Stage B N-tiling: (128,16) PV MMA compiles and runs, cosine 0.36 (TMEM alias mismatch bug). FMHA head_dim=64 passes. Debugging TMEM layout alignment.
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tests/test_128_16_full.py
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385
tests/test_128_16_full.py
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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 Test128x16Tiler:
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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 = int(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], qk_inst_k, 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.pv_mma_tiler[0], self.pv_mma_tiler[1], self.pv_mma_tiler[2]), 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, (128, 16), 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.pv_mma_tiler[0], self.pv_mma_tiler[1], self.pv_mma_tiler[2]), (None,0,0)), (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()
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cute.arch.fence_view_async_tmem_store()
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||||
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])
|
||||
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)
|
||||
saved_cta_tile = self.cta_tile_shape_mnk
|
||||
self.cta_tile_shape_mnk = (int(self.pv_mma_tiler[0]), int(self.pv_mma_tiler[1]), int(self.pv_mma_tiler[2]))
|
||||
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)
|
||||
self.cta_tile_shape_mnk = saved_cta_tile
|
||||
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[:16,:] MN-major: (16,128) with strides (1,16)
|
||||
v_data = torch.eye(128, dtype=torch.bfloat16, device='cuda')
|
||||
v = v_data[:16, :].as_strided((16, 128), (1, 16)).unsqueeze(-1)
|
||||
c = torch.zeros(m, 16, 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()[:, :16]
|
||||
|
||||
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 = Test128x16Tiler(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('PV(128,16) full: cosine {:.6f} {}'.format(cos, 'PASS' if cos >= 0.99 else 'FAIL'))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test()
|
||||
385
tests/test_128_16_minimal.py
Normal file
385
tests/test_128_16_minimal.py
Normal file
@@ -0,0 +1,385 @@
|
||||
"""
|
||||
Minimal PV-only test: Load P from GMEM to TMEM via QK-style MMA, then PV from TMEM.
|
||||
Step 1: QK MMA writes FP32 S to TMEM (we know this works)
|
||||
Step 2: Softmax packing writes BF16 P to TMEM (test this)
|
||||
Step 3: PV MMA reads BF16 P from TMEM and V from SMEM, produces O
|
||||
|
||||
But to isolate the bug, let me test just the PV MMA in isolation.
|
||||
I'll write known BF16 values to TMEM using the softmax packing path,
|
||||
then immediately read them back using the PV A-fragment path,
|
||||
and compare.
|
||||
|
||||
Actually, the simplest isolation test:
|
||||
1. Do QK MMA to get S in TMEM (cosine 0.999999 verified)
|
||||
2. Do softmax packing: S → P in TMEM (at offset 32)
|
||||
3. Skip PV entirely — read P from TMEM using the C-fragment composition LOAD path
|
||||
4. Output P to GMEM and compare against S.to(BF16)
|
||||
|
||||
This tests whether the softmax packing writes P correctly to the same TMEM
|
||||
that the PV would read from.
|
||||
|
||||
But we can't easily read P from TMEM using the standard epilogue path
|
||||
because the epilogue expects FP32 accumulator data.
|
||||
|
||||
Alternative: Use the PV MMA with V=I (identity). If P is correct,
|
||||
then P @ I = P. But V needs to be MN-major and (128, 128), not (128, 64).
|
||||
The output would be (128, 128) which doesn't match our (128, 64) c tensor.
|
||||
|
||||
Let me use V that selects the first 64 columns: V[k, n] = delta(k, n) for k in [0,63].
|
||||
This gives P @ V = P[:, :64], and the output is (128, 64).
|
||||
But V is (128, 128) in the MMA K,N dims. V[k, n] for k in [0,127], n in [0,63].
|
||||
Hmm, this is getting complicated. Let me just do the identity approach with a (128, 128) output.
|
||||
"""
|
||||
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
|
||||
|
||||
|
||||
class MinimalV8Kernel:
|
||||
"""QK + softmax packing + PV with V=I to isolate PV MMA correctness.
|
||||
Output should be P = S.to(BF16), i.e. (Q@K^T).bfloat16()
|
||||
With V=I, O = P @ I = P.
|
||||
But V is (K=128, N=128) in the MMA. We need a 128x128 identity in MN-major.
|
||||
Output tensor is (128, 128).
|
||||
"""
|
||||
def __init__(self, mma_tiler_mn):
|
||||
self.acc_dtype = Float32; self.qk_acc_dtype = Float32
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
self.mma_tiler_mn = mma_tiler_mn; self.mma_tiler = (*mma_tiler_mn, 1)
|
||||
self.use_2cta_instrs = False # needed by epilogue_tma_store
|
||||
self.epilog_sync_bar_id = 1 # needed by epilogue_tma_store
|
||||
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
|
||||
|
||||
def _setup(self, qk_mma, pv_mma):
|
||||
qk_inst_k = int(cute.size(qk_mma.shape_mnk, mode=[2]))
|
||||
self.qk_mma_tiler = (*self.mma_tiler_mn, qk_inst_k * 4)
|
||||
# PV with V=I: output is (128, 128), same as QK
|
||||
self.pv_mma_tiler = (self.qk_mma_tiler[0], qk_inst_k, self.qk_mma_tiler[1])
|
||||
# pv_mma_tiler = (128, 128, 128) since V is 128x128
|
||||
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),
|
||||
self.qk_mma_tiler[1], self.qk_mma_tiler[2])
|
||||
self.c_layout = LayoutEnum.ROW_MAJOR
|
||||
self.epi_tile = utils.sm100.compute_epilogue_tile_shape(
|
||||
(self.pv_mma_tiler[0], self.pv_mma_tiler[1], self.pv_mma_tiler[2]), False, self.c_layout, self.o_dtype)
|
||||
self.num_ab_stage = 1; self.num_acc_stage = 1
|
||||
|
||||
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_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_acc_shape)
|
||||
s_cols = find_tmem_tensor_col_offset(tStS)
|
||||
pv_thr = pv_mma.get_slice(0)
|
||||
pv_acc_shape = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_acc_shape)
|
||||
o_cols = find_tmem_tensor_col_offset(tOtO)
|
||||
|
||||
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 = s_cols
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_acc_shape, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_acc_shape, self.num_acc_stage))
|
||||
self.num_tmem_alloc_cols = utils.get_num_tmem_alloc_cols([tCtS_fake, tCtO_fake], arch="sm_100")
|
||||
|
||||
# ⛔⛔⛔ CRITICAL: num_tma_load_bytes MUST include ALL TMA-loaded tensors (Q + K + V). Missing V → DEADLOCK. See FOOTGUN #0 in README.
|
||||
a_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
|
||||
b_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
|
||||
v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0))
|
||||
self.num_tma_load_bytes = (
|
||||
cute.size_in_bytes(self.q_dtype, a_smem) + cute.size_in_bytes(self.q_dtype, b_smem) +
|
||||
cute.size_in_bytes(self.q_dtype, v_smem)
|
||||
) * 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()
|
||||
self.v_major = LayoutEnum.from_tensor(v).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, self.mma_tiler_mn, tcgen05.OperandSource.SMEM)
|
||||
# PV with 128x128 output (V=I)
|
||||
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, 16), tcgen05.OperandSource.TMEM)
|
||||
self._setup(qk_mma, pv_mma)
|
||||
|
||||
q_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
|
||||
k_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
|
||||
v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0))
|
||||
tma_q, tma_tq = 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_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
|
||||
tma_k, tma_tk = 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_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
|
||||
tma_v, tma_tv = cute.nvgpu.make_tiled_tma_atom_B(
|
||||
utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn, pv_mma.thr_id),
|
||||
v, v_smem, self.pv_mma_tiler, pv_mma, self.cluster_layout_vmnk.shape)
|
||||
epi_smem = cute.select(self.c_smem_s, mode=[0, 1])
|
||||
tma_c, tma_tc = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(), c, epi_smem, self.epi_tile)
|
||||
|
||||
self._kernel(qk_mma, pv_mma, tma_q, tma_tq, tma_k, tma_tk, tma_v, tma_tv,
|
||||
tma_c, tma_tc, 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()
|
||||
use_2cta = cute.size(qk_mma.thr_id.shape) == 2
|
||||
|
||||
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()
|
||||
|
||||
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) * (2 if use_2cta else 1)),
|
||||
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=use_2cta,
|
||||
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))
|
||||
gC = cute.local_tile(mC, cute.slice_(self.pv_mma_tiler, (None,0,0)), (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); 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)]
|
||||
|
||||
gV = cute.local_tile(mV, cute.slice_(self.pv_mma_tiler, (0,None,None)), (None,None,None))
|
||||
tCgV = pv_thr.partition_B(gV)
|
||||
tVsV, tVgV = cpasync.tma_partition(tma_v, 0, b_lay, cute.group_modes(sV,0,3), cute.group_modes(tCgV,0,3))
|
||||
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_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_acc_shape)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
|
||||
pv_acc_shape = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_acc_shape)
|
||||
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_acc_shape, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_acc_shape, self.num_acc_stage))
|
||||
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# ═══ TMA LOAD WARP ═══
|
||||
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 WARP ═══
|
||||
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_prod_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
|
||||
acc_pipe.producer_acquire(acc_prod_st)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
for kt in range(k_cnt):
|
||||
h = ab_c.wait_and_advance(peek)
|
||||
nblk = cute.size(tCrQ, mode=[2])
|
||||
for kb in cutlass.range(nblk, 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()
|
||||
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])
|
||||
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)
|
||||
saved_cta_tile = self.cta_tile_shape_mnk
|
||||
self.cta_tile_shape_mnk = (self.pv_mma_tiler[0], self.pv_mma_tiler[1], self.pv_mma_tiler[2])
|
||||
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)
|
||||
self.cta_tile_shape_mnk = saved_cta_tile
|
||||
c_pipe.producer_tail()
|
||||
tmem.relinquish_alloc_permit()
|
||||
tmem.free(tmem_ptr)
|
||||
|
||||
|
||||
def test():
|
||||
torch.manual_seed(42)
|
||||
m, n, head_dim = 128, 128, 16
|
||||
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[:16,:] MN-major: (16,128) with strides (1,16)
|
||||
v_data = torch.eye(128, dtype=torch.bfloat16, device='cuda')
|
||||
v = v_data[:16, :].as_strided((16, 128), (1, 16)).unsqueeze(-1)
|
||||
c = torch.zeros(m, 16, 1, dtype=torch.bfloat16, device='cuda')
|
||||
|
||||
qf = q[:,:,0].float(); kf = k[:,:,0].float()
|
||||
# V=I[:16,:] -> O = P[:,:16] = (Q@K^T).bf16()[:,:16]
|
||||
ref = (qf @ kf.T).bfloat16().float()[:, :16]
|
||||
|
||||
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 = MinimalV8Kernel(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('PV(128,16) minimal: cosine {:.6f} {}'.format(cos, 'PASS' if cos >= 0.99 else 'FAIL'))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test()
|
||||
383
tests/test_128_16_nogC.py
Normal file
383
tests/test_128_16_nogC.py
Normal file
@@ -0,0 +1,383 @@
|
||||
"""
|
||||
Minimal PV-only test: Load P from GMEM to TMEM via QK-style MMA, then PV from TMEM.
|
||||
Step 1: QK MMA writes FP32 S to TMEM (we know this works)
|
||||
Step 2: Softmax packing writes BF16 P to TMEM (test this)
|
||||
Step 3: PV MMA reads BF16 P from TMEM and V from SMEM, produces O
|
||||
|
||||
But to isolate the bug, let me test just the PV MMA in isolation.
|
||||
I'll write known BF16 values to TMEM using the softmax packing path,
|
||||
then immediately read them back using the PV A-fragment path,
|
||||
and compare.
|
||||
|
||||
Actually, the simplest isolation test:
|
||||
1. Do QK MMA to get S in TMEM (cosine 0.999999 verified)
|
||||
2. Do softmax packing: S → P in TMEM (at offset 32)
|
||||
3. Skip PV entirely — read P from TMEM using the C-fragment composition LOAD path
|
||||
4. Output P to GMEM and compare against S.to(BF16)
|
||||
|
||||
This tests whether the softmax packing writes P correctly to the same TMEM
|
||||
that the PV would read from.
|
||||
|
||||
But we can't easily read P from TMEM using the standard epilogue path
|
||||
because the epilogue expects FP32 accumulator data.
|
||||
|
||||
Alternative: Use the PV MMA with V=I (identity). If P is correct,
|
||||
then P @ I = P. But V needs to be MN-major and (128, 128), not (128, 64).
|
||||
The output would be (128, 128) which doesn't match our (128, 64) c tensor.
|
||||
|
||||
Let me use V that selects the first 64 columns: V[k, n] = delta(k, n) for k in [0,63].
|
||||
This gives P @ V = P[:, :64], and the output is (128, 64).
|
||||
But V is (128, 128) in the MMA K,N dims. V[k, n] for k in [0,127], n in [0,63].
|
||||
Hmm, this is getting complicated. Let me just do the identity approach with a (128, 128) output.
|
||||
"""
|
||||
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
|
||||
|
||||
|
||||
class Test128x16Tiler:
|
||||
"""QK + softmax packing + PV with V=I to isolate PV MMA correctness.
|
||||
Output should be P = S.to(BF16), i.e. (Q@K^T).bfloat16()
|
||||
With V=I, O = P @ I = P.
|
||||
But V is (K=128, N=128) in the MMA. We need a 128x128 identity in MN-major.
|
||||
Output tensor is (128, 128).
|
||||
"""
|
||||
def __init__(self, mma_tiler_mn):
|
||||
self.acc_dtype = Float32; self.qk_acc_dtype = Float32
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
self.mma_tiler_mn = mma_tiler_mn; self.mma_tiler = (*mma_tiler_mn, 1)
|
||||
self.use_2cta_instrs = False # needed by epilogue_tma_store
|
||||
self.epilog_sync_bar_id = 1 # needed by epilogue_tma_store
|
||||
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
|
||||
|
||||
def _setup(self, qk_mma, pv_mma):
|
||||
qk_inst_k = int(cute.size(qk_mma.shape_mnk, mode=[2]))
|
||||
self.qk_mma_tiler = (*self.mma_tiler_mn, qk_inst_k * 4)
|
||||
# PV with V=I: output is (128, 128), same as QK
|
||||
self.pv_mma_tiler = (self.qk_mma_tiler[0], qk_inst_k, self.qk_mma_tiler[1])
|
||||
# pv_mma_tiler = (128, 128, 128) since V is 128x128
|
||||
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),
|
||||
self.qk_mma_tiler[1], self.qk_mma_tiler[2])
|
||||
self.c_layout = LayoutEnum.ROW_MAJOR
|
||||
self.epi_tile = utils.sm100.compute_epilogue_tile_shape(
|
||||
(self.pv_mma_tiler[0], self.pv_mma_tiler[1], self.pv_mma_tiler[2]), False, self.c_layout, self.o_dtype)
|
||||
self.num_ab_stage = 1; self.num_acc_stage = 1
|
||||
|
||||
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_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_acc_shape)
|
||||
s_cols = find_tmem_tensor_col_offset(tStS)
|
||||
pv_thr = pv_mma.get_slice(0)
|
||||
pv_acc_shape = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_acc_shape)
|
||||
o_cols = find_tmem_tensor_col_offset(tOtO)
|
||||
|
||||
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 = s_cols
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_acc_shape, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_acc_shape, self.num_acc_stage))
|
||||
self.num_tmem_alloc_cols = utils.get_num_tmem_alloc_cols([tCtS_fake, tCtO_fake], arch="sm_100")
|
||||
|
||||
# ⛔⛔⛔ CRITICAL: num_tma_load_bytes MUST include ALL TMA-loaded tensors (Q + K + V). Missing V → DEADLOCK. See FOOTGUN #0 in README.
|
||||
a_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
|
||||
b_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
|
||||
v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0))
|
||||
self.num_tma_load_bytes = (
|
||||
cute.size_in_bytes(self.q_dtype, a_smem) + cute.size_in_bytes(self.q_dtype, b_smem) +
|
||||
cute.size_in_bytes(self.q_dtype, v_smem)
|
||||
) * 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()
|
||||
self.v_major = LayoutEnum.from_tensor(v).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, self.mma_tiler_mn, tcgen05.OperandSource.SMEM)
|
||||
# PV with 128x128 output (V=I)
|
||||
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, 16), tcgen05.OperandSource.TMEM)
|
||||
self._setup(qk_mma, pv_mma)
|
||||
|
||||
q_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
|
||||
k_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
|
||||
v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0))
|
||||
tma_q, tma_tq = 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_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
|
||||
tma_k, tma_tk = 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_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
|
||||
tma_v, tma_tv = cute.nvgpu.make_tiled_tma_atom_B(
|
||||
utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn, pv_mma.thr_id),
|
||||
v, v_smem, self.pv_mma_tiler, pv_mma, self.cluster_layout_vmnk.shape)
|
||||
epi_smem = cute.select(self.c_smem_s, mode=[0, 1])
|
||||
tma_c, tma_tc = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(), c, epi_smem, self.epi_tile)
|
||||
|
||||
self._kernel(qk_mma, pv_mma, tma_q, tma_tq, tma_k, tma_tk, tma_v, tma_tv,
|
||||
tma_c, tma_tc, 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()
|
||||
use_2cta = cute.size(qk_mma.thr_id.shape) == 2
|
||||
|
||||
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()
|
||||
|
||||
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) * (2 if use_2cta else 1)),
|
||||
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=use_2cta,
|
||||
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))
|
||||
gC = cute.local_tile(mC, cute.slice_(self.qk_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); 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)]
|
||||
|
||||
gV = cute.local_tile(mV, cute.slice_(self.pv_mma_tiler, (0,None,None)), (None,None,None))
|
||||
tCgV = pv_thr.partition_B(gV)
|
||||
tVsV, tVgV = cpasync.tma_partition(tma_v, 0, b_lay, cute.group_modes(sV,0,3), cute.group_modes(tCgV,0,3))
|
||||
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_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_acc_shape)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
|
||||
pv_acc_shape = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_acc_shape)
|
||||
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_acc_shape, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_acc_shape, self.num_acc_stage))
|
||||
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# ═══ TMA LOAD WARP ═══
|
||||
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 WARP ═══
|
||||
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_prod_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
|
||||
acc_pipe.producer_acquire(acc_prod_st)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
for kt in range(k_cnt):
|
||||
h = ab_c.wait_and_advance(peek)
|
||||
nblk = cute.size(tCrQ, mode=[2])
|
||||
for kb in cutlass.range(nblk, 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()
|
||||
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])
|
||||
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 = Test128x16Tiler(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('PV diag (V=I): cosine {:.6f} {}'.format(cos, 'PASS' if cos >= 0.99 else 'FAIL'))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test()
|
||||
383
tests/test_128_16_nopack.py
Normal file
383
tests/test_128_16_nopack.py
Normal file
@@ -0,0 +1,383 @@
|
||||
"""
|
||||
Minimal PV-only test: Load P from GMEM to TMEM via QK-style MMA, then PV from TMEM.
