FMHA v3: add debug variants for C9 normalization investigation
- test_fmha_v3_scalar: direct acc_scale for C6 O-rescale (no vector) - test_fmha_v3_vec_c9: TMEM vector for C9 row_sum transfer - test_fmha_v3_noop_c9: hardcoded inv_row_sum=1.0 (no normalization) - test_fmha_v3_debug: row_sum-based C9 normalization - test_fmha_v3_proper: 11-warp correction warp group (in progress) Key findings: - QK and PV C-fragments map threads to same logical rows - pv_row_sum (PV-based P read) gives cosine 0.993 for n=128 - row_sum (QK-accumulated) gives cosine 0.514 for n=128 - Noop (inv_row_sum=1.0) gives cosine 0.866 for n=128 - pv_row_sum is NOT 1.0 - it corrects PV MMA accumulator errors - The C9 normalization is essential even for single-tile case
This commit is contained in:
469
tests/unit/test_fmha_v3_debug.py
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469
tests/unit/test_fmha_v3_debug.py
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"""
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FMHA v3 + Stage C: QK -> online softmax -> PV with KV-tile interleaving.
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Stage C: row_max, exp2, O rescale, row_sum, final normalization.
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FMHA pattern P store preserved from Stage B.
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"""
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import math
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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 FmhaV3Softmax:
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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.tmem_s0_offset = 0; self.tmem_p0_offset = 32
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# P occupies [tmem_p0_offset, tmem_p0_offset + p_cols_fp32)
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# S occupies [0, qk_mma_tiler[1]) = [0, 128)
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# O must NOT overlap P. Place O after max(S end, P end), aligned to 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 # 32 + 64 = 96
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s_cols = self.qk_mma_tiler[1] # 128
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o_after = max(s_cols, p_end) # 128
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self.tmem_o0_offset = ((o_after + 31) // 32) * 32
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self.tmem_vec_offset = 0 # Reuse S region for per-row inv_row_sum vector # align to 32 = 128
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self.tmem_vec_offset = 0 # Reuse S region (free after softmax loop)
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o_cols = find_tmem_tensor_col_offset(tOtO) # footprint of O
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total = self.tmem_o0_offset + o_cols
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# Must be multiple of 32 AND power of 2
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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)); 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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self.scale_softmax_log2 = Float32(1.0 / math.sqrt(HEAD_DIM) * math.log2(math.e))
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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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# # s_k hardcoded # BROKEN in @cute.jit
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# FMHA-style V: reconstruct as (HEAD_DIM, s_k, 1) MN-major
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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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(HEAD_DIM, 128, 1),
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stride=(1, HEAD_DIM, HEAD_DIM * 128),
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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,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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pv_done_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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# --- PV read view (for MMA only, NOT for softmax store) ---
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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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pv_done_bar.arrive()
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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 WARPS (STAGE C: ONLINE SOFTMAX) =====================
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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 (QK C-fragment) ---
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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)
|
||||
tTMEM_LOADcS = thr_load.partition_D(tScS)
|
||||
|
||||
# --- P store (QK C-fragment composition, FMHA pattern) ---
|
||||
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
|
||||
tStP_layout = cute.composition(tStS.layout, cute.make_layout((self.pv_mma_tiler[0], p_cols_fp32)))
|
||||
tStP0 = cute.make_tensor(tStS.iterator + self.tmem_p0_offset, tStP_layout)
|
||||
tmem_store_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
tiled_tmem_store = tcgen05.make_tmem_copy(tmem_store_atom, tStP0)
|
||||
thr_store = tiled_tmem_store.get_slice(sfw_idx)
|
||||
tTMEM_STOREtP = thr_store.partition_D(tStP0)
|
||||
tScP_layout = cute.composition(tScS.layout, cute.make_layout((self.pv_mma_tiler[0], p_cols_fp32)))
|
||||
tScP = cute.make_tensor(tScS.iterator, tScP_layout)
|
||||
tTMEM_STOREcP = thr_store.partition_S(tScP)
|
||||
|
||||
# --- Vector TMEM (per-row row_sum storage, FMHA pattern) ---
|
||||
# composition(tStS.layout, (128, 2)) = 2 FP32 columns per logical row
|
||||
# vec[0] = row_sum (final, after loop), vec[1] = unused
|
||||
# Reuses S TMEM region (offset 0), free after softmax loop writes
|
||||
|
||||
tStS_vec_layout = cute.composition(tStS.layout, cute.make_layout((128, 2)))
|
||||
tStS_vec = cute.make_tensor(tStS.iterator + self.tmem_vec_offset, tStS_vec_layout)
|
||||
tScS_vec_layout = cute.composition(tScS.layout, cute.make_layout((128, 2)))
|
||||
tScS_vec = cute.make_tensor(tScS.iterator, tScS_vec_layout)
|
||||
tmem_store_vec_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(2)), self.qk_acc_dtype)
|
||||
tiled_tmem_store_vec = tcgen05.make_tmem_copy(tmem_store_vec_atom, tStS_vec)
|
||||
thr_tmem_store_vec = tiled_tmem_store_vec.get_slice(sfw_idx)
|
||||
tTMEM_STORE_VECtS = thr_tmem_store_vec.partition_D(tStS_vec)
|
||||
tTMEM_STORE_VECcS = thr_tmem_store_vec.partition_S(tScS_vec)
|
||||
tmem_load_vec_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(2)), self.qk_acc_dtype)
|
||||
tiled_tmem_load_vec = tcgen05.make_tmem_copy(tmem_load_vec_atom, tStS_vec)
|
||||
thr_tmem_load_vec = tiled_tmem_load_vec.get_slice(sfw_idx)
|
||||
tTMEM_LOAD_VECtS = thr_tmem_load_vec.partition_S(tStS_vec)
|
||||
tTMEM_LOAD_VECcS = thr_tmem_load_vec.partition_D(tScS_vec)
|
||||
|
||||
# --- C6: O TMEM load/store for rescale (correction_rescale pattern) ---
|
||||
corr_tile_size = 16
|
||||
cO = cute.make_identity_tensor((self.pv_mma_tiler[0], self.pv_mma_tiler[1]))
|
||||
tOcO = pv_thr.partition_C(cO)
|
||||
o_tmem_load_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.qk_acc_dtype)
|
||||
o_tmem_store_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.qk_acc_dtype)
|
||||
tOtO_i_layout = cute.composition(tOtO0.layout, cute.make_layout((128, corr_tile_size)))
|
||||
tOcO_i_layout = cute.composition(tOcO.layout, cute.make_layout((128, corr_tile_size)))
|
||||
tOtO_i = cute.make_tensor(tOtO0.iterator, tOtO_i_layout)
|
||||
tOcO_i = cute.make_tensor(tOcO.iterator, tOcO_i_layout)
|
||||
o_tiled_tmem_load = tcgen05.make_tmem_copy(o_tmem_load_atom, tOtO_i)
|
||||
o_tiled_tmem_store = tcgen05.make_tmem_copy(o_tmem_store_atom, tOtO_i)
|
||||
o_thr_load = o_tiled_tmem_load.get_slice(sfw_idx)
|
||||
o_thr_store = o_tiled_tmem_store.get_slice(sfw_idx)
|
||||
tTMEM_LOADtO = o_thr_load.partition_S(tOtO_i)
|
||||
tTMEM_LOADcO = o_thr_load.partition_D(tOcO_i)
|
||||
tTMEM_STOREtO = o_thr_store.partition_D(tOtO_i)
|
||||
o_col_tiles = self.pv_mma_tiler[1] // corr_tile_size
|
||||
|
||||
# --- C2: Per-thread row state (persist across KV tiles) ---
|
||||
row_max = -cutlass.Float32.inf
|
||||
row_sum = cutlass.Float32(0.0)
|
||||
|
||||
# --- C3: QK scale = 1/sqrt(HEAD_DIM) * log2(e) for exp2 ---
|
||||
scale = self.scale_softmax_log2
|
||||
|
||||
# =============================================================
|
||||
# Per-KV-tile online softmax loop
|
||||
# =============================================================
|
||||
for kt in range(n_kv_tiles):
|
||||
si_handle = s_cons.wait_and_advance()
|
||||
|
||||
# Load S from TMEM (FP32, QK C-fragment layout)
|
||||
tTMEM_LOADrS = cute.make_rmem_tensor(tTMEM_LOADcS.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load, tTMEM_LOADtS, tTMEM_LOADrS)
|
||||
|
||||
# --- C4: Compute tile_max via .reduce(MAX) ---
|
||||
old_row_max = row_max
|
||||
row_max = tTMEM_LOADrS.load().reduce(cute.ReductionOp.MAX, row_max, 0)
|
||||
row_max_safe = row_max
|
||||
if row_max == -cutlass.Float32.inf:
|
||||
row_max_safe = cutlass.Float32(0.0)
|
||||
|
||||
# --- C5: Compute rescale factor ---
|
||||
acc_scale = cute.math.exp2(scale * (old_row_max - row_max_safe), fastmath=True)
|
||||
|
||||
# --- C6: Rescale O in TMEM (load O, multiply by acc_scale, store O) ---
|
||||
# acc_scale belongs to QK row (N//4), but O rows are in PV partition (N).
|
||||
# Store acc_scale to vector by QK row, read by PV row.
|
||||
if kt > 0:
|
||||
pv_done_bar.arrive_and_wait()
|
||||
|
||||
# Store acc_scale to vector indexed by QK logical row
|
||||
qk_row_c6 = tTMEM_LOADcS[0][0]
|
||||
thr_vs_c6 = tiled_tmem_store_vec.get_slice(qk_row_c6)
|
||||
tVStore_c6 = thr_vs_c6.partition_D(tStS_vec)
|
||||
tVStoreSrc_c6 = thr_vs_c6.partition_S(tScS_vec)
|
||||
tVStoreRmem_c6 = cute.make_rmem_tensor(tVStoreSrc_c6.shape, self.qk_acc_dtype)
|
||||
tVStoreRmem_c6[0] = acc_scale
|
||||
cute.copy(tiled_tmem_store_vec, tVStoreRmem_c6, tVStore_c6)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# Read acc_scale from vector indexed by PV logical row
|
||||
pv_row_c6 = tTMEM_LOADcO[0][0]
|
||||
thr_vl_c6 = tiled_tmem_load_vec.get_slice(pv_row_c6)
|
||||
tVLoad_c6 = thr_vl_c6.partition_S(tStS_vec)
|
||||
tVLoadDst_c6 = thr_vl_c6.partition_D(tScS_vec)
|
||||
tVLoadRmem_c6 = cute.make_rmem_tensor(tVLoadDst_c6.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load_vec, tVLoad_c6, tVLoadRmem_c6)
|
||||
cute.arch.fence_view_async_tmem_load()
|
||||
acc_scale_pv = tVLoadRmem_c6[0]
|
||||
|
||||
tTMrO = cute.make_rmem_tensor((tTMEM_LOADcO.shape, o_col_tiles), self.qk_acc_dtype)
|
||||
for i in range(o_col_tiles):
|
||||
tTMrO_i_ = tTMrO[None, i]
|
||||
tTMrO_i_layout = cute.composition(tTMrO_i_.layout, cute.make_layout(tTMrO.shape[0]))
|
||||
tTMrO_i = cute.make_tensor(tTMrO_i_.iterator, tTMrO_i_layout)
|
||||
tTMEM_LOADtO_i = cute.make_tensor(tTMEM_LOADtO.iterator + i * corr_tile_size, tTMEM_LOADtO.layout)
|
||||
tTMEM_STOREtO_i = cute.make_tensor(tTMEM_STOREtO.iterator + i * corr_tile_size, tTMEM_STOREtO.layout)
|
||||
cute.copy(o_tiled_tmem_load, tTMEM_LOADtO_i, tTMrO_i)
|
||||
for j in cutlass.range(cute.size(tTMrO_i), vectorize=True):
|
||||
tTMrO_i[j] = tTMrO_i[j] * acc_scale_pv
|
||||
cute.copy(o_tiled_tmem_store, tTMrO_i, tTMEM_STOREtO_i)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# Rescale row_sum
|
||||
row_sum = row_sum * acc_scale
|
||||
|
||||
# --- C7: Compute P = exp2((S - row_max_safe) * scale) ---
|
||||
minus_row_max_scale = (cutlass.Float32(0.0) - row_max_safe) * scale
|
||||
|
||||
# Register bridge (FMHA pattern: FP32 backing + BF16 view)
|
||||
rP_words = cute.make_rmem_tensor(tTMEM_STOREcP.shape, self.qk_acc_dtype)
|
||||
rP_bf16 = cute.make_tensor(cute.recast_ptr(rP_words.iterator, dtype=self.q_dtype), tTMEM_LOADrS.layout)
|
||||
|
||||
frg_cnt = 4
|
||||
frg_tile = cute.size(tTMEM_LOADrS) // frg_cnt
|
||||
tTMEM_LOADrS_frg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(frg_tile))
|
||||
rP_bf16_frg = cute.logical_divide(rP_bf16, cute.make_layout(frg_tile))
|
||||
|
||||
# Scale S, compute exp2, store through register bridge
|
||||
for j in range(frg_cnt):
|
||||
for k in cutlass.range(cute.size(tTMEM_LOADrS_frg, mode=[0]), vectorize=True):
|
||||
tTMEM_LOADrS_frg[k, j] = tTMEM_LOADrS_frg[k, j] * scale + minus_row_max_scale
|
||||
tTMEM_LOADrS_frg[k, j] = cute.math.exp2(tTMEM_LOADrS_frg[k, j], fastmath=True)
|
||||
s_vec = tTMEM_LOADrS_frg[None, j].load()
|
||||
rP_bf16_frg[None, j].store(s_vec.to(self.q_dtype))
|
||||
|
||||
# Store P to TMEM
|
||||
cute.copy(tiled_tmem_store, rP_words, tTMEM_STOREtP)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
si_handle.release()
|
||||
softmax_done_bar.arrive()
|
||||
|
||||
# --- C8: Row sum accumulation (CUTLASS FMHA packed f32x2 pattern) ---
|
||||
# P values still in tTMEM_LOADrS registers.
|
||||
# 4 accumulators for 4 reduction_unroll columns.
|
||||
local_row_sum_0 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_1 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_2 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_3 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
|
||||
reduction_unroll = 4
|
||||
rfrg_tile = cute.size(tTMEM_LOADrS) // reduction_unroll
|
||||
tTMEM_LOADrS_rfrg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(rfrg_tile))
|
||||
|
||||
for j in cutlass.range_constexpr(0, cute.size(tTMEM_LOADrS_rfrg, mode=[0]), 2):
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_0, (tTMEM_LOADrS_rfrg[j, 0], tTMEM_LOADrS_rfrg[j + 1, 0]))
|
||||
local_row_sum_1 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_1, (tTMEM_LOADrS_rfrg[j, 1], tTMEM_LOADrS_rfrg[j + 1, 1]))
|
||||
local_row_sum_2 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_2, (tTMEM_LOADrS_rfrg[j, 2], tTMEM_LOADrS_rfrg[j + 1, 2]))
|
||||
local_row_sum_3 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_3, (tTMEM_LOADrS_rfrg[j, 3], tTMEM_LOADrS_rfrg[j + 1, 3]))
|
||||
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(local_row_sum_0, local_row_sum_1)
|
||||
local_row_sum_2 = cute.arch.add_packed_f32x2(local_row_sum_2, local_row_sum_3)
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(local_row_sum_0, local_row_sum_2)
|
||||
tile_sum = local_row_sum_0[0] + local_row_sum_0[1]
|
||||
|
||||
row_sum = row_sum + tile_sum
|
||||
|
||||
# --- C9: Final normalization via O TMEM rescale ---
|
||||
pv_done_bar.arrive_and_wait()
|
||||
|
||||
# Use QK-accumulated row_sum directly (DEBUG: check if row mapping matches PV)
|
||||
inv_row_sum = cutlass.Float32(1.0) / row_sum
|
||||
|
||||
# Normalize O in TMEM using PV-correct inv_row_sum
|
||||
tTMrO_final = cute.make_rmem_tensor((tTMEM_LOADcO.shape, o_col_tiles), self.qk_acc_dtype)
|
||||
for i in range(o_col_tiles):
|
||||
tTMrO_i_ = tTMrO_final[None, i]
|
||||
tTMrO_i_layout = cute.composition(tTMrO_i_.layout, cute.make_layout(tTMrO_final.shape[0]))
|
||||
tTMrO_i = cute.make_tensor(tTMrO_i_.iterator, tTMrO_i_layout)
|
||||
tTMEM_LOADtO_i = cute.make_tensor(
|
||||
tTMEM_LOADtO.iterator + i * corr_tile_size, tTMEM_LOADtO.layout)
|
||||
tTMEM_STOREtO_i = cute.make_tensor(
|
||||
tTMEM_STOREtO.iterator + i * corr_tile_size, tTMEM_STOREtO.layout)
|
||||
cute.copy(o_tiled_tmem_load, tTMEM_LOADtO_i, tTMrO_i)
|
||||
for j in cutlass.range(cute.size(tTMrO_i), vectorize=True):
|
||||
tTMrO_i[j] = tTMrO_i[j] * inv_row_sum
|
||||
cute.copy(o_tiled_tmem_store, tTMrO_i, tTMEM_STOREtO_i)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# Now O in TMEM is normalized. Use standard epilogue_tma_store with identity.
|
||||
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():
|
||||
import math
|
||||
torch.manual_seed(42)
|
||||
for n in [128, 256, 384]:
|
||||
m, hd = 128, HEAD_DIM
|
||||
q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
k = torch.randn(n, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
v = torch.randn(n, hd, dtype=torch.bfloat16, device="cuda")
|
||||
v_kernel = v.unsqueeze(-1)
|
||||
c = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
qf = q[:,:,0].float(); kf = k[:,:,0].float()
|
||||
attn = qf @ kf.T / math.sqrt(hd)
|
||||
ref = torch.softmax(attn, dim=-1) @ v.float()
|
||||
mQ = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
|
||||
mK = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
|
||||
mV = ct.from_dlpack(v_kernel).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_kernel))
|
||||
mC = ct.from_dlpack(c).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c))
|
||||
stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
|
||||
kernel = FmhaV3Softmax()
|
||||
print(f"n={n}: Compiling...", flush=True)
|
||||
compiled = cute.compile(kernel, mQ, mK, mV, mC, stream)
|
||||
print(f"n={n}: tmem: s0={kernel.tmem_s0_offset} p0={kernel.tmem_p0_offset} o0={kernel.tmem_o0_offset} vec={kernel.tmem_vec_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()
|
||||
max_err = (out - ref).abs().max().item()
|
||||
print(f"FMHA softmax n={n}: cosine {cos:.6f} max_err {max_err:.6f} {'PASS' if cos >= 0.999 else 'FAIL'}", flush=True)
|
||||
|
||||
if __name__ == "__main__":
|
||||
test()
|
||||
|
||||
|
||||
469
tests/unit/test_fmha_v3_noop_c9.py
Normal file
469
tests/unit/test_fmha_v3_noop_c9.py
Normal file
@@ -0,0 +1,469 @@
|
||||
"""
|
||||
FMHA v3 + Stage C: QK -> online softmax -> PV with KV-tile interleaving.
|
||||
Stage C: row_max, exp2, O rescale, row_sum, final normalization.
|
||||
FMHA pattern P store preserved from Stage B.
|
||||
"""
|
||||
import math
|
||||
import torch, cutlass, cutlass.cute as cute, cutlass.utils as utils, cutlass.pipeline as pipeline
|
||||
from cutlass.cute.nvgpu import cpasync, tcgen05
|
||||
from cutlass import Float32, BFloat16, Int32, Boolean, const_expr
|
||||
from cutlass.utils import LayoutEnum
|
||||
from cutlass.utils.tmem_allocator import find_tmem_tensor_col_offset
|
||||
import cuda.bindings.driver as cuda
|
||||
import cutlass.torch as ct
|
||||
|
||||
HEAD_DIM = 64
|
||||
|
||||
class FmhaV3Softmax:
|
||||
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; self.num_c_stage = 2
|
||||
|
||||
def _setup(self, qk_mma, pv_mma):
|
||||
qk_ik = cute.size(qk_mma.shape_mnk, mode=[2])
|
||||
self.qk_mma_tiler = (128, 128, qk_ik * 4)
|
||||
pv_ik = cute.size(pv_mma.shape_mnk, mode=[2])
|
||||
self.pv_mma_tiler = (128, HEAD_DIM, pv_ik * (128 // pv_ik))
|
||||
self.mma_tiler = self.qk_mma_tiler
|
||||
self.cluster_layout_vmnk = cute.tiled_divide(cute.make_layout((1,1,1)), (qk_mma.thr_id.shape,))
|
||||
self.cta_tile_shape_mnk = (self.qk_mma_tiler[0]//cute.size(qk_mma.thr_id.shape), HEAD_DIM, self.qk_mma_tiler[2])
|
||||
self.c_layout = LayoutEnum.ROW_MAJOR
|
||||
self.epi_tile = utils.sm100.compute_epilogue_tile_shape(self.cta_tile_shape_mnk, False, self.c_layout, self.o_dtype)
|
||||
self.num_ab_stage = 1; self.num_acc_stage = 1
|
||||
self.q_smem_s = utils.sm100.make_smem_layout_a(qk_mma, self.qk_mma_tiler, self.q_dtype, self.q_stage)
|
||||
self.k_smem_s = utils.sm100.make_smem_layout_b(qk_mma, self.qk_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.v_smem_s = utils.sm100.make_smem_layout_b(pv_mma, self.pv_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.c_smem_s = utils.sm100.make_smem_layout_epi(self.o_dtype, self.c_layout, self.epi_tile, 2)
|
||||
self.p_tmem_s = utils.sm100.make_smem_layout_a(pv_mma, self.pv_mma_tiler, self.q_dtype, 1)
|
||||
qk_thr = qk_mma.get_slice(0); qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
pv_thr = pv_mma.get_slice(0); pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
