Consolidate FMHA stages A/B/C into unified kernel module with SMEM-P stub
This commit is contained in:
@@ -1,8 +1,8 @@
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"""FMHA kernel: QK -> online softmax -> PV (CuTeDSL, Blackwell SM100).
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"""FMHA kernel: QK → online softmax → PV (CuTeDSL, Blackwell SM100).
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Stages A/B/C/D1. HEAD_DIM parameterized via constructor.
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PV GEMM uses SMEM for A operand (P), eliminating TMEM layout mismatch.
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P is computed in softmax warps and written to SMEM, then MMA reads from SMEM.
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Unified module consolidating Stages A/B/C (TMEM-P, hd=64) and D1 (SMEM-P, hd>64).
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use_smem_p=False (TMEM-P): P stored to TMEM via register bridge, PV reads from TMEM.
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use_smem_p=True (SMEM-P): P stored to SMEM, PV reads from SMEM (copy TODO — zeroed stub).
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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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@@ -15,17 +15,18 @@ import math
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class FmhaKernel:
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def __init__(self, head_dim=64, s_k=128, scale_softmax=None, kv_stage=2):
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def __init__(self, head_dim=64, s_k=128, scale_softmax=None, kv_stage=2, use_smem_p=False):
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self.head_dim = head_dim
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self.s_k = s_k
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self.n_kv_tiles = s_k // 128
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self.pv_n_tile = min(head_dim, 256)
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self.n_pv_tiles = head_dim // self.pv_n_tile
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self.use_smem_p = use_smem_p if use_smem_p is not None else (head_dim > 64)
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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.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 = kv_stage; self.q_stage = 1; self.num_c_stage = 2
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self.scale_softmax = scale_softmax if scale_softmax is not None else 1.0 / math.sqrt(head_dim)
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@@ -38,34 +39,65 @@ class FmhaKernel:
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pv_ik = cute.size(pv_mma.shape_mnk, mode=[2])
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self.pv_mma_tiler = (128, self.pv_n_tile, pv_ik * (128 // pv_ik))
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self.mma_tiler = self.qk_mma_tiler
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self.cluster_layout_vmnk = cute.tiled_divide(cute.make_layout((1,1,1)), (qk_mma.thr_id.shape,))
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self.cta_tile_shape_mnk = (self.qk_mma_tiler[0]//cute.size(qk_mma.thr_id.shape), self.pv_n_tile, self.qk_mma_tiler[2])
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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 = (
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self.qk_mma_tiler[0] // cute.size(qk_mma.thr_id.shape),
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self.pv_n_tile,
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self.qk_mma_tiler[2],
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)
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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.epi_tile = utils.sm100.compute_epilogue_tile_shape(
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self.cta_tile_shape_mnk, False, self.c_layout, self.o_dtype
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)
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self.num_ab_stage = 1; self.num_acc_stage = 1
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# SMEM layouts
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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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# P SMEM: always allocate (PV A-operand SMEM layout); used directly in SMEM-P, as TMEM alias in TMEM-P
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self.p_smem_s = utils.sm100.make_smem_layout_a(pv_mma, self.pv_mma_tiler, self.q_dtype, 1)
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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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# TMEM: only S (QK result). P is in SMEM, O also in TMEM.
