Clean up tests: archive superseded files, keep only essential unit tests
Kept in tests/unit/: - test_fmha_v3.py (stages A+B) - test_fmha_v3_diag.py (identity softmax, n=128+256) - test_fmha_v3_stage_c.py (real softmax, n=128 cos 0.999998) - layertest.py + cudagraph_test.py (required for every change) - infrastructure: cache, custom_op, cutedsl, router, fp4, fused, interleave Archived: 19 superseded unit tests + 10 root-level scratch files Root level: only fmha_v3_stage_c_example7.py remains (now in unit/)
This commit is contained in:
@@ -1,385 +0,0 @@
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"""
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Minimal PV-only test: Load P from GMEM to TMEM via QK-style MMA, then PV from TMEM.
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Step 1: QK MMA writes FP32 S to TMEM (we know this works)
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Step 2: Softmax packing writes BF16 P to TMEM (test this)
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Step 3: PV MMA reads BF16 P from TMEM and V from SMEM, produces O
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But to isolate the bug, let me test just the PV MMA in isolation.
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I'll write known BF16 values to TMEM using the softmax packing path,
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then immediately read them back using the PV A-fragment path,
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and compare.
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Actually, the simplest isolation test:
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1. Do QK MMA to get S in TMEM (cosine 0.999999 verified)
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2. Do softmax packing: S → P in TMEM (at offset 32)
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3. Skip PV entirely — read P from TMEM using the C-fragment composition LOAD path
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4. Output P to GMEM and compare against S.to(BF16)
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This tests whether the softmax packing writes P correctly to the same TMEM
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that the PV would read from.
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But we can't easily read P from TMEM using the standard epilogue path
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because the epilogue expects FP32 accumulator data.
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Alternative: Use the PV MMA with V=I (identity). If P is correct,
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then P @ I = P. But V needs to be MN-major and (128, 128), not (128, 64).
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The output would be (128, 128) which doesn't match our (128, 64) c tensor.
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Let me use V that selects the first 64 columns: V[k, n] = delta(k, n) for k in [0,63].
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This gives P @ V = P[:, :64], and the output is (128, 64).
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But V is (128, 128) in the MMA K,N dims. V[k, n] for k in [0,127], n in [0,63].
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Hmm, this is getting complicated. Let me just do the identity approach with a (128, 128) output.
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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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class VDiag128:
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"""QK + softmax packing + PV with V=I to isolate PV MMA correctness.
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Output should be P = S.to(BF16), i.e. (Q@K^T).bfloat16()
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With V=I, O = P @ I = P.
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But V is (K=128, N=128) in the MMA. We need a 128x128 identity in MN-major.
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Output tensor is (128, 128).
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"""
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def __init__(self, mma_tiler_mn):
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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.mma_tiler_mn = mma_tiler_mn; self.mma_tiler = (*mma_tiler_mn, 1)
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self.use_2cta_instrs = False # needed by epilogue_tma_store
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self.epilog_sync_bar_id = 1 # needed by epilogue_tma_store
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self.cluster_shape_mn = (1, 1)
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self.cta_group = tcgen05.CtaGroup.ONE
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self.epilogue_warp_id = (0, 1, 2, 3)
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self.mma_warp_id = 4; self.tma_warp_id = 5
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self.threads_per_cta = 192
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self.num_c_stage = 2
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def _setup(self, qk_mma, pv_mma):
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qk_inst_k = cute.size(qk_mma.shape_mnk, mode=[2])
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self.qk_mma_tiler = (*self.mma_tiler_mn, qk_inst_k * 4)
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# PV with V=I: output is (128, 128), same as QK
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self.pv_mma_tiler = (self.qk_mma_tiler[0], self.qk_mma_tiler[1], self.qk_mma_tiler[1])
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# pv_mma_tiler = (128, 128, 128) since V is 128x128
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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 = (
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self.qk_mma_tiler[0] // cute.size(qk_mma.thr_id.shape),
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self.qk_mma_tiler[1], 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(
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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.a_smem_s = utils.sm100.make_smem_layout_a(qk_mma, self.mma_tiler, self.q_dtype, 1)
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self.b_smem_s = utils.sm100.make_smem_layout_b(qk_mma, self.mma_tiler, self.q_dtype, 1)
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self.v_smem_s = utils.sm100.make_smem_layout_b(pv_mma, self.pv_mma_tiler, self.q_dtype, 1)
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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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self.c_smem_s = utils.sm100.make_smem_layout_epi(self.o_dtype, self.c_layout, self.epi_tile, 2)
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qk_thr = qk_mma.get_slice(0)
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qk_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
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tStS = qk_thr.make_fragment_C(qk_acc_shape)
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s_cols = find_tmem_tensor_col_offset(tStS)
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pv_thr = pv_mma.get_slice(0)
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pv_acc_shape = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
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tOtO = pv_thr.make_fragment_C(pv_acc_shape)
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o_cols = find_tmem_tensor_col_offset(tOtO)
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self.tilePlikeFP32 = self.qk_mma_tiler[1] // Float32.width * self.o_dtype.width
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self.tmem_s0_offset = 0
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self.tmem_p0_offset = 32
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self.tmem_o0_offset = s_cols
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tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_acc_shape, self.num_acc_stage))
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tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_acc_shape, self.num_acc_stage))
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self.num_tmem_alloc_cols = utils.get_num_tmem_alloc_cols([tCtS_fake, tCtO_fake], arch="sm_100")
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# ⛔⛔⛔ CRITICAL: num_tma_load_bytes MUST include ALL TMA-loaded tensors (Q + K + V). Missing V → DEADLOCK. See FOOTGUN #0 in README.
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a_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
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b_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
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v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0))
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self.num_tma_load_bytes = (
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cute.size_in_bytes(self.q_dtype, a_smem) + cute.size_in_bytes(self.q_dtype, b_smem) +
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cute.size_in_bytes(self.q_dtype, v_smem)
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) * cute.size(qk_mma.thr_id.shape)
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@cute.jit
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def __call__(self, q, k, v, c, stream):
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self.q_dtype = q.element_type; self.o_dtype = c.element_type; self.c_dtype = self.o_dtype
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self.a_major = LayoutEnum.from_tensor(q).mma_major_mode()
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self.b_major = LayoutEnum.from_tensor(k).mma_major_mode()
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self.v_major = LayoutEnum.from_tensor(v).mma_major_mode()
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self.c_layout = LayoutEnum.from_tensor(c)
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qk_mma = utils.sm100.make_trivial_tiled_mma(
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self.q_dtype, self.q_dtype, self.a_major, self.b_major,
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self.qk_acc_dtype, self.cta_group, self.mma_tiler_mn, tcgen05.OperandSource.SMEM)
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# PV with 128x128 output (V=I)
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pv_mma = utils.sm100.make_trivial_tiled_mma(
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self.q_dtype, self.q_dtype, cute.nvgpu.OperandMajorMode.K, self.v_major,
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self.qk_acc_dtype, self.cta_group, self.mma_tiler_mn, tcgen05.OperandSource.TMEM)
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self._setup(qk_mma, pv_mma)
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q_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
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k_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
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v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0))
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tma_q, tma_tq = 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_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
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tma_k, tma_tk = 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_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
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tma_v, tma_tv = 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, v_smem, self.pv_mma_tiler, pv_mma, self.cluster_layout_vmnk.shape)
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epi_smem = cute.select(self.c_smem_s, mode=[0, 1])
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tma_c, tma_tc = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(), c, epi_smem, self.epi_tile)
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self._kernel(qk_mma, pv_mma, tma_q, tma_tq, tma_k, tma_tk, tma_v, tma_tv,
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tma_c, tma_tc, self.cluster_layout_vmnk,
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self.a_smem_s, self.b_smem_s, self.v_smem_s, self.p_tmem_s, self.c_smem_s, self.epi_tile
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).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,
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tma_c, mC, cl_vmnk, a_smem_s, b_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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use_2cta = cute.size(qk_mma.thr_id.shape) == 2
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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)
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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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ab_bar: cute.struct.MemRange[cutlass.Int64, self.num_ab_stage * 2]
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mma_si_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
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holding: cutlass.Int32
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smem = utils.SmemAllocator(); st = smem.allocate(SS)
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ab_p, ab_c = pipeline.PipelineTmaUmma.create(
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barrier_storage=st.ab_bar.data_ptr(), num_stages=self.num_ab_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.num_tma_load_bytes, cta_layout_vmnk=cl_vmnk, defer_sync=True
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).make_participants()
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mma_si_prod, mma_si_cons = pipeline.PipelineUmmaAsync.create(
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barrier_storage=st.mma_si_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()
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acc_pipe = pipeline.PipelineUmmaAsync.create(
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barrier_storage=st.acc_bar.data_ptr(), num_stages=self.num_acc_stage,
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producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),
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consumer_group=pipeline.CooperativeGroup(
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pipeline.Agent.Thread, len(self.epilogue_warp_id) * (2 if use_2cta else 1)),
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cta_layout_vmnk=cl_vmnk, defer_sync=True)
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tmem_bar = pipeline.NamedBarrier(barrier_id=2,
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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,
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allocator_warp_id=self.epilogue_warp_id[0], is_two_cta=use_2cta,
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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=a_smem_s.outer, byte_alignment=128, swizzle=a_smem_s.inner)
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sK = smem.allocate_tensor(element_type=self.q_dtype, layout=b_smem_s.outer, byte_alignment=128, swizzle=b_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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gC = cute.local_tile(mC, cute.slice_(self.qk_mma_tiler, (None,None,0)), (None,None,None))
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k_cnt = cute.size(gQ, mode=[3])
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qk_thr = qk_mma.get_slice(0)
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pv_thr = pv_mma.get_slice(0)
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tCgQ = qk_thr.partition_A(gQ); tCgK = qk_thr.partition_B(gK); tCgC = qk_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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tAgQ = tAgQ[(None,0,None,0)]; tBgK = tBgK[(None,0,None,0)]
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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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tCgV = pv_thr.partition_B(gV)
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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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tVgV = tVgV[(None,0,None,0)]
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tCrQ = qk_mma.make_fragment_A(sQ); tCrK = qk_mma.make_fragment_B(sK)
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tCrV = pv_mma.make_fragment_B(sV)
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qk_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
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tStS = qk_thr.make_fragment_C(qk_acc_shape)
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tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
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pv_acc_shape = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
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tOtO = pv_thr.make_fragment_C(pv_acc_shape)
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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(
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tOrP.iterator + self.qk_acc_dtype.width // self.q_dtype.width * self.tmem_p0_offset,
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tOrP.layout)
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tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_acc_shape, self.num_acc_stage))
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tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_acc_shape, self.num_acc_stage))
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pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
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# ═══ TMA LOAD WARP ═══
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if warp_idx == self.tma_warp_id:
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ab_p.reset(); peek = ab_p.try_acquire()
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for kt in cutlass.range(k_cnt, unroll=1):
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h = ab_p.acquire_and_advance(peek)
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cute.copy(tma_q, tAgQ[(None,h.count)], tAsQ[(None,h.index)], tma_bar_ptr=h.barrier)
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cute.copy(tma_k, tBgK[(None,h.count)], tBsK[(None,h.index)], tma_bar_ptr=h.barrier)
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cute.copy(tma_v, tVgV[(None,h.count)], tVsV[(None,h.index)], tma_bar_ptr=h.barrier)
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peek = cutlass.Boolean(1)
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if h.count+1<k_cnt: peek = ab_p.try_acquire()
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ab_p.tail()
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# ═══ MMA WARP ═══
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if warp_idx == self.mma_warp_id:
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tmem.wait_for_alloc()
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ab_c.reset(); peek = ab_c.try_wait()
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s0_handle = mma_si_prod.acquire_and_advance()
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acc_prod_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
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acc_pipe.producer_acquire(acc_prod_st)
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qk_mma.set(tcgen05.Field.ACCUMULATE, False)
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for kt in range(k_cnt):
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h = ab_c.wait_and_advance(peek)
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nblk = cute.size(tCrQ, mode=[2])
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for kb in cutlass.range(nblk, unroll_full=True):
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cute.gemm(qk_mma, tStS0, tCrQ[(None,None,kb,h.index)], tCrK[(None,None,kb,h.index)], tStS0)
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qk_mma.set(tcgen05.Field.ACCUMULATE, True)
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h.release(); peek = cutlass.Boolean(1)
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if h.count+1<k_cnt: peek = ab_c.try_wait()
|
||||
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
s0_handle.commit()
|
||||
s0_handle = mma_si_prod.acquire_and_advance()
|
||||
|
||||
# PV MMA: P @ V where V=I → O = P
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
tCrV_s = tCrV[(None, None, None, 0)]
|
||||
nblk_pv = cute.size(tOrP0, mode=[2])
|
||||
if cute.arch.thread_idx()[0] == 0:
|
||||
print(f"PV: nblk={int(nblk_pv)} tCrV_s_size={int(cute.size(tCrV_s))} tOrP0_size={int(cute.size(tOrP0))}")
|
||||
for kb in cutlass.range(nblk_pv, unroll_full=True):
|
||||
cute.gemm(pv_mma, tOtO0, tOrP0[(None,None,kb)], tCrV_s[(None,None,kb)], tOtO0)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
|
||||
acc_pipe.producer_commit(acc_prod_st)
|
||||
acc_prod_st.advance()
|
||||
acc_pipe.producer_tail(acc_prod_st)
|
||||
|
||||
# ═══ 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))
|
||||
|
||||
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)
|
||||
|
||||
tStS_P_layout = cute.composition(tStS.layout, cute.make_layout((128, self.tilePlikeFP32)))
|
||||
tStS_P = cute.make_tensor(tStS.iterator + self.tmem_p0_offset, tStS_P_layout)
|
||||
tmem_store_atom = cute.make_copy_atom(
|
||||
tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
tiled_tmem_store = tcgen05.make_tmem_copy(tmem_store_atom, tStS_P)
|
||||
thr_store = tiled_tmem_store.get_slice(sfw_idx)
|
||||
tTMEM_STOREtS_x4 = thr_store.partition_D(tStS_P)
|
||||
tScS_P_layout = cute.composition(tScS.layout, cute.make_layout((128, self.tilePlikeFP32)))
|
||||
tScS_P = cute.make_tensor(tScS.iterator, tScS_P_layout)
|
||||
tTMEM_STOREcS = thr_store.partition_S(tScS_P)
|
||||
|
||||
si_handle = mma_si_cons.wait_and_advance()
|
||||
|
||||
tTMEM_LOADrS = cute.make_rmem_tensor(tTMEM_LOADcS.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load, tTMEM_LOADtS, tTMEM_LOADrS)
|
||||
tTMEM_STORErS_x4 = cute.make_rmem_tensor(tTMEM_STOREcS.shape, self.qk_acc_dtype)
|
||||
tTMEM_STORErS_x4_e = cute.make_tensor(
|
||||
cute.recast_ptr(tTMEM_STORErS_x4.iterator, dtype=self.q_dtype),
|
||||
tTMEM_LOADrS.layout)
|
||||
frg_cnt = 4
|
||||
frg_tile = cute.size(tTMEM_LOADrS) // frg_cnt
|
||||
tTMEM_LOADrS_frg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(frg_tile))
|
||||
tTMEM_STORErS_x4_e_frg = cute.logical_divide(
|
||||
tTMEM_STORErS_x4_e, cute.make_layout(frg_tile))
|
||||
for j in range(frg_cnt):
|
||||
s_vec = tTMEM_LOADrS_frg[None, j].load()
|
||||
tTMEM_STORErS_x4_e_frg[None, j].store(s_vec.to(self.q_dtype))
|
||||
cute.copy(tiled_tmem_store, tTMEM_STORErS_x4, tTMEM_STOREtS_x4)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
si_handle.release()
|
||||
|
||||
# Output epilogue
|
||||
tCtO_base = cute.make_tensor(tmem_ptr + self.tmem_o0_offset, tCtO_fake.layout)
|
||||
acc_cons_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Consumer, self.num_acc_stage)
|
||||
c_grp = pipeline.CooperativeGroup(pipeline.Agent.Thread, 32 * len(self.epilogue_warp_id))
|
||||
c_pipe = pipeline.PipelineTmaStore.create(num_stages=self.num_c_stage, producer_group=c_grp)
|
||||
acc_cons_st = utils.gemm.sm100.epilogue_tma_store(
|
||||
self, tidx, warp_idx, tma_c, tCtO_base, sC, tCgC,
|
||||
epi_tile, 0, const_expr(lambda x: x), (0,0,0), acc_cons_st, acc_pipe, c_pipe)
|
||||
c_pipe.producer_tail()
|
||||
tmem.relinquish_alloc_permit()
|
||||
tmem.free(tmem_ptr)
|
||||
|
||||
|
||||
def test():
|
||||
torch.manual_seed(42)
|
||||
m, n, head_dim = 128, 128, 64
|
||||
q = torch.randn(m, head_dim, 1, dtype=torch.bfloat16, device='cuda')
|
||||
k = torch.randn(n, head_dim, 1, dtype=torch.bfloat16, device='cuda')
|
||||
# V = identity (128x128) in MN-major: (128,128) with strides (1,128)
|
||||
v = torch.eye(128, dtype=torch.bfloat16, device='cuda')
|
||||
# MN-major: (128,128) with strides (1,128) — row is fast dim
|
||||
v = v.as_strided((128, 128), (1, 128)).unsqueeze(-1)
|
||||
c = torch.zeros(m, n, 1, dtype=torch.bfloat16, device='cuda')
|
||||
|
||||
qf = q[:,:,0].float(); kf = k[:,:,0].float()
|
||||
# With V=I and identity softmax: O = (Q@K^T).bf16() @ I = (Q@K^T).bf16()
|
||||
ref = (qf @ kf.T).bfloat16().float()
|
||||
|
||||
mQ = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
|
||||
mK = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
|
||||
mV = ct.from_dlpack(v).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v))
|
||||
mC = ct.from_dlpack(c).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c))
|
||||
stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
|
||||
kernel = VDiag128(mma_tiler_mn=(128, 128))
|
||||
print('Compiling...', flush=True)
|
||||
compiled = cute.compile(kernel, mQ, mK, mV, mC, stream)
|
||||
print('Running...', flush=True)
|
||||
compiled(mQ, mK, mV, mC, stream)
|
||||
torch.cuda.synchronize()
|
||||
out = c[:,:,0].float()
|
||||
cos = torch.nn.functional.cosine_similarity(out.flatten().unsqueeze(0), ref.flatten().unsqueeze(0)).item()
|
||||
print('VDiag(128,128): cosine {:.6f} {}'.format(cos, 'PASS' if cos >= 0.99 else 'FAIL'))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test()
|
||||
@@ -1,327 +0,0 @@
|
||||
"""
|
||||
FMHA v3: QK -> softmax -> PV with KV-tile interleaving.
|
||||
Bug 4b fix (FMHA pattern): P store uses QK C-fragment layout composition,
|
||||
NOT PV A-fragment layout. Register bridge: FP32 backing (store partition shape)
|
||||
recast to BF16 view (QK-load layout).
|
||||
"""
|
||||
import torch, cutlass, cutlass.cute as cute, cutlass.utils as utils, cutlass.pipeline as pipeline
|
||||
from cutlass.cute.nvgpu import cpasync, tcgen05
|
||||
from cutlass import Float32, BFloat16, Int32, Boolean, const_expr
|
||||
from cutlass.utils import LayoutEnum
|
||||
from cutlass.utils.tmem_allocator import find_tmem_tensor_col_offset
|
||||
import cuda.bindings.driver as cuda
|
||||
import cutlass.torch as ct
|
||||
|
||||
HEAD_DIM = 64
|
||||
|
||||
class FmhaV3:
|
||||
def __init__(self):
|
||||
self.acc_dtype = Float32; self.qk_acc_dtype = Float32
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
self.use_2cta_instrs = False; self.epilog_sync_bar_id = 1
|
||||
self.cluster_shape_mn = (1, 1); self.cta_group = tcgen05.CtaGroup.ONE
|
||||
self.epilogue_warp_id = (0,1,2,3); self.mma_warp_id = 4; self.tma_warp_id = 5
|
||||
self.threads_per_cta = 192; self.num_c_stage = 2
|
||||
self.kv_stage = 2; self.q_stage = 1; self.num_c_stage = 2
|
||||
|
||||
def _setup(self, qk_mma, pv_mma):
|
||||
qk_ik = cute.size(qk_mma.shape_mnk, mode=[2])
|
||||
self.qk_mma_tiler = (128, 128, qk_ik * 4)
|
||||
pv_ik = cute.size(pv_mma.shape_mnk, mode=[2])
|
||||
self.pv_mma_tiler = (128, HEAD_DIM, pv_ik * (128 // pv_ik))
|
||||
self.mma_tiler = self.qk_mma_tiler
|
||||
self.cluster_layout_vmnk = cute.tiled_divide(cute.make_layout((1,1,1)), (qk_mma.thr_id.shape,))
|
||||
self.cta_tile_shape_mnk = (self.qk_mma_tiler[0]//cute.size(qk_mma.thr_id.shape), HEAD_DIM, self.qk_mma_tiler[2])
|
||||
self.c_layout = LayoutEnum.ROW_MAJOR
|
||||
self.epi_tile = utils.sm100.compute_epilogue_tile_shape(self.cta_tile_shape_mnk, False, self.c_layout, self.o_dtype)
|
||||
self.num_ab_stage = 1; self.num_acc_stage = 1
|
||||
self.q_smem_s = utils.sm100.make_smem_layout_a(qk_mma, self.qk_mma_tiler, self.q_dtype, self.q_stage)
|
||||
self.k_smem_s = utils.sm100.make_smem_layout_b(qk_mma, self.qk_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.v_smem_s = utils.sm100.make_smem_layout_b(pv_mma, self.pv_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.c_smem_s = utils.sm100.make_smem_layout_epi(self.o_dtype, self.c_layout, self.epi_tile, 2)
|
||||
self.p_tmem_s = utils.sm100.make_smem_layout_a(pv_mma, self.pv_mma_tiler, self.q_dtype, 1)
|
||||
qk_thr = qk_mma.get_slice(0); qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
pv_thr = pv_mma.get_slice(0); pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
self.tmem_s0_offset = 0; self.tmem_p0_offset = 32
|
||||
# P occupies [tmem_p0_offset, tmem_p0_offset + p_cols_fp32)
|
||||
# S occupies [0, qk_mma_tiler[1]) = [0, 128)
|
||||
# O must NOT overlap P. Place O after max(S end, P end), aligned to 32.
|
||||
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
|
||||
p_end = self.tmem_p0_offset + p_cols_fp32 # 32 + 64 = 96
|
||||
s_cols = self.qk_mma_tiler[1] # 128
|
||||
o_after = max(s_cols, p_end) # 128
|
||||
self.tmem_o0_offset = ((o_after + 31) // 32) * 32 # align to 32 = 128
|
||||
o_cols = find_tmem_tensor_col_offset(tOtO) # footprint of O
|
||||
total = self.tmem_o0_offset + o_cols
|
||||
# Must be multiple of 32 AND power of 2
|
||||
self.num_tmem_alloc_cols = 1
|
||||
while self.num_tmem_alloc_cols < total:
|
||||
self.num_tmem_alloc_cols *= 2
|
||||
cta = cute.size(qk_mma.thr_id.shape)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0))
|
||||
self.q_tx_bytes = cute.size_in_bytes(self.q_dtype, q_s) * cta
|
||||
self.kv_tx_bytes = cute.size_in_bytes(self.q_dtype, k_s) * cta
|
||||
|
||||
@cute.jit
|
||||
def __call__(self, q, k, v, c, stream):
|
||||
self.q_dtype = q.element_type; self.o_dtype = c.element_type; self.c_dtype = self.o_dtype
|
||||
self.a_major = LayoutEnum.from_tensor(q).mma_major_mode()
|
||||
self.b_major = LayoutEnum.from_tensor(k).mma_major_mode()
|
||||
# FMHA-style V: reconstruct as (HEAD_DIM, s_k, 1) MN-major
|
||||
v_fmha = cute.make_tensor(
|
||||
v.iterator,
|
||||
cute.make_layout(
|
||||
(HEAD_DIM, 128, 1),
|
||||
stride=(1, HEAD_DIM, HEAD_DIM * 128),
|
||||
),
|
||||
)
|
||||
self.v_major = LayoutEnum.from_tensor(v_fmha).mma_major_mode()
|
||||
self.c_layout = LayoutEnum.from_tensor(c)
|
||||
qk_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, self.a_major, self.b_major, self.qk_acc_dtype, self.cta_group, (128,128), tcgen05.OperandSource.SMEM)
|
||||
pv_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, cute.nvgpu.OperandMajorMode.K, self.v_major, self.qk_acc_dtype, self.cta_group, (128,HEAD_DIM), tcgen05.OperandSource.TMEM)
|
||||
self._setup(qk_mma, pv_mma)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0)); v_s = cute.slice_(self.v_smem_s,(None,None,None,0))
|
||||
tma_q,mQ = cute.nvgpu.make_tiled_tma_atom_A(utils.sm100.cluster_shape_to_tma_atom_A(self.cluster_shape_mn,qk_mma.thr_id),q,q_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_k,mK = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,qk_mma.thr_id),k,k_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_v,mV = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,pv_mma.thr_id),v_fmha,v_s,self.pv_mma_tiler,pv_mma,self.cluster_layout_vmnk.shape)
|
||||
epi_s = cute.select(self.c_smem_s,mode=[0,1])
|
||||
tma_c,mC = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(),c,epi_s,self.epi_tile)
|
||||
self._kernel(qk_mma,pv_mma,tma_q,mQ,tma_k,mK,tma_v,mV,tma_c,mC,self.cluster_layout_vmnk,self.q_smem_s,self.k_smem_s,self.v_smem_s,self.p_tmem_s,self.c_smem_s,self.epi_tile).launch(grid=(1,1,1),block=[self.threads_per_cta,1,1],stream=stream)
|
||||
|
||||
@cute.kernel
|
||||
def _kernel(self, qk_mma, pv_mma, tma_q, mQ, tma_k, mK, tma_v, mV, tma_c, mC, cl_vmnk, q_smem_s, k_smem_s, v_smem_s, p_tmem_s, c_smem_s, epi_tile):
|
||||
warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx())
|
||||
tidx,_,_ = cute.arch.thread_idx()
|
||||
if warp_idx == self.tma_warp_id:
|
||||
cpasync.prefetch_descriptor(tma_q); cpasync.prefetch_descriptor(tma_k); cpasync.prefetch_descriptor(tma_v); cpasync.prefetch_descriptor(tma_c)
|
||||
|
||||
@cute.struct
|
||||
class SS:
|
||||
q_bar: cute.struct.MemRange[cutlass.Int64, self.q_stage*2]
|
||||
kv_bar: cute.struct.MemRange[cutlass.Int64, self.kv_stage*2]
|
||||
s_bar: cute.struct.MemRange[cutlass.Int64, 2]
|
||||
acc_bar: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage*2]
|
||||
tmem_dealloc: cutlass.Int64; holding: cutlass.Int32
|
||||
smem = utils.SmemAllocator(); st = smem.allocate(SS)
|
||||
|
||||
qp,qc = pipeline.PipelineTmaUmma.create(barrier_storage=st.q_bar.data_ptr(),num_stages=self.q_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.q_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
|
||||
kvp,kvc = pipeline.PipelineTmaUmma.create(barrier_storage=st.kv_bar.data_ptr(),num_stages=self.kv_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.kv_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
|
||||
s_prod,s_cons = pipeline.PipelineUmmaAsync.create(barrier_storage=st.s_bar.data_ptr(),num_stages=1,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32*len(self.epilogue_warp_id))).make_participants()
|
||||
softmax_done_bar = pipeline.NamedBarrier(barrier_id=3, num_threads=32 + 32*len(self.epilogue_warp_id))
|
||||
acc_pipe = pipeline.PipelineUmmaAsync.create(barrier_storage=st.acc_bar.data_ptr(),num_stages=self.num_acc_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,len(self.epilogue_warp_id)),cta_layout_vmnk=cl_vmnk,defer_sync=True)
|
||||
tmem_bar = pipeline.NamedBarrier(barrier_id=2,num_threads=32*len((self.mma_warp_id,*self.epilogue_warp_id)))
|
||||
tmem = utils.TmemAllocator(st.holding.ptr,barrier_for_retrieve=tmem_bar,allocator_warp_id=self.epilogue_warp_id[0],is_two_cta=cute.size(qk_mma.thr_id.shape)==2,two_cta_tmem_dealloc_mbar_ptr=st.tmem_dealloc.ptr)
|
||||
pipeline.pipeline_init_arrive(cluster_shape_mn=cl_vmnk,is_relaxed=True)
|
||||
|
||||
sQ = smem.allocate_tensor(element_type=self.q_dtype,layout=q_smem_s.outer,byte_alignment=128,swizzle=q_smem_s.inner)
|
||||
sK = smem.allocate_tensor(element_type=self.q_dtype,layout=k_smem_s.outer,byte_alignment=128,swizzle=k_smem_s.inner)
|
||||
sV = smem.allocate_tensor(element_type=self.q_dtype,layout=v_smem_s.outer,byte_alignment=128,swizzle=v_smem_s.inner)
|
||||
sC = smem.allocate_tensor(element_type=self.o_dtype,layout=c_smem_s.outer,byte_alignment=128,swizzle=c_smem_s.inner)
|
||||
|
||||
gQ = cute.local_tile(mQ,cute.slice_(self.qk_mma_tiler,(None,0,None)),(None,None,None))
|
||||
gK = cute.local_tile(mK,cute.slice_(self.qk_mma_tiler,(0,None,None)),(None,None,None))
|
||||
gV = cute.local_tile(mV,cute.slice_(self.pv_mma_tiler,(0,None,None)),(None,None,None))
|
||||
gC = cute.local_tile(mC,cute.slice_(self.pv_mma_tiler,(None,None,0)),(None,None,None))
|
||||
n_kv_tiles = cute.size(gK, mode=[3])
|
||||
|
||||
qk_thr = qk_mma.get_slice(0); pv_thr = pv_mma.get_slice(0)
|
||||
tCgQ = qk_thr.partition_A(gQ); tCgK = qk_thr.partition_B(gK)
|
||||
tCgV = pv_thr.partition_B(gV); tCgC = pv_thr.partition_C(gC)
|
||||
a_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,0,None,0)).shape)
|
||||
tAsQ,tAgQ = cpasync.tma_partition(tma_q,0,a_lay,cute.group_modes(sQ,0,3),cute.group_modes(tCgQ,0,3))
|
||||
b_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,None,0,0)).shape)
|
||||
tBsK,tBgK = cpasync.tma_partition(tma_k,0,b_lay,cute.group_modes(sK,0,3),cute.group_modes(tCgK,0,3))
|
||||
tVsV,tVgV = cpasync.tma_partition(tma_v,0,b_lay,cute.group_modes(sV,0,3),cute.group_modes(tCgV,0,3))
|
||||
tAgQ = tAgQ[(None,0,None,0)]; tBgK = tBgK[(None,0,None,0)]; tVgV = tVgV[(None,0,None,0)]
|
||||
|
||||
tCrQ = qk_mma.make_fragment_A(sQ); tCrK = qk_mma.make_fragment_B(sK)
|
||||
tCrV = pv_mma.make_fragment_B(sV)
|
||||
|
||||
qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
tOtO0 = cute.make_tensor(tOtO.iterator + self.tmem_o0_offset, tOtO.layout)
|
||||
|
||||
# --- PV read view (for MMA only, NOT for softmax store) ---
|
||||
tP = cute.make_tensor(tStS.iterator, p_tmem_s.outer)
|
||||
tOrP_base = pv_thr.make_fragment_A(tP)
|
||||
tOrP = tOrP_base[(None,None,None,0)]
|
||||
tOrP0 = cute.make_tensor(
|
||||
tOrP.iterator + self.qk_acc_dtype.width // self.q_dtype.width * self.tmem_p0_offset,
|
||||
tOrP.layout)
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_as, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_as, self.num_acc_stage))
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# TMA LOAD
|
||||
if warp_idx == self.tma_warp_id:
|
||||
qp.reset(); qh = qp.acquire_and_advance()
|
||||
cute.copy(tma_q,tAgQ[(None,qh.count)],tAsQ[(None,qh.index)],tma_bar_ptr=qh.barrier)
|
||||
qp.tail()
|
||||
kvp.reset(); pk = kvp.try_acquire()
|
||||
for kt in cutlass.range(n_kv_tiles,unroll=1):
|
||||
kh = kvp.acquire_and_advance(pk)
|
||||
cute.copy(tma_k,tBgK[(None,kh.count)],tBsK[(None,kh.index)],tma_bar_ptr=kh.barrier)
|
||||
pk = cutlass.Boolean(1)
|
||||
vh = kvp.acquire_and_advance(pk)
|
||||
cute.copy(tma_v,tVgV[(None,vh.count)],tVsV[(None,vh.index)],tma_bar_ptr=vh.barrier)
|
||||
pk = cutlass.Boolean(1)
|
||||
kvp.tail()
|
||||
|
||||
# MMA
|
||||
if warp_idx == self.mma_warp_id:
|
||||
tmem.wait_for_alloc()
|
||||
qc.reset(); qh = qc.wait_and_advance(); qh.release()
|
||||
kvc.reset(); pk = kvc.try_wait()
|
||||
acc_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
|
||||
acc_pipe.producer_acquire(acc_st)
|
||||
for kt in range(n_kv_tiles):
|
||||
kh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
sh = s_prod.acquire_and_advance()
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
for kb in cutlass.range(cute.size(tCrQ,mode=[2]), unroll_full=True):
|
||||
cute.gemm(qk_mma, tStS0, tCrQ[(None,None,kb,0)], tCrK[(None,None,kb,kh.index)], tStS0)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
sh.commit(); kh.release()
|
||||
softmax_done_bar.arrive_and_wait()
|
||||
vh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, kt != 0)
|
||||
for kb in cutlass.range(cute.size(tOrP0,mode=[2]), unroll_full=True):
|
||||
cute.gemm(pv_mma, tOtO0, tOrP0[(None,None,kb)], tCrV[(None,None,kb,vh.index)], tOtO0)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
vh.release()
|
||||
acc_pipe.producer_commit(acc_st); acc_st.advance()
|
||||
acc_pipe.producer_tail(acc_st)
|
||||
|
||||
# EPILOGUE
|
||||
if warp_idx < self.mma_warp_id:
|
||||
tmem.allocate(self.num_tmem_alloc_cols)
|
||||
tmem.wait_for_alloc()
|
||||
tmem_ptr = tmem.retrieve_ptr(self.qk_acc_dtype)
|
||||
sfw_idx = tidx % (32 * len(self.epilogue_warp_id))
|
||||
|
||||
# --- 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)
|
||||
|
||||
# S coordinate tensor (QK C-fragment)
|
||||
cS = cute.make_identity_tensor((self.qk_mma_tiler[0], self.qk_mma_tiler[1]))
|
||||
tScS = qk_thr.partition_C(cS)
|
||||
tTMEM_LOADcS = thr_load.partition_D(tScS)
|
||||
|
||||
# --- P store (QK C-fragment layout composition, FMHA pattern) ---
|
||||
# P logical columns = PV K = QK N = pv_mma_tiler[2]
|
||||
# Packed FP32 columns: BF16 pairs packed into FP32 words
|
||||
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
|
||||
# BF16: 128 * 16 / 32 = 64
|
||||
|
||||
# P TMEM destination: QK C-fragment layout composed with P sub-tile
|
||||
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,
|
||||
)
|
||||
|
||||
# P TMEM store atom and tiled copy
|
||||
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)
|
||||
|
||||
# P coordinate tensor: QK C-fragment coordinate composed with P sub-tile
|
||||
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)
|
||||
|
||||
for kt in range(n_kv_tiles):
|
||||
si_handle = s_cons.wait_and_advance()
|
||||
|
||||
# Load S from TMEM (FP32, QK C-fragment layout)
|
||||
tTMEM_LOADrS = cute.make_rmem_tensor(tTMEM_LOADcS.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load, tTMEM_LOADtS, tTMEM_LOADrS)
|
||||
|
||||
# Register bridge (FMHA pattern):
|
||||
# rP_words: FP32 backing store with store-partition shape
|
||||
# rP_bf16: BF16 view over same registers using QK-load layout
|
||||
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,
|
||||
)
|
||||
|
||||
# Fragmented load→convert→store:
|
||||
# Load S as FP32, convert to BF16, store through rP_bf16 view
|
||||
frg_cnt = 4
|
||||
frg_tile = cute.size(tTMEM_LOADrS) // frg_cnt
|
||||
tTMEM_LOADrS_frg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(frg_tile))
|
||||
rP_bf16_frg = cute.logical_divide(rP_bf16, cute.make_layout(frg_tile))
|
||||
for j in range(frg_cnt):
|
||||
s_vec = tTMEM_LOADrS_frg[None, j].load()
|
||||
rP_bf16_frg[None, j].store(s_vec.to(self.q_dtype))
|
||||
|
||||
# Copy packed FP32 backing registers to TMEM
|
||||
cute.copy(tiled_tmem_store, rP_words, tTMEM_STOREtP)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
si_handle.release()
|
||||
softmax_done_bar.arrive()
|
||||
|
||||
tCtO_base = cute.make_tensor(tmem_ptr + self.tmem_o0_offset, tCtO_fake.layout)
|
||||
acc_cons_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Consumer, self.num_acc_stage)
|
||||
c_grp = pipeline.CooperativeGroup(pipeline.Agent.Thread, 32 * len(self.epilogue_warp_id))
|
||||
c_pipe = pipeline.PipelineTmaStore.create(num_stages=self.num_c_stage, producer_group=c_grp)
|
||||
acc_cons_st = utils.gemm.sm100.epilogue_tma_store(self, tidx, warp_idx, tma_c, tCtO_base, sC, tCgC, epi_tile, 0, const_expr(lambda x: x), (0,0,0), acc_cons_st, acc_pipe, c_pipe)
|
||||
c_pipe.producer_tail()
|
||||
tmem.relinquish_alloc_permit()
|
||||
tmem.free(tmem_ptr)
|
||||
|
||||
|
||||
def test():
|
||||
torch.manual_seed(42)
|
||||
for n in [128]:
|
||||
m, hd = 128, HEAD_DIM
|
||||
q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device='cuda')
|
||||
k = torch.randn(n, hd, 1, dtype=torch.bfloat16, device='cuda')
|
||||
v = torch.randn(n, hd, dtype=torch.bfloat16, device='cuda')
|
||||
# V passed as (n, hd) row-major — FMHA-style reconstruction inside kernel
|
||||
v_kernel = v.unsqueeze(-1)
|
||||
c = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device='cuda')
|
||||
qf = q[:,:,0].float(); kf = k[:,:,0].float()
|
||||
ref = (qf @ kf.T).bfloat16().float() @ v.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 = FmhaV3()
|
||||
print(f'n={n}: Compiling...', flush=True)
|
||||
compiled = cute.compile(kernel, mQ, mK, mV, mC, stream)
|
||||
print(f'n={n}: tmem_offsets: s0={kernel.tmem_s0_offset} p0={kernel.tmem_p0_offset} o0={kernel.tmem_o0_offset} alloc={kernel.num_tmem_alloc_cols}', flush=True)
|
||||
print(f'n={n}: Running...', flush=True)
|
||||
compiled(mQ, mK, mV, mC, stream)
|
||||
torch.cuda.synchronize()
|
||||
out = c[:,:,0].float()
|
||||
cos = torch.nn.functional.cosine_similarity(out.flatten().unsqueeze(0), ref.flatten().unsqueeze(0)).item()
|
||||
print(f'FMHA v3 n={n} V=ones: cosine {cos:.6f} {"PASS" if cos >= 0.99 else "FAIL"}')
|
||||
if cos < 0.99:
|
||||
print(f' out[0,:4]={out[0,:4].tolist()} ref[0,:4]={ref[0,:4].tolist()}')
|
||||
|
||||
if __name__ == '__main__':
|
||||
test()
|
||||
@@ -1,469 +0,0 @@
|
||||
"""
|
||||
FMHA v3 + Stage C: QK -> online softmax -> PV with KV-tile interleaving.
|
||||
Stage C: row_max, exp2, O rescale, row_sum, final normalization.
|
||||
FMHA pattern P store preserved from Stage B.
|
||||
"""
|
||||
import math
|
||||
import torch, cutlass, cutlass.cute as cute, cutlass.utils as utils, cutlass.pipeline as pipeline
|
||||
from cutlass.cute.nvgpu import cpasync, tcgen05
|
||||
from cutlass import Float32, BFloat16, Int32, Boolean, const_expr
|
||||
from cutlass.utils import LayoutEnum
|
||||
from cutlass.utils.tmem_allocator import find_tmem_tensor_col_offset
|
||||
import cuda.bindings.driver as cuda
|
||||
import cutlass.torch as ct
|
||||
|
||||
HEAD_DIM = 64
|
||||
|
||||
class FmhaV3Softmax:
|
||||
def __init__(self):
|
||||
self.acc_dtype = Float32; self.qk_acc_dtype = Float32
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
self.use_2cta_instrs = False; self.epilog_sync_bar_id = 1
|
||||
self.cluster_shape_mn = (1, 1); self.cta_group = tcgen05.CtaGroup.ONE
|
||||
self.epilogue_warp_id = (0,1,2,3); self.mma_warp_id = 4; self.tma_warp_id = 5
|
||||
self.threads_per_cta = 192; self.num_c_stage = 2
|
||||
self.kv_stage = 2; self.q_stage = 1; self.num_c_stage = 2
|
||||
|
||||
def _setup(self, qk_mma, pv_mma):
|
||||
qk_ik = cute.size(qk_mma.shape_mnk, mode=[2])
|
||||
self.qk_mma_tiler = (128, 128, qk_ik * 4)
|
||||
pv_ik = cute.size(pv_mma.shape_mnk, mode=[2])
|
||||
self.pv_mma_tiler = (128, HEAD_DIM, pv_ik * (128 // pv_ik))
|
||||
self.mma_tiler = self.qk_mma_tiler
|
||||
self.cluster_layout_vmnk = cute.tiled_divide(cute.make_layout((1,1,1)), (qk_mma.thr_id.shape,))
|
||||
self.cta_tile_shape_mnk = (self.qk_mma_tiler[0]//cute.size(qk_mma.thr_id.shape), HEAD_DIM, self.qk_mma_tiler[2])
|
||||
self.c_layout = LayoutEnum.ROW_MAJOR
|
||||
self.epi_tile = utils.sm100.compute_epilogue_tile_shape(self.cta_tile_shape_mnk, False, self.c_layout, self.o_dtype)
|
||||
self.num_ab_stage = 1; self.num_acc_stage = 1
|
||||
self.q_smem_s = utils.sm100.make_smem_layout_a(qk_mma, self.qk_mma_tiler, self.q_dtype, self.q_stage)
|
||||
self.k_smem_s = utils.sm100.make_smem_layout_b(qk_mma, self.qk_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.v_smem_s = utils.sm100.make_smem_layout_b(pv_mma, self.pv_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.c_smem_s = utils.sm100.make_smem_layout_epi(self.o_dtype, self.c_layout, self.epi_tile, 2)
|
||||
self.p_tmem_s = utils.sm100.make_smem_layout_a(pv_mma, self.pv_mma_tiler, self.q_dtype, 1)
|
||||
qk_thr = qk_mma.get_slice(0); qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
pv_thr = pv_mma.get_slice(0); pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
self.tmem_s0_offset = 0; self.tmem_p0_offset = 32
|
||||
# P occupies [tmem_p0_offset, tmem_p0_offset + p_cols_fp32)
|
||||
# S occupies [0, qk_mma_tiler[1]) = [0, 128)
|
||||
# O must NOT overlap P. Place O after max(S end, P end), aligned to 32.
|
||||
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
|
||||
p_end = self.tmem_p0_offset + p_cols_fp32 # 32 + 64 = 96
|
||||
s_cols = self.qk_mma_tiler[1] # 128
|
||||
o_after = max(s_cols, p_end) # 128
|
||||
self.tmem_o0_offset = ((o_after + 31) // 32) * 32
|
||||
self.tmem_vec_offset = 0 # Reuse S region for per-row inv_row_sum vector # align to 32 = 128
|
||||
self.tmem_vec_offset = 0 # Reuse S region (free after softmax loop)
|
||||
o_cols = find_tmem_tensor_col_offset(tOtO) # footprint of O
|
||||
total = self.tmem_o0_offset + o_cols
|
||||
# Must be multiple of 32 AND power of 2
|
||||
self.num_tmem_alloc_cols = 1
|
||||
while self.num_tmem_alloc_cols < total:
|
||||
self.num_tmem_alloc_cols *= 2
|
||||
cta = cute.size(qk_mma.thr_id.shape)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0))
|
||||
self.q_tx_bytes = cute.size_in_bytes(self.q_dtype, q_s) * cta
|
||||
self.kv_tx_bytes = cute.size_in_bytes(self.q_dtype, k_s) * cta
|
||||
self.scale_softmax_log2 = Float32(1.0 / math.sqrt(HEAD_DIM) * math.log2(math.e))
|
||||
|
||||
@cute.jit
|
||||
def __call__(self, q, k, v, c, stream):
|
||||
self.q_dtype = q.element_type; self.o_dtype = c.element_type; self.c_dtype = self.o_dtype
|
||||
self.a_major = LayoutEnum.from_tensor(q).mma_major_mode()
|
||||
self.b_major = LayoutEnum.from_tensor(k).mma_major_mode()
|
||||
# # s_k hardcoded # BROKEN in @cute.jit
|
||||
# FMHA-style V: reconstruct as (HEAD_DIM, s_k, 1) MN-major
|
||||
v_fmha = cute.make_tensor(
|
||||
v.iterator,
|
||||
cute.make_layout(
|
||||
(HEAD_DIM, 128, 1),
|
||||
stride=(1, HEAD_DIM, HEAD_DIM * 128),
|
||||
),
|
||||
)
|
||||
self.v_major = LayoutEnum.from_tensor(v_fmha).mma_major_mode()
|
||||
self.c_layout = LayoutEnum.from_tensor(c)
|
||||
qk_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, self.a_major, self.b_major, self.qk_acc_dtype, self.cta_group, (128,128), tcgen05.OperandSource.SMEM)
|
||||
pv_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, cute.nvgpu.OperandMajorMode.K, self.v_major, self.qk_acc_dtype, self.cta_group, (128,HEAD_DIM), tcgen05.OperandSource.TMEM)
|
||||
self._setup(qk_mma, pv_mma)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0)); v_s = cute.slice_(self.v_smem_s,(None,None,None,0))
|
||||
tma_q,mQ = cute.nvgpu.make_tiled_tma_atom_A(utils.sm100.cluster_shape_to_tma_atom_A(self.cluster_shape_mn,qk_mma.thr_id),q,q_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_k,mK = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,qk_mma.thr_id),k,k_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_v,mV = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,pv_mma.thr_id),v_fmha,v_s,self.pv_mma_tiler,pv_mma,self.cluster_layout_vmnk.shape)
|
||||
epi_s = cute.select(self.c_smem_s,mode=[0,1])
|
||||
tma_c,mC = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(),c,epi_s,self.epi_tile)
|
||||
self._kernel(qk_mma,pv_mma,tma_q,mQ,tma_k,mK,tma_v,mV,tma_c,mC,self.cluster_layout_vmnk,self.q_smem_s,self.k_smem_s,self.v_smem_s,self.p_tmem_s,self.c_smem_s,self.epi_tile).launch(grid=(1,1,1),block=[self.threads_per_cta,1,1],stream=stream)
|
||||
|
||||
@cute.kernel
|
||||
def _kernel(self, qk_mma, pv_mma, tma_q, mQ, tma_k, mK, tma_v, mV, tma_c, mC, cl_vmnk, q_smem_s, k_smem_s, v_smem_s, p_tmem_s, c_smem_s, epi_tile):
|
||||
warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx())
|
||||
tidx,_,_ = cute.arch.thread_idx()
|
||||
if warp_idx == self.tma_warp_id:
|
||||
cpasync.prefetch_descriptor(tma_q); cpasync.prefetch_descriptor(tma_k); cpasync.prefetch_descriptor(tma_v); cpasync.prefetch_descriptor(tma_c)
|
||||
|
||||
@cute.struct
|
||||
class SS:
|
||||
q_bar: cute.struct.MemRange[cutlass.Int64, self.q_stage*2]
|
||||
kv_bar: cute.struct.MemRange[cutlass.Int64, self.kv_stage*2]
|
||||
s_bar: cute.struct.MemRange[cutlass.Int64, 2]
|
||||
acc_bar: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage*2]
|
||||
tmem_dealloc: cutlass.Int64; holding: cutlass.Int32
|
||||
smem = utils.SmemAllocator(); st = smem.allocate(SS)
|
||||
|
||||
qp,qc = pipeline.PipelineTmaUmma.create(barrier_storage=st.q_bar.data_ptr(),num_stages=self.q_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.q_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
|
||||
kvp,kvc = pipeline.PipelineTmaUmma.create(barrier_storage=st.kv_bar.data_ptr(),num_stages=self.kv_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.kv_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
|
||||
s_prod,s_cons = pipeline.PipelineUmmaAsync.create(barrier_storage=st.s_bar.data_ptr(),num_stages=1,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32*len(self.epilogue_warp_id))).make_participants()
|
||||
softmax_done_bar = pipeline.NamedBarrier(barrier_id=3, num_threads=32 + 32*len(self.epilogue_warp_id))
|
||||
pv_done_bar = pipeline.NamedBarrier(barrier_id=4, num_threads=32 + 32*len(self.epilogue_warp_id))
|
||||
acc_pipe = pipeline.PipelineUmmaAsync.create(barrier_storage=st.acc_bar.data_ptr(),num_stages=self.num_acc_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,len(self.epilogue_warp_id)),cta_layout_vmnk=cl_vmnk,defer_sync=True)
|
||||
tmem_bar = pipeline.NamedBarrier(barrier_id=2,num_threads=32*len((self.mma_warp_id,*self.epilogue_warp_id)))
|
||||
tmem = utils.TmemAllocator(st.holding.ptr,barrier_for_retrieve=tmem_bar,allocator_warp_id=self.epilogue_warp_id[0],is_two_cta=cute.size(qk_mma.thr_id.shape)==2,two_cta_tmem_dealloc_mbar_ptr=st.tmem_dealloc.ptr)
|
||||
pipeline.pipeline_init_arrive(cluster_shape_mn=cl_vmnk,is_relaxed=True)
|
||||
|
||||
sQ = smem.allocate_tensor(element_type=self.q_dtype,layout=q_smem_s.outer,byte_alignment=128,swizzle=q_smem_s.inner)
|
||||
sK = smem.allocate_tensor(element_type=self.q_dtype,layout=k_smem_s.outer,byte_alignment=128,swizzle=k_smem_s.inner)
|
||||
sV = smem.allocate_tensor(element_type=self.q_dtype,layout=v_smem_s.outer,byte_alignment=128,swizzle=v_smem_s.inner)
|
||||
sC = smem.allocate_tensor(element_type=self.o_dtype,layout=c_smem_s.outer,byte_alignment=128,swizzle=c_smem_s.inner)
|
||||
|
||||
gQ = cute.local_tile(mQ,cute.slice_(self.qk_mma_tiler,(None,0,None)),(None,None,None))
|
||||
gK = cute.local_tile(mK,cute.slice_(self.qk_mma_tiler,(0,None,None)),(None,None,None))
|
||||
gV = cute.local_tile(mV,cute.slice_(self.pv_mma_tiler,(0,None,None)),(None,None,None))
|
||||
gC = cute.local_tile(mC,cute.slice_(self.pv_mma_tiler,(None,None,0)),(None,None,None))
|
||||
n_kv_tiles = cute.size(gK, mode=[3])
|
||||
|
||||
qk_thr = qk_mma.get_slice(0); pv_thr = pv_mma.get_slice(0)
|
||||
tCgQ = qk_thr.partition_A(gQ); tCgK = qk_thr.partition_B(gK)
|
||||
tCgV = pv_thr.partition_B(gV); tCgC = pv_thr.partition_C(gC)
|
||||
a_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,0,None,0)).shape)
|
||||
tAsQ,tAgQ = cpasync.tma_partition(tma_q,0,a_lay,cute.group_modes(sQ,0,3),cute.group_modes(tCgQ,0,3))
|
||||
b_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,None,0,0)).shape)
|
||||
tBsK,tBgK = cpasync.tma_partition(tma_k,0,b_lay,cute.group_modes(sK,0,3),cute.group_modes(tCgK,0,3))
|
||||
tVsV,tVgV = cpasync.tma_partition(tma_v,0,b_lay,cute.group_modes(sV,0,3),cute.group_modes(tCgV,0,3))
|
||||
tAgQ = tAgQ[(None,0,None,0)]; tBgK = tBgK[(None,0,None,0)]; tVgV = tVgV[(None,0,None,0)]
|
||||
|
||||
tCrQ = qk_mma.make_fragment_A(sQ); tCrK = qk_mma.make_fragment_B(sK)
|
||||
tCrV = pv_mma.make_fragment_B(sV)
|
||||
|
||||
qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
tOtO0 = cute.make_tensor(tOtO.iterator + self.tmem_o0_offset, tOtO.layout)
|
||||
|
||||
# --- PV read view (for MMA only, NOT for softmax store) ---
|
||||
tP = cute.make_tensor(tStS.iterator, p_tmem_s.outer)
|
||||
tOrP_base = pv_thr.make_fragment_A(tP)
|
||||
tOrP = tOrP_base[(None,None,None,0)]
|
||||
tOrP0 = cute.make_tensor(
|
||||
tOrP.iterator + self.qk_acc_dtype.width // self.q_dtype.width * self.tmem_p0_offset,
|
||||
tOrP.layout)
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_as, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_as, self.num_acc_stage))
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# TMA LOAD
|
||||
if warp_idx == self.tma_warp_id:
|
||||
qp.reset(); qh = qp.acquire_and_advance()
|
||||
cute.copy(tma_q,tAgQ[(None,qh.count)],tAsQ[(None,qh.index)],tma_bar_ptr=qh.barrier)
|
||||
qp.tail()
|
||||
kvp.reset(); pk = kvp.try_acquire()
|
||||
for kt in cutlass.range(n_kv_tiles,unroll=1):
|
||||
kh = kvp.acquire_and_advance(pk)
|
||||
cute.copy(tma_k,tBgK[(None,kh.count)],tBsK[(None,kh.index)],tma_bar_ptr=kh.barrier)
|
||||
pk = cutlass.Boolean(1)
|
||||
vh = kvp.acquire_and_advance(pk)
|
||||
cute.copy(tma_v,tVgV[(None,vh.count)],tVsV[(None,vh.index)],tma_bar_ptr=vh.barrier)
|
||||
pk = cutlass.Boolean(1)
|
||||
kvp.tail()
|
||||
|
||||
# MMA
|
||||
if warp_idx == self.mma_warp_id:
|
||||
tmem.wait_for_alloc()
|
||||
qc.reset(); qh = qc.wait_and_advance(); qh.release()
|
||||
kvc.reset(); pk = kvc.try_wait()
|
||||
acc_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
|
||||
acc_pipe.producer_acquire(acc_st)
|
||||
for kt in range(n_kv_tiles):
|
||||
kh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
sh = s_prod.acquire_and_advance()
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
for kb in cutlass.range(cute.size(tCrQ,mode=[2]), unroll_full=True):
|
||||
cute.gemm(qk_mma, tStS0, tCrQ[(None,None,kb,0)], tCrK[(None,None,kb,kh.index)], tStS0)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
sh.commit(); kh.release()
|
||||
softmax_done_bar.arrive_and_wait()
|
||||
vh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, kt != 0)
|
||||
for kb in cutlass.range(cute.size(tOrP0,mode=[2]), unroll_full=True):
|
||||
cute.gemm(pv_mma, tOtO0, tOrP0[(None,None,kb)], tCrV[(None,None,kb,vh.index)], tOtO0)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
vh.release()
|
||||
pv_done_bar.arrive()
|
||||
acc_pipe.producer_commit(acc_st); acc_st.advance()
|
||||
acc_pipe.producer_tail(acc_st)
|
||||
|
||||
# ===================== EPILOGUE WARPS (STAGE C: ONLINE SOFTMAX) =====================
|
||||
if warp_idx < self.mma_warp_id:
|
||||
tmem.allocate(self.num_tmem_alloc_cols)
|
||||
tmem.wait_for_alloc()
|
||||
tmem_ptr = tmem.retrieve_ptr(self.qk_acc_dtype)
|
||||
sfw_idx = tidx % (32 * len(self.epilogue_warp_id))
|
||||
|
||||
# --- S load (QK C-fragment) ---
|
||||
tmem_load_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
tiled_tmem_load = tcgen05.make_tmem_copy(tmem_load_atom, tStS0)
|
||||
thr_load = tiled_tmem_load.get_slice(sfw_idx)
|
||||
tTMEM_LOADtS = thr_load.partition_S(tStS0)
|
||||
cS = cute.make_identity_tensor((self.qk_mma_tiler[0], self.qk_mma_tiler[1]))
|
||||
tScS = qk_thr.partition_C(cS)
|
||||
tTMEM_LOADcS = thr_load.partition_D(tScS)
|
||||
|
||||
# --- P store (QK C-fragment composition, FMHA pattern) ---
|
||||
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
|
||||
tStP_layout = cute.composition(tStS.layout, cute.make_layout((self.pv_mma_tiler[0], p_cols_fp32)))
|
||||
tStP0 = cute.make_tensor(tStS.iterator + self.tmem_p0_offset, tStP_layout)
|
||||
tmem_store_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
tiled_tmem_store = tcgen05.make_tmem_copy(tmem_store_atom, tStP0)
|
||||
thr_store = tiled_tmem_store.get_slice(sfw_idx)
|
||||
tTMEM_STOREtP = thr_store.partition_D(tStP0)
|
||||
tScP_layout = cute.composition(tScS.layout, cute.make_layout((self.pv_mma_tiler[0], p_cols_fp32)))
|
||||
tScP = cute.make_tensor(tScS.iterator, tScP_layout)
|
||||
tTMEM_STOREcP = thr_store.partition_S(tScP)
|
||||
|
||||
# --- Vector TMEM (per-row row_sum storage, FMHA pattern) ---
|
||||
# composition(tStS.layout, (128, 2)) = 2 FP32 columns per logical row
|
||||
# vec[0] = row_sum (final, after loop), vec[1] = unused
|
||||
# Reuses S TMEM region (offset 0), free after softmax loop writes
|
||||
|
||||
tStS_vec_layout = cute.composition(tStS.layout, cute.make_layout((128, 2)))
|
||||
tStS_vec = cute.make_tensor(tStS.iterator + self.tmem_vec_offset, tStS_vec_layout)
|
||||
tScS_vec_layout = cute.composition(tScS.layout, cute.make_layout((128, 2)))
|
||||
tScS_vec = cute.make_tensor(tScS.iterator, tScS_vec_layout)
|
||||
tmem_store_vec_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(2)), self.qk_acc_dtype)
|
||||
tiled_tmem_store_vec = tcgen05.make_tmem_copy(tmem_store_vec_atom, tStS_vec)
|
||||
thr_tmem_store_vec = tiled_tmem_store_vec.get_slice(sfw_idx)
|
||||
tTMEM_STORE_VECtS = thr_tmem_store_vec.partition_D(tStS_vec)
|
||||
tTMEM_STORE_VECcS = thr_tmem_store_vec.partition_S(tScS_vec)
|
||||
tmem_load_vec_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(2)), self.qk_acc_dtype)
|
||||
tiled_tmem_load_vec = tcgen05.make_tmem_copy(tmem_load_vec_atom, tStS_vec)
|
||||
thr_tmem_load_vec = tiled_tmem_load_vec.get_slice(sfw_idx)
|
||||
tTMEM_LOAD_VECtS = thr_tmem_load_vec.partition_S(tStS_vec)
|
||||
tTMEM_LOAD_VECcS = thr_tmem_load_vec.partition_D(tScS_vec)
|
||||
|
||||
# --- C6: O TMEM load/store for rescale (correction_rescale pattern) ---
|
||||
corr_tile_size = 16
|
||||
cO = cute.make_identity_tensor((self.pv_mma_tiler[0], self.pv_mma_tiler[1]))
|
||||
tOcO = pv_thr.partition_C(cO)
|
||||
o_tmem_load_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.qk_acc_dtype)
|
||||
o_tmem_store_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.qk_acc_dtype)
|
||||
tOtO_i_layout = cute.composition(tOtO0.layout, cute.make_layout((128, corr_tile_size)))
|
||||
tOcO_i_layout = cute.composition(tOcO.layout, cute.make_layout((128, corr_tile_size)))
|
||||
tOtO_i = cute.make_tensor(tOtO0.iterator, tOtO_i_layout)
|
||||
tOcO_i = cute.make_tensor(tOcO.iterator, tOcO_i_layout)
|
||||
o_tiled_tmem_load = tcgen05.make_tmem_copy(o_tmem_load_atom, tOtO_i)
|
||||
o_tiled_tmem_store = tcgen05.make_tmem_copy(o_tmem_store_atom, tOtO_i)
|
||||
o_thr_load = o_tiled_tmem_load.get_slice(sfw_idx)
|
||||
o_thr_store = o_tiled_tmem_store.get_slice(sfw_idx)
|
||||
tTMEM_LOADtO = o_thr_load.partition_S(tOtO_i)
|
||||
tTMEM_LOADcO = o_thr_load.partition_D(tOcO_i)
|
||||
tTMEM_STOREtO = o_thr_store.partition_D(tOtO_i)
|
||||
o_col_tiles = self.pv_mma_tiler[1] // corr_tile_size
|
||||
|
||||
# --- C2: Per-thread row state (persist across KV tiles) ---
|
||||
row_max = -cutlass.Float32.inf
|
||||
row_sum = cutlass.Float32(0.0)
|
||||
|
||||
# --- C3: QK scale = 1/sqrt(HEAD_DIM) * log2(e) for exp2 ---
|
||||
scale = self.scale_softmax_log2
|
||||
|
||||
# =============================================================
|
||||
# Per-KV-tile online softmax loop
|
||||
# =============================================================
|
||||
for kt in range(n_kv_tiles):
|
||||
si_handle = s_cons.wait_and_advance()
|
||||
|
||||
# Load S from TMEM (FP32, QK C-fragment layout)
|
||||
tTMEM_LOADrS = cute.make_rmem_tensor(tTMEM_LOADcS.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load, tTMEM_LOADtS, tTMEM_LOADrS)
|
||||
|
||||
# --- C4: Compute tile_max via .reduce(MAX) ---
|
||||
old_row_max = row_max
|
||||
row_max = tTMEM_LOADrS.load().reduce(cute.ReductionOp.MAX, row_max, 0)
|
||||
row_max_safe = row_max
|
||||
if row_max == -cutlass.Float32.inf:
|
||||
row_max_safe = cutlass.Float32(0.0)
|
||||
|
||||
# --- C5: Compute rescale factor ---
|
||||
acc_scale = cute.math.exp2(scale * (old_row_max - row_max_safe), fastmath=True)
|
||||
|
||||
# --- C6: Rescale O in TMEM (load O, multiply by acc_scale, store O) ---
|
||||
# acc_scale belongs to QK row (N//4), but O rows are in PV partition (N).
|
||||
# Store acc_scale to vector by QK row, read by PV row.
|
||||
if kt > 0:
|
||||
pv_done_bar.arrive_and_wait()
|
||||
|
||||
# Store acc_scale to vector indexed by QK logical row
|
||||
qk_row_c6 = tTMEM_LOADcS[0][0]
|
||||
thr_vs_c6 = tiled_tmem_store_vec.get_slice(qk_row_c6)
|
||||
tVStore_c6 = thr_vs_c6.partition_D(tStS_vec)
|
||||
tVStoreSrc_c6 = thr_vs_c6.partition_S(tScS_vec)
|
||||
tVStoreRmem_c6 = cute.make_rmem_tensor(tVStoreSrc_c6.shape, self.qk_acc_dtype)
|
||||
tVStoreRmem_c6[0] = acc_scale
|
||||
cute.copy(tiled_tmem_store_vec, tVStoreRmem_c6, tVStore_c6)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# Read acc_scale from vector indexed by PV logical row
|
||||
pv_row_c6 = tTMEM_LOADcO[0][0]
|
||||
thr_vl_c6 = tiled_tmem_load_vec.get_slice(pv_row_c6)
|
||||
tVLoad_c6 = thr_vl_c6.partition_S(tStS_vec)
|
||||
tVLoadDst_c6 = thr_vl_c6.partition_D(tScS_vec)
|
||||
tVLoadRmem_c6 = cute.make_rmem_tensor(tVLoadDst_c6.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load_vec, tVLoad_c6, tVLoadRmem_c6)
|
||||
cute.arch.fence_view_async_tmem_load()
|
||||
acc_scale_pv = tVLoadRmem_c6[0]
|
||||
|
||||
tTMrO = cute.make_rmem_tensor((tTMEM_LOADcO.shape, o_col_tiles), self.qk_acc_dtype)
|
||||
for i in range(o_col_tiles):
|
||||
tTMrO_i_ = tTMrO[None, i]
|
||||
tTMrO_i_layout = cute.composition(tTMrO_i_.layout, cute.make_layout(tTMrO.shape[0]))
|
||||
tTMrO_i = cute.make_tensor(tTMrO_i_.iterator, tTMrO_i_layout)
|
||||
tTMEM_LOADtO_i = cute.make_tensor(tTMEM_LOADtO.iterator + i * corr_tile_size, tTMEM_LOADtO.layout)
|
||||
tTMEM_STOREtO_i = cute.make_tensor(tTMEM_STOREtO.iterator + i * corr_tile_size, tTMEM_STOREtO.layout)
|
||||
cute.copy(o_tiled_tmem_load, tTMEM_LOADtO_i, tTMrO_i)
|
||||
for j in cutlass.range(cute.size(tTMrO_i), vectorize=True):
|
||||
tTMrO_i[j] = tTMrO_i[j] * acc_scale_pv
|
||||
cute.copy(o_tiled_tmem_store, tTMrO_i, tTMEM_STOREtO_i)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# Rescale row_sum
|
||||
row_sum = row_sum * acc_scale
|
||||
|
||||
# --- C7: Compute P = exp2((S - row_max_safe) * scale) ---
|
||||
minus_row_max_scale = (cutlass.Float32(0.0) - row_max_safe) * scale
|
||||
|
||||
# Register bridge (FMHA pattern: FP32 backing + BF16 view)
|
||||
rP_words = cute.make_rmem_tensor(tTMEM_STOREcP.shape, self.qk_acc_dtype)
|
||||
rP_bf16 = cute.make_tensor(cute.recast_ptr(rP_words.iterator, dtype=self.q_dtype), tTMEM_LOADrS.layout)
|
||||
|
||||
frg_cnt = 4
|
||||
frg_tile = cute.size(tTMEM_LOADrS) // frg_cnt
|
||||
tTMEM_LOADrS_frg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(frg_tile))
|
||||
rP_bf16_frg = cute.logical_divide(rP_bf16, cute.make_layout(frg_tile))
|
||||
|
||||
# Scale S, compute exp2, store through register bridge
|
||||
for j in range(frg_cnt):
|
||||
for k in cutlass.range(cute.size(tTMEM_LOADrS_frg, mode=[0]), vectorize=True):
|
||||
tTMEM_LOADrS_frg[k, j] = tTMEM_LOADrS_frg[k, j] * scale + minus_row_max_scale
|
||||
tTMEM_LOADrS_frg[k, j] = cute.math.exp2(tTMEM_LOADrS_frg[k, j], fastmath=True)
|
||||
s_vec = tTMEM_LOADrS_frg[None, j].load()
|
||||
rP_bf16_frg[None, j].store(s_vec.to(self.q_dtype))
|
||||
|
||||
# Store P to TMEM
|
||||
cute.copy(tiled_tmem_store, rP_words, tTMEM_STOREtP)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
si_handle.release()
|
||||
softmax_done_bar.arrive()
|
||||
|
||||
# --- C8: Row sum accumulation (CUTLASS FMHA packed f32x2 pattern) ---
|
||||
# P values still in tTMEM_LOADrS registers.
|
||||
# 4 accumulators for 4 reduction_unroll columns.
|
||||
local_row_sum_0 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_1 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_2 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_3 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
|
||||
reduction_unroll = 4
|
||||
rfrg_tile = cute.size(tTMEM_LOADrS) // reduction_unroll
|
||||
tTMEM_LOADrS_rfrg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(rfrg_tile))
|
||||
|
||||
for j in cutlass.range_constexpr(0, cute.size(tTMEM_LOADrS_rfrg, mode=[0]), 2):
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_0, (tTMEM_LOADrS_rfrg[j, 0], tTMEM_LOADrS_rfrg[j + 1, 0]))
|
||||
local_row_sum_1 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_1, (tTMEM_LOADrS_rfrg[j, 1], tTMEM_LOADrS_rfrg[j + 1, 1]))
|
||||
local_row_sum_2 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_2, (tTMEM_LOADrS_rfrg[j, 2], tTMEM_LOADrS_rfrg[j + 1, 2]))
|
||||
local_row_sum_3 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_3, (tTMEM_LOADrS_rfrg[j, 3], tTMEM_LOADrS_rfrg[j + 1, 3]))
|
||||
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(local_row_sum_0, local_row_sum_1)
|
||||
local_row_sum_2 = cute.arch.add_packed_f32x2(local_row_sum_2, local_row_sum_3)
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(local_row_sum_0, local_row_sum_2)
|
||||
tile_sum = local_row_sum_0[0] + local_row_sum_0[1]
|
||||
|
||||
row_sum = row_sum + tile_sum
|
||||
|
||||
# --- C9: Final normalization via O TMEM rescale ---
|
||||
pv_done_bar.arrive_and_wait()
|
||||
|
||||
# Use QK-accumulated row_sum directly (DEBUG: check if row mapping matches PV)
|
||||
inv_row_sum = cutlass.Float32(1.0) / row_sum
|
||||
|
||||
# Normalize O in TMEM using PV-correct inv_row_sum
|
||||
tTMrO_final = cute.make_rmem_tensor((tTMEM_LOADcO.shape, o_col_tiles), self.qk_acc_dtype)
|
||||
for i in range(o_col_tiles):
|
||||
tTMrO_i_ = tTMrO_final[None, i]
|
||||
tTMrO_i_layout = cute.composition(tTMrO_i_.layout, cute.make_layout(tTMrO_final.shape[0]))
|
||||
tTMrO_i = cute.make_tensor(tTMrO_i_.iterator, tTMrO_i_layout)
|
||||
tTMEM_LOADtO_i = cute.make_tensor(
|
||||
tTMEM_LOADtO.iterator + i * corr_tile_size, tTMEM_LOADtO.layout)
|
||||
tTMEM_STOREtO_i = cute.make_tensor(
|
||||
tTMEM_STOREtO.iterator + i * corr_tile_size, tTMEM_STOREtO.layout)
|
||||
cute.copy(o_tiled_tmem_load, tTMEM_LOADtO_i, tTMrO_i)
|
||||
for j in cutlass.range(cute.size(tTMrO_i), vectorize=True):
|
||||
tTMrO_i[j] = tTMrO_i[j] * inv_row_sum
|
||||
cute.copy(o_tiled_tmem_store, tTMrO_i, tTMEM_STOREtO_i)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# Now O in TMEM is normalized. Use standard epilogue_tma_store with identity.
|
||||
tCtO_base = cute.make_tensor(tmem_ptr + self.tmem_o0_offset, tCtO_fake.layout)
|
||||
acc_cons_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Consumer, self.num_acc_stage)
|
||||
c_grp = pipeline.CooperativeGroup(pipeline.Agent.Thread, 32 * len(self.epilogue_warp_id))
|
||||
c_pipe = pipeline.PipelineTmaStore.create(num_stages=self.num_c_stage, producer_group=c_grp)
|
||||
acc_cons_st = utils.gemm.sm100.epilogue_tma_store(
|
||||
self, tidx, warp_idx, tma_c, tCtO_base, sC, tCgC, epi_tile, 0,
|
||||
const_expr(lambda x: x),
|
||||
(0,0,0), acc_cons_st, acc_pipe, c_pipe)
|
||||
c_pipe.producer_tail()
|
||||
tmem.relinquish_alloc_permit()
|
||||
tmem.free(tmem_ptr)
|
||||
|
||||
|
||||
def test():
|
||||
import math
|
||||
torch.manual_seed(42)
|
||||
for n in [128, 256, 384]:
|
||||
m, hd = 128, HEAD_DIM
|
||||
q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
k = torch.randn(n, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
v = torch.randn(n, hd, dtype=torch.bfloat16, device="cuda")
|
||||
v_kernel = v.unsqueeze(-1)
|
||||
c = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
qf = q[:,:,0].float(); kf = k[:,:,0].float()
|
||||
attn = qf @ kf.T / math.sqrt(hd)
|
||||
ref = torch.softmax(attn, dim=-1) @ v.float()
|
||||
mQ = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
|
||||
mK = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
|
||||
mV = ct.from_dlpack(v_kernel).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_kernel))
|
||||
mC = ct.from_dlpack(c).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c))
|
||||
stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
|
||||
kernel = FmhaV3Softmax()
|
||||
print(f"n={n}: Compiling...", flush=True)
|
||||
compiled = cute.compile(kernel, mQ, mK, mV, mC, stream)
|
||||
print(f"n={n}: tmem: s0={kernel.tmem_s0_offset} p0={kernel.tmem_p0_offset} o0={kernel.tmem_o0_offset} vec={kernel.tmem_vec_offset} alloc={kernel.num_tmem_alloc_cols}", flush=True)
|
||||
print(f"n={n}: Running...", flush=True)
|
||||
compiled(mQ, mK, mV, mC, stream)
|
||||
torch.cuda.synchronize()
|
||||
out = c[:,:,0].float()
|
||||
cos = torch.nn.functional.cosine_similarity(out.flatten().unsqueeze(0), ref.flatten().unsqueeze(0)).item()
|
||||
max_err = (out - ref).abs().max().item()
|
||||
print(f"FMHA softmax n={n}: cosine {cos:.6f} max_err {max_err:.6f} {'PASS' if cos >= 0.999 else 'FAIL'}", flush=True)
|
||||
|
||||
if __name__ == "__main__":
|
||||
test()
|
||||
|
||||
|
||||
@@ -1,512 +0,0 @@
|
||||
"""
|
||||
FMHA v3 + Stage C: QK -> online softmax -> PV with KV-tile interleaving.
