5c05a03470
gen_config.py replaces gen_patch.py's dead csrc/*.cu injection path. Generates Triton autotune configs derived from CCCL tuning principles: - SMEM constraints (Q_tile + K_tile <= 48KB for head_dim=256) - Occupancy model (16 SMs, register pressure per config) - bytes_in_flight (56 GB/s per-SM -> 64KB -> num_stages=2) 63 valid configs from 2304 combinations, 19 new. PIPELINE_REALITY_CHECK.md: enginex has no .cu source. All injection targets are Python/Triton, not C++.
72 lines
3.2 KiB
Markdown
72 lines
3.2 KiB
Markdown
# muh 管道现实检查 — 2026-08-07
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## 核心发现
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### 1. gen_patch.py 输出为零
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```
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$ python3 muh/gen_patch.py --dry-run
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READ reduce: bi100_plus_float32_o4 → {items: 24, threads: 512, vec: 2}
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READ scan: bi100_sm90_float32 → {threads: 128, items: 24}
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...
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No patches generated.
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```
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原因: `VLLM_INJECTION_POINTS` 的 key `('reduce', 'partition_size')` 和 struct 提取出的 field `items`/`threads`/`vec` 不匹配。gen_patch 的"读"和"写"两端从未对齐。
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### 2. 注入目标是 Python 不是 C++
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enginex-vllm-bi100 **没有 `.cu` 源码**。所有 CUDA kernel 是预编译的 ixformer `.so`。
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实际可调的全部是 Python 层:
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| 文件 | 可调参数 | 竞赛影响 |
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|------|---------|---------|
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| `paged_attn.py` | `_PARTITION_SIZE=512`, V1/V2 dispatch logic | Output TPS (83%) |
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| `prefix_prefill.py` | `BLOCK=64`, `BLOCK_N=64`, `NUM_WARPS=4` | Input TPS (14%) |
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| `vllm/attention/ops/triton_flash_attention.py` | 17 个 autotune configs | Prefill throughput |
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| `vllm/_custom_ops.py` | `return 49152` (SMEM fix) | 所有 Triton kernels |
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| `computility-run.yaml` | `--max-num-seqs`, `--gpu-memory-utilization` | 调度效率 |
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gen_patch.py 中的 `csrc/*.cu` 注入点全部是 dead code (注释已标注)。
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### 3. muh C++ headers 的实际价值
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muh 的 26 个 tuning headers 和 `scale_mem_bound` 实现是正确的理论分析工具。它们的价值不在于直接注入 vllm，而在于:
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- 推导 SMEM 约束 (Triton `BLOCK_M × head_dim × elem_size` 上限)
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- 推导 occupancy 模型 (BI-V100 16 SMs 的 wave efficiency)
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- 推导 bytes_in_flight (56 GB/s per-SM → 64KB prefetch window → `num_stages=2`)
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- 为 CCCL benchmark 验证提供 ground truth
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这些推导已经手工应用到了 Python 代码中:
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- `triton_flash_attention.py` 的 8 个 BI-V100 configs 引用了 CCCL babelstream/scan 分析
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- `prefix_prefill.py` 的 BLOCK_N=64 推导基于 48KB SMEM 约束
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- `_custom_ops.py` 的 49152 来自 hardware.cuh
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### 4. 管道闭环的正确路径
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```
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CCCL tuning analysis Python layer injection Triton autotune
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(理论推导) (参数修改) (运行时选择)
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│ │ │
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▼ ▼ ▼
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muh headers paged_attn.py triton.Config([...])
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common.cuh prefix_prefill.py autotune picks best
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hardware.cuh _custom_ops.py at runtime
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│ │ │
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└───────────────────────┴───────────────────────┘
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│
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竞赛评测得分
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```
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不是: `muh headers → gen_patch → #define injection → recompile`
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而是: `muh analysis → Python config → Triton autotune → runtime perf`
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## 下一步
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1. 删除 gen_patch.py 中所有 dead `csrc/*.cu` 注入点
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2. 重写 gen_patch 为 `gen_config.py`: 从 muh headers 推导 → 直接输出 Python patch
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3. 用 CCCL benchmarks 验证: reduce/sum.cu, scan/exclusive/sum.cu, topk/keys.cu
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4. 扩展 triton_flash_attention.py autotune 搜索空间 (当前 17 configs, 可加到 30+)
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