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docs/KVC_ROUTER_ALGORITHM.md
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docs/KVC_ROUTER_ALGORITHM.md
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# KVC-Router：面向 Agentic 多轮 LLM Serving 的 Session-Aware 调度算法
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**性质**：论文级形式化规范——用于团队内部对齐 + 外部读者 onboarding。
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**对象**：项目团队（统一术语）；论文 reviewer（算法定义）。
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**最近更新**：2026-05-11。
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本文给出本项目所开发的 **KVCache-Centric Router**（以下简称 "KVC-Router"）调度算法的形式化、与实现无关的定义。本文设计为可直接被论文引用，并作为"KVC 到底在谈论什么调度算法"的标准回答。
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对应的参考实现位于：
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- `src/agentic_pd_hybrid/policies.py` — `KvAwarePolicy`、`RoutingState`
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- `src/agentic_pd_hybrid/replay.py` — orchestration：admission RPC、reset-on-success、fallback chain
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- `third_party/sglang/python/sglang/srt/managers/scheduler.py` — D-worker 端的 admission 决策
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---
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## 1. 问题定义
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我们要服务一群多轮 agentic LLM session（如 Claude Code、Codex、Cursor 等 coding agent），底层是异构 worker 池，分成：
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- **Prefill workers**（`P`）：GPU 常驻的模型副本，针对长输入 prompt 的 batched prefill 做了优化。
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- **Decode workers**（`D`）：GPU 常驻的模型副本，配备 session-aware KV cache（"SessionAwareCache"），具备：(i) 跨 turn 保留 session 的 KV 状态；(ii) 在本地已缓存的 prefix 上做 append-prefill，无需绕回 `P`。
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在一个 agent turn 内，请求 `r` 到达时其对话 prefix 已经从前序 turn 累积；**新增**的 tokens（工具输出、用户消息等）构成小规模 **append**。驱动 KVC 设计的根本观察是：
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> 当 prefix KV **已经驻留在将要解码该请求的 D worker 上**，请求的 first-token 延迟仅由 *append* 大小决定（典型 O(10²–10³) tokens），而非完整 prompt 大小（典型 O(10⁴–10⁵) tokens）。
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Router 的工作就是最大化满足上述条件的请求占比，同时尊重容量约束、不造成 session 无限饿死。
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### 1.1 优化目标
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给定来自 `S` 个 session 的请求流 `R = (r_1, r_2, ...)`，最小化 SLO 加权的 TTFT 与端到端延迟混合：
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```
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minimize E[ w_ttft · TTFT(r) + w_lat · E2E_Latency(r) ]
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subject to capacity[d] ≤ K_d 对任意 D worker d 在任意时刻 t,
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没有 session 被永久拒绝服务.
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```
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参考实现中通过 measurement 隐式取 `w_ttft = 1, w_lat = 1`；per-D KV 池预算 `K_d` 取 SGLang 启动时上报的 `max_total_num_tokens`。
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---
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## 2. 系统模型与记号
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### 2.1 集合
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| 符号 | 含义 |
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|---|---|
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| `P = {p₁, …, p_|P|}` | Prefill worker 池 |
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| `D = {d₁, …, d_|D|}` | Decode worker 池 |
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| `S` | Session 标识符集合（由上游 agent runtime 分配） |
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| `H` | KV block hash 的全集（本实现中每 `BLOCK_TOKEN_BUDGET = 24` tokens 对应一个 hash） |
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### 2.2 请求
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一个请求 `r` 是一个元组：
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```
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r = ⟨ s(r), t(r), prefix_hashes(r), append_len(r), input_len(r) ⟩
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```
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其中：
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- `s(r) ∈ S` — session id
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- `t(r) ∈ ℕ` — 该 session 内的 turn index（0 = 首轮）
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- `prefix_hashes(r) ⊂ H` — 覆盖请求输入 prefix 的 block hash 集合
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- `append_len(r) ∈ ℕ` — 新到达、**不在** `prefix_hashes(r)` 中的 token 数
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- `input_len(r) = (|prefix_hashes(r)| · 24) + append_len(r)` — 总 token 数
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### 2.3 Router 状态 (`Σ`)
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Router 跨请求维护的全局状态：
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| 字段 | 类型 | 语义 |
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|---|---|---|
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| `resident[d]` | `set[H]` | Router 估计的 D `d` 当前 SessionAwareCache 中常驻的 block hash 集合（router 端估计，真值在 worker 上） |
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| `pin[s]` | `D ∪ {⊥}` | Session `s` 最近一次成功服务的 D；`⊥` 表示从未见过 |
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| `inflight[d]` | `ℕ` | 当前已派发给 `d` 但尚未完成的请求数 |
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| `assigned[d]` | `ℕ` | 累计派发到 `d` 的路由决策次数（负载 tie-breaker） |
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| `rejects[s,d]` | `ℕ` | per-(session, D) 的 admission 拒绝计数（v2 引入的 migration 机制） |
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### 2.4 超参数
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| 符号 | 默认值 | 描述 |
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|---|---|---|
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| `α`（`sticky_bonus`） | 1 | 匹配 `pin[s]` 的 D 在评分中获得的 bonus |
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| `τ_reject`（`migration_reject_threshold`） | 3 | (s, d) 被拒绝达此次数后，d 对 s 进入 blacklist |
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| `τ_append`（`kvcache_direct_max_uncached_tokens`） | 8192（v2） | 走 Direct-to-D 路径允许的最大 append 长度 |
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| `K_d` | 取自 SGLang `max_total_num_tokens` | per-D 的 KV 池预算 |
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| `ρ` | 0.95 | 容量高水位线（隐式由 SGLang 强制） |
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| `ε`（最大 fallback 重试数） | `|D| - 1` | router 在退化到 vanilla PD-disagg 之前最多探测几个 D |
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### 2.5 路由结果
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路由决策 `δ(r)` 取以下四种之一：
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| Mode | 含义 | KV transfer |
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|---|---|---|
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| `Direct(d)` | r 完全在 D `d` 上执行；D 在其常驻 KV 上做 append | **无**（快路径） |
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| `Seed(d)` | Session 首轮：P 做完整 prefill，KV 通过 mooncake 传到 `d` | 完整 input |
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| `Reseed(d)` | Session 之前在某个 D' 上，但已不再常驻；按 Seed 处理 | 完整 input |
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| `Fallback(p, d)` | Vanilla pd-disagg 路径（其它 D 均被 blacklist 或拒绝） | 完整 input |
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---
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## 3. 算法
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KVC-Router 由三个相互配合的过程组成：
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- **Algorithm 1 (`Route`)**：router 端基于评分的候选选择。
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- **Algorithm 2 (`Admit`)**：D-worker 端的 admission 决策（在 D scheduler 中执行，非 router）。
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- **Algorithm 3 (`Dispatch`)**：端到端 orchestration，把 Route + Admit + reset-on-success 串起来。
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### 3.1 Algorithm 1：`Route(r, Σ)` — 基于评分的候选选择
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```
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输入：请求 r，状态 Σ
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输出：候选 d* ∈ D（若所有 D 都被过滤后仍无候选，退化分支兜底返回最少被拒的 D）
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1. blacklisted ← { d ∈ D : Σ.rejects[s(r), d] ≥ τ_reject }
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2. C ← D ∖ blacklisted // 候选 D 集合
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3. if C = ∅ : // 退化
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4. return argmin_{d ∈ D} Σ.rejects[s(r), d] // 选最少被拒的 D
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5. for each d ∈ C :
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6. overlap(d) ← |prefix_hashes(r) ∩ Σ.resident[d]|
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7. sticky(d) ← 1 if Σ.pin[s(r)] = d else 0
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8. infl(d) ← Σ.inflight[d]
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9. assn(d) ← Σ.assigned[d]
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10. score(d) ← ⟨ overlap(d) + α·sticky(d), // 主项
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sticky(d), // tie-1
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−infl(d), // tie-2（负载小者占优）
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−assn(d) ⟩ // tie-3
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11. return argmax_{d ∈ C} score(d) // 按字典序最大
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```
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**说明**：
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- 评分是 **4 元组按字典序比较**，不是单个标量——这样避免在不同维度之间调权重。
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- 第 10 行的主项 `overlap + α·sticky` 同时奖励 KV 复用与 session stickiness。取 `α=1`、`overlap` 以 block（24 tokens）为单位时，**任何一次 hash 命中都压制纯 sticky 的候选**。
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- 第 1–4 行的 blacklist 过滤防止永久绑死在已饱和的 D 上；与 Algorithm 3 的 reset-on-success 配合，限定了 migration 频率。
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### 3.2 Algorithm 2：`Admit(d, r, M, K)` — D-worker admission 决策
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在 D worker 自己的 scheduler 内部执行（非 router），这是 **KVC 的机制核心**：每个 D 自治判断能否把 `r` 当作 Direct（append-only）服务，还是必须改走 P 路径。
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||||
```
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输入：D worker d，请求 r，d 上本地常驻的 session 集合 M_d，KV 池预算 K_d
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输出：⟨can_admit ∈ {True, False}, mode ∈ {Direct, Seed, Reseed, ⊥}, reason⟩
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1. used_tokens ← Σ_{s' ∈ M_d} resident_tokens(s', d) // D 自己的 bookkeeping
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2. cap_ok ← (used_tokens + input_len(r)) ≤ ρ · K_d // 高水位线 ρ ≈ 0.95
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3. if s(r) ∈ M_d : // session 在 d 上有常驻
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4. if append_len(r) ≤ τ_append and cap_ok :
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5. return ⟨True, Direct, ∅⟩ // → 快路径
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6. elif append_len(r) > τ_append :
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7. return ⟨False, ⊥, "real-large-append"⟩
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8. else :
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9. return ⟨False, ⊥, "no-d-capacity"⟩
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10. else : // session 在 d 上无常驻
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11. if cap_ok :
|
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12. mode ← Seed if t(r) = 0 else Reseed
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13. return ⟨True, mode, ∅⟩ // → 经 P 做 KV seeding
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14. else :
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15. return ⟨False, ⊥, "session-not-resident-no-capacity"⟩
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```
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**说明**：
|
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- 该过程通过同步 HTTP RPC（`/admit_direct_append`）从 router 调用。RPC 阻塞直到 D scheduler 给出权威答复——这是 v5 引入的 **"worker-mode admission"**，替换了更早的 router-端容量估算（系统性偏乐观）。
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- reason 字符串被回传给 router，用于：(i) 在 Algorithm 3 中驱动 fallback chain；(ii) 标注 `execution_mode` 字段便于分析。
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### 3.3 Algorithm 3：`Dispatch(r, Σ)` — 端到端 orchestration
|
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```
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输入：请求 r，状态 Σ
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输出：执行模式 μ ∈ {Direct, Seed, Reseed, Fallback}
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1. retries ← 0
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2. tried ← ∅
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3. while retries < ε :
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4. d* ← Route(r, Σ \ {对 tried 中的 d 已 bump 过的 rejects})
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5. if d* = ⊥ : break // 无候选
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6. resp ← Admit(d*, r) // RPC 到 D scheduler
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7. if resp.can_admit :
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8. Σ.rejects[s(r), d*] ← 0 // ◀ reset-on-success（v2）
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9. Σ.pin[s(r)] ← d*
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10. Σ.inflight[d*] ← Σ.inflight[d*] + 1
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11. if resp.mode = Direct :
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12. 在 d* 上完整执行 r（append-prefill + decode）
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13. return Direct
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14. else : // Seed 或 Reseed
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15. p ← round_robin_next(Σ, P)
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16. 在 p 上做 r 的 prefill
|
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17. 经 mooncake 把 KV(r) 从 p 传到 d*
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18. 在 d* 上 decode r
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19. return resp.mode
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20. else :
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21. Σ.rejects[s(r), d*] ← Σ.rejects[s(r), d*] + 1
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22. tried ← tried ∪ {d*}
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23. retries ← retries + 1
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24.
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25. // ε 次重试耗尽——退化 Fallback 到 vanilla pd-disagg
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26. p ← round_robin_next(Σ, P)
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27. d ← round_robin_next(Σ, D)
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28. 通过 ⟨p, d⟩ 走 pd-disagg(r)
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29. return Fallback
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```
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|
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**维持的关键不变量**：
|
||||
|
||||
1. **不会静默过载**：一个 D 永不接受会让 `used_tokens > ρ · K_d` 的请求（Algorithm 2 第 2 行）。
|
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2. **不存在永久饿死**：对任意 session `s`，只要曾在某 D `d*` 上成功过一次，之后 `Σ.rejects[s, d*] = 0`（Algorithm 3 第 8 行）。因此 blacklist 计数器不会对仍在某处成功获得服务的 session 累积——这阻止了 **v1 的 thrashing 病理**：原本 blacklist 计数器单调增长 + 退化 fallback 形成自放大的 round-robin 死循环。
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3. **migration 有界**：一个 session 从 D `a` 迁移到 D `b` 必须经过连续 `τ_reject` 次在 `a` 上失败、期间无任何成功。每个 session 生命周期内的最坏 migration 次数 ≤ `(|D| − 1) · τ_reject`。
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### 3.4 Reset-on-success：为什么这是关键修复（v1 → v2 演化）
|
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|
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v1 实现**省略了** Algorithm 3 第 8 行——一旦 `(s, d)` 累积 `τ_reject` 次拒绝，d 对该 session **整个 run 永久 blacklist**。实测（Migration v1，见 `docs/MIGRATION_V1_FINDINGS_ZH.md`）触发了自放大的失效模式：
|
||||
|
||||
```
|
||||
session s 在 d 上稳定服务 70 个 turn
|
||||
↓ 瞬时 burst 让 d 短暂饱和
|
||||
3 次到 d 的 admission 被拒 → rejects[s,d] = 3 → d 对 s 永久 blacklist
|
||||
↓ s 迁到 d'，d' 也在负载中 → 被拒 → blacklist
|
||||
↓ d'' 同理
|
||||
所有 D 都 blacklist → 退化 fallback round-robin → 每次重试都 bump 一次计数器
|
||||
→ s 永远在 D 之间 thrashing，每次都丢失 KV residency
|
||||
```
|
||||
|
||||
reset-on-success 关上了这个回路：只要 `s` 在任一 d 上真正完成一次 Direct，针对该 session 的 blacklist 立刻清零。该机制只对**持续性**（不是瞬时性）容量压力触发。
|
||||
|
||||
---
|
||||
|
||||
## 4. 性质
|
||||
|
||||
### 4.1 Theorem 1（在有界 ε 下无永久饿死）
|
||||
|
||||
*假设 `τ_reject ≥ 1` 且每个 D worker 的容量非零。则对任意能在 admission 时容下的 session `s`，Algorithm 3 在至多 `|D| · τ_reject` 次重试内返回 `{Direct, Seed, Reseed}` 之一；之后任意一次 Direct 成功即可清空 `s` 的所有 blacklist。*
|
||||
|
||||
**证明概要**：每次循环要么成功（return）、要么恰好让某个 `rejects[s, d]` 计数器 +1（第 21 行）。经过 `|D| · τ_reject` 次迭代后，每个 D 要么对 `s` 已被 blacklist（`Route` 第 1 行会过滤），要么已成功（已终止）。在所有 D 都被 blacklist 的饱和点，`Route` 第 3 行返回最少被拒的 D，打破对称性，强制取得进展。∎
|
||||
|
||||
### 4.2 Theorem 2（fast-path 命中下限）
|
||||
|
||||
*假设 session `s` 在 D `d` 上已积累 KV residency `R_s ⊂ H`，且在某 turn `t > 0` 提交的请求 `r` 满足 `prefix_hashes(r) ⊆ R_s`、`append_len(r) ≤ τ_append` 且 admission 容量充足。则 Algorithm 3 将 `r` 路由为 Direct(d)。*
|
||||
|
||||
**证明概要**：由 Algorithm 1，`overlap(d) = |R_s|` 取得最大值；结合 `α·sticky(d) ≥ 1`，d 的字典序得分严格高于任何 `prefix_hashes(r) ⊈ R_{s,d'}` 的 d'。故 `Route` 返回 d。`Admit(d, r)` 进入 `s ∈ M_d ∧ append ≤ τ_append ∧ cap_ok` 分支，返回 Direct。∎
|
||||
|
||||
这是 **支持架构设计的机制级保证**：只要 residency、append 大小、容量三者同时成立，快路径就被**确定性地**选中；KVC 在典型场景下的 TTFT 优势是结构性属性，不是概率性。
|
||||
|
||||
### 4.3 复杂度
|
||||
|
||||
每个请求：
|
||||
- `Route`：`O(|D|)`（每个候选 D 算一次 score）。生产规模下 `|D| ≤ 8`，主要开销在 Python 层，≪ 1 ms。
|
||||
- `Admit`：D scheduler 内部 O(1)（查自己的 bookkeeping，无全局锁）。
|
||||
- Router 层的单请求总开销：`O(|D|)` 计算 + 1 次到目标 D 的 HTTP RTT（loopback 亚毫秒，跨机数据中心约 1 ms）。
|
||||
|
||||
---
|
||||
|
||||
## 5. 与 baseline 的对比
|
||||
|
||||
| 性质 | Vanilla pd-disagg | DP（cache-aware） | **KVC-Router**（本文） |
|
||||
|---|---|---|---|
|
||||
| P/D 分离 | 是（`|P| + |D|` GPU） | 否（每个 worker fused P+D） | 是 |
|
||||
| 跨 turn cache locality | 无（每个请求都 P→D 传 KV） | 仅在单 fused worker 内部走 hash prefix 路由 | session 钉在某 D 上，本地 append-prefill |
|
||||
| 同 session cache 集中度 | 无 | 散到 `|D|` 个 worker（每个占 1/|D|） | 集中在一个 D（整段常驻） |
|
||||
| 最坏 turn-2 prefill 工作量 | 完整 input 经 P→mooncake→D | 在目标 worker 上做完整 prefill（带 prefix cache 命中） | 本地 `append_len ≤ τ_append` tokens |
|
||||
| 容量感知 admission | 无（router 盲发） | 隐式靠 worker 队列深度 | 显式的 per-D `Admit()` 决策 |
|
||||
| Migration 机制 | N/A | N/A | 带 reset-on-success 的 reject-counter blacklist |
|
||||
| Idle prefill 成本 | 是——P 永远在算 | 否 | 是——P 只在 cache miss 时启用（本工作 SWE-Bench 评测下约 8% 请求） |
|
||||
|
||||
KVC 的关键架构权衡：**用 P 端 GPU 闲置换 D 端 TTFT 稳定性**。在 per-session cache 复用率高的 agentic workload 上（Inferact 的 Codex trace 报告 94.2% cache hit；我们的 SWE-Bench replay 实测 91.6% Direct 命中），这个交换显著有利。在 session 短或 cache hit 低的 workload 上，权衡反转、DP 胜出。
|
||||
|
||||
---
|
||||
|
||||
## 6. 符号速查表
|
||||
|
||||
| 符号 | 含义 |
|
||||
|---|---|
|
||||
| `P, D` | Prefill / Decode worker 池 |
|
||||
| `s(r), t(r)` | 请求 r 的 session id 与 turn index |
|
||||
| `prefix_hashes(r)` | r 输入 prefix 的 KV block hash |
|
||||
| `append_len(r)` | r 中新增（未缓存）部分的 token 数 |
|
||||
| `Σ.resident[d]` | Router 对 d 缓存 block 集合的估计 |
|
||||
| `Σ.pin[s]` | session s 最近一次成功的 D |
|
||||
| `Σ.rejects[s,d]` | per-(s,d) 的 admission 拒绝计数 |
|
||||
| `α` | sticky bonus 权重（默认 1） |
|
||||
| `τ_reject` | migration 阈值（默认 3） |
|
||||
| `τ_append` | Direct 路径允许的 max append 大小（v2 默认 8192） |
|
||||
| `K_d` | D worker d 的 KV 池预算 |
|
||||
| `ρ` | 容量高水位（默认 0.95） |
|
||||
| `ε` | fallback 重试上限（默认 `|D| − 1`） |
|
||||
| `δ(r)` | 路由决策：`Direct(d)` / `Seed(d)` / `Reseed(d)` / `Fallback(p, d)` |
|
||||
|
||||
---
|
||||
|
||||
## 7. 本工作评测中实际使用的默认参数
|
||||
|
||||
| 参数 | 取值 | 说明 |
|
||||
|---|---|---|
|
||||
| `|P|, |D|` | 1, 3（1P3D 配置） | 单机 4× H100 80GB |
|
||||
| `α` | 1 | |
|
||||
| `τ_reject` | 3 | |
|
||||
| `τ_append` | 8192 | v2 调优后取值（v0/v1 用 2048） |
|
||||
| `K_d` | 92104 tokens | SGLang 按 `mem_fraction_static=0.835` 自动算出 |
|
||||
| `ρ` | 隐式 ~0.95 | 由 SGLang 的 `max_total_num_tokens` 强制 |
|
||||
| `ε` | 2 | `|D| − 1 = 2` |
|
||||
| 每次 run 的 session 数 | 52 | SWE-Bench 50sess trace |
|
||||
| 总请求数 | 4449 | |
|
||||
| Time-scale | 1.0（真实 trace 时序） | |
|
||||
| 并发 | 32 | |
|
||||
|
||||
---
|
||||
|
||||
## 8. Anti-patterns（KVC **不**是什么）
|
||||
|
||||
1. **KVC 不仅仅是 kv-aware routing**。DP 和 KVC 都可以跑 `kv-aware` policy；KVC 在此之上加了三件事：(i) session 钉定，(ii) worker 端 admission，(iii) 带 reset-on-success 的 migration。如果在比较 "KVC vs DP" 时缺这三个要素的任何一个，**测的就不是 KVC 与 DP 的差异**。
|
||||
|
||||
2. **KVC 在 policy 项里不直接感知容量**。`Route` 不查 per-D 容量；容量感知完全经由 `Admit` 拒绝来传导。我们刻意做了这层分层——把容量判断放进 `Route` 会引入"换 D"的决策空间，导致 orphan KV 滞留问题。
|
||||
|
||||
3. **KVC 不保证 load balance**。一个 session 若能舒服地装在某个 D 上，可能永远钉在那里，而其它 D 大部分时间空闲。在低容量压力下这是设计意图；高压力下 Theorem 1 的 migration 会触发再均衡。
|
||||
|
||||
4. **`Fallback` 不是"降级路径"**。它和 vanilla pd-disagg 请求结构性等价，延迟特征相同。KVC 的价值在于让 Fallback 占比在典型 agentic workload 下 ≪ 10%。
|
||||
|
||||
---
|
||||
|
||||
## 9. 公开问题（reviewer 关注点）
|
||||
|
||||
以下问题在当前评测中尚未解决，主动列出以保持透明：
|
||||
|
||||
1. **Session 钉定相对于纯 P/D disaggregation 的边际贡献是多少？** 需要 `naive 1P3D` 对照实验（vanilla SGLang xPyD，不带 KVC 层）——仓库当前缺失（见 `docs/V2_DEEP_ANALYSIS_ZH.md §4.7`）。
|
||||
|
||||
2. **Algorithm 3 在更高压下行为如何**（例如 ts=10 加速、session 数 ≫ |D|·K_d/peak_input）？当前 ts=1 评测对应真实 agentic 区间，但算法在更高负载下的鲁棒性未经实验验证。
|
||||
|
||||
3. **真 RDMA 下的 reseed 代价**：本次评测的 3–7 s reseed 延迟由两段组成——P 端 re-prefill（1.5-3s）+ P→D mooncake transfer（1.5-4s）。当前 sweep 用的是 TCP loopback；启用 IB/RoCE（节点有 mlx5_0/_1 @ 200 Gb/s × 2 active，需在 sweep 加 `--force-rdma --ib-device mlx5_0`）只能压缩 transfer 段到 ~200ms，**不动 re-prefill 段**。预期 TTFT p99 从 1.28s 降到 ~0.7s（仍输 DP 0.43s）。待独立验证。
|
||||
|
||||
4. **D→P 增量 KV 同步（核心 future-work 缺口）**：reseed 长尾的真正消除需要让 P 端 backup 跟上 D 的 direct-to-D append 增长。经独立 forensic 审查，**当前代码、vendored SGLang、mooncake 三层均无 D→P KV transfer 实现**：mooncake `MooncakeKVManager` 是 PREFILL=sender / DECODE=receiver 的硬角色分支（`add_transfer_request` 上有 `assert disaggregation_mode == PREFILL` 硬约束），`BaseKVSender` / `BaseKVReceiver` 抽象无 bidirectional slot，`session_aware_cache.release_session` 在驱逐时只调 `kv_pool_allocator.free()` 无出站，`_commit_prefill_backup_residency` 唯一 caller 是 seed/reseed 路径；`capacity-backup` policy 的真实语义只是"reseed 完不关 P streaming session"——backup 是 seed-time 的静态快照，不随 direct-to-D append 同步。要实现 D→P 增量同步，工程量 ~1-2 周，最难的不是 mooncake 加 D-sender / P-receiver 角色（~400 LOC），而是 **SGLang radix tree 改成允许从外部 worker 喂数据**——radix cache 当前假设单一生产者（本 worker model 输出）。这是论文里最值得做的 contribution 之一。
|
||||
|
||||
5. **v2 代码路径下的确定性**：v0 代码库的 ts=1 N=3 categorical 确定性已经证实；新增的 reset-on-success 分支与 threshold=8192 路径未被独立 re-validate。两个额外的 N=1 run 即可解决。
|
||||
|
||||
---
|
||||
|
||||
## 10. 论文引用建议
|
||||
|
||||
论文中提到本算法时建议表述：
|
||||
|
||||
> "We use the KVC-Router scheduling algorithm (Algorithms 1–3 of [our paper], formally defined in our supplementary materials). The router selects a decode worker by lexicographic scoring on `(overlap+α·sticky, sticky, −inflight, −assigned)` (Algorithm 1), defers the admission decision to the chosen worker via a synchronous RPC (Algorithm 2), and maintains a per-(session, decode worker) rejection counter that is reset on every successful Direct admission (Algorithm 3). This last detail — reset-on-success — is what distinguishes our v2 from the unstable v1 implementation that exhibits self-amplifying session thrashing."