self.tmem_s0_offset = 0; self.tmem_p0_offset = 32
|
||||
# P occupies [tmem_p0_offset, tmem_p0_offset + p_cols_fp32)
|
||||
# S occupies [0, qk_mma_tiler[1]) = [0, 128)
|
||||
# O must NOT overlap P. Place O after max(S end, P end), aligned to 32.
|
||||
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
|
||||
p_end = self.tmem_p0_offset + p_cols_fp32 # 32 + 64 = 96
|
||||
s_cols = self.qk_mma_tiler[1] # 128
|
||||
o_after = max(s_cols, p_end) # 128
|
||||
self.tmem_o0_offset = ((o_after + 31) // 32) * 32
|
||||
self.tmem_vec_offset = 0 # Reuse S region for per-row inv_row_sum vector # align to 32 = 128
|
||||
self.tmem_vec_offset = 0 # Reuse S region (free after softmax loop)
|
||||
o_cols = find_tmem_tensor_col_offset(tOtO) # footprint of O
|
||||
total = self.tmem_o0_offset + o_cols
|
||||
# Must be multiple of 32 AND power of 2
|
||||
self.num_tmem_alloc_cols = 1
|
||||
while self.num_tmem_alloc_cols < total:
|
||||
self.num_tmem_alloc_cols *= 2
|
||||
cta = cute.size(qk_mma.thr_id.shape)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0))
|
||||
self.q_tx_bytes = cute.size_in_bytes(self.q_dtype, q_s) * cta
|
||||
self.kv_tx_bytes = cute.size_in_bytes(self.q_dtype, k_s) * cta
|
||||
self.scale_softmax_log2 = Float32(1.0 / math.sqrt(HEAD_DIM) * math.log2(math.e))
|
||||
|
||||
@cute.jit
|
||||
def __call__(self, q, k, v, c, stream):
|
||||
self.q_dtype = q.element_type; self.o_dtype = c.element_type; self.c_dtype = self.o_dtype
|
||||
self.a_major = LayoutEnum.from_tensor(q).mma_major_mode()
|
||||
self.b_major = LayoutEnum.from_tensor(k).mma_major_mode()
|
||||
# # s_k hardcoded # BROKEN in @cute.jit
|
||||
# FMHA-style V: reconstruct as (HEAD_DIM, s_k, 1) MN-major
|
||||
v_fmha = cute.make_tensor(
|
||||
v.iterator,
|
||||
cute.make_layout(
|
||||
(HEAD_DIM, 128, 1),
|
||||
stride=(1, HEAD_DIM, HEAD_DIM * 128),
|
||||
),
|
||||
)
|
||||
self.v_major = LayoutEnum.from_tensor(v_fmha).mma_major_mode()
|
||||
self.c_layout = LayoutEnum.from_tensor(c)
|
||||
qk_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, self.a_major, self.b_major, self.qk_acc_dtype, self.cta_group, (128,128), tcgen05.OperandSource.SMEM)
|
||||
pv_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, cute.nvgpu.OperandMajorMode.K, self.v_major, self.qk_acc_dtype, self.cta_group, (128,HEAD_DIM), tcgen05.OperandSource.TMEM)
|
||||
self._setup(qk_mma, pv_mma)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0)); v_s = cute.slice_(self.v_smem_s,(None,None,None,0))
|
||||
tma_q,mQ = cute.nvgpu.make_tiled_tma_atom_A(utils.sm100.cluster_shape_to_tma_atom_A(self.cluster_shape_mn,qk_mma.thr_id),q,q_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_k,mK = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,qk_mma.thr_id),k,k_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_v,mV = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,pv_mma.thr_id),v_fmha,v_s,self.pv_mma_tiler,pv_mma,self.cluster_layout_vmnk.shape)
|
||||
epi_s = cute.select(self.c_smem_s,mode=[0,1])
|
||||
tma_c,mC = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(),c,epi_s,self.epi_tile)
|
||||
self._kernel(qk_mma,pv_mma,tma_q,mQ,tma_k,mK,tma_v,mV,tma_c,mC,self.cluster_layout_vmnk,self.q_smem_s,self.k_smem_s,self.v_smem_s,self.p_tmem_s,self.c_smem_s,self.epi_tile).launch(grid=(1,1,1),block=[self.threads_per_cta,1,1],stream=stream)
|
||||
|
||||
@cute.kernel
|
||||
def _kernel(self, qk_mma, pv_mma, tma_q, mQ, tma_k, mK, tma_v, mV, tma_c, mC, cl_vmnk, q_smem_s, k_smem_s, v_smem_s, p_tmem_s, c_smem_s, epi_tile):
|
||||
warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx())
|
||||
tidx,_,_ = cute.arch.thread_idx()
|
||||
if warp_idx == self.tma_warp_id:
|
||||
cpasync.prefetch_descriptor(tma_q); cpasync.prefetch_descriptor(tma_k); cpasync.prefetch_descriptor(tma_v); cpasync.prefetch_descriptor(tma_c)
|
||||
|
||||
@cute.struct
|
||||
class SS:
|
||||
q_bar: cute.struct.MemRange[cutlass.Int64, self.q_stage*2]
|
||||
kv_bar: cute.struct.MemRange[cutlass.Int64, self.kv_stage*2]
|
||||
s_bar: cute.struct.MemRange[cutlass.Int64, 2]
|
||||
acc_bar: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage*2]
|
||||
tmem_dealloc: cutlass.Int64; holding: cutlass.Int32
|
||||
smem = utils.SmemAllocator(); st = smem.allocate(SS)
|
||||
|
||||
qp,qc = pipeline.PipelineTmaUmma.create(barrier_storage=st.q_bar.data_ptr(),num_stages=self.q_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.q_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
|
||||
kvp,kvc = pipeline.PipelineTmaUmma.create(barrier_storage=st.kv_bar.data_ptr(),num_stages=self.kv_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.kv_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
|
||||
s_prod,s_cons = pipeline.PipelineUmmaAsync.create(barrier_storage=st.s_bar.data_ptr(),num_stages=1,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32*len(self.epilogue_warp_id))).make_participants()
|
||||
softmax_done_bar = pipeline.NamedBarrier(barrier_id=3, num_threads=32 + 32*len(self.epilogue_warp_id))
|
||||
pv_done_bar = pipeline.NamedBarrier(barrier_id=4, 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)
|
||||
sV = smem.allocate_tensor(element_type=self.q_dtype,layout=v_smem_s.outer,byte_alignment=128,swizzle=v_smem_s.inner)
|
||||
sC = smem.allocate_tensor(element_type=self.o_dtype,layout=c_smem_s.outer,byte_alignment=128,swizzle=c_smem_s.inner)
|
||||
|
||||
gQ = cute.local_tile(mQ,cute.slice_(self.qk_mma_tiler,(None,0,None)),(None,None,None))
|
||||
gK = cute.local_tile(mK,cute.slice_(self.qk_mma_tiler,(0,None,None)),(None,None,None))
|
||||
gV = cute.local_tile(mV,cute.slice_(self.pv_mma_tiler,(0,None,None)),(None,None,None))
|
||||
gC = cute.local_tile(mC,cute.slice_(self.pv_mma_tiler,(None,None,0)),(None,None,None))
|
||||
n_kv_tiles = cute.size(gK, mode=[3])
|
||||
|
||||
qk_thr = qk_mma.get_slice(0); pv_thr = pv_mma.get_slice(0)
|
||||
tCgQ = qk_thr.partition_A(gQ); tCgK = qk_thr.partition_B(gK)
|
||||
tCgV = pv_thr.partition_B(gV); tCgC = pv_thr.partition_C(gC)
|
||||
a_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,0,None,0)).shape)
|
||||
tAsQ,tAgQ = cpasync.tma_partition(tma_q,0,a_lay,cute.group_modes(sQ,0,3),cute.group_modes(tCgQ,0,3))
|
||||
b_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,None,0,0)).shape)
|
||||
tBsK,tBgK = cpasync.tma_partition(tma_k,0,b_lay,cute.group_modes(sK,0,3),cute.group_modes(tCgK,0,3))
|
||||
tVsV,tVgV = cpasync.tma_partition(tma_v,0,b_lay,cute.group_modes(sV,0,3),cute.group_modes(tCgV,0,3))
|
||||
tAgQ = tAgQ[(None,0,None,0)]; tBgK = tBgK[(None,0,None,0)]; tVgV = tVgV[(None,0,None,0)]
|
||||
|
||||
tCrQ = qk_mma.make_fragment_A(sQ); tCrK = qk_mma.make_fragment_B(sK)
|
||||
tCrV = pv_mma.make_fragment_B(sV)
|
||||
|
||||
qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
tOtO0 = cute.make_tensor(tOtO.iterator + self.tmem_o0_offset, tOtO.layout)
|
||||
|
||||
# --- PV read view (for MMA only, NOT for softmax store) ---
|
||||
tP = cute.make_tensor(tStS.iterator, p_tmem_s.outer)
|
||||
tOrP_base = pv_thr.make_fragment_A(tP)
|
||||
tOrP = tOrP_base[(None,None,None,0)]
|
||||
tOrP0 = cute.make_tensor(
|
||||
tOrP.iterator + self.qk_acc_dtype.width // self.q_dtype.width * self.tmem_p0_offset,
|
||||
tOrP.layout)
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_as, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_as, self.num_acc_stage))
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# TMA LOAD
|
||||
if warp_idx == self.tma_warp_id:
|
||||
qp.reset(); qh = qp.acquire_and_advance()
|
||||
cute.copy(tma_q,tAgQ[(None,qh.count)],tAsQ[(None,qh.index)],tma_bar_ptr=qh.barrier)
|
||||
qp.tail()
|
||||
kvp.reset(); pk = kvp.try_acquire()
|
||||
for kt in cutlass.range(n_kv_tiles,unroll=1):
|
||||
kh = kvp.acquire_and_advance(pk)
|
||||
cute.copy(tma_k,tBgK[(None,kh.count)],tBsK[(None,kh.index)],tma_bar_ptr=kh.barrier)
|
||||
pk = cutlass.Boolean(1)
|
||||
vh = kvp.acquire_and_advance(pk)
|
||||
cute.copy(tma_v,tVgV[(None,vh.count)],tVsV[(None,vh.index)],tma_bar_ptr=vh.barrier)
|
||||
pk = cutlass.Boolean(1)
|
||||
kvp.tail()
|
||||
|
||||
# MMA
|
||||
if warp_idx == self.mma_warp_id:
|
||||
tmem.wait_for_alloc()
|
||||
qc.reset(); qh = qc.wait_and_advance(); qh.release()
|
||||
kvc.reset(); pk = kvc.try_wait()
|
||||
acc_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
|
||||
acc_pipe.producer_acquire(acc_st)
|
||||
for kt in range(n_kv_tiles):
|
||||
kh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
sh = s_prod.acquire_and_advance()
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
for kb in cutlass.range(cute.size(tCrQ,mode=[2]), unroll_full=True):
|
||||
cute.gemm(qk_mma, tStS0, tCrQ[(None,None,kb,0)], tCrK[(None,None,kb,kh.index)], tStS0)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
sh.commit(); kh.release()
|
||||
softmax_done_bar.arrive_and_wait()
|
||||
vh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, kt != 0)
|
||||
for kb in cutlass.range(cute.size(tOrP0,mode=[2]), unroll_full=True):
|
||||
cute.gemm(pv_mma, tOtO0, tOrP0[(None,None,kb)], tCrV[(None,None,kb,vh.index)], tOtO0)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
vh.release()
|
||||
pv_done_bar.arrive()
|
||||
acc_pipe.producer_commit(acc_st); acc_st.advance()
|
||||
acc_pipe.producer_tail(acc_st)
|
||||
|
||||
# ===================== EPILOGUE WARPS (STAGE C: ONLINE SOFTMAX) =====================
|
||||
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 (QK C-fragment) ---
|
||||
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 (QK C-fragment composition, FMHA pattern) ---
|
||||
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
|
||||
tStP_layout = cute.composition(tStS.layout, cute.make_layout((self.pv_mma_tiler[0], p_cols_fp32)))
|
||||
tStP0 = cute.make_tensor(tStS.iterator + self.tmem_p0_offset, tStP_layout)
|
||||
tmem_store_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
tiled_tmem_store = tcgen05.make_tmem_copy(tmem_store_atom, tStP0)
|
||||
thr_store = tiled_tmem_store.get_slice(sfw_idx)
|
||||
tTMEM_STOREtP = thr_store.partition_D(tStP0)
|
||||
tScP_layout = cute.composition(tScS.layout, cute.make_layout((self.pv_mma_tiler[0], p_cols_fp32)))
|
||||
tScP = cute.make_tensor(tScS.iterator, tScP_layout)
|
||||
tTMEM_STOREcP = thr_store.partition_S(tScP)
|
||||
|
||||
# --- Vector TMEM (per-row row_sum storage, FMHA pattern) ---
|
||||
# composition(tStS.layout, (128, 2)) = 2 FP32 columns per logical row
|
||||
# vec[0] = row_sum (final, after loop), vec[1] = unused
|
||||
# Reuses S TMEM region (offset 0), free after softmax loop writes
|
||||
|
||||
tStS_vec_layout = cute.composition(tStS.layout, cute.make_layout((128, 2)))
|
||||
tStS_vec = cute.make_tensor(tStS.iterator + self.tmem_vec_offset, tStS_vec_layout)
|
||||
tScS_vec_layout = cute.composition(tScS.layout, cute.make_layout((128, 2)))
|
||||
tScS_vec = cute.make_tensor(tScS.iterator, tScS_vec_layout)
|
||||
tmem_store_vec_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(2)), self.qk_acc_dtype)
|
||||
tiled_tmem_store_vec = tcgen05.make_tmem_copy(tmem_store_vec_atom, tStS_vec)
|
||||
thr_tmem_store_vec = tiled_tmem_store_vec.get_slice(sfw_idx)
|
||||
tTMEM_STORE_VECtS = thr_tmem_store_vec.partition_D(tStS_vec)
|
||||
tTMEM_STORE_VECcS = thr_tmem_store_vec.partition_S(tScS_vec)
|
||||
tmem_load_vec_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(2)), self.qk_acc_dtype)
|
||||
tiled_tmem_load_vec = tcgen05.make_tmem_copy(tmem_load_vec_atom, tStS_vec)
|
||||
thr_tmem_load_vec = tiled_tmem_load_vec.get_slice(sfw_idx)
|
||||
tTMEM_LOAD_VECtS = thr_tmem_load_vec.partition_S(tStS_vec)
|
||||
tTMEM_LOAD_VECcS = thr_tmem_load_vec.partition_D(tScS_vec)
|
||||
|
||||
# --- C6: O TMEM load/store for rescale (correction_rescale pattern) ---
|
||||
corr_tile_size = 16
|
||||
cO = cute.make_identity_tensor((self.pv_mma_tiler[0], self.pv_mma_tiler[1]))
|
||||
tOcO = pv_thr.partition_C(cO)
|
||||
o_tmem_load_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.qk_acc_dtype)
|
||||
o_tmem_store_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.qk_acc_dtype)
|
||||
tOtO_i_layout = cute.composition(tOtO0.layout, cute.make_layout((128, corr_tile_size)))
|
||||
tOcO_i_layout = cute.composition(tOcO.layout, cute.make_layout((128, corr_tile_size)))
|
||||
tOtO_i = cute.make_tensor(tOtO0.iterator, tOtO_i_layout)
|
||||
tOcO_i = cute.make_tensor(tOcO.iterator, tOcO_i_layout)
|
||||
o_tiled_tmem_load = tcgen05.make_tmem_copy(o_tmem_load_atom, tOtO_i)
|
||||
o_tiled_tmem_store = tcgen05.make_tmem_copy(o_tmem_store_atom, tOtO_i)
|
||||
o_thr_load = o_tiled_tmem_load.get_slice(sfw_idx)
|
||||
o_thr_store = o_tiled_tmem_store.get_slice(sfw_idx)
|
||||
tTMEM_LOADtO = o_thr_load.partition_S(tOtO_i)
|
||||
tTMEM_LOADcO = o_thr_load.partition_D(tOcO_i)
|
||||
tTMEM_STOREtO = o_thr_store.partition_D(tOtO_i)
|
||||
o_col_tiles = self.pv_mma_tiler[1] // corr_tile_size
|
||||
|
||||
# --- C2: Per-thread row state (persist across KV tiles) ---
|
||||
row_max = -cutlass.Float32.inf
|
||||
row_sum = cutlass.Float32(0.0)
|
||||
|
||||
# --- C3: QK scale = 1/sqrt(HEAD_DIM) * log2(e) for exp2 ---
|
||||
scale = self.scale_softmax_log2
|
||||
|
||||
# =============================================================
|
||||
# Per-KV-tile online softmax loop
|
||||
# =============================================================
|
||||
for kt in range(n_kv_tiles):
|
||||
si_handle = s_cons.wait_and_advance()
|
||||
|
||||
# Load S from TMEM (FP32, QK C-fragment layout)
|
||||
tTMEM_LOADrS = cute.make_rmem_tensor(tTMEM_LOADcS.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load, tTMEM_LOADtS, tTMEM_LOADrS)
|
||||
|
||||
# --- C4: Compute tile_max via .reduce(MAX) ---
|
||||
old_row_max = row_max
|
||||
row_max = tTMEM_LOADrS.load().reduce(cute.ReductionOp.MAX, row_max, 0)
|
||||
row_max_safe = row_max
|
||||
if row_max == -cutlass.Float32.inf:
|
||||
row_max_safe = cutlass.Float32(0.0)
|
||||
|
||||
# --- C5: Compute rescale factor ---
|
||||
acc_scale = cute.math.exp2(scale * (old_row_max - row_max_safe), fastmath=True)
|
||||
|
||||
# --- C6: Rescale O in TMEM (load O, multiply by acc_scale, store O) ---
|
||||
# acc_scale belongs to QK row (N//4), but O rows are in PV partition (N).
|
||||
# Store acc_scale to vector by QK row, read by PV row.
|
||||
if kt > 0:
|
||||
pv_done_bar.arrive_and_wait()
|
||||
|
||||
# Store acc_scale to vector indexed by QK logical row
|
||||
qk_row_c6 = tTMEM_LOADcS[0][0]
|
||||
thr_vs_c6 = tiled_tmem_store_vec.get_slice(qk_row_c6)
|
||||
tVStore_c6 = thr_vs_c6.partition_D(tStS_vec)
|
||||
tVStoreSrc_c6 = thr_vs_c6.partition_S(tScS_vec)
|
||||
tVStoreRmem_c6 = cute.make_rmem_tensor(tVStoreSrc_c6.shape, self.qk_acc_dtype)
|
||||
tVStoreRmem_c6[0] = acc_scale
|
||||
cute.copy(tiled_tmem_store_vec, tVStoreRmem_c6, tVStore_c6)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# Read acc_scale from vector indexed by PV logical row
|
||||
pv_row_c6 = tTMEM_LOADcO[0][0]
|
||||
thr_vl_c6 = tiled_tmem_load_vec.get_slice(pv_row_c6)
|
||||
tVLoad_c6 = thr_vl_c6.partition_S(tStS_vec)
|
||||
tVLoadDst_c6 = thr_vl_c6.partition_D(tScS_vec)
|
||||
tVLoadRmem_c6 = cute.make_rmem_tensor(tVLoadDst_c6.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load_vec, tVLoad_c6, tVLoadRmem_c6)
|
||||
cute.arch.fence_view_async_tmem_load()
|
||||
acc_scale_pv = tVLoadRmem_c6[0]
|
||||
|
||||
tTMrO = cute.make_rmem_tensor((tTMEM_LOADcO.shape, o_col_tiles), self.qk_acc_dtype)
|
||||
for i in range(o_col_tiles):
|
||||
tTMrO_i_ = tTMrO[None, i]
|
||||
tTMrO_i_layout = cute.composition(tTMrO_i_.layout, cute.make_layout(tTMrO.shape[0]))
|
||||
tTMrO_i = cute.make_tensor(tTMrO_i_.iterator, tTMrO_i_layout)
|
||||
tTMEM_LOADtO_i = cute.make_tensor(tTMEM_LOADtO.iterator + i * corr_tile_size, tTMEM_LOADtO.layout)
|
||||
tTMEM_STOREtO_i = cute.make_tensor(tTMEM_STOREtO.iterator + i * corr_tile_size, tTMEM_STOREtO.layout)
|
||||
cute.copy(o_tiled_tmem_load, tTMEM_LOADtO_i, tTMrO_i)
|
||||
for j in cutlass.range(cute.size(tTMrO_i), vectorize=True):
|
||||
tTMrO_i[j] = tTMrO_i[j] * acc_scale_pv
|
||||
cute.copy(o_tiled_tmem_store, tTMrO_i, tTMEM_STOREtO_i)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# Rescale row_sum
|
||||
row_sum = row_sum * acc_scale
|
||||
|
||||
# --- C7: Compute P = exp2((S - row_max_safe) * scale) ---
|
||||
minus_row_max_scale = (cutlass.Float32(0.0) - row_max_safe) * scale
|
||||
|
||||
# Register bridge (FMHA pattern: FP32 backing + BF16 view)
|
||||
rP_words = cute.make_rmem_tensor(tTMEM_STOREcP.shape, self.qk_acc_dtype)
|
||||
rP_bf16 = cute.make_tensor(cute.recast_ptr(rP_words.iterator, dtype=self.q_dtype), tTMEM_LOADrS.layout)
|
||||
|
||||
frg_cnt = 4
|
||||
frg_tile = cute.size(tTMEM_LOADrS) // frg_cnt
|
||||
tTMEM_LOADrS_frg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(frg_tile))
|
||||
rP_bf16_frg = cute.logical_divide(rP_bf16, cute.make_layout(frg_tile))
|
||||
|
||||
# Scale S, compute exp2, store through register bridge
|
||||
for j in range(frg_cnt):
|
||||
for k in cutlass.range(cute.size(tTMEM_LOADrS_frg, mode=[0]), vectorize=True):
|
||||
tTMEM_LOADrS_frg[k, j] = tTMEM_LOADrS_frg[k, j] * scale + minus_row_max_scale
|
||||
tTMEM_LOADrS_frg[k, j] = cute.math.exp2(tTMEM_LOADrS_frg[k, j], fastmath=True)
|
||||
s_vec = tTMEM_LOADrS_frg[None, j].load()
|
||||
rP_bf16_frg[None, j].store(s_vec.to(self.q_dtype))
|
||||
|
||||
# Store P to TMEM
|
||||
cute.copy(tiled_tmem_store, rP_words, tTMEM_STOREtP)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
si_handle.release()
|
||||
softmax_done_bar.arrive()
|
||||
|
||||
# --- C8: Row sum accumulation (CUTLASS FMHA packed f32x2 pattern) ---
|
||||
# P values still in tTMEM_LOADrS registers.
|
||||
# 4 accumulators for 4 reduction_unroll columns.
|
||||
local_row_sum_0 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_1 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_2 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_3 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
|
||||
reduction_unroll = 4
|
||||
rfrg_tile = cute.size(tTMEM_LOADrS) // reduction_unroll
|
||||
tTMEM_LOADrS_rfrg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(rfrg_tile))
|
||||
|
||||
for j in cutlass.range_constexpr(0, cute.size(tTMEM_LOADrS_rfrg, mode=[0]), 2):
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_0, (tTMEM_LOADrS_rfrg[j, 0], tTMEM_LOADrS_rfrg[j + 1, 0]))
|
||||
local_row_sum_1 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_1, (tTMEM_LOADrS_rfrg[j, 1], tTMEM_LOADrS_rfrg[j + 1, 1]))
|
||||
local_row_sum_2 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_2, (tTMEM_LOADrS_rfrg[j, 2], tTMEM_LOADrS_rfrg[j + 1, 2]))
|
||||
local_row_sum_3 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_3, (tTMEM_LOADrS_rfrg[j, 3], tTMEM_LOADrS_rfrg[j + 1, 3]))
|
||||
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(local_row_sum_0, local_row_sum_1)
|
||||
local_row_sum_2 = cute.arch.add_packed_f32x2(local_row_sum_2, local_row_sum_3)
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(local_row_sum_0, local_row_sum_2)
|
||||
tile_sum = local_row_sum_0[0] + local_row_sum_0[1]
|
||||
|
||||
row_sum = row_sum + tile_sum
|
||||
|
||||
# --- C9: Final normalization via O TMEM rescale ---
|
||||
pv_done_bar.arrive_and_wait()
|
||||
|
||||
# DEBUG: hardcoded inv_row_sum = 1.0 (no normalization)
|
||||
inv_row_sum = cutlass.Float32(1.0)
|
||||
|
||||
# Normalize O in TMEM using PV-correct inv_row_sum
|
||||
tTMrO_final = cute.make_rmem_tensor((tTMEM_LOADcO.shape, o_col_tiles), self.qk_acc_dtype)
|
||||
for i in range(o_col_tiles):
|
||||
tTMrO_i_ = tTMrO_final[None, i]
|
||||
tTMrO_i_layout = cute.composition(tTMrO_i_.layout, cute.make_layout(tTMrO_final.shape[0]))
|
||||
tTMrO_i = cute.make_tensor(tTMrO_i_.iterator, tTMrO_i_layout)
|
||||
tTMEM_LOADtO_i = cute.make_tensor(
|
||||
tTMEM_LOADtO.iterator + i * corr_tile_size, tTMEM_LOADtO.layout)
|
||||
tTMEM_STOREtO_i = cute.make_tensor(
|
||||
tTMEM_STOREtO.iterator + i * corr_tile_size, tTMEM_STOREtO.layout)
|
||||
cute.copy(o_tiled_tmem_load, tTMEM_LOADtO_i, tTMrO_i)
|
||||
for j in cutlass.range(cute.size(tTMrO_i), vectorize=True):
|
||||
tTMrO_i[j] = tTMrO_i[j] * inv_row_sum
|
||||
cute.copy(o_tiled_tmem_store, tTMrO_i, tTMEM_STOREtO_i)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# Now O in TMEM is normalized. Use standard epilogue_tma_store with identity.
