biondizzle
c395b279d2
Kept in tests/unit/: - test_fmha_v3.py (stages A+B) - test_fmha_v3_diag.py (identity softmax, n=128+256) - test_fmha_v3_stage_c.py (real softmax, n=128 cos 0.999998) - layertest.py + cudagraph_test.py (required for every change) - infrastructure: cache, custom_op, cutedsl, router, fp4, fused, interleave Archived: 19 superseded unit tests + 10 root-level scratch files Root level: only fmha_v3_stage_c_example7.py remains (now in unit/)
270 lines
13 KiB
Python
270 lines
13 KiB
Python
"""
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Debug: QK only (no PV) with KV-tile interleaving pipeline.
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Outputs P to GMEM to verify QK+softmax pipeline works.
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n=128, single KV tile, identity softmax.
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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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HEAD_DIM = 64
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class QkOnlyTest:
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def __init__(self):
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self.acc_dtype = Float32; self.qk_acc_dtype = Float32
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self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
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self.use_2cta_instrs = False; self.epilog_sync_bar_id = 1
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self.cluster_shape_mn = (1, 1); self.cta_group = tcgen05.CtaGroup.ONE
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self.epilogue_warp_id = (0,1,2,3); self.mma_warp_id = 4; self.tma_warp_id = 5
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self.threads_per_cta = 192; self.num_c_stage = 2
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self.kv_stage = 2; self.q_stage = 1
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def _setup(self, qk_mma):
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qk_ik = cute.size(qk_mma.shape_mnk, mode=[2])
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self.qk_mma_tiler = (128, 128, qk_ik * 4)
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self.mma_tiler = self.qk_mma_tiler
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self.cluster_layout_vmnk = cute.tiled_divide(cute.make_layout((1,1,1)), (qk_mma.thr_id.shape,))
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self.cta_tile_shape_mnk = (self.qk_mma_tiler[0]//cute.size(qk_mma.thr_id.shape), 128, self.qk_mma_tiler[2])
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self.c_layout = LayoutEnum.ROW_MAJOR
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self.epi_tile = utils.sm100.compute_epilogue_tile_shape(self.cta_tile_shape_mnk, False, self.c_layout, self.o_dtype)
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self.num_ab_stage = 1; self.num_acc_stage = 1
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self.q_smem_s = utils.sm100.make_smem_layout_a(qk_mma, self.qk_mma_tiler, self.q_dtype, self.q_stage)
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self.k_smem_s = utils.sm100.make_smem_layout_b(qk_mma, self.qk_mma_tiler, self.q_dtype, self.kv_stage)
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self.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); qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
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tStS = qk_thr.make_fragment_C(qk_as)
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self.tilePlikeFP32 = self.qk_mma_tiler[1] // Float32.width * self.o_dtype.width
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self.tmem_s0_offset = 0; self.tmem_p0_offset = 32
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self.tmem_o0_offset = 0 # Output is at S offset for QK-only
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tCS = qk_mma.make_fragment_C(cute.append(qk_as, self.num_acc_stage))
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self.num_tmem_alloc_cols = utils.get_num_tmem_alloc_cols([tCS], arch="sm_100")
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cta = cute.size(qk_mma.thr_id.shape)
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q_s = cute.slice_(self.q_smem_s,(None,None,None,0))
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k_s = cute.slice_(self.k_smem_s,(None,None,None,0))
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self.q_tx_bytes = cute.size_in_bytes(self.q_dtype, q_s) * cta
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self.kv_tx_bytes = cute.size_in_bytes(self.q_dtype, k_s) * cta
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@cute.jit
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def __call__(self, q, k, 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.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, (128,128), tcgen05.OperandSource.SMEM)
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self._setup(qk_mma)
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q_s = cute.slice_(self.q_smem_s,(None,None,None,0))
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k_s = cute.slice_(self.k_smem_s,(None,None,None,0))
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tma_q,mQ = cute.nvgpu.make_tiled_tma_atom_A(
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utils.sm100.cluster_shape_to_tma_atom_A(self.cluster_shape_mn,qk_mma.thr_id),
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q,q_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
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tma_k,mK = cute.nvgpu.make_tiled_tma_atom_B(
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utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,qk_mma.thr_id),
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k,k_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
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epi_s = cute.select(self.c_smem_s,mode=[0,1])
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tma_c,mC = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(),c,epi_s,self.epi_tile)
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self._kernel(qk_mma, tma_q, mQ, tma_k, mK, tma_c, mC,
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self.cluster_layout_vmnk, self.q_smem_s, self.k_smem_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, tma_q, mQ, tma_k, mK, tma_c, mC,
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cl_vmnk, q_smem_s, k_smem_s, c_smem_s, epi_tile):
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warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx())
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tidx,_,_ = cute.arch.thread_idx()
