diff --git a/tests/test_fmha_pipeline.py b/tests/test_fmha_pipeline.py new file mode 100644 index 00000000..b08e39ab --- /dev/null +++ b/tests/test_fmha_pipeline.py @@ -0,0 +1,354 @@ +""" +Stage B — FMHA-style KV-tile interleaved attention kernel. + +Following CUTLASS FMHA reference architecture: +- Q: (seq_q, head_dim) — loaded once +- K, V: tiled over sequence dimension, V overwrites K in SMEM (FMHA trick) +- For each KV-tile: + 1. TMA load K[tile] into sK SMEM + 2. QK MMA: sQ @ sK^T → S in TMEM + 3. Softmax: S → P in TMEM (with online softmax rescaling of O in TMEM) + 4. V overwrites sK SMEM (after QK, K no longer needed) + 5. PV MMA: P @ sV → O in TMEM (accumulate) +- Epilogue: divide O by row_sum, store to GMEM + +This properly handles non-(128,128) PV because V SMEM always has the correct +data for the current KV-tile — it's loaded right before PV, not stale from +the beginning. + +Warp layout: + Warp 0-3: Softmax (4 warps) + Warp 4: MMA + Warp 5: TMA load +""" +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 + + +class FmhaPipelineKernel: + def __init__(self, qk_mma_tiler, pv_mma_tiler): + self.acc_dtype = Float32 + self.qk_acc_dtype = Float32 + self.q_dtype = BFloat16 + self.o_dtype = BFloat16 + self.c_dtype = BFloat16 + self.qk_mma_tiler = qk_mma_tiler + self.pv_mma_tiler = pv_mma_tiler + 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.mma_warp_id = 4 + self.tma_warp_id = 5 + self.threads_per_cta = 192 + self.kv_stage = 2 # double-buffered KV + self.q_stage = 1 + + def _setup(self, qk_mma, pv_mma): + qk_inst_k = cute.size(qk_mma.shape_mnk, mode=[2]) + self.qk_mma_tiler = (*self.qk_mma_tiler[:2], qk_inst_k * 4) + pv_inst_k = cute.size(pv_mma.shape_mnk, mode=[2]) + self.pv_mma_tiler = (*self.pv_mma_tiler[:2], pv_inst_k * 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.epi_tile = self.pv_mma_tiler[:2] + self.cta_tile_shape_mnk = ( + self.qk_mma_tiler[0] // cute.size(qk_mma.thr_id.shape), + self.pv_mma_tiler[1], + self.qk_mma_tiler[2]) + self.c_layout = LayoutEnum.ROW_MAJOR + self.num_ab_stage = 1 + self.num_acc_stage = 1 + self.num_c_stage = 2 + + 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.p_tmem_s = utils.sm100.make_smem_layout_a(pv_mma, self.pv_mma_tiler, self.q_dtype, 1) + 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, self.num_c_stage) + + qk_thr = qk_mma.get_slice(0) + qk_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2]) + tStS = qk_thr.make_fragment_C(qk_acc_shape) + s_cols = find_tmem_tensor_col_offset(tStS) + + pv_thr = pv_mma.get_slice(0) + pv_acc_shape = pv_thr.partition_shape_C(self.pv_mma_tiler[:2]) + tOtO = pv_thr.make_fragment_C(pv_acc_shape) + o_cols = find_tmem_tensor_col_offset(tOtO) + + self.tmem_s0_offset = 0 + self.tmem_p0_offset = 32 + self.tmem_o0_offset = o_cols + self.tilePlikeFP32 = self.qk_mma_tiler[1] // Float32.width * self.o_dtype.width + + tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_acc_shape, self.num_acc_stage)) + tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_acc_shape, self.num_acc_stage)) + self.num_tmem_alloc_cols = utils.get_num_tmem_alloc_cols([tCtS_fake, tCtO_fake], arch="sm_100") + + a_smem = cute.slice_(self.q_smem_s, (None, None, None, 0)) + b_smem = cute.slice_(self.k_smem_s, (None, None, None, 0)) + self.num_tma_load_bytes = ( + cute.size_in_bytes(self.q_dtype, a_smem) + + cute.size_in_bytes(self.q_dtype, b_smem) + ) * cute.size(qk_mma.thr_id.shape) + + @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() + self.v_major = LayoutEnum.from_tensor(v).