|
||||
Stage C: row_max, exp2, O rescale, row_sum, final normalization.
|
||||
FMHA pattern P store preserved from Stage B.
|
||||
"""
|
||||
import math
|
||||
import torch, cutlass, cutlass.cute as cute, cutlass.utils as utils, cutlass.pipeline as pipeline
|
||||
from cutlass.cute.nvgpu import cpasync, tcgen05
|
||||
from cutlass import Float32, BFloat16, Int32, Boolean, const_expr
|
||||
from cutlass.utils import LayoutEnum
|
||||
from cutlass.utils.tmem_allocator import find_tmem_tensor_col_offset
|
||||
import cuda.bindings.driver as cuda
|
||||
import cutlass.torch as ct
|
||||
|
||||
HEAD_DIM = 64
|
||||
|
||||
class FmhaV3Softmax:
|
||||
def __init__(self, s_k=128):
|
||||
self.s_k = s_k
|
||||
self.acc_dtype = Float32; self.qk_acc_dtype = Float32
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
self.use_2cta_instrs = False; self.epilog_sync_bar_id = 1
|
||||
self.cluster_shape_mn = (1, 1); self.cta_group = tcgen05.CtaGroup.ONE
|
||||
self.epilogue_warp_id = (0,1,2,3); self.mma_warp_id = 4; self.tma_warp_id = 5
|
||||
self.threads_per_cta = 192; self.num_c_stage = 2
|
||||
self.kv_stage = 2; self.q_stage = 1; self.num_c_stage = 2
|
||||
|
||||
def _setup(self, qk_mma, pv_mma):
|
||||
qk_ik = cute.size(qk_mma.shape_mnk, mode=[2])
|
||||
self.qk_mma_tiler = (128, 128, qk_ik * 4)
|
||||
pv_ik = cute.size(pv_mma.shape_mnk, mode=[2])
|
||||
self.pv_mma_tiler = (128, HEAD_DIM, pv_ik * (128 // pv_ik))
|
||||
self.mma_tiler = self.qk_mma_tiler
|
||||
self.cluster_layout_vmnk = cute.tiled_divide(cute.make_layout((1,1,1)), (qk_mma.thr_id.shape,))
|
||||
self.cta_tile_shape_mnk = (self.qk_mma_tiler[0]//cute.size(qk_mma.thr_id.shape), HEAD_DIM, self.qk_mma_tiler[2])
|
||||
self.c_layout = LayoutEnum.ROW_MAJOR
|
||||
self.epi_tile = utils.sm100.compute_epilogue_tile_shape(self.cta_tile_shape_mnk, False, self.c_layout, self.o_dtype)
|
||||
self.num_ab_stage = 1; self.num_acc_stage = 1
|
||||
self.q_smem_s = utils.sm100.make_smem_layout_a(qk_mma, self.qk_mma_tiler, self.q_dtype, self.q_stage)
|
||||
self.k_smem_s = utils.sm100.make_smem_layout_b(qk_mma, self.qk_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.v_smem_s = utils.sm100.make_smem_layout_b(pv_mma, self.pv_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.c_smem_s = utils.sm100.make_smem_layout_epi(self.o_dtype, self.c_layout, self.epi_tile, 2)
|
||||
self.p_tmem_s = utils.sm100.make_smem_layout_a(pv_mma, self.pv_mma_tiler, self.q_dtype, 1)
|
||||
qk_thr = qk_mma.get_slice(0); qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
pv_thr = pv_mma.get_slice(0); pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
self.tmem_s0_offset = 0; self.tmem_p0_offset = 32
|
||||
# P occupies [tmem_p0_offset, tmem_p0_offset + p_cols_fp32)
|
||||
# S occupies [0, qk_mma_tiler[1]) = [0, 128)
|
||||
# O must NOT overlap P. Place O after max(S end, P end), aligned to 32.
|
||||
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
|
||||
p_end = self.tmem_p0_offset + p_cols_fp32 # 32 + 64 = 96
|
||||
s_cols = self.qk_mma_tiler[1] # 128
|
||||
o_after = max(s_cols, p_end) # 128
|
||||
self.tmem_o0_offset = ((o_after + 31) // 32) * 32
|
||||
self.tmem_vec_offset = 0 # Reuse S region for per-row inv_row_sum vector # align to 32 = 128
|
||||
self.tmem_vec_offset = 0 # Reuse S region (free after softmax loop)
|
||||
o_cols = find_tmem_tensor_col_offset(tOtO) # footprint of O
|
||||
total = self.tmem_o0_offset + o_cols
|
||||
# Must be multiple of 32 AND power of 2
|
||||
self.num_tmem_alloc_cols = 1
|
||||
while self.num_tmem_alloc_cols < total:
|
||||
self.num_tmem_alloc_cols *= 2
|
||||
cta = cute.size(qk_mma.thr_id.shape)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0))
|
||||
self.q_tx_bytes = cute.size_in_bytes(self.q_dtype, q_s) * cta
|
||||
self.kv_tx_bytes = cute.size_in_bytes(self.q_dtype, k_s) * cta
|
||||
self.scale_softmax_log2 = Float32(1.0 / math.sqrt(HEAD_DIM) * math.log2(math.e))
|
||||
|
||||
@cute.jit
|
||||
def __call__(self, q, k, v, c, stream):
|
||||
self.q_dtype = q.element_type; self.o_dtype = c.element_type; self.c_dtype = self.o_dtype
|
||||
self.a_major = LayoutEnum.from_tensor(q).mma_major_mode()
|
||||
self.b_major = LayoutEnum.from_tensor(k).mma_major_mode()
|
||||
# # s_k hardcoded # BROKEN in @cute.jit
|
||||
# FMHA-style V: reconstruct as (HEAD_DIM, s_k, 1) MN-major
|
||||
v_fmha = cute.make_tensor(
|
||||
v.iterator,
|
||||
cute.make_layout(
|
||||
(HEAD_DIM, 128, 1),
|
||||
stride=(1, HEAD_DIM, HEAD_DIM * 128),
|
||||
),
|
||||
)
|
||||
self.v_major = LayoutEnum.from_tensor(v_fmha).mma_major_mode()
|
||||
self.c_layout = LayoutEnum.from_tensor(c)
|
||||
qk_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, self.a_major, self.b_major, self.qk_acc_dtype, self.cta_group, (128,128), tcgen05.OperandSource.SMEM)
|
||||
pv_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, cute.nvgpu.OperandMajorMode.K, self.v_major, self.qk_acc_dtype, self.cta_group, (128,HEAD_DIM), tcgen05.OperandSource.TMEM)
|
||||
self._setup(qk_mma, pv_mma)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0)); v_s = cute.slice_(self.v_smem_s,(None,None,None,0))
|
||||
tma_q,mQ = cute.nvgpu.make_tiled_tma_atom_A(utils.sm100.cluster_shape_to_tma_atom_A(self.cluster_shape_mn,qk_mma.thr_id),q,q_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_k,mK = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,qk_mma.thr_id),k,k_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_v,mV = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,pv_mma.thr_id),v_fmha,v_s,self.pv_mma_tiler,pv_mma,self.cluster_layout_vmnk.shape)
|
||||
epi_s = cute.select(self.c_smem_s,mode=[0,1])
|
||||
tma_c,mC = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(),c,epi_s,self.epi_tile)
|
||||
self._kernel(qk_mma,pv_mma,tma_q,mQ,tma_k,mK,tma_v,mV,tma_c,mC,self.cluster_layout_vmnk,self.q_smem_s,self.k_smem_s,self.v_smem_s,self.p_tmem_s,self.c_smem_s,self.epi_tile).launch(grid=(1,1,1),block=[self.threads_per_cta,1,1],stream=stream)
|
||||
|
||||
@cute.kernel
|
||||
def _kernel(self, qk_mma, pv_mma, tma_q, mQ, tma_k, mK, tma_v, mV, tma_c, mC, cl_vmnk, q_smem_s, k_smem_s, v_smem_s, p_tmem_s, c_smem_s, epi_tile):
|
||||
warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx())
|
||||
tidx,_,_ = cute.arch.thread_idx()
|
||||
if warp_idx == self.tma_warp_id:
|
||||
cpasync.prefetch_descriptor(tma_q); cpasync.prefetch_descriptor(tma_k); cpasync.prefetch_descriptor(tma_v); cpasync.prefetch_descriptor(tma_c)
|
||||
|
||||
@cute.struct
|
||||
class SS:
|
||||
q_bar: cute.struct.MemRange[cutlass.Int64, self.q_stage*2]
|
||||
kv_bar: cute.struct.MemRange[cutlass.Int64, self.kv_stage*2]
|
||||
s_bar: cute.struct.MemRange[cutlass.Int64, 2]
|
||||
acc_bar: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage*2]
|
||||
tmem_dealloc: cutlass.Int64; holding: cutlass.Int32
|
||||
smem = utils.SmemAllocator(); st = smem.allocate(SS)
|
||||
|
||||
qp,qc = pipeline.PipelineTmaUmma.create(barrier_storage=st.q_bar.data_ptr(),num_stages=self.q_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.q_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
|
||||
kvp,kvc = pipeline.PipelineTmaUmma.create(barrier_storage=st.kv_bar.data_ptr(),num_stages=self.kv_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.kv_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
|
||||
s_prod,s_cons = pipeline.PipelineUmmaAsync.create(barrier_storage=st.s_bar.data_ptr(),num_stages=1,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32*len(self.epilogue_warp_id))).make_participants()
|
||||
softmax_done_bar = pipeline.NamedBarrier(barrier_id=3, num_threads=32 + 32*len(self.epilogue_warp_id))
|
||||
pv_done_bar = pipeline.NamedBarrier(barrier_id=4, num_threads=32 + 32*len(self.epilogue_warp_id))
|
||||
acc_pipe = pipeline.PipelineUmmaAsync.create(barrier_storage=st.acc_bar.data_ptr(),num_stages=self.num_acc_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,len(self.epilogue_warp_id)),cta_layout_vmnk=cl_vmnk,defer_sync=True)
|
||||
tmem_bar = pipeline.NamedBarrier(barrier_id=2,num_threads=32*len((self.mma_warp_id,*self.epilogue_warp_id)))
|
||||
tmem = utils.TmemAllocator(st.holding.ptr,barrier_for_retrieve=tmem_bar,allocator_warp_id=self.epilogue_warp_id[0],is_two_cta=cute.size(qk_mma.thr_id.shape)==2,two_cta_tmem_dealloc_mbar_ptr=st.tmem_dealloc.ptr)
|
||||
pipeline.pipeline_init_arrive(cluster_shape_mn=cl_vmnk,is_relaxed=True)
|
||||
|
||||
sQ = smem.allocate_tensor(element_type=self.q_dtype,layout=q_smem_s.outer,byte_alignment=128,swizzle=q_smem_s.inner)
|
||||
sK = smem.allocate_tensor(element_type=self.q_dtype,layout=k_smem_s.outer,byte_alignment=128,swizzle=k_smem_s.inner)
|
||||
sV = smem.allocate_tensor(element_type=self.q_dtype,layout=v_smem_s.outer,byte_alignment=128,swizzle=v_smem_s.inner)
|
||||
sC = smem.allocate_tensor(element_type=self.o_dtype,layout=c_smem_s.outer,byte_alignment=128,swizzle=c_smem_s.inner)
|
||||
|
||||
gQ = cute.local_tile(mQ,cute.slice_(self.qk_mma_tiler,(None,0,None)),(None,None,None))
|
||||
gK = cute.local_tile(mK,cute.slice_(self.qk_mma_tiler,(0,None,None)),(None,None,None))
|
||||
gV = cute.local_tile(mV,cute.slice_(self.pv_mma_tiler,(0,None,None)),(None,None,None))
|
||||
gC = cute.local_tile(mC,cute.slice_(self.pv_mma_tiler,(None,None,0)),(None,None,None))
|
||||
n_kv_tiles = cute.size(gK, mode=[3])
|
||||
|
||||
qk_thr = qk_mma.get_slice(0); pv_thr = pv_mma.get_slice(0)
|
||||
tCgQ = qk_thr.partition_A(gQ); tCgK = qk_thr.partition_B(gK)
|
||||
tCgV = pv_thr.partition_B(gV); tCgC = pv_thr.partition_C(gC)
|
||||
a_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,0,None,0)).shape)
|
||||
tAsQ,tAgQ = cpasync.tma_partition(tma_q,0,a_lay,cute.group_modes(sQ,0,3),cute.group_modes(tCgQ,0,3))
|
||||
b_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,None,0,0)).shape)
|
||||
tBsK,tBgK = cpasync.tma_partition(tma_k,0,b_lay,cute.group_modes(sK,0,3),cute.group_modes(tCgK,0,3))
|
||||
tVsV,tVgV = cpasync.tma_partition(tma_v,0,b_lay,cute.group_modes(sV,0,3),cute.group_modes(tCgV,0,3))
|
||||
tAgQ = tAgQ[(None,0,None,0)]; tBgK = tBgK[(None,0,None,0)]; tVgV = tVgV[(None,0,None,0)]
|
||||
|
||||
tCrQ = qk_mma.make_fragment_A(sQ); tCrK = qk_mma.make_fragment_B(sK)
|
||||
tCrV = pv_mma.make_fragment_B(sV)
|
||||
|
||||
qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
tOtO0 = cute.make_tensor(tOtO.iterator + self.tmem_o0_offset, tOtO.layout)
|
||||
|
||||
# --- PV read view (for MMA only, NOT for softmax store) ---
|
||||
tP = cute.make_tensor(tStS.iterator, p_tmem_s.outer)
|
||||
tOrP_base = pv_thr.make_fragment_A(tP)
|
||||
tOrP = tOrP_base[(None,None,None,0)]
|
||||
tOrP0 = cute.make_tensor(
|
||||
tOrP.iterator + self.qk_acc_dtype.width // self.q_dtype.width * self.tmem_p0_offset,
|
||||
tOrP.layout)
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_as, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_as, self.num_acc_stage))
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# TMA LOAD
|
||||
if warp_idx == self.tma_warp_id:
|
||||
qp.reset(); qh = qp.acquire_and_advance()
|
||||
cute.copy(tma_q,tAgQ[(None,qh.count)],tAsQ[(None,qh.index)],tma_bar_ptr=qh.barrier)
|
||||
qp.tail()
|
||||
kvp.reset(); pk = kvp.try_acquire()
|
||||
for kt in cutlass.range(n_kv_tiles,unroll=1):
|
||||
kh = kvp.acquire_and_advance(pk)
|
||||
cute.copy(tma_k,tBgK[(None,kh.count)],tBsK[(None,kh.index)],tma_bar_ptr=kh.barrier)
|
||||
pk = cutlass.Boolean(1)
|
||||
vh = kvp.acquire_and_advance(pk)
|
||||
cute.copy(tma_v,tVgV[(None,vh.count)],tVsV[(None,vh.index)],tma_bar_ptr=vh.barrier)
|
||||
pk = cutlass.Boolean(1)
|
||||
kvp.tail()
|
||||
|
||||
# MMA
|
||||
if warp_idx == self.mma_warp_id:
|
||||
tmem.wait_for_alloc()
|
||||
qc.reset(); qh = qc.wait_and_advance(); qh.release()
|
||||
kvc.reset(); pk = kvc.try_wait()
|
||||
acc_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
|
||||
acc_pipe.producer_acquire(acc_st)
|
||||
for kt in range(n_kv_tiles):
|
||||
kh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
sh = s_prod.acquire_and_advance()
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
for kb in cutlass.range(cute.size(tCrQ,mode=[2]), unroll_full=True):
|
||||
cute.gemm(qk_mma, tStS0, tCrQ[(None,None,kb,0)], tCrK[(None,None,kb,kh.index)], tStS0)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
sh.commit(); kh.release()
|
||||
softmax_done_bar.arrive_and_wait()
|
||||
vh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, kt != 0)
|
||||
for kb in cutlass.range(cute.size(tOrP0,mode=[2]), unroll_full=True):
|
||||
cute.gemm(pv_mma, tOtO0, tOrP0[(None,None,kb)], tCrV[(None,None,kb,vh.index)], tOtO0)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
vh.release()
|
||||
pv_done_bar.arrive()
|
||||
acc_pipe.producer_commit(acc_st); acc_st.advance()
|
||||
acc_pipe.producer_tail(acc_st)
|
||||
|
||||
# ===================== EPILOGUE WARPS (STAGE C: ONLINE SOFTMAX) =====================
|
||||
if warp_idx < self.mma_warp_id:
|
||||
tmem.allocate(self.num_tmem_alloc_cols)
|
||||
tmem.wait_for_alloc()
|
||||
tmem_ptr = tmem.retrieve_ptr(self.qk_acc_dtype)
|
||||
sfw_idx = tidx % (32 * len(self.epilogue_warp_id))
|
||||
|
||||
# --- S load (QK C-fragment) ---
|
||||
tmem_load_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
tiled_tmem_load = tcgen05.make_tmem_copy(tmem_load_atom, tStS0)
|
||||
thr_load = tiled_tmem_load.get_slice(sfw_idx)
|
||||
tTMEM_LOADtS = thr_load.partition_S(tStS0)
|
||||
cS = cute.make_identity_tensor((self.qk_mma_tiler[0], self.qk_mma_tiler[1]))
|
||||
tScS = qk_thr.partition_C(cS)
|
||||
tTMEM_LOADcS = thr_load.partition_D(tScS)
|
||||
|
||||
# --- P store (QK C-fragment composition, FMHA pattern) ---
|
||||
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
|
||||
tStP_layout = cute.composition(tStS.layout, cute.make_layout((self.pv_mma_tiler[0], p_cols_fp32)))
|
||||
tStP0 = cute.make_tensor(tStS.iterator + self.tmem_p0_offset, tStP_layout)
|
||||
tmem_store_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
tiled_tmem_store = tcgen05.make_tmem_copy(tmem_store_atom, tStP0)
|
||||
thr_store = tiled_tmem_store.get_slice(sfw_idx)
|
||||
tTMEM_STOREtP = thr_store.partition_D(tStP0)
|
||||
tScP_layout = cute.composition(tScS.layout, cute.make_layout((self.pv_mma_tiler[0], p_cols_fp32)))
|
||||
tScP = cute.make_tensor(tScS.iterator, tScP_layout)
|
||||
tTMEM_STOREcP = thr_store.partition_S(tScP)
|
||||
|
||||
# --- Vector TMEM (per-row row_sum storage, FMHA pattern) ---
|
||||
# composition(tStS.layout, (128, 2)) = 2 FP32 columns per logical row
|
||||
# vec[0] = row_sum (final, after loop), vec[1] = unused
|
||||
# Reuses S TMEM region (offset 0), free after softmax loop writes
|
||||
|
||||
tStS_vec_layout = cute.composition(tStS.layout, cute.make_layout((128, 2)))
|
||||
tStS_vec = cute.make_tensor(tStS.iterator + self.tmem_vec_offset, tStS_vec_layout)
|
||||
tScS_vec_layout = cute.composition(tScS.layout, cute.make_layout((128, 2)))
|
||||
tScS_vec = cute.make_tensor(tScS.iterator, tScS_vec_layout)
|
||||
tmem_store_vec_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(2)), self.qk_acc_dtype)
|
||||
tiled_tmem_store_vec = tcgen05.make_tmem_copy(tmem_store_vec_atom, tStS_vec)
|
||||
thr_tmem_store_vec = tiled_tmem_store_vec.get_slice(sfw_idx)
|
||||
tTMEM_STORE_VECtS = thr_tmem_store_vec.partition_D(tStS_vec)
|
||||
tTMEM_STORE_VECcS = thr_tmem_store_vec.partition_S(tScS_vec)
|
||||
tmem_load_vec_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(2)), self.qk_acc_dtype)
|
||||
tiled_tmem_load_vec = tcgen05.make_tmem_copy(tmem_load_vec_atom, tStS_vec)
|
||||
thr_tmem_load_vec = tiled_tmem_load_vec.get_slice(sfw_idx)
|
||||
tTMEM_LOAD_VECtS = thr_tmem_load_vec.partition_S(tStS_vec)
|
||||
tTMEM_LOAD_VECcS = thr_tmem_load_vec.partition_D(tScS_vec)
|
||||
|
||||
# --- C6: O TMEM load/store for rescale (correction_rescale pattern) ---
|
||||
corr_tile_size = 16
|
||||
cO = cute.make_identity_tensor((self.pv_mma_tiler[0], self.pv_mma_tiler[1]))
|
||||
tOcO = pv_thr.partition_C(cO)
|
||||
o_tmem_load_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.qk_acc_dtype)
|
||||
o_tmem_store_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.qk_acc_dtype)
|
||||
tOtO_i_layout = cute.composition(tOtO0.layout, cute.make_layout((128, corr_tile_size)))
|
||||
tOcO_i_layout = cute.composition(tOcO.layout, cute.make_layout((128, corr_tile_size)))
|
||||
tOtO_i = cute.make_tensor(tOtO0.iterator, tOtO_i_layout)
|
||||
tOcO_i = cute.make_tensor(tOcO.iterator, tOcO_i_layout)
|
||||
o_tiled_tmem_load = tcgen05.make_tmem_copy(o_tmem_load_atom, tOtO_i)
|
||||
o_tiled_tmem_store = tcgen05.make_tmem_copy(o_tmem_store_atom, tOtO_i)
|
||||
o_thr_load = o_tiled_tmem_load.get_slice(sfw_idx)
|
||||
o_thr_store = o_tiled_tmem_store.get_slice(sfw_idx)
|
||||
tTMEM_LOADtO = o_thr_load.partition_S(tOtO_i)
|
||||
tTMEM_LOADcO = o_thr_load.partition_D(tOcO_i)
|
||||
tTMEM_STOREtO = o_thr_store.partition_D(tOtO_i)
|
||||
o_col_tiles = self.pv_mma_tiler[1] // corr_tile_size
|
||||
|
||||
# --- C2: Per-thread row state (persist across KV tiles) ---
|
||||
row_max = -cutlass.Float32.inf
|
||||
row_sum = cutlass.Float32(0.0)
|
||||
|
||||
# --- C3: QK scale = 1/sqrt(HEAD_DIM) * log2(e) for exp2 ---
|
||||
scale = self.scale_softmax_log2
|
||||
|
||||
# =============================================================
|
||||
# Per-KV-tile online softmax loop
|
||||
# =============================================================
|
||||
for kt in range(n_kv_tiles):
|
||||
si_handle = s_cons.wait_and_advance()
|
||||
|
||||
# Load S from TMEM (FP32, QK C-fragment layout)
|
||||
tTMEM_LOADrS = cute.make_rmem_tensor(tTMEM_LOADcS.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load, tTMEM_LOADtS, tTMEM_LOADrS)
|
||||
|
||||
# --- C4: Compute tile_max via .reduce(MAX) ---
|
||||
old_row_max = row_max
|
||||
row_max = tTMEM_LOADrS.load().reduce(cute.ReductionOp.MAX, row_max, 0)
|
||||
row_max_safe = row_max
|
||||
if row_max == -cutlass.Float32.inf:
|
||||
row_max_safe = cutlass.Float32(0.0)
|
||||
|
||||
# --- C5: Compute rescale factor ---
|
||||
acc_scale = cute.math.exp2(scale * (old_row_max - row_max_safe), fastmath=True)
|
||||
|
||||
# --- C6: Rescale O in TMEM (load O, multiply by acc_scale, store O) ---
|
||||
# acc_scale belongs to QK row (N//4), but O rows are in PV partition (N).
|
||||
# Store acc_scale to vector by QK row, read by PV row.
|
||||
if kt > 0:
|
||||
pv_done_bar.arrive_and_wait()
|
||||
|
||||
# Store acc_scale to vector indexed by QK logical row
|
||||
qk_row_c6 = tTMEM_LOADcS[0][0]
|
||||
thr_vs_c6 = tiled_tmem_store_vec.get_slice(qk_row_c6)
|
||||
tVStore_c6 = thr_vs_c6.partition_D(tStS_vec)
|
||||
tVStoreSrc_c6 = thr_vs_c6.partition_S(tScS_vec)
|
||||
tVStoreRmem_c6 = cute.make_rmem_tensor(tVStoreSrc_c6.shape, self.qk_acc_dtype)
|
||||
tVStoreRmem_c6[0] = acc_scale
|
||||
cute.copy(tiled_tmem_store_vec, tVStoreRmem_c6, tVStore_c6)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# Read acc_scale from vector indexed by PV logical row
|
||||
pv_row_c6 = tTMEM_LOADcO[0][0]
|
||||
thr_vl_c6 = tiled_tmem_load_vec.get_slice(pv_row_c6)
|
||||
tVLoad_c6 = thr_vl_c6.partition_S(tStS_vec)
|
||||
tVLoadDst_c6 = thr_vl_c6.partition_D(tScS_vec)
|
||||
tVLoadRmem_c6 = cute.make_rmem_tensor(tVLoadDst_c6.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load_vec, tVLoad_c6, tVLoadRmem_c6)
|
||||
cute.arch.fence_view_async_tmem_load()
|
||||
acc_scale_pv = tVLoadRmem_c6[0]
|
||||
|
||||
tTMrO = cute.make_rmem_tensor((tTMEM_LOADcO.shape, o_col_tiles), self.qk_acc_dtype)
|
||||
for i in range(o_col_tiles):
|
||||
tTMrO_i_ = tTMrO[None, i]
|
||||
tTMrO_i_layout = cute.composition(tTMrO_i_.layout, cute.make_layout(tTMrO.shape[0]))
|
||||
tTMrO_i = cute.make_tensor(tTMrO_i_.iterator, tTMrO_i_layout)
|
||||
tTMEM_LOADtO_i = cute.make_tensor(tTMEM_LOADtO.iterator + i * corr_tile_size, tTMEM_LOADtO.layout)
|
||||
tTMEM_STOREtO_i = cute.make_tensor(tTMEM_STOREtO.iterator + i * corr_tile_size, tTMEM_STOREtO.layout)
|
||||
cute.copy(o_tiled_tmem_load, tTMEM_LOADtO_i, tTMrO_i)
|
||||
for j in cutlass.range(cute.size(tTMrO_i), vectorize=True):
|
||||
tTMrO_i[j] = tTMrO_i[j] * acc_scale_pv
|
||||
cute.copy(o_tiled_tmem_store, tTMrO_i, tTMEM_STOREtO_i)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# Rescale row_sum
|
||||
row_sum = row_sum * acc_scale
|
||||
|
||||
# --- C7: Compute P = exp2((S - row_max_safe) * scale) ---
|
||||
minus_row_max_scale = (cutlass.Float32(0.0) - row_max_safe) * scale
|
||||
|
||||
# Register bridge (FMHA pattern: FP32 backing + BF16 view)
|
||||
rP_words = cute.make_rmem_tensor(tTMEM_STOREcP.shape, self.qk_acc_dtype)
|
||||
rP_bf16 = cute.make_tensor(cute.recast_ptr(rP_words.iterator, dtype=self.q_dtype), tTMEM_LOADrS.layout)
|
||||
|
||||
frg_cnt = 4
|
||||
frg_tile = cute.size(tTMEM_LOADrS) // frg_cnt
|
||||
tTMEM_LOADrS_frg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(frg_tile))
|
||||
rP_bf16_frg = cute.logical_divide(rP_bf16, cute.make_layout(frg_tile))
|
||||
|
||||
# Scale S, compute exp2, store through register bridge
|
||||
for j in range(frg_cnt):
|
||||
for k in cutlass.range(cute.size(tTMEM_LOADrS_frg, mode=[0]), vectorize=True):
|
||||
tTMEM_LOADrS_frg[k, j] = tTMEM_LOADrS_frg[k, j] * scale + minus_row_max_scale
|
||||
tTMEM_LOADrS_frg[k, j] = cute.math.exp2(tTMEM_LOADrS_frg[k, j], fastmath=True)
|
||||
s_vec = tTMEM_LOADrS_frg[None, j].load()
|
||||
rP_bf16_frg[None, j].store(s_vec.to(self.q_dtype))
|
||||
|
||||
# Store P to TMEM
|
||||
cute.copy(tiled_tmem_store, rP_words, tTMEM_STOREtP)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
si_handle.release()
|
||||
softmax_done_bar.arrive()
|
||||
|
||||
# --- C8: Row sum accumulation (CUTLASS FMHA packed f32x2 pattern) ---
|
||||
# P values still in tTMEM_LOADrS registers.
|
||||
# 4 accumulators for 4 reduction_unroll columns.
|
||||
local_row_sum_0 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_1 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_2 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_3 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
|
||||
reduction_unroll = 4
|
||||
rfrg_tile = cute.size(tTMEM_LOADrS) // reduction_unroll
|
||||
tTMEM_LOADrS_rfrg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(rfrg_tile))
|
||||
|
||||
for j in cutlass.range_constexpr(0, cute.size(tTMEM_LOADrS_rfrg, mode=[0]), 2):
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_0, (tTMEM_LOADrS_rfrg[j, 0], tTMEM_LOADrS_rfrg[j + 1, 0]))
|
||||
local_row_sum_1 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_1, (tTMEM_LOADrS_rfrg[j, 1], tTMEM_LOADrS_rfrg[j + 1, 1]))
|
||||
local_row_sum_2 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_2, (tTMEM_LOADrS_rfrg[j, 2], tTMEM_LOADrS_rfrg[j + 1, 2]))
|
||||
local_row_sum_3 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_3, (tTMEM_LOADrS_rfrg[j, 3], tTMEM_LOADrS_rfrg[j + 1, 3]))
|
||||
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(local_row_sum_0, local_row_sum_1)
|
||||
local_row_sum_2 = cute.arch.add_packed_f32x2(local_row_sum_2, local_row_sum_3)
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(local_row_sum_0, local_row_sum_2)
|
||||
tile_sum = local_row_sum_0[0] + local_row_sum_0[1]
|
||||
|
||||
row_sum = row_sum + tile_sum
|
||||
|
||||
# --- C9: Final normalization via O TMEM rescale ---
|
||||
pv_done_bar.arrive_and_wait()
|
||||
|
||||
# Compute inv_row_sum from P in TMEM using PV partition.
|
||||
# P was stored by softmax loop into TMEM at offset tmem_p0_offset.
|
||||
# PV partition maps thread N to PV row N, so reading P via PV partition
|
||||
# gives the correct per-row P values to sum.
|
||||
# This avoids the QK→PV row mapping mismatch (QK: N->N//4, PV: N->N).
|
||||
|
||||
# P is stored as BF16 in TMEM at tmem_p0_offset.
|
||||
# We need to read it via PV TMEM load and sum the values.
|
||||
# P has shape (128, HEAD_DIM//2) in FP32 columns (64 BF16 = 32 FP32 cols).
|
||||
# Use the P TMEM load partition (PV A-fragment read).
|
||||
|
||||
# Actually, P was stored via QK C-fragment store (St32x32bOp Repetition(32)).
|
||||
# To read it via PV partition, we need a PV-partitioned load from the P region.
|
||||
# Let's use the same o_tiled_tmem_load but pointed at P's TMEM offset.
|
||||
|
||||
# P occupies TMEM columns [tmem_p0_offset, tmem_p0_offset + p_cols_fp32)
|
||||
# In the PV C-fragment, P is the A-fragment. We can use tOrP0's layout.
|
||||
# tOrP0 was set up with offset for PV MMA read.
|
||||
|
||||
# Simpler: sum O across columns to get unnormalized row sum, then normalize.
|
||||
# For V=identity, O = P@V = sum(P per row). So O.sum(dim=-1) = row_sum.
|
||||
# For arbitrary V, O = P@V. O.sum(dim=-1) = sum_j(P@V)[j] = sum_j(sum_i P[i]*V[i,j])
|
||||
# This is NOT sum(P). So this trick only works for V=identity.
|
||||
|
||||
# Correct approach: read P from TMEM, sum it per PV row.
|
||||
# P is at TMEM offset tmem_p0_offset, stored as BF16 with St32x32bOp.
|
||||
# P shape in TMEM: 128 rows x (HEAD_DIM BF16 = 32 FP32 cols)
|
||||
# We can read P using Ld32x32bOp(Repetition(corr_tile_size)) via PV O-partition.
|
||||
|
||||
# Use PV O TMEM load to read from P region instead of O region
|
||||
p_col_tiles = p_cols_fp32 // corr_tile_size # 32 // 16 = 2
|
||||
pv_row_sum = cutlass.Float32(0.0)
|
||||
for i in range(p_col_tiles):
|
||||
# Read P tile from TMEM at P offset (not O offset)
|
||||
tTMEM_LOADtP_i = cute.make_tensor(
|
||||
tTMEM_LOADtO.iterator + (self.tmem_p0_offset - self.tmem_o0_offset) + i * corr_tile_size,
|
||||
tTMEM_LOADtO.layout)
|
||||
tTMrP_i = cute.make_rmem_tensor(tTMEM_LOADcO.shape, self.qk_acc_dtype)
|
||||
cute.copy(o_tiled_tmem_load, tTMEM_LOADtP_i, tTMrP_i)
|
||||
# Use .reduce(SUM) instead of scalar accumulation (vectorizer can't handle scalar in vectorized loop)
|
||||
tile_p_sum = tTMrP_i.load().reduce(cute.ReductionOp.ADD, cutlass.Float32(0.0), 0)
|
||||
pv_row_sum = pv_row_sum + tile_p_sum
|
||||
|
||||
inv_row_sum = cutlass.Float32(1.0) / pv_row_sum
|
||||
|
||||
# Normalize O in TMEM using PV-correct inv_row_sum
|
||||
tTMrO_final = cute.make_rmem_tensor((tTMEM_LOADcO.shape, o_col_tiles), self.qk_acc_dtype)
|
||||
for i in range(o_col_tiles):
|
||||
tTMrO_i_ = tTMrO_final[None, i]
|
||||
tTMrO_i_layout = cute.composition(tTMrO_i_.layout, cute.make_layout(tTMrO_final.shape[0]))
|
||||
tTMrO_i = cute.make_tensor(tTMrO_i_.iterator, tTMrO_i_layout)
|
||||
tTMEM_LOADtO_i = cute.make_tensor(
|
||||
tTMEM_LOADtO.iterator + i * corr_tile_size, tTMEM_LOADtO.layout)
|
||||
tTMEM_STOREtO_i = cute.make_tensor(
|
||||
tTMEM_STOREtO.iterator + i * corr_tile_size, tTMEM_STOREtO.layout)
|
||||
cute.copy(o_tiled_tmem_load, tTMEM_LOADtO_i, tTMrO_i)
|
||||
for j in cutlass.range(cute.size(tTMrO_i), vectorize=True):
|
||||
tTMrO_i[j] = tTMrO_i[j] * inv_row_sum
|
||||
cute.copy(o_tiled_tmem_store, tTMrO_i, tTMEM_STOREtO_i)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# Now O in TMEM is normalized. Use standard epilogue_tma_store with identity.
|
||||
tCtO_base = cute.make_tensor(tmem_ptr + self.tmem_o0_offset, tCtO_fake.layout)
|
||||
acc_cons_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Consumer, self.num_acc_stage)
|
||||
c_grp = pipeline.CooperativeGroup(pipeline.Agent.Thread, 32 * len(self.epilogue_warp_id))
|
||||
c_pipe = pipeline.PipelineTmaStore.create(num_stages=self.num_c_stage, producer_group=c_grp)
|
||||
acc_cons_st = utils.gemm.sm100.epilogue_tma_store(
|
||||
self, tidx, warp_idx, tma_c, tCtO_base, sC, tCgC, epi_tile, 0,
|
||||
const_expr(lambda x: x),
|
||||
(0,0,0), acc_cons_st, acc_pipe, c_pipe)
|
||||
c_pipe.producer_tail()
|
||||
tmem.relinquish_alloc_permit()
|
||||
tmem.free(tmem_ptr)
|
||||
|
||||
|
||||
def test():
|
||||
import math
|
||||
torch.manual_seed(42)
|
||||
for n in [128, 256, 384]:
|
||||
m, hd = 128, HEAD_DIM
|
||||
q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
k = torch.randn(n, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
v = torch.randn(n, hd, dtype=torch.bfloat16, device="cuda")
|
||||
v_kernel = v.unsqueeze(-1)
|
||||
c = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
qf = q[:,:,0].float(); kf = k[:,:,0].float()
|
||||
attn = qf @ kf.T / math.sqrt(hd)
|
||||
ref = torch.softmax(attn, dim=-1) @ v.float()
|
||||
mQ = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
|
||||
mK = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
|
||||
mV = ct.from_dlpack(v_kernel).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_kernel))
|
||||
mC = ct.from_dlpack(c).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c))
|
||||
stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
|
||||
kernel = FmhaV3Softmax(s_k=n)
|
||||
print(f"n={n}: Compiling...", flush=True)
|
||||
compiled = cute.compile(kernel, mQ, mK, mV, mC, stream)
|
||||
print(f"n={n}: tmem: s0={kernel.tmem_s0_offset} p0={kernel.tmem_p0_offset} o0={kernel.tmem_o0_offset} vec={kernel.tmem_vec_offset} alloc={kernel.num_tmem_alloc_cols}", flush=True)
|
||||
print(f"n={n}: Running...", flush=True)
|
||||
compiled(mQ, mK, mV, mC, stream)
|
||||
torch.cuda.synchronize()
|
||||
out = c[:,:,0].float()
|
||||
cos = torch.nn.functional.cosine_similarity(out.flatten().unsqueeze(0), ref.flatten().unsqueeze(0)).item()
|
||||
max_err = (out - ref).abs().max().item()
|
||||
print(f"FMHA softmax n={n}: cosine {cos:.6f} max_err {max_err:.6f} {'PASS' if cos >= 0.999 else 'FAIL'}", flush=True)
|
||||
|
||||
if __name__ == "__main__":
|
||||
test()
|
||||
|
||||
|
||||
@@ -1,469 +0,0 @@
|
||||
"""
|
||||
FMHA v3 + Stage C: QK -> online softmax -> PV with KV-tile interleaving.
|
||||
Stage C: row_max, exp2, O rescale, row_sum, final normalization.
|
||||
FMHA pattern P store preserved from Stage B.
|
||||
"""
|
||||
import math
|
||||
import torch, cutlass, cutlass.cute as cute, cutlass.utils as utils, cutlass.pipeline as pipeline
|
||||
from cutlass.cute.nvgpu import cpasync, tcgen05
|
||||
from cutlass import Float32, BFloat16, Int32, Boolean, const_expr
|
||||
from cutlass.utils import LayoutEnum
|
||||
from cutlass.utils.tmem_allocator import find_tmem_tensor_col_offset
|
||||
import cuda.bindings.driver as cuda
|
||||
import cutlass.torch as ct
|
||||
|
||||
HEAD_DIM = 64
|
||||
|
||||
class FmhaV3Softmax:
|
||||
def __init__(self):
|
||||
self.acc_dtype = Float32; self.qk_acc_dtype = Float32
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
self.use_2cta_instrs = False; self.epilog_sync_bar_id = 1
|
||||
self.cluster_shape_mn = (1, 1); self.cta_group = tcgen05.CtaGroup.ONE
|
||||
self.epilogue_warp_id = (0,1,2,3); self.mma_warp_id = 4; self.tma_warp_id = 5
|
||||
self.threads_per_cta = 192; self.num_c_stage = 2
|
||||
self.kv_stage = 2; self.q_stage = 1; self.num_c_stage = 2
|
||||
|
||||
def _setup(self, qk_mma, pv_mma):
|
||||
qk_ik = cute.size(qk_mma.shape_mnk, mode=[2])
|
||||
self.qk_mma_tiler = (128, 128, qk_ik * 4)
|
||||
pv_ik = cute.size(pv_mma.shape_mnk, mode=[2])
|
||||
self.pv_mma_tiler = (128, HEAD_DIM, pv_ik * (128 // pv_ik))
|
||||
self.mma_tiler = self.qk_mma_tiler
|
||||
self.cluster_layout_vmnk = cute.tiled_divide(cute.make_layout((1,1,1)), (qk_mma.thr_id.shape,))
|
||||
self.cta_tile_shape_mnk = (self.qk_mma_tiler[0]//cute.size(qk_mma.thr_id.shape), HEAD_DIM, self.qk_mma_tiler[2])
|
||||
self.c_layout = LayoutEnum.ROW_MAJOR
|
||||
self.epi_tile = utils.sm100.compute_epilogue_tile_shape(self.cta_tile_shape_mnk, False, self.c_layout, self.o_dtype)
|
||||
self.num_ab_stage = 1; self.num_acc_stage = 1
|
||||
self.q_smem_s = utils.sm100.make_smem_layout_a(qk_mma, self.qk_mma_tiler, self.q_dtype, self.q_stage)
|
||||
self.k_smem_s = utils.sm100.make_smem_layout_b(qk_mma, self.qk_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.v_smem_s = utils.sm100.make_smem_layout_b(pv_mma, self.pv_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.c_smem_s = utils.sm100.make_smem_layout_epi(self.o_dtype, self.c_layout, self.epi_tile, 2)
|
||||
self.p_tmem_s = utils.sm100.make_smem_layout_a(pv_mma, self.pv_mma_tiler, self.q_dtype, 1)
|
||||
qk_thr = qk_mma.get_slice(0); qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
pv_thr = pv_mma.get_slice(0); pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
self.tmem_s0_offset = 0; self.tmem_p0_offset = 32
|
||||
# P occupies [tmem_p0_offset, tmem_p0_offset + p_cols_fp32)
|
||||
# S occupies [0, qk_mma_tiler[1]) = [0, 128)
|
||||
# O must NOT overlap P. Place O after max(S end, P end), aligned to 32.
|
||||
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
|
||||
p_end = self.tmem_p0_offset + p_cols_fp32 # 32 + 64 = 96
|
||||
s_cols = self.qk_mma_tiler[1] # 128
|
||||
o_after = max(s_cols, p_end) # 128
|
||||
self.tmem_o0_offset = ((o_after + 31) // 32) * 32
|
||||
self.tmem_vec_offset = 0 # Reuse S region for per-row inv_row_sum vector # align to 32 = 128
|
||||
self.tmem_vec_offset = 0 # Reuse S region (free after softmax loop)
|
||||
o_cols = find_tmem_tensor_col_offset(tOtO) # footprint of O
|
||||
total = self.tmem_o0_offset + o_cols
|
||||
# Must be multiple of 32 AND power of 2
|
||||
self.num_tmem_alloc_cols = 1
|
||||
while self.num_tmem_alloc_cols < total:
|
||||
self.num_tmem_alloc_cols *= 2
|
||||
cta = cute.size(qk_mma.thr_id.shape)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0))
|
||||
self.q_tx_bytes = cute.size_in_bytes(self.q_dtype, q_s) * cta
|
||||
self.kv_tx_bytes = cute.size_in_bytes(self.q_dtype, k_s) * cta
|
||||
self.scale_softmax_log2 = Float32(1.0 / math.sqrt(HEAD_DIM) * math.log2(math.e))
|
||||
|
||||
@cute.jit
|
||||
def __call__(self, q, k, v, c, stream):
|
||||
self.q_dtype = q.element_type; self.o_dtype = c.element_type; self.c_dtype = self.o_dtype
|
||||
self.a_major = LayoutEnum.from_tensor(q).mma_major_mode()
|
||||
self.b_major = LayoutEnum.from_tensor(k).mma_major_mode()
|
||||
# # s_k hardcoded # BROKEN in @cute.jit
|
||||
# FMHA-style V: reconstruct as (HEAD_DIM, s_k, 1) MN-major
|
||||
v_fmha = cute.make_tensor(
|
||||
v.iterator,
|
||||
cute.make_layout(
|
||||
(HEAD_DIM, 128, 1),
|
||||
stride=(1, HEAD_DIM, HEAD_DIM * 128),
|
||||
),
|
||||
)
|
||||
self.v_major = LayoutEnum.from_tensor(v_fmha).mma_major_mode()
|
||||
self.c_layout = LayoutEnum.from_tensor(c)
|
||||
qk_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, self.a_major, self.b_major, self.qk_acc_dtype, self.cta_group, (128,128), tcgen05.OperandSource.SMEM)
|
||||
pv_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, cute.nvgpu.OperandMajorMode.K, self.v_major, self.qk_acc_dtype, self.cta_group, (128,HEAD_DIM), tcgen05.OperandSource.TMEM)
|
||||
self._setup(qk_mma, pv_mma)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0)); v_s = cute.slice_(self.v_smem_s,(None,None,None,0))
|
||||
tma_q,mQ = cute.nvgpu.make_tiled_tma_atom_A(utils.sm100.cluster_shape_to_tma_atom_A(self.cluster_shape_mn,qk_mma.thr_id),q,q_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_k,mK = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,qk_mma.thr_id),k,k_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_v,mV = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,pv_mma.thr_id),v_fmha,v_s,self.pv_mma_tiler,pv_mma,self.cluster_layout_vmnk.shape)
|
||||
epi_s = cute.select(self.c_smem_s,mode=[0,1])
|
||||
tma_c,mC = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(),c,epi_s,self.epi_tile)
|
||||
self._kernel(qk_mma,pv_mma,tma_q,mQ,tma_k,mK,tma_v,mV,tma_c,mC,self.cluster_layout_vmnk,self.q_smem_s,self.k_smem_s,self.v_smem_s,self.p_tmem_s,self.c_smem_s,self.epi_tile).launch(grid=(1,1,1),block=[self.threads_per_cta,1,1],stream=stream)
|
||||
|
||||
@cute.kernel
|
||||
def _kernel(self, qk_mma, pv_mma, tma_q, mQ, tma_k, mK, tma_v, mV, tma_c, mC, cl_vmnk, q_smem_s, k_smem_s, v_smem_s, p_tmem_s, c_smem_s, epi_tile):
|
||||
warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx())
|
||||
tidx,_,_ = cute.arch.thread_idx()
|
||||
if warp_idx == self.tma_warp_id:
|
||||
cpasync.prefetch_descriptor(tma_q); cpasync.prefetch_descriptor(tma_k); cpasync.prefetch_descriptor(tma_v); cpasync.prefetch_descriptor(tma_c)
|
||||
|
||||
@cute.struct
|
||||
class SS:
|
||||
q_bar: cute.struct.MemRange[cutlass.Int64, self.q_stage*2]
|
||||
kv_bar: cute.struct.MemRange[cutlass.Int64, self.kv_stage*2]
|
||||
s_bar: cute.struct.MemRange[cutlass.Int64, 2]
|
||||
acc_bar: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage*2]
|
||||
tmem_dealloc: cutlass.Int64; holding: cutlass.Int32
|
||||
smem = utils.SmemAllocator(); st = smem.allocate(SS)
|
||||
|
||||
qp,qc = pipeline.PipelineTmaUmma.create(barrier_storage=st.q_bar.data_ptr(),num_stages=self.q_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.q_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
|
||||
kvp,kvc = pipeline.PipelineTmaUmma.create(barrier_storage=st.kv_bar.data_ptr(),num_stages=self.kv_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.kv_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
|
||||
s_prod,s_cons = pipeline.PipelineUmmaAsync.create(barrier_storage=st.s_bar.data_ptr(),num_stages=1,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32*len(self.epilogue_warp_id))).make_participants()
|
||||
softmax_done_bar = pipeline.NamedBarrier(barrier_id=3, num_threads=32 + 32*len(self.epilogue_warp_id))
|
||||
pv_done_bar = pipeline.NamedBarrier(barrier_id=4, num_threads=32 + 32*len(self.epilogue_warp_id))
|
||||
acc_pipe = pipeline.PipelineUmmaAsync.create(barrier_storage=st.acc_bar.data_ptr(),num_stages=self.num_acc_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,len(self.epilogue_warp_id)),cta_layout_vmnk=cl_vmnk,defer_sync=True)
|
||||
tmem_bar = pipeline.NamedBarrier(barrier_id=2,num_threads=32*len((self.mma_warp_id,*self.epilogue_warp_id)))
|
||||
tmem = utils.TmemAllocator(st.holding.ptr,barrier_for_retrieve=tmem_bar,allocator_warp_id=self.epilogue_warp_id[0],is_two_cta=cute.size(qk_mma.thr_id.shape)==2,two_cta_tmem_dealloc_mbar_ptr=st.tmem_dealloc.ptr)
|
||||
pipeline.pipeline_init_arrive(cluster_shape_mn=cl_vmnk,is_relaxed=True)
|
||||
|
||||
sQ = smem.allocate_tensor(element_type=self.q_dtype,layout=q_smem_s.outer,byte_alignment=128,swizzle=q_smem_s.inner)
|
||||
sK = smem.allocate_tensor(element_type=self.q_dtype,layout=k_smem_s.outer,byte_alignment=128,swizzle=k_smem_s.inner)
|
||||
sV = smem.allocate_tensor(element_type=self.q_dtype,layout=v_smem_s.outer,byte_alignment=128,swizzle=v_smem_s.inner)
|
||||
sC = smem.allocate_tensor(element_type=self.o_dtype,layout=c_smem_s.outer,byte_alignment=128,swizzle=c_smem_s.inner)
|
||||
|
||||
gQ = cute.local_tile(mQ,cute.slice_(self.qk_mma_tiler,(None,0,None)),(None,None,None))
|
||||
gK = cute.local_tile(mK,cute.slice_(self.qk_mma_tiler,(0,None,None)),(None,None,None))
|
||||
gV = cute.local_tile(mV,cute.slice_(self.pv_mma_tiler,(0,None,None)),(None,None,None))
|
||||
gC = cute.local_tile(mC,cute.slice_(self.pv_mma_tiler,(None,None,0)),(None,None,None))
|
||||
n_kv_tiles = cute.size(gK, mode=[3])
|
||||
|
||||
qk_thr = qk_mma.get_slice(0); pv_thr = pv_mma.get_slice(0)
|
||||
tCgQ = qk_thr.partition_A(gQ); tCgK = qk_thr.partition_B(gK)
|
||||
tCgV = pv_thr.partition_B(gV); tCgC = pv_thr.partition_C(gC)
|
||||
a_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,0,None,0)).shape)
|
||||
tAsQ,tAgQ = cpasync.tma_partition(tma_q,0,a_lay,cute.group_modes(sQ,0,3),cute.group_modes(tCgQ,0,3))
|
||||
b_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,None,0,0)).shape)
|
||||
tBsK,tBgK = cpasync.tma_partition(tma_k,0,b_lay,cute.group_modes(sK,0,3),cute.group_modes(tCgK,0,3))
|
||||
tVsV,tVgV = cpasync.tma_partition(tma_v,0,b_lay,cute.group_modes(sV,0,3),cute.group_modes(tCgV,0,3))
|
||||
tAgQ = tAgQ[(None,0,None,0)]; tBgK = tBgK[(None,0,None,0)]; tVgV = tVgV[(None,0,None,0)]
|
||||
|
||||
tCrQ = qk_mma.make_fragment_A(sQ); tCrK = qk_mma.make_fragment_B(sK)
|
||||
tCrV = pv_mma.make_fragment_B(sV)
|
||||
|
||||
qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
tOtO0 = cute.make_tensor(tOtO.iterator + self.tmem_o0_offset, tOtO.layout)
|
||||
|
||||
# --- PV read view (for MMA only, NOT for softmax store) ---
|
||||
tP = cute.make_tensor(tStS.iterator, p_tmem_s.outer)
|
||||
tOrP_base = pv_thr.make_fragment_A(tP)
|
||||
tOrP = tOrP_base[(None,None,None,0)]
|
||||
tOrP0 = cute.make_tensor(
|
||||
tOrP.iterator + self.qk_acc_dtype.width // self.q_dtype.width * self.tmem_p0_offset,
|
||||
tOrP.layout)
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_as, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_as, self.num_acc_stage))
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# TMA LOAD
|
||||
if warp_idx == self.tma_warp_id:
|
||||
qp.reset(); qh = qp.acquire_and_advance()
|
||||
cute.copy(tma_q,tAgQ[(None,qh.count)],tAsQ[(None,qh.index)],tma_bar_ptr=qh.barrier)
|
||||
qp.tail()
|
||||
kvp.reset(); pk = kvp.try_acquire()
|
||||
for kt in cutlass.range(n_kv_tiles,unroll=1):
|
||||
kh = kvp.acquire_and_advance(pk)
|
||||
cute.copy(tma_k,tBgK[(None,kh.count)],tBsK[(None,kh.index)],tma_bar_ptr=kh.barrier)
|
||||
pk = cutlass.Boolean(1)
|
||||
vh = kvp.acquire_and_advance(pk)
|
||||
cute.copy(tma_v,tVgV[(None,vh.count)],tVsV[(None,vh.index)],tma_bar_ptr=vh.barrier)
|
||||
pk = cutlass.Boolean(1)
|
||||
kvp.tail()
|
||||
|
||||
# MMA
|
||||
if warp_idx == self.mma_warp_id:
|
||||
tmem.wait_for_alloc()
|
||||
qc.reset(); qh = qc.wait_and_advance(); qh.release()
|
||||
kvc.reset(); pk = kvc.try_wait()
|
||||
acc_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
|
||||
acc_pipe.producer_acquire(acc_st)
|
||||
for kt in range(n_kv_tiles):
|
||||
kh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
sh = s_prod.acquire_and_advance()
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
for kb in cutlass.range(cute.size(tCrQ,mode=[2]), unroll_full=True):
|
||||
cute.gemm(qk_mma, tStS0, tCrQ[(None,None,kb,0)], tCrK[(None,None,kb,kh.index)], tStS0)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
sh.commit(); kh.release()
|
||||
softmax_done_bar.arrive_and_wait()
|
||||
vh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, kt != 0)
|
||||
for kb in cutlass.range(cute.size(tOrP0,mode=[2]), unroll_full=True):
|
||||
cute.gemm(pv_mma, tOtO0, tOrP0[(None,None,kb)], tCrV[(None,None,kb,vh.index)], tOtO0)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
vh.release()
|
||||
pv_done_bar.arrive()
|
||||
acc_pipe.producer_commit(acc_st); acc_st.advance()
|
||||
acc_pipe.producer_tail(acc_st)
|
||||
|
||||
# ===================== EPILOGUE WARPS (STAGE C: ONLINE SOFTMAX) =====================
|
||||
if warp_idx < self.mma_warp_id:
|
||||
tmem.allocate(self.num_tmem_alloc_cols)
|
||||
tmem.wait_for_alloc()
|
||||
tmem_ptr = tmem.retrieve_ptr(self.qk_acc_dtype)
|
||||
sfw_idx = tidx % (32 * len(self.epilogue_warp_id))
|
||||
|
||||
# --- S load (QK C-fragment) ---
|
||||
tmem_load_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
tiled_tmem_load = tcgen05.make_tmem_copy(tmem_load_atom, tStS0)
|
||||
thr_load = tiled_tmem_load.get_slice(sfw_idx)
|
||||
tTMEM_LOADtS = thr_load.partition_S(tStS0)
|
||||
cS = cute.make_identity_tensor((self.qk_mma_tiler[0], self.qk_mma_tiler[1]))
|
||||
tScS = qk_thr.partition_C(cS)
|
||||
tTMEM_LOADcS = thr_load.partition_D(tScS)
|
||||
|
||||
# --- P store (QK C-fragment composition, FMHA pattern) ---
|
||||
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
|
||||
tStP_layout = cute.composition(tStS.layout, cute.make_layout((self.pv_mma_tiler[0], p_cols_fp32)))
|
||||
tStP0 = cute.make_tensor(tStS.iterator + self.tmem_p0_offset, tStP_layout)
|
||||
tmem_store_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
tiled_tmem_store = tcgen05.make_tmem_copy(tmem_store_atom, tStP0)
|
||||
thr_store = tiled_tmem_store.get_slice(sfw_idx)
|
||||
tTMEM_STOREtP = thr_store.partition_D(tStP0)
|
||||
tScP_layout = cute.composition(tScS.layout, cute.make_layout((self.pv_mma_tiler[0], p_cols_fp32)))
|
||||
tScP = cute.make_tensor(tScS.iterator, tScP_layout)
|
||||
tTMEM_STOREcP = thr_store.partition_S(tScP)
|
||||
|
||||
# --- Vector TMEM (per-row row_sum storage, FMHA pattern) ---
|
||||
# composition(tStS.layout, (128, 2)) = 2 FP32 columns per logical row
|
||||
# vec[0] = row_sum (final, after loop), vec[1] = unused
|
||||
# Reuses S TMEM region (offset 0), free after softmax loop writes
|
||||
|
||||
tStS_vec_layout = cute.composition(tStS.layout, cute.make_layout((128, 2)))
|
||||
tStS_vec = cute.make_tensor(tStS.iterator + self.tmem_vec_offset, tStS_vec_layout)
|
||||
tScS_vec_layout = cute.composition(tScS.layout, cute.make_layout((128, 2)))
|
||||
tScS_vec = cute.make_tensor(tScS.iterator, tScS_vec_layout)
|
||||
tmem_store_vec_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(2)), self.qk_acc_dtype)
|
||||
tiled_tmem_store_vec = tcgen05.make_tmem_copy(tmem_store_vec_atom, tStS_vec)
|
||||
thr_tmem_store_vec = tiled_tmem_store_vec.get_slice(sfw_idx)
|
||||
tTMEM_STORE_VECtS = thr_tmem_store_vec.partition_D(tStS_vec)
|
||||
tTMEM_STORE_VECcS = thr_tmem_store_vec.partition_S(tScS_vec)
|
||||
tmem_load_vec_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(2)), self.qk_acc_dtype)
|
||||
tiled_tmem_load_vec = tcgen05.make_tmem_copy(tmem_load_vec_atom, tStS_vec)
|
||||
thr_tmem_load_vec = tiled_tmem_load_vec.get_slice(sfw_idx)
|
||||
tTMEM_LOAD_VECtS = thr_tmem_load_vec.partition_S(tStS_vec)
|
||||
tTMEM_LOAD_VECcS = thr_tmem_load_vec.partition_D(tScS_vec)
|
||||
|
||||
# --- C6: O TMEM load/store for rescale (correction_rescale pattern) ---
|
||||
corr_tile_size = 16
|
||||
cO = cute.make_identity_tensor((self.pv_mma_tiler[0], self.pv_mma_tiler[1]))
|
||||
tOcO = pv_thr.partition_C(cO)
|
||||
o_tmem_load_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.qk_acc_dtype)
|
||||
o_tmem_store_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.qk_acc_dtype)
|
||||
tOtO_i_layout = cute.composition(tOtO0.layout, cute.make_layout((128, corr_tile_size)))
|
||||
tOcO_i_layout = cute.composition(tOcO.layout, cute.make_layout((128, corr_tile_size)))
|
||||
tOtO_i = cute.make_tensor(tOtO0.iterator, tOtO_i_layout)
|
||||
tOcO_i = cute.make_tensor(tOcO.iterator, tOcO_i_layout)
|
||||
o_tiled_tmem_load = tcgen05.make_tmem_copy(o_tmem_load_atom, tOtO_i)
|
||||
o_tiled_tmem_store = tcgen05.make_tmem_copy(o_tmem_store_atom, tOtO_i)
|
||||
o_thr_load = o_tiled_tmem_load.get_slice(sfw_idx)
|
||||
o_thr_store = o_tiled_tmem_store.get_slice(sfw_idx)
|
||||
tTMEM_LOADtO = o_thr_load.partition_S(tOtO_i)
|
||||
tTMEM_LOADcO = o_thr_load.partition_D(tOcO_i)
|
||||
tTMEM_STOREtO = o_thr_store.partition_D(tOtO_i)
|
||||
o_col_tiles = self.pv_mma_tiler[1] // corr_tile_size
|
||||
|
||||
# --- C2: Per-thread row state (persist across KV tiles) ---
|
||||
row_max = -cutlass.Float32.inf
|
||||
row_sum = cutlass.Float32(0.0)
|
||||
|
||||
# --- C3: QK scale = 1/sqrt(HEAD_DIM) * log2(e) for exp2 ---
|
||||
scale = self.scale_softmax_log2
|
||||
|
||||
# =============================================================
|
||||
# Per-KV-tile online softmax loop
|
||||
# =============================================================
|
||||
for kt in range(n_kv_tiles):
|
||||
si_handle = s_cons.wait_and_advance()
|
||||
|
||||
# Load S from TMEM (FP32, QK C-fragment layout)
|
||||
tTMEM_LOADrS = cute.make_rmem_tensor(tTMEM_LOADcS.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load, tTMEM_LOADtS, tTMEM_LOADrS)
|
||||
|
||||
# --- C4: Compute tile_max via .reduce(MAX) ---
|
||||
old_row_max = row_max
|
||||
row_max = tTMEM_LOADrS.load().reduce(cute.ReductionOp.MAX, row_max, 0)
|
||||
row_max_safe = row_max
|
||||
if row_max == -cutlass.Float32.inf:
|
||||
row_max_safe = cutlass.Float32(0.0)
|
||||
|
||||
# --- C5: Compute rescale factor ---
|
||||
acc_scale = cute.math.exp2(scale * (old_row_max - row_max_safe), fastmath=True)
|
||||
|
||||
# --- C6: Rescale O in TMEM (load O, multiply by acc_scale, store O) ---
|
||||
# acc_scale belongs to QK row (N//4), but O rows are in PV partition (N).
|
||||
# Store acc_scale to vector by QK row, read by PV row.
|
||||
if kt > 0:
|
||||
pv_done_bar.arrive_and_wait()
|
||||
|
||||
# Store acc_scale to vector indexed by QK logical row
|
||||
qk_row_c6 = tTMEM_LOADcS[0][0]
|
||||
thr_vs_c6 = tiled_tmem_store_vec.get_slice(qk_row_c6)
|
||||
tVStore_c6 = thr_vs_c6.partition_D(tStS_vec)
|
||||
tVStoreSrc_c6 = thr_vs_c6.partition_S(tScS_vec)
|
||||
tVStoreRmem_c6 = cute.make_rmem_tensor(tVStoreSrc_c6.shape, self.qk_acc_dtype)
|
||||
tVStoreRmem_c6[0] = acc_scale
|
||||
cute.copy(tiled_tmem_store_vec, tVStoreRmem_c6, tVStore_c6)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# Read acc_scale from vector indexed by PV logical row
|
||||
pv_row_c6 = tTMEM_LOADcO[0][0]
|
||||
thr_vl_c6 = tiled_tmem_load_vec.get_slice(pv_row_c6)
|
||||
tVLoad_c6 = thr_vl_c6.partition_S(tStS_vec)
|
||||
tVLoadDst_c6 = thr_vl_c6.partition_D(tScS_vec)
|
||||
tVLoadRmem_c6 = cute.make_rmem_tensor(tVLoadDst_c6.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load_vec, tVLoad_c6, tVLoadRmem_c6)
|
||||
cute.arch.fence_view_async_tmem_load()
|
||||
acc_scale_pv = tVLoadRmem_c6[0]
|
||||
|
||||
tTMrO = cute.make_rmem_tensor((tTMEM_LOADcO.shape, o_col_tiles), self.qk_acc_dtype)
|
||||
for i in range(o_col_tiles):
|
||||
tTMrO_i_ = tTMrO[None, i]
|
||||
tTMrO_i_layout = cute.composition(tTMrO_i_.layout, cute.make_layout(tTMrO.shape[0]))
|
||||
tTMrO_i = cute.make_tensor(tTMrO_i_.iterator, tTMrO_i_layout)
|
||||
tTMEM_LOADtO_i = cute.make_tensor(tTMEM_LOADtO.iterator + i * corr_tile_size, tTMEM_LOADtO.layout)
|
||||
tTMEM_STOREtO_i = cute.make_tensor(tTMEM_STOREtO.iterator + i * corr_tile_size, tTMEM_STOREtO.layout)
|
||||
cute.copy(o_tiled_tmem_load, tTMEM_LOADtO_i, tTMrO_i)
|
||||
for j in cutlass.range(cute.size(tTMrO_i), vectorize=True):
|
||||
tTMrO_i[j] = tTMrO_i[j] * acc_scale_pv
|
||||
cute.copy(o_tiled_tmem_store, tTMrO_i, tTMEM_STOREtO_i)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# Rescale row_sum
|
||||
row_sum = row_sum * acc_scale
|
||||
|
||||
# --- C7: Compute P = exp2((S - row_max_safe) * scale) ---
|
||||
minus_row_max_scale = (cutlass.Float32(0.0) - row_max_safe) * scale
|
||||
|
||||
# Register bridge (FMHA pattern: FP32 backing + BF16 view)
|
||||
rP_words = cute.make_rmem_tensor(tTMEM_STOREcP.shape, self.qk_acc_dtype)
|
||||
rP_bf16 = cute.make_tensor(cute.recast_ptr(rP_words.iterator, dtype=self.q_dtype), tTMEM_LOADrS.layout)
|
||||
|
||||
frg_cnt = 4
|
||||
frg_tile = cute.size(tTMEM_LOADrS) // frg_cnt
|
||||
tTMEM_LOADrS_frg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(frg_tile))
|
||||
rP_bf16_frg = cute.logical_divide(rP_bf16, cute.make_layout(frg_tile))
|
||||
|
||||
# Scale S, compute exp2, store through register bridge
|
||||
for j in range(frg_cnt):
|
||||
for k in cutlass.range(cute.size(tTMEM_LOADrS_frg, mode=[0]), vectorize=True):
|
||||
tTMEM_LOADrS_frg[k, j] = tTMEM_LOADrS_frg[k, j] * scale + minus_row_max_scale
|
||||
tTMEM_LOADrS_frg[k, j] = cute.math.exp2(tTMEM_LOADrS_frg[k, j], fastmath=True)
|
||||
s_vec = tTMEM_LOADrS_frg[None, j].load()
|
||||
rP_bf16_frg[None, j].store(s_vec.to(self.q_dtype))
|
||||
|
||||
# Store P to TMEM
|
||||
cute.copy(tiled_tmem_store, rP_words, tTMEM_STOREtP)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
si_handle.release()
|
||||
softmax_done_bar.arrive()
|
||||
|
||||
# --- C8: Row sum accumulation (CUTLASS FMHA packed f32x2 pattern) ---
|
||||
# P values still in tTMEM_LOADrS registers.
|
||||
# 4 accumulators for 4 reduction_unroll columns.
|
||||
local_row_sum_0 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_1 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_2 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_3 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
|
||||
reduction_unroll = 4
|
||||
rfrg_tile = cute.size(tTMEM_LOADrS) // reduction_unroll
|
||||
tTMEM_LOADrS_rfrg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(rfrg_tile))
|
||||
|
||||
for j in cutlass.range_constexpr(0, cute.size(tTMEM_LOADrS_rfrg, mode=[0]), 2):
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_0, (tTMEM_LOADrS_rfrg[j, 0], tTMEM_LOADrS_rfrg[j + 1, 0]))
|
||||
local_row_sum_1 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_1, (tTMEM_LOADrS_rfrg[j, 1], tTMEM_LOADrS_rfrg[j + 1, 1]))
|
||||
local_row_sum_2 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_2, (tTMEM_LOADrS_rfrg[j, 2], tTMEM_LOADrS_rfrg[j + 1, 2]))
|
||||
local_row_sum_3 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_3, (tTMEM_LOADrS_rfrg[j, 3], tTMEM_LOADrS_rfrg[j + 1, 3]))
|
||||
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(local_row_sum_0, local_row_sum_1)
|
||||
local_row_sum_2 = cute.arch.add_packed_f32x2(local_row_sum_2, local_row_sum_3)
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(local_row_sum_0, local_row_sum_2)
|
||||
tile_sum = local_row_sum_0[0] + local_row_sum_0[1]
|
||||
|
||||
row_sum = row_sum + tile_sum
|
||||
|
||||
# --- C9: Final normalization via O TMEM rescale ---
|
||||
pv_done_bar.arrive_and_wait()
|
||||
|
||||
# DEBUG: hardcoded inv_row_sum = 1.0 (no normalization)
|
||||
inv_row_sum = cutlass.Float32(1.0)
|
||||
|
||||
# Normalize O in TMEM using PV-correct inv_row_sum
|
||||
tTMrO_final = cute.make_rmem_tensor((tTMEM_LOADcO.shape, o_col_tiles), self.qk_acc_dtype)
|
||||
for i in range(o_col_tiles):
|
||||
tTMrO_i_ = tTMrO_final[None, i]
|
||||
tTMrO_i_layout = cute.composition(tTMrO_i_.layout, cute.make_layout(tTMrO_final.shape[0]))
|
||||
tTMrO_i = cute.make_tensor(tTMrO_i_.iterator, tTMrO_i_layout)
|
||||
tTMEM_LOADtO_i = cute.make_tensor(
|
||||
tTMEM_LOADtO.iterator + i * corr_tile_size, tTMEM_LOADtO.layout)
|
||||
tTMEM_STOREtO_i = cute.make_tensor(
|
||||
tTMEM_STOREtO.iterator + i * corr_tile_size, tTMEM_STOREtO.layout)
|
||||
cute.copy(o_tiled_tmem_load, tTMEM_LOADtO_i, tTMrO_i)
|
||||
for j in cutlass.range(cute.size(tTMrO_i), vectorize=True):
|
||||
tTMrO_i[j] = tTMrO_i[j] * inv_row_sum
|
||||
cute.copy(o_tiled_tmem_store, tTMrO_i, tTMEM_STOREtO_i)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# Now O in TMEM is normalized. Use standard epilogue_tma_store with identity.
|
||||
tCtO_base = cute.make_tensor(tmem_ptr + self.tmem_o0_offset, tCtO_fake.layout)
|
||||
acc_cons_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Consumer, self.num_acc_stage)
|
||||
c_grp = pipeline.CooperativeGroup(pipeline.Agent.Thread, 32 * len(self.epilogue_warp_id))
|
||||
c_pipe = pipeline.PipelineTmaStore.create(num_stages=self.num_c_stage, producer_group=c_grp)
|
||||
acc_cons_st = utils.gemm.sm100.epilogue_tma_store(
|
||||
self, tidx, warp_idx, tma_c, tCtO_base, sC, tCgC, epi_tile, 0,
|
||||
const_expr(lambda x: x),
|
||||
(0,0,0), acc_cons_st, acc_pipe, c_pipe)
|
||||
c_pipe.producer_tail()
|
||||
tmem.relinquish_alloc_permit()
|
||||
tmem.free(tmem_ptr)
|
||||
|
||||
|
||||
def test():
|
||||
import math
|
||||
torch.manual_seed(42)
|
||||
for n in [128, 256, 384]:
|
||||
m, hd = 128, HEAD_DIM
|
||||
q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
k = torch.randn(n, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
v = torch.randn(n, hd, dtype=torch.bfloat16, device="cuda")
|
||||
v_kernel = v.unsqueeze(-1)
|
||||
c = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
qf = q[:,:,0].float(); kf = k[:,:,0].float()
|
||||
attn = qf @ kf.T / math.sqrt(hd)
|
||||
ref = torch.softmax(attn, dim=-1) @ v.float()
|
||||
mQ = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
|
||||
mK = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
|
||||
mV = ct.from_dlpack(v_kernel).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_kernel))
|
||||
mC = ct.from_dlpack(c).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c))
|
||||
stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
|
||||
kernel = FmhaV3Softmax()
|
||||
print(f"n={n}: Compiling...", flush=True)
|
||||
compiled = cute.compile(kernel, mQ, mK, mV, mC, stream)
|
||||
print(f"n={n}: tmem: s0={kernel.tmem_s0_offset} p0={kernel.tmem_p0_offset} o0={kernel.tmem_o0_offset} vec={kernel.tmem_vec_offset} alloc={kernel.num_tmem_alloc_cols}", flush=True)
|
||||
print(f"n={n}: Running...", flush=True)
|
||||
compiled(mQ, mK, mV, mC, stream)
|
||||
torch.cuda.synchronize()
|
||||
out = c[:,:,0].float()
|
||||
cos = torch.nn.functional.cosine_similarity(out.flatten().unsqueeze(0), ref.flatten().unsqueeze(0)).item()
|
||||
max_err = (out - ref).abs().max().item()
|
||||
print(f"FMHA softmax n={n}: cosine {cos:.6f} max_err {max_err:.6f} {'PASS' if cos >= 0.999 else 'FAIL'}", flush=True)
|
||||
|
||||
if __name__ == "__main__":
|
||||
test()
|
||||
|
||||
|
||||
@@ -1,587 +0,0 @@
|
||||
"""
|
||||
FMHA v3 + Stage C: QK -> online softmax -> PV with KV-tile interleaving.
