FMHA Stage-C: real softmax + O normalization in 6-warp layout
- Replace identity softmax with online softmax (row_max, exp2 scaling, P store) - Add row_sum accumulation from P values - After softmax loop, normalize O in TMEM by 1/row_sum using TMEM load/modify/store - Then epilogue writes normalized O from TMEM to GMEM - Reference test uses softmax(Q@K^T/sqrt(d))@V
This commit is contained in:
@@ -1,13 +1,8 @@
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"""
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FMHA v3 Stage-C Full: Production Blackwell pipeline with real softmax + correction.
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Architecture (12-warps, matches CUTLASS FMHA):
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softmax warps 0-3 : S(TMEM) -> softmax -> P(TMEM), vec(TMEM)
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correction warps 4-7 : vec(TMEM) + O(TMEM) -> corrected O(SMEM)
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MMA warp 8 : QK and PV
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TMA/load warp 9 : Q/K/V load
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epilogue warp 10 : corrected O SMEM -> GMEM via TMA
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empty warp 11 : tmem dealloc mbar init
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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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@@ -21,26 +16,16 @@ import math
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HEAD_DIM = 64
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class FmhaV3StageC:
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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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def __init__(self, scale_softmax=None):
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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.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.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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# Softmax scaling
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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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self.scale_output = 1.0
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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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@@ -56,20 +41,27 @@ class FmhaV3StageC:
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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, self.epi_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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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; s_cols = self.qk_mma_tiler[1]
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o_after = max(s_cols, p_end)
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self.tmem_o0_offset = ((o_after + 31) // 32) * 32
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o_cols = find_tmem_tensor_col_offset(tOtO); total = self.tmem_o0_offset + o_cols
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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: self.num_tmem_alloc_cols *= 2
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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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@@ -84,8 +76,8 @@ class FmhaV3StageC:
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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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(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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@@ -95,130 +87,143 @@ class FmhaV3StageC:
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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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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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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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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_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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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_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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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_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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qp,qc = pipeline.PipelineTmaUmma.create(barrier_storage=st.q_bar.data_ptr(),num_stages=self.q_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.q_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
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kvp,kvc = pipeline.PipelineTmaUmma.create(barrier_storage=st.kv_bar.data_ptr(),num_stages=self.kv_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.kv_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
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s_prod,s_cons = pipeline.PipelineUmmaAsync.create(barrier_storage=st.s_bar.data_ptr(),num_stages=1,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32*len(self.epilogue_warp_id))).make_participants()
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softmax_done_bar = pipeline.NamedBarrier(barrier_id=3, num_threads=32 + 32*len(self.epilogue_warp_id))
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acc_pipe = pipeline.PipelineUmmaAsync.create(barrier_storage=st.acc_bar.data_ptr(),num_stages=self.num_acc_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,len(self.epilogue_warp_id)),cta_layout_vmnk=cl_vmnk,defer_sync=True)
