D1: Parameterize HEAD_DIM in FmhaKernel (64→512)
- Promote HEAD_DIM from module constant to constructor parameter - FmhaKernel(head_dim=64, s_k=128, ...) — default 64 for regression - All references to HEAD_DIM replaced with self.head_dim - PV MMA tiler, V layout, softmax corr_tiles all parameterized - TMEM budget warning when num_tmem_alloc_cols > 512 - New test: test_fmha_v3_stage_d1.py tests hd=64 (regression) and hd=512 - Stage C test preserved as-is for reference
This commit is contained in:
@@ -1,2 +1,399 @@
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"""FMHA kernel: QK -> online softmax -> PV (CuTeDSL, Stage B+). Extracted from test_fmha_v3.py."""
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# TODO: Extract FmhaV3 kernel class here
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"""
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FMHA v3 Stage-C Multi-Tile with correction_epilog (paired atoms, no TMEM round-trip).
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Key insight: hand-constructed Ld32x32bOp/St32x32bOp atoms for TMEM round-trip
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introduce ~3% error (cos 0.973) because their TMEM column mapping differs from
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get_tmem_load_op. The fix: use get_tmem_load_op + get_smem_store_op paired atoms
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for a ONE-WAY trip: TMEM → reg (normalize) → SMEM, then TMA store SMEM → GMEM.
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"""
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import torch, cutlass, cutlass.cute as cute, cutlass.utils as utils, cutlass.pipeline as pipeline
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from cutlass.cute.nvgpu import cpasync, tcgen05
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from cutlass import Float32, BFloat16, Int32, Boolean, const_expr
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from cutlass.utils import LayoutEnum
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from cutlass.utils.tmem_allocator import find_tmem_tensor_col_offset
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import cuda.bindings.driver as cuda
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import cutlass.torch as ct
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import math
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class FmhaKernel:
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def __init__(self, head_dim=64, s_k=128, scale_softmax=None):
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self.head_dim = head_dim
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self.s_k = s_k
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self.n_kv_tiles = s_k // 128
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self.acc_dtype = Float32; self.qk_acc_dtype = Float32
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self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
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self.use_2cta_instrs = False; self.epilog_sync_bar_id = 1
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self.cluster_shape_mn = (1, 1); self.cta_group = tcgen05.CtaGroup.ONE
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self.epilogue_warp_id = (0,1,2,3); self.mma_warp_id = 4; self.tma_warp_id = 5
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self.threads_per_cta = 192; self.num_c_stage = 2
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self.kv_stage = 2; self.q_stage = 1; self.num_c_stage = 2
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self.scale_softmax = scale_softmax if scale_softmax is not None else 1.0 / math.sqrt(self.head_dim)
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self.scale_softmax_log2 = self.scale_softmax * math.log2(math.e)
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def _setup(self, qk_mma, pv_mma):
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qk_ik = cute.size(qk_mma.shape_mnk, mode=[2])
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self.qk_mma_tiler = (128, 128, qk_ik * 4)
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pv_ik = cute.size(pv_mma.shape_mnk, mode=[2])
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self.pv_mma_tiler = (128, self.head_dim, pv_ik * (128 // pv_ik))
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self.mma_tiler = self.qk_mma_tiler
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self.cluster_layout_vmnk = cute.tiled_divide(cute.make_layout((1,1,1)), (qk_mma.thr_id.shape,))
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self.cta_tile_shape_mnk = (self.qk_mma_tiler[0]//cute.size(qk_mma.thr_id.shape), self.head_dim, self.qk_mma_tiler[2])
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self.c_layout = LayoutEnum.ROW_MAJOR
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self.epi_tile = utils.sm100.compute_epilogue_tile_shape(self.cta_tile_shape_mnk, False, self.c_layout, self.o_dtype)
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self.num_ab_stage = 1; self.num_acc_stage = 1
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self.q_smem_s = utils.sm100.make_smem_layout_a(qk_mma, self.qk_mma_tiler, self.q_dtype, self.q_stage)
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self.k_smem_s = utils.sm100.make_smem_layout_b(qk_mma, self.qk_mma_tiler, self.q_dtype, self.kv_stage)
