docs: KVC v1-v4 debug journey + raise session soft_cap to 16
Document the iterative debugging from v1 (broken KVC) through v4
(routing fixed + session cap raised), with code-level analysis of
the two main bugs encountered:
1. v2 root cause (mis-diagnosed previously as `allow_local_prefill`):
`--policy default` for KVC mechanism caused replay's round-robin
policy and the PD router's round-robin to diverge, sending requests
with `session_params` to a D worker that did not have the session
open. Resulted in 56-61% truncation with finish_reason
"session id X does not exist".
Fix: use `--policy kv-aware` (sweep_tp1_v3_kvaware.sh) so replay
emits `x-smg-target-worker` and PD router uses consistent_hashing.
2. v3 new bottleneck: `pd-router-fallback-large-append-session-cap`
dominated 52-65% of requests. Root cause was hardcoded
`min(4, ...)` in `_decode_session_soft_cap`. With 7 D workers x 4
sessions = 28 slots for 52 trace sessions, ~24 sessions starved
permanently (bimodal direct-to-D rate of 0% or 99%).
Fix: raise the cap to 16 (replay.py).
Also includes the v3 finding that direct-to-d-session path P50=0.495s
and TTFT P50=0.043s already beats the 8-way DP baseline (0.65s/0.093s)
- the KVC core mechanism works when fallback paths are avoided.
Files:
- docs/KVC_DEBUG_JOURNEY_V1_TO_V4.md: full journey + code location index
- docs/SWEBENCH_EXPERIMENT_{PROGRESS,RESULTS}.md: prior session notes
- scripts/sweep_tp1_v{2,3,4}*.sh: experiment driver scripts
- src/agentic_pd_hybrid/replay.py: cap 4 -> 16, audit fields
- src/agentic_pd_hybrid/pd_router.py: strip session_params from prefill
- src/agentic_pd_hybrid/metrics.py: truncated_request_count
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
kzlin
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docs/KVC_DEBUG_JOURNEY_V1_TO_V4.md
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docs/KVC_DEBUG_JOURNEY_V1_TO_V4.md
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# KVC 实验踩坑记录与代码 Bug 分析（v1 → v4）
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记录从 v1 到 v4 KVC 实验的踩坑过程、错误诊断、以及最终定位的代码 bug。
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模型: Qwen3-30B-A3B (TP1)，硬件: 单节点 8×H100 80GB。
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Trace: `qwen35-swebench-50sess.jsonl`（4449 请求，52 sessions）。
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## TL;DR
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| 版本 | 关键变化 | 截断率 | direct-to-D 占比 | P50 | 主要瓶颈 |
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|------|----------|:---:|:---:|:---:|----------|
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| v1 (smoke / 早期) | mechanism 跑通 | - | - | - | - |
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| v2 | KVC + `--policy default` | **56.8% / 61.4%** | <0.1% | 0.08s* | Routing 错位（默认策略） |
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| v3 | KVC + `--policy kv-aware` | **0.9%** | 30-42% | 1.5-1.8s | session-cap fallback (52-65%) |
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| v4 | v3 + soft_cap 4→16 | (待数据) | (待数据) | (待数据) | (待数据) |
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`*` v2 的 P50 是假数字——超过半数请求只生成 1 个 token 就被 abort。
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## v2 踩坑：Default policy 与 KVC 机制根本不兼容
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### 表象
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`scripts/sweep_tp1_v2_fixed.sh` 跑出来：
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- Exp1（8-way DP，baseline）：4449/4449 成功，P50=0.65s，error=0
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- Exp2（1P7D KVC）：**2524 truncated (56.8%)**，18 errors，P50=0.08s* (假)
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- Exp3（2P6D KVC）：**2733 truncated (61.4%)**，17 errors，P50=0.08s* (假)
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每个截断请求 `actual_output_tokens=1`，`finish_reason="abort: session id X does not exist"`。
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### 错误的早期诊断
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之前 `RESULTS_SUMMARY.md` 把锅扣在 SGLang 的 `--disaggregation-decode-allow-local-prefill` flag 上，认为是 D worker 在有 `bootstrap_room` 时仍然做了 local prefill。这个诊断**完全错误**——查 `scheduler.py:1975-1980` 的 `_should_allow_local_prefill_on_decode`：
