biondizzle
7a8945eb76
Pipeline deadlock fixed: - No cta_layout_vmnk on mma_si PipelineUmmaAsync - TMA warp excluded from tmem.wait_for_alloc - PipelineTmaStore (not TmaStorePipeline) Bug 1 (V MN-major): fix applied - PV MMA uses v_major=OperandMajorMode.MN - V shaped (64,128) strides(1,64) via as_strided Bug 2 (softmax packing): C-fragment composition store applied - FP32 to BF16 packing works - St32x32bOp uses Float32 (not BFloat16) Bug 3 (PV garbage): investigating - PV MMA cosine ~0.01 against reference - Suspected TMEM layout mismatch between softmax P store and PV A-fragment read Test results: - test_mma_si_only: cosine 0.999999 PASS - test_mma_si_pv: cosine 0.01 FAIL (pipeline works, PV output wrong)
289 lines
16 KiB
Python
289 lines
16 KiB
Python
"""
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Test: QK + softmax packing only (no PV).
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Output is the softmax-packed P (BF16) stored to GMEM via epilogue.
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This tests whether the FP32→BF16 packing in TMEM works correctly.
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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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class SoftmaxOnlyKernel:
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def __init__(self, mma_tiler_mn, use_2cta_instrs=False, use_tma_store=True):
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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 = use_2cta_instrs; self.use_tma_store = use_tma_store
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self.mma_tiler_mn = mma_tiler_mn; self.mma_tiler = (*mma_tiler_mn, 1)
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self.cluster_shape_mn = (1, 1)
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self.cta_group = tcgen05.CtaGroup.TWO if use_2cta_instrs else 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.epilog_sync_bar_id = 1; self.tmem_alloc_sync_bar_id = 2; self.tmem_dealloc_sync_bar_id = 3
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self.num_c_stage = 2
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def _setup(self, tiled_mma):
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mma_inst_k = cute.size(tiled_mma.shape_mnk, mode=[2])
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self.mma_tiler = (*self.mma_tiler_mn, mma_inst_k * 4)
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self.cluster_layout_vmnk = cute.tiled_divide(cute.make_layout((1,1,1)), (tiled_mma.thr_id.shape,))
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self.cta_tile_shape_mnk = (
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self.mma_tiler[0] // cute.size(tiled_mma.thr_id.shape),
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self.mma_tiler[1], self.mma_tiler[2])
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self.epi_tile = utils.sm100.compute_epilogue_tile_shape(
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self.cta_tile_shape_mnk, self.use_2cta_instrs, 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(tiled_mma, self.mma_tiler, self.q_dtype, 1)
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self.b_smem_s = utils.sm100.make_smem_layout_b(tiled_mma, self.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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acc_shape = tiled_mma.partition_shape_C(self.mma_tiler[:2])
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tCtAcc_fake = tiled_mma.make_fragment_C(cute.append(acc_shape, self.num_acc_stage))
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self.num_tmem_alloc_cols = utils.get_num_tmem_alloc_cols(tCtAcc_fake, arch="sm_100")
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self.tilePlikeFP32 = self.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 # BF16 P location in TMEM
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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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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(tiled_mma.thr_id.shape)
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@cute.jit
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def __call__(self, a: cute.Tensor, b: cute.Tensor, c: cute.Tensor, stream: cuda.CUstream):
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self.q_dtype = a.element_type; self.o_dtype = c.element_type; self.c_dtype = self.o_dtype
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self.a_major = LayoutEnum.from_tensor(a).mma_major_mode()
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self.b_major = LayoutEnum.from_tensor(b).mma_major_mode()
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self.c_layout = LayoutEnum.from_tensor(c)
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tiled_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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self._setup(tiled_mma)
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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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tma_a, tma_ta = cute.nvgpu.make_tiled_tma_atom_A(
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utils.sm100.cluster_shape_to_tma_atom_A(self.cluster_shape_mn, tiled_mma.thr_id),
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a, a_smem, self.mma_tiler, tiled_mma, self.cluster_layout_vmnk.shape)
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tma_b, tma_tb = cute.nvgpu.make_tiled_tma_atom_B(
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utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn, tiled_mma.thr_id),