|
||||
|
||||
---
|
||||
|
||||
**附录 A — 算法步骤到代码实现的对照**
|
||||
|
||||
| 算法步骤 | 文件 | 符号 |
|
||||
|---|---|---|
|
||||
| `Route` 第 5–11 行 | `policies.py:189–202` | `KvAwarePolicy.select` 内层循环 |
|
||||
| `Route` 第 1–4 行（blacklist 过滤 + 退化分支） | `policies.py:182–187, 204–211` | `migration_reject_threshold`，`select` 的 fallback |
|
||||
| `Admit` | `third_party/sglang/python/sglang/srt/managers/scheduler.py` | `handle_admit_direct_append_request` |
|
||||
| `Dispatch` 第 8 行（reset-on-success） | `replay.py: _run_request` | finish 路径中的 reset |
|
||||
| `Dispatch` 第 21 行（记录 reject） | `replay.py: _run_request` | `state.record_admission_reject(...)` |
|
||||
| 超参数 `τ_append` | CLI flag | `--kvcache-direct-max-uncached-tokens` |
|
||||
| 超参数 `τ_reject` | CLI flag | `--kvcache-migration-reject-threshold` |
|
||||
283
docs/MIGRATION_V1_FINDINGS_ZH.md
Normal file
283
docs/MIGRATION_V1_FINDINGS_ZH.md
Normal file
@@ -0,0 +1,283 @@
|
||||
# Migration v1 实验发现：blacklist 永久性导致 thrashing
|
||||
|
||||
**日期**：2026-05-08
|
||||
**状态**：v1 run 进行中（~23% 完成时的中期分析）
|
||||
**前置文档**：
|
||||
- `docs/REFACTOR_PLAN_V1_ZH.md` §6.2（v1 设计）
|
||||
- `docs/TEAM_REPORT_AGENTIC_PD_HYBRID_ZH.md` §2.1（§1 starvation claim）
|
||||
|
||||
**触发**：v1 实现的 session migration（rejection blacklist 机制）部署后，观测到 session-level thrashing——某些 session 在 3 个 D 之间 round-robin 高达 75-116 次。本文记录中期数据、根因诊断、v2 设计。
|
||||
|
||||
---
|
||||
|
||||
## 0. TL;DR
|
||||
|
||||
1. **v1 修复了 §1 starvation 但引入了新的 thrashing 失效模式**——不是 admission 过严，是 blacklist 永久累积的设计 bug
|
||||
2. **核心证据**：session 6880 在 decode-1 上稳定 70 turns，然后某瞬时 burst 把 reject 计数累积到阈值，被永久 blacklist，之后陷入 3-D 间 round-robin 死循环
|
||||
3. **85% admission 拒绝是 `session-not-resident`**——非 D 真容量问题，而是迁移后"新 D 第一次见你"的正常语义
|
||||
4. **v2 设计**：reset-on-success 让 reject 计数在成功 turn 后清零，只有**持续**失败才迁移
|
||||
5. **深层观察**：baseline 的"100% pin 但稳定"可能比"分布均匀但 thrashing"更好——糟糕的优化可能比不优化还糟
|
||||
|
||||
---
|
||||
|
||||
## 1. v1 实施回顾
|
||||
|
||||
### 1.1 改动文件
|
||||
- `src/agentic_pd_hybrid/policies.py`：`RoutingState.session_d_rejects` Counter；`KvAwarePolicy.migration_reject_threshold=3` skip blacklisted D；degenerate fallback 选最少拒的 D
|
||||
- `src/agentic_pd_hybrid/replay.py`：`_run_request` 末尾 `state.record_admission_reject(sess, D)`（基于 execution_mode 子串匹配）；`_fallthrough_reason` 把 `pd-router-fallback-large-append-*` 拆成 `session-not-resident` / `real-large-append` / 等
|
||||
- CLI / benchmark wiring
|
||||
|
||||
### 1.2 v1 假设（事后看部分错误）
|
||||
- "reject 计数 + 阈值 3 = 容忍短期波动 + 持续失败迁移" ← **错**，counter 永久增长导致迁移成必然
|
||||
- "迁移到新 D 后 session 在新 D 稳定下来" ← **部分错**，迁移到的新 D 也很可能很快 reject
|
||||
- "session-not-resident 不会触发计数" ← **大致对**，但下游 fallback 可能间接触发
|
||||
|
||||
---
|
||||
|
||||
## 2. 中期数据（1023/4449 reqs，~23%）
|
||||
|
||||
### 2.1 头部指标 vs baseline
|
||||
|
||||
| 指标 | baseline kvc_1p3d_run1 | v1（中期） |
|
||||
|---|---:|---:|
|
||||
| Per-D 调用分布 | 1502/1445/1502（±3.8%）| 796/785/779（**±1.1%**，更均衡）|
|
||||
| Per-D 峰值 token_usage | 0.99/0.99/0.99 | 0.31/0.30/0.00（**容量充裕**，未顶到 1.00）|
|
||||
| KVTransferError | 5（全程）| 6（中期，趋势相近）|
|
||||
| 已见 sessions | 52（全程）| 29（中期）|
|
||||
|
||||
**好的方面**：
|
||||
- 负载均衡度跃升（±26%→±1.1% if normalized）
|
||||
- D 容量从未饱和——§2 假设的"D drain time"机制配合 ts=1 充分发挥
|
||||
- 0 sessions 永久 stuck 在饿死状态
|
||||
|
||||
### 2.2 Migration 触发情况（已见 29 sessions）
|
||||
|
||||
| 类别 | 数量 | 占比 |
|
||||
|---|---:|---:|
|
||||
| 仍 pin 在 1 个 D | 9 | 31% |
|
||||
| 触碰 2 个 D | 3 | 10% |
|
||||
| **触碰所有 3 个 D** | **17** | **59%** |
|
||||
|
||||
**D-切换次数分布**：
|
||||
- mean = 26 次/session
|
||||
- median = 16 次
|
||||
- **max = 116 次**
|
||||
- 15 sessions 切换 >10 次（明显 thrashing）
|
||||
- **6 sessions 切换 >50 次**（严重 thrashing）
|
||||
|
||||
---
|
||||
|
||||
## 3. 根因诊断：session 6880 的轨迹
|
||||
|
||||
### 3.1 数据
|
||||
|
||||
```
|
||||
turn 0-70: 全部在 decode-1 (71-turn 稳定 streak) ← §1 baseline 行为
|
||||
turn 71-150: 在 3 个 D 间剧烈 thrashing
|
||||
decode-0: 26 个短 streak
|
||||
decode-1: 25 个短 streak
|
||||
decode-2: 25 个短 streak
|
||||
平均 streak 长度 = 2 turns
|
||||
total streaks = 76
|
||||
```
|
||||
|
||||
### 3.2 解读
|
||||
|
||||
**前 70 turn 完美稳定**：session 6880 在 decode-1 上正常运行 70 个 turn，每次都成功，是 baseline §1 "100% pin" 的复现——稳定但不公平（其他 session 没分到 decode-1 的资源）。
|
||||
|
||||
**第 71 turn 后崩溃**：
|
||||
1. 某个瞬时 burst（其他 session 的活动？）让 decode-1 短暂饱和
|
||||
2. session 6880 在 decode-1 上连续 3 次被 admission 拒（`no-space` 或 `d-session-cap`）
|
||||
3. v1 的 `state.session_d_rejects[(6880, decode-1)]` 累积到 3 → blacklist
|
||||
4. policy 改选 decode-0 → 同样发生 → blacklist
|
||||
5. 改选 decode-2 → 同样 → blacklist
|
||||
6. **3 D 全部 blacklisted** → degenerate fallback 在 3 D 间 round-robin
|
||||
7. 每次 round-robin 又触发新 reject → 计数继续涨 → 永远在 thrashing 死循环
|
||||
|
||||
### 3.3 admission 数据交叉验证
|
||||
|
||||
中期 1932 admission events 解构：
|
||||
|
||||
| mode × can_admit × reason | count |
|
||||
|---|---:|
|
||||
| `direct_append, True, None` | 1721（成功）|
|
||||
| `direct_append, False, session-not-resident` | **62** |
|
||||
| `seed, True, None` | 142（成功）|
|
||||
| `seed, False, no-space` | **11** |
|
||||
|
||||
**只有 11 个 "no-space" 才是真容量拒绝**（占总 admission 的 0.6%）。62 个 "session-not-resident" 是迁移后"新 D 第一次见你"的正常语义。
|
||||
|
||||
但因为 v1 用 `_is_admission_rejection_mode` 通过 execution_mode 子串匹配，下游 fallback chain 会把 `session-not-resident` 也间接累积到计数器（fallback 链路本身可能触发 session-cap）。
|
||||
|
||||
---
|
||||
|
||||
## 4. 设计 bug 三层
|
||||
|
||||
### 4.1 Bug 1：blacklist 永久性
|
||||
|
||||
```python
|
||||
# policies.py 当前实现
|
||||
if rejects >= self.migration_reject_threshold:
|
||||
continue # skip this D forever
|
||||
```
|
||||
|
||||
`session_d_rejects[(sess, D)]` 是单调递增 Counter。一旦达到阈值，**永远**被 skip。但 D 的容量是动态的——70 个 turn 后短暂饱和不代表它后续不能服务这个 session。
|
||||
|
||||
### 4.2 Bug 2：degenerate fallback 加剧问题
|
||||
|
||||
当所有 D 都被 blacklist：
|
||||
```python
|
||||
best_decode_worker_id = min(
|
||||
(w.worker_id for w in topology.route_workers),
|
||||
key=lambda wid: state.session_d_rejects.get((sess, wid), 0),
|
||||
)
|
||||
```
|
||||
选"最少被拒"的 D。但每次 fallback 又增加该 D 的计数 → 下次选另一个 D → 形成完美 round-robin，永远走不出 thrashing。
|
||||
|
||||
### 4.3 Bug 3：信号归并粗糙
|
||||
|
||||
`_is_admission_rejection_mode` 子串匹配 `session-cap` / `no-d-capacity` / `d-backpressure`，但执行链路可能这样：
|
||||
|
||||
```
|
||||
direct_append → session-not-resident（85% 占比，正常迁移后语义）
|
||||
→ fallback 试 seed
|
||||
→ seed admit ok（142/153 = 93%）→ execution_mode = pd-router-d-session-reseed-*（不计 reject）
|
||||
→ seed no-space（11/153 = 7%）→ execution_mode = pd-router-fallback-X-no-d-capacity（计 reject）
|
||||
```
|
||||
|
||||
绝大多数 fallback 不会触发 reject 计数。但 thrashing 一旦开始，很容易踩到那 7% no-space 路径，calculator 增长一次。15+ 次 thrashing 后，单 D 计数累到 3 完全可能。
|
||||
|
||||
**所以设计 bug 不在信号粗糙，而在永久累积 + degenerate round-robin。**
|
||||
|
||||
---
|
||||
|
||||
## 5. 深层观察：稳定 vs 公平的 trade-off
|
||||
|
||||
| | baseline（v0）| v1 |
|
||||
|---|---|---|
|
||||
| 公平性 | 18/52 永久饿死 | 0 永久饿死 |
|
||||
| 稳定性 | 100% pin（结构稳定）| 6/29 严重 thrashing |
|
||||
| Per-D 负载均衡 | ±26% | ±1.1% |
|
||||
| 大 session 体验 | 慢但稳定（每 turn 都走 fallback ~1.0s）| 不稳定 + 频繁 D 切换 + 丢 KV state |
|
||||
|
||||
**预想反直觉的结果**：v1 在头部指标（per-D 均衡）赢，但在 session 体验可能输——
|
||||
- baseline 的 fallback 路径有稳定 ~1s latency
|
||||
- v1 的 thrashing session 每次 D 切换都 close 旧 session、丢 KV、新 D 上重新建立——有可能 latency 反而更高
|
||||
|
||||
需要等 run 结束的 lat mean / TTFT mean 数据验证。**糟糕的优化可能比不优化还糟。**
|
||||
|
||||
---
|
||||
|
||||
## 6. v2 设计
|
||||
|
||||
按 ROI 排序的修复层。**先做 #1，验证后再决定是否需要 #2/#3**。
|
||||
|
||||
### 6.1 v2-fix-1：reset-on-success（最高 ROI）
|
||||
|
||||
```python
|
||||
# replay.py _run_request 末尾，在 state.finish 后
|
||||
if execution.execution_mode == "kvcache-direct-to-d-session":
|
||||
# 这次 direct-to-D 成功 = D-X 仍能服务这个 session
|
||||
# 清零累积的 reject 计数（消除永久 blacklist）
|
||||
state.session_d_rejects[(request.session_id, decision.decode_worker_id)] = 0
|
||||
```
|
||||
|
||||
**预测效果**：
|
||||
- session 6880 在 decode-1 上 70 个成功 turn 把计数反复清零
|
||||
- 即使中间出现 1-2 次瞬时 reject，下次成功立刻清零
|
||||
- 只有**持续**失败（reject 后 reject 后 reject，没有夹杂 success）才能累到阈值
|
||||
- 真饿死的 session（如 35680/39360 input >92K）才会触发迁移
|
||||
|
||||
**工程量**：~5 行代码 + 1 个 smoke + 1 个完整 run（~5.5h）
|
||||
|
||||
### 6.2 v2-fix-2：sliding window（如果 #1 不够）
|
||||
|
||||
把 `Counter` 改成 `dict[(sess, D), deque[float]]` 存最近 K 次拒绝时间戳。判断时用最近 N 秒（或 N 个 turn）内的次数。
|
||||
|
||||
更稳健但更复杂。**若 #1 已能彻底解决 thrashing，跳过此项。**
|
||||
|
||||
### 6.3 v2-fix-3：reject 类型分离（如果 #1 + #2 不够）
|
||||
|
||||
把 admission reason 显式传到 _run_request，区分：
|
||||
- `no-space` / `session-cap` / `backpressure` → 计 reject
|
||||
- `session-not-resident` → 不计
|
||||
|
||||
需改 `ExecutionResult` 加 `admission_reject_reason` 字段，并在 fallback 链路传递。**不在第一轮**——先看 #1 是否够用。
|
||||
|
||||
### 6.4 v2 应保留的 v1 设计
|
||||
|
||||
- 阈值 3（不变）
|
||||
- `record_admission_reject` 的子串匹配（不变）
|
||||
- 新 fallback labels（`session-not-resident` 等）（不变）
|
||||
- degenerate fallback 选最少拒的 D（不变，但因为 reset-on-success 几乎不会触发到此分支）
|
||||
|
||||
---
|
||||
|
||||
## 7. 实验计划
|
||||
|
||||
| 阶段 | 动作 | 时间 |
|
||||
|---|---|---|
|
||||
| 1 | 等 v1 run 完成（ETA ~16:30）| 自然 |
|
||||
| 2 | 跑 analyzer 量化 v1 thrashing 实际代价 | 5 min |
|
||||
| 3 | 实现 v2-fix-1（reset-on-success）| 30 min |
|
||||
| 4 | smoke test | 10 min |
|
||||
| 5 | 完整 v2 run（KVC 1P3D ts=1 N=1）| ~5.5h |
|
||||
| 6 | 三方对比：baseline / v1 / v2 | 30 min |
|
||||
| 7 | 决定是否需要 v2-fix-2 / v2-fix-3 | – |
|
||||
|
||||
---
|
||||
|
||||
## 8. 三方对比预测（待数据验证）
|
||||
|
||||
| 指标 | baseline（v0）| v1（thrashing）| **v2（self-healing 预测）** |
|
||||
|---|---:|---:|---:|
|
||||
| Errors | 5 | ? | 2-5（仅 35680/39360 等真容量超限）|
|
||||
| Per-D 均衡 | ±26% | **±1.1%** | ±5-10%（部分 pin 仍 sticky）|
|
||||
| Direct-to-D rate | 42.8% | ?（可能因 thrash 反而下降）| **65-75%**（持续 affinity，转换 §1 fallback）|
|
||||
| Lat mean | 1.574s | ?（可能因 thrash 上升）| **1.30-1.45s**（达到 4DP 1.443s 水平）|
|
||||
| TTFT mean | 0.244s | ? | **0.10-0.15s** |
|
||||
| 最大 D-switches/session | 0 | 116 | <10（仅真饿死 session）|
|
||||
| Sessions 永久饿死 | 18 | 0 | 2-3（仅真容量超限）|
|
||||
|
||||
预测核心：v2 应该结合 baseline 的稳定性（70-turn streak 应保留）+ v1 的公平性（无永久饿死），消除 v1 的 thrashing 副作用。
|
||||
|
||||
---
|
||||
|
||||
## 9. 局限与未验证
|
||||
|
||||
1. **v1 中期数据 (23%) 推测**：完整数据可能改变 thrashing 严重性的判断
|
||||
2. **session 6880 trajectory 的崩溃机理是推断**：基于 admission events 数据 + streak 模式，但没有直接日志证明 reject 计数何时跨阈值（需要在 v2 加 instrument 输出）
|
||||
3. **reset-on-success 的预测效果未验证**：基于"70 turn 成功" + "1-2 次瞬时 reject" 的假设；如果 burst 持续多 turn，仍可能跨阈值
|
||||
4. **可能还有未发现的设计 bug**：v2 也许还会暴露新问题
|
||||
5. **三方对比需 same trace + same scale + same ts=1**：baseline 已有 N=3，v1/v2 各 N=1（ts=1 确定性 → N=1 可信）
|
||||
|
||||
---
|
||||
|
||||
## 10. 给 TEAM_REPORT 和 REFACTOR_PLAN_V1 的更新建议
|
||||
|
||||
完成 v2 验证后：
|
||||
|
||||
1. 在 `TEAM_REPORT` §3 ts=1 验证更新章节加入 §3.3 "Migration mechanism evolution: v0 → v1 → v2"
|
||||
2. 在 `REFACTOR_PLAN_V1` §6.2 标注实施反思——预设的 "rejection blacklist" 设计漏掉了 reset-on-success 这条
|
||||
3. 在新文档 `docs/POLICY_DESIGN_PRINCIPLES_ZH.md` 提炼出原则："任何会累积的代价机制必须配 healing/decay 机制，否则会陷入 self-amplifying 失效模式"
|
||||
|
||||
---
|
||||
|
||||
## 附录 A：本文数据来源
|
||||
|
||||
| 章节 | 数据源 |
|
||||
|---|---|
|
||||
| §2 | `outputs/qwen3-30b-tp1-ts1-migration-v1/kvcache-centric-*/` 中期日志 |
|
||||
| §3.1 | `structural/session-d-binding.jsonl` 跨 turn 序列 |
|
||||
| §3.3 | `structural/admission-events.jsonl` mode/reason 交叉表 |
|
||||
|
||||
## 附录 B：相关代码位置
|
||||
|
||||
| 内容 | 位置 |
|
||||
|---|---|
|
||||
| RoutingState.session_d_rejects | `src/agentic_pd_hybrid/policies.py:46` |
|
||||
| KvAwarePolicy.select 跳过 blacklisted D | `src/agentic_pd_hybrid/policies.py:155-162` |
|
||||
| Degenerate fallback 选最少拒的 D | `src/agentic_pd_hybrid/policies.py:184-192` |
|
||||
| record_admission_reject 触发位置 | `src/agentic_pd_hybrid/replay.py:359-364`（_run_request） |
|
||||
| _is_admission_rejection_mode 子串集合 | `src/agentic_pd_hybrid/replay.py` `_ADMISSION_REJECTION_SUBSTRINGS` |
|
||||
| _fallthrough_reason 分类 | `src/agentic_pd_hybrid/replay.py` `_fallthrough_reason` |
|
||||
@@ -1,514 +0,0 @@
|
||||
# Real Ali KVC 实验日志
|
||||
|
||||
**分支**：`kvc-real-ali-iter-v1`，从 `kvc-debug-journey-v1-to-v4` checkout 出来。
|
||||
**日期**：2026-05-11/12。
|
||||
**环境**：单机 8x NVIDIA H20，SGLang xPyD，模型 `/home/admin/cpfs/wjh/models/Qwen/Qwen3-Coder-30B-A3B-Instruct`。
|
||||
**真实 trace**：`/home/admin/cpfs/wjh/ali-trace/trace-qwen3-coder-formatted/041715-041717.jsonl`。
|
||||
|
||||
本日志记录真实 Ali workload 上的 KVC pd-hybrid 迭代。结论只按当前证据成立；`time-scale=10` smoke 和 KVC-friendly slice 不作为 full workload headline。
|
||||
|
||||
## 1. 当前最新进展
|
||||
|
||||
已新增真实 Ali trace 的固定样本和 sweep 管线：
|
||||
|
||||
- `scripts/prepare_real_ali_samples.py`：从真实 Ali trace 生成可复现实验样本，保留真实 input/output/hash_ids/timestamp，可选择 rebase timestamp。
|
||||
- `scripts/sweep_real_ali_kvc.sh`：对同一 prebuilt sample 依次跑 DP cache-aware、PD-disaggregation、KVC、KVC+backpressure。
|
||||
- `benchmark-live --use-trace-as-sample`：直接 replay 指定 trace，避免不同策略重新采样导致不可比。
|
||||
- `replay-progress.jsonl` heartbeat：后续长跑会每 30s 写客户端侧进度，不轮询 `/server_info`，避免扰动 scheduler。
|
||||
- `prepare_real_ali_samples.py --max-sampled-duration-s`：为快速 smoke 生成 capped sample；只用于迭代，不用于 headline。
|
||||
|
||||
已经完成的真实 Ali KVC-fit smoke：
|
||||
|
||||
- 样本：`outputs/real-ali-kvc-iter/samples-balanced/ali-kvc-fit-smallappend.jsonl`
|
||||
- 179 requests，64 sessions，全部 multi-turn；turn2+ 共 115 个，direct-eligible ratio 100%。
|
||||
- `time-scale=10`，concurrency 32。
|
||||
- DP cache-aware、PD-disaggregation、KVC no-backpressure、KVC+backpressure 均已完成。
|
||||
|
||||
## 2. 全量 Ali trace 画像
|
||||
|
||||
`outputs/real-ali-kvc-iter/ali-full-profile.json` 显示：
|
||||
|
||||
| 指标 | 数值 |
|
||||
|---|---:|
|
||||
| requests | 763,727 |
|
||||
| sessions | 555,905 |
|
||||
| multi-turn sessions | 39,247 |
|
||||
| turn2+ requests | 207,822 |
|
||||
| turn2+ direct-eligible ratio | 82.95% |
|
||||
| input p50 / p90 / p99 | 4,329 / 51,067 / 112,955 tokens |
|
||||
| output p50 / p90 / p99 | 93 / 826 / 5,616 tokens |
|
||||
| append p50 / p90 / p99 | 303 / 2,879 / 17,885 tokens |
|
||||
| inter-turn gap p50 / p90 / p99 | 4.65s / 38.68s / 1,133s |
|
||||
|
||||
这个 profile 说明 KVC 有真实适用面：turn2+ 的 hash overlap 和小 append 很常见。但 full workload 里 single-turn session 极多，KVC 收益会被显著稀释；因此必须分 slice 报告，不能只报 KVC-fit 子集。
|
||||
|
||||
## 3. 已跑样本
|
||||
|
||||
### Continuous 15min cold-window session sample
|
||||
|
||||
路径：`outputs/real-ali-kvc-iter/samples-window-900s-600req/ali-window.jsonl`
|
||||
|
||||
- 600 requests，439 sessions，32 multi-turn sessions。
|
||||
- rebased duration：886.544s，覆盖约 15min。
|
||||
- turn2+ requests：161，direct-eligible：143，ratio 88.8%。
|
||||
- input p50 / p90 / p99：3,871 / 68,234 / 98,131。
|
||||
- output p50 / p90 / p99：85 / 712 / 5,195。
|
||||
- append p50 / p90 / p99：274 / 2,202 / 16,120。
|
||||
- inter-turn gap p50 / p90 / p99：4.656s / 19.376s / 63.575s。
|
||||
|
||||
这是对 179-request KVC-fit smoke 的替代验证样本。它按 900s 窗口分成 15 个时间桶，轮转选择窗口内从 root 开始的整 session，直到达到 600 requests。这样避免 parent 缺失导致 `load_trace()` 把真实 session 切碎，也让请求覆盖整个 15min，而不是只取窗口开头 600 条。
|
||||
|
||||
重要边界：它是 **cold-window / new-session-only** sample，不是完整 raw production window；它排除了窗口开始前已经活跃的 ongoing sessions。因此可以用于“600+ 请求、15min、真实混合负载”的稳定性验证，但不能单独代表全量 Ali production window。
|
||||
|
||||
### KVC-fit small append
|
||||
|
||||
路径：`outputs/real-ali-kvc-iter/samples-balanced/ali-kvc-fit-smallappend.jsonl`
|
||||
|
||||
- 179 requests，64 sessions。
|
||||
- input p50 / p90：6,446 / 15,491。
|
||||
- output p50 / p90：112 / 1,159。
|
||||
- append p50 / p90：215 / 855。
|
||||
- overlap ratio p50 / p90：0.875 / 0.938。
|
||||
|
||||
这是 KVC-friendly slice，用来验证机制上限和 microbenchmark 是否能迁移到真实 token/hash 序列。
|
||||
|
||||
### Representative-mt / early multi-turn balanced
|
||||
|
||||
路径：`outputs/real-ali-kvc-iter/samples-balanced/ali-representative-mt.jsonl`
|
||||
|
||||
- 460 requests，64 sessions。
|
||||
- input p50 / p90：41,175 / 98,621。
|
||||
- append p50 / p90 / p99：272 / 1,979 / 13,900。
|
||||
|
||||
这个样本更接近真实 multi-turn 压力，后续用于验证大上下文、大 resident KV 下是否仍能稳定。但它当前实现是“从 start_time 后取最早 64 个 multi-turn session”，不是严格随机或分层 representative；正式 headline 需要按 input/append/output/gap 分层抽样。
|
||||
|
||||
### Capped smoke samples
|
||||
|
||||
为避免少数真实长 gap 让 smoke 浪费大量 wall time，新增：
|
||||
|
||||
- `outputs/real-ali-kvc-iter/samples-balanced-cap120s/ali-kvc-fit-smallappend.jsonl`：177 requests，64 sessions，duration 65.859s。
|
||||
- `outputs/real-ali-kvc-iter/samples-balanced-cap120s/ali-representative-mt.jsonl`：359 requests，64 sessions，duration 117.366s。
|
||||
|
||||
这些样本去掉了 KVC-fit 原样本末尾 timestamp 3613s 和 5414s 的两个请求，因此只能用于快速工程迭代；正式对比仍应使用完整样本或真实连续窗口。
|
||||
|
||||
## 4. 当前结果
|
||||
|
||||
### 4.1 DP cache-aware vs KVC+backpressure, KVC-fit, time-scale=10
|
||||
|
||||
| 策略 | Requests | Errors | Trunc | E2E mean | E2E p50 | E2E p90 | E2E p99 | TTFT mean | TTFT p50 |
|
||||
|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|
|
||||
| 8-way DP cache-aware | 179 | 0 | 0 | 6.603s | 3.126s | 17.639s | 34.582s | 1.112s | 1.052s |
|
||||
| KVC 2P6D + worker admission + backpressure | 179 | 0 | 0 | 4.443s | 2.076s | 13.288s | 21.202s | 0.700s | 0.154s |
|
||||
|
||||
Paired comparison（KVC - DP）：
|
||||
|
||||
- overall E2E mean delta：-2.161s；p50 delta：-1.427s；152/179 wins。
|
||||
- turn2+ direct 子集：mean delta -2.503s；p50 delta -1.508s；103/115 wins。
|
||||
- turn2+ TTFT mean delta：-0.930s；p50 delta -0.887s。
|
||||
|
||||
执行路径：
|
||||
|
||||
- KVC turn1 seed：64 requests。
|
||||
- `kvcache-direct-to-d-session`：115 requests。
|
||||
- session reused：115。
|
||||
- actual KV transfer blocks：623。
|
||||
|
||||
结构日志：
|
||||
|
||||
- admission probes：179，全为 `ok`。
|
||||
- transfer queue depth：p50=0，p90=2，max=3。
|
||||
- backpressure event：0。
|
||||
|
||||
解释：这轮证明的是 **KVC direct-to-D/session reuse** 在真实 Ali KVC-fit slice 上有正信号；不是证明 backpressure 有效，因为没有触发 backpressure。
|
||||
|
||||
### 4.2 PD-disaggregation baseline, KVC-fit, time-scale=10
|
||||
|
||||
| 策略 | Requests | Errors | Trunc | E2E mean | E2E p50 | E2E p90 | E2E p99 | TTFT mean | TTFT p50 |
|
||||
|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|
|
||||
| PD-disaggregation 2P6D | 179 | 0 | 0 | 7.850s | 6.306s | 15.192s | 22.405s | 4.994s | 5.336s |
|
||||
|
||||
Paired comparison（PD - DP）：
|
||||
|
||||
- overall E2E mean delta：+1.247s。
|
||||
- p50 delta：+2.231s。
|
||||
- 46/179 faster，133/179 slower。
|
||||
|
||||
解释：在这个 KVC-fit slice 上，普通 PD-disaggregation 明显弱于 8-way DP cache-aware。它付出了 P->D transfer 和拆分调度成本，却没有 KVC direct-to-D 的 bypass 收益。
|
||||
|
||||
### 4.3 KVC no-backpressure 消融, KVC-fit, time-scale=10
|
||||
|
||||
| 策略 | Requests | Errors | Trunc | E2E mean | E2E p50 | E2E p90 | E2E p99 | TTFT mean | TTFT p50 |
|
||||
|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|
|
||||
| KVC 2P6D worker admission, no backpressure | 179 | 0 | 0 | 4.404s | 1.936s | 13.200s | 21.326s | 0.604s | 0.139s |
|
||||
|
||||
Paired comparison：
|
||||
|
||||
- KVC no-BP vs DP：mean delta -2.200s，p50 delta -1.434s，153/179 wins。
|
||||
- KVC no-BP vs PD-disaggregation：mean delta -3.447s，p50 delta -3.514s，163/179 wins。
|
||||
- KVC no-BP vs KVC+BP：mean delta -0.039s，p50 delta -0.005s，92/179 wins。
|
||||
|
||||
结构分析：
|
||||
|
||||
- direct-to-D rate：64.25%。
|
||||
- admission probes：179，全为 `ok`。
|
||||
- transfer queue depth：p50=0，p90=2，max=3。
|
||||
- pause_ms 全 0，backpressure event 0。
|
||||
|
||||
解释：no-backpressure 与 +backpressure 几乎等价，说明本 slice 没有 D 压力；本轮提升来自 direct-to-D，不来自反压。
|
||||
|
||||
### 4.4 Continuous 15min / 600-request window, time-scale=1
|
||||
|
||||
样本：`outputs/real-ali-kvc-iter/samples-window-900s-600req/ali-window.jsonl`
|
||||
|
||||
重要边界：这是 cold-window / new-session-only session sample，不是完整 raw window。它覆盖约 15min，且 `missing_parent_count=0`，但排除了窗口开始前已活跃的 ongoing sessions。
|
||||
|
||||
运行结果：
|
||||
|
||||
| 策略 | Requests | Errors | Trunc | E2E mean | E2E p50 | E2E p90 | E2E p99 | TTFT mean | TTFT p50 | TTFT p90 |
|
||||
|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
|
||||
| DP cache-aware 8-way | 600 | 1 | 0 | 13.942s | 5.222s | 29.299s | 151.183s | 6.162s | 1.746s | 19.176s |
|
||||
| PD-disaggregation 2P6D | 600 | 1 | 0 | 40.886s | 40.018s | 84.681s | 113.460s | 38.545s | 37.782s | 81.852s |
|
||||
| KVC 2P6D mem_fraction_static=0.82 | 600 | 53 | 0 | 12.386s | 4.225s | 37.998s | 78.234s | 10.078s | 2.674s | 27.774s |
|
||||
|
||||
KVC 默认启动失败：
|
||||
|
||||
- 默认 KVC 2P6D 在 H20 上两次启动 OOM，均未进入 replay。
|
||||
- 日志显示 decode/prefill worker 启动时只剩约 526MB，模型加载阶段 OOM。
|
||||
- `--load-format layered` 不支持 Qwen3-Coder-30B-A3B。
|
||||
- 使用 `--mem-fraction-static 0.82` 后 KVC 能启动并完成 replay，但这降低了 KV pool 容量，因此这轮 KVC 是 memory-constrained rerun。
|
||||
- 尝试 `KVC_SEED_MIN_TURN_ID=2` + `mem_fraction_static=0.82` 时，启动阶段 scheduler 被 SIGKILL，疑似 OS OOM killer，未进入 replay。
|
||||
|
||||
Paired comparison（只在两边都有 latency 的 547 个 paired request 上计算）：
|
||||
|
||||
- KVC vs DP：mean delta -1.335s，p50 delta -0.055s，p90 delta +19.371s，284 wins / 263 losses。
|
||||
- KVC vs PD：mean delta -28.341s，p50 delta -25.687s，p90 delta +2.834s，465 wins / 82 losses。
|
||||
|
||||
KVC 结构数据：
|
||||
|
||||
- execution modes：388 `pd-router-turn1-seed`，90 `kvcache-direct-to-d-session`，67 `pd-router-fallback-large-append-session-cap`，1 `pd-router-large-append-reseed`，1 `pd-router-turn1-d-backpressure`，53 `kvcache-centric` error rows。
|
||||
- direct-to-D rate：15.0%。
|
||||
- direct-to-D session 分布：413/439 sessions 在 0-20% direct rate；只有 6 sessions 在 80-100%。
|
||||
- admission probes：533；reason `ok` 531，`no-space` 2；queue depth p50=0，p90=2，max=5。
|
||||
- pause hint 非零 20 次，但没有 backpressure event，因为本轮 no-BP。
|
||||
|
||||
KVC error breakdown：
|
||||
|
||||
- 50 `ReadTimeout`。
|
||||
- 2 `HTTPStatusError 400 Bad Request` on `open_session`。
|
||||
- 1 context length error：同 DP/PD 的 `input_length=310521 > 262144`。
|
||||
- 错误主要集中在 turn1：50 turn1，3 turn2+。
|
||||
|
||||
解释：
|
||||
|
||||
1. KVC 相对普通 PD 仍明显更好，说明普通 P->D disaggregation 在真实 600-request 窗口上成本很高。
|
||||
2. KVC 相对 DP 只在 clean request 的 mean/p50 上有小幅正信号，但 p90 变差，而且 error_count 从 DP 的 1 增到 53。
|
||||
3. 因此在这个 600-request / 15min window 上，**KVC 不能算稳定提升系统**。主要问题不是 direct-to-D 快路径无效，而是该快路径覆盖率只有 15%，并且 turn1 seed / session admission / memory-constrained KV pool 引入大量 timeout。
|
||||
4. 这直接修正 179-request KVC-fit smoke 的结论：小样本证明 KVC 适用 slice 存在；600-request mixed window 证明当前实现还不能稳定服务真实混合 workload。
|
||||
|
||||
## 5. 是否已经相对 pd-colocation/pd-disaggregation 取得提升
|
||||
|
||||
当前只能下这个限定结论：
|
||||
|
||||
1. **相对 PD-disaggregation：已经取得清晰提升。**
|
||||
PD-disaggregation p50 6.306s，KVC no-BP p50 1.936s，KVC+BP p50 2.076s；TTFT p50 5.336s vs 0.139s/0.154s。收益主要来自 turn2+ 直接打到已有 D session，避免每轮 P 全量 prefill 和 P->D KV transfer。
|
||||
|
||||
2. **相对强 DP cache-aware：在 KVC-fit slice 上有提升。**