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```python
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def _should_allow_local_prefill_on_decode(self, req: Req) -> bool:
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return (
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self.disaggregation_mode == DisaggregationMode.DECODE
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and self.server_args.disaggregation_decode_allow_local_prefill
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and req.bootstrap_room is None # ← 有 bootstrap_room 不会走 local prefill
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)
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```
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KVC reseed 路径的请求都带 `bootstrap_room`，根本不会触发 local prefill。
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### 实际根因：Replay 与 PD Router 的 round-robin 错位
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实验脚本里 KVC 用 `--policy default`，而 baseline 用 `--policy kv-aware`。
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看 `benchmark.py:287-300` 这两者的差别巨大：
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```python
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def _decode_policy_for(policy_name: str) -> str:
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if policy_name == "sticky": return "manual"
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if policy_name == "kv-aware": return "consistent_hashing"
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return "round_robin" # default
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def _header_mode_for(policy_name: str) -> str:
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if policy_name == "sticky": return "routing-key"
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if policy_name == "kv-aware": return "target-worker"
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return "none" # default
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```
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`default` policy + KVC 机制下：
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1. Replay policy（`policies.py:DefaultPolicy`）round-robin 选一个 D，比如 D-3
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2. Replay 在 D-3 上 `open_session(session_id=X)`（`replay.py:1722-1731`）
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3. Replay 通过 PD Router 发请求（带 `session_params`），但 `header_mode=none`，**不发任何 routing header**
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4. PD Router (`pd_router.py:_select_decode_index`) 看到 `decode_policy=round_robin`，用**自己独立的计数器**round-robin，发到了 D-5
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5. D-5 的 scheduler 看到 `session_params` 里有 session_id，但自己的 `session_controller` 里没这个 session（session 在 D-3 上）→ abort with `"Invalid request: session id X does not exist"` (`scheduler.py:1824-1836`)
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两个独立的 round-robin 计数器只要一次错位（任何并发或 direct-to-D 绕过 router 的请求都会引起）就永远对不上。
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### 为什么 turn 0 不出问题？
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Turn 0 走 `_invoke_plain_router`（`replay.py:1894`），不带 `session_params`，作为普通 PD disagg 请求处理，发到任何 D 都行。Turn 1+ 才开始走带 session_params 的 KVC 路径，撞上路由错位。
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### 数据特征验证（per-session pattern）
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```
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session 11360 (58 turns): pattern = .TTTTT.TTTTTTT.TTTTTT... ← turn 0 OK，1+ 全 T
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session 18720 (87 turns): pattern = .TTTTTTTTTTTTTTTTTT...
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```
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每个 D worker 收到了全部 52 个 session 的请求（理想情况下应该是 ~7-8 个/D，因为 round-robin 把 session 完全打散）。
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### 修复
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唯一正确的修复是把 KVC 的 policy 从 `default` 改成 `kv-aware`：
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```diff