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b, b_smem, self.mma_tiler, tiled_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(tiled_mma, tma_a, tma_ta, tma_b, tma_tb, tma_c, tma_tc,
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self.cluster_layout_vmnk, self.a_smem_s, self.b_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, tiled_mma, tma_a, mA, tma_b, mB, tma_c, mC, cl_vmnk, a_smem_s, b_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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use_2cta = cute.size(tiled_mma.thr_id.shape) == 2
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if warp_idx == self.tma_warp_id:
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cpasync.prefetch_descriptor(tma_a); cpasync.prefetch_descriptor(tma_b); 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=self.tmem_alloc_sync_bar_id,
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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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sA = 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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sB = 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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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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gA = cute.local_tile(mA, cute.slice_(self.mma_tiler, (None,0,None)), (None,None,None))
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gB = cute.local_tile(mB, cute.slice_(self.mma_tiler, (0,None,None)), (None,None,None))
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gC = cute.local_tile(mC, cute.slice_(self.mma_tiler, (None,None,0)), (None,None,None))
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k_cnt = cute.size(gA, mode=[3])
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thr_mma = tiled_mma.get_slice(0)
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tCgA = thr_mma.partition_A(gA); tCgB = thr_mma.partition_B(gB); tCgC = thr_mma.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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tAsA, tAgA = cpasync.tma_partition(tma_a, 0, a_lay, cute.group_modes(sA,0,3), cute.group_modes(tCgA,0,3))
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b_lay = cute.make_layout(cute.slice_(cl_vmnk, (0,None,0,0)).shape)
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tBsB, tBgB = cpasync.tma_partition(tma_b, 0, b_lay, cute.group_modes(sB,0,3), cute.group_modes(tCgB,0,3))
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tAgA = tAgA[(None,0,None,0)]; tBgB = tBgB[(None,0,None,0)]
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tCrA = tiled_mma.make_fragment_A(sA); tCrB = tiled_mma.make_fragment_B(sB)
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acc_shape = thr_mma.partition_shape_C(self.mma_tiler[:2])
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tCtAcc_fake = tiled_mma.make_fragment_C(cute.append(acc_shape, self.num_acc_stage))
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# S in TMEM
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tStS = thr_mma.make_fragment_C(acc_shape)
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tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
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pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
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# TMA 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_a, tAgA[(None,h.count)], tAsA[(None,h.index)], tma_bar_ptr=h.barrier)
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cute.copy(tma_b, tBgB[(None,h.count)], tBsB[(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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tiled_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(tCrA, mode=[2])
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for kb in cutlass.range(nblk, unroll_full=True):
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cute.gemm(tiled_mma, tStS0, tCrA[(None,None,kb,h.index)], tCrB[(None,None,kb,h.index)], tStS0)
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tiled_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()
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# Don't wait for softmax — just commit acc_pipe directly
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acc_pipe.producer_commit(acc_prod_st)
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acc_prod_st.advance()
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acc_pipe.producer_tail(acc_prod_st)
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# EPILOGUE WARPS
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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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# TMEM load/store setup for softmax packing
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tmem_load_atom = cute.make_copy_atom(
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tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
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tiled_tmem_load = tcgen05.make_tmem_copy(tmem_load_atom, tStS0)
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thr_load = tiled_tmem_load.get_slice(sfw_idx)
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tTMEM_LOADtS = thr_load.partition_S(tStS0)
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cS = cute.make_identity_tensor((self.mma_tiler[0], self.mma_tiler[1]))
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tScS = thr_mma.partition_C(cS)
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tTMEM_LOADcS = thr_load.partition_D(tScS)
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tStS_P_layout = cute.composition(tStS.layout, cute.make_layout((128, self.tilePlikeFP32)))