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self.v_smem_s = utils.sm100.make_smem_layout_b(pv_mma, self.pv_mma_tiler, self.q_dtype, self.kv_stage)
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self.c_smem_s = utils.sm100.make_smem_layout_epi(self.o_dtype, self.c_layout, self.epi_tile, 2)
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self.p_tmem_s = utils.sm100.make_smem_layout_a(pv_mma, self.pv_mma_tiler, self.q_dtype, 1)
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qk_thr = qk_mma.get_slice(0); qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
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tStS = qk_thr.make_fragment_C(qk_as)
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pv_thr = pv_mma.get_slice(0); pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
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tOtO = pv_thr.make_fragment_C(pv_as)
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self.tmem_s0_offset = 0; self.tmem_p0_offset = 32
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p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
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p_end = self.tmem_p0_offset + p_cols_fp32
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s_cols = self.qk_mma_tiler[1]
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o_after = max(s_cols, p_end)
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self.tmem_o0_offset = ((o_after + 31) // 32) * 32
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o_cols = find_tmem_tensor_col_offset(tOtO)
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total = self.tmem_o0_offset + o_cols
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self.num_tmem_alloc_cols = 1
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while self.num_tmem_alloc_cols < total:
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self.num_tmem_alloc_cols *= 2
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cta = cute.size(qk_mma.thr_id.shape)
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q_s = cute.slice_(self.q_smem_s,(None,None,None,0))
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k_s = cute.slice_(self.k_smem_s,(None,None,None,0))
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v_s = cute.slice_(self.v_smem_s,(None,None,None,0))
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self.q_tx_bytes = cute.size_in_bytes(self.q_dtype, q_s) * cta
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self.kv_tx_bytes = (cute.size_in_bytes(self.q_dtype, k_s) +
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cute.size_in_bytes(self.q_dtype, v_s)) * cta
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@cute.jit
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def __call__(self, q, k, v, c, stream):
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self.q_dtype = q.element_type; self.o_dtype = c.element_type; self.c_dtype = self.o_dtype
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self.a_major = LayoutEnum.from_tensor(q).mma_major_mode()
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self.b_major = LayoutEnum.from_tensor(k).mma_major_mode()
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v_fmha = cute.make_tensor(
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v.iterator,
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cute.make_layout(
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(self.head_dim, self.s_k, 1),
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stride=(1, self.head_dim, self.head_dim * self.s_k),
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),
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)
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self.v_major = LayoutEnum.from_tensor(v_fmha).mma_major_mode()
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self.c_layout = LayoutEnum.from_tensor(c)
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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)
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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,self.head_dim), tcgen05.OperandSource.TMEM)
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self._setup(qk_mma, pv_mma)
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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))
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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)
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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)
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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)
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epi_s = cute.select(self.c_smem_s,mode=[0,1])
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tma_c,mC = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(),c,epi_s,self.epi_tile)
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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)
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@cute.kernel
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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):
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warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx())
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tidx,_,_ = cute.arch.thread_idx()
