15753cb68d
Key findings from full source code analysis: - enginex ships .so + Python + Triton, NO .cu source - gen_patch.py VLLM_INJECTION_POINTS all dead (confirmed in code) - Real optimization: prefix_prefill.py + paged_attn.py Triton params - bi100_configs.json: 22 flash_attn + 9 prefill + 5 moe configs done - muh 27 headers average 21% coverage of CCCL (3618 vs 17000+ lines) - cccl_upstream already has everything needed, no full clone required - bench_bi100.py written but needs BI-V100 hardware to produce real data
125 lines
5.3 KiB
Markdown
125 lines
5.3 KiB
Markdown
# project_6 真实状态报告
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生成时间: 2026-08-05, commit 96f6465
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## 一句话总结
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**enginex 没有 .cu 源码，gen_patch 的 C++ injection 管道全部失效。** 实际可用的优化路径只有 Python/Triton 层面的参数调优。muh 的 27 个 C++ tuning headers 是正确的架构设计，但在竞赛引擎上无处注入。
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---
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## 1. 竞赛引擎的致命事实
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```
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gen_patch.py 第 47 行:
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WARNING: ALL csrc/*.cu targets are DEAD — files do not exist.
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enginex-vllm-bi100-qwen36 ships: Python + precompiled .so + Triton.
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No .cu source files. gen_patch patches have zero effect.
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```
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enginex 交付物 = Python 文件 + 预编译 .so + Triton kernels。
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不提供 C 源码 → 无法修改 CUDA kernel → C++ tuning header 无法注入到 vllm 的编译产物里。
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**真正的优化路径:**
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- Triton kernels (prefix_prefill.py, paged_attn.py): 可以改 BLOCK、NUM_WARPS 等 JIT 参数
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- Python 配置层 (computility-run.yaml): max_model_len、gpu_memory_utilization 等
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- 模型适配 (qwen3_5.py): MoE routing、attention 实现
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## 2. 已有的 benchmark 数据 (真实的)
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| 算法域 | 已跑配置数 | 来源 |
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|--------|-----------|------|
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| flash_attn | 22 configs | bi100_configs.json, SMEM 约束扫描 |
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| prefill (Triton) | 9 configs | bi100_configs.json, BLOCK×NUM_WARPS |
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| MoE | 5 configs | bi100_configs.json, BLOCK_SIZE_M |
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| reduce/scan/topk CUB | 0 | bench_bi100.py 已写但需要 BI-V100 硬件才能跑 |
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## 3. muh C++ headers vs CCCL 覆盖率
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| 算法 | muh 行数 | CCCL 行数 | 覆盖率 | 竞赛优先级 |
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|------|---------|---------|--------|-----------|
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| reduce | 297 | 478 | 62% | **P0** — Output TPS 83% 权重 |
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| scan | 352 | 1525 | 23% | **P0** — softmax 累积 |
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| topk | 113 | 121 | 93% | **P0** — sampling 路径 |
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| transform | 185 | 549 | 33% | P1 — RMSNorm/SiLU |
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| select_if | 459 | 2729 | 16% | P1 — token filtering |
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| radix_sort | 222 | 2381 | 9% | P1 — full sort path |
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| scan_by_key | 145 | 2008 | 7% | P1 — per-seq softmax |
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| reduce_by_key | 171 | 1735 | 9% | P1 — score aggregation |
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| unique_by_key | 166 | 1539 | 10% | P1 — KV cache dedup |
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| 其余 18 个 | 33-189 | 78-788 | 10-65% | P2 |
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总计: muh 3618 行 vs CCCL 17000+ 行 = 平均 21% 覆盖率
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## 4. CCCL 资产完整性
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cccl_upstream/ 34MB, 3432 files — 是精选提取, 不是 full clone。
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**已有 (竞赛必需的全有):**
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- 27/27 tuning headers ✓
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- 32/32 dispatch implementations ✓
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- 25/25 agent kernels ✓
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- 60/60 Thrust examples ✓
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- 243 CUB tests ✓
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- 78 CUB benchmark .cu files ✓
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- 230 Thrust tests ✓
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- 48 Thrust benchmark algorithms ✓
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**不需要 full clone。** 缺的 ~21000 文件是 CI/CD、cudax、Python bindings、docs。
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## 5. 真正的行动路径
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### 短期 (功能测试通过)
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竞赛门控: 50+ 功能测试全通过 + 效果偏差 ≤ ±4%
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关键文件:
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- `computility-run.yaml` — 控制 vllm 启动参数
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- `qwen3_6_scripts/qwen3_5.py` (588行) — MoE 模型适配
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- `prefix_prefill.py` — Triton prefill kernel, 可调 BLOCK/NUM_WARPS
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- `paged_attn.py` — Triton decode kernel
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### 中期 (性能优化)
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目标: Token 吞吐加权值 ≥ 8000
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```
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加权值 = Output_TPS × 16.796 + Input_TPS × 2.799 + Cache_TPS × 0.56
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```
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**Output TPS (83%):** decode kernel → paged_attn.py Triton 参数优化
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**Input TPS (14%):** prefill kernel → prefix_prefill.py Triton 参数优化
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**Cache TPS (3%):** prefix caching 配置
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### 长期 (如果能编译 C++)
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如果能获取 EngineX 的 C 编译环境:
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- muh C++ headers 可以直接注入
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- bench_bi100.py 的 CUB parameter sweep 可以在 BI-V100 上跑
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- 这条路 ROI 最高但依赖竞赛方提供编译链
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## 6. 代码架构
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```
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project_6/
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├── computility-run.yaml ← 竞赛提交配置 (直接影响评测)
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├── baseline.muh ← muh 格式的 vllm 配置
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├── Dockerfile ← 竞赛镜像构建
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├── cccl_upstream/ ← CCCL 精选 (34MB, 3432 files)
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│ ├── cub/ ← CUB: dispatch/tuning/agent/test/bench
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│ ├── thrust/ ← Thrust: examples/testing/benchmarks
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│ └── libcudacxx/ ← CUDA 标准库
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├── muh/ ← kernel tuning 框架 (544KB)
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│ ├── include/muh/tuning/ ← 27 个 BI-V100 tuning headers
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│ ├── bench_bi100.py ← CUB parameter sweep runner
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│ ├── gen_patch.py ← vllm patch 生成 (C++ 注入点已死)
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│ ├── gen_yaml.py ← computility-run.yaml 生成
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│ └── parse.py ← .muh 配置解析器
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├── muh_kernel_map.py ← CCCL 算法 → vllm kernel 映射
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├── muh_dispatch.py ← 运行时 policy 分派
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├── vllm/ ← vllm 引擎源码 (11MB Python)
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├── vllm_adapter/ ← Qwen3.5 模型适配 + 部署脚本
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├── qwen3_6_scripts/ ← Qwen3.6 patch 集合 (576KB, 25+ patches)
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├── prefix_prefill.py ← Triton prefill kernel (可调优)
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├── paged_attn.py ← Triton decode kernel (可调优)
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├── attention.py ← Attention 实现
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└── enginex-vllm-bi100-qwen36-main.zip ← 竞赛基础引擎 (97MB)
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```
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