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- --policy default
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+ --policy kv-aware
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```
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`KvAwarePolicy` (`policies.py:146-187`) 做两件事：
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1. 用 `_overlap_blocks` + `sticky_bonus` 给每个 D 打分，session 自然粘在同一个 D（**session 亲和性**）
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2. `header_mode=target-worker`，发 `x-smg-target-worker` header
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3. PD Router 用 `consistent_hashing` 模式，看到 header 就直接用，不再 round-robin
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## v3 改 kv-aware policy 后：路由对了，但新瓶颈出现
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`scripts/sweep_tp1_v3_kvaware.sh` 把所有 KVC 实验改成 `--policy kv-aware`，结果：
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| 指标 | v2 1P7D (default) | **v3 1P7D (kv-aware)** | v3 2P6D | 8-way DP baseline |
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|------|:---:|:---:|:---:|:---:|
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| 截断 | 56.8% | **0.9%** | 0.9% | 1.5% |
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| Errors | 18 | 363 (8.2%) | 9 | 0 |
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| Mean | 4.74s | 4.88s | 3.58s | 1.43s |
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| P50 | 0.08s* (假) | 1.75s | 1.52s | 0.65s |
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| P90 | 12.14s | 12.67s | 9.23s | 3.61s |
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| TTFT P50 | - | 0.36s | 0.33s | 0.09s |
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✅ **截断从 56.8% 降到 0.9%，路由问题彻底解决**。
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❌ 但 P50 仍然是 baseline 的 2-3 倍。
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### Direct-to-D 路径表现优秀（KVC 该有的样子）
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按 execution_mode 拆开看：
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| 路径 | Exp1 1P7D 占比 | Exp1 1P7D P50 | Exp1 1P7D TTFT P50 |
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|------|:---:|:---:|:---:|
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| `kvcache-direct-to-d-session` ✨ | 42.0% | **0.495s** | **0.043s** |
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| `pd-router-fallback-large-append-session-cap` 🔥 | **52.6%** | 5.6s | 3.7s |
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Direct-to-D 路径下：
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- P50 = 0.495s（**比 baseline 0.65s 快 25%**）
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- TTFT P50 = 0.043s（**比 baseline 0.093s 快 2 倍**）
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- KV transfer = 0（无 P 介入，纯 D 上 append-prefill）
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这才是 KVC 真正的价值。但只有 30-42% 请求走到这条路。
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### 新瓶颈：session-cap fallback 占了 52-65%
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`pd-router-fallback-large-append-session-cap` 占 1P7D 的 52.6%、2P6D 的 65.4%。这条路径意味着 router 想开新 session 在 D 上，但 admission 拒绝了（"d-session-cap"），只好回退到 plain router（P 全量 prefill + 传给 D，无 session 复用）。
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### Bimodal session 分布（starvation）
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| Session | Total turns | Direct-to-D | Session-cap fallback |
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|---------|:---:|:---:|:---:|
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| 22080 | 129 | **98%** | 0% |
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| 3840 | 118 | **97%** | 0% |
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| 70560 | 150 | **0%** | **99%** |
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| 39360 | 148 | **0%** | **99%** |
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| 61600 | 117 | **0%** | **99%** |
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要么完全幸运，要么完全饿死——典型的双峰分布。
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### 根因：硬编码 cap=4
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看 `replay.py:_decode_session_soft_cap` 原始代码：
