374 lines
20 KiB
Python
374 lines
20 KiB
Python
"""
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Diagnostic: PV with (128,64) output.
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Key fix: compute epilogue tile from PV cta_tile_shape, not QK.
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V[d,k] = (d+1)*(k+1), MN-major. Check element-level patterns.
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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, OperandMajorMode
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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 DiagLayoutKernel:
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def __init__(self, mma_tiler_mn, head_dim):
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self.head_dim = head_dim
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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
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self.epilog_sync_bar_id = 1
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self.cluster_shape_mn = (1, 1)
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self.cta_group = tcgen05.CtaGroup.ONE
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self.epilogue_warp_id = (0, 1, 2, 3)
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self.mma_warp_id = 4; self.tma_warp_id = 5
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self.threads_per_cta = 192
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self.num_c_stage = 2
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def _setup(self, qk_mma, pv_mma):
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qk_inst_k = cute.size(qk_mma.shape_mnk, mode=[2])
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self.qk_mma_tiler = (*self.mma_tiler_mn, qk_inst_k * 4)
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self.pv_mma_tiler = (self.qk_mma_tiler[0], self.qk_mma_tiler[2], self.qk_mma_tiler[1])
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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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# QK cta tile
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self.qk_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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# PV cta tile — for epilogue, this is what matters
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self.pv_cta_tile_shape_mnk = (
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self.pv_mma_tiler[0] // cute.size(pv_mma.thr_id.shape),
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self.pv_mma_tiler[1], self.pv_mma_tiler[2])
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self.c_layout = LayoutEnum.ROW_MAJOR
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# Compute epi_tile from PV cta_tile, not QK
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self.epi_tile = utils.sm100.compute_epilogue_tile_shape(
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self.pv_cta_tile_shape_mnk, self.use_2cta_instrs, self.c_layout, self.o_dtype)
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print(f"[SETUP] qk_mma_tiler={self.qk_mma_tiler}, pv_mma_tiler={self.pv_mma_tiler}")
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print(f"[SETUP] qk_cta_tile={self.qk_cta_tile_shape_mnk}, pv_cta_tile={self.pv_cta_tile_shape_mnk}")
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print(f"[SETUP] epi_tile={self.epi_tile}")
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self.cta_tile_shape_mnk = self.pv_cta_tile_shape_mnk
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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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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_mma_tiler_mn = (self.mma_tiler_mn[0], self.head_dim)
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pv_mma = utils.sm100.make_trivial_tiled_mma(
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self.q_dtype, self.q_dtype, OperandMajorMode.K, self.v_major,
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self.qk_acc_dtype, self.cta_group, pv_mma_tiler_mn, tcgen05.OperandSource.TMEM)
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self._setup(qk_mma, pv_mma)
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q_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
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k_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
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v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0))
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tma_q, tma_tq = cute.nvgpu.make_tiled_tma_atom_A(
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utils.sm100.cluster_shape_to_tma_atom_A(self.cluster_shape_mn, qk_mma.thr_id),
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q, q_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
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tma_k, tma_tk = cute.nvgpu.make_tiled_tma_atom_B(
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utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn, qk_mma.thr_id),
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k, k_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
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tma_v, tma_tv = cute.nvgpu.make_tiled_tma_atom_B(
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utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn, pv_mma.thr_id),
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v, v_smem, self.pv_mma_tiler, pv_mma, self.cluster_layout_vmnk.shape)
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epi_smem = cute.select(self.c_smem_s, mode=[0, 1])
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tma_c, tma_tc = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(), c, epi_smem, self.epi_tile)
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self._kernel(qk_mma, pv_mma, tma_q, tma_tq, tma_k, tma_tk, tma_v, tma_tv,
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tma_c, tma_tc, self.cluster_layout_vmnk,
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self.a_smem_s, self.b_smem_s, self.v_smem_s, self.p_tmem_s, self.c_smem_s, self.epi_tile
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).launch(grid=(1,1,1), block=[self.threads_per_cta,1,1], stream=stream)
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@cute.kernel
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def _kernel(self, qk_mma, pv_mma, tma_q, mQ, tma_k, mK, tma_v, mV,
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tma_c, mC, cl_vmnk, a_smem_s, b_smem_s, v_smem_s, p_tmem_s, c_smem_s, epi_tile):
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warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx())
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tidx, _, _ = cute.arch.thread_idx()
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use_2cta = cute.size(qk_mma.thr_id.shape) == 2
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if warp_idx == self.tma_warp_id:
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cpasync.prefetch_descriptor(tma_q); cpasync.prefetch_descriptor(tma_k)
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cpasync.prefetch_descriptor(tma_v); cpasync.prefetch_descriptor(tma_c)
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@cute.struct
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class SS:
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ab_bar: cute.struct.MemRange[cutlass.Int64, self.num_ab_stage * 2]
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mma_si_bar: cute.struct.MemRange[cutlass.Int64, 2]
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acc_bar: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage * 2]
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tmem_dealloc: cutlass.Int64
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holding: cutlass.Int32
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smem = utils.SmemAllocator(); st = smem.allocate(SS)
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ab_p, ab_c = pipeline.PipelineTmaUmma.create(
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barrier_storage=st.ab_bar.data_ptr(), num_stages=self.num_ab_stage,
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producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),
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consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread, 1),
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tx_count=self.num_tma_load_bytes, cta_layout_vmnk=cl_vmnk, defer_sync=True
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).make_participants()
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mma_si_prod, mma_si_cons = pipeline.PipelineUmmaAsync.create(
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barrier_storage=st.mma_si_bar.data_ptr(), num_stages=1,
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producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),
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consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread, 32 * len(self.epilogue_warp_id)),
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).make_participants()
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acc_pipe = pipeline.PipelineUmmaAsync.create(
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barrier_storage=st.acc_bar.data_ptr(), num_stages=self.num_acc_stage,
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producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),
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consumer_group=pipeline.CooperativeGroup(
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pipeline.Agent.Thread, len(self.epilogue_warp_id) * (2 if use_2cta else 1)),
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cta_layout_vmnk=cl_vmnk, defer_sync=True)
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tmem_bar = pipeline.NamedBarrier(barrier_id=2,
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num_threads=32 * len((self.mma_warp_id, *self.epilogue_warp_id)))
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tmem = utils.TmemAllocator(st.holding.ptr, barrier_for_retrieve=tmem_bar,
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allocator_warp_id=self.epilogue_warp_id[0], is_two_cta=use_2cta,
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two_cta_tmem_dealloc_mbar_ptr=st.tmem_dealloc.ptr)
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pipeline.pipeline_init_arrive(cluster_shape_mn=cl_vmnk, is_relaxed=True)
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sQ = smem.allocate_tensor(element_type=self.q_dtype, layout=a_smem_s.outer, byte_alignment=128, swizzle=a_smem_s.inner)
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sK = smem.allocate_tensor(element_type=self.q_dtype, layout=b_smem_s.outer, byte_alignment=128, swizzle=b_smem_s.inner)
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sV = smem.allocate_tensor(element_type=self.q_dtype, layout=v_smem_s.outer, byte_alignment=128, swizzle=v_smem_s.inner)
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sC = smem.allocate_tensor(element_type=self.o_dtype, layout=c_smem_s.outer, byte_alignment=128, swizzle=c_smem_s.inner)
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gQ = cute.local_tile(mQ, cute.slice_(self.qk_mma_tiler, (None,0,None)), (None,None,None))
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gK = cute.local_tile(mK, cute.slice_(self.qk_mma_tiler, (0,None,None)), (None,None,None))
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gC = cute.local_tile(mC, cute.slice_(self.pv_mma_tiler, (None,0,None)), (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 = pv_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()
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s0_handle = mma_si_prod.acquire_and_advance()
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pv_mma.set(tcgen05.Field.ACCUMULATE, False)
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tCrV_s = tCrV[(None, None, None, 0)]
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nblk_pv = cute.size(tOrP0, mode=[2])
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for kb in cutlass.range(nblk_pv, unroll_full=True):
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cute.gemm(pv_mma, tOtO0, tOrP0[(None,None,kb)], tCrV_s[(None,None,kb)], tOtO0)
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pv_mma.set(tcgen05.Field.ACCUMULATE, True)
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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_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.qk_mma_tiler[0], self.qk_mma_tiler[1]))
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tScS = qk_thr.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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|
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si_handle = mma_si_cons.wait_and_advance()
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|
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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()
|
|
|
|
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_diag_v():
|
|
torch.manual_seed(42)
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|
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_data = torch.zeros(head_dim, n, dtype=torch.bfloat16, device='cuda')
|
|
for d in range(head_dim):
|
|
for k_idx in range(n):
|
|
v_data[d, k_idx] = (d + 1) * (k_idx + 1)
|
|
v = v_data.as_strided((head_dim, n), (1, head_dim)).unsqueeze(-1)
|
|
|
|
c = torch.zeros(m, head_dim, 1, dtype=torch.bfloat16, device='cuda')
|
|
|
|
qf = q[:,:,0].float()
|
|
kf = k[:,:,0].float()
|
|
vf = v_data.float()
|
|
ref = (qf @ kf.T).bfloat16().float() @ vf.T
|
|
|
|
mQ = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
|
|
mK = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
|
|
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 = DiagLayoutKernel(mma_tiler_mn=(128, 128), head_dim=head_dim)
|
|
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()
|
|
max_err = (out - ref).abs().max().item()
|
|
print('Diag V test (128x64 PV, epi from PV cta_tile): cosine {:.6f}, max_err {:.6f} {}'.format(
|
|
cos, max_err, 'PASS' if cos >= 0.99 else 'FAIL'))
|
|
|
|
if cos < 0.99:
|
|
print('\n=== Element-level diagnostics ===')
|
|
for m_idx in [0, 1, 63, 127]:
|
|
for d_idx in [0, 1, 31, 63]:
|
|
print(f' O[{m_idx},{d_idx}] = {out[m_idx,d_idx]:.4f}, ref = {ref[m_idx,d_idx]:.4f}')
|
|
print(f'\n O[0,:5] = {out[0,:5].tolist()}')
|
|
print(f' ref[0,:5] = {ref[0,:5].tolist()}')
|
|
print(f' O[:5,0] = {out[:5,0].tolist()}')
|
|
print(f' ref[:5,0] = {ref[:5,0].tolist()}')
|
|
|
|
if __name__ == '__main__':
|
|
test_diag_v()
|