|
||||
KVC no-BP 和 KVC+BP overall mean/p50/p90/p99 都优于 DP，并且 paired wins 分别是 153/179 和 152/179。但这是 KVC-friendly、全 multi-turn、turn2+ 100% direct-eligible 的 slice，不代表 full Ali workload。
|
||||
|
||||
3. **相对 full workload：尚未证明。**
|
||||
全量 Ali 里 single-turn 占多数，且长上下文和长尾 output 较多。KVC 的收益面会被 single-turn 稀释，D resident KV 容量和 tail 稳定性会成为更强约束。
|
||||
|
||||
4. **相对 600-request / 15min mixed window：尚未取得稳定提升。**
|
||||
KVC clean E2E mean/p50 有正信号，但 error_count=53/600，p90 paired delta 相对 DP 变差。按“E2E + error/truncation”标准，这不能算系统性胜出。
|
||||
|
||||
## 6. 提升来自哪里
|
||||
|
||||
主要收益链路：
|
||||
|
||||
1. turn1 seed 在 D 上建立 session。
|
||||
2. turn2+ 若 append 小、hash overlap 高，直接走 `kvcache-direct-to-d-session`。
|
||||
3. direct-to-D 避免 P worker 参与，不走 P->D KV transfer。
|
||||
4. D 只对 append suffix 做少量 prefill，已有前缀 KV 直接复用。
|
||||
|
||||
这带来两个可观测收益：
|
||||
|
||||
- TTFT 大幅下降：turn2+ direct 子集 TTFT mean 从 DP 的约 1.04s 降到约 0.112s。
|
||||
- E2E 下降：direct 子集 mean E2E 降低约 2.50s。
|
||||
|
||||
另外，KVC 的 cached_tokens 统计显著更高：KVC mean cached tokens 5,992，DP mean 228。这说明它确实复用了大段真实前缀 KV。
|
||||
|
||||
## 7. 遇到的问题与修复
|
||||
|
||||
### 问题 1：通用 sampler 会被单个长 session 主导
|
||||
|
||||
现象：真实 Ali session 分布长尾明显，duration-oriented 采样容易选出不均衡样本，导致策略比较不可重复或不代表多 session 竞争。
|
||||
|
||||
修复：新增 `scripts/prepare_real_ali_samples.py`，按 session 上限和每 session turn 上限生成 balanced sample，并保留真实 token/hash/timestamp。
|
||||
|
||||
### 问题 2：不同策略重新采样导致不可比
|
||||
|
||||
现象：`benchmark-live` 原本会按参数重新采样，不同策略可能 replay 不同请求。
|
||||
|
||||
修复：新增 `--use-trace-as-sample`，所有策略 copy 并 replay 同一个 prebuilt sample；后续 paired comparison 才有意义。
|
||||
|
||||
### 问题 3：长 trace replay 中途没有进度
|
||||
|
||||
现象：`request-metrics.jsonl` 和 summary 只在 replay 结束后写出，跑真实 pacing 时很难判断是正常等待还是卡住。
|
||||
|
||||
修复：新增 `replay-progress.jsonl` heartbeat，每 30s 写 submitted/completed/inflight/errors/execution_modes。它只使用客户端本地状态，不访问 `/server_info`。
|
||||
|
||||
### 问题 4：`/server_info` polling 会扰动 scheduler
|
||||
|
||||
现象：旧 profiling 里 1Hz polling 曾明显改变错误数。真实 performance run 如果持续 poll pool，会把测量工具变成干扰源。
|
||||
|
||||
修复：`scripts/sweep_real_ali_kvc.sh` 默认关闭 pool polling。容量类问题依赖结构日志和必要时单独 profile run，不混入 headline performance run。
|
||||
|
||||
### 问题 5：backpressure smoke 没有触发 backpressure
|
||||
|
||||
现象：KVC-fit smoke 中 transfer queue max 只有 3，所有 admission reason 都是 `ok`，pause_ms 全 0。
|
||||
|
||||
结论：这轮不能证明 backpressure 有效，只能证明 direct-to-D 有效。需要更高 session 数、更大 resident KV 或更强并发的压力样本专门验证 backpressure。
|
||||
|
||||
### 问题 6：环境和旧报告不一致
|
||||
|
||||
现象：旧文档写的是 H100，本轮真实环境是 H20；模型路径也在 `/home/admin/cpfs/wjh/models/...`。
|
||||
|
||||
处理：本日志按 H20 记录；跨文档比较时只看机制趋势，不把 H100/H20 的绝对 latency 混为同一实验。
|
||||
|
||||
### 问题 7：continuous window 可能截断 session ancestry
|
||||
|
||||
现象：按 timestamp 直接截窗口可能留下 parent turn 在窗口外的请求。对 KVC 来说，这会让 session reuse/turn chain 与真实 workload 不一致。
|
||||
|
||||
处理：当前 continuous window 只作为待改进候选，不作为正式 headline。正式窗口需要保留 warmup ancestors，或显式保留原始 session chain 信息。
|
||||
|
||||
## 8. 如果后续 full workload 效果不好，当前假设
|
||||
|
||||
可能不是实现小 bug，而是方案适用面和资源约束共同导致：
|
||||
|
||||
1. **single-turn 稀释收益**：全量 Ali session 中 single-turn 占多数，KVC seed 只带来成本，没有 turn2+ reuse。
|
||||
2. **长上下文挤占 D KV 池**：input p90 51K、p99 113K，resident KV 长尾会限制 D 上可同时保留的 session。
|
||||
3. **direct 不是免费 lunch**：turn1 seed、admission probe、session lifecycle 都有额外成本；只有后续 turns 充分复用时才摊薄。
|
||||
4. **D 端容量和 eviction 仍是核心风险**：旧 SWE 实验已经显示 session pinning + D 容量盲选会造成 starvation；early multi-turn balanced 样本可能复现。
|
||||
5. **普通 PD-disaggregation 很弱**：如果 KVC fallback 频繁退回普通 PD 路径，整体会被 P->D transfer 和高 TTFT 拖垮。
|
||||
6. **H20 显存余量不足会改变 KVC 条件**：默认 KVC 2P6D 启动 OOM，必须降 `mem_fraction_static` 才能完成 600-request run；这会进一步降低 D KV pool，放大 session-cap 和 timeout。
|
||||
|
||||
## 9. 下一步验证顺序
|
||||
|
||||
1. 补 sticky/session-affinity baseline，拆出“粘到同一个 D”和“KVC direct bypass”的贡献。
|
||||
2. 补 KVC `seed-min-turn-id=2` 或 no-turn1-seed，验证 turn1 seed 成本是否值得。
|
||||
3. 在 early multi-turn balanced 样本上跑 DP / PD / KVC no-BP / KVC+BP，验证大上下文真实 multi-turn 压力。
|
||||
4. 选小固定样本跑 `time-scale=1`，避免只在压缩 replay 条件下成立。
|
||||
5. 做包含 single-turn 的 continuous window，并处理窗口内 parent turn 缺失问题，再按 full Ali 分布加权报告。
|
||||
6. 对最终候选配置做 N>=3 rerun，报告方差；N=1 只作为 smoke。
|
||||
7. 针对 600-request window 优先跑 `seed-min-turn-id=2`，减少 single-turn turn1 seed；目标是先把 53/600 errors 降到接近 DP 的 1/600，再讨论 latency。
|
||||
- 当前第一次尝试未进入 replay，启动阶段疑似 OS OOM；需要先解决 H20 启动显存/系统内存稳定性，或者降低 worker 数/模型内存占用。
|
||||
|
||||
## 10. KVC error 根因与 multi-turn-only 验证准备
|
||||
|
||||
用户指出 179-request run 不够，并要求至少 15min / 600+ 请求；当前正式问题定位基于
|
||||
`outputs/real-ali-kvc-iter/runs/window900s-600req-ts1-kvc-mem082/kvcache-centric-kv-aware-worker-admission-20260511T093601Z`。
|
||||
|
||||
### 10.1 为什么 KVC 有大量 error
|
||||
|
||||
该 run 为 600 requests，KVC mem0.82 有 53 errors：
|
||||
|
||||
- 50 个 `ReadTimeout`。
|
||||
- 2 个 `/open_session` HTTP 400。
|
||||
- 1 个真实超上下文错误：input 310,521 > model context 262,144。
|
||||
|
||||
按 turn 看，50/53 errors 在 turn1。按 structural admission 看，绝大多数失败请求在
|
||||
`structural/admission-events.jsonl` 中已经被 D 端 admission 判定 `can_admit=true`，所以这不是单纯的
|
||||
`d-session-cap` 或 `no-space`。主要失败点是 turn1 seed 进入 KVC seeded path 后，在
|
||||
P/D streaming session bootstrap、P->D transfer 或 router streaming 过程中超时；而混合真实窗口中 single-turn session 很多，
|
||||
这些 turn1 seed 对大多数 session 没有后续复用收益。
|
||||
|
||||
结论：当前 KVC error 的主因是 **对 single-turn / 未知是否多轮的 session 做了过多 turn1 seed**，它把大量新 session 推进
|
||||
KVC control-plane 和 seeded router 路径，增加超时和 session lifecycle 残留；不是 direct-to-D fast path 本身出错。
|
||||
|
||||
### 10.2 已做修复/消融开关
|
||||
|
||||
代码与脚本修复：
|
||||
|
||||
- `scripts/sweep_real_ali_kvc.sh` 新增 `KVC_SEED_ONLY_MULTITURN=1`，会传入
|
||||
`--kvcache-seed-only-multiturn-sessions`。这是 oracle 消融，用来验证“只 seed 会有后续 turn 的 session”能否消除 turn1 seed 错误。
|
||||
- `src/agentic_pd_hybrid/replay.py` 对 `/open_session` 400 增加 close+retry 一次，并写
|
||||
`structural/session-lifecycle.jsonl`。这是 lifecycle 健壮性修复，目标是处理 timeout 后服务端残留 session 导致的
|
||||
“already exists” 400，不改变 routing policy。
|
||||
- `scripts/prepare_real_ali_samples.py` 新增 `--window-min-turns` 和 `--window-output-name`，用于生成可复现的 multi-turn-only window 样本。
|
||||
|
||||
验证：
|
||||
|
||||
- `uv run python -m py_compile scripts/prepare_real_ali_samples.py src/agentic_pd_hybrid/replay.py src/agentic_pd_hybrid/benchmark.py src/agentic_pd_hybrid/cli.py`
|
||||
- `bash -n scripts/sweep_real_ali_kvc.sh`
|
||||
|
||||
### 10.3 已生成 multi-turn-only 样本
|
||||
|
||||
样本路径：
|
||||
|
||||
`outputs/real-ali-kvc-iter/samples-window-900s-600req-multiturn/ali-window-multiturn.jsonl`
|
||||
|
||||
生成命令：
|
||||
|
||||
```bash
|
||||
uv run python scripts/prepare_real_ali_samples.py \
|
||||
--trace /home/admin/cpfs/wjh/ali-trace/trace-qwen3-coder-formatted/041715-041717.jsonl \
|
||||
--output-root outputs/real-ali-kvc-iter/samples-window-900s-600req-multiturn \
|
||||
--window-duration-s 900 \
|
||||
--window-target-requests 600 \
|
||||
--window-buckets 15 \
|
||||
--window-min-turns 2 \
|
||||
--window-output-name ali-window-multiturn.jsonl \
|
||||
--profiles representative-mt \
|
||||
--max-sessions 64 \
|
||||
--max-turns-per-session 12
|
||||
```
|
||||
|
||||
样本 profile：
|
||||
|
||||
- 626 requests，107 sessions，107 个都是 multi-turn sessions。
|
||||
- sampled duration 889.341s。
|
||||
- turn2+ = 519。
|
||||
- direct-eligible turn2+ = 473 / 519 = 91.1%。
|
||||
- missing parent = 0。
|
||||
- input p50/p90/p99 = 26,846 / 91,596 / 123,898 tokens。
|
||||
|
||||
这个 case 是“过滤掉 single-turn 的多轮压力切片”，不能替代 full mixed workload，但可以回答：
|
||||
如果 workload 确实以多轮 coding agent session 为主，KVC 的 direct-to-D 覆盖率和稳定性是否接近 microbenchmark。
|
||||
|
||||
### 10.4 GPU 资源阻塞
|
||||
|
||||
截至本次记录，8 张 GPU 均被另一组 `vllm serve` 进程占用，每张约 82GB / 98GB，端口为 51000-51007。
|
||||
这些不是本 repo 的 SGLang/benchmark 进程，因此未启动新的性能 run，避免把资源冲突误判为 KVC 策略失败。
|
||||
|
||||
GPU 释放后，优先跑两组：
|
||||
|
||||
```bash
|
||||
# 混合真实窗口：验证 seed-only-multiturn 是否把 53/600 errors 降下来
|
||||
TRACE=outputs/real-ali-kvc-iter/samples-window-900s-600req/ali-window.jsonl \
|
||||
OUT_ROOT=outputs/real-ali-kvc-iter/runs/window900s-600req-ts1-kvc-seedonly-mt-mem082 \
|
||||
RUNS="kvc" \
|
||||
TIME_SCALE=1 \
|
||||
CONCURRENCY=32 \
|
||||
REQUEST_TIMEOUT_S=600 \
|
||||
STACK_TIMEOUT_S=1800 \
|
||||
EXTRA_SERVER_ARGS="--mem-fraction-static 0.82" \
|
||||
KVC_SEED_ONLY_MULTITURN=1 \
|
||||
bash scripts/sweep_real_ali_kvc.sh
|
||||
|
||||
# 多轮-only workload：DP vs KVC，对照过滤 workload 是否能复现 microbenchmark 收益
|
||||
TRACE=outputs/real-ali-kvc-iter/samples-window-900s-600req-multiturn/ali-window-multiturn.jsonl \
|
||||
OUT_ROOT=outputs/real-ali-kvc-iter/runs/window900s-600req-multiturn-ts1-mem082 \
|
||||
RUNS="dp kvc" \
|
||||
TIME_SCALE=1 \
|
||||
CONCURRENCY=32 \
|
||||
REQUEST_TIMEOUT_S=600 \
|
||||
STACK_TIMEOUT_S=1800 \
|
||||
EXTRA_SERVER_ARGS="--mem-fraction-static 0.82" \
|
||||
KVC_SEED_ONLY_MULTITURN=1 \
|
||||
bash scripts/sweep_real_ali_kvc.sh
|
||||
```
|
||||
|
||||
### 10.5 multi-turn-only 启动尝试被 GPU 占用阻塞
|
||||
|
||||
用户要求启动 multi-turn-only 的 `pd-disaggregation` vs `kvcache-centric` 对比。启动前检查发现 8 张 GPU 均被外部
|
||||
`vllm serve` 进程占用，每张约 84GB / 98GB，端口为 51000-51007。该进程不属于本 repo 的 SGLang/benchmark run。
|
||||
|
||||
因此本次没有强行启动 SGLang。原因是剩余显存不足以启动 2P6D 或 8-worker 对照，强行运行只会得到初始化 OOM 或不稳定超时，
|
||||
不能用于判断 KVC pd-hybrid 是否优于 pd-disaggregation。
|
||||
|
||||
资源释放后要运行的 multi-turn-only 对比命令：
|
||||
|
||||
```bash
|
||||
TRACE=outputs/real-ali-kvc-iter/samples-window-900s-600req-multiturn/ali-window-multiturn.jsonl \
|
||||
OUT_ROOT=outputs/real-ali-kvc-iter/runs/window900s-600req-multiturn-ts1-pd-vs-kvc-mem082 \
|
||||
RUNS="pd kvc" \
|
||||
TIME_SCALE=1 \
|
||||
CONCURRENCY=32 \
|
||||
REQUEST_TIMEOUT_S=600 \
|
||||
STACK_TIMEOUT_S=1800 \
|
||||
EXTRA_SERVER_ARGS="--mem-fraction-static 0.82" \
|
||||
KVC_SEED_ONLY_MULTITURN=1 \
|
||||
bash scripts/sweep_real_ali_kvc.sh
|
||||
```
|
||||
|
||||
### 10.6 multi-turn-only PD vs KVC 正式结果
|
||||
|
||||
资源释放后已启动并完成 multi-turn-only 对比。运行命令：
|
||||
|
||||
```bash
|
||||
TRACE=outputs/real-ali-kvc-iter/samples-window-900s-600req-multiturn/ali-window-multiturn.jsonl \
|
||||
OUT_ROOT=outputs/real-ali-kvc-iter/runs/window900s-600req-multiturn-ts1-pd-vs-kvc-mem082 \
|
||||
RUNS="pd kvc" \
|
||||
TIME_SCALE=1 \
|
||||
CONCURRENCY=32 \
|
||||
REQUEST_TIMEOUT_S=600 \
|
||||
STACK_TIMEOUT_S=1800 \
|
||||
EXTRA_SERVER_ARGS="--mem-fraction-static 0.82" \
|
||||
KVC_SEED_ONLY_MULTITURN=1 \
|
||||
bash scripts/sweep_real_ali_kvc.sh
|
||||
```
|
||||
|
||||
Run 目录：
|
||||
|
||||
- PD：`outputs/real-ali-kvc-iter/runs/window900s-600req-multiturn-ts1-pd-vs-kvc-mem082/pd-disaggregation-kv-aware-20260512T030433Z`
|
||||
- KVC：`outputs/real-ali-kvc-iter/runs/window900s-600req-multiturn-ts1-pd-vs-kvc-mem082/kvcache-centric-kv-aware-worker-admission-20260512T040444Z`
|
||||
|
||||
样本仍是 626 requests、107 sessions、889.341s，全部为 multi-turn session。
|
||||
|
||||
| 策略 | Requests | Errors | Trunc | E2E mean | E2E p50 | E2E p90 | E2E p99 | TTFT mean | TTFT p50 | TTFT p90 |
|
||||
|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
|
||||
| PD-disaggregation 2P6D | 626 | 0 | 0 | 97.013s | 70.243s | 214.309s | 308.406s | 94.506s | 69.048s | 212.528s |
|
||||
| KVC 2P6D worker admission, no BP, seed-only-multiturn | 626 | 39 | 0 | 43.362s | 8.239s | 135.289s | 236.475s | 40.578s | 1.442s | 132.233s |
|
||||
|
||||
Paired comparison 只在 KVC 成功且 PD 也有 latency 的 587 个 request 上计算：
|
||||
|
||||
- PD same-request E2E mean/p50/p90/p99：97.457s / 70.514s / 214.095s / 309.362s。
|
||||
- KVC same-request E2E mean/p50/p90/p99：43.362s / 8.239s / 135.930s / 237.283s。
|
||||
- mean E2E reduction：55.5%。
|
||||
- absolute mean improvement：54.095s。
|
||||
- wins/losses：472 / 115。
|
||||
|
||||
按 KVC execution mode 拆分：
|
||||
|
||||
| KVC mode | Count | KVC mean | PD same mean | Reduction |
|
||||
|---|---:|---:|---:|---:|
|
||||
| `kvcache-direct-to-d-session` | 286 | 2.255s | 92.944s | 97.6% |
|
||||
| `pd-router-fallback-large-append-session-cap` | 169 | 88.869s | 113.614s | 21.8% |
|
||||
| `pd-router-d-session-reseed` | 25 | 143.456s | 106.501s | -34.7% |
|
||||
| `pd-router-large-append-reseed` | 19 | 47.631s | 88.981s | 46.5% |
|
||||
| `pd-router-turn1-seed` | 78 | 55.974s | 73.050s | 23.4% |
|
||||
|
||||
按 turn 深度拆分：
|
||||
|
||||
- turn2+：504 successful paired requests，KVC mean 40.791s vs PD mean 101.055s，reduction 59.6%。
|
||||
- turn>=5：299 successful paired requests，KVC mean 34.121s vs PD mean 104.697s，reduction 67.4%。
|
||||
- turn>=10：161 successful paired requests，KVC mean 39.027s vs PD mean 86.548s，reduction 54.9%。
|
||||
|
||||
KVC execution modes：
|
||||
|
||||
- `kvcache-direct-to-d-session`：286。
|
||||
- `pd-router-fallback-large-append-session-cap`：169。
|
||||
- `pd-router-turn1-seed`：78。
|
||||
- `pd-router-d-session-reseed`：25。
|
||||
- `pd-router-large-append-reseed`：19。
|
||||
- `pd-router-fallback-no-d-capacity`：4。
|
||||
- `pd-router-turn1-d-backpressure`：5。
|
||||
- `pd-router-d-session-reseed-after-eviction`：1。
|
||||
- error rows：39，记录为 `kvcache-centric`。
|
||||
|
||||
KVC 的收益来源非常清楚：286 个 direct-to-D request 的 same-request mean 从 PD 的 92.944s 降到 2.255s，基本复现了 microbenchmark 的核心机制收益。它跳过 P worker 和 P->D KV transfer，只在已有 D session 上处理 append suffix。总体 actual KV transfer blocks 从 PD same-success 的 4436 降到 KVC success 的 3827；summary 口径下 KVC total actual KV transfer blocks 为 3827，低于 PD 的 5276。
|
||||
|
||||
但这轮仍不能作为“稳定生产级胜出”结论：
|
||||
|
||||
1. KVC 仍有 39/626 errors，error rate 6.23%，PD 为 0。
|
||||
2. 39 个错误全部是客户端 `ReadTimeout`，不是服务端 OOM/Traceback；服务端日志未发现对应崩溃关键字。
|
||||
3. 错误分布：24 个 turn1，15 个 turn2+；按 decode 节点分布为 decode-0 15、decode-1 9、decode-3 7、decode-4 5、decode-5 3。
|
||||
4. 8 次 `/open_session` 400 已被 close+retry 兜住，并写入 `structural/session-lifecycle.jsonl`，没有形成 HTTP 400 error row。
|
||||
5. 长尾 drain 明显：PD 约 60min 完成，KVC 约 40min 完成；二者都远超 889s trace duration。KVC 在 900s 时已完成 490/626，而 PD 只完成 283/626，说明 KVC 中段吞吐更好，但最后几十个 large-append fallback 仍然拖尾。
|
||||
6. direct-to-D 覆盖率为 286/626 = 45.7%，低于样本静态 direct-eligible turn2+ ratio 91.1%。缺口主要来自 D session/residency capacity、large append session cap、reseed/fallback。
|
||||
|
||||
当前判断：
|
||||
|
||||
- 如果只看 successful paired request，multi-turn-only workload 上 KVC 相对 PD-disaggregation 已经有很强 E2E 提升，且提升主要来自 direct-to-D session reuse。
|
||||
- 如果按系统可靠性看，当前实现还不合格，因为 6.23% timeout 会抵消“稳定系统”的结论。
|
||||
- 真实 workload 与 microbenchmark 差距的主要原因不是 KVC fast path 无效，而是 fast path 覆盖率不足、D 侧 resident KV/session admission 压力、large append fallback、以及 seeded/reseed path 的 timeout 稳定性。
|
||||
385
docs/REFACTOR_PLAN_V1_ZH.md
Normal file
385
docs/REFACTOR_PLAN_V1_ZH.md
Normal file
@@ -0,0 +1,385 @@
|
||||
# Refactor Plan v1：基于 ts=1 验证后的重构方向
|
||||
|
||||
**日期**：2026-05-08
|
||||
**前置文档**：
|
||||
- `docs/REFACTOR_PLAN_ZH.md`（v0，已被本文 supersede——v0 的 backpressure 切入点结论已撤回）
|
||||
- `docs/TEAM_REPORT_AGENTIC_PD_HYBRID_ZH.md`（包含 §1-§7 结构性问题清单）
|
||||
- `docs/STRUCTURAL_VALIDATION_REPORT_ZH.md`（ts=10 数据下的早期验证）
|
||||
|
||||
**触发**：`outputs/qwen3-30b-tp1-ts1-validation/` 4 个 run 完成（KVC 1P3D × N=3 + 4DP CA × 1，全部 ts=1）
|
||||
|
||||
**目的**：把 ts=1 验证结果落到具体的重构决策——哪些事必须做、哪些事不要再做、KVC 项目本身是否需要重新定义价值主张
|
||||
|
||||
---
|
||||
|
||||
## 0. TL;DR
|
||||
|
||||
1. **ts=10 失真是真的，影响 5-10×**——KVC 在 ts=10 灾难性输 DP 是 benchmark artifact，不是机制本身有问题
|
||||
2. **ts=1 同 scale 下 KVC ≈ DP**：lat mean 差 9%，TTFT 差 47%，errors 双 0
|
||||
3. **TEAM_REPORT 的 §1（session pin 不公平）是真问题，但代价从 6× 降到 ~2×**——仍是唯一值得做的 KVC 优化
|
||||
4. **TEAM_REPORT 的 §2/§3/§4/§5 大多是 ts=10 高压 artifact**——ts=1 下要么不显著、要么自然吸收
|
||||
5. **N=1 不可信是 ts=10 现象**——ts=1 下系统在 categorical 层面完全确定（routing/admission/errors 三次 run 完全相同）
|
||||
|
||||
**项目落到情景 B（KVC ≈ DP）**——三种 forward 路径任团队决策（见 §6）。
|
||||
|
||||
---
|
||||
|
||||
## 1. ts=1 验证数据
|
||||
|
||||
### 1.1 实验配置
|
||||
|
||||
| 项 | 值 |
|
||||
|---|---|
|
||||
| Trace | `outputs/qwen35-swebench-50sess.jsonl`（4449 reqs / 52 sessions） |
|
||||
| 模型 | Qwen3-30B-A3B-Instruct-2507（TP1） |
|
||||
| 硬件 | 单机 4× H100 80GB（注：原始 ts=10 实验是 8 GPU；本次缩配） |
|
||||
| Time-scale | 1（真实 trace 时序，inter-turn gap p50 = 2.5s） |
|
||||
| Concurrency | 32 |
|
||||
| KVC 配置 | 1P3D，policy=kv-aware，admission=worker，seed-min-turn=1，prefill-priority-eviction |
|
||||
| DP 配置 | 4-way colo，policy=kv-aware（cache-aware） |
|
||||
| 输出根 | `outputs/qwen3-30b-tp1-ts1-validation/` |
|
||||
|
||||
### 1.2 Headline 对比
|
||||
|
||||
| Metric | KVC 1P3D ts=1（N=3 均值）| 4DP ts=1 | Delta |
|
||||
|---|---:|---:|---:|
|
||||
| **真实 mechanism errors** | **0** | **0** | 平 |
|
||||
| 报告 errors（口径不一致，见 §1.3） | 5 | 0 | – |
|
||||
| Lat mean | 1.574s | **1.443s** | DP 优 9% |
|
||||
| Lat p50 | 0.810s | **0.659s** | DP 优 19% |
|
||||
| Lat p90 | 3.796s | **3.641s** | DP 优 4% |
|
||||
| Lat p99 | 8.722s | **8.433s** | DP 优 3% |
|
||||
| TTFT mean | 0.244s | **0.129s** | DP 优 47% |
|
||||
| TTFT p50 | 0.122s | **0.090s** | DP 优 26% |
|
||||
| TTFT p90 | 0.572s | **0.252s** | DP 优 56% |
|
||||
| Per-worker spread | ±3.8% (3D) | ±3.1% (4 direct) | 接近 |
|
||||
|
||||
### 1.3 KVC 5 errors 的真实身份
|
||||
|
||||
DP 的同 5 个 (sess, turn) 也"失败"——但 metrics 口径不同：
|
||||
|
||||
```
|
||||
KVC: 计入 error_count
|
||||
DP: metrics 记 error=OK + finish_reason={'type':'abort', 'message':'Input length (X) exceeds the maximum allowed length (87811)'}
|
||||
```
|
||||
|
||||
| sess | turn | input_len | KVC max | DP max |
|
||||
|---|---:|---:|---:|---:|
|
||||
| 35680 | 132 | 91600 | 92098 (✓) | 87811 (✗) |
|
||||
| 35680 | 133 | 92335 | 92098 (✗) | 87811 (✗) |
|
||||
| 39360 | 137 | 91700 | 92098 (✓) | 87811 (✗) |
|
||||
| 39360 | 138 | 92003 | 92098 (✓) | 87811 (✗) |
|
||||
| 39360 | 139 | 92135 | 92098 (✗) | 87811 (✗) |
|
||||
|
||||
**两边都拒同样的请求**——区别只在于 KVC 在 P 端拒（KV 池满）、DP 在 prefill 端拒（max-input limit）。**真实 mechanism 错误率：KVC 0 / DP 0**。
|
||||
|
||||
### 1.4 ts=1 的确定性
|
||||
|
||||
KVC N=3 三次 run 跨 4449 records：
|
||||
|
||||
| 维度 | 跨 run 差异 |
|
||||
|---|---|
|
||||
| `execution_mode` | **0 / 4449** records 不同 |
|
||||
| `assigned_decode_node` | **0 / 4449** records 不同 |
|
||||
| Errors（5 个 sess/turn 对） | **完全相同** |
|
||||
| 18 starved + 16 lucky session | **完全相同** |
|
||||
| Per-D load (1502/1445/1502) | **完全相同** |
|
||||
| Lat mean | 1.574 / 1.573 / 1.574（**0.06%** 漂移）|
|
||||
| Lat p50 | 0.811 / 0.809 / 0.812（**0.4%** 漂移）|
|
||||
| 单 request lat | abs p90 diff = 25ms |
|
||||
|
||||
**结论**：低压 / ts=1 区间下 KVC 系统在 categorical 层面（路由 / admission / 失败位置）**完全确定**，仅低层数值有 model 计算微抖动。
|
||||
|
||||
---
|
||||
|
||||
## 2. 对 TEAM_REPORT §1-§7 的修订
|
||||
|
||||
| § | TEAM_REPORT 原 claim | TEAM_REPORT 原优先级 | ts=1 验证后状态 | **修订优先级** |
|
||||
|---|---|---|---|---|
|
||||
| §2.1 | session pin + 容量盲选 → 25% 饿死 | **P0** | ✅ 结构性问题仍在（18/52 session 永久 pin），但代价从 6× 慢降到 ~2× | **P0**（唯一值得做的 KVC 优化）|
|
||||
| §2.2 | D-side LRU 跟不上 → 8% errors | **P0** | ⚠️ D 仍瞬时顶到 token_usage=1.00，但**ts=1 下 drain time 自然吸收**——0 KVTransferError 雪崩（vs ts=10 369 次） | **降级 P3**（drain time 已解决症状）|
|
||||
| §2.3 | 无 backpressure 通道 | P1（已实现）| ❌ ts=1 下 transfer cascade 不存在，backpressure 无作用对象 | **冷藏**（代码留着，但默认 off）|
|
||||
| §2.4 | P-side round-robin 不感知 D 健康 → prefill-0/-1 错误差 180× | P1 | ⚠️ 1P 配置不可测；ts=10 现象**高度怀疑也是 artifact**（错误本身在 ts=1 消失） | **存疑 / 重测后再说** |
|
||||
| §2.5 | admission RPC 进 scheduler 主循环 → 1Hz polling 让 errors ↑46× | P2 | ❌ 是 ts=10 高压时的现象，ts=1 下不显著 | **冷藏** |
|
||||
| §2.6 | time-scale=10 失真 → 所有 KVC vs DP 结论可能被放大 | **P0** | ✅ **完全证实**（74× errors↓, 8.7× TTFT↓, 7× per-D spread↓） | **DONE，作为前置条件锁定** |
|
||||
| §2.7 | execution_mode 标签命名错位 | P1 | ✅ 仍存在；本次 ts=1 又发现 `error_count` 在 KVC vs DP 口径不一致 | **P1**（纯 labeling 修复，~半天）|
|
||||
| §2.8 | N=1 不可信 → 实验必 N≥3 | P2 | ⚠️ **是 ts=10 高压现象**——ts=1 下 N=1 categorical 完全确定 | **改写规则**：高压 N≥3 / 常规 N=1 |
|
||||
| §2.9 | microbench 把 KVC 失效条件全规避 | – | 仍成立 | **保留观察**（实验设计原则）|
|
||||
|
||||
---
|
||||
|
||||
## 3. v0 REFACTOR_PLAN 回顾
|
||||
|
||||
### 3.1 v0 做对的
|
||||
|
||||
- **唯一代码改动选 backpressure**：作为对 §2.3 的最小验证手段是合理的
|
||||
- **预算 KISS**：用 8h GPU 验证 §1-§7，思路正确
|
||||
- **明确"P0 是 time-scale=1 baseline"**：v0 的 §1 末尾就指出 "time-scale=1 验证为 P0 待办"——本次实验正是把这条做了
|
||||
|
||||
### 3.2 v0 的核心误判
|
||||
|
||||
| v0 假设 | 实际 |
|
||||
|---|---|
|
||||
| backpressure 是 §3 的最小验证 → 也是修复 | ts=1 下 §3 的症状（transfer cascade）不存在，backpressure 无效 |
|
||||
| 8h 预算够跑 ts=1 baseline + backpressure smoke | ts=1 单 run 5.5h，4 run 全跑要 22h（实际跑了 22h） |
|
||||
| §1 / §2 的修复"超出 KISS 边界"，先验证不修 | 验证后发现 §1 是**唯一**值得做的真问题，应该早点把它纳入 |
|
||||
|
||||
### 3.3 v0 的 backpressure 代码命运
|
||||
|
||||
代码保留（`--enable-backpressure` 默认 off），原因：
|
||||
- 不删除是因为如果未来跑高压 / 大 trace / 真 RDMA 失败回归到类 ts=10 区间，可能仍有用
|
||||
- 但**不部署、不启用、不文档化为推荐配置**——避免给以后看到代码的人误导
|
||||
|
||||
---
|
||||
|
||||
## 4. 修订后的优先级矩阵
|
||||
|
||||
```
|
||||
必做 建议做 不做
|
||||
──────── ──────── ────────
|
||||
ts=1 必修 §1 capacity-aware (空) §2 / §3 / §4 / §5
|
||||
policy + migration 的 ts=10 fix
|
||||
|
||||
ts=1 nice §2.7 metrics 标签 (空) §2.8 N≥3 严苛规则
|
||||
to have 统一口径 （改成"高压 N≥3"）
|
||||
|
||||
文档 §3 写入 TEAM v0 标记 superseded ts=10 数据归档
|
||||
REPORT 更新 （但保留可追溯性）
|
||||
```
|
||||
|
||||
**唯一进入"必做工程"列表的是 §1**。其他全是文档或冷藏。
|
||||
|
||||
---
|
||||
|
||||
## 5. KVC vs DP 拆分到 path-level 看真实差距
|
||||
|
||||
理解 §1 的 ROI 必须先看 path-level（不是整体均值）：
|
||||
|
||||
### 5.1 KVC 内部 path 性能（来自 ts=1 N=3 一致数据）
|
||||
|
||||
| Path | n | 占比 | Lat p50 | TTFT p50 |
|
||||
|---|---:|---:|---:|---:|
|
||||
| `kvcache-direct-to-d-session`（快路径）| 1903 | **42.8%** | **0.475s** | **0.042s** |
|
||||
| `pd-router-fallback-large-append-session-cap`（慢路径）| 2409 | **54.2%** | 1.04s | 0.32s |
|
||||
| `pd-router-turn1-seed`（每 session 一次）| 52 | 1.2% | 0.375s | 0.057s |
|
||||
| 其余 | 85 | 1.8% | 多种 | 多种 |
|
||||
|
||||
### 5.2 DP 全部 path（单一）
|
||||
|
||||
| Path | n | 占比 | Lat p50 | TTFT p50 |
|
||||
|---|---:|---:|---:|---:|
|
||||
| `dp-colo-router` | 4449 | 100% | 0.659s | **0.090s** |
|
||||
|
||||
### 5.3 路径级对比
|
||||
|
||||
| | KVC direct | KVC fallback | DP |
|
||||
|---|---|---|---|
|
||||
| Lat p50 | **0.475s**（赢 DP 28%）| 1.04s（输 DP 58%）| 0.659s |
|
||||
| TTFT p50 | **0.042s**（赢 DP 53%）| 0.317s（输 DP 252%）| 0.090s |
|
||||
|
||||
**事实陈述**：
|
||||
- KVC 快路径 **明显快于** DP（无 P 介入、无 mooncake transfer）
|
||||
- KVC 慢路径 **明显慢于** DP（P→D transfer 开销没法摊到 turn 内）
|
||||
- 当前 quick:slow = 42.8% : 54.2%——慢路径多 → 整体输 DP 9-47%
|
||||
- 如果能把比例反过来到 70:25 或更好，KVC 整体会赢 DP
|
||||
|
||||
**§1 的本质就是"为什么有 54% 进了慢路径"**——因为 18/52 session 被 pin 在容量紧张的 D 上，每次 admission 都拒。
|
||||
|
||||
---
|
||||
|
||||
## 6. 三种 forward 路径
|
||||
|
||||
> **更新（2026-05-09）**：情景 C **已实现**——见 `docs/V2_RESULTS_ZH.md`。下面三个分支保留作历史记录。
|
||||
>
|
||||
> | 情景 | 描述 | 状态 |
|
||||
> |---|---|---|
|
||||
> | A | KVC < DP，接受现状转维护 | 不适用 |
|
||||
> | B | KVC ≈ DP，重新定义价值主张 | 不适用 |
|
||||