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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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# TMEM layout depends on path
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qk_thr = qk_mma.get_slice(0)
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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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pv_thr = pv_mma.get_slice(0); pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
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pv_thr = pv_mma.get_slice(0)
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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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self.tmem_s0_offset = 0; self.tmem_o0_offset = 0 # S and O share TMEM (sequential)
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s_cols = self.qk_mma_tiler[1]
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o_cols = find_tmem_tensor_col_offset(tOtO)
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total = max(s_cols, o_cols)
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if not self.use_smem_p:
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# TMEM-P: S at 0, P at 32, O after P and S
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self.tmem_s0_offset = 0
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self.tmem_p0_offset = 32
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s_cols = self.qk_mma_tiler[1]
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p_cols = self.pv_mma_tiler[1] * self.q_dtype.width // self.qk_acc_dtype.width
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self.tmem_o0_offset = max(s_cols, p_cols)
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o_cols = find_tmem_tensor_col_offset(tOtO)
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total = self.tmem_o0_offset + o_cols
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else:
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# SMEM-P: S and O share TMEM (sequential, no P in TMEM)
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self.tmem_s0_offset = 0
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self.tmem_o0_offset = 0
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s_cols = self.qk_mma_tiler[1]
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o_cols = find_tmem_tensor_col_offset(tOtO)
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total = max(s_cols, o_cols)
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self.num_tmem_alloc_cols = 1
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while self.num_tmem_alloc_cols < total:
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self.num_tmem_alloc_cols *= 2
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if self.num_tmem_alloc_cols > 512:
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print(f"⚠️ TMEM BUDGET: {self.num_tmem_alloc_cols} cols (hd={hd})")
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# P TMEM alias (PV A-operand viewed as TMEM for partition mapping)
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self.p_tmem_s = tStS # reuses QK C-fragment TMEM layout for P partition
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# TMA bytes
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cta = cute.size(qk_mma.thr_id.shape)
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q_s = cute.slice_(self.q_smem_s,(None,None,None,0))
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k_s = cute.slice_(self.k_smem_s,(None,None,None,0))
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v_s = cute.slice_(self.v_smem_s,(None,None,None,0))
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q_s = cute.slice_(self.q_smem_s, (None, None, None, 0))
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k_s = cute.slice_(self.k_smem_s, (None, None, None, 0))
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v_s = cute.slice_(self.v_smem_s, (None, None, None, 0))
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self.q_tx_bytes = cute.size_in_bytes(self.q_dtype, q_s) * cta
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self.kv_tx_bytes = (cute.size_in_bytes(self.q_dtype, k_s) + cute.size_in_bytes(self.q_dtype, v_s)) * cta
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@@ -78,72 +110,164 @@ class FmhaKernel:
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v_fmha = cute.make_tensor(v.iterator, cute.make_layout((v_n, self.s_k, 1), stride=(1, v_n, v_n * self.s_k)))
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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, self.a_major, self.v_major, self.qk_acc_dtype, self.cta_group, (128,self.pv_n_tile), tcgen05.OperandSource.SMEM)
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qk_mma = utils.sm100.make_trivial_tiled_mma(
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self.q_dtype, self.q_dtype, self.a_major, self.b_major, self.qk_acc_dtype,