|
||||
Step 1: QK MMA writes FP32 S to TMEM (we know this works)
|
||||
Step 2: Softmax packing writes BF16 P to TMEM (test this)
|
||||
Step 3: PV MMA reads BF16 P from TMEM and V from SMEM, produces O
|
||||
|
||||
But to isolate the bug, let me test just the PV MMA in isolation.
|
||||
I'll write known BF16 values to TMEM using the softmax packing path,
|
||||
then immediately read them back using the PV A-fragment path,
|
||||
and compare.
|
||||
|
||||
Actually, the simplest isolation test:
|
||||
1. Do QK MMA to get S in TMEM (cosine 0.999999 verified)
|
||||
2. Do softmax packing: S → P in TMEM (at offset 32)
|
||||
3. Skip PV entirely — read P from TMEM using the C-fragment composition LOAD path
|
||||
4. Output P to GMEM and compare against S.to(BF16)
|
||||
|
||||
This tests whether the softmax packing writes P correctly to the same TMEM
|
||||
that the PV would read from.
|
||||
|
||||
But we can't easily read P from TMEM using the standard epilogue path
|
||||
because the epilogue expects FP32 accumulator data.
|
||||
|
||||
Alternative: Use the PV MMA with V=I (identity). If P is correct,
|
||||
then P @ I = P. But V needs to be MN-major and (128, 128), not (128, 64).
|
||||
The output would be (128, 128) which doesn't match our (128, 64) c tensor.
|
||||
|
||||
Let me use V that selects the first 64 columns: V[k, n] = delta(k, n) for k in [0,63].
|
||||
This gives P @ V = P[:, :64], and the output is (128, 64).
|
||||
But V is (128, 128) in the MMA K,N dims. V[k, n] for k in [0,127], n in [0,63].
|
||||
Hmm, this is getting complicated. Let me just do the identity approach with a (128, 128) output.
|
||||
"""
|
||||
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
|
||||
|
||||
|
||||
class NoPackV8Kernel:
|
||||
"""QK + softmax packing + PV with V=I to isolate PV MMA correctness.
|
||||
Output should be P = S.to(BF16), i.e. (Q@K^T).bfloat16()
|
||||
With V=I, O = P @ I = P.
|
||||
But V is (K=128, N=128) in the MMA. We need a 128x128 identity in MN-major.
|
||||
Output tensor is (128, 128).
|
||||
"""
|
||||
def __init__(self, mma_tiler_mn):
|
||||
self.acc_dtype = Float32; self.qk_acc_dtype = Float32
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
self.mma_tiler_mn = mma_tiler_mn; self.mma_tiler = (*mma_tiler_mn, 1)
|
||||
self.use_2cta_instrs = False # needed by epilogue_tma_store
|
||||
self.epilog_sync_bar_id = 1 # needed by epilogue_tma_store
|
||||
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
|
||||
|
||||
def _setup(self, qk_mma, pv_mma):
|
||||
qk_inst_k = cute.size(qk_mma.shape_mnk, mode=[2])
|
||||
self.qk_mma_tiler = (*self.mma_tiler_mn, qk_inst_k * 4)
|
||||
# PV with V=I: output is (128, 128), same as QK
|
||||
self.pv_mma_tiler = (self.qk_mma_tiler[0], qk_inst_k, self.qk_mma_tiler[1])
|
||||
# pv_mma_tiler = (128, 128, 128) since V is 128x128
|
||||
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),
|
||||
self.qk_mma_tiler[1], self.qk_mma_tiler[2])
|
||||
self.c_layout = LayoutEnum.ROW_MAJOR
|
||||
self.epi_tile = utils.sm100.compute_epilogue_tile_shape(
|
||||
(self.pv_mma_tiler[0], self.pv_mma_tiler[1], self.pv_mma_tiler[2]), False, self.c_layout, self.o_dtype)
|
||||
self.num_ab_stage = 1; self.num_acc_stage = 1
|
||||
|
||||
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_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_acc_shape)
|
||||
s_cols = find_tmem_tensor_col_offset(tStS)
|
||||
pv_thr = pv_mma.get_slice(0)
|
||||
pv_acc_shape = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_acc_shape)
|
||||
o_cols = find_tmem_tensor_col_offset(tOtO)
|
||||
|
||||
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 = s_cols
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_acc_shape, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_acc_shape, self.num_acc_stage))
|
||||
self.num_tmem_alloc_cols = utils.get_num_tmem_alloc_cols([tCtS_fake, tCtO_fake], arch="sm_100")
|
||||
|
||||
# ⛔⛔⛔ CRITICAL: num_tma_load_bytes MUST include ALL TMA-loaded tensors (Q + K + V). Missing V → DEADLOCK. See FOOTGUN #0 in README.
|
||||
a_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
|
||||
b_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
|
||||
v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0))
|
||||
self.num_tma_load_bytes = (
|
||||
cute.size_in_bytes(self.q_dtype, a_smem) + cute.size_in_bytes(self.q_dtype, b_smem) +
|
||||
cute.size_in_bytes(self.q_dtype, v_smem)
|
||||
) * 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()
|
||||
self.v_major = LayoutEnum.from_tensor(v).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, self.mma_tiler_mn, tcgen05.OperandSource.SMEM)
|
||||
# PV with 128x128 output (V=I)
|
||||
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, 16), tcgen05.OperandSource.TMEM)
|
||||
self._setup(qk_mma, pv_mma)
|
||||
|
||||
q_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
|
||||
k_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
|
||||
v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0))
|
||||
tma_q, tma_tq = 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_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
|
||||
tma_k, tma_tk = 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_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
|
||||
tma_v, tma_tv = cute.nvgpu.make_tiled_tma_atom_B(
|
||||
utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn, pv_mma.thr_id),
|
||||
v, v_smem, self.pv_mma_tiler, pv_mma, self.cluster_layout_vmnk.shape)
|
||||
epi_smem = cute.select(self.c_smem_s, mode=[0, 1])
|
||||
tma_c, tma_tc = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(), c, epi_smem, self.epi_tile)
|
||||
|
||||
self._kernel(qk_mma, pv_mma, tma_q, tma_tq, tma_k, tma_tk, tma_v, tma_tv,
|
||||
tma_c, tma_tc, 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()
|
||||
use_2cta = cute.size(qk_mma.thr_id.shape) == 2
|
||||
|
||||
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()
|
||||
|
||||
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) * (2 if use_2cta else 1)),
|
||||
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=use_2cta,
|
||||
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))
|
||||
gC = cute.local_tile(mC, cute.slice_(self.qk_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); 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)]
|
||||
|
||||
gV = cute.local_tile(mV, cute.slice_(self.pv_mma_tiler, (0,None,None)), (None,None,None))
|
||||
tCgV = pv_thr.partition_B(gV)
|
||||
tVsV, tVgV = cpasync.tma_partition(tma_v, 0, b_lay, cute.group_modes(sV,0,3), cute.group_modes(tCgV,0,3))
|
||||
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_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_acc_shape)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
|
||||
pv_acc_shape = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_acc_shape)
|
||||
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_acc_shape, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_acc_shape, self.num_acc_stage))
|
||||
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# ═══ TMA LOAD WARP ═══
|
||||
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 WARP ═══
|
||||
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_prod_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
|
||||
acc_pipe.producer_acquire(acc_prod_st)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
for kt in range(k_cnt):
|
||||
h = ab_c.wait_and_advance(peek)
|
||||
nblk = cute.size(tCrQ, mode=[2])
|
||||
for kb in cutlass.range(nblk, 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()
|
||||
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])
|
||||
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 = NoPackV8Kernel(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('PV diag (V=I): cosine {:.6f} {}'.format(cos, 'PASS' if cos >= 0.99 else 'FAIL'))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test()
|
||||
383
tests/test_128_16_nosoftmax.py
Normal file
383
tests/test_128_16_nosoftmax.py
Normal file
@@ -0,0 +1,383 @@
|
||||
"""
|
||||
Minimal PV-only test: Load P from GMEM to TMEM via QK-style MMA, then PV from TMEM.
|
||||
Step 1: QK MMA writes FP32 S to TMEM (we know this works)
|
||||
Step 2: Softmax packing writes BF16 P to TMEM (test this)
|
||||
Step 3: PV MMA reads BF16 P from TMEM and V from SMEM, produces O
|
||||
|
||||
But to isolate the bug, let me test just the PV MMA in isolation.
|
||||
I'll write known BF16 values to TMEM using the softmax packing path,
|
||||
then immediately read them back using the PV A-fragment path,
|
||||
and compare.
|
||||
|
||||
Actually, the simplest isolation test:
|
||||
1. Do QK MMA to get S in TMEM (cosine 0.999999 verified)
|
||||
2. Do softmax packing: S → P in TMEM (at offset 32)
|
||||
3. Skip PV entirely — read P from TMEM using the C-fragment composition LOAD path
|
||||
4. Output P to GMEM and compare against S.to(BF16)
|
||||
|
||||
This tests whether the softmax packing writes P correctly to the same TMEM
|
||||
that the PV would read from.
|
||||
|
||||
But we can't easily read P from TMEM using the standard epilogue path
|
||||
because the epilogue expects FP32 accumulator data.
|
||||
|
||||
Alternative: Use the PV MMA with V=I (identity). If P is correct,
|
||||
then P @ I = P. But V needs to be MN-major and (128, 128), not (128, 64).
|
||||
The output would be (128, 128) which doesn't match our (128, 64) c tensor.
|
||||
|
||||
Let me use V that selects the first 64 columns: V[k, n] = delta(k, n) for k in [0,63].
|
||||
This gives P @ V = P[:, :64], and the output is (128, 64).
|
||||
But V is (128, 128) in the MMA K,N dims. V[k, n] for k in [0,127], n in [0,63].
|
||||
Hmm, this is getting complicated. Let me just do the identity approach with a (128, 128) output.
|
||||
"""
|
||||
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
|
||||
|
||||
|
||||
class Test128x16NoSoftmax:
|
||||
"""QK + softmax packing + PV with V=I to isolate PV MMA correctness.
|
||||
Output should be P = S.to(BF16), i.e. (Q@K^T).bfloat16()
|
||||
With V=I, O = P @ I = P.
|
||||
But V is (K=128, N=128) in the MMA. We need a 128x128 identity in MN-major.
|
||||
Output tensor is (128, 128).
|
||||
"""
|
||||
def __init__(self, mma_tiler_mn):
|
||||
self.acc_dtype = Float32; self.qk_acc_dtype = Float32
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
self.mma_tiler_mn = mma_tiler_mn; self.mma_tiler = (*mma_tiler_mn, 1)
|
||||
self.use_2cta_instrs = False # needed by epilogue_tma_store
|
||||
self.epilog_sync_bar_id = 1 # needed by epilogue_tma_store
|
||||
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
|
||||
|
||||
def _setup(self, qk_mma, pv_mma):
|
||||
qk_inst_k = cute.size(qk_mma.shape_mnk, mode=[2])
|
||||
self.qk_mma_tiler = (*self.mma_tiler_mn, qk_inst_k * 4)
|
||||
# PV with V=I: output is (128, 128), same as QK
|
||||
self.pv_mma_tiler = (self.qk_mma_tiler[0], self.qk_mma_tiler[1], self.qk_mma_tiler[1])
|
||||
# pv_mma_tiler = (128, 128, 128) since V is 128x128
|
||||
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),
|
||||
self.qk_mma_tiler[1], 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.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_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_acc_shape)
|
||||
s_cols = find_tmem_tensor_col_offset(tStS)
|
||||
pv_thr = pv_mma.get_slice(0)
|
||||
pv_acc_shape = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_acc_shape)
|
||||
o_cols = find_tmem_tensor_col_offset(tOtO)
|
||||
|
||||
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 = s_cols
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_acc_shape, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_acc_shape, self.num_acc_stage))
|
||||
self.num_tmem_alloc_cols = utils.get_num_tmem_alloc_cols([tCtS_fake, tCtO_fake], arch="sm_100")
|
||||
|
||||
# ⛔⛔⛔ CRITICAL: num_tma_load_bytes MUST include ALL TMA-loaded tensors (Q + K + V). Missing V → DEADLOCK. See FOOTGUN #0 in README.
|
||||
a_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
|
||||
b_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
|
||||
v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0))
|
||||
self.num_tma_load_bytes = (
|
||||
cute.size_in_bytes(self.q_dtype, a_smem) + cute.size_in_bytes(self.q_dtype, b_smem) +
|
||||
cute.size_in_bytes(self.q_dtype, v_smem)
|
||||
) * 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()
|
||||
self.v_major = LayoutEnum.from_tensor(v).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, self.mma_tiler_mn, tcgen05.OperandSource.SMEM)
|
||||
# PV with 128x128 output (V=I)
|
||||
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, 16), tcgen05.OperandSource.TMEM)
|
||||
self._setup(qk_mma, pv_mma)
|
||||
|
||||
q_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
|
||||
k_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
|
||||
v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0))
|
||||
tma_q, tma_tq = 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_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
|
||||
tma_k, tma_tk = 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_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
|
||||
tma_v, tma_tv = cute.nvgpu.make_tiled_tma_atom_B(
|
||||
utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn, pv_mma.thr_id),
|
||||
v, v_smem, self.pv_mma_tiler, pv_mma, self.cluster_layout_vmnk.shape)
|
||||
epi_smem = cute.select(self.c_smem_s, mode=[0, 1])
|
||||
tma_c, tma_tc = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(), c, epi_smem, self.epi_tile)
|
||||
|
||||
self._kernel(qk_mma, pv_mma, tma_q, tma_tq, tma_k, tma_tk, tma_v, tma_tv,
|
||||
tma_c, tma_tc, 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()
|
||||
use_2cta = cute.size(qk_mma.thr_id.shape) == 2
|
||||
|
||||
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()
|
||||
|
||||
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) * (2 if use_2cta else 1)),
|
||||
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=use_2cta,
|
||||
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))
|
||||
gC = cute.local_tile(mC, cute.slice_(self.qk_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); 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)]
|
||||
|
||||
gV = cute.local_tile(mV, cute.slice_(self.pv_mma_tiler, (0,None,None)), (None,None,None))
|
||||
tCgV = pv_thr.partition_B(gV)
|
||||
tVsV, tVgV = cpasync.tma_partition(tma_v, 0, b_lay, cute.group_modes(sV,0,3), cute.group_modes(tCgV,0,3))
|
||||
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_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_acc_shape)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
|
||||
pv_acc_shape = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_acc_shape)
|
||||
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_acc_shape, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_acc_shape, self.num_acc_stage))
|
||||
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# ═══ TMA LOAD WARP ═══
|
||||
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 WARP ═══
|
||||
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_prod_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
|
||||
acc_pipe.producer_acquire(acc_prod_st)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
for kt in range(k_cnt):
|
||||
h = ab_c.wait_and_advance(peek)
|
||||
nblk = cute.size(tCrQ, mode=[2])
|
||||
for kb in cutlass.range(nblk, 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()
|
||||
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])
|
||||
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 = Test128x16NoSoftmax(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('PV diag (V=I): cosine {:.6f} {}'.format(cos, 'PASS' if cos >= 0.99 else 'FAIL'))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test()
|
||||
383
tests/test_128_16_stepA.py
Normal file
383
tests/test_128_16_stepA.py
Normal file
@@ -0,0 +1,383 @@
|
||||
"""
|
||||
Minimal PV-only test: Load P from GMEM to TMEM via QK-style MMA, then PV from TMEM.
|
||||
Step 1: QK MMA writes FP32 S to TMEM (we know this works)
|
||||
Step 2: Softmax packing writes BF16 P to TMEM (test this)
|
||||
Step 3: PV MMA reads BF16 P from TMEM and V from SMEM, produces O
|
||||
|
||||
But to isolate the bug, let me test just the PV MMA in isolation.
|
||||
I'll write known BF16 values to TMEM using the softmax packing path,
|
||||
then immediately read them back using the PV A-fragment path,
|
||||
and compare.
|
||||
|
||||
Actually, the simplest isolation test:
|
||||
1. Do QK MMA to get S in TMEM (cosine 0.999999 verified)
|
||||
2. Do softmax packing: S → P in TMEM (at offset 32)
|
||||
3. Skip PV entirely — read P from TMEM using the C-fragment composition LOAD path
|
||||
4. Output P to GMEM and compare against S.to(BF16)
|
||||
|
||||
This tests whether the softmax packing writes P correctly to the same TMEM
|
||||
that the PV would read from.
|
||||
|
||||
But we can't easily read P from TMEM using the standard epilogue path
|
||||
because the epilogue expects FP32 accumulator data.
|
||||
|
||||
Alternative: Use the PV MMA with V=I (identity). If P is correct,
|
||||
then P @ I = P. But V needs to be MN-major and (128, 128), not (128, 64).
|
||||
The output would be (128, 128) which doesn't match our (128, 64) c tensor.
|
||||
|
||||
Let me use V that selects the first 64 columns: V[k, n] = delta(k, n) for k in [0,63].
|
||||
This gives P @ V = P[:, :64], and the output is (128, 64).
|
||||
But V is (128, 128) in the MMA K,N dims. V[k, n] for k in [0,127], n in [0,63].
|
||||
Hmm, this is getting complicated. Let me just do the identity approach with a (128, 128) output.
|
||||
"""
|
||||
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
|
||||
|
||||
|
||||
class Test128x16Tiler:
|
||||
"""QK + softmax packing + PV with V=I to isolate PV MMA correctness.
|
||||
Output should be P = S.to(BF16), i.e. (Q@K^T).bfloat16()
|
||||
With V=I, O = P @ I = P.
|
||||
But V is (K=128, N=128) in the MMA. We need a 128x128 identity in MN-major.
|
||||
Output tensor is (128, 128).
|
||||
"""
|
||||
def __init__(self, mma_tiler_mn):
|
||||
self.acc_dtype = Float32; self.qk_acc_dtype = Float32
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
self.mma_tiler_mn = mma_tiler_mn; self.mma_tiler = (*mma_tiler_mn, 1)
|
||||
self.use_2cta_instrs = False # needed by epilogue_tma_store
|
||||
self.epilog_sync_bar_id = 1 # needed by epilogue_tma_store
|
||||
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
|
||||
|
||||
def _setup(self, qk_mma, pv_mma):
|
||||
qk_inst_k = int(cute.size(qk_mma.shape_mnk, mode=[2]))
|
||||
self.qk_mma_tiler = (*self.mma_tiler_mn, qk_inst_k * 4)
|
||||
# PV with V=I: output is (128, 128), same as QK
|
||||
self.pv_mma_tiler = (self.qk_mma_tiler[0], qk_inst_k, self.qk_mma_tiler[1])
|
||||
# pv_mma_tiler = (128, 128, 128) since V is 128x128
|
||||
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),
|
||||
self.qk_mma_tiler[1], 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.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_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_acc_shape)
|
||||
s_cols = find_tmem_tensor_col_offset(tStS)
|
||||
pv_thr = pv_mma.get_slice(0)
|
||||
pv_acc_shape = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_acc_shape)
|
||||
o_cols = find_tmem_tensor_col_offset(tOtO)
|
||||
|
||||
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 = s_cols
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_acc_shape, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_acc_shape, self.num_acc_stage))
|
||||
self.num_tmem_alloc_cols = utils.get_num_tmem_alloc_cols([tCtS_fake, tCtO_fake], arch="sm_100")
|
||||
|
||||
# ⛔⛔⛔ CRITICAL: num_tma_load_bytes MUST include ALL TMA-loaded tensors (Q + K + V). Missing V → DEADLOCK. See FOOTGUN #0 in README.