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tmem_bar = pipeline.NamedBarrier(barrier_id=2,num_threads=32*len((self.mma_warp_id,*self.epilogue_warp_id)))
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tmem = utils.TmemAllocator(st.holding.ptr,barrier_for_retrieve=tmem_bar,allocator_warp_id=self.epilogue_warp_id[0],is_two_cta=cute.size(qk_mma.thr_id.shape)==2,two_cta_tmem_dealloc_mbar_ptr=st.tmem_dealloc.ptr)
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pipeline.pipeline_init_arrive(cluster_shape_mn=cl_vmnk,is_relaxed=True)
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sQ = smem.allocate_tensor(element_type=self.q_dtype, layout=q_smem_s.outer, byte_alignment=128, swizzle=q_smem_s.inner)
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sK = smem.allocate_tensor(element_type=self.q_dtype, layout=k_smem_s.outer, byte_alignment=128, swizzle=k_smem_s.inner)
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sV = smem.allocate_tensor(element_type=self.q_dtype, layout=v_smem_s.outer, byte_alignment=128, swizzle=v_smem_s.inner)
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sC = smem.allocate_tensor(element_type=self.o_dtype, layout=c_smem_s.outer, byte_alignment=128, swizzle=c_smem_s.inner)
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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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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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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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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)
|
||||
|
||||
# --- 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)
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_as, 1))
|
||||
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 WARP (9) ====================
|
||||
# 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)
|
||||
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):
|
||||
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)
|
||||
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)
|
||||
cute.copy(tma_v,tVgV[(None,vh.count)],tVsV[(None,vh.index)],tma_bar_ptr=vh.barrier)
|
||||
pk = cutlass.Boolean(1)
|
||||
kvp.tail()
|
||||
|
||||
# ==================== MMA WARP (8) ====================
|
||||
# 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):
|
||||
# QK -> S
|
||||
kh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
sh = mma_s_prod.acquire_and_advance()
|
||||
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)
|
||||
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()
|
||||
# PV -> O
|
||||
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)
|
||||
oh = mma_corr_prod.acquire_and_advance()
|
||||
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)
|
||||
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(); oh.commit(); vh.release()
|
||||
mma_s_prod.tail(); mma_corr_prod.tail()
|
||||
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))
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
vh.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()
|
||||
sfw_idx = tidx % (32 * len(self.softmax_warp_ids))
|
||||
# S load setup
|
||||
# SOFTMAX + EPILOGUE (warps 0-3)
|
||||
# Step 1: Real online softmax: load S, compute row_max, exp2 scale, store P
|
||||
# Step 2: After all KV tiles, normalize O in TMEM by row_sum
|
||||
# Step 3: Epilogue writes normalized O from TMEM to GMEM
|
||||
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 layout) ---
|
||||
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 layout composition, FMHA pattern)
|
||||
|
||||
# --- P store (QK C-fragment layout 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)
|
||||
@@ -227,48 +232,38 @@ class FmhaV3StageC:
|
||||
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
|
||||
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)
|
||||
tScP = cute.make_tensor(tScS.iterator, tScP_layout)
|
||||
tTMEM_STOREcP = thr_store.partition_S(tScP)
|
||||
|
||||
# --- Online softmax loop ---
|
||||
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()
|
||||
si_handle = s_cons.wait_and_advance()
|
||||
|
||||
# Load S from TMEM
|
||||
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
|
||||
|
||||
# Update row_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 = Float32(0.0)
|
||||
# Vec = [old_max, new_max]
|
||||
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()
|
||||
# P = exp2((S - new_max) * scale_log2) via register bridge
|
||||
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
|
||||
# Scale existing row_sum
|
||||
|
||||
# Scale existing row_sum: row_sum *= exp2((old_max - new_max) * scale_log2)
|
||||
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
|
||||
|
||||
# P = exp2((S - new_max) * scale_log2) via register bridge
|
||||
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
|
||||
|
||||
frg_cnt = 4
|
||||
frg_tile = cute.size(tTMEM_LOADrS) // frg_cnt
|
||||
tTMEM_LOADrS_frg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(frg_tile))
|
||||
@@ -279,39 +274,19 @@ class FmhaV3StageC:
|
||||
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))
|
||||
# Accumulate row_sum from P values
|
||||
for k in cutlass.range(cute.size(tTMEM_LOADrS_frg, mode=[0])):
|
||||
row_sum = row_sum + tTMEM_LOADrS_frg[k, j]
|
||||
|
||||
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()
|
||||
softmax_done_bar.arrive()
|
||||
|
||||
# Final vec = [row_sum, row_max] for correction epilog
|
||||
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
|
||||
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_layout = cute.composition(tScS.layout, cute.make_layout((128, 2)))
|
||||
tScS_vec = cute.make_tensor(tScS.iterator, tScS_vec_layout)
|
||||
tTMEM_LOAD_VECcS = thr_load_vec.partition_D(tScS_vec)
