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dylanyunlon 19879ccaae docs: pipeline ground truth — CCCL parity audit, SM100 benchmark data, injection status

Key findings:
- scale_mem_bound: 11/11 FULL PARITY with CCCL
- 27/27 tuning headers complete
- All vllm injection points already deployed via Python modifications
- gen_patch.py role: verification tool (enginex has no .cu source)
- CCCL SM100 benchmark values extracted to JSON for reference

2026-08-07 07:20:34 +00:00

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muh Pipeline Ground Truth — 2026-08-07

管道实际状态（不是设计稿，是已部署代码的真实描述）

scale_mem_bound: FULL PARITY ✓

11/11测试用例与CCCL cub::detail::scale_mem_bound 完全匹配。返回值顺序 {items_per_thread, threads_per_block} — items-first，与CCCL一致。

C++ Tuning Headers: 27/27 ✓

所有26个算法（+common）都有bi100 header，policy_selector::operator() 接受 hardware_capability 参数。SMEM overflow保护覆盖所有type_size。

Injection现状（enginex没有.cu源码）

注入位置	状态	值	commit
prefix_prefill.py BLOCK	✓ 已手动修改	BLOCK=64, WARPS=4	多个commit
paged_attn.py _PARTITION_SIZE	✓ 保持默认	512	—
paged_attn.py V1/V2 dispatch	✓ 已手动修改	use_v1 threshold	`cbd1f08`
_custom_ops.py SMEM	✓ 已手动修改	48KB	`16f0b30`
triton_flash_attention.py	✓ 已添加BI-V100 configs	BLOCK=32/64	多个commit
protocol.py 兼容性	✓ 已修复	max_completion_tokens等	`2c353da`

gen_patch.py 角色

设计时期望: C++ header → unified diff → vllm .cu文件实际情况: enginex只有Python + .so, 没有.cu源码当前角色: 文档工具 + 验证（确认header值与已部署Python代码一致）

CCCL SM100 Benchmark数据（从源码提取，已存入cccl_sm100_benchmark_values.json）

Reduce (paged_attention score reduction, Output TPS 83%权重):

float32+plus: items=16, threads=512, vec=2, speedup=[1.061, 1.000, 1.065, 1.167]
float64+plus: items=16, threads=640, vec=1, speedup=[1.018, 1.000, 1.016, 1.057]

Scan (softmax prefix-sum):

4B lookback: items=22, threads=384, delay=1904ns/dcid=6/l2w=830, speedup=[1.148, 0.997, 1.140, 1.463]
8B lookback: items=23, threads=416, delay=772ns/dcid=5/l2w=710, speedup=[1.089, 1.016, 1.086, 1.265]

muh BI-V100适配:

reduce float32: items=24(+50%), threads=512(=), vec=2(=) → 补偿16 SMs
scan 4B: 通过scale_mem_bound自动适配(items=22 @4B安全, @8B降级到16)
delay参数: ns×0.5, l2w×0.6 (启发式, 待实测)

竞赛门槛

功能测试: 50+ TC, 项目看板14个FEA item覆盖
效果测试: benchmark偏差 ≤ ±4%
性能测试: Token吞吐加权值 ≥ 8000
- Output TPS × 16.796 (83%) → reduce/scan/topk
- Input TPS × 2.799 (14%) → scan/transform
- Cache TPS × 0.56 (3%) → batch_memcpy

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