FMHA Stage-C2: production 12-warp pipeline with correction warps
- Softmax warps (0-3): S→softmax→P, vec=[old_max,new_max]→TMEM - Correction warps (4-7): O rescale in TMEM, final normalize by row_sum - MMA warp (8): QK→S, PV→O with pipeline chaining - TMA warp (9): Q/K/V load - Epilogue warp (10): O TMEM→GMEM via epilogue_tma_store - Empty warp (11): tmem dealloc mbar init - Pipeline: mma_s→softmax→s_corr→correction→corr_epi→epilogue + mma_corr→correction - Supports multi-tile KV with online O rescale - Follows CUTLASS FMHA correction_rescale pattern exactly
This commit is contained in:
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tests/unit/test_fmha_v3_stage_c2.py
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tests/unit/test_fmha_v3_stage_c2.py
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"""
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FMHA v3 Stage-C: Real softmax + O normalization.
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Builds on the 12w identity-softmax test by replacing identity softmax with
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online softmax (row_max, exp2 scaling, P store) and adding O normalization
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by row_sum before the epilogue writes to GMEM.
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"""
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import torch, cutlass, cutlass.cute as cute, cutlass.utils as utils, cutlass.pipeline as pipeline
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from cutlass.cute.nvgpu import cpasync, tcgen05
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from cutlass import Float32, BFloat16, Int32, Boolean, const_expr
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from cutlass.utils import LayoutEnum
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from cutlass.utils.tmem_allocator import find_tmem_tensor_col_offset
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import cuda.bindings.driver as cuda
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import cutlass.torch as ct
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import math
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HEAD_DIM = 64
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class FmhaV3StageC2:
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def __init__(self, s_k=128, scale_softmax=None):
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self.s_k = s_k
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self.acc_dtype = Float32; self.qk_acc_dtype = Float32; self.pv_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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# 12-warp layout
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self.softmax_warp_ids = (0, 1, 2, 3)
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self.correction_warp_ids = (4, 5, 6, 7)
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self.mma_warp_id = 8; self.tma_warp_id = 9
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self.epilogue_warp_id = 10; self.empty_warp_id = 11
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self.threads_per_cta = 32 * 12
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# Pipeline stages
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self.mma_softmax_stage = 1; self.softmax_corr_stage = 1
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self.mma_corr_stage = 2; self.epi_stage = 2
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# TMA stages
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self.kv_stage = 2; self.q_stage = 1; self.num_c_stage = 2
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# Softmax
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self.scale_softmax = scale_softmax if scale_softmax is not None else 1.0 / math.sqrt(HEAD_DIM)
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self.scale_softmax_log2 = self.scale_softmax * math.log2(math.e)
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def _setup(self, qk_mma, pv_mma):
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qk_ik = cute.size(qk_mma.shape_mnk, mode=[2])
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self.qk_mma_tiler = (128, 128, qk_ik * 4)
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pv_ik = cute.size(pv_mma.shape_mnk, mode=[2])
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self.pv_mma_tiler = (128, 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_vec0_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 # align to 32 = 128
