FMHA v3: KV-tile interleaving pipeline - QK works, Bug 4b blocks PV
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283
tests/test_fmha_v3.py
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283
tests/test_fmha_v3.py
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"""
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FMHA v3: QK -> softmax -> PV with KV-tile interleaving.
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Bug 4b fix: tP uses QK C-fragment layout so PV reads from same TMEM columns softmax writes.
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V=ones test: O[i,j] = sum_j(S[i,:].bf16())
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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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HEAD_DIM = 64
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class FmhaV3:
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def __init__(self):
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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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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, 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), 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.tilePlikeFP32 = self.qk_mma_tiler[1] // Float32.width * self.o_dtype.width
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self.tmem_s0_offset = 0; self.tmem_p0_offset = 32
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self.tmem_o0_offset = find_tmem_tensor_col_offset(tOtO)
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tCS = qk_mma.make_fragment_C(cute.append(qk_as, self.num_acc_stage))
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tCO = pv_mma.make_fragment_C(cute.append(pv_as, self.num_acc_stage))
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self.num_tmem_alloc_cols = utils.get_num_tmem_alloc_cols([tCS, tCO], arch="sm_100")
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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)); k_s = cute.slice_(self.k_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) * 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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self.v_major = LayoutEnum.from_tensor(v).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,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,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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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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# BUG 4b FIX: Use QK C-fragment layout for tP so PV A-fragment reads
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# from the same TMEM columns that softmax writes P to.
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# Softmax writes P using tStS_P (QK C-fragment composition).
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# BUG 4b: Use p_tmem_s layout for tP so PV A-fragment reads from correct TMEM columns
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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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tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_as, self.num_acc_stage))
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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
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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,qh.count)],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(n_kv_tiles,unroll=1):
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kh = kvp.acquire_and_advance(pk)
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cute.copy(tma_k,tBgK[(None,kh.count)],tBsK[(None,kh.index)],tma_bar_ptr=kh.barrier)
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pk = cutlass.Boolean(1)
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vh = kvp.acquire_and_advance(pk)
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cute.copy(tma_v,tVgV[(None,vh.count)],tVsV[(None,vh.index)],tma_bar_ptr=vh.barrier)
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pk = cutlass.Boolean(1)
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kvp.tail()
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# MMA
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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(n_kv_tiles):
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kh = 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,kh.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(); kh.release()
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softmax_done_bar.arrive_and_wait()
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vh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
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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,vh.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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vh.release()
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acc_pipe.producer_commit(acc_st); acc_st.advance()
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acc_pipe.producer_tail(acc_st)
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# EPILOGUE
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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
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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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# BUG 4b FIX: P store uses PV A-fragment's TMEM layout from tOrP0.
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# tOrP0 is 3D — flatten to 2D for make_tmem_store compatibility.
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# We create a 2D tensor from tOrP0's pointer with a flat layout.
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tilePlikeFP32_pv = self.pv_mma_tiler[1] // Float32.width * self.o_dtype.width
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# Flatten tOrP0 from 3D to 2D: (M, K_frag*K_blocks) → (M, K_total)
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tOrP0_shape = tOrP0.shape
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k_total = cute.size(tOrP0_shape, mode=[1]) * cute.size(tOrP0_shape, mode=[2])
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m_total = cute.size(tOrP0_shape, mode=[0])
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# Recast to FP32: BF16 pairs → FP32, so K_FP32 = K_total / 2
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k_fp32 = k_total // 2
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tOrP0_2d_fp32 = cute.make_tensor(
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cute.recast_ptr(tOrP0.iterator, dtype=self.qk_acc_dtype),
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cute.composition(
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cute.make_layout((m_total, k_fp32)),
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tOrP0.layout
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),
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)
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tmem_store_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
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tiled_tmem_store = tcgen05.make_tmem_copy(tmem_store_atom, tOrP0_2d_fp32)
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thr_store = tiled_tmem_store.get_slice(sfw_idx)
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tTMEM_STOREtP = thr_store.partition_D(tOrP0_2d_fp32)
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# 2D coordinate tensor — use thr_store's partition (NOT qk_thr)
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# so source and dest have matching element distributions
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cP = cute.make_identity_tensor((m_total, k_fp32))
|
||||
tTMEM_STOREcP = thr_store.partition_S(cP)
|
||||
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)
|
||||
tTMEM_STORErP = cute.make_rmem_tensor(tTMEM_STOREcP.shape, self.qk_acc_dtype)
|
||||
tTMEM_STORErP_e = cute.make_tensor(cute.recast_ptr(tTMEM_STORErP.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_STORErP_e_frg = cute.logical_divide(tTMEM_STORErP_e, cute.make_layout(frg_tile))
|
||||
for j in range(frg_cnt):
|
||||
s_vec = tTMEM_LOADrS_frg[None, j].load()
|
||||
tTMEM_STORErP_e_frg[None, j].store(s_vec.to(self.q_dtype))
|
||||
cute.copy(tiled_tmem_store, tTMEM_STORErP, tTMEM_STOREtP)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
si_handle.release()
|
||||
softmax_done_bar.arrive()
|
||||
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, dtype=torch.bfloat16, device='cuda')
|
||||
v = v.as_strided((n, hd), (1, n)).unsqueeze(-1)
|
||||
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 = FmhaV3()
|
||||
print(f'n={n}: Compiling...', flush=True)
|
||||
compiled = cute.compile(kernel, mQ, mK, mV, mC, stream)
|
||||
print(f'n={n}: tmem_offsets: s0={kernel.tmem_s0_offset} p0={kernel.tmem_p0_offset} o0={kernel.tmem_o0_offset} alloc={kernel.num_tmem_alloc_cols}', flush=True)
|
||||
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 v3 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()
|
||||
252
tests/test_qk_softmax.py
Normal file
252
tests/test_qk_softmax.py
Normal file
@@ -0,0 +1,252 @@
|
||||
"""
|
||||
Debug: QK + identity softmax, output P (BF16) to GMEM.