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```python
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def _decode_session_soft_cap(...) -> int:
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target_tokens = max(1, _estimate_session_resident_tokens(request))
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usable_capacity_tokens = _usable_capacity_tokens(residency, server_url)
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...
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if usable_capacity_tokens <= 0:
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return 4
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return max(1, min(4, usable_capacity_tokens // target_tokens))
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# ^^^ 硬编码上限 4
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```
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7 个 D × 每个 D 最多 4 个 session = **28 个 session slot 总容量**。Trace 有 52 个 session → 24 个 session 永远抢不到 slot。
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启动期 race condition 决定了哪些 session 是"幸运儿"——前 28 个挤进来的 session 的所有后续 turn 都走 direct-to-D（快）；剩下 24 个 session 永远走 session-cap fallback（慢）。
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## v4 改进：把硬 cap 从 4 提到 16
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`replay.py:_decode_session_soft_cap` 一行修改：
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```diff
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- if usable_capacity_tokens <= 0:
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- return 4
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- return max(1, min(4, usable_capacity_tokens // target_tokens))
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+ if usable_capacity_tokens <= 0:
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+ return 16
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+ return max(1, min(16, usable_capacity_tokens // target_tokens))
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```
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7 D × 16 = 112 个 slot，远超 52 个 session 需求。预期 session-cap fallback 占比降到 <10%，整体 P50 向 direct-to-D 的 0.46s 收敛。
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实际数据见 `outputs/qwen3-30b-tp1-v4-cap16/`。
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## 后续可以考虑的更深方案：让 D 自己决定 admission
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v4 的硬 cap 抬高只是把数字调大，实际容量管理还是 replay 自己估算。代码里 `replay.py:_decode_session_soft_cap` 用 `target_tokens = input + output`（基于当前请求的 size）估算每个 session footprint，但：
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- agentic context 越攒越长，target_tokens 动态增长，cap 会随之缩小
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- 多个并发请求查询时 replay 视图会过期
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- replay 自己写了 LRU eviction 逻辑（`_reserve_decode_session_capacity_from_router_state`），与 SGLang 内部的 `maybe_trim_decode_session_cache` 重复且永远滞后
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SGLang 已经提供 `/session_cache/admit_direct_append` 端点（`scheduler.py:3497`），D worker 自己回答能不能 admit，并且查询时**主动调用 LRU eviction**。但这个端点只在 direct-to-D 路径用，seed/reseed 路径用的是 replay 自己估算的 soft_cap。
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理想方案是扩展端点支持 `seed_new` / `reseed` 模式，replay 完全交给 D 决策——但这需要 SGLang 侧 patch + replay 重构（~200 行），工程量比 v4 大得多。
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## 关键文件与代码位置索引
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| 现象 | 代码位置 |
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|------|----------|
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| Replay policy round-robin | `policies.py:63-67` `RoutingState.next_decode_worker_id` |
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| KV-aware policy（session 亲和） | `policies.py:146-187` `KvAwarePolicy.select` |
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| PD router decode 选择 | `pd_router.py:51-74` `_select_decode_index` |
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| Header 构建 | `replay.py:2407-2424` `_build_headers` |
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| Policy → router config 映射 | `benchmark.py:287-300` `_decode_policy_for/_header_mode_for` |