|
||||
a_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
|
||||
b_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
|
||||
v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0))
|
||||
self.num_tma_load_bytes = (
|
||||
cute.size_in_bytes(self.q_dtype, a_smem) + cute.size_in_bytes(self.q_dtype, b_smem) +
|
||||
cute.size_in_bytes(self.q_dtype, v_smem)
|
||||
) * 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()
|
||||
self.v_major = LayoutEnum.from_tensor(v).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, self.mma_tiler_mn, tcgen05.OperandSource.SMEM)
|
||||
# PV with 128x128 output (V=I)
|
||||
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, 16), tcgen05.OperandSource.TMEM)
|
||||
self._setup(qk_mma, pv_mma)
|
||||
|
||||
q_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
|
||||
k_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
|
||||
v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0))
|
||||
tma_q, tma_tq = 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_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
|
||||
tma_k, tma_tk = 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_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
|
||||
tma_v, tma_tv = cute.nvgpu.make_tiled_tma_atom_B(
|
||||
utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn, pv_mma.thr_id),
|
||||
v, v_smem, self.pv_mma_tiler, pv_mma, self.cluster_layout_vmnk.shape)
|
||||
epi_smem = cute.select(self.c_smem_s, mode=[0, 1])
|
||||
tma_c, tma_tc = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(), c, epi_smem, self.epi_tile)
|
||||
|
||||
self._kernel(qk_mma, pv_mma, tma_q, tma_tq, tma_k, tma_tk, tma_v, tma_tv,
|
||||
tma_c, tma_tc, 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()
|
||||
use_2cta = cute.size(qk_mma.thr_id.shape) == 2
|
||||
|
||||
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()
|
||||
|
||||
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) * (2 if use_2cta else 1)),
|
||||
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=use_2cta,
|
||||
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))
|
||||
gC = cute.local_tile(mC, cute.slice_(self.qk_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); 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)]
|
||||
|
||||
gV = cute.local_tile(mV, cute.slice_(self.pv_mma_tiler, (0,None,None)), (None,None,None))
|
||||
tCgV = pv_thr.partition_B(gV)
|
||||
tVsV, tVgV = cpasync.tma_partition(tma_v, 0, b_lay, cute.group_modes(sV,0,3), cute.group_modes(tCgV,0,3))
|
||||
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_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_acc_shape)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
|
||||
pv_acc_shape = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_acc_shape)
|
||||
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_acc_shape, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_acc_shape, self.num_acc_stage))
|
||||
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# ═══ TMA LOAD WARP ═══
|
||||
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 WARP ═══
|
||||
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_prod_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
|
||||
acc_pipe.producer_acquire(acc_prod_st)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
for kt in range(k_cnt):
|
||||
h = ab_c.wait_and_advance(peek)
|
||||
nblk = cute.size(tCrQ, mode=[2])
|
||||
for kb in cutlass.range(nblk, 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()
|
||||
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])
|
||||
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 = Test128x16Tiler(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('PV diag (V=I): cosine {:.6f} {}'.format(cos, 'PASS' if cos >= 0.99 else 'FAIL'))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test()
|
||||
383
tests/test_128_16_stepB.py
Normal file
383
tests/test_128_16_stepB.py
Normal file
@@ -0,0 +1,383 @@
|
||||
"""
|
||||
Minimal PV-only test: Load P from GMEM to TMEM via QK-style MMA, then PV from TMEM.
|
||||
Step 1: QK MMA writes FP32 S to TMEM (we know this works)
|
||||
Step 2: Softmax packing writes BF16 P to TMEM (test this)
|
||||
Step 3: PV MMA reads BF16 P from TMEM and V from SMEM, produces O
|
||||
|
||||
But to isolate the bug, let me test just the PV MMA in isolation.
|
||||
I'll write known BF16 values to TMEM using the softmax packing path,
|
||||
then immediately read them back using the PV A-fragment path,
|
||||
and compare.
|
||||
|
||||
Actually, the simplest isolation test:
|
||||
1. Do QK MMA to get S in TMEM (cosine 0.999999 verified)
|
||||
2. Do softmax packing: S → P in TMEM (at offset 32)
|
||||
3. Skip PV entirely — read P from TMEM using the C-fragment composition LOAD path
|
||||
4. Output P to GMEM and compare against S.to(BF16)
|
||||
|
||||
This tests whether the softmax packing writes P correctly to the same TMEM
|
||||
that the PV would read from.
|
||||
|
||||
But we can't easily read P from TMEM using the standard epilogue path
|
||||
because the epilogue expects FP32 accumulator data.
|
||||
|
||||
Alternative: Use the PV MMA with V=I (identity). If P is correct,
|
||||
then P @ I = P. But V needs to be MN-major and (128, 128), not (128, 64).
|
||||
The output would be (128, 128) which doesn't match our (128, 64) c tensor.
|
||||
|
||||
Let me use V that selects the first 64 columns: V[k, n] = delta(k, n) for k in [0,63].
|
||||
This gives P @ V = P[:, :64], and the output is (128, 64).
|
||||
But V is (128, 128) in the MMA K,N dims. V[k, n] for k in [0,127], n in [0,63].
|
||||
Hmm, this is getting complicated. Let me just do the identity approach with a (128, 128) output.
|
||||
"""
|
||||
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
|
||||
|
||||
|
||||
class Test128x16Tiler:
|
||||
"""QK + softmax packing + PV with V=I to isolate PV MMA correctness.
|
||||
Output should be P = S.to(BF16), i.e. (Q@K^T).bfloat16()
|
||||
With V=I, O = P @ I = P.
|
||||
But V is (K=128, N=128) in the MMA. We need a 128x128 identity in MN-major.
|
||||
Output tensor is (128, 128).
|
||||
"""
|
||||
def __init__(self, mma_tiler_mn):
|
||||
self.acc_dtype = Float32; self.qk_acc_dtype = Float32
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
self.mma_tiler_mn = mma_tiler_mn; self.mma_tiler = (*mma_tiler_mn, 1)
|
||||
self.use_2cta_instrs = False # needed by epilogue_tma_store
|
||||
self.epilog_sync_bar_id = 1 # needed by epilogue_tma_store
|
||||
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
|
||||
|
||||
def _setup(self, qk_mma, pv_mma):
|
||||
qk_inst_k = int(cute.size(qk_mma.shape_mnk, mode=[2]))
|
||||
self.qk_mma_tiler = (*self.mma_tiler_mn, qk_inst_k * 4)
|
||||
# PV with V=I: output is (128, 128), same as QK
|
||||
self.pv_mma_tiler = (self.qk_mma_tiler[0], qk_inst_k, self.qk_mma_tiler[1])
|
||||
# pv_mma_tiler = (128, 128, 128) since V is 128x128
|
||||
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),
|
||||
self.qk_mma_tiler[1], self.qk_mma_tiler[2])
|
||||
self.c_layout = LayoutEnum.ROW_MAJOR
|
||||
self.epi_tile = utils.sm100.compute_epilogue_tile_shape(
|
||||
(self.pv_mma_tiler[0], self.pv_mma_tiler[1], self.pv_mma_tiler[2]), False, self.c_layout, self.o_dtype)
|
||||
self.num_ab_stage = 1; self.num_acc_stage = 1
|
||||
|
||||
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_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_acc_shape)
|
||||
s_cols = find_tmem_tensor_col_offset(tStS)
|
||||
pv_thr = pv_mma.get_slice(0)
|
||||
pv_acc_shape = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_acc_shape)
|
||||
o_cols = find_tmem_tensor_col_offset(tOtO)
|
||||
|
||||
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 = s_cols
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_acc_shape, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_acc_shape, self.num_acc_stage))
|
||||
self.num_tmem_alloc_cols = utils.get_num_tmem_alloc_cols([tCtS_fake, tCtO_fake], arch="sm_100")
|
||||
|
||||
# ⛔⛔⛔ CRITICAL: num_tma_load_bytes MUST include ALL TMA-loaded tensors (Q + K + V). Missing V → DEADLOCK. See FOOTGUN #0 in README.
|
||||
a_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
|
||||
b_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
|
||||
v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0))
|
||||
self.num_tma_load_bytes = (
|
||||
cute.size_in_bytes(self.q_dtype, a_smem) + cute.size_in_bytes(self.q_dtype, b_smem) +
|
||||
cute.size_in_bytes(self.q_dtype, v_smem)
|
||||
) * 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()
|
||||
self.v_major = LayoutEnum.from_tensor(v).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, self.mma_tiler_mn, tcgen05.OperandSource.SMEM)
|
||||
# PV with 128x128 output (V=I)
|
||||
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, 16), tcgen05.OperandSource.TMEM)
|
||||
self._setup(qk_mma, pv_mma)
|
||||
|
||||
q_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
|
||||
k_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
|
||||
v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0))
|
||||
tma_q, tma_tq = 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_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
|
||||
tma_k, tma_tk = 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_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
|
||||
tma_v, tma_tv = cute.nvgpu.make_tiled_tma_atom_B(
|
||||
utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn, pv_mma.thr_id),
|
||||
v, v_smem, self.pv_mma_tiler, pv_mma, self.cluster_layout_vmnk.shape)
|
||||
epi_smem = cute.select(self.c_smem_s, mode=[0, 1])
|
||||
tma_c, tma_tc = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(), c, epi_smem, self.epi_tile)
|
||||
|
||||
self._kernel(qk_mma, pv_mma, tma_q, tma_tq, tma_k, tma_tk, tma_v, tma_tv,
|
||||
tma_c, tma_tc, 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()
|
||||
use_2cta = cute.size(qk_mma.thr_id.shape) == 2
|
||||
|
||||
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()
|
||||
|
||||
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) * (2 if use_2cta else 1)),
|
||||
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=use_2cta,
|
||||
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))
|
||||
gC = cute.local_tile(mC, cute.slice_(self.qk_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); 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)]
|
||||
|
||||
gV = cute.local_tile(mV, cute.slice_(self.pv_mma_tiler, (0,None,None)), (None,None,None))
|
||||
tCgV = pv_thr.partition_B(gV)
|
||||
tVsV, tVgV = cpasync.tma_partition(tma_v, 0, b_lay, cute.group_modes(sV,0,3), cute.group_modes(tCgV,0,3))
|
||||
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_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_acc_shape)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
|
||||
pv_acc_shape = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_acc_shape)
|
||||
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_acc_shape, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_acc_shape, self.num_acc_stage))
|
||||
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# ═══ TMA LOAD WARP ═══
|
||||
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 WARP ═══
|
||||
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_prod_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
|
||||
acc_pipe.producer_acquire(acc_prod_st)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
for kt in range(k_cnt):
|
||||
h = ab_c.wait_and_advance(peek)
|
||||
nblk = cute.size(tCrQ, mode=[2])
|
||||
for kb in cutlass.range(nblk, 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()
|
||||
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])
|
||||
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 = Test128x16Tiler(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('PV diag (V=I): cosine {:.6f} {}'.format(cos, 'PASS' if cos >= 0.99 else 'FAIL'))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test()
|
||||
383
tests/test_128_16_stepC.py
Normal file
383
tests/test_128_16_stepC.py
Normal file
@@ -0,0 +1,383 @@
|
||||
"""
|
||||
Minimal PV-only test: Load P from GMEM to TMEM via QK-style MMA, then PV from TMEM.
|
||||
Step 1: QK MMA writes FP32 S to TMEM (we know this works)
|
||||
Step 2: Softmax packing writes BF16 P to TMEM (test this)
|
||||
Step 3: PV MMA reads BF16 P from TMEM and V from SMEM, produces O
|
||||
|
||||
But to isolate the bug, let me test just the PV MMA in isolation.
|
||||
I'll write known BF16 values to TMEM using the softmax packing path,
|
||||
then immediately read them back using the PV A-fragment path,
|
||||
and compare.
|
||||
|
||||
Actually, the simplest isolation test:
|
||||
1. Do QK MMA to get S in TMEM (cosine 0.999999 verified)
|
||||
2. Do softmax packing: S → P in TMEM (at offset 32)
|
||||
3. Skip PV entirely — read P from TMEM using the C-fragment composition LOAD path
|
||||
4. Output P to GMEM and compare against S.to(BF16)
|
||||
|
||||
This tests whether the softmax packing writes P correctly to the same TMEM
|
||||
that the PV would read from.
|
||||
|
||||
But we can't easily read P from TMEM using the standard epilogue path
|
||||
because the epilogue expects FP32 accumulator data.
|
||||
|
||||
Alternative: Use the PV MMA with V=I (identity). If P is correct,
|
||||
then P @ I = P. But V needs to be MN-major and (128, 128), not (128, 64).
|
||||
The output would be (128, 128) which doesn't match our (128, 64) c tensor.
|
||||
|
||||
Let me use V that selects the first 64 columns: V[k, n] = delta(k, n) for k in [0,63].
|
||||
This gives P @ V = P[:, :64], and the output is (128, 64).
|
||||
But V is (128, 128) in the MMA K,N dims. V[k, n] for k in [0,127], n in [0,63].
|
||||
Hmm, this is getting complicated. Let me just do the identity approach with a (128, 128) output.
|
||||
"""
|
||||
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
|
||||
|
||||
|
||||
class Test128x16Tiler:
|
||||
"""QK + softmax packing + PV with V=I to isolate PV MMA correctness.
|
||||
Output should be P = S.to(BF16), i.e. (Q@K^T).bfloat16()
|
||||
With V=I, O = P @ I = P.
|
||||
But V is (K=128, N=128) in the MMA. We need a 128x128 identity in MN-major.
|
||||
Output tensor is (128, 128).
|
||||
"""
|
||||
def __init__(self, mma_tiler_mn):
|
||||
self.acc_dtype = Float32; self.qk_acc_dtype = Float32
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
self.mma_tiler_mn = mma_tiler_mn; self.mma_tiler = (*mma_tiler_mn, 1)
|
||||
self.use_2cta_instrs = False # needed by epilogue_tma_store
|
||||
self.epilog_sync_bar_id = 1 # needed by epilogue_tma_store
|
||||
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
|
||||
|
||||
def _setup(self, qk_mma, pv_mma):
|
||||
qk_inst_k = int(cute.size(qk_mma.shape_mnk, mode=[2]))
|
||||
self.qk_mma_tiler = (*self.mma_tiler_mn, qk_inst_k * 4)
|
||||
# PV with V=I: output is (128, 128), same as QK
|
||||
self.pv_mma_tiler = (self.qk_mma_tiler[0], qk_inst_k, self.qk_mma_tiler[1])
|
||||
# pv_mma_tiler = (128, 128, 128) since V is 128x128
|
||||
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),
|
||||
self.qk_mma_tiler[1], self.qk_mma_tiler[2])
|
||||
self.c_layout = LayoutEnum.ROW_MAJOR
|
||||
self.epi_tile = utils.sm100.compute_epilogue_tile_shape(
|
||||
(self.pv_mma_tiler[0], self.pv_mma_tiler[1], self.pv_mma_tiler[2]), False, self.c_layout, self.o_dtype)
|
||||
self.num_ab_stage = 1; self.num_acc_stage = 1
|
||||
|
||||
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_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_acc_shape)
|
||||
s_cols = find_tmem_tensor_col_offset(tStS)
|
||||
pv_thr = pv_mma.get_slice(0)
|
||||
pv_acc_shape = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_acc_shape)
|
||||
o_cols = find_tmem_tensor_col_offset(tOtO)
|
||||
|
||||
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 = s_cols
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_acc_shape, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_acc_shape, self.num_acc_stage))
|
||||
self.num_tmem_alloc_cols = utils.get_num_tmem_alloc_cols([tCtS_fake, tCtO_fake], arch="sm_100")
|
||||
|
||||
# ⛔⛔⛔ CRITICAL: num_tma_load_bytes MUST include ALL TMA-loaded tensors (Q + K + V). Missing V → DEADLOCK. See FOOTGUN #0 in README.
|
||||
a_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
|
||||
b_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
|
||||
v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0))
|
||||
self.num_tma_load_bytes = (
|
||||
cute.size_in_bytes(self.q_dtype, a_smem) + cute.size_in_bytes(self.q_dtype, b_smem) +
|
||||
cute.size_in_bytes(self.q_dtype, v_smem)
|
||||
) * 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()
|
||||
self.v_major = LayoutEnum.from_tensor(v).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, self.mma_tiler_mn, tcgen05.OperandSource.SMEM)
|
||||
# PV with 128x128 output (V=I)
|
||||
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, 16), tcgen05.OperandSource.TMEM)
|
||||
self._setup(qk_mma, pv_mma)
|
||||
|
||||
q_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
|
||||
k_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
|
||||
v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0))
|
||||
tma_q, tma_tq = 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_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
|
||||
tma_k, tma_tk = 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_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
|
||||
tma_v, tma_tv = cute.nvgpu.make_tiled_tma_atom_B(
|
||||
utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn, pv_mma.thr_id),
|
||||
v, v_smem, self.pv_mma_tiler, pv_mma, self.cluster_layout_vmnk.shape)
|
||||
epi_smem = cute.select(self.c_smem_s, mode=[0, 1])
|
||||
tma_c, tma_tc = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(), c, epi_smem, self.epi_tile)
|
||||
|
||||
self._kernel(qk_mma, pv_mma, tma_q, tma_tq, tma_k, tma_tk, tma_v, tma_tv,
|
||||
tma_c, tma_tc, 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()
|
||||
use_2cta = cute.size(qk_mma.thr_id.shape) == 2
|
||||
|
||||
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()
|
||||
|
||||
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) * (2 if use_2cta else 1)),
|
||||
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=use_2cta,
|
||||
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))
|
||||
gC = cute.local_tile(mC, cute.slice_(self.pv_mma_tiler, (None,0,0)), (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); 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)]
|
||||
|
||||
gV = cute.local_tile(mV, cute.slice_(self.pv_mma_tiler, (0,None,None)), (None,None,None))
|
||||
tCgV = pv_thr.partition_B(gV)
|
||||
tVsV, tVgV = cpasync.tma_partition(tma_v, 0, b_lay, cute.group_modes(sV,0,3), cute.group_modes(tCgV,0,3))
|
||||
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_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_acc_shape)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
|
||||
pv_acc_shape = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_acc_shape)
|
||||
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_acc_shape, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_acc_shape, self.num_acc_stage))
|
||||
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# ═══ TMA LOAD WARP ═══
|
||||
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 WARP ═══
|
||||
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_prod_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
|
||||
acc_pipe.producer_acquire(acc_prod_st)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
for kt in range(k_cnt):
|
||||
h = ab_c.wait_and_advance(peek)
|
||||
nblk = cute.size(tCrQ, mode=[2])
|
||||
for kb in cutlass.range(nblk, 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()
|
||||
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])
|
||||
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 = Test128x16Tiler(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('PV diag (V=I): cosine {:.6f} {}'.format(cos, 'PASS' if cos >= 0.99 else 'FAIL'))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test()
|
||||
386
tests/test_128_16_stepD.py
Normal file
386
tests/test_128_16_stepD.py
Normal file
@@ -0,0 +1,386 @@
|
||||
"""
|
||||
Minimal PV-only test: Load P from GMEM to TMEM via QK-style MMA, then PV from TMEM.