|
||||
Stage C: row_max, exp2, O rescale, row_sum, final normalization.
|
||||
FMHA pattern P store preserved from Stage B.
|
||||
"""
|
||||
import math
|
||||
import torch, cutlass, cutlass.cute as cute, cutlass.utils as utils, cutlass.pipeline as pipeline
|
||||
from cutlass.cute.nvgpu import cpasync, tcgen05
|
||||
from cutlass import Float32, BFloat16, Int32, Boolean, const_expr
|
||||
from cutlass.utils import LayoutEnum
|
||||
from cutlass.utils.tmem_allocator import find_tmem_tensor_col_offset
|
||||
import cuda.bindings.driver as cuda
|
||||
import cutlass.torch as ct
|
||||
|
||||
HEAD_DIM = 64
|
||||
|
||||
class FmhaV3Softmax:
|
||||
def __init__(self, s_k: int = 128):
|
||||
self.s_k = s_k
|
||||
self.acc_dtype = Float32; self.qk_acc_dtype = Float32
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
self.use_2cta_instrs = False; self.epilog_sync_bar_id = 1
|
||||
self.cluster_shape_mn = (1, 1); self.cta_group = tcgen05.CtaGroup.ONE
|
||||
self.epilogue_warp_id = (0,1,2,3); self.mma_warp_id = 4; self.tma_warp_id = 5
|
||||
self.threads_per_cta = 192; self.num_c_stage = 2
|
||||
self.kv_stage = 2; self.q_stage = 1; self.num_c_stage = 2
|
||||
|
||||
def _setup(self, qk_mma, pv_mma):
|
||||
qk_ik = cute.size(qk_mma.shape_mnk, mode=[2])
|
||||
self.qk_mma_tiler = (128, 128, qk_ik * 4)
|
||||
pv_ik = cute.size(pv_mma.shape_mnk, mode=[2])
|
||||
self.pv_mma_tiler = (128, HEAD_DIM, pv_ik * (128 // pv_ik))
|
||||
self.mma_tiler = self.qk_mma_tiler
|
||||
self.cluster_layout_vmnk = cute.tiled_divide(cute.make_layout((1,1,1)), (qk_mma.thr_id.shape,))
|
||||
self.cta_tile_shape_mnk = (self.qk_mma_tiler[0]//cute.size(qk_mma.thr_id.shape), HEAD_DIM, self.qk_mma_tiler[2])
|
||||
self.c_layout = LayoutEnum.ROW_MAJOR
|
||||
self.epi_tile = utils.sm100.compute_epilogue_tile_shape(self.cta_tile_shape_mnk, False, self.c_layout, self.o_dtype)
|
||||
self.num_ab_stage = 1; self.num_acc_stage = 1
|
||||
self.q_smem_s = utils.sm100.make_smem_layout_a(qk_mma, self.qk_mma_tiler, self.q_dtype, self.q_stage)
|
||||
self.k_smem_s = utils.sm100.make_smem_layout_b(qk_mma, self.qk_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.v_smem_s = utils.sm100.make_smem_layout_b(pv_mma, self.pv_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.c_smem_s = utils.sm100.make_smem_layout_epi(self.o_dtype, self.c_layout, self.epi_tile, 2)
|
||||
self.p_tmem_s = utils.sm100.make_smem_layout_a(pv_mma, self.pv_mma_tiler, self.q_dtype, 1)
|
||||
qk_thr = qk_mma.get_slice(0); qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
pv_thr = pv_mma.get_slice(0); pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
self.tmem_s0_offset = 0; self.tmem_p0_offset = 32
|
||||
# P occupies [tmem_p0_offset, tmem_p0_offset + p_cols_fp32)
|
||||
# S occupies [0, qk_mma_tiler[1]) = [0, 128)
|
||||
# O must NOT overlap P. Place O after max(S end, P end), aligned to 32.
|
||||
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
|
||||
p_end = self.tmem_p0_offset + p_cols_fp32 # 32 + 64 = 96
|
||||
s_cols = self.qk_mma_tiler[1] # 128
|
||||
o_after = max(s_cols, p_end) # 128
|
||||
self.tmem_o0_offset = ((o_after + 31) // 32) * 32
|
||||
self.tmem_vec_offset = 0 # Reuse S region for per-row inv_row_sum vector # align to 32 = 128
|
||||
self.tmem_vec_offset = 0 # Reuse S region (free after softmax loop)
|
||||
o_cols = find_tmem_tensor_col_offset(tOtO) # footprint of O
|
||||
total = self.tmem_o0_offset + o_cols
|
||||
# Must be multiple of 32 AND power of 2
|
||||
self.num_tmem_alloc_cols = 1
|
||||
while self.num_tmem_alloc_cols < total:
|
||||
self.num_tmem_alloc_cols *= 2
|
||||
cta = cute.size(qk_mma.thr_id.shape)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0))
|
||||
self.q_tx_bytes = cute.size_in_bytes(self.q_dtype, q_s) * cta
|
||||
self.kv_tx_bytes = cute.size_in_bytes(self.q_dtype, k_s) * cta
|
||||
self.scale_softmax_log2 = Float32(1.0 / math.sqrt(HEAD_DIM) * math.log2(math.e))
|
||||
|
||||
@cute.jit
|
||||
def __call__(self, q, k, v, c, stream):
|
||||
self.q_dtype = q.element_type; self.o_dtype = c.element_type; self.c_dtype = self.o_dtype
|
||||
self.a_major = LayoutEnum.from_tensor(q).mma_major_mode()
|
||||
self.b_major = LayoutEnum.from_tensor(k).mma_major_mode()
|
||||
# # s_k hardcoded # BROKEN in @cute.jit
|
||||
# FMHA-style V: reconstruct as (HEAD_DIM, s_k, 1) MN-major
|
||||
v_fmha = cute.make_tensor(
|
||||
v.iterator,
|
||||
cute.make_layout(
|
||||
(HEAD_DIM, self.s_k, 1),
|
||||
stride=(1, HEAD_DIM, HEAD_DIM * self.s_k),
|
||||
),
|
||||
)
|
||||
self.v_major = LayoutEnum.from_tensor(v_fmha).mma_major_mode()
|
||||
self.c_layout = LayoutEnum.from_tensor(c)
|
||||
qk_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, self.a_major, self.b_major, self.qk_acc_dtype, self.cta_group, (128,128), tcgen05.OperandSource.SMEM)
|
||||
pv_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, cute.nvgpu.OperandMajorMode.K, self.v_major, self.qk_acc_dtype, self.cta_group, (128,HEAD_DIM), tcgen05.OperandSource.TMEM)
|
||||
self._setup(qk_mma, pv_mma)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0)); v_s = cute.slice_(self.v_smem_s,(None,None,None,0))
|
||||
tma_q,mQ = cute.nvgpu.make_tiled_tma_atom_A(utils.sm100.cluster_shape_to_tma_atom_A(self.cluster_shape_mn,qk_mma.thr_id),q,q_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_k,mK = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,qk_mma.thr_id),k,k_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_v,mV = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,pv_mma.thr_id),v_fmha,v_s,self.pv_mma_tiler,pv_mma,self.cluster_layout_vmnk.shape)
|
||||
epi_s = cute.select(self.c_smem_s,mode=[0,1])
|
||||
tma_c,mC = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(),c,epi_s,self.epi_tile)
|
||||
self._kernel(qk_mma,pv_mma,tma_q,mQ,tma_k,mK,tma_v,mV,tma_c,mC,self.cluster_layout_vmnk,self.q_smem_s,self.k_smem_s,self.v_smem_s,self.p_tmem_s,self.c_smem_s,self.epi_tile).launch(grid=(1,1,1),block=[self.threads_per_cta,1,1],stream=stream)
|
||||
|
||||
@cute.kernel
|
||||
def _kernel(self, qk_mma, pv_mma, tma_q, mQ, tma_k, mK, tma_v, mV, tma_c, mC, cl_vmnk, q_smem_s, k_smem_s, v_smem_s, p_tmem_s, c_smem_s, epi_tile):
|
||||
warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx())
|
||||
tidx,_,_ = cute.arch.thread_idx()
|
||||
if warp_idx == self.tma_warp_id:
|
||||
cpasync.prefetch_descriptor(tma_q); cpasync.prefetch_descriptor(tma_k); cpasync.prefetch_descriptor(tma_v); cpasync.prefetch_descriptor(tma_c)
|
||||
|
||||
@cute.struct
|
||||
class SS:
|
||||
q_bar: cute.struct.MemRange[cutlass.Int64, self.q_stage*2]
|
||||
kv_bar: cute.struct.MemRange[cutlass.Int64, self.kv_stage*2]
|
||||
s_bar: cute.struct.MemRange[cutlass.Int64, 2]
|
||||
acc_bar: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage*2]
|
||||
tmem_dealloc: cutlass.Int64; holding: cutlass.Int32
|
||||
smem = utils.SmemAllocator(); st = smem.allocate(SS)
|
||||
|
||||
qp,qc = pipeline.PipelineTmaUmma.create(barrier_storage=st.q_bar.data_ptr(),num_stages=self.q_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.q_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
|
||||
kvp,kvc = pipeline.PipelineTmaUmma.create(barrier_storage=st.kv_bar.data_ptr(),num_stages=self.kv_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.kv_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
|
||||
s_prod,s_cons = pipeline.PipelineUmmaAsync.create(barrier_storage=st.s_bar.data_ptr(),num_stages=1,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32*len(self.epilogue_warp_id))).make_participants()
|
||||
softmax_done_bar = pipeline.NamedBarrier(barrier_id=3, num_threads=32 + 32*len(self.epilogue_warp_id))
|
||||
pv_done_bar = pipeline.NamedBarrier(barrier_id=4, num_threads=32 + 32*len(self.epilogue_warp_id))
|
||||
vec_handoff_bar = pipeline.NamedBarrier(barrier_id=5, num_threads=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,1),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32*len(self.epilogue_warp_id)),cta_layout_vmnk=cl_vmnk,defer_sync=True)
|
||||
tmem_bar = pipeline.NamedBarrier(barrier_id=2,num_threads=32*len((self.mma_warp_id,*self.epilogue_warp_id)))
|
||||
tmem = utils.TmemAllocator(st.holding.ptr,barrier_for_retrieve=tmem_bar,allocator_warp_id=self.epilogue_warp_id[0],is_two_cta=cute.size(qk_mma.thr_id.shape)==2,two_cta_tmem_dealloc_mbar_ptr=st.tmem_dealloc.ptr)
|
||||
pipeline.pipeline_init_arrive(cluster_shape_mn=cl_vmnk,is_relaxed=True)
|
||||
|
||||
sQ = smem.allocate_tensor(element_type=self.q_dtype,layout=q_smem_s.outer,byte_alignment=128,swizzle=q_smem_s.inner)
|
||||
sK = smem.allocate_tensor(element_type=self.q_dtype,layout=k_smem_s.outer,byte_alignment=128,swizzle=k_smem_s.inner)
|
||||
sV = smem.allocate_tensor(element_type=self.q_dtype,layout=v_smem_s.outer,byte_alignment=128,swizzle=v_smem_s.inner)
|
||||
sC = smem.allocate_tensor(element_type=self.o_dtype,layout=c_smem_s.outer,byte_alignment=128,swizzle=c_smem_s.inner)
|
||||
|
||||
gQ = cute.local_tile(mQ,cute.slice_(self.qk_mma_tiler,(None,0,None)),(None,None,None))
|
||||
gK = cute.local_tile(mK,cute.slice_(self.qk_mma_tiler,(0,None,None)),(None,None,None))
|
||||
gV = cute.local_tile(mV,cute.slice_(self.pv_mma_tiler,(0,None,None)),(None,None,None))
|
||||
gC = cute.local_tile(mC,cute.slice_(self.pv_mma_tiler,(None,None,0)),(None,None,None))
|
||||
n_kv_tiles = cute.size(gK, mode=[3])
|
||||
|
||||
qk_thr = qk_mma.get_slice(0); pv_thr = pv_mma.get_slice(0)
|
||||
tCgQ = qk_thr.partition_A(gQ); tCgK = qk_thr.partition_B(gK)
|
||||
tCgV = pv_thr.partition_B(gV); tCgC = pv_thr.partition_C(gC)
|
||||
a_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,0,None,0)).shape)
|
||||
tAsQ,tAgQ = cpasync.tma_partition(tma_q,0,a_lay,cute.group_modes(sQ,0,3),cute.group_modes(tCgQ,0,3))
|
||||
b_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,None,0,0)).shape)
|
||||
tBsK,tBgK = cpasync.tma_partition(tma_k,0,b_lay,cute.group_modes(sK,0,3),cute.group_modes(tCgK,0,3))
|
||||
tVsV,tVgV = cpasync.tma_partition(tma_v,0,b_lay,cute.group_modes(sV,0,3),cute.group_modes(tCgV,0,3))
|
||||
tAgQ = tAgQ[(None,0,None,0)]; tBgK = tBgK[(None,0,None,0)]; tVgV = tVgV[(None,0,None,0)]
|
||||
|
||||
tCrQ = qk_mma.make_fragment_A(sQ); tCrK = qk_mma.make_fragment_B(sK)
|
||||
tCrV = pv_mma.make_fragment_B(sV)
|
||||
|
||||
qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
tOtO0 = cute.make_tensor(tOtO.iterator + self.tmem_o0_offset, tOtO.layout)
|
||||
|
||||
# --- PV read view (for MMA only, NOT for softmax store) ---
|
||||
tP = cute.make_tensor(tStS.iterator, p_tmem_s.outer)
|
||||
tOrP_base = pv_thr.make_fragment_A(tP)
|
||||
tOrP = tOrP_base[(None,None,None,0)]
|
||||
tOrP0 = cute.make_tensor(
|
||||
tOrP.iterator + self.qk_acc_dtype.width // self.q_dtype.width * self.tmem_p0_offset,
|
||||
tOrP.layout)
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_as, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_as, self.num_acc_stage))
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# TMA LOAD
|
||||
if warp_idx == self.tma_warp_id:
|
||||
qp.reset(); qh = qp.acquire_and_advance()
|
||||
cute.copy(tma_q,tAgQ[(None,qh.count)],tAsQ[(None,qh.index)],tma_bar_ptr=qh.barrier)
|
||||
qp.tail()
|
||||
kvp.reset(); pk = kvp.try_acquire()
|
||||
for kt in cutlass.range(n_kv_tiles,unroll=1):
|
||||
kh = kvp.acquire_and_advance(pk)
|
||||
cute.copy(tma_k,tBgK[(None,kh.count)],tBsK[(None,kh.index)],tma_bar_ptr=kh.barrier)
|
||||
pk = cutlass.Boolean(1)
|
||||
vh = kvp.acquire_and_advance(pk)
|
||||
cute.copy(tma_v,tVgV[(None,vh.count)],tVsV[(None,vh.index)],tma_bar_ptr=vh.barrier)
|
||||
pk = cutlass.Boolean(1)
|
||||
kvp.tail()
|
||||
|
||||
# MMA
|
||||
if warp_idx == self.mma_warp_id:
|
||||
tmem.wait_for_alloc()
|
||||
qc.reset(); qh = qc.wait_and_advance(); qh.release()
|
||||
kvc.reset(); pk = kvc.try_wait()
|
||||
acc_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
|
||||
acc_pipe.producer_acquire(acc_st)
|
||||
for kt in range(n_kv_tiles):
|
||||
kh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
sh = s_prod.acquire_and_advance()
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
for kb in cutlass.range(cute.size(tCrQ,mode=[2]), unroll_full=True):
|
||||
cute.gemm(qk_mma, tStS0, tCrQ[(None,None,kb,0)], tCrK[(None,None,kb,kh.index)], tStS0)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
sh.commit(); kh.release()
|
||||
softmax_done_bar.arrive_and_wait()
|
||||
vh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, kt != 0)
|
||||
for kb in cutlass.range(cute.size(tOrP0,mode=[2]), unroll_full=True):
|
||||
cute.gemm(pv_mma, tOtO0, tOrP0[(None,None,kb)], tCrV[(None,None,kb,vh.index)], tOtO0)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
vh.release()
|
||||
pv_done_bar.arrive()
|
||||
acc_pipe.producer_commit(acc_st); acc_st.advance()
|
||||
acc_pipe.producer_tail(acc_st)
|
||||
|
||||
# ===================== EPILOGUE WARPS (STAGE C: ONLINE SOFTMAX) =====================
|
||||
if warp_idx < self.mma_warp_id:
|
||||
tmem.allocate(self.num_tmem_alloc_cols)
|
||||
tmem.wait_for_alloc()
|
||||
tmem_ptr = tmem.retrieve_ptr(self.qk_acc_dtype)
|
||||
sfw_idx = tidx % (32 * len(self.epilogue_warp_id))
|
||||
|
||||
# --- S load (QK C-fragment) ---
|
||||
tmem_load_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
tiled_tmem_load = tcgen05.make_tmem_copy(tmem_load_atom, tStS0)
|
||||
thr_load = tiled_tmem_load.get_slice(sfw_idx)
|
||||
tTMEM_LOADtS = thr_load.partition_S(tStS0)
|
||||
cS = cute.make_identity_tensor((self.qk_mma_tiler[0], self.qk_mma_tiler[1]))
|
||||
tScS = qk_thr.partition_C(cS)
|
||||
tTMEM_LOADcS = thr_load.partition_D(tScS)
|
||||
|
||||
# --- P store (QK C-fragment composition, FMHA pattern) ---
|
||||
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
|
||||
tStP_layout = cute.composition(tStS.layout, cute.make_layout((self.pv_mma_tiler[0], p_cols_fp32)))
|
||||
tStP0 = cute.make_tensor(tStS.iterator + self.tmem_p0_offset, tStP_layout)
|
||||
tmem_store_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
tiled_tmem_store = tcgen05.make_tmem_copy(tmem_store_atom, tStP0)
|
||||
thr_store = tiled_tmem_store.get_slice(sfw_idx)
|
||||
tTMEM_STOREtP = thr_store.partition_D(tStP0)
|
||||
tScP_layout = cute.composition(tScS.layout, cute.make_layout((self.pv_mma_tiler[0], p_cols_fp32)))
|
||||
tScP = cute.make_tensor(tScS.iterator, tScP_layout)
|
||||
tTMEM_STOREcP = thr_store.partition_S(tScP)
|
||||
|
||||
# --- Vector TMEM (per-row row_sum storage, FMHA pattern) ---
|
||||
# composition(tStS.layout, (128, 2)) = 2 FP32 columns per logical row
|
||||
# vec[0] = row_sum (final, after loop), vec[1] = unused
|
||||
# Reuses S TMEM region (offset 0), free after softmax loop writes
|
||||
|
||||
tStS_vec_layout = cute.composition(tStS.layout, cute.make_layout((128, 2)))
|
||||
tStS_vec = cute.make_tensor(tStS.iterator + self.tmem_vec_offset, tStS_vec_layout)
|
||||
tScS_vec_layout = cute.composition(tScS.layout, cute.make_layout((128, 2)))
|
||||
tScS_vec = cute.make_tensor(tScS.iterator, tScS_vec_layout)
|
||||
tmem_store_vec_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(2)), self.qk_acc_dtype)
|
||||
tiled_tmem_store_vec = tcgen05.make_tmem_copy(tmem_store_vec_atom, tStS_vec)
|
||||
thr_tmem_store_vec = tiled_tmem_store_vec.get_slice(sfw_idx)
|
||||
tTMEM_STORE_VECtS = thr_tmem_store_vec.partition_D(tStS_vec)
|
||||
tTMEM_STORE_VECcS = thr_tmem_store_vec.partition_S(tScS_vec)
|
||||
tmem_load_vec_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(2)), self.qk_acc_dtype)
|
||||
tiled_tmem_load_vec = tcgen05.make_tmem_copy(tmem_load_vec_atom, tStS_vec)
|
||||
thr_tmem_load_vec = tiled_tmem_load_vec.get_slice(sfw_idx)
|
||||
tTMEM_LOAD_VECtS = thr_tmem_load_vec.partition_S(tStS_vec)
|
||||
tTMEM_LOAD_VECcS = thr_tmem_load_vec.partition_D(tScS_vec)
|
||||
|
||||
# --- C6: O TMEM load/store for rescale (correction_rescale pattern) ---
|
||||
corr_tile_size = 16
|
||||
cO = cute.make_identity_tensor((self.pv_mma_tiler[0], self.pv_mma_tiler[1]))
|
||||
tOcO = pv_thr.partition_C(cO)
|
||||
o_tmem_load_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.qk_acc_dtype)
|
||||
o_tmem_store_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.qk_acc_dtype)
|
||||
tOtO_i_layout = cute.composition(tOtO0.layout, cute.make_layout((128, corr_tile_size)))
|
||||
tOcO_i_layout = cute.composition(tOcO.layout, cute.make_layout((128, corr_tile_size)))
|
||||
tOtO_i = cute.make_tensor(tOtO0.iterator, tOtO_i_layout)
|
||||
tOcO_i = cute.make_tensor(tOcO.iterator, tOcO_i_layout)
|
||||
o_tiled_tmem_load = tcgen05.make_tmem_copy(o_tmem_load_atom, tOtO_i)
|
||||
o_tiled_tmem_store = tcgen05.make_tmem_copy(o_tmem_store_atom, tOtO_i)
|
||||
o_thr_load = o_tiled_tmem_load.get_slice(sfw_idx)
|
||||
o_thr_store = o_tiled_tmem_store.get_slice(sfw_idx)
|
||||
tTMEM_LOADtO = o_thr_load.partition_S(tOtO_i)
|
||||
tTMEM_LOADcO = o_thr_load.partition_D(tOcO_i)
|
||||
tTMEM_STOREtO = o_thr_store.partition_D(tOtO_i)
|
||||
o_col_tiles = self.pv_mma_tiler[1] // corr_tile_size
|
||||
|
||||
# --- C2: Per-QK-fragment-row state (persist across KV tiles) ---
|
||||
# The QK TMEM load fragment is logically 4 rows x 32 columns for each
|
||||
# softmax thread. The old scalar row_max/row_sum reduced across all
|
||||
# 4 rows and therefore produced a row_sum around 4.0. Keep one
|
||||
# online-softmax state per local QK row.
|
||||
qk_frg_cnt = 4
|
||||
qk_frg_tile = cute.size(tTMEM_LOADcS) // qk_frg_cnt
|
||||
tTMEM_LOADcS_frg = cute.logical_divide(tTMEM_LOADcS, cute.make_layout(qk_frg_tile))
|
||||
|
||||
qk_row0 = tTMEM_LOADcS_frg[0, 0][0]
|
||||
qk_row1 = tTMEM_LOADcS_frg[0, 1][0]
|
||||
qk_row2 = tTMEM_LOADcS_frg[0, 2][0]
|
||||
qk_row3 = tTMEM_LOADcS_frg[0, 3][0]
|
||||
|
||||
row_max0 = -cutlass.Float32.inf
|
||||
row_max1 = -cutlass.Float32.inf
|
||||
row_max2 = -cutlass.Float32.inf
|
||||
row_max3 = -cutlass.Float32.inf
|
||||
|
||||
row_sum0 = cutlass.Float32(0.0)
|
||||
row_sum1 = cutlass.Float32(0.0)
|
||||
row_sum2 = cutlass.Float32(0.0)
|
||||
row_sum3 = cutlass.Float32(0.0)
|
||||
|
||||
# --- C3: QK scale = 1/sqrt(HEAD_DIM) * log2(e) for exp2 ---
|
||||
scale = self.scale_softmax_log2
|
||||
|
||||
# =============================================================
|
||||
# Per-KV-tile online softmax loop
|
||||
# =============================================================
|
||||
for kt in range(n_kv_tiles):
|
||||
si_handle = s_cons.wait_and_advance()
|
||||
|
||||
# Load S from TMEM (FP32, QK C-fragment layout). Because the
|
||||
# vector buffer reuses the S columns, all softmax threads must
|
||||
# finish this load before any thread writes vector data.
|
||||
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()
|
||||
vec_handoff_bar.arrive_and_wait()
|
||||
|
||||
frg_cnt = 4
|
||||
frg_tile = cute.size(tTMEM_LOADrS) // frg_cnt
|
||||
tTMEM_LOADrS_frg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(frg_tile))
|
||||
|
||||
# --- C4: Compute tile_max independently for each local QK row ---
|
||||
old_row_max0 = row_max0
|
||||
old_row_max1 = row_max1
|
||||
old_row_max2 = row_max2
|
||||
old_row_max3 = row_max3
|
||||
|
||||
row_max0 = tTMEM_LOADrS_frg[None, 0].load().reduce(cute.ReductionOp.MAX, row_max0, 0)
|
||||
row_max1 = tTMEM_LOADrS_frg[None, 1].load().reduce(cute.ReductionOp.MAX, row_max1, 0)
|
||||
row_max2 = tTMEM_LOADrS_frg[None, 2].load().reduce(cute.ReductionOp.MAX, row_max2, 0)
|
||||
row_max3 = tTMEM_LOADrS_frg[None, 3].load().reduce(cute.ReductionOp.MAX, row_max3, 0)
|
||||
|
||||
row_max0_safe = row_max0
|
||||
row_max1_safe = row_max1
|
||||
row_max2_safe = row_max2
|
||||
row_max3_safe = row_max3
|
||||
if row_max0 == -cutlass.Float32.inf:
|
||||
row_max0_safe = cutlass.Float32(0.0)
|
||||
if row_max1 == -cutlass.Float32.inf:
|
||||
row_max1_safe = cutlass.Float32(0.0)
|
||||
if row_max2 == -cutlass.Float32.inf:
|
||||
row_max2_safe = cutlass.Float32(0.0)
|
||||
if row_max3 == -cutlass.Float32.inf:
|
||||
row_max3_safe = cutlass.Float32(0.0)
|
||||
|
||||
# --- C5: Per-row O-rescale factors for the already-accumulated O ---
|
||||
acc_scale0 = cute.math.exp2(scale * (old_row_max0 - row_max0_safe), fastmath=True)
|
||||
acc_scale1 = cute.math.exp2(scale * (old_row_max1 - row_max1_safe), fastmath=True)
|
||||
acc_scale2 = cute.math.exp2(scale * (old_row_max2 - row_max2_safe), fastmath=True)
|
||||
acc_scale3 = cute.math.exp2(scale * (old_row_max3 - row_max3_safe), fastmath=True)
|
||||
|
||||
# --- C6: Rescale O in TMEM using a row-indexed vector handoff ---
|
||||
# Store per-QK-row acc_scale into vec[row, 0], then read vec[pv_row, 0]
|
||||
# from the PV/O partition. This is the CUTLASS-style vector bridge,
|
||||
# but folded into the same four softmax warps, so it needs an
|
||||
# explicit warpgroup barrier between store and load.
|
||||
if kt > 0:
|
||||
pv_done_bar.arrive_and_wait()
|
||||
|
||||
thr_vs0 = tiled_tmem_store_vec.get_slice(qk_row0)
|
||||
tVStore0 = thr_vs0.partition_D(tStS_vec)
|
||||
tVStoreSrc0 = thr_vs0.partition_S(tScS_vec)
|
||||
rVec0 = cute.make_rmem_tensor(tVStoreSrc0.shape, self.qk_acc_dtype)
|
||||
rVec0[0] = acc_scale0
|
||||
rVec0[1] = row_max0_safe
|
||||
cute.copy(tiled_tmem_store_vec, rVec0, tVStore0)
|
||||
|
||||
thr_vs1 = tiled_tmem_store_vec.get_slice(qk_row1)
|
||||
tVStore1 = thr_vs1.partition_D(tStS_vec)
|
||||
tVStoreSrc1 = thr_vs1.partition_S(tScS_vec)
|
||||
rVec1 = cute.make_rmem_tensor(tVStoreSrc1.shape, self.qk_acc_dtype)
|
||||
rVec1[0] = acc_scale1
|
||||
rVec1[1] = row_max1_safe
|
||||
cute.copy(tiled_tmem_store_vec, rVec1, tVStore1)
|
||||
|
||||
thr_vs2 = tiled_tmem_store_vec.get_slice(qk_row2)
|
||||
tVStore2 = thr_vs2.partition_D(tStS_vec)
|
||||
tVStoreSrc2 = thr_vs2.partition_S(tScS_vec)
|
||||
rVec2 = cute.make_rmem_tensor(tVStoreSrc2.shape, self.qk_acc_dtype)
|
||||
rVec2[0] = acc_scale2
|
||||
rVec2[1] = row_max2_safe
|
||||
cute.copy(tiled_tmem_store_vec, rVec2, tVStore2)
|
||||
|
||||
thr_vs3 = tiled_tmem_store_vec.get_slice(qk_row3)
|
||||
tVStore3 = thr_vs3.partition_D(tStS_vec)
|
||||
tVStoreSrc3 = thr_vs3.partition_S(tScS_vec)
|
||||
rVec3 = cute.make_rmem_tensor(tVStoreSrc3.shape, self.qk_acc_dtype)
|
||||
rVec3[0] = acc_scale3
|
||||
rVec3[1] = row_max3_safe
|
||||
cute.copy(tiled_tmem_store_vec, rVec3, tVStore3)
|
||||
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
vec_handoff_bar.arrive_and_wait()
|
||||
|
||||
pv_row = tTMEM_LOADcO[0][0]
|
||||
thr_vl = tiled_tmem_load_vec.get_slice(pv_row)
|
||||
tVLoad = thr_vl.partition_S(tStS_vec)
|
||||
tVLoadDst = thr_vl.partition_D(tScS_vec)
|
||||
rVecPV = cute.make_rmem_tensor(tVLoadDst.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load_vec, tVLoad, rVecPV)
|
||||
cute.arch.fence_view_async_tmem_load()
|
||||
acc_scale_pv = rVecPV[0]
|
||||
|
||||
tTMrO = cute.make_rmem_tensor((tTMEM_LOADcO.shape, o_col_tiles), self.qk_acc_dtype)
|
||||
for i in range(o_col_tiles):
|
||||
tTMrO_i_ = tTMrO[None, i]
|
||||
tTMrO_i_layout = cute.composition(tTMrO_i_.layout, cute.make_layout(tTMrO.shape[0]))
|
||||
tTMrO_i = cute.make_tensor(tTMrO_i_.iterator, tTMrO_i_layout)
|
||||
tTMEM_LOADtO_i = cute.make_tensor(tTMEM_LOADtO.iterator + i * corr_tile_size, tTMEM_LOADtO.layout)
|
||||
tTMEM_STOREtO_i = cute.make_tensor(tTMEM_STOREtO.iterator + i * corr_tile_size, tTMEM_STOREtO.layout)
|
||||
cute.copy(o_tiled_tmem_load, tTMEM_LOADtO_i, tTMrO_i)
|
||||
for j in cutlass.range(cute.size(tTMrO_i), vectorize=True):
|
||||
tTMrO_i[j] = tTMrO_i[j] * acc_scale_pv
|
||||
cute.copy(o_tiled_tmem_store, tTMrO_i, tTMEM_STOREtO_i)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# Rescale the four online row sums.
|
||||
row_sum0 = row_sum0 * acc_scale0
|
||||
row_sum1 = row_sum1 * acc_scale1
|
||||
row_sum2 = row_sum2 * acc_scale2
|
||||
row_sum3 = row_sum3 * acc_scale3
|
||||
|
||||
# --- C7: Compute P = exp2((S - row_max[row]) * scale), per row ---
|
||||
minus_row_max_scale0 = (cutlass.Float32(0.0) - row_max0_safe) * scale
|
||||
minus_row_max_scale1 = (cutlass.Float32(0.0) - row_max1_safe) * scale
|
||||
minus_row_max_scale2 = (cutlass.Float32(0.0) - row_max2_safe) * scale
|
||||
minus_row_max_scale3 = (cutlass.Float32(0.0) - row_max3_safe) * scale
|
||||
|
||||
rP_words = cute.make_rmem_tensor(tTMEM_STOREcP.shape, self.qk_acc_dtype)
|
||||
rP_bf16 = cute.make_tensor(cute.recast_ptr(rP_words.iterator, dtype=self.q_dtype), tTMEM_LOADrS.layout)
|
||||
rP_bf16_frg = cute.logical_divide(rP_bf16, cute.make_layout(frg_tile))
|
||||
|
||||
for k in cutlass.range(cute.size(tTMEM_LOADrS_frg, mode=[0]), vectorize=True):
|
||||
tTMEM_LOADrS_frg[k, 0] = tTMEM_LOADrS_frg[k, 0] * scale + minus_row_max_scale0
|
||||
tTMEM_LOADrS_frg[k, 0] = cute.math.exp2(tTMEM_LOADrS_frg[k, 0], fastmath=True)
|
||||
s_vec0 = tTMEM_LOADrS_frg[None, 0].load()
|
||||
rP_bf16_frg[None, 0].store(s_vec0.to(self.q_dtype))
|
||||
|
||||
for k in cutlass.range(cute.size(tTMEM_LOADrS_frg, mode=[0]), vectorize=True):
|
||||
tTMEM_LOADrS_frg[k, 1] = tTMEM_LOADrS_frg[k, 1] * scale + minus_row_max_scale1
|
||||
tTMEM_LOADrS_frg[k, 1] = cute.math.exp2(tTMEM_LOADrS_frg[k, 1], fastmath=True)
|
||||
s_vec1 = tTMEM_LOADrS_frg[None, 1].load()
|
||||
rP_bf16_frg[None, 1].store(s_vec1.to(self.q_dtype))
|
||||
|
||||
for k in cutlass.range(cute.size(tTMEM_LOADrS_frg, mode=[0]), vectorize=True):
|
||||
tTMEM_LOADrS_frg[k, 2] = tTMEM_LOADrS_frg[k, 2] * scale + minus_row_max_scale2
|
||||
tTMEM_LOADrS_frg[k, 2] = cute.math.exp2(tTMEM_LOADrS_frg[k, 2], fastmath=True)
|
||||
s_vec2 = tTMEM_LOADrS_frg[None, 2].load()
|
||||
rP_bf16_frg[None, 2].store(s_vec2.to(self.q_dtype))
|
||||
|
||||
for k in cutlass.range(cute.size(tTMEM_LOADrS_frg, mode=[0]), vectorize=True):
|
||||
tTMEM_LOADrS_frg[k, 3] = tTMEM_LOADrS_frg[k, 3] * scale + minus_row_max_scale3
|
||||
tTMEM_LOADrS_frg[k, 3] = cute.math.exp2(tTMEM_LOADrS_frg[k, 3], fastmath=True)
|
||||
s_vec3 = tTMEM_LOADrS_frg[None, 3].load()
|
||||
rP_bf16_frg[None, 3].store(s_vec3.to(self.q_dtype))
|
||||
|
||||
# Store P to TMEM.
|
||||
cute.copy(tiled_tmem_store, rP_words, tTMEM_STOREtP)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
si_handle.release()
|
||||
softmax_done_bar.arrive()
|
||||
|
||||
# --- C8: Row sum accumulation, independently for each local QK row ---
|
||||
tile_sum0 = tTMEM_LOADrS_frg[None, 0].load().reduce(cute.ReductionOp.ADD, cutlass.Float32(0.0), 0)
|
||||
tile_sum1 = tTMEM_LOADrS_frg[None, 1].load().reduce(cute.ReductionOp.ADD, cutlass.Float32(0.0), 0)
|
||||
tile_sum2 = tTMEM_LOADrS_frg[None, 2].load().reduce(cute.ReductionOp.ADD, cutlass.Float32(0.0), 0)
|
||||
tile_sum3 = tTMEM_LOADrS_frg[None, 3].load().reduce(cute.ReductionOp.ADD, cutlass.Float32(0.0), 0)
|
||||
|
||||
row_sum0 = row_sum0 + tile_sum0
|
||||
row_sum1 = row_sum1 + tile_sum1
|
||||
row_sum2 = row_sum2 + tile_sum2
|
||||
row_sum3 = row_sum3 + tile_sum3
|
||||
|
||||
# --- C9: Final normalization via row-indexed TMEM vector ---
|
||||
# Wait for the final PV MMA to finish producing O.
|
||||
pv_done_bar.arrive_and_wait()
|
||||
|
||||
# Publish final row_sum per QK row into vec[row, 0].
|
||||
thr_vs0 = tiled_tmem_store_vec.get_slice(qk_row0)
|
||||
tVStore0 = thr_vs0.partition_D(tStS_vec)
|
||||
tVStoreSrc0 = thr_vs0.partition_S(tScS_vec)
|
||||
rVec0 = cute.make_rmem_tensor(tVStoreSrc0.shape, self.qk_acc_dtype)
|
||||
rVec0[0] = row_sum0
|
||||
rVec0[1] = row_max0
|
||||
cute.copy(tiled_tmem_store_vec, rVec0, tVStore0)
|
||||
|
||||
thr_vs1 = tiled_tmem_store_vec.get_slice(qk_row1)
|
||||
tVStore1 = thr_vs1.partition_D(tStS_vec)
|
||||
tVStoreSrc1 = thr_vs1.partition_S(tScS_vec)
|
||||
rVec1 = cute.make_rmem_tensor(tVStoreSrc1.shape, self.qk_acc_dtype)
|
||||
rVec1[0] = row_sum1
|
||||
rVec1[1] = row_max1
|
||||
cute.copy(tiled_tmem_store_vec, rVec1, tVStore1)
|
||||
|
||||
thr_vs2 = tiled_tmem_store_vec.get_slice(qk_row2)
|
||||
tVStore2 = thr_vs2.partition_D(tStS_vec)
|
||||
tVStoreSrc2 = thr_vs2.partition_S(tScS_vec)
|
||||
rVec2 = cute.make_rmem_tensor(tVStoreSrc2.shape, self.qk_acc_dtype)
|
||||
rVec2[0] = row_sum2
|
||||
rVec2[1] = row_max2
|
||||
cute.copy(tiled_tmem_store_vec, rVec2, tVStore2)
|
||||
|
||||
thr_vs3 = tiled_tmem_store_vec.get_slice(qk_row3)
|
||||
tVStore3 = thr_vs3.partition_D(tStS_vec)
|
||||
tVStoreSrc3 = thr_vs3.partition_S(tScS_vec)
|
||||
rVec3 = cute.make_rmem_tensor(tVStoreSrc3.shape, self.qk_acc_dtype)
|
||||
rVec3[0] = row_sum3
|
||||
rVec3[1] = row_max3
|
||||
cute.copy(tiled_tmem_store_vec, rVec3, tVStore3)
|
||||
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
vec_handoff_bar.arrive_and_wait()
|
||||
|
||||
# Read the correct row_sum for this PV/O row and normalize O.
|
||||
pv_row_final = tTMEM_LOADcO[0][0]
|
||||
thr_vl_final = tiled_tmem_load_vec.get_slice(pv_row_final)
|
||||
tVLoadFinal = thr_vl_final.partition_S(tStS_vec)
|
||||
tVLoadFinalDst = thr_vl_final.partition_D(tScS_vec)
|
||||
rVecFinal = cute.make_rmem_tensor(tVLoadFinalDst.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load_vec, tVLoadFinal, rVecFinal)
|
||||
cute.arch.fence_view_async_tmem_load()
|
||||
|
||||
inv_row_sum = cutlass.Float32(1.0) / rVecFinal[0]
|
||||
|
||||
tTMrO_final = cute.make_rmem_tensor((tTMEM_LOADcO.shape, o_col_tiles), self.qk_acc_dtype)
|
||||
for i in range(o_col_tiles):
|
||||
tTMrO_i_ = tTMrO_final[None, i]
|
||||
tTMrO_i_layout = cute.composition(tTMrO_i_.layout, cute.make_layout(tTMrO_final.shape[0]))
|
||||
tTMrO_i = cute.make_tensor(tTMrO_i_.iterator, tTMrO_i_layout)
|
||||
tTMEM_LOADtO_i = cute.make_tensor(
|
||||
tTMEM_LOADtO.iterator + i * corr_tile_size, tTMEM_LOADtO.layout)
|
||||
tTMEM_STOREtO_i = cute.make_tensor(
|
||||
tTMEM_STOREtO.iterator + i * corr_tile_size, tTMEM_STOREtO.layout)
|
||||
cute.copy(o_tiled_tmem_load, tTMEM_LOADtO_i, tTMrO_i)
|
||||
for j in cutlass.range(cute.size(tTMrO_i), vectorize=True):
|
||||
tTMrO_i[j] = tTMrO_i[j] * inv_row_sum
|
||||
cute.copy(o_tiled_tmem_store, tTMrO_i, tTMEM_STOREtO_i)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# Now O in TMEM is normalized. Use standard epilogue_tma_store with identity.
|
||||
tCtO_base = cute.make_tensor(tmem_ptr + self.tmem_o0_offset, tCtO_fake.layout)
|
||||
acc_cons_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Consumer, self.num_acc_stage)
|
||||
c_grp = pipeline.CooperativeGroup(pipeline.Agent.Thread, 32 * len(self.epilogue_warp_id))
|
||||
c_pipe = pipeline.PipelineTmaStore.create(num_stages=self.num_c_stage, producer_group=c_grp)
|
||||
acc_cons_st = utils.gemm.sm100.epilogue_tma_store(
|
||||
self, tidx, warp_idx, tma_c, tCtO_base, sC, tCgC, epi_tile, 0,
|
||||
const_expr(lambda x: x),
|
||||
(0,0,0), acc_cons_st, acc_pipe, c_pipe)
|
||||
c_pipe.producer_tail()
|
||||
tmem.relinquish_alloc_permit()
|
||||
tmem.free(tmem_ptr)
|
||||
|
||||
|
||||
def test():
|
||||
import math
|
||||
torch.manual_seed(42)
|
||||
for n in [128, 256, 384]:
|
||||
m, hd = 128, HEAD_DIM
|
||||
q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
k = torch.randn(n, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
v = torch.randn(n, hd, dtype=torch.bfloat16, device="cuda")
|
||||
v_kernel = v.unsqueeze(-1)
|
||||
c = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
qf = q[:,:,0].float(); kf = k[:,:,0].float()
|
||||
attn = qf @ kf.T / math.sqrt(hd)
|
||||
ref = torch.softmax(attn, dim=-1) @ v.float()
|
||||
mQ = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
|
||||
mK = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
|
||||
mV = ct.from_dlpack(v_kernel).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_kernel))
|
||||
mC = ct.from_dlpack(c).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c))
|
||||
stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
|
||||
kernel = FmhaV3Softmax(s_k=n)
|
||||
print(f"n={n}: Compiling...", flush=True)
|
||||
compiled = cute.compile(kernel, mQ, mK, mV, mC, stream)
|
||||
print(f"n={n}: tmem: s0={kernel.tmem_s0_offset} p0={kernel.tmem_p0_offset} o0={kernel.tmem_o0_offset} vec={kernel.tmem_vec_offset} alloc={kernel.num_tmem_alloc_cols}", flush=True)
|
||||
print(f"n={n}: Running...", flush=True)
|
||||
compiled(mQ, mK, mV, mC, stream)
|
||||
torch.cuda.synchronize()
|
||||
out = c[:,:,0].float()
|
||||
cos = torch.nn.functional.cosine_similarity(out.flatten().unsqueeze(0), ref.flatten().unsqueeze(0)).item()
|
||||
max_err = (out - ref).abs().max().item()
|
||||
print(f"FMHA softmax n={n}: cosine {cos:.6f} max_err {max_err:.6f} {'PASS' if cos >= 0.999 else 'FAIL'}", flush=True)
|
||||
|
||||
if __name__ == "__main__":
|
||||
test()
|
||||
|
||||
@@ -1,465 +0,0 @@
|
||||
"""
|
||||
FMHA v3 + Stage C: QK -> online softmax -> PV with KV-tile interleaving.
|
||||
Stage C: row_max, exp2, O rescale, row_sum, final normalization.
|
||||
FMHA pattern P store preserved from Stage B.
|
||||
"""
|
||||
import math
|
||||
import torch, cutlass, cutlass.cute as cute, cutlass.utils as utils, cutlass.pipeline as pipeline
|
||||
from cutlass.cute.nvgpu import cpasync, tcgen05
|
||||
from cutlass import Float32, BFloat16, Int32, Boolean, const_expr
|
||||
from cutlass.utils import LayoutEnum
|
||||
from cutlass.utils.tmem_allocator import find_tmem_tensor_col_offset
|
||||
import cuda.bindings.driver as cuda
|
||||
import cutlass.torch as ct
|
||||
|
||||
HEAD_DIM = 64
|
||||
|
||||
class FmhaV3Softmax:
|
||||
def __init__(self, s_k: int = 128):
|
||||
self.s_k = s_k
|
||||
self.acc_dtype = Float32; self.qk_acc_dtype = Float32
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
self.use_2cta_instrs = False; self.epilog_sync_bar_id = 1
|
||||
self.cluster_shape_mn = (1, 1); self.cta_group = tcgen05.CtaGroup.ONE
|
||||
self.epilogue_warp_id = (0,1,2,3); self.mma_warp_id = 4; self.tma_warp_id = 5
|
||||
self.threads_per_cta = 192; self.num_c_stage = 2
|
||||
self.kv_stage = 2; self.q_stage = 1; self.num_c_stage = 2
|
||||
|
||||
def _setup(self, qk_mma, pv_mma):
|
||||
qk_ik = cute.size(qk_mma.shape_mnk, mode=[2])
|
||||
self.qk_mma_tiler = (128, 128, qk_ik * 4)
|
||||
pv_ik = cute.size(pv_mma.shape_mnk, mode=[2])
|
||||
self.pv_mma_tiler = (128, HEAD_DIM, pv_ik * (128 // pv_ik))
|
||||
self.mma_tiler = self.qk_mma_tiler
|
||||
self.cluster_layout_vmnk = cute.tiled_divide(cute.make_layout((1,1,1)), (qk_mma.thr_id.shape,))
|
||||
self.cta_tile_shape_mnk = (self.qk_mma_tiler[0]//cute.size(qk_mma.thr_id.shape), HEAD_DIM, self.qk_mma_tiler[2])
|
||||
self.c_layout = LayoutEnum.ROW_MAJOR
|
||||
self.epi_tile = utils.sm100.compute_epilogue_tile_shape(self.cta_tile_shape_mnk, False, self.c_layout, self.o_dtype)
|
||||
self.num_ab_stage = 1; self.num_acc_stage = 1
|
||||
self.q_smem_s = utils.sm100.make_smem_layout_a(qk_mma, self.qk_mma_tiler, self.q_dtype, self.q_stage)
|
||||
self.k_smem_s = utils.sm100.make_smem_layout_b(qk_mma, self.qk_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.v_smem_s = utils.sm100.make_smem_layout_b(pv_mma, self.pv_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.c_smem_s = utils.sm100.make_smem_layout_epi(self.o_dtype, self.c_layout, self.epi_tile, 2)
|
||||
self.p_tmem_s = utils.sm100.make_smem_layout_a(pv_mma, self.pv_mma_tiler, self.q_dtype, 1)
|
||||
qk_thr = qk_mma.get_slice(0); qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
pv_thr = pv_mma.get_slice(0); pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
self.tmem_s0_offset = 0; self.tmem_p0_offset = 32
|
||||
# P occupies [tmem_p0_offset, tmem_p0_offset + p_cols_fp32)
|
||||
# S occupies [0, qk_mma_tiler[1]) = [0, 128)
|
||||
# O must NOT overlap P. Place O after max(S end, P end), aligned to 32.
|
||||
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
|
||||
p_end = self.tmem_p0_offset + p_cols_fp32 # 32 + 64 = 96
|
||||
s_cols = self.qk_mma_tiler[1] # 128
|
||||
o_after = max(s_cols, p_end) # 128
|
||||
self.tmem_o0_offset = ((o_after + 31) // 32) * 32
|
||||
self.tmem_vec_offset = 0 # Reuse S region for per-row inv_row_sum vector # align to 32 = 128
|
||||
self.tmem_vec_offset = 0 # Reuse S region (free after softmax loop)
|
||||
o_cols = find_tmem_tensor_col_offset(tOtO) # footprint of O
|
||||
total = self.tmem_o0_offset + o_cols
|
||||
# Must be multiple of 32 AND power of 2
|
||||
self.num_tmem_alloc_cols = 1
|
||||
while self.num_tmem_alloc_cols < total:
|
||||
self.num_tmem_alloc_cols *= 2
|
||||
cta = cute.size(qk_mma.thr_id.shape)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0))
|
||||
self.q_tx_bytes = cute.size_in_bytes(self.q_dtype, q_s) * cta
|
||||
self.kv_tx_bytes = cute.size_in_bytes(self.q_dtype, k_s) * cta
|
||||
self.scale_softmax_log2 = Float32(1.0 / math.sqrt(HEAD_DIM) * math.log2(math.e))
|
||||
|
||||
@cute.jit
|
||||
def __call__(self, q, k, v, c, stream):
|
||||
self.q_dtype = q.element_type; self.o_dtype = c.element_type; self.c_dtype = self.o_dtype
|
||||
self.a_major = LayoutEnum.from_tensor(q).mma_major_mode()
|
||||
self.b_major = LayoutEnum.from_tensor(k).mma_major_mode()
|
||||
# # s_k hardcoded # BROKEN in @cute.jit
|
||||
# FMHA-style V: reconstruct as (HEAD_DIM, s_k, 1) MN-major
|
||||
v_fmha = cute.make_tensor(
|
||||
v.iterator,
|
||||
cute.make_layout(
|
||||
(HEAD_DIM, self.s_k, 1),
|
||||
stride=(1, HEAD_DIM, HEAD_DIM * self.s_k),
|
||||
),
|
||||
)
|
||||
self.v_major = LayoutEnum.from_tensor(v_fmha).mma_major_mode()
|
||||
self.c_layout = LayoutEnum.from_tensor(c)
|
||||
qk_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, self.a_major, self.b_major, self.qk_acc_dtype, self.cta_group, (128,128), tcgen05.OperandSource.SMEM)
|
||||
pv_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, cute.nvgpu.OperandMajorMode.K, self.v_major, self.qk_acc_dtype, self.cta_group, (128,HEAD_DIM), tcgen05.OperandSource.TMEM)
|
||||
self._setup(qk_mma, pv_mma)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0)); v_s = cute.slice_(self.v_smem_s,(None,None,None,0))
|
||||
tma_q,mQ = cute.nvgpu.make_tiled_tma_atom_A(utils.sm100.cluster_shape_to_tma_atom_A(self.cluster_shape_mn,qk_mma.thr_id),q,q_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_k,mK = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,qk_mma.thr_id),k,k_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_v,mV = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,pv_mma.thr_id),v_fmha,v_s,self.pv_mma_tiler,pv_mma,self.cluster_layout_vmnk.shape)
|
||||
epi_s = cute.select(self.c_smem_s,mode=[0,1])
|
||||
tma_c,mC = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(),c,epi_s,self.epi_tile)
|
||||
self._kernel(qk_mma,pv_mma,tma_q,mQ,tma_k,mK,tma_v,mV,tma_c,mC,self.cluster_layout_vmnk,self.q_smem_s,self.k_smem_s,self.v_smem_s,self.p_tmem_s,self.c_smem_s,self.epi_tile).launch(grid=(1,1,1),block=[self.threads_per_cta,1,1],stream=stream)
|
||||
|
||||
@cute.kernel
|
||||
def _kernel(self, qk_mma, pv_mma, tma_q, mQ, tma_k, mK, tma_v, mV, tma_c, mC, cl_vmnk, q_smem_s, k_smem_s, v_smem_s, p_tmem_s, c_smem_s, epi_tile):
|
||||
warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx())
|
||||
tidx,_,_ = cute.arch.thread_idx()
|
||||
if warp_idx == self.tma_warp_id:
|
||||
cpasync.prefetch_descriptor(tma_q); cpasync.prefetch_descriptor(tma_k); cpasync.prefetch_descriptor(tma_v); cpasync.prefetch_descriptor(tma_c)
|
||||
|
||||
@cute.struct
|
||||
class SS:
|
||||
q_bar: cute.struct.MemRange[cutlass.Int64, self.q_stage*2]
|
||||
kv_bar: cute.struct.MemRange[cutlass.Int64, self.kv_stage*2]
|
||||
s_bar: cute.struct.MemRange[cutlass.Int64, 2]
|
||||
acc_bar: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage*2]
|
||||
tmem_dealloc: cutlass.Int64; holding: cutlass.Int32
|
||||
smem = utils.SmemAllocator(); st = smem.allocate(SS)
|
||||
|
||||
qp,qc = pipeline.PipelineTmaUmma.create(barrier_storage=st.q_bar.data_ptr(),num_stages=self.q_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.q_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
|
||||
kvp,kvc = pipeline.PipelineTmaUmma.create(barrier_storage=st.kv_bar.data_ptr(),num_stages=self.kv_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.kv_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
|
||||
s_prod,s_cons = pipeline.PipelineUmmaAsync.create(barrier_storage=st.s_bar.data_ptr(),num_stages=1,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32*len(self.epilogue_warp_id))).make_participants()
|
||||
softmax_done_bar = pipeline.NamedBarrier(barrier_id=3, num_threads=32 + 32*len(self.epilogue_warp_id))
|
||||
pv_done_bar = pipeline.NamedBarrier(barrier_id=4, num_threads=32 + 32*len(self.epilogue_warp_id))
|
||||
vec_handoff_bar = pipeline.NamedBarrier(barrier_id=5, num_threads=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,1),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32*len(self.epilogue_warp_id)),cta_layout_vmnk=cl_vmnk,defer_sync=True)
|
||||
tmem_bar = pipeline.NamedBarrier(barrier_id=2,num_threads=32*len((self.mma_warp_id,*self.epilogue_warp_id)))
|
||||
tmem = utils.TmemAllocator(st.holding.ptr,barrier_for_retrieve=tmem_bar,allocator_warp_id=self.epilogue_warp_id[0],is_two_cta=cute.size(qk_mma.thr_id.shape)==2,two_cta_tmem_dealloc_mbar_ptr=st.tmem_dealloc.ptr)
|
||||
pipeline.pipeline_init_arrive(cluster_shape_mn=cl_vmnk,is_relaxed=True)
|
||||
|
||||
sQ = smem.allocate_tensor(element_type=self.q_dtype,layout=q_smem_s.outer,byte_alignment=128,swizzle=q_smem_s.inner)
|
||||
sK = smem.allocate_tensor(element_type=self.q_dtype,layout=k_smem_s.outer,byte_alignment=128,swizzle=k_smem_s.inner)
|
||||
sV = smem.allocate_tensor(element_type=self.q_dtype,layout=v_smem_s.outer,byte_alignment=128,swizzle=v_smem_s.inner)
|
||||
sC = smem.allocate_tensor(element_type=self.o_dtype,layout=c_smem_s.outer,byte_alignment=128,swizzle=c_smem_s.inner)
|
||||
|
||||
gQ = cute.local_tile(mQ,cute.slice_(self.qk_mma_tiler,(None,0,None)),(None,None,None))
|
||||
gK = cute.local_tile(mK,cute.slice_(self.qk_mma_tiler,(0,None,None)),(None,None,None))
|
||||
gV = cute.local_tile(mV,cute.slice_(self.pv_mma_tiler,(0,None,None)),(None,None,None))
|
||||
gC = cute.local_tile(mC,cute.slice_(self.pv_mma_tiler,(None,None,0)),(None,None,None))
|
||||
n_kv_tiles = cute.size(gK, mode=[3])
|
||||
|
||||
qk_thr = qk_mma.get_slice(0); pv_thr = pv_mma.get_slice(0)
|
||||
tCgQ = qk_thr.partition_A(gQ); tCgK = qk_thr.partition_B(gK)
|
||||
tCgV = pv_thr.partition_B(gV); tCgC = pv_thr.partition_C(gC)
|
||||
a_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,0,None,0)).shape)
|
||||
tAsQ,tAgQ = cpasync.tma_partition(tma_q,0,a_lay,cute.group_modes(sQ,0,3),cute.group_modes(tCgQ,0,3))
|
||||
b_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,None,0,0)).shape)
|
||||
tBsK,tBgK = cpasync.tma_partition(tma_k,0,b_lay,cute.group_modes(sK,0,3),cute.group_modes(tCgK,0,3))
|
||||
tVsV,tVgV = cpasync.tma_partition(tma_v,0,b_lay,cute.group_modes(sV,0,3),cute.group_modes(tCgV,0,3))
|
||||
tAgQ = tAgQ[(None,0,None,0)]; tBgK = tBgK[(None,0,None,0)]; tVgV = tVgV[(None,0,None,0)]
|
||||
|
||||
tCrQ = qk_mma.make_fragment_A(sQ); tCrK = qk_mma.make_fragment_B(sK)
|
||||
tCrV = pv_mma.make_fragment_B(sV)
|
||||
|
||||
qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
tOtO0 = cute.make_tensor(tOtO.iterator + self.tmem_o0_offset, tOtO.layout)
|
||||
|
||||
# --- PV read view (for MMA only, NOT for softmax store) ---
|
||||
tP = cute.make_tensor(tStS.iterator, p_tmem_s.outer)
|
||||
tOrP_base = pv_thr.make_fragment_A(tP)
|
||||
tOrP = tOrP_base[(None,None,None,0)]
|
||||
tOrP0 = cute.make_tensor(
|
||||
tOrP.iterator + self.qk_acc_dtype.width // self.q_dtype.width * self.tmem_p0_offset,
|
||||
tOrP.layout)
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_as, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_as, self.num_acc_stage))
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# TMA LOAD
|
||||
if warp_idx == self.tma_warp_id:
|
||||
qp.reset(); qh = qp.acquire_and_advance()
|
||||
cute.copy(tma_q,tAgQ[(None,qh.count)],tAsQ[(None,qh.index)],tma_bar_ptr=qh.barrier)
|
||||
qp.tail()
|
||||
kvp.reset(); pk = kvp.try_acquire()
|
||||
for kt in cutlass.range(n_kv_tiles,unroll=1):
|
||||
kh = kvp.acquire_and_advance(pk)
|
||||
cute.copy(tma_k,tBgK[(None,kh.count)],tBsK[(None,kh.index)],tma_bar_ptr=kh.barrier)
|
||||
pk = cutlass.Boolean(1)
|
||||
vh = kvp.acquire_and_advance(pk)
|
||||
cute.copy(tma_v,tVgV[(None,vh.count)],tVsV[(None,vh.index)],tma_bar_ptr=vh.barrier)
|
||||
pk = cutlass.Boolean(1)
|
||||
kvp.tail()
|
||||
|
||||
# MMA
|
||||
if warp_idx == self.mma_warp_id:
|
||||
tmem.wait_for_alloc()
|
||||
qc.reset(); qh = qc.wait_and_advance(); qh.release()
|
||||
kvc.reset(); pk = kvc.try_wait()
|
||||
acc_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
|
||||
acc_pipe.producer_acquire(acc_st)
|
||||
for kt in range(n_kv_tiles):
|
||||
kh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
sh = s_prod.acquire_and_advance()
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
for kb in cutlass.range(cute.size(tCrQ,mode=[2]), unroll_full=True):
|
||||
cute.gemm(qk_mma, tStS0, tCrQ[(None,None,kb,0)], tCrK[(None,None,kb,kh.index)], tStS0)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
sh.commit(); kh.release()
|
||||
# softmax_done_bar skipped
|
||||
vh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, kt != 0)
|
||||
for kb in cutlass.range(cute.size(tOrP0,mode=[2]), unroll_full=True):
|
||||
cute.gemm(pv_mma, tOtO0, tOrP0[(None,None,kb)], tCrV[(None,None,kb,vh.index)], tOtO0)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
vh.release()
|
||||
# pv_done_bar skipped
|
||||
acc_pipe.producer_commit(acc_st); acc_st.advance()
|
||||
acc_pipe.producer_tail(acc_st)
|
||||
|
||||
# ===================== EPILOGUE WARPS (STAGE C: ONLINE SOFTMAX) =====================
|
||||
if warp_idx < self.mma_warp_id:
|
||||
tmem.allocate(self.num_tmem_alloc_cols)
|
||||
tmem.wait_for_alloc()
|
||||
tmem_ptr = tmem.retrieve_ptr(self.qk_acc_dtype)
|
||||
sfw_idx = tidx % (32 * len(self.epilogue_warp_id))
|
||||
|
||||
# --- S load (QK C-fragment) ---
|
||||
tmem_load_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
tiled_tmem_load = tcgen05.make_tmem_copy(tmem_load_atom, tStS0)
|
||||
thr_load = tiled_tmem_load.get_slice(sfw_idx)
|
||||
tTMEM_LOADtS = thr_load.partition_S(tStS0)
|
||||
cS = cute.make_identity_tensor((self.qk_mma_tiler[0], self.qk_mma_tiler[1]))
|
||||
tScS = qk_thr.partition_C(cS)
|
||||
tTMEM_LOADcS = thr_load.partition_D(tScS)
|
||||
|
||||
# --- P store (QK C-fragment composition, FMHA pattern) ---
|
||||
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
|
||||
tStP_layout = cute.composition(tStS.layout, cute.make_layout((self.pv_mma_tiler[0], p_cols_fp32)))
|
||||
tStP0 = cute.make_tensor(tStS.iterator + self.tmem_p0_offset, tStP_layout)
|
||||
tmem_store_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
tiled_tmem_store = tcgen05.make_tmem_copy(tmem_store_atom, tStP0)
|
||||
thr_store = tiled_tmem_store.get_slice(sfw_idx)
|
||||
tTMEM_STOREtP = thr_store.partition_D(tStP0)
|
||||
tScP_layout = cute.composition(tScS.layout, cute.make_layout((self.pv_mma_tiler[0], p_cols_fp32)))
|
||||
tScP = cute.make_tensor(tScS.iterator, tScP_layout)
|
||||
tTMEM_STOREcP = thr_store.partition_S(tScP)
|
||||
|
||||
# --- Vector TMEM (per-row row_sum storage, FMHA pattern) ---
|
||||
# composition(tStS.layout, (128, 2)) = 2 FP32 columns per logical row
|
||||
# vec[0] = row_sum (final, after loop), vec[1] = unused
|
||||
# Reuses S TMEM region (offset 0), free after softmax loop writes
|
||||
|
||||
tStS_vec_layout = cute.composition(tStS.layout, cute.make_layout((128, 2)))
|
||||
tStS_vec = cute.make_tensor(tStS.iterator + self.tmem_vec_offset, tStS_vec_layout)
|
||||
tScS_vec_layout = cute.composition(tScS.layout, cute.make_layout((128, 2)))
|
||||
tScS_vec = cute.make_tensor(tScS.iterator, tScS_vec_layout)
|
||||
tmem_store_vec_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(2)), self.qk_acc_dtype)
|
||||
tiled_tmem_store_vec = tcgen05.make_tmem_copy(tmem_store_vec_atom, tStS_vec)
|
||||
thr_tmem_store_vec = tiled_tmem_store_vec.get_slice(sfw_idx)
|
||||
tTMEM_STORE_VECtS = thr_tmem_store_vec.partition_D(tStS_vec)
|
||||
tTMEM_STORE_VECcS = thr_tmem_store_vec.partition_S(tScS_vec)
|
||||
tmem_load_vec_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(2)), self.qk_acc_dtype)
|
||||
tiled_tmem_load_vec = tcgen05.make_tmem_copy(tmem_load_vec_atom, tStS_vec)
|
||||
thr_tmem_load_vec = tiled_tmem_load_vec.get_slice(sfw_idx)
|
||||
tTMEM_LOAD_VECtS = thr_tmem_load_vec.partition_S(tStS_vec)
|
||||
tTMEM_LOAD_VECcS = thr_tmem_load_vec.partition_D(tScS_vec)
|
||||
|
||||
# --- C6: O TMEM load/store for rescale (correction_rescale pattern) ---
|
||||
corr_tile_size = 16
|
||||
cO = cute.make_identity_tensor((self.pv_mma_tiler[0], self.pv_mma_tiler[1]))
|
||||
tOcO = pv_thr.partition_C(cO)
|
||||
o_tmem_load_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.qk_acc_dtype)
|
||||
o_tmem_store_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.qk_acc_dtype)
|
||||
tOtO_i_layout = cute.composition(tOtO0.layout, cute.make_layout((128, corr_tile_size)))
|
||||
tOcO_i_layout = cute.composition(tOcO.layout, cute.make_layout((128, corr_tile_size)))
|
||||
tOtO_i = cute.make_tensor(tOtO0.iterator, tOtO_i_layout)
|
||||
tOcO_i = cute.make_tensor(tOcO.iterator, tOcO_i_layout)
|
||||
o_tiled_tmem_load = tcgen05.make_tmem_copy(o_tmem_load_atom, tOtO_i)
|
||||
o_tiled_tmem_store = tcgen05.make_tmem_copy(o_tmem_store_atom, tOtO_i)
|
||||
o_thr_load = o_tiled_tmem_load.get_slice(sfw_idx)
|
||||
o_thr_store = o_tiled_tmem_store.get_slice(sfw_idx)
|
||||
tTMEM_LOADtO = o_thr_load.partition_S(tOtO_i)
|
||||
tTMEM_LOADcO = o_thr_load.partition_D(tOcO_i)
|
||||
tTMEM_STOREtO = o_thr_store.partition_D(tOtO_i)
|
||||
o_col_tiles = self.pv_mma_tiler[1] // corr_tile_size
|
||||
|
||||
# --- C2: Per-QK-fragment-row state (persist across KV tiles) ---
|
||||
# The QK TMEM load fragment is logically 4 rows x 32 columns for each
|
||||
# softmax thread. The old scalar row_max/row_sum reduced across all
|
||||
# 4 rows and therefore produced a row_sum around 4.0. Keep one
|
||||
# online-softmax state per local QK row.
|
||||
qk_frg_cnt = 4
|
||||
qk_frg_tile = cute.size(tTMEM_LOADcS) // qk_frg_cnt
|
||||
tTMEM_LOADcS_frg = cute.logical_divide(tTMEM_LOADcS, cute.make_layout(qk_frg_tile))
|
||||
|
||||
qk_row0 = tTMEM_LOADcS_frg[0, 0][0]
|
||||
qk_row1 = tTMEM_LOADcS_frg[0, 1][0]
|
||||
qk_row2 = tTMEM_LOADcS_frg[0, 2][0]
|
||||
qk_row3 = tTMEM_LOADcS_frg[0, 3][0]
|
||||
|
||||
row_max0 = -cutlass.Float32.inf
|
||||
row_max1 = -cutlass.Float32.inf
|
||||
row_max2 = -cutlass.Float32.inf
|
||||
row_max3 = -cutlass.Float32.inf
|
||||
|
||||
row_sum0 = cutlass.Float32(0.0)
|
||||
row_sum1 = cutlass.Float32(0.0)
|
||||
row_sum2 = cutlass.Float32(0.0)
|
||||
row_sum3 = cutlass.Float32(0.0)
|
||||
|
||||
# --- C3: QK scale = 1/sqrt(HEAD_DIM) * log2(e) for exp2 ---
|
||||
scale = self.scale_softmax_log2
|
||||
|
||||
# =============================================================
|
||||
# Per-KV-tile online softmax loop
|
||||
# =============================================================
|
||||
for kt in range(n_kv_tiles):
|
||||
si_handle = s_cons.wait_and_advance()
|
||||
|
||||
# Load S from TMEM (FP32, QK C-fragment layout). Because the
|
||||
# vector buffer reuses the S columns, all softmax threads must
|
||||
# finish this load before any thread writes vector data.
|
||||
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()
|
||||
# vec_handoff_bar skipped
|
||||
|
||||
frg_cnt = 4
|
||||
frg_tile = cute.size(tTMEM_LOADrS) // frg_cnt
|
||||
tTMEM_LOADrS_frg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(frg_tile))
|
||||
|
||||
# --- C4: Compute tile_max independently for each local QK row ---
|
||||
old_row_max0 = row_max0
|
||||
old_row_max1 = row_max1
|
||||
old_row_max2 = row_max2
|
||||
old_row_max3 = row_max3
|
||||
|
||||
# Per-row max: explicit loop (avoid .load().reduce on sliced tensor)
|
||||
for k in cutlass.range(cute.size(tTMEM_LOADrS_frg, mode=[0])):
|
||||
row_max0 = cutlass.Float32.max(row_max0, tTMEM_LOADrS_frg[k, 0])
|
||||
row_max1 = cutlass.Float32.max(row_max1, tTMEM_LOADrS_frg[k, 1])
|
||||
row_max2 = cutlass.Float32.max(row_max2, tTMEM_LOADrS_frg[k, 2])
|
||||
row_max3 = cutlass.Float32.max(row_max3, tTMEM_LOADrS_frg[k, 3])
|
||||
|
||||
row_max0_safe = row_max0
|
||||
row_max1_safe = row_max1
|
||||
row_max2_safe = row_max2
|
||||
row_max3_safe = row_max3
|
||||
if row_max0 == -cutlass.Float32.inf:
|
||||
row_max0_safe = cutlass.Float32(0.0)
|
||||
if row_max1 == -cutlass.Float32.inf:
|
||||
row_max1_safe = cutlass.Float32(0.0)
|
||||
if row_max2 == -cutlass.Float32.inf:
|
||||
row_max2_safe = cutlass.Float32(0.0)
|
||||
if row_max3 == -cutlass.Float32.inf:
|
||||
row_max3_safe = cutlass.Float32(0.0)
|
||||
|
||||
# --- C5: Per-row O-rescale factors for the already-accumulated O ---
|
||||
acc_scale0 = cute.math.exp2(scale * (old_row_max0 - row_max0_safe), fastmath=True)
|
||||
acc_scale1 = cute.math.exp2(scale * (old_row_max1 - row_max1_safe), fastmath=True)
|
||||
acc_scale2 = cute.math.exp2(scale * (old_row_max2 - row_max2_safe), fastmath=True)
|
||||
acc_scale3 = cute.math.exp2(scale * (old_row_max3 - row_max3_safe), fastmath=True)
|
||||
|
||||
# --- C6: SKIPPED (minimal test) ---
|
||||
if kt > 0:
|
||||
pass # pv_done_bar skipped
|
||||
else:
|
||||
pass # pv_done_bar skipped
|
||||
|
||||
# --- C7: Compute P = exp2((S - row_max[row]) * scale), per row ---
|
||||
minus_row_max_scale0 = (cutlass.Float32(0.0) - row_max0_safe) * scale
|
||||
minus_row_max_scale1 = (cutlass.Float32(0.0) - row_max1_safe) * scale
|
||||
minus_row_max_scale2 = (cutlass.Float32(0.0) - row_max2_safe) * scale
|
||||
minus_row_max_scale3 = (cutlass.Float32(0.0) - row_max3_safe) * scale
|
||||
|
||||
rP_words = cute.make_rmem_tensor(tTMEM_STOREcP.shape, self.qk_acc_dtype)
|
||||
rP_bf16 = cute.make_tensor(cute.recast_ptr(rP_words.iterator, dtype=self.q_dtype), tTMEM_LOADrS.layout)
|
||||
rP_bf16_frg = cute.logical_divide(rP_bf16, cute.make_layout(frg_tile))
|
||||
|
||||
for k in cutlass.range(cute.size(tTMEM_LOADrS_frg, mode=[0]), vectorize=True):
|
||||
tTMEM_LOADrS_frg[k, 0] = tTMEM_LOADrS_frg[k, 0] * scale + minus_row_max_scale0
|
||||
tTMEM_LOADrS_frg[k, 0] = cute.math.exp2(tTMEM_LOADrS_frg[k, 0], fastmath=True)
|
||||
s_vec0 = tTMEM_LOADrS_frg[None, 0].load()
|
||||
rP_bf16_frg[None, 0].store(s_vec0.to(self.q_dtype))
|
||||
|
||||
for k in cutlass.range(cute.size(tTMEM_LOADrS_frg, mode=[0]), vectorize=True):
|
||||
tTMEM_LOADrS_frg[k, 1] = tTMEM_LOADrS_frg[k, 1] * scale + minus_row_max_scale1
|
||||
tTMEM_LOADrS_frg[k, 1] = cute.math.exp2(tTMEM_LOADrS_frg[k, 1], fastmath=True)
|
||||
s_vec1 = tTMEM_LOADrS_frg[None, 1].load()
|
||||
rP_bf16_frg[None, 1].store(s_vec1.to(self.q_dtype))
|
||||
|
||||
for k in cutlass.range(cute.size(tTMEM_LOADrS_frg, mode=[0]), vectorize=True):
|
||||
tTMEM_LOADrS_frg[k, 2] = tTMEM_LOADrS_frg[k, 2] * scale + minus_row_max_scale2
|
||||
tTMEM_LOADrS_frg[k, 2] = cute.math.exp2(tTMEM_LOADrS_frg[k, 2], fastmath=True)
|
||||
s_vec2 = tTMEM_LOADrS_frg[None, 2].load()
|
||||
rP_bf16_frg[None, 2].store(s_vec2.to(self.q_dtype))
|
||||
|
||||
for k in cutlass.range(cute.size(tTMEM_LOADrS_frg, mode=[0]), vectorize=True):
|
||||
tTMEM_LOADrS_frg[k, 3] = tTMEM_LOADrS_frg[k, 3] * scale + minus_row_max_scale3
|
||||
tTMEM_LOADrS_frg[k, 3] = cute.math.exp2(tTMEM_LOADrS_frg[k, 3], fastmath=True)
|
||||
s_vec3 = tTMEM_LOADrS_frg[None, 3].load()
|
||||
rP_bf16_frg[None, 3].store(s_vec3.to(self.q_dtype))
|
||||
|
||||
# Store P to TMEM.
|
||||
cute.copy(tiled_tmem_store, rP_words, tTMEM_STOREtP)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
si_handle.release()
|
||||
# softmax_done_bar skipped
|
||||
|
||||
# --- C8: Row sum accumulation, independently for each local QK row ---
|
||||
# Per-row sum: explicit loop
|
||||
tile_sum0 = cutlass.Float32(0.0)
|
||||
tile_sum1 = cutlass.Float32(0.0)
|
||||
tile_sum2 = cutlass.Float32(0.0)
|
||||
tile_sum3 = cutlass.Float32(0.0)
|
||||
for k in cutlass.range(cute.size(tTMEM_LOADrS_frg, mode=[0])):
|
||||
tile_sum0 = tile_sum0 + tTMEM_LOADrS_frg[k, 0]
|
||||
tile_sum1 = tile_sum1 + tTMEM_LOADrS_frg[k, 1]
|
||||
tile_sum2 = tile_sum2 + tTMEM_LOADrS_frg[k, 2]
|
||||
tile_sum3 = tile_sum3 + tTMEM_LOADrS_frg[k, 3]
|
||||
|
||||
row_sum0 = row_sum0 + tile_sum0
|
||||
row_sum1 = row_sum1 + tile_sum1
|
||||
row_sum2 = row_sum2 + tile_sum2
|
||||
row_sum3 = row_sum3 + tile_sum3
|
||||
|
||||
# --- C9: SKIPPED (minimal test) ---
|
||||
# pv_done_bar skipped
|
||||
tCtO_base = cute.make_tensor(tmem_ptr + self.tmem_o0_offset, tCtO_fake.layout)
|
||||
acc_cons_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Consumer, self.num_acc_stage)
|
||||
c_grp = pipeline.CooperativeGroup(pipeline.Agent.Thread, 32 * len(self.epilogue_warp_id))
|
||||
c_pipe = pipeline.PipelineTmaStore.create(num_stages=self.num_c_stage, producer_group=c_grp)
|
||||
acc_cons_st = utils.gemm.sm100.epilogue_tma_store(
|
||||
self, tidx, warp_idx, tma_c, tCtO_base, sC, tCgC, epi_tile, 0,
|
||||
const_expr(lambda x: x),
|
||||
(0,0,0), acc_cons_st, acc_pipe, c_pipe)
|
||||
c_pipe.producer_tail()
|
||||
tmem.relinquish_alloc_permit()
|
||||
tmem.free(tmem_ptr)
|
||||
|
||||
|
||||
def test():
|
||||
import math
|
||||
torch.manual_seed(42)
|
||||
for n in [128, 256, 384]:
|
||||
m, hd = 128, HEAD_DIM
|
||||
q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
k = torch.randn(n, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
v = torch.randn(n, hd, dtype=torch.bfloat16, device="cuda")
|
||||
v_kernel = v.unsqueeze(-1)
|
||||
c = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
qf = q[:,:,0].float(); kf = k[:,:,0].float()
|
||||
attn = qf @ kf.T / math.sqrt(hd)
|
||||
ref = torch.softmax(attn, dim=-1) @ v.float()
|
||||
mQ = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
|
||||
mK = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
|
||||
mV = ct.from_dlpack(v_kernel).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_kernel))
|
||||
mC = ct.from_dlpack(c).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c))
|
||||
stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
|
||||
kernel = FmhaV3Softmax(s_k=n)
|
||||
print(f"n={n}: Compiling...", flush=True)
|
||||
compiled = cute.compile(kernel, mQ, mK, mV, mC, stream)
|
||||
print(f"n={n}: tmem: s0={kernel.tmem_s0_offset} p0={kernel.tmem_p0_offset} o0={kernel.tmem_o0_offset} vec={kernel.tmem_vec_offset} alloc={kernel.num_tmem_alloc_cols}", flush=True)
|
||||
print(f"n={n}: Running...", flush=True)
|
||||
compiled(mQ, mK, mV, mC, stream)
|
||||
torch.cuda.synchronize()
|
||||
out = c[:,:,0].float()
|
||||
cos = torch.nn.functional.cosine_similarity(out.flatten().unsqueeze(0), ref.flatten().unsqueeze(0)).item()
|
||||
max_err = (out - ref).abs().max().item()
|
||||
print(f"FMHA softmax n={n}: cosine {cos:.6f} max_err {max_err:.6f} {'PASS' if cos >= 0.999 else 'FAIL'}", flush=True)
|
||||
|
||||
if __name__ == "__main__":
|
||||
test()
|
||||
|
||||
@@ -1,416 +0,0 @@
|
||||
"""
|
||||
FMHA v3 Proper: 11-warp with correction warp group + epilogue warp.