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self.cta_group, (128, 128), tcgen05.OperandSource.SMEM,
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)
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pv_src = tcgen05.OperandSource.SMEM if self.use_smem_p else tcgen05.OperandSource.TMEM
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pv_mma = utils.sm100.make_trivial_tiled_mma(
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self.q_dtype, self.q_dtype, self.a_major, self.v_major, self.qk_acc_dtype,
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self.cta_group, (128, self.pv_n_tile), pv_src,
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)
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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_smem_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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q_s = cute.slice_(self.q_smem_s, (None, None, None, 0))
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k_s = cute.slice_(self.k_smem_s, (None, None, None, 0))
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v_s = cute.slice_(self.v_smem_s, (None, None, None, 0))
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tma_q, mQ = cute.nvgpu.make_tiled_tma_atom_A(
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utils.sm100.cluster_shape_to_tma_atom_A(self.cluster_shape_mn, qk_mma.thr_id),
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q, q_s, self.qk_mma_tiler, qk_mma, self.cluster_layout_vmnk.shape,
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)
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tma_k, mK = cute.nvgpu.make_tiled_tma_atom_B(
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utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn, qk_mma.thr_id),
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k, k_s, self.qk_mma_tiler, qk_mma, self.cluster_layout_vmnk.shape,
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)
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tma_v, mV = cute.nvgpu.make_tiled_tma_atom_B(
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utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn, pv_mma.thr_id),
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v_fmha, v_s, self.pv_mma_tiler, pv_mma, self.cluster_layout_vmnk.shape,
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)
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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(
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qk_mma, pv_mma, tma_q, mQ, tma_k, mK, tma_v, mV, tma_c, mC,
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self.cluster_layout_vmnk,
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self.q_smem_s, self.k_smem_s, self.v_smem_s, self.p_smem_s, self.c_smem_s,
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self.epi_tile,
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).launch(
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grid=(1, 1, 1), block=[self.threads_per_cta, 1, 1], stream=stream,
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)
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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_smem_s, c_smem_s, epi_tile):
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def _kernel(
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self, qk_mma, pv_mma, tma_q, mQ, tma_k, mK, tma_v, mV, tma_c, mC,
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cl_vmnk, q_smem_s, k_smem_s, v_smem_s, p_smem_s, c_smem_s, epi_tile,
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):
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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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use_smem_p = self.use_smem_p
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# ── TMA warp: prefetch descriptors ──
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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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cpasync.prefetch_descriptor(tma_q)
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cpasync.prefetch_descriptor(tma_k)
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cpasync.prefetch_descriptor(tma_v)
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cpasync.prefetch_descriptor(tma_c)
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# ── Shared storage ──
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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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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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acc_bar: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage * 2]
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tmem_dealloc: cutlass.Int64; holding: cutlass.Int32
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smem = utils.SmemAllocator(); st = smem.allocate(SS)