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if warp_idx == self.tma_warp_id:
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cpasync.prefetch_descriptor(tma_q); cpasync.prefetch_descriptor(tma_k)
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cpasync.prefetch_descriptor(tma_c)
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@cute.struct
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class SS:
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q_bar: cute.struct.MemRange[cutlass.Int64, self.q_stage*2]
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kv_bar: cute.struct.MemRange[cutlass.Int64, self.kv_stage*2]
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s_bar: cute.struct.MemRange[cutlass.Int64, 2]
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acc_bar: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage*2]
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tmem_dealloc: cutlass.Int64; holding: cutlass.Int32
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smem = utils.SmemAllocator(); st = smem.allocate(SS)
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qp,qc = pipeline.PipelineTmaUmma.create(
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barrier_storage=st.q_bar.data_ptr(), num_stages=self.q_stage,
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producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),
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consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),
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tx_count=self.q_tx_bytes, cta_layout_vmnk=cl_vmnk, defer_sync=True
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).make_participants()
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kvp,kvc = pipeline.PipelineTmaUmma.create(
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barrier_storage=st.kv_bar.data_ptr(), num_stages=self.kv_stage,
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producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),
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consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),
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tx_count=self.kv_tx_bytes, cta_layout_vmnk=cl_vmnk, defer_sync=True
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).make_participants()
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s_prod,s_cons = pipeline.PipelineUmmaAsync.create(
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barrier_storage=st.s_bar.data_ptr(), num_stages=1,
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producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),
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consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread, 32*len(self.epilogue_warp_id))
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).make_participants()
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softmax_done_bar = pipeline.NamedBarrier(barrier_id=3, num_threads=32 + 32*len(self.epilogue_warp_id))
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acc_pipe = pipeline.PipelineUmmaAsync.create(
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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(pipeline.Agent.Thread, len(self.epilogue_warp_id)),
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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],
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is_two_cta=cute.size(qk_mma.thr_id.shape)==2,
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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=q_smem_s.outer, byte_alignment=128, swizzle=q_smem_s.inner)
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sK = smem.allocate_tensor(element_type=self.q_dtype, layout=k_smem_s.outer, byte_alignment=128, swizzle=k_smem_s.inner)
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sC = smem.allocate_tensor(element_type=self.o_dtype, layout=c_smem_s.outer, byte_alignment=128, swizzle=c_smem_s.inner)
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gQ = cute.local_tile(mQ, cute.slice_(self.qk_mma_tiler,(None,0,None)), (None,None,None))
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gK = cute.local_tile(mK, cute.slice_(self.qk_mma_tiler,(0,None,None)), (None,None,None))
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gC = cute.local_tile(mC, cute.slice_(self.qk_mma_tiler,(None,None,0)), (None,None,None))
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n_kv_tiles = cute.size(gK, mode=[3])
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qk_thr = qk_mma.get_slice(0)
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tCgQ = qk_thr.partition_A(gQ); tCgK = qk_thr.partition_B(gK)
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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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tCrQ = qk_mma.make_fragment_A(sQ); tCrK = qk_mma.make_fragment_B(sK)
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qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
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tStS = qk_thr.make_fragment_C(qk_as)
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tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
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tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_as, self.num_acc_stage))
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pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
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# TMA LOAD
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if warp_idx == self.tma_warp_id:
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qp.reset(); qh = qp.acquire_and_advance()
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cute.copy(tma_q, tAgQ[(None,qh.count)], tAsQ[(None,qh.index)], tma_bar_ptr=qh.barrier)
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qp.tail()
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kvp.reset(); pk = kvp.try_acquire()
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for kt in cutlass.range(n_kv_tiles, unroll=1):
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kh = kvp.acquire_and_advance(pk)
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cute.copy(tma_k, tBgK[(None,kh.count)], tBsK[(None,kh.index)], tma_bar_ptr=kh.barrier)
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pk = cutlass.Boolean(1)
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kvp.tail()
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# MMA
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if warp_idx == self.mma_warp_id:
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tmem.wait_for_alloc()
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qc.reset(); qh = qc.wait_and_advance(); qh.release()