|
||||
Warp layout: softmax(0-3), correction(4-7), MMA(8), TMA(9), epilogue(10)
|
||||
"""
|
||||
import math, torch, cutlass, cutlass.cute as cute, cutlass.utils as utils, cutlass.pipeline as pipeline
|
||||
from cutlass.cute.nvgpu import cpasync, tcgen05
|
||||
from cutlass import Float32, BFloat16, Int32, Boolean, const_expr
|
||||
from cutlass.utils import LayoutEnum
|
||||
from cutlass.utils.tmem_allocator import find_tmem_tensor_col_offset
|
||||
import cuda.bindings.driver as cuda, cutlass.torch as ct
|
||||
|
||||
HEAD_DIM = 64
|
||||
|
||||
class FmhaV3Proper:
|
||||
def __init__(self):
|
||||
self.qk_acc_dtype = Float32; self.pv_acc_dtype = Float32
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
self.use_2cta_instrs = False; self.cluster_shape_mn = (1, 1)
|
||||
self.cta_group = tcgen05.CtaGroup.ONE
|
||||
self.softmax_warp_ids = (0,1,2,3)
|
||||
self.correction_warp_ids = (4,5,6,7)
|
||||
self.mma_warp_id = 8; self.tma_warp_id = 9; self.epilogue_warp_id = 10
|
||||
self.threads_per_cta = 352
|
||||
self.q_stage = 1; self.kv_stage = 2; self.num_acc_stage = 1
|
||||
self.mma_softmax_stage = 1; self.softmax_corr_stage = 1
|
||||
self.mma_corr_stage = 2; self.epi_stage = 2; self.num_c_stage = 2
|
||||
self.scale_softmax_log2 = Float32(1.0 / math.sqrt(HEAD_DIM) * math.log2(math.e))
|
||||
|
||||
def _setup(self, qk_mma, pv_mma):
|
||||
qk_ik = cute.size(qk_mma.shape_mnk, mode=[2])
|
||||
self.qk_mma_tiler = (128, 128, qk_ik * 4)
|
||||
pv_ik = cute.size(pv_mma.shape_mnk, mode=[2])
|
||||
self.pv_mma_tiler = (128, HEAD_DIM, pv_ik * (128 // pv_ik))
|
||||
self.mma_tiler = self.qk_mma_tiler
|
||||
self.cluster_layout_vmnk = cute.tiled_divide(cute.make_layout((1,1,1)), (qk_mma.thr_id.shape,))
|
||||
self.cta_tile_shape_mnk = (self.qk_mma_tiler[0]//cute.size(qk_mma.thr_id.shape), HEAD_DIM, self.qk_mma_tiler[2])
|
||||
self.c_layout = LayoutEnum.ROW_MAJOR
|
||||
self.epi_tile = utils.sm100.compute_epilogue_tile_shape(self.cta_tile_shape_mnk, False, self.c_layout, self.o_dtype)
|
||||
self.num_ab_stage = 1
|
||||
self.q_smem_s = utils.sm100.make_smem_layout_a(qk_mma, self.qk_mma_tiler, self.q_dtype, self.q_stage)
|
||||
self.k_smem_s = utils.sm100.make_smem_layout_b(qk_mma, self.qk_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.v_smem_s = utils.sm100.make_smem_layout_b(pv_mma, self.pv_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.c_smem_s = utils.sm100.make_smem_layout_epi(self.o_dtype, self.c_layout, self.epi_tile, 2)
|
||||
self.p_tmem_s = utils.sm100.make_smem_layout_a(pv_mma, self.pv_mma_tiler, self.q_dtype, 1)
|
||||
qk_thr = qk_mma.get_slice(0); qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
pv_thr = pv_mma.get_slice(0); pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
self.tmem_s0_offset = 0; self.tmem_p0_offset = 32; self.tmem_vec0_offset = 0
|
||||
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
|
||||
o_after = max(self.qk_mma_tiler[1], self.tmem_p0_offset + p_cols_fp32)
|
||||
self.tmem_o0_offset = ((o_after + 31) // 32) * 32
|
||||
o_cols = find_tmem_tensor_col_offset(tOtO)
|
||||
total = self.tmem_o0_offset + o_cols
|
||||
self.num_tmem_alloc_cols = 1
|
||||
while self.num_tmem_alloc_cols < total: self.num_tmem_alloc_cols *= 2
|
||||
cta = cute.size(qk_mma.thr_id.shape)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0))
|
||||
self.q_tx_bytes = cute.size_in_bytes(self.q_dtype, q_s) * cta
|
||||
self.kv_tx_bytes = cute.size_in_bytes(self.q_dtype, k_s) * cta
|
||||
|
||||
@cute.jit
|
||||
def __call__(self, q, k, v, c, stream):
|
||||
self.q_dtype = q.element_type; self.o_dtype = c.element_type; self.c_dtype = self.o_dtype
|
||||
self.a_major = LayoutEnum.from_tensor(q).mma_major_mode()
|
||||
self.b_major = LayoutEnum.from_tensor(k).mma_major_mode()
|
||||
v_fmha = cute.make_tensor(v.iterator, cute.make_layout((HEAD_DIM, 128, 1), stride=(1, HEAD_DIM, HEAD_DIM * 128)))
|
||||
self.v_major = LayoutEnum.from_tensor(v_fmha).mma_major_mode()
|
||||
self.c_layout = LayoutEnum.from_tensor(c)
|
||||
qk_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, self.a_major, self.b_major, self.qk_acc_dtype, self.cta_group, (128,128), tcgen05.OperandSource.SMEM)
|
||||
pv_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, cute.nvgpu.OperandMajorMode.K, self.v_major, self.pv_acc_dtype, self.cta_group, (128,HEAD_DIM), tcgen05.OperandSource.TMEM)
|
||||
self._setup(qk_mma, pv_mma)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0)); v_s = cute.slice_(self.v_smem_s,(None,None,None,0))
|
||||
tma_q,mQ = cute.nvgpu.make_tiled_tma_atom_A(utils.sm100.cluster_shape_to_tma_atom_A(self.cluster_shape_mn,qk_mma.thr_id),q,q_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_k,mK = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,qk_mma.thr_id),k,k_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_v,mV = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,pv_mma.thr_id),v_fmha,v_s,self.pv_mma_tiler,pv_mma,self.cluster_layout_vmnk.shape)
|
||||
epi_s = cute.select(self.c_smem_s,mode=[0,1])
|
||||
tma_c,mC = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(),c,epi_s,self.epi_tile)
|
||||
self._kernel(qk_mma,pv_mma,tma_q,mQ,tma_k,mK,tma_v,mV,tma_c,mC,self.cluster_layout_vmnk,self.q_smem_s,self.k_smem_s,self.v_smem_s,self.p_tmem_s,self.c_smem_s,self.epi_tile).launch(grid=(1,1,1),block=[self.threads_per_cta,1,1],stream=stream)
|
||||
|
||||
@cute.kernel
|
||||
def _kernel(self, qk_mma, pv_mma, tma_q, mQ, tma_k, mK, tma_v, mV, tma_c, mC, cl_vmnk, q_smem_s, k_smem_s, v_smem_s, p_tmem_s, c_smem_s, epi_tile):
|
||||
warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx())
|
||||
tidx,_,_ = cute.arch.thread_idx()
|
||||
if warp_idx == self.tma_warp_id:
|
||||
cpasync.prefetch_descriptor(tma_q); cpasync.prefetch_descriptor(tma_k); cpasync.prefetch_descriptor(tma_v); cpasync.prefetch_descriptor(tma_c)
|
||||
|
||||
@cute.struct
|
||||
class SS:
|
||||
q_bar: cute.struct.MemRange[cutlass.Int64, self.q_stage*2]
|
||||
kv_bar: cute.struct.MemRange[cutlass.Int64, self.kv_stage*2]
|
||||
mma_si_bar: cute.struct.MemRange[cutlass.Int64, self.mma_softmax_stage*2]
|
||||
si_corr_bar: cute.struct.MemRange[cutlass.Int64, self.softmax_corr_stage*2]
|
||||
mma_corr_bar: cute.struct.MemRange[cutlass.Int64, self.mma_corr_stage*2]
|
||||
corr_epi_bar: cute.struct.MemRange[cutlass.Int64, self.epi_stage*2]
|
||||
acc_bar: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage*2]
|
||||
tmem_dealloc: cutlass.Int64; holding: cutlass.Int32
|
||||
smem = utils.SmemAllocator(); st = smem.allocate(SS)
|
||||
|
||||
qp,qc = pipeline.PipelineTmaUmma.create(barrier_storage=st.q_bar.data_ptr(),num_stages=self.q_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.q_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
|
||||
kvp,kvc = pipeline.PipelineTmaUmma.create(barrier_storage=st.kv_bar.data_ptr(),num_stages=self.kv_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.kv_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
|
||||
mma_si_prod,mma_si_cons = pipeline.PipelineUmmaAsync.create(barrier_storage=st.mma_si_bar.data_ptr(),num_stages=self.mma_softmax_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32*len(self.softmax_warp_ids))).make_participants()
|
||||
si_corr_prod,si_corr_cons = pipeline.PipelineAsync.create(barrier_storage=st.si_corr_bar.data_ptr(),num_stages=self.softmax_corr_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32*len(self.softmax_warp_ids)),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32*len(self.correction_warp_ids))).make_participants()
|
||||
mma_corr_prod,mma_corr_cons = pipeline.PipelineUmmaAsync.create(barrier_storage=st.mma_corr_bar.data_ptr(),num_stages=self.mma_corr_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32*len(self.correction_warp_ids))).make_participants()
|
||||
corr_epi_prod,corr_epi_cons = pipeline.PipelineAsync.create(barrier_storage=st.corr_epi_bar.data_ptr(),num_stages=self.epi_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32*len(self.correction_warp_ids)),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32)).make_participants()
|
||||
acc_pipe = pipeline.PipelineUmmaAsync.create(barrier_storage=st.acc_bar.data_ptr(),num_stages=self.num_acc_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,len(self.correction_warp_ids)),cta_layout_vmnk=cl_vmnk,defer_sync=True)
|
||||
tmem_bar = pipeline.NamedBarrier(barrier_id=2,num_threads=32*len((self.mma_warp_id,*self.softmax_warp_ids)))
|
||||
tmem = utils.TmemAllocator(st.holding.ptr,barrier_for_retrieve=tmem_bar,allocator_warp_id=self.softmax_warp_ids[0],is_two_cta=cute.size(qk_mma.thr_id.shape)==2,two_cta_tmem_dealloc_mbar_ptr=st.tmem_dealloc.ptr)
|
||||
pipeline.pipeline_init_arrive(cluster_shape_mn=cl_vmnk,is_relaxed=True)
|
||||
|
||||
sQ = smem.allocate_tensor(element_type=self.q_dtype,layout=q_smem_s.outer,byte_alignment=128,swizzle=q_smem_s.inner)
|
||||
sK = smem.allocate_tensor(element_type=self.q_dtype,layout=k_smem_s.outer,byte_alignment=128,swizzle=k_smem_s.inner)
|
||||
sV = smem.allocate_tensor(element_type=self.q_dtype,layout=v_smem_s.outer,byte_alignment=128,swizzle=v_smem_s.inner)
|
||||
sC = smem.allocate_tensor(element_type=self.o_dtype,layout=c_smem_s.outer,byte_alignment=128,swizzle=c_smem_s.inner)
|
||||
gQ = cute.local_tile(mQ,cute.slice_(self.qk_mma_tiler,(None,0,None)),(None,None,None))
|
||||
gK = cute.local_tile(mK,cute.slice_(self.qk_mma_tiler,(0,None,None)),(None,None,None))
|
||||
gV = cute.local_tile(mV,cute.slice_(self.pv_mma_tiler,(0,None,None)),(None,None,None))
|
||||
gC = cute.local_tile(mC,cute.slice_(self.pv_mma_tiler,(None,None,0)),(None,None,None))
|
||||
n_kv_tiles = cute.size(gK, mode=[3])
|
||||
qk_thr = qk_mma.get_slice(0); pv_thr = pv_mma.get_slice(0)
|
||||
tCgQ = qk_thr.partition_A(gQ); tCgK = qk_thr.partition_B(gK); tCgV = pv_thr.partition_B(gV); tCgC = pv_thr.partition_C(gC)
|
||||
a_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,0,None,0)).shape)
|
||||
tAsQ,tAgQ = cpasync.tma_partition(tma_q,0,a_lay,cute.group_modes(sQ,0,3),cute.group_modes(tCgQ,0,3))
|
||||
b_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,None,0,0)).shape)
|
||||
tBsK,tBgK = cpasync.tma_partition(tma_k,0,b_lay,cute.group_modes(sK,0,3),cute.group_modes(tCgK,0,3))
|
||||
tVsV,tVgV = cpasync.tma_partition(tma_v,0,b_lay,cute.group_modes(sV,0,3),cute.group_modes(tCgV,0,3))
|
||||
tAgQ = tAgQ[(None,0,None,0)]; tBgK = tBgK[(None,0,None,0)]; tVgV = tVgV[(None,0,None,0)]
|
||||
tCrQ = qk_mma.make_fragment_A(sQ); tCrK = qk_mma.make_fragment_B(sK); tCrV = pv_mma.make_fragment_B(sV)
|
||||
qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2]); tStS = qk_thr.make_fragment_C(qk_as)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2]); tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
tOtO0 = cute.make_tensor(tOtO.iterator + self.tmem_o0_offset, tOtO.layout)
|
||||
tP = cute.make_tensor(tStS.iterator, p_tmem_s.outer)
|
||||
tOrP_base = pv_thr.make_fragment_A(tP); tOrP = tOrP_base[(None,None,None,0)]
|
||||
tOrP0 = cute.make_tensor(tOrP.iterator + self.qk_acc_dtype.width // self.q_dtype.width * self.tmem_p0_offset, tOrP.layout)
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_as, self.num_acc_stage))
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# TMA
|
||||
if warp_idx == self.tma_warp_id:
|
||||
qp.reset(); qh = qp.acquire_and_advance()
|
||||
cute.copy(tma_q,tAgQ[(None,qh.count)],tAsQ[(None,qh.index)],tma_bar_ptr=qh.barrier); qp.tail()
|
||||
kvp.reset(); pk = kvp.try_acquire()
|
||||
for kt in cutlass.range(n_kv_tiles,unroll=1):
|
||||
kh = kvp.acquire_and_advance(pk); cute.copy(tma_k,tBgK[(None,kh.count)],tBsK[(None,kh.index)],tma_bar_ptr=kh.barrier); pk = cutlass.Boolean(1)
|
||||
vh = kvp.acquire_and_advance(pk); cute.copy(tma_v,tVgV[(None,vh.count)],tVsV[(None,vh.index)],tma_bar_ptr=vh.barrier); pk = cutlass.Boolean(1)
|
||||
kvp.tail()
|
||||
|
||||
# MMA
|
||||
if warp_idx == self.mma_warp_id:
|
||||
tmem.wait_for_alloc()
|
||||
qc.reset(); qh = qc.wait_and_advance(); qh.release()
|
||||
kvc.reset(); pk = kvc.try_wait()
|
||||
acc_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
|
||||
acc_pipe.producer_acquire(acc_st)
|
||||
for kt in range(n_kv_tiles):
|
||||
kh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
sh = mma_si_prod.acquire_and_advance()
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
for kb in cutlass.range(cute.size(tCrQ,mode=[2]), unroll_full=True):
|
||||
cute.gemm(qk_mma, tStS0, tCrQ[(None,None,kb,0)], tCrK[(None,None,kb,kh.index)], tStS0)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store(); sh.commit(); kh.release()
|
||||
if kt > 0:
|
||||
o_handle = mma_corr_cons.wait_and_advance(); o_handle.release()
|
||||
sh2 = mma_si_prod.acquire_and_advance()
|
||||
vh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, kt != 0)
|
||||
for kb in cutlass.range(cute.size(tOrP0,mode=[2]), unroll_full=True):
|
||||
cute.gemm(pv_mma, tOtO0, tOrP0[(None,None,kb)], tCrV[(None,None,kb,vh.index)], tOtO0)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store(); vh.release()
|
||||
o_prod_h = mma_corr_prod.acquire_and_advance(); o_prod_h.commit()
|
||||
o_handle = mma_corr_cons.wait_and_advance(); o_handle.release()
|
||||
acc_pipe.producer_commit(acc_st); acc_st.advance(); acc_pipe.producer_tail(acc_st)
|
||||
|
||||
# SOFTMAX (warps 0-3)
|
||||
if warp_idx < len(self.softmax_warp_ids):
|
||||
tmem.allocate(self.num_tmem_alloc_cols); tmem.wait_for_alloc()
|
||||
tmem_ptr = tmem.retrieve_ptr(self.qk_acc_dtype)
|
||||
sfw_idx = tidx % (32 * len(self.softmax_warp_ids))
|
||||
scale = self.scale_softmax_log2
|
||||
tmem_load_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
tiled_tmem_load = tcgen05.make_tmem_copy(tmem_load_atom, tStS0)
|
||||
thr_load = tiled_tmem_load.get_slice(sfw_idx)
|
||||
tTMEM_LOADtS = thr_load.partition_S(tStS0)
|
||||
cS = cute.make_identity_tensor((self.qk_mma_tiler[0], self.qk_mma_tiler[1]))
|
||||
tScS = qk_thr.partition_C(cS); tTMEM_LOADcS = thr_load.partition_D(tScS)
|
||||
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
|
||||
tStP_layout = cute.composition(tStS.layout, cute.make_layout((self.pv_mma_tiler[0], p_cols_fp32)))
|
||||
tStP0 = cute.make_tensor(tStS.iterator + self.tmem_p0_offset, tStP_layout)
|
||||
tmem_store_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
tiled_tmem_store = tcgen05.make_tmem_copy(tmem_store_atom, tStP0)
|
||||
thr_store = tiled_tmem_store.get_slice(sfw_idx)
|
||||
tTMEM_STOREtP = thr_store.partition_D(tStP0)
|
||||
tScP_layout = cute.composition(tScS.layout, cute.make_layout((self.pv_mma_tiler[0], p_cols_fp32)))
|
||||
tScP = cute.make_tensor(tScS.iterator, tScP_layout); tTMEM_STOREcP = thr_store.partition_S(tScP)
|
||||
tStS_vec_layout = cute.composition(tStS.layout, cute.make_layout((128, 2)))
|
||||
tStS_vec = cute.make_tensor(tStS.iterator + self.tmem_vec0_offset, tStS_vec_layout)
|
||||
tScS_vec_layout = cute.composition(tScS.layout, cute.make_layout((128, 2)))
|
||||
tScS_vec = cute.make_tensor(tScS.iterator, tScS_vec_layout)
|
||||
tmem_store_vec_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(2)), self.qk_acc_dtype)
|
||||
tiled_tmem_store_vec = tcgen05.make_tmem_copy(tmem_store_vec_atom, tStS_vec)
|
||||
thr_tmem_store_vec = tiled_tmem_store_vec.get_slice(sfw_idx)
|
||||
tTMEM_STORE_VECtS = thr_tmem_store_vec.partition_D(tStS_vec)
|
||||
tTMEM_STORE_VECcS = thr_tmem_store_vec.partition_S(tScS_vec)
|
||||
row_max = -cutlass.Float32.inf; row_sum = cutlass.Float32(0.0)
|
||||
for kt in range(n_kv_tiles):
|
||||
si_handle = mma_si_cons.wait_and_advance()
|
||||
tTMEM_LOADrS = cute.make_rmem_tensor(tTMEM_LOADcS.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load, tTMEM_LOADtS, tTMEM_LOADrS)
|
||||
old_row_max = row_max
|
||||
row_max = tTMEM_LOADrS.load().reduce(cute.ReductionOp.MAX, row_max, 0)
|
||||
row_max_safe = row_max
|
||||
if row_max == -cutlass.Float32.inf: row_max_safe = cutlass.Float32(0.0)
|
||||
vec_handle = si_corr_prod.acquire_and_advance()
|
||||
tTMEM_STORE_VECrS = cute.make_rmem_tensor(tTMEM_STORE_VECcS.shape, self.qk_acc_dtype)
|
||||
tTMEM_STORE_VECrS[0] = old_row_max; tTMEM_STORE_VECrS[1] = row_max_safe
|
||||
cute.copy(tiled_tmem_store_vec, tTMEM_STORE_VECrS, tTMEM_STORE_VECtS)
|
||||
cute.arch.fence_view_async_tmem_store(); vec_handle.commit()
|
||||
minus_row_max_scale = (cutlass.Float32(0.0) - row_max_safe) * scale
|
||||
rP_words = cute.make_rmem_tensor(tTMEM_STOREcP.shape, self.qk_acc_dtype)
|
||||
rP_bf16 = cute.make_tensor(cute.recast_ptr(rP_words.iterator, dtype=self.q_dtype), tTMEM_LOADrS.layout)
|
||||
frg_cnt = 4; frg_tile = cute.size(tTMEM_LOADrS) // frg_cnt
|
||||
tTMEM_LOADrS_frg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(frg_tile))
|
||||
rP_bf16_frg = cute.logical_divide(rP_bf16, cute.make_layout(frg_tile))
|
||||
for j in range(frg_cnt):
|
||||
for k in cutlass.range(cute.size(tTMEM_LOADrS_frg, mode=[0]), vectorize=True):
|
||||
tTMEM_LOADrS_frg[k, j] = tTMEM_LOADrS_frg[k, j] * scale + minus_row_max_scale
|
||||
tTMEM_LOADrS_frg[k, j] = cute.math.exp2(tTMEM_LOADrS_frg[k, j], fastmath=True)
|
||||
s_vec = tTMEM_LOADrS_frg[None, j].load(); rP_bf16_frg[None, j].store(s_vec.to(self.q_dtype))
|
||||
cute.copy(tiled_tmem_store, rP_words, tTMEM_STOREtP)
|
||||
cute.arch.fence_view_async_tmem_store(); si_handle.release()
|
||||
acc_scale = cute.math.exp2(scale * (old_row_max - row_max_safe), fastmath=True)
|
||||
row_sum = row_sum * acc_scale
|
||||
local_row_sum_0 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_1 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_2 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_3 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
reduction_unroll = 4; rfrg_tile = cute.size(tTMEM_LOADrS) // reduction_unroll
|
||||
tTMEM_LOADrS_rfrg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(rfrg_tile))
|
||||
for j in cutlass.range_constexpr(0, cute.size(tTMEM_LOADrS_rfrg, mode=[0]), 2):
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(local_row_sum_0, (tTMEM_LOADrS_rfrg[j, 0], tTMEM_LOADrS_rfrg[j+1, 0]))
|
||||
local_row_sum_1 = cute.arch.add_packed_f32x2(local_row_sum_1, (tTMEM_LOADrS_rfrg[j, 1], tTMEM_LOADrS_rfrg[j+1, 1]))
|
||||
local_row_sum_2 = cute.arch.add_packed_f32x2(local_row_sum_2, (tTMEM_LOADrS_rfrg[j, 2], tTMEM_LOADrS_rfrg[j+1, 2]))
|
||||
local_row_sum_3 = cute.arch.add_packed_f32x2(local_row_sum_3, (tTMEM_LOADrS_rfrg[j, 3], tTMEM_LOADrS_rfrg[j+1, 3]))
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(local_row_sum_0, local_row_sum_1)
|
||||
local_row_sum_2 = cute.arch.add_packed_f32x2(local_row_sum_2, local_row_sum_3)
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(local_row_sum_0, local_row_sum_2)
|
||||
row_sum = row_sum + local_row_sum_0[0] + local_row_sum_0[1]
|
||||
# Final vector: (row_sum, row_max)
|
||||
vec_handle = si_corr_prod.acquire_and_advance()
|
||||
tTMEM_STORE_VECrS = cute.make_rmem_tensor(tTMEM_STORE_VECcS.shape, self.qk_acc_dtype)
|
||||
tTMEM_STORE_VECrS[0] = row_sum; tTMEM_STORE_VECrS[1] = row_max
|
||||
cute.copy(tiled_tmem_store_vec, tTMEM_STORE_VECrS, tTMEM_STORE_VECtS)
|
||||
cute.arch.fence_view_async_tmem_store(); vec_handle.commit()
|
||||
si_handle = mma_si_cons.wait_and_advance(); si_corr_prod.acquire(); si_handle.release()
|
||||
tmem.relinquish_alloc_permit()
|
||||
|
||||
# CORRECTION (warps 4-7)
|
||||
if warp_idx >= len(self.softmax_warp_ids) and warp_idx < len(self.softmax_warp_ids) + len(self.correction_warp_ids):
|
||||
corr_idx = tidx % (32 * len(self.correction_warp_ids))
|
||||
scale = self.scale_softmax_log2
|
||||
# Create tScS from common-scope qk_thr (same as softmax section)
|
||||
cS_corr = cute.make_identity_tensor((self.qk_mma_tiler[0], self.qk_mma_tiler[1]))
|
||||
tScS = qk_thr.partition_C(cS_corr)
|
||||
tStS_vec_layout = cute.composition(tStS.layout, cute.make_layout((128, 2)))
|
||||
tStS_vec = cute.make_tensor(tStS.iterator + self.tmem_vec0_offset, tStS_vec_layout)
|
||||
tScS_vec_layout = cute.composition(tScS.layout, cute.make_layout((128, 2)))
|
||||
tScS_vec = cute.make_tensor(tScS.iterator, tScS_vec_layout)
|
||||
tmem_load_vec_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(2)), self.qk_acc_dtype)
|
||||
tiled_tmem_load_vec = tcgen05.make_tmem_copy(tmem_load_vec_atom, tStS_vec)
|
||||
thr_tmem_load_vec = tiled_tmem_load_vec.get_slice(corr_idx)
|
||||
tTMEM_LOAD_VECtS = thr_tmem_load_vec.partition_S(tStS_vec)
|
||||
tTMEM_LOAD_VECcS = thr_tmem_load_vec.partition_D(tScS_vec)
|
||||
corr_tile_size = 16
|
||||
cO = cute.make_identity_tensor((self.pv_mma_tiler[0], self.pv_mma_tiler[1]))
|
||||
tOcO = pv_thr.partition_C(cO)
|
||||
tOtO_i_layout = cute.composition(tOtO0.layout, cute.make_layout((128, corr_tile_size)))
|
||||
tOcO_i_layout = cute.composition(tOcO.layout, cute.make_layout((128, corr_tile_size)))
|
||||
tOtO_i = cute.make_tensor(tOtO0.iterator, tOtO_i_layout)
|
||||
tOcO_i = cute.make_tensor(tOcO.iterator, tOcO_i_layout)
|
||||
o_tmem_load_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.pv_acc_dtype)
|
||||
o_tmem_store_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.pv_acc_dtype)
|
||||
o_tiled_tmem_load = tcgen05.make_tmem_copy(o_tmem_load_atom, tOtO_i)
|
||||
o_tiled_tmem_store = tcgen05.make_tmem_copy(o_tmem_store_atom, tOtO_i)
|
||||
o_thr_load = o_tiled_tmem_load.get_slice(corr_idx)
|
||||
o_thr_store = o_tiled_tmem_store.get_slice(corr_idx)
|
||||
tTMEM_LOADtO = o_thr_load.partition_S(tOtO_i)
|
||||
tTMEM_LOADcO = o_thr_load.partition_D(tOcO_i)
|
||||
tTMEM_STOREtO = o_thr_store.partition_D(tOtO_i)
|
||||
o_col_tiles = self.pv_mma_tiler[1] // corr_tile_size
|
||||
|
||||
# Ignore first vec (no rescale for first PV)
|
||||
vec_handle = si_corr_cons.wait_and_advance()
|
||||
vec_handle.release()
|
||||
|
||||
for kt in range(n_kv_tiles):
|
||||
if kt > 0:
|
||||
# Wait for vector (old_max, new_max) from softmax
|
||||
vec_handle = si_corr_cons.wait_and_advance()
|
||||
tTMEM_LOAD_VECrS = cute.make_rmem_tensor(tTMEM_LOAD_VECcS.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load_vec, tTMEM_LOAD_VECtS, tTMEM_LOAD_VECrS)
|
||||
corr_scale_ = scale * (tTMEM_LOAD_VECrS[0] - tTMEM_LOAD_VECrS[1])
|
||||
corr_scale = cute.math.exp2(corr_scale_, fastmath=True)
|
||||
|
||||
# Wait for O from MMA
|
||||
o_handle = mma_corr_cons.wait_and_advance()
|
||||
|
||||
# correction_rescale: O *= corr_scale in TMEM
|
||||
tTMrO = cute.make_rmem_tensor((tTMEM_LOADcO.shape, o_col_tiles), self.pv_acc_dtype)
|
||||
for i in range(o_col_tiles):
|
||||
tTMrO_i_ = tTMrO[None, i]
|
||||
tTMrO_i_layout = cute.composition(tTMrO_i_.layout, cute.make_layout(tTMrO.shape[0]))
|
||||
tTMrO_i = cute.make_tensor(tTMrO_i_.iterator, tTMrO_i_layout)
|
||||
tTMEM_LOADtO_i = cute.make_tensor(tTMEM_LOADtO.iterator + i * corr_tile_size, tTMEM_LOADtO.layout)
|
||||
tTMEM_STOREtO_i = cute.make_tensor(tTMEM_STOREtO.iterator + i * corr_tile_size, tTMEM_STOREtO.layout)
|
||||
cute.copy(o_tiled_tmem_load, tTMEM_LOADtO_i, tTMrO_i)
|
||||
for j in cutlass.range(cute.size(tTMrO_i), vectorize=True):
|
||||
tTMrO_i[j] = tTMrO_i[j] * corr_scale
|
||||
cute.copy(o_tiled_tmem_store, tTMrO_i, tTMEM_STOREtO_i)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
vec_handle.release()
|
||||
o_handle.release()
|
||||
|
||||
# --- correction_epilog: final normalize O /= row_sum ---
|
||||
# Wait for final vector (row_sum, row_max) from softmax
|
||||
vec_handle = si_corr_cons.wait_and_advance()
|
||||
tTMEM_LOAD_VECrS = cute.make_rmem_tensor(tTMEM_LOAD_VECcS.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load_vec, tTMEM_LOAD_VECtS, tTMEM_LOAD_VECrS)
|
||||
cute.arch.fence_view_async_tmem_load()
|
||||
vec_handle.release()
|
||||
inv_row_sum = cutlass.Float32(1.0) / tTMEM_LOAD_VECrS[0]
|
||||
|
||||
# Wait for final O from MMA
|
||||
o_handle = mma_corr_cons.wait_and_advance()
|
||||
epi_handle = corr_epi_prod.acquire_and_advance()
|
||||
|
||||
# Final normalize O in TMEM
|
||||
tTMrO = cute.make_rmem_tensor((tTMEM_LOADcO.shape, o_col_tiles), self.pv_acc_dtype)
|
||||
for i in range(o_col_tiles):
|
||||
tTMrO_i_ = tTMrO[None, i]
|
||||
tTMrO_i_layout = cute.composition(tTMrO_i_.layout, cute.make_layout(tTMrO.shape[0]))
|
||||
tTMrO_i = cute.make_tensor(tTMrO_i_.iterator, tTMrO_i_layout)
|
||||
tTMEM_LOADtO_i = cute.make_tensor(tTMEM_LOADtO.iterator + i * corr_tile_size, tTMEM_LOADtO.layout)
|
||||
tTMEM_STOREtO_i = cute.make_tensor(tTMEM_STOREtO.iterator + i * corr_tile_size, tTMEM_STOREtO.layout)
|
||||
cute.copy(o_tiled_tmem_load, tTMEM_LOADtO_i, tTMrO_i)
|
||||
for j in cutlass.range(cute.size(tTMrO_i), vectorize=True):
|
||||
tTMrO_i[j] = tTMrO_i[j] * inv_row_sum
|
||||
cute.copy(o_tiled_tmem_store, tTMrO_i, tTMEM_STOREtO_i)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
o_handle.release()
|
||||
epi_handle.commit()
|
||||
|
||||
# --- EPILOGUE WARP (warp 10) - TMA store O ---
|
||||
# After correction normalizes O in TMEM, the epilogue reads O from TMEM,
|
||||
# writes to SMEM, then TMA stores from SMEM to GMEM.
|
||||
# For now, the softmax warps (which have tmem_ptr) handle the TMA store
|
||||
# after correction signals completion. This matches our working 6-warp code's
|
||||
# epilogue_tma_store pattern.
|
||||
# The epilogue warp (warp 10) just waits for the signal and does TMA store.
|
||||
# Since it doesn't have tmem_ptr, we need a different approach.
|
||||
# Simplest: let the softmax warps also do the TMA store after correction
|
||||
# signals O is ready. But softmax warps already exited...
|
||||
#
|
||||
# Alternative: the epilogue warp uses acc_pipe + epilogue_tma_store
|
||||
# which reads from TMEM directly.
|
||||
# For initial test: skip epilogue TMA store, just verify correction works.
|
||||
# Then add TMA store via a separate mechanism.
|
||||
#
|
||||
# Actually, looking at our working 6-warp code, the epilogue_tma_store
|
||||
# reads from tCtO_base which is a TMEM tensor at tmem_ptr + offset.
|
||||
# The epilogue warp doesn't have tmem_ptr. BUT it can create the same
|
||||
# tensor if it knows the address. The MMA warp has it from alloc_tmem.
|
||||
#
|
||||
# For the initial version, let the softmax warps do TMA store
|
||||
# (they have tmem_ptr) after waiting for correction to finish.
|
||||
# This is a temporary simplification.
|
||||
|
||||
if warp_idx == self.epilogue_warp_id:
|
||||
epi_handle = corr_epi_cons.wait_and_advance()
|
||||
epi_handle.release()
|
||||
|
||||
|
||||
def test():
|
||||
import math
|
||||
torch.manual_seed(42)
|
||||
for n in [128, 256, 384]:
|
||||
m, hd = 128, HEAD_DIM
|
||||
q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
k = torch.randn(n, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
v = torch.randn(n, hd, dtype=torch.bfloat16, device="cuda")
|
||||
v_kernel = v.unsqueeze(-1)
|
||||
c = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
qf = q[:,:,0].float(); kf = k[:,:,0].float()
|
||||
attn = qf @ kf.T / math.sqrt(hd)
|
||||
ref = torch.softmax(attn, dim=-1) @ v.float()
|
||||
mQ = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
|
||||
mK = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
|
||||
mV = ct.from_dlpack(v_kernel).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_kernel))
|
||||
mC = ct.from_dlpack(c).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c))
|
||||
stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
|
||||
kernel = FmhaV3Proper()
|
||||
print(f"n={n}: Compiling...", flush=True)
|
||||
compiled = cute.compile(kernel, mQ, mK, mV, mC, stream)
|
||||
print(f"n={n}: tmem: s0={kernel.tmem_s0_offset} p0={kernel.tmem_p0_offset} o0={kernel.tmem_o0_offset} vec={kernel.tmem_vec0_offset} alloc={kernel.num_tmem_alloc_cols}", flush=True)
|
||||
print(f"n={n}: Running...", flush=True)
|
||||
compiled(mQ, mK, mV, mC, stream)
|
||||
torch.cuda.synchronize()
|
||||
out = c[:,:,0].float()
|
||||
cos = torch.nn.functional.cosine_similarity(out.flatten().unsqueeze(0), ref.flatten().unsqueeze(0)).item()
|
||||
max_err = (out - ref).abs().max().item()
|
||||
print(f"FMHA proper n={n}: cosine {cos:.6f} max_err {max_err:.6f} {'PASS' if cos >= 0.999 else 'FAIL'}", flush=True)
|
||||
|
||||
if __name__ == "__main__":
|
||||
test()
|
||||
@@ -1,484 +0,0 @@
|
||||
"""
|
||||
FMHA v3 + Stage C: QK -> online softmax -> PV with KV-tile interleaving.
|
||||
Stage C: row_max, exp2, O rescale, row_sum, final normalization.
|
||||
FMHA pattern P store preserved from Stage B.
|
||||
"""
|
||||
import math
|
||||
import torch, cutlass, cutlass.cute as cute, cutlass.utils as utils, cutlass.pipeline as pipeline
|
||||
from cutlass.cute.nvgpu import cpasync, tcgen05
|
||||
from cutlass import Float32, BFloat16, Int32, Boolean, const_expr
|
||||
from cutlass.utils import LayoutEnum
|
||||
from cutlass.utils.tmem_allocator import find_tmem_tensor_col_offset
|
||||
import cuda.bindings.driver as cuda
|
||||
import cutlass.torch as ct
|
||||
|
||||
HEAD_DIM = 64
|
||||
|
||||
class FmhaV3Softmax:
|
||||
def __init__(self):
|
||||
self.acc_dtype = Float32; self.qk_acc_dtype = Float32
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
self.use_2cta_instrs = False; self.epilog_sync_bar_id = 1
|
||||
self.cluster_shape_mn = (1, 1); self.cta_group = tcgen05.CtaGroup.ONE
|
||||
self.epilogue_warp_id = (0,1,2,3); self.mma_warp_id = 4; self.tma_warp_id = 5
|
||||
self.threads_per_cta = 192; self.num_c_stage = 2
|
||||
self.kv_stage = 2; self.q_stage = 1; self.num_c_stage = 2
|
||||
|
||||
def _setup(self, qk_mma, pv_mma):
|
||||
qk_ik = cute.size(qk_mma.shape_mnk, mode=[2])
|
||||
self.qk_mma_tiler = (128, 128, qk_ik * 4)
|
||||
pv_ik = cute.size(pv_mma.shape_mnk, mode=[2])
|
||||
self.pv_mma_tiler = (128, HEAD_DIM, pv_ik * (128 // pv_ik))
|
||||
self.mma_tiler = self.qk_mma_tiler
|
||||
self.cluster_layout_vmnk = cute.tiled_divide(cute.make_layout((1,1,1)), (qk_mma.thr_id.shape,))
|
||||
self.cta_tile_shape_mnk = (self.qk_mma_tiler[0]//cute.size(qk_mma.thr_id.shape), HEAD_DIM, self.qk_mma_tiler[2])
|
||||
self.c_layout = LayoutEnum.ROW_MAJOR
|
||||
self.epi_tile = utils.sm100.compute_epilogue_tile_shape(self.cta_tile_shape_mnk, False, self.c_layout, self.o_dtype)
|
||||
self.num_ab_stage = 1; self.num_acc_stage = 1
|
||||
self.q_smem_s = utils.sm100.make_smem_layout_a(qk_mma, self.qk_mma_tiler, self.q_dtype, self.q_stage)
|
||||
self.k_smem_s = utils.sm100.make_smem_layout_b(qk_mma, self.qk_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.v_smem_s = utils.sm100.make_smem_layout_b(pv_mma, self.pv_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.c_smem_s = utils.sm100.make_smem_layout_epi(self.o_dtype, self.c_layout, self.epi_tile, 2)
|
||||
self.p_tmem_s = utils.sm100.make_smem_layout_a(pv_mma, self.pv_mma_tiler, self.q_dtype, 1)
|
||||
qk_thr = qk_mma.get_slice(0); qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
pv_thr = pv_mma.get_slice(0); pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
self.tmem_s0_offset = 0; self.tmem_p0_offset = 32
|
||||
# P occupies [tmem_p0_offset, tmem_p0_offset + p_cols_fp32)
|
||||
# S occupies [0, qk_mma_tiler[1]) = [0, 128)
|
||||
# O must NOT overlap P. Place O after max(S end, P end), aligned to 32.
|
||||
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
|
||||
p_end = self.tmem_p0_offset + p_cols_fp32 # 32 + 64 = 96
|
||||
s_cols = self.qk_mma_tiler[1] # 128
|
||||
o_after = max(s_cols, p_end) # 128
|
||||
self.tmem_o0_offset = ((o_after + 31) // 32) * 32
|
||||
self.tmem_vec_offset = 0 # Reuse S region for per-row inv_row_sum vector # align to 32 = 128
|
||||
self.tmem_vec_offset = 0 # Reuse S region (free after softmax loop)
|
||||
o_cols = find_tmem_tensor_col_offset(tOtO) # footprint of O
|
||||
total = self.tmem_o0_offset + o_cols
|
||||
# Must be multiple of 32 AND power of 2
|
||||
self.num_tmem_alloc_cols = 1
|
||||
while self.num_tmem_alloc_cols < total:
|
||||
self.num_tmem_alloc_cols *= 2
|
||||
cta = cute.size(qk_mma.thr_id.shape)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0))
|
||||
self.q_tx_bytes = cute.size_in_bytes(self.q_dtype, q_s) * cta
|
||||
self.kv_tx_bytes = cute.size_in_bytes(self.q_dtype, k_s) * cta
|
||||
self.scale_softmax_log2 = Float32(1.0 / math.sqrt(HEAD_DIM) * math.log2(math.e))
|
||||
|
||||
@cute.jit
|
||||
def __call__(self, q, k, v, c, stream):
|
||||
self.q_dtype = q.element_type; self.o_dtype = c.element_type; self.c_dtype = self.o_dtype
|
||||
self.a_major = LayoutEnum.from_tensor(q).mma_major_mode()
|
||||
self.b_major = LayoutEnum.from_tensor(k).mma_major_mode()
|
||||
# # s_k hardcoded # BROKEN in @cute.jit
|
||||
# FMHA-style V: reconstruct as (HEAD_DIM, s_k, 1) MN-major
|
||||
v_fmha = cute.make_tensor(
|
||||
v.iterator,
|
||||
cute.make_layout(
|
||||
(HEAD_DIM, 128, 1),
|
||||
stride=(1, HEAD_DIM, HEAD_DIM * 128),
|
||||
),
|
||||
)
|
||||
self.v_major = LayoutEnum.from_tensor(v_fmha).mma_major_mode()
|
||||
self.c_layout = LayoutEnum.from_tensor(c)
|
||||
qk_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, self.a_major, self.b_major, self.qk_acc_dtype, self.cta_group, (128,128), tcgen05.OperandSource.SMEM)
|
||||
pv_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, cute.nvgpu.OperandMajorMode.K, self.v_major, self.qk_acc_dtype, self.cta_group, (128,HEAD_DIM), tcgen05.OperandSource.TMEM)
|
||||
self._setup(qk_mma, pv_mma)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0)); v_s = cute.slice_(self.v_smem_s,(None,None,None,0))
|
||||
tma_q,mQ = cute.nvgpu.make_tiled_tma_atom_A(utils.sm100.cluster_shape_to_tma_atom_A(self.cluster_shape_mn,qk_mma.thr_id),q,q_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_k,mK = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,qk_mma.thr_id),k,k_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_v,mV = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,pv_mma.thr_id),v_fmha,v_s,self.pv_mma_tiler,pv_mma,self.cluster_layout_vmnk.shape)
|
||||
epi_s = cute.select(self.c_smem_s,mode=[0,1])
|
||||
tma_c,mC = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(),c,epi_s,self.epi_tile)
|
||||
self._kernel(qk_mma,pv_mma,tma_q,mQ,tma_k,mK,tma_v,mV,tma_c,mC,self.cluster_layout_vmnk,self.q_smem_s,self.k_smem_s,self.v_smem_s,self.p_tmem_s,self.c_smem_s,self.epi_tile).launch(grid=(1,1,1),block=[self.threads_per_cta,1,1],stream=stream)
|
||||
|
||||
@cute.kernel
|
||||
def _kernel(self, qk_mma, pv_mma, tma_q, mQ, tma_k, mK, tma_v, mV, tma_c, mC, cl_vmnk, q_smem_s, k_smem_s, v_smem_s, p_tmem_s, c_smem_s, epi_tile):
|
||||
warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx())
|
||||
tidx,_,_ = cute.arch.thread_idx()
|
||||
if warp_idx == self.tma_warp_id:
|
||||
cpasync.prefetch_descriptor(tma_q); cpasync.prefetch_descriptor(tma_k); cpasync.prefetch_descriptor(tma_v); cpasync.prefetch_descriptor(tma_c)
|
||||
|
||||
@cute.struct
|
||||
class SS:
|
||||
q_bar: cute.struct.MemRange[cutlass.Int64, self.q_stage*2]
|
||||
kv_bar: cute.struct.MemRange[cutlass.Int64, self.kv_stage*2]
|
||||
s_bar: cute.struct.MemRange[cutlass.Int64, 2]
|
||||
acc_bar: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage*2]
|
||||
tmem_dealloc: cutlass.Int64; holding: cutlass.Int32
|
||||
smem = utils.SmemAllocator(); st = smem.allocate(SS)
|
||||
|
||||
qp,qc = pipeline.PipelineTmaUmma.create(barrier_storage=st.q_bar.data_ptr(),num_stages=self.q_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.q_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
|
||||
kvp,kvc = pipeline.PipelineTmaUmma.create(barrier_storage=st.kv_bar.data_ptr(),num_stages=self.kv_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.kv_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
|
||||
s_prod,s_cons = pipeline.PipelineUmmaAsync.create(barrier_storage=st.s_bar.data_ptr(),num_stages=1,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32*len(self.epilogue_warp_id))).make_participants()
|
||||
softmax_done_bar = pipeline.NamedBarrier(barrier_id=3, num_threads=32 + 32*len(self.epilogue_warp_id))
|
||||
pv_done_bar = pipeline.NamedBarrier(barrier_id=4, num_threads=32 + 32*len(self.epilogue_warp_id))
|
||||
acc_pipe = pipeline.PipelineUmmaAsync.create(barrier_storage=st.acc_bar.data_ptr(),num_stages=self.num_acc_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,len(self.epilogue_warp_id)),cta_layout_vmnk=cl_vmnk,defer_sync=True)
|
||||
tmem_bar = pipeline.NamedBarrier(barrier_id=2,num_threads=32*len((self.mma_warp_id,*self.epilogue_warp_id)))
|
||||
tmem = utils.TmemAllocator(st.holding.ptr,barrier_for_retrieve=tmem_bar,allocator_warp_id=self.epilogue_warp_id[0],is_two_cta=cute.size(qk_mma.thr_id.shape)==2,two_cta_tmem_dealloc_mbar_ptr=st.tmem_dealloc.ptr)
|
||||
pipeline.pipeline_init_arrive(cluster_shape_mn=cl_vmnk,is_relaxed=True)
|
||||
|
||||
sQ = smem.allocate_tensor(element_type=self.q_dtype,layout=q_smem_s.outer,byte_alignment=128,swizzle=q_smem_s.inner)
|
||||
sK = smem.allocate_tensor(element_type=self.q_dtype,layout=k_smem_s.outer,byte_alignment=128,swizzle=k_smem_s.inner)
|
||||
sV = smem.allocate_tensor(element_type=self.q_dtype,layout=v_smem_s.outer,byte_alignment=128,swizzle=v_smem_s.inner)
|
||||
sC = smem.allocate_tensor(element_type=self.o_dtype,layout=c_smem_s.outer,byte_alignment=128,swizzle=c_smem_s.inner)
|
||||
|
||||
gQ = cute.local_tile(mQ,cute.slice_(self.qk_mma_tiler,(None,0,None)),(None,None,None))
|
||||
gK = cute.local_tile(mK,cute.slice_(self.qk_mma_tiler,(0,None,None)),(None,None,None))
|
||||
gV = cute.local_tile(mV,cute.slice_(self.pv_mma_tiler,(0,None,None)),(None,None,None))
|
||||
gC = cute.local_tile(mC,cute.slice_(self.pv_mma_tiler,(None,None,0)),(None,None,None))
|
||||
n_kv_tiles = cute.size(gK, mode=[3])
|
||||
|
||||
qk_thr = qk_mma.get_slice(0); pv_thr = pv_mma.get_slice(0)
|
||||
tCgQ = qk_thr.partition_A(gQ); tCgK = qk_thr.partition_B(gK)
|
||||
tCgV = pv_thr.partition_B(gV); tCgC = pv_thr.partition_C(gC)
|
||||
a_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,0,None,0)).shape)
|
||||
tAsQ,tAgQ = cpasync.tma_partition(tma_q,0,a_lay,cute.group_modes(sQ,0,3),cute.group_modes(tCgQ,0,3))
|
||||
b_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,None,0,0)).shape)
|
||||
tBsK,tBgK = cpasync.tma_partition(tma_k,0,b_lay,cute.group_modes(sK,0,3),cute.group_modes(tCgK,0,3))
|
||||
tVsV,tVgV = cpasync.tma_partition(tma_v,0,b_lay,cute.group_modes(sV,0,3),cute.group_modes(tCgV,0,3))
|
||||
tAgQ = tAgQ[(None,0,None,0)]; tBgK = tBgK[(None,0,None,0)]; tVgV = tVgV[(None,0,None,0)]
|
||||
|
||||
tCrQ = qk_mma.make_fragment_A(sQ); tCrK = qk_mma.make_fragment_B(sK)
|
||||
tCrV = pv_mma.make_fragment_B(sV)
|
||||
|
||||
qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
tOtO0 = cute.make_tensor(tOtO.iterator + self.tmem_o0_offset, tOtO.layout)
|
||||
|
||||
# --- PV read view (for MMA only, NOT for softmax store) ---
|
||||
tP = cute.make_tensor(tStS.iterator, p_tmem_s.outer)
|
||||
tOrP_base = pv_thr.make_fragment_A(tP)
|
||||
tOrP = tOrP_base[(None,None,None,0)]
|
||||
tOrP0 = cute.make_tensor(
|
||||
tOrP.iterator + self.qk_acc_dtype.width // self.q_dtype.width * self.tmem_p0_offset,
|
||||
tOrP.layout)
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_as, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_as, self.num_acc_stage))
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# TMA LOAD
|
||||
if warp_idx == self.tma_warp_id:
|
||||
qp.reset(); qh = qp.acquire_and_advance()
|
||||
cute.copy(tma_q,tAgQ[(None,qh.count)],tAsQ[(None,qh.index)],tma_bar_ptr=qh.barrier)
|
||||
qp.tail()
|
||||
kvp.reset(); pk = kvp.try_acquire()
|
||||
for kt in cutlass.range(n_kv_tiles,unroll=1):
|
||||
kh = kvp.acquire_and_advance(pk)
|
||||
cute.copy(tma_k,tBgK[(None,kh.count)],tBsK[(None,kh.index)],tma_bar_ptr=kh.barrier)
|
||||
pk = cutlass.Boolean(1)
|
||||
vh = kvp.acquire_and_advance(pk)
|
||||
cute.copy(tma_v,tVgV[(None,vh.count)],tVsV[(None,vh.index)],tma_bar_ptr=vh.barrier)
|
||||
pk = cutlass.Boolean(1)
|
||||
kvp.tail()
|
||||
|
||||
# MMA
|
||||
if warp_idx == self.mma_warp_id:
|
||||
tmem.wait_for_alloc()
|
||||
qc.reset(); qh = qc.wait_and_advance(); qh.release()
|
||||
kvc.reset(); pk = kvc.try_wait()
|
||||
acc_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
|
||||
acc_pipe.producer_acquire(acc_st)
|
||||
for kt in range(n_kv_tiles):
|
||||
kh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
sh = s_prod.acquire_and_advance()
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
for kb in cutlass.range(cute.size(tCrQ,mode=[2]), unroll_full=True):
|
||||
cute.gemm(qk_mma, tStS0, tCrQ[(None,None,kb,0)], tCrK[(None,None,kb,kh.index)], tStS0)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
sh.commit(); kh.release()
|
||||
softmax_done_bar.arrive_and_wait()
|
||||
vh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, kt != 0)
|
||||
for kb in cutlass.range(cute.size(tOrP0,mode=[2]), unroll_full=True):
|
||||
cute.gemm(pv_mma, tOtO0, tOrP0[(None,None,kb)], tCrV[(None,None,kb,vh.index)], tOtO0)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
vh.release()
|
||||
pv_done_bar.arrive()
|
||||
acc_pipe.producer_commit(acc_st); acc_st.advance()
|
||||
acc_pipe.producer_tail(acc_st)
|
||||
|
||||
# ===================== EPILOGUE WARPS (STAGE C: ONLINE SOFTMAX) =====================
|
||||
if warp_idx < self.mma_warp_id:
|
||||
tmem.allocate(self.num_tmem_alloc_cols)
|
||||
tmem.wait_for_alloc()
|
||||
tmem_ptr = tmem.retrieve_ptr(self.qk_acc_dtype)
|
||||
sfw_idx = tidx % (32 * len(self.epilogue_warp_id))
|
||||
|
||||
# --- S load (QK C-fragment) ---
|
||||
tmem_load_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
tiled_tmem_load = tcgen05.make_tmem_copy(tmem_load_atom, tStS0)
|
||||
thr_load = tiled_tmem_load.get_slice(sfw_idx)
|
||||
tTMEM_LOADtS = thr_load.partition_S(tStS0)
|
||||
cS = cute.make_identity_tensor((self.qk_mma_tiler[0], self.qk_mma_tiler[1]))
|
||||
tScS = qk_thr.partition_C(cS)
|
||||
tTMEM_LOADcS = thr_load.partition_D(tScS)
|
||||
|
||||
# --- P store (QK C-fragment composition, FMHA pattern) ---
|
||||
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
|
||||
tStP_layout = cute.composition(tStS.layout, cute.make_layout((self.pv_mma_tiler[0], p_cols_fp32)))
|
||||
tStP0 = cute.make_tensor(tStS.iterator + self.tmem_p0_offset, tStP_layout)
|
||||
tmem_store_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
tiled_tmem_store = tcgen05.make_tmem_copy(tmem_store_atom, tStP0)
|
||||
thr_store = tiled_tmem_store.get_slice(sfw_idx)
|
||||
tTMEM_STOREtP = thr_store.partition_D(tStP0)
|
||||
tScP_layout = cute.composition(tScS.layout, cute.make_layout((self.pv_mma_tiler[0], p_cols_fp32)))
|
||||
tScP = cute.make_tensor(tScS.iterator, tScP_layout)
|
||||
tTMEM_STOREcP = thr_store.partition_S(tScP)
|
||||
|
||||
# --- Vector TMEM (per-row row_sum storage, FMHA pattern) ---
|
||||
# composition(tStS.layout, (128, 2)) = 2 FP32 columns per logical row
|
||||
# vec[0] = row_sum (final, after loop), vec[1] = unused
|
||||
# Reuses S TMEM region (offset 0), free after softmax loop writes
|
||||
|
||||
tStS_vec_layout = cute.composition(tStS.layout, cute.make_layout((128, 2)))
|
||||
tStS_vec = cute.make_tensor(tStS.iterator + self.tmem_vec_offset, tStS_vec_layout)
|
||||
tScS_vec_layout = cute.composition(tScS.layout, cute.make_layout((128, 2)))
|
||||
tScS_vec = cute.make_tensor(tScS.iterator, tScS_vec_layout)
|
||||
tmem_store_vec_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(2)), self.qk_acc_dtype)
|
||||
tiled_tmem_store_vec = tcgen05.make_tmem_copy(tmem_store_vec_atom, tStS_vec)
|
||||
thr_tmem_store_vec = tiled_tmem_store_vec.get_slice(sfw_idx)
|
||||
tTMEM_STORE_VECtS = thr_tmem_store_vec.partition_D(tStS_vec)
|
||||
tTMEM_STORE_VECcS = thr_tmem_store_vec.partition_S(tScS_vec)
|
||||
tmem_load_vec_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(2)), self.qk_acc_dtype)
|
||||
tiled_tmem_load_vec = tcgen05.make_tmem_copy(tmem_load_vec_atom, tStS_vec)
|
||||
thr_tmem_load_vec = tiled_tmem_load_vec.get_slice(sfw_idx)
|
||||
tTMEM_LOAD_VECtS = thr_tmem_load_vec.partition_S(tStS_vec)
|
||||
tTMEM_LOAD_VECcS = thr_tmem_load_vec.partition_D(tScS_vec)
|
||||
|
||||
# --- C6: O TMEM load/store for rescale (correction_rescale pattern) ---
|
||||
corr_tile_size = 16
|
||||
cO = cute.make_identity_tensor((self.pv_mma_tiler[0], self.pv_mma_tiler[1]))
|
||||
tOcO = pv_thr.partition_C(cO)
|
||||
o_tmem_load_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.qk_acc_dtype)
|
||||
o_tmem_store_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.qk_acc_dtype)
|
||||
tOtO_i_layout = cute.composition(tOtO0.layout, cute.make_layout((128, corr_tile_size)))
|
||||
tOcO_i_layout = cute.composition(tOcO.layout, cute.make_layout((128, corr_tile_size)))
|
||||
tOtO_i = cute.make_tensor(tOtO0.iterator, tOtO_i_layout)
|
||||
tOcO_i = cute.make_tensor(tOcO.iterator, tOcO_i_layout)
|
||||
o_tiled_tmem_load = tcgen05.make_tmem_copy(o_tmem_load_atom, tOtO_i)
|
||||
o_tiled_tmem_store = tcgen05.make_tmem_copy(o_tmem_store_atom, tOtO_i)
|
||||
o_thr_load = o_tiled_tmem_load.get_slice(sfw_idx)
|
||||
o_thr_store = o_tiled_tmem_store.get_slice(sfw_idx)
|
||||
tTMEM_LOADtO = o_thr_load.partition_S(tOtO_i)
|
||||
tTMEM_LOADcO = o_thr_load.partition_D(tOcO_i)
|
||||
tTMEM_STOREtO = o_thr_store.partition_D(tOtO_i)
|
||||
o_col_tiles = self.pv_mma_tiler[1] // corr_tile_size
|
||||
|
||||
# --- C2: Per-thread row state (persist across KV tiles) ---
|
||||
row_max = -cutlass.Float32.inf
|
||||
row_sum = cutlass.Float32(0.0)
|
||||
|
||||
# --- C3: QK scale = 1/sqrt(HEAD_DIM) * log2(e) for exp2 ---
|
||||
scale = self.scale_softmax_log2
|
||||
|
||||
# =============================================================
|
||||
# Per-KV-tile online softmax loop
|
||||
# =============================================================
|
||||
for kt in range(n_kv_tiles):
|
||||
si_handle = s_cons.wait_and_advance()
|
||||
|
||||
# Load S from TMEM (FP32, QK C-fragment layout)
|
||||
tTMEM_LOADrS = cute.make_rmem_tensor(tTMEM_LOADcS.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load, tTMEM_LOADtS, tTMEM_LOADrS)
|
||||
|
||||
# --- C4: Compute tile_max via .reduce(MAX) ---
|
||||
old_row_max = row_max
|
||||
row_max = tTMEM_LOADrS.load().reduce(cute.ReductionOp.MAX, row_max, 0)
|
||||
row_max_safe = row_max
|
||||
if row_max == -cutlass.Float32.inf:
|
||||
row_max_safe = cutlass.Float32(0.0)
|
||||
|
||||
# --- C5: Compute rescale factor ---
|
||||
acc_scale = cute.math.exp2(scale * (old_row_max - row_max_safe), fastmath=True)
|
||||
|
||||
# --- C6: Rescale O in TMEM (load O, multiply by acc_scale, store O) ---
|
||||
# acc_scale belongs to QK row (N//4), but O rows are in PV partition (N).
|
||||
# Store acc_scale to vector by QK row, read by PV row.
|
||||
if kt > 0:
|
||||
pv_done_bar.arrive_and_wait()
|
||||
|
||||
# Store acc_scale to vector indexed by QK logical row
|
||||
qk_row_c6 = tTMEM_LOADcS[0][0]
|
||||
thr_vs_c6 = tiled_tmem_store_vec.get_slice(qk_row_c6)
|
||||
tVStore_c6 = thr_vs_c6.partition_D(tStS_vec)
|
||||
tVStoreSrc_c6 = thr_vs_c6.partition_S(tScS_vec)
|
||||
tVStoreRmem_c6 = cute.make_rmem_tensor(tVStoreSrc_c6.shape, self.qk_acc_dtype)
|
||||
tVStoreRmem_c6[0] = acc_scale
|
||||
cute.copy(tiled_tmem_store_vec, tVStoreRmem_c6, tVStore_c6)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# Read acc_scale from vector indexed by PV logical row
|
||||
pv_row_c6 = tTMEM_LOADcO[0][0]
|
||||
thr_vl_c6 = tiled_tmem_load_vec.get_slice(pv_row_c6)
|
||||
tVLoad_c6 = thr_vl_c6.partition_S(tStS_vec)
|
||||
tVLoadDst_c6 = thr_vl_c6.partition_D(tScS_vec)
|
||||
tVLoadRmem_c6 = cute.make_rmem_tensor(tVLoadDst_c6.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load_vec, tVLoad_c6, tVLoadRmem_c6)
|
||||
cute.arch.fence_view_async_tmem_load()
|
||||
acc_scale_pv = tVLoadRmem_c6[0]
|
||||
|
||||
tTMrO = cute.make_rmem_tensor((tTMEM_LOADcO.shape, o_col_tiles), self.qk_acc_dtype)
|
||||
for i in range(o_col_tiles):
|
||||
tTMrO_i_ = tTMrO[None, i]
|
||||
tTMrO_i_layout = cute.composition(tTMrO_i_.layout, cute.make_layout(tTMrO.shape[0]))
|
||||
tTMrO_i = cute.make_tensor(tTMrO_i_.iterator, tTMrO_i_layout)
|
||||
tTMEM_LOADtO_i = cute.make_tensor(tTMEM_LOADtO.iterator + i * corr_tile_size, tTMEM_LOADtO.layout)
|
||||
tTMEM_STOREtO_i = cute.make_tensor(tTMEM_STOREtO.iterator + i * corr_tile_size, tTMEM_STOREtO.layout)
|
||||
cute.copy(o_tiled_tmem_load, tTMEM_LOADtO_i, tTMrO_i)
|
||||
for j in cutlass.range(cute.size(tTMrO_i), vectorize=True):
|
||||
tTMrO_i[j] = tTMrO_i[j] * acc_scale_pv
|
||||
cute.copy(o_tiled_tmem_store, tTMrO_i, tTMEM_STOREtO_i)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# Rescale row_sum
|
||||
row_sum = row_sum * acc_scale
|
||||
|
||||
# --- C7: Compute P = exp2((S - row_max_safe) * scale) ---
|
||||
minus_row_max_scale = (cutlass.Float32(0.0) - row_max_safe) * scale
|
||||
|
||||
# Register bridge (FMHA pattern: FP32 backing + BF16 view)
|
||||
rP_words = cute.make_rmem_tensor(tTMEM_STOREcP.shape, self.qk_acc_dtype)
|
||||
rP_bf16 = cute.make_tensor(cute.recast_ptr(rP_words.iterator, dtype=self.q_dtype), tTMEM_LOADrS.layout)
|
||||
|
||||
frg_cnt = 4
|
||||
frg_tile = cute.size(tTMEM_LOADrS) // frg_cnt
|
||||
tTMEM_LOADrS_frg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(frg_tile))
|
||||
rP_bf16_frg = cute.logical_divide(rP_bf16, cute.make_layout(frg_tile))
|
||||
|
||||
# Scale S, compute exp2, store through register bridge
|
||||
for j in range(frg_cnt):
|
||||
for k in cutlass.range(cute.size(tTMEM_LOADrS_frg, mode=[0]), vectorize=True):
|
||||
tTMEM_LOADrS_frg[k, j] = tTMEM_LOADrS_frg[k, j] * scale + minus_row_max_scale
|
||||
tTMEM_LOADrS_frg[k, j] = cute.math.exp2(tTMEM_LOADrS_frg[k, j], fastmath=True)
|
||||
s_vec = tTMEM_LOADrS_frg[None, j].load()
|
||||
rP_bf16_frg[None, j].store(s_vec.to(self.q_dtype))
|
||||
|
||||
# Store P to TMEM
|
||||
cute.copy(tiled_tmem_store, rP_words, tTMEM_STOREtP)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
si_handle.release()
|
||||
softmax_done_bar.arrive()
|
||||
|
||||
# --- C8: Row sum accumulation (CUTLASS FMHA packed f32x2 pattern) ---
|
||||
# P values still in tTMEM_LOADrS registers.
|
||||
# 4 accumulators for 4 reduction_unroll columns.
|
||||
local_row_sum_0 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_1 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_2 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_3 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
|
||||
reduction_unroll = 4
|
||||
rfrg_tile = cute.size(tTMEM_LOADrS) // reduction_unroll
|
||||
tTMEM_LOADrS_rfrg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(rfrg_tile))
|
||||
|
||||
for j in cutlass.range_constexpr(0, cute.size(tTMEM_LOADrS_rfrg, mode=[0]), 2):
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_0, (tTMEM_LOADrS_rfrg[j, 0], tTMEM_LOADrS_rfrg[j + 1, 0]))
|
||||
local_row_sum_1 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_1, (tTMEM_LOADrS_rfrg[j, 1], tTMEM_LOADrS_rfrg[j + 1, 1]))
|
||||
local_row_sum_2 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_2, (tTMEM_LOADrS_rfrg[j, 2], tTMEM_LOADrS_rfrg[j + 1, 2]))
|
||||
local_row_sum_3 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_3, (tTMEM_LOADrS_rfrg[j, 3], tTMEM_LOADrS_rfrg[j + 1, 3]))
|
||||
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(local_row_sum_0, local_row_sum_1)
|
||||
local_row_sum_2 = cute.arch.add_packed_f32x2(local_row_sum_2, local_row_sum_3)
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(local_row_sum_0, local_row_sum_2)
|
||||
tile_sum = local_row_sum_0[0] + local_row_sum_0[1]
|
||||
|
||||
row_sum = row_sum + tile_sum
|
||||
|
||||
# --- C9: Final normalization via O TMEM rescale ---
|
||||
pv_done_bar.arrive_and_wait()
|
||||
|
||||
# Compute inv_row_sum by reading P from TMEM using PV A-fragment layout.
|
||||
# P was stored by softmax into TMEM at tmem_p0_offset.
|
||||
# The PV A-fragment (tOrP0) correctly maps thread N to PV row N's P values.
|
||||
# Sum P per PV row to get the unnormalized row_sum.
|
||||
# Since P = exp2((S - max) * scale) (unnormalized), row_sum = sum(P) per row.
|
||||
# Then O_normalized = O_unnormalized / row_sum.
|
||||
|
||||
# Read P using PV A-fragment (the same layout PV MMA uses to read P)
|
||||
# tOrP0 is already set up for PV MMA input.
|
||||
# We need to sum its values per thread.
|
||||
pv_row_sum = cutlass.Float32(0.0)
|
||||
for kb in cutlass.range(cute.size(tOrP0, mode=[2])):
|
||||
p_frag = tOrP0[(None, None, kb)]
|
||||
for j in cutlass.range(cute.size(p_frag), vectorize=True):
|
||||
pv_row_sum = pv_row_sum + p_frag[j]
|
||||
|
||||
inv_row_sum = cutlass.Float32(1.0) / pv_row_sum
|
||||
|
||||
# Normalize O in TMEM using PV-correct inv_row_sum
|
||||
tTMrO_final = cute.make_rmem_tensor((tTMEM_LOADcO.shape, o_col_tiles), self.qk_acc_dtype)
|
||||
for i in range(o_col_tiles):
|
||||
tTMrO_i_ = tTMrO_final[None, i]
|
||||
tTMrO_i_layout = cute.composition(tTMrO_i_.layout, cute.make_layout(tTMrO_final.shape[0]))
|
||||
tTMrO_i = cute.make_tensor(tTMrO_i_.iterator, tTMrO_i_layout)
|
||||
tTMEM_LOADtO_i = cute.make_tensor(
|
||||
tTMEM_LOADtO.iterator + i * corr_tile_size, tTMEM_LOADtO.layout)
|
||||
tTMEM_STOREtO_i = cute.make_tensor(
|
||||
tTMEM_STOREtO.iterator + i * corr_tile_size, tTMEM_STOREtO.layout)
|
||||
cute.copy(o_tiled_tmem_load, tTMEM_LOADtO_i, tTMrO_i)
|
||||
for j in cutlass.range(cute.size(tTMrO_i), vectorize=True):
|
||||
tTMrO_i[j] = tTMrO_i[j] * inv_row_sum
|
||||
cute.copy(o_tiled_tmem_store, tTMrO_i, tTMEM_STOREtO_i)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# Now O in TMEM is normalized. Use standard epilogue_tma_store with identity.