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qp,qc = pipeline.PipelineTmaUmma.create(barrier_storage=st.q_bar.data_ptr(),num_stages=self.q_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.q_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
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kvp,kvc = pipeline.PipelineTmaUmma.create(barrier_storage=st.kv_bar.data_ptr(),num_stages=self.kv_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.kv_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
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s_prod,s_cons = pipeline.PipelineUmmaAsync.create(barrier_storage=st.s_bar.data_ptr(),num_stages=1,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32*len(self.epilogue_warp_id))).make_participants()
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softmax_done_bar = pipeline.NamedBarrier(barrier_id=3, num_threads=32 + 32*len(self.epilogue_warp_id))
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final_o_bar = pipeline.NamedBarrier(barrier_id=4, num_threads=32 + 32*len(self.epilogue_warp_id))
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acc_pipe = pipeline.PipelineUmmaAsync.create(barrier_storage=st.acc_bar.data_ptr(),num_stages=self.num_acc_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,len(self.epilogue_warp_id)),cta_layout_vmnk=cl_vmnk,defer_sync=True)
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tmem_bar = pipeline.NamedBarrier(barrier_id=2,num_threads=32*len((self.mma_warp_id,*self.epilogue_warp_id)))
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tmem = utils.TmemAllocator(st.holding.ptr,barrier_for_retrieve=tmem_bar,allocator_warp_id=self.epilogue_warp_id[0],is_two_cta=cute.size(qk_mma.thr_id.shape)==2,two_cta_tmem_dealloc_mbar_ptr=st.tmem_dealloc.ptr)
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pipeline.pipeline_init_arrive(cluster_shape_mn=cl_vmnk,is_relaxed=True)
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sQ = smem.allocate_tensor(element_type=self.q_dtype,layout=q_smem_s.outer,byte_alignment=128,swizzle=q_smem_s.inner)
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sK = smem.allocate_tensor(element_type=self.q_dtype,layout=k_smem_s.outer,byte_alignment=128,swizzle=k_smem_s.inner)
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sV = smem.allocate_tensor(element_type=self.q_dtype,layout=v_smem_s.outer,byte_alignment=128,swizzle=v_smem_s.inner)
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sP = smem.allocate_tensor(element_type=self.q_dtype,layout=p_smem_s.outer,byte_alignment=128,swizzle=p_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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qp, qc = pipeline.PipelineTmaUmma.create(
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barrier_storage=st.q_bar.data_ptr(), num_stages=self.q_stage,
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producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),
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consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread, 1),
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tx_count=self.q_tx_bytes, cta_layout_vmnk=cl_vmnk, defer_sync=True,
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).make_participants()
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kvp, kvc = pipeline.PipelineTmaUmma.create(
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barrier_storage=st.kv_bar.data_ptr(), num_stages=self.kv_stage,
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producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),
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consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread, 1),
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tx_count=self.kv_tx_bytes, cta_layout_vmnk=cl_vmnk, defer_sync=True,
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).make_participants()
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s_prod, s_cons = pipeline.PipelineUmmaAsync.create(
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barrier_storage=st.s_bar.data_ptr(), num_stages=1,
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producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),
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consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread, 32 * len(self.epilogue_warp_id)),
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).make_participants()
|
||||
softmax_done_bar = pipeline.NamedBarrier(barrier_id=3, num_threads=32 + 32 * len(self.epilogue_warp_id))
|
||||
final_o_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)
|
||||
|
||||
# ── SMEM tensors ──
|
||||
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)
|
||||
sP = smem.allocate_tensor(element_type=self.q_dtype, layout=p_smem_s.outer, byte_alignment=128, swizzle=p_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)
|
||||
|
||||
# ── Gmem tensors ──
|
||||
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))