|
||||
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():
|
||||
import math
|
||||
torch.manual_seed(42)
|
||||
for n in [128, 256, 384]:
|
||||
m, hd = 128, HEAD_DIM
|
||||
q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
k = torch.randn(n, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
v = torch.randn(n, hd, dtype=torch.bfloat16, device="cuda")
|
||||
v_kernel = v.unsqueeze(-1)
|
||||
c = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
qf = q[:,:,0].float(); kf = k[:,:,0].float()
|
||||
attn = qf @ kf.T / math.sqrt(hd)
|
||||
ref = torch.softmax(attn, dim=-1) @ v.float()
|
||||
mQ = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
|
||||
mK = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
|
||||
mV = ct.from_dlpack(v_kernel).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_kernel))
|
||||
mC = ct.from_dlpack(c).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c))
|
||||
stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
|
||||
kernel = FmhaV3Softmax()
|
||||
print(f"n={n}: Compiling...", flush=True)
|
||||
compiled = cute.compile(kernel, mQ, mK, mV, mC, stream)
|
||||
print(f"n={n}: tmem: s0={kernel.tmem_s0_offset} p0={kernel.tmem_p0_offset} o0={kernel.tmem_o0_offset} vec={kernel.tmem_vec_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()
|
||||
max_err = (out - ref).abs().max().item()
|
||||
print(f"FMHA softmax n={n}: cosine {cos:.6f} max_err {max_err:.6f} {'PASS' if cos >= 0.999 else 'FAIL'}", flush=True)
|
||||
|
||||
if __name__ == "__main__":
|
||||
test()
|
||||
|
||||
|
||||
416
tests/unit/test_fmha_v3_proper.py
Normal file
416
tests/unit/test_fmha_v3_proper.py
Normal file
@@ -0,0 +1,416 @@
|
||||
"""
|
||||
FMHA v3 Proper: 11-warp with correction warp group + epilogue warp.
|
||||
Warp layout: softmax(0-3), correction(4-7), MMA(8), TMA(9), epilogue(10)
|
||||
"""
|
||||
import math, torch, cutlass, cutlass.cute as cute, cutlass.utils as utils, cutlass.pipeline as pipeline
|
||||
from cutlass.cute.nvgpu import cpasync, tcgen05
|
||||
from cutlass import Float32, BFloat16, Int32, Boolean, const_expr
|
||||
from cutlass.utils import LayoutEnum
|
||||
from cutlass.utils.tmem_allocator import find_tmem_tensor_col_offset
|
||||
import cuda.bindings.driver as cuda, cutlass.torch as ct
|
||||
|
||||
HEAD_DIM = 64
|
||||
|
||||
class FmhaV3Proper:
|
||||
def __init__(self):
|
||||
self.qk_acc_dtype = Float32; self.pv_acc_dtype = Float32
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
self.use_2cta_instrs = False; self.cluster_shape_mn = (1, 1)
|
||||
self.cta_group = tcgen05.CtaGroup.ONE
|
||||
self.softmax_warp_ids = (0,1,2,3)
|
||||
self.correction_warp_ids = (4,5,6,7)
|
||||
self.mma_warp_id = 8; self.tma_warp_id = 9; self.epilogue_warp_id = 10
|
||||
self.threads_per_cta = 352
|
||||
self.q_stage = 1; self.kv_stage = 2; self.num_acc_stage = 1
|
||||
self.mma_softmax_stage = 1; self.softmax_corr_stage = 1
|
||||
self.mma_corr_stage = 2; self.epi_stage = 2; self.num_c_stage = 2
|
||||
self.scale_softmax_log2 = Float32(1.0 / math.sqrt(HEAD_DIM) * math.log2(math.e))
|
||||
|
||||
def _setup(self, qk_mma, pv_mma):
|
||||
qk_ik = cute.size(qk_mma.shape_mnk, mode=[2])
|
||||
self.qk_mma_tiler = (128, 128, qk_ik * 4)
|
||||
pv_ik = cute.size(pv_mma.shape_mnk, mode=[2])
|
||||
self.pv_mma_tiler = (128, HEAD_DIM, pv_ik * (128 // pv_ik))
|
||||
self.mma_tiler = self.qk_mma_tiler
|
||||
self.cluster_layout_vmnk = cute.tiled_divide(cute.make_layout((1,1,1)), (qk_mma.thr_id.shape,))
|
||||
self.cta_tile_shape_mnk = (self.qk_mma_tiler[0]//cute.size(qk_mma.thr_id.shape), HEAD_DIM, self.qk_mma_tiler[2])
|
||||
self.c_layout = LayoutEnum.ROW_MAJOR
|
||||
self.epi_tile = utils.sm100.compute_epilogue_tile_shape(self.cta_tile_shape_mnk, False, self.c_layout, self.o_dtype)
|
||||
self.num_ab_stage = 1
|
||||
self.q_smem_s = utils.sm100.make_smem_layout_a(qk_mma, self.qk_mma_tiler, self.q_dtype, self.q_stage)
|
||||
self.k_smem_s = utils.sm100.make_smem_layout_b(qk_mma, self.qk_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.v_smem_s = utils.sm100.make_smem_layout_b(pv_mma, self.pv_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.c_smem_s = utils.sm100.make_smem_layout_epi(self.o_dtype, self.c_layout, self.epi_tile, 2)
|
||||
self.p_tmem_s = utils.sm100.make_smem_layout_a(pv_mma, self.pv_mma_tiler, self.q_dtype, 1)
|
||||
qk_thr = qk_mma.get_slice(0); qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
pv_thr = pv_mma.get_slice(0); pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
self.tmem_s0_offset = 0; self.tmem_p0_offset = 32; self.tmem_vec0_offset = 0
|
||||
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
|
||||
o_after = max(self.qk_mma_tiler[1], self.tmem_p0_offset + p_cols_fp32)
|
||||
self.tmem_o0_offset = ((o_after + 31) // 32) * 32
|
||||
o_cols = find_tmem_tensor_col_offset(tOtO)
|
||||
total = self.tmem_o0_offset + o_cols
|
||||
self.num_tmem_alloc_cols = 1
|
||||
while self.num_tmem_alloc_cols < total: self.num_tmem_alloc_cols *= 2
|
||||
cta = cute.size(qk_mma.thr_id.shape)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0))
|
||||
self.q_tx_bytes = cute.size_in_bytes(self.q_dtype, q_s) * cta
|
||||
self.kv_tx_bytes = cute.size_in_bytes(self.q_dtype, k_s) * cta
|
||||
|
||||
@cute.jit
|
||||
def __call__(self, q, k, v, c, stream):
|
||||
self.q_dtype = q.element_type; self.o_dtype = c.element_type; self.c_dtype = self.o_dtype
|
||||
self.a_major = LayoutEnum.from_tensor(q).mma_major_mode()
|
||||
self.b_major = LayoutEnum.from_tensor(k).mma_major_mode()
|
||||
v_fmha = cute.make_tensor(v.iterator, cute.make_layout((HEAD_DIM, 128, 1), stride=(1, HEAD_DIM, HEAD_DIM * 128)))
|
||||
self.v_major = LayoutEnum.from_tensor(v_fmha).mma_major_mode()
|
||||
self.c_layout = LayoutEnum.from_tensor(c)
|
||||
qk_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, self.a_major, self.b_major, self.qk_acc_dtype, self.cta_group, (128,128), tcgen05.OperandSource.SMEM)
|
||||
pv_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, cute.nvgpu.OperandMajorMode.K, self.v_major, self.pv_acc_dtype, self.cta_group, (128,HEAD_DIM), tcgen05.OperandSource.TMEM)
|
||||
self._setup(qk_mma, pv_mma)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0)); v_s = cute.slice_(self.v_smem_s,(None,None,None,0))
|
||||
tma_q,mQ = cute.nvgpu.make_tiled_tma_atom_A(utils.sm100.cluster_shape_to_tma_atom_A(self.cluster_shape_mn,qk_mma.thr_id),q,q_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_k,mK = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,qk_mma.thr_id),k,k_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_v,mV = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,pv_mma.thr_id),v_fmha,v_s,self.pv_mma_tiler,pv_mma,self.cluster_layout_vmnk.shape)
|
||||
epi_s = cute.select(self.c_smem_s,mode=[0,1])
|
||||
tma_c,mC = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(),c,epi_s,self.epi_tile)
|
||||
self._kernel(qk_mma,pv_mma,tma_q,mQ,tma_k,mK,tma_v,mV,tma_c,mC,self.cluster_layout_vmnk,self.q_smem_s,self.k_smem_s,self.v_smem_s,self.p_tmem_s,self.c_smem_s,self.epi_tile).launch(grid=(1,1,1),block=[self.threads_per_cta,1,1],stream=stream)
|
||||
|
||||
@cute.kernel
|
||||
def _kernel(self, qk_mma, pv_mma, tma_q, mQ, tma_k, mK, tma_v, mV, tma_c, mC, cl_vmnk, q_smem_s, k_smem_s, v_smem_s, p_tmem_s, c_smem_s, epi_tile):
|
||||
warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx())
|
||||
tidx,_,_ = cute.arch.thread_idx()
|
||||
if warp_idx == self.tma_warp_id:
|
||||
cpasync.prefetch_descriptor(tma_q); cpasync.prefetch_descriptor(tma_k); cpasync.prefetch_descriptor(tma_v); cpasync.prefetch_descriptor(tma_c)
|
||||
|
||||
@cute.struct
|
||||
class SS:
|
||||
q_bar: cute.struct.MemRange[cutlass.Int64, self.q_stage*2]
|
||||
kv_bar: cute.struct.MemRange[cutlass.Int64, self.kv_stage*2]
|
||||
mma_si_bar: cute.struct.MemRange[cutlass.Int64, self.mma_softmax_stage*2]
|
||||
si_corr_bar: cute.struct.MemRange[cutlass.Int64, self.softmax_corr_stage*2]
|
||||
mma_corr_bar: cute.struct.MemRange[cutlass.Int64, self.mma_corr_stage*2]
|
||||
corr_epi_bar: cute.struct.MemRange[cutlass.Int64, self.epi_stage*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()
|
||||
mma_si_prod,mma_si_cons = pipeline.PipelineUmmaAsync.create(barrier_storage=st.mma_si_bar.data_ptr(),num_stages=self.mma_softmax_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32*len(self.softmax_warp_ids))).make_participants()
|
||||
si_corr_prod,si_corr_cons = pipeline.PipelineAsync.create(barrier_storage=st.si_corr_bar.data_ptr(),num_stages=self.softmax_corr_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32*len(self.softmax_warp_ids)),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32*len(self.correction_warp_ids))).make_participants()
|
||||
mma_corr_prod,mma_corr_cons = pipeline.PipelineUmmaAsync.create(barrier_storage=st.mma_corr_bar.data_ptr(),num_stages=self.mma_corr_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32*len(self.correction_warp_ids))).make_participants()
|
||||
corr_epi_prod,corr_epi_cons = pipeline.PipelineAsync.create(barrier_storage=st.corr_epi_bar.data_ptr(),num_stages=self.epi_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32*len(self.correction_warp_ids)),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32)).make_participants()
|
||||
acc_pipe = pipeline.PipelineUmmaAsync.create(barrier_storage=st.acc_bar.data_ptr(),num_stages=self.num_acc_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,len(self.correction_warp_ids)),cta_layout_vmnk=cl_vmnk,defer_sync=True)
|
||||
tmem_bar = pipeline.NamedBarrier(barrier_id=2,num_threads=32*len((self.mma_warp_id,*self.softmax_warp_ids)))
|
||||
tmem = utils.TmemAllocator(st.holding.ptr,barrier_for_retrieve=tmem_bar,allocator_warp_id=self.softmax_warp_ids[0],is_two_cta=cute.size(qk_mma.thr_id.shape)==2,two_cta_tmem_dealloc_mbar_ptr=st.tmem_dealloc.ptr)
|
||||
pipeline.pipeline_init_arrive(cluster_shape_mn=cl_vmnk,is_relaxed=True)
|
||||
|
||||
sQ = smem.allocate_tensor(element_type=self.q_dtype,layout=q_smem_s.outer,byte_alignment=128,swizzle=q_smem_s.inner)
|
||||
sK = smem.allocate_tensor(element_type=self.q_dtype,layout=k_smem_s.outer,byte_alignment=128,swizzle=k_smem_s.inner)
|
||||
sV = smem.allocate_tensor(element_type=self.q_dtype,layout=v_smem_s.outer,byte_alignment=128,swizzle=v_smem_s.inner)
|
||||
sC = smem.allocate_tensor(element_type=self.o_dtype,layout=c_smem_s.outer,byte_alignment=128,swizzle=c_smem_s.inner)
|
||||
gQ = cute.local_tile(mQ,cute.slice_(self.qk_mma_tiler,(None,0,None)),(None,None,None))
|
||||
gK = cute.local_tile(mK,cute.slice_(self.qk_mma_tiler,(0,None,None)),(None,None,None))
|
||||
gV = cute.local_tile(mV,cute.slice_(self.pv_mma_tiler,(0,None,None)),(None,None,None))
|
||||
gC = cute.local_tile(mC,cute.slice_(self.pv_mma_tiler,(None,None,0)),(None,None,None))
|
||||
n_kv_tiles = cute.size(gK, mode=[3])
|
||||
qk_thr = qk_mma.get_slice(0); pv_thr = pv_mma.get_slice(0)
|
||||
tCgQ = qk_thr.partition_A(gQ); tCgK = qk_thr.partition_B(gK); tCgV = pv_thr.partition_B(gV); tCgC = pv_thr.partition_C(gC)
|
||||
a_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,0,None,0)).shape)
|
||||
tAsQ,tAgQ = cpasync.tma_partition(tma_q,0,a_lay,cute.group_modes(sQ,0,3),cute.group_modes(tCgQ,0,3))
|
||||
b_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,None,0,0)).shape)
|
||||
tBsK,tBgK = cpasync.tma_partition(tma_k,0,b_lay,cute.group_modes(sK,0,3),cute.group_modes(tCgK,0,3))
|
||||
tVsV,tVgV = cpasync.tma_partition(tma_v,0,b_lay,cute.group_modes(sV,0,3),cute.group_modes(tCgV,0,3))
|
||||
tAgQ = tAgQ[(None,0,None,0)]; tBgK = tBgK[(None,0,None,0)]; tVgV = tVgV[(None,0,None,0)]
|
||||
tCrQ = qk_mma.make_fragment_A(sQ); tCrK = qk_mma.make_fragment_B(sK); tCrV = pv_mma.make_fragment_B(sV)
|
||||
qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2]); tStS = qk_thr.make_fragment_C(qk_as)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2]); tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
tOtO0 = cute.make_tensor(tOtO.iterator + self.tmem_o0_offset, tOtO.layout)
|
||||
tP = cute.make_tensor(tStS.iterator, p_tmem_s.outer)
|
||||
tOrP_base = pv_thr.make_fragment_A(tP); tOrP = tOrP_base[(None,None,None,0)]
|
||||
tOrP0 = cute.make_tensor(tOrP.iterator + self.qk_acc_dtype.width // self.q_dtype.width * self.tmem_p0_offset, tOrP.layout)
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_as, self.num_acc_stage))
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# TMA
|
||||
if warp_idx == self.tma_warp_id:
|
||||
qp.reset(); qh = qp.acquire_and_advance()
|
||||
cute.copy(tma_q,tAgQ[(None,qh.count)],tAsQ[(None,qh.index)],tma_bar_ptr=qh.barrier); qp.tail()
|
||||
kvp.reset(); pk = kvp.try_acquire()
|
||||
for kt in cutlass.range(n_kv_tiles,unroll=1):
|
||||
kh = kvp.acquire_and_advance(pk); cute.copy(tma_k,tBgK[(None,kh.count)],tBsK[(None,kh.index)],tma_bar_ptr=kh.barrier); pk = cutlass.Boolean(1)
|
||||
vh = kvp.acquire_and_advance(pk); cute.copy(tma_v,tVgV[(None,vh.count)],tVsV[(None,vh.index)],tma_bar_ptr=vh.barrier); pk = cutlass.Boolean(1)
|
||||
kvp.tail()
|
||||
|
||||
# MMA
|
||||
if warp_idx == self.mma_warp_id:
|
||||
tmem.wait_for_alloc()
|
||||
qc.reset(); qh = qc.wait_and_advance(); qh.release()
|
||||
kvc.reset(); pk = kvc.try_wait()
|
||||
acc_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
|
||||
acc_pipe.producer_acquire(acc_st)
|
||||
for kt in range(n_kv_tiles):
|
||||
kh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
sh = mma_si_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()
|
||||
if kt > 0:
|
||||
o_handle = mma_corr_cons.wait_and_advance(); o_handle.release()
|
||||
sh2 = mma_si_prod.acquire_and_advance()
|
||||
vh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, kt != 0)
|
||||
for kb in cutlass.range(cute.size(tOrP0,mode=[2]), unroll_full=True):
|
||||
cute.gemm(pv_mma, tOtO0, tOrP0[(None,None,kb)], tCrV[(None,None,kb,vh.index)], tOtO0)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store(); vh.release()
|
||||
o_prod_h = mma_corr_prod.acquire_and_advance(); o_prod_h.commit()
|
||||
o_handle = mma_corr_cons.wait_and_advance(); o_handle.release()
|
||||
acc_pipe.producer_commit(acc_st); acc_st.advance(); acc_pipe.producer_tail(acc_st)
|
||||
|
||||
# SOFTMAX (warps 0-3)
|
||||
if warp_idx < len(self.softmax_warp_ids):
|
||||
tmem.allocate(self.num_tmem_alloc_cols); tmem.wait_for_alloc()
|
||||
tmem_ptr = tmem.retrieve_ptr(self.qk_acc_dtype)
|
||||
sfw_idx = tidx % (32 * len(self.softmax_warp_ids))
|
||||
scale = self.scale_softmax_log2
|
||||
tmem_load_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
tiled_tmem_load = tcgen05.make_tmem_copy(tmem_load_atom, tStS0)
|
||||
thr_load = tiled_tmem_load.get_slice(sfw_idx)
|
||||
tTMEM_LOADtS = thr_load.partition_S(tStS0)
|
||||
cS = cute.make_identity_tensor((self.qk_mma_tiler[0], self.qk_mma_tiler[1]))
|
||||
tScS = qk_thr.partition_C(cS); tTMEM_LOADcS = thr_load.partition_D(tScS)
|
||||
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
|
||||
tStP_layout = cute.composition(tStS.layout, cute.make_layout((self.pv_mma_tiler[0], p_cols_fp32)))
|
||||
tStP0 = cute.make_tensor(tStS.iterator + self.tmem_p0_offset, tStP_layout)
|
||||
tmem_store_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
tiled_tmem_store = tcgen05.make_tmem_copy(tmem_store_atom, tStP0)
|
||||
thr_store = tiled_tmem_store.get_slice(sfw_idx)
|
||||
tTMEM_STOREtP = thr_store.partition_D(tStP0)
|
||||
tScP_layout = cute.composition(tScS.layout, cute.make_layout((self.pv_mma_tiler[0], p_cols_fp32)))
|
||||
tScP = cute.make_tensor(tScS.iterator, tScP_layout); tTMEM_STOREcP = thr_store.partition_S(tScP)