|
||||
# O load/store for correction_rescale (matching CUTLASS pattern)
|
||||
# --- Normalize O in TMEM by row_sum ---
|
||||
# O is at tmem_o0_offset in TMEM. Load each element, divide by row_sum, store back.
|
||||
# Use the O TMEM layout (pv_thr C-fragment) for the load/store.
|
||||
# We need a tiled TMEM copy for O.
|
||||
cO = cute.make_identity_tensor((self.pv_mma_tiler[0], self.pv_mma_tiler[1]))
|
||||
tOcO = pv_thr.partition_C(cO)
|
||||
corr_tile_size = 16
|
||||
@@ -319,98 +294,38 @@ class FmhaV3StageC:
|
||||
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)
|
||||
tmem_load_o_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.acc_dtype)
|
||||
tmem_store_o_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.acc_dtype)
|
||||
tiled_tmem_load_o = tcgen05.make_tmem_copy(tmem_load_o_atom, tOtO_i)
|
||||
tiled_tmem_store_o = tcgen05.make_tmem_copy(tmem_store_o_atom, tOtO_i)
|
||||
thr_load_o = tiled_tmem_load_o.get_slice(corr_idx)
|
||||
thr_store_o = tiled_tmem_store_o.get_slice(corr_idx)
|
||||
thr_load_o = tiled_tmem_load_o.get_slice(sfw_idx)
|
||||
thr_store_o = tiled_tmem_store_o.get_slice(sfw_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)
|
||||
|
||||
# First vec has no previous O to rescale
|
||||
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]
|
||||
# scale = exp2((old_max - new_max) * scale_log2)
|
||||
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_i_ = cute.make_rmem_tensor(tTMEM_LOAD_OcO.shape, self.pv_acc_dtype)
|
||||
tTMrO_i_layout = cute.composition(tTMrO_i_.layout, cute.make_layout(tTMEM_LOAD_OcO.shape[0]))
|
||||
tTMrO_i = cute.make_tensor(tTMrO_i_.iterator, tTMrO_i_layout)
|
||||
cute.copy(tiled_tmem_load_o, tTMEM_LOAD_O_i, tTMrO_i)
|
||||
for k in cutlass.range(cute.size(tTMrO_i), vectorize=True):
|
||||
tTMrO_i[k] = tTMrO_i[k] * corr_scale
|
||||
cute.copy(tiled_tmem_store_o, tTMrO_i, 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
|
||||
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()
|
||||
|
||||
# Correction epilog: load O from TMEM, normalize, convert to BF16, write SMEM
|
||||
# Following CUTLASS correction_epilog pattern
|
||||
corr_tile_size_epi = 32 * 8 // self.o_dtype.width
|
||||
tOsO = pv_thr.partition_C(sC)
|
||||
tOcO_epi = pv_thr.partition_C(cO)
|
||||
tOtO_i_epi = cute.logical_divide(tOtO, cute.make_layout((128, corr_tile_size_epi)))
|
||||
tOcO_i_epi = cute.logical_divide(tOcO_epi, cute.make_layout((128, corr_tile_size_epi)))
|
||||
tOsO_i = cute.logical_divide(tOsO, cute.make_layout((128, corr_tile_size_epi)))
|
||||
|
||||
epi_subtile = (self.epi_tile[0], corr_tile_size_epi)
|
||||
tmem_copy_atom = utils.sm100.get_tmem_load_op(self.pv_mma_tiler, self.c_layout, self.o_dtype, self.pv_acc_dtype, epi_subtile, use_2cta_instrs=False)
|
||||
tiled_tmem_load_epi = tcgen05.make_tmem_copy(tmem_copy_atom, tOtO_i_epi[(None, None), 0])
|
||||
thr_tmem_load_epi = tiled_tmem_load_epi.get_slice(corr_idx)
|
||||
smem_copy_atom = utils.sm100.get_smem_store_op(self.c_layout, self.o_dtype, self.pv_acc_dtype, tiled_tmem_load_epi)
|
||||
tiled_smem_store = cute.make_tiled_copy_D(smem_copy_atom, tiled_tmem_load_epi)
|
||||
|
||||