|
||||
|
||||
# ── Thread partitions ──
|
||||
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)]
|
||||
|
||||
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)]
|
||||
|
||||
# Register fragments
|
||||
tCrQ = qk_mma.make_fragment_A(sQ); tCrK = qk_mma.make_fragment_B(sK)
|
||||
tCrV = pv_mma.make_fragment_B(sV); tCrP = pv_mma.make_fragment_A(sP)
|
||||
# TMEM: S (QK result)
|
||||
tCrV = pv_mma.make_fragment_B(sV)
|
||||
tCrP = pv_mma.make_fragment_A(sP) # used in SMEM-P path
|
||||
|
||||
# ── TMEM: S (QK result) ──
|
||||
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)
|
||||
# TMEM: O (PV result) — same offset as S (sequential, no overlap)
|
||||
|
||||
# ── TMEM: O (PV result) ──
|
||||
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)
|
||||
|
||||
# ── TMEM-P path: PV A-operand from TMEM ──
|
||||
if not use_smem_p:
|
||||
tP = cute.make_tensor(tStS.iterator, self.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 LOAD warp =====
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
# TMA LOAD WARP
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
if warp_idx == self.tma_warp_id:
|
||||
qp.reset(); qh = qp.acquire_and_advance()
|
||||
cute.copy(tma_q, tAgQ[(None, Int32(0))], tAsQ[(None, qh.index)], tma_bar_ptr=qh.barrier)
|
||||
@@ -156,42 +280,57 @@ class FmhaKernel:
|
||||
pk = cutlass.Boolean(1)
|
||||
kvp.tail()
|
||||
|
||||
# ===== MMA warp =====
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
# MMA WARP
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
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(self.n_kv_tiles):
|
||||
kvh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
sh = s_prod.acquire_and_advance()
|
||||
|
||||
# QK GEMM → S in TMEM
|
||||
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,kvh.index)], tStS0)
|
||||
cute.gemm(qk_mma, tStS0, tCrQ[(None, None, kb, 0)], tCrK[(None, None, kb, kvh.index)], tStS0)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
sh.commit()
|
||||
softmax_done_bar.arrive_and_wait()
|
||||
# PV GEMM: P from SMEM, V from SMEM → O in TMEM
|
||||
|
||||
# PV GEMM → O in TMEM
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, kt != 0)
|
||||
for kb in cutlass.range(cute.size(tCrP, mode=[2]), unroll_full=True):
|
||||
cute.gemm(pv_mma, tOtO0, tCrP[(None,None,kb,0)], tCrV[(None,None,kb,kvh.index)], tOtO0)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
if not use_smem_p:
|
||||
# TMEM-P: P from TMEM
|
||||
for kb in cutlass.range(cute.size(tOrP0, mode=[2]), unroll_full=True):
|
||||
cute.gemm(pv_mma, tOtO0, tOrP0[(None, None, kb, 0)], tCrV[(None, None, kb, kvh.index)], tOtO0)
|
||||
else:
|
||||
# SMEM-P: P from SMEM
|
||||
for kb in cutlass.range(cute.size(tCrP, mode=[2]), unroll_full=True):
|
||||
cute.gemm(pv_mma, tOtO0, tCrP[(None, None, kb, 0)], tCrV[(None, None, kb, kvh.index)], tOtO0)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
kvh.release()
|
||||
|
||||
acc_pipe.producer_commit(acc_st); acc_st.advance()
|
||||
final_o_bar.arrive()
|
||||
acc_pipe.producer_tail(acc_st)
|
||||
|
||||
# ===== SOFTMAX + EPILOGUE warps =====
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
# SOFTMAX + EPILOGUE WARPS
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
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 atoms
|
||||
|
||||
# ── 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)
|
||||
@@ -200,30 +339,57 @@ class FmhaKernel:
|
||||
tScS = qk_thr.partition_C(cS)
|
||||
tTMEM_LOADcS = thr_load.partition_D(tScS)
|
||||
|
||||
# P → SMEM: use make_tiled_copy_C for register→SMEM (standard epilogue pattern)
|
||||
# The P values are the A operand of PV, written to SMEM so the MMA can read them
|
||||
p_s = cute.slice_(p_smem_s,(None,None,None,0))
|
||||
tCrP_smem = pv_thr.partition_A(sP) # PV thread → SMEM partition for P (A operand)
|
||||
tCrP_reg = pv_mma.make_fragment_A(sP) # register fragment matching SMEM layout
|
||||
tiled_p_copy = cute.make_tiled_copy_C(pv_mma, tCrP_smem, p_s, 1)
|
||||
# ── TMEM-P: P store setup (register bridge) ──
|
||||
if not use_smem_p:
|
||||
p_cols_fp32 = self.pv_mma_tiler[1] * 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)
|
||||
|
||||
# Online softmax state
|
||||
# ── SMEM-P: P → SMEM copy setup (TODO: proper QK→PV partition remap) ──
|
||||
if use_smem_p:
|
||||
# TODO: make_tiled_copy_C(store_atom, qk_mma) to partition threads by QK's C-fragment
|
||||
# For now, zero sP as a stub — PV will read garbage/zero
|
||||
pass
|
||||
|
||||
# ── O rescale / normalization setup (correction_rescale atoms) ──
|
||||
corr_tile_size = 16
|
||||
o_rescale_atom_ld = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
o_rescale_atom_st = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
o_rescale_layout = cute.composition(tStS.layout, cute.make_layout((self.pv_mma_tiler[0], corr_tile_size)))
|
||||
tiled_o_ld = tcgen05.make_tmem_copy(o_rescale_atom_ld, cute.make_tensor(tStS.iterator + self.tmem_o0_offset, o_rescale_layout))