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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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@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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# 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, self.s_k, 1),
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stride=(1, HEAD_DIM, HEAD_DIM * self.s_k),
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),
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)
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self.v_major = LayoutEnum.from_tensor(v_fmha).mma_major_mode()
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self.c_layout = LayoutEnum.from_tensor(c)
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qk_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, self.a_major, self.b_major, self.qk_acc_dtype, self.cta_group, (128,128), tcgen05.OperandSource.SMEM)
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pv_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, cute.nvgpu.OperandMajorMode.K, self.v_major, self.qk_acc_dtype, self.cta_group, (128,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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@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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mma_s_bar: cute.struct.MemRange[cutlass.Int64, self.mma_softmax_stage * 2]
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s_corr_bar: cute.struct.MemRange[cutlass.Int64, self.softmax_corr_stage * 2]
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mma_corr_bar: cute.struct.MemRange[cutlass.Int64, self.mma_corr_stage * 2]
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corr_epi_bar: cute.struct.MemRange[cutlass.Int64, self.epi_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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def cg(n): return pipeline.CooperativeGroup(pipeline.Agent.Thread, n)
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qp, qc = pipeline.PipelineTmaUmma.create(barrier_storage=st.q_bar.data_ptr(), num_stages=self.q_stage, producer_group=cg(1), consumer_group=cg(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=cg(1), consumer_group=cg(1), tx_count=self.kv_tx_bytes, cta_layout_vmnk=cl_vmnk, defer_sync=True).make_participants()
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# MMA → Softmax: S ready
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mma_s_prod, mma_s_cons = pipeline.PipelineUmmaAsync.create(barrier_storage=st.mma_s_bar.data_ptr(), num_stages=self.mma_softmax_stage, producer_group=cg(1), consumer_group=cg(32 * len(self.softmax_warp_ids)), cta_layout_vmnk=cl_vmnk, defer_sync=True).make_participants()
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# Softmax → Correction: vec ready
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s_corr_prod, s_corr_cons = pipeline.PipelineAsync.create(barrier_storage=st.s_corr_bar.data_ptr(), num_stages=self.softmax_corr_stage, producer_group=cg(32 * len(self.softmax_warp_ids)), consumer_group=cg(32 * len(self.correction_warp_ids))).make_participants()
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# MMA → Correction: O ready
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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=cg(1), consumer_group=cg(32 * len(self.correction_warp_ids)), cta_layout_vmnk=cl_vmnk, defer_sync=True).make_participants()
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# Correction → Epilogue: O in SMEM ready
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corr_epi_prod, corr_epi_cons = pipeline.PipelineAsync.create(barrier_storage=st.corr_epi_bar.data_ptr(), num_stages=self.epi_stage, producer_group=cg(32 * len(self.correction_warp_ids)), consumer_group=cg(32)).make_participants()
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# TMEM alloc barrier: softmax + correction + MMA
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tmem_bar = pipeline.NamedBarrier(barrier_id=2, num_threads=32 * len((*self.softmax_warp_ids, *self.correction_warp_ids, self.mma_warp_id)))
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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)
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if warp_idx == self.empty_warp_id:
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cute.arch.mbarrier_init(st.tmem_dealloc, 32 * len((*self.softmax_warp_ids, *self.correction_warp_ids)))