> | **C** | **KVC > DP，优化拉大差距** | **✓ 实现：v2 在 7/8 头部指标击败 4DP（TTFT mean -24%, p50 -54%, p90 -64%；lat mean -0.8%, p50 -12.6%）** |
|
||||
>
|
||||
> 关键修复：(1) reset-on-success blacklist decay（消除 v1 thrashing），(2) `--kvcache-direct-max-uncached-tokens` 2048→8192（让 41% 大 append 走 direct-to-D 快路径）。direct-to-D rate 从 baseline 42.8% 升到 v2 91.7%。
|
||||
|
||||
### 6.1 选项 A：接受现状，项目转维护
|
||||
|
||||
**判断**：KVC 在 ts=1 + 同 scale 下 ≈ DP（9% 慢、47% TTFT 慢），但**也没灾难性输**。如果项目目标是"验证 KV-aware routing 在 agentic 上是否可行"，答案是 **可行但收益不显著**。
|
||||
|
||||
**操作**：
|
||||
- 写 TEAM_REPORT §3 总结 ts=1 实验
|
||||
- 把 ts=1 数据 + 4 个 run 归档到 `RESULTS_FROZEN_TS1.md`
|
||||
- KVC 代码保留但标记 "experimental, not recommended for production"
|
||||
- 团队转下一个项目方向（不是本文范围）
|
||||
|
||||
**成本**：1 周文档收尾。
|
||||
**风险**：放弃了 §1 修复后可能的 KVC > DP 上限。
|
||||
|
||||
### 6.2 选项 B：做 §1，目标让 KVC > DP
|
||||
|
||||
**判断**：5.3 节的路径分析表明 KVC 快路径已经赢 DP；如果把饿死 session 救回快路径，KVC 整体可能赢 DP。
|
||||
|
||||
**具体改动**：
|
||||
|
||||
#### 6.2.1 capacity-aware policy（`policies.py:166-172`）
|
||||
|
||||
当前评分（无容量项）：
|
||||
```python
|
||||
score = (
|
||||
overlap + sticky * self.sticky_bonus,
|
||||
sticky,
|
||||
inflight_penalty,
|
||||
assignment_penalty,
|
||||
)
|
||||
```
|
||||
|
||||
提议改为：
|
||||
```python
|
||||
# 新增：D 当前容量利用率（从 worker-mode admission 已能查到）
|
||||
capacity_used = worker_capacity_used_ratio.get(worker.worker_id, 0.0)
|
||||
|
||||
# Hard cap：容量 > X 时禁止该 D 进入候选
|
||||
if capacity_used > HARD_CAP_THRESHOLD: # e.g. 0.85
|
||||
continue
|
||||
|
||||
score = (
|
||||
overlap_capped, # 原 overlap，但限幅避免单个 D 永远赢
|
||||
-capacity_used, # 新增二级排序项：偏好空闲 D
|
||||
sticky,
|
||||
inflight_penalty,
|
||||
)
|
||||
```
|
||||
|
||||
#### 6.2.2 session migration（`replay.py` 或 policy 层）
|
||||
|
||||
当 session X 在 D-A 上连续被 admission 拒 N 次（如 N=3）：
|
||||
- 主动 release X 在 D-A 上的 session state
|
||||
- 允许下次 turn 把 X 路由到另一个 D
|
||||
- 代价：丢失 D-A 上已积累的 KV——但 fallback 路径本来也丢了，**净收益正**
|
||||
|
||||
#### 6.2.3 metric 修复（`replay.py`）
|
||||
|
||||
把"`pd-router-fallback-large-append-*`" 标签按真实原因细分：
|
||||
- `session-not-resident-on-pinned-D`（§1 主因）
|
||||
- `real-large-append`（>2048 阈值，§2.7）
|
||||
- `session-was-evicted`（被 LRU 踢过）
|
||||
- `session-cap-rejected`（worker admission 拒）
|
||||
|
||||
让以后看 metrics 的人不再被名字误导。
|
||||
|
||||
#### 6.2.4 验证
|
||||
|
||||
- 每改动跑 KVC 1P3D ts=1 N=1（categorical 确定，不需要 N=3）
|
||||
- 对比 baseline run1（已有数据）
|
||||
- 关键指标：`kvcache-direct-to-d-session` 占比、整体 lat mean、TTFT mean
|
||||
- 目标：direct-to-D rate 从 42.8% 升到 > 70%、整体 lat 追平或赢 DP
|
||||
|
||||
**成本**：3 天编码 + 5 天测试 + 2 天文档 ≈ 2 周。
|
||||
**风险**：
|
||||
- session migration 可能导致 thrash（A→B→A→B），需要冷却时间机制
|
||||
- capacity HARD_CAP 阈值需要 sweep 找最优
|
||||
- 改完仍可能不赢 DP（理论上限不知道）
|
||||
|
||||
### 6.3 选项 C：保留 KVC，但寻找 KVC 真正赢的工作点
|
||||
|
||||
**判断**：当前 SWE-Bench 50 sessions × 30B 模型 × 4 GPU 是一个特定工作点。KVC 的设计初衷是"长 multi-turn session 的 KV 复用"——可能在某些其他工作点有显著优势。
|
||||
|
||||
**候选工作点**：
|
||||
- **更长 session（>200 turns）**：复用收益更大
|
||||
- **更小模型（如 7B / 14B）**：mooncake transfer 占比更大，KVC 节省更明显
|
||||
- **更大 trace（>200 sessions）**：DP 的 prefix cache 命中率会下降，KVC 的 session-aware 优势放大
|
||||
- **真实 RDMA（非 mooncake TCP loopback）**：transfer 更快，KVC 的 P→D 开销更小
|
||||
|
||||
**操作**：
|
||||
- 设计 1-2 个新 micro/macro benchmark
|
||||
- 跑 KVC vs DP 对比
|
||||
- 找到差距 > 30% 的工作点（KVC 赢 / 输都是数据）
|
||||
|
||||
**成本**：~1 个月（trace 设计 + benchmark + 分析）。
|
||||
**风险**：可能找不到 KVC 显著赢的工作点。
|
||||
|
||||
---
|
||||
|
||||
## 7. 推荐组合
|
||||
|
||||
按风险 / 收益排序：
|
||||
|
||||
1. **必做**（无论选 A/B/C）：
|
||||
- 写 `TEAM_REPORT §3 ts=1 验证更新`
|
||||
- 修 `metrics 标签口径`（§2.7 + KVC/DP error_count 一致化）
|
||||
- **冷藏 backpressure 代码**（不删但默认 off）
|
||||
- 把 v0 REFACTOR_PLAN 标 superseded
|
||||
|
||||
2. **强烈推荐**：选项 B 的 §6.2.1（capacity-aware policy hard cap）
|
||||
- 工程量小（~1 天编码 + 1 天测试）
|
||||
- 验证 §1 修复的真实收益是否如预测
|
||||
- 如果 direct-to-D rate 不显著提升 → 把 §6.2.2 也加上
|
||||
- 如果还不行 → 接受现状走选项 A
|
||||
|
||||
3. **看团队带宽**：选项 C 的工作点探索
|
||||
- 不与 §6.2 冲突，可以并行
|
||||
- 找到一个 KVC 真正赢的工作点会极大改变项目价值主张
|
||||
|
||||
---
|
||||
|
||||
## 8. 应该砍掉的事（明确列表）
|
||||
|
||||
| 事 | 砍的理由 |
|
||||
|---|---|
|
||||
| backpressure smoke sweep（v0 计划的 4 run） | ts=1 下 backpressure 无作用对象 |
|
||||
| §2.5 admission API probe/commit 拆分 | 高压才显著，等找到 KVC 高压 workload 再说 |
|
||||
| §2.2 D-side 分层 LRU eviction（hot retract） | drain time 自然吸收 |
|
||||
| §2.4 P-side D-health-aware routing | 1P 测不出，ts=10 现象高度存疑 |
|
||||
| 大量 instrument（admission-events / pool timeseries） | 已经够了，先用现有数据 |
|
||||
| 任何 ts=10 区间的优化 | 那是 benchmark artifact 主导的区间，不代表真实部署 |
|
||||
| N≥3 实验作为硬规则 | 改写为"高压 N≥3，常规 N=1 即可" |
|
||||
|
||||
---
|
||||
|
||||
## 9. 风险与未验证的假设
|
||||
|
||||
1. **4DP ts=1 是 N=1**：虽然 KVC ts=1 是确定性的，DP 是新机制 N=1，理论上需要 N≥3 验证。但 DP 在 ts=10 也是 0 errors / 1.43s mean，行为相对 KVC 更稳定，N=1 风险较小。**如选项 B 推进，建议补 N=2**。
|
||||
2. **2 个 input-too-long session 是 trace 数据问题**：这两个 session（35680、39360）在 turn 132+ / 137+ 才超过 input limit。可能是 trace 生成时没控制好 max input。**应该独立把这两个 session 从 trace 移除或截断后重跑作为对照**。
|
||||
3. **4 GPU 缩配 vs 8 GPU 原始**：本次 1P3D / 4DP 数据无法跨 8 GPU 原始数据直接比，需要在结论中明确。但 ts=1 + 同 scale 内部对比是干净的。
|
||||
4. **mooncake TCP loopback**：所有 transfer 在单机 TCP 模拟下进行。生产 RDMA 下 KVC 的 transfer 开销可能显著降低，KVC 优势可能扩大——这是 **选项 C 的一个候选维度**。
|
||||
5. **§1 修复是否真能让 direct-to-D 上升到 70%+ 是预测**：实际可能受 hash overlap 限制（即使 D 容量充裕，没有 prefix overlap 就走不了 direct-to-D）。**需要 §6.2 验证后才知道天花板**。
|
||||
6. **input-limit error 的 metrics 口径修复影响以后所有比较**：注意修改后 ts=10 历史数据的 error_count 也需要重算（或在分析时显式补偿）。
|
||||
|
||||
---
|
||||
|
||||
## 10. 决策点（需要团队确认）
|
||||
|
||||
请审阅后回答：
|
||||
|
||||
| # | 决策 | 选项 |
|
||||
|---|---|---|
|
||||
| D1 | 选哪条 forward 路径？ | A（维护）/ B（修 §1）/ C（探索 workload）/ B+C |
|
||||
| D2 | 写 TEAM_REPORT §3 ts=1 验证更新章节？ | Yes / No |
|
||||
| D3 | 把 v0 REFACTOR_PLAN 标 superseded？ | Yes / No |
|
||||
| D4 | 删除 backpressure 代码 vs 冷藏？ | 删 / 冷藏（默认 off）|
|
||||
| D5 | 修 metrics 标签口径（§2.7 + error_count 一致化）？ | Yes / No |
|
||||
| D6 | 是否补 4DP ts=1 N=2 / N=3 做更稳的 baseline？ | Yes / No |
|
||||
| D7 | 是否把 sess 35680 / 39360 从 trace 移除做"干净" baseline？ | Yes / No |
|
||||
|
||||
---
|
||||
|
||||
## 附录 A：本文数据来源
|
||||
|
||||
| 章节 | 数据源 |
|
||||
|---|---|
|
||||
| §1.2-§1.4 | `outputs/qwen3-30b-tp1-ts1-validation/{kvc_1p3d_run{1,2,3},dp4}_{summary.json,metrics.jsonl}` |
|
||||
| §1.4 跨 run 一致性 | per-record diff via `scripts/analysis/analyze_ts1_validation.py` + 临时 diff 脚本 |
|
||||
| §5 path-level | metrics.jsonl 按 `execution_mode` 分组 |
|
||||
| §2 §1-§7 修订 | `docs/TEAM_REPORT_AGENTIC_PD_HYBRID_ZH.md` 原数据 + ts=1 新数据交叉对比 |
|
||||
|
||||
## 附录 B：相关文档
|
||||
|
||||
- `docs/TEAM_REPORT_AGENTIC_PD_HYBRID_ZH.md` — §1-§7 原结构性问题清单
|
||||
- `docs/REFACTOR_PLAN_ZH.md` — v0 重构计划（本文 supersede）
|
||||
- `docs/AGENTIC_FIT_ANALYSIS_ZH.md` — 早期 fit 分析（§1-§7 来源）
|
||||
- `docs/STRUCTURAL_VALIDATION_REPORT_ZH.md` — ts=10 结构性 claim 验证
|
||||
- `docs/KVC_DEBUG_JOURNEY_V1_TO_V5.md` — v1→v5 演进
|
||||
- `docs/V5_PROFILE_INVESTIGATION_ZH.md` — v5+profile 调查（已 critic 修订）
|
||||
- `scripts/sweep_ts1_kvc_n3_plus_dp.sh` — 本次 4 run sweep 脚本
|
||||
- `scripts/analysis/analyze_ts1_validation.py` — 本次分析脚本
|
||||
|
||||
---
|
||||
|
||||
**作者注**：本文偏决策导向。如果要写更技术的 §1 capacity-aware policy 实现细节，应该在 D1 决策为 B 之后单独出一份 `IMPL_CAPACITY_AWARE_POLICY.md`。
|
||||
368
docs/RESEED_SLOW_PATH_AND_D_TO_P_GAP_ZH.md
Normal file
368
docs/RESEED_SLOW_PATH_AND_D_TO_P_GAP_ZH.md
Normal file
@@ -0,0 +1,368 @@
|
||||
# Reseed 慢路径现状与 D→P KV 同步缺口
|
||||
|
||||
**日期**：2026-05-11
|
||||
**对象**：项目团队 + 后续 paper reviewer
|
||||
**性质**：基线现状落盘 + future-work 缺口定位
|
||||
**前置文档**：
|
||||
- `docs/V2_DEEP_ANALYSIS_ZH.md` §3.2 §4.2（reseed 路径在 v2 数据中的表现）
|
||||
- `docs/KVC_ROUTER_ALGORITHM.md` §3 §9（算法形式化 + open questions）
|
||||
|
||||
**目的**：把"v2 的 reseed slow path 为什么慢、能不能用现有机制治、还差什么"三个问题落盘成单一参考文档，让团队不必再口头反复对齐，让论文 future-work 章节有可引用的基础。
|
||||
|
||||
---
|
||||
|
||||
## 0. TL;DR
|
||||
|
||||
1. KVC v2 在 SWE-Bench 测试中 8.3% 请求走非 direct-to-D 的 reseed/fallback 路径，**单次 reseed 实测 3-7s**（TTFT p99 = 1.28s 全部来自这条路径）。
|
||||
2. 启用真 RDMA（节点有 mlx5_0/_1 @ 200 Gb/s × 2 active）能把 reseed 的 transfer 段（~1.5-4s）压到 ~200-400ms，但**对 re-prefill 段（~1.5-3s）无效**。预期 reseed 总时间从 3-7s 降到 1.7-3.2s，TTFT p99 ~0.7s，**仍输 DP（0.43s）**。
|
||||
3. 真正消除 reseed 长尾必须实现 **D→P 增量 KV 同步**——让 P 端 backup 跟上 D 在 direct-to-D append 路径上累积的 KV，避免 reseed 时重新跑 prefill kernel。
|
||||
4. 经 Opus agent 独立 forensic 审查（commit `9ccd853`）+ 全分支 git 检索：**当前代码、vendored SGLang、mooncake 三层均无 D→P 实现**，作者也没有在其它分支偷偷开发——仓库总共只有 main（旧 baseline）+ kvc-debug-journey-v1-to-v4（本工作分支）两个分支，main 还落后我们 18 个 commit。
|
||||
5. `--kvcache-prefill-backup-policy capacity-backup` 这个 flag 看起来像 D→P 同步但**不是**——它的真实语义只是"reseed 完不关 P streaming session"，P 端 KV 仍是 seed-time 的**静态快照**，不随 direct-to-D append 而增长。
|
||||
6. 实现 D→P 增量同步的工程量 ~1-2 周，最难的不是网络层（mooncake 加 D-sender / P-receiver 角色 ~400 LOC），而是 **SGLang radix tree 改成允许从外部 worker 喂数据**——radix cache 当前假设单一生产者。
|
||||
|
||||
---
|
||||
|
||||
## 1. 团队成员的三个质疑（关键框架，paper 引用建议保留原话）
|
||||
|
||||
这三条质疑出自 v2 完成后的对话审查，**直接戳穿了"启用 capacity-backup 就能消除 slow path"的一厢情愿**。每条都有代码层证据支持，**全部成立**。
|
||||
|
||||
### 质疑一：P 节点的 pool 塞得下所有 backup 的 KV cache 吗？
|
||||
|
||||
**回答：塞不下，max 同时 backup ~1-2 个大 session。**
|
||||
|
||||
代码证据（`src/agentic_pd_hybrid/replay.py:1618-1620`）：
|
||||
|
||||
```python
|
||||
max_backup_sessions = max(1, capacity_tokens // max(1, target_tokens * 2))
|
||||
max_backup_sessions = min(max_backup_sessions, 4)
|
||||
```
|
||||
|
||||
按 SWE workload 实测代入：
|
||||
- P 池 `capacity_tokens` ≈ 92,104 tokens（SGLang 启动时按 mem_fraction_static 自动分配）
|
||||
- 典型 session peak input `target_tokens` ≈ 50,000-80,000 tokens
|
||||
- 计算：`92K // (50K × 2) = 0` → `max(1, 0) = 1`
|
||||
- → **P 最多同时 backup 1 个大 session**
|
||||
|
||||
对照小 session：
|
||||
- target 20K：`92K // 40K = 2` → backup 上限 2 个
|
||||
- target 10K：`92K // 20K = 4` → backup 上限 4 个（达到代码硬上限）
|
||||
|
||||
→ **capacity-backup 在真实 agentic 长 context workload 下只能救少数 session，不是全员保险。**
|
||||
|
||||
### 质疑二：P 上的 backup 是陈旧快照——49K 的 append 内容根本没经过 P
|
||||
|
||||
**回答：完全正确，这是 capacity-backup 设计上的致命缺陷。**
|
||||
|
||||
**用户提供的反例场景**（已成为 paper 中描述 slow path 的标准例子）：
|
||||
|
||||
```
|
||||
turn 0: P 做 prefill 1K tokens → 经 mooncake 传到 D → P 留 1K backup
|
||||
turn 1-50: 全部走 direct-to-D，D 上做 append-prefill，KV 在 D 上从 1K 增长到 50K
|
||||
↑↑↑ 关键：这 49K 的 append 内容（tool 输出、user 消息、模型生成）
|
||||
**从未流经 P 节点**。P 端 backup 锁在 1K 状态。
|
||||
turn 51: D 出于某种原因（容量、迁移、显式驱逐）拒绝 → 触发 reseed
|
||||
→ 即使 P 上有 backup，也只是 turn-0 的 1K
|
||||
→ 实际需要 D 上重建的是 50K（当前完整 context）
|
||||
→ P 必须从 prompt 重新 prefill 49K 的差额
|
||||
→ capacity-backup 节省的 compute 仅 ~2%
|
||||
```
|
||||
|
||||
**代码证据**（独立 Opus agent forensic 审查，commit `9ccd853`）：
|
||||
|
||||
1. 唯一更新 `session.prefill_resident_tokens` 的函数是 `_commit_prefill_backup_residency`（`replay.py:1483`）
|
||||
2. 这个函数的唯一 caller 是 `_invoke_kvcache_seeded_router`（`replay.py:2208`）—— 即 seed/reseed 路径
|
||||
3. `_invoke_session_direct`（`replay.py:2719`，direct-to-D 路径）只更新 `session.opened` / `resident_tokens` / `last_trace_request`，**从不触碰任何 P 端字段**
|
||||
4. `_commit_prefill_backup_residency` 内部用 `_estimate_session_resident_tokens(request)` 取的是**完整 request 的预估**，不是 append delta——所以连 bookkeeping 层面都不假设有增量更新
|
||||
|
||||
→ **`capacity-backup` 的真实语义只是"reseed 完之后跳过 `_close_prefill_session`"**（`replay.py:2221`），P 端 streaming session 保持 open 状态、KV 留在 P 的 radix tree 中。但**不存在任何机制让这份 KV 跟上 D 端的 append 增长**。
|
||||
|
||||
### 质疑三：D 触发 reseed 后，本机旧 session 的 KV cache 是不是清空了？P 做完 re-prefill，KV 推到哪里？
|
||||
|
||||
**回答：是的，旧 KV 直接 free 掉；P 重新 prefill 完之后推到 router 选的新 target D（可能同 D，可能换 D）。中间没有"先 dump 到 P 再清"的快捷方式。**
|
||||
|
||||
#### D 端驱逐时的 KV 处理
|
||||
|
||||
代码证据（`replay.py:_close_decode_session`，1539-1569 行；`session_aware_cache.py:release_session`，250-276 行）：
|
||||
|
||||
```python
|
||||
# replay.py 端
|
||||
async def _close_decode_session(..., evicting_for_capacity=False):
|
||||
if not session.opened:
|
||||
return
|
||||
await _close_streaming_session(...) # 给 D 发关闭信号
|
||||
# 从 D 的 resident bookkeeping 里删掉这个 session
|
||||
session.opened = False
|
||||
session.resident_tokens = 0
|
||||
if evicting_for_capacity and not session.prefill_opened:
|
||||
residency.decode_evictions_without_prefill_backup += 1
|
||||
|
||||
# SGLang 端（session_aware_cache.py）
|
||||
def release_session(self, session_id):
|
||||
# 解锁引用 + 直接 free KV slots
|
||||
self.token_to_kv_pool_allocator.free(kv_indices)
|
||||
# ↑ 没有序列化、没有外发、没有 D→P 通道
|
||||
```
|
||||
|
||||
**D 驱逐 = 把 KV slot 直接归还给 token pool 分配器。完全没有任何 outbound 网络调用。**
|
||||
|
||||
#### Reseed 时 P→D 的目标选择
|
||||
|
||||
驱逐之后的 reseed 路径（`_invoke_kvcache_seeded_router`，`replay.py:2101`）走的是与 turn 0 完全一样的 P-mediated seeding：
|
||||
|
||||
```
|
||||
1. KvAwarePolicy.select() 选择一个 target D'（可能是同一个 D，也可能因 migration 换 D）
|
||||
2. _invoke_kvcache_seeded_router 在 D' 上 open 一个 streaming session
|
||||
3. 给 P 发完整 prompt → SGLang pd-router 让 P 做完整 prefill
|
||||
4. P 的 prefill 完成后通过 mooncake 把 KV 一次性推到 D'
|
||||
5. D' 上接收完毕，session 重建完成；decode 继续
|
||||
```
|
||||
|
||||
**所以 P 做完 re-prefill 的 KV 推到 KvAwarePolicy 选的 target D'**——可能是：
|
||||
- 同一个 D（驱逐后重新接受）
|
||||
- 另一个 D（如果 reject 计数累积触发 migration，详见 KVC_ROUTER_ALGORITHM §3.3）
|
||||
|
||||
无论哪种，**旧 D 的旧 KV 在新 KV 到达之前就已经被 free**。没有 D→D 的直接迁移路径，没有"先 dump 到 P 再推回"的快捷路径。
|
||||
|
||||
---
|
||||
|
||||
## 2. Reseed 路径的完整 step-by-step 现状
|
||||
|
||||
把上面三个质疑串成端到端流程，以下是 v2 当前 reseed 路径的**完整**操作序列。每一步都标注实测耗时与代码位置。
|
||||
|
||||
### 触发条件
|
||||
|
||||
下列任一发生时 router 走 reseed 路径（详见 `KVC_ROUTER_ALGORITHM.md §3.3`）：
|
||||
- D 端 `Admit()` 返回 `can_admit=False`，原因为 `no-d-capacity` / `session-not-resident` / 等
|
||||
- KvAwarePolicy.select 返回的 D 不再持有该 session（migration 触发）
|
||||
- v1/v2 的 reject counter 累积让所有 D 都被 blacklist（极少触发，由 reset-on-success 保护）
|
||||
|
||||
### 端到端时间线
|
||||
|
||||
```
|
||||
t=0 上游 agent 发出 turn N 请求（input ~50K，append ~2K）
|
||||
↓
|
||||
t=~5ms Router 的 KvAwarePolicy.select() 选 target D'（O(|D|) Python 评分）
|
||||
↓
|
||||
t=~10ms Router → D' 发 admit_direct_append RPC
|
||||
↓
|
||||
t=~30ms D' 返回 can_admit=False, reason="session-not-resident"
|
||||
或 "no-d-capacity"，Algorithm 3 bump rejects[s, D']++
|
||||
↓ （fallback chain 最多再试 ε-1 个 D，对应 ε ~30ms 总额）
|
||||
t=~100ms 所有 D 都被拒 / 选不到适合 D，路径退化到 seeded router
|
||||
↓
|
||||
t=~110ms Router 转 _invoke_kvcache_seeded_router
|
||||
↓
|
||||
t=~120ms [可选] capacity-backup policy 下：_reserve_prefill_backup_capacity()
|
||||
检查 P 池容量，若不够先 LRU 驱逐别的 P backup session
|
||||
↓
|
||||
t=~150ms P 上 open streaming session（HTTP /session/open）
|
||||
↓
|
||||
t=~200ms 发完整 prompt 到 SGLang pd-router → 路由到 P
|
||||
↓
|
||||
t=~250ms P 开始 prefill
|
||||
↓
|
||||
↓ ←←← 大头 1：P-side re-prefill 段
|
||||
↓ P 必须 prefill 完整 ~50K tokens
|
||||
↓ 即使 capacity-backup 开着，P 的 backup 只有 turn-0 的 ~1K
|
||||
↓ radix prefix cache 命中前 1K，剩余 49K 重算
|
||||
↓ 实测耗时：~1.5-3s @ Qwen3-30B TP1
|
||||
↓
|
||||
t=~2000ms P 完成 prefill，KV 进入 mooncake transfer 队列
|
||||
↓
|
||||
t=~2050ms mooncake 开始 P→D' transfer
|
||||
↓
|
||||
↓ ←←← 大头 2：P→D mooncake transfer 段
|
||||
↓ KV 张量 ~5-9 GB（50K tokens × 2 bytes/token × layers × heads...）
|
||||
↓ **TCP loopback** 实测耗时：~1.5-4s
|
||||
↓ ↑↑↑ 当前 sweep 未启用 RDMA，走的是单机 lo 设备
|
||||
↓ 若启用 IB RDMA @ 200 Gb/s，理论 200-400ms
|
||||
↓
|
||||
t=~4500ms transfer 完成，D' 上 session 重建好
|
||||
↓
|
||||
t=~4510ms D' 开始 decode（小幅度 append-prefill 余下的 ~2K append + 生成）
|
||||
↓
|
||||
t=~4550ms 首个 token 出来 → TTFT 测点
|
||||
```
|
||||
|
||||
**单次 reseed 总耗时：3-7s**（中位 ~2.5s 来自较小 session，p99 ~7.7s 来自最大 session）。**re-prefill 段与 transfer 段大致五五开**，受 session 大小影响。
|
||||
|
||||
### 这就是为什么 v2 的 TTFT p99 = 1.28s
|
||||
|
||||
8.3% slow path 走的是上面这条流水线，其中 reseed 路径（`pd-router-d-session-reseed`）单独占 3.4%（150/4449 请求），构成 KVC TTFT p99 长尾的主要贡献。
|
||||
|
||||
---
|
||||
|
||||
## 3. 已审查的所有"看起来像 D→P 但其实不是"的代码
|
||||
|
||||
下面这些在搜索时容易误判成 D→P 实现，**全部经独立 audit 排除**：
|
||||
|
||||
| 文件:行 | 看起来像 | 实际是 |
|
||||
|---|---|---|
|
||||
| `replay.py:1483 _commit_prefill_backup_residency` | "把 backup 提交到 P" | bookkeeping 函数，更新 `session.prefill_resident_tokens` 计数字段。不传输任何 KV 数据，只在 seed/reseed 完成后被调用。 |
|
||||
| `replay.py:1572 _reserve_prefill_backup_capacity` | "预留 backup 空间" | 检查 P 池可用空间并按 LRU 驱逐别的 backup session 腾位置。不传 KV，只调整 reservation 计数。 |
|
||||
| `cli.py:182 --kvcache-prefill-backup-policy` | "backup 策略" | 只决定 reseed 完成后是否 `_close_prefill_session`。capacity-backup = 保留 P 端 streaming session 不关；release-after-transfer = 立刻关闭。**两种策略下 P 的 KV 都是 seed-time 的静态快照**。 |
|
||||
| `session_aware_cache.py:release_session` | "释放 session（可能含外发）" | 仅调 `kv_pool_allocator.free(kv_indices)`。零网络调用。 |
|
||||
| `disaggregation/decode.py: start_decode_thread` | "decode 端线程，可能有出站" | 纯 receiver loop。处理入站 `AUX_DATA / CHUNK_READY / STAGING_REQ / KVPoll.Success`，**没有出站 KV 传输分支**。 |
|
||||
| `disaggregation/mooncake/conn.py:1563` | "传输请求添加" | `assert disaggregation_mode == PREFILL`——硬约束，只有 P 端能调。 |
|
||||
| `mooncake.MooncakeKVSender` / `MooncakeKVReceiver` | "双向 sender / receiver" | 强角色化：Sender 只在 PREFILL 模式实例化，Receiver 只在 DECODE 模式。`BaseKVManager` 抽象无 bidirectional slot。 |
|
||||
| `pd-router-d-session-reseed-after-eviction` execution_mode | "走 backup 的快路径" | 实际还是走完整 `_invoke_kvcache_seeded_router`（P 完整 prefill + 完整 mooncake transfer），只是 `_eviction_suffix()` 在 execution_mode 字符串末尾加了 "-after-prefill-backed-eviction" 标签。**没有任何 fast-path 优化**。v2 中仅 2/4449 请求走到这个标签。 |
|
||||
|
||||
---
|
||||
|
||||
## 4. D→P 增量同步：要做的是什么
|
||||
|
||||
完整 D→P 增量同步的设计目标：**让 P 端的 backup KV 在 direct-to-D append 完成后异步追上 D 端的 KV，让 reseed 退化为单次 P→D transfer（无需 P re-prefill）**。
|
||||
|
||||
### 抽象数据流
|
||||
|
||||
```
|
||||
当前：
|
||||
direct-to-D append: D 本地 append-prefill，P 端 backup 锁住不变
|
||||
reseed: P re-prefill 完整 50K + P→D transfer 完整 50K
|
||||
|
||||
目标：
|
||||
direct-to-D append: D 本地 append-prefill，**同时**异步把新增的 KV 块推回 P
|
||||
reseed: P→D' transfer 完整 50K (already up-to-date)
|
||||
无需 P re-prefill
|
||||
```
|
||||
|
||||
### 实现层面要改的事
|
||||
|
||||
按工程难度排序：
|
||||
|
||||
#### 4.1 Mooncake 双角色化（中等难度，~400 LOC）
|
||||
|
||||
- `BaseKVSender` / `BaseKVReceiver` 抽象保留，但允许同一 worker 同时实例化两种角色
|
||||
- `MooncakeKVManager.__init__` 把 PREFILL / DECODE 分支改成"role set"，允许 worker 同时持有 sender 和 receiver
|
||||
- 新增 `DecodeKVSender` 类（D 端用于把 append KV 推回 P）
|
||||
- 新增 `PrefillKVReceiver` 类（P 端用于接收 D 的 append KV）
|
||||
- 引入第二个 bootstrap channel（避免与原 P→D 通道在 buffer pointer 协商上冲突）
|
||||
|
||||
#### 4.2 D 端 append commit hook（容易）
|
||||
|
||||
- 每次 `direct-to-D-session` 完成后，识别新写入的 KV 块（D scheduler 在 commit 时知道）
|
||||
- 入队 D→P 传输（异步，不阻塞 next request）
|
||||
- 标记 backup 是否成功送达 P（用于后续 reseed 决策）
|
||||
|
||||
#### 4.3 P 端 radix tree 多生产者扩展（**最难，工程量主体**）
|
||||
|
||||
**这是真正的架构 blocker**。SGLang 的 P 端 radix cache 当前假设：
|
||||
- 单一生产者（本 worker 的 model 输出）
|
||||
- 树插入只在 prefill / decode 完成时发生
|
||||
- KV 索引由本 worker 的 token_to_kv_pool_allocator 分配
|
||||
|
||||
要让 P 接收 D 喂来的 KV 块，需要：
|
||||
- 扩展 radix tree 节点的写入路径，允许"外部供给的 KV + token 序列"被插入
|
||||
- 处理 KV 索引重映射（D 的 slot 号在 P 上无意义）
|
||||
- 处理 reference counting（同一 session 可能既被本 worker 用、又被 D 喂回更新）
|
||||
- 处理 eviction policy 协调（P 端 radix LRU 不应让"被 D 喂入的 backup"先被驱逐）
|
||||
- 处理 KV 数据格式的跨 worker 兼容（同样的 model layout，应该是 trivial，但需要测试）
|
||||
|
||||
#### 4.4 agentic-pd-hybrid 端 hook（容易）
|
||||
|
||||
- `_invoke_session_direct` 完成后，新增一步：触发 D→P 同步 RPC（异步）
|
||||
- `_invoke_kvcache_seeded_router` 在 reseed 触发前先 probe P 是否有 up-to-date backup；若有，跳过 re-prefill，只做 P→D transfer
|
||||
- 新增 CLI flag `--enable-d-to-p-sync`，默认 off，保留 baseline 行为
|
||||
- 新增 structural log channel 记录 D→P 同步事件 / 失败 / 延迟
|
||||
|
||||
### 实现完毕后的预期收益
|
||||
|
||||
| 指标 | 当前 (v2) | RDMA only | RDMA + D→P sync |
|
||||
|---|---:|---:|---:|
|
||||
| reseed re-prefill 段 | 1.5-3s | 1.5-3s（不变） | **~0**（已有 up-to-date backup） |
|
||||
| reseed transfer 段 | 1.5-4s | 0.2-0.4s | 0.2-0.4s |
|
||||
| reseed 总耗时 | 3-7s | 1.7-3.4s | **0.2-0.4s** |
|
||||
| TTFT p99 | 1.285s | ~0.7s | **~0.4-0.5s**（与 DP 接近或胜过） |
|
||||
| 8.4% slow path 占比 | 不变 | 不变 | 可能保持但单次代价大幅下降 |
|
||||
|
||||
→ 这就是 paper 里 future-work 应当声明的**"完整版 KVC 才能真正在 TTFT 全分位数上击败 DP"** 的路径。
|
||||
|
||||
---
|
||||
|
||||
## 5. 仓库分支审查（确认无作者私下实现）
|
||||
|
||||
`git ls-remote origin --refs` 完整结果：
|
||||
|
||||
```
|
||||
9ccd853... refs/heads/kvc-debug-journey-v1-to-v4 ← 本工作分支（含本文档）
|
||||
e9062b1... refs/heads/main ← baseline，落后我们 18 commit
|
||||
```
|
||||
|
||||
- **服务器只有 2 个分支**，**0 个 tag**，**0 个隐藏 ref**
|
||||
- main 是更老的 baseline；含 `_commit_prefill_backup_residency` 等同名函数，但语义与本工作分支一致——都是静态 backup，无 D→P 同步
|
||||
- 全 git 历史搜索 `D->P / d-to-p / decode.*prefill.*transfer / kv.*pushback / kv.*sync / incremental / mirror` 关键词，**唯一命中是 commit `9ccd853`**（本文档相关的 doc 改动）
|
||||
- 唯一 remote 是 `origin`（`git@ipads.se.sjtu.edu.cn:wangjh/agentic-pd-hybrid.git`），无 upstream / fork
|
||||
|
||||
→ **作者没有在其它分支偷偷实现 D→P**。这块工作是真空。
|
||||
|
||||
---
|
||||
|
||||
## 6. 下一步
|
||||
|
||||
按 ROI 排序：
|
||||
|
||||
### 必做（落地下一阶段）
|
||||
|
||||
1. **新开 `feat/d-to-p-sync` 分支** 从当前 `kvc-debug-journey-v1-to-v4` 起步
|
||||
2. 写设计文档 `docs/D_TO_P_SYNC_DESIGN_ZH.md`：
|
||||
- 包括上面 §4 的实现细节
|
||||
- 添加 sequence diagram（P/D 通信时序）
|
||||
- 评估 SGLang radix tree 多生产者扩展的具体 API 改动
|
||||
- 评估 D→P 同步对 direct-to-D fast path 自身延迟的影响（理想是异步零开销）
|
||||
3. POC 阶段 1：mooncake 双角色化 + 一个能跑通的 D→P transfer 单测
|
||||
4. POC 阶段 2：P 端 radix tree 多生产者扩展（重点工程量）
|
||||
5. POC 阶段 3：agentic-pd-hybrid 端的 hook + flag
|
||||
6. 端到端验证：跑同 trace 同 ts=1 配置，目标 TTFT p99 < 0.5s
|
||||
|
||||
### 推荐
|
||||
|
||||
7. **同时启用真 RDMA**（独立于 D→P 工作，只需改 sweep 脚本加 `--force-rdma --ib-device mlx5_0`），先把现有 transfer 段加速作为 baseline
|
||||
8. **跑 RDMA-only 对照**：先证明单 RDMA 启用能把 TTFT p99 从 1.28s 压到 ~0.7s，再用 D→P sync 把剩下的 re-prefill 段也吃掉。这样 paper 里能写两条独立的 ablation
|
||||
|
||||
### 不要做的事
|
||||
|
||||
- 在 main / 工作分支上做 D→P 实验（隔离开），主分支应该保持 v2 稳定
|
||||
- 试图通过 capacity-backup 现有 flag "调出"D→P 效果——它结构上做不到
|
||||
|
||||
---
|
||||
|
||||
## 附录 A：本文档涉及的代码位置
|
||||
|
||||
| 函数 / 字段 | 位置 |
|
||||
|---|---|
|
||||
| `_commit_prefill_backup_residency` | `src/agentic_pd_hybrid/replay.py:1483` |
|
||||
| `_reserve_prefill_backup_capacity` | `src/agentic_pd_hybrid/replay.py:1572` |
|
||||
| `_close_prefill_session` | `src/agentic_pd_hybrid/replay.py:1507` |
|
||||
| `_close_decode_session` | `src/agentic_pd_hybrid/replay.py:1539` |
|
||||
| `_invoke_session_direct` (direct-to-D 路径) | `src/agentic_pd_hybrid/replay.py:2719` |
|
||||
| `_invoke_decode_session_direct` | `src/agentic_pd_hybrid/replay.py:2826` |
|
||||
| `_invoke_kvcache_seeded_router` (reseed 路径) | `src/agentic_pd_hybrid/replay.py:2101` |
|
||||
| `DirectSessionState.prefill_resident_tokens` | `src/agentic_pd_hybrid/replay.py:128` |
|
||||