|
||||
Tests the full QK -> softmax -> TMEM pipeline without PV.
|
||||
Uses the QK C-fragment store (like FMHA).
|
||||
Output = S.bf16() which is (Q@K^T).bfloat16(), shape (128, 128).
|
||||
"""
|
||||
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 QkSoftmaxTest:
|
||||
def __init__(self):
|
||||
self.acc_dtype = Float32; self.qk_acc_dtype = Float32
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
self.use_2cta_instrs = False; self.epilog_sync_bar_id = 1
|
||||
self.cluster_shape_mn = (1, 1); self.cta_group = tcgen05.CtaGroup.ONE
|
||||
self.epilogue_warp_id = (0,1,2,3); self.mma_warp_id = 4; self.tma_warp_id = 5
|
||||
self.threads_per_cta = 192; self.num_c_stage = 2
|
||||
self.kv_stage = 2; self.q_stage = 1
|
||||
|
||||
def _setup(self, qk_mma):
|
||||
qk_ik = cute.size(qk_mma.shape_mnk, mode=[2])
|
||||
self.qk_mma_tiler = (128, 128, qk_ik * 4)
|
||||
self.mma_tiler = self.qk_mma_tiler
|
||||
self.cluster_layout_vmnk = cute.tiled_divide(cute.make_layout((1,1,1)), (qk_mma.thr_id.shape,))
|
||||
self.cta_tile_shape_mnk = (self.qk_mma_tiler[0]//cute.size(qk_mma.thr_id.shape), 128, self.qk_mma_tiler[2])
|
||||
self.c_layout = LayoutEnum.ROW_MAJOR
|
||||
self.epi_tile = utils.sm100.compute_epilogue_tile_shape(self.cta_tile_shape_mnk, False, self.c_layout, self.o_dtype)
|
||||
self.num_ab_stage = 1; self.num_acc_stage = 1
|
||||
self.q_smem_s = utils.sm100.make_smem_layout_a(qk_mma, self.qk_mma_tiler, self.q_dtype, self.q_stage)
|
||||
self.k_smem_s = utils.sm100.make_smem_layout_b(qk_mma, self.qk_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.c_smem_s = utils.sm100.make_smem_layout_epi(self.o_dtype, self.c_layout, self.epi_tile, 2)
|
||||
qk_thr = qk_mma.get_slice(0); qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
self.tilePlikeFP32 = self.qk_mma_tiler[1] // Float32.width * self.o_dtype.width
|
||||
self.tmem_s0_offset = 0; self.tmem_p0_offset = 32; self.tmem_o0_offset = 0
|
||||
tCS = qk_mma.make_fragment_C(cute.append(qk_as, self.num_acc_stage))
|
||||
self.num_tmem_alloc_cols = utils.get_num_tmem_alloc_cols([tCS], arch="sm_100")
|
||||
cta = cute.size(qk_mma.thr_id.shape)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0))
|
||||
self.q_tx_bytes = cute.size_in_bytes(self.q_dtype, q_s) * cta
|
||||
self.kv_tx_bytes = cute.size_in_bytes(self.q_dtype, k_s) * cta
|
||||
|
||||
@cute.jit
|
||||
def __call__(self, q, k, c, stream):
|
||||
self.q_dtype = q.element_type; self.o_dtype = c.element_type; self.c_dtype = self.o_dtype
|
||||
self.a_major = LayoutEnum.from_tensor(q).mma_major_mode()
|
||||
self.b_major = LayoutEnum.from_tensor(k).mma_major_mode()
|
||||
self.c_layout = LayoutEnum.from_tensor(c)
|
||||
qk_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, self.a_major, self.b_major, self.qk_acc_dtype, self.cta_group, (128,128), tcgen05.OperandSource.SMEM)
|
||||
self._setup(qk_mma)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0))
|
||||