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, self.qk_mma_tiler[:2], + 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, self.pv_mma_tiler[:2], + tcgen05.OperandSource.TMEM) + self._setup(qk_mma, pv_mma) + + q_smem = cute.slice_(self.q_smem_s, (None, None, None, 0)) + k_smem = cute.slice_(self.k_smem_s, (None, None, None, 0)) + v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0)) + + tma_q, tma_tq = 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_smem, self.qk_mma_tiler, qk_mma, self.cluster_layout_vmnk.shape) + tma_k, tma_tk = cute.nvgpu.make_tma_atom_B( + utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn, qk_mma.thr_id), + k, k_smem, self.qk_mma_tiler, qk_mma, self.cluster_layout_vmnk.shape) + tma_v, tma_tv = cute.nvgpu.make_tma_atom_B( + utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn, pv_mma.thr_id), + v, v_smem, self.pv_mma_tiler, pv_mma, self.cluster_layout_vmnk.shape) + + epi_smem = cute.select(self.c_smem_s, mode=[0, 1]) + tma_c, tma_tc = cpasync.make_tiled_tma_atom( + cpasync.CopyBulkTensorTileS2GOp(), c, epi_smem, self.epi_tile) + + self._kernel( + qk_mma, pv_mma, tma_q, tma_tq, tma_k, tma_tk, tma_v, tma_tv, + tma_c, tma_tc, 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() + use_2cta = cute.size(qk_mma.thr_id.shape) == 2 + + 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] + mma_si_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) + + q_prod, q_cons = 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.num_tma_load_bytes, cta_layout_vmnk=cl_vmnk, defer_sync=True + ).make_participants() + + kv_prod, kv_cons = 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.num_tma_load_bytes, cta_layout_vmnk=cl_vmnk, defer_sync=True + ).make_participants() + + mma_si_prod, mma_si_cons = pipeline.PipelineUmmaAsync.create( + barrier_storage=st.mma_si_bar.data_ptr(), num_stages=1, + producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread), + consumer_group=pipeline.CooperativeGroup( + pipeline.Agent.Thread, 32 * len(self.softmax_warp_ids)), + ).make_participants() + + 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.softmax_warp_ids) * (2 if use_2cta else 1)), + cta_layout_vmnk=cl_vmnk, defer_sync=True) + + tmem_bar = pipeline.NamedBarrier( + barrier_id=2, + num_threads=32 * len((self.mma_warp_id, *self.softmax_warp_ids))) + tmem = utils.TmemAllocator( + st.holding.ptr, barrier_for_retrieve=tmem_bar, + allocator_warp_id=self.softmax_warp_ids[0], is_two_cta=use_2cta, + 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) + # V overwrites K SMEM (FMHA trick) + sV_ptr = cute.recast_ptr(sK.iterator, v_smem_s.inner) + sV = cute.make_tensor(sV_ptr, v_smem_s.outer) + 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_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2]) + tStS = qk_thr.make_fragment_C(qk_acc_shape) + tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout) + + pv_acc_shape = pv_thr.partition_shape_C(self.pv_mma_tiler[:2]) + tOtO = pv_thr.make_fragment_C(pv_acc_shape) + 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_acc_shape, self.num_acc_stage)) + tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_acc_shape, self.num_acc_stage)) + + pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk) + + # ═══ TMA LOAD WARP ═══ + if warp_idx == self.tma_warp_id: + # Load Q once + q_prod.reset() + qh = q_prod.acquire_and_advance() + cute.copy(tma_q, tAgQ[(None, qh.count)], tAsQ[(None, qh.index)], tma_bar_ptr=qh.barrier) + q_prod.tail() + + # Load KV tiles: for each tile, load K then V + # K and V share SMEM, so V overwrites K after QK consumes it + kv_prod.reset() + peek = kv_prod.try_acquire() + for kt in cutlass.range(n_kv_tiles, unroll=1): + # Load K[tile] + kvh = kv_prod.acquire_and_advance(peek) + cute.copy(tma_k, tBgK[(None, kvh.count)], tBsK[(None, kvh.index)], tma_bar_ptr=kvh.barrier) + # Load V[tile] into the SAME SMEM (overwrites K after QK) + # Wait — we need QK to finish before V overwrites K. + # FMHA uses a SEPARATE pipeline entry for V. The MMA warp + # consumes K first (QK), then V (PV). The pipeline ordering + # ensures V doesn't overwrite K before QK is done. + cute.copy(tma_v, tVgV[(None, kvh.count)], tVsV[(None, kvh.index)], tma_bar_ptr=kvh.barrier) + peek = cutlass.Boolean(1) + if kvh.count + 1 < 2 * n_kv_tiles: + peek = kv_prod.try_acquire() + kv_prod.tail() + + # ═══ MMA WARP ═══ + if warp_idx == self.mma_warp_id: + tmem.wait_for_alloc() + + q_cons.reset() + qh = q_cons.wait_and_advance() + qh.release() + + kv_cons.reset() + peek = kv_cons.try_wait() + + acc_prod_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage) + acc_pipe.producer_acquire(acc_prod_st) + + for kt in range(n_kv_tiles): + # Wait for K[tile] + kvh = kv_cons.wait_and_advance(peek) + peek = cutlass.Boolean(1) + + # ─── QK: Q @ K[tile]^T → S ─── + s0_handle = mma_si_prod.acquire_and_advance() + qk_mma.set(tcgen05.Field.ACCUMULATE, kt != 0) + nblk = cute.size(tCrQ, mode=[2]) + for kb in cutlass.range(nblk, unroll_full=True): + cute.gemm(qk_mma, tStS0, + tCrQ[(None, None, kb, 0)], + tCrK[(None, None, kb, kvh.index)], + tStS0) + qk_mma.set(tcgen05.Field.ACCUMULATE, True) + cute.arch.fence_view_async_tmem_store() + s0_handle.commit() + + # ─── Wait for softmax: S → P done ─── + s0_handle = mma_si_prod.acquire_and_advance() + + # ─── Wait for V[tile] ─── + vvh = kv_cons.wait_and_advance(peek) + peek = cutlass.Boolean(1) + + # ─── PV: P @ V[tile] → O ─── + pv_mma.set(tcgen05.Field.ACCUMULATE, kt != 0) + nblk_pv = cute.size(tOrP0, mode=[2]) + for kb in cutlass.range(nblk_pv, unroll_full=True): + cute.gemm(pv_mma, tOtO0, + tOrP0[(None, None, kb)], + tCrV[(None, None, kb, vvh.index)], + tOtO0) + pv_mma.set(tcgen05.Field.ACCUMULATE, True) + + kvh.release() + vvh.release() + + acc_pipe.producer_commit(acc_prod_st) + acc_prod_st.advance() + acc_pipe.producer_tail(acc_prod_st) + + # ═══ SOFTMAX WARPS ═══ + 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.softmax_warp_ids)) + + tmem_load_atom = cute.make_copy_atom( + tcgen05.copy.Ld32x32bOp(tcgen \ No newline at end of file diff --git a/tests/test_fmha_v1.py b/tests/test_fmha_v1.py new file mode 100644 index 00000000..f9cb6b9b --- /dev/null +++ b/tests/test_fmha_v1.py @@ -0,0 +1,251 @@ +""" +FMHA Pipeline v1: Modified v30 with pv_mma_tiler=(128,64) for head_dim=64. +Step 1: Test if the V SMEM layout works for head_dim=64 PV. +""" +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 PvHeadDimKernel: + 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 + + 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]) + # PV tiler: (M, N, K) = (QK_M, head_dim, QK_N) + # K of PV = sequence per tile = QK's N = 128 + pv_nphases = 128 // pv_ik # 128/16 = 8 k-phases + self.pv_mma_tiler = (128, HEAD_DIM, pv_ik * pv_nphases) # (128, 64, 128) + 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.a_smem_s = utils.sm100.make_smem_layout_a(qk_mma, self.mma_tiler, self.q_dtype, 1) + self.b_smem_s = utils.sm100.make_smem_layout_b(qk_mma, self.mma_tiler, self.q_dtype, 1) + self.v_smem_s = utils.sm100.make_smem_layout_b(pv_mma, self.pv_mma_tiler, self.q_dtype, 1) + # Diagnostic: print V SMEM size + v_s = cute.slice_(self.v_smem_s, (None,None,None,0)) + v_sz = cute.size_in_bytes(self.q_dtype, v_s) + print(f"[DIAG] pv_mma_tiler={self.pv_mma_tiler} V SMEM per stage: {v_sz} bytes ({v_sz//2} BF16)") + self.p_tmem_s = utils.sm100.make_smem_layout_a(pv_mma, self.pv_mma_tiler, self.q_dtype, 1) + 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) + 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.