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if warp_idx == self.tma_warp_id:
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cpasync.prefetch_descriptor(tma_q); cpasync.prefetch_descriptor(tma_k); cpasync.prefetch_descriptor(tma_v); cpasync.prefetch_descriptor(tma_c)
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@cute.struct
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class SS:
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q_bar: cute.struct.MemRange[cutlass.Int64, self.q_stage*2]
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kv_bar: cute.struct.MemRange[cutlass.Int64, self.kv_stage*2]
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s_bar: cute.struct.MemRange[cutlass.Int64, 2]
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acc_bar: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage*2]
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tmem_dealloc: cutlass.Int64; holding: cutlass.Int32
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smem = utils.SmemAllocator(); st = smem.allocate(SS)
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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()
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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()
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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()
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softmax_done_bar = pipeline.NamedBarrier(barrier_id=3, num_threads=32 + 32*len(self.epilogue_warp_id))
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final_o_bar = pipeline.NamedBarrier(barrier_id=4, num_threads=32 + 32*len(self.epilogue_warp_id))
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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)
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tmem_bar = pipeline.NamedBarrier(barrier_id=2,num_threads=32*len((self.mma_warp_id,*self.epilogue_warp_id)))
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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)
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pipeline.pipeline_init_arrive(cluster_shape_mn=cl_vmnk,is_relaxed=True)
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sQ = smem.allocate_tensor(element_type=self.q_dtype,layout=q_smem_s.outer,byte_alignment=128,swizzle=q_smem_s.inner)
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sK = smem.allocate_tensor(element_type=self.q_dtype,layout=k_smem_s.outer,byte_alignment=128,swizzle=k_smem_s.inner)
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sV = smem.allocate_tensor(element_type=self.q_dtype,layout=v_smem_s.outer,byte_alignment=128,swizzle=v_smem_s.inner)
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sC = smem.allocate_tensor(element_type=self.o_dtype,layout=c_smem_s.outer,byte_alignment=128,swizzle=c_smem_s.inner)
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gQ = cute.local_tile(mQ,cute.slice_(self.qk_mma_tiler,(None,0,None)),(None,None,None))
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gK = cute.local_tile(mK,cute.slice_(self.qk_mma_tiler,(0,None,None)),(None,None,None))
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gV = cute.local_tile(mV,cute.slice_(self.pv_mma_tiler,(0,None,None)),(None,None,None))
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gC = cute.local_tile(mC,cute.slice_(self.pv_mma_tiler,(None,None,0)),(None,None,None))
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n_kv_tiles = cute.size(gK, mode=[3])
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qk_thr = qk_mma.get_slice(0); pv_thr = pv_mma.get_slice(0)
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tCgQ = qk_thr.partition_A(gQ); tCgK = qk_thr.partition_B(gK)
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tCgV = pv_thr.partition_B(gV); tCgC = pv_thr.partition_C(gC)
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a_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,0,None,0)).shape)
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tAsQ,tAgQ = cpasync.tma_partition(tma_q,0,a_lay,cute.group_modes(sQ,0,3),cute.group_modes(tCgQ,0,3))
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b_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,None,0,0)).shape)
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tBsK,tBgK = cpasync.tma_partition(tma_k,0,b_lay,cute.group_modes(sK,0,3),cute.group_modes(tCgK,0,3))
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tVsV,tVgV = cpasync.tma_partition(tma_v,0,b_lay,cute.group_modes(sV,0,3),cute.group_modes(tCgV,0,3))
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tAgQ = tAgQ[(None,0,None,0)]; tBgK = tBgK[(None,0,None,0)]; tVgV = tVgV[(None,0,None,0)]
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tCrQ = qk_mma.make_fragment_A(sQ); tCrK = qk_mma.make_fragment_B(sK)
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tCrV = pv_mma.make_fragment_B(sV)
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qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
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tStS = qk_thr.make_fragment_C(qk_as)
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tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
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pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
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tOtO = pv_thr.make_fragment_C(pv_as)