|
||||
Step 1: QK MMA writes FP32 S to TMEM (we know this works)
|
||||
Step 2: Softmax packing writes BF16 P to TMEM (test this)
|
||||
Step 3: PV MMA reads BF16 P from TMEM and V from SMEM, produces O
|
||||
|
||||
But to isolate the bug, let me test just the PV MMA in isolation.
|
||||
I'll write known BF16 values to TMEM using the softmax packing path,
|
||||
then immediately read them back using the PV A-fragment path,
|
||||
and compare.
|
||||
|
||||
Actually, the simplest isolation test:
|
||||
1. Do QK MMA to get S in TMEM (cosine 0.999999 verified)
|
||||
2. Do softmax packing: S → P in TMEM (at offset 32)
|
||||
3. Skip PV entirely — read P from TMEM using the C-fragment composition LOAD path
|
||||
4. Output P to GMEM and compare against S.to(BF16)
|
||||
|
||||
This tests whether the softmax packing writes P correctly to the same TMEM
|
||||
that the PV would read from.
|
||||
|
||||
But we can't easily read P from TMEM using the standard epilogue path
|
||||
because the epilogue expects FP32 accumulator data.
|
||||
|
||||
Alternative: Use the PV MMA with V=I (identity). If P is correct,
|
||||
then P @ I = P. But V needs to be MN-major and (128, 128), not (128, 64).
|
||||
The output would be (128, 128) which doesn't match our (128, 64) c tensor.
|
||||
|
||||
Let me use V that selects the first 64 columns: V[k, n] = delta(k, n) for k in [0,63].
|
||||
This gives P @ V = P[:, :64], and the output is (128, 64).
|
||||
But V is (128, 128) in the MMA K,N dims. V[k, n] for k in [0,127], n in [0,63].
|
||||
Hmm, this is getting complicated. Let me just do the identity approach with a (128, 128) output.
|
||||
"""
|
||||
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
|
||||
|
||||
|
||||
class Test128x16Tiler:
|
||||
"""QK + softmax packing + PV with V=I to isolate PV MMA correctness.
|
||||
Output should be P = S.to(BF16), i.e. (Q@K^T).bfloat16()
|
||||
With V=I, O = P @ I = P.
|
||||
But V is (K=128, N=128) in the MMA. We need a 128x128 identity in MN-major.
|
||||
Output tensor is (128, 128).
|
||||
"""
|
||||
def __init__(self, mma_tiler_mn):
|
||||
self.acc_dtype = Float32; self.qk_acc_dtype = Float32
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
self.mma_tiler_mn = mma_tiler_mn; self.mma_tiler = (*mma_tiler_mn, 1)
|
||||
self.use_2cta_instrs = False # needed by epilogue_tma_store
|
||||
self.epilog_sync_bar_id = 1 # needed by epilogue_tma_store
|
||||
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
|
||||
|
||||
def _setup(self, qk_mma, pv_mma):
|
||||
qk_inst_k = int(cute.size(qk_mma.shape_mnk, mode=[2]))
|
||||
self.qk_mma_tiler = (*self.mma_tiler_mn, qk_inst_k * 4)
|
||||
# PV with V=I: output is (128, 128), same as QK
|
||||
self.pv_mma_tiler = (self.qk_mma_tiler[0], qk_inst_k, self.qk_mma_tiler[1])
|
||||
# pv_mma_tiler = (128, 128, 128) since V is 128x128
|
||||
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),
|
||||
self.qk_mma_tiler[1], self.qk_mma_tiler[2])
|
||||
self.c_layout = LayoutEnum.ROW_MAJOR
|
||||
self.epi_tile = utils.sm100.compute_epilogue_tile_shape(
|
||||
(self.pv_mma_tiler[0], self.pv_mma_tiler[1], self.pv_mma_tiler[2]), False, self.c_layout, self.o_dtype)
|
||||
self.num_ab_stage = 1; self.num_acc_stage = 1
|
||||
|
||||
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_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_acc_shape)
|
||||
s_cols = find_tmem_tensor_col_offset(tStS)
|
||||
pv_thr = pv_mma.get_slice(0)
|
||||
pv_acc_shape = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_acc_shape)
|
||||
o_cols = find_tmem_tensor_col_offset(tOtO)
|
||||
|
||||
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 = s_cols
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_acc_shape, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_acc_shape, self.num_acc_stage))
|
||||
self.num_tmem_alloc_cols = utils.get_num_tmem_alloc_cols([tCtS_fake, tCtO_fake], arch="sm_100")
|
||||
|
||||
# ⛔⛔⛔ CRITICAL: num_tma_load_bytes MUST include ALL TMA-loaded tensors (Q + K + V). Missing V → DEADLOCK. See FOOTGUN #0 in README.
|
||||
a_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
|
||||
b_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
|
||||
v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0))
|
||||
self.num_tma_load_bytes = (
|
||||
cute.size_in_bytes(self.q_dtype, a_smem) + cute.size_in_bytes(self.q_dtype, b_smem) +
|
||||
cute.size_in_bytes(self.q_dtype, v_smem)
|
||||
) * 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()
|
||||
self.v_major = LayoutEnum.from_tensor(v).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, self.mma_tiler_mn, tcgen05.OperandSource.SMEM)
|
||||
# PV with 128x128 output (V=I)
|
||||
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, 16), tcgen05.OperandSource.TMEM)
|
||||
self._setup(qk_mma, pv_mma)
|
||||
|
||||
q_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
|
||||
k_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
|
||||
v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0))
|
||||
tma_q, tma_tq = 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_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
|
||||
tma_k, tma_tk = 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_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
|
||||
tma_v, tma_tv = cute.nvgpu.make_tiled_tma_atom_B(
|
||||
utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn, pv_mma.thr_id),
|
||||
v, v_smem, self.pv_mma_tiler, pv_mma, self.cluster_layout_vmnk.shape)
|
||||
epi_smem = cute.select(self.c_smem_s, mode=[0, 1])
|
||||
tma_c, tma_tc = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(), c, epi_smem, self.epi_tile)
|
||||
|
||||
self._kernel(qk_mma, pv_mma, tma_q, tma_tq, tma_k, tma_tk, tma_v, tma_tv,
|
||||
tma_c, tma_tc, 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()
|
||||
use_2cta = cute.size(qk_mma.thr_id.shape) == 2
|
||||
|
||||
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()
|
||||
|
||||
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) * (2 if use_2cta else 1)),
|
||||
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=use_2cta,
|
||||
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))
|
||||
gC = cute.local_tile(mC, cute.slice_(self.pv_mma_tiler, (None,0,0)), (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); 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)]
|
||||
|
||||
gV = cute.local_tile(mV, cute.slice_(self.pv_mma_tiler, (0,None,None)), (None,None,None))
|
||||
tCgV = pv_thr.partition_B(gV)
|
||||
tVsV, tVgV = cpasync.tma_partition(tma_v, 0, b_lay, cute.group_modes(sV,0,3), cute.group_modes(tCgV,0,3))
|
||||
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_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_acc_shape)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
|
||||
pv_acc_shape = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_acc_shape)
|
||||
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_acc_shape, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_acc_shape, self.num_acc_stage))
|
||||
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# ═══ TMA LOAD WARP ═══
|
||||
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 WARP ═══
|
||||
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_prod_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
|
||||
acc_pipe.producer_acquire(acc_prod_st)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
for kt in range(k_cnt):
|
||||
h = ab_c.wait_and_advance(peek)
|
||||
nblk = cute.size(tCrQ, mode=[2])
|
||||
for kb in cutlass.range(nblk, 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()
|
||||
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])
|
||||
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)
|
||||
saved_cta_tile = self.cta_tile_shape_mnk
|
||||
self.cta_tile_shape_mnk = (self.pv_mma_tiler[0], self.pv_mma_tiler[1], self.pv_mma_tiler[2])
|
||||
acc_cons_st = utils.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)
|
||||
self.cta_tile_shape_mnk = saved_cta_tile
|
||||
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 = Test128x16Tiler(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('PV diag (V=I): cosine {:.6f} {}'.format(cos, 'PASS' if cos >= 0.99 else 'FAIL'))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test()
|
||||
385
tests/test_128_16_stepE.py
Normal file
385
tests/test_128_16_stepE.py
Normal file
@@ -0,0 +1,385 @@
|
||||
"""
|
||||
Minimal PV-only test: Load P from GMEM to TMEM via QK-style MMA, then PV from TMEM.
|
||||
Step 1: QK MMA writes FP32 S to TMEM (we know this works)
|
||||
Step 2: Softmax packing writes BF16 P to TMEM (test this)
|
||||
Step 3: PV MMA reads BF16 P from TMEM and V from SMEM, produces O
|
||||
|
||||
But to isolate the bug, let me test just the PV MMA in isolation.
|
||||
I'll write known BF16 values to TMEM using the softmax packing path,
|
||||
then immediately read them back using the PV A-fragment path,
|
||||
and compare.
|
||||
|
||||
Actually, the simplest isolation test:
|
||||
1. Do QK MMA to get S in TMEM (cosine 0.999999 verified)
|
||||
2. Do softmax packing: S → P in TMEM (at offset 32)
|
||||
3. Skip PV entirely — read P from TMEM using the C-fragment composition LOAD path
|
||||
4. Output P to GMEM and compare against S.to(BF16)
|
||||
|
||||
This tests whether the softmax packing writes P correctly to the same TMEM
|
||||
that the PV would read from.
|
||||
|
||||
But we can't easily read P from TMEM using the standard epilogue path
|
||||
because the epilogue expects FP32 accumulator data.
|
||||
|
||||
Alternative: Use the PV MMA with V=I (identity). If P is correct,
|
||||
then P @ I = P. But V needs to be MN-major and (128, 128), not (128, 64).
|
||||
The output would be (128, 128) which doesn't match our (128, 64) c tensor.
|
||||
|
||||
Let me use V that selects the first 64 columns: V[k, n] = delta(k, n) for k in [0,63].
|
||||
This gives P @ V = P[:, :64], and the output is (128, 64).
|
||||
But V is (128, 128) in the MMA K,N dims. V[k, n] for k in [0,127], n in [0,63].
|
||||
Hmm, this is getting complicated. Let me just do the identity approach with a (128, 128) output.
|
||||
"""
|
||||
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
|
||||
|
||||
|
||||
class Test128x16Tiler:
|
||||
"""QK + softmax packing + PV with V=I to isolate PV MMA correctness.
|
||||
Output should be P = S.to(BF16), i.e. (Q@K^T).bfloat16()
|
||||
With V=I, O = P @ I = P.
|
||||
But V is (K=128, N=128) in the MMA. We need a 128x128 identity in MN-major.
|
||||
Output tensor is (128, 128).
|
||||
"""
|
||||
def __init__(self, mma_tiler_mn):
|
||||
self.acc_dtype = Float32; self.qk_acc_dtype = Float32
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
self.mma_tiler_mn = mma_tiler_mn; self.mma_tiler = (*mma_tiler_mn, 1)
|
||||
self.use_2cta_instrs = False # needed by epilogue_tma_store
|
||||
self.epilog_sync_bar_id = 1 # needed by epilogue_tma_store
|
||||
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
|
||||
|
||||
def _setup(self, qk_mma, pv_mma):
|
||||
qk_inst_k = int(cute.size(qk_mma.shape_mnk, mode=[2]))
|
||||
self.qk_mma_tiler = (*self.mma_tiler_mn, qk_inst_k * 4)
|
||||
# PV with V=I: output is (128, 128), same as QK
|
||||
self.pv_mma_tiler = (self.qk_mma_tiler[0], qk_inst_k, self.qk_mma_tiler[1])
|
||||
# pv_mma_tiler = (128, 128, 128) since V is 128x128
|
||||
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),
|
||||
self.qk_mma_tiler[1], self.qk_mma_tiler[2])
|
||||
self.c_layout = LayoutEnum.ROW_MAJOR
|
||||
self.epi_tile = utils.sm100.compute_epilogue_tile_shape(
|
||||
(self.pv_mma_tiler[0], self.pv_mma_tiler[1], self.pv_mma_tiler[2]), False, self.c_layout, self.o_dtype)
|
||||
self.num_ab_stage = 1; self.num_acc_stage = 1
|
||||
|
||||
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_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_acc_shape)
|
||||
s_cols = find_tmem_tensor_col_offset(tStS)
|
||||
pv_thr = pv_mma.get_slice(0)
|
||||
pv_acc_shape = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_acc_shape)
|
||||
o_cols = find_tmem_tensor_col_offset(tOtO)
|
||||
|
||||
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 = s_cols
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_acc_shape, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_acc_shape, self.num_acc_stage))
|
||||
self.num_tmem_alloc_cols = utils.get_num_tmem_alloc_cols([tCtS_fake, tCtO_fake], arch="sm_100")
|
||||
|
||||
# ⛔⛔⛔ CRITICAL: num_tma_load_bytes MUST include ALL TMA-loaded tensors (Q + K + V). Missing V → DEADLOCK. See FOOTGUN #0 in README.
|
||||
a_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
|
||||
b_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
|
||||
v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0))
|
||||
self.num_tma_load_bytes = (
|
||||
cute.size_in_bytes(self.q_dtype, a_smem) + cute.size_in_bytes(self.q_dtype, b_smem) +
|
||||
cute.size_in_bytes(self.q_dtype, v_smem)
|
||||
) * 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()
|
||||
self.v_major = LayoutEnum.from_tensor(v).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, self.mma_tiler_mn, tcgen05.OperandSource.SMEM)
|
||||
# PV with 128x128 output (V=I)
|
||||
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, 16), tcgen05.OperandSource.TMEM)
|
||||
self._setup(qk_mma, pv_mma)
|
||||
|
||||
q_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
|
||||
k_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
|
||||
v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0))
|
||||
tma_q, tma_tq = 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_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
|
||||
tma_k, tma_tk = 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_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
|
||||
tma_v, tma_tv = cute.nvgpu.make_tiled_tma_atom_B(
|
||||
utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn, pv_mma.thr_id),
|
||||
v, v_smem, self.pv_mma_tiler, pv_mma, self.cluster_layout_vmnk.shape)
|
||||
epi_smem = cute.select(self.c_smem_s, mode=[0, 1])
|
||||
tma_c, tma_tc = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(), c, epi_smem, self.epi_tile)
|
||||
|
||||
self._kernel(qk_mma, pv_mma, tma_q, tma_tq, tma_k, tma_tk, tma_v, tma_tv,
|
||||
tma_c, tma_tc, 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()
|
||||
use_2cta = cute.size(qk_mma.thr_id.shape) == 2
|
||||
|
||||
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()
|
||||
|
||||
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) * (2 if use_2cta else 1)),
|
||||
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=use_2cta,
|
||||
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))
|
||||
gC = cute.local_tile(mC, cute.slice_(self.pv_mma_tiler, (None,0,0)), (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); 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)]
|
||||
|
||||
gV = cute.local_tile(mV, cute.slice_(self.pv_mma_tiler, (0,None,None)), (None,None,None))
|
||||
tCgV = pv_thr.partition_B(gV)
|
||||
tVsV, tVgV = cpasync.tma_partition(tma_v, 0, b_lay, cute.group_modes(sV,0,3), cute.group_modes(tCgV,0,3))
|
||||
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_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_acc_shape)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
|
||||
pv_acc_shape = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_acc_shape)
|
||||
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_acc_shape, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_acc_shape, self.num_acc_stage))
|
||||
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# ═══ TMA LOAD WARP ═══
|
||||
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 WARP ═══
|
||||
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_prod_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
|
||||
acc_pipe.producer_acquire(acc_prod_st)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
for kt in range(k_cnt):
|
||||
h = ab_c.wait_and_advance(peek)
|
||||
nblk = cute.size(tCrQ, mode=[2])
|
||||
for kb in cutlass.range(nblk, 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()
|
||||
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])
|
||||
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)
|
||||
saved_cta_tile = self.cta_tile_shape_mnk
|
||||
self.cta_tile_shape_mnk = (self.pv_mma_tiler[0], self.pv_mma_tiler[1], self.pv_mma_tiler[2])
|
||||
acc_cons_st = utils.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)
|
||||
self.cta_tile_shape_mnk = saved_cta_tile
|
||||
c_pipe.producer_tail()
|
||||
tmem.relinquish_alloc_permit()
|
||||
tmem.free(tmem_ptr)
|
||||
|
||||
|
||||
def test():
|
||||
torch.manual_seed(42)
|
||||
m, n, head_dim = 128, 128, 16
|
||||
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[:16,:] MN-major: (16,128) with strides (1,16)
|
||||
v_data = torch.eye(128, dtype=torch.bfloat16, device='cuda')
|
||||
v = v_data[:16, :].as_strided((16, 128), (1, 16)).unsqueeze(-1)
|
||||
c = torch.zeros(m, 16, 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()[:, :16]
|
||||
|
||||
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 = Test128x16Tiler(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('PV diag (V=I): cosine {:.6f} {}'.format(cos, 'PASS' if cos >= 0.99 else 'FAIL'))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test()
|
||||
383
tests/test_128_16_tiler.py
Normal file
383
tests/test_128_16_tiler.py
Normal file
@@ -0,0 +1,383 @@
|
||||
"""
|
||||
Minimal PV-only test: Load P from GMEM to TMEM via QK-style MMA, then PV from TMEM.
|
||||
Step 1: QK MMA writes FP32 S to TMEM (we know this works)
|
||||
Step 2: Softmax packing writes BF16 P to TMEM (test this)
|
||||
Step 3: PV MMA reads BF16 P from TMEM and V from SMEM, produces O
|
||||
|
||||
But to isolate the bug, let me test just the PV MMA in isolation.
|
||||
I'll write known BF16 values to TMEM using the softmax packing path,
|
||||
then immediately read them back using the PV A-fragment path,
|
||||
and compare.
|
||||
|
||||
Actually, the simplest isolation test:
|
||||
1. Do QK MMA to get S in TMEM (cosine 0.999999 verified)
|
||||
2. Do softmax packing: S → P in TMEM (at offset 32)
|
||||
3. Skip PV entirely — read P from TMEM using the C-fragment composition LOAD path
|
||||
4. Output P to GMEM and compare against S.to(BF16)
|
||||
|
||||
This tests whether the softmax packing writes P correctly to the same TMEM
|
||||
that the PV would read from.
|
||||
|
||||
But we can't easily read P from TMEM using the standard epilogue path
|
||||
because the epilogue expects FP32 accumulator data.