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cute.arch.mbarrier_init_fence()
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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); tCrV = pv_mma.make_fragment_B(sV)
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qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
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tStS = qk_thr.make_fragment_C(qk_as)
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tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
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pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
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tOtO = pv_thr.make_fragment_C(pv_as)
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tOtO0 = cute.make_tensor(tOtO.iterator + self.tmem_o0_offset, tOtO.layout)
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tP = cute.make_tensor(tStS.iterator, p_tmem_s.outer)
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tOrP_base = pv_thr.make_fragment_A(tP)
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tOrP = tOrP_base[(None, None, None, 0)]
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tOrP0 = cute.make_tensor(tOrP.iterator + self.qk_acc_dtype.width // self.q_dtype.width * self.tmem_p0_offset, tOrP.layout)
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tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_as, 1))
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tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_as, 1))
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pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
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# ==================== TMA WARP (9) ====================
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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 WARP (8) ====================
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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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for kt in range(n_kv_tiles):
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# QK -> S
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kh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
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sh = mma_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(); sh.commit(); kh.release()
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# PV -> O (softmax consumes S and produces P between these two)
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vh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
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oh = mma_corr_prod.acquire_and_advance()
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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(); oh.commit(); vh.release()
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mma_s_prod.tail(); mma_corr_prod.tail()
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cute.arch.relinquish_tmem_alloc_permit()
|
||||
cute.arch.mbarrier_wait(st.tmem_dealloc, 0)
|
||||
tmem_ptr = cute.arch.retrieve_tmem_ptr(self.qk_acc_dtype, alignment=16, ptr_to_buffer_holding_addr=st.holding)
|
||||
cute.arch.dealloc_tmem(tmem_ptr, Int32(self.num_tmem_alloc_cols))
|
||||
|
||||
# ==================== 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))
|
||||
|
||||
# S load setup
|
||||
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 setup (QK C-fragment composition)
|
||||
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)))
|
||||
tTMEM_STOREcP = thr_store.partition_S(cute.make_tensor(tScS.iterator, tScP_layout))
|
||||
|
||||
# Vec store setup ([old_max, new_max] per iteration, [row_sum, row_max] at end)
|
||||
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)
|
||||
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_store_vec = tiled_tmem_store_vec.get_slice(sfw_idx)
|
||||
tTMEM_STORE_VECtS = thr_store_vec.partition_D(tStS_vec)
|
||||
tScS_vec_layout = cute.composition(tScS.layout, cute.make_layout((128, 2)))
|
||||
tScS_vec = cute.make_tensor(tScS.iterator, tScS_vec_layout)
|
||||
tTMEM_STORE_VECcS = thr_store_vec.partition_S(tScS_vec)
|
||||
|
||||
row_max = -Float32.inf; row_sum = Float32(0.0)
|
||||
vec_handle = s_corr_prod.acquire_and_advance()
|
||||
scale_log2 = Float32(self.scale_softmax_log2)
|
||||
|
||||
for kt in range(n_kv_tiles):
|
||||
si_handle = mma_s_cons.wait_and_advance()
|
||||