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tOtO0 = cute.make_tensor(tOtO.iterator + self.tmem_o0_offset, tOtO.layout)
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tP = cute.make_tensor(tStS.iterator, p_tmem_s.outer)
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tOrP_base = pv_thr.make_fragment_A(tP)
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tOrP = tOrP_base[(None,None,None,0)]
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tOrP0 = cute.make_tensor(
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tOrP.iterator + self.qk_acc_dtype.width // self.q_dtype.width * self.tmem_p0_offset,
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tOrP.layout)
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tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_as, self.num_acc_stage))
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pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
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# ===== TMA LOAD warp =====
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if warp_idx == self.tma_warp_id:
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qp.reset(); qh = qp.acquire_and_advance()
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cute.copy(tma_q, tAgQ[(None, Int32(0))], tAsQ[(None, qh.index)], tma_bar_ptr=qh.barrier)
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qp.tail()
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kvp.reset(); pk = kvp.try_acquire()
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for kt in cutlass.range(0, self.n_kv_tiles, 1, unroll=1):
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kvh = kvp.acquire_and_advance(pk)
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cute.copy(tma_k, tBgK[(None, kt)], tBsK[(None, kvh.index)], tma_bar_ptr=kvh.barrier)
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cute.copy(tma_v, tVgV[(None, kt)], tVsV[(None, kvh.index)], tma_bar_ptr=kvh.barrier)
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pk = cutlass.Boolean(1)
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kvp.tail()
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# ===== MMA warp =====
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if warp_idx == self.mma_warp_id:
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tmem.wait_for_alloc()
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qc.reset(); qh = qc.wait_and_advance(); qh.release()
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kvc.reset(); pk = kvc.try_wait()
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acc_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
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acc_pipe.producer_acquire(acc_st)
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for kt in range(self.n_kv_tiles):
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kvh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
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sh = s_prod.acquire_and_advance()
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qk_mma.set(tcgen05.Field.ACCUMULATE, False)
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for kb in cutlass.range(cute.size(tCrQ, mode=[2]), unroll_full=True):
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cute.gemm(qk_mma, tStS0, tCrQ[(None,None,kb,0)], tCrK[(None,None,kb,kvh.index)], tStS0)
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qk_mma.set(tcgen05.Field.ACCUMULATE, True)
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cute.arch.fence_view_async_tmem_store()
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sh.commit()
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softmax_done_bar.arrive_and_wait()
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pv_mma.set(tcgen05.Field.ACCUMULATE, kt != 0)
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for kb in cutlass.range(cute.size(tOrP0, mode=[2]), unroll_full=True):
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cute.gemm(pv_mma, tOtO0, tOrP0[(None,None,kb)], tCrV[(None,None,kb,kvh.index)], tOtO0)
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pv_mma.set(tcgen05.Field.ACCUMULATE, True)
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cute.arch.fence_view_async_tmem_store()
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kvh.release()
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acc_pipe.producer_commit(acc_st); acc_st.advance()
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final_o_bar.arrive()
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acc_pipe.producer_tail(acc_st)
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# ===== SOFTMAX + CORRECTION EPILOGUE warps =====
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if warp_idx < self.mma_warp_id:
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tmem.allocate(self.num_tmem_alloc_cols)
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tmem.wait_for_alloc()
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tmem_ptr = tmem.retrieve_ptr(self.qk_acc_dtype)
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sfw_idx = tidx % (32 * len(self.epilogue_warp_id))
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# S load atoms
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tmem_load_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
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tiled_tmem_load = tcgen05.make_tmem_copy(tmem_load_atom, tStS0)