|
||||
|
||||
Alternative: Use the PV MMA with V=I (identity). If P is correct,
|
||||
then P @ I = P. But V needs to be MN-major and (128, 128), not (128, 64).
|
||||
The output would be (128, 128) which doesn't match our (128, 64) c tensor.
|
||||
|
||||
Let me use V that selects the first 64 columns: V[k, n] = delta(k, n) for k in [0,63].
|
||||
This gives P @ V = P[:, :64], and the output is (128, 64).
|
||||
But V is (128, 128) in the MMA K,N dims. V[k, n] for k in [0,127], n in [0,63].
|
||||
Hmm, this is getting complicated. Let me just do the identity approach with a (128, 128) output.
|
||||
"""
|
||||
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
|
||||
|
||||
|
||||
class Test128x16Tiler:
|
||||
"""QK + softmax packing + PV with V=I to isolate PV MMA correctness.
|
||||
Output should be P = S.to(BF16), i.e. (Q@K^T).bfloat16()
|
||||
With V=I, O = P @ I = P.
|
||||
But V is (K=128, N=128) in the MMA. We need a 128x128 identity in MN-major.
|
||||
Output tensor is (128, 128).
|
||||
"""
|
||||
def __init__(self, mma_tiler_mn):
|
||||
self.acc_dtype = Float32; self.qk_acc_dtype = Float32
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
self.mma_tiler_mn = mma_tiler_mn; self.mma_tiler = (*mma_tiler_mn, 1)
|
||||
self.use_2cta_instrs = False # needed by epilogue_tma_store
|
||||
self.epilog_sync_bar_id = 1 # needed by epilogue_tma_store
|
||||
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
|
||||
|
||||
def _setup(self, qk_mma, pv_mma):
|
||||
qk_inst_k = int(cute.size(qk_mma.shape_mnk, mode=[2]))
|
||||
self.qk_mma_tiler = (*self.mma_tiler_mn, qk_inst_k * 4)
|
||||
# PV with V=I: output is (128, 128), same as QK
|
||||
self.pv_mma_tiler = (self.qk_mma_tiler[0], qk_inst_k, self.qk_mma_tiler[1])
|
||||
# pv_mma_tiler = (128, 128, 128) since V is 128x128
|
||||
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),
|
||||
self.qk_mma_tiler[1], 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.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_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_acc_shape)
|
||||
s_cols = find_tmem_tensor_col_offset(tStS)
|
||||
pv_thr = pv_mma.get_slice(0)
|
||||
pv_acc_shape = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_acc_shape)
|
||||
o_cols = find_tmem_tensor_col_offset(tOtO)
|
||||
|
||||
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 = s_cols
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_acc_shape, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_acc_shape, self.num_acc_stage))
|
||||
self.num_tmem_alloc_cols = utils.get_num_tmem_alloc_cols([tCtS_fake, tCtO_fake], arch="sm_100")
|
||||
|
||||
# ⛔⛔⛔ CRITICAL: num_tma_load_bytes MUST include ALL TMA-loaded tensors (Q + K + V). Missing V → DEADLOCK. See FOOTGUN #0 in README.
|
||||
a_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
|
||||
b_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
|
||||
v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0))
|
||||
self.num_tma_load_bytes = (
|
||||
cute.size_in_bytes(self.q_dtype, a_smem) + cute.size_in_bytes(self.q_dtype, b_smem) +
|
||||
cute.size_in_bytes(self.q_dtype, v_smem)
|
||||
) * 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()
|
||||
self.v_major = LayoutEnum.from_tensor(v).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, self.mma_tiler_mn, tcgen05.OperandSource.SMEM)
|
||||
# PV with 128x128 output (V=I)
|
||||
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, self.mma_tiler_mn, tcgen05.OperandSource.TMEM)
|
||||
self._setup(qk_mma, pv_mma)
|
||||
|
||||
q_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
|
||||
k_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
|
||||
v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0))
|
||||
tma_q, tma_tq = 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_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
|
||||
tma_k, tma_tk = 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_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
|
||||
tma_v, tma_tv = cute.nvgpu.make_tiled_tma_atom_B(
|
||||
utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn, pv_mma.thr_id),
|
||||
v, v_smem, self.pv_mma_tiler, pv_mma, self.cluster_layout_vmnk.shape)
|
||||
epi_smem = cute.select(self.c_smem_s, mode=[0, 1])
|
||||
tma_c, tma_tc = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(), c, epi_smem, self.epi_tile)
|
||||
|
||||
self._kernel(qk_mma, pv_mma, tma_q, tma_tq, tma_k, tma_tk, tma_v, tma_tv,
|
||||
tma_c, tma_tc, 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()
|
||||
use_2cta = cute.size(qk_mma.thr_id.shape) == 2
|
||||
|
||||
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()
|
||||
|
||||
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) * (2 if use_2cta else 1)),
|
||||
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=use_2cta,
|
||||
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))
|
||||
gC = cute.local_tile(mC, cute.slice_(self.qk_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); 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)]
|
||||
|
||||
gV = cute.local_tile(mV, cute.slice_(self.pv_mma_tiler, (0,None,None)), (None,None,None))
|
||||
tCgV = pv_thr.partition_B(gV)
|
||||
tVsV, tVgV = cpasync.tma_partition(tma_v, 0, b_lay, cute.group_modes(sV,0,3), cute.group_modes(tCgV,0,3))
|
||||
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_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_acc_shape)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
|
||||
pv_acc_shape = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_acc_shape)
|
||||
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_acc_shape, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_acc_shape, self.num_acc_stage))
|
||||
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# ═══ TMA LOAD WARP ═══
|
||||
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 WARP ═══
|
||||
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_prod_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
|
||||
acc_pipe.producer_acquire(acc_prod_st)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
for kt in range(k_cnt):
|
||||
h = ab_c.wait_and_advance(peek)
|
||||
nblk = cute.size(tCrQ, mode=[2])
|
||||
for kb in cutlass.range(nblk, 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()
|
||||
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])
|
||||
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 = Test128x16Tiler(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('PV diag (V=I): cosine {:.6f} {}'.format(cos, 'PASS' if cos >= 0.99 else 'FAIL'))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test()
|
||||
385
tests/test_128_16_v8.py
Normal file
385
tests/test_128_16_v8.py
Normal file
@@ -0,0 +1,385 @@
|
||||
"""
|
||||
Minimal PV-only test: Load P from GMEM to TMEM via QK-style MMA, then PV from TMEM.
|
||||
Step 1: QK MMA writes FP32 S to TMEM (we know this works)
|
||||
Step 2: Softmax packing writes BF16 P to TMEM (test this)
|
||||
Step 3: PV MMA reads BF16 P from TMEM and V from SMEM, produces O
|
||||
|
||||
But to isolate the bug, let me test just the PV MMA in isolation.
|
||||
I'll write known BF16 values to TMEM using the softmax packing path,
|
||||
then immediately read them back using the PV A-fragment path,
|
||||
and compare.
|
||||
|
||||
Actually, the simplest isolation test:
|
||||
1. Do QK MMA to get S in TMEM (cosine 0.999999 verified)
|
||||
2. Do softmax packing: S → P in TMEM (at offset 32)
|
||||
3. Skip PV entirely — read P from TMEM using the C-fragment composition LOAD path
|
||||
4. Output P to GMEM and compare against S.to(BF16)
|
||||
|
||||
This tests whether the softmax packing writes P correctly to the same TMEM
|
||||
that the PV would read from.
|
||||
|
||||
But we can't easily read P from TMEM using the standard epilogue path
|
||||
because the epilogue expects FP32 accumulator data.
|
||||
|
||||
Alternative: Use the PV MMA with V=I (identity). If P is correct,
|
||||
then P @ I = P. But V needs to be MN-major and (128, 128), not (128, 64).
|
||||
The output would be (128, 128) which doesn't match our (128, 64) c tensor.
|
||||
|
||||
Let me use V that selects the first 64 columns: V[k, n] = delta(k, n) for k in [0,63].
|
||||
This gives P @ V = P[:, :64], and the output is (128, 64).
|
||||
But V is (128, 128) in the MMA K,N dims. V[k, n] for k in [0,127], n in [0,63].
|
||||
Hmm, this is getting complicated. Let me just do the identity approach with a (128, 128) output.
|
||||
"""
|
||||
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
|
||||
|
||||
|
||||
class Test128x16Tiler:
|
||||
"""QK + softmax packing + PV with V=I to isolate PV MMA correctness.
|
||||
Output should be P = S.to(BF16), i.e. (Q@K^T).bfloat16()
|
||||
With V=I, O = P @ I = P.
|
||||
But V is (K=128, N=128) in the MMA. We need a 128x128 identity in MN-major.
|
||||
Output tensor is (128, 128).
|
||||
"""
|
||||
def __init__(self, mma_tiler_mn):
|
||||
self.acc_dtype = Float32; self.qk_acc_dtype = Float32
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
self.mma_tiler_mn = mma_tiler_mn; self.mma_tiler = (*mma_tiler_mn, 1)
|
||||
self.use_2cta_instrs = False # needed by epilogue_tma_store
|
||||
self.epilog_sync_bar_id = 1 # needed by epilogue_tma_store
|
||||
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
|
||||
|
||||
def _setup(self, qk_mma, pv_mma):
|
||||
qk_inst_k = int(cute.size(qk_mma.shape_mnk, mode=[2]))
|
||||
self.qk_mma_tiler = (*self.mma_tiler_mn, qk_inst_k * 4)
|
||||
# PV with V=I: output is (128, 128), same as QK
|
||||
self.pv_mma_tiler = (self.qk_mma_tiler[0], qk_inst_k, self.qk_mma_tiler[1])
|
||||
# pv_mma_tiler = (128, 128, 128) since V is 128x128
|
||||
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),
|
||||
self.qk_mma_tiler[1], self.qk_mma_tiler[2])
|
||||
self.c_layout = LayoutEnum.ROW_MAJOR
|
||||
self.epi_tile = utils.sm100.compute_epilogue_tile_shape(
|
||||
(self.pv_mma_tiler[0], self.pv_mma_tiler[1], self.pv_mma_tiler[2]), False, self.c_layout, self.o_dtype)
|
||||
self.num_ab_stage = 1; self.num_acc_stage = 1
|
||||
|
||||
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_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_acc_shape)
|
||||
s_cols = find_tmem_tensor_col_offset(tStS)
|
||||
pv_thr = pv_mma.get_slice(0)
|
||||
pv_acc_shape = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_acc_shape)
|
||||
o_cols = find_tmem_tensor_col_offset(tOtO)
|
||||
|
||||
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 = s_cols
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_acc_shape, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_acc_shape, self.num_acc_stage))
|
||||
self.num_tmem_alloc_cols = utils.get_num_tmem_alloc_cols([tCtS_fake, tCtO_fake], arch="sm_100")
|
||||
|
||||
# ⛔⛔⛔ CRITICAL: num_tma_load_bytes MUST include ALL TMA-loaded tensors (Q + K + V). Missing V → DEADLOCK. See FOOTGUN #0 in README.
|
||||
a_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
|
||||
b_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
|
||||
v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0))
|
||||
self.num_tma_load_bytes = (
|
||||
cute.size_in_bytes(self.q_dtype, a_smem) + cute.size_in_bytes(self.q_dtype, b_smem) +
|
||||
cute.size_in_bytes(self.q_dtype, v_smem)
|
||||
) * 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()
|
||||
self.v_major = LayoutEnum.from_tensor(v).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, self.mma_tiler_mn, tcgen05.OperandSource.SMEM)
|
||||
# PV with 128x128 output (V=I)
|
||||
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, 16), tcgen05.OperandSource.TMEM)
|
||||
self._setup(qk_mma, pv_mma)
|
||||
|
||||
q_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
|
||||
k_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
|
||||
v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0))
|
||||
tma_q, tma_tq = 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_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
|
||||
tma_k, tma_tk = 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_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
|
||||
tma_v, tma_tv = cute.nvgpu.make_tiled_tma_atom_B(
|
||||
utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn, pv_mma.thr_id),
|
||||
v, v_smem, self.pv_mma_tiler, pv_mma, self.cluster_layout_vmnk.shape)
|
||||
epi_smem = cute.select(self.c_smem_s, mode=[0, 1])
|
||||
tma_c, tma_tc = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(), c, epi_smem, self.epi_tile)
|
||||
|
||||
self._kernel(qk_mma, pv_mma, tma_q, tma_tq, tma_k, tma_tk, tma_v, tma_tv,
|
||||
tma_c, tma_tc, 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()
|
||||
use_2cta = cute.size(qk_mma.thr_id.shape) == 2
|
||||
|
||||
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()
|
||||
|
||||
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) * (2 if use_2cta else 1)),
|
||||
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=use_2cta,
|
||||
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))
|
||||
gC = cute.local_tile(mC, cute.slice_(self.pv_mma_tiler, (None,0,0)), (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); 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)]
|
||||
|
||||
gV = cute.local_tile(mV, cute.slice_(self.pv_mma_tiler, (0,None,None)), (None,None,None))
|
||||
tCgV = pv_thr.partition_B(gV)
|
||||
tVsV, tVgV = cpasync.tma_partition(tma_v, 0, b_lay, cute.group_modes(sV,0,3), cute.group_modes(tCgV,0,3))
|
||||
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_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_acc_shape)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
|
||||
pv_acc_shape = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_acc_shape)
|
||||
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_acc_shape, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_acc_shape, self.num_acc_stage))
|
||||
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# ═══ TMA LOAD WARP ═══
|
||||
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 WARP ═══
|
||||
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_prod_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
|
||||
acc_pipe.producer_acquire(acc_prod_st)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
for kt in range(k_cnt):
|
||||
h = ab_c.wait_and_advance(peek)
|
||||
nblk = cute.size(tCrQ, mode=[2])
|
||||
for kb in cutlass.range(nblk, 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()
|
||||
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])
|
||||
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)
|
||||
saved_cta_tile = self.cta_tile_shape_mnk
|
||||
self.cta_tile_shape_mnk = (self.pv_mma_tiler[0], self.pv_mma_tiler[1], self.pv_mma_tiler[2])
|
||||
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)
|
||||
self.cta_tile_shape_mnk = saved_cta_tile
|
||||
c_pipe.producer_tail()
|
||||
tmem.relinquish_alloc_permit()
|
||||
tmem.free(tmem_ptr)
|
||||
|
||||
|
||||
def test():
|
||||
torch.manual_seed(42)
|
||||
m, n, head_dim = 128, 128, 16
|
||||
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[:16,:] MN-major: (16,128) with strides (1,16)
|
||||
v_data = torch.eye(128, dtype=torch.bfloat16, device='cuda')
|
||||
v = v_data[:16, :].as_strided((16, 128), (1, 16)).unsqueeze(-1)
|
||||
c = torch.zeros(m, 16, 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()[:, :16]
|
||||
|
||||
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 = Test128x16Tiler(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('PV(128,16) v8: cosine {:.6f} {}'.format(cos, 'PASS' if cos >= 0.99 else 'FAIL'))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test()
|
||||
399
tests/test_stage_b_ntile_v1.py
Normal file
399
tests/test_stage_b_ntile_v1.py
Normal file
@@ -0,0 +1,399 @@
|
||||
"""
|
||||
Stage B with N-tiling: QK + identity softmax + PV with (128,16) PV MMA.
|
||||
Single subtile test: verify the C→A TMEM bridge works for (128,16) PV.
|
||||
|
||||
pv_mma_tiler = (128, 16, 128). K=128 matches QK's N=128 → C/A bridge preserved.
|
||||
V is (16, 128) MN-major — first 16 rows of the head_dim.
|
||||
Output is (128, 16) — should be the first 16 columns of (Q@K^T).bf16().
|
||||
|
||||
If this passes, the TMEM layout mismatch (Bug 4) is fixed by N-tiling.
|
||||
Then we extend to the full (128, head_dim) output with the N-subtile loop.
|
||||
"""
|
||||
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
|
||||
|
||||
|
||||
class StageBSingleSubtileKernel:
|
||||
"""Single (128,16) PV subtile test. Output should be (Q@K^T).bf16()[:, :16]."""
|
||||
def __init__(self, mma_tiler_mn):
|
||||
self.acc_dtype = Float32; self.qk_acc_dtype = Float32
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
self.mma_tiler_mn = mma_tiler_mn; self.mma_tiler = (*mma_tiler_mn, 1)
|
||||
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
|
||||
|
||||
def _setup(self, qk_mma, pv_mma):
|
||||
qk_inst_k = cute.size(qk_mma.shape_mnk, mode=[2])
|
||||
self.qk_mma_tiler = (*self.mma_tiler_mn, qk_inst_k * 4)
|
||||
|
||||
# PV MMA tiler: (M=128, N=16, K=128)
|
||||
# N=16 = qk_inst_k (one subtile of head_dim)
|
||||
# K=128 = qk_mma_tiler[1] (QK's N → PV's K, preserves C/A bridge)
|
||||
self.pv_mma_tiler = (
|
||||
self.qk_mma_tiler[0], # M = 128
|
||||
qk_inst_k, # N = 16
|
||||
self.qk_mma_tiler[1], # K = 128
|
||||
)
|
||||
|
||||
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,))
|
||||
|
||||
# For the epilogue: use PV's cta_tile_shape_mnk and epi_tile
|
||||
self.cta_tile_shape_mnk = (
|
||||
self.pv_mma_tiler[0] // cute.size(pv_mma.thr_id.shape),
|
||||
self.pv_mma_tiler[1], self.pv_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.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_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_acc_shape)
|
||||
s_cols = find_tmem_tensor_col_offset(tStS)
|
||||
|
||||
pv_thr = pv_mma.get_slice(0)
|
||||
pv_acc_shape = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_acc_shape)
|
||||
o_cols = find_tmem_tensor_col_offset(tOtO)
|
||||
|
||||
self.tilePlikeFP32 = self.qk_mma_tiler[1] // Float32.width * self.o_dtype.width # = 64
|
||||
self.tmem_s0_offset = 0
|
||||
self.tmem_p0_offset = 32
|
||||
self.tmem_o0_offset = s_cols
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_acc_shape, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_acc_shape, self.num_acc_stage))
|
||||
self.num_tmem_alloc_cols = utils.get_num_tmem_alloc_cols([tCtS_fake, tCtO_fake], arch="sm_100")
|
||||
|
||||
a_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
|
||||
b_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
|
||||
v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0))
|
||||
self.num_tma_load_bytes = (
|
||||
cute.size_in_bytes(self.q_dtype, a_smem)
|
||||
+ cute.size_in_bytes(self.q_dtype, b_smem)
|
||||
+ cute.size_in_bytes(self.q_dtype, v_smem)
|
||||
) * 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()
|
||||
self.v_major = LayoutEnum.from_tensor(v).mma_major_mode()
|
||||
self.c_layout = LayoutEnum.from_tensor(c)
|
||||
|
||||
# PV MMA tiler: (M=128, N=16, K=128) — N=16 is one subtile, K=128 matches QK N
|
||||
# This must be computed before make_trivial_tiled_mma
|
||||
self.pv_mma_tiler_mn = (128, 16) # (M, N) for PV MMA
|
||||
|
||||
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, self.mma_tiler_mn, tcgen05.OperandSource.SMEM)
|
||||
# PV MMA with (128, 16) output, P from TMEM
|
||||
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, self.pv_mma_tiler_mn, tcgen05.OperandSource.TMEM)
|
||||
self._setup(qk_mma, pv_mma)
|
||||
|
||||
q_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
|
||||
k_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
|
||||
v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0))
|
||||
tma_q, tma_tq = 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_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
|
||||
tma_k, tma_tk = 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_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
|
||||
tma_v, tma_tv = cute.nvgpu.make_tiled_tma_atom_B(
|
||||
utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn, pv_mma.thr_id),
|
||||
v, v_smem, self.pv_mma_tiler, pv_mma, self.cluster_layout_vmnk.shape)
|
||||
epi_smem = cute.select(self.c_smem_s, mode=[0, 1])
|
||||
tma_c, tma_tc = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(), c, epi_smem, self.epi_tile)
|
||||
|
||||
self._kernel(qk_mma, pv_mma, tma_q, tma_tq, tma_k, tma_tk, tma_v, tma_tv,
|
||||
tma_c, tma_tc, 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()
|
||||
use_2cta = cute.size(qk_mma.thr_id.shape) == 2
|
||||
|
||||
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()
|
||||
|
||||
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) * (2 if use_2cta else 1)),
|
||||
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=use_2cta,
|
||||
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))
|
||||
# Output is (128, 16) — one PV subtile
|
||||
gC = cute.local_tile(mC, cute.select(self.pv_mma_tiler, mode=[0,1]), (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)
|
||||
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))
|
||||
tAgQ = tAgQ[(None,0,None,0)]; tBgK = tBgK[(None,0,None,0)]