tma_q,mQ = cute.nvgpu.make_tiled_tma_atom_A(utils.sm100.cluster_shape_to_tma_atom_A(self.cluster_shape_mn,qk_mma.thr_id),q,q_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_k,mK = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,qk_mma.thr_id),k,k_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
epi_s = cute.select(self.c_smem_s,mode=[0,1])
|
||||
tma_c,mC = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(),c,epi_s,self.epi_tile)
|
||||
self._kernel(qk_mma,tma_q,mQ,tma_k,mK,tma_c,mC,self.cluster_layout_vmnk,self.q_smem_s,self.k_smem_s,self.c_smem_s,self.epi_tile).launch(grid=(1,1,1),block=[self.threads_per_cta,1,1],stream=stream)
|
||||
|
||||
@cute.kernel
|
||||
def _kernel(self, qk_mma, tma_q, mQ, tma_k, mK, tma_c, mC, cl_vmnk, q_smem_s, k_smem_s, c_smem_s, epi_tile):
|
||||
warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx())
|
||||
tidx,_,_ = cute.arch.thread_idx()
|
||||
if warp_idx == self.tma_warp_id:
|
||||
cpasync.prefetch_descriptor(tma_q); cpasync.prefetch_descriptor(tma_k); cpasync.prefetch_descriptor(tma_c)
|
||||
|
||||
@cute.struct
|
||||
class SS:
|
||||
q_bar: cute.struct.MemRange[cutlass.Int64, self.q_stage*2]
|
||||
kv_bar: cute.struct.MemRange[cutlass.Int64, self.kv_stage*2]
|
||||
s_bar: cute.struct.MemRange[cutlass.Int64, 2]
|
||||
acc_bar: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage*2]
|
||||
tmem_dealloc: cutlass.Int64; holding: cutlass.Int32
|
||||
|
||||
smem = utils.SmemAllocator(); st = smem.allocate(SS)
|
||||
|
||||
qp,qc = pipeline.PipelineTmaUmma.create(barrier_storage=st.q_bar.data_ptr(),num_stages=self.q_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.q_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
|
||||
kvp,kvc = pipeline.PipelineTmaUmma.create(barrier_storage=st.kv_bar.data_ptr(),num_stages=self.kv_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.kv_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
|
||||
|
||||
s_prod,s_cons = pipeline.PipelineUmmaAsync.create(barrier_storage=st.s_bar.data_ptr(),num_stages=1,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32*len(self.epilogue_warp_id))).make_participants()
|
||||
|
||||
# P-ready: softmax -> MMA signal using NamedBarrier
|
||||
softmax_done_bar = pipeline.NamedBarrier(barrier_id=3, num_threads=32 + 32*len(self.epilogue_warp_id))
|
||||
|
||||
acc_pipe = pipeline.PipelineUmmaAsync.create(barrier_storage=st.acc_bar.data_ptr(),num_stages=self.num_acc_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,len(self.epilogue_warp_id)),cta_layout_vmnk=cl_vmnk,defer_sync=True)
|
||||
tmem_bar = pipeline.NamedBarrier(barrier_id=2,num_threads=32*len((self.mma_warp_id,*self.epilogue_warp_id)))
|
||||
tmem = utils.TmemAllocator(st.holding.ptr,barrier_for_retrieve=tmem_bar,allocator_warp_id=self.epilogue_warp_id[0],is_two_cta=cute.size(qk_mma.thr_id.shape)==2,two_cta_tmem_dealloc_mbar_ptr=st.tmem_dealloc.ptr)
|
||||
pipeline.pipeline_init_arrive(cluster_shape_mn=cl_vmnk,is_relaxed=True)
|
||||
|
||||
sQ = smem.allocate_tensor(element_type=self.q_dtype,layout=q_smem_s.outer,byte_alignment=128,swizzle=q_smem_s.inner)
|
||||