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| Session admission 软 cap | `replay.py:889-905` `_decode_session_soft_cap` |
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| 已有的 D 侧 admission 端点 | `scheduler.py:3497-3580` `admit_direct_append` |
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| Session 在 D 上找不到的报错 | `scheduler.py:1824-1836` |
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| `_should_allow_local_prefill_on_decode` | `scheduler.py:1975-1980` |
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| Reseed 流程入口 | `replay.py:1665-1809` `_invoke_kvcache_seeded_router` |
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| Direct-to-D 流程 | `replay.py:2351-2398` `_invoke_decode_session_direct` |
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## 经验教训
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1. **policy 和 mechanism 是两个正交维度**——`--policy default` 不是"无脑默认值"，它真的是 round-robin 无 session 亲和性。KVC 机制必须配 session 亲和的 policy。
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2. **不要无脑相信前一个 agent 的 RESULTS_SUMMARY**——v2 的诊断（"local prefill bug"）和实际 finish_reason（"session id does not exist"）完全对不上。任何错误诊断必须用 finish_reason、execution_mode 这些原始字段交叉验证。
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3. **bimodal 分布是 starvation 的强信号**——v3 数据里某些 session 100% 走快路径、某些 100% 走慢路径，几乎肯定是某种"先到先得"的资源竞争。看到这种模式立刻去找硬编码 cap 或全局共享资源。
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4. **测量要看分组而非整体均值**——v3 整体 P50=1.5s 看似比 baseline 慢，但拆开看 direct-to-D 子集 P50=0.495s 已经反超 baseline。整体均值被 fallback 路径拖累，但 KVC 的核心价值是真实存在的。
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docs/SWEBENCH_EXPERIMENT_PROGRESS.md
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docs/SWEBENCH_EXPERIMENT_PROGRESS.md
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# SWE-Bench PD Hybrid Experiment Progress
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## 实验目标
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在单节点 8xH100 上复现 agentic-pd-hybrid 三种 serving mechanism，对比 Qwen3.5-35B-A3B 在 SWE-Bench 500 instance agentic trajectory 上的性能。
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## 硬件环境
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- 8x H100 80GB (NVLink 互联, 2 NUMA nodes: GPU 0-3 / GPU 4-7)
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- 无 RDMA/IB 设备
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- Transfer backend: **mooncake TCP** (nixl UCX 因 pip 包缺少 CUDA 支持导致 segfault，已放弃)
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## 实验矩阵
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| 实验 | Mechanism | Workers | GPU 分配 | Router | Policy |
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|------|-----------|---------|----------|--------|--------|
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| A | pd-disaggregation | 1P + 1D (TP4 each) | P: 0-3, D: 4-7 | Yes | default |
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| B | pd-colo | 2 direct (TP4 each) | D0: 0-3, D1: 4-7 | No | default |
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| C | kvcache-centric | 1P + 1D (TP4 each) | P: 0-3, D: 4-7 | Yes | default |
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## 测试负载
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- 源数据: `simm-swe-bench/outputs/20260416-205833-hicache-qwen35-verified-0-500/audit.jsonl`
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- 39,417 lines (turns), 497 unique instances (sessions)
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- 每个 instance 8-150 turns (均值 79.3)
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- 转换为 agentic-pd-hybrid trace 格式: `outputs/qwen35-swebench-500.jsonl`
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## 关键发现
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### Transfer Backend 选择
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- **nixl (UCX)**: pip 安装的 nixl_cu12 包自带的 UCX 库没有 CUDA 支持，导致 GPU memory registration 时 segfault。系统 UCX (/opt/hpcx/ucx) 有 CUDA 支持但因 RPATH 无法被 NIXL 使用。
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- **mooncake (TCP)**: 可用。需要两处修改:
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1. `third_party/sglang/.../mooncake_transfer_engine.py`: 从环境变量 `MOONCAKE_PROTOCOL` 读取协议，而非硬编码 `"rdma"`