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kvc.reset(); pk = kvc.try_wait()
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acc_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
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acc_pipe.producer_acquire(acc_st)
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for kt in range(n_kv_tiles):
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kh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)
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# QK only, accumulate across KV tiles
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sh = s_prod.acquire_and_advance()
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qk_mma.set(tcgen05.Field.ACCUMULATE, kt != 0)
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for kb in cutlass.range(cute.size(tCrQ,mode=[2]), unroll_full=True):
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cute.gemm(qk_mma, tStS0,
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tCrQ[(None,None,kb,0)], tCrK[(None,None,kb,kh.index)], tStS0)
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qk_mma.set(tcgen05.Field.ACCUMULATE, True)
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cute.arch.fence_view_async_tmem_store()
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sh.commit()
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kh.release()
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# Wait for softmax (identity: just signal done)
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softmax_done_bar.arrive_and_wait()
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acc_pipe.producer_commit(acc_st); acc_st.advance()
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acc_pipe.producer_tail(acc_st)
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# EPILOGUE
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if warp_idx < self.mma_warp_id:
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tmem.allocate(self.num_tmem_alloc_cols)
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tmem.wait_for_alloc()
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tmem_ptr = tmem.retrieve_ptr(self.qk_acc_dtype)
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sfw_idx = tidx % (32 * len(self.epilogue_warp_id))
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for kt in range(n_kv_tiles):
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si_handle = s_cons.wait_and_advance()
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# Identity softmax: no-op, just signal MMA
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si_handle.release()
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softmax_done_bar.arrive()
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# Output S (QK result) to GMEM
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tCtS_base = cute.make_tensor(tmem_ptr + self.tmem_s0_offset, tCtS_fake.layout)
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acc_cons_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Consumer, self.num_acc_stage)
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c_grp = pipeline.CooperativeGroup(pipeline.Agent.Thread, 32 * len(self.epilogue_warp_id))
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c_pipe = pipeline.PipelineTmaStore.create(num_stages=self.num_c_stage, producer_group=c_grp)
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acc_cons_st = utils.gemm.sm100.epilogue_tma_store(
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self, tidx, warp_idx, tma_c, tCtS_base, sC, tCgC,
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epi_tile, 0, const_expr(lambda x: x), (0,0,0), acc_cons_st, acc_pipe, c_pipe)
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c_pipe.producer_tail()
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tmem.relinquish_alloc_permit()
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tmem.free(tmem_ptr)
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def test():
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torch.manual_seed(42)
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n = 128
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m, hd = 128, HEAD_DIM
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q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device='cuda')
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k = torch.randn(n, hd, 1, dtype=torch.bfloat16, device='cuda')
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c = torch.zeros(m, n, 1, dtype=torch.bfloat16, device='cuda') # (128, 128) output = S
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qf = q[:,:,0].float(); kf = k[:,:,0].float()
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ref = (qf @ kf.T)
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mQ = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
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mK = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
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mC = ct.from_dlpack(c).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c))
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stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
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kernel = QkOnlyTest()
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print('Compiling...', flush=True)
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compiled = cute.compile(kernel, mQ, mK, mC, stream)
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print('Running...', flush=True)
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compiled(mQ, mK, mC, stream)
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torch.cuda.synchronize()
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out = c[:,:,0].float()
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cos = torch.nn.functional.cosine_similarity(out.flatten().unsqueeze(0), ref.flatten().unsqueeze(0)).item()
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print(f'QK-only n={n}: cosine {cos:.6f} {"PASS" if cos >= 0.99 else "FAIL"}')
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if cos < 0.99:
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print(f' out[0,:4]={out[0,:4].tolist()} ref[0,:4]={ref[0,:4].tolist()}')
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print(f' out stats: min={out.min().item():.4f} max={out.max().item():.4f}')
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print(f' ref stats: min={ref.min().item():.4f} max={ref.max().item():.4f}')
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if __name__ == '__main__':
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test()
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