sK = smem.allocate_tensor(element_type=self.q_dtype,layout=k_smem_s.outer,byte_alignment=128,swizzle=k_smem_s.inner)
|
||||
sC = smem.allocate_tensor(element_type=self.o_dtype,layout=c_smem_s.outer,byte_alignment=128,swizzle=c_smem_s.inner)
|
||||
|
||||
gQ = cute.local_tile(mQ,cute.slice_(self.qk_mma_tiler,(None,0,None)),(None,None,None))
|
||||
gK = cute.local_tile(mK,cute.slice_(self.qk_mma_tiler,(0,None,None)),(None,None,None))
|
||||
gC = cute.local_tile(mC,cute.slice_(self.qk_mma_tiler,(None,None,0)),(None,None,None))
|
||||
n_kv_tiles = cute.size(gK, mode=[3])
|
||||
|
||||
qk_thr = qk_mma.get_slice(0)
|
||||
tCgQ = qk_thr.partition_A(gQ); tCgK = qk_thr.partition_B(gK); tCgC = qk_thr.partition_C(gC)
|
||||
a_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,0,None,0)).shape)
|
||||
tAsQ,tAgQ = cpasync.tma_partition(tma_q,0,a_lay,cute.group_modes(sQ,0,3),cute.group_modes(tCgQ,0,3))
|
||||
b_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,None,0,0)).shape)
|
||||
tBsK,tBgK = cpasync.tma_partition(tma_k,0,b_lay,cute.group_modes(sK,0,3),cute.group_modes(tCgK,0,3))
|
||||
tAgQ = tAgQ[(None,0,None,0)]; tBgK = tBgK[(None,0,None,0)]
|
||||
|
||||
tCrQ = qk_mma.make_fragment_A(sQ); tCrK = qk_mma.make_fragment_B(sK)
|
||||
|
||||
qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_as, self.num_acc_stage))
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# TMA LOAD
|
||||
if warp_idx == self.tma_warp_id:
|
||||
qp.reset(); qh = qp.acquire_and_advance()
|
||||
cute.copy(tma_q,tAgQ[(None,qh.count)],tAsQ[(None,qh.index)],tma_bar_ptr=qh.barrier)
|
||||
qp.tail()
|
||||
kvp.reset(); pk = kvp.try_acquire()
|
||||
for kt in cutlass.range(n_kv_tiles,unroll=1):
|
||||
kh = kvp.acquire_and_advance(pk)
|
||||
cute.copy(tma_k,tBgK[(None,kh.count)],tBsK[(None,kh.index)],tma_bar_ptr=kh.barrier)
|
||||
pk = cutlass.Boolean(1)
|
||||
kvp.tail()
|
||||
|
||||
# MMA: QK, then wait for softmax, then signal done
|
||||
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()
|
||||
|
||||
# Wait for softmax to finish
|
||||
softmax_done_bar.arrive_and_wait()
|
||||
|
||||
# After all tiles: S contains the identity-softmax'd result
|
||||
# But we want to output P (written by softmax at p0 offset)
|
||||
# For this test, output what's at tmem_s0_offset (the final S accumulator)
|
||||
acc_pipe.producer_commit(acc_st); acc_st.advance()
|
||||
acc_pipe.producer_tail(acc_st)
|
||||
|
||||
# EPILOGUE: identity softmax + output
|
||||
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))
|
||||
|
||||
# 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
|
||||
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 = s_cons.wait_and_advance()
|
||||
|
||||
# Load S
|
||||
tTMEM_LOADrS = cute.make_rmem_tensor(tTMEM_LOADcS.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load, tTMEM_LOADtS, tTMEM_LOADrS)
|
||||
|
||||
# Identity softmax: FP32 S -> BF16 P, write to TMEM at p0 offset
|
||||
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()
|
||||
# Signal MMA
|
||||
softmax_done_bar.arrive()