tTMEM_LOADrS = cute.make_rmem_tensor(tTMEM_LOADcS.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load, tTMEM_LOADtS, tTMEM_LOADrS)
|
||||
cute.arch.fence_view_async_tmem_load()
|
||||
|
||||
# Row max (element-wise fmax)
|
||||
old_row_max = row_max
|
||||
frg_cnt = 4
|
||||
frg_tile = cute.size(tTMEM_LOADrS) // frg_cnt
|
||||
tTMEM_LOADrS_frg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(frg_tile))
|
||||
for j in range(frg_cnt):
|
||||
for k in range(cute.size(tTMEM_LOADrS_frg, mode=[0])):
|
||||
row_max = cute.arch.fmax(row_max, tTMEM_LOADrS_frg[k, j] * scale_log2)
|
||||
|
||||
row_max_safe = row_max
|
||||
if row_max == -cutlass.Float32.inf: row_max_safe = Float32(0.0)
|
||||
|
||||
# Vec = [old_max, new_max] for correction
|
||||
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()
|
||||
|
||||
# Scale row_sum and compute P
|
||||
acc_scale_ = scale_log2 * (old_row_max - row_max_safe)
|
||||
acc_scale = cute.math.exp2(acc_scale_, fastmath=True)
|
||||
if old_row_max == -cutlass.Float32.inf: acc_scale = Float32(0.0)
|
||||
row_sum *= acc_scale
|
||||
rP_words = cute.make_rmem_tensor(tTMEM_STOREcP.shape, self.qk_acc_dtype)
|
||||
rP_bf16 = cute.make_tensor(cute.recast_ptr(rP_words.iterator, dtype=self.q_dtype), tTMEM_LOADrS.layout)
|
||||
minus_row_max_scale = (Float32(0.0) - row_max_safe) * scale_log2
|
||||
rP_bf16_frg = cute.logical_divide(rP_bf16, cute.make_layout(frg_tile))
|
||||
for j in range(frg_cnt):
|
||||
for k in range(cute.size(tTMEM_LOADrS_frg, mode=[0])):
|
||||
tTMEM_LOADrS_frg[k, j] = tTMEM_LOADrS_frg[k, j] * scale_log2 + minus_row_max_scale
|
||||
tTMEM_LOADrS_frg[k, j] = cute.math.exp2(tTMEM_LOADrS_frg[k, j], fastmath=True)
|
||||
row_sum = row_sum + tTMEM_LOADrS_frg[k, j]
|
||||
s_vec = tTMEM_LOADrS_frg[None, j].load()
|
||||
rP_bf16_frg[None, j].store(s_vec.to(self.q_dtype))
|
||||
|
||||
cute.copy(tiled_tmem_store, rP_words, tTMEM_STOREtP)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
si_handle.release()
|
||||
vec_handle = s_corr_prod.acquire_and_advance()
|
||||
|
||||
# Final vec = [row_sum, row_max]
|
||||
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()
|
||||
s_corr_prod.acquire() # balance final pipe step
|
||||
s_corr_prod.tail()
|
||||
cute.arch.mbarrier_arrive(st.tmem_dealloc)
|
||||
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):
|
||||
tmem.wait_for_alloc()
|
||||
corr_idx = tidx % (32 * len(self.correction_warp_ids))
|
||||
# Vec load setup
|
||||
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)
|
||||
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_load_vec = tiled_tmem_load_vec.get_slice(corr_idx)
|
||||
tTMEM_LOAD_VECtS = thr_load_vec.partition_S(tStS_vec)
|
||||
tScS_vec = cute.make_tensor(tScS.iterator, cute.composition(tScS.layout, cute.make_layout((128, 2))))
|
||||
tTMEM_LOAD_VECcS = thr_load_vec.partition_D(tScS_vec)
|
||||
# O rescale setup (matching CUTLASS correction_rescale)
|
||||
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(tOtO.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(tOtO.iterator, tOtO_i_layout)
|
||||
tOcO_i = cute.make_tensor(tOcO.iterator, tOcO_i_layout)
|
||||
tmem_load_o_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.pv_acc_dtype)
|
||||
tmem_store_o_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.pv_acc_dtype)
|
||||
tiled_tmem_load_o = tcgen05.make_tmem_copy(tmem_load_o_atom, tOtO_i)
|
||||
tiled_tmem_store_o = tcgen05.make_tmem_copy(tmem_store_o_atom, tOtO_i)
|
||||
thr_load_o = tiled_tmem_load_o.get_slice(corr_idx)
|
||||
thr_store_o = tiled_tmem_store_o.get_slice(corr_idx)
|
||||
tTMEM_LOAD_OtO = thr_load_o.partition_S(tOtO_i)
|
||||
tTMEM_LOAD_OcO = thr_load_o.partition_D(tOcO_i)
|
||||
tTMEM_STORE_OtO = thr_store_o.partition_D(tOtO_i)
|
||||
scale_log2 = Float32(self.scale_softmax_log2)
|
||||
|
||||
# Correction rescale loop: for each KV tile (except first), rescale O
|
||||
first_vec = s_corr_cons.wait_and_advance(); first_vec.release()
|
||||
for kt in range(n_kv_tiles - 1):
|
||||
vec = s_corr_cons.wait_and_advance()
|
||||
# Read vec = [old_max, new_max]
|
||||
tTMEM_LOAD_VECrS = cute.make_rmem_tensor(tTMEM_LOAD_VECcS.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load_vec, tTMEM_LOAD_VECtS, tTMEM_LOAD_VECrS)
|
||||
cute.arch.fence_view_async_tmem_load()
|
||||
old_max = tTMEM_LOAD_VECrS[0]; new_max = tTMEM_LOAD_VECrS[1]
|
||||
corr_scale = cute.math.exp2(scale_log2 * (old_max - new_max), fastmath=True)
|
||||
# Wait for O from MMA, rescale O in TMEM
|
||||
o_handle = mma_corr_cons.wait_and_advance()
|
||||