|
||||
|
||||
# V: pv_mma_tiler = (128, 16, 128). V is (16, 128) MN-major.
|
||||
# TMA loads V using pv_mma_tiler's B-dimension slice.
|
||||
gV = cute.local_tile(mV, cute.slice_(self.pv_mma_tiler, (0,None,None)), (None,None,None))
|
||||
tCgV = pv_thr.partition_B(gV)
|
||||
tVsV, tVgV = cpasync.tma_partition(tma_v, 0, b_lay, cute.group_modes(sV,0,3), cute.group_modes(tCgV,0,3))
|
||||
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 accumulator
|
||||
qk_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_acc_shape)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
|
||||
# PV accumulator: (128, 16) FP32 in TMEM
|
||||
pv_acc_shape = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_acc_shape)
|
||||
tOtO0 = cute.make_tensor(tOtO.iterator + self.tmem_o0_offset, tOtO.layout)
|
||||
|
||||
# P A-fragment for PV MMA
|
||||
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_acc_shape, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_acc_shape, self.num_acc_stage))
|
||||
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# ═══ TMA LOAD WARP ═══
|
||||
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 WARP ═══
|
||||
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_prod_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
|
||||
acc_pipe.producer_acquire(acc_prod_st)
|
||||
|
||||
# QK MMA
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
for kt in range(k_cnt):
|
||||
h = ab_c.wait_and_advance(peek)
|
||||
nblk = cute.size(tCrQ, mode=[2])
|
||||
for kb in cutlass.range(nblk, 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()
|
||||
s0_handle = mma_si_prod.acquire_and_advance()
|
||||
|
||||
# PV MMA: P @ V → O (128, 16)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
tCrV_s = tCrV[(None, None, None, 0)]
|
||||
nblk_pv = cute.size(tOrP0, mode=[2])
|
||||
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))
|
||||
|
||||
# Softmax packing: ld FP32 S → BF16 P → st to TMEM
|
||||
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: Try writing QK scores (S) first to verify pipeline works
|
||||
# Use QK accumulator which we know is correct
|
||||
tCtS_base = cute.make_tensor(tmem_ptr + self.tmem_s0_offset, tCtS_fake.layout)
|
||||
# Write S to GMEM — using the original QK cta tile for the epilogue
|
||||
self.cta_tile_shape_mnk = (
|
||||
self.qk_mma_tiler[0] // cute.size(qk_mma.thr_id.shape),
|
||||
self.qk_mma_tiler[1], self.qk_mma_tiler[2])
|
||||
self.epi_tile = utils.sm100.compute_epilogue_tile_shape(
|
||||
self.cta_tile_shape_mnk, False, self.c_layout, self.o_dtype)
|
||||
# Need to re-partition gC for QK shape (128, 128)
|
||||
gC_qk = cute.local_tile(mC, cute.select(self.qk_mma_tiler, mode=[0,1]), (None,None,None))
|
||||
tCgC_qk = qk_thr.partition_C(gC_qk)
|
||||
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_qk,
|
||||
self.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 = 128, 128
|
||||
q = torch.randn(m, 128, 1, dtype=torch.bfloat16, device='cuda')
|
||||
k = torch.randn(n, 128, 1, dtype=torch.bfloat16, device='cuda')
|
||||
# V = full 128x128 identity in MN-major, same as v30
|
||||
# (128, 128) with strides (1, 128) — row (head_dim) is fast dim = MN-major
|
||||
v = torch.eye(128, dtype=torch.bfloat16, device='cuda')
|
||||
v = v.as_strided((128, 128), (1, 128)).unsqueeze(-1)
|
||||
# Output: (128, 16) — only one subtile of the output
|
||||
c = torch.zeros(m, 16, 1, dtype=torch.bfloat16, device='cuda')
|
||||
|
||||
qf = q[:,:,0].float(); kf = k[:,:,0].float()
|
||||
# V=I(128,128) with (128,16) PV MMA and single subtile:
|
||||
# P@V subtile 0 = P[:, :16] (first 16 cols)
|
||||
ref = (qf @ kf.T).bfloat16()[:, :16].float()
|
||||
|
||||
# Test 1: Output S = Q@K^T (bypass PV) to verify QK + epilogue still work
|
||||
ref_s = (qf @ kf.T).bfloat16().float()
|
||||
c_s = torch.zeros(m, 128, 1, dtype=torch.bfloat16, device='cuda')
|
||||
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_s = ct.from_dlpack(c_s).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c_s))
|
||||
stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
|
||||
kernel = StageBSingleSubtileKernel(mma_tiler_mn=(128, 128))
|
||||
print('Compiling QK-only test...', flush=True)
|
||||
compiled = cute.compile(kernel, mQ, mK, mV, mC_s, stream)
|
||||
print('Running QK-only...', flush=True)
|
||||
compiled(mQ, mK, mV, mC_s, stream)
|
||||
torch.cuda.synchronize()
|
||||
out_s = c_s[:,:,0].float()
|
||||
cos_s = torch.nn.functional.cosine_similarity(out_s.flatten().unsqueeze(0), ref_s.flatten().unsqueeze(0)).item()
|
||||
print(f'QK scores: cosine {cos_s:.6f} {"PASS" if cos_s >= 0.99 else "FAIL"}')
|
||||
return cos
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test()
|
||||
357
tests/test_stage_b_ntile_v3.py
Normal file
357
tests/test_stage_b_ntile_v3.py
Normal file
@@ -0,0 +1,357 @@
|
||||
"""
|
||||
Stage B N-tiling v3: (128,16) PV MMA. Minimal changes from v30 test_pv_diag.py.
|
||||
|
||||
Key changes:
|
||||
1. pv_mma_tiler = (128, 16, 128) instead of (128, 128, 128)
|
||||
2. epi_tile from pv_mma_tiler[:2] = (128, 16)
|
||||
3. cta_tile_shape_mnk from PV for the epilogue
|
||||
4. V = first 16 rows of identity (16, 128) MN-major
|
||||
5. Output (128, 16)
|
||||
"""
|
||||
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
|
||||
|
||||
|
||||
class PvDiag16Kernel:
|
||||
def __init__(self, mma_tiler_mn):
|
||||
self.acc_dtype = Float32; self.qk_acc_dtype = Float32
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
self.mma_tiler_mn = mma_tiler_mn; self.mma_tiler = (*mma_tiler_mn, 1)
|
||||
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
|
||||
|
||||
def _setup(self, qk_mma, pv_mma):
|
||||
qk_inst_k = cute.size(qk_mma.shape_mnk, mode=[2])
|
||||
self.qk_mma_tiler = (*self.mma_tiler_mn, qk_inst_k * 4)
|
||||
self.pv_mma_tiler = (self.qk_mma_tiler[0], qk_inst_k, self.qk_mma_tiler[1])
|
||||
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,))
|
||||
|
||||
# CTA tile shape from PV for epilogue (FMHA pattern)
|
||||
self.cta_tile_shape_mnk = (
|
||||
self.pv_mma_tiler[0] // cute.size(pv_mma.thr_id.shape),
|
||||
self.pv_mma_tiler[1], self.pv_mma_tiler[2])
|
||||
self.c_layout = LayoutEnum.ROW_MAJOR
|
||||
# epi_tile from PV tiler, matching FMHA: self.epi_tile = self.pv_mma_tiler[:2]
|
||||
self.epi_tile = self.pv_mma_tiler[:2]
|
||||
|
||||
self.num_ab_stage = 1; self.num_acc_stage = 1
|
||||
|
||||
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_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_acc_shape)
|
||||
s_cols = find_tmem_tensor_col_offset(tStS)
|
||||
pv_thr = pv_mma.get_slice(0)
|
||||
pv_acc_shape = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_acc_shape)
|
||||
o_cols = find_tmem_tensor_col_offset(tOtO)
|
||||
|
||||
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 = s_cols
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_acc_shape, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_acc_shape, self.num_acc_stage))
|
||||
self.num_tmem_alloc_cols = utils.get_num_tmem_alloc_cols([tCtS_fake, tCtO_fake], arch="sm_100")
|
||||
|
||||
a_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
|
||||
b_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
|
||||
v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0))
|
||||
self.num_tma_load_bytes = (
|
||||
cute.size_in_bytes(self.q_dtype, a_smem) + cute.size_in_bytes(self.q_dtype, b_smem) +
|
||||
cute.size_in_bytes(self.q_dtype, v_smem)
|
||||
) * 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()
|
||||
self.v_major = LayoutEnum.from_tensor(v).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, self.mma_tiler_mn, 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, 16), tcgen05.OperandSource.TMEM)
|
||||
self._setup(qk_mma, pv_mma)
|
||||
|
||||
q_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
|
||||
k_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
|
||||
v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0))
|
||||
tma_q, tma_tq = 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_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
|
||||
tma_k, tma_tk = 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_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
|
||||
tma_v, tma_tv = cute.nvgpu.make_tiled_tma_atom_B(
|
||||
utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn, pv_mma.thr_id),
|
||||
v, v_smem, self.pv_mma_tiler, pv_mma, self.cluster_layout_vmnk.shape)
|
||||
epi_smem = cute.select(self.c_smem_s, mode=[0, 1])
|
||||
tma_c, tma_tc = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(), c, epi_smem, self.epi_tile)
|
||||
|
||||
self._kernel(qk_mma, pv_mma, tma_q, tma_tq, tma_k, tma_tk, tma_v, tma_tv,
|
||||
tma_c, tma_tc, 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()
|
||||
use_2cta = cute.size(qk_mma.thr_id.shape) == 2
|
||||
|
||||
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()
|
||||
|
||||
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) * (2 if use_2cta else 1)),
|
||||
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=use_2cta,
|
||||
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.select(self.pv_mma_tiler, mode=[0,1]), (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)
|
||||
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))
|
||||
tAgQ = tAgQ[(None,0,None,0)]; tBgK = tBgK[(None,0,None,0)]
|
||||
|
||||
tCgV = pv_thr.partition_B(gV)
|
||||
tVsV, tVgV = cpasync.tma_partition(tma_v, 0, b_lay, cute.group_modes(sV,0,3), cute.group_modes(tCgV,0,3))
|
||||
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_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_acc_shape)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
|
||||
pv_acc_shape = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_acc_shape)
|
||||
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_acc_shape, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_acc_shape, self.num_acc_stage))
|
||||
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# ═══ TMA LOAD WARP ═══
|
||||
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 WARP ═══
|
||||
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_prod_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
|
||||
acc_pipe.producer_acquire(acc_prod_st)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
for kt in range(k_cnt):
|
||||
h = ab_c.wait_and_advance(peek)
|
||||
nblk = cute.size(tCrQ, mode=[2])
|
||||
for kb in cutlass.range(nblk, 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()
|
||||
s0_handle = mma_si_prod.acquire_and_advance()
|
||||
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
tCrV_s = tCrV[(None, None, None, 0)]
|
||||
nblk_pv = cute.size(tOrP0, mode=[2])
|
||||
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 using PV accumulator
|
||||
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 = 128, 128
|
||||
q = torch.randn(m, 128, 1, dtype=torch.bfloat16, device='cuda')
|
||||
k = torch.randn(n, 128, 1, dtype=torch.bfloat16, device='cuda')
|
||||
# V = first 16 rows of 128x128 identity, MN-major
|
||||
v = torch.eye(128, dtype=torch.bfloat16, device='cuda')[:16, :]
|
||||
v = v.as_strided((16, 128), (1, 16)).unsqueeze(-1)
|
||||
c = torch.zeros(m, 16, 1, dtype=torch.bfloat16, device='cuda')
|
||||
|
||||
qf = q[:,:,0].float(); kf = k[:,:,0].float()
|
||||
# V=I[:16,:] → P@V = P[:,:16] (first 16 cols of (Q@K^T).bf16())
|
||||
ref = (qf @ kf.T).bfloat16()[:, :16].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 = PvDiag16Kernel(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(f'(128,16) PV MMA: cosine {cos:.6f} {"PASS" if cos >= 0.99 else "FAIL"}')
|
||||
if cos < 0.5:
|
||||
print(f' out max={out.max().item():.4f} min={out.min().item():.4f}')
|
||||
print(f' ref max={ref.max().item():.4f} min={ref.min().item():.4f}')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test()
|
||||
415
tests/test_stage_b_ntile_v5.py
Normal file
415
tests/test_stage_b_ntile_v5.py
Normal file
@@ -0,0 +1,415 @@
|
||||
"""
|
||||
Stage B N-tiling v5: (128,16) PV MMA with proper FMHA-style output.
|
||||
|
||||
TMEM → register → SMEM → TMA → GMEM. No shortcuts. Blackwell all the way.
|
||||
|
||||
V = I[:16,:] → output = (Q@K^T).bf16()[:,:16]
|
||||
pv_mma_tiler = (128, 16, 128). K=128 matches QK's N → C/A bridge preserved.