|
||||
|
||||
# Output: read from p0 offset (BF16 P values, but we read as FP32)
|
||||
# We need to output the BF16 P values. Read from p0 and convert.
|
||||
# Actually, output the S accumulator (at s0) for now to verify QK works
|
||||
tCtS_base = cute.make_tensor(tmem_ptr + self.tmem_s0_offset, tCtS_fake.layout)
|
||||
acc_cons_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Consumer, self.num_acc_stage)
|
||||
c_grp = pipeline.CooperativeGroup(pipeline.Agent.Thread, 32 * len(self.epilogue_warp_id))
|
||||
c_pipe = pipeline.PipelineTmaStore.create(num_stages=self.num_c_stage, producer_group=c_grp)
|
||||
acc_cons_st = utils.gemm.sm100.epilogue_tma_store(self, tidx, warp_idx, tma_c, tCtS_base, sC, tCgC, epi_tile, 0, const_expr(lambda x: x), (0,0,0), acc_cons_st, acc_pipe, c_pipe)
|
||||
c_pipe.producer_tail()
|
||||
tmem.relinquish_alloc_permit()
|
||||
tmem.free(tmem_ptr)
|
||||
|
||||
|
||||
def test():
|
||||
torch.manual_seed(42)
|
||||
n = 128; m, hd = 128, HEAD_DIM
|
||||
q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device='cuda')
|
||||
k = torch.randn(n, hd, 1, dtype=torch.bfloat16, device='cuda')
|
||||
c = torch.zeros(m, n, 1, dtype=torch.bfloat16, device='cuda')
|
||||
qf = q[:,:,0].float(); kf = k[:,:,0].float()
|
||||
ref = (qf @ kf.T)
|
||||
mQ = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
|
||||
mK = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
|
||||
mC = ct.from_dlpack(c).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c))
|
||||
stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
|
||||
kernel = QkSoftmaxTest()
|
||||
print('Compiling...', flush=True)
|
||||
compiled = cute.compile(kernel, mQ, mK, mC, stream)
|
||||
print('Running...', flush=True)
|
||||
compiled(mQ, mK, mC, stream)
|
||||
torch.cuda.synchronize()
|
||||
out = c[:,:,0].float()
|
||||
cos = torch.nn.functional.cosine_similarity(out.flatten().unsqueeze(0), ref.flatten().unsqueeze(0)).item()
|
||||
print(f'QK+softmax n={n}: cosine {cos:.6f} {"PASS" if cos >= 0.99 else "FAIL"}')
|
||||
if cos < 0.99:
|
||||
print(f' out[0,:4]={out[0,:4].tolist()} ref[0,:4]={ref[0,:4].tolist()}')
|
||||
|
||||
if __name__ == '__main__':
|
||||
test()
|
||||
269
tests/test_qkonly.py
Normal file
269
tests/test_qkonly.py
Normal file
@@ -0,0 +1,269 @@
|
||||
"""
|
||||
Debug: QK only (no PV) with KV-tile interleaving pipeline.
|
||||
Outputs P to GMEM to verify QK+softmax pipeline works.
|
||||
n=128, single KV tile, identity softmax.
|
||||
"""
|
||||
import torch, cutlass, cutlass.cute as cute, cutlass.utils as utils, cutlass.pipeline as pipeline
|
||||
from cutlass.cute.nvgpu import cpasync, tcgen05
|
||||
from cutlass import Float32, BFloat16, Int32, Boolean, const_expr
|
||||
from cutlass.utils import LayoutEnum
|
||||
from cutlass.utils.tmem_allocator import find_tmem_tensor_col_offset
|
||||
import cuda.bindings.driver as cuda
|
||||
import cutlass.torch as ct
|
||||
|
||||
HEAD_DIM = 64
|
||||
|
||||
|
||||
class QkOnlyTest:
|
||||
def __init__(self):
|
||||
self.acc_dtype = Float32; self.qk_acc_dtype = Float32
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
self.use_2cta_instrs = False; self.epilog_sync_bar_id = 1
|
||||
self.cluster_shape_mn = (1, 1); self.cta_group = tcgen05.CtaGroup.ONE
|
||||
self.epilogue_warp_id = (0,1,2,3); self.mma_warp_id = 4; self.tma_warp_id = 5
|
||||
self.threads_per_cta = 192; self.num_c_stage = 2
|
||||
self.kv_stage = 2; self.q_stage = 1
|
||||
|
||||
def _setup(self, qk_mma):
|
||||
qk_ik = cute.size(qk_mma.shape_mnk, mode=[2])
|
||||
self.qk_mma_tiler = (128, 128, qk_ik * 4)
|
||||
self.mma_tiler = self.qk_mma_tiler
|
||||
self.cluster_layout_vmnk = cute.tiled_divide(cute.make_layout((1,1,1)), (qk_mma.thr_id.shape,))
|
||||
self.cta_tile_shape_mnk = (self.qk_mma_tiler[0]//cute.size(qk_mma.thr_id.shape), 128, self.qk_mma_tiler[2])