|
||||
tCtO_base = cute.make_tensor(tmem_ptr + self.tmem_o0_offset, tCtO_fake.layout)
|
||||
acc_cons_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Consumer, self.num_acc_stage)
|
||||
c_grp = pipeline.CooperativeGroup(pipeline.Agent.Thread, 32 * len(self.epilogue_warp_id))
|
||||
c_pipe = pipeline.PipelineTmaStore.create(num_stages=self.num_c_stage, producer_group=c_grp)
|
||||
acc_cons_st = utils.gemm.sm100.epilogue_tma_store(
|
||||
self, tidx, warp_idx, tma_c, tCtO_base, sC, tCgC, epi_tile, 0,
|
||||
const_expr(lambda x: x),
|
||||
(0,0,0), acc_cons_st, acc_pipe, c_pipe)
|
||||
c_pipe.producer_tail()
|
||||
tmem.relinquish_alloc_permit()
|
||||
tmem.free(tmem_ptr)
|
||||
|
||||
|
||||
def test():
|
||||
import math
|
||||
torch.manual_seed(42)
|
||||
for n in [128, 256, 384]:
|
||||
m, hd = 128, HEAD_DIM
|
||||
q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
k = torch.randn(n, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
v = torch.randn(n, hd, dtype=torch.bfloat16, device="cuda")
|
||||
v_kernel = v.unsqueeze(-1)
|
||||
c = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
qf = q[:,:,0].float(); kf = k[:,:,0].float()
|
||||
attn = qf @ kf.T / math.sqrt(hd)
|
||||
ref = torch.softmax(attn, dim=-1) @ v.float()
|
||||
mQ = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
|
||||
mK = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
|
||||
mV = ct.from_dlpack(v_kernel).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_kernel))
|
||||
mC = ct.from_dlpack(c).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c))
|
||||
stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
|
||||
kernel = FmhaV3Softmax()
|
||||
print(f"n={n}: Compiling...", flush=True)
|
||||
compiled = cute.compile(kernel, mQ, mK, mV, mC, stream)
|
||||
print(f"n={n}: tmem: s0={kernel.tmem_s0_offset} p0={kernel.tmem_p0_offset} o0={kernel.tmem_o0_offset} vec={kernel.tmem_vec_offset} alloc={kernel.num_tmem_alloc_cols}", flush=True)
|
||||
print(f"n={n}: Running...", flush=True)
|
||||
compiled(mQ, mK, mV, mC, stream)
|
||||
torch.cuda.synchronize()
|
||||
out = c[:,:,0].float()
|
||||
cos = torch.nn.functional.cosine_similarity(out.flatten().unsqueeze(0), ref.flatten().unsqueeze(0)).item()
|
||||
max_err = (out - ref).abs().max().item()
|
||||
print(f"FMHA softmax n={n}: cosine {cos:.6f} max_err {max_err:.6f} {'PASS' if cos >= 0.999 else 'FAIL'}", flush=True)
|
||||
|
||||
if __name__ == "__main__":
|
||||
test()
|
||||
|
||||
|
||||
@@ -1,493 +0,0 @@
|
||||
"""
|
||||
FMHA v3 + Stage C: QK -> online softmax -> PV with KV-tile interleaving.
|
||||
Stage C: row_max, exp2, O rescale, row_sum, final normalization.
|
||||
FMHA pattern P store preserved from Stage B.
|
||||
"""
|
||||
import math
|
||||
import torch, cutlass, cutlass.cute as cute, cutlass.utils as utils, cutlass.pipeline as pipeline
|
||||
from cutlass.cute.nvgpu import cpasync, tcgen05
|
||||
from cutlass import Float32, BFloat16, Int32, Boolean, const_expr
|
||||
from cutlass.utils import LayoutEnum
|
||||
from cutlass.utils.tmem_allocator import find_tmem_tensor_col_offset
|
||||
import cuda.bindings.driver as cuda
|
||||
import cutlass.torch as ct
|
||||
|
||||
HEAD_DIM = 64
|
||||
|
||||
class FmhaV3Softmax:
|
||||
def __init__(self):
|
||||
self.acc_dtype = Float32; self.qk_acc_dtype = Float32
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
self.use_2cta_instrs = False; self.epilog_sync_bar_id = 1
|
||||
self.cluster_shape_mn = (1, 1); self.cta_group = tcgen05.CtaGroup.ONE
|
||||
self.epilogue_warp_id = (0,1,2,3); self.mma_warp_id = 4; self.tma_warp_id = 5
|
||||
self.threads_per_cta = 192; self.num_c_stage = 2
|
||||
self.kv_stage = 2; self.q_stage = 1; self.num_c_stage = 2
|
||||
|
||||
def _setup(self, qk_mma, pv_mma):
|
||||
qk_ik = cute.size(qk_mma.shape_mnk, mode=[2])
|
||||
self.qk_mma_tiler = (128, 128, qk_ik * 4)
|
||||
pv_ik = cute.size(pv_mma.shape_mnk, mode=[2])
|
||||
self.pv_mma_tiler = (128, HEAD_DIM, pv_ik * (128 // pv_ik))
|
||||
self.mma_tiler = self.qk_mma_tiler
|
||||
self.cluster_layout_vmnk = cute.tiled_divide(cute.make_layout((1,1,1)), (qk_mma.thr_id.shape,))
|
||||
self.cta_tile_shape_mnk = (self.qk_mma_tiler[0]//cute.size(qk_mma.thr_id.shape), HEAD_DIM, self.qk_mma_tiler[2])
|
||||
self.c_layout = LayoutEnum.ROW_MAJOR
|
||||
self.epi_tile = utils.sm100.compute_epilogue_tile_shape(self.cta_tile_shape_mnk, False, self.c_layout, self.o_dtype)
|
||||
self.num_ab_stage = 1; self.num_acc_stage = 1
|
||||
self.q_smem_s = utils.sm100.make_smem_layout_a(qk_mma, self.qk_mma_tiler, self.q_dtype, self.q_stage)
|
||||
self.k_smem_s = utils.sm100.make_smem_layout_b(qk_mma, self.qk_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.v_smem_s = utils.sm100.make_smem_layout_b(pv_mma, self.pv_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.c_smem_s = utils.sm100.make_smem_layout_epi(self.o_dtype, self.c_layout, self.epi_tile, 2)
|
||||
self.p_tmem_s = utils.sm100.make_smem_layout_a(pv_mma, self.pv_mma_tiler, self.q_dtype, 1)
|
||||
qk_thr = qk_mma.get_slice(0); qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
pv_thr = pv_mma.get_slice(0); pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
self.tmem_s0_offset = 0; self.tmem_p0_offset = 32
|
||||
# P occupies [tmem_p0_offset, tmem_p0_offset + p_cols_fp32)
|
||||
# S occupies [0, qk_mma_tiler[1]) = [0, 128)
|
||||
# O must NOT overlap P. Place O after max(S end, P end), aligned to 32.
|
||||
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
|
||||
p_end = self.tmem_p0_offset + p_cols_fp32 # 32 + 64 = 96
|
||||
s_cols = self.qk_mma_tiler[1] # 128
|
||||
o_after = max(s_cols, p_end) # 128
|
||||
self.tmem_o0_offset = ((o_after + 31) // 32) * 32
|
||||
self.tmem_vec_offset = 0 # Reuse S region for per-row inv_row_sum vector # align to 32 = 128
|
||||
self.tmem_vec_offset = 0 # Reuse S region (free after softmax loop)
|
||||
o_cols = find_tmem_tensor_col_offset(tOtO) # footprint of O
|
||||
total = self.tmem_o0_offset + o_cols
|
||||
# Must be multiple of 32 AND power of 2
|
||||
self.num_tmem_alloc_cols = 1
|
||||
while self.num_tmem_alloc_cols < total:
|
||||
self.num_tmem_alloc_cols *= 2
|
||||
cta = cute.size(qk_mma.thr_id.shape)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0))
|
||||
self.q_tx_bytes = cute.size_in_bytes(self.q_dtype, q_s) * cta
|
||||
self.kv_tx_bytes = cute.size_in_bytes(self.q_dtype, k_s) * cta
|
||||
self.scale_softmax_log2 = Float32(1.0 / math.sqrt(HEAD_DIM) * math.log2(math.e))
|
||||
|
||||
@cute.jit
|
||||
def __call__(self, q, k, v, c, stream):
|
||||
self.q_dtype = q.element_type; self.o_dtype = c.element_type; self.c_dtype = self.o_dtype
|
||||
self.a_major = LayoutEnum.from_tensor(q).mma_major_mode()
|
||||
self.b_major = LayoutEnum.from_tensor(k).mma_major_mode()
|
||||
# # s_k hardcoded # BROKEN in @cute.jit
|
||||
# FMHA-style V: reconstruct as (HEAD_DIM, s_k, 1) MN-major
|
||||
v_fmha = cute.make_tensor(
|
||||
v.iterator,
|
||||
cute.make_layout(
|
||||
(HEAD_DIM, 128, 1),
|
||||
stride=(1, HEAD_DIM, HEAD_DIM * 128),
|
||||
),
|
||||
)
|
||||
self.v_major = LayoutEnum.from_tensor(v_fmha).mma_major_mode()
|
||||
self.c_layout = LayoutEnum.from_tensor(c)
|
||||
qk_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, self.a_major, self.b_major, self.qk_acc_dtype, self.cta_group, (128,128), tcgen05.OperandSource.SMEM)
|
||||
pv_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, cute.nvgpu.OperandMajorMode.K, self.v_major, self.qk_acc_dtype, self.cta_group, (128,HEAD_DIM), tcgen05.OperandSource.TMEM)
|
||||
self._setup(qk_mma, pv_mma)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0)); v_s = cute.slice_(self.v_smem_s,(None,None,None,0))
|
||||
tma_q,mQ = cute.nvgpu.make_tiled_tma_atom_A(utils.sm100.cluster_shape_to_tma_atom_A(self.cluster_shape_mn,qk_mma.thr_id),q,q_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_k,mK = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,qk_mma.thr_id),k,k_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_v,mV = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,pv_mma.thr_id),v_fmha,v_s,self.pv_mma_tiler,pv_mma,self.cluster_layout_vmnk.shape)
|
||||
epi_s = cute.select(self.c_smem_s,mode=[0,1])
|
||||
tma_c,mC = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(),c,epi_s,self.epi_tile)
|
||||
self._kernel(qk_mma,pv_mma,tma_q,mQ,tma_k,mK,tma_v,mV,tma_c,mC,self.cluster_layout_vmnk,self.q_smem_s,self.k_smem_s,self.v_smem_s,self.p_tmem_s,self.c_smem_s,self.epi_tile).launch(grid=(1,1,1),block=[self.threads_per_cta,1,1],stream=stream)
|
||||
|
||||
@cute.kernel
|
||||
def _kernel(self, qk_mma, pv_mma, tma_q, mQ, tma_k, mK, tma_v, mV, tma_c, mC, cl_vmnk, q_smem_s, k_smem_s, v_smem_s, p_tmem_s, c_smem_s, epi_tile):
|
||||
warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx())
|
||||
tidx,_,_ = cute.arch.thread_idx()
|
||||
if warp_idx == self.tma_warp_id:
|
||||
cpasync.prefetch_descriptor(tma_q); cpasync.prefetch_descriptor(tma_k); cpasync.prefetch_descriptor(tma_v); cpasync.prefetch_descriptor(tma_c)
|
||||
|
||||
@cute.struct
|
||||
class SS:
|
||||
q_bar: cute.struct.MemRange[cutlass.Int64, self.q_stage*2]
|
||||
kv_bar: cute.struct.MemRange[cutlass.Int64, self.kv_stage*2]
|
||||
s_bar: cute.struct.MemRange[cutlass.Int64, 2]
|
||||
acc_bar: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage*2]
|
||||
tmem_dealloc: cutlass.Int64; holding: cutlass.Int32
|
||||
smem = utils.SmemAllocator(); st = smem.allocate(SS)
|
||||
|
||||
qp,qc = pipeline.PipelineTmaUmma.create(barrier_storage=st.q_bar.data_ptr(),num_stages=self.q_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.q_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
|
||||
kvp,kvc = pipeline.PipelineTmaUmma.create(barrier_storage=st.kv_bar.data_ptr(),num_stages=self.kv_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.kv_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
|
||||
s_prod,s_cons = pipeline.PipelineUmmaAsync.create(barrier_storage=st.s_bar.data_ptr(),num_stages=1,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32*len(self.epilogue_warp_id))).make_participants()
|
||||
softmax_done_bar = pipeline.NamedBarrier(barrier_id=3, num_threads=32 + 32*len(self.epilogue_warp_id))
|
||||
pv_done_bar = pipeline.NamedBarrier(barrier_id=4, num_threads=32 + 32*len(self.epilogue_warp_id))
|
||||
acc_pipe = pipeline.PipelineUmmaAsync.create(barrier_storage=st.acc_bar.data_ptr(),num_stages=self.num_acc_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,len(self.epilogue_warp_id)),cta_layout_vmnk=cl_vmnk,defer_sync=True)
|
||||
tmem_bar = pipeline.NamedBarrier(barrier_id=2,num_threads=32*len((self.mma_warp_id,*self.epilogue_warp_id)))
|
||||
tmem = utils.TmemAllocator(st.holding.ptr,barrier_for_retrieve=tmem_bar,allocator_warp_id=self.epilogue_warp_id[0],is_two_cta=cute.size(qk_mma.thr_id.shape)==2,two_cta_tmem_dealloc_mbar_ptr=st.tmem_dealloc.ptr)
|
||||
pipeline.pipeline_init_arrive(cluster_shape_mn=cl_vmnk,is_relaxed=True)
|
||||
|
||||
sQ = smem.allocate_tensor(element_type=self.q_dtype,layout=q_smem_s.outer,byte_alignment=128,swizzle=q_smem_s.inner)
|
||||
sK = smem.allocate_tensor(element_type=self.q_dtype,layout=k_smem_s.outer,byte_alignment=128,swizzle=k_smem_s.inner)
|
||||
sV = smem.allocate_tensor(element_type=self.q_dtype,layout=v_smem_s.outer,byte_alignment=128,swizzle=v_smem_s.inner)
|
||||
sC = smem.allocate_tensor(element_type=self.o_dtype,layout=c_smem_s.outer,byte_alignment=128,swizzle=c_smem_s.inner)
|
||||
|
||||
gQ = cute.local_tile(mQ,cute.slice_(self.qk_mma_tiler,(None,0,None)),(None,None,None))
|
||||
gK = cute.local_tile(mK,cute.slice_(self.qk_mma_tiler,(0,None,None)),(None,None,None))
|
||||
gV = cute.local_tile(mV,cute.slice_(self.pv_mma_tiler,(0,None,None)),(None,None,None))
|
||||
gC = cute.local_tile(mC,cute.slice_(self.pv_mma_tiler,(None,None,0)),(None,None,None))
|
||||
n_kv_tiles = cute.size(gK, mode=[3])
|
||||
|
||||
qk_thr = qk_mma.get_slice(0); pv_thr = pv_mma.get_slice(0)
|
||||
tCgQ = qk_thr.partition_A(gQ); tCgK = qk_thr.partition_B(gK)
|
||||
tCgV = pv_thr.partition_B(gV); tCgC = pv_thr.partition_C(gC)
|
||||
a_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,0,None,0)).shape)
|
||||
tAsQ,tAgQ = cpasync.tma_partition(tma_q,0,a_lay,cute.group_modes(sQ,0,3),cute.group_modes(tCgQ,0,3))
|
||||
b_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,None,0,0)).shape)
|
||||
tBsK,tBgK = cpasync.tma_partition(tma_k,0,b_lay,cute.group_modes(sK,0,3),cute.group_modes(tCgK,0,3))
|
||||
tVsV,tVgV = cpasync.tma_partition(tma_v,0,b_lay,cute.group_modes(sV,0,3),cute.group_modes(tCgV,0,3))
|
||||
tAgQ = tAgQ[(None,0,None,0)]; tBgK = tBgK[(None,0,None,0)]; tVgV = tVgV[(None,0,None,0)]
|
||||
|
||||
tCrQ = qk_mma.make_fragment_A(sQ); tCrK = qk_mma.make_fragment_B(sK)
|
||||
tCrV = pv_mma.make_fragment_B(sV)
|
||||
|
||||
qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
tOtO0 = cute.make_tensor(tOtO.iterator + self.tmem_o0_offset, tOtO.layout)
|
||||
|
||||
# --- PV read view (for MMA only, NOT for softmax store) ---
|
||||
tP = cute.make_tensor(tStS.iterator, p_tmem_s.outer)
|
||||
tOrP_base = pv_thr.make_fragment_A(tP)
|
||||
tOrP = tOrP_base[(None,None,None,0)]
|
||||
tOrP0 = cute.make_tensor(
|
||||
tOrP.iterator + self.qk_acc_dtype.width // self.q_dtype.width * self.tmem_p0_offset,
|
||||
tOrP.layout)
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_as, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_as, self.num_acc_stage))
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# TMA LOAD
|
||||
if warp_idx == self.tma_warp_id:
|
||||
qp.reset(); qh = qp.acquire_and_advance()
|
||||
cute.copy(tma_q,tAgQ[(None,qh.count)],tAsQ[(None,qh.index)],tma_bar_ptr=qh.barrier)
|
||||
qp.tail()
|
||||
kvp.reset(); pk = kvp.try_acquire()
|
||||
for kt in cutlass.range(n_kv_tiles,unroll=1):
|
||||
kh = kvp.acquire_and_advance(pk)
|
||||
cute.copy(tma_k,tBgK[(None,kh.count)],tBsK[(None,kh.index)],tma_bar_ptr=kh.barrier)
|
||||
pk = cutlass.Boolean(1)
|
||||
vh = kvp.acquire_and_advance(pk)
|
||||
cute.copy(tma_v,tVgV[(None,vh.count)],tVsV[(None,vh.index)],tma_bar_ptr=vh.barrier)
|
||||
pk = cutlass.Boolean(1)
|
||||
kvp.tail()
|
||||
|
||||
# MMA
|
||||
if warp_idx == self.mma_warp_id:
|
||||
tmem.wait_for_alloc()
|
||||
qc.reset(); qh = qc.wait_and_advance(); qh.release()
|
||||
kvc.reset(); pk = kvc.try_wait()
|
||||
acc_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
|
||||
acc_pipe.producer_acquire(acc_st)
|
||||
for kt in range(n_kv_tiles):
|
||||
kh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
sh = s_prod.acquire_and_advance()
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
for kb in cutlass.range(cute.size(tCrQ,mode=[2]), unroll_full=True):
|
||||
cute.gemm(qk_mma, tStS0, tCrQ[(None,None,kb,0)], tCrK[(None,None,kb,kh.index)], tStS0)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
sh.commit(); kh.release()
|
||||
softmax_done_bar.arrive_and_wait()
|
||||
vh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, kt != 0)
|
||||
for kb in cutlass.range(cute.size(tOrP0,mode=[2]), unroll_full=True):
|
||||
cute.gemm(pv_mma, tOtO0, tOrP0[(None,None,kb)], tCrV[(None,None,kb,vh.index)], tOtO0)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
vh.release()
|
||||
pv_done_bar.arrive()
|
||||
acc_pipe.producer_commit(acc_st); acc_st.advance()
|
||||
acc_pipe.producer_tail(acc_st)
|
||||
|
||||
# ===================== EPILOGUE WARPS (STAGE C: ONLINE SOFTMAX) =====================
|
||||
if warp_idx < self.mma_warp_id:
|
||||
tmem.allocate(self.num_tmem_alloc_cols)
|
||||
tmem.wait_for_alloc()
|
||||
tmem_ptr = tmem.retrieve_ptr(self.qk_acc_dtype)
|
||||
sfw_idx = tidx % (32 * len(self.epilogue_warp_id))
|
||||
|
||||
# --- S load (QK C-fragment) ---
|
||||
tmem_load_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
tiled_tmem_load = tcgen05.make_tmem_copy(tmem_load_atom, tStS0)
|
||||
thr_load = tiled_tmem_load.get_slice(sfw_idx)
|
||||
tTMEM_LOADtS = thr_load.partition_S(tStS0)
|
||||
cS = cute.make_identity_tensor((self.qk_mma_tiler[0], self.qk_mma_tiler[1]))
|
||||
tScS = qk_thr.partition_C(cS)
|
||||
tTMEM_LOADcS = thr_load.partition_D(tScS)
|
||||
|
||||
# --- P store (QK C-fragment composition, FMHA pattern) ---
|
||||
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
|
||||
tStP_layout = cute.composition(tStS.layout, cute.make_layout((self.pv_mma_tiler[0], p_cols_fp32)))
|
||||
tStP0 = cute.make_tensor(tStS.iterator + self.tmem_p0_offset, tStP_layout)
|
||||
tmem_store_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
tiled_tmem_store = tcgen05.make_tmem_copy(tmem_store_atom, tStP0)
|
||||
thr_store = tiled_tmem_store.get_slice(sfw_idx)
|
||||
tTMEM_STOREtP = thr_store.partition_D(tStP0)
|
||||
tScP_layout = cute.composition(tScS.layout, cute.make_layout((self.pv_mma_tiler[0], p_cols_fp32)))
|
||||
tScP = cute.make_tensor(tScS.iterator, tScP_layout)
|
||||
tTMEM_STOREcP = thr_store.partition_S(tScP)
|
||||
|
||||
# --- Vector TMEM (per-row row_sum storage, FMHA pattern) ---
|
||||
# composition(tStS.layout, (128, 2)) = 2 FP32 columns per logical row
|
||||
# vec[0] = row_sum (final, after loop), vec[1] = unused
|
||||
# Reuses S TMEM region (offset 0), free after softmax loop writes
|
||||
|
||||
tStS_vec_layout = cute.composition(tStS.layout, cute.make_layout((128, 2)))
|
||||
tStS_vec = cute.make_tensor(tStS.iterator + self.tmem_vec_offset, tStS_vec_layout)
|
||||
tScS_vec_layout = cute.composition(tScS.layout, cute.make_layout((128, 2)))
|
||||
tScS_vec = cute.make_tensor(tScS.iterator, tScS_vec_layout)
|
||||
tmem_store_vec_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(2)), self.qk_acc_dtype)
|
||||
tiled_tmem_store_vec = tcgen05.make_tmem_copy(tmem_store_vec_atom, tStS_vec)
|
||||
thr_tmem_store_vec = tiled_tmem_store_vec.get_slice(sfw_idx)
|
||||
tTMEM_STORE_VECtS = thr_tmem_store_vec.partition_D(tStS_vec)
|
||||
tTMEM_STORE_VECcS = thr_tmem_store_vec.partition_S(tScS_vec)
|
||||
tmem_load_vec_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(2)), self.qk_acc_dtype)
|
||||
tiled_tmem_load_vec = tcgen05.make_tmem_copy(tmem_load_vec_atom, tStS_vec)
|
||||
thr_tmem_load_vec = tiled_tmem_load_vec.get_slice(sfw_idx)
|
||||
tTMEM_LOAD_VECtS = thr_tmem_load_vec.partition_S(tStS_vec)
|
||||
tTMEM_LOAD_VECcS = thr_tmem_load_vec.partition_D(tScS_vec)
|
||||
|
||||
# --- C6: O TMEM load/store for rescale (correction_rescale pattern) ---
|
||||
corr_tile_size = 16
|
||||
cO = cute.make_identity_tensor((self.pv_mma_tiler[0], self.pv_mma_tiler[1]))
|
||||
tOcO = pv_thr.partition_C(cO)
|
||||
o_tmem_load_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.qk_acc_dtype)
|
||||
o_tmem_store_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.qk_acc_dtype)
|
||||
tOtO_i_layout = cute.composition(tOtO0.layout, cute.make_layout((128, corr_tile_size)))
|
||||
tOcO_i_layout = cute.composition(tOcO.layout, cute.make_layout((128, corr_tile_size)))
|
||||
tOtO_i = cute.make_tensor(tOtO0.iterator, tOtO_i_layout)
|
||||
tOcO_i = cute.make_tensor(tOcO.iterator, tOcO_i_layout)
|
||||
o_tiled_tmem_load = tcgen05.make_tmem_copy(o_tmem_load_atom, tOtO_i)
|
||||
o_tiled_tmem_store = tcgen05.make_tmem_copy(o_tmem_store_atom, tOtO_i)
|
||||
o_thr_load = o_tiled_tmem_load.get_slice(sfw_idx)
|
||||
o_thr_store = o_tiled_tmem_store.get_slice(sfw_idx)
|
||||
tTMEM_LOADtO = o_thr_load.partition_S(tOtO_i)
|
||||
tTMEM_LOADcO = o_thr_load.partition_D(tOcO_i)
|
||||
tTMEM_STOREtO = o_thr_store.partition_D(tOtO_i)
|
||||
o_col_tiles = self.pv_mma_tiler[1] // corr_tile_size
|
||||
|
||||
# --- C2: Per-thread row state (persist across KV tiles) ---
|
||||
row_max = -cutlass.Float32.inf
|
||||
row_sum = cutlass.Float32(0.0)
|
||||
|
||||
# --- C3: QK scale = 1/sqrt(HEAD_DIM) * log2(e) for exp2 ---
|
||||
scale = self.scale_softmax_log2
|
||||
|
||||
# =============================================================
|
||||
# Per-KV-tile online softmax loop
|
||||
# =============================================================
|
||||
for kt in range(n_kv_tiles):
|
||||
si_handle = s_cons.wait_and_advance()
|
||||
|
||||
# Load S from TMEM (FP32, QK C-fragment layout)
|
||||
tTMEM_LOADrS = cute.make_rmem_tensor(tTMEM_LOADcS.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load, tTMEM_LOADtS, tTMEM_LOADrS)
|
||||
|
||||
# --- C4: Compute tile_max via .reduce(MAX) ---
|
||||
old_row_max = row_max
|
||||
row_max = tTMEM_LOADrS.load().reduce(cute.ReductionOp.MAX, row_max, 0)
|
||||
row_max_safe = row_max
|
||||
if row_max == -cutlass.Float32.inf:
|
||||
row_max_safe = cutlass.Float32(0.0)
|
||||
|
||||
# --- C5: Compute rescale factor ---
|
||||
acc_scale = cute.math.exp2(scale * (old_row_max - row_max_safe), fastmath=True)
|
||||
|
||||
# --- C6: Rescale O in TMEM (direct scalar approach) ---
|
||||
# Each softmax thread computes acc_scale from its QK row_max.
|
||||
# In the QK C-fragment with 128 threads and 128 rows, thread N = row N.
|
||||
# In the PV C-fragment with 128 threads and 128 rows, thread N = row N.
|
||||
# So acc_scale for thread N's QK row = acc_scale for thread N's PV row.
|
||||
# Use acc_scale directly (no vector indirection needed).
|
||||
if kt > 0:
|
||||
pv_done_bar.arrive_and_wait()
|
||||
|
||||
acc_scale_pv = acc_scale # Direct scalar
|
||||
|
||||
tTMrO = cute.make_rmem_tensor((tTMEM_LOADcO.shape, o_col_tiles), self.qk_acc_dtype)
|
||||
for i in range(o_col_tiles):
|
||||
tTMrO_i_ = tTMrO[None, i]
|
||||
tTMrO_i_layout = cute.composition(tTMrO_i_.layout, cute.make_layout(tTMrO.shape[0]))
|
||||
tTMrO_i = cute.make_tensor(tTMrO_i_.iterator, tTMrO_i_layout)
|
||||
tTMEM_LOADtO_i = cute.make_tensor(tTMEM_LOADtO.iterator + i * corr_tile_size, tTMEM_LOADtO.layout)
|
||||
tTMEM_STOREtO_i = cute.make_tensor(tTMEM_STOREtO.iterator + i * corr_tile_size, tTMEM_STOREtO.layout)
|
||||
cute.copy(o_tiled_tmem_load, tTMEM_LOADtO_i, tTMrO_i)
|
||||
for j in cutlass.range(cute.size(tTMrO_i), vectorize=True):
|
||||
tTMrO_i[j] = tTMrO_i[j] * acc_scale_pv
|
||||
cute.copy(o_tiled_tmem_store, tTMrO_i, tTMEM_STOREtO_i)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# # --- C7: Compute P = exp2((S - row_max_safe) * scale) ---
|
||||
minus_row_max_scale = (cutlass.Float32(0.0) - row_max_safe) * scale
|
||||
|
||||
# Register bridge (FMHA pattern: FP32 backing + BF16 view)
|
||||
rP_words = cute.make_rmem_tensor(tTMEM_STOREcP.shape, self.qk_acc_dtype)
|
||||
rP_bf16 = cute.make_tensor(cute.recast_ptr(rP_words.iterator, dtype=self.q_dtype), tTMEM_LOADrS.layout)
|
||||
|
||||
frg_cnt = 4
|
||||
frg_tile = cute.size(tTMEM_LOADrS) // frg_cnt
|
||||
tTMEM_LOADrS_frg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(frg_tile))
|
||||
rP_bf16_frg = cute.logical_divide(rP_bf16, cute.make_layout(frg_tile))
|
||||
|
||||
# Scale S, compute exp2, store through register bridge
|
||||
for j in range(frg_cnt):
|
||||
for k in cutlass.range(cute.size(tTMEM_LOADrS_frg, mode=[0]), vectorize=True):
|
||||
tTMEM_LOADrS_frg[k, j] = tTMEM_LOADrS_frg[k, j] * scale + minus_row_max_scale
|
||||
tTMEM_LOADrS_frg[k, j] = cute.math.exp2(tTMEM_LOADrS_frg[k, j], fastmath=True)
|
||||
s_vec = tTMEM_LOADrS_frg[None, j].load()
|
||||
rP_bf16_frg[None, j].store(s_vec.to(self.q_dtype))
|
||||
|
||||
# Store P to TMEM
|
||||
cute.copy(tiled_tmem_store, rP_words, tTMEM_STOREtP)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
si_handle.release()
|
||||
softmax_done_bar.arrive()
|
||||
|
||||
# --- C8: Row sum accumulation (CUTLASS FMHA packed f32x2 pattern) ---
|
||||
# P values still in tTMEM_LOADrS registers.
|
||||
# 4 accumulators for 4 reduction_unroll columns.
|
||||
local_row_sum_0 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_1 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_2 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_3 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
|
||||
reduction_unroll = 4
|
||||
rfrg_tile = cute.size(tTMEM_LOADrS) // reduction_unroll
|
||||
tTMEM_LOADrS_rfrg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(rfrg_tile))
|
||||
|
||||
for j in cutlass.range_constexpr(0, cute.size(tTMEM_LOADrS_rfrg, mode=[0]), 2):
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_0, (tTMEM_LOADrS_rfrg[j, 0], tTMEM_LOADrS_rfrg[j + 1, 0]))
|
||||
local_row_sum_1 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_1, (tTMEM_LOADrS_rfrg[j, 1], tTMEM_LOADrS_rfrg[j + 1, 1]))
|
||||
local_row_sum_2 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_2, (tTMEM_LOADrS_rfrg[j, 2], tTMEM_LOADrS_rfrg[j + 1, 2]))
|
||||
local_row_sum_3 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_3, (tTMEM_LOADrS_rfrg[j, 3], tTMEM_LOADrS_rfrg[j + 1, 3]))
|
||||
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(local_row_sum_0, local_row_sum_1)
|
||||
local_row_sum_2 = cute.arch.add_packed_f32x2(local_row_sum_2, local_row_sum_3)
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(local_row_sum_0, local_row_sum_2)
|
||||
tile_sum = local_row_sum_0[0] + local_row_sum_0[1]
|
||||
|
||||
row_sum = row_sum + tile_sum
|
||||
|
||||
# --- C9: Final normalization via O TMEM rescale ---
|
||||
pv_done_bar.arrive_and_wait()
|
||||
|
||||
# Compute inv_row_sum from P in TMEM using PV partition.
|
||||
# P was stored by softmax loop into TMEM at offset tmem_p0_offset.
|
||||
# PV partition maps thread N to PV row N, so reading P via PV partition
|
||||
# gives the correct per-row P values to sum.
|
||||
# This avoids the QK→PV row mapping mismatch (QK: N->N//4, PV: N->N).
|
||||
|
||||
# P is stored as BF16 in TMEM at tmem_p0_offset.
|
||||
# We need to read it via PV TMEM load and sum the values.
|
||||
# P has shape (128, HEAD_DIM//2) in FP32 columns (64 BF16 = 32 FP32 cols).
|
||||
# Use the P TMEM load partition (PV A-fragment read).
|
||||
|
||||
# Actually, P was stored via QK C-fragment store (St32x32bOp Repetition(32)).
|
||||
# To read it via PV partition, we need a PV-partitioned load from the P region.
|
||||
# Let's use the same o_tiled_tmem_load but pointed at P's TMEM offset.
|
||||
|
||||
# P occupies TMEM columns [tmem_p0_offset, tmem_p0_offset + p_cols_fp32)
|
||||
# In the PV C-fragment, P is the A-fragment. We can use tOrP0's layout.
|
||||
# tOrP0 was set up with offset for PV MMA read.
|
||||
|
||||
# Simpler: sum O across columns to get unnormalized row sum, then normalize.
|
||||
# For V=identity, O = P@V = sum(P per row). So O.sum(dim=-1) = row_sum.
|
||||
# For arbitrary V, O = P@V. O.sum(dim=-1) = sum_j(P@V)[j] = sum_j(sum_i P[i]*V[i,j])
|
||||
# This is NOT sum(P). So this trick only works for V=identity.
|
||||
|
||||
# Correct approach: read P from TMEM, sum it per PV row.
|
||||
# P is at TMEM offset tmem_p0_offset, stored as BF16 with St32x32bOp.
|
||||
# P shape in TMEM: 128 rows x (HEAD_DIM BF16 = 32 FP32 cols)
|
||||
# We can read P using Ld32x32bOp(Repetition(corr_tile_size)) via PV O-partition.
|
||||
|
||||
# Use PV O TMEM load to read from P region instead of O region
|
||||
p_col_tiles = p_cols_fp32 // corr_tile_size # 32 // 16 = 2
|
||||
pv_row_sum = cutlass.Float32(0.0)
|
||||
for i in range(p_col_tiles):
|
||||
# Read P tile from TMEM at P offset (not O offset)
|
||||
tTMEM_LOADtP_i = cute.make_tensor(
|
||||
tTMEM_LOADtO.iterator + (self.tmem_p0_offset - self.tmem_o0_offset) + i * corr_tile_size,
|
||||
tTMEM_LOADtO.layout)
|
||||
tTMrP_i = cute.make_rmem_tensor(tTMEM_LOADcO.shape, self.qk_acc_dtype)
|
||||
cute.copy(o_tiled_tmem_load, tTMEM_LOADtP_i, tTMrP_i)
|
||||
# Use .reduce(SUM) instead of scalar accumulation (vectorizer can't handle scalar in vectorized loop)
|
||||
tile_p_sum = tTMrP_i.load().reduce(cute.ReductionOp.ADD, cutlass.Float32(0.0), 0)
|
||||
pv_row_sum = pv_row_sum + tile_p_sum
|
||||
|
||||
inv_row_sum = cutlass.Float32(1.0) / pv_row_sum
|
||||
|
||||
# Normalize O in TMEM using PV-correct inv_row_sum
|
||||
tTMrO_final = cute.make_rmem_tensor((tTMEM_LOADcO.shape, o_col_tiles), self.qk_acc_dtype)
|
||||
for i in range(o_col_tiles):
|
||||
tTMrO_i_ = tTMrO_final[None, i]
|
||||
tTMrO_i_layout = cute.composition(tTMrO_i_.layout, cute.make_layout(tTMrO_final.shape[0]))
|
||||
tTMrO_i = cute.make_tensor(tTMrO_i_.iterator, tTMrO_i_layout)
|
||||
tTMEM_LOADtO_i = cute.make_tensor(
|
||||
tTMEM_LOADtO.iterator + i * corr_tile_size, tTMEM_LOADtO.layout)
|
||||
tTMEM_STOREtO_i = cute.make_tensor(
|
||||
tTMEM_STOREtO.iterator + i * corr_tile_size, tTMEM_STOREtO.layout)
|
||||
cute.copy(o_tiled_tmem_load, tTMEM_LOADtO_i, tTMrO_i)
|
||||
for j in cutlass.range(cute.size(tTMrO_i), vectorize=True):
|
||||
tTMrO_i[j] = tTMrO_i[j] * inv_row_sum
|
||||
cute.copy(o_tiled_tmem_store, tTMrO_i, tTMEM_STOREtO_i)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# Now O in TMEM is normalized. Use standard epilogue_tma_store with identity.
|
||||
tCtO_base = cute.make_tensor(tmem_ptr + self.tmem_o0_offset, tCtO_fake.layout)
|
||||
acc_cons_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Consumer, self.num_acc_stage)
|
||||
c_grp = pipeline.CooperativeGroup(pipeline.Agent.Thread, 32 * len(self.epilogue_warp_id))
|
||||
c_pipe = pipeline.PipelineTmaStore.create(num_stages=self.num_c_stage, producer_group=c_grp)
|
||||
acc_cons_st = utils.gemm.sm100.epilogue_tma_store(
|
||||
self, tidx, warp_idx, tma_c, tCtO_base, sC, tCgC, epi_tile, 0,
|
||||
const_expr(lambda x: x),
|
||||
(0,0,0), acc_cons_st, acc_pipe, c_pipe)
|
||||
c_pipe.producer_tail()
|
||||
tmem.relinquish_alloc_permit()
|
||||
tmem.free(tmem_ptr)
|
||||
|
||||
|
||||
def test():
|
||||
import math
|
||||
torch.manual_seed(42)
|
||||
for n in [128, 256, 384]:
|
||||
m, hd = 128, HEAD_DIM
|
||||
q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
k = torch.randn(n, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
v = torch.randn(n, hd, dtype=torch.bfloat16, device="cuda")
|
||||
v_kernel = v.unsqueeze(-1)
|
||||
c = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
qf = q[:,:,0].float(); kf = k[:,:,0].float()
|
||||
attn = qf @ kf.T / math.sqrt(hd)
|
||||
ref = torch.softmax(attn, dim=-1) @ v.float()
|
||||
mQ = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
|
||||
mK = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
|
||||
mV = ct.from_dlpack(v_kernel).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_kernel))
|
||||
mC = ct.from_dlpack(c).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c))
|
||||
stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
|
||||
kernel = FmhaV3Softmax()
|
||||
print(f"n={n}: Compiling...", flush=True)
|
||||
compiled = cute.compile(kernel, mQ, mK, mV, mC, stream)
|
||||
print(f"n={n}: tmem: s0={kernel.tmem_s0_offset} p0={kernel.tmem_p0_offset} o0={kernel.tmem_o0_offset} vec={kernel.tmem_vec_offset} alloc={kernel.num_tmem_alloc_cols}", flush=True)
|
||||
print(f"n={n}: Running...", flush=True)
|
||||
compiled(mQ, mK, mV, mC, stream)
|
||||
torch.cuda.synchronize()
|
||||
out = c[:,:,0].float()
|
||||
cos = torch.nn.functional.cosine_similarity(out.flatten().unsqueeze(0), ref.flatten().unsqueeze(0)).item()
|
||||
max_err = (out - ref).abs().max().item()
|
||||
print(f"FMHA softmax n={n}: cosine {cos:.6f} max_err {max_err:.6f} {'PASS' if cos >= 0.999 else 'FAIL'}", flush=True)
|
||||
|
||||
if __name__ == "__main__":
|
||||
test()
|
||||
|
||||
|
||||
@@ -1,518 +0,0 @@
|
||||
"""
|
||||
FMHA v3 + Stage C: QK -> online softmax -> PV with KV-tile interleaving.
|
||||
Stage C: row_max, exp2, O rescale, row_sum, final normalization.
|
||||
FMHA pattern P store preserved from Stage B.
|
||||
"""
|
||||
import math
|
||||
import torch, cutlass, cutlass.cute as cute, cutlass.utils as utils, cutlass.pipeline as pipeline
|
||||
from cutlass.cute.nvgpu import cpasync, tcgen05
|
||||
from cutlass import Float32, BFloat16, Int32, Boolean, const_expr
|
||||
from cutlass.utils import LayoutEnum
|
||||
from cutlass.utils.tmem_allocator import find_tmem_tensor_col_offset
|
||||
import cuda.bindings.driver as cuda
|
||||
import cutlass.torch as ct
|
||||
|
||||
HEAD_DIM = 64
|
||||
|
||||
class FmhaV3Softmax:
|
||||
def __init__(self):
|
||||
self.acc_dtype = Float32; self.qk_acc_dtype = Float32
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
self.use_2cta_instrs = False; self.epilog_sync_bar_id = 1
|
||||
self.cluster_shape_mn = (1, 1); self.cta_group = tcgen05.CtaGroup.ONE
|
||||
self.epilogue_warp_id = (0,1,2,3); self.mma_warp_id = 4; self.tma_warp_id = 5
|
||||
self.threads_per_cta = 192; self.num_c_stage = 2
|
||||
self.kv_stage = 2; self.q_stage = 1; self.num_c_stage = 2
|
||||
|
||||
def _setup(self, qk_mma, pv_mma):
|
||||
qk_ik = cute.size(qk_mma.shape_mnk, mode=[2])
|
||||
self.qk_mma_tiler = (128, 128, qk_ik * 4)
|
||||
pv_ik = cute.size(pv_mma.shape_mnk, mode=[2])
|
||||
self.pv_mma_tiler = (128, HEAD_DIM, pv_ik * (128 // pv_ik))
|
||||
self.mma_tiler = self.qk_mma_tiler
|
||||
self.cluster_layout_vmnk = cute.tiled_divide(cute.make_layout((1,1,1)), (qk_mma.thr_id.shape,))
|
||||
self.cta_tile_shape_mnk = (self.qk_mma_tiler[0]//cute.size(qk_mma.thr_id.shape), HEAD_DIM, self.qk_mma_tiler[2])
|
||||
self.c_layout = LayoutEnum.ROW_MAJOR
|
||||
self.epi_tile = utils.sm100.compute_epilogue_tile_shape(self.cta_tile_shape_mnk, False, self.c_layout, self.o_dtype)
|
||||
self.num_ab_stage = 1; self.num_acc_stage = 1
|
||||
self.q_smem_s = utils.sm100.make_smem_layout_a(qk_mma, self.qk_mma_tiler, self.q_dtype, self.q_stage)
|
||||
self.k_smem_s = utils.sm100.make_smem_layout_b(qk_mma, self.qk_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.v_smem_s = utils.sm100.make_smem_layout_b(pv_mma, self.pv_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.c_smem_s = utils.sm100.make_smem_layout_epi(self.o_dtype, self.c_layout, self.epi_tile, 2)
|
||||
self.p_tmem_s = utils.sm100.make_smem_layout_a(pv_mma, self.pv_mma_tiler, self.q_dtype, 1)
|
||||
qk_thr = qk_mma.get_slice(0); qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
pv_thr = pv_mma.get_slice(0); pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
self.tmem_s0_offset = 0; self.tmem_p0_offset = 32
|
||||
# P occupies [tmem_p0_offset, tmem_p0_offset + p_cols_fp32)
|
||||
# S occupies [0, qk_mma_tiler[1]) = [0, 128)
|
||||
# O must NOT overlap P. Place O after max(S end, P end), aligned to 32.
|
||||
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
|
||||
p_end = self.tmem_p0_offset + p_cols_fp32 # 32 + 64 = 96
|
||||
s_cols = self.qk_mma_tiler[1] # 128
|
||||
o_after = max(s_cols, p_end) # 128
|
||||
self.tmem_o0_offset = ((o_after + 31) // 32) * 32
|
||||
self.tmem_vec_offset = 0 # Reuse S region for per-row inv_row_sum vector # align to 32 = 128
|
||||
self.tmem_vec_offset = 0 # Reuse S region (free after softmax loop)
|
||||
o_cols = find_tmem_tensor_col_offset(tOtO) # footprint of O
|
||||
total = self.tmem_o0_offset + o_cols
|
||||
# Must be multiple of 32 AND power of 2
|
||||
self.num_tmem_alloc_cols = 1
|
||||
while self.num_tmem_alloc_cols < total:
|
||||
self.num_tmem_alloc_cols *= 2
|
||||
cta = cute.size(qk_mma.thr_id.shape)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0))
|
||||
self.q_tx_bytes = cute.size_in_bytes(self.q_dtype, q_s) * cta
|
||||
self.kv_tx_bytes = cute.size_in_bytes(self.q_dtype, k_s) * cta
|
||||
self.scale_softmax_log2 = Float32(1.0 / math.sqrt(HEAD_DIM) * math.log2(math.e))
|
||||
|
||||
@cute.jit
|
||||
def __call__(self, q, k, v, c, stream):
|
||||
self.q_dtype = q.element_type; self.o_dtype = c.element_type; self.c_dtype = self.o_dtype
|
||||
self.a_major = LayoutEnum.from_tensor(q).mma_major_mode()
|
||||
self.b_major = LayoutEnum.from_tensor(k).mma_major_mode()
|
||||
# # s_k hardcoded # BROKEN in @cute.jit
|
||||
# FMHA-style V: reconstruct as (HEAD_DIM, s_k, 1) MN-major
|
||||
v_fmha = cute.make_tensor(
|
||||
v.iterator,
|
||||
cute.make_layout(
|
||||
(HEAD_DIM, 128, 1),
|
||||
stride=(1, HEAD_DIM, HEAD_DIM * 128),
|
||||
),
|
||||
)
|
||||
self.v_major = LayoutEnum.from_tensor(v_fmha).mma_major_mode()
|
||||
self.c_layout = LayoutEnum.from_tensor(c)
|
||||
qk_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, self.a_major, self.b_major, self.qk_acc_dtype, self.cta_group, (128,128), tcgen05.OperandSource.SMEM)
|
||||
pv_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, cute.nvgpu.OperandMajorMode.K, self.v_major, self.qk_acc_dtype, self.cta_group, (128,HEAD_DIM), tcgen05.OperandSource.TMEM)
|
||||
self._setup(qk_mma, pv_mma)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0)); v_s = cute.slice_(self.v_smem_s,(None,None,None,0))
|
||||
tma_q,mQ = cute.nvgpu.make_tiled_tma_atom_A(utils.sm100.cluster_shape_to_tma_atom_A(self.cluster_shape_mn,qk_mma.thr_id),q,q_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_k,mK = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,qk_mma.thr_id),k,k_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_v,mV = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,pv_mma.thr_id),v_fmha,v_s,self.pv_mma_tiler,pv_mma,self.cluster_layout_vmnk.shape)
|
||||
epi_s = cute.select(self.c_smem_s,mode=[0,1])
|
||||
tma_c,mC = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(),c,epi_s,self.epi_tile)
|
||||
self._kernel(qk_mma,pv_mma,tma_q,mQ,tma_k,mK,tma_v,mV,tma_c,mC,self.cluster_layout_vmnk,self.q_smem_s,self.k_smem_s,self.v_smem_s,self.p_tmem_s,self.c_smem_s,self.epi_tile).launch(grid=(1,1,1),block=[self.threads_per_cta,1,1],stream=stream)
|
||||
|
||||
@cute.kernel
|
||||
def _kernel(self, qk_mma, pv_mma, tma_q, mQ, tma_k, mK, tma_v, mV, tma_c, mC, cl_vmnk, q_smem_s, k_smem_s, v_smem_s, p_tmem_s, c_smem_s, epi_tile):
|
||||
warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx())
|
||||
tidx,_,_ = cute.arch.thread_idx()
|
||||
if warp_idx == self.tma_warp_id:
|
||||
cpasync.prefetch_descriptor(tma_q); cpasync.prefetch_descriptor(tma_k); cpasync.prefetch_descriptor(tma_v); cpasync.prefetch_descriptor(tma_c)
|
||||
|
||||
@cute.struct
|
||||
class SS:
|
||||
q_bar: cute.struct.MemRange[cutlass.Int64, self.q_stage*2]
|
||||
kv_bar: cute.struct.MemRange[cutlass.Int64, self.kv_stage*2]
|
||||
s_bar: cute.struct.MemRange[cutlass.Int64, 2]
|
||||
acc_bar: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage*2]
|
||||
tmem_dealloc: cutlass.Int64; holding: cutlass.Int32
|
||||
smem = utils.SmemAllocator(); st = smem.allocate(SS)
|
||||
|
||||
qp,qc = pipeline.PipelineTmaUmma.create(barrier_storage=st.q_bar.data_ptr(),num_stages=self.q_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.q_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
|
||||
kvp,kvc = pipeline.PipelineTmaUmma.create(barrier_storage=st.kv_bar.data_ptr(),num_stages=self.kv_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.kv_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
|
||||
s_prod,s_cons = pipeline.PipelineUmmaAsync.create(barrier_storage=st.s_bar.data_ptr(),num_stages=1,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32*len(self.epilogue_warp_id))).make_participants()
|
||||
softmax_done_bar = pipeline.NamedBarrier(barrier_id=3, num_threads=32 + 32*len(self.epilogue_warp_id))
|
||||
pv_done_bar = pipeline.NamedBarrier(barrier_id=4, num_threads=32 + 32*len(self.epilogue_warp_id))
|
||||
acc_pipe = pipeline.PipelineUmmaAsync.create(barrier_storage=st.acc_bar.data_ptr(),num_stages=self.num_acc_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,len(self.epilogue_warp_id)),cta_layout_vmnk=cl_vmnk,defer_sync=True)
|
||||
tmem_bar = pipeline.NamedBarrier(barrier_id=2,num_threads=32*len((self.mma_warp_id,*self.epilogue_warp_id)))
|
||||
tmem = utils.TmemAllocator(st.holding.ptr,barrier_for_retrieve=tmem_bar,allocator_warp_id=self.epilogue_warp_id[0],is_two_cta=cute.size(qk_mma.thr_id.shape)==2,two_cta_tmem_dealloc_mbar_ptr=st.tmem_dealloc.ptr)
|
||||
pipeline.pipeline_init_arrive(cluster_shape_mn=cl_vmnk,is_relaxed=True)
|
||||
|
||||
sQ = smem.allocate_tensor(element_type=self.q_dtype,layout=q_smem_s.outer,byte_alignment=128,swizzle=q_smem_s.inner)
|
||||
sK = smem.allocate_tensor(element_type=self.q_dtype,layout=k_smem_s.outer,byte_alignment=128,swizzle=k_smem_s.inner)
|
||||
sV = smem.allocate_tensor(element_type=self.q_dtype,layout=v_smem_s.outer,byte_alignment=128,swizzle=v_smem_s.inner)
|
||||
sC = smem.allocate_tensor(element_type=self.o_dtype,layout=c_smem_s.outer,byte_alignment=128,swizzle=c_smem_s.inner)
|
||||
|
||||
gQ = cute.local_tile(mQ,cute.slice_(self.qk_mma_tiler,(None,0,None)),(None,None,None))
|
||||
gK = cute.local_tile(mK,cute.slice_(self.qk_mma_tiler,(0,None,None)),(None,None,None))
|
||||
gV = cute.local_tile(mV,cute.slice_(self.pv_mma_tiler,(0,None,None)),(None,None,None))
|
||||
gC = cute.local_tile(mC,cute.slice_(self.pv_mma_tiler,(None,None,0)),(None,None,None))
|
||||
n_kv_tiles = cute.size(gK, mode=[3])
|
||||
|
||||
qk_thr = qk_mma.get_slice(0); pv_thr = pv_mma.get_slice(0)
|
||||
tCgQ = qk_thr.partition_A(gQ); tCgK = qk_thr.partition_B(gK)
|
||||
tCgV = pv_thr.partition_B(gV); tCgC = pv_thr.partition_C(gC)
|
||||
a_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,0,None,0)).shape)
|
||||
tAsQ,tAgQ = cpasync.tma_partition(tma_q,0,a_lay,cute.group_modes(sQ,0,3),cute.group_modes(tCgQ,0,3))
|
||||
b_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,None,0,0)).shape)
|
||||
tBsK,tBgK = cpasync.tma_partition(tma_k,0,b_lay,cute.group_modes(sK,0,3),cute.group_modes(tCgK,0,3))
|
||||
tVsV,tVgV = cpasync.tma_partition(tma_v,0,b_lay,cute.group_modes(sV,0,3),cute.group_modes(tCgV,0,3))
|
||||
tAgQ = tAgQ[(None,0,None,0)]; tBgK = tBgK[(None,0,None,0)]; tVgV = tVgV[(None,0,None,0)]
|
||||
|
||||
tCrQ = qk_mma.make_fragment_A(sQ); tCrK = qk_mma.make_fragment_B(sK)
|
||||
tCrV = pv_mma.make_fragment_B(sV)
|
||||
|
||||
qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
tOtO0 = cute.make_tensor(tOtO.iterator + self.tmem_o0_offset, tOtO.layout)
|
||||
|
||||
# --- PV read view (for MMA only, NOT for softmax store) ---
|
||||
tP = cute.make_tensor(tStS.iterator, p_tmem_s.outer)
|
||||
tOrP_base = pv_thr.make_fragment_A(tP)
|
||||
tOrP = tOrP_base[(None,None,None,0)]
|
||||
tOrP0 = cute.make_tensor(
|
||||
tOrP.iterator + self.qk_acc_dtype.width // self.q_dtype.width * self.tmem_p0_offset,
|
||||
tOrP.layout)
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_as, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_as, self.num_acc_stage))
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# TMA LOAD
|
||||
if warp_idx == self.tma_warp_id:
|
||||
qp.reset(); qh = qp.acquire_and_advance()
|
||||
cute.copy(tma_q,tAgQ[(None,qh.count)],tAsQ[(None,qh.index)],tma_bar_ptr=qh.barrier)
|
||||
qp.tail()
|
||||
kvp.reset(); pk = kvp.try_acquire()
|
||||
for kt in cutlass.range(n_kv_tiles,unroll=1):
|
||||
kh = kvp.acquire_and_advance(pk)
|
||||
cute.copy(tma_k,tBgK[(None,kh.count)],tBsK[(None,kh.index)],tma_bar_ptr=kh.barrier)
|
||||
pk = cutlass.Boolean(1)
|
||||
vh = kvp.acquire_and_advance(pk)
|
||||
cute.copy(tma_v,tVgV[(None,vh.count)],tVsV[(None,vh.index)],tma_bar_ptr=vh.barrier)
|
||||
pk = cutlass.Boolean(1)
|
||||
kvp.tail()
|
||||
|
||||
# MMA
|
||||
if warp_idx == self.mma_warp_id:
|
||||
tmem.wait_for_alloc()
|
||||
qc.reset(); qh = qc.wait_and_advance(); qh.release()
|
||||
kvc.reset(); pk = kvc.try_wait()
|
||||
acc_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
|
||||
acc_pipe.producer_acquire(acc_st)
|
||||
for kt in range(n_kv_tiles):
|
||||
kh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
sh = s_prod.acquire_and_advance()
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
for kb in cutlass.range(cute.size(tCrQ,mode=[2]), unroll_full=True):
|
||||
cute.gemm(qk_mma, tStS0, tCrQ[(None,None,kb,0)], tCrK[(None,None,kb,kh.index)], tStS0)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
sh.commit(); kh.release()
|
||||
softmax_done_bar.arrive_and_wait()
|
||||
vh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, kt != 0)
|
||||
for kb in cutlass.range(cute.size(tOrP0,mode=[2]), unroll_full=True):
|
||||
cute.gemm(pv_mma, tOtO0, tOrP0[(None,None,kb)], tCrV[(None,None,kb,vh.index)], tOtO0)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
vh.release()
|
||||
pv_done_bar.arrive()
|
||||
acc_pipe.producer_commit(acc_st); acc_st.advance()
|
||||
acc_pipe.producer_tail(acc_st)
|
||||
|
||||
# ===================== EPILOGUE WARPS (STAGE C: ONLINE SOFTMAX) =====================
|
||||
if warp_idx < self.mma_warp_id:
|
||||
tmem.allocate(self.num_tmem_alloc_cols)
|
||||
tmem.wait_for_alloc()
|
||||
tmem_ptr = tmem.retrieve_ptr(self.qk_acc_dtype)
|
||||
sfw_idx = tidx % (32 * len(self.epilogue_warp_id)) # DEBUG: print fragment shapes (only from thread 0)
|
||||
if sfw_idx == 0:
|
||||
print(f"DEBUG sfw_idx=0: tTMEM_LOADcS shape={tTMEM_LOADcS.shape} size={cute.size(tTMEM_LOADcS)}")
|
||||
print(f"DEBUG sfw_idx=0: tScS shape={tScS.shape} size={cute.size(tScS)}")
|
||||
# Check which rows thread 0 handles
|
||||
for i in range(min(4, cute.size(tScS, mode=[0]))):
|
||||
row_col = tScS[i][0]
|
||||
print(f" tScS[{i}][0] = {row_col}")
|
||||
|
||||
# --- S load (QK C-fragment) ---
|
||||
tmem_load_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
tiled_tmem_load = tcgen05.make_tmem_copy(tmem_load_atom, tStS0)
|
||||
thr_load = tiled_tmem_load.get_slice(sfw_idx)
|
||||
tTMEM_LOADtS = thr_load.partition_S(tStS0)
|
||||
cS = cute.make_identity_tensor((self.qk_mma_tiler[0], self.qk_mma_tiler[1]))
|
||||
tScS = qk_thr.partition_C(cS)
|
||||
tTMEM_LOADcS = thr_load.partition_D(tScS)
|
||||
|
||||
# --- P store (QK C-fragment composition, FMHA pattern) ---
|
||||
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
|
||||
tStP_layout = cute.composition(tStS.layout, cute.make_layout((self.pv_mma_tiler[0], p_cols_fp32)))
|
||||
tStP0 = cute.make_tensor(tStS.iterator + self.tmem_p0_offset, tStP_layout)
|
||||
tmem_store_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
tiled_tmem_store = tcgen05.make_tmem_copy(tmem_store_atom, tStP0)
|
||||
thr_store = tiled_tmem_store.get_slice(sfw_idx)
|
||||
tTMEM_STOREtP = thr_store.partition_D(tStP0)
|
||||
tScP_layout = cute.composition(tScS.layout, cute.make_layout((self.pv_mma_tiler[0], p_cols_fp32)))
|
||||
tScP = cute.make_tensor(tScS.iterator, tScP_layout)
|
||||
tTMEM_STOREcP = thr_store.partition_S(tScP)
|
||||
|
||||
# --- Vector TMEM (per-row row_sum storage, FMHA pattern) ---
|
||||
# composition(tStS.layout, (128, 2)) = 2 FP32 columns per logical row
|
||||
# vec[0] = row_sum (final, after loop), vec[1] = unused
|
||||
# Reuses S TMEM region (offset 0), free after softmax loop writes
|
||||
|
||||
tStS_vec_layout = cute.composition(tStS.layout, cute.make_layout((128, 2)))
|
||||
tStS_vec = cute.make_tensor(tStS.iterator + self.tmem_vec_offset, tStS_vec_layout)
|
||||
tScS_vec_layout = cute.composition(tScS.layout, cute.make_layout((128, 2)))
|
||||
tScS_vec = cute.make_tensor(tScS.iterator, tScS_vec_layout)
|
||||
tmem_store_vec_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(2)), self.qk_acc_dtype)
|
||||
tiled_tmem_store_vec = tcgen05.make_tmem_copy(tmem_store_vec_atom, tStS_vec)
|
||||
thr_tmem_store_vec = tiled_tmem_store_vec.get_slice(sfw_idx)
|
||||
tTMEM_STORE_VECtS = thr_tmem_store_vec.partition_D(tStS_vec)
|
||||
tTMEM_STORE_VECcS = thr_tmem_store_vec.partition_S(tScS_vec)
|
||||
tmem_load_vec_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(2)), self.qk_acc_dtype)
|
||||
tiled_tmem_load_vec = tcgen05.make_tmem_copy(tmem_load_vec_atom, tStS_vec)
|
||||
thr_tmem_load_vec = tiled_tmem_load_vec.get_slice(sfw_idx)
|
||||
tTMEM_LOAD_VECtS = thr_tmem_load_vec.partition_S(tStS_vec)
|
||||
tTMEM_LOAD_VECcS = thr_tmem_load_vec.partition_D(tScS_vec)
|
||||
|
||||
# --- C6: O TMEM load/store for rescale (correction_rescale pattern) ---
|
||||
corr_tile_size = 16
|
||||
cO = cute.make_identity_tensor((self.pv_mma_tiler[0], self.pv_mma_tiler[1]))
|
||||
tOcO = pv_thr.partition_C(cO)
|
||||
o_tmem_load_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.qk_acc_dtype)
|
||||
o_tmem_store_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.qk_acc_dtype)
|
||||
tOtO_i_layout = cute.composition(tOtO0.layout, cute.make_layout((128, corr_tile_size)))
|
||||
tOcO_i_layout = cute.composition(tOcO.layout, cute.make_layout((128, corr_tile_size)))
|
||||
tOtO_i = cute.make_tensor(tOtO0.iterator, tOtO_i_layout)
|
||||
tOcO_i = cute.make_tensor(tOcO.iterator, tOcO_i_layout)
|
||||
o_tiled_tmem_load = tcgen05.make_tmem_copy(o_tmem_load_atom, tOtO_i)
|
||||
o_tiled_tmem_store = tcgen05.make_tmem_copy(o_tmem_store_atom, tOtO_i)
|
||||
o_thr_load = o_tiled_tmem_load.get_slice(sfw_idx)
|
||||
o_thr_store = o_tiled_tmem_store.get_slice(sfw_idx)
|
||||
tTMEM_LOADtO = o_thr_load.partition_S(tOtO_i)
|
||||
tTMEM_LOADcO = o_thr_load.partition_D(tOcO_i)
|
||||
tTMEM_STOREtO = o_thr_store.partition_D(tOtO_i)
|
||||
o_col_tiles = self.pv_mma_tiler[1] // corr_tile_size
|
||||
|
||||
# --- C2: Per-thread row state (persist across KV tiles) ---
|
||||
row_max = -cutlass.Float32.inf
|
||||
row_sum = cutlass.Float32(0.0)
|
||||
|
||||
# --- C3: QK scale = 1/sqrt(HEAD_DIM) * log2(e) for exp2 ---
|
||||
scale = self.scale_softmax_log2
|
||||
|
||||
# =============================================================
|
||||
# Per-KV-tile online softmax loop
|
||||
# =============================================================
|
||||
for kt in range(n_kv_tiles):
|
||||
si_handle = s_cons.wait_and_advance()
|
||||
|
||||
# Load S from TMEM (FP32, QK C-fragment layout)
|
||||
tTMEM_LOADrS = cute.make_rmem_tensor(tTMEM_LOADcS.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load, tTMEM_LOADtS, tTMEM_LOADrS)
|
||||
|
||||
# --- C4: Compute tile_max via .reduce(MAX) ---
|
||||
old_row_max = row_max
|
||||
row_max = tTMEM_LOADrS.load().reduce(cute.ReductionOp.MAX, row_max, 0)
|
||||
row_max_safe = row_max
|
||||
if row_max == -cutlass.Float32.inf:
|
||||
row_max_safe = cutlass.Float32(0.0)
|
||||
|
||||
# --- C5: Compute rescale factor ---
|
||||
acc_scale = cute.math.exp2(scale * (old_row_max - row_max_safe), fastmath=True)
|
||||
|
||||
# --- C6: Rescale O in TMEM (load O, multiply by acc_scale, store O) ---
|
||||
# acc_scale belongs to QK row (N//4), but O rows are in PV partition (N).
|
||||
# Store acc_scale to vector by QK row, read by PV row.
|
||||
if kt > 0:
|
||||
pv_done_bar.arrive_and_wait()
|
||||
|
||||
# Store acc_scale to vector indexed by QK logical row
|
||||
qk_row_c6 = tTMEM_LOADcS[0][0]
|
||||
thr_vs_c6 = tiled_tmem_store_vec.get_slice(qk_row_c6)
|
||||
tVStore_c6 = thr_vs_c6.partition_D(tStS_vec)
|
||||
tVStoreSrc_c6 = thr_vs_c6.partition_S(tScS_vec)
|
||||
tVStoreRmem_c6 = cute.make_rmem_tensor(tVStoreSrc_c6.shape, self.qk_acc_dtype)
|
||||
tVStoreRmem_c6[0] = acc_scale
|
||||
cute.copy(tiled_tmem_store_vec, tVStoreRmem_c6, tVStore_c6)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# Read acc_scale from vector indexed by PV logical row
|
||||
pv_row_c6 = tTMEM_LOADcO[0][0]
|
||||
thr_vl_c6 = tiled_tmem_load_vec.get_slice(pv_row_c6)
|
||||
tVLoad_c6 = thr_vl_c6.partition_S(tStS_vec)
|
||||
tVLoadDst_c6 = thr_vl_c6.partition_D(tScS_vec)
|
||||
tVLoadRmem_c6 = cute.make_rmem_tensor(tVLoadDst_c6.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load_vec, tVLoad_c6, tVLoadRmem_c6)
|
||||
cute.arch.fence_view_async_tmem_load()
|
||||
acc_scale_pv = tVLoadRmem_c6[0]
|
||||
|
||||
tTMrO = cute.make_rmem_tensor((tTMEM_LOADcO.shape, o_col_tiles), self.qk_acc_dtype)
|
||||
for i in range(o_col_tiles):
|
||||
tTMrO_i_ = tTMrO[None, i]
|
||||
tTMrO_i_layout = cute.composition(tTMrO_i_.layout, cute.make_layout(tTMrO.shape[0]))
|
||||
tTMrO_i = cute.make_tensor(tTMrO_i_.iterator, tTMrO_i_layout)
|
||||
tTMEM_LOADtO_i = cute.make_tensor(tTMEM_LOADtO.iterator + i * corr_tile_size, tTMEM_LOADtO.layout)
|
||||
tTMEM_STOREtO_i = cute.make_tensor(tTMEM_STOREtO.iterator + i * corr_tile_size, tTMEM_STOREtO.layout)
|
||||
cute.copy(o_tiled_tmem_load, tTMEM_LOADtO_i, tTMrO_i)
|
||||
for j in cutlass.range(cute.size(tTMrO_i), vectorize=True):
|
||||
tTMrO_i[j] = tTMrO_i[j] * acc_scale_pv
|
||||
cute.copy(o_tiled_tmem_store, tTMrO_i, tTMEM_STOREtO_i)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# Rescale row_sum
|
||||
row_sum = row_sum * acc_scale
|
||||
|
||||
# --- C7: Compute P = exp2((S - row_max_safe) * scale) ---
|
||||
minus_row_max_scale = (cutlass.Float32(0.0) - row_max_safe) * scale
|
||||
|
||||
# Register bridge (FMHA pattern: FP32 backing + BF16 view)
|
||||
rP_words = cute.make_rmem_tensor(tTMEM_STOREcP.shape, self.qk_acc_dtype)
|
||||
rP_bf16 = cute.make_tensor(cute.recast_ptr(rP_words.iterator, dtype=self.q_dtype), tTMEM_LOADrS.layout)
|
||||
|
||||
frg_cnt = 4
|
||||
frg_tile = cute.size(tTMEM_LOADrS) // frg_cnt
|
||||
tTMEM_LOADrS_frg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(frg_tile))
|
||||
rP_bf16_frg = cute.logical_divide(rP_bf16, cute.make_layout(frg_tile))
|
||||
|
||||
# Scale S, compute exp2, store through register bridge
|
||||
for j in range(frg_cnt):
|
||||
for k in cutlass.range(cute.size(tTMEM_LOADrS_frg, mode=[0]), vectorize=True):
|
||||
tTMEM_LOADrS_frg[k, j] = tTMEM_LOADrS_frg[k, j] * scale + minus_row_max_scale
|
||||
tTMEM_LOADrS_frg[k, j] = cute.math.exp2(tTMEM_LOADrS_frg[k, j], fastmath=True)
|
||||
s_vec = tTMEM_LOADrS_frg[None, j].load()
|
||||
rP_bf16_frg[None, j].store(s_vec.to(self.q_dtype))
|
||||
|
||||
# Store P to TMEM
|
||||
cute.copy(tiled_tmem_store, rP_words, tTMEM_STOREtP)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
si_handle.release()
|
||||
softmax_done_bar.arrive()
|
||||
|
||||
# --- C8: Row sum accumulation (CUTLASS FMHA packed f32x2 pattern) ---
|
||||
# P values still in tTMEM_LOADrS registers.
|
||||
# 4 accumulators for 4 reduction_unroll columns.
|
||||
local_row_sum_0 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_1 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_2 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_3 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
|
||||
reduction_unroll = 4
|
||||
rfrg_tile = cute.size(tTMEM_LOADrS) // reduction_unroll
|
||||
tTMEM_LOADrS_rfrg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(rfrg_tile))
|
||||
|
||||
for j in cutlass.range_constexpr(0, cute.size(tTMEM_LOADrS_rfrg, mode=[0]), 2):
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_0, (tTMEM_LOADrS_rfrg[j, 0], tTMEM_LOADrS_rfrg[j + 1, 0]))
|
||||
local_row_sum_1 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_1, (tTMEM_LOADrS_rfrg[j, 1], tTMEM_LOADrS_rfrg[j + 1, 1]))
|
||||
local_row_sum_2 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_2, (tTMEM_LOADrS_rfrg[j, 2], tTMEM_LOADrS_rfrg[j + 1, 2]))
|
||||
local_row_sum_3 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_3, (tTMEM_LOADrS_rfrg[j, 3], tTMEM_LOADrS_rfrg[j + 1, 3]))
|
||||
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(local_row_sum_0, local_row_sum_1)
|
||||
local_row_sum_2 = cute.arch.add_packed_f32x2(local_row_sum_2, local_row_sum_3)
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(local_row_sum_0, local_row_sum_2)
|
||||
tile_sum = local_row_sum_0[0] + local_row_sum_0[1]
|
||||
|
||||
row_sum = row_sum + tile_sum
|
||||
|
||||
# --- C9: Final normalization via O TMEM rescale ---
|
||||
pv_done_bar.arrive_and_wait()
|
||||
|
||||
# Compute inv_row_sum from P in TMEM using PV partition.
|
||||
# P was stored by softmax loop into TMEM at offset tmem_p0_offset.
|
||||
# PV partition maps thread N to PV row N, so reading P via PV partition
|
||||
# gives the correct per-row P values to sum.
|
||||
# This avoids the QK→PV row mapping mismatch (QK: N->N//4, PV: N->N).
|
||||
|
||||
# P is stored as BF16 in TMEM at tmem_p0_offset.
|
||||
# We need to read it via PV TMEM load and sum the values.
|
||||
# P has shape (128, HEAD_DIM//2) in FP32 columns (64 BF16 = 32 FP32 cols).
|
||||
# Use the P TMEM load partition (PV A-fragment read).
|
||||
|
||||
# Actually, P was stored via QK C-fragment store (St32x32bOp Repetition(32)).
|
||||
# To read it via PV partition, we need a PV-partitioned load from the P region.
|
||||
# Let's use the same o_tiled_tmem_load but pointed at P's TMEM offset.
|
||||
|
||||
# P occupies TMEM columns [tmem_p0_offset, tmem_p0_offset + p_cols_fp32)
|
||||
# In the PV C-fragment, P is the A-fragment. We can use tOrP0's layout.
|
||||
# tOrP0 was set up with offset for PV MMA read.
|
||||
|
||||
# Simpler: sum O across columns to get unnormalized row sum, then normalize.
|
||||
# For V=identity, O = P@V = sum(P per row). So O.sum(dim=-1) = row_sum.
|
||||
# For arbitrary V, O = P@V. O.sum(dim=-1) = sum_j(P@V)[j] = sum_j(sum_i P[i]*V[i,j])
|
||||
# This is NOT sum(P). So this trick only works for V=identity.
|
||||
|
||||
# Correct approach: read P from TMEM, sum it per PV row.
|
||||
# P is at TMEM offset tmem_p0_offset, stored as BF16 with St32x32bOp.
|
||||
# P shape in TMEM: 128 rows x (HEAD_DIM BF16 = 32 FP32 cols)
|
||||
# We can read P using Ld32x32bOp(Repetition(corr_tile_size)) via PV O-partition.
|
||||
|
||||
# Use PV O TMEM load to read from P region instead of O region
|
||||
p_col_tiles = p_cols_fp32 // corr_tile_size # 32 // 16 = 2
|
||||
pv_row_sum = cutlass.Float32(0.0)
|
||||
for i in range(p_col_tiles):
|
||||
# Read P tile from TMEM at P offset (not O offset)
|
||||
tTMEM_LOADtP_i = cute.make_tensor(
|
||||
tTMEM_LOADtO.iterator + (self.tmem_p0_offset - self.tmem_o0_offset) + i * corr_tile_size,
|
||||
tTMEM_LOADtO.layout)
|
||||
tTMrP_i = cute.make_rmem_tensor(tTMEM_LOADcO.shape, self.qk_acc_dtype)
|
||||
cute.copy(o_tiled_tmem_load, tTMEM_LOADtP_i, tTMrP_i)
|
||||
# Use .reduce(SUM) instead of scalar accumulation (vectorizer can't handle scalar in vectorized loop)
|
||||
tile_p_sum = tTMrP_i.load().reduce(cute.ReductionOp.ADD, cutlass.Float32(0.0), 0)
|
||||
pv_row_sum = pv_row_sum + tile_p_sum
|
||||
|
||||
inv_row_sum = cutlass.Float32(1.0) / pv_row_sum
|
||||
|
||||
# Normalize O in TMEM using PV-correct inv_row_sum
|
||||
tTMrO_final = cute.make_rmem_tensor((tTMEM_LOADcO.shape, o_col_tiles), self.qk_acc_dtype)
|
||||
for i in range(o_col_tiles):
|
||||
tTMrO_i_ = tTMrO_final[None, i]
|
||||
tTMrO_i_layout = cute.composition(tTMrO_i_.layout, cute.make_layout(tTMrO_final.shape[0]))
|
||||
tTMrO_i = cute.make_tensor(tTMrO_i_.iterator, tTMrO_i_layout)
|
||||
tTMEM_LOADtO_i = cute.make_tensor(
|
||||
tTMEM_LOADtO.iterator + i * corr_tile_size, tTMEM_LOADtO.layout)
|
||||
tTMEM_STOREtO_i = cute.make_tensor(
|
||||
tTMEM_STOREtO.iterator + i * corr_tile_size, tTMEM_STOREtO.layout)
|
||||
cute.copy(o_tiled_tmem_load, tTMEM_LOADtO_i, tTMrO_i)
|
||||
for j in cutlass.range(cute.size(tTMrO_i), vectorize=True):
|
||||
tTMrO_i[j] = tTMrO_i[j] * inv_row_sum
|
||||
cute.copy(o_tiled_tmem_store, tTMrO_i, tTMEM_STOREtO_i)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# Now O in TMEM is normalized. Use standard epilogue_tma_store with identity.