|
||||
tStS_vec_layout = cute.composition(tStS.layout, cute.make_layout((128, 2)))
|
||||
tStS_vec = cute.make_tensor(tStS.iterator + self.tmem_vec0_offset, tStS_vec_layout)
|
||||
tScS_vec_layout = cute.composition(tScS.layout, cute.make_layout((128, 2)))
|
||||
tScS_vec = cute.make_tensor(tScS.iterator, tScS_vec_layout)
|
||||
tmem_store_vec_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(2)), self.qk_acc_dtype)
|
||||
tiled_tmem_store_vec = tcgen05.make_tmem_copy(tmem_store_vec_atom, tStS_vec)
|
||||
thr_tmem_store_vec = tiled_tmem_store_vec.get_slice(sfw_idx)
|
||||
tTMEM_STORE_VECtS = thr_tmem_store_vec.partition_D(tStS_vec)
|
||||
tTMEM_STORE_VECcS = thr_tmem_store_vec.partition_S(tScS_vec)
|
||||
row_max = -cutlass.Float32.inf; row_sum = cutlass.Float32(0.0)
|
||||
for kt in range(n_kv_tiles):
|
||||
si_handle = mma_si_cons.wait_and_advance()
|
||||
tTMEM_LOADrS = cute.make_rmem_tensor(tTMEM_LOADcS.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load, tTMEM_LOADtS, tTMEM_LOADrS)
|
||||
old_row_max = row_max
|
||||
row_max = tTMEM_LOADrS.load().reduce(cute.ReductionOp.MAX, row_max, 0)
|
||||
row_max_safe = row_max
|
||||
if row_max == -cutlass.Float32.inf: row_max_safe = cutlass.Float32(0.0)
|
||||
vec_handle = si_corr_prod.acquire_and_advance()
|
||||
tTMEM_STORE_VECrS = cute.make_rmem_tensor(tTMEM_STORE_VECcS.shape, self.qk_acc_dtype)
|
||||
tTMEM_STORE_VECrS[0] = old_row_max; tTMEM_STORE_VECrS[1] = row_max_safe
|
||||
cute.copy(tiled_tmem_store_vec, tTMEM_STORE_VECrS, tTMEM_STORE_VECtS)
|
||||
cute.arch.fence_view_async_tmem_store(); vec_handle.commit()
|
||||
minus_row_max_scale = (cutlass.Float32(0.0) - row_max_safe) * scale
|
||||
rP_words = cute.make_rmem_tensor(tTMEM_STOREcP.shape, self.qk_acc_dtype)
|
||||
rP_bf16 = cute.make_tensor(cute.recast_ptr(rP_words.iterator, dtype=self.q_dtype), tTMEM_LOADrS.layout)
|
||||
frg_cnt = 4; frg_tile = cute.size(tTMEM_LOADrS) // frg_cnt
|
||||
tTMEM_LOADrS_frg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(frg_tile))
|
||||
rP_bf16_frg = cute.logical_divide(rP_bf16, cute.make_layout(frg_tile))
|
||||
for j in range(frg_cnt):
|
||||
for k in cutlass.range(cute.size(tTMEM_LOADrS_frg, mode=[0]), vectorize=True):
|
||||
tTMEM_LOADrS_frg[k, j] = tTMEM_LOADrS_frg[k, j] * scale + minus_row_max_scale
|
||||
tTMEM_LOADrS_frg[k, j] = cute.math.exp2(tTMEM_LOADrS_frg[k, j], fastmath=True)
|
||||
s_vec = tTMEM_LOADrS_frg[None, j].load(); rP_bf16_frg[None, j].store(s_vec.to(self.q_dtype))
|
||||
cute.copy(tiled_tmem_store, rP_words, tTMEM_STOREtP)
|
||||
cute.arch.fence_view_async_tmem_store(); si_handle.release()
|
||||
acc_scale = cute.math.exp2(scale * (old_row_max - row_max_safe), fastmath=True)
|
||||
row_sum = row_sum * acc_scale
|
||||
local_row_sum_0 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_1 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_2 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_3 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
reduction_unroll = 4; rfrg_tile = cute.size(tTMEM_LOADrS) // reduction_unroll
|
||||
tTMEM_LOADrS_rfrg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(rfrg_tile))
|
||||
for j in cutlass.range_constexpr(0, cute.size(tTMEM_LOADrS_rfrg, mode=[0]), 2):
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(local_row_sum_0, (tTMEM_LOADrS_rfrg[j, 0], tTMEM_LOADrS_rfrg[j+1, 0]))
|
||||
local_row_sum_1 = cute.arch.add_packed_f32x2(local_row_sum_1, (tTMEM_LOADrS_rfrg[j, 1], tTMEM_LOADrS_rfrg[j+1, 1]))
|
||||
local_row_sum_2 = cute.arch.add_packed_f32x2(local_row_sum_2, (tTMEM_LOADrS_rfrg[j, 2], tTMEM_LOADrS_rfrg[j+1, 2]))
|
||||
local_row_sum_3 = cute.arch.add_packed_f32x2(local_row_sum_3, (tTMEM_LOADrS_rfrg[j, 3], tTMEM_LOADrS_rfrg[j+1, 3]))
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(local_row_sum_0, local_row_sum_1)
|
||||
local_row_sum_2 = cute.arch.add_packed_f32x2(local_row_sum_2, local_row_sum_3)
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(local_row_sum_0, local_row_sum_2)
|
||||
row_sum = row_sum + local_row_sum_0[0] + local_row_sum_0[1]
|
||||
# Final vector: (row_sum, row_max)
|
||||
vec_handle = si_corr_prod.acquire_and_advance()
|
||||
tTMEM_STORE_VECrS = cute.make_rmem_tensor(tTMEM_STORE_VECcS.shape, self.qk_acc_dtype)
|
||||
tTMEM_STORE_VECrS[0] = row_sum; tTMEM_STORE_VECrS[1] = row_max
|
||||
cute.copy(tiled_tmem_store_vec, tTMEM_STORE_VECrS, tTMEM_STORE_VECtS)
|
||||
cute.arch.fence_view_async_tmem_store(); vec_handle.commit()
|
||||
si_handle = mma_si_cons.wait_and_advance(); si_corr_prod.acquire(); si_handle.release()
|
||||
tmem.relinquish_alloc_permit()
|
||||
|
||||
# CORRECTION (warps 4-7)
|
||||
if warp_idx >= len(self.softmax_warp_ids) and warp_idx < len(self.softmax_warp_ids) + len(self.correction_warp_ids):
|
||||
corr_idx = tidx % (32 * len(self.correction_warp_ids))
|
||||
scale = self.scale_softmax_log2
|
||||
# Create tScS from common-scope qk_thr (same as softmax section)
|
||||
cS_corr = cute.make_identity_tensor((self.qk_mma_tiler[0], self.qk_mma_tiler[1]))
|
||||
tScS = qk_thr.partition_C(cS_corr)
|
||||
tStS_vec_layout = cute.composition(tStS.layout, cute.make_layout((128, 2)))
|
||||
tStS_vec = cute.make_tensor(tStS.iterator + self.tmem_vec0_offset, tStS_vec_layout)
|
||||
tScS_vec_layout = cute.composition(tScS.layout, cute.make_layout((128, 2)))
|
||||
tScS_vec = cute.make_tensor(tScS.iterator, tScS_vec_layout)
|
||||
tmem_load_vec_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(2)), self.qk_acc_dtype)
|
||||
tiled_tmem_load_vec = tcgen05.make_tmem_copy(tmem_load_vec_atom, tStS_vec)
|
||||
thr_tmem_load_vec = tiled_tmem_load_vec.get_slice(corr_idx)
|
||||
tTMEM_LOAD_VECtS = thr_tmem_load_vec.partition_S(tStS_vec)
|
||||
tTMEM_LOAD_VECcS = thr_tmem_load_vec.partition_D(tScS_vec)
|
||||
corr_tile_size = 16
|
||||
cO = cute.make_identity_tensor((self.pv_mma_tiler[0], self.pv_mma_tiler[1]))
|
||||
tOcO = pv_thr.partition_C(cO)
|
||||
tOtO_i_layout = cute.composition(tOtO0.layout, cute.make_layout((128, corr_tile_size)))
|
||||
tOcO_i_layout = cute.composition(tOcO.layout, cute.make_layout((128, corr_tile_size)))
|
||||
tOtO_i = cute.make_tensor(tOtO0.iterator, tOtO_i_layout)
|
||||
tOcO_i = cute.make_tensor(tOcO.iterator, tOcO_i_layout)
|
||||
o_tmem_load_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.pv_acc_dtype)
|
||||
o_tmem_store_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.pv_acc_dtype)
|
||||
o_tiled_tmem_load = tcgen05.make_tmem_copy(o_tmem_load_atom, tOtO_i)
|
||||
o_tiled_tmem_store = tcgen05.make_tmem_copy(o_tmem_store_atom, tOtO_i)
|
||||
o_thr_load = o_tiled_tmem_load.get_slice(corr_idx)
|
||||
o_thr_store = o_tiled_tmem_store.get_slice(corr_idx)
|
||||
tTMEM_LOADtO = o_thr_load.partition_S(tOtO_i)
|
||||
tTMEM_LOADcO = o_thr_load.partition_D(tOcO_i)
|
||||
tTMEM_STOREtO = o_thr_store.partition_D(tOtO_i)
|
||||
o_col_tiles = self.pv_mma_tiler[1] // corr_tile_size
|
||||
|
||||
# Ignore first vec (no rescale for first PV)
|
||||
vec_handle = si_corr_cons.wait_and_advance()
|
||||
vec_handle.release()
|
||||
|
||||
for kt in range(n_kv_tiles):
|
||||
if kt > 0:
|
||||
# Wait for vector (old_max, new_max) from softmax
|
||||
vec_handle = si_corr_cons.wait_and_advance()
|
||||
tTMEM_LOAD_VECrS = cute.make_rmem_tensor(tTMEM_LOAD_VECcS.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load_vec, tTMEM_LOAD_VECtS, tTMEM_LOAD_VECrS)
|
||||
corr_scale_ = scale * (tTMEM_LOAD_VECrS[0] - tTMEM_LOAD_VECrS[1])
|
||||
corr_scale = cute.math.exp2(corr_scale_, fastmath=True)
|
||||
|
||||
# Wait for O from MMA
|
||||
o_handle = mma_corr_cons.wait_and_advance()
|
||||
|
||||
# correction_rescale: O *= corr_scale in TMEM
|
||||
tTMrO = cute.make_rmem_tensor((tTMEM_LOADcO.shape, o_col_tiles), self.pv_acc_dtype)
|
||||
for i in range(o_col_tiles):
|
||||
tTMrO_i_ = tTMrO[None, i]
|
||||
tTMrO_i_layout = cute.composition(tTMrO_i_.layout, cute.make_layout(tTMrO.shape[0]))
|
||||
tTMrO_i = cute.make_tensor(tTMrO_i_.iterator, tTMrO_i_layout)
|
||||
tTMEM_LOADtO_i = cute.make_tensor(tTMEM_LOADtO.iterator + i * corr_tile_size, tTMEM_LOADtO.layout)
|
||||
tTMEM_STOREtO_i = cute.make_tensor(tTMEM_STOREtO.iterator + i * corr_tile_size, tTMEM_STOREtO.layout)
|
||||
cute.copy(o_tiled_tmem_load, tTMEM_LOADtO_i, tTMrO_i)
|
||||
for j in cutlass.range(cute.size(tTMrO_i), vectorize=True):
|
||||
tTMrO_i[j] = tTMrO_i[j] * corr_scale
|
||||
cute.copy(o_tiled_tmem_store, tTMrO_i, tTMEM_STOREtO_i)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
vec_handle.release()
|
||||
o_handle.release()
|
||||
|
||||
# --- correction_epilog: final normalize O /= row_sum ---
|
||||
# Wait for final vector (row_sum, row_max) from softmax
|
||||
vec_handle = si_corr_cons.wait_and_advance()
|
||||
tTMEM_LOAD_VECrS = cute.make_rmem_tensor(tTMEM_LOAD_VECcS.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load_vec, tTMEM_LOAD_VECtS, tTMEM_LOAD_VECrS)
|
||||
cute.arch.fence_view_async_tmem_load()
|
||||
vec_handle.release()
|
||||
inv_row_sum = cutlass.Float32(1.0) / tTMEM_LOAD_VECrS[0]
|
||||
|
||||
# Wait for final O from MMA
|
||||
o_handle = mma_corr_cons.wait_and_advance()
|
||||
epi_handle = corr_epi_prod.acquire_and_advance()
|
||||
|
||||
# Final normalize O in TMEM
|
||||
tTMrO = cute.make_rmem_tensor((tTMEM_LOADcO.shape, o_col_tiles), self.pv_acc_dtype)
|
||||
for i in range(o_col_tiles):
|
||||
tTMrO_i_ = tTMrO[None, i]
|
||||
tTMrO_i_layout = cute.composition(tTMrO_i_.layout, cute.make_layout(tTMrO.shape[0]))
|
||||
tTMrO_i = cute.make_tensor(tTMrO_i_.iterator, tTMrO_i_layout)
|
||||
tTMEM_LOADtO_i = cute.make_tensor(tTMEM_LOADtO.iterator + i * corr_tile_size, tTMEM_LOADtO.layout)
|
||||
tTMEM_STOREtO_i = cute.make_tensor(tTMEM_STOREtO.iterator + i * corr_tile_size, tTMEM_STOREtO.layout)
|
||||
cute.copy(o_tiled_tmem_load, tTMEM_LOADtO_i, tTMrO_i)
|
||||
for j in cutlass.range(cute.size(tTMrO_i), vectorize=True):
|
||||
tTMrO_i[j] = tTMrO_i[j] * inv_row_sum
|
||||
cute.copy(o_tiled_tmem_store, tTMrO_i, tTMEM_STOREtO_i)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
o_handle.release()
|
||||
epi_handle.commit()
|
||||
|
||||
# --- EPILOGUE WARP (warp 10) - TMA store O ---
|
||||
# After correction normalizes O in TMEM, the epilogue reads O from TMEM,
|
||||
# writes to SMEM, then TMA stores from SMEM to GMEM.
|
||||
# For now, the softmax warps (which have tmem_ptr) handle the TMA store
|
||||
# after correction signals completion. This matches our working 6-warp code's
|
||||
# epilogue_tma_store pattern.
|
||||
# The epilogue warp (warp 10) just waits for the signal and does TMA store.
|
||||
# Since it doesn't have tmem_ptr, we need a different approach.
|
||||
# Simplest: let the softmax warps also do the TMA store after correction
|
||||
# signals O is ready. But softmax warps already exited...
|
||||
#
|
||||
# Alternative: the epilogue warp uses acc_pipe + epilogue_tma_store
|
||||
# which reads from TMEM directly.
|
||||
# For initial test: skip epilogue TMA store, just verify correction works.
|
||||
# Then add TMA store via a separate mechanism.
|
||||
#
|
||||
# Actually, looking at our working 6-warp code, the epilogue_tma_store
|
||||
# reads from tCtO_base which is a TMEM tensor at tmem_ptr + offset.
|
||||
# The epilogue warp doesn't have tmem_ptr. BUT it can create the same
|
||||
# tensor if it knows the address. The MMA warp has it from alloc_tmem.
|
||||
#
|
||||
# For the initial version, let the softmax warps do TMA store
|
||||
# (they have tmem_ptr) after waiting for correction to finish.
|
||||
# This is a temporary simplification.
|
||||
|
||||
if warp_idx == self.epilogue_warp_id:
|
||||
epi_handle = corr_epi_cons.wait_and_advance()
|
||||
epi_handle.release()
|
||||
|
||||
|
||||
def test():
|
||||
import math
|
||||
torch.manual_seed(42)
|
||||
for n in [128, 256, 384]:
|
||||
m, hd = 128, HEAD_DIM
|
||||
q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
k = torch.randn(n, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
v = torch.randn(n, hd, dtype=torch.bfloat16, device="cuda")
|
||||
v_kernel = v.unsqueeze(-1)
|
||||
c = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
qf = q[:,:,0].float(); kf = k[:,:,0].float()
|
||||
attn = qf @ kf.T / math.sqrt(hd)
|
||||
ref = torch.softmax(attn, dim=-1) @ v.float()
|
||||
mQ = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
|
||||
mK = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
|
||||
mV = ct.from_dlpack(v_kernel).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_kernel))
|
||||
mC = ct.from_dlpack(c).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c))
|
||||
stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
|
||||
kernel = FmhaV3Proper()
|
||||
print(f"n={n}: Compiling...", flush=True)
|
||||
compiled = cute.compile(kernel, mQ, mK, mV, mC, stream)
|
||||
print(f"n={n}: tmem: s0={kernel.tmem_s0_offset} p0={kernel.tmem_p0_offset} o0={kernel.tmem_o0_offset} vec={kernel.tmem_vec0_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()
|
||||
max_err = (out - ref).abs().max().item()
|
||||
print(f"FMHA proper n={n}: cosine {cos:.6f} max_err {max_err:.6f} {'PASS' if cos >= 0.999 else 'FAIL'}", flush=True)
|
||||
|
||||
if __name__ == "__main__":
|
||||
test()
|
||||
493
tests/unit/test_fmha_v3_scalar.py
Normal file
493
tests/unit/test_fmha_v3_scalar.py
Normal file
@@ -0,0 +1,493 @@
|
||||
"""
|
||||
FMHA v3 + Stage C: QK -> online softmax -> PV with KV-tile interleaving.
|
||||
Stage C: row_max, exp2, O rescale, row_sum, final normalization.
|
||||
FMHA pattern P store preserved from Stage B.
|
||||
"""
|
||||
import math
|
||||
import torch, cutlass, cutlass.cute as cute, cutlass.utils as utils, cutlass.pipeline as pipeline
|
||||
from cutlass.cute.nvgpu import cpasync, tcgen05
|
||||
from cutlass import Float32, BFloat16, Int32, Boolean, const_expr
|
||||
from cutlass.utils import LayoutEnum
|
||||
from cutlass.utils.tmem_allocator import find_tmem_tensor_col_offset
|
||||
import cuda.bindings.driver as cuda
|
||||
import cutlass.torch as ct
|
||||
|
||||
HEAD_DIM = 64
|
||||
|
||||
class FmhaV3Softmax:
|
||||
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; self.num_c_stage = 2
|
||||
|
||||
def _setup(self, qk_mma, pv_mma):
|
||||
qk_ik = cute.size(qk_mma.shape_mnk, mode=[2])
|
||||
self.qk_mma_tiler = (128, 128, qk_ik * 4)
|
||||
pv_ik = cute.size(pv_mma.shape_mnk, mode=[2])
|
||||
self.pv_mma_tiler = (128, HEAD_DIM, pv_ik * (128 // pv_ik))
|
||||
self.mma_tiler = self.qk_mma_tiler
|
||||
self.cluster_layout_vmnk = cute.tiled_divide(cute.make_layout((1,1,1)), (qk_mma.thr_id.shape,))
|
||||
self.cta_tile_shape_mnk = (self.qk_mma_tiler[0]//cute.size(qk_mma.thr_id.shape), HEAD_DIM, self.qk_mma_tiler[2])
|
||||
self.c_layout = LayoutEnum.ROW_MAJOR
|
||||
self.epi_tile = utils.sm100.compute_epilogue_tile_shape(self.cta_tile_shape_mnk, False, self.c_layout, self.o_dtype)
|
||||
self.num_ab_stage = 1; self.num_acc_stage = 1
|
||||
self.q_smem_s = utils.sm100.make_smem_layout_a(qk_mma, self.qk_mma_tiler, self.q_dtype, self.q_stage)
|
||||
self.k_smem_s = utils.sm100.make_smem_layout_b(qk_mma, self.qk_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.v_smem_s = utils.sm100.make_smem_layout_b(pv_mma, self.pv_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.c_smem_s = utils.sm100.make_smem_layout_epi(self.o_dtype, self.c_layout, self.epi_tile, 2)
|
||||
self.p_tmem_s = utils.sm100.make_smem_layout_a(pv_mma, self.pv_mma_tiler, self.q_dtype, 1)
|
||||
qk_thr = qk_mma.get_slice(0); qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
pv_thr = pv_mma.get_slice(0); pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