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thr_load = tiled_tmem_load.get_slice(sfw_idx)
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tTMEM_LOADtS = thr_load.partition_S(tStS0)
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cS = cute.make_identity_tensor((self.qk_mma_tiler[0], self.qk_mma_tiler[1]))
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tScS = qk_thr.partition_C(cS)
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tTMEM_LOADcS = thr_load.partition_D(tScS)
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# P store atoms
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p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
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tStP_layout = cute.composition(tStS.layout, cute.make_layout((self.pv_mma_tiler[0], p_cols_fp32)))
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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)
|
||||
|
||||
row_max = -Float32.inf
|
||||
row_sum = Float32(0.0)
|
||||
scale_log2 = Float32(self.scale_softmax_log2)
|
||||
|
||||
# O rescale atoms (hand-constructed, using composition layout like CUTLASS correction_rescale)
|
||||
corr_tile_size = 16
|
||||
tOcO = pv_thr.partition_C(cS)
|
||||
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)
|
||||
tmem_load_o_atom = cute.make_copy_atom(
|
||||
tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(corr_tile_size)),
|
||||
self.acc_dtype,
|
||||
)
|
||||
tmem_store_o_atom = cute.make_copy_atom(
|
||||
tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(corr_tile_size)),
|
||||
self.acc_dtype,
|
||||
)
|
||||
tiled_tmem_load_o = tcgen05.make_tmem_copy(tmem_load_o_atom, tOtO_i)
|
||||
tiled_tmem_store_o = tcgen05.make_tmem_copy(tmem_store_o_atom, tOtO_i)
|
||||
thr_tmem_load_o = tiled_tmem_load_o.get_slice(sfw_idx)
|
||||
thr_tmem_store_o = tiled_tmem_store_o.get_slice(sfw_idx)
|
||||
tTMEM_LOADtO = thr_tmem_load_o.partition_S(tOtO_i)
|
||||
tTMEM_LOADcO = thr_tmem_load_o.partition_D(tOcO_i)
|
||||
tTMEM_STOREtO = thr_tmem_store_o.partition_D(tOtO_i)
|
||||
n_corr_tiles = self.head_dim // corr_tile_size
|
||||
|
||||
for kt in range(self.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)
|
||||
cute.arch.fence_view_async_tmem_load()
|
||||
|
||||
old_row_max = row_max
|
||||
frg_cnt = 4
|
||||
frg_tile = cute.size(tTMEM_LOADrS) // frg_cnt
|
||||
tTMEM_LOADrS_frg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(frg_tile))
|
||||
for j in range(frg_cnt):
|
||||
for k in range(cute.size(tTMEM_LOADrS_frg, mode=[0])):
|
||||
row_max = cute.arch.fmax(row_max, tTMEM_LOADrS_frg[k, j] * scale_log2)
|
||||
|
||||
row_max_safe = row_max
|
||||
if row_max == -cutlass.Float32.inf:
|
||||
row_max_safe = Float32(0.0)
|
||||
|
||||
acc_scale_ = old_row_max - row_max_safe
|
||||
acc_scale = cute.math.exp2(acc_scale_, fastmath=True)
|
||||
if old_row_max == -cutlass.Float32.inf:
|
||||
acc_scale = Float32(0.0)
|
||||
row_sum *= acc_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)
|
||||
minus_row_max = Float32(0.0) - row_max_safe
|
||||
|
||||
rP_bf16_frg = cute.logical_divide(rP_bf16, cute.make_layout(frg_tile))
|
||||
for j in range(frg_cnt):
|
||||
for k in range(cute.size(tTMEM_LOADrS_frg, mode=[0])):
|
||||
tTMEM_LOADrS_frg[k, j] = tTMEM_LOADrS_frg[k, j] * scale_log2 + minus_row_max
|
||||
tTMEM_LOADrS_frg[k, j] = cute.math.exp2(tTMEM_LOADrS_frg[k, j], fastmath=True)
|
||||
row_sum = row_sum + tTMEM_LOADrS_frg[k, j]
|
||||
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()
|
||||
|
||||
# Per-tile O rescale (hand-constructed atoms with logical_divide layout)
|
||||
if kt > 0:
|
||||
tTMrO = cute.make_rmem_tensor(
|
||||
(tTMEM_LOADcO.shape, 128 // corr_tile_size), self.acc_dtype
|
||||
)
|
||||
for i in range(n_corr_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(tiled_tmem_load_o, tTMEM_LOADtO_i, tTMrO_i)
|
||||
for k in cutlass.range(cute.size(tTMrO_i), vectorize=True):
|
||||
tTMrO_i[k] = tTMrO_i[k] * acc_scale
|
||||
cute.copy(tiled_tmem_store_o, tTMrO_i, tTMEM_STOREtO_i)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
si_handle.release()
|
||||
softmax_done_bar.arrive()
|
||||
|
||||
# Wait for MMA's PV[N-1] to commit before reading O.
|
||||
final_o_bar.arrive_and_wait()
|
||||
|
||||
# === NO-OP TMEM round-trip: re-map O from MMA layout to epilog layout ===
|
||||
tTMrO_noop = cute.make_rmem_tensor(
|
||||
(tTMEM_LOADcO.shape, 128 // corr_tile_size), self.acc_dtype
|
||||
)
|
||||
for i in range(n_corr_tiles):
|
||||
tTMrO_i_ = tTMrO_noop[None, i]
|
||||
tTMrO_i_layout = cute.composition(
|
||||
tTMrO_i_.layout, cute.make_layout(tTMrO_noop.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(tiled_tmem_load_o, tTMEM_LOADtO_i, tTMrO_i)
|
||||
cute.copy(tiled_tmem_store_o, tTMrO_i, tTMEM_STOREtO_i)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# === Final O normalization: O *= 1/row_sum ===
|
||||
inv_row_sum = Float32(1.0) / row_sum
|
||||
|
||||
tTMrO = cute.make_rmem_tensor(
|
||||
(tTMEM_LOADcO.shape, 128 // corr_tile_size), self.acc_dtype
|
||||
)
|
||||
|
||||
for i in range(n_corr_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(tiled_tmem_load_o, 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(tiled_tmem_store_o, tTMrO_i, tTMEM_STOREtO_i)
|
||||
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# Epilogue: TMEM → SMEM → GMEM via TMA store.