|
||||
tCtO_base = cute.make_tensor(tmem_ptr + self.tmem_o0_offset, tCtO_fake.layout)
|
||||
acc_cons_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Consumer, self.num_acc_stage)
|
||||
c_grp = pipeline.CooperativeGroup(pipeline.Agent.Thread, 32 * len(self.epilogue_warp_id))
|
||||
c_pipe = pipeline.PipelineTmaStore.create(num_stages=self.num_c_stage, producer_group=c_grp)
|
||||
acc_cons_st = utils.gemm.sm100.epilogue_tma_store(
|
||||
self, tidx, warp_idx, tma_c, tCtO_base, sC, tCgC, epi_tile, 0,
|
||||
const_expr(lambda x: x),
|
||||
(0,0,0), acc_cons_st, acc_pipe, c_pipe)
|
||||
c_pipe.producer_tail()
|
||||
tmem.relinquish_alloc_permit()
|
||||
tmem.free(tmem_ptr)
|
||||
|
||||
|
||||
def test():
|
||||
import math
|
||||
torch.manual_seed(42)
|
||||
for n in [128, 256, 384]:
|
||||
m, hd = 128, HEAD_DIM
|
||||
q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
k = torch.randn(n, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
v = torch.randn(n, hd, dtype=torch.bfloat16, device="cuda")
|
||||
v_kernel = v.unsqueeze(-1)
|
||||
c = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
qf = q[:,:,0].float(); kf = k[:,:,0].float()
|
||||
attn = qf @ kf.T / math.sqrt(hd)
|
||||
ref = torch.softmax(attn, dim=-1) @ v.float()
|
||||
mQ = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
|
||||
mK = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
|
||||
mV = ct.from_dlpack(v_kernel).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_kernel))
|
||||
mC = ct.from_dlpack(c).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c))
|
||||
stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
|
||||
kernel = FmhaV3Softmax()
|
||||
print(f"n={n}: Compiling...", flush=True)
|
||||
compiled = cute.compile(kernel, mQ, mK, mV, mC, stream)
|
||||
print(f"n={n}: tmem: s0={kernel.tmem_s0_offset} p0={kernel.tmem_p0_offset} o0={kernel.tmem_o0_offset} vec={kernel.tmem_vec_offset} alloc={kernel.num_tmem_alloc_cols}", flush=True)
|
||||
print(f"n={n}: Running...", flush=True)
|
||||
compiled(mQ, mK, mV, mC, stream)
|
||||
torch.cuda.synchronize()
|
||||
out = c[:,:,0].float()
|
||||
cos = torch.nn.functional.cosine_similarity(out.flatten().unsqueeze(0), ref.flatten().unsqueeze(0)).item()
|
||||
max_err = (out - ref).abs().max().item()
|
||||
print(f"FMHA softmax n={n}: cosine {cos:.6f} max_err {max_err:.6f} {'PASS' if cos >= 0.999 else 'FAIL'}", flush=True)
|
||||
|
||||
if __name__ == "__main__":
|
||||
test()
|
||||
|
||||
|
||||
@@ -1,511 +0,0 @@
|
||||
"""
|
||||
FMHA v3 + Stage C: QK -> online softmax -> PV with KV-tile interleaving.
|
||||
Stage C: row_max, exp2, O rescale, row_sum, final normalization.
|
||||
FMHA pattern P store preserved from Stage B.
|
||||
"""
|
||||
import math
|
||||
import torch, cutlass, cutlass.cute as cute, cutlass.utils as utils, cutlass.pipeline as pipeline
|
||||
from cutlass.cute.nvgpu import cpasync, tcgen05
|
||||
from cutlass import Float32, BFloat16, Int32, Boolean, const_expr
|
||||
from cutlass.utils import LayoutEnum
|
||||
from cutlass.utils.tmem_allocator import find_tmem_tensor_col_offset
|
||||
import cuda.bindings.driver as cuda
|
||||
import cutlass.torch as ct
|
||||
|
||||
HEAD_DIM = 64
|
||||
|
||||
class FmhaV3Softmax:
|
||||
def __init__(self):
|
||||
self.acc_dtype = Float32; self.qk_acc_dtype = Float32
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
self.use_2cta_instrs = False; self.epilog_sync_bar_id = 1
|
||||
self.cluster_shape_mn = (1, 1); self.cta_group = tcgen05.CtaGroup.ONE
|
||||
self.epilogue_warp_id = (0,1,2,3); self.mma_warp_id = 4; self.tma_warp_id = 5
|
||||
self.threads_per_cta = 192; self.num_c_stage = 2
|
||||
self.kv_stage = 2; self.q_stage = 1; self.num_c_stage = 2
|
||||
|
||||
def _setup(self, qk_mma, pv_mma):
|
||||
qk_ik = cute.size(qk_mma.shape_mnk, mode=[2])
|
||||
self.qk_mma_tiler = (128, 128, qk_ik * 4)
|
||||
pv_ik = cute.size(pv_mma.shape_mnk, mode=[2])
|
||||
self.pv_mma_tiler = (128, HEAD_DIM, pv_ik * (128 // pv_ik))
|
||||
self.mma_tiler = self.qk_mma_tiler
|
||||
self.cluster_layout_vmnk = cute.tiled_divide(cute.make_layout((1,1,1)), (qk_mma.thr_id.shape,))
|
||||
self.cta_tile_shape_mnk = (self.qk_mma_tiler[0]//cute.size(qk_mma.thr_id.shape), HEAD_DIM, self.qk_mma_tiler[2])
|
||||
self.c_layout = LayoutEnum.ROW_MAJOR
|
||||
self.epi_tile = utils.sm100.compute_epilogue_tile_shape(self.cta_tile_shape_mnk, False, self.c_layout, self.o_dtype)
|
||||
self.num_ab_stage = 1; self.num_acc_stage = 1
|
||||
self.q_smem_s = utils.sm100.make_smem_layout_a(qk_mma, self.qk_mma_tiler, self.q_dtype, self.q_stage)
|
||||
self.k_smem_s = utils.sm100.make_smem_layout_b(qk_mma, self.qk_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.v_smem_s = utils.sm100.make_smem_layout_b(pv_mma, self.pv_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.c_smem_s = utils.sm100.make_smem_layout_epi(self.o_dtype, self.c_layout, self.epi_tile, 2)
|
||||
self.p_tmem_s = utils.sm100.make_smem_layout_a(pv_mma, self.pv_mma_tiler, self.q_dtype, 1)
|
||||
qk_thr = qk_mma.get_slice(0); qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
pv_thr = pv_mma.get_slice(0); pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
self.tmem_s0_offset = 0; self.tmem_p0_offset = 32
|
||||
# P occupies [tmem_p0_offset, tmem_p0_offset + p_cols_fp32)
|
||||
# S occupies [0, qk_mma_tiler[1]) = [0, 128)
|
||||
# O must NOT overlap P. Place O after max(S end, P end), aligned to 32.
|
||||
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
|
||||
p_end = self.tmem_p0_offset + p_cols_fp32 # 32 + 64 = 96
|
||||
s_cols = self.qk_mma_tiler[1] # 128
|
||||
o_after = max(s_cols, p_end) # 128
|
||||
self.tmem_o0_offset = ((o_after + 31) // 32) * 32
|
||||
self.tmem_vec_offset = 0 # Reuse S region for per-row inv_row_sum vector # align to 32 = 128
|
||||
self.tmem_vec_offset = 0 # Reuse S region (free after softmax loop)
|
||||
o_cols = find_tmem_tensor_col_offset(tOtO) # footprint of O
|
||||
total = self.tmem_o0_offset + o_cols
|
||||
# Must be multiple of 32 AND power of 2
|
||||
self.num_tmem_alloc_cols = 1
|
||||
while self.num_tmem_alloc_cols < total:
|
||||
self.num_tmem_alloc_cols *= 2
|
||||
cta = cute.size(qk_mma.thr_id.shape)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0))
|
||||
self.q_tx_bytes = cute.size_in_bytes(self.q_dtype, q_s) * cta
|
||||
self.kv_tx_bytes = cute.size_in_bytes(self.q_dtype, k_s) * cta
|
||||
self.scale_softmax_log2 = Float32(1.0 / math.sqrt(HEAD_DIM) * math.log2(math.e))
|
||||
|
||||
@cute.jit
|
||||
def __call__(self, q, k, v, c, stream):
|
||||
self.q_dtype = q.element_type; self.o_dtype = c.element_type; self.c_dtype = self.o_dtype
|
||||
self.a_major = LayoutEnum.from_tensor(q).mma_major_mode()
|
||||
self.b_major = LayoutEnum.from_tensor(k).mma_major_mode()
|
||||
# # s_k hardcoded # BROKEN in @cute.jit
|
||||
# FMHA-style V: reconstruct as (HEAD_DIM, s_k, 1) MN-major
|
||||
v_fmha = cute.make_tensor(
|
||||
v.iterator,
|
||||
cute.make_layout(
|
||||
(HEAD_DIM, 128, 1),
|
||||
stride=(1, HEAD_DIM, HEAD_DIM * 128),
|
||||
),
|
||||
)
|
||||
self.v_major = LayoutEnum.from_tensor(v_fmha).mma_major_mode()
|
||||
self.c_layout = LayoutEnum.from_tensor(c)
|
||||
qk_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, self.a_major, self.b_major, self.qk_acc_dtype, self.cta_group, (128,128), tcgen05.OperandSource.SMEM)
|
||||
pv_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, cute.nvgpu.OperandMajorMode.K, self.v_major, self.qk_acc_dtype, self.cta_group, (128,HEAD_DIM), tcgen05.OperandSource.TMEM)
|
||||
self._setup(qk_mma, pv_mma)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0)); v_s = cute.slice_(self.v_smem_s,(None,None,None,0))
|
||||
tma_q,mQ = cute.nvgpu.make_tiled_tma_atom_A(utils.sm100.cluster_shape_to_tma_atom_A(self.cluster_shape_mn,qk_mma.thr_id),q,q_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_k,mK = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,qk_mma.thr_id),k,k_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_v,mV = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,pv_mma.thr_id),v_fmha,v_s,self.pv_mma_tiler,pv_mma,self.cluster_layout_vmnk.shape)
|
||||
epi_s = cute.select(self.c_smem_s,mode=[0,1])
|
||||
tma_c,mC = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(),c,epi_s,self.epi_tile)
|
||||
self._kernel(qk_mma,pv_mma,tma_q,mQ,tma_k,mK,tma_v,mV,tma_c,mC,self.cluster_layout_vmnk,self.q_smem_s,self.k_smem_s,self.v_smem_s,self.p_tmem_s,self.c_smem_s,self.epi_tile).launch(grid=(1,1,1),block=[self.threads_per_cta,1,1],stream=stream)
|
||||
|
||||
@cute.kernel
|
||||
def _kernel(self, qk_mma, pv_mma, tma_q, mQ, tma_k, mK, tma_v, mV, tma_c, mC, cl_vmnk, q_smem_s, k_smem_s, v_smem_s, p_tmem_s, c_smem_s, epi_tile):
|
||||
warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx())
|
||||
tidx,_,_ = cute.arch.thread_idx()
|
||||
if warp_idx == self.tma_warp_id:
|
||||
cpasync.prefetch_descriptor(tma_q); cpasync.prefetch_descriptor(tma_k); cpasync.prefetch_descriptor(tma_v); cpasync.prefetch_descriptor(tma_c)
|
||||
|
||||
@cute.struct
|
||||
class SS:
|
||||
q_bar: cute.struct.MemRange[cutlass.Int64, self.q_stage*2]
|
||||
kv_bar: cute.struct.MemRange[cutlass.Int64, self.kv_stage*2]
|
||||
s_bar: cute.struct.MemRange[cutlass.Int64, 2]
|
||||
acc_bar: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage*2]
|
||||
tmem_dealloc: cutlass.Int64; holding: cutlass.Int32
|
||||
smem = utils.SmemAllocator(); st = smem.allocate(SS)
|
||||
|
||||
qp,qc = pipeline.PipelineTmaUmma.create(barrier_storage=st.q_bar.data_ptr(),num_stages=self.q_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.q_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
|
||||
kvp,kvc = pipeline.PipelineTmaUmma.create(barrier_storage=st.kv_bar.data_ptr(),num_stages=self.kv_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.kv_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
|
||||
s_prod,s_cons = pipeline.PipelineUmmaAsync.create(barrier_storage=st.s_bar.data_ptr(),num_stages=1,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32*len(self.epilogue_warp_id))).make_participants()
|
||||
softmax_done_bar = pipeline.NamedBarrier(barrier_id=3, num_threads=32 + 32*len(self.epilogue_warp_id))
|
||||
pv_done_bar = pipeline.NamedBarrier(barrier_id=4, num_threads=32 + 32*len(self.epilogue_warp_id))
|
||||
acc_pipe = pipeline.PipelineUmmaAsync.create(barrier_storage=st.acc_bar.data_ptr(),num_stages=self.num_acc_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,len(self.epilogue_warp_id)),cta_layout_vmnk=cl_vmnk,defer_sync=True)
|
||||
tmem_bar = pipeline.NamedBarrier(barrier_id=2,num_threads=32*len((self.mma_warp_id,*self.epilogue_warp_id)))
|
||||
tmem = utils.TmemAllocator(st.holding.ptr,barrier_for_retrieve=tmem_bar,allocator_warp_id=self.epilogue_warp_id[0],is_two_cta=cute.size(qk_mma.thr_id.shape)==2,two_cta_tmem_dealloc_mbar_ptr=st.tmem_dealloc.ptr)
|
||||
pipeline.pipeline_init_arrive(cluster_shape_mn=cl_vmnk,is_relaxed=True)
|
||||
|
||||
sQ = smem.allocate_tensor(element_type=self.q_dtype,layout=q_smem_s.outer,byte_alignment=128,swizzle=q_smem_s.inner)
|
||||
sK = smem.allocate_tensor(element_type=self.q_dtype,layout=k_smem_s.outer,byte_alignment=128,swizzle=k_smem_s.inner)
|
||||
sV = smem.allocate_tensor(element_type=self.q_dtype,layout=v_smem_s.outer,byte_alignment=128,swizzle=v_smem_s.inner)
|
||||
sC = smem.allocate_tensor(element_type=self.o_dtype,layout=c_smem_s.outer,byte_alignment=128,swizzle=c_smem_s.inner)
|
||||
|
||||
gQ = cute.local_tile(mQ,cute.slice_(self.qk_mma_tiler,(None,0,None)),(None,None,None))
|
||||
gK = cute.local_tile(mK,cute.slice_(self.qk_mma_tiler,(0,None,None)),(None,None,None))
|
||||
gV = cute.local_tile(mV,cute.slice_(self.pv_mma_tiler,(0,None,None)),(None,None,None))
|
||||
gC = cute.local_tile(mC,cute.slice_(self.pv_mma_tiler,(None,None,0)),(None,None,None))
|
||||
n_kv_tiles = cute.size(gK, mode=[3])
|
||||
|
||||
qk_thr = qk_mma.get_slice(0); pv_thr = pv_mma.get_slice(0)
|
||||
tCgQ = qk_thr.partition_A(gQ); tCgK = qk_thr.partition_B(gK)
|
||||
tCgV = pv_thr.partition_B(gV); tCgC = pv_thr.partition_C(gC)
|
||||
a_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,0,None,0)).shape)
|
||||
tAsQ,tAgQ = cpasync.tma_partition(tma_q,0,a_lay,cute.group_modes(sQ,0,3),cute.group_modes(tCgQ,0,3))
|
||||
b_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,None,0,0)).shape)
|
||||
tBsK,tBgK = cpasync.tma_partition(tma_k,0,b_lay,cute.group_modes(sK,0,3),cute.group_modes(tCgK,0,3))
|
||||
tVsV,tVgV = cpasync.tma_partition(tma_v,0,b_lay,cute.group_modes(sV,0,3),cute.group_modes(tCgV,0,3))
|
||||
tAgQ = tAgQ[(None,0,None,0)]; tBgK = tBgK[(None,0,None,0)]; tVgV = tVgV[(None,0,None,0)]
|
||||
|
||||
tCrQ = qk_mma.make_fragment_A(sQ); tCrK = qk_mma.make_fragment_B(sK)
|
||||
tCrV = pv_mma.make_fragment_B(sV)
|
||||
|
||||
qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
tOtO0 = cute.make_tensor(tOtO.iterator + self.tmem_o0_offset, tOtO.layout)
|
||||
|
||||
# --- PV read view (for MMA only, NOT for softmax store) ---
|
||||
tP = cute.make_tensor(tStS.iterator, p_tmem_s.outer)
|
||||
tOrP_base = pv_thr.make_fragment_A(tP)
|
||||
tOrP = tOrP_base[(None,None,None,0)]
|
||||
tOrP0 = cute.make_tensor(
|
||||
tOrP.iterator + self.qk_acc_dtype.width // self.q_dtype.width * self.tmem_p0_offset,
|
||||
tOrP.layout)
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_as, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_as, self.num_acc_stage))
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# TMA LOAD
|
||||
if warp_idx == self.tma_warp_id:
|
||||
qp.reset(); qh = qp.acquire_and_advance()
|
||||
cute.copy(tma_q,tAgQ[(None,qh.count)],tAsQ[(None,qh.index)],tma_bar_ptr=qh.barrier)
|
||||
qp.tail()
|
||||
kvp.reset(); pk = kvp.try_acquire()
|
||||
for kt in cutlass.range(n_kv_tiles,unroll=1):
|
||||
kh = kvp.acquire_and_advance(pk)
|
||||
cute.copy(tma_k,tBgK[(None,kh.count)],tBsK[(None,kh.index)],tma_bar_ptr=kh.barrier)
|
||||
pk = cutlass.Boolean(1)
|
||||
vh = kvp.acquire_and_advance(pk)
|
||||
cute.copy(tma_v,tVgV[(None,vh.count)],tVsV[(None,vh.index)],tma_bar_ptr=vh.barrier)
|
||||
pk = cutlass.Boolean(1)
|
||||
kvp.tail()
|
||||
|
||||
# MMA
|
||||
if warp_idx == self.mma_warp_id:
|
||||
tmem.wait_for_alloc()
|
||||
qc.reset(); qh = qc.wait_and_advance(); qh.release()
|
||||
kvc.reset(); pk = kvc.try_wait()
|
||||
acc_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
|
||||
acc_pipe.producer_acquire(acc_st)
|
||||
for kt in range(n_kv_tiles):
|
||||
kh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
sh = s_prod.acquire_and_advance()
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
for kb in cutlass.range(cute.size(tCrQ,mode=[2]), unroll_full=True):
|
||||
cute.gemm(qk_mma, tStS0, tCrQ[(None,None,kb,0)], tCrK[(None,None,kb,kh.index)], tStS0)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
sh.commit(); kh.release()
|
||||
softmax_done_bar.arrive_and_wait()
|
||||
vh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, kt != 0)
|
||||
for kb in cutlass.range(cute.size(tOrP0,mode=[2]), unroll_full=True):
|
||||
cute.gemm(pv_mma, tOtO0, tOrP0[(None,None,kb)], tCrV[(None,None,kb,vh.index)], tOtO0)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
vh.release()
|
||||
pv_done_bar.arrive()
|
||||
acc_pipe.producer_commit(acc_st); acc_st.advance()
|
||||
acc_pipe.producer_tail(acc_st)
|
||||
|
||||
# ===================== EPILOGUE WARPS (STAGE C: ONLINE SOFTMAX) =====================
|
||||
if warp_idx < self.mma_warp_id:
|
||||
tmem.allocate(self.num_tmem_alloc_cols)
|
||||
tmem.wait_for_alloc()
|
||||
tmem_ptr = tmem.retrieve_ptr(self.qk_acc_dtype)
|
||||
sfw_idx = tidx % (32 * len(self.epilogue_warp_id))
|
||||
|
||||
# --- S load (QK C-fragment) ---
|
||||
tmem_load_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
tiled_tmem_load = tcgen05.make_tmem_copy(tmem_load_atom, tStS0)
|
||||
thr_load = tiled_tmem_load.get_slice(sfw_idx)
|
||||
tTMEM_LOADtS = thr_load.partition_S(tStS0)
|
||||
cS = cute.make_identity_tensor((self.qk_mma_tiler[0], self.qk_mma_tiler[1]))
|
||||
tScS = qk_thr.partition_C(cS)
|
||||
tTMEM_LOADcS = thr_load.partition_D(tScS)
|
||||
|
||||
# --- P store (QK C-fragment composition, FMHA pattern) ---
|
||||
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
|
||||
tStP_layout = cute.composition(tStS.layout, cute.make_layout((self.pv_mma_tiler[0], p_cols_fp32)))
|
||||
tStP0 = cute.make_tensor(tStS.iterator + self.tmem_p0_offset, tStP_layout)
|
||||
tmem_store_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
tiled_tmem_store = tcgen05.make_tmem_copy(tmem_store_atom, tStP0)
|
||||
thr_store = tiled_tmem_store.get_slice(sfw_idx)
|
||||
tTMEM_STOREtP = thr_store.partition_D(tStP0)
|
||||
tScP_layout = cute.composition(tScS.layout, cute.make_layout((self.pv_mma_tiler[0], p_cols_fp32)))
|
||||
tScP = cute.make_tensor(tScS.iterator, tScP_layout)
|
||||
tTMEM_STOREcP = thr_store.partition_S(tScP)
|
||||
|
||||
# --- Vector TMEM (per-row row_sum storage, FMHA pattern) ---
|
||||
# composition(tStS.layout, (128, 2)) = 2 FP32 columns per logical row
|
||||
# vec[0] = row_sum (final, after loop), vec[1] = unused
|
||||
# Reuses S TMEM region (offset 0), free after softmax loop writes
|
||||
|
||||
tStS_vec_layout = cute.composition(tStS.layout, cute.make_layout((128, 2)))
|
||||
tStS_vec = cute.make_tensor(tStS.iterator + self.tmem_vec_offset, tStS_vec_layout)
|
||||
tScS_vec_layout = cute.composition(tScS.layout, cute.make_layout((128, 2)))
|
||||
tScS_vec = cute.make_tensor(tScS.iterator, tScS_vec_layout)
|
||||
tmem_store_vec_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(2)), self.qk_acc_dtype)
|
||||
tiled_tmem_store_vec = tcgen05.make_tmem_copy(tmem_store_vec_atom, tStS_vec)
|
||||
thr_tmem_store_vec = tiled_tmem_store_vec.get_slice(sfw_idx)
|
||||
tTMEM_STORE_VECtS = thr_tmem_store_vec.partition_D(tStS_vec)
|
||||
tTMEM_STORE_VECcS = thr_tmem_store_vec.partition_S(tScS_vec)
|
||||
tmem_load_vec_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(2)), self.qk_acc_dtype)
|
||||
tiled_tmem_load_vec = tcgen05.make_tmem_copy(tmem_load_vec_atom, tStS_vec)
|
||||
thr_tmem_load_vec = tiled_tmem_load_vec.get_slice(sfw_idx)
|
||||
tTMEM_LOAD_VECtS = thr_tmem_load_vec.partition_S(tStS_vec)
|
||||
tTMEM_LOAD_VECcS = thr_tmem_load_vec.partition_D(tScS_vec)
|
||||
|
||||
# --- C6: O TMEM load/store for rescale (correction_rescale pattern) ---
|
||||
corr_tile_size = 16
|
||||
cO = cute.make_identity_tensor((self.pv_mma_tiler[0], self.pv_mma_tiler[1]))
|
||||
tOcO = pv_thr.partition_C(cO)
|
||||
o_tmem_load_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.qk_acc_dtype)
|
||||
o_tmem_store_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.qk_acc_dtype)
|
||||
tOtO_i_layout = cute.composition(tOtO0.layout, cute.make_layout((128, corr_tile_size)))
|
||||
tOcO_i_layout = cute.composition(tOcO.layout, cute.make_layout((128, corr_tile_size)))
|
||||
tOtO_i = cute.make_tensor(tOtO0.iterator, tOtO_i_layout)
|
||||
tOcO_i = cute.make_tensor(tOcO.iterator, tOcO_i_layout)
|
||||
o_tiled_tmem_load = tcgen05.make_tmem_copy(o_tmem_load_atom, tOtO_i)
|
||||
o_tiled_tmem_store = tcgen05.make_tmem_copy(o_tmem_store_atom, tOtO_i)
|
||||
o_thr_load = o_tiled_tmem_load.get_slice(sfw_idx)
|
||||
o_thr_store = o_tiled_tmem_store.get_slice(sfw_idx)
|
||||
tTMEM_LOADtO = o_thr_load.partition_S(tOtO_i)
|
||||
tTMEM_LOADcO = o_thr_load.partition_D(tOcO_i)
|
||||
tTMEM_STOREtO = o_thr_store.partition_D(tOtO_i)
|
||||
o_col_tiles = self.pv_mma_tiler[1] // corr_tile_size
|
||||
|
||||
# --- C2: Per-thread row state (persist across KV tiles) ---
|
||||
row_max = -cutlass.Float32.inf
|
||||
row_sum = cutlass.Float32(0.0)
|
||||
|
||||
# --- C3: QK scale = 1/sqrt(HEAD_DIM) * log2(e) for exp2 ---
|
||||
scale = self.scale_softmax_log2
|
||||
|
||||
# =============================================================
|
||||
# Per-KV-tile online softmax loop
|
||||
# =============================================================
|
||||
for kt in range(n_kv_tiles):
|
||||
si_handle = s_cons.wait_and_advance()
|
||||
|
||||
# Load S from TMEM (FP32, QK C-fragment layout)
|
||||
tTMEM_LOADrS = cute.make_rmem_tensor(tTMEM_LOADcS.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load, tTMEM_LOADtS, tTMEM_LOADrS)
|
||||
|
||||
# --- C4: Compute tile_max via .reduce(MAX) ---
|
||||
old_row_max = row_max
|
||||
row_max = tTMEM_LOADrS.load().reduce(cute.ReductionOp.MAX, row_max, 0)
|
||||
row_max_safe = row_max
|
||||
if row_max == -cutlass.Float32.inf:
|
||||
row_max_safe = cutlass.Float32(0.0)
|
||||
|
||||
# --- C5: Compute rescale factor ---
|
||||
acc_scale = cute.math.exp2(scale * (old_row_max - row_max_safe), fastmath=True)
|
||||
|
||||
# --- C6: Rescale O in TMEM (load O, multiply by acc_scale, store O) ---
|
||||
# acc_scale belongs to QK row (N//4), but O rows are in PV partition (N).
|
||||
# Store acc_scale to vector by QK row, read by PV row.
|
||||
if kt > 0:
|
||||
pv_done_bar.arrive_and_wait()
|
||||
|
||||
# Store acc_scale to vector indexed by QK logical row
|
||||
qk_row_c6 = tTMEM_LOADcS[0][0]
|
||||
thr_vs_c6 = tiled_tmem_store_vec.get_slice(qk_row_c6)
|
||||
tVStore_c6 = thr_vs_c6.partition_D(tStS_vec)
|
||||
tVStoreSrc_c6 = thr_vs_c6.partition_S(tScS_vec)
|
||||
tVStoreRmem_c6 = cute.make_rmem_tensor(tVStoreSrc_c6.shape, self.qk_acc_dtype)
|
||||
tVStoreRmem_c6[0] = acc_scale
|
||||
cute.copy(tiled_tmem_store_vec, tVStoreRmem_c6, tVStore_c6)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# Read acc_scale from vector indexed by PV logical row
|
||||
pv_row_c6 = tTMEM_LOADcO[0][0]
|
||||
thr_vl_c6 = tiled_tmem_load_vec.get_slice(pv_row_c6)
|
||||
tVLoad_c6 = thr_vl_c6.partition_S(tStS_vec)
|
||||
tVLoadDst_c6 = thr_vl_c6.partition_D(tScS_vec)
|
||||
tVLoadRmem_c6 = cute.make_rmem_tensor(tVLoadDst_c6.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load_vec, tVLoad_c6, tVLoadRmem_c6)
|
||||
cute.arch.fence_view_async_tmem_load()
|
||||
acc_scale_pv = tVLoadRmem_c6[0]
|
||||
|
||||
tTMrO = cute.make_rmem_tensor((tTMEM_LOADcO.shape, o_col_tiles), self.qk_acc_dtype)
|
||||
for i in range(o_col_tiles):
|
||||
tTMrO_i_ = tTMrO[None, i]
|
||||
tTMrO_i_layout = cute.composition(tTMrO_i_.layout, cute.make_layout(tTMrO.shape[0]))
|
||||
tTMrO_i = cute.make_tensor(tTMrO_i_.iterator, tTMrO_i_layout)
|
||||
tTMEM_LOADtO_i = cute.make_tensor(tTMEM_LOADtO.iterator + i * corr_tile_size, tTMEM_LOADtO.layout)
|
||||
tTMEM_STOREtO_i = cute.make_tensor(tTMEM_STOREtO.iterator + i * corr_tile_size, tTMEM_STOREtO.layout)
|
||||
cute.copy(o_tiled_tmem_load, tTMEM_LOADtO_i, tTMrO_i)
|
||||
for j in cutlass.range(cute.size(tTMrO_i), vectorize=True):
|
||||
tTMrO_i[j] = tTMrO_i[j] * acc_scale_pv
|
||||
cute.copy(o_tiled_tmem_store, tTMrO_i, tTMEM_STOREtO_i)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# Rescale row_sum
|
||||
row_sum = row_sum * acc_scale
|
||||
|
||||
# --- C7: Compute P = exp2((S - row_max_safe) * scale) ---
|
||||
minus_row_max_scale = (cutlass.Float32(0.0) - row_max_safe) * scale
|
||||
|
||||
# Register bridge (FMHA pattern: FP32 backing + BF16 view)
|
||||
rP_words = cute.make_rmem_tensor(tTMEM_STOREcP.shape, self.qk_acc_dtype)
|
||||
rP_bf16 = cute.make_tensor(cute.recast_ptr(rP_words.iterator, dtype=self.q_dtype), tTMEM_LOADrS.layout)
|
||||
|
||||
frg_cnt = 4
|
||||
frg_tile = cute.size(tTMEM_LOADrS) // frg_cnt
|
||||
tTMEM_LOADrS_frg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(frg_tile))
|
||||
rP_bf16_frg = cute.logical_divide(rP_bf16, cute.make_layout(frg_tile))
|
||||
|
||||
# Scale S, compute exp2, store through register bridge
|
||||
for j in range(frg_cnt):
|
||||
for k in cutlass.range(cute.size(tTMEM_LOADrS_frg, mode=[0]), vectorize=True):
|
||||
tTMEM_LOADrS_frg[k, j] = tTMEM_LOADrS_frg[k, j] * scale + minus_row_max_scale
|
||||
tTMEM_LOADrS_frg[k, j] = cute.math.exp2(tTMEM_LOADrS_frg[k, j], fastmath=True)
|
||||
s_vec = tTMEM_LOADrS_frg[None, j].load()
|
||||
rP_bf16_frg[None, j].store(s_vec.to(self.q_dtype))
|
||||
|
||||
# Store P to TMEM
|
||||
cute.copy(tiled_tmem_store, rP_words, tTMEM_STOREtP)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
si_handle.release()
|
||||
softmax_done_bar.arrive()
|
||||
|
||||
# --- C8: Row sum accumulation (CUTLASS FMHA packed f32x2 pattern) ---
|
||||
# P values still in tTMEM_LOADrS registers.
|
||||
# 4 accumulators for 4 reduction_unroll columns.
|
||||
local_row_sum_0 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_1 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_2 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_3 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
|
||||
reduction_unroll = 4
|
||||
rfrg_tile = cute.size(tTMEM_LOADrS) // reduction_unroll
|
||||
tTMEM_LOADrS_rfrg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(rfrg_tile))
|
||||
|
||||
for j in cutlass.range_constexpr(0, cute.size(tTMEM_LOADrS_rfrg, mode=[0]), 2):
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_0, (tTMEM_LOADrS_rfrg[j, 0], tTMEM_LOADrS_rfrg[j + 1, 0]))
|
||||
local_row_sum_1 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_1, (tTMEM_LOADrS_rfrg[j, 1], tTMEM_LOADrS_rfrg[j + 1, 1]))
|
||||
local_row_sum_2 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_2, (tTMEM_LOADrS_rfrg[j, 2], tTMEM_LOADrS_rfrg[j + 1, 2]))
|
||||
local_row_sum_3 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_3, (tTMEM_LOADrS_rfrg[j, 3], tTMEM_LOADrS_rfrg[j + 1, 3]))
|
||||
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(local_row_sum_0, local_row_sum_1)
|
||||
local_row_sum_2 = cute.arch.add_packed_f32x2(local_row_sum_2, local_row_sum_3)
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(local_row_sum_0, local_row_sum_2)
|
||||
tile_sum = local_row_sum_0[0] + local_row_sum_0[1]
|
||||
|
||||
row_sum = row_sum + tile_sum
|
||||
|
||||
# --- C9: Final normalization via O TMEM rescale ---
|
||||
pv_done_bar.arrive_and_wait()
|
||||
|
||||
# Compute inv_row_sum from P in TMEM using PV partition.
|
||||
# P was stored by softmax loop into TMEM at offset tmem_p0_offset.
|
||||
# PV partition maps thread N to PV row N, so reading P via PV partition
|
||||
# gives the correct per-row P values to sum.
|
||||
# This avoids the QK→PV row mapping mismatch (QK: N->N//4, PV: N->N).
|
||||
|
||||
# P is stored as BF16 in TMEM at tmem_p0_offset.
|
||||
# We need to read it via PV TMEM load and sum the values.
|
||||
# P has shape (128, HEAD_DIM//2) in FP32 columns (64 BF16 = 32 FP32 cols).
|
||||
# Use the P TMEM load partition (PV A-fragment read).
|
||||
|
||||
# Actually, P was stored via QK C-fragment store (St32x32bOp Repetition(32)).
|
||||
# To read it via PV partition, we need a PV-partitioned load from the P region.
|
||||
# Let's use the same o_tiled_tmem_load but pointed at P's TMEM offset.
|
||||
|
||||
# P occupies TMEM columns [tmem_p0_offset, tmem_p0_offset + p_cols_fp32)
|
||||
# In the PV C-fragment, P is the A-fragment. We can use tOrP0's layout.
|
||||
# tOrP0 was set up with offset for PV MMA read.
|
||||
|
||||
# Simpler: sum O across columns to get unnormalized row sum, then normalize.
|
||||
# For V=identity, O = P@V = sum(P per row). So O.sum(dim=-1) = row_sum.
|
||||
# For arbitrary V, O = P@V. O.sum(dim=-1) = sum_j(P@V)[j] = sum_j(sum_i P[i]*V[i,j])
|
||||
# This is NOT sum(P). So this trick only works for V=identity.
|
||||
|
||||
# Correct approach: read P from TMEM, sum it per PV row.
|
||||
# P is at TMEM offset tmem_p0_offset, stored as BF16 with St32x32bOp.
|
||||
# P shape in TMEM: 128 rows x (HEAD_DIM BF16 = 32 FP32 cols)
|
||||
# We can read P using Ld32x32bOp(Repetition(corr_tile_size)) via PV O-partition.
|
||||
|
||||
# Use PV O TMEM load to read from P region instead of O region
|
||||
p_col_tiles = p_cols_fp32 // corr_tile_size # 32 // 16 = 2
|
||||
pv_row_sum = cutlass.Float32(0.0)
|
||||
for i in range(p_col_tiles):
|
||||
# Read P tile from TMEM at P offset (not O offset)
|
||||
tTMEM_LOADtP_i = cute.make_tensor(
|
||||
tTMEM_LOADtO.iterator + (self.tmem_p0_offset - self.tmem_o0_offset) + i * corr_tile_size,
|
||||
tTMEM_LOADtO.layout)
|
||||
tTMrP_i = cute.make_rmem_tensor(tTMEM_LOADcO.shape, self.qk_acc_dtype)
|
||||
cute.copy(o_tiled_tmem_load, tTMEM_LOADtP_i, tTMrP_i)
|
||||
# Use .reduce(SUM) instead of scalar accumulation (vectorizer can't handle scalar in vectorized loop)
|
||||
tile_p_sum = tTMrP_i.load().reduce(cute.ReductionOp.ADD, cutlass.Float32(0.0), 0)
|
||||
pv_row_sum = pv_row_sum + tile_p_sum
|
||||
|
||||
inv_row_sum = cutlass.Float32(1.0) / pv_row_sum
|
||||
|
||||
# Normalize O in TMEM using PV-correct inv_row_sum
|
||||
tTMrO_final = cute.make_rmem_tensor((tTMEM_LOADcO.shape, o_col_tiles), self.qk_acc_dtype)
|
||||
for i in range(o_col_tiles):
|
||||
tTMrO_i_ = tTMrO_final[None, i]
|
||||
tTMrO_i_layout = cute.composition(tTMrO_i_.layout, cute.make_layout(tTMrO_final.shape[0]))
|
||||
tTMrO_i = cute.make_tensor(tTMrO_i_.iterator, tTMrO_i_layout)
|
||||
tTMEM_LOADtO_i = cute.make_tensor(
|
||||
tTMEM_LOADtO.iterator + i * corr_tile_size, tTMEM_LOADtO.layout)
|
||||
tTMEM_STOREtO_i = cute.make_tensor(
|
||||
tTMEM_STOREtO.iterator + i * corr_tile_size, tTMEM_STOREtO.layout)
|
||||
cute.copy(o_tiled_tmem_load, tTMEM_LOADtO_i, tTMrO_i)
|
||||
for j in cutlass.range(cute.size(tTMrO_i), vectorize=True):
|
||||
tTMrO_i[j] = tTMrO_i[j] * inv_row_sum
|
||||
cute.copy(o_tiled_tmem_store, tTMrO_i, tTMEM_STOREtO_i)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# Now O in TMEM is normalized. Use standard epilogue_tma_store with identity.
|
||||
tCtO_base = cute.make_tensor(tmem_ptr + self.tmem_o0_offset, tCtO_fake.layout)
|
||||
acc_cons_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Consumer, self.num_acc_stage)
|
||||
c_grp = pipeline.CooperativeGroup(pipeline.Agent.Thread, 32 * len(self.epilogue_warp_id))
|
||||
c_pipe = pipeline.PipelineTmaStore.create(num_stages=self.num_c_stage, producer_group=c_grp)
|
||||
acc_cons_st = utils.gemm.sm100.epilogue_tma_store(
|
||||
self, tidx, warp_idx, tma_c, tCtO_base, sC, tCgC, epi_tile, 0,
|
||||
const_expr(lambda x: x),
|
||||
(0,0,0), acc_cons_st, acc_pipe, c_pipe)
|
||||
c_pipe.producer_tail()
|
||||
tmem.relinquish_alloc_permit()
|
||||
tmem.free(tmem_ptr)
|
||||
|
||||
|
||||
def test():
|
||||
import math
|
||||
torch.manual_seed(42)
|
||||
for n in [128, 256, 384]:
|
||||
m, hd = 128, HEAD_DIM
|
||||
q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
k = torch.randn(n, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
v = torch.randn(n, hd, dtype=torch.bfloat16, device="cuda")
|
||||
v_kernel = v.unsqueeze(-1)
|
||||
c = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
qf = q[:,:,0].float(); kf = k[:,:,0].float()
|
||||
attn = qf @ kf.T / math.sqrt(hd)
|
||||
ref = torch.softmax(attn, dim=-1) @ v.float()
|
||||
mQ = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
|
||||
mK = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
|
||||
mV = ct.from_dlpack(v_kernel).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_kernel))
|
||||
mC = ct.from_dlpack(c).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c))
|
||||
stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
|
||||
kernel = FmhaV3Softmax()
|
||||
print(f"n={n}: Compiling...", flush=True)
|
||||
compiled = cute.compile(kernel, mQ, mK, mV, mC, stream)
|
||||
print(f"n={n}: tmem: s0={kernel.tmem_s0_offset} p0={kernel.tmem_p0_offset} o0={kernel.tmem_o0_offset} vec={kernel.tmem_vec_offset} alloc={kernel.num_tmem_alloc_cols}", flush=True)
|
||||
print(f"n={n}: Running...", flush=True)
|
||||
compiled(mQ, mK, mV, mC, stream)
|
||||
torch.cuda.synchronize()
|
||||
out = c[:,:,0].float()
|
||||
cos = torch.nn.functional.cosine_similarity(out.flatten().unsqueeze(0), ref.flatten().unsqueeze(0)).item()
|
||||
max_err = (out - ref).abs().max().item()
|
||||
print(f"FMHA softmax n={n}: cosine {cos:.6f} max_err {max_err:.6f} {'PASS' if cos >= 0.999 else 'FAIL'}", flush=True)
|
||||
|
||||
if __name__ == "__main__":
|
||||
test()
|
||||
|
||||
|
||||
@@ -1,8 +1,44 @@
|
||||
"""
|
||||
FMHA v3: QK -> softmax -> PV with KV-tile interleaving.
|
||||
Bug 4b fix (FMHA pattern): P store uses QK C-fragment layout composition,
|
||||
NOT PV A-fragment layout. Register bridge: FP32 backing (store partition shape)
|
||||
recast to BF16 view (QK-load layout).
|
||||
FMHA v3 Stage-C Multi-Tile (paired TMEM/SMEM atoms, reference-style epilogue).
|
||||
|
||||
Two structural rules we had to learn the hard way:
|
||||
|
||||
(A) Pipeline handle's `.count` is NOT a GMEM tile coordinate. Whatever it is at
|
||||
runtime (phase, wrapped slot index, internal state), it is not a global
|
||||
tile counter and TMA copies don't consume it as one. Use the loop
|
||||
induction variable for GMEM, handle.index for SMEM.
|
||||
|
||||
(B) Hand-constructed TMEM load/store atoms (Ld32x32bOp + St32x32bOp built
|
||||
independently) DO NOT preserve register tile shape across a round-trip.
|
||||
A no-op TMEM-load-then-TMEM-store visibly corrupts data. Use the paired
|
||||
atoms from `utils.sm100.get_tmem_load_op` + `get_smem_store_op` — they
|
||||
are configured together for the same (mma_tiler, layout, dtype) combo
|
||||
and the register tile shape lines up. This is what the CUTLASS Blackwell
|
||||
FMHA reference does in `correction_epilog`.
|
||||
|
||||
Kernel structure:
|
||||
|
||||
1. Combined K+V pipeline (tx_count = K_bytes + V_bytes; one acquire per kt;
|
||||
K and V share the same barrier slot). SMEM slot via kvh.index, GMEM via
|
||||
the cutlass.range loop variable.
|
||||
|
||||
2. Reference-style epilogue (TMEM → reg → scale by 1/row_sum → FP32→BF16 in
|
||||
reg → SMEM via paired atoms → TMA SMEM→GMEM). One pass, no TMEM
|
||||
round-trip, no `epilogue_tma_store` helper. Inline TMA store + named
|
||||
barrier sync to substitute for what the helper would have done.
|
||||
|
||||
3. Online softmax row_max / row_sum tracking is correct, but the per-tile
|
||||
in-place TMEM O rescale (multiplying existing O by exp2(old_max - new_max)
|
||||
before PV[kt]) is currently DISABLED. Fixing that requires applying the
|
||||
same paired-atom pattern to a separate scratch SMEM buffer and bouncing
|
||||
PV's accumulator through it, which is substantial work. For now, the
|
||||
kernel is correct when row_max growth across tiles is mild. Long n with
|
||||
pronounced max growth will drift; the fix path is well-defined.
|
||||
|
||||
4. final_o_bar (32 MMA + 128 softmax threads). MMA arrives between
|
||||
acc_pipe.producer_commit and producer_tail; softmax arrives_and_waits
|
||||
before reading O. Order: producer_commit → final_o_bar.arrive() →
|
||||
producer_tail (reverse deadlocks).
|
||||
"""
|
||||
import torch, cutlass, cutlass.cute as cute, cutlass.utils as utils, cutlass.pipeline as pipeline
|
||||
from cutlass.cute.nvgpu import cpasync, tcgen05
|
||||
@@ -11,11 +47,16 @@ from cutlass.utils import LayoutEnum
|
||||
from cutlass.utils.tmem_allocator import find_tmem_tensor_col_offset
|
||||
import cuda.bindings.driver as cuda
|
||||
import cutlass.torch as ct
|
||||
import math
|
||||
|
||||
HEAD_DIM = 64
|
||||
|
||||
class FmhaV3:
|
||||
def __init__(self):
|
||||
|
||||
class FmhaV3StageCMulti:
|
||||
def __init__(self, s_k=128, scale_softmax=None):
|
||||
# s_k MUST equal actual sequence length n.
|
||||
self.s_k = s_k
|
||||
self.n_kv_tiles = s_k // 128
|
||||
self.acc_dtype = Float32; self.qk_acc_dtype = Float32
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
self.use_2cta_instrs = False; self.epilog_sync_bar_id = 1
|
||||
@@ -23,6 +64,8 @@ class FmhaV3:
|
||||
self.epilogue_warp_id = (0,1,2,3); self.mma_warp_id = 4; self.tma_warp_id = 5
|
||||
self.threads_per_cta = 192; self.num_c_stage = 2
|
||||
self.kv_stage = 2; self.q_stage = 1; self.num_c_stage = 2
|
||||
self.scale_softmax = scale_softmax if scale_softmax is not None else 1.0 / math.sqrt(HEAD_DIM)
|
||||
self.scale_softmax_log2 = self.scale_softmax * math.log2(math.e)
|
||||
|
||||
def _setup(self, qk_mma, pv_mma):
|
||||
qk_ik = cute.size(qk_mma.shape_mnk, mode=[2])
|
||||
@@ -45,36 +88,35 @@ class FmhaV3:
|
||||
pv_thr = pv_mma.get_slice(0); pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
self.tmem_s0_offset = 0; self.tmem_p0_offset = 32
|
||||
# P occupies [tmem_p0_offset, tmem_p0_offset + p_cols_fp32)
|
||||
# S occupies [0, qk_mma_tiler[1]) = [0, 128)
|
||||
# O must NOT overlap P. Place O after max(S end, P end), aligned to 32.
|
||||
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
|
||||
p_end = self.tmem_p0_offset + p_cols_fp32 # 32 + 64 = 96
|
||||
s_cols = self.qk_mma_tiler[1] # 128
|
||||
o_after = max(s_cols, p_end) # 128
|
||||
self.tmem_o0_offset = ((o_after + 31) // 32) * 32 # align to 32 = 128
|
||||
o_cols = find_tmem_tensor_col_offset(tOtO) # footprint of O
|
||||
p_end = self.tmem_p0_offset + p_cols_fp32
|
||||
s_cols = self.qk_mma_tiler[1]
|
||||
o_after = max(s_cols, p_end)
|
||||
self.tmem_o0_offset = ((o_after + 31) // 32) * 32
|
||||
o_cols = find_tmem_tensor_col_offset(tOtO)
|
||||
total = self.tmem_o0_offset + o_cols
|
||||
# Must be multiple of 32 AND power of 2
|
||||
self.num_tmem_alloc_cols = 1
|
||||
while self.num_tmem_alloc_cols < total:
|
||||
self.num_tmem_alloc_cols *= 2
|
||||
cta = cute.size(qk_mma.thr_id.shape)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0))
|
||||
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))
|
||||
self.q_tx_bytes = cute.size_in_bytes(self.q_dtype, q_s) * cta
|
||||
self.kv_tx_bytes = cute.size_in_bytes(self.q_dtype, k_s) * cta
|
||||
# Combined barrier: tx_count covers BOTH K and V transfers per acquire.
|
||||
self.kv_tx_bytes = (cute.size_in_bytes(self.q_dtype, k_s) +
|
||||
cute.size_in_bytes(self.q_dtype, v_s)) * cta
|
||||
|
||||
@cute.jit
|
||||
def __call__(self, q, k, v, c, stream):
|
||||
self.q_dtype = q.element_type; self.o_dtype = c.element_type; self.c_dtype = self.o_dtype
|
||||
self.a_major = LayoutEnum.from_tensor(q).mma_major_mode()
|
||||
self.b_major = LayoutEnum.from_tensor(k).mma_major_mode()
|
||||
# FMHA-style V: reconstruct as (HEAD_DIM, s_k, 1) MN-major
|
||||
v_fmha = cute.make_tensor(
|
||||
v.iterator,
|
||||
cute.make_layout(
|
||||
(HEAD_DIM, 128, 1),
|
||||
stride=(1, HEAD_DIM, HEAD_DIM * 128),
|
||||
(HEAD_DIM, self.s_k, 1),
|
||||
stride=(1, HEAD_DIM, HEAD_DIM * self.s_k),
|
||||
),
|
||||
)
|
||||
self.v_major = LayoutEnum.from_tensor(v_fmha).mma_major_mode()
|
||||
@@ -107,9 +149,13 @@ class FmhaV3:
|
||||
smem = utils.SmemAllocator(); st = smem.allocate(SS)
|
||||
|
||||
qp,qc = pipeline.PipelineTmaUmma.create(barrier_storage=st.q_bar.data_ptr(),num_stages=self.q_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.q_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
|
||||
# Combined K+V pipeline: each stage carries BOTH K and V loaded together.
|
||||
kvp,kvc = pipeline.PipelineTmaUmma.create(barrier_storage=st.kv_bar.data_ptr(),num_stages=self.kv_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.kv_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
|
||||
s_prod,s_cons = pipeline.PipelineUmmaAsync.create(barrier_storage=st.s_bar.data_ptr(),num_stages=1,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32*len(self.epilogue_warp_id))).make_participants()
|
||||
softmax_done_bar = pipeline.NamedBarrier(barrier_id=3, num_threads=32 + 32*len(self.epilogue_warp_id))
|
||||
# Final-O sync: MMA arrives between producer_commit and producer_tail;
|
||||
# softmax arrives_and_waits before reading O for the final normalize.
|
||||
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)
|
||||
@@ -134,7 +180,7 @@ class FmhaV3:
|
||||
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)]
|
||||
tAgQ = tAgQ[(None,0,None,0)]; tBgK = tBgK[(None,None,0,0)]; tVgV = tVgV[(None,0,None,0)]
|
||||
|
||||
tCrQ = qk_mma.make_fragment_A(sQ); tCrK = qk_mma.make_fragment_B(sK)
|
||||
tCrV = pv_mma.make_fragment_B(sV)
|
||||
@@ -146,7 +192,6 @@ class FmhaV3:
|
||||
tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
tOtO0 = cute.make_tensor(tOtO.iterator + self.tmem_o0_offset, tOtO.layout)
|
||||
|
||||
# --- PV read view (for MMA only, NOT for softmax store) ---
|
||||
tP = cute.make_tensor(tStS.iterator, p_tmem_s.outer)
|
||||
tOrP_base = pv_thr.make_fragment_A(tP)
|
||||
tOrP = tOrP_base[(None,None,None,0)]
|
||||
@@ -158,22 +203,25 @@ class FmhaV3:
|
||||
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
|
||||
# ===== TMA LOAD warp =====
|
||||
# NOTE: using kt from cutlass.range works for n=128 (single tile).
|
||||
# Multi-tile (n>128) loads from tile 0 only — the JIT constant-folds kt.
|
||||
# TODO: fix multi-tile TMA indexing (kv_coord pattern from diag test).
|
||||
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, Int32(0))], tAsQ[(None, qh.index)], tma_bar_ptr=qh.barrier)
|
||||
qp.tail()
|
||||
kvp.reset(); pk = kvp.try_acquire()
|
||||
for kt in cutlass.range(n_kv_tiles,unroll=1):
|
||||
kh = kvp.acquire_and_advance(pk)
|
||||
cute.copy(tma_k,tBgK[(None,kh.count)],tBsK[(None,kh.index)],tma_bar_ptr=kh.barrier)
|
||||
pk = cutlass.Boolean(1)
|
||||
vh = kvp.acquire_and_advance(pk)
|
||||
cute.copy(tma_v,tVgV[(None,vh.count)],tVsV[(None,vh.index)],tma_bar_ptr=vh.barrier)
|
||||
for kt in cutlass.range(0, n_kv_tiles, 1, unroll=1):
|
||||
kvh = kvp.acquire_and_advance(pk)
|
||||
cute.copy(tma_k, tBgK[(None, kt)], tBsK[(None, kvh.index)], tma_bar_ptr=kvh.barrier)
|
||||
cute.copy(tma_v, tVgV[(None, kt)], tVsV[(None, kvh.index)], tma_bar_ptr=kvh.barrier)
|
||||
pk = cutlass.Boolean(1)
|
||||
kvp.tail()
|
||||
|
||||
# MMA
|
||||
# ===== MMA warp =====
|
||||
# One wait per kt; same slot index used for both K (QK) and V (PV).
|
||||
# Release happens AFTER PV — combined slot stays held across QK+PV.
|
||||
if warp_idx == self.mma_warp_id:
|
||||
tmem.wait_for_alloc()
|
||||
qc.reset(); qh = qc.wait_and_advance(); qh.release()
|
||||
@@ -181,147 +229,269 @@ class FmhaV3:
|
||||
acc_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
|
||||
acc_pipe.producer_acquire(acc_st)
|
||||
for kt in range(n_kv_tiles):
|
||||
kh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
kvh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
sh = s_prod.acquire_and_advance()
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
for kb in cutlass.range(cute.size(tCrQ,mode=[2]), unroll_full=True):
|
||||
cute.gemm(qk_mma, tStS0, tCrQ[(None,None,kb,0)], tCrK[(None,None,kb,kh.index)], tStS0)
|
||||
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)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
sh.commit(); kh.release()
|
||||
sh.commit()
|
||||
softmax_done_bar.arrive_and_wait()
|
||||
vh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, kt != 0)
|
||||
for kb in cutlass.range(cute.size(tOrP0,mode=[2]), unroll_full=True):
|
||||
cute.gemm(pv_mma, tOtO0, tOrP0[(None,None,kb)], tCrV[(None,None,kb,vh.index)], tOtO0)
|
||||
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,kvh.index)], tOtO0)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
vh.release()
|
||||
kvh.release()
|
||||
acc_pipe.producer_commit(acc_st); acc_st.advance()
|
||||
# Signal softmax FIRST so it can run normalize + epilogue. Then
|
||||
# wait for the epilogue's consumer-release in producer_tail.
|
||||
# Reverse order deadlocks: producer_tail blocks waiting for
|
||||
# consumer release; softmax blocks at final_o_bar waiting for
|
||||
# MMA arrive; the epilogue (which does the release) is gated
|
||||
# behind softmax's final_o_bar wait. Cycle.
|
||||
final_o_bar.arrive()
|
||||
acc_pipe.producer_tail(acc_st)
|
||||
|
||||
# EPILOGUE
|
||||
# ===== 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 (QK C-fragment layout) ---
|
||||
# S load
|
||||
tmem_load_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
tiled_tmem_load = tcgen05.make_tmem_copy(tmem_load_atom, tStS0)
|
||||
thr_load = tiled_tmem_load.get_slice(sfw_idx)
|
||||
tTMEM_LOADtS = thr_load.partition_S(tStS0)
|
||||
|
||||
# S coordinate tensor (QK C-fragment)
|
||||
cS = cute.make_identity_tensor((self.qk_mma_tiler[0], self.qk_mma_tiler[1]))
|
||||
tScS = qk_thr.partition_C(cS)
|
||||
tTMEM_LOADcS = thr_load.partition_D(tScS)
|
||||
|
||||
# --- P store (QK C-fragment layout composition, FMHA pattern) ---
|
||||
# P logical columns = PV K = QK N = pv_mma_tiler[2]
|
||||
# Packed FP32 columns: BF16 pairs packed into FP32 words
|
||||
# P store
|
||||
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
|
||||
# BF16: 128 * 16 / 32 = 64
|
||||
|
||||
# P TMEM destination: QK C-fragment layout composed with P sub-tile
|
||||
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,
|
||||
)
|
||||
|
||||
# P TMEM store atom and tiled copy
|
||||
tmem_store_atom = cute.make_copy_atom(
|
||||
tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(32)),
|
||||
self.qk_acc_dtype,
|
||||
)
|
||||
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)
|
||||
|
||||
# P coordinate tensor: QK C-fragment coordinate composed with P sub-tile
|
||||
tScP_layout = cute.composition(
|
||||
tScS.layout,
|
||||
cute.make_layout((self.pv_mma_tiler[0], p_cols_fp32)),
|
||||
)
|
||||
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)
|
||||
|
||||
row_max = -Float32.inf
|
||||
row_sum = Float32(0.0)
|
||||
scale_log2 = Float32(self.scale_softmax_log2)
|
||||
|
||||
# Per-tile softmax loop.
|
||||
# Online softmax row_max/row_sum tracking is maintained, but the
|
||||
# in-place TMEM O rescale (which would multiply existing O by
|
||||
# exp2(old_max - new_max) before PV[kt]) is DISABLED — this is the
|
||||
# correctness compromise for hand-paired TMEM atoms not working.
|
||||
# The fix path is to integrate the rescale into the same paired
|
||||
# tmem_load/smem_store epilogue pattern we use below for normalize.
|
||||
# For now: kernel is correct when row_max growth across tiles is
|
||||
# mild (typical for short n with random data); for very long n
|
||||
# the missing rescale shows as accuracy drift.
|
||||
for kt in range(n_kv_tiles):
|
||||
si_handle = s_cons.wait_and_advance()
|
||||
|
||||
# Load S from TMEM (FP32, QK C-fragment layout)
|
||||
# Load S[kt]
|
||||
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()
|
||||
|
||||
# Register bridge (FMHA pattern):
|
||||
# rP_words: FP32 backing store with store-partition shape
|
||||
# rP_bf16: BF16 view over same registers using QK-load layout
|
||||
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,
|
||||
)
|
||||
|
||||
# Fragmented load→convert→store:
|
||||
# Load S as FP32, convert to BF16, store through rP_bf16 view
|
||||
# Pass 1: update row_max (in log2-domain, fused with scale).
|
||||
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)
|
||||
|
||||
# row_sum rescale (correct even without O rescale — row_sum
|
||||
# is a register variable, not in TMEM).
|
||||
# row_max is already in scaled domain, so no extra scale_log2.
|
||||
acc_scale_ = 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
|
||||
|
||||
# Pass 2: P = exp2((S - new_max) * log2), accumulate row_sum,
|
||||
# store BF16 P through the FP32-backed 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 = Float32(0.0) - row_max_safe
|
||||
|
||||
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
|
||||
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))
|
||||
|
||||
# Copy packed FP32 backing registers to TMEM
|
||||
cute.copy(tiled_tmem_store, rP_words, tTMEM_STOREtP)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
si_handle.release()
|
||||
softmax_done_bar.arrive()
|
||||
|
||||
# === Reference-style scaled epilogue (no TMEM round-trip) ===
|
||||
#
|
||||
# Pattern (mirrors CUTLASS Blackwell FMHA reference's
|
||||
# correction_epilog): for each column sub-tile,
|
||||
# 1. TMEM -> registers via PAIRED tmem_load atom
|
||||
# 2. scale in registers (1/row_sum)
|
||||
# 3. FP32 -> BF16 conversion in registers
|
||||
# 4. registers -> SMEM via PAIRED smem_store atom
|
||||
# Then TMA SMEM -> GMEM as a separate step.
|
||||
#
|
||||
# Critical: the load and store atoms MUST be a matched pair.
|
||||
# Independently constructed Ld32x32bOp + St32x32bOp atoms (the
|
||||
# previous code) don't preserve the register tile shape, so even a
|
||||
# no-op load+store corrupts data. Using utils.blackwell_helpers
|
||||
# (sm100_utils) gives a paired set keyed to the same epi_subtile.
|
||||
|
||||
# Wait for MMA's PV[N-1] to commit before reading O.
|
||||
final_o_bar.arrive_and_wait()
|
||||
|
||||
# === O normalization via TMEM load → scale → TMEM store ===
|
||||
# Matches CUTLASS reference's correction_rescale pattern exactly.
|
||||
|
||||
corr_tile_size = 16
|
||||
|
||||
cO = cute.make_identity_tensor((self.pv_mma_tiler[0], self.pv_mma_tiler[1]))
|
||||
tOcO = pv_thr.partition_C(cO)
|
||||
|
||||
tOtO_i_layout = cute.composition(tOtO0.layout, cute.make_layout((128, corr_tile_size)))
|
||||
tOcO_i_layout = cute.composition(tOcO.layout, cute.make_layout((128, corr_tile_size)))
|
||||
|
||||
tOtO_i = cute.make_tensor(tOtO0.iterator, tOtO_i_layout)
|
||||
tOcO_i = cute.make_tensor(tOcO.iterator, tOcO_i_layout)
|
||||
|
||||
tmem_load_atom = cute.make_copy_atom(
|
||||
tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(corr_tile_size)),
|
||||
self.acc_dtype,
|
||||
)
|
||||
tmem_store_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_atom, tOtO_i)
|
||||
tiled_tmem_store_o = tcgen05.make_tmem_copy(tmem_store_atom, tOtO_i)
|
||||
|
||||
thr_tmem_load_o = tiled_tmem_load_o.get_slice(sfw_idx)
|
||||
thr_tmem_store_o = tiled_tmem_store_o.get_slice(sfw_idx)
|
||||
|
||||
tTMEM_LOADtO = thr_tmem_load_o.partition_S(tOtO_i)
|
||||
tTMEM_LOADcO = thr_tmem_load_o.partition_D(tOcO_i)
|
||||
tTMEM_STOREtO = thr_tmem_store_o.partition_D(tOtO_i)
|
||||
|
||||
# 2D register tensor: (frg_shape, n_corr_tiles)
|
||||
tTMrO = cute.make_rmem_tensor(
|
||||
(tTMEM_LOADcO.shape, 128 // corr_tile_size), self.acc_dtype
|
||||
)
|
||||
|
||||
inv_row_sum = Float32(1.0) / row_sum
|
||||
|
||||
for i in range(HEAD_DIM // corr_tile_size):
|
||||
tTMrO_i_ = tTMrO[None, i]
|
||||
tTMrO_i_layout = cute.composition(
|
||||
tTMrO_i_.layout, cute.make_layout(tTMrO.shape[0])
|
||||
)
|
||||
tTMrO_i = cute.make_tensor(tTMrO_i_.iterator, tTMrO_i_layout)
|
||||
tTMEM_LOADtO_i = cute.make_tensor(
|
||||
tTMEM_LOADtO.iterator + i * corr_tile_size, tTMEM_LOADtO.layout
|
||||
)
|
||||
tTMEM_STOREtO_i = cute.make_tensor(
|
||||
tTMEM_STOREtO.iterator + i * corr_tile_size, tTMEM_STOREtO.layout
|
||||
)
|
||||
|
||||
cute.copy(tiled_tmem_load_o, tTMEM_LOADtO_i, tTMrO_i)
|
||||
for j in cutlass.range(cute.size(tTMrO_i), vectorize=True):
|
||||
tTMrO_i[j] = tTMrO_i[j] * inv_row_sum
|
||||
cute.copy(tiled_tmem_store_o, tTMrO_i, tTMEM_STOREtO_i)
|
||||
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# Standard epilogue: TMEM → SMEM → GMEM via TMA store.
|
||||
# O in TMEM is now scaled by 1/row_sum.
|
||||
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)
|
||||
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)
|
||||
acc_cons_st = utils.gemm.sm100.epilogue_tma_store(
|
||||
self, tidx, warp_idx, tma_c, tCtO_base, sC, tCgC, epi_tile,
|
||||
0, const_expr(lambda x: x), (0, 0, 0),
|
||||
acc_cons_st, acc_pipe, c_pipe,
|
||||
)
|
||||
c_pipe.producer_tail()
|
||||
|
||||
tmem.relinquish_alloc_permit()
|
||||
tmem.free(tmem_ptr)
|
||||
|
||||
|
||||
def test():
|
||||
torch.manual_seed(42)
|
||||
for n in [128]:
|
||||
for n in [128, 256, 512, 1024]:
|
||||
torch.manual_seed(42)
|
||||
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 passed as (n, hd) row-major — FMHA-style reconstruction inside kernel
|
||||
v_kernel = v.unsqueeze(-1)
|
||||
c = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device='cuda')
|
||||
qf = q[:,:,0].float(); kf = k[:,:,0].float()
|
||||
ref = (qf @ kf.T).bfloat16().float() @ v.float()
|
||||
|
||||
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 = FmhaV3()
|
||||
|
||||
# Each n requires its own compiled kernel (s_k is compile-time).
|
||||
kernel = FmhaV3StageCMulti(s_k=n)
|
||||
print(f'n={n}: Compiling...', flush=True)
|
||||
compiled = cute.compile(kernel, mQ, mK, mV, mC, stream)
|
||||
print(f'n={n}: tmem_offsets: s0={kernel.tmem_s0_offset} p0={kernel.tmem_p0_offset} o0={kernel.tmem_o0_offset} alloc={kernel.num_tmem_alloc_cols}', flush=True)
|
||||
print(f'n={n}: Running...', flush=True)
|
||||
print(f'n={n}: tmem s0={kernel.tmem_s0_offset} p0={kernel.tmem_p0_offset} '
|
||||
f'o0={kernel.tmem_o0_offset} alloc={kernel.num_tmem_alloc_cols} '
|
||||
f'kv_tx_bytes={kernel.kv_tx_bytes}', flush=True)
|
||||
compiled(mQ, mK, mV, mC, stream)
|
||||
torch.cuda.synchronize()
|
||||
out = c[:,:,0].float()
|
||||
cos = torch.nn.functional.cosine_similarity(out.flatten().unsqueeze(0), ref.flatten().unsqueeze(0)).item()
|
||||
print(f'FMHA v3 n={n} V=ones: cosine {cos:.6f} {"PASS" if cos >= 0.99 else "FAIL"}')
|
||||
|
||||
out = c[:, :, 0].float()
|
||||
cos = torch.nn.functional.cosine_similarity(
|
||||
out.flatten().unsqueeze(0), ref.flatten().unsqueeze(0)
|
||||
).item()
|
||||
max_abs = (out - ref).abs().max().item()
|
||||
n_tiles = n // 128
|
||||
print(f'FMHA Stage-C Multi n={n} ({n_tiles} kv tiles): '
|
||||
f'cos {cos:.6f} max_abs {max_abs:.4f} '
|
||||
f'{"PASS" if cos >= 0.99 else "FAIL"}')
|
||||
if cos < 0.99:
|
||||
print(f' out[0,:4]={out[0,:4].tolist()} ref[0,:4]={ref[0,:4].tolist()}')
|
||||
print(f' out[0,:4]={out[0,:4].tolist()}')
|
||||
print(f' ref[0,:4]={ref[0,:4].tolist()}')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test()
|
||||
test()
|
||||
@@ -1,466 +0,0 @@
|
||||
"""
|
||||
FMHA v3 Stage-C: Real softmax + O normalization.
|
||||
Builds on the 12w identity-softmax test by replacing identity softmax with
|
||||
online softmax (row_max, exp2 scaling, P store) and adding O normalization
|
||||
by row_sum before the epilogue writes to GMEM.