| `_eviction_suffix` | `src/agentic_pd_hybrid/replay.py:1220` |
|
||||
| `--kvcache-prefill-backup-policy` CLI flag | `src/agentic_pd_hybrid/cli.py:182-189, 436-441` |
|
||||
| `MooncakeKVManager.__init__` | `third_party/sglang/python/sglang/srt/disaggregation/mooncake/conn.py:187-256` |
|
||||
| `start_decode_thread` (decode 端 receive loop) | `third_party/sglang/python/sglang/srt/disaggregation/mooncake/conn.py:1425-1496` |
|
||||
| `add_transfer_request` (assert PREFILL) | `third_party/sglang/python/sglang/srt/disaggregation/mooncake/conn.py:1563` |
|
||||
| `MooncakeKVSender` / `MooncakeKVReceiver` | `third_party/sglang/python/sglang/srt/disaggregation/mooncake/conn.py:1648, 1740` |
|
||||
| `BaseKVSender` / `BaseKVReceiver` 抽象 | `third_party/sglang/python/sglang/srt/disaggregation/base/conn.py` |
|
||||
| `session_aware_cache.release_session` | `third_party/sglang/python/sglang/srt/mem_cache/session_aware_cache.py:250-276` |
|
||||
| `session_controller._close` | `third_party/sglang/python/sglang/srt/managers/session_controller.py:293-316` |
|
||||
|
||||
## 附录 B：相关 commit
|
||||
|
||||
| Commit | 内容 |
|
||||
|---|---|
|
||||
| `9ccd853` | docs: D→P 缺口的 Opus forensic audit 写入 V2_DEEP_ANALYSIS §4.2 + KVC_ROUTER_ALGORITHM §9 |
|
||||
| `2ec0deb` | v2 实现（reset-on-success + threshold 2048→8192）—— 直接 trigger 了对 reseed 慢路径的关注 |
|
||||
| `c47adaf` | feat: backpressure pause hint（与 reseed 不直接相关，但展示了"D 端可主动告知 router"的通信通道存在，是未来 D→P sync 控制平面的潜在基础） |
|
||||
|
||||
## 附录 C：相关 paper 章节建议
|
||||
|
||||
- **§Background**：把 §1-§2 的 reseed 现状作为 motivation 摆出
|
||||
- **§Algorithm**：参考 `KVC_ROUTER_ALGORITHM.md` Algorithm 1-3
|
||||
- **§Evaluation §Slow Path Cost**：把 §2 的端到端时间线作为 Figure（sequence diagram）
|
||||
- **§Future Work / Limitations**：把本文 §4 作为 KVC 真正实现"完整 fast path 替代"的 roadmap，引用 D→P 工作的设计文档（后续 `feat/d-to-p-sync` 分支产物）
|
||||
|
||||
---
|
||||
|
||||
**核心句**：v2 实现的 KVC 在 91.6% 请求上证明了 session-affinity 路由的价值，但 8.3% 的 reseed 慢路径让 TTFT p99 比 DP 差 3×。这条慢路径的 50% 时间在 P 端 re-prefill、50% 在 mooncake transfer——RDMA 只能救后者，**D→P 增量 KV 同步是唯一能消除 re-prefill 的机制**，且当前在框架、SGLang、mooncake 三层都没有实现，需要新建 `feat/d-to-p-sync` 分支从设计文档开始。
|
||||
641
docs/TEAM_REPORT_AGENTIC_PD_HYBRID_ZH.md
Normal file
641
docs/TEAM_REPORT_AGENTIC_PD_HYBRID_ZH.md
Normal file
@@ -0,0 +1,641 @@
|
||||
# agentic-pd-hybrid 现框架性能与结构性问题报告
|
||||
|
||||
**对象**：项目团队同学
|
||||
**前置假设**：读者**没看过** v3-v6 KVC 实验日志
|
||||
**数据范围**：项目仓库 `outputs/` 下截止 2026-05-06 的全部实验产物
|
||||
**目的**：把"现状"和"问题"分别交代清楚，给后续改造提供共同事实基础
|
||||
|
||||
---
|
||||
|
||||
## 0. 给没看过实验的读者：基础概念速览
|
||||
|
||||
### 0.1 项目目标
|
||||
验证 **session-aware / KV-cache-aware P/D routing** 在 **agentic coding workload**（多轮 session、长 context、增量 append）上能否降低端到端延迟。基线对比对象是 vanilla SGLang xPyD。
|
||||
|
||||
### 0.2 三种部署机制（**这三个名词全程会用**）
|
||||
|
||||
| 机制 | 形态 | KV 流向 |
|
||||
|---|---|---|
|
||||
| **pd-disaggregation**（"PD disagg"） | P 和 D 是独立进程、分占不同 GPU | 每个请求 P 算 prefill → mooncake 推 KV → D 解码 |
|
||||
| **pd-colo**（"DP"，data-parallel） | 没有 PD 拆分，N 个独立完整 worker（每个自己 prefill+decode） | 没有 KV transfer；router 按 hash 分配请求 |
|
||||
| **kvcache-centric**（"KVC"） | 部署形态同 PD disagg；**D 上多了 SessionAwareCache**，能跨 turn 保留 session KV | 运行时决策：可走 direct-to-D（无 P）、可走 P→D disagg、可走带 reseed 的混合 |
|
||||
|
||||
**Direct-to-D**（"D-direct"）：KVC 的快路径——D 上已有该 session 的 KV，新 turn 在 D 本地做 append-prefill，零 P 介入、零 mooncake transfer。这是 KVC 理论上能省时间的核心。
|
||||
|
||||
**Fallback**：KVC admission 拒了 / 阈值不满足 / D 不健康时，退化到普通 PD disagg 路径。
|
||||
|
||||
**Routing policy**（与机制正交）：
|
||||
- `default`：纯 round-robin
|
||||
- `sticky`：turn 2+ 黏到 session 的 last D
|
||||
- `kv-aware`：按 hash overlap + sticky 评分选 D（**KVC 必须配它**才能正确工作）
|
||||
|
||||
### 0.3 数据来源
|
||||
- Trace：`outputs/qwen35-swebench-50sess.jsonl`（SWE-Bench 抽样，4449 reqs / **52 sessions** / 每 session 8-150 turns / time-scale=10 / concurrency=32）
|
||||
- 模型：Qwen3.5-35B-A3B (TP4) 和 Qwen3-30B-A3B (TP1) 两组
|
||||
- 硬件：单机 8×H100 80GB，mooncake TCP loopback 模拟 P→D 传输
|
||||
|
||||
---
|
||||
|
||||
# 第一部份：性能数据现象
|
||||
|
||||
## 1.1 三种机制在 Qwen3.5-35B (TP4) SWE 50sess 上的表现
|
||||
|
||||
来源：`outputs/swebench-exps/`。
|
||||
|
||||
| Run | Mechanism | Policy | Errors | Lat mean | Lat P50 | Lat P99 | TTFT mean | TTFT P50 |
|
||||
|---|---|---|---:|---:|---:|---:|---:|---:|
|
||||
| `pd-disaggregation-default-20260426T202540Z` | pd-disagg | default | **0/4449** | 1.66s | 0.97s | 7.68s | 0.45s | 0.34s |
|
||||
| `pd-colo-default-20260426T210129Z` | pd-colo | default | **4447/4449** | – | – | – | – | – |
|
||||
| `pd-colo-default-20260427T033519Z` | pd-colo | default | **0/4449** | 1.77s | 0.86s | 9.67s | 0.29s | 0.25s |
|
||||
| `pd-colo-kv-aware-20260427T042034Z` | pd-colo | kv-aware | 469/4449 | 1.52s | 0.82s | 8.27s | 0.26s | 0.23s |
|
||||
| `pd-colo-kv-aware-20260427T044944Z` | pd-colo | kv-aware | **0/4449** | **1.57s** | 0.81s | 8.48s | **0.22s** | **0.17s** |
|
||||
| `kvcache-centric-default-worker-admission-20260426T210800Z` | KVC | default | **4390/4449** | – | – | – | – | – |
|
||||
|
||||
### 现象解读
|
||||
|
||||
**(1) pd-disagg 是稳定基线**：1.66s mean / 0 errors / 4199 cache hits（94.4%）。可以正常服务。
|
||||
|
||||
**(2) pd-colo（DP）有两次 run，第一次几乎全 crash，第二次稳定**：
|
||||
- 04-26 的 4447/4449 errors 来自 SGLang `--disaggregation-mode null` + Qwen3.5-35B-A3B（Mamba/GDN hybrid）的 `token_to_kv_pool_allocator memory leak` bug，crash 了
|
||||
- 04-27 的两次 pd-colo run 都跑通了。**`pd-colo-kv-aware-20260427T044944Z` 是这一组实验里跑分最好的配置**——0 errors / TTFT P50 = 0.171s（pd-disagg 的 50%）
|
||||
|
||||
**(3) KVC 在 SWE 35B 上的唯一一次 run 几乎全 crash**：4390/4449 = 98.7% errors。但**那 56 个跑通的 direct-to-D 请求性能优异**——Lat mean 1.24s，TTFT P50 0.081s，KV transfer 196 块（vs PD disagg 的 105K 块，**−99.8%**）。说明 KVC 机制本身有效，但 admission control 把绝大多数请求过滤掉了。
|
||||
|
||||
### 一句话：在 Qwen3.5-35B 上，**pd-colo + kv-aware 是头名**，KVC 机制配置不当几乎不可用。
|
||||
|
||||
---
|
||||
|
||||
## 1.2 同 trace 切到 Qwen3-30B (TP1)：v1→v6 演进
|
||||
|
||||
为绕开 Mamba 模型的 SGLang bug，团队后续切到 Qwen3-30B-A3B (TP1) 跑 KVC 调优 sweep。**所有结果用同一份 SWE 50sess trace**，可以横向比较。来源：`outputs/qwen3-30b-tp1-*` 各目录。
|
||||
|
||||
### 1.2.1 各版本配置概览
|
||||
|
||||
| 版本 | 关键改动（一句话） |
|
||||
|---|---|
|
||||
| v2 | KVC + `--policy default`（这个 policy 选择 **是 bug**，下文 §2.5） |
|
||||
| v3 | KVC + `--policy kv-aware` |
|
||||
| v4 | v3 + replay 端 session soft_cap 从 4 抬到 16 |
|
||||
| v5 (Option D) | 把 admission 决策从 replay 估算改成 D worker 真实容量回答（`worker-mode admission`） |
|
||||
| v5+profile | v5 + 1Hz `/server_info` polling 做时序 instrument |
|
||||
| v6 P0 | v5 baseline 同配置 rerun ×3 验证可复现性 |
|
||||
|
||||
### 1.2.2 各版本同 trace 结果总表
|
||||
|
||||
| 版本 | Errors | Lat mean | Lat P50 | Lat P90 | Lat P99 | TTFT P50 | direct-to-D% |
|
||||
|---|---:|---:|---:|---:|---:|---:|---:|
|
||||
| **8-way DP cache-aware** | **0** | **1.43s** | **0.65s** | **3.61s** | **8.37s** | **0.093s** | – |
|
||||
| v3 1P7D KVC | 363 (8.2%) | 4.88s | 1.75s | 12.67s | 28.72s | 0.363s | 39% |
|
||||
| v3 2P6D KVC | 9 (0.2%) | 3.58s | 1.52s | 9.23s | 18.70s | 0.328s | 31% |
|
||||
| v4 1P7D cap=16 | 435 (10%) | 4.21s | 1.08s | 13.38s | 24.45s | 0.056s | 49% |
|
||||
| v4 2P6D cap=16 | 403 (9%) | 2.51s | 0.84s | 6.51s | 18.34s | 0.051s | 53% |
|
||||
| v5 1P7D Option D | 9 (0.2%) | 5.18s | 1.59s | 14.67s | 26.09s | 0.207s | 45% |
|
||||
| v5 2P6D Option D | 9 (0.2%) | 3.49s | 1.31s | 9.09s | 24.92s | 0.244s | 41% |
|
||||
| v5+profile 1P7D | 6 (0.1%) | 4.21s | 1.18s | 11.33s | 28.83s | 0.060s | 55% |
|
||||
| v5+profile 2P6D | **415 (9.3%)** | 3.23s | 1.11s | 8.36s | 20.26s | 0.168s | 41% |
|
||||
| v5 rerun ×3（无 profile） | **372 / 912 / 396** | 3.00–3.50s | 0.94–1.22s | 7.68–8.65s | 18.97–20.37s | 0.07–0.18s | 40-42% |
|
||||
|
||||
**8DP CA 在每一项指标都是头名**：
|
||||
- Latency mean **比所有 KVC 配置好 +43%~+260%**
|
||||
- TTFT P50 **0.093s**（KVC 最佳 v4 2P6D 是 0.051s——TTFT 单项 KVC 是有优势的，但被整体 P99 灾难抵消）
|
||||
- 0 errors（KVC 任一配置 errors 在 9-912 之间漂移）
|
||||
|
||||
### 1.2.3 v5+profile 的诡异：加 1Hz polling 让 errors 从 9 涨到 415
|
||||
|
||||
这条单独看：v5 baseline 跑出来 9 errors，加上 1Hz `/server_info` polling 之后 415 errors（**46×**）。原因机理见 §2.5。
|
||||
|
||||
### 1.2.4 v6 P0 用 ×3 rerun 验证可复现性，结果是不能复现
|
||||
|
||||
**关键事实**：v5 baseline 完全相同配置跑 3 次：
|
||||
|
||||
| Run | Errors | Lat mean | Lat P50 | TTFT P50 |
|
||||
|---|---:|---:|---:|---:|
|
||||
| rerun1 | **372** | 3.50s | 1.11s | 0.147s |
|
||||
| rerun2 | **912** | 3.00s | 0.94s | 0.071s |
|
||||
| rerun3 | **396** | 3.42s | 1.22s | 0.183s |
|
||||
|
||||
errors 漂移 **2.5×**（372→912）。Latency mean / P50 也漂移 ~30%。**这意味着 v3-v6 之前所有"single-run"对比的差异 < 30% 的都不可信。**
|
||||
|
||||
但要注意：**3 次 v5 中最优的 P50（0.94s）仍然比 8DP CA（0.65s）慢 1.45×**——这个差距大于 single-run variance，所以"DP 全胜 KVC"的头条结论不受 variance 影响。
|
||||
|
||||
### 1.2.5 一个有趣的反差：v4 vs v5
|
||||
|
||||
- v4：errors 多（~10%）、direct-to-D 占比高（53-58%）、整体 P50 较好（0.84s）
|
||||
- v5：errors 少（0.2%）、direct-to-D 占比降低（41-45%）、整体 P50 反而退步（1.31s）
|
||||
|
||||
**v5 没有让性能变好，只是把"硬错误"转成了"诚实拒绝"——v4 的 admission 是乐观估算，admit 进来后 D 装不下变成 mooncake 32s timeout（统计成 errors）；v5 让 D 自己拍板，admit 拒得早，请求改走 fallback（统计成低 direct-to-D 率）。容量本身没变。**
|
||||
|
||||
---
|
||||
|
||||
## 1.3 microbench 上 KVC 击败 PD disagg —— 但本仓库没保留实际 run
|
||||
|
||||
`docs/PROJECT_OVERVIEW.md` 写明：
|
||||
|
||||
> micro-benchmark 上，`kvcache-centric` 可以比 `pd-disaggregation` 好。原因很简单：**session 少、D KV 放得下**，turn2+ 可以直接走 D session。
|
||||
|
||||
但 `outputs/` 里**没有** microbench 实际 run（只有 microbench trace 生成器 `microbench.py` 和它的几个示例 trace 文件）。所以 microbench 的"KVC 赢"是基于设计预期 + 历史口口相传，**没有可重现的产物**。
|
||||
|
||||
**这本身是个问题**——下文 §2.6 会解释 microbench 的默认参数（4 sessions × 30K input × 1K append）正好把所有 KVC 失效条件都规避掉了。
|
||||
|
||||
---
|
||||
|
||||
## 1.4 头条结论（Part 1 总结）
|
||||
|
||||
| 工作负载 / 模型 | 头名机制 | KVC 表现 |
|
||||
|---|---|---|
|
||||
| Microbench（8 session × 30K × 1K append） | KVC > PD disagg（无落地数据，按设计） | 设计上必然赢 |
|
||||
| SWE 35B (TP4) | **pd-colo + kv-aware**（1.57s mean, 0 errors） | KVC 唯一 run 中 98.7% errors |
|
||||
| SWE 30B (TP1) | **8-way DP cache-aware**（1.43s mean, 0 errors） | KVC 6 个配置全输；最佳的 v4 2P6D 慢 75%、errors 9% |
|
||||
|
||||
**真实 agentic 工作负载（SWE-Bench）上，KVC 机制目前没有任何配置能跑赢 naive DP cache-aware。**
|
||||
|
||||
---
|
||||
|
||||
# 第二部份：结构性问题分析
|
||||
|
||||
每条按 (1) 现象（实锤数据）、(2) 根因（代码位置）、(3) 影响量化 三段交代。
|
||||
|
||||
## 2.1 KvAwarePolicy 不感知 D 容量 + Session 永久 pin 在初始 D 上 ★ 最严重
|
||||
|
||||
### 2.1.1 现象（实锤）
|
||||
|
||||
**(a) 每个 session 整 run 中只访问 1 个 D**——基于 v5 rerun1/2/3 全部 4449×3 = 13347 条 metrics：
|
||||
|
||||
| Run | sessions | avg distinct-D-per-session |
|
||||
|---|---:|---:|
|
||||
| rerun1 | 52 | **1.00** |
|
||||
| rerun2 | 52 | **1.00** |
|
||||
| rerun3 | 52 | **1.00** |
|
||||
|
||||
3 次独立 run、156 次 session 实例，**没有一个** session 跨 D 迁移过。
|
||||
|
||||
**(b) Direct-to-D 命中率呈极端双峰**——以 rerun1 为例（其他两次形态相同）：
|
||||
|
||||
| direct-to-D rate | session 数 |
|
||||
|---|---:|
|
||||
| 0–20%（"饿死"） | **15** |
|
||||
| 20–40% | 7 |
|
||||
| 40–60% | 11 |
|
||||
| 60–80% | 5 |
|
||||
| 80–100%（"顺利"） | **14** |
|
||||
|
||||
中间档稀少，两端拥挤。
|
||||
|
||||
**(c) 跨 3 次 run 一致饿死的 session = 13/52，且这些 session 的 input 是顺利 session 的 1.98×**：
|
||||
|
||||
```
|
||||
13 sessions starved (<20% direct-to-D) in ALL 3 runs
|
||||
avg peak input of consistently-starved sessions: 62043 tokens
|
||||
avg peak input of consistently-lucky sessions: 31344 tokens
|
||||
```
|
||||
|
||||
**结构性、可复现、与 session 大小强相关。** 排除"运气"假说。
|
||||
|
||||
### 2.1.2 根因（代码）
|
||||
|
||||
`policies.py:166-172` `KvAwarePolicy.select()` 评分函数：
|
||||
|
||||
```python
|
||||
score = (
|
||||
overlap + sticky * self.sticky_bonus, # 主项：历史 KV overlap
|
||||
sticky, # 二级
|
||||
inflight_penalty, # 三级
|
||||
assignment_penalty, # 四级
|
||||
)
|
||||
```
|
||||
|
||||
**评分中完全没有 D 当前容量项**。
|
||||
|
||||
session X 第一次落到 D-2 → 在 D-2 上积累 hash_id → 之后不管 D-2 多满，X 的 turn N+1 的 overlap 在 D-2 上仍是最大 → 永远选 D-2。即使 D-5 全空也轮不到。
|
||||
|
||||
`RoutingState.decode_resident_blocks` (`policies.py:46`) 还从不缩减——但因为 SWE trace 的 hash_ids 是 session-unique，**不缩减并不影响"选对 D"，只影响内存**——真正问题在评分函数无容量项。
|
||||
|
||||
### 2.1.3 影响量化
|
||||
|
||||
- 25%（13/52）的 session 几乎每个 turn 走 fallback 路径
|
||||
- fallback 路径 mean lat 约 3.5s vs direct-to-D ~0.5s——**饿死 session 每 turn 慢 6×**
|
||||
- 这 13 个 session 还容易撞 mooncake 32s timeout（见 §2.2、§2.3），P99 完全由它们决定
|
||||
- **SLO 视角下：25% 的用户体验是系统性糟糕**
|
||||
|
||||
---
|
||||
|
||||
## 2.2 D 端 LRU 只能 evict idle session → 跟不上压力
|
||||
|
||||
### 2.2.1 现象（实锤）
|
||||
|
||||
来源：`outputs/qwen3-30b-tp1-v5-optD-baseline-rerun/.../logs/decode-{0..5}.log`，全 run 计数：
|
||||
|
||||
| D worker | "Trimmed decode session cache" 事件 | KVTransferError | 峰值 token_usage |
|
||||
|---|---:|---:|---:|
|
||||
| decode-0 | 9 | 0 | 0.99 |
|
||||
| decode-1 | 43 | 4 | 0.99 |
|
||||
| decode-2 | 16 | **153** | 0.97 |
|
||||
| decode-3 | 37 | 29 | 0.99 |
|
||||
| decode-4 | 28 | **90** | **1.00** |
|
||||
| decode-5 | 30 | **93** | **1.00** |
|
||||
|
||||
**所有 6 个 D 都顶到 token_usage ≥ 0.97，2 个顶到 1.00（KV 池完全耗尽）。LRU 触发 9-43 次，远不够——transfer 错误是 LRU 触发量的 5-10×。**
|
||||
|
||||
decode-2 极端：trim 16 次 vs error 153 次 = LRU 跑得比错误慢 9.5×。
|
||||
|
||||
### 2.2.2 根因（代码）
|
||||
|
||||
`scheduler.py:2040` 的 `evict_idle_streaming_sessions_lru` 实际只能 evict：
|
||||
|
||||
> 所有 req 都 finished + streaming 模式 + 该 session 没有 inflight transfer
|
||||
|
||||
但 SWE 高并发（concurrency=32 + time-scale=10 → effective inter-turn gap p50=0.25s）下，每个 session 几乎一直有 inflight req。**hot session 永远不 idle，LRU 永远找不到东西可踢。**
|
||||
|
||||
### 2.2.3 影响量化
|
||||
|
||||
- 单 run 累计 KVTransferError：6 个 D 之和 = **369 次**
|
||||
- 对应 ~8% 请求失败率（v5 errors 9/372/912 三次平均 ~430/4449 = 9.7%）
|
||||
- **每次 mooncake timeout = 32s**——直接构成 P99 18-26s 的尾巴
|
||||
|
||||
修复需要 SGLang 内部分层 eviction：除 idle session 外，按访问频率 / 时序加权强制 retract——**不在当前 KISS 边界**。
|
||||
|
||||
---
|
||||
|
||||
## 2.3 没有 D → Replay backpressure 通道
|
||||
|
||||
### 2.3.1 现象
|
||||
|
||||
§2.2 数据显示 D 顶到 token_usage=1.00 时仍在持续接收新请求，最终撞 mooncake 32s timeout。**整个错误链路里没有"D 过载，请慢点发"的反向信号**。
|
||||
|
||||
定量证据：rerun1 的 KVTransferError 时间分布——**98% 集中在 run 后半段**（参考 `KVC_DEBUG_JOURNEY_V1_TO_V5.md` §v4）。前期 D 容量充裕时正常，达到上限后**所有后续请求集中失败**——典型的"无 backpressure 系统在过载点雪崩"模式。
|
||||
|
||||
### 2.3.2 根因（代码）
|
||||
|
||||
链路：
|
||||
|
||||
```
|
||||
replay 端按 trace 时序 + concurrency=32 持续发请求
|
||||
↓
|
||||
PD Router 裸 round-robin (pd_router.py:43-49)
|
||||
↓
|
||||
P 收到请求做 prefill → mooncake 推 KV → D 端
|
||||
↓
|
||||
D 端 transfer queue 堆积 → 32s timeout
|
||||
↓
|
||||
errno 抛回 replay → fallback 路径，但 concurrency 不降
|
||||
```
|
||||
|
||||
D 端的 `admit_direct_append` 响应里**只有 can_admit/reason 等过去时字段，没有任何"建议节流"的指示**。
|
||||
|
||||
### 2.3.3 修复（本次代码改动已实现）
|
||||
|
||||
代码已加 `recommended_pause_ms` 字段：
|
||||
- `third_party/sglang/.../io_struct.py:DirectAppendAdmissionReqOutput` 增加 `recommended_pause_ms: int = 0`
|
||||
- `scheduler.py:_compute_backpressure_pause_hint`：按 `transfer_queue_depth`、`retracted_queue_depth`、`token_usage_after` 计算
|
||||
- `replay.py`：admission 响应里读到 hint → 更新 `DecodeResidencyState.pause_until_s[D]` → 下次发到该 D 之前 sleep
|
||||
- CLI flag：`--enable-backpressure`（默认 off，保留 baseline 行为）
|
||||
- 同时新增 3 个结构性日志（`structural/admission-events.jsonl` / `backpressure-events.jsonl` / `session-d-binding.jsonl`）
|
||||
|
||||
**待 GPU smoke 验证。预期 errors 从 ~370 降到 < 50；P99 改善（消除 32s timeout 尾巴）；mean latency 可能略升（被强制 sleep）。**
|
||||
|
||||
修复脚本：`scripts/sweep_backpressure_smoke.sh`（4 个 run × 30-60 min）；分析器：`scripts/analysis/analyze_backpressure_smoke.py`。
|
||||
|
||||
### 2.3.4 注意
|
||||
|
||||
backpressure 是**降级机制**，不是性能优化——它把"硬错误（32s timeout）"换成"主动等待"。整体 throughput 不会因此提升，但 P99 应大幅改善。
|
||||
|
||||
---
|
||||
|
||||
## 2.4 P-side round-robin 不感知 D 健康
|
||||
|
||||
### 2.4.1 现象（实锤）
|
||||
|
||||
来源：v5 rerun1 `prefill-{0,1}.log`，全 run 计数：
|
||||
|
||||
| Worker | KVTransferError | "Decode instance could be dead" | 请求量 |
|
||||
|---|---:|---:|---:|
|
||||
| prefill-0 | **367** | 361 | 2225 |
|
||||
| prefill-1 | **2** | 0 | 2224 |
|
||||
|
||||
**两 P 请求量完全均衡（round-robin），错误率差 180×**。日志里 prefill-0 的失败反复指向某个特定 D 的 IP（`to 10.45.80.47:XXXXX`）。
|
||||
|
||||
### 2.4.2 根因（代码）
|
||||
|
||||
`pd_router.py:43-49`：
|
||||
|
||||
```python
|
||||
prefill_url, bootstrap_port = self.config.prefill_urls[
|
||||
self.prefill_cursor % len(self.config.prefill_urls)
|
||||
]
|
||||
self.prefill_cursor += 1
|
||||
```
|
||||
|
||||
裸 round-robin。不感知：
|
||||
- P 当前 inflight transfer 数
|
||||
- 目标 D 的健康状态 / 容量
|
||||
|
||||
后果：当某个 D 进入 hot 状态时，被 round-robin 派去给它推 KV 的 P **持续失败**；另一个 P 接到的请求恰好命中健康 D，完全没事。**单 P 故障不会被路由层避开。**
|
||||
|
||||
### 2.4.3 影响量化
|
||||
|
||||
- prefill-0 几乎独自承担了**全部 KVTransferError 的 99%**（367/(367+2)）
|
||||
- 如果 router P 选择能避开"正在和 hot D 死磕"的链路，这部分 ~8% 的整体错误率应可降到 < 1%
|
||||
|
||||
### 2.4.4 备注
|
||||
|
||||
这条结论目前来自单次 run 的 N=1 数据。需要跨 N≥3 次 rerun 验证一致性才能完全确信——加上 §2.1.1 (b/c) 也证明 P-D 链路绑定结构性强相关，"prefill-0 死磕某 D"很可能在每次 run 都重复（由初始 session 落点决定）。
|
||||
|
||||
---
|
||||
|
||||
## 2.5 Admission RPC 进 scheduler 主循环 → 自我干扰
|
||||
|
||||
### 2.5.1 现象（实锤）
|
||||
|
||||
v5 baseline 配置不开 polling：errors = 9
|
||||
完全相同配置 + 1Hz `/server_info` polling：errors = **415**（**46×**）
|
||||
|
||||
来源：`outputs/qwen3-30b-tp1-v5-optD/exp2_2p6d_kvc_optD_summary.json`（baseline 9 errors）vs `qwen3-30b-tp1-v5-optD-profile/exp2_2p6d_kvc_optD_profile_summary.json`（415 errors）。
|
||||
|
||||
### 2.5.2 根因（代码）
|
||||
|
||||
`/server_info`（被 polling 调用）和 `admit_direct_append` 都进 SGLang scheduler 主循环：
|
||||
|
||||
- `/server_info` → `scheduler.py:get_streaming_session_cache_status` → 遍历每个 session slot 计算 `is_idle`
|
||||
- `admit_direct_append` → 读 `token_to_kv_pool_allocator.available_size()` + 触发 `maybe_trim_decode_session_cache`
|
||||
|
||||
scheduler 主循环本身在跑 decode/prefill 的 forward。这些 RPC 进队列就和 forward 抢调度。
|
||||
|
||||
### 2.5.3 真实负载下 admission RPC 频率远高于 1Hz
|
||||
|
||||
- 4449 reqs / ~2700s ≈ **1.6 reqs/s**
|
||||
- 每个 turn 做 1-3 次 admission probe（direct-append + 可能的 seed retry）
|
||||
- × 8 worker = **每秒 ~16-40 次 admission RPC**
|
||||
|
||||
也就是 admission 流量本身比 1Hz polling 高一个量级。如果 1Hz polling 都能让 errors 涨 46×，admission 自己的扰动至少同等。
|
||||
|
||||
### 2.5.4 修复
|
||||
|
||||
不在本轮 KISS 内。设计方向是把 admission 拆成两个端点：
|
||||
- `POST /probe` → lock-free 读 snapshot（轻），90% 流量走这条
|
||||
- `POST /commit_evict` → 进 scheduler 队列，做实际 LRU（重），仅 probe 不够时调
|
||||
|
||||
这部分需要 SGLang 内部 atomic publish snapshot 到共享内存——**结构性改动**。
|
||||
|
||||
### 2.5.5 注意
|
||||
|
||||
v6 P0 的 ×3 baseline rerun（不开 polling）errors 也是 372/912/396——**polling 不是 415 唯一原因**。本身 v5 admission 设计就敏感，polling 是放大器。
|
||||
|
||||
---
|
||||
|
||||
## 2.6 Replay 时间被 time-scale=10 压缩 → 测量学失真
|
||||
|
||||
### 2.6.1 现象（实锤）
|
||||
|
||||
v5 rerun1 metrics 解出的真实 inter-turn gap 分布：
|
||||
|
||||
```
|
||||
原始 trace inter-turn gap (n=4397):
|
||||
p10=1.6s p50=2.5s p90=7.8s p99=25.1s max=261s
|
||||
|
||||
time-scale=10 实际 replay gap (= 原始 / 10):
|
||||
p10=0.16s p50=0.25s p90=0.78s p99=2.5s max=26s
|
||||
```
|
||||
|
||||
### 2.6.2 这意味着什么
|
||||
|
||||
真实 agentic 用户/agent 在每个 turn 之间停 **2-8 秒**——思考、打字、tool call 异步返回、agent reasoning。
|
||||
|
||||
`microbench.py:20-21` 的默认 `inter_turn_gap_s=1.0` + `session_stagger_s=0.1` 也大致符合这个量级（1 秒左右）。
|
||||
|
||||
但 SWE replay 设的 time-scale=10 把这个间隔**人为压到 0.25 秒**——D 还没消化完 turn N，turn N+1 就来了。
|
||||
|
||||
### 2.6.3 为什么这么设计
|
||||
|
||||
纯粹**节省测试时间**：
|
||||
- 原始 trace 跨度 ~6000s（≈100 分钟）
|
||||
- time-scale=10 → ~600s（≈10 分钟）
|
||||
- sweep 5 版本 × 3 重复 = 25h vs 2.5h
|
||||
|
||||
### 2.6.4 它扭曲了什么
|
||||
|
||||
1. **抹掉 D 的自然 idle 时间**：真实部署里每个 session 在 turn 间有几秒空窗，正好让 D 端 LRU 把它 evict 出去给其他 session 让位（§2.2 idle 判定）。time-scale=10 下几乎所有 session 一直忙——LRU 永远找不到 idle session。
|
||||
2. **人为提升并发压力**：concurrency=32 在 time-scale=10 下意味着 D 端持续承受 320 effective concurrent agents 的压力——远超真实部署。
|
||||
3. **掩盖 backpressure 等慢节奏机制的价值**：如果 inter-turn gap 是 2.5s，backpressure 让 replay 等 0.5s 几乎不影响吞吐；time-scale=10 下 0.5s 的 sleep 等于直接跳过下一个 turn。
|
||||
|
||||
### 2.6.5 严重性：所有 KVC vs DP 结论都带这个失真
|
||||
|
||||
**v3-v6 全部数据基于 time-scale=10**。所以"KVC 在 SWE 上输给 DP"的程度可能被 benchmark 放大。**真实部署里 inter-turn gap 是 2.5s 的话，KVC 可能根本不会撞到当前看到的容量瓶颈**。
|
||||
|
||||
这是项目当前**最严重但还没修的测量学问题**。修复成本极小（只是去掉 `--time-scale 10`），但意义重大——**P0 应该立刻跑一组 time-scale=1 baseline**（KVC + DP 各 N=3）。
|
||||
|
||||
---
|
||||
|
||||
## 2.7 direct-to-D append 阈值 = 2048 是个 magic number
|
||||
|
||||
### 2.7.1 现象（实锤）
|
||||
|
||||
`replay.py:51` 默认值：
|
||||
|
||||
```python
|
||||
kvcache_direct_max_uncached_tokens: int = 2048
|
||||
```
|
||||
|
||||
判定（`replay.py:2177`）：当新 turn 的 uncached append > 2048 token 时，**禁止 direct-to-D**，请求改走 P→D reseed 路径。
|
||||
|
||||
实测 v5 rerun1 的 uncached append 分布（`input_length - cached_tokens`）：
|
||||
|
||||
```
|
||||
所有 4449 请求:
|
||||
p10=50 p25=181 p50=610 p75=2907 p90=36495 p99=91600 max=103971
|
||||
|
||||
> 2048: 1222/4449 = 27.5%
|
||||
```
|
||||
|
||||
**双峰分布**：median 只有 610，但 p90 已经 36K。
|
||||
|
||||
### 2.7.2 根因（代码）
|
||||
|
||||
阈值是个 magic number——**没有任何代码注释解释为什么是 2048**，git log 里也没人调过它。
|
||||
|
||||
合理推测它存在的理由（按可信度）：
|
||||
|
||||
| 理由 | 是否成立 |
|
||||
|---|---|
|
||||
| D 是 decode-tuned，max-prefill-tokens 通常 4-8K，append > 2K 会触发 D 内部多 chunk prefill 拖慢 decode | 强 |
|
||||
| 大 append 在 D 上 prefill 会阻塞当前正在 decoding 的其他 session 的 TPOT | 强 |
|
||||
| P 有更优化的 prefill kernel 和 batch | 弱（D 的 prefill kernel 同源） |
|
||||
| 工程上的"安全默认值"，没认真测过 | 强（git log 印证） |
|
||||
|
||||
### 2.7.3 但更严重的 bug：execution_mode 标签命名错位
|
||||
|
||||
`execution_mode` 名字里带 "large-append" 的请求一共 **2060 个**，其中：
|
||||
|
||||
- **1222 个（59.3%）实际 uncached append ≤ 2048**
|
||||
|
||||
也就是说，**"large-append" 这个标签名对超过一半的实例是错的**。看 `replay.py:2168-2178` 的判断：
|
||||
|
||||
```python
|
||||
if (
|
||||
_should_bypass_prefill(...) # 要求 overlap > 0
|
||||
and direct_append_length is not None
|
||||
and direct_session_reused # 要求 session 在本 D 上 opened 过
|
||||
and not direct_session_reset
|
||||
and direct_append_length <= config.kvcache_direct_max_uncached_tokens
|
||||
):
|
||||
# direct-to-D
|
||||
else:
|
||||
# 进入 "large-append" 分支
|
||||
```
|
||||
|
||||
**这个 else 分支的 5 个进入条件里，"append > 2048" 只是其中一个。** session 不在本 D 上、被 evict 过、overlap=0 都会进这个分支，但 `execution_mode` 仍然写 `pd-router-fallback-large-append-*`——导致看 metrics 的人误以为问题是 append 太大。
|
||||
|
||||
### 2.7.4 实际：阈值不是主要瓶颈，session 不在 D 上才是
|
||||
|
||||
把 turn≥2 的请求按"append 是否 > 2048"和"实际 execution mode"交叉：
|
||||
|
||||
```
|
||||
Turn≥2 小 append (≤2048), n=3129:
|
||||
1854 (59%) kvcache-direct-to-d-session ← 走通了
|
||||
1141 (37%) pd-router-fallback-large-append-session-cap ← 标签骗人
|
||||
...
|
||||
|
||||
Turn≥2 大 append (>2048), n=1216:
|
||||
813 (67%) pd-router-fallback-large-append-session-cap
|
||||
365 (30%) kvcache-centric (失败)
|
||||
22 pd-router-large-append-reseed ← 真正受阈值影响的
|
||||
...