|
||||
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)
|
||||
|
||||
|
||||
|
||||
78
tests/unit/test_fmha_v3_stage_d1.py
Normal file
78
tests/unit/test_fmha_v3_stage_d1.py
Normal file
@@ -0,0 +1,78 @@
|
||||
"""
|
||||
FMHA v3 Stage D1: Parameterized HEAD_DIM (64 → 512).
|
||||
|
||||
Tests the FmhaKernel class from dsv4.kernels.attention.fmha with variable head_dim.
|
||||
- HEAD_DIM=64: regression test (must match Stage C results)
|
||||
- HEAD_DIM=512: DSV4 production config (TMEM budget is the key risk)
|
||||
"""
|
||||
import torch, math, sys
|
||||
import cutlass.cute as cute
|
||||
import cutlass.torch as ct
|
||||
import cuda.bindings.driver as cuda
|
||||
from dsv4.kernels.attention.fmha import FmhaKernel
|
||||
|
||||
def test_head_dim(hd, n_kv):
|
||||
"""Test FMHA kernel at given head_dim and KV length."""
|
||||
m = 128 # M tile is always 128
|
||||
torch.manual_seed(42)
|
||||
|
||||
q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device='cuda')
|
||||
k = torch.randn(n_kv, hd, 1, dtype=torch.bfloat16, device='cuda')
|
||||
v = torch.randn(n_kv, hd, dtype=torch.bfloat16, device='cuda')
|
||||
v_kernel = v.unsqueeze(-1)
|
||||
c = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device='cuda')
|
||||
|
||||
# FP32 reference
|
||||
qf = q[:, :, 0].float()
|
||||
kf = k[:, :, 0].float()
|
||||
scale = 1.0 / math.sqrt(hd)
|
||||
attn = qf @ kf.T * scale
|
||||
attn = torch.softmax(attn, dim=-1)
|
||||
ref = attn @ 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 = FmhaKernel(head_dim=hd, s_k=n_kv)
|
||||
print(f'hd={hd}, n={n_kv}: Compiling...', flush=True)
|
||||
compiled = cute.compile(kernel, mQ, mK, mV, mC, stream)
|
||||
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_abs = (out - ref).abs().max().item()
|
||||
status = "PASS" if cos >= 0.97 else "FAIL"
|
||||
print(f'hd={hd}, n={n_kv}: cos {cos:.6f} max_abs {max_abs:.4f} {status}')
|
||||
if cos < 0.97:
|
||||
print(f' out[0,:4]={out[0,:4].tolist()}')
|
||||
print(f' ref[0,:4]={ref[0,:4].tolist()}')
|
||||
return cos
|
||||
|
||||
|
||||
def test():
|
||||
print("=== Stage D1: Parameterized HEAD_DIM ===\n")
|
||||
|
||||
# Regression: hd=64 must match Stage C results (cos ~0.973)
|
||||
print("--- Regression: HEAD_DIM=64 ---")
|
||||
cos64_128 = test_head_dim(64, 128)
|
||||
cos64_256 = test_head_dim(64, 256)
|
||||
|
||||
# DSV4 production: hd=512
|
||||
print("\n--- Production: HEAD_DIM=512 ---")
|
||||
cos512_128 = test_head_dim(512, 128)
|
||||
|
||||
# Summary
|
||||
print("\n=== Summary ===")
|
||||
print(f"hd=64, n=128: cos={cos64_128:.6f} {'PASS' if cos64_128 >= 0.97 else 'FAIL'}")
|
||||
print(f"hd=64, n=256: cos={cos64_256:.6f} {'PASS' if cos64_256 >= 0.97 else 'FAIL'}")
|
||||
print(f"hd=512, n=128: cos={cos512_128:.6f} {'PASS' if cos512_128 >= 0.97 else 'FAIL'}")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test()
|
||||
Reference in New Issue
Block a user