n, cos, max_err, status), flush=True) - -if __name__ == "__main__": - test()