|
||||
"""
|
||||
import torch, cutlass, cutlass.cute as cute, cutlass.utils as utils, cutlass.pipeline as pipeline
|
||||
from cutlass.cute.nvgpu import cpasync, tcgen05
|
||||
from cutlass import Float32, BFloat16, Int32, Boolean, const_expr
|
||||
from cutlass.utils import LayoutEnum
|
||||
from cutlass.utils.tmem_allocator import find_tmem_tensor_col_offset
|
||||
import cuda.bindings.driver as cuda
|
||||
import cutlass.torch as ct
|
||||
import math
|
||||
|
||||
HEAD_DIM = 64
|
||||
|
||||
class FmhaV3StageC2:
|
||||
def __init__(self, s_k=128, scale_softmax=None):
|
||||
self.s_k = s_k
|
||||
self.acc_dtype = Float32; self.qk_acc_dtype = Float32; self.pv_acc_dtype = Float32
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
self.use_2cta_instrs = False; self.epilog_sync_bar_id = 1
|
||||
self.cluster_shape_mn = (1, 1); self.cta_group = tcgen05.CtaGroup.ONE
|
||||
# 12-warp layout
|
||||
self.softmax_warp_ids = (0, 1, 2, 3)
|
||||
self.correction_warp_ids = (4, 5, 6, 7)
|
||||
self.mma_warp_id = 8; self.tma_warp_id = 9
|
||||
self.epilogue_warp_id = (10,); self.empty_warp_id = 11
|
||||
self.threads_per_cta = 32 * 12
|
||||
# Pipeline stages
|
||||
self.mma_softmax_stage = 1; self.softmax_corr_stage = 1
|
||||
self.mma_corr_stage = 2; self.epi_stage = 2
|
||||
# TMA stages
|
||||
self.kv_stage = 2; self.q_stage = 1; self.num_c_stage = 2
|
||||
# Softmax
|
||||
self.scale_softmax = scale_softmax if scale_softmax is not None else 1.0 / math.sqrt(HEAD_DIM)
|
||||
self.scale_softmax_log2 = self.scale_softmax * math.log2(math.e)
|
||||
|
||||
def _setup(self, qk_mma, pv_mma):
|
||||
qk_ik = cute.size(qk_mma.shape_mnk, mode=[2])
|
||||
self.qk_mma_tiler = (128, 128, qk_ik * 4)
|
||||
pv_ik = cute.size(pv_mma.shape_mnk, mode=[2])
|
||||
self.pv_mma_tiler = (128, HEAD_DIM, pv_ik * (128 // pv_ik))
|
||||
self.mma_tiler = self.qk_mma_tiler
|
||||
self.cluster_layout_vmnk = cute.tiled_divide(cute.make_layout((1,1,1)), (qk_mma.thr_id.shape,))
|
||||
self.cta_tile_shape_mnk = (self.qk_mma_tiler[0]//cute.size(qk_mma.thr_id.shape), HEAD_DIM, self.qk_mma_tiler[2])
|
||||
self.c_layout = LayoutEnum.ROW_MAJOR
|
||||
self.epi_tile = utils.sm100.compute_epilogue_tile_shape(self.cta_tile_shape_mnk, False, self.c_layout, self.o_dtype)
|
||||
self.num_ab_stage = 1; self.num_acc_stage = 1
|
||||
self.q_smem_s = utils.sm100.make_smem_layout_a(qk_mma, self.qk_mma_tiler, self.q_dtype, self.q_stage)
|
||||
self.k_smem_s = utils.sm100.make_smem_layout_b(qk_mma, self.qk_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.v_smem_s = utils.sm100.make_smem_layout_b(pv_mma, self.pv_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.c_smem_s = utils.sm100.make_smem_layout_epi(self.o_dtype, self.c_layout, self.epi_tile, 2)
|
||||
self.p_tmem_s = utils.sm100.make_smem_layout_a(pv_mma, self.pv_mma_tiler, self.q_dtype, 1)
|
||||
qk_thr = qk_mma.get_slice(0); qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
pv_thr = pv_mma.get_slice(0); pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
self.tmem_s0_offset = 0; self.tmem_vec0_offset = 0; self.tmem_p0_offset = 32
|
||||
# P occupies [tmem_p0_offset, tmem_p0_offset + p_cols_fp32)
|
||||
# S occupies [0, qk_mma_tiler[1]) = [0, 128)
|
||||
# O must NOT overlap P. Place O after max(S end, P end), aligned to 32.
|
||||
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
|
||||
p_end = self.tmem_p0_offset + p_cols_fp32 # 32 + 64 = 96
|
||||
s_cols = self.qk_mma_tiler[1] # 128
|
||||
o_after = max(s_cols, p_end) # 128
|
||||
self.tmem_o0_offset = ((o_after + 31) // 32) * 32 # align to 32 = 128
|
||||
o_cols = find_tmem_tensor_col_offset(tOtO) # footprint of O
|
||||
total = self.tmem_o0_offset + o_cols
|
||||
# Must be multiple of 32 AND power of 2
|
||||
self.num_tmem_alloc_cols = 1
|
||||
while self.num_tmem_alloc_cols < total:
|
||||
self.num_tmem_alloc_cols *= 2
|
||||
cta = cute.size(qk_mma.thr_id.shape)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0))
|
||||
self.q_tx_bytes = cute.size_in_bytes(self.q_dtype, q_s) * cta
|
||||
self.kv_tx_bytes = cute.size_in_bytes(self.q_dtype, k_s) * cta
|
||||
|
||||
@cute.jit
|
||||
def __call__(self, q, k, v, c, stream):
|
||||
self.q_dtype = q.element_type; self.o_dtype = c.element_type; self.c_dtype = self.o_dtype
|
||||
self.a_major = LayoutEnum.from_tensor(q).mma_major_mode()
|
||||
self.b_major = LayoutEnum.from_tensor(k).mma_major_mode()
|
||||
# FMHA-style V: reconstruct as (HEAD_DIM, s_k, 1) MN-major
|
||||
v_fmha = cute.make_tensor(
|
||||
v.iterator,
|
||||
cute.make_layout(
|
||||
(HEAD_DIM, self.s_k, 1),
|
||||
stride=(1, HEAD_DIM, HEAD_DIM * self.s_k),
|
||||
),
|
||||
)
|
||||
self.v_major = LayoutEnum.from_tensor(v_fmha).mma_major_mode()
|
||||
self.c_layout = LayoutEnum.from_tensor(c)
|
||||
qk_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, self.a_major, self.b_major, self.qk_acc_dtype, self.cta_group, (128,128), tcgen05.OperandSource.SMEM)
|
||||
pv_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, cute.nvgpu.OperandMajorMode.K, self.v_major, self.qk_acc_dtype, self.cta_group, (128,HEAD_DIM), tcgen05.OperandSource.TMEM)
|
||||
self._setup(qk_mma, pv_mma)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0)); v_s = cute.slice_(self.v_smem_s,(None,None,None,0))
|
||||
tma_q,mQ = cute.nvgpu.make_tiled_tma_atom_A(utils.sm100.cluster_shape_to_tma_atom_A(self.cluster_shape_mn,qk_mma.thr_id),q,q_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_k,mK = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,qk_mma.thr_id),k,k_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_v,mV = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,pv_mma.thr_id),v_fmha,v_s,self.pv_mma_tiler,pv_mma,self.cluster_layout_vmnk.shape)
|
||||
epi_s = cute.select(self.c_smem_s,mode=[0,1])
|
||||
tma_c,mC = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(),c,epi_s,self.epi_tile)
|
||||
self._kernel(qk_mma,pv_mma,tma_q,mQ,tma_k,mK,tma_v,mV,tma_c,mC,self.cluster_layout_vmnk,self.q_smem_s,self.k_smem_s,self.v_smem_s,self.p_tmem_s,self.c_smem_s,self.epi_tile).launch(grid=(1,1,1),block=[self.threads_per_cta,1,1],stream=stream)
|
||||
|
||||
@cute.kernel
|
||||
def _kernel(self, qk_mma, pv_mma, tma_q, mQ, tma_k, mK, tma_v, mV, tma_c, mC, cl_vmnk, q_smem_s, k_smem_s, v_smem_s, p_tmem_s, c_smem_s, epi_tile):
|
||||
warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx())
|
||||
tidx, _, _ = cute.arch.thread_idx()
|
||||
if warp_idx == self.tma_warp_id:
|
||||
cpasync.prefetch_descriptor(tma_q); cpasync.prefetch_descriptor(tma_k); cpasync.prefetch_descriptor(tma_v); cpasync.prefetch_descriptor(tma_c)
|
||||
|
||||
@cute.struct
|
||||
class SS:
|
||||
q_bar: cute.struct.MemRange[cutlass.Int64, self.q_stage * 2]
|
||||
kv_bar: cute.struct.MemRange[cutlass.Int64, self.kv_stage * 2]
|
||||
mma_s_bar: cute.struct.MemRange[cutlass.Int64, self.mma_softmax_stage * 2]
|
||||
s_corr_bar: cute.struct.MemRange[cutlass.Int64, self.softmax_corr_stage * 2]
|
||||
mma_corr_bar: cute.struct.MemRange[cutlass.Int64, self.mma_corr_stage * 2]
|
||||
corr_epi_bar: cute.struct.MemRange[cutlass.Int64, self.epi_stage * 2]
|
||||
acc_bar: cute.struct.MemRange[cutlass.Int64, 2]
|
||||
tmem_dealloc: cutlass.Int64; holding: cutlass.Int32
|
||||
|
||||
smem = utils.SmemAllocator(); st = smem.allocate(SS)
|
||||
def cg(n): return pipeline.CooperativeGroup(pipeline.Agent.Thread, n)
|
||||
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()
|
||||
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()
|
||||
# MMA → Softmax: S ready
|
||||
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()
|
||||
# Softmax → Correction: vec ready
|
||||
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()
|
||||
# MMA → Correction: O ready
|
||||
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()
|
||||
# Correction → Epilogue: O in SMEM ready
|
||||
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()
|
||||
# Accumulator pipeline for epilogue (full pipeline, not participants)
|
||||
acc_pipe = pipeline.PipelineUmmaAsync.create(barrier_storage=st.acc_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)), cta_layout_vmnk=cl_vmnk, defer_sync=True)
|
||||
# TMEM alloc barrier: softmax + correction + MMA + epilogue
|
||||
tmem_bar = pipeline.NamedBarrier(barrier_id=2, num_threads=32 * len((*self.softmax_warp_ids, *self.correction_warp_ids, self.mma_warp_id, self.epilogue_warp_id)))
|
||||
# Softmax done barrier: MMA waits for softmax to produce P before starting PV
|
||||
softmax_done_bar = pipeline.NamedBarrier(barrier_id=3, num_threads=32 * len(self.softmax_warp_ids) + 32)
|
||||
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)
|
||||
if warp_idx == self.empty_warp_id:
|
||||
cute.arch.mbarrier_init(st.tmem_dealloc, 32 * len((*self.softmax_warp_ids, *self.correction_warp_ids)))
|
||||
cute.arch.mbarrier_init_fence()
|
||||
pipeline.pipeline_init_arrive(cluster_shape_mn=cl_vmnk, is_relaxed=True)
|
||||
|
||||
sQ = smem.allocate_tensor(element_type=self.q_dtype, layout=q_smem_s.outer, byte_alignment=128, swizzle=q_smem_s.inner)
|
||||
sK = smem.allocate_tensor(element_type=self.q_dtype, layout=k_smem_s.outer, byte_alignment=128, swizzle=k_smem_s.inner)
|
||||
sV = smem.allocate_tensor(element_type=self.q_dtype, layout=v_smem_s.outer, byte_alignment=128, swizzle=v_smem_s.inner)
|
||||
sC = smem.allocate_tensor(element_type=self.o_dtype, layout=c_smem_s.outer, byte_alignment=128, swizzle=c_smem_s.inner)
|
||||
|
||||
gQ = cute.local_tile(mQ, cute.slice_(self.qk_mma_tiler, (None, 0, None)), (None, None, None))
|
||||
gK = cute.local_tile(mK, cute.slice_(self.qk_mma_tiler, (0, None, None)), (None, None, None))
|
||||
gV = cute.local_tile(mV, cute.slice_(self.pv_mma_tiler, (0, None, None)), (None, None, None))
|
||||
gC = cute.local_tile(mC, cute.slice_(self.pv_mma_tiler, (None, None, 0)), (None, None, None))
|
||||
n_kv_tiles = cute.size(gK, mode=[3])
|
||||
|
||||
qk_thr = qk_mma.get_slice(0); pv_thr = pv_mma.get_slice(0)
|
||||
tCgQ = qk_thr.partition_A(gQ); tCgK = qk_thr.partition_B(gK)
|
||||
tCgV = pv_thr.partition_B(gV); tCgC = pv_thr.partition_C(gC)
|
||||
a_lay = cute.make_layout(cute.slice_(cl_vmnk, (0, 0, None, 0)).shape)
|
||||
tAsQ, tAgQ = cpasync.tma_partition(tma_q, 0, a_lay, cute.group_modes(sQ, 0, 3), cute.group_modes(tCgQ, 0, 3))
|
||||
b_lay = cute.make_layout(cute.slice_(cl_vmnk, (0, None, 0, 0)).shape)
|
||||
tBsK, tBgK = cpasync.tma_partition(tma_k, 0, b_lay, cute.group_modes(sK, 0, 3), cute.group_modes(tCgK, 0, 3))
|
||||
tVsV, tVgV = cpasync.tma_partition(tma_v, 0, b_lay, cute.group_modes(sV, 0, 3), cute.group_modes(tCgV, 0, 3))
|
||||
tAgQ = tAgQ[(None, 0, None, 0)]; tBgK = tBgK[(None, 0, None, 0)]; tVgV = tVgV[(None, 0, None, 0)]
|
||||
|
||||
tCrQ = qk_mma.make_fragment_A(sQ); tCrK = qk_mma.make_fragment_B(sK); tCrV = pv_mma.make_fragment_B(sV)
|
||||
qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
tOtO0 = cute.make_tensor(tOtO.iterator + self.tmem_o0_offset, tOtO.layout)
|
||||
tP = cute.make_tensor(tStS.iterator, p_tmem_s.outer)
|
||||
tOrP_base = pv_thr.make_fragment_A(tP)
|
||||
tOrP = tOrP_base[(None, None, None, 0)]
|
||||
tOrP0 = cute.make_tensor(tOrP.iterator + self.qk_acc_dtype.width // self.q_dtype.width * self.tmem_p0_offset, tOrP.layout)
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_as, 1))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_as, 1))
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# ==================== TMA WARP (9) ====================
|
||||
if warp_idx == self.tma_warp_id:
|
||||
qp.reset(); qh = qp.acquire_and_advance()
|
||||
cute.copy(tma_q, tAgQ[(None, qh.count)], tAsQ[(None, qh.index)], tma_bar_ptr=qh.barrier)
|
||||
qp.tail()
|
||||
kvp.reset(); pk = kvp.try_acquire()
|
||||
for kt in cutlass.range(n_kv_tiles, unroll=1):
|
||||
kh = kvp.acquire_and_advance(pk)
|
||||
cute.copy(tma_k, tBgK[(None, kh.count)], tBsK[(None, kh.index)], tma_bar_ptr=kh.barrier)
|
||||
pk = cutlass.Boolean(1)
|
||||
vh = kvp.acquire_and_advance(pk)
|
||||
cute.copy(tma_v, tVgV[(None, vh.count)], tVsV[(None, vh.index)], tma_bar_ptr=vh.barrier)
|
||||
pk = cutlass.Boolean(1)
|
||||
kvp.tail()
|
||||
|
||||
# ==================== MMA WARP (8) ====================
|
||||
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()
|
||||
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()
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
for kb in cutlass.range(cute.size(tCrQ, mode=[2]), unroll_full=True):
|
||||
cute.gemm(qk_mma, tStS0, tCrQ[(None, None, kb, 0)], tCrK[(None, None, kb, kh.index)], tStS0)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store(); sh.commit(); kh.release()
|
||||
# PV -> O (wait for softmax to produce P)
|
||||
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)
|
||||
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))
|
||||
|
||||
# ==================== 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()
|
||||
softmax_done_bar.arrive()
|
||||
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 (compute cS from scratch for correction warps)
|
||||
cS = cute.make_identity_tensor((self.qk_mma_tiler[0], self.qk_mma_tiler[1]))
|
||||
tScS = qk_thr.partition_C(cS)
|
||||
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[0]:
|
||||
tmem.wait_for_alloc()
|
||||
tmem_ptr = tmem.retrieve_ptr(self.qk_acc_dtype)
|
||||
epi_handle = corr_epi_cons.wait_and_advance()
|
||||
# Signal acc_pipe that O is ready (correction already normalized in TMEM)
|
||||
acc_prod_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, 1)
|
||||
acc_pipe.producer_acquire(acc_prod_st)
|
||||
acc_pipe.producer_commit(acc_prod_st); acc_prod_st.advance()
|
||||
acc_pipe.producer_tail(acc_prod_st)
|
||||
# Write O from TMEM to GMEM via epilogue_tma_store
|
||||
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_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()
|
||||
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()
|
||||
@@ -1,454 +0,0 @@
|
||||
"""
|
||||
FMHA v3 Stage-C: Real online softmax with full multi-tile support.
|
||||
|
||||
Integrated from:
|
||||
- test_fmha_v3.py (Stage A+B: QK→identity softmax→PV)
|
||||
- test_fmha_v3_stage_c_full.py (Stage C: single-tile real softmax)
|
||||
- fmha_v3_stage_c_example3.py (Multi-tile: combined K+V barrier, O rescale, final_o_bar)
|
||||
|
||||
Architecture (6-warp, 192 threads):
|
||||
Warps 0-3: Softmax + Epilogue (row_max, exp2, P store, O rescale, O normalize, TMA store)
|
||||
Warp 4: MMA (QK GEMM, PV GEMM)
|
||||
Warp 5: TMA (Q, K, V load)
|
||||
|
||||
Multi-tile key changes vs single-tile:
|
||||
1. Combined K+V barrier: one acquire_and_advance per kt, both K and V share kvh.barrier.
|
||||
kvh.count == kt naturally — no interleaving, no Python int in TMA coordinates.
|
||||
2. kv_tx_bytes covers BOTH K and V transfers per stage.
|
||||
3. V FMHA layout uses s_k as the sequence dimension (compile-time, not hardcoded 128).
|
||||
4. MMA: same slot index (kvh.index) for both K (QK) and V (PV). Release after PV.
|
||||
5. O rescale for kt > 0: exp2((old_max - new_max) * scale_log2) on O in TMEM.
|
||||
6. final_o_bar: MMA arrives between producer_commit and producer_tail;
|
||||
softmax arrives_and_wait before reading O for final normalize.
|
||||
7. s_k is a constructor parameter — each sequence length requires its own compiled kernel.
|
||||
"""
|
||||
import torch, cutlass, cutlass.cute as cute, cutlass.utils as utils, cutlass.pipeline as pipeline
|
||||
from cutlass.cute.nvgpu import cpasync, tcgen05
|
||||
from cutlass import Float32, BFloat16, Int32, Boolean, const_expr
|
||||
from cutlass.utils import LayoutEnum
|
||||
from cutlass.utils.tmem_allocator import find_tmem_tensor_col_offset
|
||||
import cuda.bindings.driver as cuda
|
||||
import cutlass.torch as ct
|
||||
import math
|
||||
|
||||
HEAD_DIM = 64
|
||||
|
||||
|
||||
class FmhaV3StageC:
|
||||
def __init__(self, s_k=128, scale_softmax=None):
|
||||
# s_k MUST equal actual sequence length n (compile-time constant for V layout).
|
||||
self.s_k = s_k
|
||||
self.n_kv_tiles = s_k // 128 # Python int — needed for range() unrolling
|
||||
self.acc_dtype = Float32; self.qk_acc_dtype = Float32
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
self.use_2cta_instrs = False; self.epilog_sync_bar_id = 1
|
||||
self.cluster_shape_mn = (1, 1); self.cta_group = tcgen05.CtaGroup.ONE
|
||||
self.epilogue_warp_id = (0,1,2,3); self.mma_warp_id = 4; self.tma_warp_id = 5
|
||||
self.threads_per_cta = 192; self.num_c_stage = 2
|
||||
self.kv_stage = 2; self.q_stage = 1; self.num_c_stage = 2
|
||||
self.scale_softmax = scale_softmax if scale_softmax is not None else 1.0 / math.sqrt(HEAD_DIM)
|
||||
self.scale_softmax_log2 = self.scale_softmax * math.log2(math.e)
|
||||
|
||||
def _setup(self, qk_mma, pv_mma):
|
||||
qk_ik = cute.size(qk_mma.shape_mnk, mode=[2])
|
||||
self.qk_mma_tiler = (128, 128, qk_ik * 4)
|
||||
pv_ik = cute.size(pv_mma.shape_mnk, mode=[2])
|
||||
self.pv_mma_tiler = (128, HEAD_DIM, pv_ik * (128 // pv_ik))
|
||||
self.mma_tiler = self.qk_mma_tiler
|
||||
self.cluster_layout_vmnk = cute.tiled_divide(cute.make_layout((1,1,1)), (qk_mma.thr_id.shape,))
|
||||
self.cta_tile_shape_mnk = (self.qk_mma_tiler[0]//cute.size(qk_mma.thr_id.shape), HEAD_DIM, self.qk_mma_tiler[2])
|
||||
self.c_layout = LayoutEnum.ROW_MAJOR
|
||||
self.epi_tile = utils.sm100.compute_epilogue_tile_shape(self.cta_tile_shape_mnk, False, self.c_layout, self.o_dtype)
|
||||
self.num_ab_stage = 1; self.num_acc_stage = 1
|
||||
self.q_smem_s = utils.sm100.make_smem_layout_a(qk_mma, self.qk_mma_tiler, self.q_dtype, self.q_stage)
|
||||
self.k_smem_s = utils.sm100.make_smem_layout_b(qk_mma, self.qk_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.v_smem_s = utils.sm100.make_smem_layout_b(pv_mma, self.pv_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.c_smem_s = utils.sm100.make_smem_layout_epi(self.o_dtype, self.c_layout, self.epi_tile, 2)
|
||||
self.p_tmem_s = utils.sm100.make_smem_layout_a(pv_mma, self.pv_mma_tiler, self.q_dtype, 1)
|
||||
qk_thr = qk_mma.get_slice(0); qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
pv_thr = pv_mma.get_slice(0); pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
self.tmem_s0_offset = 0; self.tmem_p0_offset = 32
|
||||
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
|
||||
p_end = self.tmem_p0_offset + p_cols_fp32
|
||||
s_cols = self.qk_mma_tiler[1]
|
||||
o_after = max(s_cols, p_end)
|
||||
self.tmem_o0_offset = ((o_after + 31) // 32) * 32
|
||||
o_cols = find_tmem_tensor_col_offset(tOtO)
|
||||
total = self.tmem_o0_offset + o_cols
|
||||
self.num_tmem_alloc_cols = 1
|
||||
while self.num_tmem_alloc_cols < total:
|
||||
self.num_tmem_alloc_cols *= 2
|
||||
cta = cute.size(qk_mma.thr_id.shape)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0))
|
||||
k_s = cute.slice_(self.k_smem_s,(None,None,None,0))
|
||||
v_s = cute.slice_(self.v_smem_s,(None,None,None,0))
|
||||
self.q_tx_bytes = cute.size_in_bytes(self.q_dtype, q_s) * cta
|
||||
# Combined barrier: tx_count covers BOTH K and V transfers per acquire.
|
||||
self.kv_tx_bytes = (cute.size_in_bytes(self.q_dtype, k_s) +
|
||||
cute.size_in_bytes(self.q_dtype, v_s)) * cta
|
||||
|
||||
@cute.jit
|
||||
def __call__(self, q, k, v, c, stream):
|
||||
self.q_dtype = q.element_type; self.o_dtype = c.element_type; self.c_dtype = self.o_dtype
|
||||
self.a_major = LayoutEnum.from_tensor(q).mma_major_mode()
|
||||
self.b_major = LayoutEnum.from_tensor(k).mma_major_mode()
|
||||
# FMHA-style V: reconstruct as (HEAD_DIM, s_k, 1) MN-major
|
||||
# s_k is compile-time — must match actual sequence length n.
|
||||
v_fmha = cute.make_tensor(
|
||||
v.iterator,
|
||||
cute.make_layout(
|
||||
(HEAD_DIM, self.s_k, 1),
|
||||
stride=(1, HEAD_DIM, HEAD_DIM * self.s_k),
|
||||
),
|
||||
)
|
||||
self.v_major = LayoutEnum.from_tensor(v_fmha).mma_major_mode()
|
||||
self.c_layout = LayoutEnum.from_tensor(c)
|
||||
qk_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, self.a_major, self.b_major, self.qk_acc_dtype, self.cta_group, (128,128), tcgen05.OperandSource.SMEM)
|
||||
pv_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, cute.nvgpu.OperandMajorMode.K, self.v_major, self.qk_acc_dtype, self.cta_group, (128,HEAD_DIM), tcgen05.OperandSource.TMEM)
|
||||
self._setup(qk_mma, pv_mma)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0)); v_s = cute.slice_(self.v_smem_s,(None,None,None,0))
|
||||
tma_q,mQ = cute.nvgpu.make_tiled_tma_atom_A(utils.sm100.cluster_shape_to_tma_atom_A(self.cluster_shape_mn,qk_mma.thr_id),q,q_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_k,mK = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,qk_mma.thr_id),k,k_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_v,mV = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,pv_mma.thr_id),v_fmha,v_s,self.pv_mma_tiler,pv_mma,self.cluster_layout_vmnk.shape)
|
||||
epi_s = cute.select(self.c_smem_s,mode=[0,1])
|
||||
tma_c,mC = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(),c,epi_s,self.epi_tile)
|
||||
self._kernel(qk_mma,pv_mma,tma_q,mQ,tma_k,mK,tma_v,mV,tma_c,mC,self.cluster_layout_vmnk,self.q_smem_s,self.k_smem_s,self.v_smem_s,self.p_tmem_s,self.c_smem_s,self.epi_tile).launch(grid=(1,1,1),block=[self.threads_per_cta,1,1],stream=stream)
|
||||
|
||||
@cute.kernel
|
||||
def _kernel(self, qk_mma, pv_mma, tma_q, mQ, tma_k, mK, tma_v, mV, tma_c, mC, cl_vmnk, q_smem_s, k_smem_s, v_smem_s, p_tmem_s, c_smem_s, epi_tile):
|
||||
warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx())
|
||||
tidx,_,_ = cute.arch.thread_idx()
|
||||
if warp_idx == self.tma_warp_id:
|
||||
cpasync.prefetch_descriptor(tma_q); cpasync.prefetch_descriptor(tma_k); cpasync.prefetch_descriptor(tma_v); cpasync.prefetch_descriptor(tma_c)
|
||||
|
||||
@cute.struct
|
||||
class SS:
|
||||
q_bar: cute.struct.MemRange[cutlass.Int64, self.q_stage*2]
|
||||
kv_bar: cute.struct.MemRange[cutlass.Int64, self.kv_stage*2]
|
||||
s_bar: cute.struct.MemRange[cutlass.Int64, 2]
|
||||
acc_bar: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage*2]
|
||||
tmem_dealloc: cutlass.Int64; holding: cutlass.Int32
|
||||
smem = utils.SmemAllocator(); st = smem.allocate(SS)
|
||||
|
||||
qp,qc = pipeline.PipelineTmaUmma.create(barrier_storage=st.q_bar.data_ptr(),num_stages=self.q_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.q_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
|
||||
# Combined K+V pipeline: each stage carries BOTH K and V loaded together.
|
||||
# One acquire per kt → kvh.count == kt (pipeline state value, accepted by cute.copy).
|
||||
# No interleaving problem. No Python int in TMA coordinates.
|
||||
kvp,kvc = pipeline.PipelineTmaUmma.create(barrier_storage=st.kv_bar.data_ptr(),num_stages=self.kv_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.kv_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
|
||||
s_prod,s_cons = pipeline.PipelineUmmaAsync.create(barrier_storage=st.s_bar.data_ptr(),num_stages=1,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32*len(self.epilogue_warp_id))).make_participants()
|
||||
softmax_done_bar = pipeline.NamedBarrier(barrier_id=3, num_threads=32 + 32*len(self.epilogue_warp_id))
|
||||
# Final-O sync: MMA arrives between producer_commit and producer_tail;
|
||||
# softmax arrives_and_waits before reading O for the final normalize.
|
||||
# This prevents softmax from racing MMA's PV[N-1] and dividing a
|
||||
# partially-accumulated O by row_sum.
|
||||
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)
|
||||
|
||||
sQ = smem.allocate_tensor(element_type=self.q_dtype,layout=q_smem_s.outer,byte_alignment=128,swizzle=q_smem_s.inner)
|
||||
sK = smem.allocate_tensor(element_type=self.q_dtype,layout=k_smem_s.outer,byte_alignment=128,swizzle=k_smem_s.inner)
|
||||
sV = smem.allocate_tensor(element_type=self.q_dtype,layout=v_smem_s.outer,byte_alignment=128,swizzle=v_smem_s.inner)
|
||||
sC = smem.allocate_tensor(element_type=self.o_dtype,layout=c_smem_s.outer,byte_alignment=128,swizzle=c_smem_s.inner)
|
||||
|
||||
gQ = cute.local_tile(mQ,cute.slice_(self.qk_mma_tiler,(None,0,None)),(None,None,None))
|
||||
gK = cute.local_tile(mK,cute.slice_(self.qk_mma_tiler,(0,None,None)),(None,None,None))
|
||||
gV = cute.local_tile(mV,cute.slice_(self.pv_mma_tiler,(0,None,None)),(None,None,None))
|
||||
gC = cute.local_tile(mC,cute.slice_(self.pv_mma_tiler,(None,None,0)),(None,None,None))
|
||||
n_kv_tiles = cute.size(gK, mode=[3])
|
||||
|
||||
qk_thr = qk_mma.get_slice(0); pv_thr = pv_mma.get_slice(0)
|
||||
tCgQ = qk_thr.partition_A(gQ); tCgK = qk_thr.partition_B(gK)
|
||||
tCgV = pv_thr.partition_B(gV); tCgC = pv_thr.partition_C(gC)
|
||||
a_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,0,None,0)).shape)
|
||||
tAsQ,tAgQ = cpasync.tma_partition(tma_q,0,a_lay,cute.group_modes(sQ,0,3),cute.group_modes(tCgQ,0,3))
|
||||
b_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,None,0,0)).shape)
|
||||
tBsK,tBgK = cpasync.tma_partition(tma_k,0,b_lay,cute.group_modes(sK,0,3),cute.group_modes(tCgK,0,3))
|
||||
tVsV,tVgV = cpasync.tma_partition(tma_v,0,b_lay,cute.group_modes(sV,0,3),cute.group_modes(tCgV,0,3))
|
||||
# GMEM slices: K uses mode 1 for GMEM iter → (None,None,0,0) keeps it free
|
||||
# V uses mode 2 for GMEM iter → (None,0,None,0) keeps it free
|
||||
# Q has 1 tile → (None,0,None,0) hardcode is fine
|
||||
# CRITICAL: K from QK MMA B-partition has GMEM iter at mode 1, NOT mode 2!
|
||||
# (None,0,None,0) for K hardcodes mode 1 to 0 → always loads tile 0.
|
||||
# (None,None,0,0) for K keeps mode 1 free → correct multi-tile loading.
|
||||
# Proven by diag test: (None,0,None,0) gives cos 0.711, (None,None,0,0) gives 0.999999.
|
||||
tAgQ = tAgQ[(None,0,None,0)] # Q: 1 tile
|
||||
tBgK = tBgK[(None,None,0,0)] # K: keep mode 1 (GMEM iter) free
|
||||
tVgV = tVgV[(None,0,None,0)] # V: keep mode 2 (GMEM iter) free
|
||||
|
||||
tCrQ = qk_mma.make_fragment_A(sQ); tCrK = qk_mma.make_fragment_B(sK)
|
||||
tCrV = pv_mma.make_fragment_B(sV)
|
||||
|
||||
qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
tOtO0 = cute.make_tensor(tOtO.iterator + self.tmem_o0_offset, tOtO.layout)
|
||||
|
||||
# --- PV read view (for MMA only, NOT for softmax store) ---
|
||||
tP = cute.make_tensor(tStS.iterator, p_tmem_s.outer)
|
||||
tOrP_base = pv_thr.make_fragment_A(tP)
|
||||
tOrP = tOrP_base[(None,None,None,0)]
|
||||
tOrP0 = cute.make_tensor(
|
||||
tOrP.iterator + self.qk_acc_dtype.width // self.q_dtype.width * self.tmem_p0_offset,
|
||||
tOrP.layout)
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_as, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_as, self.num_acc_stage))
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# ===== TMA LOAD warp =====
|
||||
# GMEM tile coordinate: manual Int32 counter (kv_coord). SMEM slot: kvh.index.
|
||||
# Pipeline handle .count is NOT a usable GMEM coordinate.
|
||||
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)
|
||||
qp.tail()
|
||||
kvp.reset(); pk = kvp.try_acquire()
|
||||
# Use cutlass.range with Python int n_kv_tiles for proper pipeline
|
||||
# semantics (acquire/release). Wrap kt in Int32() for TMA coordinate.
|
||||
for kt in cutlass.range(self.n_kv_tiles, unroll=1):
|
||||
coord = Int32(kt)
|
||||
kvh = kvp.acquire_and_advance(pk)
|
||||
cute.copy(tma_k, tBgK[(None, coord)], tBsK[(None, kvh.index)], tma_bar_ptr=kvh.barrier)
|
||||
cute.copy(tma_v, tVgV[(None, coord)], tVsV[(None, kvh.index)], tma_bar_ptr=kvh.barrier)
|
||||
pk = cutlass.Boolean(1)
|
||||
kvp.tail()
|
||||
|
||||
# ===== MMA warp =====
|
||||
# One wait per kt; same slot index used for both K (QK) and V (PV).
|
||||
# Release happens AFTER PV — combined slot stays held across QK+PV.
|
||||
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_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)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
sh.commit()
|
||||
softmax_done_bar.arrive_and_wait()
|
||||
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,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()
|
||||
# Signal softmax FIRST so it can run normalize + epilogue. Then
|
||||
# wait for the epilogue's consumer-release in producer_tail.
|
||||
# Reverse order deadlocks: producer_tail blocks waiting for
|
||||
# consumer release; softmax blocks at final_o_bar waiting for
|
||||
# MMA arrive; the epilogue (which does the release) is gated
|
||||
# behind softmax's final_o_bar wait. Cycle.
|
||||
final_o_bar.arrive()
|
||||
acc_pipe.producer_tail(acc_st)
|
||||
|
||||
# ===== 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 (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 (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)
|
||||
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)
|
||||
|
||||
# O rescale / normalize path
|
||||
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
|
||||
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.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(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)
|
||||
|
||||
o_col_tiles = self.pv_mma_tiler[1] // corr_tile_size
|
||||
|
||||
row_max = -Float32.inf
|
||||
row_sum = Float32(0.0)
|
||||
scale_log2 = Float32(self.scale_softmax_log2)
|
||||
|
||||
for kt in range(self.n_kv_tiles):
|
||||
si_handle = s_cons.wait_and_advance()
|
||||
|
||||
# Load S[kt]
|
||||
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()
|
||||
|
||||
# Pass 1: update row_max
|
||||
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)
|
||||
|
||||
# acc_scale for both row_sum rescale and O rescale.
|
||||
# row_max is already in log2(scaled) space (S * scale_log2),
|
||||
# so the difference old_row_max - row_max_safe is already the
|
||||
# correct exponent for exp2. No extra scale_log2 factor.
|
||||
acc_scale = cute.math.exp2(old_row_max - row_max_safe, fastmath=True)
|
||||
if old_row_max == -cutlass.Float32.inf:
|
||||
acc_scale = Float32(0.0)
|
||||
row_sum *= acc_scale
|
||||
|
||||
# Pass 2: P = exp2((S - new_max) * log2), accumulate row_sum,
|
||||
# store BF16 P through the FP32-backed 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
|
||||
|
||||
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()
|
||||
|
||||
# O rescale TEMPORARILY DISABLED for debugging NaN
|
||||
# if kt > 0:
|
||||
# cute.arch.fence_view_async_tmem_load()
|
||||
# for i in range(o_col_tiles):
|
||||
# ...
|
||||
# cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
si_handle.release()
|
||||
softmax_done_bar.arrive()
|
||||
|
||||
# Wait for MMA's last PV to commit before reading O for normalize.
|
||||
# Without this barrier softmax can race MMA's PV[N-1].
|
||||
final_o_bar.arrive_and_wait()
|
||||
|
||||
# Final O = O / row_sum
|
||||
inv_row_sum = Float32(1.0) / row_sum
|
||||
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.acc_dtype)
|
||||
cute.copy(tiled_tmem_load_o, tTMEM_LOAD_O_i, tTMrO)
|
||||
cute.arch.fence_view_async_tmem_load()
|
||||
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()
|
||||
|
||||
# Epilogue: TMEM -> SMEM -> GMEM via TMA store
|
||||
tCtO_base = cute.make_tensor(tmem_ptr + self.tmem_o0_offset, tCtO_fake.layout)
|
||||
acc_cons_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Consumer, self.num_acc_stage)
|
||||
c_grp = pipeline.CooperativeGroup(pipeline.Agent.Thread, 32 * len(self.epilogue_warp_id))
|
||||
c_pipe = pipeline.PipelineTmaStore.create(num_stages=self.num_c_stage, producer_group=c_grp)
|
||||
acc_cons_st = utils.gemm.sm100.epilogue_tma_store(self, tidx, warp_idx, tma_c, tCtO_base, sC, tCgC, epi_tile, 0, const_expr(lambda x: x), (0,0,0), acc_cons_st, acc_pipe, c_pipe)
|
||||
c_pipe.producer_tail()
|
||||
tmem.relinquish_alloc_permit()
|
||||
tmem.free(tmem_ptr)
|
||||
|
||||
|
||||
def test():
|
||||
torch.manual_seed(42)
|
||||
for n in [128, 256, 512, 1024]:
|
||||
torch.manual_seed(42)
|
||||
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')
|
||||
|
||||
# PyTorch reference
|
||||
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)
|
||||
|
||||
# Each n requires its own compiled kernel (s_k is compile-time).
|
||||
kernel = FmhaV3StageC(s_k=n)
|
||||
print(f'n={n}: Compiling...', flush=True)
|
||||
compiled = cute.compile(kernel, mQ, mK, mV, mC, stream)
|
||||
print(f'n={n}: tmem s0={kernel.tmem_s0_offset} p0={kernel.tmem_p0_offset} '
|
||||
f'o0={kernel.tmem_o0_offset} alloc={kernel.num_tmem_alloc_cols} '
|
||||
f'kv_tx_bytes={kernel.kv_tx_bytes}', flush=True)
|
||||
compiled(mQ, mK, mV, mC, stream)
|
||||
torch.cuda.synchronize()
|
||||
|
||||
out = c[:, :, 0].float()
|
||||
has_nan = out.isnan().any().item()
|
||||
has_inf = out.isinf().any().item()
|
||||
cos = torch.nn.functional.cosine_similarity(
|
||||
out.flatten().unsqueeze(0), ref.flatten().unsqueeze(0)
|
||||
).item()
|
||||
max_abs = (out - ref).abs().max().item()
|
||||
n_tiles = n // 128
|
||||
print(f'FMHA Stage-C n={n} ({n_tiles} kv tiles): '
|
||||
f'cos {cos:.6f} max_abs {max_abs:.4f} '
|
||||
f'nan={has_nan} inf={has_inf} '
|
||||
f'{"PASS" if cos >= 0.99 else "FAIL"}')
|
||||
if cos < 0.99:
|
||||
print(f' out[0,:4]={out[0,:4].tolist()}')
|
||||
print(f' ref[0,:4]={ref[0,:4].tolist()}')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test()
|
||||
@@ -1,305 +0,0 @@
|
||||
"""
|
||||
FMHA v3 Stage-C minimal: 12-warps, identity softmax, identity correction.
|
||||
Validates pipeline chain: mma_s, s_corr, mma_corr, corr_epi.
|
||||
"""
|
||||
import torch, cutlass, cutlass.cute as cute, cutlass.utils as utils, cutlass.pipeline as pipeline
|
||||
from cutlass.cute.nvgpu import cpasync, tcgen05
|
||||
from cutlass import Float32, BFloat16, Int32, Boolean, const_expr
|
||||
from cutlass.utils import LayoutEnum
|
||||
from cutlass.utils.tmem_allocator import find_tmem_tensor_col_offset
|
||||
import cuda.bindings.driver as cuda
|
||||
import cutlass.torch as ct
|
||||
|
||||
HEAD_DIM = 64
|
||||
|
||||
class FmhaV3StageCMin:
|
||||
def __init__(self, s_k=128):
|
||||
self.s_k = s_k
|
||||
self.acc_dtype = Float32; self.qk_acc_dtype = Float32
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
self.use_2cta_instrs = False; self.epilog_sync_bar_id = 1
|
||||
self.cluster_shape_mn = (1, 1); self.cta_group = tcgen05.CtaGroup.ONE
|
||||
self.softmax_warp_ids = (0,1,2,3)
|
||||
self.correction_warp_ids = (4,5,6,7)
|
||||
self.mma_warp_id = 8; self.tma_warp_id = 9
|
||||
self.epilogue_warp_id = (10,)
|
||||
self.epi_warp_id = 10; self.empty_warp_id = 11
|
||||
self.threads_per_cta = 32 * 12
|
||||
self.mma_softmax_stage = 1; self.softmax_corr_stage = 1
|
||||
self.mma_corr_stage = 2; self.epi_stage = 2
|
||||
self.kv_stage = 2; self.q_stage = 1; self.num_c_stage = 2
|
||||
|
||||
def _setup(self, qk_mma, pv_mma):
|
||||
qk_ik = cute.size(qk_mma.shape_mnk, mode=[2])
|
||||
self.qk_mma_tiler = (128, 128, qk_ik * 4)
|
||||
pv_ik = cute.size(pv_mma.shape_mnk, mode=[2])
|
||||
self.pv_mma_tiler = (128, HEAD_DIM, pv_ik * (128 // pv_ik))
|
||||
self.mma_tiler = self.qk_mma_tiler
|
||||
self.cluster_layout_vmnk = cute.tiled_divide(cute.make_layout((1,1,1)), (qk_mma.thr_id.shape,))
|
||||
self.cta_tile_shape_mnk = (self.qk_mma_tiler[0]//cute.size(qk_mma.thr_id.shape), HEAD_DIM, self.qk_mma_tiler[2])
|
||||
self.c_layout = LayoutEnum.ROW_MAJOR
|
||||
self.epi_tile = utils.sm100.compute_epilogue_tile_shape(self.cta_tile_shape_mnk, False, self.c_layout, self.o_dtype)
|
||||
self.num_ab_stage = 1; self.num_acc_stage = 1
|
||||
self.q_smem_s = utils.sm100.make_smem_layout_a(qk_mma, self.qk_mma_tiler, self.q_dtype, self.q_stage)
|
||||
self.k_smem_s = utils.sm100.make_smem_layout_b(qk_mma, self.qk_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.v_smem_s = utils.sm100.make_smem_layout_b(pv_mma, self.pv_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.c_smem_s = utils.sm100.make_smem_layout_epi(self.o_dtype, self.c_layout, self.epi_tile, 2)
|
||||
self.p_tmem_s = utils.sm100.make_smem_layout_a(pv_mma, self.pv_mma_tiler, self.q_dtype, 1)
|
||||
qk_thr = qk_mma.get_slice(0); qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
pv_thr = pv_mma.get_slice(0); pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
self.tmem_s0_offset = 0; self.tmem_p0_offset = 32
|
||||
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
|
||||
p_end = self.tmem_p0_offset + p_cols_fp32
|
||||
s_cols = self.qk_mma_tiler[1]; o_after = max(s_cols, p_end)
|
||||
self.tmem_o0_offset = ((o_after + 31) // 32) * 32
|
||||
o_cols = find_tmem_tensor_col_offset(tOtO)
|
||||
total = self.tmem_o0_offset + o_cols
|
||||
self.num_tmem_alloc_cols = 1
|
||||
while self.num_tmem_alloc_cols < total:
|
||||
self.num_tmem_alloc_cols *= 2
|
||||
cta = cute.size(qk_mma.thr_id.shape)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0))
|
||||
k_s = cute.slice_(self.k_smem_s,(None,None,None,0))
|
||||
self.q_tx_bytes = cute.size_in_bytes(self.q_dtype, q_s) * cta
|
||||
self.kv_tx_bytes = cute.size_in_bytes(self.q_dtype, k_s) * cta
|
||||
|
||||
@cute.jit
|
||||
def __call__(self, q, k, v, c, stream):
|
||||
self.q_dtype = q.element_type; self.o_dtype = c.element_type; self.c_dtype = self.o_dtype
|
||||
self.a_major = LayoutEnum.from_tensor(q).mma_major_mode()
|
||||
self.b_major = LayoutEnum.from_tensor(k).mma_major_mode()
|
||||
v_fmha = cute.make_tensor(v.iterator, cute.make_layout((HEAD_DIM, self.s_k, 1), stride=(1, HEAD_DIM, HEAD_DIM * self.s_k)))
|
||||
self.v_major = LayoutEnum.from_tensor(v_fmha).mma_major_mode()
|
||||
self.c_layout = LayoutEnum.from_tensor(c)
|
||||
qk_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, self.a_major, self.b_major, self.qk_acc_dtype, self.cta_group, (128,128), tcgen05.OperandSource.SMEM)
|
||||
pv_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, cute.nvgpu.OperandMajorMode.K, self.v_major, self.qk_acc_dtype, self.cta_group, (128,HEAD_DIM), tcgen05.OperandSource.TMEM)
|
||||
self._setup(qk_mma, pv_mma)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0)); v_s = cute.slice_(self.v_smem_s,(None,None,None,0))
|
||||
tma_q,mQ = cute.nvgpu.make_tiled_tma_atom_A(utils.sm100.cluster_shape_to_tma_atom_A(self.cluster_shape_mn,qk_mma.thr_id),q,q_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_k,mK = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,qk_mma.thr_id),k,k_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_v,mV = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,pv_mma.thr_id),v_fmha,v_s,self.pv_mma_tiler,pv_mma,self.cluster_layout_vmnk.shape)
|
||||
epi_s = cute.select(self.c_smem_s,mode=[0,1])
|
||||
tma_c,mC = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(),c,epi_s,self.epi_tile)
|
||||
self._kernel(qk_mma,pv_mma,tma_q,mQ,tma_k,mK,tma_v,mV,tma_c,mC,self.cluster_layout_vmnk,self.q_smem_s,self.k_smem_s,self.v_smem_s,self.p_tmem_s,self.c_smem_s,self.epi_tile).launch(grid=(1,1,1),block=[self.threads_per_cta,1,1],stream=stream)
|
||||
|
||||
@cute.kernel
|
||||
def _kernel(self, qk_mma, pv_mma, tma_q, mQ, tma_k, mK, tma_v, mV, tma_c, mC, cl_vmnk, q_smem_s, k_smem_s, v_smem_s, p_tmem_s, c_smem_s, epi_tile):
|
||||
warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx())
|
||||
tidx,_,_ = cute.arch.thread_idx()
|
||||
if warp_idx == self.tma_warp_id:
|
||||
cpasync.prefetch_descriptor(tma_q); cpasync.prefetch_descriptor(tma_k); cpasync.prefetch_descriptor(tma_v); cpasync.prefetch_descriptor(tma_c)
|
||||
@cute.struct
|
||||
class SS:
|
||||
q_bar: cute.struct.MemRange[cutlass.Int64, self.q_stage*2]
|
||||
kv_bar: cute.struct.MemRange[cutlass.Int64, self.kv_stage*2]
|
||||
mma_s_bar: cute.struct.MemRange[cutlass.Int64, self.mma_softmax_stage*2]
|
||||
s_corr_bar: cute.struct.MemRange[cutlass.Int64, self.softmax_corr_stage*2]
|
||||
mma_corr_bar: cute.struct.MemRange[cutlass.Int64, self.mma_corr_stage*2]
|
||||
corr_epi_bar: cute.struct.MemRange[cutlass.Int64, self.epi_stage*2]
|
||||
tmem_dealloc: cutlass.Int64
|
||||
holding: cutlass.Int32
|
||||
smem = utils.SmemAllocator(); st = smem.allocate(SS)
|
||||
def cg(n): return pipeline.CooperativeGroup(pipeline.Agent.Thread, n)
|
||||
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()
|
||||
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()
|
||||
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()
|
||||
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()
|
||||
mma_corr_pipe = 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)
|
||||
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()
|
||||
tmem_bar = pipeline.NamedBarrier(barrier_id=2,num_threads=32*len((*self.softmax_warp_ids,*self.correction_warp_ids,self.mma_warp_id)))
|
||||
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)
|
||||
if warp_idx == self.empty_warp_id:
|
||||
cute.arch.mbarrier_init(st.tmem_dealloc, 32*len((*self.softmax_warp_ids,*self.correction_warp_ids)))
|
||||
cute.arch.mbarrier_init_fence()
|
||||
pipeline.pipeline_init_arrive(cluster_shape_mn=cl_vmnk,is_relaxed=True)
|
||||
sQ = smem.allocate_tensor(element_type=self.q_dtype,layout=q_smem_s.outer,byte_alignment=128,swizzle=q_smem_s.inner)
|
||||
sK = smem.allocate_tensor(element_type=self.q_dtype,layout=k_smem_s.outer,byte_alignment=128,swizzle=k_smem_s.inner)
|
||||
sV = smem.allocate_tensor(element_type=self.q_dtype,layout=v_smem_s.outer,byte_alignment=128,swizzle=v_smem_s.inner)
|
||||
sC = smem.allocate_tensor(element_type=self.o_dtype,layout=c_smem_s.outer,byte_alignment=128,swizzle=c_smem_s.inner)
|
||||
gQ = cute.local_tile(mQ,cute.slice_(self.qk_mma_tiler,(None,0,None)),(None,None,None))
|
||||
gK = cute.local_tile(mK,cute.slice_(self.qk_mma_tiler,(0,None,None)),(None,None,None))
|
||||
gV = cute.local_tile(mV,cute.slice_(self.pv_mma_tiler,(0,None,None)),(None,None,None))
|
||||
gC = cute.local_tile(mC,cute.slice_(self.pv_mma_tiler,(None,None,0)),(None,None,None))
|
||||
n_kv_tiles = cute.size(gK, mode=[3])
|
||||
qk_thr = qk_mma.get_slice(0); pv_thr = pv_mma.get_slice(0)
|
||||
tCgQ = qk_thr.partition_A(gQ); tCgK = qk_thr.partition_B(gK)
|
||||
tCgV = pv_thr.partition_B(gV); tCgC = pv_thr.partition_C(gC)
|
||||
a_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,0,None,0)).shape)
|
||||
tAsQ,tAgQ = cpasync.tma_partition(tma_q,0,a_lay,cute.group_modes(sQ,0,3),cute.group_modes(tCgQ,0,3))
|
||||
b_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,None,0,0)).shape)
|
||||
tBsK,tBgK = cpasync.tma_partition(tma_k,0,b_lay,cute.group_modes(sK,0,3),cute.group_modes(tCgK,0,3))
|
||||
tVsV,tVgV = cpasync.tma_partition(tma_v,0,b_lay,cute.group_modes(sV,0,3),cute.group_modes(tCgV,0,3))
|
||||
tAgQ = tAgQ[(None,0,None,0)]; tBgK = tBgK[(None,0,None,0)]; tVgV = tVgV[(None,0,None,0)]
|
||||
tCrQ = qk_mma.make_fragment_A(sQ); tCrK = qk_mma.make_fragment_B(sK)
|
||||
tCrV = pv_mma.make_fragment_B(sV)
|
||||
qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
tOtO0 = cute.make_tensor(tOtO.iterator + self.tmem_o0_offset, tOtO.layout)
|
||||
tP = cute.make_tensor(tStS.iterator, p_tmem_s.outer)
|
||||
tOrP_base = pv_thr.make_fragment_A(tP)
|
||||
tOrP = tOrP_base[(None,None,None,0)]
|
||||
tOrP0 = cute.make_tensor(tOrP.iterator + self.qk_acc_dtype.width // self.q_dtype.width * self.tmem_p0_offset, tOrP.layout)
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_as, 1))
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# ==================== TMA WARP (9) ====================
|
||||
if warp_idx == self.tma_warp_id:
|
||||
qp.reset(); qh = qp.acquire_and_advance()
|
||||
cute.copy(tma_q,tAgQ[(None,qh.count)],tAsQ[(None,qh.index)],tma_bar_ptr=qh.barrier)
|
||||
qp.tail()
|
||||
kvp.reset(); pk = kvp.try_acquire()
|
||||
for kt in cutlass.range(n_kv_tiles,unroll=1):
|
||||
kh = kvp.acquire_and_advance(pk)
|
||||
cute.copy(tma_k,tBgK[(None,kh.count)],tBsK[(None,kh.index)],tma_bar_ptr=kh.barrier)
|
||||
pk = cutlass.Boolean(1)
|
||||
vh = kvp.acquire_and_advance(pk)
|
||||
cute.copy(tma_v,tVgV[(None,vh.count)],tVsV[(None,vh.index)],tma_bar_ptr=vh.barrier)
|
||||
pk = cutlass.Boolean(1)
|
||||
kvp.tail()
|
||||
|
||||
# ==================== MMA WARP (8) ====================
|
||||
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()
|
||||
for kt in range(n_kv_tiles):
|
||||
kh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
sh = mma_s_prod.acquire_and_advance()
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
for kb in cutlass.range(cute.size(tCrQ,mode=[2]), unroll_full=True):
|
||||
cute.gemm(qk_mma, tStS0, tCrQ[(None,None,kb,0)], tCrK[(None,None,kb,kh.index)], tStS0)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
sh.commit(); kh.release()
|
||||
# MMA waits for softmax to produce P (softmax consumes S, releases when P ready)
|
||||
# In the pipeline model, the S release by softmax IS the P-ready signal
|
||||
# But with PipelineUmmaAsync, the consumer release releases the producer handle
|
||||
# So after the softmax releases, the MMA can acquire the next S handle
|
||||
|
||||
vh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
oh = mma_corr_pipe.producer_acquire(pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.mma_corr_stage))
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, kt != 0)
|
||||
for kb in cutlass.range(cute.size(tOrP0,mode=[2]), unroll_full=True):
|
||||
cute.gemm(pv_mma, tOtO0, tOrP0[(None,None,kb)], tCrV[(None,None,kb,vh.index)], tOtO0)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
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))
|
||||
|
||||
# ==================== SOFTMAX WARPS (0-3) — identity ====================
|
||||
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))
|
||||
tmem_load_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
tiled_tmem_load = tcgen05.make_tmem_copy(tmem_load_atom, tStS0)
|
||||
thr_load = tiled_tmem_load.get_slice(sfw_idx)
|
||||
tTMEM_LOADtS = thr_load.partition_S(tStS0)
|
||||
cS = cute.make_identity_tensor((self.qk_mma_tiler[0], self.qk_mma_tiler[1]))
|
||||
tScS = qk_thr.partition_C(cS)
|
||||
tTMEM_LOADcS = thr_load.partition_D(tScS)
|
||||
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
|
||||
tStP_layout = cute.composition(tStS.layout, cute.make_layout((self.pv_mma_tiler[0], p_cols_fp32)))
|
||||
tStP0 = cute.make_tensor(tStS.iterator + self.tmem_p0_offset, tStP_layout)
|
||||
tmem_store_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
tiled_tmem_store = tcgen05.make_tmem_copy(tmem_store_atom, tStP0)
|
||||
thr_store = tiled_tmem_store.get_slice(sfw_idx)
|
||||
tTMEM_STOREtP = thr_store.partition_D(tStP0)
|
||||
tScP_layout = cute.composition(tScS.layout, cute.make_layout((self.pv_mma_tiler[0], p_cols_fp32)))
|
||||
tTMEM_STOREcP = thr_store.partition_S(cute.make_tensor(tScS.iterator, tScP_layout))
|
||||
vec_handle = s_corr_prod.acquire_and_advance()
|
||||
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()
|
||||
rP_words = cute.make_rmem_tensor(tTMEM_STOREcP.shape, self.qk_acc_dtype)
|
||||
rP_bf16 = cute.make_tensor(cute.recast_ptr(rP_words.iterator, dtype=self.q_dtype), tTMEM_LOADrS.layout)
|
||||
frg_cnt = 4
|
||||
frg_tile = cute.size(tTMEM_LOADrS) // frg_cnt
|
||||
tTMEM_LOADrS_frg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(frg_tile))
|
||||
rP_bf16_frg = cute.logical_divide(rP_bf16, cute.make_layout(frg_tile))
|
||||
for j in range(frg_cnt):
|
||||
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()
|
||||
vec_handle.commit()
|
||||
si_handle.release()
|
||||
vec_handle = s_corr_prod.acquire_and_advance()
|
||||
s_corr_prod.tail()
|
||||
cute.arch.mbarrier_arrive(st.tmem_dealloc)
|
||||
tmem.relinquish_alloc_permit()
|
||||
|
||||
# ==================== CORRECTION WARPS (4-7) — identity, no epilogue ====================
|
||||
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))
|
||||
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()
|
||||
o = mma_corr_cons.wait_and_advance()
|
||||
o.release()
|
||||
vec.release()
|
||||
final_vec = s_corr_cons.wait_and_advance()
|
||||
final_vec.release()
|
||||
final_o = mma_corr_cons.wait_and_advance()
|
||||
# Write O from TMEM to output using the epilogue pipeline
|
||||
epi_handle = corr_epi_prod.acquire_and_advance()
|
||||
tmem_ptr = tmem.retrieve_ptr(self.qk_acc_dtype)
|
||||
tCtO_base = cute.make_tensor(tmem_ptr + self.tmem_o0_offset, tCtO_fake.layout)
|
||||
# Use epilogue_tma_store with a fresh consumer state
|
||||
# The acc_pipe is the pipeline we already consumed from, but
|
||||
# epilogue_tma_store wants a consumer. Since we already have O,
|
||||
# skip the acc_pipe wait by using a dummy pipeline.
|
||||
# Actually, just do a simple TMA copy from sC
|
||||
# For the minimal test, just signal the epilogue and move on
|
||||
epi_handle.commit()
|
||||
final_o.release()
|
||||
cute.arch.mbarrier_arrive(st.tmem_dealloc)
|
||||
|
||||
# ==================== EPILOGUE WARP (10) ====================
|
||||
if warp_idx == self.epi_warp_id:
|
||||
epi_handle = corr_epi_cons.wait_and_advance()
|
||||
epi_handle.release()
|
||||
|
||||
|
||||
def test():
|
||||
torch.manual_seed(42)
|
||||
for n in [128]:
|
||||
m, hd = 128, HEAD_DIM
|
||||
q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device='cuda')
|
||||
k = torch.randn(n, hd, 1, dtype=torch.bfloat16, device='cuda')
|
||||
v = torch.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()
|
||||
ref = (qf @ kf.T).bfloat16().float() @ 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 = FmhaV3StageCMin(s_k=n)
|
||||
print(f'n={n}: Compiling...', flush=True)
|
||||
compiled = cute.compile(kernel, mQ, mK, mV, mC, stream)
|
||||
print(f'n={n}: Running...', flush=True)
|
||||
compiled(mQ, mK, mV, mC, stream)
|
||||
torch.cuda.synchronize()
|
||||
out = c[:,:,0].float()
|
||||
cos = torch.nn.functional.cosine_similarity(out.flatten().unsqueeze(0), ref.flatten().unsqueeze(0)).item()
|
||||
print(f'FMHA stage-C min n={n}: cosine {cos:.6f}')
|
||||
|
||||
if __name__ == '__main__':
|
||||
test()
|
||||
@@ -1,488 +0,0 @@
|
||||
"""
|
||||
FMHA v3 + Stage C: QK -> online softmax -> PV with KV-tile interleaving.
|
||||
Stage C: row_max, exp2, O rescale, row_sum, final normalization.
|
||||
FMHA pattern P store preserved from Stage B.
|
||||
"""
|
||||
import math
|
||||
import torch, cutlass, cutlass.cute as cute, cutlass.utils as utils, cutlass.pipeline as pipeline
|
||||
from cutlass.cute.nvgpu import cpasync, tcgen05
|
||||
from cutlass import Float32, BFloat16, Int32, Boolean, const_expr
|
||||
from cutlass.utils import LayoutEnum
|
||||
from cutlass.utils.tmem_allocator import find_tmem_tensor_col_offset
|
||||
import cuda.bindings.driver as cuda
|
||||
import cutlass.torch as ct
|
||||
|
||||
HEAD_DIM = 64
|
||||
|
||||
class FmhaV3Softmax:
|
||||
def __init__(self):
|
||||
self.acc_dtype = Float32; self.qk_acc_dtype = Float32
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
self.use_2cta_instrs = False; self.epilog_sync_bar_id = 1
|
||||
self.cluster_shape_mn = (1, 1); self.cta_group = tcgen05.CtaGroup.ONE
|
||||
self.epilogue_warp_id = (0,1,2,3); self.mma_warp_id = 4; self.tma_warp_id = 5
|
||||
self.threads_per_cta = 192; self.num_c_stage = 2
|
||||
self.kv_stage = 2; self.q_stage = 1; self.num_c_stage = 2
|
||||
|
||||
def _setup(self, qk_mma, pv_mma):
|
||||
qk_ik = cute.size(qk_mma.shape_mnk, mode=[2])
|
||||
self.qk_mma_tiler = (128, 128, qk_ik * 4)
|
||||
pv_ik = cute.size(pv_mma.shape_mnk, mode=[2])
|
||||
self.pv_mma_tiler = (128, HEAD_DIM, pv_ik * (128 // pv_ik))
|
||||
self.mma_tiler = self.qk_mma_tiler
|
||||
self.cluster_layout_vmnk = cute.tiled_divide(cute.make_layout((1,1,1)), (qk_mma.thr_id.shape,))
|
||||
self.cta_tile_shape_mnk = (self.qk_mma_tiler[0]//cute.size(qk_mma.thr_id.shape), HEAD_DIM, self.qk_mma_tiler[2])
|
||||
self.c_layout = LayoutEnum.ROW_MAJOR
|
||||
self.epi_tile = utils.sm100.compute_epilogue_tile_shape(self.cta_tile_shape_mnk, False, self.c_layout, self.o_dtype)
|
||||
self.num_ab_stage = 1; self.num_acc_stage = 1
|
||||
self.q_smem_s = utils.sm100.make_smem_layout_a(qk_mma, self.qk_mma_tiler, self.q_dtype, self.q_stage)
|
||||
self.k_smem_s = utils.sm100.make_smem_layout_b(qk_mma, self.qk_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.v_smem_s = utils.sm100.make_smem_layout_b(pv_mma, self.pv_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.c_smem_s = utils.sm100.make_smem_layout_epi(self.o_dtype, self.c_layout, self.epi_tile, 2)
|
||||
self.p_tmem_s = utils.sm100.make_smem_layout_a(pv_mma, self.pv_mma_tiler, self.q_dtype, 1)
|
||||
qk_thr = qk_mma.get_slice(0); qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
pv_thr = pv_mma.get_slice(0); pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
self.tmem_s0_offset = 0; self.tmem_p0_offset = 32
|
||||
# P occupies [tmem_p0_offset, tmem_p0_offset + p_cols_fp32)
|
||||
# S occupies [0, qk_mma_tiler[1]) = [0, 128)
|
||||
# O must NOT overlap P. Place O after max(S end, P end), aligned to 32.
|
||||
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
|
||||
p_end = self.tmem_p0_offset + p_cols_fp32 # 32 + 64 = 96
|
||||
s_cols = self.qk_mma_tiler[1] # 128
|
||||
o_after = max(s_cols, p_end) # 128
|
||||
self.tmem_o0_offset = ((o_after + 31) // 32) * 32
|
||||
self.tmem_vec_offset = 0 # Reuse S region for per-row inv_row_sum vector # align to 32 = 128
|
||||
self.tmem_vec_offset = 0 # Reuse S region (free after softmax loop)
|
||||
o_cols = find_tmem_tensor_col_offset(tOtO) # footprint of O
|
||||
total = self.tmem_o0_offset + o_cols
|
||||
# Must be multiple of 32 AND power of 2
|
||||
self.num_tmem_alloc_cols = 1
|
||||
while self.num_tmem_alloc_cols < total:
|
||||
self.num_tmem_alloc_cols *= 2
|
||||
cta = cute.size(qk_mma.thr_id.shape)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0))
|
||||
self.q_tx_bytes = cute.size_in_bytes(self.q_dtype, q_s) * cta
|
||||
self.kv_tx_bytes = cute.size_in_bytes(self.q_dtype, k_s) * cta
|
||||
self.scale_softmax_log2 = Float32(1.0 / math.sqrt(HEAD_DIM) * math.log2(math.e))
|
||||
|
||||
@cute.jit
|
||||
def __call__(self, q, k, v, c, stream):
|
||||
self.q_dtype = q.element_type; self.o_dtype = c.element_type; self.c_dtype = self.o_dtype
|
||||
self.a_major = LayoutEnum.from_tensor(q).mma_major_mode()
|
||||
self.b_major = LayoutEnum.from_tensor(k).mma_major_mode()
|
||||
# # s_k hardcoded # BROKEN in @cute.jit
|
||||
# FMHA-style V: reconstruct as (HEAD_DIM, s_k, 1) MN-major
|
||||
v_fmha = cute.make_tensor(
|
||||
v.iterator,
|
||||
cute.make_layout(
|
||||
(HEAD_DIM, 128, 1),
|
||||
stride=(1, HEAD_DIM, HEAD_DIM * 128),
|
||||
),
|
||||
)
|
||||
self.v_major = LayoutEnum.from_tensor(v_fmha).mma_major_mode()
|
||||
self.c_layout = LayoutEnum.from_tensor(c)
|
||||
qk_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, self.a_major, self.b_major, self.qk_acc_dtype, self.cta_group, (128,128), tcgen05.OperandSource.SMEM)
|
||||
pv_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, cute.nvgpu.OperandMajorMode.K, self.v_major, self.qk_acc_dtype, self.cta_group, (128,HEAD_DIM), tcgen05.OperandSource.TMEM)
|
||||
self._setup(qk_mma, pv_mma)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0)); v_s = cute.slice_(self.v_smem_s,(None,None,None,0))
|
||||
tma_q,mQ = cute.nvgpu.make_tiled_tma_atom_A(utils.sm100.cluster_shape_to_tma_atom_A(self.cluster_shape_mn,qk_mma.thr_id),q,q_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_k,mK = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,qk_mma.thr_id),k,k_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_v,mV = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,pv_mma.thr_id),v_fmha,v_s,self.pv_mma_tiler,pv_mma,self.cluster_layout_vmnk.shape)
|
||||
epi_s = cute.select(self.c_smem_s,mode=[0,1])
|
||||
tma_c,mC = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(),c,epi_s,self.epi_tile)
|
||||
self._kernel(qk_mma,pv_mma,tma_q,mQ,tma_k,mK,tma_v,mV,tma_c,mC,self.cluster_layout_vmnk,self.q_smem_s,self.k_smem_s,self.v_smem_s,self.p_tmem_s,self.c_smem_s,self.epi_tile).launch(grid=(1,1,1),block=[self.threads_per_cta,1,1],stream=stream)
|
||||
|
||||
@cute.kernel
|
||||
def _kernel(self, qk_mma, pv_mma, tma_q, mQ, tma_k, mK, tma_v, mV, tma_c, mC, cl_vmnk, q_smem_s, k_smem_s, v_smem_s, p_tmem_s, c_smem_s, epi_tile):
|
||||
warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx())
|
||||
tidx,_,_ = cute.arch.thread_idx()
|
||||
if warp_idx == self.tma_warp_id:
|
||||
cpasync.prefetch_descriptor(tma_q); cpasync.prefetch_descriptor(tma_k); cpasync.prefetch_descriptor(tma_v); cpasync.prefetch_descriptor(tma_c)
|
||||
|
||||
@cute.struct
|
||||
class SS:
|
||||
q_bar: cute.struct.MemRange[cutlass.Int64, self.q_stage*2]
|
||||
kv_bar: cute.struct.MemRange[cutlass.Int64, self.kv_stage*2]
|
||||
s_bar: cute.struct.MemRange[cutlass.Int64, 2]
|
||||
acc_bar: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage*2]
|
||||
tmem_dealloc: cutlass.Int64; holding: cutlass.Int32
|
||||
smem = utils.SmemAllocator(); st = smem.allocate(SS)
|
||||
|
||||
qp,qc = pipeline.PipelineTmaUmma.create(barrier_storage=st.q_bar.data_ptr(),num_stages=self.q_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.q_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
|
||||
kvp,kvc = pipeline.PipelineTmaUmma.create(barrier_storage=st.kv_bar.data_ptr(),num_stages=self.kv_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.kv_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
|
||||
s_prod,s_cons = pipeline.PipelineUmmaAsync.create(barrier_storage=st.s_bar.data_ptr(),num_stages=1,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32*len(self.epilogue_warp_id))).make_participants()
|
||||
softmax_done_bar = pipeline.NamedBarrier(barrier_id=3, num_threads=32 + 32*len(self.epilogue_warp_id))
|
||||
pv_done_bar = pipeline.NamedBarrier(barrier_id=4, num_threads=32 + 32*len(self.epilogue_warp_id))
|
||||
acc_pipe = pipeline.PipelineUmmaAsync.create(barrier_storage=st.acc_bar.data_ptr(),num_stages=self.num_acc_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,len(self.epilogue_warp_id)),cta_layout_vmnk=cl_vmnk,defer_sync=True)
|
||||
tmem_bar = pipeline.NamedBarrier(barrier_id=2,num_threads=32*len((self.mma_warp_id,*self.epilogue_warp_id)))
|
||||
tmem = utils.TmemAllocator(st.holding.ptr,barrier_for_retrieve=tmem_bar,allocator_warp_id=self.epilogue_warp_id[0],is_two_cta=cute.size(qk_mma.thr_id.shape)==2,two_cta_tmem_dealloc_mbar_ptr=st.tmem_dealloc.ptr)
|
||||
pipeline.pipeline_init_arrive(cluster_shape_mn=cl_vmnk,is_relaxed=True)
|
||||
|
||||
sQ = smem.allocate_tensor(element_type=self.q_dtype,layout=q_smem_s.outer,byte_alignment=128,swizzle=q_smem_s.inner)
|
||||
sK = smem.allocate_tensor(element_type=self.q_dtype,layout=k_smem_s.outer,byte_alignment=128,swizzle=k_smem_s.inner)
|
||||
sV = smem.allocate_tensor(element_type=self.q_dtype,layout=v_smem_s.outer,byte_alignment=128,swizzle=v_smem_s.inner)
|
||||
sC = smem.allocate_tensor(element_type=self.o_dtype,layout=c_smem_s.outer,byte_alignment=128,swizzle=c_smem_s.inner)
|
||||
|
||||
gQ = cute.local_tile(mQ,cute.slice_(self.qk_mma_tiler,(None,0,None)),(None,None,None))
|
||||
gK = cute.local_tile(mK,cute.slice_(self.qk_mma_tiler,(0,None,None)),(None,None,None))
|
||||
gV = cute.local_tile(mV,cute.slice_(self.pv_mma_tiler,(0,None,None)),(None,None,None))
|
||||
gC = cute.local_tile(mC,cute.slice_(self.pv_mma_tiler,(None,None,0)),(None,None,None))
|
||||
n_kv_tiles = cute.size(gK, mode=[3])
|
||||
|
||||
qk_thr = qk_mma.get_slice(0); pv_thr = pv_mma.get_slice(0)
|
||||
tCgQ = qk_thr.partition_A(gQ); tCgK = qk_thr.partition_B(gK)
|
||||
tCgV = pv_thr.partition_B(gV); tCgC = pv_thr.partition_C(gC)
|
||||
a_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,0,None,0)).shape)
|
||||
tAsQ,tAgQ = cpasync.tma_partition(tma_q,0,a_lay,cute.group_modes(sQ,0,3),cute.group_modes(tCgQ,0,3))
|
||||
b_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,None,0,0)).shape)
|
||||
tBsK,tBgK = cpasync.tma_partition(tma_k,0,b_lay,cute.group_modes(sK,0,3),cute.group_modes(tCgK,0,3))
|
||||
tVsV,tVgV = cpasync.tma_partition(tma_v,0,b_lay,cute.group_modes(sV,0,3),cute.group_modes(tCgV,0,3))
|
||||
tAgQ = tAgQ[(None,0,None,0)]; tBgK = tBgK[(None,0,None,0)]; tVgV = tVgV[(None,0,None,0)]
|
||||
|
||||
tCrQ = qk_mma.make_fragment_A(sQ); tCrK = qk_mma.make_fragment_B(sK)
|
||||
tCrV = pv_mma.make_fragment_B(sV)
|
||||
|
||||
qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
tOtO0 = cute.make_tensor(tOtO.iterator + self.tmem_o0_offset, tOtO.layout)
|
||||
|
||||
# --- PV read view (for MMA only, NOT for softmax store) ---
|
||||
tP = cute.make_tensor(tStS.iterator, p_tmem_s.outer)
|
||||
tOrP_base = pv_thr.make_fragment_A(tP)
|
||||
tOrP = tOrP_base[(None,None,None,0)]
|
||||
tOrP0 = cute.make_tensor(
|
||||
tOrP.iterator + self.qk_acc_dtype.width // self.q_dtype.width * self.tmem_p0_offset,
|
||||
tOrP.layout)
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_as, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_as, self.num_acc_stage))
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# TMA LOAD
|
||||
if warp_idx == self.tma_warp_id:
|
||||
qp.reset(); qh = qp.acquire_and_advance()
|
||||
cute.copy(tma_q,tAgQ[(None,qh.count)],tAsQ[(None,qh.index)],tma_bar_ptr=qh.barrier)
|
||||
qp.tail()
|
||||
kvp.reset(); pk = kvp.try_acquire()
|
||||
for kt in cutlass.range(n_kv_tiles,unroll=1):
|
||||
kh = kvp.acquire_and_advance(pk)
|
||||
cute.copy(tma_k,tBgK[(None,kh.count)],tBsK[(None,kh.index)],tma_bar_ptr=kh.barrier)
|
||||
pk = cutlass.Boolean(1)
|
||||
vh = kvp.acquire_and_advance(pk)
|
||||
cute.copy(tma_v,tVgV[(None,vh.count)],tVsV[(None,vh.index)],tma_bar_ptr=vh.barrier)
|
||||
pk = cutlass.Boolean(1)
|
||||
kvp.tail()
|
||||
|
||||
# MMA
|
||||
if warp_idx == self.mma_warp_id:
|
||||
tmem.wait_for_alloc()
|
||||
qc.reset(); qh = qc.wait_and_advance(); qh.release()
|
||||
kvc.reset(); pk = kvc.try_wait()
|
||||
acc_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
|
||||
acc_pipe.producer_acquire(acc_st)
|
||||
for kt in range(n_kv_tiles):
|
||||
kh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
sh = s_prod.acquire_and_advance()
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
for kb in cutlass.range(cute.size(tCrQ,mode=[2]), unroll_full=True):
|
||||
cute.gemm(qk_mma, tStS0, tCrQ[(None,None,kb,0)], tCrK[(None,None,kb,kh.index)], tStS0)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
sh.commit(); kh.release()
|
||||
softmax_done_bar.arrive_and_wait()
|
||||
vh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, kt != 0)
|
||||
for kb in cutlass.range(cute.size(tOrP0,mode=[2]), unroll_full=True):
|
||||
cute.gemm(pv_mma, tOtO0, tOrP0[(None,None,kb)], tCrV[(None,None,kb,vh.index)], tOtO0)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
vh.release()
|
||||
pv_done_bar.arrive()
|
||||
acc_pipe.producer_commit(acc_st); acc_st.advance()
|
||||
acc_pipe.producer_tail(acc_st)
|
||||
|
||||
# ===================== EPILOGUE WARPS (STAGE C: ONLINE SOFTMAX) =====================
|
||||
if warp_idx < self.mma_warp_id:
|
||||
tmem.allocate(self.num_tmem_alloc_cols)
|
||||
tmem.wait_for_alloc()
|
||||
tmem_ptr = tmem.retrieve_ptr(self.qk_acc_dtype)
|
||||
sfw_idx = tidx % (32 * len(self.epilogue_warp_id))
|
||||
|
||||
# --- S load (QK C-fragment) ---
|
||||
tmem_load_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
tiled_tmem_load = tcgen05.make_tmem_copy(tmem_load_atom, tStS0)
|
||||
thr_load = tiled_tmem_load.get_slice(sfw_idx)
|
||||
tTMEM_LOADtS = thr_load.partition_S(tStS0)
|
||||
cS = cute.make_identity_tensor((self.qk_mma_tiler[0], self.qk_mma_tiler[1]))
|
||||
tScS = qk_thr.partition_C(cS)
|
||||
tTMEM_LOADcS = thr_load.partition_D(tScS)
|
||||
|
||||
# --- P store (QK C-fragment composition, FMHA pattern) ---
|
||||
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
|
||||
tStP_layout = cute.composition(tStS.layout, cute.make_layout((self.pv_mma_tiler[0], p_cols_fp32)))
|
||||
tStP0 = cute.make_tensor(tStS.iterator + self.tmem_p0_offset, tStP_layout)
|
||||
tmem_store_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
tiled_tmem_store = tcgen05.make_tmem_copy(tmem_store_atom, tStP0)
|
||||
thr_store = tiled_tmem_store.get_slice(sfw_idx)
|
||||
tTMEM_STOREtP = thr_store.partition_D(tStP0)
|
||||
tScP_layout = cute.composition(tScS.layout, cute.make_layout((self.pv_mma_tiler[0], p_cols_fp32)))
|
||||
tScP = cute.make_tensor(tScS.iterator, tScP_layout)
|
||||
tTMEM_STOREcP = thr_store.partition_S(tScP)
|
||||
|
||||
# --- Vector TMEM (per-row row_sum storage, FMHA pattern) ---
|
||||
# composition(tStS.layout, (128, 2)) = 2 FP32 columns per logical row
|
||||
# vec[0] = row_sum (final, after loop), vec[1] = unused
|
||||
# Reuses S TMEM region (offset 0), free after softmax loop writes
|
||||
|
||||
tStS_vec_layout = cute.composition(tStS.layout, cute.make_layout((128, 2)))
|
||||
tStS_vec = cute.make_tensor(tStS.iterator + self.tmem_vec_offset, tStS_vec_layout)
|
||||
tScS_vec_layout = cute.composition(tScS.layout, cute.make_layout((128, 2)))
|
||||
tScS_vec = cute.make_tensor(tScS.iterator, tScS_vec_layout)
|
||||
tmem_store_vec_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(2)), self.qk_acc_dtype)
|
||||
tiled_tmem_store_vec = tcgen05.make_tmem_copy(tmem_store_vec_atom, tStS_vec)
|
||||
thr_tmem_store_vec = tiled_tmem_store_vec.get_slice(sfw_idx)
|
||||
tTMEM_STORE_VECtS = thr_tmem_store_vec.partition_D(tStS_vec)
|
||||
tTMEM_STORE_VECcS = thr_tmem_store_vec.partition_S(tScS_vec)
|
||||
tmem_load_vec_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(2)), self.qk_acc_dtype)
|
||||
tiled_tmem_load_vec = tcgen05.make_tmem_copy(tmem_load_vec_atom, tStS_vec)
|
||||
thr_tmem_load_vec = tiled_tmem_load_vec.get_slice(sfw_idx)
|
||||
tTMEM_LOAD_VECtS = thr_tmem_load_vec.partition_S(tStS_vec)
|
||||
tTMEM_LOAD_VECcS = thr_tmem_load_vec.partition_D(tScS_vec)
|
||||
|
||||
# --- C6: O TMEM load/store for rescale (correction_rescale pattern) ---
|
||||
corr_tile_size = 16
|
||||
cO = cute.make_identity_tensor((self.pv_mma_tiler[0], self.pv_mma_tiler[1]))
|
||||
tOcO = pv_thr.partition_C(cO)
|
||||
o_tmem_load_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.qk_acc_dtype)
|
||||
o_tmem_store_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), self.qk_acc_dtype)
|
||||
tOtO_i_layout = cute.composition(tOtO0.layout, cute.make_layout((128, corr_tile_size)))
|
||||
tOcO_i_layout = cute.composition(tOcO.layout, cute.make_layout((128, corr_tile_size)))
|
||||
tOtO_i = cute.make_tensor(tOtO0.iterator, tOtO_i_layout)
|
||||
tOcO_i = cute.make_tensor(tOcO.iterator, tOcO_i_layout)
|
||||
o_tiled_tmem_load = tcgen05.make_tmem_copy(o_tmem_load_atom, tOtO_i)
|
||||
o_tiled_tmem_store = tcgen05.make_tmem_copy(o_tmem_store_atom, tOtO_i)
|
||||
o_thr_load = o_tiled_tmem_load.get_slice(sfw_idx)
|
||||
o_thr_store = o_tiled_tmem_store.get_slice(sfw_idx)
|
||||
tTMEM_LOADtO = o_thr_load.partition_S(tOtO_i)
|
||||
tTMEM_LOADcO = o_thr_load.partition_D(tOcO_i)
|
||||
tTMEM_STOREtO = o_thr_store.partition_D(tOtO_i)
|
||||
o_col_tiles = self.pv_mma_tiler[1] // corr_tile_size
|
||||
|
||||
# --- C2: Per-thread row state (persist across KV tiles) ---
|
||||
row_max = -cutlass.Float32.inf
|
||||
row_sum = cutlass.Float32(0.0)
|
||||
|
||||
# --- C3: QK scale = 1/sqrt(HEAD_DIM) * log2(e) for exp2 ---
|
||||
scale = self.scale_softmax_log2
|
||||
|
||||
# =============================================================
|
||||
# Per-KV-tile online softmax loop
|
||||
# =============================================================
|
||||
for kt in range(n_kv_tiles):
|
||||
si_handle = s_cons.wait_and_advance()
|
||||
|
||||
# Load S from TMEM (FP32, QK C-fragment layout)
|
||||
tTMEM_LOADrS = cute.make_rmem_tensor(tTMEM_LOADcS.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load, tTMEM_LOADtS, tTMEM_LOADrS)
|
||||
|
||||
# --- C4: Compute tile_max via .reduce(MAX) ---
|
||||
old_row_max = row_max
|
||||
row_max = tTMEM_LOADrS.load().reduce(cute.ReductionOp.MAX, row_max, 0)
|
||||
row_max_safe = row_max
|
||||
if row_max == -cutlass.Float32.inf:
|
||||
row_max_safe = cutlass.Float32(0.0)
|
||||
|
||||
# --- C5: Compute rescale factor ---
|
||||
acc_scale = cute.math.exp2(scale * (old_row_max - row_max_safe), fastmath=True)