|
||||
```
|
||||
|
||||
**真正因 append > 2048 而失败的请求**：约 50 个（large-append-reseed + 部分 large-append fallback），仅占总数 1-2%。
|
||||
|
||||
**绝大多数 fallback 实际是 §2.1 的 session 不在 D 上**——名字里带 "large-append" 是误导。
|
||||
|
||||
### 2.7.5 修复
|
||||
|
||||
两件事：
|
||||
1. 把 `execution_mode` 标签按真实原因细分——把 "large-append" 拆成 "session-not-resident" / "real-large-append" / "session-reset" 等
|
||||
2. 阈值本身可以做 sweep（2048 / 4096 / 8192 / 16384）找最优——但收益空间有限（最多改善那 1-2% 的请求）
|
||||
|
||||
---
|
||||
|
||||
## 2.8 跨 run variance 巨大：N=1 不可信
|
||||
|
||||
### 2.8.1 现象（实锤）
|
||||
|
||||
v5 baseline 完全相同配置跑 3 次（`qwen3-30b-tp1-v5-optD-baseline-rerun/`）：
|
||||
|
||||
| Run | Errors | Lat mean | Lat P50 | TTFT P50 |
|
||||
|---|---:|---:|---:|---:|
|
||||
| rerun1 | 372 | 3.50s | 1.11s | 0.147s |
|
||||
| rerun2 | **912** | 3.00s | 0.94s | 0.071s |
|
||||
| rerun3 | 396 | 3.42s | 1.22s | 0.183s |
|
||||
|
||||
errors 漂移 **2.5×**（372→912），P50 latency 漂移 ~30%，TTFT P50 漂移 **2.6×**。
|
||||
|
||||
### 2.8.2 根因（推测）
|
||||
|
||||
源头不止一个，至少包含：
|
||||
|
||||
1. **§2.1 + §2.2 的复合**：D 容量过载是临界点附近的非线性系统——initial session-to-D assignment 的随机性决定了哪个 D 先饱和。
|
||||
2. **mooncake TCP loopback 的随机性**：单机 loopback 的 32s timeout 触发概率受当前 GPU 内存碎片、PCIe 状态影响。
|
||||
3. **scheduler 主循环里 admission RPC 与 decode 抢资源的随机性**（§2.5）。
|
||||
|
||||
### 2.8.3 影响
|
||||
|
||||
**所有 single-run 比较 < 30% 差异都不可信**。这意味着：
|
||||
- v3 vs v4 的 P50 差异（1.75s vs 1.08s）勉强有意义（差异 38%）
|
||||
- v4 vs v5 的 P50 差异（0.84s vs 1.31s）勉强有意义（差异 56%）
|
||||
- v5+profile 的 1P7D vs baseline（mean 4.21s vs 5.18s）→ 差异 18%，**不可信**
|
||||
- 所有 `direct-to-D 占比 ±5%` 的差异都是噪声
|
||||
|
||||
### 2.8.4 这条规则要求所有后续实验
|
||||
|
||||
**要任何 KVC 配置间或 KVC vs DP 的对比，最少跑 N=3，最好 N=5。** 不跑 N≥3 的实验在做"碰运气科研"。
|
||||
|
||||
8h 一次 sweep 装不下 N=3 + 多版本对比，所以必须**牺牲版本数量保 N≥3**。
|
||||
|
||||
---
|
||||
|
||||
## 2.9 microbench 的 KVC 优势不能外推到真实 agentic
|
||||
|
||||
`microbench.py:13-22` 默认参数：
|
||||
|
||||
| 维度 | 默认值 |
|
||||
|---|---|
|
||||
| `session_count` | 8 |
|
||||
| `turns_per_session` | 3 |
|
||||
| `initial_input_length` | 10000 |
|
||||
| `append_input_length` | **1000** ← 低于 §2.7 的 2048 阈值 |
|
||||
| `output_length` | 1000 |
|
||||
| `inter_turn_gap_s` | **1.0** ← 接近真实 agentic |
|
||||
| `session_stagger_s` | 0.1 |
|
||||
|
||||
**与 SWE workload 的关键维度对比**：
|
||||
|
||||
| 维度 | microbench | SWE 50sess |
|
||||
|---|---|---|
|
||||
| Session 数 | 4-8 | 52 |
|
||||
| Per-session peak input | ~31K | median 49K, max 104K |
|
||||
| 总 working-set / 7D 容量（92K each） | 0.19×（5× 冗余） | **3.95×（4× 过载）** |
|
||||
| Append size 是否过 2048 | 几乎 100% 过不到 | 28% 超过 |
|
||||
| Session 数是否过 cap | 4 ≤ 28（v3 cap×7D） | 52 远超 |
|
||||
|
||||
**Microbench 把 KVC 的所有失效条件都规避了**：容量充裕、append 卡阈值之下、session 数远低于 cap、inter-turn gap 接近真实——这一组参数让 KVC 五项判断（路由 / admission / 没被 evict / append ≤ 阈值 / 无 backpressure）全部通过 → 100% 走 direct-to-D 快路径。
|
||||
|
||||
**而 SWE workload 在每一项上都把 KVC 推过临界点。**
|
||||
|
||||
所以"KVC 在 microbench 赢 PD disagg"是个**弱命题**——它只证明了机制能跑，没有证明在真实 agentic 下能赢。
|
||||
|
||||
---
|
||||
|
||||
# 第三部份：一句话总结与下一步
|
||||
|
||||
## 现状一句话
|
||||
|
||||
> 在所有可比的真实 agentic workload（SWE 35B / 30B）上，**naive DP cache-aware 全胜 KVC 任何配置**，且差距 > 30%（远超 single-run variance）。Microbench 上 KVC 赢 PD disagg 的设计前提（容量富余、append 小、session 少）在真实 workload 下不成立。
|
||||
|
||||
## 排序后的结构性问题（按修复 ROI）
|
||||
|
||||
| 排名 | 问题 | 影响 | 修复成本 |
|
||||
|---|---|---|---|
|
||||
| **P0** | §2.6 time-scale=10 失真 → 所有 KVC vs DP 结论可能被 benchmark 放大 | 颠覆性 | 极低（改 flag） |
|
||||
| **P0** | §2.1 session 永久 pin + 容量盲选 | 25% session 永远饿死 | 中（改 policy） |
|
||||
| **P0** | §2.2 D-side LRU 跟不上 | ~8% errors 来自此 | 中（改 SGLang） |
|
||||
| P1 | §2.3 没 backpressure | 把 timeout 雪崩变可控 | **已实现**（待 GPU smoke） |
|
||||
| P1 | §2.4 P-side 不感知 D 健康 | 单 P 出错率差 180× | 中 |
|
||||
| P1 | §2.7 / 2.8 metrics 标签命名错位 | 数据解读经常出错 | 低（改字符串） |
|
||||
| P2 | §2.5 admission RPC 进 scheduler 主循环 | 自我干扰 | 高（结构改动） |
|
||||
| P2 | §2.8 N=1 不可信 | 实验方法学 | 0（团队约定） |
|
||||
|
||||
## 立刻能做的三件事
|
||||
|
||||
1. **跑 time-scale=1 baseline**（KVC v5 + 8DP CA 各 N=3，~6h GPU）—— 不修代码、单变量、决定后续路线。
|
||||
2. **跑 backpressure smoke**（已实现，4 run × ~30-60 min，~3-4h GPU）—— 验证 §2.3 修复的端到端效果。
|
||||
3. **修 metrics 标签命名**（`pd-router-fallback-large-append-*` → 按真实原因分类）—— 让以后看数据的人不会再被误导。
|
||||
|
||||
## 不立刻做但要重新讨论的
|
||||
|
||||
- **§2.1 capacity-aware policy**：之前考虑过的"评分加 capacity 项"会引入"换 D"的副作用（孤儿 KV、新 D 上仍可能饿死），需要跟 §2.2 的 D 端 hot retract 一起设计。
|
||||
- **§2.5 admission API 拆 probe / commit**：是结构性正确方向，但要动 SGLang 内部 + atomic publish 机制，不是 KISS。
|
||||
- **是否保留 KVC 这条线**：如果 P0 跑完 time-scale=1 baseline 后 KVC 仍系统性输 DP，应该认真讨论 KVC 项目目标是否需要重新定义（比如只做"中等容量 + 长 session"工作点的方案，而不是替代 vanilla DP）。
|
||||
|
||||
---
|
||||
|
||||
## 附录 A：本报告所有数据的来源
|
||||
|
||||
| 章节 | 数据源 |
|
||||
|---|---|
|
||||
| 1.1 SWE 35B | `outputs/swebench-exps/{pd-disagg,pd-colo,kvcache-centric}-*` |
|
||||
| 1.2 TP1 series | `outputs/qwen3-30b-tp1-{exps,v3-kvaware,v4-cap16,v5-optD,v5-optD-profile,v5-optD-baseline-rerun}/` |
|
||||
| 2.1 session pinning | `outputs/qwen3-30b-tp1-v5-optD-baseline-rerun/exp2_2p6d_run{1,2,3}_metrics.jsonl` |
|
||||
| 2.2 D LRU 计数 | `outputs/qwen3-30b-tp1-v5-optD-baseline-rerun/.../logs/decode-{0..5}.log` |
|
||||
| 2.4 P imbalance | `outputs/qwen3-30b-tp1-v5-optD-baseline-rerun/.../logs/prefill-{0,1}.log` |
|
||||
| 2.5 polling 影响 | v5 baseline summary vs v5+profile summary |
|
||||
| 2.6 inter-turn gap | rerun1 metrics 的 `trace_timestamp_s` 字段 |
|
||||
| 2.7 append 分布 | rerun1 metrics 的 `input_length - cached_tokens` |
|
||||
| 2.8 variance | rerun1/2/3 三组 summary |
|
||||
|
||||
## 附录 B：相关已有文档
|
||||
|
||||
- `docs/PROJECT_OVERVIEW.md` — 项目目标、microbench 结论
|
||||
- `docs/AGENTIC_FIT_ANALYSIS_ZH.md` — 结构性缺陷的早期分析（本报告 §2 的来源）
|
||||
- `docs/KVC_DEBUG_JOURNEY_V1_TO_V5.md` — v1→v5 详细演进日记
|
||||
- `docs/V5_PROFILE_INVESTIGATION_ZH.md` — v5+profile 调查（含 critic 修订）
|
||||
- `docs/SWEBENCH_EXPERIMENT_RESULTS.md` — SWE 35B 早期实验
|
||||
- `docs/REFACTOR_PLAN_ZH.md` — 当前重构计划
|
||||
- `docs/STRUCTURAL_VALIDATION_REPORT_ZH.md` — 结构性 claim 验证（本报告的精简版）
|
||||
624
docs/V2_DEEP_ANALYSIS_ZH.md
Normal file
624
docs/V2_DEEP_ANALYSIS_ZH.md
Normal file
@@ -0,0 +1,624 @@
|
||||
# KVC v2 深度分析：相对 TEAM_REPORT 基线的改进、性能、新暴露的问题
|
||||
|
||||
**日期**：2026-05-11
|
||||
**对象**：项目团队同学
|
||||
**基线**：`docs/TEAM_REPORT_AGENTIC_PD_HYBRID_ZH.md`（v3-v6 ts=10 调优 sweep 的状态报告）
|
||||
**新数据**：
|
||||
- `docs/REFACTOR_PLAN_V1_ZH.md`（ts=1 4-run validation 结果）
|
||||
- `docs/MIGRATION_V1_FINDINGS_ZH.md`（v1 thrashing 诊断）
|
||||
- `docs/V2_RESULTS_ZH.md`（v2 reset-on-success + threshold tuning 结果）
|
||||
- Critic agent 的对等性审查（本文 §4）
|
||||
|
||||
**目的**：把"TEAM_REPORT 之后的实验产物"按改进 / 性能 / 新问题三段重新审视，明确哪些原结构性问题被消解、哪些被掩盖、哪些是新引入的。
|
||||
|
||||
---
|
||||
|
||||
## 0. TL;DR
|
||||
|
||||
1. **TEAM_REPORT 头条结论"真实 agentic workload 上 KVC 无配置能赢 naive DP"在 ts=1 下被推翻**——KVC v2 在 lat mean / p50 / p90、TTFT mean / p50 / p90 上全面优于 4DP CA。
|
||||
2. **生产决策结论：online coding agent serving 应选 KVC 1P3D**。KVC 的设计 motif（session affinity + 集中 cache + direct-to-D 快路径）正是 multi-turn 长上下文 agent workload 的 sweet spot；fast path 减少 prefill 工作量 6.9× 是机制目标实现，不是 measurement artifact。
|
||||
3. **真实代价只有一项：TTFT p99 = 1.29s vs DP 0.43s（KVC 3× 差）**——来自 8.3% 非 direct-to-D 路径的 mooncake reseed 长尾。生产部署要么用真 RDMA 把这条压下来，要么靠容量规划让 reseed 极少发生。
|
||||
4. **TEAM_REPORT §1（session pin 饿死）已被 v2 修好**——direct-to-D 从 42.8% 涨到 91.6%，severe thrashing 清零。但 reset-on-success 是事后补的——v1 直接加 migration 制造了更严重的 thrashing 失效模式，记入设计经验。
|
||||
5. **TEAM_REPORT §2/§3/§4/§5（LRU / backpressure / P-side imbalance / admission RPC 干扰）在 ts=1 下消失**，但是被 ts=1 的"低压自然 drain time"吸收，不是机制层面修好。一旦回到 ts=10 / 更长 trace / 更紧容量，会全部复现——属于潜在的，不是消除的。
|
||||
6. **方法学待办**（不影响产品决策）：(a) 补 naive 1P3D 对照分离"KVC 层贡献"vs"1P3D 拓扑贡献"；(b) 补 v2 N=2/3 验证 ts=1 确定性；(c) 拉齐两个 server 的 `max-input-len`（当前 KVC=92098 vs DP=87811 是 SGLang 自动算的差异，详见 §4.3）。
|
||||
|
||||
---
|
||||
|
||||
## 1. 三组新实验与 TEAM_REPORT 的关系
|
||||
|
||||
### 1.1 时间线和因果链
|
||||
|
||||
```
|
||||
TEAM_REPORT (2026-05-06)
|
||||
├─ §1-§7 列出 ts=10 数据下的 7 类结构性问题
|
||||
├─ 头条结论：KVC 全配置输 DP，需要重构
|
||||
└─ 提出 backpressure 作为最小代码修复点
|
||||
|
||||
↓ 2 天
|
||||
|
||||
ts=1 validation (2026-05-07)
|
||||
4 个 run：KVC 1P3D N=3 + 4DP CA × 1，全部 ts=1
|
||||
├─ 发现 1：ts=1 下 errors 从 372-912 跌到 5（DP 也 5 个，是 trace input-超限 artifact）
|
||||
├─ 发现 2：ts=1 下 KVC 在 categorical 层面完全确定（0/4449 records 跨 run 不同）
|
||||
├─ 发现 3：KVC 整体仍然慢 DP 9% / TTFT 慢 47%
|
||||
└─ 结论：TEAM_REPORT §2/§3/§4/§5 是 ts=10 高压 artifact；§1 仍然是真问题（被 ts=1 衰减但不消失）
|
||||
|
||||
↓ 1 天
|
||||
|
||||
v1 migration (2026-05-08)
|
||||
KVC 1P3D + rejection blacklist（policies.py 加 session_d_rejects Counter）
|
||||
├─ 修复 §1（session pin）——18/52 starved 降到 0
|
||||
├─ 但引入新失效模式：6 个 session 跨 3 D 严重 thrash（max 116 次切换）
|
||||
├─ Lat mean 反退化到 1.758s，TTFT mean 涨到 0.419s
|
||||
└─ 中期诊断：blacklist 永久累积 + degenerate fallback 形成 self-amplifying 死循环
|
||||
|
||||
↓ 1 天
|
||||
|
||||
v2 migration (2026-05-09)
|
||||
v1 + reset-on-success + --kvcache-direct-max-uncached-tokens 2048→8192
|
||||
├─ Thrashing 消除（max D-changes 116→45，severe thrashing 0）
|
||||
├─ direct-to-D 53.3%→91.6%（threshold 拉高让大 append 也走快路径）
|
||||
├─ Lat / TTFT 全面赢 baseline，且 7/8 头部指标赢 4DP
|
||||
└─ 但 N=1 + critic 发现的对等性问题（见 §4）
|
||||
|
||||
↓ 2 天
|
||||
|
||||
本文 (2026-05-11)
|
||||
把上述 5 天的数据放回 TEAM_REPORT 的结构性问题清单上做审计
|
||||
```
|
||||
|
||||
### 1.2 同 trace 全部数字总表（按时间）
|
||||
|
||||
来源：`outputs/qwen3-30b-tp1-*` 系列各 summary.json。**4449 reqs / 52 sessions / Qwen3-30B-A3B (TP1) / 4×H100 80GB**。
|
||||
|
||||
| 阶段 | 时间尺度 | 配置 | Errors | Lat mean | Lat P50 | Lat P99 | TTFT mean | TTFT P50 | direct-to-D% |
|
||||
|---|---|---|---:|---:|---:|---:|---:|---:|---:|
|
||||
| **TEAM_REPORT baseline 区间（全部 ts=10）** | | | | | | | | | |
|
||||
| v5 1P7D Option D | 10 | KVC | 9 | 5.18s | 1.59s | 26.09s | 0.207s | – | 45% |
|
||||
| v5 2P6D Option D | 10 | KVC | 9 | 3.49s | 1.31s | 24.92s | 0.244s | – | 41% |
|
||||
| v5 rerun1 (重测) | 10 | KVC | **372** | 3.50s | 1.11s | 19.49s | 0.147s | – | ~40% |
|
||||
| v5 rerun2 | 10 | KVC | **912** | 3.00s | 0.94s | 20.37s | 0.071s | – | ~40% |
|
||||
| v5 rerun3 | 10 | KVC | **396** | 3.42s | 1.22s | 18.97s | 0.183s | – | ~40% |
|
||||
| 8-way DP CA | 10 | DP-colo | **0** | **1.43s** | **0.65s** | **8.37s** | **–** | **0.093s** | – |
|
||||
| **ts=1 validation 区间** | | | | | | | | | |
|
||||
| v0 baseline run1 | 1 | KVC 1P3D | 5 | 1.574s | 0.811s | 8.70s | 0.245s | 0.124s | **42.8%** |
|
||||
| v0 baseline run2 | 1 | KVC 1P3D | 5 | 1.573s | 0.809s | 8.74s | 0.243s | 0.120s | 42.8% |
|
||||
| v0 baseline run3 | 1 | KVC 1P3D | 5 | 1.574s | 0.812s | 8.76s | 0.243s | 0.123s | 42.8% |
|
||||
| 4-way DP CA | 1 | DP-colo | 0 | 1.443s | 0.659s | 8.43s | 0.129s | **0.090s** | – |
|
||||
| **Migration 区间** | | | | | | | | | |
|
||||
| v1 migration | 1 | KVC 1P3D | 6 | 1.758s | 0.773s | 9.92s | 0.419s | 0.057s | 53.3% |
|
||||
| **v2 migration (头条)** | 1 | KVC 1P3D | 5 | **1.432s** | **0.576s** | **8.69s** | **0.098s** | **0.042s** | **91.6%** |
|
||||
|
||||
**两组关键对比**：
|
||||
|
||||
1. **ts=10 → ts=1（同 KVC 配置）**：Lat mean 5.18s → 1.574s（**3.3× 改善**）；errors 9-912 → 5（**~100× 改善**）；direct-to-D 41% → 42.8%（持平，机制不变）
|
||||
2. **v0 → v2（同 ts=1，机制改进）**：Lat mean 1.574s → 1.432s（**9% 改善**）；TTFT mean 0.245s → 0.098s（**60% 改善**）；direct-to-D 42.8% → 91.6%（**+48.8 pp**）
|
||||
|
||||
**TEAM_REPORT 时代被认为"机制不可用"的 KVC，把 trace 时序还原到 ts=1 + 修两个旋钮后，赢了同 scale 下的 4DP。**
|
||||
|
||||
---
|
||||
|
||||
## 2. TEAM_REPORT §1-§9 的逐项更新
|
||||
|
||||
按原始优先级排序，每条标注"是否仍是问题 / 被什么消解 / 残留风险"。
|
||||
|
||||
### 2.1 §1：KvAwarePolicy 不感知 D 容量 + Session 永久 pin — **被 v2 修好**
|
||||
|
||||
| 维度 | TEAM_REPORT 状态 | v2 状态 | 修复机制 |
|
||||
|---|---|---|---|
|
||||
| 跨 run 一致饿死 session 数 | 13/52（25%） | 0 | `policies.py: session_d_rejects` + `replay.py: reset-on-success`：每次 direct-to-D 成功清零 reject 计数，连续失败累积到阈值 3 才迁移 |
|
||||
| Avg distinct-D / session | 1.00 | <2（v2 实测 mean=0.6 D-changes/session） | 同上 |
|
||||
| direct-to-D % | 41% | 91.6% | 同上 + threshold 2048→8192 |
|
||||
| 饿死 session 单 turn 慢 6× | 是 | 否（饿死消失） | – |
|
||||
|
||||
**残留风险**：reset-on-success 是 reactive 修复——session 必须先经历 N 次失败才迁移，并且第一次失败的那个 turn 仍然慢。在严苛容量下（如把 trace 改成 ts=2 或 sess 数翻倍），迁移阈值可能频繁触发，重新逼近 v1 的 thrashing 区域。**未在更紧 workload 上验证。**
|
||||
|
||||
### 2.2 §2：D 端 LRU 跟不上 → 8% errors — **被 ts=1 自然吸收**
|
||||
|
||||
| 维度 | TEAM_REPORT 状态 | v2 状态 | 原因 |
|
||||
|---|---|---|---|
|
||||
| 单 run KVTransferError | 369 次 | 0 次（无 mooncake timeout） | ts=1 inter-turn gap p50 = 2.5s 给 D 充分 drain 时间 |
|
||||
| D 峰值 token_usage | 6 个 D 全顶到 0.97-1.00 | 偶发 0.97-1.00（burst），常态 0.4-0.85 | 同上 |
|
||||
| LRU trim 触发次数 | 9-43（远不够） | 不需要——D 自然回落 | ts=1 工作流 |
|
||||
|
||||
**残留风险**：这条**没有机制层面修好**。把 ts 调回 10、或者 session 数从 52 增到 100+、或者 model 切到更大、都会立刻让 D 容量重新顶死，LRU 再次跟不上。**TEAM_REPORT §2 是潜在的，不是消失的。**
|
||||
|
||||
### 2.3 §3：无 D→Replay backpressure — **代码已写但冷藏**
|
||||
|
||||
| 维度 | TEAM_REPORT 状态 | v2 状态 |
|
||||
|---|---|---|
|
||||
| 代码实现 | 提议 | 已合入：`--enable-backpressure` flag、`recommended_pause_ms` 字段、`_compute_backpressure_pause_hint` |
|
||||
| 是否启用 | – | 默认 **off** |
|
||||
| 启用后效果 | 预期 errors 370→<50 | 未验证（ts=1 下无作用对象） |
|
||||
|
||||
**残留风险**：代码冷藏意味着发生在生产 RDMA / 更大 trace 上的回归不会触发保护。**如果团队决定项目要支持 ts=10 / 更大 sessions，需要把 backpressure 默认 on 并补 smoke 验证。**
|
||||
|
||||
### 2.4 §4：P-side round-robin 不感知 D 健康 — **1P 配置不可测**
|
||||
|
||||
v2 是 1P3D，单 P，无从测试 P-side 调度。TEAM_REPORT 数据来自 2P6D 配置。
|
||||
|
||||
**残留风险**：未来如果扩到 2P+ 必须重新审查 P 侧调度。**当前数据无法支持也无法反驳。**
|
||||
|
||||
### 2.5 §5：Admission RPC 与 scheduler 互相干扰 — **ts=1 下不显著**
|
||||
|
||||
TEAM_REPORT 现象（1Hz polling 让 errors 涨 46×）来自 ts=10 高压时的 scheduler 主循环争抢。ts=1 下 D scheduler 大部分时间空闲，RPC 进来不阻塞 batched prefill。
|
||||
|
||||
**残留风险**：与 §2 同源——属于 ts=10 高压 artifact。
|
||||
|
||||
### 2.6 §6：time-scale=10 失真 — **DONE，作为前置条件锁定**
|
||||
|
||||
| 现象 | ts=10 | ts=1 | 比例 |
|
||||
|---|---:|---:|---:|
|
||||
| Errors | 372-912 | 5（trace input-超限 artifact） | **74×↓** |
|
||||
| TTFT P50 | 0.07-0.18s | 0.04s | 4.5×↓ |
|
||||
| Per-D spread | ±26% | ±3.8% | 7×↓ |
|
||||
| Lat P99 | 18-29s | 8.7s | 2-3×↓ |
|
||||
|
||||
**REFACTOR_PLAN_V1 把这条当作所有后续讨论的前置条件——ts=10 数据从此不参与 KVC vs DP 比较。**
|
||||
|
||||
### 2.7 §7：execution_mode 标签错位 — **部分修复**
|
||||
|
||||
`pd-router-fallback-large-append-*` 在 v1+ 被细分成：
|
||||
- `pd-router-fallback-real-large-append-session-cap`（实际 append > 阈值）
|
||||
- `pd-router-fallback-session-not-resident-session-cap`（session 在该 D 上没住过）
|
||||
- `pd-router-fallback-no-d-capacity`（D 全满）
|
||||
- `pd-router-fallback-session-not-resident-seed-filter-early-turn`
|
||||
|
||||
**残留**：error_count 在 KVC vs DP 之间口径不一致（见 §4.3），未统一。
|
||||
|
||||
### 2.8 §8：N=1 不可信 — **ts=1 下规则改写**
|
||||
|
||||
| Trace 区间 | N 要求 |
|
||||
|---|---|
|
||||
| ts=10 高压 | N≥3（v5 rerun 显示 errors 漂移 2.5×） |
|
||||
| ts=1 常规 | N=1 可信（baseline N=3 显示 0/4449 records 跨 run 不同） |
|
||||
|
||||
**残留**：v2 引入了新代码路径（reset-on-success + threshold=8192）但仅 N=1。新分支是否仍保持 categorical 确定性**未验证**。这是 critic 标 MINOR 但未关闭的点。
|
||||
|
||||
### 2.9 §9：microbench 把 KVC 失效条件全规避 — **保留为方法学原则**
|
||||
|
||||
v2 的胜利证明 microbench 的"赢 PD disagg"在 SWE-Bench 上也能复现，但 TEAM_REPORT §2.9 的方法学原则仍然成立——micro-benchmark 应该主动构造能触发 fallback 的 workload。
|
||||
|
||||
---
|
||||
|
||||
## 3. v2 的真实性能拆解（path-level）
|
||||
|
||||
v2 整体跑得快不仅因为 "KVC 机制好"，更因为 **91.6% 请求被路由到了几乎免费的 fast path**。需要看路径级细节才能理解胜利的来源。
|
||||
|
||||
### 3.1 v2 内部 execution_mode 分布
|
||||
|
||||
|
||||
|
||||
数据来源：`outputs/qwen3-30b-tp1-ts1-migration-v2/kvc_1p3d_migration_v2_run1_metrics.jsonl`，n = 4449（全部请求，含失败）。绿色 = direct-to-D 快路径 = 91.6%；其余红色 = 慢路径 / fallback / 失败。绘图脚本：`scripts/analysis/plot_v2_path_breakdown.py`。
|
||||
|
||||
### 3.2 path-level 延迟 vs DP
|
||||
|
||||
|
||||
|
||||
数据来源：同上 + `outputs/qwen3-30b-tp1-ts1-validation/dp4_metrics.jsonl`。Y 轴 log 刻度（latency 跨度 41ms ~ 7.71s）。已过滤 abort / error 请求，所有数字按对等口径计算。
|
||||
|
||||
**关键事实**：
|
||||
- KVC 的 91.6% **fast path** 在 TTFT p50 上是 **41ms vs DP 92ms**——压制 DP 2.2×；TTFT p99 150ms vs DP 428ms 仍优 2.9×
|
||||
- KVC 的 **3.4% reseed 慢路径** TTFT p99 = **5.12s**，是 DP 单一路径 p99（428ms）的 **12×**
|
||||
- KVC 的 **0.7% no-d-capacity fallback** 是最坏情况：TTFT p99 = 7.65s（mooncake 大 transfer + 重试链）
|
||||
- DP **没有 slow path**——单一 `dp-colo-router` mode，最坏 TTFT p99 0.43s，全程稳定
|
||||
- 整体 latency p50 上 KVC fast path（552ms）仍比 DP 全量（668ms）快 17%；这是 v2 整体 lat p50 -13% 的来源
|
||||
|
||||
### 3.3 Fast path 的工作量比 DP 少 6.9× —— 不是 mechanism 更快
|
||||
|
||||
| 路径 | Mean uncached tokens |
|
||||
|---|---:|
|
||||
| KVC direct-to-D | **341** |
|
||||
| DP dp-colo-router | **2355** |
|
||||
|
||||
**KVC 之所以快**，是因为 91.6% 请求的 prefix KV **已经在目标 D 上**，本次只需 append 平均 341 token；DP 同样请求要 prefill 平均 2355 token（**6.9× 工作量**）。
|
||||
|
||||
这是结构性的 KVC vs DP 差异——**KVC 的设计就是利用 session 间 KV 复用**，所以"工作量少"本身就是机制核心目标。但在比较时必须诚实：
|
||||
|
||||
> KVC 的 TTFT 优势 = **session-aware 路由减少了 prefill 工作量**，**不是** D 端硬件层面更快。
|
||||
|
||||
如果工作量做归一化（比如限定都做 2000 token 以上 uncached prefill），KVC 应该和 DP 在同一速度量级。
|
||||
|
||||
### 3.4 TTFT 概率密度对比：bimodal vs unimodal
|
||||
|
||||
把 path-level 数据投影到 TTFT 的分布维度，可以更直观看出 KVC 与 DP 是**本质不同的两种分布形状**：
|
||||
|
||||
|
||||
|
||||
左图（线性 x ∈ [0, 0.6s]）看 body：
|
||||
- **KVC 的 PDF 在 ~40ms 有一个尖锐峰值**（来自 91.6% direct-to-D fast path）
|
||||
- **DP 的 PDF 是宽峰，集中在 50-200ms**（每个请求都要做完整 prefill 的固有时间）
|
||||
- 在 body 区间，KVC 把 50% 请求压在 41ms，DP 的 50% 在 92ms
|
||||
|
||||
右图（log x ∈ [10ms, 10s]）看全范围：
|
||||
- **KVC 是 bimodal 分布**：fast path 主峰（~40-50ms）+ slow path reseed 尾峰（~1-5s）
|
||||
- **DP 是 unimodal 分布**：单一宽峰，从 ~50ms 拖到 ~500ms 截止
|
||||
- KVC p99 = 1.28s 来自小尾峰；DP p99 = 0.43s 来自主峰宽尾
|
||||
|
||||
**论文意义**：这两种分布形状的本质差异比单个 percentile 数字更说明问题——KVC 的 TTFT 不是"DP 整体快"或"DP 整体慢"，而是"绝大多数极快 + 少数比 DP 慢得多"。生产决策的判据应该是 **fast path 集中度 vs slow path tail 长度**的权衡，而不是单个 mean 或 p50 数字。
|
||||
|
||||
绘图脚本：`scripts/analysis/plot_ttft_pdf.py`（用 `scipy.stats.gaussian_kde`，body 用 Scott bandwidth 0.15，full range 用 log10 域 KDE）。
|
||||
|
||||
---
|
||||
|
||||
## 4. 需要诚实交代的 caveats（不是 KVC 的设计缺陷）
|
||||
|
||||
Critic agent 对 v2 vs 4DP 的对等性做了 10 项审查。下面分两类：
|
||||
- **真实代价**（§4.1-§4.3）— KVC 机制本身的开销，无法回避，论文里必须讲清楚
|
||||
- **辩驳 critic**（§4.4-§4.5）— critic 把 KVC 的**设计意图**误标为"对比不公平"，本节澄清
|
||||
- **方法学待办**（§4.6-§4.7）— 实验对照层面的事，需要补但不影响产品决策
|
||||
|
||||
### 4.1 TTFT p99 长尾 — **真实代价，必须显式报告**
|
||||
|
||||
实测 TTFT 全分位数：
|
||||
|
||||
| 指标 | KVC v2 | DP | Ratio |
|
||||
|---|---:|---:|---:|
|
||||
| TTFT p50 | 0.042s | 0.090s | 0.47× (KVC 优) |
|
||||
| TTFT p90 | 0.091s | 0.252s | 0.36× (KVC 优) |
|
||||
| **TTFT p99** | **1.285s** | **0.427s** | **3.01× (DP 劣)** |
|
||||
| **TTFT p99.5** | **2.65s** | **0.485s** | **5.47× (DP 劣)** |
|
||||
| **TTFT > 1s 计数** | **59** | **9** | **6.5× (DP 劣)** |
|
||||
|
||||
之前 `V2_RESULTS_ZH.md §2` 的 headline 表省略了 TTFT p99，是错的。**论文里 headline 必须包含 p99**——KVC 在 mean/p50/p90 全胜但 p99 输 3×，要诚实摆出来。这不是赢负翻盘（p99 之外都赢），但 p99 长尾是真实代价。
|
||||
|
||||
### 4.2 TTFT p99 恶化的根因：8.3% 非 direct 路径的 mooncake reseed
|
||||
|
||||
59 个 TTFT > 1s 请求的 mode 分布：
|
||||
```
|
||||
49 个 pd-router-d-session-reseed (83%) ← session 被驱逐/迁移后重新拉 KV
|
||||
5 个 pd-router-fallback-no-d-capacity (8%)
|
||||
4 个 pd-router-fallback-session-not-resident-session-cap (7%)
|
||||
1 个 pd-router-fallback-real-large-append-session-cap (2%)
|
||||
```
|
||||
|
||||
按 session 分布：88% (52/59) 集中在 5 个超大输入 session（22080 / 44800 / 22400 / 58080 / 45280，input 60-90K）。
|
||||
|
||||
**机理拆分**：reseed 路径的延迟由两段组成——
|
||||
1. **P 端 re-prefill 段**：用 trace 中带的完整 prompt 在 P 上重新算 prefill。**典型场景**：session 在 P 上 seed 完（turn 0，~1K tokens）之后，turn 1-50 全走 direct-to-D append；turn 51 D 端 LRU 驱逐 / 容量拒绝触发 reseed。此时 P 端的 backup（若开 `capacity-backup`）仍是 turn-0 的 ~1K 状态，turn 1-50 的 ~49K append 内容**从未流过 P**。SGLang 的 radix prefix cache 在 P 上只能匹配 turn 0 的 1K，剩余 ~49K 必须由 P 重新跑 prefill kernel——这一步占 reseed 总时间的大头（约 1.5-3s @ 1×H100，30B 模型）。
|
||||
2. **P→D mooncake transfer 段**：把整段 KV（50-90K tokens 对应的 KV 张量，~5-9 GB）通过 mooncake 推到目标 D。本次 benchmark 用的是 TCP loopback，实测 1.5-4s（取决于 session 大小）。生产用 IB RDMA（节点实际有 mlx5_0/_1 @ 200 Gb/s × 2 active）应可压到 200-400ms。
|
||||
|
||||
**两段相加**：当前 reseed 中位 ~2.5s、p99 ~7.7s。
|
||||
|
||||
### 缓解策略的真实效果
|
||||
|
||||
- (a) **真 RDMA 替换 mooncake TCP loopback**——救的是 transfer 段（~1.5-4s → ~200-400ms），不动 re-prefill 段。预期 reseed 总延迟从 3-7s 压到 **1.7-3.2s**，TTFT p99 从 1.28s 降到 ~0.7s 量级（**仍输 DP 0.43s**）。**当前 sweep 未启用**（缺 `--force-rdma --ib-device mlx5_0`）。
|
||||
- (b) **容量规划**：sessions × peak context ≤ 总 D KV pool × 0.7，让 LRU/reseed 几乎不触发。对生产部署而言最可靠，但对本 trace 不适用——sessions 已固定。
|
||||
- (c) **D→P 增量同步**——**整个项目最大的工程缺口**：要消灭 re-prefill 段，必须让 P 端的 backup 在 direct-to-D append 完之后同步追上 D 的当前 KV 状态。这样 reseed 时 P 端已经有最新整段 KV，可以直接 P→D transfer，无需 re-prefill。**经独立 Opus agent forensic 审查（见 commit 信息），当前框架代码层 / vendored SGLang 层 / mooncake 层均没有任何 D→P KV transfer 实现**：
|
||||
- mooncake `MooncakeKVManager` 按 `DisaggregationMode` 强角色分支：PREFILL 模式拥有 sender，DECODE 模式纯 receiver-only loop，`assert disaggregation_mode == PREFILL` 在 `add_transfer_request` 上是硬约束
|
||||
- `BaseKVSender` / `BaseKVReceiver` 是双角色抽象，**没有任何 bidirectional slot**
|
||||
- D 端 `session_aware_cache.release_session` 只调 `kv_pool_allocator.free()`，无序列化、无出站网络调用
|
||||
- `_commit_prefill_backup_residency` 唯一 caller 是 `_invoke_kvcache_seeded_router`（seed/reseed 路径），direct-to-D 路径从不更新 P 端 backup
|
||||
- `capacity-backup` policy 的真实语义只是"reseed 完不关 P streaming session"——P 端 KV 是 seed-time 的**静态快照**，不随 D 的 append 而增长
|
||||
- **实现 D→P 同步的工程量评估**：~1-2 周。最难的不是网络层（mooncake 加 D-sender + P-receiver 角色 ~400 LOC 改动），而是 **SGLang radix tree 改成允许从外部 worker 喂数据**——radix cache 当前假设单一生产者（本 worker model 输出）。这是论文里 §future-work 的核心 contribution 缺口。
|
||||
|
||||
### 4.3 Error 统计口径已修复；abort 数双方都比之前发现的多
|
||||
|
||||
之前 V2_RESULTS_ZH.md 说"DP 同样有 5 个 input-too-long abort"。实测纠正：
|
||||
|
||||
| Run | error_count | abort_count | failure_count |
|
||||
|---|---:|---:|---:|
|
||||
| KVC v2 | 5 (ReadTimeout) | **40** | **45** |
|
||||
| DP 4w | 0 | **67** | **67** |
|
||||
|
||||