o_col_tiles = self.pv_mma_tiler[1] // corr_tile_size
|
||||
for i in range(o_col_tiles):
|
||||
tTMEM_LOAD_O_i = cute.make_tensor(tTMEM_LOAD_OtO.iterator + i * corr_tile_size, tTMEM_LOAD_OtO.layout)
|
||||
tTMEM_STORE_O_i = cute.make_tensor(tTMEM_STORE_OtO.iterator + i * corr_tile_size, tTMEM_STORE_OtO.layout)
|
||||
tTMrO = cute.make_rmem_tensor(tTMEM_LOAD_OcO.shape, self.pv_acc_dtype)
|
||||
cute.copy(tiled_tmem_load_o, tTMEM_LOAD_O_i, tTMrO)
|
||||
for k in cutlass.range(cute.size(tTMrO), vectorize=True):
|
||||
tTMrO[k] = tTMrO[k] * corr_scale
|
||||
cute.copy(tiled_tmem_store_o, tTMrO, tTMEM_STORE_O_i)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
o_handle.release(); vec.release()
|
||||
|
||||
# Final: read [row_sum, row_max], normalize O, write to SMEM via epilogue_tma_store
|
||||
final_vec = s_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()
|
||||
row_sum = tTMEM_LOAD_VECrS[0]; row_max = tTMEM_LOAD_VECrS[1]
|
||||
final_vec.release()
|
||||
|
||||
final_o = mma_corr_cons.wait_and_advance()
|
||||
epi_handle = corr_epi_prod.acquire_and_advance()
|
||||
|
||||
# Normalize O in TMEM by 1/row_sum
|
||||
inv_row_sum = Float32(1.0) / row_sum
|
||||
for i in range(self.pv_mma_tiler[1] // corr_tile_size):
|
||||
tTMEM_LOAD_O_i = cute.make_tensor(tTMEM_LOAD_OtO.iterator + i * corr_tile_size, tTMEM_LOAD_OtO.layout)
|
||||
tTMEM_STORE_O_i = cute.make_tensor(tTMEM_STORE_OtO.iterator + i * corr_tile_size, tTMEM_STORE_OtO.layout)
|
||||
tTMrO = cute.make_rmem_tensor(tTMEM_LOAD_OcO.shape, self.pv_acc_dtype)
|
||||
cute.copy(tiled_tmem_load_o, tTMEM_LOAD_O_i, tTMrO)
|
||||
for k in cutlass.range(cute.size(tTMrO), vectorize=True):
|
||||
tTMrO[k] = tTMrO[k] * inv_row_sum
|
||||
cute.copy(tiled_tmem_store_o, tTMrO, tTMEM_STORE_O_i)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
final_o.release()
|
||||
epi_handle.commit()
|
||||
cute.arch.mbarrier_arrive(st.tmem_dealloc)
|
||||
|
||||
# ==================== EPILOGUE WARP (10) ====================
|
||||
if warp_idx == self.epilogue_warp_id:
|
||||
tmem.wait_for_alloc()
|
||||
tmem.allocate(self.num_tmem_alloc_cols)
|
||||
tmem_ptr = tmem.retrieve_ptr(self.qk_acc_dtype)
|
||||
# Wait for correction to finish normalizing O
|
||||
epi_handle = corr_epi_cons.wait_and_advance()
|
||||
# Use epilogue_tma_store to write O from TMEM to GMEM
|
||||
tCtO_base = cute.make_tensor(tmem_ptr + self.tmem_o0_offset, tCtO_fake.layout)
|
||||
acc_cons_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Consumer, 1)
|
||||
c_grp = pipeline.CooperativeGroup(pipeline.Agent.Thread, 32)
|
||||
c_pipe = pipeline.PipelineTmaStore.create(num_stages=self.num_c_stage, producer_group=c_grp)
|
||||
acc_pipe = 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=c_grp, cta_layout_vmnk=cl_vmnk, defer_sync=True)
|
||||
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()
|
||||
epi_handle.release()
|
||||
tmem.relinquish_alloc_permit()
|
||||
tmem.free(tmem_ptr)
|
||||
def test():
|
||||
torch.manual_seed(42)
|
||||
for n in [128]:
|
||||
for seed in [42, 123, 999]:
|
||||
torch.manual_seed(seed)
|
||||
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()
|
||||
scale = 1.0 / math.sqrt(hd)
|
||||
attn = qf @ kf.T * scale
|
||||
attn = torch.softmax(attn, dim=-1)
|
||||
ref = attn @ v.float()
|
||||
mQ = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
|
||||
mK = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
|
||||
mV = ct.from_dlpack(v_kernel).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_kernel))
|
||||
mC = ct.from_dlpack(c).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c))
|
||||
stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
|
||||
kernel = FmhaV3StageC2()
|
||||
if seed == 42:
|
||||
print(f'seed={seed}: Compiling...', flush=True)
|
||||
compiled = cute.compile(kernel, mQ, mK, mV, mC, stream)
|
||||
if seed == 42:
|
||||
print(f'tmem_offsets: s0={kernel.tmem_s0_offset} p0={kernel.tmem_p0_offset} o0={kernel.tmem_o0_offset} alloc={kernel.num_tmem_alloc_cols}', flush=True)
|
||||
compiled(mQ, mK, mV, mC, stream)
|
||||
torch.cuda.synchronize()
|
||||
out = c[:,:,0].float()
|
||||
cos = torch.nn.functional.cosine_similarity(out.flatten().unsqueeze(0), ref.flatten().unsqueeze(0)).item()
|
||||
print(f'FMHA Stage-C n={n} seed={seed}: cosine {cos:.6f} {"PASS" if cos >= 0.99 else "FAIL"}')
|
||||
if cos < 0.99:
|
||||
print(f' out[0,:4]={out[0,:4].tolist()} ref[0,:4]={ref[0,:4].tolist()}')
|
||||
|
||||
if __name__ == '__main__':
|
||||
test()
|
||||
Reference in New Issue
Block a user