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2. `src/agentic_pd_hybrid/stack.py`: 当 `transfer_backend == "mooncake"` 且非 `force_rdma` 时，自动设置 `MOONCAKE_PROTOCOL=tcp`
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### 代码修改记录
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1. **`third_party/sglang/python/sglang/srt/distributed/device_communicators/mooncake_transfer_engine.py`**
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- 将 `"rdma"` 硬编码改为 `os.environ.get("MOONCAKE_PROTOCOL", "rdma")`
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2. **`src/agentic_pd_hybrid/stack.py`**
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- 在 `_build_process_env()` 中添加: mooncake 非 force_rdma 时默认设置 `MOONCAKE_PROTOCOL=tcp`
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3. **`scripts/convert_audit_to_trace.py`** (新建)
|
||||
- 将 sibench audit.jsonl 转换为 agentic-pd-hybrid trace 格式
|
||||
|
||||
## 实验进度
|
||||
|
||||
- [x] Step 0: 环境准备 (uv sync, nixl/mooncake 安装)
|
||||
- [x] Step 1: Trace 格式转换 (39,417 lines 验证通过)
|
||||
- [x] Step 2: Smoke test (pd-disaggregation, mooncake TCP, 100 requests) — **通过**
|
||||
- 100/100 requests, 0 errors
|
||||
- Mean latency: 1.53s, P50: 0.77s, P90: 2.82s
|
||||
- TTFT: mean 0.49s, P50 0.29s; TPOT: mean 4.7ms
|
||||
- 91/100 cache hits
|
||||
- [x] Step 3a: 实验 A 全量尝试 (39K reqs, 497 sessions) — **中止**
|
||||
- Run dir: `outputs/swebench-exps/pd-disaggregation-default-20260426T171113Z` (无metrics,被kill)
|
||||
- 前 90% 完成 ~80min (~8-10 req/s), 但尾部 D 侧 KV cache 98% 饱和
|
||||
- 497 并发 session 争抢 D 侧 token 空间, mamba 80-93 sessions 无法 drain
|
||||
- **教训**: 1P+1D (TP4) 无法支撑 497 并发 session, 需减少 session 数量或降低 concurrency
|
||||
- [x] Step 3b: 实验 A — pd-disaggregation (52 sessions, 4449 reqs, concurrency=32) — **完成**
|
||||
- Run dir: `outputs/swebench-exps/pd-disaggregation-default-20260426T202540Z`
|
||||
- Trace: `outputs/qwen35-swebench-50sess.jsonl` (10% sample, 52 sessions)
|
||||
- **结果**: 4449/4449 成功, 0 errors
|
||||
- Latency: mean=1.66s, P50=0.97s, P90=3.64s, P99=7.68s
|
||||
- TTFT: mean=0.45s, P50=0.34s, P90=0.88s
|
||||
- TPOT: mean=5.2ms, P50=5.2ms
|
||||
- Cache hit: 4199/4449 (94.4%)
|
||||
- [x] Step 4: 实验 B — pd-colo — **失败: SGLang bug**
|
||||
- Run dir: `outputs/swebench-exps/pd-colo-default-20260426T210129Z`
|
||||
- **Bug**: `--disaggregation-mode null` (colocation) 下 Qwen3.5-35B-A3B 模型触发 token_to_kv_pool_allocator 内存泄漏
|
||||
- 错误: `ValueError: token_to_kv_pool_allocator memory leak detected!`
|
||||
- 两个 direct worker 在处理 ~5 个请求后均 crash (Scheduler exception)
|
||||
- **结论**: 当前 vendored SGLang v0.5.10 不支持 Qwen3.5-35B-A3B 的 colocation 模式
|
||||
- [x] Step 5: 实验 C — kvcache-centric — **完成 (高错误率)**
|
||||
- Run dir: `outputs/swebench-exps/kvcache-centric-default-worker-admission-20260426T210800Z`
|
||||
- 4390/4449 errors (98.7%) — admission control 过于保守
|
||||
- 59 成功请求: mean latency 1.24s (比 pd-disagg 快 25%), TTFT 0.18s (快 60%)
|
||||
- 详细分析见 `docs/SWEBENCH_EXPERIMENT_RESULTS.md`
|
||||
- [x] Step 6: 结果对比分析 — **完成**
|
||||
- 完整报告: `docs/SWEBENCH_EXPERIMENT_RESULTS.md`
|
||||
|
||||
## 启动脚本
|
||||
|
||||
- `scripts/run_exp_a_pd_disagg.sh` — 实验 A
|
||||
- `scripts/run_exp_b_pd_colo.sh` — 实验 B
|
||||
- `scripts/run_exp_c_kvcache_centric.sh` — 实验 C
|
||||
- `scripts/convert_audit_to_trace.py` — Trace 转换
|
||||
|
||||
## 已知风险
|
||||
|
||||
1. Qwen3.5-35B-A3B TP4 可用 mem ~12GB/GPU (after model + CUDA graph)，长 session (150 turns) 可能 OOM
|
||||
2. mooncake TCP loopback 延迟远低于真实跨机，结果偏乐观
|
||||
3. 原始 trace 时间跨度 ~6000s，全量回放非常耗时
|
||||
121
docs/SWEBENCH_EXPERIMENT_RESULTS.md
Normal file
121
docs/SWEBENCH_EXPERIMENT_RESULTS.md
Normal file
@@ -0,0 +1,121 @@
|
||||
# SWE-Bench PD Hybrid Experiment Results
|
||||
|
||||
## 实验配置
|
||||
|
||||
- **模型**: Qwen3.5-35B-A3B (MoE, 35B total / 3B active), TP4
|
||||
- **硬件**: 8x H100 80GB, NVLink, 单节点
|
||||
- **Transfer backend**: mooncake TCP (loopback)
|
||||
- **Trace**: 52 sessions, 4,449 requests (10% sample of SWE-Bench 500 instances)
|
||||
- **时间压缩**: time-scale=10, concurrency-limit=32
|
||||
|
||||
## 结果汇总
|
||||
|
||||
### Experiment A: pd-disaggregation (baseline)
|
||||
|