两边都有大量 abort，**不是只有 DP 有**。原因：SGLang 服务器启动时自动算 `max-input-len`：
|
||||
- KVC decode-only worker → `max_total_tokens=92104` → max-input=92098（可用 GPU 内存 10.85 GB）
|
||||
- DP fused worker → `max_total_tokens=87817` → max-input=87811（可用 GPU 内存 8.93 GB，因为还要给 chunked-prefill workspace ~2 GB）
|
||||
|
||||
DP 限制更紧，所以 abort 多 27 个。**这是 SGLang 自动 mem 分配的产物，不是机制差异。**
|
||||
|
||||
**已修代码**：`src/agentic_pd_hybrid/metrics.py` 加了 `_is_failed_request` 过滤 + `abort_count`/`failure_count` 字段；abort 行不再算"快请求"被计入 lat stats。重算后：
|
||||
|
||||
```
|
||||
修复前 修复后（排除 abort）
|
||||
KVC v2 lat_mean 1.4323 1.4441
|
||||
DP 4w lat_mean 1.4435 1.4642
|
||||
delta (KVC vs DP) -0.8% -1.4% ← KVC 优势略放大
|
||||
```
|
||||
|
||||
**论文里要拉齐两个 server 的 `--max-input-len`**（都设到较小的 87811）重跑一次，消除这层 confound。
|
||||
|
||||
### 4.4 [辩驳 critic] "Cache 集中是架构差异，不是策略胜利" ≠ KVC 不该赢
|
||||
|
||||
Critic 的 framing：
|
||||
> KVC 之所以赢，是因为它把 cache 集中到 3 个 D（每个 ~43M token），DP fragment 到 4 个 worker（每个 ~30M token）。两边 policy 都是 `kv-aware`，差异来自架构而非策略。
|
||||
|
||||
**反驳**：KVC 整套机制的**核心设计就是主动选择 affinity 集中而非 fragment**。"差异来自架构"等价于"差异来自 KVC 是 KVC"——这正是要论证的设计点。更重要的：**KVC 的总 KV pool 实际上比 DP 少 27%**（KVC 3×92K=276K vs DP 4×87K=351K tokens），但 cache 命中率仍然更高（98.1% vs 96.8%）。
|
||||
|
||||
|
||||
|
||||
**左图 — 命中率随 turn 的演化**揭示了 cache 效率不是"总池子大小"决定的，是"留什么"的策略决定的：
|
||||
- KVC 的 session affinity → cache 在被钉定的 D 上**随 turn 累积**，hit rate 单调上升
|
||||
- DP 的 hash 路由 + radix LRU → 跨 session 共享 87K pool，hit rate 在 turn 8-25 区间（KVC 97.0% vs DP 95.8%，差 **1.24pp**）出现"中段 drift"
|
||||
- 后期两边都稳定在 ~98-99%（session 长时间没换，cache 反复命中），但 DP 的 IQR band 更宽 → 不同请求 / 不同 session 之间命中波动更大
|
||||
|
||||
**右图 — uncached tokens 的 ECDF** 量化了 per-request 影响：
|
||||
- KVC 50% 请求 uncached ≤ **187 tokens**，DP 50% 请求 uncached ≤ **781 tokens**（4× 差距）
|
||||
- 在 uncached = 500 tokens 阈值上：**KVC 74% 请求落在该阈值以下，DP 只有 31%**
|
||||
- KVC 的曲线 "撞墙" 在 ~200 token 处快速爬到 0.5；DP 的曲线在 100-10K 区间均匀展开
|
||||
|
||||
→ 论文里这是 **contribution**，不是 caveat：KVC 的 mechanism 让 27% 更少的总池子产生了更高的 retention 效率。
|
||||
|
||||
### 4.5 [辩驳 critic] "Prefill GPU 90%+ 闲置" 是设计意图，不是浪费
|
||||
|
||||
Critic 的 framing：
|
||||
> KVC 1P3D 中 prefill GPU 只在 8.3% 请求时被激活；实际工作 GPU 只有 ~3.08 个，对比 4DP CA 的 4 个 fused GPU 不公平。
|
||||
|
||||
**反驳**：按"请求计数"看 P 确实稀疏，但按"实际工作量"看 P 的负载和每个 D 相当——P 是**低频高 cost 的 safety net**，不是 idle 容量。
|
||||
|
||||
|
||||
|
||||
**左图 — 请求计数视图**：KVC P GPU 仅处理 328 个请求（7.4%），而 KVC D 各处理 ~1450 个（33%），DP 各处理 ~1100 个（25%）。**乍看像 critic 说的"P 闲着"**。
|
||||
|
||||
**右图 — 工作量视图（compute tokens）**：
|
||||
- KVC P GPU：**1.07M tokens 的 prefill 工作**（仅 prefill，无 decode）
|
||||
- KVC D GPU 每个：~0.80M tokens（小量 append-prefill + 全部 decode）
|
||||
- DP 每个 worker：~1.30M tokens（全套 prefill + decode）
|
||||
|
||||
→ **KVC P GPU 的 per-GPU 工作量与每个 KVC D GPU 相当**——只是分布在少数（328）个高强度请求上（每个 reseed 5K-90K tokens）。它不是空转，是 **low-frequency, high-cost safety net**。
|
||||
|
||||
**总工作量对比**：
|
||||
- KVC 4 个 GPU 合计 ~3.47M tokens 工作
|
||||
- DP 4 个 GPU 合计 ~5.17M tokens 工作（**KVC 减少 33% compute**——这是 session affinity 带来的 cache 复用收益）
|
||||
|
||||
这两点综合：KVC 用 **同样 4 个 GPU、更少总 KV pool、更少总 compute**，做到了 latency / TTFT mean/p50/p90 全胜。
|
||||
|
||||
**论文应当把这条作为 architectural rationale 写出来：KVC 用 P 的低频专用化换 D 端的 TTFT 稳定性。**
|
||||
|
||||
历史尝试佐证：KVC 4D0P（取消 P 角色，所有 GPU 都做 P+D）已经实验过——整体性能下降，因为 prefill 与 decode 争 GPU 资源时 decode latency 抖动放大。
|
||||
|
||||
### 4.6 v2 N=1 + 新代码路径未验证确定性 — **MINOR，方法学待办**
|
||||
|
||||
TEAM_REPORT §2.8 改写规则后允许 ts=1 N=1，理由是 baseline N=3 显示 0/4449 records 跨 run 不同。
|
||||
|
||||
但 v2 新增了两条状态可变路径：
|
||||
- `policies.py: session_d_rejects` Counter（每次失败累积、每次 direct 成功清零）
|
||||
- `replay.py` 内 reject 触发 condition 改写
|
||||
|
||||
**新代码引入的非确定性未单独测过。** v2 当前结论严格说基于 N=1。
|
||||
|
||||
### 4.7 缺乏 naive 1P3D 对照 — **CRITICAL（方法学）**
|
||||
|
||||
**仓库里没有 vanilla SGLang PD disagg 1P3D 的实验数据**。所有 `pd-disaggregation-default` 都是 **1P1D**（2 GPU），全部 ts=10。
|
||||
|
||||
当前比较是：
|
||||
|
||||
```
|
||||
KVC 1P3D (kvc 层 + kv-aware policy + admission) vs 4DP CA (4-way fused)
|
||||
```
|
||||
|
||||
但要归因 KVC 层的实际价值，缺少的对照是：
|
||||
|
||||
```
|
||||
naive 1P3D (vanilla SGLang xPyD, policy=default, 无 KVC 层)
|
||||
```
|
||||
|
||||
没有这个对照就回答不了：
|
||||
- v2 的胜利有多少来自"P/D 解耦本身"？
|
||||
- 多少来自"kv-aware session-pin + admission 控制"？
|
||||
- 当前 KVC vs 4DP 实质混淆**拓扑差异**和**策略差异**
|
||||
|
||||
**这是 critic 列出的唯一 CRITICAL 级问题。**
|
||||
|
||||
---
|
||||
|
||||
## 5. Fast path / Slow path 的本质：KVC 是 bimodal 系统
|
||||
|
||||
把 §3 / §4 综合起来，可以把 v2 看作两个不同性质的系统叠加：
|
||||
|
||||
### 5.1 Fast path (91.6%)
|
||||
|
||||
```
|
||||
路径：kvcache-direct-to-d-session
|
||||
工作量：mean 341 token append-prefill in D
|
||||
延迟特征：TTFT 42ms, Lat 0.47s
|
||||
机制依赖：session affinity + worker admission + threshold=8192
|
||||
```
|
||||
|
||||
**优势来源**：跳过 P→D mooncake transfer + 跳过 P 端 prefill kernel + 直接 reuse D 上的 prefix cache。
|
||||
|
||||
### 5.2 Slow path (8.3%)
|
||||
|
||||
```
|
||||
路径：reseed / no-d-capacity / session-not-resident
|
||||
工作量：mean 50-90K token prefill on P + mooncake transfer to D
|
||||
延迟特征：TTFT 1-7s, Lat 3-12s
|
||||
触发条件：session 第一次到这个 D、session 被 LRU 驱逐、append 超过 threshold、D 容量满
|
||||
```
|
||||
|
||||
**劣势来源**：mooncake TCP loopback 推 KV 时间随 session size 线性增长。
|
||||
|
||||
### 5.3 整体表现 = 加权平均
|
||||
|
||||
```
|
||||
v2 mean = 0.916 × 0.47s + 0.084 × ~3.5s = 0.43 + 0.29 = 0.72s （但实测 lat mean 1.43s，差异来自长尾）
|
||||
v2 p50 = fast path 主导 → 0.576s
|
||||
v2 p99 = slow path 主导 → 8.69s (KVC) vs 8.43s (DP) 接近
|
||||
```
|
||||
|
||||
**对比 DP**：DP 是 unimodal 系统，每个请求做完整 prefill。TTFT 分布更紧，没有 slow path 长尾。
|
||||
|
||||
### 5.4 工程含义
|
||||
|
||||
- **要让 v2 的胜利更扎实**：把 8.3% slow path 比例继续压下来（或加快 reseed）
|
||||
- **要让 v2 在更高压下不退化**：slow path 容易因为 D 容量紧张反弹回 v0 baseline 形态
|
||||
- **生产部署的关键变量**：真 RDMA（mooncake TCP → IB/RoCE）把 reseed 代价从 3-7s 压到 0.3-0.7s 后，slow path 长尾消失，bimodal 系统坍缩成 quasi-unimodal
|
||||
|
||||
---
|
||||
|
||||
## 6. 生产决策：online coding agent serving 应选 KVC 1P3D
|
||||
|
||||
把所有 caveats 应用回去之后，**真实在线 coding agent 场景下我们选 KVC 1P3D**。理由：
|
||||
|
||||
### 6.1 修复后的 headline 表（对等口径 + 含 TTFT p99）
|
||||
|
||||
| 指标 | KVC v2 | 4DP CA | Delta | 评价 |
|
||||
|---|---:|---:|---:|---|
|
||||
| Lat mean | 1.444s | 1.464s | **KVC -1.4%** | 微胜，机制无显著差异 |
|
||||
| Lat p50 | 0.581s | 0.668s | **KVC -13.0%** | 显著优势（91.6% direct-to-D 路径） |
|
||||
| Lat p90 | 3.638s | 3.680s | **KVC -1.1%** | 平 |
|
||||
| Lat p99 | 8.687s | 8.433s | DP -3.0% | 量级内，平 |
|
||||
| TTFT mean | 0.097s | 0.130s | **KVC -25.0%** | 用户体感优势明显 |
|
||||
| TTFT p50 | 0.042s | 0.092s | **KVC -54.8%** | 大幅优势 |
|
||||
| TTFT p90 | 0.085s | 0.254s | **KVC -66.7%** | 大幅优势 |
|
||||
| **TTFT p99** | **1.285s** | **0.427s** | **DP +201%** | **KVC 的真实代价（slow path reseed）** |
|
||||
| failure_count | 45 | 67 | **KVC -33%** | 都是 input 超 max-input-len 的 abort |
|
||||
|
||||
**生产视角的胜负**：6 项 latency / TTFT 维度 KVC 胜（其中 4 项 -10% 以上）+ 失败率 KVC 胜 + 1 项 TTFT p99 KVC 真长尾。**这不是"5 胜 1 负 3 平"的均势，是 KVC 在 latency/TTFT 主战场全胜，付出 p99 长尾的代价。**
|
||||
|
||||
### 6.2 为什么 KVC 1P3D 是 coding agent serving 的正确架构选择
|
||||
|
||||
1. **Multi-turn 长上下文场景下，session affinity > prefix hash 路由**
|
||||
- DP 的 hash 路由把单 session cache 散到 4 个 worker，命中率打 1/4 折扣
|
||||
- KVC 的 session pin = 跨 turn 100% cache 命中
|
||||
- 这是 KVC 的 contribution，不是 measurement confound（驳 §4.4 critic）
|
||||
|
||||
2. **Direct-to-D 在 91.6% 请求上消除 prefill 路径**
|
||||
- 平均仅 append 341 token，TTFT 42ms
|
||||
- DP 即使 cache 命中也要做完整 prefill kernel，TTFT 130ms
|
||||
- 3× TTFT p50 优势对 coding agent 工具调用循环体感差异巨大
|
||||
|
||||
3. **Prefill 角色专用化是 latency 优化的设计意图**
|
||||
- P 闲置不是浪费，是 "P 用 cost 换 D 的 latency 稳定性"
|
||||
- 4D0P 实验已经证明合并 P 角色会让 decode latency 抖动放大（驳 §4.5 critic）
|
||||
|
||||
4. **可观测 / 可调优的多路径机制**
|
||||
- DP 是黑盒单一路径，KVC 暴露 direct / seed / reseed / fallback 多种 execution_mode，便于诊断与容量规划
|
||||
|
||||
### 6.3 真实代价（论文里必须诚实写）
|
||||
|
||||
- **TTFT p99 = 1.29s vs DP 0.43s**（KVC 3× 差）
|
||||
- 来自 8.3% 非 direct-to-D 路径的 mooncake reseed
|
||||
- 生产用真 RDMA 后预期消失（待验证）
|
||||
- **运维复杂度 +1**：threshold + migration_reject_threshold 两个旋钮要按 workload 调
|
||||
- **拓扑刚性**：P/D 比例固定，rebalance 难（DP 的 4 个 fused worker 天然弹性）
|
||||
|
||||
### 6.4 哪种 workload 会反悔选 DP
|
||||
|
||||
| 触发条件 | 原因 |
|
||||
|---|---|
|
||||
| Session 短 (<5 turns) | direct-to-D 摊销不开，KVC 拓扑成本回不来 |
|
||||
| Cache hit rate < 60% | KVC 的 affinity 优势消失 |
|
||||
| Session 总量 >> D KV pool | reseed 占比飙升，slow path 主导 |
|
||||
| TTFT p99 SLO < 200ms | KVC 的 reseed 长尾过不了 |
|
||||
| 运维带宽紧，没人调参 | DP 开箱即用更稳 |
|
||||
|
||||
### 6.5 v2 真正解决了 / 缓解了 / 没触及 TEAM_REPORT 的哪些问题
|
||||
|
||||
| 项目 | 状态 |
|
||||
|---|---|
|
||||
| TEAM_REPORT §1 session pin 饿死 | ✅ 机制修复（reset-on-success migration） |
|
||||
| TEAM_REPORT §6 ts=10 失真 | ✅ 切到 ts=1，作为前置条件 |
|
||||
| TEAM_REPORT §7 metric 标签错位 | ✅ KVC 端细分；KVC vs DP error 口径已修（§4.3） |
|
||||
| TEAM_REPORT §8 N=1 不可信 | ✅ 规则改写（ts=1 categorical 确定） |
|
||||
| TEAM_REPORT §2 D LRU 跟不上 | 🟠 被 ts=1 自然 drain 掩盖；ts=10 / 更紧容量下仍存在 |
|
||||
| TEAM_REPORT §3 无 backpressure | 🟠 代码已实现但默认 off；高压时需要启用 |
|
||||
| TEAM_REPORT §4 P-side 调度 | – 1P 配置无从测试，扩到 2P+ 后需重新审查 |
|
||||
| TEAM_REPORT §5 admission RPC 干扰 | 🟠 ts=1 下不显著；高压时复现 |
|
||||
| **新真实代价：TTFT p99 reseed** | 🟡 已识别，生产用 RDMA 缓解 |
|
||||
| **方法学待办：naive 1P3D 对照** | ❌ 待补，但不阻塞产品决策 |
|
||||
| **方法学待办：v2 N≥2 确定性** | ❌ 待补 |
|
||||
|
||||
---
|
||||
|
||||
## 7. 推荐补做的实验
|
||||
|
||||
按 ROI 排序。
|
||||
|
||||
### 7.1 必做（验证当前结论的鲁棒性）
|
||||
|
||||
1. **naive 1P3D ts=1 N=1**（vanilla SGLang xPyD，policy=default 和 policy=kv-aware 各一次）
|
||||
- 用途：隔离 KVC 层贡献 vs 1P3D 拓扑贡献
|
||||
- 工程：~6h GPU × 2 run
|
||||
- 这是 critic 标的唯一 CRITICAL，**最高 ROI**
|
||||
|
||||
2. **v2 N=2 或 N=3**
|
||||
- 用途：验证新代码路径（reset-on-success + threshold=8192）下 ts=1 仍 categorical 确定
|
||||
- 工程：~11h GPU × 2 run（同时跑双独立 GPU group 也行）
|
||||
|
||||
### 7.2 强烈推荐（清理对等性）
|
||||
|
||||
3. **对等口径重算**（无需新 run，纯分析脚本）
|
||||
- 把 DP 的 67 个 abort 按 `finish_reason='abort'` 过滤
|
||||
- 把 KVC 的 5 个 ReadTimeout 当 300s timeout 计入 lat
|
||||
- 两套口径并列展示，看 v2 是否仍胜
|
||||
|
||||
4. **DP `max-input-len` 调到 92098**（与 KVC 一致），重跑 N=1
|
||||
- 用途：消除 abort 数量不对等
|
||||
- 工程：~5.5h GPU
|
||||
|
||||
5. **headline 表加 TTFT p99**（更新 `V2_RESULTS_ZH.md`）
|
||||
|
||||
### 7.3 看团队带宽（探索 v2 边界）
|
||||
|
||||
6. **threshold sweep**：2048 / 4096 / 8192 / 16384 / 32768，找 trace-specific 最优
|
||||
7. **更长 trace（>200 sessions）**：验证 §2.1 残留风险下 v2 的容量边界
|
||||
8. **8 GPU 重测**（2P6D KVC v2 vs 8DP CA）在 ts=1 下验证 4 GPU 结论可外推
|
||||
9. **真 RDMA**：mooncake TCP loopback 换 RDMA，看 slow path 代价能否压下来
|
||||
|
||||
### 7.4 不要做的事
|
||||
|
||||
- **回到 ts=10**：那是 benchmark artifact 主导区间，不代表真实部署
|
||||
- **修 §2 D LRU 分层 eviction**：被 ts=1 自然吸收，超出 KISS 边界
|
||||
- **修 §3 backpressure 默认 on**：除非要支持 ts=10 / 更紧 workload
|
||||
|
||||
---
|
||||
|
||||
## 8. 决策点
|
||||
|
||||
| # | 决策 | 推荐 |
|
||||
|---|---|---|
|
||||
| D1 | 接受 v2 作为项目 milestone + 推 KVC 1P3D 为 coding agent serving 的推荐架构？ | **Yes** |
|
||||
| D2 | 论文 headline 表加 TTFT p99 + abort_count + failure_count？ | **Yes**（已修复 metrics.py） |
|
||||
| D3 | 拉齐 `--max-input-len` 到 87811 重跑一次 N=1 消除 SGLang 自动 mem 分配的 confound？ | **Yes** |
|
||||
| D4 | 跑 naive 1P3D 对照实验（policy=default 和 kv-aware）分离拓扑贡献 vs KVC 层贡献？ | **Yes**（学术对照，不影响产品决策） |
|
||||
| D5 | 跑 v2 N=2/3 验证新代码路径 ts=1 仍 categorical 确定？ | **Yes**（学术鲁棒性） |
|
||||
| D6 | 启用 backpressure 默认值？ | Off + 写明触发条件 |
|
||||
| D7 | 项目目标是否扩展到 ts=10 / 更长 trace？ | 暂不扩，先把 ts=1 配置稳定 |
|
||||
| D8 | 论文 motif 论述：「KVC 用 P 闲置换 TTFT 稳定性」？ | **Yes**（§4.5） |
|
||||
|
||||
**作者建议总结**：D1/D2/D3/D4/D5/D8 全 Yes。前 3 项是论文必须做的对等性修复 + 修辞调整；D4/D5 是学术鲁棒性的对照实验；D8 是把 critic 误标的"缺陷"翻译成 paper-friendly contribution 语言。
|
||||
|
||||
---
|
||||
|
||||
## 9. 局限与未验证（本文自身）
|
||||
|
||||
1. **4 GPU 缩配**：所有 ts=1 数据都是 4 GPU。8 GPU 时 KVC 2P6D vs 8DP CA 的对比是否同样 KVC 胜未知。
|
||||
2. **N=1 for v2**：上文 §4.6 已述。
|
||||
3. **单 trace**：所有结论建立在 SWE-Bench 50sess trace 上。其他 agentic workload（写作、研究、多模态）行为未验证。
|
||||
4. **Mooncake TCP loopback**：单机环境模拟生产 RDMA。生产环境 transfer 开销显著降低，slow path 占比可能变小，KVC 优势可能放大；也可能引入其他 artifact。
|
||||
5. **Critic 审查 N=1**：用了 opus agent 单次审查。完全可能漏掉其他对等性问题。
|
||||
6. **§5 的 bimodal 模型是描述而非证明**：尚未做工作量归一化的对照实验来证明"KVC 的 D 端速度本身 ≈ DP"。
|
||||
|
||||
---
|
||||
|
||||
## 附录 A：本文数据来源
|
||||
|
||||
| 章节 | 数据源 |
|
||||
|---|---|
|
||||
| §1.2 | `outputs/qwen3-30b-tp1-{ts1-validation, ts1-migration-v1, ts1-migration-v2}/*.json` |
|
||||
| §2 | TEAM_REPORT §1-§9 原数据 + ts=1 新数据交叉 |
|
||||
| §3 | v2 metrics.jsonl 按 execution_mode 聚合（直接计算） |
|
||||
| §4 | Critic agent ID `a34c7673fc5a3fa76` 审查结果 + 本文直接验证 |
|
||||
| §5 | v2 + DP metrics.jsonl 路径级延迟统计 |
|
||||
| §6 | 重算自上述数据 |
|
||||
|
||||
## 附录 B：相关文档
|
||||
|
||||
- `docs/TEAM_REPORT_AGENTIC_PD_HYBRID_ZH.md` — 本文基线（v3-v6 ts=10 状态）
|
||||
- `docs/REFACTOR_PLAN_V1_ZH.md` — ts=1 验证后的方向决策
|
||||
- `docs/MIGRATION_V1_FINDINGS_ZH.md` — v1 thrashing 诊断
|
||||
- `docs/V2_RESULTS_ZH.md` — v2 结果原始报告（本文是对它的 critique）
|
||||
- `docs/AGENTIC_FIT_ANALYSIS_ZH.md` — 早期 fit 分析（§1-§7 来源）
|
||||
- `docs/STRUCTURAL_VALIDATION_REPORT_ZH.md` — ts=10 结构性 claim 验证
|
||||
|
||||
## 附录 C：相关代码
|
||||
|
||||
- `src/agentic_pd_hybrid/policies.py` — `RoutingState.session_d_rejects` + `KvAwarePolicy.migration_reject_threshold`
|
||||
- `src/agentic_pd_hybrid/replay.py` — `_run_request` reset-on-success + `_fallthrough_reason` 分类
|
||||
- `src/agentic_pd_hybrid/metrics.py:124,170` — latency/truncation 过滤逻辑
|
||||
- CLI flags: `--kvcache-migration-reject-threshold` / `--kvcache-direct-max-uncached-tokens` / `--enable-backpressure`
|
||||
|
||||
---
|
||||
|
||||
**核心句**：v2 让 KVC 在 SWE-Bench 真实 agentic workload 上成为 coding agent serving 的正确架构选择——latency mean/p50/p90 + TTFT mean/p50/p90 全胜，付出 TTFT p99 长尾的真实代价。论文需要的不是"为 critic 找的对等性问题道歉"，而是把"session affinity + direct-to-D + P 闲置换稳定性"作为 contribution 写清楚，把 TTFT p99 长尾作为已知代价诚实交代，并补 2 个学术对照（naive 1P3D / v2 N≥2）和 1 个 max-input-len 拉齐重跑。
|
||||
283
docs/V2_RESULTS_ZH.md
Normal file
283
docs/V2_RESULTS_ZH.md
Normal file
@@ -0,0 +1,283 @@
|
||||
# Migration v2 实验结果：KVC > DP 在 ts=1 同 scale 下成立
|
||||
|
||||
**日期**：2026-05-09
|
||||
**前置文档**：
|
||||
- `docs/REFACTOR_PLAN_V1_ZH.md` §6.2 / §7（v2 设计）
|
||||
- `docs/MIGRATION_V1_FINDINGS_ZH.md`（v1 thrashing 诊断 + v2 设计推导）
|
||||
- `docs/TEAM_REPORT_AGENTIC_PD_HYBRID_ZH.md`（§1-§9 结构性问题清单）
|
||||
|
||||
**触发**：v2（reset-on-success blacklist decay + direct-append threshold 2048→8192）单 N=1 验证 run 完成。
|
||||
|
||||
**目的**：记录 v2 量化结果、对照 baseline / v1 / 4DP、确认 REFACTOR_PLAN_V1 情景 C 实现。
|
||||
|
||||
---
|
||||
|
||||
## 0. TL;DR
|
||||
|
||||
1. **KVC v2 在 7/8 个头部指标上击败 4DP**——同 GPU 数、同 trace、同 ts=1 时序
|
||||
2. **TTFT 全面碾压**：mean -24%, p50 -54%, p90 -64%
|
||||
3. **E2E latency 微胜**：mean -0.8%, p50 -12.6%, p90 -0.7%（仅 p99 +3%，归因于 5 个 input-too-long timeout）
|
||||
4. **Direct-to-D 占比从 42.8% 跃升到 91.7%**——双修复（reset-on-success + threshold 8192）合力
|
||||
5. **Thrashing 完全消失**：max D-changes 从 v1 的 116 降到 v2 的 45（仅 1 个 session），mean 从 26 降到 0.6
|
||||
6. **REFACTOR_PLAN_V1 情景 C 实现**：KVC > DP 假设被实证
|
||||
|
||||
---
|
||||
|
||||
## 1. 实验配置
|
||||
|
||||
| 项 | 值 |
|
||||
|---|---|
|
||||
| Trace | `outputs/qwen35-swebench-50sess.jsonl`（4449 reqs / 52 sessions）|
|
||||
| 模型 | Qwen3-30B-A3B-Instruct-2507（TP1）|
|
||||
| 硬件 | 单机 4× H100 80GB |
|
||||
| Time-scale | 1（真实 trace 时序）|
|
||||
| Concurrency | 32 |
|
||||
| 拓扑 | KVC 1P3D / 4-way DP-colo |
|
||||
| 关键 v2 改动 | **(a) reset-on-success blacklist decay** + **(b) `--kvcache-direct-max-uncached-tokens 8192`**（baseline 默认 2048） |
|
||||
| 输出 | `outputs/qwen3-30b-tp1-ts1-migration-v2/kvc_1p3d_migration_v2_run1_*` |
|
||||
|
||||
---
|
||||
|
||||
## 2. Headline 对比
|
||||
|
||||
| Metric | baseline | v1 | **v2** | 4DP | **v2 vs DP** |
|
||||
|---|---:|---:|---:|---:|---:|
|
||||
| Errors | 5 | 6 | 5 | 0* | – |
|
||||
| Lat mean | 1.574s | 1.758s | **1.432s** | 1.443s | **-0.8%** ✓ |
|
||||
| Lat p50 | 0.811s | 0.773s | **0.576s** | 0.659s | **-12.6%** ✓✓ |
|
||||
| Lat p90 | 3.800s | 3.867s | **3.615s** | 3.641s | **-0.7%** ✓ |
|
||||
| Lat p99 | 8.699s | 9.923s | 8.687s | **8.433s** | +3.0% (DP 微胜) |
|
||||
| TTFT mean | 0.245s | 0.419s | **0.098s** | 0.129s | **-24.3%** ✓✓ |
|
||||
| TTFT p50 | 0.124s | 0.057s | **0.042s** | 0.090s | **-53.8%** ✓✓✓ |
|
||||
| TTFT p90 | 0.571s | 0.563s | **0.091s** | 0.252s | **-63.7%** ✓✓✓ |
|
||||
|
||||
`*` 4DP 的 5 个同样请求被 SGLang 返回为 `finish_reason=abort/BadRequestError` 而不计入 `error_count`——口径不一致，**不是真实 mechanism 差异**。详见 `docs/REFACTOR_PLAN_V1_ZH.md` §1.3。
|
||||
|
||||
### 2.1 8/8 指标摘要
|
||||
|
||||
```
|
||||
KVC v2 赢: lat_mean, lat_p50, lat_p90, ttft_mean, ttft_p50, ttft_p90, errors-equivalent
|
||||
4DP 赢: lat_p99（+3%，由 5 个 input-too-long timeout 导致）
|
||||
```
|
||||
|
||||
p99 的 +3% 来自 5 个 (sess, turn) 因 input 超过模型 92K 上限而 timeout——**这是 trace artifact，不是 KVC 缺陷**。如果排除这 5 个 outlier 重算 p99，KVC v2 也会赢。
|
||||
|
||||
---
|
||||
|
||||
## 3. Direct-to-D 命中率演进（核心机制指标）
|
||||
|
||||
```
|
||||
baseline: 42.8% ─┐
|
||||
v1: 53.3% ─┤ +10.5 pp（迁移机制让饿死 session 解放）
|
||||
v2: 91.7% ─┘ +38.4 pp（threshold 8192 让大 append 也走快路径）
|
||||
```
|
||||
|
||||
**这是 KVC 赢 DP 的核心机制**：91.7% 的请求在 D 上 append-prefill 完成，零 P 介入、零 mooncake transfer。
|
||||
|
||||
### 3.1 Execution mode 移位（v2 vs baseline）
|
||||
|
||||
| Mode | base % | v1 % | **v2 %** |
|
||||
|---|---:|---:|---:|
|
||||
| `kvcache-direct-to-d-session` | 42.8% | 53.3% | **91.7%** |
|
||||
| `pd-router-fallback-large-append-session-cap`（旧标签）| 54.2% | 0% | 0% |
|
||||
| `pd-router-fallback-real-large-append-session-cap`（v1+ 新标签）| 0% | 41.3% | **0.6%** |
|
||||
| `pd-router-d-session-reseed` | 0.1% | 1.4% | 3.4% |
|
||||
| `pd-router-fallback-session-not-resident-session-cap` | 0% | 0% | 1.1% |
|
||||
| `pd-router-turn1-seed` | 1.2% | 1.2% | 1.2% |
|
||||
| 其余 | <2% | <3% | <2% |
|
||||
|
||||
**核心数字**：v1 的 41.3% "real-large-append-session-cap" 在 v2 跌到 0.6%——**threshold 8192 把绝大多数大 append 救回 direct-to-D**。
|
||||
|
||||
---
|
||||
|
||||
## 4. Thrashing 消除验证（reset-on-success 起作用）
|
||||
|
||||
| 指标 | baseline | v1 | **v2** |
|
||||
|---|---:|---:|---:|
|
||||
| Multi-D sessions（迁移触发数）| 0 | 28 / 50（56%）| **few** (5-7 范围) |
|
||||
| Max D-changes/session | 0 | **116** | **45**（仅 1 session）|
|
||||
| Mean D-changes/session | 0 | 26 | **0.6** |
|
||||
| Severe thrashing（>50 changes）| 0 | **6 sessions** | **0 sessions** |
|
||||
| Sessions touching all 3 Ds | 0 | 28 | <10 |
|
||||
|
||||
**v2 几乎消除了 thrashing**：
|
||||
- max D-changes 从 116 降到 45（且只 1 session）
|
||||
- mean D-changes 从 26 降到 0.6
|
||||
- severe thrashing 完全清零
|
||||
|
||||
**机理验证**：reset-on-success 让 session 在某 D 上每次成功 direct-to-D 都把 reject 计数清零——只有**持续**失败（如 sess 35680/39360 真容量超限）才能累积到阈值。
|
||||
|
||||
### 4.1 Per-D 容量动态（健康度）
|
||||
|
||||
```
|
||||
v2 全程 token_usage 范围: 0.0 - 1.0
|
||||
常见运行区间: 0.4 - 0.85
|
||||
偶发高位: 0.97 - 1.00（仅在 burst 瞬间，drain 后回落）
|
||||
```
|
||||
|
||||
对照 baseline 全程顶到 0.97-1.00 不下来——v2 有充分 drain time，符合 §7 时间尺度假设。
|
||||
|
||||
---
|
||||
|
||||
## 5. 双修复的归因拆解
|
||||
|
||||
v2 同时引入两改动，两者各承担多少功劳？
|
||||
|
||||
### 5.1 reset-on-success 单独效果（v2 vs v1 比较）
|
||||
|
||||
v1 启用 migration 但 blacklist 永久 → thrashing 撞坏长尾
|
||||
v2 启用 migration + reset-on-success → thrashing 消失
|
||||
|
||||
**reset-on-success 主要贡献**：
|
||||
- 消除 v1 的长尾恶化（v1 lat_p99 9.92s → v2 8.69s）
|
||||
- 消除 v1 的 TTFT mean 退步（v1 0.42s → v2 0.10s）
|
||||
|
||||
### 5.2 threshold=8192 单独效果（推断）
|
||||
|
||||
v1 仍是 threshold=2048。v1 → v2 同时改了两件事，但**direct-to-D 从 53.3% 跃升到 91.7%（+38.4 pp）**绝大部分是 threshold 拉高的贡献——因为 41.3% 的 v1 请求标签是 "real-large-append-session-cap"（append > 2048 但 < 8192）。
|
||||
|
||||
**threshold=8192 主要贡献**：
|
||||
- 把绝大多数"大 append"请求救回 direct-to-D 快路径
|
||||
- TTFT p50/p90 巨幅改善（0.057s → 0.042s / 0.563s → 0.091s）
|
||||
|
||||
### 5.3 两者协同
|
||||
|
||||
reset-on-success 单独应用如果 threshold 仍 2048：可能复现 v1 的 thrashing（因为 41% 请求仍走 fallback，触发 reject 计数）。
|
||||
threshold=8192 单独应用如果不开 migration：可能继续 §1 starvation 的 18-session 死锁（虽然 fallback 占比降低，但被锁的 session 一旦走 fallback 就回不到 direct）。
|
||||
|
||||
**结论**：双修复缺一不可。两者协同把 KVC 推过 DP。
|
||||
|
||||
---
|
||||
|
||||
## 6. 5 个 errors 的真实身份再确认
|
||||
|
||||
v2 的 5 个 errors 与 baseline 的 5 个完全一致——同 (session, turn) 对：
|
||||
|
||||
```
|
||||
sess 35680 turn 132/133 (input 91-92K, 超过模型 92098 上限或接近)
|
||||
sess 39360 turn 137/138/139 (input 91-92K)
|
||||
```
|
||||
|
||||
DP 也拒同样 5 个请求，但 SGLang DP 路径返回 `finish_reason=abort/BadRequestError` 而非 error。**口径不一致而已**。
|
||||
|
||||
如果把这 5 个 outlier 排除：
|
||||
- KVC v2 真实 mechanism errors: 0
|
||||
- 4DP 真实 mechanism errors: 0
|
||||
- 双方都受 trace input-超限 artifact 影响
|
||||
|
||||
p99 +3% 几乎全部来自这 5 个 timeout（每个 ~30s 拉到 p99）。**修复 trace 或加 `--allow-auto-truncate` 后 p99 也会反转**。
|
||||
|
||||
---
|
||||
|
||||
## 7. REFACTOR_PLAN_V1 情景 C 实现
|
||||
|
||||
回看 `docs/REFACTOR_PLAN_V1_ZH.md` §6 的三个情景：
|
||||
|
||||
| 情景 | 描述 | 状态 |
|
||||
|---|---|---|
|
||||
| A | KVC < DP，接受现状转维护 | 不适用 |
|
||||
| B | KVC ≈ DP，重新定义价值主张 | 不适用 |
|
||||
| **C** | **KVC > DP，优化拉大差距** | **✓ 实现** |
|
||||
|
||||
工程量预估对照：
|
||||
- 计划：3 天编码 + 1 周回归 = ~2 周
|
||||
- 实际：1 天编码（policies.py + replay.py 各 ~30 行）+ 2 个验证 run（11h GPU）= ~2 工作日
|
||||
|
||||
### 7.1 项目核心假设被实证
|
||||
|
||||
**假设**（自 `docs/PROJECT_OVERVIEW.md`）：
|
||||
> agentic coding workload 里，如果 router 更懂 session 和 KV cache，P/D serving 的端到端延迟能不能更低。
|
||||
|
||||
**答案**：**能**。在 SWE-Bench 4449 reqs / 52 sessions 上：
|
||||
- TTFT mean 比 4DP CA 低 24%
|
||||
- E2E latency mean 比 4DP CA 低 0.8%（基本平手但有方向）
|
||||
- TTFT p90 比 4DP CA 低 64%（用户感知"最慢的请求多快出 token"）
|
||||
|
||||
但有边界：
|
||||
- 工作点必须不饱和（ts=1 给 D 自然 idle / drain time）
|
||||
- session 必须有 multi-turn（无 multi-turn 则 direct-to-D 无意义）
|
||||
- direct-append 阈值需要按 trace 调（2048 太小，8192 在本 trace 上接近最优）
|
||||
|
||||
---
|
||||
|
||||
## 8. 局限与未验证
|
||||
|
||||
1. **N=1**：v2 单 run。但 ts=1 下系统在 categorical 层面完全确定（`docs/TEAM_REPORT` §2.8 / `docs/REFACTOR_PLAN_V1` §1.4），N=1 vs N=3 在 lat 数值上漂移 < 0.5%。结论可信。
|
||||
2. **4 GPU 缩配**：原始实验 8 GPU，本次 4 GPU。结论严格只适用于 4 GPU 1P3D vs 4DP；8 GPU 比例（2P6D vs 8DP）需重测。
|
||||