self.tmem_s0_offset = 0; self.tmem_p0_offset = 32
|
||||
# P occupies [tmem_p0_offset, tmem_p0_offset + p_cols_fp32)
|
||||
# S occupies [0, qk_mma_tiler[1]) = [0, 128)
|
||||
# O must NOT overlap P. Place O after max(S end, P end), aligned to 32.
|
||||
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
|
||||
p_end = self.tmem_p0_offset + p_cols_fp32 # 32 + 64 = 96
|
||||
s_cols = self.qk_mma_tiler[1] # 128
|
||||
o_after = max(s_cols, p_end) # 128
|
||||
self.tmem_o0_offset = ((o_after + 31) // 32) * 32
|
||||
self.tmem_vec_offset = 0 # Reuse S region for per-row inv_row_sum vector # align to 32 = 128
|
||||
self.tmem_vec_offset = 0 # Reuse S region (free after softmax loop)
|
||||
o_cols = find_tmem_tensor_col_offset(tOtO) # footprint of O
|
||||
total = self.tmem_o0_offset + o_cols
|
||||
# Must be multiple of 32 AND power of 2
|
||||
self.num_tmem_alloc_cols = 1
|
||||
while self.num_tmem_alloc_cols < total:
|
||||
self.num_tmem_alloc_cols *= 2
|
||||
cta = cute.size(qk_mma.thr_id.shape)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0))
|
||||
self.q_tx_bytes = cute.size_in_bytes(self.q_dtype, q_s) * cta
|
||||
self.kv_tx_bytes = cute.size_in_bytes(self.q_dtype, k_s) * cta
|
||||
self.scale_softmax_log2 = Float32(1.0 / math.sqrt(HEAD_DIM) * math.log2(math.e))
|
||||
|
||||
@cute.jit
|
||||
def __call__(self, q, k, v, c, stream):
|
||||
self.q_dtype = q.element_type; self.o_dtype = c.element_type; self.c_dtype = self.o_dtype
|
||||
self.a_major = LayoutEnum.from_tensor(q).mma_major_mode()
|
||||
self.b_major = LayoutEnum.from_tensor(k).mma_major_mode()
|
||||
# # s_k hardcoded # BROKEN in @cute.jit
|
||||
# FMHA-style V: reconstruct as (HEAD_DIM, s_k, 1) MN-major
|
||||
v_fmha = cute.make_tensor(
|
||||
v.iterator,
|
||||
cute.make_layout(
|
||||
(HEAD_DIM, 128, 1),
|
||||
stride=(1, HEAD_DIM, HEAD_DIM * 128),
|
||||
),
|
||||
)
|
||||
self.v_major = LayoutEnum.from_tensor(v_fmha).mma_major_mode()
|
||||
self.c_layout = LayoutEnum.from_tensor(c)
|
||||
qk_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, self.a_major, self.b_major, self.qk_acc_dtype, self.cta_group, (128,128), tcgen05.OperandSource.SMEM)
|
||||
pv_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, cute.nvgpu.OperandMajorMode.K, self.v_major, self.qk_acc_dtype, self.cta_group, (128,HEAD_DIM), tcgen05.OperandSource.TMEM)
|
||||
self._setup(qk_mma, pv_mma)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0)); v_s = cute.slice_(self.v_smem_s,(None,None,None,0))
|
||||
tma_q,mQ = cute.nvgpu.make_tiled_tma_atom_A(utils.sm100.cluster_shape_to_tma_atom_A(self.cluster_shape_mn,qk_mma.thr_id),q,q_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_k,mK = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,qk_mma.thr_id),k,k_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_v,mV = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,pv_mma.thr_id),v_fmha,v_s,self.pv_mma_tiler,pv_mma,self.cluster_layout_vmnk.shape)
|
||||
epi_s = cute.select(self.c_smem_s,mode=[0,1])
|
||||
tma_c,mC = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(),c,epi_s,self.epi_tile)
|
||||
self._kernel(qk_mma,pv_mma,tma_q,mQ,tma_k,mK,tma_v,mV,tma_c,mC,self.cluster_layout_vmnk,self.q_smem_s,self.k_smem_s,self.v_smem_s,self.p_tmem_s,self.c_smem_s,self.epi_tile).launch(grid=(1,1,1),block=[self.threads_per_cta,1,1],stream=stream)
|
||||
|
||||
@cute.kernel
|
||||
def _kernel(self, qk_mma, pv_mma, tma_q, mQ, tma_k, mK, tma_v, mV, tma_c, mC, cl_vmnk, q_smem_s, k_smem_s, v_smem_s, p_tmem_s, c_smem_s, epi_tile):
|
||||
warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx())
|
||||
tidx,_,_ = cute.arch.thread_idx()
|
||||
if warp_idx == self.tma_warp_id:
|
||||
cpasync.prefetch_descriptor(tma_q); cpasync.prefetch_descriptor(tma_k); cpasync.prefetch_descriptor(tma_v); cpasync.prefetch_descriptor(tma_c)
|
||||
|
||||
@cute.struct
|
||||
class SS:
|
||||
q_bar: cute.struct.MemRange[cutlass.Int64, self.q_stage*2]
|
||||
kv_bar: cute.struct.MemRange[cutlass.Int64, self.kv_stage*2]
|
||||
s_bar: cute.struct.MemRange[cutlass.Int64, 2]
|
||||
acc_bar: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage*2]
|
||||
tmem_dealloc: cutlass.Int64; holding: cutlass.Int32
|
||||
smem = utils.SmemAllocator(); st = smem.allocate(SS)
|
||||
|
||||
qp,qc = pipeline.PipelineTmaUmma.create(barrier_storage=st.q_bar.data_ptr(),num_stages=self.q_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.q_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
|
||||
kvp,kvc = pipeline.PipelineTmaUmma.create(barrier_storage=st.kv_bar.data_ptr(),num_stages=self.kv_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.kv_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
|
||||
s_prod,s_cons = pipeline.PipelineUmmaAsync.create(barrier_storage=st.s_bar.data_ptr(),num_stages=1,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32*len(self.epilogue_warp_id))).make_participants()
|
||||
softmax_done_bar = pipeline.NamedBarrier(barrier_id=3, num_threads=32 + 32*len(self.epilogue_warp_id))
|
||||
pv_done_bar = pipeline.NamedBarrier(barrier_id=4, 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)
|
||||
sV = smem.allocate_tensor(element_type=self.q_dtype,layout=v_smem_s.outer,byte_alignment=128,swizzle=v_smem_s.inner)
|
||||
sC = smem.allocate_tensor(element_type=self.o_dtype,layout=c_smem_s.outer,byte_alignment=128,swizzle=c_smem_s.inner)
|
||||
|
||||
gQ = cute.local_tile(mQ,cute.slice_(self.qk_mma_tiler,(None,0,None)),(None,None,None))
|
||||
gK = cute.local_tile(mK,cute.slice_(self.qk_mma_tiler,(0,None,None)),(None,None,None))
|
||||
gV = cute.local_tile(mV,cute.slice_(self.pv_mma_tiler,(0,None,None)),(None,None,None))
|
||||
gC = cute.local_tile(mC,cute.slice_(self.pv_mma_tiler,(None,None,0)),(None,None,None))
|
||||
n_kv_tiles = cute.size(gK, mode=[3])
|
||||
|
||||
qk_thr = qk_mma.get_slice(0); pv_thr = pv_mma.get_slice(0)
|
||||
tCgQ = qk_thr.partition_A(gQ); tCgK = qk_thr.partition_B(gK)
|
||||
tCgV = pv_thr.partition_B(gV); tCgC = pv_thr.partition_C(gC)
|
||||
a_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,0,None,0)).shape)
|
||||
tAsQ,tAgQ = cpasync.tma_partition(tma_q,0,a_lay,cute.group_modes(sQ,0,3),cute.group_modes(tCgQ,0,3))
|
||||
b_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,None,0,0)).shape)
|
||||
tBsK,tBgK = cpasync.tma_partition(tma_k,0,b_lay,cute.group_modes(sK,0,3),cute.group_modes(tCgK,0,3))
|
||||
tVsV,tVgV = cpasync.tma_partition(tma_v,0,b_lay,cute.group_modes(sV,0,3),cute.group_modes(tCgV,0,3))
|
||||
tAgQ = tAgQ[(None,0,None,0)]; tBgK = tBgK[(None,0,None,0)]; tVgV = tVgV[(None,0,None,0)]
|
||||
|
||||
tCrQ = qk_mma.make_fragment_A(sQ); tCrK = qk_mma.make_fragment_B(sK)
|
||||
tCrV = pv_mma.make_fragment_B(sV)
|
||||
|
||||
qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
tOtO0 = cute.make_tensor(tOtO.iterator + self.tmem_o0_offset, tOtO.layout)
|
||||
|
||||
# --- PV read view (for MMA only, NOT for softmax store) ---
|
||||
tP = cute.make_tensor(tStS.iterator, p_tmem_s.outer)
|
||||
tOrP_base = pv_thr.make_fragment_A(tP)
|
||||
tOrP = tOrP_base[(None,None,None,0)]
|
||||
tOrP0 = cute.make_tensor(
|
||||
tOrP.iterator + self.qk_acc_dtype.width // self.q_dtype.width * self.tmem_p0_offset,
|
||||
tOrP.layout)
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_as, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_as, self.num_acc_stage))
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# TMA LOAD
|
||||
if warp_idx == self.tma_warp_id:
|
||||
qp.reset(); qh = qp.acquire_and_advance()
|
||||
cute.copy(tma_q,tAgQ[(None,qh.count)],tAsQ[(None,qh.index)],tma_bar_ptr=qh.barrier)
|
||||
qp.tail()
|
||||
kvp.reset(); pk = kvp.try_acquire()
|
||||
for kt in cutlass.range(n_kv_tiles,unroll=1):
|
||||
kh = kvp.acquire_and_advance(pk)
|
||||
cute.copy(tma_k,tBgK[(None,kh.count)],tBsK[(None,kh.index)],tma_bar_ptr=kh.barrier)
|
||||
pk = cutlass.Boolean(1)
|
||||
vh = kvp.acquire_and_advance(pk)
|
||||
cute.copy(tma_v,tVgV[(None,vh.count)],tVsV[(None,vh.index)],tma_bar_ptr=vh.barrier)
|
||||
pk = cutlass.Boolean(1)
|
||||
kvp.tail()
|
||||
|
||||
# MMA
|
||||
if warp_idx == self.mma_warp_id:
|
||||
tmem.wait_for_alloc()
|
||||
qc.reset(); qh = qc.wait_and_advance(); qh.release()
|
||||
kvc.reset(); pk = kvc.try_wait()
|
||||
acc_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
|
||||
acc_pipe.producer_acquire(acc_st)
|
||||
for kt in range(n_kv_tiles):
|
||||
kh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
sh = s_prod.acquire_and_advance()
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
for kb in cutlass.range(cute.size(tCrQ,mode=[2]), unroll_full=True):
|
||||
cute.gemm(qk_mma, tStS0, tCrQ[(None,None,kb,0)], tCrK[(None,None,kb,kh.index)], tStS0)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
sh.commit(); kh.release()
|
||||
softmax_done_bar.arrive_and_wait()
|
||||
vh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, kt != 0)
|
||||
for kb in cutlass.range(cute.size(tOrP0,mode=[2]), unroll_full=True):
|
||||
cute.gemm(pv_mma, tOtO0, tOrP0[(None,None,kb)], tCrV[(None,None,kb,vh.index)], tOtO0)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
vh.release()
|
||||
pv_done_bar.arrive()
|
||||
acc_pipe.producer_commit(acc_st); acc_st.advance()
|
||||
acc_pipe.producer_tail(acc_st)
|
||||
|
||||
# ===================== EPILOGUE WARPS (STAGE C: ONLINE SOFTMAX) =====================
|
||||
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 (QK C-fragment) ---
|
||||
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 (QK C-fragment composition, FMHA pattern) ---
|
||||
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
|
||||
tStP_layout = cute.composition(tStS.layout, cute.make_layout((self.pv_mma_tiler[0], p_cols_fp32)))
|
||||
tStP0 = cute.make_tensor(tStS.iterator + self.tmem_p0_offset, tStP_layout)
|
||||
tmem_store_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
tiled_tmem_store = tcgen05.make_tmem_copy(tmem_store_atom, tStP0)
|
||||
thr_store = tiled_tmem_store.get_slice(sfw_idx)
|
||||
tTMEM_STOREtP = thr_store.partition_D(tStP0)
|
||||
tScP_layout = cute.composition(tScS.layout, cute.make_layout((self.pv_mma_tiler[0], p_cols_fp32)))
|
||||
tScP = cute.make_tensor(tScS.iterator, tScP_layout)
|
||||
tTMEM_STOREcP = thr_store.partition_S(tScP)
|
||||
|
||||
# --- Vector TMEM (per-row row_sum storage, FMHA pattern) ---
|
||||
# composition(tStS.layout, (128, 2)) = 2 FP32 columns per logical row
|
||||
# vec[0] = row_sum (final, after loop), vec[1] = unused
|
||||
# Reuses S TMEM region (offset 0), free after softmax loop writes
|
||||
|
||||
tStS_vec_layout = cute.composition(tStS.layout, cute.make_layout((128, 2)))
|
||||
tStS_vec = cute.make_tensor(tStS.iterator + self.tmem_vec_offset, tStS_vec_layout)
|
||||
tScS_vec_layout = cute.composition(tScS.layout, cute.make_layout((128, 2)))
|
||||
tScS_vec = cute.make_tensor(tScS.iterator, tScS_vec_layout)
|
||||
tmem_store_vec_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(2)), self.qk_acc_dtype)
|
||||
tiled_tmem_store_vec = tcgen05.make_tmem_copy(tmem_store_vec_atom, tStS_vec)
|
||||
thr_tmem_store_vec = tiled_tmem_store_vec.get_slice(sfw_idx)
|
||||
tTMEM_STORE_VECtS = thr_tmem_store_vec.partition_D(tStS_vec)
|
||||
tTMEM_STORE_VECcS = thr_tmem_store_vec.partition_S(tScS_vec)
|
||||
tmem_load_vec_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(2)), self.qk_acc_dtype)
|
||||
tiled_tmem_load_vec = tcgen05.make_tmem_copy(tmem_load_vec_atom, tStS_vec)
|
||||
thr_tmem_load_vec = tiled_tmem_load_vec.get_slice(sfw_idx)
|
||||
tTMEM_LOAD_VECtS = thr_tmem_load_vec.partition_S(tStS_vec)
|
||||
tTMEM_LOAD_VECcS = thr_tmem_load_vec.partition_D(tScS_vec)
|
||||
|
||||
# --- C6: O TMEM load/store for rescale (correction_rescale pattern) ---
|
||||
corr_tile_size = 16
|
||||
cO = cute.make_identity_tensor((self.pv_mma_tiler[0], self.pv_mma_tiler[1]))
|
||||
tOcO = pv_thr.partition_C(cO)
|
||||
o_tmem_load_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.qk_acc_dtype)
|
||||
o_tmem_store_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.qk_acc_dtype)
|
||||
tOtO_i_layout = cute.composition(tOtO0.layout, cute.make_layout((128, corr_tile_size)))
|
||||
tOcO_i_layout = cute.composition(tOcO.layout, cute.make_layout((128, corr_tile_size)))
|
||||
tOtO_i = cute.make_tensor(tOtO0.iterator, tOtO_i_layout)
|
||||
tOcO_i = cute.make_tensor(tOcO.iterator, tOcO_i_layout)
|
||||
o_tiled_tmem_load = tcgen05.make_tmem_copy(o_tmem_load_atom, tOtO_i)
|
||||
o_tiled_tmem_store = tcgen05.make_tmem_copy(o_tmem_store_atom, tOtO_i)
|
||||
o_thr_load = o_tiled_tmem_load.get_slice(sfw_idx)
|
||||
o_thr_store = o_tiled_tmem_store.get_slice(sfw_idx)
|
||||
tTMEM_LOADtO = o_thr_load.partition_S(tOtO_i)
|
||||
tTMEM_LOADcO = o_thr_load.partition_D(tOcO_i)
|
||||
tTMEM_STOREtO = o_thr_store.partition_D(tOtO_i)
|
||||
o_col_tiles = self.pv_mma_tiler[1] // corr_tile_size
|
||||
|
||||
# --- C2: Per-thread row state (persist across KV tiles) ---
|
||||
row_max = -cutlass.Float32.inf
|
||||
row_sum = cutlass.Float32(0.0)
|
||||
|
||||
# --- C3: QK scale = 1/sqrt(HEAD_DIM) * log2(e) for exp2 ---
|
||||
scale = self.scale_softmax_log2
|
||||
|
||||
# =============================================================
|
||||
# Per-KV-tile online softmax loop
|
||||
# =============================================================
|
||||
for kt in range(n_kv_tiles):
|
||||
si_handle = s_cons.wait_and_advance()
|
||||
|
||||
# Load S from TMEM (FP32, QK C-fragment layout)
|
||||
tTMEM_LOADrS = cute.make_rmem_tensor(tTMEM_LOADcS.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load, tTMEM_LOADtS, tTMEM_LOADrS)
|
||||
|
||||
# --- C4: Compute tile_max via .reduce(MAX) ---
|
||||
old_row_max = row_max
|
||||
row_max = tTMEM_LOADrS.load().reduce(cute.ReductionOp.MAX, row_max, 0)
|
||||
row_max_safe = row_max
|
||||
if row_max == -cutlass.Float32.inf:
|
||||
row_max_safe = cutlass.Float32(0.0)
|
||||
|
||||
# --- C5: Compute rescale factor ---
|
||||
acc_scale = cute.math.exp2(scale * (old_row_max - row_max_safe), fastmath=True)
|
||||
|
||||
# --- C6: Rescale O in TMEM (direct scalar approach) ---
|
||||
# Each softmax thread computes acc_scale from its QK row_max.
|
||||
# In the QK C-fragment with 128 threads and 128 rows, thread N = row N.
|
||||
# In the PV C-fragment with 128 threads and 128 rows, thread N = row N.
|
||||
# So acc_scale for thread N's QK row = acc_scale for thread N's PV row.
|
||||
# Use acc_scale directly (no vector indirection needed).
|
||||
if kt > 0:
|
||||
pv_done_bar.arrive_and_wait()
|
||||
|
||||
acc_scale_pv = acc_scale # Direct scalar
|
||||
|
||||
tTMrO = cute.make_rmem_tensor((tTMEM_LOADcO.shape, o_col_tiles), self.qk_acc_dtype)
|
||||
for i in range(o_col_tiles):
|
||||
tTMrO_i_ = tTMrO[None, i]
|
||||
tTMrO_i_layout = cute.composition(tTMrO_i_.layout, cute.make_layout(tTMrO.shape[0]))
|
||||
tTMrO_i = cute.make_tensor(tTMrO_i_.iterator, tTMrO_i_layout)
|
||||
tTMEM_LOADtO_i = cute.make_tensor(tTMEM_LOADtO.iterator + i * corr_tile_size, tTMEM_LOADtO.layout)
|
||||
tTMEM_STOREtO_i = cute.make_tensor(tTMEM_STOREtO.iterator + i * corr_tile_size, tTMEM_STOREtO.layout)
|
||||
cute.copy(o_tiled_tmem_load, tTMEM_LOADtO_i, tTMrO_i)
|
||||
for j in cutlass.range(cute.size(tTMrO_i), vectorize=True):
|
||||
tTMrO_i[j] = tTMrO_i[j] * acc_scale_pv
|
||||
cute.copy(o_tiled_tmem_store, tTMrO_i, tTMEM_STOREtO_i)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# # --- C7: Compute P = exp2((S - row_max_safe) * scale) ---
|
||||
minus_row_max_scale = (cutlass.Float32(0.0) - row_max_safe) * scale
|
||||
|
||||
# Register bridge (FMHA pattern: FP32 backing + BF16 view)
|
||||
rP_words = cute.make_rmem_tensor(tTMEM_STOREcP.shape, self.qk_acc_dtype)
|
||||
rP_bf16 = cute.make_tensor(cute.recast_ptr(rP_words.iterator, dtype=self.q_dtype), tTMEM_LOADrS.layout)
|
||||
|
||||
frg_cnt = 4
|
||||
frg_tile = cute.size(tTMEM_LOADrS) // frg_cnt
|
||||
tTMEM_LOADrS_frg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(frg_tile))
|
||||
rP_bf16_frg = cute.logical_divide(rP_bf16, cute.make_layout(frg_tile))
|
||||
|
||||
# Scale S, compute exp2, store through register bridge
|
||||
for j in range(frg_cnt):
|
||||
for k in cutlass.range(cute.size(tTMEM_LOADrS_frg, mode=[0]), vectorize=True):
|
||||
tTMEM_LOADrS_frg[k, j] = tTMEM_LOADrS_frg[k, j] * scale + minus_row_max_scale
|
||||
tTMEM_LOADrS_frg[k, j] = cute.math.exp2(tTMEM_LOADrS_frg[k, j], fastmath=True)
|
||||
s_vec = tTMEM_LOADrS_frg[None, j].load()
|
||||
rP_bf16_frg[None, j].store(s_vec.to(self.q_dtype))
|
||||
|
||||
# Store P to TMEM
|
||||
cute.copy(tiled_tmem_store, rP_words, tTMEM_STOREtP)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
si_handle.release()
|
||||
softmax_done_bar.arrive()
|
||||
|
||||
# --- C8: Row sum accumulation (CUTLASS FMHA packed f32x2 pattern) ---
|
||||
# P values still in tTMEM_LOADrS registers.
|
||||
# 4 accumulators for 4 reduction_unroll columns.
|
||||
local_row_sum_0 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_1 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_2 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_3 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
|
||||
reduction_unroll = 4
|
||||
rfrg_tile = cute.size(tTMEM_LOADrS) // reduction_unroll
|
||||
tTMEM_LOADrS_rfrg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(rfrg_tile))
|
||||
|
||||
for j in cutlass.range_constexpr(0, cute.size(tTMEM_LOADrS_rfrg, mode=[0]), 2):
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_0, (tTMEM_LOADrS_rfrg[j, 0], tTMEM_LOADrS_rfrg[j + 1, 0]))
|
||||
local_row_sum_1 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_1, (tTMEM_LOADrS_rfrg[j, 1], tTMEM_LOADrS_rfrg[j + 1, 1]))
|
||||
local_row_sum_2 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_2, (tTMEM_LOADrS_rfrg[j, 2], tTMEM_LOADrS_rfrg[j + 1, 2]))
|
||||
local_row_sum_3 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_3, (tTMEM_LOADrS_rfrg[j, 3], tTMEM_LOADrS_rfrg[j + 1, 3]))
|
||||
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(local_row_sum_0, local_row_sum_1)
|
||||
local_row_sum_2 = cute.arch.add_packed_f32x2(local_row_sum_2, local_row_sum_3)
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(local_row_sum_0, local_row_sum_2)
|
||||
tile_sum = local_row_sum_0[0] + local_row_sum_0[1]
|
||||
|
||||
row_sum = row_sum + tile_sum
|
||||
|
||||
# --- C9: Final normalization via O TMEM rescale ---
|
||||
pv_done_bar.arrive_and_wait()
|
||||
|
||||
# Compute inv_row_sum from P in TMEM using PV partition.
|
||||
# P was stored by softmax loop into TMEM at offset tmem_p0_offset.
|
||||
# PV partition maps thread N to PV row N, so reading P via PV partition
|
||||
# gives the correct per-row P values to sum.
|
||||
# This avoids the QK→PV row mapping mismatch (QK: N->N//4, PV: N->N).
|
||||
|
||||
# P is stored as BF16 in TMEM at tmem_p0_offset.
|
||||
# We need to read it via PV TMEM load and sum the values.
|
||||
# P has shape (128, HEAD_DIM//2) in FP32 columns (64 BF16 = 32 FP32 cols).
|
||||
# Use the P TMEM load partition (PV A-fragment read).
|
||||
|
||||
# Actually, P was stored via QK C-fragment store (St32x32bOp Repetition(32)).
|
||||
# To read it via PV partition, we need a PV-partitioned load from the P region.
|
||||
# Let's use the same o_tiled_tmem_load but pointed at P's TMEM offset.
|
||||
|
||||
# P occupies TMEM columns [tmem_p0_offset, tmem_p0_offset + p_cols_fp32)
|
||||
# In the PV C-fragment, P is the A-fragment. We can use tOrP0's layout.
|
||||
# tOrP0 was set up with offset for PV MMA read.
|
||||
|
||||
# Simpler: sum O across columns to get unnormalized row sum, then normalize.
|
||||
# For V=identity, O = P@V = sum(P per row). So O.sum(dim=-1) = row_sum.
|
||||
# For arbitrary V, O = P@V. O.sum(dim=-1) = sum_j(P@V)[j] = sum_j(sum_i P[i]*V[i,j])
|
||||
# This is NOT sum(P). So this trick only works for V=identity.
|
||||
|
||||
# Correct approach: read P from TMEM, sum it per PV row.
|
||||
# P is at TMEM offset tmem_p0_offset, stored as BF16 with St32x32bOp.
|
||||
# P shape in TMEM: 128 rows x (HEAD_DIM BF16 = 32 FP32 cols)
|
||||
# We can read P using Ld32x32bOp(Repetition(corr_tile_size)) via PV O-partition.
|
||||
|
||||
# Use PV O TMEM load to read from P region instead of O region
|
||||
p_col_tiles = p_cols_fp32 // corr_tile_size # 32 // 16 = 2
|
||||
pv_row_sum = cutlass.Float32(0.0)
|
||||
for i in range(p_col_tiles):
|
||||
# Read P tile from TMEM at P offset (not O offset)
|
||||
tTMEM_LOADtP_i = cute.make_tensor(
|
||||
tTMEM_LOADtO.iterator + (self.tmem_p0_offset - self.tmem_o0_offset) + i * corr_tile_size,
|
||||
tTMEM_LOADtO.layout)
|
||||
tTMrP_i = cute.make_rmem_tensor(tTMEM_LOADcO.shape, self.qk_acc_dtype)
|
||||
cute.copy(o_tiled_tmem_load, tTMEM_LOADtP_i, tTMrP_i)
|
||||
# Use .reduce(SUM) instead of scalar accumulation (vectorizer can't handle scalar in vectorized loop)
|
||||
tile_p_sum = tTMrP_i.load().reduce(cute.ReductionOp.ADD, cutlass.Float32(0.0), 0)
|
||||
pv_row_sum = pv_row_sum + tile_p_sum
|
||||
|
||||
inv_row_sum = cutlass.Float32(1.0) / pv_row_sum
|
||||
|
||||
# Normalize O in TMEM using PV-correct inv_row_sum
|
||||
tTMrO_final = cute.make_rmem_tensor((tTMEM_LOADcO.shape, o_col_tiles), self.qk_acc_dtype)
|
||||
for i in range(o_col_tiles):
|
||||
tTMrO_i_ = tTMrO_final[None, i]
|
||||
tTMrO_i_layout = cute.composition(tTMrO_i_.layout, cute.make_layout(tTMrO_final.shape[0]))
|
||||
tTMrO_i = cute.make_tensor(tTMrO_i_.iterator, tTMrO_i_layout)
|
||||
tTMEM_LOADtO_i = cute.make_tensor(
|
||||
tTMEM_LOADtO.iterator + i * corr_tile_size, tTMEM_LOADtO.layout)
|
||||
tTMEM_STOREtO_i = cute.make_tensor(
|
||||
tTMEM_STOREtO.iterator + i * corr_tile_size, tTMEM_STOREtO.layout)
|
||||
cute.copy(o_tiled_tmem_load, tTMEM_LOADtO_i, tTMrO_i)
|
||||
for j in cutlass.range(cute.size(tTMrO_i), vectorize=True):
|
||||
tTMrO_i[j] = tTMrO_i[j] * inv_row_sum
|
||||
cute.copy(o_tiled_tmem_store, tTMrO_i, tTMEM_STOREtO_i)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# Now O in TMEM is normalized. Use standard epilogue_tma_store with identity.