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 = 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") + a = cute.slice_(self.a_smem_s,(None,None,None,0)); b = cute.slice_(self.b_smem_s,(None,None,None,0)) + vs = cute.slice_(self.v_smem_s,(None,None,None,0)) + self.num_tma_load_bytes = (cute.size_in_bytes(self.q_dtype,a)+cute.size_in_bytes(self.q_dtype,b)+cute.size_in_bytes(self.q_dtype,vs))*cute.size(qk_mma.thr_id.shape) + + @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() + self.v_major = LayoutEnum.from_tensor(v).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.a_smem_s,(None,None,None,0)); k_s = cute.slice_(self.b_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.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.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.a_smem_s,self.b_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, a_smem_s, b_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: + ab_bar: cute.struct.MemRange[cutlass.Int64, self.num_ab_stage*2] + mma_si_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) + ab_p,ab_c = pipeline.PipelineTmaUmma.create(barrier_storage=st.ab_bar.data_ptr(),num_stages=self.num_ab_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.num_tma_load_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants() + mma_si_prod,mma_si_cons = pipeline.PipelineUmmaAsync.create(barrier_storage=st.mma_si_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() + 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)*(2 if cute.size(qk_mma.thr_id.shape)==2 else 1)),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=a_smem_s.outer,byte_alignment=128,swizzle=a_smem_s.inner) + sK = smem.allocate_tensor(element_type=self.q_dtype,layout=b_smem_s.outer,byte_alignment=128,swizzle=b_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)) + gC = cute.local_tile(mC,cute.slice_(self.pv_mma_tiler,(None,None,0)),(None,None,None)) + k_cnt = cute.size(gQ,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); 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)) + tAgQ = tAgQ[(None,0,None,0)]; tBgK = tBgK[(None,0,None,0)] + gV = cute.local_tile(mV,cute.slice_(self.pv_mma_tiler,(0,None,None)),(None,None,None)) + tCgV = pv_thr.partition_B(gV) + tVsV,tVgV = cpasync.tma_partition(tma_v,0,b_lay,cute.group_modes(sV,0,3),cute.group_modes(tCgV,0,3)) + 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) + + if warp_idx == self.tma_warp_id: + ab_p.reset(); peek = ab_p.try_acquire() + for kt in cutlass.range(k_cnt,unroll=1): + h = ab_p.acquire_and_advance(peek) + cute.copy(tma_q,tAgQ[(None,h.count)],tAsQ[(None,h.index)],tma_bar_ptr=h.barrier) + cute.copy(tma_k,tBgK[(None,h.count)],tBsK[(None,h.index)],tma_bar_ptr=h.barrier) + cute.copy(tma_v,tVgV[(None,h.count)],tVsV[(None,h.index)],tma_bar_ptr=h.barrier) + peek = cutlass.Boolean(1) + if h.count+1= 0.99 else "FAIL"}') + if cos < 0.99: + print(f' out[0,:4] = {out[0,:4].tolist()}') + print(f' ref[0,:4] = {ref[0,:4].tolist()}') + +if __name__ == '__main__': + test() diff --git a/tests/test_fmha_v2.py b/tests/test_fmha_v2.py new file mode 100644 index 00000000..873ecdf2 --- /dev/null +++ b/tests/test_fmha_v2.py @@ -0,0 +1,245 @@ +""" +FMHA Pipeline v2: KV-tile interleaved with V overwriting K in SMEM. +K and V use separate pipeline entries from the same kv pipeline. +MMA: wait for K → QK → wait for softmax → wait for V → PV per KV-tile. +""" +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 FmhaKernel: + 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, 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.p_tmem_s = utils.sm100.make_smem_layout_a(pv_mma, self.pv_mma_tiler, self.q_dtype, 1) + 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) + 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.