|
||||
tiled_o_st = tcgen05.make_tmem_copy(o_rescale_atom_st, cute.make_tensor(tStS.iterator + self.tmem_o0_offset, o_rescale_layout))
|
||||
|
||||
# ── Online softmax state ──
|
||||
row_max = -Float32.inf
|
||||
row_sum = Float32(0.0)
|
||||
scale_log2 = Float32(self.scale_softmax_log2)
|
||||
|
||||
# ── Softmax loop ──
|
||||
for kt in range(self.n_kv_tiles):
|
||||
si_handle = s_cons.wait_and_advance()
|
||||
tTMEM_LOADrS = cute.make_rmem_tensor(tTMEM_LOADcS.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load, tTMEM_LOADtS, tTMEM_LOADrS)
|
||||
cute.arch.fence_view_async_tmem_load()
|
||||
|
||||
old_row_max = row_max
|
||||
frg_cnt = 4
|
||||
frg_tile = cute.size(tTMEM_LOADrS) // frg_cnt
|
||||
tTMEM_LOADrS_frg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(frg_tile))
|
||||
|
||||
# Row max
|
||||
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)
|
||||
@@ -234,26 +400,72 @@ class FmhaKernel:
|
||||
row_sum *= acc_scale
|
||||
minus_row_max = Float32(0.0) - row_max_safe
|
||||
|
||||
# Compute P = exp2(S * scale - row_max), convert to BF16, write to SMEM
|
||||
for j in range(frg_cnt):
|
||||
for k in range(cute.size(tTMEM_LOADrS_frg, mode=[0])):
|
||||
tTMEM_LOADrS_frg[k, j] = tTMEM_LOADrS_frg[k, j] * scale_log2 + minus_row_max
|
||||
tTMEM_LOADrS_frg[k, j] = cute.math.exp2(tTMEM_LOADrS_frg[k, j], fastmath=True)
|
||||
row_sum = row_sum + tTMEM_LOADrS_frg[k, j]
|
||||
# Softmax + P store
|
||||
if not use_smem_p:
|
||||
# TMEM-P: register bridge — 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,
|
||||
)
|
||||
rP_bf16_frg = cute.logical_divide(rP_bf16, cute.make_layout(frg_tile))
|
||||
|
||||
# Write P to SMEM using PV A-operand partition
|
||||
# TODO: proper element mapping from QK→PV partition
|
||||
for j in cutlass.range(cute.size(tCrP_reg), vectorize=True):
|
||||
tCrP_reg[j] = BFloat16(0.0)
|
||||
cute.copy(tiled_p_copy, tCrP_reg, tCrP_smem)
|
||||
cute.arch.fence_proxy("async.shared", space="cta")
|
||||
for j in range(frg_cnt):
|
||||
for k in range(cute.size(tTMEM_LOADrS_frg, mode=[0])):
|
||||
tTMEM_LOADrS_frg[k, j] = tTMEM_LOADrS_frg[k, j] * scale_log2 + minus_row_max
|
||||
tTMEM_LOADrS_frg[k, j] = cute.math.exp2(tTMEM_LOADrS_frg[k, j], fastmath=True)
|
||||
row_sum = row_sum + tTMEM_LOADrS_frg[k, j]
|
||||
s_vec = tTMEM_LOADrS_frg[None, j].load()
|
||||
rP_bf16_frg[None, j].store(s_vec.to(self.q_dtype))
|
||||
|
||||
cute.copy(tiled_tmem_store, rP_words, tTMEM_STOREtP)
|
||||
else:
|
||||
# SMEM-P: compute softmax, write P to SMEM (TODO)
|
||||
for j in range(frg_cnt):
|
||||
for k in range(cute.size(tTMEM_LOADrS_frg, mode=[0])):
|
||||
tTMEM_LOADrS_frg[k, j] = tTMEM_LOADrS_frg[k, j] * scale_log2 + minus_row_max
|
||||
tTMEM_LOADrS_frg[k, j] = cute.math.exp2(tTMEM_LOADrS_frg[k, j], fastmath=True)
|
||||
row_sum = row_sum + tTMEM_LOADrS_frg[k, j]
|
||||
|
||||
# TODO: Write P to SMEM using make_tiled_copy_C(store_atom, qk_mma)
|
||||
# to partition threads by QK's C-fragment, then copy to p_smem_s layout.
|
||||
# STUB: zero P in SMEM for now
|
||||
for j in cutlass.range(cute.size(sP), vectorize=True):
|
||||
sP[j] = BFloat16(0.0)
|
||||
cute.arch.fence_proxy("async.shared", space="cta")
|
||||
|
||||
si_handle.release()
|
||||
softmax_done_bar.arrive()
|
||||
|
||||
# Wait for MMA's final PV
|
||||
# ── Per-tile O rescale (multiply O by acc_scale when kt > 0) ──
|
||||
if kt > 0:
|
||||
thr_ld = tiled_o_ld.get_slice(sfw_idx)
|
||||
thr_st = tiled_o_st.get_slice(sfw_idx)
|
||||
tOrO_src = thr_ld.partition_S(cute.make_tensor(tStS.iterator + self.tmem_o0_offset, o_rescale_layout))
|
||||
tOrO_dst = thr_st.partition_D(cute.make_tensor(tStS.iterator + self.tmem_o0_offset, o_rescale_layout))
|
||||
rO = cute.make_rmem_tensor(tOrO_src.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_o_ld, tOrO_src, rO)
|
||||
for i in cutlass.range(cute.size(rO), vectorize=True):
|
||||
rO[i] = rO[i] * acc_scale
|
||||
cute.copy(tiled_o_st, rO, tOrO_dst)
|
||||
|
||||
# ── Wait for MMA's final PV GEMM ──
|
||||
final_o_bar.arrive_and_wait()
|
||||
# Epilogue: raw PV output (unnormalized)
|
||||
|
||||
# ── O normalization: multiply O by 1/row_sum (TMEM round-trip) ──
|
||||
inv_row_sum = Float32(1.0) / row_sum
|
||||
thr_ld = tiled_o_ld.get_slice(sfw_idx)
|
||||
thr_st = tiled_o_st.get_slice(sfw_idx)
|
||||
tOrO_src = thr_ld.partition_S(cute.make_tensor(tStS.iterator + self.tmem_o0_offset, o_rescale_layout))
|
||||
tOrO_dst = thr_st.partition_D(cute.make_tensor(tStS.iterator + self.tmem_o0_offset, o_rescale_layout))
|
||||
rO = cute.make_rmem_tensor(tOrO_src.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_o_ld, tOrO_src, rO)
|
||||
for i in cutlass.range(cute.size(rO), vectorize=True):
|
||||
rO[i] = rO[i] * inv_row_sum
|
||||
cute.copy(tiled_o_st, rO, tOrO_dst)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# ── Epilogue: TMA store O → global ──
|
||||
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))
|
||||
|
||||
Reference in New Issue
Block a user