|
||||
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():
|
||||
import math
|
||||
torch.manual_seed(42)
|
||||
for n in [128, 256, 384]:
|
||||
m, hd = 128, HEAD_DIM
|
||||
q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
k = torch.randn(n, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
v = torch.randn(n, hd, dtype=torch.bfloat16, device="cuda")
|
||||
v_kernel = v.unsqueeze(-1)
|
||||
c = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
qf = q[:,:,0].float(); kf = k[:,:,0].float()
|
||||
attn = qf @ kf.T / math.sqrt(hd)
|
||||
ref = torch.softmax(attn, dim=-1) @ v.float()
|
||||
mQ = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
|
||||
mK = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
|
||||
mV = ct.from_dlpack(v_kernel).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_kernel))
|
||||
mC = ct.from_dlpack(c).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c))
|
||||
stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
|
||||
kernel = FmhaV3Softmax()
|
||||
print(f"n={n}: Compiling...", flush=True)
|
||||
compiled = cute.compile(kernel, mQ, mK, mV, mC, stream)
|
||||
print(f"n={n}: tmem: s0={kernel.tmem_s0_offset} p0={kernel.tmem_p0_offset} o0={kernel.tmem_o0_offset} vec={kernel.tmem_vec_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()
|
||||
max_err = (out - ref).abs().max().item()
|
||||
print(f"FMHA softmax n={n}: cosine {cos:.6f} max_err {max_err:.6f} {'PASS' if cos >= 0.999 else 'FAIL'}", flush=True)
|
||||
|
||||
if __name__ == "__main__":
|
||||
test()
|
||||
|
||||
|
||||
518
tests/unit/test_fmha_v3_shapes.py
Normal file
518
tests/unit/test_fmha_v3_shapes.py
Normal file
@@ -0,0 +1,518 @@
|
||||
"""
|
||||
FMHA v3 + Stage C: QK -> online softmax -> PV with KV-tile interleaving.
|
||||
Stage C: row_max, exp2, O rescale, row_sum, final normalization.
|
||||
FMHA pattern P store preserved from Stage B.
|
||||
"""
|
||||
import math
|
||||
import torch, cutlass, cutlass.cute as cute, cutlass.utils as utils, cutlass.pipeline as pipeline
|
||||
from cutlass.cute.nvgpu import cpasync, tcgen05
|
||||
from cutlass import Float32, BFloat16, Int32, Boolean, const_expr
|
||||
from cutlass.utils import LayoutEnum
|
||||
from cutlass.utils.tmem_allocator import find_tmem_tensor_col_offset
|
||||
import cuda.bindings.driver as cuda
|
||||
import cutlass.torch as ct
|
||||
|
||||
HEAD_DIM = 64
|
||||
|
||||
class FmhaV3Softmax:
|
||||
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; self.num_c_stage = 2
|
||||
|
||||
def _setup(self, qk_mma, pv_mma):
|
||||
qk_ik = cute.size(qk_mma.shape_mnk, mode=[2])
|
||||
self.qk_mma_tiler = (128, 128, qk_ik * 4)
|
||||
pv_ik = cute.size(pv_mma.shape_mnk, mode=[2])
|
||||
self.pv_mma_tiler = (128, HEAD_DIM, pv_ik * (128 // pv_ik))
|
||||
self.mma_tiler = self.qk_mma_tiler
|
||||
self.cluster_layout_vmnk = cute.tiled_divide(cute.make_layout((1,1,1)), (qk_mma.thr_id.shape,))
|
||||
self.cta_tile_shape_mnk = (self.qk_mma_tiler[0]//cute.size(qk_mma.thr_id.shape), HEAD_DIM, self.qk_mma_tiler[2])
|
||||
self.c_layout = LayoutEnum.ROW_MAJOR
|
||||
self.epi_tile = utils.sm100.compute_epilogue_tile_shape(self.cta_tile_shape_mnk, False, self.c_layout, self.o_dtype)
|
||||
self.num_ab_stage = 1; self.num_acc_stage = 1
|
||||
self.q_smem_s = utils.sm100.make_smem_layout_a(qk_mma, self.qk_mma_tiler, self.q_dtype, self.q_stage)
|
||||
self.k_smem_s = utils.sm100.make_smem_layout_b(qk_mma, self.qk_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.v_smem_s = utils.sm100.make_smem_layout_b(pv_mma, self.pv_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.c_smem_s = utils.sm100.make_smem_layout_epi(self.o_dtype, self.c_layout, self.epi_tile, 2)
|
||||
self.p_tmem_s = utils.sm100.make_smem_layout_a(pv_mma, self.pv_mma_tiler, self.q_dtype, 1)
|
||||
qk_thr = qk_mma.get_slice(0); qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
pv_thr = pv_mma.get_slice(0); pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
self.tmem_s0_offset = 0; self.tmem_p0_offset = 32
|
||||
# P occupies [tmem_p0_offset, tmem_p0_offset + p_cols_fp32)
|
||||
# S occupies [0, qk_mma_tiler[1]) = [0, 128)
|
||||
# O must NOT overlap P. Place O after max(S end, P end), aligned to 32.
|
||||
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
|
||||
p_end = self.tmem_p0_offset + p_cols_fp32 # 32 + 64 = 96
|
||||
s_cols = self.qk_mma_tiler[1] # 128
|
||||
o_after = max(s_cols, p_end) # 128
|
||||
self.tmem_o0_offset = ((o_after + 31) // 32) * 32
|
||||
self.tmem_vec_offset = 0 # Reuse S region for per-row inv_row_sum vector # align to 32 = 128
|
||||
self.tmem_vec_offset = 0 # Reuse S region (free after softmax loop)
|
||||
o_cols = find_tmem_tensor_col_offset(tOtO) # footprint of O
|
||||
total = self.tmem_o0_offset + o_cols
|
||||
# Must be multiple of 32 AND power of 2
|
||||
self.num_tmem_alloc_cols = 1
|
||||
while self.num_tmem_alloc_cols < total:
|
||||
self.num_tmem_alloc_cols *= 2
|
||||
cta = cute.size(qk_mma.thr_id.shape)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0))
|
||||
self.q_tx_bytes = cute.size_in_bytes(self.q_dtype, q_s) * cta
|
||||
self.kv_tx_bytes = cute.size_in_bytes(self.q_dtype, k_s) * cta
|
||||
self.scale_softmax_log2 = Float32(1.0 / math.sqrt(HEAD_DIM) * math.log2(math.e))
|
||||
|
||||
@cute.jit
|
||||
def __call__(self, q, k, v, c, stream):
|
||||
self.q_dtype = q.element_type; self.o_dtype = c.element_type; self.c_dtype = self.o_dtype
|
||||
self.a_major = LayoutEnum.from_tensor(q).mma_major_mode()
|
||||
self.b_major = LayoutEnum.from_tensor(k).mma_major_mode()
|
||||
# # s_k hardcoded # BROKEN in @cute.jit
|
||||
# FMHA-style V: reconstruct as (HEAD_DIM, s_k, 1) MN-major
|
||||
v_fmha = cute.make_tensor(
|
||||
v.iterator,
|
||||
cute.make_layout(
|
||||
(HEAD_DIM, 128, 1),
|
||||
stride=(1, HEAD_DIM, HEAD_DIM * 128),
|
||||
),
|
||||
)
|
||||
self.v_major = LayoutEnum.from_tensor(v_fmha).mma_major_mode()
|
||||
self.c_layout = LayoutEnum.from_tensor(c)
|
||||
qk_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, self.a_major, self.b_major, self.qk_acc_dtype, self.cta_group, (128,128), tcgen05.OperandSource.SMEM)
|
||||
pv_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, cute.nvgpu.OperandMajorMode.K, self.v_major, self.qk_acc_dtype, self.cta_group, (128,HEAD_DIM), tcgen05.OperandSource.TMEM)
|
||||
self._setup(qk_mma, pv_mma)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0)); v_s = cute.slice_(self.v_smem_s,(None,None,None,0))
|
||||
tma_q,mQ = cute.nvgpu.make_tiled_tma_atom_A(utils.sm100.cluster_shape_to_tma_atom_A(self.cluster_shape_mn,qk_mma.thr_id),q,q_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_k,mK = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,qk_mma.thr_id),k,k_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_v,mV = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,pv_mma.thr_id),v_fmha,v_s,self.pv_mma_tiler,pv_mma,self.cluster_layout_vmnk.shape)
|
||||
epi_s = cute.select(self.c_smem_s,mode=[0,1])
|
||||
tma_c,mC = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(),c,epi_s,self.epi_tile)
|
||||
self._kernel(qk_mma,pv_mma,tma_q,mQ,tma_k,mK,tma_v,mV,tma_c,mC,self.cluster_layout_vmnk,self.q_smem_s,self.k_smem_s,self.v_smem_s,self.p_tmem_s,self.c_smem_s,self.epi_tile).launch(grid=(1,1,1),block=[self.threads_per_cta,1,1],stream=stream)
|
||||
|
||||
@cute.kernel
|
||||
def _kernel(self, qk_mma, pv_mma, tma_q, mQ, tma_k, mK, tma_v, mV, tma_c, mC, cl_vmnk, q_smem_s, k_smem_s, v_smem_s, p_tmem_s, c_smem_s, epi_tile):
|
||||
warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx())
|
||||
tidx,_,_ = cute.arch.thread_idx()
|
||||
if warp_idx == self.tma_warp_id:
|
||||
cpasync.prefetch_descriptor(tma_q); cpasync.prefetch_descriptor(tma_k); cpasync.prefetch_descriptor(tma_v); cpasync.prefetch_descriptor(tma_c)
|
||||
|
||||
@cute.struct
|
||||
class SS:
|
||||
q_bar: cute.struct.MemRange[cutlass.Int64, self.q_stage*2]
|
||||
kv_bar: cute.struct.MemRange[cutlass.Int64, self.kv_stage*2]
|
||||
s_bar: cute.struct.MemRange[cutlass.Int64, 2]
|
||||
acc_bar: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage*2]
|
||||
tmem_dealloc: cutlass.Int64; holding: cutlass.Int32
|
||||
smem = utils.SmemAllocator(); st = smem.allocate(SS)
|
||||
|
||||
qp,qc = pipeline.PipelineTmaUmma.create(barrier_storage=st.q_bar.data_ptr(),num_stages=self.q_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.q_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
|
||||
kvp,kvc = pipeline.PipelineTmaUmma.create(barrier_storage=st.kv_bar.data_ptr(),num_stages=self.kv_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.kv_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
|
||||
s_prod,s_cons = pipeline.PipelineUmmaAsync.create(barrier_storage=st.s_bar.data_ptr(),num_stages=1,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32*len(self.epilogue_warp_id))).make_participants()
|
||||
softmax_done_bar = pipeline.NamedBarrier(barrier_id=3, num_threads=32 + 32*len(self.epilogue_warp_id))
|
||||
pv_done_bar = pipeline.NamedBarrier(barrier_id=4, 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)
|
||||
sV = smem.allocate_tensor(element_type=self.q_dtype,layout=v_smem_s.outer,byte_alignment=128,swizzle=v_smem_s.inner)
|
||||
sC = smem.allocate_tensor(element_type=self.o_dtype,layout=c_smem_s.outer,byte_alignment=128,swizzle=c_smem_s.inner)
|
||||
|
||||
gQ = cute.local_tile(mQ,cute.slice_(self.qk_mma_tiler,(None,0,None)),(None,None,None))
|
||||
gK = cute.local_tile(mK,cute.slice_(self.qk_mma_tiler,(0,None,None)),(None,None,None))
|
||||
gV = cute.local_tile(mV,cute.slice_(self.pv_mma_tiler,(0,None,None)),(None,None,None))
|
||||
gC = cute.local_tile(mC,cute.slice_(self.pv_mma_tiler,(None,None,0)),(None,None,None))
|
||||
n_kv_tiles = cute.size(gK, mode=[3])
|
||||
|
||||
qk_thr = qk_mma.get_slice(0); pv_thr = pv_mma.get_slice(0)
|
||||
tCgQ = qk_thr.partition_A(gQ); tCgK = qk_thr.partition_B(gK)
|
||||
tCgV = pv_thr.partition_B(gV); tCgC = pv_thr.partition_C(gC)
|
||||
a_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,0,None,0)).shape)
|
||||
tAsQ,tAgQ = cpasync.tma_partition(tma_q,0,a_lay,cute.group_modes(sQ,0,3),cute.group_modes(tCgQ,0,3))
|
||||
b_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,None,0,0)).shape)
|
||||
tBsK,tBgK = cpasync.tma_partition(tma_k,0,b_lay,cute.group_modes(sK,0,3),cute.group_modes(tCgK,0,3))
|
||||
tVsV,tVgV = cpasync.tma_partition(tma_v,0,b_lay,cute.group_modes(sV,0,3),cute.group_modes(tCgV,0,3))
|
||||
tAgQ = tAgQ[(None,0,None,0)]; tBgK = tBgK[(None,0,None,0)]; tVgV = tVgV[(None,0,None,0)]
|
||||
|
||||
tCrQ = qk_mma.make_fragment_A(sQ); tCrK = qk_mma.make_fragment_B(sK)
|
||||
tCrV = pv_mma.make_fragment_B(sV)
|
||||
|
||||
qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
tOtO0 = cute.make_tensor(tOtO.iterator + self.tmem_o0_offset, tOtO.layout)
|
||||
|
||||
# --- PV read view (for MMA only, NOT for softmax store) ---
|
||||
tP = cute.make_tensor(tStS.iterator, p_tmem_s.outer)
|
||||
tOrP_base = pv_thr.make_fragment_A(tP)
|
||||
tOrP = tOrP_base[(None,None,None,0)]
|
||||
tOrP0 = cute.make_tensor(
|
||||
tOrP.iterator + self.qk_acc_dtype.width // self.q_dtype.width * self.tmem_p0_offset,
|
||||
tOrP.layout)
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_as, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_as, self.num_acc_stage))
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# TMA LOAD
|
||||
if warp_idx == self.tma_warp_id:
|
||||
qp.reset(); qh = qp.acquire_and_advance()
|
||||
cute.copy(tma_q,tAgQ[(None,qh.count)],tAsQ[(None,qh.index)],tma_bar_ptr=qh.barrier)
|
||||
qp.tail()
|
||||
kvp.reset(); pk = kvp.try_acquire()
|
||||
for kt in cutlass.range(n_kv_tiles,unroll=1):
|
||||
kh = kvp.acquire_and_advance(pk)
|
||||
cute.copy(tma_k,tBgK[(None,kh.count)],tBsK[(None,kh.index)],tma_bar_ptr=kh.barrier)
|
||||
pk = cutlass.Boolean(1)
|
||||
vh = kvp.acquire_and_advance(pk)
|
||||
cute.copy(tma_v,tVgV[(None,vh.count)],tVsV[(None,vh.index)],tma_bar_ptr=vh.barrier)
|
||||
pk = cutlass.Boolean(1)
|
||||
kvp.tail()
|
||||
|
||||
# MMA
|
||||
if warp_idx == self.mma_warp_id:
|
||||
tmem.wait_for_alloc()
|
||||
qc.reset(); qh = qc.wait_and_advance(); qh.release()
|
||||
kvc.reset(); pk = kvc.try_wait()
|
||||
acc_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
|
||||
acc_pipe.producer_acquire(acc_st)
|
||||
for kt in range(n_kv_tiles):
|
||||
kh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
sh = s_prod.acquire_and_advance()
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
for kb in cutlass.range(cute.size(tCrQ,mode=[2]), unroll_full=True):
|
||||
cute.gemm(qk_mma, tStS0, tCrQ[(None,None,kb,0)], tCrK[(None,None,kb,kh.index)], tStS0)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
sh.commit(); kh.release()
|
||||
softmax_done_bar.arrive_and_wait()
|
||||
vh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, kt != 0)
|
||||
for kb in cutlass.range(cute.size(tOrP0,mode=[2]), unroll_full=True):
|
||||
cute.gemm(pv_mma, tOtO0, tOrP0[(None,None,kb)], tCrV[(None,None,kb,vh.index)], tOtO0)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
vh.release()
|
||||
pv_done_bar.arrive()
|
||||
acc_pipe.producer_commit(acc_st); acc_st.advance()
|
||||
acc_pipe.producer_tail(acc_st)
|
||||
|
||||
# ===================== EPILOGUE WARPS (STAGE C: ONLINE SOFTMAX) =====================
|
||||
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)) # DEBUG: print fragment shapes (only from thread 0)
|
||||
if sfw_idx == 0:
|
||||
print(f"DEBUG sfw_idx=0: tTMEM_LOADcS shape={tTMEM_LOADcS.shape} size={cute.size(tTMEM_LOADcS)}")
|
||||
print(f"DEBUG sfw_idx=0: tScS shape={tScS.shape} size={cute.size(tScS)}")
|
||||
# Check which rows thread 0 handles
|
||||
for i in range(min(4, cute.size(tScS, mode=[0]))):
|
||||
row_col = tScS[i][0]
|
||||
print(f" tScS[{i}][0] = {row_col}")
|
||||
|
||||
# --- S load (QK C-fragment) ---
|
||||
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 (QK C-fragment composition, FMHA pattern) ---
|
||||
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
|
||||
tStP_layout = cute.composition(tStS.layout, cute.make_layout((self.pv_mma_tiler[0], p_cols_fp32)))
|
||||
tStP0 = cute.make_tensor(tStS.iterator + self.tmem_p0_offset, tStP_layout)
|
||||
tmem_store_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
tiled_tmem_store = tcgen05.make_tmem_copy(tmem_store_atom, tStP0)
|
||||
thr_store = tiled_tmem_store.get_slice(sfw_idx)
|
||||
tTMEM_STOREtP = thr_store.partition_D(tStP0)
|
||||
tScP_layout = cute.composition(tScS.layout, cute.make_layout((self.pv_mma_tiler[0], p_cols_fp32)))
|
||||
tScP = cute.make_tensor(tScS.iterator, tScP_layout)
|
||||
tTMEM_STOREcP = thr_store.partition_S(tScP)
|
||||
|
||||
# --- Vector TMEM (per-row row_sum storage, FMHA pattern) ---
|
||||
# composition(tStS.layout, (128, 2)) = 2 FP32 columns per logical row
|
||||
# vec[0] = row_sum (final, after loop), vec[1] = unused
|
||||
# Reuses S TMEM region (offset 0), free after softmax loop writes
|
||||
|
||||
tStS_vec_layout = cute.composition(tStS.layout, cute.make_layout((128, 2)))
|
||||
tStS_vec = cute.make_tensor(tStS.iterator + self.tmem_vec_offset, tStS_vec_layout)
|
||||
tScS_vec_layout = cute.composition(tScS.layout, cute.make_layout((128, 2)))
|
||||
tScS_vec = cute.make_tensor(tScS.iterator, tScS_vec_layout)
|
||||
tmem_store_vec_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(2)), self.qk_acc_dtype)
|
||||
tiled_tmem_store_vec = tcgen05.make_tmem_copy(tmem_store_vec_atom, tStS_vec)
|
||||
thr_tmem_store_vec = tiled_tmem_store_vec.get_slice(sfw_idx)
|
||||
tTMEM_STORE_VECtS = thr_tmem_store_vec.partition_D(tStS_vec)
|
||||
tTMEM_STORE_VECcS = thr_tmem_store_vec.partition_S(tScS_vec)
|
||||
tmem_load_vec_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(2)), self.qk_acc_dtype)
|
||||
tiled_tmem_load_vec = tcgen05.make_tmem_copy(tmem_load_vec_atom, tStS_vec)
|
||||
thr_tmem_load_vec = tiled_tmem_load_vec.get_slice(sfw_idx)
|
||||
tTMEM_LOAD_VECtS = thr_tmem_load_vec.partition_S(tStS_vec)
|
||||
tTMEM_LOAD_VECcS = thr_tmem_load_vec.partition_D(tScS_vec)
|
||||
|
||||
# --- C6: O TMEM load/store for rescale (correction_rescale pattern) ---
|
||||
corr_tile_size = 16
|
||||
cO = cute.make_identity_tensor((self.pv_mma_tiler[0], self.pv_mma_tiler[1]))
|
||||
tOcO = pv_thr.partition_C(cO)
|
||||
o_tmem_load_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.qk_acc_dtype)
|
||||
o_tmem_store_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.qk_acc_dtype)
|
||||
tOtO_i_layout = cute.composition(tOtO0.layout, cute.make_layout((128, corr_tile_size)))
|
||||
tOcO_i_layout = cute.composition(tOcO.layout, cute.make_layout((128, corr_tile_size)))
|
||||
tOtO_i = cute.make_tensor(tOtO0.iterator, tOtO_i_layout)
|
||||
tOcO_i = cute.make_tensor(tOcO.iterator, tOcO_i_layout)
|
||||
o_tiled_tmem_load = tcgen05.make_tmem_copy(o_tmem_load_atom, tOtO_i)
|
||||
o_tiled_tmem_store = tcgen05.make_tmem_copy(o_tmem_store_atom, tOtO_i)
|
||||
o_thr_load = o_tiled_tmem_load.get_slice(sfw_idx)
|
||||
o_thr_store = o_tiled_tmem_store.get_slice(sfw_idx)
|
||||
tTMEM_LOADtO = o_thr_load.partition_S(tOtO_i)
|
||||
tTMEM_LOADcO = o_thr_load.partition_D(tOcO_i)
|
||||
tTMEM_STOREtO = o_thr_store.partition_D(tOtO_i)
|
||||
o_col_tiles = self.pv_mma_tiler[1] // corr_tile_size
|
||||
|
||||
# --- C2: Per-thread row state (persist across KV tiles) ---
|
||||
row_max = -cutlass.Float32.inf
|
||||
row_sum = cutlass.Float32(0.0)
|
||||
|
||||
# --- C3: QK scale = 1/sqrt(HEAD_DIM) * log2(e) for exp2 ---
|
||||
scale = self.scale_softmax_log2
|
||||
|
||||
# =============================================================
|
||||
# Per-KV-tile online softmax loop
|
||||
# =============================================================
|
||||
for kt in range(n_kv_tiles):
|
||||
si_handle = s_cons.wait_and_advance()
|
||||
|
||||
# Load S from TMEM (FP32, QK C-fragment layout)
|
||||
tTMEM_LOADrS = cute.make_rmem_tensor(tTMEM_LOADcS.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load, tTMEM_LOADtS, tTMEM_LOADrS)
|
||||
|
||||
# --- C4: Compute tile_max via .reduce(MAX) ---
|
||||
old_row_max = row_max
|
||||
row_max = tTMEM_LOADrS.load().reduce(cute.ReductionOp.MAX, row_max, 0)
|
||||
row_max_safe = row_max
|
||||
if row_max == -cutlass.Float32.inf:
|
||||
row_max_safe = cutlass.Float32(0.0)
|
||||
|
||||
# --- C5: Compute rescale factor ---
|
||||
acc_scale = cute.math.exp2(scale * (old_row_max - row_max_safe), fastmath=True)