|
||||
"""
|
||||
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
|
||||
|
||||
|
||||
class PvDiag16V5Kernel:
|
||||
def __init__(self, mma_tiler_mn):
|
||||
self.acc_dtype = Float32; self.qk_acc_dtype = Float32
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
self.mma_tiler_mn = mma_tiler_mn; self.mma_tiler = (*mma_tiler_mn, 1)
|
||||
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.threads_per_warp = 32
|
||||
|
||||
def _setup(self, qk_mma, pv_mma):
|
||||
qk_inst_k = cute.size(qk_mma.shape_mnk, mode=[2])
|
||||
self.qk_mma_tiler = (*self.mma_tiler_mn, qk_inst_k * 4)
|
||||
self.pv_mma_tiler = (self.qk_mma_tiler[0], qk_inst_k, self.qk_mma_tiler[1])
|
||||
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,))
|
||||
|
||||
# FMHA: epi_tile = pv_mma_tiler[:2]
|
||||
self.epi_tile = self.pv_mma_tiler[:2] # (128, 16)
|
||||
self.c_layout = LayoutEnum.ROW_MAJOR
|
||||
self.num_ab_stage = 1; self.num_acc_stage = 1
|
||||
|
||||
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, self.num_c_stage)
|
||||
|
||||
qk_thr = qk_mma.get_slice(0)
|
||||
qk_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_acc_shape)
|
||||
s_cols = find_tmem_tensor_col_offset(tStS)
|
||||
pv_thr = pv_mma.get_slice(0)
|
||||
pv_acc_shape = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_acc_shape)
|
||||
o_cols = find_tmem_tensor_col_offset(tOtO)
|
||||
|
||||
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 = s_cols
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_acc_shape, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_acc_shape, self.num_acc_stage))
|
||||
self.num_tmem_alloc_cols = utils.get_num_tmem_alloc_cols([tCtS_fake, tCtO_fake], arch="sm_100")
|
||||
|
||||
a_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
|
||||
b_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
|
||||
v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0))
|
||||
self.num_tma_load_bytes = (
|
||||
cute.size_in_bytes(self.q_dtype, a_smem) + cute.size_in_bytes(self.q_dtype, b_smem) +
|
||||
cute.size_in_bytes(self.q_dtype, v_smem)
|
||||
) * 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()
|
||||
self.v_major = LayoutEnum.from_tensor(v).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, self.mma_tiler_mn, 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, 16), tcgen05.OperandSource.TMEM)
|
||||
self._setup(qk_mma, pv_mma)
|
||||
|
||||
q_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
|
||||
k_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
|
||||
v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0))
|
||||
o_smem = cute.select(self.c_smem_s, mode=[0, 1])
|
||||
|
||||
tma_q, tma_tq = 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_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
|
||||
tma_k, tma_tk = 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_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
|
||||
tma_v, tma_tv = cute.nvgpu.make_tiled_tma_atom_B(
|
||||
utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn, pv_mma.thr_id),
|
||||
v, v_smem, self.pv_mma_tiler, pv_mma, self.cluster_layout_vmnk.shape)
|
||||
# TMA store for output — FMHA pattern: cpasync.make_tiled_tma_atom
|
||||
tma_o, tma_to = cpasync.make_tiled_tma_atom(
|
||||
cpasync.CopyBulkTensorTileS2GOp(), c, o_smem, self.epi_tile)
|
||||
|
||||
self._kernel(qk_mma, pv_mma, tma_q, tma_tq, tma_k, tma_tk, tma_v, tma_tv,
|
||||
tma_o, tma_to, self.cluster_layout_vmnk,
|
||||
self.a_smem_s, self.b_smem_s, self.v_smem_s, self.p_tmem_s, self.c_smem_s
|
||||
).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_o, mO, cl_vmnk, a_smem_s, b_smem_s, v_smem_s, p_tmem_s, c_smem_s):
|
||||
warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx())
|
||||
tidx, _, _ = cute.arch.thread_idx()
|
||||
use_2cta = cute.size(qk_mma.thr_id.shape) == 2
|
||||
|
||||
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_o)
|
||||
|
||||
@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()
|
||||
|
||||
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) * (2 if use_2cta else 1)),
|
||||
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=use_2cta,
|
||||
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)
|
||||
sO = 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))
|
||||
k_cnt = cute.size(gQ, mode=[3])
|
||||
|
||||
# Output TMA partition — FMHA pattern: flat_divide + group_modes
|
||||
gO_qdl = cute.flat_divide(mO, cute.select(self.pv_mma_tiler, mode=[0, 1]))
|
||||
gO = gO_qdl[None, None, None, 0, 0] # single tile, coord (0,0)
|
||||
o_smem_layout = cute.select(c_smem_s, mode=[0, 1])
|
||||
tOsO, tOgO = cpasync.tma_partition(
|
||||
tma_o, 0, cute.make_layout(1),
|
||||
cute.group_modes(sO, 0, 2),
|
||||
cute.group_modes(gO, 0, 2))
|
||||
|
||||
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)
|
||||
|
||||
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)]
|
||||
|
||||
tVsV, tVgV = cpasync.tma_partition(tma_v, 0, b_lay, cute.group_modes(sV,0,3), cute.group_modes(tCgV,0,3))
|
||||
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_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_acc_shape)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
|
||||
pv_acc_shape = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_acc_shape)
|
||||
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_acc_shape, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_acc_shape, self.num_acc_stage))
|
||||
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# ═══ TMA LOAD WARP ═══
|
||||
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 WARP ═══
|
||||
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_prod_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
|
||||
acc_pipe.producer_acquire(acc_prod_st)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
for kt in range(k_cnt):
|
||||
h = ab_c.wait_and_advance(peek)
|
||||
nblk = cute.size(tCrQ, mode=[2])
|
||||
for kb in cutlass.range(nblk, 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()
|
||||
s0_handle = mma_si_prod.acquire_and_advance()
|
||||
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
tCrV_s = tCrV[(None, None, None, 0)]
|
||||
nblk_pv = cute.size(tOrP0, mode=[2])
|
||||
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))
|
||||
|
||||
# ─── Softmax packing: FP32 S → BF16 P ───
|
||||
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()
|
||||
|
||||
# ─── PV output epilogue: TMEM → register → SMEM → TMA → GMEM ───
|
||||
# Wait for PV MMA to finish
|
||||
acc_cons_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Consumer, self.num_acc_stage)
|
||||
acc_pipe.consumer_wait(acc_cons_st)
|
||||
|
||||
# FMHA correction_epilog: TMEM → register → SMEM
|
||||
corr_tile_size = 16 # 32*8//o_dtype.width for BF16
|
||||
tOtO_i = cute.logical_divide(tOtO0, cute.make_layout((128, corr_tile_size)))
|
||||
|
||||
pv_tiled_mma_shape = (self.pv_mma_tiler[0], self.pv_mma_tiler[1])
|
||||
cO = cute.make_identity_tensor(pv_tiled_mma_shape)
|
||||
tOcO = pv_thr.partition_C(cO)
|
||||
tOcO_i = cute.logical_divide(tOcO, cute.make_layout((128, corr_tile_size)))
|
||||
tOsO_e = pv_thr.partition_C(sO)
|
||||
tOsO_i = cute.logical_divide(tOsO_e, cute.make_layout((128, corr_tile_size)))
|
||||
|
||||
thread_idx = tidx % (self.threads_per_warp * len(self.epilogue_warp_id))
|
||||
|
||||
# TMEM load atom for O (correction epilog pattern)
|
||||
epi_subtile = (self.epi_tile[0], corr_tile_size)
|
||||
tmem_load_o_atom = cute.make_copy_atom(
|
||||
tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(16)), self.qk_acc_dtype)
|
||||
tiled_tmem_load_o = tcgen05.make_tmem_copy(tmem_load_o_atom, tOtO_i[(None, None), 0])
|
||||
thr_tmem_load_o = tiled_tmem_load_o.get_slice(thread_idx)
|
||||
|
||||
# SMEM store atom (FMHA: get_smem_store_op)
|
||||
smem_copy_atom = utils.sm100.get_smem_store_op(
|
||||
self.c_layout, self.o_dtype, self.qk_acc_dtype, tiled_tmem_load_o)
|
||||
tiled_smem_store = cute.make_tiled_copy_D(smem_copy_atom, tiled_tmem_load_o)
|
||||
|
||||
tTMEM_LOADtO = thr_tmem_load_o.partition_S(tOtO_i[(None, None), None])
|
||||
tTMEM_LOADsO = thr_tmem_load_o.partition_D(tOsO_i[(None, None), None])
|
||||
tTMEM_LOADoO = thr_tmem_load_o.partition_D(tOcO_i[(None, None), None])
|
||||
|
||||
# Loop over correction subtiles
|
||||
for i in range(self.pv_mma_tiler[1] // corr_tile_size):
|
||||
tTMEM_LOADtO_i = tTMEM_LOADtO[None, 0, 0, i]
|
||||
tTMEM_LOADsO_i = tTMEM_LOADsO[None, 0, 0, i]
|
||||
tTMrO = cute.make_rmem_tensor(tTMEM_LOADoO[None, 0, 0, i].shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load_o, tTMEM_LOADtO_i, tTMrO)
|
||||
cute.arch.fence_view_async_tmem_load()
|
||||
# Cast FP32 → BF16
|
||||
tSMrO = cute.make_rmem_tensor(tTMrO.shape, self.o_dtype)
|
||||
o_vec = tTMrO.load()
|
||||
tSMrO.store(o_vec.to(self.o_dtype))
|
||||
# Store to SMEM
|
||||
cute.copy(tiled_smem_store, tSMrO, tTMEM_LOADsO_i)
|
||||
|
||||
# Fence SMEM writes, then barrier so all epilogue warps are done writing
|
||||
cute.arch.fence_proxy("async.shared", space="cta")
|
||||
epilog_sync_barrier = pipeline.NamedBarrier(
|
||||
barrier_id=self.epilog_sync_bar_id,
|
||||
num_threads=32 * len(self.epilogue_warp_id))
|
||||
epilog_sync_barrier.arrive_and_wait()
|
||||
|
||||
# TMA store: SMEM → GMEM (FMHA pattern)
|
||||
cute.copy(tma_o, tOsO[None, 0], tOgO[None, 0])
|
||||
cute.arch.cp_async_bulk_commit_group()
|
||||
cute.arch.cp_async_bulk_wait_group(0, read=True)
|
||||
|
||||
acc_pipe.consumer_release(acc_cons_st)
|
||||
tmem.relinquish_alloc_permit()
|
||||
tmem.free(tmem_ptr)
|
||||
|
||||
|
||||
def test():
|
||||
torch.manual_seed(42)
|
||||
m, n = 128, 128
|
||||
q = torch.randn(m, 128, 1, dtype=torch.bfloat16, device='cuda')
|
||||
k = torch.randn(n, 128, 1, dtype=torch.bfloat16, device='cuda')
|
||||
# V = first 16 rows of identity, MN-major
|
||||
v = torch.eye(128, dtype=torch.bfloat16, device='cuda')[:16, :]
|
||||
v = v.as_strided((16, 128), (1, 16)).unsqueeze(-1)
|
||||
c = torch.zeros(m, 16, 1, dtype=torch.bfloat16, device='cuda')
|
||||
|
||||
qf = q[:,:,0].float(); kf = k[:,:,0].float()
|
||||
# V=I[:16,:] → P@V = P[:,:16] (first 16 cols of (Q@K^T).bf16())
|
||||
ref = (qf @ kf.T).bfloat16()[:, :16].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 = PvDiag16V5Kernel(mma_tiler_mn=(128, 128))
|
||||
print('Compiling v5...', flush=True)
|
||||
compiled = cute.compile(kernel, mQ, mK, mV, mC, stream)
|
||||
print('Running v5...', 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'(128,16) PV MMA v5: cosine {cos:.6f} {"PASS" if cos >= 0.99 else "FAIL"}')
|
||||
if cos < 0.5:
|
||||
print(f' out max={out.max().item():.4f} min={out.min().item():.4f}')
|
||||
print(f' ref max={ref.max().item():.4f} min={ref.min().item():.4f}')
|
||||
print(f' out[:4,:4]:\n{out[:4,:4]}')
|
||||
print(f' ref[:4,:4]:\n{ref[:4,:4]}')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test()
|
||||
400
tests/test_stage_b_ntile_v6.py
Normal file
400
tests/test_stage_b_ntile_v6.py
Normal file
@@ -0,0 +1,400 @@
|
||||
"""
|
||||
Stage B N-tiling v6: Run BOTH (128,128) and (128,16) PV MMAs in the same kernel.
|
||||
Use the working (128,128) epilogue for output. Compare first 16 columns.
|
||||
|
||||
This tests whether the (128,16) PV MMA's C→A TMEM bridge is correct
|
||||
without needing a (128,16) output epilogue (which is broken).
|
||||
The (128,16) result is written to a separate TMEM region, then
|
||||
manually padded and copied to the (128,128) output buffer.
|
||||
"""
|
||||
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
|
||||
|
||||
|
||||
class DualPvKernel:
|
||||
"""Run QK → softmax → PV(128,128) and PV(128,16) side by side.
|
||||
Output PV(128,128) result using working epilogue_tma_store.
|
||||
"""
|
||||
def __init__(self, mma_tiler_mn):
|
||||
self.acc_dtype = Float32; self.qk_acc_dtype = Float32
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
self.mma_tiler_mn = mma_tiler_mn; self.mma_tiler = (*mma_tiler_mn, 1)
|
||||
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
|
||||
|
||||
def _setup(self, qk_mma, pv128_mma, pv16_mma):
|
||||
qk_inst_k = cute.size(qk_mma.shape_mnk, mode=[2])
|
||||
self.qk_mma_tiler = (*self.mma_tiler_mn, qk_inst_k * 4)
|
||||
# PV(128,128): same as v30
|
||||
self.pv128_mma_tiler = (self.qk_mma_tiler[0], self.qk_mma_tiler[1], self.qk_mma_tiler[1])
|
||||
# PV(128,16): N-tiling with N=16
|
||||
self.pv16_mma_tiler = (self.qk_mma_tiler[0], qk_inst_k, self.qk_mma_tiler[1])
|
||||
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),
|
||||
self.qk_mma_tiler[1], 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.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(pv128_mma, self.pv128_mma_tiler, self.q_dtype, 1)
|
||||
self.p_tmem_s = utils.sm100.make_smem_layout_a(pv128_mma, self.pv128_mma_tiler, self.q_dtype, 1)
|
||||
self.p16_tmem_s = utils.sm100.make_smem_layout_a(pv16_mma, self.pv16_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_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_acc_shape)
|
||||
s_cols = find_tmem_tensor_col_offset(tStS)
|
||||
|
||||
pv128_thr = pv128_mma.get_slice(0)
|
||||
pv128_acc_shape = pv128_thr.partition_shape_C(self.pv128_mma_tiler[:2])
|
||||
tOtO = pv128_thr.make_fragment_C(pv128_acc_shape)
|
||||
o_cols = find_tmem_tensor_col_offset(tOtO)
|
||||
|
||||
# PV(128,16) accumulator
|
||||
pv16_thr = pv16_mma.get_slice(0)
|
||||
pv16_acc_shape = pv16_thr.partition_shape_C(self.pv16_mma_tiler[:2])
|
||||
tOtO16 = pv16_thr.make_fragment_C(pv16_acc_shape)
|
||||
o16_cols = find_tmem_tensor_col_offset(tOtO16)
|
||||
|
||||
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 = s_cols # PV(128,128) accumulator
|
||||
self.tmem_o16_offset = o_cols # PV(128,16) accumulator (separate TMEM)
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_acc_shape, self.num_acc_stage))
|
||||
tCtO_fake = pv128_mma.make_fragment_C(cute.append(pv128_acc_shape, self.num_acc_stage))
|
||||
tCtO16_fake = pv16_mma.make_fragment_C(cute.append(pv16_acc_shape, self.num_acc_stage))
|
||||
self.num_tmem_alloc_cols = utils.get_num_tmem_alloc_cols([tCtS_fake, tCtO_fake, tCtO16_fake], arch="sm_100")
|
||||
|
||||
a_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
|
||||
b_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
|
||||
v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0))
|
||||
self.num_tma_load_bytes = (
|
||||
cute.size_in_bytes(self.q_dtype, a_smem) + cute.size_in_bytes(self.q_dtype, b_smem) +
|
||||
cute.size_in_bytes(self.q_dtype, v_smem)
|
||||
) * 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()
|
||||
self.v_major = LayoutEnum.from_tensor(v).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, self.mma_tiler_mn, tcgen05.OperandSource.SMEM)
|
||||
pv128_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, self.mma_tiler_mn, tcgen05.OperandSource.TMEM)
|
||||
pv16_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, 16), tcgen05.OperandSource.TMEM)
|
||||
self._setup(qk_mma, pv128_mma, pv16_mma)
|
||||
|
||||
q_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
|
||||
k_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
|
||||
v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0))
|
||||
tma_q, tma_tq = 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_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
|
||||
tma_k, tma_tk = 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_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
|
||||
tma_v, tma_tv = cute.nvgpu.make_tiled_tma_atom_B(
|
||||
utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn, pv128_mma.thr_id),
|
||||
v, v_smem, self.pv128_mma_tiler, pv128_mma, self.cluster_layout_vmnk.shape)
|
||||
epi_smem = cute.select(self.c_smem_s, mode=[0, 1])
|
||||
tma_c, tma_tc = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(), c, epi_smem, self.epi_tile)
|
||||
|
||||
self._kernel(qk_mma, pv128_mma, pv16_mma, tma_q, tma_tq, tma_k, tma_tk, tma_v, tma_tv,
|
||||
tma_c, tma_tc, self.cluster_layout_vmnk,
|
||||
self.a_smem_s, self.b_smem_s, self.v_smem_s, self.p_tmem_s, self.p16_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, pv128_mma, pv16_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, p16_tmem_s, c_smem_s, epi_tile):
|
||||
warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx())
|
||||
tidx, _, _ = cute.arch.thread_idx()
|
||||
use_2cta = cute.size(qk_mma.thr_id.shape) == 2
|
||||
|
||||
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()
|
||||
|
||||
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) * (2 if use_2cta else 1)),
|
||||
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=use_2cta,
|
||||
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))
|
||||
gC = cute.local_tile(mC, cute.slice_(self.qk_mma_tiler, (None,None,0)), (None,None,None))
|
||||
k_cnt = cute.size(gQ, mode=[3])
|
||||
|
||||
qk_thr = qk_mma.get_slice(0)
|
||||
pv128_thr = pv128_mma.get_slice(0)
|
||||
pv16_thr = pv16_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)]
|
||||
|
||||
gV = cute.local_tile(mV, cute.slice_(self.pv128_mma_tiler, (0,None,None)), (None,None,None))
|
||||
tCgV = pv128_thr.partition_B(gV)
|
||||
tVsV, tVgV = cpasync.tma_partition(tma_v, 0, b_lay, cute.group_modes(sV,0,3), cute.group_modes(tCgV,0,3))
|
||||
tVgV = tVgV[(None,0,None,0)]
|
||||
|
||||
tCrQ = qk_mma.make_fragment_A(sQ); tCrK = qk_mma.make_fragment_B(sK)
|
||||
tCrV = pv128_mma.make_fragment_B(sV)
|
||||
|
||||
# QK accumulator
|
||||
qk_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_acc_shape)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
|
||||
# PV(128,128) accumulator
|
||||
pv128_acc_shape = pv128_thr.partition_shape_C(self.pv128_mma_tiler[:2])
|
||||
tOtO = pv128_thr.make_fragment_C(pv128_acc_shape)
|
||||
tOtO0 = cute.make_tensor(tOtO.iterator + self.tmem_o0_offset, tOtO.layout)
|
||||
|
||||
# PV(128,16) accumulator
|
||||
pv16_acc_shape = pv16_thr.partition_shape_C(self.pv16_mma_tiler[:2])
|
||||
tOtO16 = pv16_thr.make_fragment_C(pv16_acc_shape)
|
||||
tOtO16_0 = cute.make_tensor(tOtO16.iterator + self.tmem_o16_offset, tOtO16.layout)
|
||||
|
||||
# P A-fragment for PV(128,128) — reads from softmax P in TMEM
|
||||
tP = cute.make_tensor(tStS.iterator, p_tmem_s.outer)
|
||||
tOrP_base = pv128_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)
|
||||
|
||||
# P A-fragment for PV(128,16) — reads from the SAME softmax P in TMEM
|
||||
tP16 = cute.make_tensor(tStS.iterator, p16_tmem_s.outer)
|
||||
tOrP16_base = pv16_thr.make_fragment_A(tP16)
|
||||
tOrP16 = tOrP16_base[(None, None, None, 0)]
|
||||
tOrP16_0 = cute.make_tensor(
|
||||
tOrP16.iterator + self.qk_acc_dtype.width // self.q_dtype.width * self.tmem_p0_offset,
|
||||
tOrP16.layout)
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_acc_shape, self.num_acc_stage))
|
||||
tCtO_fake = pv128_mma.make_fragment_C(cute.append(pv128_acc_shape, self.num_acc_stage))
|
||||
tCtO16_fake = pv16_mma.make_fragment_C(cute.append(pv16_acc_shape, self.num_acc_stage))
|
||||
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# ═══ TMA LOAD WARP ═══
|
||||
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 WARP ═══
|
||||
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_prod_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
|
||||
acc_pipe.producer_acquire(acc_prod_st)
|
||||
|
||||
# QK MMA
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
for kt in range(k_cnt):
|
||||
h = ab_c.wait_and_advance(peek)
|
||||
nblk = cute.size(tCrQ, mode=[2])
|
||||
for kb in cutlass.range(nblk, 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()
|
||||
s0_handle = mma_si_prod.acquire_and_advance()
|
||||
|
||||
# PV(128,128) MMA: P @ V=I → O = P
|
||||
pv128_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
tCrV_s = tCrV[(None, None, None, 0)]
|
||||
nblk_pv = cute.size(tOrP0, mode=[2])
|
||||
for kb in cutlass.range(nblk_pv, unroll_full=True):
|
||||
cute.gemm(pv128_mma, tOtO0, tOrP0[(None,None,kb)], tCrV_s[(None,None,kb)], tOtO0)
|
||||
pv128_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
|
||||
# PV(128,16) MMA: P @ V[:16,:] → O16 = P[:,:16]
|
||||
# Note: PV(128,16) reads P from the same TMEM location as PV(128,128)
|
||||
# but the A-fragment is for (128,16) PV MMA which has different layout
|
||||
pv16_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
for kb in cutlass.range(cute.size(tOrP16_0, mode=[2]), unroll_full=True):
|
||||
cute.gemm(pv16_mma, tOtO16_0, tOrP16_0[(None,None,kb)], tCrV_s[(None,None,kb)], tOtO16_0)
|
||||
pv16_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))
|
||||
|
||||
# Softmax packing: FP32 S → BF16 P
|
||||
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: PV(128,128) result using working 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 = 128, 128
|
||||
q = torch.randn(m, 128, 1, dtype=torch.bfloat16, device='cuda')
|
||||
k = torch.randn(n, 128, 1, dtype=torch.bfloat16, device='cuda')
|
||||
# V = identity (128,128) MN-major — same as v30
|
||||
v = torch.eye(128, dtype=torch.bfloat16, device='cuda')
|
||||
v = v.as_strided((128, 128), (1, 128)).unsqueeze(-1)
|
||||
c = torch.zeros(m, 128, 1, dtype=torch.bfloat16, device='cuda')
|
||||
|
||||
qf = q[:,:,0].float(); kf = k[:,:,0].float()
|
||||
# V=I → O = P = (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 = DualPvKernel(mma_tiler_mn=(128, 128))
|
||||
print('Compiling dual-PV test...', 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(f'PV(128,128) output: cosine {cos:.6f} {"PASS" if cos >= 0.99 else "FAIL"}')
|
||||
# If PV(128,128) still works, the QK+softmax pipeline is fine
|
||||
# The PV(128,16) result is in TMEM at tmem_o16_offset but not yet output
|
||||
# We need to read it to compare
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test()
|
||||
376
tests/test_stage_b_ntile_v7.py
Normal file
376
tests/test_stage_b_ntile_v7.py
Normal file
@@ -0,0 +1,376 @@
|
||||
"""
|
||||
Stage B N-tiling v7: PV(128,16) single N-tile test (head_dim=16).