||||
| Metric | Value |
|
||||
|--------|-------|
|
||||
| Run dir | `pd-disaggregation-default-20260426T202540Z` |
|
||||
| Requests | 4,449 / 4,449 (100%) |
|
||||
| Errors | 0 |
|
||||
| **Mean Latency** | **1.662s** |
|
||||
| P50 Latency | 0.973s |
|
||||
| P90 Latency | 3.644s |
|
||||
| P99 Latency | 7.676s |
|
||||
| Mean TTFT | 0.445s |
|
||||
| P50 TTFT | 0.340s |
|
||||
| P90 TTFT | 0.880s |
|
||||
| Mean TPOT | 5.20ms |
|
||||
| Cache Hit Rate | 94.4% (4199/4449) |
|
||||
| Mean Cached Tokens | 27,794 |
|
||||
| KV Transfer Blocks | 105,235 |
|
||||
|
||||
### Experiment B: pd-colo (colocation) — FAILED
|
||||
|
||||
| Metric | Value |
|
||||
|--------|-------|
|
||||
| Run dir | `pd-colo-default-20260426T210129Z` |
|
||||
| Status | **CRASHED** |
|
||||
| Error | `token_to_kv_pool_allocator memory leak detected!` |
|
||||
| Root Cause | SGLang v0.5.10 `--disaggregation-mode null` 与 Qwen3.5-35B-A3B (Mamba/GDN hybrid) 不兼容 |
|
||||
| Requests | ~10 / 4,449 (0.2%) |
|
||||
|
||||
**结论**: 当前 vendored SGLang 不支持此模型的 colocation 模式。需要修复 token_to_kv_pool_allocator 中 Mamba 模型的内存管理。
|
||||
|
||||
### Experiment C: kvcache-centric (session-aware PD)
|
||||
|
||||
| Metric | Value |
|
||||
|--------|-------|
|
||||
| Run dir | `kvcache-centric-default-worker-admission-20260426T210800Z` |
|
||||
| Requests | 4,449 total |
|
||||
| **Errors** | **4,390 (98.7%)** |
|
||||
| Successful | 59 (1.3%) |
|
||||
| Mean Latency (success) | 1.238s |
|
||||
| P50 Latency (success) | 0.484s |
|
||||
| P90 Latency (success) | 2.550s |
|
||||
| Mean TTFT (success) | 0.179s |
|
||||
| P50 TTFT (success) | 0.081s |
|
||||
| Mean TPOT (success) | 4.70ms |
|
||||
| Direct-to-D Sessions | 56 |
|
||||
| KV Transfer (actual) | 196 blocks (vs 105,235 planned) |
|
||||
|
||||
**Execution Mode 分布**:
|
||||
- `kvcache-centric` (failed): 4,390
|
||||
- `kvcache-direct-to-d-session` (success): 56
|
||||
- `pd-router-*` variants: 3
|
||||
|
||||
## 关键分析
|
||||
|
||||
### 1. pd-disaggregation (A) — 稳定可靠
|
||||
|
||||
- 100% 成功率，0 错误
|
||||
- Mean latency 1.66s 合理 (包含 P→D KV transfer 开销)
|
||||
- 94.4% cache hit 说明 prefix cache 在 P 侧工作良好
|
||||
- KV transfer 105K blocks = 主要开销来源
|
||||
- **适合生产使用**
|
||||
|
||||
### 2. pd-colo (B) — 不可用
|
||||
|
||||
- Qwen3.5-35B-A3B 的 Mamba/GDN hybrid 架构在 `disaggregation-mode null` 下触发内存泄漏
|
||||
- 这是 SGLang 的 bug，不是 agentic-pd-hybrid 的问题
|
||||
- **需要 SGLang 修复后重新测试**
|
||||
|
||||
### 3. kvcache-centric (C) — Admission 过于保守
|
||||
|
||||
- 98.7% 错误率说明 admission control 拒绝了几乎所有请求
|
||||
- `kvcache-seed-min-turn-id=2` 过滤了 turn 1 的 seed（正确行为）
|
||||
- 但绝大多数 turn 2+ 请求也走 `kvcache-centric` 模式后失败
|
||||
- 可能原因:
|
||||
- Worker admission 查询发现 D 侧没有对应 session 的 KV cache（因为 turn 1 没有 seed）
|
||||
- D 侧 transfer queue 积压导致 admission 拒绝
|
||||
- 成功的 56 个 `direct-to-d-session` 请求表现优异: TTFT 0.08s (P50), 比 pd-disagg 的 0.34s 快 4x
|
||||
- **需要调优 admission 参数，或使用 `kvcache-seed-min-turn-id=1` 允许 turn 1 seed**
|
||||
|
||||
### 4. kvcache-centric 成功请求 vs pd-disaggregation 对比
|
||||
|
||||
| Metric | pd-disagg (A) | kvcache-centric (C, success only) | Delta |
|
||||
|--------|:---:|:---:|:---:|
|
||||
| Mean Latency | 1.662s | 1.238s | **-25.5%** |
|
||||
| P50 Latency | 0.973s | 0.484s | **-50.3%** |
|
||||
| Mean TTFT | 0.445s | 0.179s | **-59.8%** |
|
||||
| P50 TTFT | 0.340s | 0.081s | **-76.2%** |
|
||||
| Mean TPOT | 5.20ms | 4.70ms | -9.6% |
|
||||
| Actual KV Transfer | 105,235 blk | 196 blk | **-99.8%** |
|
||||
|
||||
**当 kvcache-centric 成功时，性能提升显著：**
|
||||
- TTFT 降低 60-76% (D 侧直接 append，无需 P→D transfer)
|
||||
- 端到端 latency 降低 25-50%
|
||||
- KV transfer 减少 99.8%
|
||||
|
||||
## 后续建议
|
||||
|
||||
1. **修复 pd-colo**: 提交 SGLang issue 关于 Mamba/GDN 模型在 disaggregation-mode null 下的内存泄漏
|
||||
2. **调优 kvcache-centric admission**:
|
||||
- 尝试 `--kvcache-seed-min-turn-id 1` 允许 turn 1 seed
|
||||
- 放宽 `--kvcache-seed-max-decode-transfer-queue-reqs` 阈值
|
||||
- 使用 `--kvcache-admission-mode router` (shadow state, 不在 critical path)
|
||||
3. **增加 D 侧内存**: 调整 `--mem-fraction-static` 给 KV cache 更多空间
|
||||
4. **多 P/D 配置**: 测试 2P2D (TP2) 配置以增加并行度
|
||||
|
||||
## 实验日期
|
||||
|
||||
2026-04-27
|
||||
Reference in New Issue
Block a user