|
||||
|
||||
# --- C6: Rescale O in TMEM (load O, multiply by acc_scale, store O) ---
|
||||
# acc_scale belongs to QK row (N//4), but O rows are in PV partition (N).
|
||||
# Store acc_scale to vector by QK row, read by PV row.
|
||||
if kt > 0:
|
||||
pv_done_bar.arrive_and_wait()
|
||||
|
||||
# Store acc_scale to vector indexed by QK logical row
|
||||
qk_row_c6 = tTMEM_LOADcS[0][0]
|
||||
thr_vs_c6 = tiled_tmem_store_vec.get_slice(qk_row_c6)
|
||||
tVStore_c6 = thr_vs_c6.partition_D(tStS_vec)
|
||||
tVStoreSrc_c6 = thr_vs_c6.partition_S(tScS_vec)
|
||||
tVStoreRmem_c6 = cute.make_rmem_tensor(tVStoreSrc_c6.shape, self.qk_acc_dtype)
|
||||
tVStoreRmem_c6[0] = acc_scale
|
||||
cute.copy(tiled_tmem_store_vec, tVStoreRmem_c6, tVStore_c6)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# Read acc_scale from vector indexed by PV logical row
|
||||
pv_row_c6 = tTMEM_LOADcO[0][0]
|
||||
thr_vl_c6 = tiled_tmem_load_vec.get_slice(pv_row_c6)
|
||||
tVLoad_c6 = thr_vl_c6.partition_S(tStS_vec)
|
||||
tVLoadDst_c6 = thr_vl_c6.partition_D(tScS_vec)
|
||||
tVLoadRmem_c6 = cute.make_rmem_tensor(tVLoadDst_c6.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load_vec, tVLoad_c6, tVLoadRmem_c6)
|
||||
cute.arch.fence_view_async_tmem_load()
|
||||
acc_scale_pv = tVLoadRmem_c6[0]
|
||||
|
||||
tTMrO = cute.make_rmem_tensor((tTMEM_LOADcO.shape, o_col_tiles), self.qk_acc_dtype)
|
||||
for i in range(o_col_tiles):
|
||||
tTMrO_i_ = tTMrO[None, i]
|
||||
tTMrO_i_layout = cute.composition(tTMrO_i_.layout, cute.make_layout(tTMrO.shape[0]))
|
||||
tTMrO_i = cute.make_tensor(tTMrO_i_.iterator, tTMrO_i_layout)
|
||||
tTMEM_LOADtO_i = cute.make_tensor(tTMEM_LOADtO.iterator + i * corr_tile_size, tTMEM_LOADtO.layout)
|
||||
tTMEM_STOREtO_i = cute.make_tensor(tTMEM_STOREtO.iterator + i * corr_tile_size, tTMEM_STOREtO.layout)
|
||||
cute.copy(o_tiled_tmem_load, tTMEM_LOADtO_i, tTMrO_i)
|
||||
for j in cutlass.range(cute.size(tTMrO_i), vectorize=True):
|
||||
tTMrO_i[j] = tTMrO_i[j] * acc_scale_pv
|
||||
cute.copy(o_tiled_tmem_store, tTMrO_i, tTMEM_STOREtO_i)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# Rescale row_sum
|
||||
row_sum = row_sum * acc_scale
|
||||
|
||||
# --- C7: Compute P = exp2((S - row_max_safe) * scale) ---
|
||||
minus_row_max_scale = (cutlass.Float32(0.0) - row_max_safe) * scale
|
||||
|
||||
# Register bridge (FMHA pattern: FP32 backing + BF16 view)
|
||||
rP_words = cute.make_rmem_tensor(tTMEM_STOREcP.shape, self.qk_acc_dtype)
|
||||
rP_bf16 = cute.make_tensor(cute.recast_ptr(rP_words.iterator, dtype=self.q_dtype), tTMEM_LOADrS.layout)
|
||||
|
||||
frg_cnt = 4
|
||||
frg_tile = cute.size(tTMEM_LOADrS) // frg_cnt
|
||||
tTMEM_LOADrS_frg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(frg_tile))
|
||||
rP_bf16_frg = cute.logical_divide(rP_bf16, cute.make_layout(frg_tile))
|
||||
|
||||
# Scale S, compute exp2, store through register bridge
|
||||
for j in range(frg_cnt):
|
||||
for k in cutlass.range(cute.size(tTMEM_LOADrS_frg, mode=[0]), vectorize=True):
|
||||
tTMEM_LOADrS_frg[k, j] = tTMEM_LOADrS_frg[k, j] * scale + minus_row_max_scale
|
||||
tTMEM_LOADrS_frg[k, j] = cute.math.exp2(tTMEM_LOADrS_frg[k, j], fastmath=True)
|
||||
s_vec = tTMEM_LOADrS_frg[None, j].load()
|
||||
rP_bf16_frg[None, j].store(s_vec.to(self.q_dtype))
|
||||
|
||||
# Store P to TMEM
|
||||
cute.copy(tiled_tmem_store, rP_words, tTMEM_STOREtP)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
si_handle.release()
|
||||
softmax_done_bar.arrive()
|
||||
|
||||
# --- C8: Row sum accumulation (CUTLASS FMHA packed f32x2 pattern) ---
|
||||
# P values still in tTMEM_LOADrS registers.
|
||||
# 4 accumulators for 4 reduction_unroll columns.
|
||||
local_row_sum_0 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_1 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_2 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
local_row_sum_3 = (cutlass.Float32(0.0), cutlass.Float32(0.0))
|
||||
|
||||
reduction_unroll = 4
|
||||
rfrg_tile = cute.size(tTMEM_LOADrS) // reduction_unroll
|
||||
tTMEM_LOADrS_rfrg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(rfrg_tile))
|
||||
|
||||
for j in cutlass.range_constexpr(0, cute.size(tTMEM_LOADrS_rfrg, mode=[0]), 2):
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_0, (tTMEM_LOADrS_rfrg[j, 0], tTMEM_LOADrS_rfrg[j + 1, 0]))
|
||||
local_row_sum_1 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_1, (tTMEM_LOADrS_rfrg[j, 1], tTMEM_LOADrS_rfrg[j + 1, 1]))
|
||||
local_row_sum_2 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_2, (tTMEM_LOADrS_rfrg[j, 2], tTMEM_LOADrS_rfrg[j + 1, 2]))
|
||||
local_row_sum_3 = cute.arch.add_packed_f32x2(
|
||||
local_row_sum_3, (tTMEM_LOADrS_rfrg[j, 3], tTMEM_LOADrS_rfrg[j + 1, 3]))
|
||||
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(local_row_sum_0, local_row_sum_1)
|
||||
local_row_sum_2 = cute.arch.add_packed_f32x2(local_row_sum_2, local_row_sum_3)
|
||||
local_row_sum_0 = cute.arch.add_packed_f32x2(local_row_sum_0, local_row_sum_2)
|
||||
tile_sum = local_row_sum_0[0] + local_row_sum_0[1]
|
||||
|
||||
row_sum = row_sum + tile_sum
|
||||
|
||||
# --- C9: Final normalization via O TMEM rescale ---
|
||||
pv_done_bar.arrive_and_wait()
|
||||
|
||||
# Write row_sum to TMEM vector using QK partition (correct row mapping)
|
||||
qk_row_c9 = tTMEM_LOADcS[0][0]
|
||||
thr_vs_c9 = tiled_tmem_store_vec.get_slice(qk_row_c9)
|
||||
tVStore_c9 = thr_vs_c9.partition_D(tStS_vec)
|
||||
tVStoreSrc_c9 = thr_vs_c9.partition_S(tScS_vec)
|
||||
tVStoreRmem_c9 = cute.make_rmem_tensor(tVStoreSrc_c9.shape, self.qk_acc_dtype)
|
||||
tVStoreRmem_c9[0] = row_sum
|
||||
cute.copy(tiled_tmem_store_vec, tVStoreRmem_c9, tVStore_c9)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# Read row_sum from TMEM vector using PV partition (correct for O rows)
|
||||
pv_row_c9 = tTMEM_LOADcO[0][0]
|
||||
thr_vl_c9 = tiled_tmem_load_vec.get_slice(pv_row_c9)
|
||||
tVLoad_c9 = thr_vl_c9.partition_S(tStS_vec)
|
||||
tVLoadDst_c9 = thr_vl_c9.partition_D(tScS_vec)
|
||||
tVLoadRmem_c9 = cute.make_rmem_tensor(tVLoadDst_c9.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load_vec, tVLoad_c9, tVLoadRmem_c9)
|
||||
cute.arch.fence_view_async_tmem_load()
|
||||
pv_row_sum = tVLoadRmem_c9[0]
|
||||
|
||||
inv_row_sum = cutlass.Float32(1.0) / pv_row_sum
|
||||
|
||||
# Normalize O in TMEM using PV-correct inv_row_sum
|
||||
tTMrO_final = cute.make_rmem_tensor((tTMEM_LOADcO.shape, o_col_tiles), self.qk_acc_dtype)
|
||||
for i in range(o_col_tiles):
|
||||
tTMrO_i_ = tTMrO_final[None, i]
|
||||
tTMrO_i_layout = cute.composition(tTMrO_i_.layout, cute.make_layout(tTMrO_final.shape[0]))
|
||||
tTMrO_i = cute.make_tensor(tTMrO_i_.iterator, tTMrO_i_layout)
|
||||
tTMEM_LOADtO_i = cute.make_tensor(
|
||||
tTMEM_LOADtO.iterator + i * corr_tile_size, tTMEM_LOADtO.layout)
|
||||
tTMEM_STOREtO_i = cute.make_tensor(
|
||||
tTMEM_STOREtO.iterator + i * corr_tile_size, tTMEM_STOREtO.layout)
|
||||
cute.copy(o_tiled_tmem_load, tTMEM_LOADtO_i, tTMrO_i)
|
||||
for j in cutlass.range(cute.size(tTMrO_i), vectorize=True):
|
||||
tTMrO_i[j] = tTMrO_i[j] * inv_row_sum
|
||||
cute.copy(o_tiled_tmem_store, tTMrO_i, tTMEM_STOREtO_i)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
# Now O in TMEM is normalized. Use standard epilogue_tma_store with identity.
|
||||
tCtO_base = cute.make_tensor(tmem_ptr + self.tmem_o0_offset, tCtO_fake.layout)
|
||||
acc_cons_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Consumer, self.num_acc_stage)
|
||||
c_grp = pipeline.CooperativeGroup(pipeline.Agent.Thread, 32 * len(self.epilogue_warp_id))
|
||||
c_pipe = pipeline.PipelineTmaStore.create(num_stages=self.num_c_stage, producer_group=c_grp)
|
||||
acc_cons_st = utils.gemm.sm100.epilogue_tma_store(
|
||||
self, tidx, warp_idx, tma_c, tCtO_base, sC, tCgC, epi_tile, 0,
|
||||
const_expr(lambda x: x),
|
||||
(0,0,0), acc_cons_st, acc_pipe, c_pipe)
|
||||
c_pipe.producer_tail()
|
||||
tmem.relinquish_alloc_permit()
|
||||
tmem.free(tmem_ptr)
|
||||
|
||||
|
||||
def test():
|
||||
import math
|
||||
torch.manual_seed(42)
|
||||
for n in [128, 256, 384]:
|
||||
m, hd = 128, HEAD_DIM
|
||||
q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
k = torch.randn(n, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
v = torch.randn(n, hd, dtype=torch.bfloat16, device="cuda")
|
||||
v_kernel = v.unsqueeze(-1)
|
||||
c = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device="cuda")
|
||||
qf = q[:,:,0].float(); kf = k[:,:,0].float()
|
||||
attn = qf @ kf.T / math.sqrt(hd)
|
||||
ref = torch.softmax(attn, dim=-1) @ v.float()
|
||||
mQ = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
|
||||
mK = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
|
||||
mV = ct.from_dlpack(v_kernel).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_kernel))
|
||||
mC = ct.from_dlpack(c).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c))
|
||||
stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
|
||||
kernel = FmhaV3Softmax()
|
||||
print(f"n={n}: Compiling...", flush=True)
|
||||
compiled = cute.compile(kernel, mQ, mK, mV, mC, stream)
|
||||
print(f"n={n}: tmem: s0={kernel.tmem_s0_offset} p0={kernel.tmem_p0_offset} o0={kernel.tmem_o0_offset} vec={kernel.tmem_vec_offset} alloc={kernel.num_tmem_alloc_cols}", flush=True)
|
||||
print(f"n={n}: Running...", flush=True)
|
||||
compiled(mQ, mK, mV, mC, stream)
|
||||
torch.cuda.synchronize()
|
||||
out = c[:,:,0].float()
|
||||
cos = torch.nn.functional.cosine_similarity(out.flatten().unsqueeze(0), ref.flatten().unsqueeze(0)).item()
|
||||
max_err = (out - ref).abs().max().item()
|
||||
print(f"FMHA softmax n={n}: cosine {cos:.6f} max_err {max_err:.6f} {'PASS' if cos >= 0.999 else 'FAIL'}", flush=True)
|
||||
|
||||
if __name__ == "__main__":
|
||||
test()
|
||||
|
||||
|
||||
@@ -1,257 +0,0 @@
|
||||
"""
|
||||
Test (128,64) PV WITH identity softmax, single AB pipeline.
|
||||
This is test_pv64.py but we add a print to see if softmax actually writes.
|
||||
The key: if P/A alias works (proven above), then 0.67 must be from softmax
|
||||
writing to wrong columns or V being wrong.
|
||||
"""
|
||||
import torch, cutlass, cutlass.cute as cute, cutlass.utils as utils, cutlass.pipeline as pipeline
|
||||
from cutlass.cute.nvgpu import cpasync, tcgen05
|
||||
from cutlass import Float32, BFloat16, Int32, Boolean, const_expr
|
||||
from cutlass.utils import LayoutEnum
|
||||
from cutlass.utils.tmem_allocator import find_tmem_tensor_col_offset
|
||||
import cuda.bindings.driver as cuda
|
||||
import cutlass.torch as ct
|
||||
|
||||
HEAD_DIM = 64
|
||||
|
||||
class Pv64WithSoftmax:
|
||||
def __init__(self):
|
||||
self.acc_dtype = Float32; self.qk_acc_dtype = Float32
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
self.use_2cta_instrs = False; self.epilog_sync_bar_id = 1
|
||||
self.cluster_shape_mn = (1, 1); self.cta_group = tcgen05.CtaGroup.ONE
|
||||
self.epilogue_warp_id = (0,1,2,3); self.mma_warp_id = 4; self.tma_warp_id = 5
|
||||
self.threads_per_cta = 192; self.num_c_stage = 2
|
||||
self.num_ab_stage = 1; self.num_acc_stage = 1
|
||||
|
||||
def _setup(self, qk_mma, pv_mma):
|
||||
qk_ik = cute.size(qk_mma.shape_mnk, mode=[2])
|
||||
self.qk_mma_tiler = (128, 128, qk_ik * 4)
|
||||
pv_ik = cute.size(pv_mma.shape_mnk, mode=[2])
|
||||
self.pv_mma_tiler = (128, HEAD_DIM, pv_ik * (128 // pv_ik))
|
||||
self.mma_tiler = self.qk_mma_tiler
|
||||
self.cluster_layout_vmnk = cute.tiled_divide(cute.make_layout((1,1,1)), (qk_mma.thr_id.shape,))
|
||||
self.cta_tile_shape_mnk = (self.qk_mma_tiler[0]//cute.size(qk_mma.thr_id.shape), HEAD_DIM, self.qk_mma_tiler[2])
|
||||
self.c_layout = LayoutEnum.ROW_MAJOR
|
||||
self.epi_tile = utils.sm100.compute_epilogue_tile_shape(self.cta_tile_shape_mnk, False, self.c_layout, self.o_dtype)
|
||||
self.a_smem_s = utils.sm100.make_smem_layout_a(qk_mma, self.mma_tiler, self.q_dtype, 1)
|
||||
self.b_smem_s = utils.sm100.make_smem_layout_b(qk_mma, self.mma_tiler, self.q_dtype, 1)
|
||||
self.v_smem_s = utils.sm100.make_smem_layout_b(pv_mma, self.pv_mma_tiler, self.q_dtype, 1)
|
||||
self.p_tmem_s = utils.sm100.make_smem_layout_a(pv_mma, self.pv_mma_tiler, self.q_dtype, 1)
|
||||
self.c_smem_s = utils.sm100.make_smem_layout_epi(self.o_dtype, self.c_layout, self.epi_tile, 2)
|
||||
qk_thr = qk_mma.get_slice(0); qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
pv_thr = pv_mma.get_slice(0); pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
self.tilePlikeFP32 = self.qk_mma_tiler[1] // Float32.width * self.o_dtype.width
|
||||
self.tmem_s0_offset = 0; self.tmem_p0_offset = 32
|
||||
p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width
|
||||
p_end = self.tmem_p0_offset + p_cols_fp32
|
||||
s_cols = self.qk_mma_tiler[1]
|
||||
o_after = max(s_cols, p_end)
|
||||
self.tmem_o0_offset = ((o_after + 31) // 32) * 32
|
||||
tCS = qk_mma.make_fragment_C(cute.append(qk_as, self.num_acc_stage))
|
||||
tCO = pv_mma.make_fragment_C(cute.append(pv_as, self.num_acc_stage))
|
||||
self.num_tmem_alloc_cols = utils.get_num_tmem_alloc_cols([tCS, tCO], arch="sm_100")
|
||||
a_s = cute.slice_(self.a_smem_s,(None,None,None,0)); b_s = cute.slice_(self.b_smem_s,(None,None,None,0))
|
||||
v_s = cute.slice_(self.v_smem_s,(None,None,None,0))
|
||||
self.num_tma_load_bytes = (cute.size_in_bytes(self.q_dtype,a_s)+cute.size_in_bytes(self.q_dtype,b_s)+cute.size_in_bytes(self.q_dtype,v_s))*cute.size(qk_mma.thr_id.shape)
|
||||
|
||||
@cute.jit
|
||||
def __call__(self, q, k, v, c, stream):
|
||||
self.q_dtype = q.element_type; self.o_dtype = c.element_type; self.c_dtype = self.o_dtype
|
||||
self.a_major = LayoutEnum.from_tensor(q).mma_major_mode()
|
||||
self.b_major = LayoutEnum.from_tensor(k).mma_major_mode()
|
||||
v_fmha = cute.make_tensor(
|
||||
v.iterator,
|
||||
cute.make_layout(
|
||||
(HEAD_DIM, 128, 1),
|
||||
stride=(1, HEAD_DIM, HEAD_DIM * 128),
|
||||
),
|
||||
)
|
||||
self.v_major = LayoutEnum.from_tensor(v_fmha).mma_major_mode()
|
||||
self.c_layout = LayoutEnum.from_tensor(c)
|
||||
qk_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, self.a_major, self.b_major, self.qk_acc_dtype, self.cta_group, (128,128), tcgen05.OperandSource.SMEM)
|
||||
pv_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, cute.nvgpu.OperandMajorMode.K, self.v_major, self.qk_acc_dtype, self.cta_group, (128,HEAD_DIM), tcgen05.OperandSource.TMEM)
|
||||
self._setup(qk_mma, pv_mma)
|
||||
q_s = cute.slice_(self.a_smem_s,(None,None,None,0)); k_s = cute.slice_(self.b_smem_s,(None,None,None,0))
|
||||
v_s = cute.slice_(self.v_smem_s,(None,None,None,0))
|
||||
tma_q,mQ = cute.nvgpu.make_tiled_tma_atom_A(utils.sm100.cluster_shape_to_tma_atom_A(self.cluster_shape_mn,qk_mma.thr_id),q,q_s,self.mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_k,mK = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,qk_mma.thr_id),k,k_s,self.mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_v,mV = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,pv_mma.thr_id),v_fmha,v_s,self.pv_mma_tiler,pv_mma,self.cluster_layout_vmnk.shape)
|
||||
epi_s = cute.select(self.c_smem_s,mode=[0,1])
|
||||
tma_c,mC = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(),c,epi_s,self.epi_tile)
|
||||
self._kernel(qk_mma,pv_mma,tma_q,mQ,tma_k,mK,tma_v,mV,tma_c,mC,self.cluster_layout_vmnk,self.a_smem_s,self.b_smem_s,self.v_smem_s,self.p_tmem_s,self.c_smem_s,self.epi_tile).launch(grid=(1,1,1),block=[self.threads_per_cta,1,1],stream=stream)
|
||||
|
||||
@cute.kernel
|
||||
def _kernel(self, qk_mma, pv_mma, tma_q, mQ, tma_k, mK, tma_v, mV, tma_c, mC, cl_vmnk, a_smem_s, b_smem_s, v_smem_s, p_tmem_s, c_smem_s, epi_tile):
|
||||
warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx())
|
||||
tidx,_,_ = cute.arch.thread_idx()
|
||||
if warp_idx == self.tma_warp_id:
|
||||
cpasync.prefetch_descriptor(tma_q); cpasync.prefetch_descriptor(tma_k)
|
||||
cpasync.prefetch_descriptor(tma_v); cpasync.prefetch_descriptor(tma_c)
|
||||
@cute.struct
|
||||
class SS:
|
||||
ab_bar: cute.struct.MemRange[cutlass.Int64, self.num_ab_stage*2]
|
||||
mma_si_bar: cute.struct.MemRange[cutlass.Int64, 2]
|
||||
acc_bar: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage*2]
|
||||
tmem_dealloc: cutlass.Int64; holding: cutlass.Int32
|
||||
smem = utils.SmemAllocator(); st = smem.allocate(SS)
|
||||
ab_p,ab_c = pipeline.PipelineTmaUmma.create(barrier_storage=st.ab_bar.data_ptr(),num_stages=self.num_ab_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.num_tma_load_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
|
||||
mma_si_prod,mma_si_cons = pipeline.PipelineUmmaAsync.create(barrier_storage=st.mma_si_bar.data_ptr(),num_stages=1,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32*len(self.epilogue_warp_id))).make_participants()
|
||||
softmax_done_bar = pipeline.NamedBarrier(barrier_id=3, num_threads=32 + 32*len(self.epilogue_warp_id))
|
||||
acc_pipe = pipeline.PipelineUmmaAsync.create(barrier_storage=st.acc_bar.data_ptr(),num_stages=self.num_acc_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,len(self.epilogue_warp_id)),cta_layout_vmnk=cl_vmnk,defer_sync=True)
|
||||
tmem_bar = pipeline.NamedBarrier(barrier_id=2,num_threads=32*len((self.mma_warp_id,*self.epilogue_warp_id)))
|
||||
tmem = utils.TmemAllocator(st.holding.ptr,barrier_for_retrieve=tmem_bar,allocator_warp_id=self.epilogue_warp_id[0],is_two_cta=cute.size(qk_mma.thr_id.shape)==2,two_cta_tmem_dealloc_mbar_ptr=st.tmem_dealloc.ptr)
|
||||
pipeline.pipeline_init_arrive(cluster_shape_mn=cl_vmnk,is_relaxed=True)
|
||||
sQ = smem.allocate_tensor(element_type=self.q_dtype,layout=a_smem_s.outer,byte_alignment=128,swizzle=a_smem_s.inner)
|
||||
sK = smem.allocate_tensor(element_type=self.q_dtype,layout=b_smem_s.outer,byte_alignment=128,swizzle=b_smem_s.inner)
|
||||
sV = smem.allocate_tensor(element_type=self.q_dtype,layout=v_smem_s.outer,byte_alignment=128,swizzle=v_smem_s.inner)
|
||||
sC = smem.allocate_tensor(element_type=self.o_dtype,layout=c_smem_s.outer,byte_alignment=128,swizzle=c_smem_s.inner)
|
||||
gQ = cute.local_tile(mQ,cute.slice_(self.qk_mma_tiler,(None,0,None)),(None,None,None))
|
||||
gK = cute.local_tile(mK,cute.slice_(self.qk_mma_tiler,(0,None,None)),(None,None,None))
|
||||
gV = cute.local_tile(mV,cute.slice_(self.pv_mma_tiler,(0,None,None)),(None,None,None))
|
||||
gC = cute.local_tile(mC,cute.slice_(self.pv_mma_tiler,(None,None,0)),(None,None,None))
|
||||
k_cnt = cute.size(gQ, mode=[3])
|
||||
qk_thr = qk_mma.get_slice(0); pv_thr = pv_mma.get_slice(0)
|
||||
tCgQ = qk_thr.partition_A(gQ); tCgK = qk_thr.partition_B(gK)
|
||||
tCgV = pv_thr.partition_B(gV); tCgC = pv_thr.partition_C(gC)
|
||||
a_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,0,None,0)).shape)
|
||||
tAsQ,tAgQ = cpasync.tma_partition(tma_q,0,a_lay,cute.group_modes(sQ,0,3),cute.group_modes(tCgQ,0,3))
|
||||
b_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,None,0,0)).shape)
|
||||
tBsK,tBgK = cpasync.tma_partition(tma_k,0,b_lay,cute.group_modes(sK,0,3),cute.group_modes(tCgK,0,3))
|
||||
tVsV,tVgV = cpasync.tma_partition(tma_v,0,b_lay,cute.group_modes(sV,0,3),cute.group_modes(tCgV,0,3))
|
||||
tAgQ = tAgQ[(None,0,None,0)]; tBgK = tBgK[(None,0,None,0)]; tVgV = tVgV[(None,0,None,0)]
|
||||
tCrQ = qk_mma.make_fragment_A(sQ); tCrK = qk_mma.make_fragment_B(sK)
|
||||
tCrV = pv_mma.make_fragment_B(sV)
|
||||
qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
tStS0 = cute.make_tensor(tStS.iterator+self.tmem_s0_offset,tStS.layout)
|
||||
pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_as)
|
||||
tOtO0 = cute.make_tensor(tOtO.iterator+self.tmem_o0_offset,tOtO.layout)
|
||||
tP = cute.make_tensor(tStS.iterator, p_tmem_s.outer)
|
||||
tOrP_base = pv_thr.make_fragment_A(tP)
|
||||
tOrP = tOrP_base[(None,None,None,0)]
|
||||
tOrP0 = cute.make_tensor(tOrP.iterator+self.qk_acc_dtype.width//self.q_dtype.width*self.tmem_p0_offset,tOrP.layout)
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_as,self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_as,self.num_acc_stage))
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# TMA LOAD
|
||||
if warp_idx == self.tma_warp_id:
|
||||
ab_p.reset(); peek = ab_p.try_acquire()
|
||||
for kt in cutlass.range(k_cnt,unroll=1):
|
||||
h = ab_p.acquire_and_advance(peek)
|
||||
cute.copy(tma_q,tAgQ[(None,h.count)],tAsQ[(None,h.index)],tma_bar_ptr=h.barrier)
|
||||
cute.copy(tma_k,tBgK[(None,h.count)],tBsK[(None,h.index)],tma_bar_ptr=h.barrier)
|
||||
cute.copy(tma_v,tVgV[(None,h.count)],tVsV[(None,h.index)],tma_bar_ptr=h.barrier)
|
||||
peek = cutlass.Boolean(1)
|
||||
if h.count+1<k_cnt: peek = ab_p.try_acquire()
|
||||
ab_p.tail()
|
||||
|
||||
# MMA
|
||||
if warp_idx == self.mma_warp_id:
|
||||
tmem.wait_for_alloc()
|
||||
ab_c.reset(); peek = ab_c.try_wait()
|
||||
s0_handle = mma_si_prod.acquire_and_advance()
|
||||
acc_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer,self.num_acc_stage)
|
||||
acc_pipe.producer_acquire(acc_st)
|
||||
# QK
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
for kt in range(k_cnt):
|
||||
h = ab_c.wait_and_advance(peek)
|
||||
for kb in cutlass.range(cute.size(tCrQ,mode=[2]),unroll_full=True):
|
||||
cute.gemm(qk_mma,tStS0,tCrQ[(None,None,kb,h.index)],tCrK[(None,None,kb,h.index)],tStS0)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
h.release(); peek = cutlass.Boolean(1)
|
||||
if h.count+1<k_cnt: peek = ab_c.try_wait()
|
||||
cute.arch.fence_view_async_tmem_store(); s0_handle.commit()
|
||||
softmax_done_bar.arrive_and_wait()
|
||||
# PV
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
tCrV_s = tCrV[(None,None,None,0)]
|
||||
if tidx == 0: print(f"PV64: nblk={int(cute.size(tOrP0,mode=[2]))} tOrP0_s={int(cute.size(tOrP0))}")
|
||||
for kb in cutlass.range(cute.size(tOrP0,mode=[2]),unroll_full=True):
|
||||
cute.gemm(pv_mma,tOtO0,tOrP0[(None,None,kb)],tCrV_s[(None,None,kb)],tOtO0)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
acc_pipe.producer_commit(acc_st); acc_st.advance(); acc_pipe.producer_tail(acc_st)
|
||||
|
||||
# EPILOGUE (softmax + epilogue)
|
||||
if warp_idx < self.mma_warp_id:
|
||||
tmem.allocate(self.num_tmem_alloc_cols)
|
||||
tmem.wait_for_alloc()
|
||||
tmem_ptr = tmem.retrieve_ptr(self.qk_acc_dtype)
|
||||
sfw_idx = tidx % (32 * len(self.epilogue_warp_id))
|
||||
# S load
|
||||
tmem_load_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
tiled_tmem_load = tcgen05.make_tmem_copy(tmem_load_atom, tStS0)
|
||||
thr_load = tiled_tmem_load.get_slice(sfw_idx)
|
||||
tTMEM_LOADtS = thr_load.partition_S(tStS0)
|
||||
cS = cute.make_identity_tensor((self.qk_mma_tiler[0], self.qk_mma_tiler[1]))
|
||||
tScS = qk_thr.partition_C(cS)
|
||||
tTMEM_LOADcS = thr_load.partition_D(tScS)
|
||||
# P store (QK C-fragment composition)
|
||||
tStS_P_layout = cute.composition(tStS.layout, cute.make_layout((128, self.tilePlikeFP32)))
|
||||
tStS_P = cute.make_tensor(tStS.iterator + self.tmem_p0_offset, tStS_P_layout)
|
||||
tmem_store_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
tiled_tmem_store = tcgen05.make_tmem_copy(tmem_store_atom, tStS_P)
|
||||
thr_store = tiled_tmem_store.get_slice(sfw_idx)
|
||||
tTMEM_STOREtS_x4 = thr_store.partition_D(tStS_P)
|
||||
tScS_P_layout = cute.composition(tScS.layout, cute.make_layout((128, self.tilePlikeFP32)))
|
||||
tScS_P = cute.make_tensor(tScS.iterator, tScS_P_layout)
|
||||
tTMEM_STOREcS = thr_store.partition_S(tScS_P)
|
||||
# Softmax
|
||||
si_handle = mma_si_cons.wait_and_advance()
|
||||
tTMEM_LOADrS = cute.make_rmem_tensor(tTMEM_LOADcS.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load, tTMEM_LOADtS, tTMEM_LOADrS)
|
||||
tTMEM_STORErS_x4 = cute.make_rmem_tensor(tTMEM_STOREcS.shape, self.qk_acc_dtype)
|
||||
tTMEM_STORErS_x4_e = cute.make_tensor(cute.recast_ptr(tTMEM_STORErS_x4.iterator, dtype=self.q_dtype), tTMEM_LOADrS.layout)
|
||||
frg_cnt = 4; frg_tile = cute.size(tTMEM_LOADrS) // frg_cnt
|
||||
tTMEM_LOADrS_frg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(frg_tile))
|
||||
tTMEM_STORErS_x4_e_frg = cute.logical_divide(tTMEM_STORErS_x4_e, cute.make_layout(frg_tile))
|
||||
for j in range(frg_cnt):
|
||||
s_vec = tTMEM_LOADrS_frg[None, j].load()
|
||||
tTMEM_STORErS_x4_e_frg[None, j].store(s_vec.to(self.q_dtype))
|
||||
cute.copy(tiled_tmem_store, tTMEM_STORErS_x4, tTMEM_STOREtS_x4)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
si_handle.release()
|
||||
softmax_done_bar.arrive()
|
||||
# Epilogue
|
||||
tCtO_base = cute.make_tensor(tmem_ptr + self.tmem_o0_offset, tCtO_fake.layout)
|
||||
acc_cons_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Consumer, self.num_acc_stage)
|
||||
c_grp = pipeline.CooperativeGroup(pipeline.Agent.Thread, 32 * len(self.epilogue_warp_id))
|
||||
c_pipe = pipeline.PipelineTmaStore.create(num_stages=self.num_c_stage, producer_group=c_grp)
|
||||
acc_cons_st = utils.gemm.sm100.epilogue_tma_store(self, tidx, warp_idx, tma_c, tCtO_base, sC, tCgC, epi_tile, 0, const_expr(lambda x: x), (0,0,0), acc_cons_st, acc_pipe, c_pipe)
|
||||
c_pipe.producer_tail()
|
||||
tmem.relinquish_alloc_permit()
|
||||
tmem.free(tmem_ptr)
|
||||
|
||||
|
||||
def test():
|
||||
torch.manual_seed(42)
|
||||
m, n, hd = 128, 128, HEAD_DIM
|
||||
q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device='cuda')
|
||||
k = torch.randn(n, hd, 1, dtype=torch.bfloat16, device='cuda')
|
||||
v = torch.ones(n, hd, dtype=torch.bfloat16, device='cuda')
|
||||
v_kernel = v.unsqueeze(-1)
|
||||
c = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device='cuda')
|
||||
qf = q[:,:,0].float(); kf = k[:,:,0].float()
|
||||
ref = (qf @ kf.T).bfloat16().float() @ v.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 = Pv64WithSoftmax()
|
||||
print("Compiling...", flush=True)
|
||||
compiled = cute.compile(kernel, mQ, mK, mV, mC, stream)
|
||||
print(f"tilePlikeFP32={kernel.tilePlikeFP32} s0={kernel.tmem_s0_offset} p0={kernel.tmem_p0_offset} o0={kernel.tmem_o0_offset}", 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"PV64 with softmax: cosine {cos:.6f} {"PASS" if cos >= 0.99 else "FAIL"}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
test()
|
||||
@@ -1,252 +0,0 @@
|
||||
"""
|
||||
Debug: QK + identity softmax, output P (BF16) to GMEM.
|
||||
Tests the full QK -> softmax -> TMEM pipeline without PV.
|
||||
Uses the QK C-fragment store (like FMHA).
|
||||
Output = S.bf16() which is (Q@K^T).bfloat16(), shape (128, 128).
|
||||
"""
|
||||
import torch, cutlass, cutlass.cute as cute, cutlass.utils as utils, cutlass.pipeline as pipeline
|
||||
from cutlass.cute.nvgpu import cpasync, tcgen05
|
||||
from cutlass import Float32, BFloat16, Int32, Boolean, const_expr
|
||||
from cutlass.utils import LayoutEnum
|
||||
from cutlass.utils.tmem_allocator import find_tmem_tensor_col_offset
|
||||
import cuda.bindings.driver as cuda
|
||||
import cutlass.torch as ct
|
||||
|
||||
HEAD_DIM = 64
|
||||
|
||||
class QkSoftmaxTest:
|
||||
def __init__(self):
|
||||
self.acc_dtype = Float32; self.qk_acc_dtype = Float32
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
self.use_2cta_instrs = False; self.epilog_sync_bar_id = 1
|
||||
self.cluster_shape_mn = (1, 1); self.cta_group = tcgen05.CtaGroup.ONE
|
||||
self.epilogue_warp_id = (0,1,2,3); self.mma_warp_id = 4; self.tma_warp_id = 5
|
||||
self.threads_per_cta = 192; self.num_c_stage = 2
|
||||
self.kv_stage = 2; self.q_stage = 1
|
||||
|
||||
def _setup(self, qk_mma):
|
||||
qk_ik = cute.size(qk_mma.shape_mnk, mode=[2])
|
||||
self.qk_mma_tiler = (128, 128, qk_ik * 4)
|
||||
self.mma_tiler = self.qk_mma_tiler
|
||||
self.cluster_layout_vmnk = cute.tiled_divide(cute.make_layout((1,1,1)), (qk_mma.thr_id.shape,))
|
||||
self.cta_tile_shape_mnk = (self.qk_mma_tiler[0]//cute.size(qk_mma.thr_id.shape), 128, self.qk_mma_tiler[2])
|
||||
self.c_layout = LayoutEnum.ROW_MAJOR
|
||||
self.epi_tile = utils.sm100.compute_epilogue_tile_shape(self.cta_tile_shape_mnk, False, self.c_layout, self.o_dtype)
|
||||
self.num_ab_stage = 1; self.num_acc_stage = 1
|
||||
self.q_smem_s = utils.sm100.make_smem_layout_a(qk_mma, self.qk_mma_tiler, self.q_dtype, self.q_stage)
|
||||
self.k_smem_s = utils.sm100.make_smem_layout_b(qk_mma, self.qk_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.c_smem_s = utils.sm100.make_smem_layout_epi(self.o_dtype, self.c_layout, self.epi_tile, 2)
|
||||
qk_thr = qk_mma.get_slice(0); qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
self.tilePlikeFP32 = self.qk_mma_tiler[1] // Float32.width * self.o_dtype.width
|
||||
self.tmem_s0_offset = 0; self.tmem_p0_offset = 32; self.tmem_o0_offset = 0
|
||||
tCS = qk_mma.make_fragment_C(cute.append(qk_as, self.num_acc_stage))
|
||||
self.num_tmem_alloc_cols = utils.get_num_tmem_alloc_cols([tCS], arch="sm_100")
|
||||
cta = cute.size(qk_mma.thr_id.shape)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0))
|
||||
self.q_tx_bytes = cute.size_in_bytes(self.q_dtype, q_s) * cta
|
||||
self.kv_tx_bytes = cute.size_in_bytes(self.q_dtype, k_s) * cta
|
||||
|
||||
@cute.jit
|
||||
def __call__(self, q, k, c, stream):
|
||||
self.q_dtype = q.element_type; self.o_dtype = c.element_type; self.c_dtype = self.o_dtype
|
||||
self.a_major = LayoutEnum.from_tensor(q).mma_major_mode()
|
||||
self.b_major = LayoutEnum.from_tensor(k).mma_major_mode()
|
||||
self.c_layout = LayoutEnum.from_tensor(c)
|
||||
qk_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, self.a_major, self.b_major, self.qk_acc_dtype, self.cta_group, (128,128), tcgen05.OperandSource.SMEM)
|
||||
self._setup(qk_mma)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0)); k_s = cute.slice_(self.k_smem_s,(None,None,None,0))
|
||||
tma_q,mQ = cute.nvgpu.make_tiled_tma_atom_A(utils.sm100.cluster_shape_to_tma_atom_A(self.cluster_shape_mn,qk_mma.thr_id),q,q_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_k,mK = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,qk_mma.thr_id),k,k_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
epi_s = cute.select(self.c_smem_s,mode=[0,1])
|
||||
tma_c,mC = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(),c,epi_s,self.epi_tile)
|
||||
self._kernel(qk_mma,tma_q,mQ,tma_k,mK,tma_c,mC,self.cluster_layout_vmnk,self.q_smem_s,self.k_smem_s,self.c_smem_s,self.epi_tile).launch(grid=(1,1,1),block=[self.threads_per_cta,1,1],stream=stream)
|
||||
|
||||
@cute.kernel
|
||||
def _kernel(self, qk_mma, tma_q, mQ, tma_k, mK, tma_c, mC, cl_vmnk, q_smem_s, k_smem_s, c_smem_s, epi_tile):
|
||||
warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx())
|
||||
tidx,_,_ = cute.arch.thread_idx()
|
||||
if warp_idx == self.tma_warp_id:
|
||||
cpasync.prefetch_descriptor(tma_q); cpasync.prefetch_descriptor(tma_k); cpasync.prefetch_descriptor(tma_c)
|
||||
|
||||
@cute.struct
|
||||
class SS:
|
||||
q_bar: cute.struct.MemRange[cutlass.Int64, self.q_stage*2]
|
||||
kv_bar: cute.struct.MemRange[cutlass.Int64, self.kv_stage*2]
|
||||
s_bar: cute.struct.MemRange[cutlass.Int64, 2]
|
||||
acc_bar: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage*2]
|
||||
tmem_dealloc: cutlass.Int64; holding: cutlass.Int32
|
||||
|
||||
smem = utils.SmemAllocator(); st = smem.allocate(SS)
|
||||
|
||||
qp,qc = pipeline.PipelineTmaUmma.create(barrier_storage=st.q_bar.data_ptr(),num_stages=self.q_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.q_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
|
||||
kvp,kvc = pipeline.PipelineTmaUmma.create(barrier_storage=st.kv_bar.data_ptr(),num_stages=self.kv_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),tx_count=self.kv_tx_bytes,cta_layout_vmnk=cl_vmnk,defer_sync=True).make_participants()
|
||||
|
||||
s_prod,s_cons = pipeline.PipelineUmmaAsync.create(barrier_storage=st.s_bar.data_ptr(),num_stages=1,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,32*len(self.epilogue_warp_id))).make_participants()
|
||||
|
||||
# P-ready: softmax -> MMA signal using NamedBarrier
|
||||
softmax_done_bar = pipeline.NamedBarrier(barrier_id=3, num_threads=32 + 32*len(self.epilogue_warp_id))
|
||||
|
||||
acc_pipe = pipeline.PipelineUmmaAsync.create(barrier_storage=st.acc_bar.data_ptr(),num_stages=self.num_acc_stage,producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,len(self.epilogue_warp_id)),cta_layout_vmnk=cl_vmnk,defer_sync=True)
|
||||
tmem_bar = pipeline.NamedBarrier(barrier_id=2,num_threads=32*len((self.mma_warp_id,*self.epilogue_warp_id)))
|
||||
tmem = utils.TmemAllocator(st.holding.ptr,barrier_for_retrieve=tmem_bar,allocator_warp_id=self.epilogue_warp_id[0],is_two_cta=cute.size(qk_mma.thr_id.shape)==2,two_cta_tmem_dealloc_mbar_ptr=st.tmem_dealloc.ptr)
|
||||
pipeline.pipeline_init_arrive(cluster_shape_mn=cl_vmnk,is_relaxed=True)
|
||||
|
||||
sQ = smem.allocate_tensor(element_type=self.q_dtype,layout=q_smem_s.outer,byte_alignment=128,swizzle=q_smem_s.inner)
|
||||
sK = smem.allocate_tensor(element_type=self.q_dtype,layout=k_smem_s.outer,byte_alignment=128,swizzle=k_smem_s.inner)
|
||||
sC = smem.allocate_tensor(element_type=self.o_dtype,layout=c_smem_s.outer,byte_alignment=128,swizzle=c_smem_s.inner)
|
||||
|
||||
gQ = cute.local_tile(mQ,cute.slice_(self.qk_mma_tiler,(None,0,None)),(None,None,None))
|
||||
gK = cute.local_tile(mK,cute.slice_(self.qk_mma_tiler,(0,None,None)),(None,None,None))
|
||||
gC = cute.local_tile(mC,cute.slice_(self.qk_mma_tiler,(None,None,0)),(None,None,None))
|
||||
n_kv_tiles = cute.size(gK, mode=[3])
|
||||
|
||||
qk_thr = qk_mma.get_slice(0)
|
||||
tCgQ = qk_thr.partition_A(gQ); tCgK = qk_thr.partition_B(gK); tCgC = qk_thr.partition_C(gC)
|
||||
a_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,0,None,0)).shape)
|
||||
tAsQ,tAgQ = cpasync.tma_partition(tma_q,0,a_lay,cute.group_modes(sQ,0,3),cute.group_modes(tCgQ,0,3))
|
||||
b_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,None,0,0)).shape)
|
||||
tBsK,tBgK = cpasync.tma_partition(tma_k,0,b_lay,cute.group_modes(sK,0,3),cute.group_modes(tCgK,0,3))
|
||||
tAgQ = tAgQ[(None,0,None,0)]; tBgK = tBgK[(None,0,None,0)]
|
||||
|
||||
tCrQ = qk_mma.make_fragment_A(sQ); tCrK = qk_mma.make_fragment_B(sK)
|
||||
|
||||
qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_as, self.num_acc_stage))
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# TMA LOAD
|
||||
if warp_idx == self.tma_warp_id:
|
||||
qp.reset(); qh = qp.acquire_and_advance()
|
||||
cute.copy(tma_q,tAgQ[(None,qh.count)],tAsQ[(None,qh.index)],tma_bar_ptr=qh.barrier)
|
||||
qp.tail()
|
||||
kvp.reset(); pk = kvp.try_acquire()
|
||||
for kt in cutlass.range(n_kv_tiles,unroll=1):
|
||||
kh = kvp.acquire_and_advance(pk)
|
||||
cute.copy(tma_k,tBgK[(None,kh.count)],tBsK[(None,kh.index)],tma_bar_ptr=kh.barrier)
|
||||
pk = cutlass.Boolean(1)
|
||||
kvp.tail()
|
||||
|
||||
# MMA: QK, then wait for softmax, then signal done
|
||||
if warp_idx == self.mma_warp_id:
|
||||
tmem.wait_for_alloc()
|
||||
qc.reset(); qh = qc.wait_and_advance(); qh.release()
|
||||
kvc.reset(); pk = kvc.try_wait()
|
||||
|
||||
acc_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
|
||||
acc_pipe.producer_acquire(acc_st)
|
||||
|
||||
for kt in range(n_kv_tiles):
|
||||
kh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
|
||||
sh = s_prod.acquire_and_advance()
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
for kb in cutlass.range(cute.size(tCrQ,mode=[2]), unroll_full=True):
|
||||
cute.gemm(qk_mma, tStS0, tCrQ[(None,None,kb,0)], tCrK[(None,None,kb,kh.index)], tStS0)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
sh.commit()
|
||||
kh.release()
|
||||
|
||||
# Wait for softmax to finish
|
||||
softmax_done_bar.arrive_and_wait()
|
||||
|
||||
# After all tiles: S contains the identity-softmax'd result
|
||||
# But we want to output P (written by softmax at p0 offset)
|
||||
# For this test, output what's at tmem_s0_offset (the final S accumulator)
|
||||
acc_pipe.producer_commit(acc_st); acc_st.advance()
|
||||
acc_pipe.producer_tail(acc_st)
|
||||
|
||||
# EPILOGUE: identity softmax + output
|
||||
if warp_idx < self.mma_warp_id:
|
||||
tmem.allocate(self.num_tmem_alloc_cols)
|
||||
tmem.wait_for_alloc()
|
||||
tmem_ptr = tmem.retrieve_ptr(self.qk_acc_dtype)
|
||||
sfw_idx = tidx % (32 * len(self.epilogue_warp_id))
|
||||
|
||||
# S load
|
||||
tmem_load_atom = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
tiled_tmem_load = tcgen05.make_tmem_copy(tmem_load_atom, tStS0)
|
||||
thr_load = tiled_tmem_load.get_slice(sfw_idx)
|
||||
tTMEM_LOADtS = thr_load.partition_S(tStS0)
|
||||
cS = cute.make_identity_tensor((self.qk_mma_tiler[0], self.qk_mma_tiler[1]))
|
||||
tScS = qk_thr.partition_C(cS)
|
||||
tTMEM_LOADcS = thr_load.partition_D(tScS)
|
||||
|
||||
# P store
|
||||
tStS_P_layout = cute.composition(tStS.layout, cute.make_layout((128, self.tilePlikeFP32)))
|
||||
tStS_P = cute.make_tensor(tStS.iterator + self.tmem_p0_offset, tStS_P_layout)
|
||||
tmem_store_atom = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
tiled_tmem_store = tcgen05.make_tmem_copy(tmem_store_atom, tStS_P)
|
||||
thr_store = tiled_tmem_store.get_slice(sfw_idx)
|
||||
tTMEM_STOREtS_x4 = thr_store.partition_D(tStS_P)
|
||||
tScS_P_layout = cute.composition(tScS.layout, cute.make_layout((128, self.tilePlikeFP32)))
|
||||
tScS_P = cute.make_tensor(tScS.iterator, tScS_P_layout)
|
||||
tTMEM_STOREcS = thr_store.partition_S(tScS_P)
|
||||
|
||||
for kt in range(n_kv_tiles):
|
||||
si_handle = s_cons.wait_and_advance()
|
||||
|
||||
# Load S
|
||||
tTMEM_LOADrS = cute.make_rmem_tensor(tTMEM_LOADcS.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load, tTMEM_LOADtS, tTMEM_LOADrS)
|
||||
|
||||
# Identity softmax: FP32 S -> BF16 P, write to TMEM at p0 offset
|
||||
tTMEM_STORErS_x4 = cute.make_rmem_tensor(tTMEM_STOREcS.shape, self.qk_acc_dtype)
|
||||
tTMEM_STORErS_x4_e = cute.make_tensor(cute.recast_ptr(tTMEM_STORErS_x4.iterator, dtype=self.q_dtype), tTMEM_LOADrS.layout)
|
||||
|
||||
frg_cnt = 4; frg_tile = cute.size(tTMEM_LOADrS) // frg_cnt
|
||||
tTMEM_LOADrS_frg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(frg_tile))
|
||||
tTMEM_STORErS_x4_e_frg = cute.logical_divide(tTMEM_STORErS_x4_e, cute.make_layout(frg_tile))
|
||||
for j in range(frg_cnt):
|
||||
s_vec = tTMEM_LOADrS_frg[None, j].load()
|
||||
tTMEM_STORErS_x4_e_frg[None, j].store(s_vec.to(self.q_dtype))
|
||||
cute.copy(tiled_tmem_store, tTMEM_STORErS_x4, tTMEM_STOREtS_x4)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
|
||||
si_handle.release()
|
||||
# Signal MMA
|
||||
softmax_done_bar.arrive()
|
||||
|
||||
# Output: read from p0 offset (BF16 P values, but we read as FP32)
|
||||
# We need to output the BF16 P values. Read from p0 and convert.
|
||||
# Actually, output the S accumulator (at s0) for now to verify QK works
|
||||
tCtS_base = cute.make_tensor(tmem_ptr + self.tmem_s0_offset, tCtS_fake.layout)
|
||||
acc_cons_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Consumer, self.num_acc_stage)
|
||||
c_grp = pipeline.CooperativeGroup(pipeline.Agent.Thread, 32 * len(self.epilogue_warp_id))
|
||||
c_pipe = pipeline.PipelineTmaStore.create(num_stages=self.num_c_stage, producer_group=c_grp)
|
||||
acc_cons_st = utils.gemm.sm100.epilogue_tma_store(self, tidx, warp_idx, tma_c, tCtS_base, sC, tCgC, epi_tile, 0, const_expr(lambda x: x), (0,0,0), acc_cons_st, acc_pipe, c_pipe)
|
||||
c_pipe.producer_tail()
|
||||
tmem.relinquish_alloc_permit()
|
||||
tmem.free(tmem_ptr)
|
||||
|
||||
|
||||
def test():
|
||||
torch.manual_seed(42)
|
||||
n = 128; m, hd = 128, HEAD_DIM
|
||||
q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device='cuda')
|
||||
k = torch.randn(n, hd, 1, dtype=torch.bfloat16, device='cuda')
|
||||
c = torch.zeros(m, n, 1, dtype=torch.bfloat16, device='cuda')
|
||||
qf = q[:,:,0].float(); kf = k[:,:,0].float()
|
||||
ref = (qf @ kf.T)
|
||||
mQ = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
|
||||
mK = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
|
||||
mC = ct.from_dlpack(c).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c))
|
||||
stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
|
||||
kernel = QkSoftmaxTest()
|
||||
print('Compiling...', flush=True)
|
||||
compiled = cute.compile(kernel, mQ, mK, mC, stream)
|
||||
print('Running...', flush=True)
|
||||
compiled(mQ, mK, mC, stream)
|
||||
torch.cuda.synchronize()
|
||||
out = c[:,:,0].float()
|
||||
cos = torch.nn.functional.cosine_similarity(out.flatten().unsqueeze(0), ref.flatten().unsqueeze(0)).item()
|
||||
print(f'QK+softmax n={n}: cosine {cos:.6f} {"PASS" if cos >= 0.99 else "FAIL"}')
|
||||
if cos < 0.99:
|
||||
print(f' out[0,:4]={out[0,:4].tolist()} ref[0,:4]={ref[0,:4].tolist()}')
|
||||
|
||||
if __name__ == '__main__':
|
||||
test()
|
||||
@@ -1,269 +0,0 @@
|
||||
"""
|
||||
Debug: QK only (no PV) with KV-tile interleaving pipeline.
|
||||
Outputs P to GMEM to verify QK+softmax pipeline works.
|
||||
n=128, single KV tile, identity softmax.
|
||||
"""
|
||||
import torch, cutlass, cutlass.cute as cute, cutlass.utils as utils, cutlass.pipeline as pipeline
|
||||
from cutlass.cute.nvgpu import cpasync, tcgen05
|
||||
from cutlass import Float32, BFloat16, Int32, Boolean, const_expr
|
||||
from cutlass.utils import LayoutEnum
|
||||
from cutlass.utils.tmem_allocator import find_tmem_tensor_col_offset
|
||||
import cuda.bindings.driver as cuda
|
||||
import cutlass.torch as ct
|
||||
|
||||
HEAD_DIM = 64
|
||||
|
||||
|
||||
class QkOnlyTest:
|
||||
def __init__(self):
|
||||
self.acc_dtype = Float32; self.qk_acc_dtype = Float32
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
self.use_2cta_instrs = False; self.epilog_sync_bar_id = 1
|
||||
self.cluster_shape_mn = (1, 1); self.cta_group = tcgen05.CtaGroup.ONE
|
||||
self.epilogue_warp_id = (0,1,2,3); self.mma_warp_id = 4; self.tma_warp_id = 5
|
||||
self.threads_per_cta = 192; self.num_c_stage = 2
|
||||
self.kv_stage = 2; self.q_stage = 1
|
||||
|
||||
def _setup(self, qk_mma):
|
||||
qk_ik = cute.size(qk_mma.shape_mnk, mode=[2])
|
||||
self.qk_mma_tiler = (128, 128, qk_ik * 4)
|
||||
self.mma_tiler = self.qk_mma_tiler
|
||||
self.cluster_layout_vmnk = cute.tiled_divide(cute.make_layout((1,1,1)), (qk_mma.thr_id.shape,))
|
||||
self.cta_tile_shape_mnk = (self.qk_mma_tiler[0]//cute.size(qk_mma.thr_id.shape), 128, self.qk_mma_tiler[2])
|
||||
self.c_layout = LayoutEnum.ROW_MAJOR
|
||||
self.epi_tile = utils.sm100.compute_epilogue_tile_shape(self.cta_tile_shape_mnk, False, self.c_layout, self.o_dtype)
|
||||
self.num_ab_stage = 1; self.num_acc_stage = 1
|
||||
self.q_smem_s = utils.sm100.make_smem_layout_a(qk_mma, self.qk_mma_tiler, self.q_dtype, self.q_stage)
|
||||
self.k_smem_s = utils.sm100.make_smem_layout_b(qk_mma, self.qk_mma_tiler, self.q_dtype, self.kv_stage)
|
||||
self.c_smem_s = utils.sm100.make_smem_layout_epi(self.o_dtype, self.c_layout, self.epi_tile, 2)
|
||||
|
||||
qk_thr = qk_mma.get_slice(0); qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
|
||||
self.tilePlikeFP32 = self.qk_mma_tiler[1] // Float32.width * self.o_dtype.width
|
||||
self.tmem_s0_offset = 0; self.tmem_p0_offset = 32
|
||||
self.tmem_o0_offset = 0 # Output is at S offset for QK-only
|
||||
|
||||
tCS = qk_mma.make_fragment_C(cute.append(qk_as, self.num_acc_stage))
|
||||
self.num_tmem_alloc_cols = utils.get_num_tmem_alloc_cols([tCS], arch="sm_100")
|
||||
|
||||
cta = cute.size(qk_mma.thr_id.shape)
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0))
|
||||
k_s = cute.slice_(self.k_smem_s,(None,None,None,0))
|
||||
self.q_tx_bytes = cute.size_in_bytes(self.q_dtype, q_s) * cta
|
||||
self.kv_tx_bytes = cute.size_in_bytes(self.q_dtype, k_s) * cta
|
||||
|
||||
@cute.jit
|
||||
def __call__(self, q, k, c, stream):
|
||||
self.q_dtype = q.element_type; self.o_dtype = c.element_type; self.c_dtype = self.o_dtype
|
||||
self.a_major = LayoutEnum.from_tensor(q).mma_major_mode()
|
||||
self.b_major = LayoutEnum.from_tensor(k).mma_major_mode()
|
||||
self.c_layout = LayoutEnum.from_tensor(c)
|
||||
|
||||
qk_mma = utils.sm100.make_trivial_tiled_mma(
|
||||
self.q_dtype, self.q_dtype, self.a_major, self.b_major,
|
||||
self.qk_acc_dtype, self.cta_group, (128,128), tcgen05.OperandSource.SMEM)
|
||||
self._setup(qk_mma)
|
||||
|
||||
q_s = cute.slice_(self.q_smem_s,(None,None,None,0))
|
||||
k_s = cute.slice_(self.k_smem_s,(None,None,None,0))
|
||||
|
||||
tma_q,mQ = cute.nvgpu.make_tiled_tma_atom_A(
|
||||
utils.sm100.cluster_shape_to_tma_atom_A(self.cluster_shape_mn,qk_mma.thr_id),
|
||||
q,q_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
tma_k,mK = cute.nvgpu.make_tiled_tma_atom_B(
|
||||
utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,qk_mma.thr_id),
|
||||
k,k_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
|
||||
epi_s = cute.select(self.c_smem_s,mode=[0,1])
|
||||
tma_c,mC = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(),c,epi_s,self.epi_tile)
|
||||
|
||||
self._kernel(qk_mma, tma_q, mQ, tma_k, mK, tma_c, mC,
|
||||
self.cluster_layout_vmnk, self.q_smem_s, self.k_smem_s, self.c_smem_s, self.epi_tile
|
||||
).launch(grid=(1,1,1), block=[self.threads_per_cta,1,1], stream=stream)
|
||||
|
||||
@cute.kernel
|
||||
def _kernel(self, qk_mma, tma_q, mQ, tma_k, mK, tma_c, mC,
|
||||
cl_vmnk, q_smem_s, k_smem_s, c_smem_s, epi_tile):
|
||||
warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx())
|
||||
tidx,_,_ = cute.arch.thread_idx()
|
||||
|
||||
if warp_idx == self.tma_warp_id:
|
||||
cpasync.prefetch_descriptor(tma_q); cpasync.prefetch_descriptor(tma_k)
|
||||
cpasync.prefetch_descriptor(tma_c)
|
||||
|
||||
@cute.struct
|
||||
class SS:
|
||||
q_bar: cute.struct.MemRange[cutlass.Int64, self.q_stage*2]
|
||||
kv_bar: cute.struct.MemRange[cutlass.Int64, self.kv_stage*2]
|
||||
s_bar: cute.struct.MemRange[cutlass.Int64, 2]
|
||||
acc_bar: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage*2]
|
||||
tmem_dealloc: cutlass.Int64; holding: cutlass.Int32
|
||||
|
||||
smem = utils.SmemAllocator(); st = smem.allocate(SS)
|
||||
|
||||
qp,qc = pipeline.PipelineTmaUmma.create(
|
||||
barrier_storage=st.q_bar.data_ptr(), num_stages=self.q_stage,
|
||||
producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),
|
||||
consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),
|
||||
tx_count=self.q_tx_bytes, cta_layout_vmnk=cl_vmnk, defer_sync=True
|
||||
).make_participants()
|
||||
|
||||
kvp,kvc = pipeline.PipelineTmaUmma.create(
|
||||
barrier_storage=st.kv_bar.data_ptr(), num_stages=self.kv_stage,
|
||||
producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),
|
||||
consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),
|
||||
tx_count=self.kv_tx_bytes, cta_layout_vmnk=cl_vmnk, defer_sync=True
|
||||
).make_participants()
|
||||
|
||||
s_prod,s_cons = pipeline.PipelineUmmaAsync.create(
|
||||
barrier_storage=st.s_bar.data_ptr(), num_stages=1,
|
||||
producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),
|
||||
consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread, 32*len(self.epilogue_warp_id))
|
||||
).make_participants()
|
||||
|
||||
softmax_done_bar = pipeline.NamedBarrier(barrier_id=3, num_threads=32 + 32*len(self.epilogue_warp_id))
|
||||
|
||||
acc_pipe = pipeline.PipelineUmmaAsync.create(
|
||||
barrier_storage=st.acc_bar.data_ptr(), num_stages=self.num_acc_stage,
|
||||
producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),
|
||||
consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread, len(self.epilogue_warp_id)),
|
||||
cta_layout_vmnk=cl_vmnk, defer_sync=True)
|
||||
|
||||
tmem_bar = pipeline.NamedBarrier(barrier_id=2,
|
||||
num_threads=32*len((self.mma_warp_id,*self.epilogue_warp_id)))
|
||||
tmem = utils.TmemAllocator(st.holding.ptr, barrier_for_retrieve=tmem_bar,
|
||||
allocator_warp_id=self.epilogue_warp_id[0],
|
||||
is_two_cta=cute.size(qk_mma.thr_id.shape)==2,
|
||||
two_cta_tmem_dealloc_mbar_ptr=st.tmem_dealloc.ptr)
|
||||
|
||||
pipeline.pipeline_init_arrive(cluster_shape_mn=cl_vmnk, is_relaxed=True)
|
||||
|
||||
sQ = smem.allocate_tensor(element_type=self.q_dtype, layout=q_smem_s.outer, byte_alignment=128, swizzle=q_smem_s.inner)
|
||||
sK = smem.allocate_tensor(element_type=self.q_dtype, layout=k_smem_s.outer, byte_alignment=128, swizzle=k_smem_s.inner)
|
||||
sC = smem.allocate_tensor(element_type=self.o_dtype, layout=c_smem_s.outer, byte_alignment=128, swizzle=c_smem_s.inner)
|
||||
|
||||
gQ = cute.local_tile(mQ, cute.slice_(self.qk_mma_tiler,(None,0,None)), (None,None,None))
|
||||
gK = cute.local_tile(mK, cute.slice_(self.qk_mma_tiler,(0,None,None)), (None,None,None))
|
||||
gC = cute.local_tile(mC, cute.slice_(self.qk_mma_tiler,(None,None,0)), (None,None,None))
|
||||
n_kv_tiles = cute.size(gK, mode=[3])
|
||||
|
||||
qk_thr = qk_mma.get_slice(0)
|
||||
tCgQ = qk_thr.partition_A(gQ); tCgK = qk_thr.partition_B(gK)
|
||||
tCgC = qk_thr.partition_C(gC)
|
||||
|
||||
a_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,0,None,0)).shape)
|
||||
tAsQ,tAgQ = cpasync.tma_partition(tma_q,0,a_lay,cute.group_modes(sQ,0,3),cute.group_modes(tCgQ,0,3))
|
||||
b_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,None,0,0)).shape)
|
||||
tBsK,tBgK = cpasync.tma_partition(tma_k,0,b_lay,cute.group_modes(sK,0,3),cute.group_modes(tCgK,0,3))
|
||||
tAgQ = tAgQ[(None,0,None,0)]; tBgK = tBgK[(None,0,None,0)]
|
||||
|
||||
tCrQ = qk_mma.make_fragment_A(sQ); tCrK = qk_mma.make_fragment_B(sK)
|
||||
|
||||
qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_as)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_as, self.num_acc_stage))
|
||||
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# TMA LOAD
|
||||
if warp_idx == self.tma_warp_id:
|
||||
qp.reset(); qh = qp.acquire_and_advance()
|
||||
cute.copy(tma_q, tAgQ[(None,qh.count)], tAsQ[(None,qh.index)], tma_bar_ptr=qh.barrier)
|
||||
qp.tail()
|
||||
|
||||
kvp.reset(); pk = kvp.try_acquire()
|
||||
for kt in cutlass.range(n_kv_tiles, unroll=1):
|
||||
kh = kvp.acquire_and_advance(pk)
|
||||
cute.copy(tma_k, tBgK[(None,kh.count)], tBsK[(None,kh.index)], tma_bar_ptr=kh.barrier)
|
||||
pk = cutlass.Boolean(1)
|
||||
kvp.tail()
|
||||
|
||||
# MMA
|
||||
if warp_idx == self.mma_warp_id:
|
||||
tmem.wait_for_alloc()
|
||||
qc.reset(); qh = qc.wait_and_advance(); qh.release()
|
||||
kvc.reset(); pk = kvc.try_wait()
|
||||
|
||||
acc_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
|
||||
acc_pipe.producer_acquire(acc_st)
|
||||
|
||||
for kt in range(n_kv_tiles):
|
||||
kh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
|
||||
|
||||
# QK only, accumulate across KV tiles
|
||||
sh = s_prod.acquire_and_advance()
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, kt != 0)
|
||||
for kb in cutlass.range(cute.size(tCrQ,mode=[2]), unroll_full=True):
|
||||
cute.gemm(qk_mma, tStS0,
|
||||
tCrQ[(None,None,kb,0)], tCrK[(None,None,kb,kh.index)], tStS0)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
sh.commit()
|
||||
kh.release()
|
||||
|
||||
# Wait for softmax (identity: just signal done)
|
||||
softmax_done_bar.arrive_and_wait()
|
||||
|
||||
acc_pipe.producer_commit(acc_st); acc_st.advance()
|
||||
acc_pipe.producer_tail(acc_st)
|
||||
|
||||
# EPILOGUE
|
||||
if warp_idx < self.mma_warp_id:
|
||||
tmem.allocate(self.num_tmem_alloc_cols)
|
||||
tmem.wait_for_alloc()
|
||||
tmem_ptr = tmem.retrieve_ptr(self.qk_acc_dtype)
|
||||
sfw_idx = tidx % (32 * len(self.epilogue_warp_id))
|
||||
|
||||
for kt in range(n_kv_tiles):
|
||||
si_handle = s_cons.wait_and_advance()
|
||||
# Identity softmax: no-op, just signal MMA
|
||||
si_handle.release()
|
||||
softmax_done_bar.arrive()
|
||||
|
||||
# Output S (QK result) to GMEM
|
||||
tCtS_base = cute.make_tensor(tmem_ptr + self.tmem_s0_offset, tCtS_fake.layout)
|
||||
acc_cons_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Consumer, self.num_acc_stage)
|
||||
c_grp = pipeline.CooperativeGroup(pipeline.Agent.Thread, 32 * len(self.epilogue_warp_id))
|
||||
c_pipe = pipeline.PipelineTmaStore.create(num_stages=self.num_c_stage, producer_group=c_grp)
|
||||
acc_cons_st = utils.gemm.sm100.epilogue_tma_store(
|
||||
self, tidx, warp_idx, tma_c, tCtS_base, sC, tCgC,
|
||||
epi_tile, 0, const_expr(lambda x: x), (0,0,0), acc_cons_st, acc_pipe, c_pipe)
|
||||
c_pipe.producer_tail()
|
||||
tmem.relinquish_alloc_permit()
|
||||
tmem.free(tmem_ptr)
|
||||
|
||||
|
||||
def test():
|
||||
torch.manual_seed(42)
|
||||
n = 128
|
||||
m, hd = 128, HEAD_DIM
|
||||
q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device='cuda')
|
||||
k = torch.randn(n, hd, 1, dtype=torch.bfloat16, device='cuda')
|
||||
c = torch.zeros(m, n, 1, dtype=torch.bfloat16, device='cuda') # (128, 128) output = S
|
||||
|
||||
qf = q[:,:,0].float(); kf = k[:,:,0].float()
|
||||
ref = (qf @ kf.T)
|
||||
|
||||
mQ = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
|
||||
mK = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
|
||||
mC = ct.from_dlpack(c).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c))
|
||||
stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
|
||||
|
||||
kernel = QkOnlyTest()
|
||||
print('Compiling...', flush=True)
|
||||
compiled = cute.compile(kernel, mQ, mK, mC, stream)
|
||||
print('Running...', flush=True)
|
||||
compiled(mQ, mK, mC, stream)
|
||||
torch.cuda.synchronize()
|
||||
out = c[:,:,0].float()
|
||||
cos = torch.nn.functional.cosine_similarity(out.flatten().unsqueeze(0), ref.flatten().unsqueeze(0)).item()
|
||||
print(f'QK-only n={n}: cosine {cos:.6f} {"PASS" if cos >= 0.99 else "FAIL"}')
|
||||
if cos < 0.99:
|
||||
print(f' out[0,:4]={out[0,:4].tolist()} ref[0,:4]={ref[0,:4].tolist()}')
|
||||
print(f' out stats: min={out.min().item():.4f} max={out.max().item():.4f}')
|
||||
print(f' ref stats: min={ref.min().item():.4f} max={ref.max().item():.4f}')
|
||||
|
||||
if __name__ == '__main__':
|
||||
test()
|
||||
Reference in New Issue
Block a user