|
||||
self.c_layout = LayoutEnum.ROW_MAJOR
|
||||
self.epi_tile = utils.sm100.compute_epilogue_tile_shape(self.cta_tile_shape_mnk, False, self.c_layout, self.o_dtype)
|
||||
self.num_ab_stage = 1; self.num_acc_stage = 1
|
||||
self.q_smem_s = utils.sm100.make_smem_layout_a(qk_mma, self.qk_mma_tiler, self.q_dtype, self.q_stage)
|
||||
self.k_smem_s = utils.sm100.make_smem_layout_b(qk_mma, self.qk_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.c_smem_s = utils.sm100.make_smem_layout_epi(self.o_dtype, self.c_layout, self.epi_tile, 2)
|
||||
|
||||
qk_thr = qk_mma.get_slice(0); qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
|
||||
self.tilePlikeFP32 = self.qk_mma_tiler[1] // Float32.width * self.o_dtype.width
|
||||
self.tmem_s0_offset = 0; self.tmem_p0_offset = 32
|
||||
self.tmem_o0_offset = 0 # Output is at S offset for QK-only
|
||||
|
||||
tCS = qk_mma.make_fragment_C(cute.append(qk_as, self.num_acc_stage))
|
||||
self.num_tmem_alloc_cols = utils.get_num_tmem_alloc_cols([tCS], arch="sm_100")
|
||||
|
||||
cta = cute.size(qk_mma.thr_id.shape)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0))
|
||||
k_s = cute.slice_(self.k_smem_s,(None,None,None,0))
|
||||
self.q_tx_bytes = cute.size_in_bytes(self.q_dtype, q_s) * cta
|
||||
self.kv_tx_bytes = cute.size_in_bytes(self.q_dtype, k_s) * cta
|
||||
|
||||
@cute.jit
|
||||
def __call__(self, q, k, c, stream):
|
||||
self.q_dtype = q.element_type; self.o_dtype = c.element_type; self.c_dtype = self.o_dtype
|
||||
self.a_major = LayoutEnum.from_tensor(q).mma_major_mode()
|
||||
self.b_major = LayoutEnum.from_tensor(k).mma_major_mode()
|
||||
self.c_layout = LayoutEnum.from_tensor(c)
|
||||
|
||||
qk_mma = utils.sm100.make_trivial_tiled_mma(
|
||||
self.q_dtype, self.q_dtype, self.a_major, self.b_major,
|
||||
self.qk_acc_dtype, self.cta_group, (128,128), tcgen05.OperandSource.SMEM)
|
||||
self._setup(qk_mma)
|
||||
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0))
|
||||
k_s = cute.slice_(self.k_smem_s,(None,None,None,0))
|
||||
|
||||
tma_q,mQ = cute.nvgpu.make_tiled_tma_atom_A(
|
||||
utils.sm100.cluster_shape_to_tma_atom_A(self.cluster_shape_mn,qk_mma.thr_id),
|
||||
q,q_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_k,mK = cute.nvgpu.make_tiled_tma_atom_B(
|
||||
utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,qk_mma.thr_id),
|
||||
k,k_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
epi_s = cute.select(self.c_smem_s,mode=[0,1])
|
||||
tma_c,mC = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(),c,epi_s,self.epi_tile)
|
||||
|
||||
self._kernel(qk_mma, tma_q, mQ, tma_k, mK, tma_c, mC,
|
||||
self.cluster_layout_vmnk, self.q_smem_s, self.k_smem_s, self.c_smem_s, self.epi_tile
|
||||
).launch(grid=(1,1,1), block=[self.threads_per_cta,1,1], stream=stream)
|
||||
|
||||
@cute.kernel
|
||||
def _kernel(self, qk_mma, tma_q, mQ, tma_k, mK, tma_c, mC,
|
||||
cl_vmnk, q_smem_s, k_smem_s, c_smem_s, epi_tile):
|
||||
warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx())
|
||||
tidx,_,_ = cute.arch.thread_idx()
|
||||
|
||||
if warp_idx == self.tma_warp_id:
|
||||
cpasync.prefetch_descriptor(tma_q); cpasync.prefetch_descriptor(tma_k)
|
||||
cpasync.prefetch_descriptor(tma_c)
|
||||
|
||||
@cute.struct
|
||||
class SS:
|
||||
q_bar: cute.struct.MemRange[cutlass.Int64, self.q_stage*2]
|
||||
kv_bar: cute.struct.MemRange[cutlass.Int64, self.kv_stage*2]
|
||||
s_bar: cute.struct.MemRange[cutlass.Int64, 2]
|
||||
acc_bar: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage*2]
|
||||
tmem_dealloc: cutlass.Int64; holding: cutlass.Int32
|
||||
|
||||
smem = utils.SmemAllocator(); st = smem.allocate(SS)
|
||||
|
||||
qp,qc = pipeline.PipelineTmaUmma.create(
|
||||
barrier_storage=st.q_bar.data_ptr(), num_stages=self.q_stage,
|
||||
producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),
|
||||
consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),
|
||||
tx_count=self.q_tx_bytes, cta_layout_vmnk=cl_vmnk, defer_sync=True
|
||||
).make_participants()
|
||||
|
||||
kvp,kvc = pipeline.PipelineTmaUmma.create(
|
||||
barrier_storage=st.kv_bar.data_ptr(), num_stages=self.kv_stage,
|
||||
producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),
|
||||
consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),
|
||||
tx_count=self.kv_tx_bytes, cta_layout_vmnk=cl_vmnk, defer_sync=True
|
||||
).make_participants()
|
||||
|
||||
s_prod,s_cons = pipeline.PipelineUmmaAsync.create(
|
||||
barrier_storage=st.s_bar.data_ptr(), num_stages=1,
|
||||
producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),
|
||||
consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread, 32*len(self.epilogue_warp_id))
|
||||
).make_participants()
|
||||
|
||||
softmax_done_bar = pipeline.NamedBarrier(barrier_id=3, num_threads=32 + 32*len(self.epilogue_warp_id))
|
||||
|
||||
acc_pipe = pipeline.PipelineUmmaAsync.create(
|
||||
barrier_storage=st.acc_bar.data_ptr(), num_stages=self.num_acc_stage,
|
||||
producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),
|
||||
consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread, len(self.epilogue_warp_id)),
|
||||
cta_layout_vmnk=cl_vmnk, defer_sync=True)
|
||||
|
||||
tmem_bar = pipeline.NamedBarrier(barrier_id=2,
|
||||
num_threads=32*len((self.mma_warp_id,*self.epilogue_warp_id)))
|
||||
tmem = utils.TmemAllocator(st.holding.ptr, barrier_for_retrieve=tmem_bar,
|
||||
allocator_warp_id=self.epilogue_warp_id[0],
|
||||
is_two_cta=cute.size(qk_mma.thr_id.shape)==2,
|
||||
two_cta_tmem_dealloc_mbar_ptr=st.tmem_dealloc.ptr)
|
||||
|
||||
pipeline.pipeline_init_arrive(cluster_shape_mn=cl_vmnk, is_relaxed=True)
|
||||
|
||||
sQ = smem.allocate_tensor(element_type=self.q_dtype, layout=q_smem_s.outer, byte_alignment=128, swizzle=q_smem_s.inner)
|
||||
sK = smem.allocate_tensor(element_type=self.q_dtype, layout=k_smem_s.outer, byte_alignment=128, swizzle=k_smem_s.inner)
|
||||
sC = smem.allocate_tensor(element_type=self.o_dtype, layout=c_smem_s.outer, byte_alignment=128, swizzle=c_smem_s.inner)
|
||||
|
||||
gQ = cute.local_tile(mQ, cute.slice_(self.qk_mma_tiler,(None,0,None)), (None,None,None))
|
||||
gK = cute.local_tile(mK, cute.slice_(self.qk_mma_tiler,(0,None,None)), (None,None,None))
|
||||
gC = cute.local_tile(mC, cute.slice_(self.qk_mma_tiler,(None,None,0)), (None,None,None))
|
||||
n_kv_tiles = cute.size(gK, mode=[3])
|
||||
|
||||
qk_thr = qk_mma.get_slice(0)
|
||||
tCgQ = qk_thr.partition_A(gQ); tCgK = qk_thr.partition_B(gK)
|
||||
tCgC = qk_thr.partition_C(gC)
|
||||
|
||||
a_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,0,None,0)).shape)
|
||||
tAsQ,tAgQ = cpasync.tma_partition(tma_q,0,a_lay,cute.group_modes(sQ,0,3),cute.group_modes(tCgQ,0,3))
|
||||
b_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,None,0,0)).shape)
|
||||
tBsK,tBgK = cpasync.tma_partition(tma_k,0,b_lay,cute.group_modes(sK,0,3),cute.group_modes(tCgK,0,3))
|
||||
tAgQ = tAgQ[(None,0,None,0)]; tBgK = tBgK[(None,0,None,0)]
|
||||
|
||||
tCrQ = qk_mma.make_fragment_A(sQ); tCrK = qk_mma.make_fragment_B(sK)
|
||||
|
||||
qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_as, self.num_acc_stage))
|
||||
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# TMA LOAD
|
||||
if warp_idx == self.tma_warp_id:
|
||||
qp.reset(); qh = qp.acquire_and_advance()
|
||||
cute.copy(tma_q, tAgQ[(None,qh.count)], tAsQ[(None,qh.index)], tma_bar_ptr=qh.barrier)
|
||||
qp.tail()
|
||||
|
||||
kvp.reset(); pk = kvp.try_acquire()
|
||||
for kt in cutlass.range(n_kv_tiles, unroll=1):
|
||||
kh = kvp.acquire_and_advance(pk)
|
||||
cute.copy(tma_k, tBgK[(None,kh.count)], tBsK[(None,kh.index)], tma_bar_ptr=kh.barrier)