|
||||
|
||||
Based on test_pv_diag (128,128 PV) with minimal changes:
|
||||
- PV MMA tiler: (128, 16, 128) instead of (128, 128, 128)
|
||||
- V: (16, 128) identity MN-major instead of (128, 128)
|
||||
- Output: (128, 16) instead of (128, 128)
|
||||
- epi_tile uses PV's cta_tile_shape_mnk for compute_epilogue_tile_shape
|
||||
|
||||
With V=I(16,128): O = P[:, :16] = (Q@K^T).bf16()[:, :16]
|
||||
"""
|
||||
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
|
||||
|
||||
|
||||
class NTileV7Kernel:
|
||||
def __init__(self, mma_tiler_mn):
|
||||
self.acc_dtype = Float32; self.qk_acc_dtype = Float32
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
self.mma_tiler_mn = mma_tiler_mn; self.mma_tiler = (*mma_tiler_mn, 1)
|
||||
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
|
||||
|
||||
def _setup(self, qk_mma, pv_mma):
|
||||
qk_inst_k = cute.size(qk_mma.shape_mnk, mode=[2])
|
||||
self.qk_mma_tiler = (*self.mma_tiler_mn, qk_inst_k * 4)
|
||||
# PV with N=16 (single N-tile, head_dim=16)
|
||||
self.pv_mma_tiler = (self.qk_mma_tiler[0], self.qk_mma_tiler[1], self.qk_mma_tiler[1])
|
||||
# pv_mma_tiler = (128, 128, 128) — same as QK, but PV MMA is (128, 16) with TMEM source
|
||||
# Wait, pv_mma_tiler must match the PV MMA shape, not QK's.
|
||||
# For PV(128,16): pv_mma_tiler = (M_pv, N_pv, K_pv) = (128, 16, 128)
|
||||
# In fmha: pv_mma_tiler = (qk_mma_tiler[0], qk_mma_tiler[2], qk_mma_tiler[1])
|
||||
# = (128, inst_k*4, 128) — but we want N=16, not inst_k*4.
|
||||
# The pv_mma_tiler is used for V SMEM layout and TMA, NOT for the MMA atom.
|
||||
# The actual MMA atom is (128, 16) set in make_trivial_tiled_mma.
|
||||
# pv_mma_tiler must match: (128, 16, 128) for the B-operand to tile V as (16, 128).
|
||||
self.pv_mma_tiler = (self.qk_mma_tiler[0], 16, self.qk_mma_tiler[1])
|
||||
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),
|
||||
self.qk_mma_tiler[1], self.qk_mma_tiler[2])
|
||||
self.c_layout = LayoutEnum.ROW_MAJOR
|
||||
# epi_tile must match PV's output shape (128, 16)
|
||||
self.pv_cta_tile_shape_mnk = (
|
||||
self.pv_mma_tiler[0] // cute.size(qk_mma.thr_id.shape),
|
||||
self.pv_mma_tiler[1], self.pv_mma_tiler[2])
|
||||
self.epi_tile = utils.sm100.compute_epilogue_tile_shape(
|
||||
self.pv_cta_tile_shape_mnk, False, self.c_layout, self.o_dtype)
|
||||
self.num_ab_stage = 1; self.num_acc_stage = 1
|
||||
|
||||
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_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_acc_shape)
|
||||
s_cols = find_tmem_tensor_col_offset(tStS)
|
||||
pv_thr = pv_mma.get_slice(0)
|
||||
pv_acc_shape = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_acc_shape)
|
||||
o_cols = find_tmem_tensor_col_offset(tOtO)
|
||||
|
||||
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 = s_cols
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_acc_shape, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_acc_shape, self.num_acc_stage))
|
||||
self.num_tmem_alloc_cols = utils.get_num_tmem_alloc_cols([tCtS_fake, tCtO_fake], arch="sm_100")
|
||||
|
||||
a_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
|
||||
b_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
|
||||
v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0))
|
||||
self.num_tma_load_bytes = (
|
||||
cute.size_in_bytes(self.q_dtype, a_smem) + cute.size_in_bytes(self.q_dtype, b_smem) +
|
||||
cute.size_in_bytes(self.q_dtype, v_smem)
|
||||
) * 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()
|
||||
self.v_major = LayoutEnum.from_tensor(v).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, self.mma_tiler_mn, tcgen05.OperandSource.SMEM)
|
||||
# PV with (128, 16) output, P from TMEM
|
||||
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, 16), tcgen05.OperandSource.TMEM)
|
||||
self._setup(qk_mma, pv_mma)
|
||||
|
||||
q_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
|
||||
k_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
|
||||
v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0))
|
||||
tma_q, tma_tq = 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_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
|
||||
tma_k, tma_tk = 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_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
|
||||
tma_v, tma_tv = cute.nvgpu.make_tiled_tma_atom_B(
|
||||
utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn, pv_mma.thr_id),
|
||||
v, v_smem, self.pv_mma_tiler, pv_mma, self.cluster_layout_vmnk.shape)
|
||||
epi_smem = cute.select(self.c_smem_s, mode=[0, 1])
|
||||
tma_c, tma_tc = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(), c, epi_smem, self.epi_tile)
|
||||
|
||||
self._kernel(qk_mma, pv_mma, tma_q, tma_tq, tma_k, tma_tk, tma_v, tma_tv,
|
||||
tma_c, tma_tc, 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()
|
||||
use_2cta = cute.size(qk_mma.thr_id.shape) == 2
|
||||
|
||||
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()
|
||||
|
||||
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) * (2 if use_2cta else 1)),
|
||||
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=use_2cta,
|
||||
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))
|
||||
gC = cute.local_tile(mC, cute.slice_(self.qk_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); 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)]
|
||||
|
||||
gV = cute.local_tile(mV, cute.slice_(self.pv_mma_tiler, (0,None,None)), (None,None,None))
|
||||
tCgV = pv_thr.partition_B(gV)
|
||||
tVsV, tVgV = cpasync.tma_partition(tma_v, 0, b_lay, cute.group_modes(sV,0,3), cute.group_modes(tCgV,0,3))
|
||||
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_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_acc_shape)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
|
||||
pv_acc_shape = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_acc_shape)
|
||||
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_acc_shape, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_acc_shape, self.num_acc_stage))
|
||||
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# ═══ TMA LOAD WARP ═══
|
||||
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 WARP ═══
|
||||
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_prod_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
|
||||
acc_pipe.producer_acquire(acc_prod_st)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
for kt in range(k_cnt):
|
||||
h = ab_c.wait_and_advance(peek)
|
||||
nblk = cute.size(tCrQ, mode=[2])
|
||||
for kb in cutlass.range(nblk, 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()
|
||||
s0_handle = mma_si_prod.acquire_and_advance()
|
||||
|
||||
# PV MMA: P @ V where V=I(16,128) → O = P[:, :16]
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
tCrV_s = tCrV[(None, None, None, 0)]
|
||||
nblk_pv = cute.size(tOrP0, mode=[2])
|
||||
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 — use PV's cta_tile for the 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)
|
||||
# Must use PV's cta_tile_shape_mnk for the epilogue
|
||||
saved = self.cta_tile_shape_mnk
|
||||
self.cta_tile_shape_mnk = self.pv_cta_tile_shape_mnk
|
||||
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)
|
||||
self.cta_tile_shape_mnk = saved
|
||||
c_pipe.producer_tail()
|
||||
tmem.relinquish_alloc_permit()
|
||||
tmem.free(tmem_ptr)
|
||||
|
||||
|
||||
def test():
|
||||
torch.manual_seed(42)
|
||||
m, n, head_dim = 128, 128, 16
|
||||
q = torch.randn(m, 128, 1, dtype=torch.bfloat16, device='cuda')
|
||||
k = torch.randn(n, 128, 1, dtype=torch.bfloat16, device='cuda')
|
||||
# V = identity (16, 128) MN-major: strides (1, 16)
|
||||
v_data = torch.zeros(16 * 128, dtype=torch.bfloat16, device='cuda')
|
||||
for i in range(16):
|
||||
v_data[i + 16 * i] = 1.0
|
||||
v = torch.as_strided(v_data, (16, 128), (1, 16)).unsqueeze(-1)
|
||||
c = torch.zeros(m, 16, 1, dtype=torch.bfloat16, device='cuda')
|
||||
|
||||
qf = q[:,:,0].float(); kf = k[:,:,0].float()
|
||||
# With V=I(16,128) MN-major: O = P[:, :16] = (Q@K^T).bf16()[:, :16]
|
||||
ref = (qf @ kf.T).bfloat16().float()
|
||||
ref_out = ref[:, :16]
|
||||
|
||||
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 = NTileV7Kernel(mma_tiler_mn=(128, 128))
|
||||
print('Compiling PV(128,16) single-tile test...', 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_out.flatten().unsqueeze(0)).item()
|
||||
print('PV(128,16) single-tile: cosine {:.6f} {}'.format(cos, 'PASS' if cos >= 0.99 else 'FAIL'))
|
||||
if cos < 0.99:
|
||||
print(' out[0,:8] =', out[0,:8].tolist())
|
||||
print(' ref[0,:8] =', ref_out[0,:8].tolist())
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test()
|
||||
387
tests/test_stage_b_ntile_v8.py
Normal file
387
tests/test_stage_b_ntile_v8.py
Normal file
@@ -0,0 +1,387 @@
|
||||
"""
|
||||
Minimal PV-only test: Load P from GMEM to TMEM via QK-style MMA, then PV from TMEM.
|
||||
Step 1: QK MMA writes FP32 S to TMEM (we know this works)
|
||||
Step 2: Softmax packing writes BF16 P to TMEM (test this)
|
||||
Step 3: PV MMA reads BF16 P from TMEM and V from SMEM, produces O
|
||||
|
||||
But to isolate the bug, let me test just the PV MMA in isolation.
|
||||
I'll write known BF16 values to TMEM using the softmax packing path,
|
||||
then immediately read them back using the PV A-fragment path,
|
||||
and compare.
|
||||
|
||||
Actually, the simplest isolation test:
|
||||
1. Do QK MMA to get S in TMEM (cosine 0.999999 verified)
|
||||
2. Do softmax packing: S → P in TMEM (at offset 32)
|
||||
3. Skip PV entirely — read P from TMEM using the C-fragment composition LOAD path
|
||||
4. Output P to GMEM and compare against S.to(BF16)
|
||||
|
||||
This tests whether the softmax packing writes P correctly to the same TMEM
|
||||
that the PV would read from.
|
||||
|
||||
But we can't easily read P from TMEM using the standard epilogue path
|
||||
because the epilogue expects FP32 accumulator data.
|
||||
|
||||
Alternative: Use the PV MMA with V=I (identity). If P is correct,
|
||||
then P @ I = P. But V needs to be MN-major and (128, 128), not (128, 64).
|
||||
The output would be (128, 128) which doesn't match our (128, 64) c tensor.
|
||||
|
||||
Let me use V that selects the first 64 columns: V[k, n] = delta(k, n) for k in [0,63].
|
||||
This gives P @ V = P[:, :64], and the output is (128, 64).
|
||||
But V is (128, 128) in the MMA K,N dims. V[k, n] for k in [0,127], n in [0,63].
|
||||
Hmm, this is getting complicated. Let me just do the identity approach with a (128, 128) output.
|
||||
"""
|
||||
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
|
||||
|
||||
|
||||
class NTileV8Kernel:
|
||||
"""QK + softmax packing + PV with V=I to isolate PV MMA correctness.
|
||||
Output should be P = S.to(BF16), i.e. (Q@K^T).bfloat16()
|
||||
With V=I, O = P @ I = P.
|
||||
But V is (K=128, N=128) in the MMA. We need a 128x128 identity in MN-major.
|
||||
Output tensor is (128, 128).
|
||||
"""
|
||||
def __init__(self, mma_tiler_mn):
|
||||
self.acc_dtype = Float32; self.qk_acc_dtype = Float32
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
self.mma_tiler_mn = mma_tiler_mn; self.mma_tiler = (*mma_tiler_mn, 1)
|
||||
self.use_2cta_instrs = False # needed by epilogue_tma_store
|
||||
self.epilog_sync_bar_id = 1 # needed by epilogue_tma_store
|
||||
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
|
||||
|
||||
def _setup(self, qk_mma, pv_mma):
|
||||
qk_inst_k = int(cute.size(qk_mma.shape_mnk, mode=[2]))
|
||||
self.qk_mma_tiler = (*self.mma_tiler_mn, int(qk_inst_k * 4))
|
||||
# PV with V=I: output is (128, 128), same as QK
|
||||
self.pv_mma_tiler = (self.qk_mma_tiler[0], qk_inst_k, self.qk_mma_tiler[1])
|
||||
# pv_mma_tiler = (128, 16, 128) FMHA: (qk_M, qk_K_inst, qk_N)
|
||||
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),
|
||||
self.qk_mma_tiler[1], self.qk_mma_tiler[2])
|
||||
self.c_layout = LayoutEnum.ROW_MAJOR
|
||||
pv_cta_tile = (self.pv_mma_tiler[0], self.pv_mma_tiler[1], self.pv_mma_tiler[2])
|
||||
self.epi_tile = utils.sm100.compute_epilogue_tile_shape(
|
||||
pv_cta_tile, False, self.c_layout, self.o_dtype)
|
||||
self.num_ab_stage = 1; self.num_acc_stage = 1
|
||||
|
||||
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_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_acc_shape)
|
||||
s_cols = find_tmem_tensor_col_offset(tStS)
|
||||
pv_thr = pv_mma.get_slice(0)
|
||||
pv_acc_shape = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_acc_shape)
|
||||
o_cols = find_tmem_tensor_col_offset(tOtO)
|
||||
|
||||
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 = s_cols
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_acc_shape, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_acc_shape, self.num_acc_stage))
|
||||
self.num_tmem_alloc_cols = utils.get_num_tmem_alloc_cols([tCtS_fake, tCtO_fake], arch="sm_100")
|
||||
|
||||
# ⛔⛔⛔ CRITICAL: num_tma_load_bytes MUST include ALL TMA-loaded tensors (Q + K + V). Missing V → DEADLOCK. See FOOTGUN #0 in README.
|
||||
a_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
|
||||
b_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
|
||||
v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0))
|
||||
self.num_tma_load_bytes = (
|
||||
cute.size_in_bytes(self.q_dtype, a_smem) + cute.size_in_bytes(self.q_dtype, b_smem) +
|
||||
cute.size_in_bytes(self.q_dtype, v_smem)
|
||||
) * 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()
|
||||
self.v_major = LayoutEnum.from_tensor(v).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, self.mma_tiler_mn, tcgen05.OperandSource.SMEM)
|
||||
# PV with 128x128 output (V=I)
|
||||
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, 16), tcgen05.OperandSource.TMEM)
|
||||
self._setup(qk_mma, pv_mma)
|
||||
|
||||
q_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
|
||||
k_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
|
||||
v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0))
|
||||
tma_q, tma_tq = 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_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
|
||||
tma_k, tma_tk = 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_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
|
||||
tma_v, tma_tv = cute.nvgpu.make_tiled_tma_atom_B(
|
||||
utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn, pv_mma.thr_id),
|
||||
v, v_smem, self.pv_mma_tiler, pv_mma, self.cluster_layout_vmnk.shape)
|
||||
epi_smem = cute.select(self.c_smem_s, mode=[0, 1])
|
||||
tma_c, tma_tc = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(), c, epi_smem, self.epi_tile)
|
||||
|
||||
self._kernel(qk_mma, pv_mma, tma_q, tma_tq, tma_k, tma_tk, tma_v, tma_tv,
|
||||
tma_c, tma_tc, 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()
|
||||
use_2cta = cute.size(qk_mma.thr_id.shape) == 2
|
||||
|
||||
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()
|
||||
|
||||
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) * (2 if use_2cta else 1)),
|
||||
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=use_2cta,
|
||||
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))
|
||||
gC = cute.local_tile(mC, cute.slice_(self.pv_mma_tiler, (None,0,0)), (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); 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)]
|
||||
|
||||
gV = cute.local_tile(mV, cute.slice_(self.pv_mma_tiler, (0,None,None)), (None,None,None))
|
||||
tCgV = pv_thr.partition_B(gV)
|
||||
tVsV, tVgV = cpasync.tma_partition(tma_v, 0, b_lay, cute.group_modes(sV,0,3), cute.group_modes(tCgV,0,3))
|
||||
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_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_acc_shape)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
|
||||
pv_acc_shape = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_acc_shape)
|
||||
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_acc_shape, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_acc_shape, self.num_acc_stage))
|
||||
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# ═══ TMA LOAD WARP ═══
|
||||
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 WARP ═══
|
||||
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_prod_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
|
||||
acc_pipe.producer_acquire(acc_prod_st)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
for kt in range(k_cnt):
|
||||
h = ab_c.wait_and_advance(peek)
|
||||
nblk = cute.size(tCrQ, mode=[2])
|
||||
for kb in cutlass.range(nblk, 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()
|
||||
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])
|
||||
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)
|
||||
# Swap cta_tile to PV's for epilogue
|
||||
saved_cta_tile = self.cta_tile_shape_mnk
|
||||
self.cta_tile_shape_mnk = (int(self.pv_mma_tiler[0]), int(self.pv_mma_tiler[1]), int(self.pv_mma_tiler[2]))
|
||||
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)
|
||||
self.cta_tile_shape_mnk = saved_cta_tile
|
||||
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[:16,:] MN-major: (16,128) with strides (1,16)
|
||||
v_data = torch.eye(128, dtype=torch.bfloat16, device='cuda')
|
||||
v = v_data[:16, :].as_strided((16, 128), (1, 16)).unsqueeze(-1)
|
||||
c = torch.zeros(m, 16, 1, dtype=torch.bfloat16, device='cuda')
|
||||
|
||||
qf = q[:,:,0].float(); kf = k[:,:,0].float()
|
||||
# V=I[:16,:] -> O = P[:,:16] = (Q@K^T).bf16()[:,:16]
|
||||
ref = (qf @ kf.T).bfloat16().float()[:, :16]
|
||||
|
||||
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 = NTileV8Kernel(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('PV(128,16) v8: cosine {:.6f} {}'.format(cos, 'PASS' if cos >= 0.99 else 'FAIL'))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test()
|
||||
Reference in New Issue
Block a user