tTMEM_LOAD_EPItO = thr_tmem_load_epi.partition_S(tOtO_i_epi[(None, None), None])
|
||||
tTMEM_LOAD_EPIdS = thr_tmem_load_epi.partition_D(tOsO_i[(None, None), None])
|
||||
tTMEM_LOAD_EPIdO = thr_tmem_load_epi.partition_D(tOcO_i_epi[(None, None), None])
|
||||
|
||||
inv_row_sum = Float32(1.0) / row_sum
|
||||
for i in range(self.pv_mma_tiler[1] // corr_tile_size_epi):
|
||||
tTMrO = cute.make_rmem_tensor(tTMEM_LOAD_EPIdO[None, 0, 0, i].shape, self.pv_acc_dtype)
|
||||
cute.copy(tiled_tmem_load_epi, tTMEM_LOAD_EPItO[None, 0, 0, i], tTMrO)
|
||||
for k in cutlass.range(cute.size(tTMrO), vectorize=True):
|
||||
tTMrO[k] = tTMrO[k] * inv_row_sum
|
||||
tSMrO = cute.make_rmem_tensor(tTMrO.shape, self.o_dtype)
|
||||
tSMrO.store(tTMrO.load().to(self.o_dtype))
|
||||
cute.copy(tiled_smem_store, tSMrO, tTMEM_LOAD_EPIdS[None, 0, 0, i])
|
||||
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_i_ = cute.make_rmem_tensor(tTMEM_LOAD_OcO.shape, self.acc_dtype)
|
||||
tTMrO_i_layout = cute.composition(tTMrO_i_.layout, cute.make_layout(tTMEM_LOAD_OcO.shape[0]))
|
||||
tTMrO_i = cute.make_tensor(tTMrO_i_.iterator, tTMrO_i_layout)
|
||||
cute.copy(tiled_tmem_load_o, tTMEM_LOAD_O_i, tTMrO_i)
|
||||
for k in cutlass.range(cute.size(tTMrO_i), vectorize=True):
|
||||
tTMrO_i[k] = tTMrO_i[k] * inv_row_sum
|
||||
cute.copy(tiled_tmem_store_o, tTMrO_i, tTMEM_STORE_O_i)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
cute.arch.fence_proxy("async.shared", space="cta")
|
||||
final_o.release()
|
||||
epi_handle.commit()
|
||||
cute.arch.mbarrier_arrive(st.tmem_dealloc)
|
||||
|
||||
# ==================== EPILOGUE WARP (10) ====================
|
||||
if warp_idx == self.epilogue_warp_id:
|
||||
epi_handle = corr_epi_cons.wait_and_advance()
|
||||
# TMA store O from SMEM to GMEM
|
||||
cute.copy(tma_c, sC, tCgC[(None, 0)])
|
||||
cute.arch.cp_async_bulk_commit_group()
|
||||
cute.arch.cp_async_bulk_wait_group(0, read=True)
|
||||
epi_handle.release()
|
||||
# --- Epilogue: 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, 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():
|
||||
@@ -422,7 +337,7 @@ def test():
|
||||
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')
|
||||
# Reference: softmax(Q @ K^T) @ V
|
||||
# Reference: softmax(Q @ K^T / sqrt(d)) @ V
|
||||
qf = q[:,:,0].float(); kf = k[:,:,0].float()
|
||||
scale = 1.0 / math.sqrt(hd)
|
||||
attn = qf @ kf.T * scale
|
||||
@@ -433,10 +348,10 @@ def test():
|
||||
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 = FmhaV3StageC(s_k=n)
|
||||
kernel = FmhaV3StageC()
|
||||
print(f'n={n}: Compiling...', flush=True)
|
||||
compiled = cute.compile(kernel, mQ, mK, mV, mC, stream)
|
||||
print(f'n={n}: tmem_offsets: s0={kernel.tmem_s0_offset} vec0={kernel.tmem_vec0_offset} p0={kernel.tmem_p0_offset} o0={kernel.tmem_o0_offset} alloc={kernel.num_tmem_alloc_cols}', flush=True)
|
||||
print(f'n={n}: tmem_offsets: s0={kernel.tmem_s0_offset} p0={kernel.tmem_p0_offset} o0={kernel.tmem_o0_offset} alloc={kernel.num_tmem_alloc_cols}', flush=True)
|
||||
print(f'n={n}: Running...', flush=True)
|
||||
compiled(mQ, mK, mV, mC, stream)
|
||||
torch.cuda.synchronize()
|
||||
|
||||
Reference in New Issue
Block a user