|
||||
pk = cutlass.Boolean(1)
|
||||
kvp.tail()
|
||||
|
||||
# MMA
|
||||
if warp_idx == self.mma_warp_id:
|
||||
tmem.wait_for_alloc()
|
||||
qc.reset(); qh = qc.wait_and_advance(); qh.release()
|
||||
kvc.reset(); pk = kvc.try_wait()
|
||||
|
||||
acc_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
|
||||
acc_pipe.producer_acquire(acc_st)
|
||||
|
||||
for kt in range(n_kv_tiles):
|
||||
kh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
|
||||
# QK only, accumulate across KV tiles
|
||||
sh = s_prod.acquire_and_advance()
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, kt != 0)
|
||||
for kb in cutlass.range(cute.size(tCrQ,mode=[2]), unroll_full=True):
|
||||
cute.gemm(qk_mma, tStS0,
|
||||
tCrQ[(None,None,kb,0)], tCrK[(None,None,kb,kh.index)], tStS0)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
sh.commit()
|
||||
kh.release()
|
||||
|
||||
# Wait for softmax (identity: just signal done)
|
||||
softmax_done_bar.arrive_and_wait()
|
||||
|
||||
acc_pipe.producer_commit(acc_st); acc_st.advance()
|
||||
acc_pipe.producer_tail(acc_st)
|
||||
|
||||
# EPILOGUE
|
||||
if warp_idx < self.mma_warp_id:
|
||||
tmem.allocate(self.num_tmem_alloc_cols)
|
||||
tmem.wait_for_alloc()
|
||||
tmem_ptr = tmem.retrieve_ptr(self.qk_acc_dtype)
|
||||
sfw_idx = tidx % (32 * len(self.epilogue_warp_id))
|
||||
|
||||
for kt in range(n_kv_tiles):
|
||||
si_handle = s_cons.wait_and_advance()
|
||||
# Identity softmax: no-op, just signal MMA
|
||||
si_handle.release()
|
||||
softmax_done_bar.arrive()
|
||||
|
||||
# Output S (QK result) to GMEM
|
||||
tCtS_base = cute.make_tensor(tmem_ptr + self.tmem_s0_offset, tCtS_fake.layout)
|
||||
acc_cons_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Consumer, self.num_acc_stage)
|
||||
c_grp = pipeline.CooperativeGroup(pipeline.Agent.Thread, 32 * len(self.epilogue_warp_id))
|
||||
c_pipe = pipeline.PipelineTmaStore.create(num_stages=self.num_c_stage, producer_group=c_grp)
|
||||
acc_cons_st = utils.gemm.sm100.epilogue_tma_store(
|
||||
self, tidx, warp_idx, tma_c, tCtS_base, sC, tCgC,
|
||||
epi_tile, 0, const_expr(lambda x: x), (0,0,0), acc_cons_st, acc_pipe, c_pipe)
|
||||
c_pipe.producer_tail()
|
||||
tmem.relinquish_alloc_permit()
|
||||
tmem.free(tmem_ptr)
|
||||
|
||||
|
||||
def test():
|
||||
torch.manual_seed(42)
|
||||
n = 128
|
||||
m, hd = 128, HEAD_DIM
|
||||
q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device='cuda')
|
||||
k = torch.randn(n, hd, 1, dtype=torch.bfloat16, device='cuda')
|
||||
c = torch.zeros(m, n, 1, dtype=torch.bfloat16, device='cuda') # (128, 128) output = S
|
||||
|
||||
qf = q[:,:,0].float(); kf = k[:,:,0].float()
|
||||
ref = (qf @ kf.T)
|
||||
|
||||
mQ = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
|
||||
mK = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
|
||||
mC = ct.from_dlpack(c).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c))
|
||||
stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
|
||||
|
||||
kernel = QkOnlyTest()
|
||||
print('Compiling...', flush=True)
|
||||
compiled = cute.compile(kernel, mQ, mK, mC, stream)
|
||||
print('Running...', flush=True)
|
||||
compiled(mQ, mK, mC, stream)
|
||||
torch.cuda.synchronize()
|
||||
out = c[:,:,0].float()
|
||||
cos = torch.nn.functional.cosine_similarity(out.flatten().unsqueeze(0), ref.flatten().unsqueeze(0)).item()
|
||||
print(f'QK-only n={n}: cosine {cos:.6f} {"PASS" if cos >= 0.99 else "FAIL"}')
|
||||
if cos < 0.99:
|
||||
print(f' out[0,:4]={out[0,:4].tolist()} ref[0,:4]={ref[0,:4].tolist()}')
|
||||
print(f' out stats: min={out.min().item():.4f} max={out.max().item():.4f}')
|
||||
print(f' ref stats: min={ref.min().item():.4f} max={ref.max().item():.4f}')
|
||||
|
||||
if __name__ == '__main__':
|
||||
test()
|
||||
Reference in New Issue
Block a user