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 = 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") + a = cute.slice_(self.q_smem_s,(None,None,None,0)); b = cute.slice_(self.k_smem_s,(None,None,None,0)) + self.num_tma_load_bytes = (cute.size_in_bytes(self.q_dtype,a)+cute.size_in_bytes(self.q_dtype,b))*cute.size(qk_mma.thr_id.shape) + + @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() + self.v_major = LayoutEnum.from_tensor(v).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,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] + mma_si_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.num_tma_load_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.num_tma_load_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants() + mma_si_prod,mma_si_cons = pipeline.PipelineUmmaAsync.create(barrier_storage=st.mma_si_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() + 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) + # V overwrites K SMEM + sV = cute.make_tensor(cute.recast_ptr(sK.iterator, v_smem_s.inner), v_smem_s.outer) + 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 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) + vh = kvp.acquire_and_advance(cutlass.Boolean(1)) + 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 = mma_si_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() + sh = mma_si_prod.acquire_and_advance() # wait softmax + vh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1) # wait V + 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) + kh.release(); vh.release() + 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)) + 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) + tStS_P_layout = cute.composition(tStS.layout, cute.make_layout((128, self.tilePlikeFP32))) + tStS_P = cute.make_tensor(tStS.iterator + self.tmem_p0_offset, tStS_P_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, tStS_P) + thr_store = tiled_tmem_store.get_slice(sfw_idx) + tTMEM_STOREtS_x4 = thr_store.partition_D(tStS_P) + tScS_P_layout = cute.composition(tScS.layout, cute.make_layout((128, self.tilePlikeFP32))) + tScS_P = cute.make_tensor(tScS.iterator, tScS_P_layout) + tTMEM_STOREcS = thr_store.partition_S(tScS_P) + for kt in range(n_kv_tiles): + si_handle = mma_si_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) + tTMEM_STORErS_x4 = cute.make_rmem_tensor(tTMEM_STOREcS.shape, self.qk_acc_dtype) + tTMEM_STORErS_x4_e = cute.make_tensor(cute.recast_ptr(tTMEM_STORErS_x4.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)) + tTMEM_STORErS_x4_e_frg = cute.logical_divide(tTMEM_STORErS_x4_e, cute.make_layout(frg_tile)) + for j in range(frg_cnt): + s_vec = tTMEM_LOADrS_frg[None, j].load() + tTMEM_STORErS_x4_e_frg[None, j].store(s_vec.to(self.q_dtype)) + cute.copy(tiled_tmem_store, tTMEM_STORErS_x4, tTMEM_STOREtS_x4) + cute.arch.fence_view_async_tmem_store() + si_handle.release() + 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(): + 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.ones(n, hd, 1, dtype=torch.bfloat16, device='cuda') + c = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device='cuda') + qf = q[:,:,0].float(); kf = k[:,:,0].float() + ref = (qf @ kf.T).bfloat16().float() @ v[:,:,0].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).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v)) + 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 = FmhaKernel() + print(f'n={n}: Compiling...', flush=True) + compiled = cute.compile(kernel, mQ, mK, mV, mC, stream) + 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() + print(f'FMHA v2 n={n} V=ones: 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()}') + +if __name__ == '__main__': + test()