|
||||
|
||||
# --- C6: Rescale O in TMEM (load O, multiply by acc_scale, store O) ---
|
||||
# acc_scale belongs to QK row (N//4), but O rows are in PV partition (N).
|
||||
# Store acc_scale to vector by QK row, read by PV row.
|
||||
if kt > 0:
|
||||
pv_done_bar.arrive_and_wait()
|
||||
|
||||
# Store acc_scale to vector indexed by QK logical row
|
||||
qk_row_c6 = tTMEM_LOADcS[0][0]
|
||||
thr_vs_c6 = tiled_tmem_store_vec.get_slice(qk_row_c6)
|
||||
tVStore_c6 = thr_vs_c6.partition_D(tStS_vec)
|
||||
tVStoreSrc_c6 = thr_vs_c6.partition_S(tScS_vec)
|
||||
tVStoreRmem_c6 = cute.make_rmem_tensor(tVStoreSrc_c6.shape, self.qk_acc_dtype)
|
||||
tVStoreRmem_c6[0] = acc_scale
|
||||
cute.copy(tiled_tmem_store_vec, tVStoreRmem_c6, tVStore_c6)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# Read acc_scale from vector indexed by PV logical row
|
||||
pv_row_c6 = tTMEM_LOADcO[0][0]
|
||||
thr_vl_c6 = tiled_tmem_load_vec.get_slice(pv_row_c6)
|
||||
tVLoad_c6 = thr_vl_c6.partition_S(tStS_vec)
|
||||
tVLoadDst_c6 = thr_vl_c6.partition_D(tScS_vec)
|
||||
tVLoadRmem_c6 = cute.make_rmem_tensor(tVLoadDst_c6.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load_vec, tVLoad_c6, tVLoadRmem_c6)
|
||||
cute.arch.fence_view_async_tmem_load()
|
||||
acc_scale_pv = tVLoadRmem_c6[0]
|
||||
|
||||
tTMrO = cute.make_rmem_tensor((tTMEM_LOADcO.shape, o_col_tiles), self.qk_acc_dtype)
|
||||
for i in range(o_col_tiles):
|
||||
tTMrO_i_ = tTMrO[None, i]
|
||||
tTMrO_i_layout = cute.composition(tTMrO_i_.layout, cute.make_layout(tTMrO.shape[0]))
|
||||
tTMrO_i = cute.make_tensor(tTMrO_i_.iterator, tTMrO_i_layout)
|
||||
tTMEM_LOADtO_i = cute.make_tensor(tTMEM_LOADtO.iterator + i * corr_tile_size, tTMEM_LOADtO.layout)
|
||||
tTMEM_STOREtO_i = cute.make_tensor(tTMEM_STOREtO.iterator + i * corr_tile_size, tTMEM_STOREtO.layout)
|
||||
cute.copy(o_tiled_tmem_load, tTMEM_LOADtO_i, tTMrO_i)
|
||||
for j in cutlass.range(cute.size(tTMrO_i), vectorize=True):
|
||||
tTMrO_i[j] = tTMrO_i[j] * acc_scale_pv
|
||||
cute.copy(o_tiled_tmem_store, tTMrO_i, tTMEM_STOREtO_i)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# Rescale row_sum
|
||||
row_sum = row_sum * acc_scale
|
||||
|
||||
# --- C7: Compute P = exp2((S - row_max_safe) * scale) ---
|
||||
minus_row_max_scale = (cutlass.Float32(0.0) - row_max_safe) * scale
|
||||
|
||||
# Register bridge (FMHA pattern: FP32 backing + BF16 view)
|
||||
rP_words = cute.make_rmem_tensor(tTMEM_STOREcP.shape, self.qk_acc_dtype)
|
||||
rP_bf16 = cute.make_tensor(cute.recast_ptr(rP_words.iterator, dtype=self.q_dtype), tTMEM_LOADrS.layout)
|
||||
|
||||
frg_cnt = 4
|
||||
frg_tile = cute.size(tTMEM_LOADrS) // frg_cnt
|
||||
tTMEM_LOADrS_frg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(frg_tile))
|
||||
rP_bf16_frg = cute.logical_divide(rP_bf16, cute.make_layout(frg_tile))
|
||||
|
||||
# Scale S, compute exp2, store through register bridge
|
||||
for j in range(frg_cnt):
|
||||
for k in cutlass.range(cute.size(tTMEM_LOADrS_frg, mode=[0]), vectorize=True):
|
||||
tTMEM_LOADrS_frg[k, j] = tTMEM_LOADrS_frg[k, j] * scale + minus_row_max_scale
|
||||
tTMEM_LOADrS_frg[k, j] = cute.math.exp2(tTMEM_LOADrS_frg[k, j], fastmath=True)
|
||||
s_vec = tTMEM_LOADrS_frg[None, j].load()
|
||||
rP_bf16_frg[None, j].store(s_vec.to(self.q_dtype))
|
||||
|
||||
# Store P to TMEM
|
||||
cute.copy(tiled_tmem_store, rP_words, tTMEM_STOREtP)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
si_handle.release()
|
||||
softmax_done_bar.arrive()
|
||||
|
||||
# --- C8: Row sum accumulation (CUTLASS FMHA packed f32x2 pattern) ---
|
||||
# P values still in tTMEM_LOADrS registers.
|
||||
# 4 accumulators for 4 reduction_unroll columns.
|
||||
local_row_sum_0 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_1 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_2 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_3 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
|
||||
reduction_unroll = 4
|
||||
rfrg_tile = cute.size(tTMEM_LOADrS) // reduction_unroll
|
||||
tTMEM_LOADrS_rfrg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(rfrg_tile))
|
||||
|
||||
for j in cutlass.range_constexpr(0, cute.size(tTMEM_LOADrS_rfrg, mode=[0]), 2):
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_0, (tTMEM_LOADrS_rfrg[j, 0], tTMEM_LOADrS_rfrg[j + 1, 0]))
|
||||
local_row_sum_1 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_1, (tTMEM_LOADrS_rfrg[j, 1], tTMEM_LOADrS_rfrg[j + 1, 1]))
|
||||
local_row_sum_2 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_2, (tTMEM_LOADrS_rfrg[j, 2], tTMEM_LOADrS_rfrg[j + 1, 2]))
|
||||
local_row_sum_3 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_3, (tTMEM_LOADrS_rfrg[j, 3], tTMEM_LOADrS_rfrg[j + 1, 3]))
|
||||
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(local_row_sum_0, local_row_sum_1)
|
||||
local_row_sum_2 = cute.arch.add_packed_f32x2(local_row_sum_2, local_row_sum_3)
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(local_row_sum_0, local_row_sum_2)
|
||||
tile_sum = local_row_sum_0[0] + local_row_sum_0[1]
|
||||
|
||||
row_sum = row_sum + tile_sum
|
||||
|
||||
# --- C9: Final normalization via O TMEM rescale ---
|
||||
pv_done_bar.arrive_and_wait()
|
||||
|
||||
# Compute inv_row_sum from P in TMEM using PV partition.
|
||||
# P was stored by softmax loop into TMEM at offset tmem_p0_offset.
|
||||
# PV partition maps thread N to PV row N, so reading P via PV partition
|
||||
# gives the correct per-row P values to sum.
|
||||
# This avoids the QK→PV row mapping mismatch (QK: N->N//4, PV: N->N).
|
||||
|
||||
# P is stored as BF16 in TMEM at tmem_p0_offset.
|
||||
# We need to read it via PV TMEM load and sum the values.
|
||||
# P has shape (128, HEAD_DIM//2) in FP32 columns (64 BF16 = 32 FP32 cols).
|
||||
# Use the P TMEM load partition (PV A-fragment read).
|
||||
|
||||
# Actually, P was stored via QK C-fragment store (St32x32bOp Repetition(32)).
|
||||
# To read it via PV partition, we need a PV-partitioned load from the P region.
|
||||
# Let's use the same o_tiled_tmem_load but pointed at P's TMEM offset.
|
||||
|
||||
# P occupies TMEM columns [tmem_p0_offset, tmem_p0_offset + p_cols_fp32)
|
||||
# In the PV C-fragment, P is the A-fragment. We can use tOrP0's layout.
|
||||
# tOrP0 was set up with offset for PV MMA read.
|
||||
|
||||
# Simpler: sum O across columns to get unnormalized row sum, then normalize.
|
||||
# For V=identity, O = P@V = sum(P per row). So O.sum(dim=-1) = row_sum.
|
||||
# For arbitrary V, O = P@V. O.sum(dim=-1) = sum_j(P@V)[j] = sum_j(sum_i P[i]*V[i,j])
|
||||
# This is NOT sum(P). So this trick only works for V=identity.
|
||||
|
||||
# Correct approach: read P from TMEM, sum it per PV row.
|
||||
# P is at TMEM offset tmem_p0_offset, stored as BF16 with St32x32bOp.
|
||||
# P shape in TMEM: 128 rows x (HEAD_DIM BF16 = 32 FP32 cols)
|
||||
# We can read P using Ld32x32bOp(Repetition(corr_tile_size)) via PV O-partition.
|
||||
|
||||
# Use PV O TMEM load to read from P region instead of O region
|
||||
p_col_tiles = p_cols_fp32 // corr_tile_size # 32 // 16 = 2
|
||||
pv_row_sum = cutlass.Float32(0.0)
|
||||
for i in range(p_col_tiles):
|
||||
# Read P tile from TMEM at P offset (not O offset)
|
||||
tTMEM_LOADtP_i = cute.make_tensor(
|
||||
tTMEM_LOADtO.iterator + (self.tmem_p0_offset - self.tmem_o0_offset) + i * corr_tile_size,
|
||||
tTMEM_LOADtO.layout)
|
||||
tTMrP_i = cute.make_rmem_tensor(tTMEM_LOADcO.shape, self.qk_acc_dtype)
|
||||
cute.copy(o_tiled_tmem_load, tTMEM_LOADtP_i, tTMrP_i)
|
||||
# Use .reduce(SUM) instead of scalar accumulation (vectorizer can't handle scalar in vectorized loop)
|
||||
tile_p_sum = tTMrP_i.load().reduce(cute.ReductionOp.ADD, cutlass.Float32(0.0), 0)
|
||||
pv_row_sum = pv_row_sum + tile_p_sum
|
||||
|
||||
inv_row_sum = cutlass.Float32(1.0) / pv_row_sum
|
||||
|
||||
# Normalize O in TMEM using PV-correct inv_row_sum
|
||||
tTMrO_final = cute.make_rmem_tensor((tTMEM_LOADcO.shape, o_col_tiles), self.qk_acc_dtype)
|
||||
for i in range(o_col_tiles):
|
||||
tTMrO_i_ = tTMrO_final[None, i]
|
||||
tTMrO_i_layout = cute.composition(tTMrO_i_.layout, cute.make_layout(tTMrO_final.shape[0]))
|
||||
tTMrO_i = cute.make_tensor(tTMrO_i_.iterator, tTMrO_i_layout)
|
||||
tTMEM_LOADtO_i = cute.make_tensor(
|
||||
tTMEM_LOADtO.iterator + i * corr_tile_size, tTMEM_LOADtO.layout)
|
||||
tTMEM_STOREtO_i = cute.make_tensor(
|
||||
tTMEM_STOREtO.iterator + i * corr_tile_size, tTMEM_STOREtO.layout)
|
||||
cute.copy(o_tiled_tmem_load, tTMEM_LOADtO_i, tTMrO_i)
|
||||
for j in cutlass.range(cute.size(tTMrO_i), vectorize=True):
|
||||
tTMrO_i[j] = tTMrO_i[j] * inv_row_sum
|
||||
cute.copy(o_tiled_tmem_store, tTMrO_i, tTMEM_STOREtO_i)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# Now O in TMEM is normalized. Use standard epilogue_tma_store with identity.
|
||||
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():
|
||||
import math
|
||||
torch.manual_seed(42)
|
||||
for n in [128, 256, 384]:
|
||||
m, hd = 128, HEAD_DIM
|
||||
q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
k = torch.randn(n, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
v = torch.randn(n, hd, dtype=torch.bfloat16, device="cuda")
|
||||
v_kernel = v.unsqueeze(-1)
|
||||
c = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
qf = q[:,:,0].float(); kf = k[:,:,0].float()
|
||||
attn = qf @ kf.T / math.sqrt(hd)
|
||||
ref = torch.softmax(attn, dim=-1) @ v.float()
|
||||
mQ = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
|
||||
mK = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
|
||||
mV = ct.from_dlpack(v_kernel).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_kernel))
|
||||
mC = ct.from_dlpack(c).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c))
|
||||
stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
|
||||
kernel = FmhaV3Softmax()
|
||||
print(f"n={n}: Compiling...", flush=True)
|
||||
compiled = cute.compile(kernel, mQ, mK, mV, mC, stream)
|
||||
print(f"n={n}: tmem: s0={kernel.tmem_s0_offset} p0={kernel.tmem_p0_offset} o0={kernel.tmem_o0_offset} vec={kernel.tmem_vec_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()
|
||||
max_err = (out - ref).abs().max().item()
|
||||
print(f"FMHA softmax n={n}: cosine {cos:.6f} max_err {max_err:.6f} {'PASS' if cos >= 0.999 else 'FAIL'}", flush=True)
|
||||
|
||||
if __name__ == "__main__":
|
||||
test()
|
||||
|
||||
|
||||
488
tests/unit/test_fmha_v3_vec_c9.py
Normal file
488
tests/unit/test_fmha_v3_vec_c9.py
Normal file
@@ -0,0 +1,488 @@
|
||||
"""
|
||||
FMHA v3 + Stage C: QK -> online softmax -> PV with KV-tile interleaving.
|
||||
Stage C: row_max, exp2, O rescale, row_sum, final normalization.
|
||||
FMHA pattern P store preserved from Stage B.
|
||||
"""
|
||||
import math
|
||||
import torch, cutlass, cutlass.cute as cute, cutlass.utils as utils, cutlass.pipeline as pipeline
|
||||
from cutlass.cute.nvgpu import cpasync, tcgen05
|
||||
from cutlass import Float32, BFloat16, Int32, Boolean, const_expr
|
||||
from cutlass.utils import LayoutEnum
|
||||
from cutlass.utils.tmem_allocator import find_tmem_tensor_col_offset
|
||||
import cuda.bindings.driver as cuda
|
||||
import cutlass.torch as ct
|
||||
|
||||
HEAD_DIM = 64
|
||||
|
||||
class FmhaV3Softmax:
|
||||
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; self.num_c_stage = 2
|
||||
|
||||
def _setup(self, qk_mma, pv_mma):
|
||||
qk_ik = cute.size(qk_mma.shape_mnk, mode=[2])
|
||||
self.qk_mma_tiler = (128, 128, qk_ik * 4)
|
||||
pv_ik = cute.size(pv_mma.shape_mnk, mode=[2])
|
||||
self.pv_mma_tiler = (128, HEAD_DIM, pv_ik * (128 // pv_ik))
|
||||
self.mma_tiler = self.qk_mma_tiler
|
||||
self.cluster_layout_vmnk = cute.tiled_divide(cute.make_layout((1,1,1)), (qk_mma.thr_id.shape,))
|
||||
self.cta_tile_shape_mnk = (self.qk_mma_tiler[0]//cute.size(qk_mma.thr_id.shape), HEAD_DIM, self.qk_mma_tiler[2])
|
||||
self.c_layout = LayoutEnum.ROW_MAJOR
|
||||
self.epi_tile = utils.sm100.compute_epilogue_tile_shape(self.cta_tile_shape_mnk, False, self.c_layout, self.o_dtype)
|
||||
self.num_ab_stage = 1; self.num_acc_stage = 1
|
||||
self.q_smem_s = utils.sm100.make_smem_layout_a(qk_mma, self.qk_mma_tiler, self.q_dtype, self.q_stage)
|
||||
self.k_smem_s = utils.sm100.make_smem_layout_b(qk_mma, self.qk_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.v_smem_s = utils.sm100.make_smem_layout_b(pv_mma, self.pv_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.c_smem_s = utils.sm100.make_smem_layout_epi(self.o_dtype, self.c_layout, self.epi_tile, 2)
|
||||
self.p_tmem_s = utils.sm100.make_smem_layout_a(pv_mma, self.pv_mma_tiler, self.q_dtype, 1)
|
||||
qk_thr = qk_mma.get_slice(0); qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
pv_thr = pv_mma.get_slice(0); pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