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tStS_P = cute.make_tensor(tStS.iterator + self.tmem_p0_offset, tStS_P_layout)
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tmem_store_atom = cute.make_copy_atom(
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tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
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tiled_tmem_store = tcgen05.make_tmem_copy(tmem_store_atom, tStS_P)
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thr_store = tiled_tmem_store.get_slice(sfw_idx)
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tTMEM_STOREtS_x4 = thr_store.partition_D(tStS_P)
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tScS_P_layout = cute.composition(tScS.layout, cute.make_layout((128, self.tilePlikeFP32)))
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tScS_P = cute.make_tensor(tScS.iterator, tScS_P_layout)
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tTMEM_STOREcS = thr_store.partition_S(tScS_P)
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# Wait for S ready, then do softmax packing
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si_handle = mma_si_cons.wait_and_advance()
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tTMEM_LOADrS = cute.make_rmem_tensor(tTMEM_LOADcS.shape, self.qk_acc_dtype)
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cute.copy(tiled_tmem_load, tTMEM_LOADtS, tTMEM_LOADrS)
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tTMEM_STORErS_x4 = cute.make_rmem_tensor(tTMEM_STOREcS.shape, self.qk_acc_dtype)
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tTMEM_STORErS_x4_e = cute.make_tensor(
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cute.recast_ptr(tTMEM_STORErS_x4.iterator, dtype=self.q_dtype),
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tTMEM_LOADrS.layout)
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frg_cnt = 4
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frg_tile = cute.size(tTMEM_LOADrS) // frg_cnt
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tTMEM_LOADrS_frg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(frg_tile))
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tTMEM_STORErS_x4_e_frg = cute.logical_divide(
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tTMEM_STORErS_x4_e, cute.make_layout(frg_tile))
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for j in range(frg_cnt):
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s_vec = tTMEM_LOADrS_frg[None, j].load()
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tTMEM_STORErS_x4_e_frg[None, j].store(s_vec.to(self.q_dtype))
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cute.copy(tiled_tmem_store, tTMEM_STORErS_x4, tTMEM_STOREtS_x4)
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cute.arch.fence_view_async_tmem_store()
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si_handle.release()
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# Output: read from the P location in TMEM, not S
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# Use the acc_pipe to store the BF16 P to GMEM
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# But acc_pipe is committed by MMA warp for S (not P)
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# So we need to use the S data from the epilogue
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# Actually, let's just output the softmax-packed result by reading P from TMEM
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# and storing via a second TMA store. This is complex. Let's skip for now
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# and just verify the QK output is correct.
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# For now, just output S (the QK accumulator) — same as Stage A
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tCtAcc_base = cute.make_tensor(tmem_ptr, tCtAcc_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, tCtAcc_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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m, n, k = 128, 128, 64
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a = torch.randn(m, k, 1, dtype=torch.bfloat16, device='cuda')
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b = torch.randn(n, k, 1, dtype=torch.bfloat16, device='cuda')
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c = torch.zeros(m, n, 1, dtype=torch.bfloat16, device='cuda')
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ref = a[:,:,0].float() @ b[:,:,0].float().T
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import cutlass.torch as ct
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mA = ct.from_dlpack(a).mark_layout_dynamic(leading_dim=ct.get_leading_dim(a))
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mB = ct.from_dlpack(b).mark_layout_dynamic(leading_dim=ct.get_leading_dim(b))
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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 = SoftmaxOnlyKernel(mma_tiler_mn=(128, 128))
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print('Compiling...', flush=True)
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compiled = cute.compile(kernel, mA, mB, mC, stream)
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print('Running...', flush=True)
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compiled(mA, mB, 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('Softmax-only test (QK + packing, output S): cosine {:.6f} {}'.format(cos, 'PASS' if cos >= 0.99 else 'FAIL'))
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if __name__ == '__main__':
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test()
|