D1.2: TMEM budget probe script for hd=64,128,256,512
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100
tests/unit/test_tmem_budget.py
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100
tests/unit/test_tmem_budget.py
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"""TMEM column budget probe for FMHA at various head_dims.
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Prints find_tmem_tensor_col_offset(tOtO) and related shapes so we can
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plan the SMEM-P path and verify TMEM fits in 512 columns at hd=512.
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"""
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import torch, math
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import cutlass, cutlass.cute as cute, cutlass.utils as utils, cutlass.nvgpu.tcgen05 as tcgen05
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from cutlass import BFloat16, Float32, LayoutEnum
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from cutlass.utils.tmem_allocator import find_tmem_tensor_col_offset
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def probe_hd(hd):
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print(f"\n=== HEAD_DIM={hd} ===")
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# QK MMA: always (128, 128)
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qk_mma = utils.sm100.make_trivial_tiled_mma(
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BFloat16, BFloat16,
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LayoutEnum.ROW_MAJOR, LayoutEnum.ROW_MAJOR,
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Float32, tcgen05.CtaGroup.ONE, (128, 128),
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tcgen05.OperandSource.SMEM,
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)
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qk_thr = qk_mma.get_slice(0)
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qk_as = qk_thr.partition_shape_C((128, 128))
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tStS = qk_thr.make_fragment_C(qk_as)
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s_cols = find_tmem_tensor_col_offset(tStS)
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print(f" QK C-fragment: qk_as={qk_as}, tStS.layout shape={cute.shape(tStS)}, s_cols={s_cols}")
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# PV MMA: (128, hd)
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# TMEM-P path
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pv_mma_tmem = utils.sm100.make_trivial_tiled_mma(
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BFloat16, BFloat16,
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LayoutEnum.ROW_MAJOR, LayoutEnum.ROW_MAJOR,
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Float32, tcgen05.CtaGroup.ONE, (128, hd),
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tcgen05.OperandSource.TMEM,
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)
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pv_thr_tmem = pv_mma_tmem.get_slice(0)
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pv_as_tmem = pv_thr_tmem.partition_shape_C((128, hd))
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tOtO_tmem = pv_thr_tmem.make_fragment_C(pv_as_tmem)
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o_cols_tmem = find_tmem_tensor_col_offset(tOtO_tmem)
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print(f" PV C-fragment (TMEM-P): pv_as={pv_as_tmem}, tOtO.layout shape={cute.shape(tOtO_tmem)}, o_cols={o_cols_tmem}")
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# SMEM-P path (PV from SMEM)
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pv_mma_smem = utils.sm100.make_trivial_tiled_mma(
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BFloat16, BFloat16,
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LayoutEnum.ROW_MAJOR, LayoutEnum.ROW_MAJOR,
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Float32, tcgen05.CtaGroup.ONE, (128, hd),
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tcgen05.OperandSource.SMEM,
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)
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pv_thr_smem = pv_mma_smem.get_slice(0)
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pv_as_smem = pv_thr_smem.partition_shape_C((128, hd))
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tOtO_smem = pv_thr_smem.make_fragment_C(pv_as_smem)
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o_cols_smem = find_tmem_tensor_col_offset(tOtO_smem)
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print(f" PV C-fragment (SMEM-P): pv_as={pv_as_smem}, tOtO.layout shape={cute.shape(tOtO_smem)}, o_cols={o_cols_smem}")
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# P columns in TMEM (TMEM-P path only)
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# pv_mma_tiler[2] is the K-dim of the PV MMA, which determines P cols
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# At hd=64: pv_mma_tiler = (128, 64, 128), pv_mma_tiler[2] = 128
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# p_cols_fp32 = 128 * 16 / 32 = 64
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pv_mma_tiler = (128, hd, 128) # assuming s_k=128
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p_cols_fp32 = pv_mma_tiler[2] * BFloat16.width // Float32.width
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print(f" P cols (FP32): {p_cols_fp32} (pv_mma_tiler[2]={pv_mma_tiler[2]})")
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# TMEM budget calculation
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print(f" --- TMEM Budget ---")
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print(f" S cols: {s_cols}")
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print(f" P cols (TMEM-P): {p_cols_fp32}")
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print(f" O cols: {o_cols_tmem} (TMEM-P) / {o_cols_smem} (SMEM-P)")
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# TMEM-P: S at 0, P at 32, O after max(S, P_end)
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tmem_p0 = 32
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p_end = tmem_p0 + p_cols_fp32
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o_after = max(s_cols, p_end)
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tmem_o0_tmem_p = ((o_after + 31) // 32) * 32
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total_tmem_p = tmem_o0_tmem_p + o_cols_tmem
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print(f" TMEM-P total: S(0) + P({tmem_p0}) + O({tmem_o0_tmem_p}) + O_size({o_cols_tmem}) = {total_tmem_p} / 512 cols {'✅' if total_tmem_p <= 512 else '❌ OVER BUDGET'}")
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# SMEM-P: P not in TMEM. S and O sequential (S consumed before O written).
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# Best case: O at 0 (reuses S space), total = max(s_cols, o_cols)
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total_smem_p = o_cols_smem # O starts at 0
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print(f" SMEM-P total (O at 0, reuses S): {total_smem_p} / 512 cols {'✅' if total_smem_p <= 512 else '❌ OVER BUDGET'}")
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# Split-PV: if hd > 256, process (128, 256) PV tiles
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if hd > 256:
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pv_n_tile = 256
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pv_mma_split = utils.sm100.make_trivial_tiled_mma(
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BFloat16, BFloat16,
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LayoutEnum.ROW_MAJOR, LayoutEnum.ROW_MAJOR,
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Float32, tcgen05.CtaGroup.ONE, (128, pv_n_tile),
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tcgen05.OperandSource.SMEM,
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)
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pv_thr_split = pv_mma_split.get_slice(0)
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pv_as_split = pv_thr_split.partition_shape_C((128, pv_n_tile))
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tOtO_split = pv_thr_split.make_fragment_C(pv_as_split)
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o_cols_split = find_tmem_tensor_col_offset(tOtO_split)
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total_split = o_cols_split
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print(f" Split-PV (128, {pv_n_tile}) O cols: {o_cols_split}, total SMEM-P: {total_split} / 512 {'✅' if total_split <= 512 else '❌ OVER BUDGET'}")
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if __name__ == '__main__':
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for hd in [64, 128, 256, 512]:
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probe_hd(hd)
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