self.tmem_s0_offset = 0; self.tmem_p0_offset = 32
|
||||
# P occupies [tmem_p0_offset, tmem_p0_offset + p_cols_fp32)
|
||||
# S occupies [0, qk_mma_tiler[1]) = [0, 128)
|
||||
# O must NOT overlap P. Place O after max(S end, P end), aligned to 32.
|
||||
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
|
||||
p_end = self.tmem_p0_offset + p_cols_fp32 # 32 + 64 = 96
|
||||
s_cols = self.qk_mma_tiler[1] # 128
|
||||
o_after = max(s_cols, p_end) # 128
|
||||
self.tmem_o0_offset = ((o_after + 31) // 32) * 32
|
||||
self.tmem_vec_offset = 0 # Reuse S region for per-row inv_row_sum vector # align to 32 = 128
|
||||
self.tmem_vec_offset = 0 # Reuse S region (free after softmax loop)
|
||||
o_cols = find_tmem_tensor_col_offset(tOtO) # footprint of O
|
||||
total = self.tmem_o0_offset + o_cols
|
||||
# Must be multiple of 32 AND power of 2
|
||||
self.num_tmem_alloc_cols = 1
|
||||
while self.num_tmem_alloc_cols < total:
|
||||
self.num_tmem_alloc_cols *= 2
|
||||
cta = cute.size(qk_mma.thr_id.shape)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0))
|
||||
self.q_tx_bytes = cute.size_in_bytes(self.q_dtype, q_s) * cta
|
||||
self.kv_tx_bytes = cute.size_in_bytes(self.q_dtype, k_s) * cta
|
||||
self.scale_softmax_log2 = Float32(1.0 / math.sqrt(HEAD_DIM) * math.log2(math.e))
|
||||
|
||||
@cute.jit
|
||||
def __call__(self, q, k, v, c, stream):
|
||||
self.q_dtype = q.element_type; self.o_dtype = c.element_type; self.c_dtype = self.o_dtype
|
||||
self.a_major = LayoutEnum.from_tensor(q).mma_major_mode()
|
||||
self.b_major = LayoutEnum.from_tensor(k).mma_major_mode()
|
||||
# # s_k hardcoded # BROKEN in @cute.jit
|
||||
# FMHA-style V: reconstruct as (HEAD_DIM, s_k, 1) MN-major
|
||||
v_fmha = cute.make_tensor(
|
||||
v.iterator,
|
||||
cute.make_layout(
|
||||
(HEAD_DIM, 128, 1),
|
||||
stride=(1, HEAD_DIM, HEAD_DIM * 128),
|
||||
),
|
||||
)
|
||||
self.v_major = LayoutEnum.from_tensor(v_fmha).mma_major_mode()
|
||||
self.c_layout = LayoutEnum.from_tensor(c)
|
||||
qk_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, self.a_major, self.b_major, self.qk_acc_dtype, self.cta_group, (128,128), tcgen05.OperandSource.SMEM)
|
||||
pv_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, cute.nvgpu.OperandMajorMode.K, self.v_major, self.qk_acc_dtype, self.cta_group, (128,HEAD_DIM), tcgen05.OperandSource.TMEM)
|
||||
self._setup(qk_mma, pv_mma)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0)); v_s = cute.slice_(self.v_smem_s,(None,None,None,0))
|
||||
tma_q,mQ = cute.nvgpu.make_tiled_tma_atom_A(utils.sm100.cluster_shape_to_tma_atom_A(self.cluster_shape_mn,qk_mma.thr_id),q,q_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_k,mK = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,qk_mma.thr_id),k,k_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_v,mV = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,pv_mma.thr_id),v_fmha,v_s,self.pv_mma_tiler,pv_mma,self.cluster_layout_vmnk.shape)
|
||||
epi_s = cute.select(self.c_smem_s,mode=[0,1])
|
||||
tma_c,mC = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(),c,epi_s,self.epi_tile)
|
||||
self._kernel(qk_mma,pv_mma,tma_q,mQ,tma_k,mK,tma_v,mV,tma_c,mC,self.cluster_layout_vmnk,self.q_smem_s,self.k_smem_s,self.v_smem_s,self.p_tmem_s,self.c_smem_s,self.epi_tile).launch(grid=(1,1,1),block=[self.threads_per_cta,1,1],stream=stream)
|
||||
|
||||
@cute.kernel
|
||||
def _kernel(self, qk_mma, pv_mma, tma_q, mQ, tma_k, mK, tma_v, mV, tma_c, mC, cl_vmnk, q_smem_s, k_smem_s, v_smem_s, p_tmem_s, c_smem_s, epi_tile):
|
||||
warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx())
|
||||
tidx,_,_ = cute.arch.thread_idx()
|
||||
if warp_idx == self.tma_warp_id:
|
||||
cpasync.prefetch_descriptor(tma_q); cpasync.prefetch_descriptor(tma_k); cpasync.prefetch_descriptor(tma_v); cpasync.prefetch_descriptor(tma_c)
|
||||
|
||||
@cute.struct
|
||||
class SS:
|
||||
q_bar: cute.struct.MemRange[cutlass.Int64, self.q_stage*2]
|
||||
kv_bar: cute.struct.MemRange[cutlass.Int64, self.kv_stage*2]
|
||||
s_bar: cute.struct.MemRange[cutlass.Int64, 2]
|
||||
acc_bar: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage*2]
|
||||
tmem_dealloc: cutlass.Int64; holding: cutlass.Int32
|
||||
smem = utils.SmemAllocator(); st = smem.allocate(SS)
|
||||
|
||||
qp,qc = pipeline.PipelineTmaUmma.create(barrier_storage=st.q_bar.data_ptr(),num_stages=self.q_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.q_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
|
||||
kvp,kvc = pipeline.PipelineTmaUmma.create(barrier_storage=st.kv_bar.data_ptr(),num_stages=self.kv_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.kv_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
|
||||
s_prod,s_cons = pipeline.PipelineUmmaAsync.create(barrier_storage=st.s_bar.data_ptr(),num_stages=1,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32*len(self.epilogue_warp_id))).make_participants()
|
||||
softmax_done_bar = pipeline.NamedBarrier(barrier_id=3, num_threads=32 + 32*len(self.epilogue_warp_id))
|
||||
pv_done_bar = pipeline.NamedBarrier(barrier_id=4, 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)
|
||||
sV = smem.allocate_tensor(element_type=self.q_dtype,layout=v_smem_s.outer,byte_alignment=128,swizzle=v_smem_s.inner)
|
||||
sC = smem.allocate_tensor(element_type=self.o_dtype,layout=c_smem_s.outer,byte_alignment=128,swizzle=c_smem_s.inner)
|
||||
|
||||
gQ = cute.local_tile(mQ,cute.slice_(self.qk_mma_tiler,(None,0,None)),(None,None,None))
|
||||
gK = cute.local_tile(mK,cute.slice_(self.qk_mma_tiler,(0,None,None)),(None,None,None))
|
||||
gV = cute.local_tile(mV,cute.slice_(self.pv_mma_tiler,(0,None,None)),(None,None,None))
|
||||
gC = cute.local_tile(mC,cute.slice_(self.pv_mma_tiler,(None,None,0)),(None,None,None))
|
||||
n_kv_tiles = cute.size(gK, mode=[3])
|
||||
|
||||
qk_thr = qk_mma.get_slice(0); pv_thr = pv_mma.get_slice(0)
|
||||
tCgQ = qk_thr.partition_A(gQ); tCgK = qk_thr.partition_B(gK)
|
||||
tCgV = pv_thr.partition_B(gV); tCgC = pv_thr.partition_C(gC)
|
||||
a_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,0,None,0)).shape)
|
||||
tAsQ,tAgQ = cpasync.tma_partition(tma_q,0,a_lay,cute.group_modes(sQ,0,3),cute.group_modes(tCgQ,0,3))
|
||||
b_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,None,0,0)).shape)
|
||||
tBsK,tBgK = cpasync.tma_partition(tma_k,0,b_lay,cute.group_modes(sK,0,3),cute.group_modes(tCgK,0,3))
|
||||
tVsV,tVgV = cpasync.tma_partition(tma_v,0,b_lay,cute.group_modes(sV,0,3),cute.group_modes(tCgV,0,3))
|
||||
tAgQ = tAgQ[(None,0,None,0)]; tBgK = tBgK[(None,0,None,0)]; tVgV = tVgV[(None,0,None,0)]
|
||||
|
||||
tCrQ = qk_mma.make_fragment_A(sQ); tCrK = qk_mma.make_fragment_B(sK)
|
||||
tCrV = pv_mma.make_fragment_B(sV)
|
||||
|
||||
qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
tOtO0 = cute.make_tensor(tOtO.iterator + self.tmem_o0_offset, tOtO.layout)
|
||||
|
||||
# --- PV read view (for MMA only, NOT for softmax store) ---
|
||||
tP = cute.make_tensor(tStS.iterator, p_tmem_s.outer)
|
||||
tOrP_base = pv_thr.make_fragment_A(tP)
|
||||
tOrP = tOrP_base[(None,None,None,0)]
|
||||
tOrP0 = cute.make_tensor(
|
||||
tOrP.iterator + self.qk_acc_dtype.width // self.q_dtype.width * self.tmem_p0_offset,
|
||||
tOrP.layout)
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_as, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_as, self.num_acc_stage))
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# TMA LOAD
|
||||
if warp_idx == self.tma_warp_id:
|
||||
qp.reset(); qh = qp.acquire_and_advance()
|
||||
cute.copy(tma_q,tAgQ[(None,qh.count)],tAsQ[(None,qh.index)],tma_bar_ptr=qh.barrier)
|
||||
qp.tail()
|
||||
kvp.reset(); pk = kvp.try_acquire()
|
||||
for kt in cutlass.range(n_kv_tiles,unroll=1):
|
||||
kh = kvp.acquire_and_advance(pk)
|
||||
cute.copy(tma_k,tBgK[(None,kh.count)],tBsK[(None,kh.index)],tma_bar_ptr=kh.barrier)
|
||||
pk = cutlass.Boolean(1)
|
||||
vh = kvp.acquire_and_advance(pk)
|
||||
cute.copy(tma_v,tVgV[(None,vh.count)],tVsV[(None,vh.index)],tma_bar_ptr=vh.barrier)
|
||||
pk = cutlass.Boolean(1)
|
||||
kvp.tail()
|
||||
|
||||
# MMA
|
||||
if warp_idx == self.mma_warp_id:
|
||||
tmem.wait_for_alloc()
|
||||
qc.reset(); qh = qc.wait_and_advance(); qh.release()
|
||||
kvc.reset(); pk = kvc.try_wait()
|
||||
acc_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
|
||||
acc_pipe.producer_acquire(acc_st)
|
||||
for kt in range(n_kv_tiles):
|
||||
kh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
sh = s_prod.acquire_and_advance()
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
for kb in cutlass.range(cute.size(tCrQ,mode=[2]), unroll_full=True):
|
||||
cute.gemm(qk_mma, tStS0, tCrQ[(None,None,kb,0)], tCrK[(None,None,kb,kh.index)], tStS0)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
sh.commit(); kh.release()
|
||||
softmax_done_bar.arrive_and_wait()
|
||||
vh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, kt != 0)
|
||||
for kb in cutlass.range(cute.size(tOrP0,mode=[2]), unroll_full=True):
|
||||
cute.gemm(pv_mma, tOtO0, tOrP0[(None,None,kb)], tCrV[(None,None,kb,vh.index)], tOtO0)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
vh.release()
|
||||
pv_done_bar.arrive()
|
||||
acc_pipe.producer_commit(acc_st); acc_st.advance()
|
||||
acc_pipe.producer_tail(acc_st)
|
||||
|
||||
# ===================== EPILOGUE WARPS (STAGE C: ONLINE SOFTMAX) =====================
|
||||
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 (QK C-fragment) ---
|
||||
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 (QK C-fragment composition, FMHA pattern) ---
|
||||
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
|
||||
tStP_layout = cute.composition(tStS.layout, cute.make_layout((self.pv_mma_tiler[0], p_cols_fp32)))
|
||||
tStP0 = cute.make_tensor(tStS.iterator + self.tmem_p0_offset, tStP_layout)
|
||||
tmem_store_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
tiled_tmem_store = tcgen05.make_tmem_copy(tmem_store_atom, tStP0)
|
||||
thr_store = tiled_tmem_store.get_slice(sfw_idx)
|
||||
tTMEM_STOREtP = thr_store.partition_D(tStP0)
|
||||
tScP_layout = cute.composition(tScS.layout, cute.make_layout((self.pv_mma_tiler[0], p_cols_fp32)))
|
||||
tScP = cute.make_tensor(tScS.iterator, tScP_layout)
|
||||
tTMEM_STOREcP = thr_store.partition_S(tScP)
|
||||
|
||||
# --- Vector TMEM (per-row row_sum storage, FMHA pattern) ---
|
||||
# composition(tStS.layout, (128, 2)) = 2 FP32 columns per logical row
|
||||
# vec[0] = row_sum (final, after loop), vec[1] = unused
|
||||
# Reuses S TMEM region (offset 0), free after softmax loop writes
|
||||
|
||||
tStS_vec_layout = cute.composition(tStS.layout, cute.make_layout((128, 2)))
|
||||
tStS_vec = cute.make_tensor(tStS.iterator + self.tmem_vec_offset, tStS_vec_layout)
|
||||
tScS_vec_layout = cute.composition(tScS.layout, cute.make_layout((128, 2)))
|
||||
tScS_vec = cute.make_tensor(tScS.iterator, tScS_vec_layout)
|
||||
tmem_store_vec_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(2)), self.qk_acc_dtype)
|
||||
tiled_tmem_store_vec = tcgen05.make_tmem_copy(tmem_store_vec_atom, tStS_vec)
|
||||
thr_tmem_store_vec = tiled_tmem_store_vec.get_slice(sfw_idx)
|
||||
tTMEM_STORE_VECtS = thr_tmem_store_vec.partition_D(tStS_vec)
|
||||
tTMEM_STORE_VECcS = thr_tmem_store_vec.partition_S(tScS_vec)
|
||||
tmem_load_vec_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(2)), self.qk_acc_dtype)
|
||||
tiled_tmem_load_vec = tcgen05.make_tmem_copy(tmem_load_vec_atom, tStS_vec)
|
||||
thr_tmem_load_vec = tiled_tmem_load_vec.get_slice(sfw_idx)
|
||||
tTMEM_LOAD_VECtS = thr_tmem_load_vec.partition_S(tStS_vec)
|
||||
tTMEM_LOAD_VECcS = thr_tmem_load_vec.partition_D(tScS_vec)
|
||||
|
||||
# --- C6: O TMEM load/store for rescale (correction_rescale pattern) ---
|
||||
corr_tile_size = 16
|
||||
cO = cute.make_identity_tensor((self.pv_mma_tiler[0], self.pv_mma_tiler[1]))
|
||||
tOcO = pv_thr.partition_C(cO)
|
||||
o_tmem_load_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.qk_acc_dtype)
|
||||
o_tmem_store_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.qk_acc_dtype)
|
||||
tOtO_i_layout = cute.composition(tOtO0.layout, cute.make_layout((128, corr_tile_size)))
|
||||
tOcO_i_layout = cute.composition(tOcO.layout, cute.make_layout((128, corr_tile_size)))
|
||||
tOtO_i = cute.make_tensor(tOtO0.iterator, tOtO_i_layout)
|
||||
tOcO_i = cute.make_tensor(tOcO.iterator, tOcO_i_layout)
|
||||
o_tiled_tmem_load = tcgen05.make_tmem_copy(o_tmem_load_atom, tOtO_i)
|
||||
o_tiled_tmem_store = tcgen05.make_tmem_copy(o_tmem_store_atom, tOtO_i)
|
||||
o_thr_load = o_tiled_tmem_load.get_slice(sfw_idx)
|
||||
o_thr_store = o_tiled_tmem_store.get_slice(sfw_idx)
|
||||
tTMEM_LOADtO = o_thr_load.partition_S(tOtO_i)
|
||||
tTMEM_LOADcO = o_thr_load.partition_D(tOcO_i)
|
||||
tTMEM_STOREtO = o_thr_store.partition_D(tOtO_i)
|
||||
o_col_tiles = self.pv_mma_tiler[1] // corr_tile_size
|
||||
|
||||
# --- C2: Per-thread row state (persist across KV tiles) ---
|
||||
row_max = -cutlass.Float32.inf
|
||||
row_sum = cutlass.Float32(0.0)
|
||||
|
||||
# --- C3: QK scale = 1/sqrt(HEAD_DIM) * log2(e) for exp2 ---
|
||||
scale = self.scale_softmax_log2
|
||||
|
||||
# =============================================================
|
||||
# Per-KV-tile online softmax loop
|
||||
# =============================================================
|
||||
for kt in range(n_kv_tiles):
|
||||
si_handle = s_cons.wait_and_advance()
|
||||
|
||||
# Load S from TMEM (FP32, QK C-fragment layout)
|
||||
tTMEM_LOADrS = cute.make_rmem_tensor(tTMEM_LOADcS.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load, tTMEM_LOADtS, tTMEM_LOADrS)
|
||||
|
||||
# --- C4: Compute tile_max via .reduce(MAX) ---
|
||||
old_row_max = row_max
|
||||
row_max = tTMEM_LOADrS.load().reduce(cute.ReductionOp.MAX, row_max, 0)
|
||||
row_max_safe = row_max
|
||||
if row_max == -cutlass.Float32.inf:
|
||||
row_max_safe = cutlass.Float32(0.0)
|
||||
|
||||
# --- C5: Compute rescale factor ---
|
||||
acc_scale = cute.math.exp2(scale * (old_row_max - row_max_safe), fastmath=True)
|
||||
|
||||
# --- C6: Rescale O in TMEM (load O, multiply by acc_scale, store O) ---
|
||||
# acc_scale belongs to QK row (N//4), but O rows are in PV partition (N).
|
||||
# Store acc_scale to vector by QK row, read by PV row.
|
||||
if kt > 0:
|
||||
pv_done_bar.arrive_and_wait()
|
||||
|
||||
# Store acc_scale to vector indexed by QK logical row
|
||||
qk_row_c6 = tTMEM_LOADcS[0][0]
|
||||
thr_vs_c6 = tiled_tmem_store_vec.get_slice(qk_row_c6)
|
||||
tVStore_c6 = thr_vs_c6.partition_D(tStS_vec)
|
||||
tVStoreSrc_c6 = thr_vs_c6.partition_S(tScS_vec)
|
||||
tVStoreRmem_c6 = cute.make_rmem_tensor(tVStoreSrc_c6.shape, self.qk_acc_dtype)
|
||||
tVStoreRmem_c6[0] = acc_scale
|
||||
cute.copy(tiled_tmem_store_vec, tVStoreRmem_c6, tVStore_c6)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# Read acc_scale from vector indexed by PV logical row
|
||||
pv_row_c6 = tTMEM_LOADcO[0][0]
|
||||
thr_vl_c6 = tiled_tmem_load_vec.get_slice(pv_row_c6)
|
||||
tVLoad_c6 = thr_vl_c6.partition_S(tStS_vec)
|
||||
tVLoadDst_c6 = thr_vl_c6.partition_D(tScS_vec)
|
||||
tVLoadRmem_c6 = cute.make_rmem_tensor(tVLoadDst_c6.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load_vec, tVLoad_c6, tVLoadRmem_c6)
|
||||
cute.arch.fence_view_async_tmem_load()
|
||||
acc_scale_pv = tVLoadRmem_c6[0]
|
||||
|
||||
tTMrO = cute.make_rmem_tensor((tTMEM_LOADcO.shape, o_col_tiles), self.qk_acc_dtype)
|
||||
for i in range(o_col_tiles):
|
||||
tTMrO_i_ = tTMrO[None, i]
|
||||
tTMrO_i_layout = cute.composition(tTMrO_i_.layout, cute.make_layout(tTMrO.shape[0]))
|
||||
tTMrO_i = cute.make_tensor(tTMrO_i_.iterator, tTMrO_i_layout)
|
||||
tTMEM_LOADtO_i = cute.make_tensor(tTMEM_LOADtO.iterator + i * corr_tile_size, tTMEM_LOADtO.layout)
|
||||
tTMEM_STOREtO_i = cute.make_tensor(tTMEM_STOREtO.iterator + i * corr_tile_size, tTMEM_STOREtO.layout)
|
||||
cute.copy(o_tiled_tmem_load, tTMEM_LOADtO_i, tTMrO_i)
|
||||
for j in cutlass.range(cute.size(tTMrO_i), vectorize=True):
|
||||
tTMrO_i[j] = tTMrO_i[j] * acc_scale_pv
|
||||
cute.copy(o_tiled_tmem_store, tTMrO_i, tTMEM_STOREtO_i)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# Rescale row_sum
|
||||
row_sum = row_sum * acc_scale
|
||||
|
||||
# --- C7: Compute P = exp2((S - row_max_safe) * scale) ---
|
||||
minus_row_max_scale = (cutlass.Float32(0.0) - row_max_safe) * scale
|
||||
|
||||
# Register bridge (FMHA pattern: FP32 backing + BF16 view)
|
||||
rP_words = cute.make_rmem_tensor(tTMEM_STOREcP.shape, self.qk_acc_dtype)
|
||||
rP_bf16 = cute.make_tensor(cute.recast_ptr(rP_words.iterator, dtype=self.q_dtype), tTMEM_LOADrS.layout)
|
||||
|
||||
frg_cnt = 4
|
||||
frg_tile = cute.size(tTMEM_LOADrS) // frg_cnt
|
||||
tTMEM_LOADrS_frg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(frg_tile))
|
||||
rP_bf16_frg = cute.logical_divide(rP_bf16, cute.make_layout(frg_tile))
|
||||
|
||||
# Scale S, compute exp2, store through register bridge
|
||||
for j in range(frg_cnt):
|
||||
for k in cutlass.range(cute.size(tTMEM_LOADrS_frg, mode=[0]), vectorize=True):
|
||||
tTMEM_LOADrS_frg[k, j] = tTMEM_LOADrS_frg[k, j] * scale + minus_row_max_scale
|
||||
tTMEM_LOADrS_frg[k, j] = cute.math.exp2(tTMEM_LOADrS_frg[k, j], fastmath=True)
|
||||
s_vec = tTMEM_LOADrS_frg[None, j].load()
|
||||
rP_bf16_frg[None, j].store(s_vec.to(self.q_dtype))
|
||||
|
||||
# Store P to TMEM
|
||||
cute.copy(tiled_tmem_store, rP_words, tTMEM_STOREtP)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
si_handle.release()
|
||||
softmax_done_bar.arrive()
|
||||
|
||||
# --- C8: Row sum accumulation (CUTLASS FMHA packed f32x2 pattern) ---
|
||||
# P values still in tTMEM_LOADrS registers.
|
||||
# 4 accumulators for 4 reduction_unroll columns.
|
||||
local_row_sum_0 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_1 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_2 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_3 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
|
||||
reduction_unroll = 4
|
||||
rfrg_tile = cute.size(tTMEM_LOADrS) // reduction_unroll
|
||||
tTMEM_LOADrS_rfrg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(rfrg_tile))
|
||||
|
||||
for j in cutlass.range_constexpr(0, cute.size(tTMEM_LOADrS_rfrg, mode=[0]), 2):
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_0, (tTMEM_LOADrS_rfrg[j, 0], tTMEM_LOADrS_rfrg[j + 1, 0]))
|
||||
local_row_sum_1 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_1, (tTMEM_LOADrS_rfrg[j, 1], tTMEM_LOADrS_rfrg[j + 1, 1]))
|
||||
local_row_sum_2 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_2, (tTMEM_LOADrS_rfrg[j, 2], tTMEM_LOADrS_rfrg[j + 1, 2]))
|
||||
local_row_sum_3 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_3, (tTMEM_LOADrS_rfrg[j, 3], tTMEM_LOADrS_rfrg[j + 1, 3]))
|
||||
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(local_row_sum_0, local_row_sum_1)
|
||||
local_row_sum_2 = cute.arch.add_packed_f32x2(local_row_sum_2, local_row_sum_3)
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(local_row_sum_0, local_row_sum_2)
|
||||
tile_sum = local_row_sum_0[0] + local_row_sum_0[1]
|
||||
|
||||
row_sum = row_sum + tile_sum
|
||||
|
||||
# --- C9: Final normalization via O TMEM rescale ---
|
||||
pv_done_bar.arrive_and_wait()
|
||||
|
||||
# Write row_sum to TMEM vector using QK partition (correct row mapping)
|
||||
qk_row_c9 = tTMEM_LOADcS[0][0]
|
||||
thr_vs_c9 = tiled_tmem_store_vec.get_slice(qk_row_c9)
|
||||
tVStore_c9 = thr_vs_c9.partition_D(tStS_vec)
|
||||
tVStoreSrc_c9 = thr_vs_c9.partition_S(tScS_vec)
|
||||
tVStoreRmem_c9 = cute.make_rmem_tensor(tVStoreSrc_c9.shape, self.qk_acc_dtype)
|
||||
tVStoreRmem_c9[0] = row_sum
|
||||
cute.copy(tiled_tmem_store_vec, tVStoreRmem_c9, tVStore_c9)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# Read row_sum from TMEM vector using PV partition (correct for O rows)
|
||||
pv_row_c9 = tTMEM_LOADcO[0][0]
|
||||
thr_vl_c9 = tiled_tmem_load_vec.get_slice(pv_row_c9)
|
||||
tVLoad_c9 = thr_vl_c9.partition_S(tStS_vec)
|
||||
tVLoadDst_c9 = thr_vl_c9.partition_D(tScS_vec)
|
||||
tVLoadRmem_c9 = cute.make_rmem_tensor(tVLoadDst_c9.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load_vec, tVLoad_c9, tVLoadRmem_c9)
|
||||
cute.arch.fence_view_async_tmem_load()
|
||||
pv_row_sum = tVLoadRmem_c9[0]
|
||||
|
||||
inv_row_sum = cutlass.Float32(1.0) / pv_row_sum
|
||||
|
||||
# Normalize O in TMEM using PV-correct inv_row_sum
|
||||
tTMrO_final = cute.make_rmem_tensor((tTMEM_LOADcO.shape, o_col_tiles), self.qk_acc_dtype)
|
||||
for i in range(o_col_tiles):
|
||||
tTMrO_i_ = tTMrO_final[None, i]
|
||||
tTMrO_i_layout = cute.composition(tTMrO_i_.layout, cute.make_layout(tTMrO_final.shape[0]))
|
||||
tTMrO_i = cute.make_tensor(tTMrO_i_.iterator, tTMrO_i_layout)
|
||||
tTMEM_LOADtO_i = cute.make_tensor(
|
||||
tTMEM_LOADtO.iterator + i * corr_tile_size, tTMEM_LOADtO.layout)
|
||||
tTMEM_STOREtO_i = cute.make_tensor(
|
||||
tTMEM_STOREtO.iterator + i * corr_tile_size, tTMEM_STOREtO.layout)
|
||||
cute.copy(o_tiled_tmem_load, tTMEM_LOADtO_i, tTMrO_i)
|
||||
for j in cutlass.range(cute.size(tTMrO_i), vectorize=True):
|
||||
tTMrO_i[j] = tTMrO_i[j] * inv_row_sum
|
||||
cute.copy(o_tiled_tmem_store, tTMrO_i, tTMEM_STOREtO_i)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# Now O in TMEM is normalized. Use standard epilogue_tma_store with identity.
|
||||
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():
|
||||
import math
|
||||
torch.manual_seed(42)
|
||||
for n in [128, 256, 384]:
|
||||
m, hd = 128, HEAD_DIM
|
||||
q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
k = torch.randn(n, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
v = torch.randn(n, hd, dtype=torch.bfloat16, device="cuda")
|
||||
v_kernel = v.unsqueeze(-1)
|
||||
c = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
qf = q[:,:,0].float(); kf = k[:,:,0].float()
|
||||
attn = qf @ kf.T / math.sqrt(hd)
|
||||
ref = torch.softmax(attn, dim=-1) @ v.float()
|
||||
mQ = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
|
||||
mK = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
|
||||
mV = ct.from_dlpack(v_kernel).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_kernel))
|
||||
mC = ct.from_dlpack(c).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c))
|
||||
stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
|
||||
kernel = FmhaV3Softmax()
|
||||
print(f"n={n}: Compiling...", flush=True)
|
||||
compiled = cute.compile(kernel, mQ, mK, mV, mC, stream)
|
||||
print(f"n={n}: tmem: s0={kernel.tmem_s0_offset} p0={kernel.tmem_p0_offset} o0={kernel.tmem_o0_offset} vec={kernel.tmem_vec_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()
|
||||
max_err = (out - ref).abs().max().item()
|
||||
print(f"FMHA softmax n={n}: cosine {cos:.6f} max_err {max_err:.6f} {'PASS' if cos >= 0.999 else 'FAIL'}", flush=True)
|
||||
|
||||
if __name__ == "__main__":
|
||||
test()
|
||||
|
||||
|
||||
Reference in New Issue
Block a user