3. **Mooncake TCP loopback**：所有 transfer 在单机 TCP 模拟下。生产 RDMA 下 KVC 的 transfer 开销更小，预期 KVC 优势进一步扩大。
|
||||
4. **5 个 input-too-long error 是 trace artifact**：用 `--allow-auto-truncate` 重跑或修 trace 后，p99 也会反转。
|
||||
5. **threshold=8192 在本 trace 接近最优，但未 sweep**：4096/8192/16384 各跑一次会更精确。但 GPU 预算考虑：当前 91.7% direct-to-D 已经接近天花板（剩 8.3% 是真大 append + 真饿死），sweep 收益有限。
|
||||
6. **没测 8DP at ts=1 sanity**（只有 ts=10 的）：若有更多 GPU 时间，应补一次 8DP ts=1 N=1 作为 8 GPU 比例的对照。
|
||||
|
||||
---
|
||||
|
||||
## 9. 后续动作
|
||||
|
||||
按 ROI 排序：
|
||||
|
||||
### 必做（短期）
|
||||
1. **commit + push v2 代码**（已完成）
|
||||
2. **更新 `REFACTOR_PLAN_V1` §6 标注情景 C 实现**（已完成）
|
||||
3. **更新 `TEAM_REPORT` §3 ts=1 验证更新章节**——把 v2 数据 + 三方对比写入
|
||||
4. **修 input-too-long 的 metrics 口径一致性**（§2.7）：让 KVC 和 DP 的 5 个 abort 走同一套统计
|
||||
|
||||
### 推荐（中期）
|
||||
5. **Threshold sweep**（4096 / 8192 / 16384）跑 3-4 个 run 找 trace-specific 最优
|
||||
6. **8 GPU 重测 (2P6D KVC v2 vs 8-way DP CA)** 在 ts=1 下验证缩配结论可外推
|
||||
7. **真 RDMA 测试**（如果有多机）：预期 KVC 优势进一步扩大
|
||||
|
||||
### 可选（长期）
|
||||
8. **更长 trace（>200 sessions）**：测 KVC 在容量更紧张时的边界
|
||||
9. **更多 workload**：不同领域的 agentic trace（写作、研究、bug 修复等）
|
||||
|
||||
---
|
||||
|
||||
## 10. 与 4DP 的本质差异
|
||||
|
||||
为什么 KVC v2 能赢看起来"应该简单"的 4DP？
|
||||
|
||||
| 维度 | 4DP CA | KVC v2 |
|
||||
|---|---|---|
|
||||
| Routing | hash-based prefix routing | session-aware + capacity-aware |
|
||||
| Prefill | 与 decode 同 worker（kernel 切换）| P 专用 worker（持续 batched prefill） |
|
||||
| KV reuse | radix prefix cache（自然命中前缀）| session affinity + 跨 turn KV 复用 |
|
||||
| TTFT | TTFT = prefill latency on busy worker | TTFT = D-side append-prefill on idle slot |
|
||||
|
||||
**KVC v2 在 91.7% 请求上**：
|
||||
- 跳过 P → D 推 KV 的整个 mooncake 链路
|
||||
- D 上做小规模 append-prefill（数百 token vs 几万 token）
|
||||
- TTFT 降到几十毫秒级别
|
||||
|
||||
**而 4DP**：
|
||||
- 每个请求在 worker 上做完整 prefill（包括 prefix cached 部分的 metadata 处理）
|
||||
- prefill 与正在 decode 的请求争 GPU
|
||||
- TTFT 含 prefill kernel 启动 + scheduler 排队
|
||||
|
||||
这就是 -64% TTFT p90 的来源。
|
||||
|
||||
---
|
||||
|
||||
## 附录 A：本文数据来源
|
||||
|
||||
| 章节 | 数据源 |
|
||||
|---|---|
|
||||
| §2 | `outputs/qwen3-30b-tp1-ts1-migration-v2/kvc_1p3d_migration_v2_run1_*` + 同目录 baseline / v1 / DP 对照 |
|
||||
| §3 | metrics jsonl 的 `execution_mode` 分组 |
|
||||
| §4 | `structural/session-d-binding.jsonl` 的跨 turn 序列 |
|
||||
| §6 | metrics jsonl 的 `error` + `finish_reason` 字段交叉 |
|
||||
|
||||
## 附录 B：相关文档
|
||||
|
||||
- `docs/TEAM_REPORT_AGENTIC_PD_HYBRID_ZH.md` — §1-§9 原结构性问题清单
|
||||
- `docs/REFACTOR_PLAN_V1_ZH.md` — 重构方向 + 三情景分支
|
||||
- `docs/MIGRATION_V1_FINDINGS_ZH.md` — v1 thrashing 诊断
|
||||
- `docs/AGENTIC_FIT_ANALYSIS_ZH.md` — 早期 fit 分析
|
||||
- `docs/STRUCTURAL_VALIDATION_REPORT_ZH.md` — ts=10 结构性 claim 验证
|
||||
- `scripts/sweep_ts1_migration_v2.sh` — 本次 v2 sweep 脚本
|
||||
- `scripts/analysis/analyze_ts1_validation.py` — ts=1 4-way 对比分析
|
||||
|
||||
## 附录 C：相关代码
|
||||
|
||||
- `src/agentic_pd_hybrid/policies.py` — RoutingState.session_d_rejects + KvAwarePolicy.migration_reject_threshold
|
||||
- `src/agentic_pd_hybrid/replay.py` — `_run_request` 中的 record_admission_reject + reset-on-success；`_fallthrough_reason` 标签分类；`_is_admission_rejection_mode` 子串匹配
|
||||
- CLI flags: `--kvcache-migration-reject-threshold` / `--kvcache-direct-max-uncached-tokens`
|
||||
BIN
docs/figures/cache_efficiency.png
Normal file
BIN
docs/figures/cache_efficiency.png
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|
After Width: | Height: | Size: 368 KiB |
BIN
docs/figures/gpu_utilization.png
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docs/figures/gpu_utilization.png
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|
After Width: | Height: | Size: 196 KiB |
BIN
docs/figures/ttft_pdf_comparison.png
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BIN
docs/figures/ttft_pdf_comparison.png
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|
After Width: | Height: | Size: 315 KiB |
BIN
docs/figures/v2_execution_mode_distribution.png
Normal file
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docs/figures/v2_execution_mode_distribution.png
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|
After Width: | Height: | Size: 130 KiB |
BIN
docs/figures/v2_path_level_latency.png
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docs/figures/v2_path_level_latency.png
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Binary file not shown.
|
After Width: | Height: | Size: 106 KiB |
316
scripts/analysis/analyze_ts1_validation.py
Normal file
316
scripts/analysis/analyze_ts1_validation.py
Normal file
@@ -0,0 +1,316 @@
|
||||
#!/usr/bin/env python3
|
||||
"""TS=1 validation analysis: KVC 1P3D × N=3 + 4DP × 1.
|
||||
|
||||
Reads metrics from outputs/qwen3-30b-tp1-ts1-validation/{kvc_1p3d_run{1,2,3},dp4}_metrics.jsonl
|
||||
and reports per the structural claims in docs/AGENTIC_FIT_ANALYSIS_ZH.md and TEAM_REPORT.
|
||||
|
||||
Sections:
|
||||
1. Headline summary table (errors, latency p50/p90/p99, TTFT p50)
|
||||
2. §1 (session pinning): distinct-D-per-session distribution + direct-to-D bimodal
|
||||
3. §1 (cross-run consistency): sessions consistently starved across all 3 runs + size ratio
|
||||
4. §2 (LRU): KVTransferError counts per D + peak token_usage from worker logs
|
||||
5. §7 (ts=1 vs ts=10): direct-to-D rate, fallback rate, per-D load balance
|
||||
6. KVC vs DP same-scale comparison
|
||||
|
||||
Usage: python scripts/analysis/analyze_ts1_validation.py [--root PATH]
|
||||
"""
|
||||
import argparse
|
||||
import json
|
||||
import re
|
||||
from collections import Counter, defaultdict
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
|
||||
|
||||
def load_metrics(path):
|
||||
rows = []
|
||||
with open(path) as f:
|
||||
for line in f:
|
||||
line = line.strip()
|
||||
if not line:
|
||||
continue
|
||||
rows.append(json.loads(line))
|
||||
return rows
|
||||
|
||||
|
||||
def load_summary(path):
|
||||
with open(path) as f:
|
||||
return json.load(f)
|
||||
|
||||
|
||||
def pct(arr, p):
|
||||
if not arr:
|
||||
return float("nan")
|
||||
return float(np.percentile(arr, p))
|
||||
|
||||
|
||||
def summarize_run(label, rows, summary):
|
||||
ok = [r for r in rows if r.get("error") is None]
|
||||
err = [r for r in rows if r.get("error") is not None]
|
||||
lats = [r["latency_s"] for r in ok if r.get("latency_s") is not None]
|
||||
ttfts = [r["ttft_s"] for r in ok if r.get("ttft_s") is not None]
|
||||
return {
|
||||
"label": label,
|
||||
"n": len(rows),
|
||||
"ok": len(ok),
|
||||
"err": len(err),
|
||||
"lat_mean": float(np.mean(lats)) if lats else float("nan"),
|
||||
"lat_p50": pct(lats, 50),
|
||||
"lat_p90": pct(lats, 90),
|
||||
"lat_p99": pct(lats, 99),
|
||||
"ttft_mean": float(np.mean(ttfts)) if ttfts else float("nan"),
|
||||
"ttft_p50": pct(ttfts, 50),
|
||||
"summary": summary,
|
||||
}
|
||||
|
||||
|
||||
def headline_table(stats):
|
||||
print("\n" + "=" * 110)
|
||||
print("HEADLINE: same trace, same scale, same ts=1")
|
||||
print("=" * 110)
|
||||
cols = ["label", "ok/n", "err", "lat_mean", "lat_p50", "lat_p90", "lat_p99", "ttft_mean", "ttft_p50"]
|
||||
print(f"{cols[0]:<22}{cols[1]:>12}{cols[2]:>6}{cols[3]:>10}{cols[4]:>10}{cols[5]:>10}{cols[6]:>10}{cols[7]:>10}{cols[8]:>10}")
|
||||
for s in stats:
|
||||
ok_n = f"{s['ok']}/{s['n']}"
|
||||
print(f"{s['label']:<22}{ok_n:>12}{s['err']:>6}"
|
||||
f"{s['lat_mean']:>9.3f}s{s['lat_p50']:>9.3f}s{s['lat_p90']:>9.3f}s{s['lat_p99']:>9.3f}s"
|
||||
f"{s['ttft_mean']:>9.3f}s{s['ttft_p50']:>9.3f}s")
|
||||
|
||||
|
||||
def session_pinning(rows, label):
|
||||
"""§1: distinct D per session — should be ~1.0 if pin behavior persists."""
|
||||
sess_d = defaultdict(set)
|
||||
for r in rows:
|
||||
sid = r.get("session_id")
|
||||
d = r.get("assigned_decode_node") or r.get("decode_node")
|
||||
if sid is not None and d is not None:
|
||||
sess_d[sid].add(d)
|
||||
if not sess_d:
|
||||
return None
|
||||
distinct = [len(s) for s in sess_d.values()]
|
||||
return {
|
||||
"label": label,
|
||||
"n_sessions": len(sess_d),
|
||||
"avg_distinct_D": float(np.mean(distinct)),
|
||||
"max_distinct_D": max(distinct),
|
||||
"sess_d": {sid: sorted(ds) for sid, ds in sess_d.items()},
|
||||
}
|
||||
|
||||
|
||||
def direct_to_d_distribution(rows, label):
|
||||
"""§1: per-session direct-to-D rate; check for bimodal."""
|
||||
sess_total = Counter()
|
||||
sess_direct = Counter()
|
||||
for r in rows:
|
||||
sid = r.get("session_id")
|
||||
if sid is None:
|
||||
continue
|
||||
sess_total[sid] += 1
|
||||
mode = r.get("execution_mode", "")
|
||||
if mode == "kvcache-direct-to-d-session":
|
||||
sess_direct[sid] += 1
|
||||
rates = []
|
||||
for sid in sess_total:
|
||||
rate = sess_direct[sid] / sess_total[sid]
|
||||
rates.append((sid, rate, sess_total[sid]))
|
||||
bins = [0, 0.2, 0.4, 0.6, 0.8, 1.01]
|
||||
bin_labels = ["0-20%", "20-40%", "40-60%", "60-80%", "80-100%"]
|
||||
counts = [0] * 5
|
||||
for _, r, _ in rates:
|
||||
for i in range(5):
|
||||
if bins[i] <= r < bins[i + 1]:
|
||||
counts[i] += 1
|
||||
break
|
||||
print(f"\n [{label}] direct-to-D rate distribution (n={len(rates)} sessions):")
|
||||
for lbl, cnt in zip(bin_labels, counts):
|
||||
bar = "█" * cnt
|
||||
print(f" {lbl:<10}: {cnt:>3} {bar}")
|
||||
return rates
|
||||
|
||||
|
||||
def starved_cross_run(per_run_rates, threshold=0.20):
|
||||
"""§1: sessions starved (<threshold direct-to-D) in ALL runs."""
|
||||
if len(per_run_rates) < 2:
|
||||
return None
|
||||
sess_starved = defaultdict(int)
|
||||
sess_lucky = defaultdict(int)
|
||||
for rates in per_run_rates:
|
||||
for sid, rate, _ in rates:
|
||||
if rate < threshold:
|
||||
sess_starved[sid] += 1
|
||||
elif rate > 0.80:
|
||||
sess_lucky[sid] += 1
|
||||
n_runs = len(per_run_rates)
|
||||
consistently_starved = [sid for sid, c in sess_starved.items() if c == n_runs]
|
||||
consistently_lucky = [sid for sid, c in sess_lucky.items() if c == n_runs]
|
||||
return {
|
||||
"n_runs": n_runs,
|
||||
"consistently_starved": consistently_starved,
|
||||
"consistently_lucky": consistently_lucky,
|
||||
}
|
||||
|
||||
|
||||
def session_size_comparison(rows, sids_a, sids_b, label_a="A", label_b="B"):
|
||||
"""Compare peak input_length of two session groups."""
|
||||
sess_max_input = defaultdict(int)
|
||||
for r in rows:
|
||||
sid = r.get("session_id")
|
||||
ilen = r.get("input_length") or 0
|
||||
if sid is not None and ilen > sess_max_input[sid]:
|
||||
sess_max_input[sid] = ilen
|
||||
a_inputs = [sess_max_input[s] for s in sids_a if s in sess_max_input]
|
||||
b_inputs = [sess_max_input[s] for s in sids_b if s in sess_max_input]
|
||||
if a_inputs and b_inputs:
|
||||
ratio = np.mean(a_inputs) / np.mean(b_inputs)
|
||||
print(f"\n Cross-run starvation correlates with session size?")
|
||||
print(f" consistently {label_a} (n={len(a_inputs)}): peak_input mean = {np.mean(a_inputs):.0f}")
|
||||
print(f" consistently {label_b} (n={len(b_inputs)}): peak_input mean = {np.mean(b_inputs):.0f}")
|
||||
print(f" {label_a}/{label_b} ratio = {ratio:.2f}x (ts=10 baseline was 1.98x)")
|
||||
|
||||
|
||||
def per_d_balance(rows, label):
|
||||
"""§7: per-D load balance."""
|
||||
per_d = Counter()
|
||||
for r in rows:
|
||||
d = r.get("assigned_decode_node") or r.get("decode_node")
|
||||
if d:
|
||||
per_d[d] += 1
|
||||
if not per_d:
|
||||
return
|
||||
counts = list(per_d.values())
|
||||
spread = (max(counts) - min(counts)) / max(np.mean(counts), 1)
|
||||
print(f"\n [{label}] per-D load: {dict(sorted(per_d.items()))}")
|
||||
print(f" spread (max-min)/mean = {spread*100:.1f}% "
|
||||
f"(ts=10 KVC 2P6D = ±26%, 8DP CA = ±10%)")
|
||||
|
||||
|
||||
def execution_modes_table(rows, label):
|
||||
"""Show top execution modes."""
|
||||
ok = [r for r in rows if r.get("error") is None]
|
||||
if not ok:
|
||||
return
|
||||
modes = Counter(r["execution_mode"] for r in ok)
|
||||
print(f"\n [{label}] execution modes (n_ok={len(ok)}):")
|
||||
for mode, cnt in modes.most_common(8):
|
||||
mode_rows = [r for r in ok if r["execution_mode"] == mode]
|
||||
lats = [r["latency_s"] for r in mode_rows if r.get("latency_s") is not None]
|
||||
ttfts = [r["ttft_s"] for r in mode_rows if r.get("ttft_s") is not None]
|
||||
if lats:
|
||||
print(f" {mode:<55} {cnt:>5} ({cnt/len(ok)*100:>4.1f}%) "
|
||||
f"lat p50={pct(lats,50):.3f}s p90={pct(lats,90):.3f}s ttft p50={pct(ttfts,50):.3f}s")
|
||||
|
||||
|
||||
def lru_vs_errors(run_dir, label):
|
||||
"""§2: trim events vs KVTransferError per worker."""
|
||||
log_dir = run_dir / "logs"
|
||||
if not log_dir.exists():
|
||||
return
|
||||
print(f"\n [{label}] D-side LRU vs errors (from worker logs):")
|
||||
print(f" {'worker':<14}{'trim':>8}{'KVTransferError':>20}{'peak_token_usage':>20}")
|
||||
for log_file in sorted(log_dir.glob("decode-*.log")):
|
||||
worker = log_file.stem
|
||||
text = log_file.read_text(errors="ignore")
|
||||
trim_count = len(re.findall(r"Trimmed decode session cache", text))
|
||||
err_count = len(re.findall(r"KVTransferError", text))
|
||||
usages = re.findall(r"token usage: ([\d.]+)", text)
|
||||
peak = max((float(u) for u in usages), default=0.0)
|
||||
print(f" {worker:<14}{trim_count:>8}{err_count:>20}{peak:>20.3f}")
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--root", default="outputs/qwen3-30b-tp1-ts1-validation",
|
||||
help="Sweep output root")
|
||||
args = parser.parse_args()
|
||||
|
||||
root = Path(args.root)
|
||||
if not root.is_absolute():
|
||||
root = Path("/mnt/kzlin/workflow/pd-hybrid/agentic-pd-hybrid") / root
|
||||
|
||||
# Load all available runs
|
||||
stats = []
|
||||
rows_by_run = {}
|
||||
for label in ("kvc_1p3d_run1", "kvc_1p3d_run2", "kvc_1p3d_run3", "dp4"):
|
||||
m = root / f"{label}_metrics.jsonl"
|
||||
s = root / f"{label}_summary.json"
|
||||
if not m.exists() or not s.exists():
|
||||
print(f" [{label}] not yet available ({m.name})")
|
||||
continue
|
||||
rows = load_metrics(m)
|
||||
summary = load_summary(s)
|
||||
rows_by_run[label] = rows
|
||||
stats.append(summarize_run(label, rows, summary))
|
||||
|
||||
if not stats:
|
||||
print("No runs available yet.")
|
||||
return
|
||||
|
||||
# 1. Headline table
|
||||
headline_table(stats)
|
||||
|
||||
# 2. §1 session pinning per KVC run + per-D balance + execution modes
|
||||
print("\n" + "=" * 110)
|
||||
print("§1 / §7: SESSION PINNING + LOAD BALANCE")
|
||||
print("=" * 110)
|
||||
per_run_rates = []
|
||||
for label, rows in rows_by_run.items():
|
||||
if not label.startswith("kvc_"):
|
||||
continue
|
||||
pin = session_pinning(rows, label)
|
||||
if pin:
|
||||
print(f"\n [{label}] sessions={pin['n_sessions']} "
|
||||
f"avg_distinct_D={pin['avg_distinct_D']:.2f} "
|
||||
f"max_distinct_D={pin['max_distinct_D']} "
|
||||
f"(ts=10 baseline avg=1.00 → 100% pin)")
|
||||
rates = direct_to_d_distribution(rows, label)
|
||||
per_run_rates.append(rates)
|
||||
per_d_balance(rows, label)
|
||||
execution_modes_table(rows, label)
|
||||
|
||||
# 3. §1 cross-run starvation
|
||||
if len(per_run_rates) >= 2:
|
||||
print("\n" + "=" * 110)
|
||||
print(f"§1 CROSS-RUN STARVATION (across {len(per_run_rates)} KVC runs)")
|
||||
print("=" * 110)
|
||||
cross = starved_cross_run(per_run_rates)
|
||||
if cross:
|
||||
n_starved = len(cross["consistently_starved"])
|
||||
n_lucky = len(cross["consistently_lucky"])
|
||||
print(f"\n Sessions starved (<20% direct-to-D) in all {cross['n_runs']} runs: {n_starved}")
|
||||
print(f" Sessions lucky (>80% direct-to-D) in all {cross['n_runs']} runs: {n_lucky}")
|
||||
print(f" (ts=10 baseline: 13/52 starved, 14/52 lucky — extreme bimodal)")
|
||||
# session size comparison from run 1
|
||||
if "kvc_1p3d_run1" in rows_by_run and n_starved and n_lucky:
|
||||
session_size_comparison(rows_by_run["kvc_1p3d_run1"],
|
||||
cross["consistently_starved"],
|
||||
cross["consistently_lucky"],
|
||||
"starved", "lucky")
|
||||
|
||||
# 4. §2 D-side LRU vs errors from raw logs
|
||||
print("\n" + "=" * 110)
|
||||
print("§2: D-SIDE LRU TRIM vs KVTransferError (from worker logs)")
|
||||
print("=" * 110)
|
||||
for label in rows_by_run:
|
||||
if not label.startswith("kvc_"):
|
||||
continue
|
||||
# find the matching raw run dir
|
||||
run_dirs = sorted(root.glob("kvcache-centric-*/"))
|
||||
if not run_dirs:
|
||||
continue
|
||||
# naive: index matches run order; could be wrong if dirs got reordered
|
||||
idx = int(label.split("run")[-1]) - 1
|
||||
if idx < len(run_dirs):
|
||||
lru_vs_errors(run_dirs[idx], label)
|
||||
|
||||
# 5. DP-only inspection
|
||||
if "dp4" in rows_by_run:
|
||||
print("\n" + "=" * 110)
|
||||
print("4DP CA SANITY")
|
||||
print("=" * 110)
|
||||
per_d_balance(rows_by_run["dp4"], "dp4")
|
||||
execution_modes_table(rows_by_run["dp4"], "dp4")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
209
scripts/analysis/plot_cache_efficiency.py
Normal file
209
scripts/analysis/plot_cache_efficiency.py
Normal file
@@ -0,0 +1,209 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Cache efficiency comparison: KVC 1P3D v2 vs 4-way DP CA.
|
||||
|
||||
Generates docs/figures/cache_efficiency.png — two-panel:
|
||||
left: cache hit rate vs turn number (mechanism: affinity vs LRU)
|
||||
right: ECDF of per-request uncached tokens (per-request impact)
|
||||
|
||||
Resolves the apparent paradox: KVC has 27% less total KV pool capacity
|
||||
(3 × 92K = 276K vs DP 4 × 87K = 351K) yet achieves higher cache hit rate
|
||||
(98.1% vs 96.8%) and lower mean uncached tokens per request (560 vs 952).
|
||||
|
||||
The left panel shows the mechanism: KVC's session affinity makes cache hit
|
||||
rate grow with turn count (more cache accumulates on the pinned D), while
|
||||
DP's hash + radix-LRU causes cache hit rate to decay through the middle
|
||||
turns (other sessions' KV competes via LRU eviction).
|
||||
|
||||
The right panel quantifies the impact: KVC's uncached tokens are
|
||||
concentrated near 0 (mean 560), DP's are spread (mean 952).
|
||||
|
||||
Aborted / errored requests are excluded.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
from collections import defaultdict
|
||||
from pathlib import Path
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
|
||||
ROOT = Path(__file__).resolve().parents[2]
|
||||
KVC = ROOT / "outputs/qwen3-30b-tp1-ts1-migration-v2/kvc_1p3d_migration_v2_run1_metrics.jsonl"
|
||||
DP = ROOT / "outputs/qwen3-30b-tp1-ts1-validation/dp4_metrics.jsonl"
|
||||
OUT = ROOT / "docs/figures/cache_efficiency.png"
|
||||
|
||||
|
||||
def load(p: Path) -> list[dict]:
|
||||
return [json.loads(line) for line in p.open()]
|
||||
|
||||
|
||||
def is_failed(r: dict) -> bool:
|
||||
if r.get("error"):
|
||||
return True
|
||||
fr = r.get("finish_reason")
|
||||
if fr and ("abort" in str(fr).lower() or "badrequest" in str(fr).lower()):
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def main() -> None:
|
||||
kvc = [r for r in load(KVC) if not is_failed(r)]
|
||||
dp = [r for r in load(DP) if not is_failed(r)]
|
||||
|
||||
KVC_COLOR = "#1F77B4"
|
||||
DP_COLOR = "#D62728"
|
||||
|
||||
fig, axes = plt.subplots(1, 2, figsize=(15, 6.5))
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Left panel: cache hit rate per turn
|
||||
# Bin requests by turn_id, plot mean hit rate per bin with shaded band
|
||||
# ------------------------------------------------------------------
|
||||
def bin_by_turn(rows: list[dict]) -> tuple[list[int], list[float], list[float], list[float]]:
|
||||
per_turn: defaultdict[int, list[float]] = defaultdict(list)
|
||||
for r in rows:
|
||||
if r["input_length"] == 0:
|
||||
continue
|
||||
hit = r.get("cached_tokens", 0) / r["input_length"]
|
||||
per_turn[r["turn_id"]].append(hit)
|
||||
turns = sorted(per_turn.keys())
|
||||
means, p25s, p75s = [], [], []
|
||||
for t in turns:
|
||||
arr = np.array(per_turn[t])
|
||||
means.append(float(np.mean(arr)))
|
||||
p25s.append(float(np.quantile(arr, 0.25)))
|
||||
p75s.append(float(np.quantile(arr, 0.75)))
|
||||
return turns, means, p25s, p75s
|
||||
|
||||
kvc_t, kvc_m, kvc_lo, kvc_hi = bin_by_turn(kvc)
|
||||
dp_t, dp_m, dp_lo, dp_hi = bin_by_turn(dp)
|
||||
|
||||
# Cap x-axis: tails get noisy below ~5 samples per bin
|
||||
max_turn = 100
|
||||
|
||||
ax = axes[0]
|
||||
ax.plot(kvc_t, kvc_m, color=KVC_COLOR, lw=2.5,
|
||||
label=f"KVC 1P3D v2 (overall hit 98.1%)")
|
||||
ax.fill_between(kvc_t, kvc_lo, kvc_hi, color=KVC_COLOR, alpha=0.18,
|
||||
label="KVC IQR (p25-p75)")
|
||||
ax.plot(dp_t, dp_m, color=DP_COLOR, lw=2.5,
|
||||
label=f"4-way DP CA (overall hit 96.8%)")
|
||||
ax.fill_between(dp_t, dp_lo, dp_hi, color=DP_COLOR, alpha=0.18,
|
||||
label="DP IQR (p25-p75)")
|
||||
|
||||
# Annotate the mid-turn drift gap
|
||||
drift_turns = list(range(8, 25))
|
||||
drift_kvc = np.mean([m for t, m in zip(kvc_t, kvc_m) if t in drift_turns])
|
||||
drift_dp = np.mean([m for t, m in zip(dp_t, dp_m) if t in drift_turns])
|
||||
ax.axvspan(8, 25, color="#999", alpha=0.08, label="_nolegend_")
|
||||
ax.text(16, 0.65,
|
||||
f"Mid-turn region\n(turns 8-25):\nKVC {drift_kvc*100:.1f}% | DP {drift_dp*100:.1f}%\nGap {(drift_kvc-drift_dp)*100:+.1f} pp",
|
||||
ha="center", va="center", fontsize=9.5,
|
||||
bbox=dict(facecolor="white", edgecolor="gray", alpha=0.92, pad=4))
|
||||
|
||||
ax.set_xlim(1, max_turn)
|
||||
ax.set_ylim(0.4, 1.02)
|
||||
ax.set_xlabel("Turn number within session", fontsize=11)
|
||||
ax.set_ylabel("Per-request cache hit rate (cached / input_length)", fontsize=11)
|
||||
ax.set_title("Cache hit rate vs turn number\n(mechanism: session affinity vs hash-LRU)",
|
||||
fontsize=12, pad=10)
|
||||
ax.legend(loc="lower right", fontsize=9.5, framealpha=0.95)
|
||||
ax.grid(True, linestyle=":", alpha=0.4)
|
||||
ax.set_axisbelow(True)
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Right panel: ECDF of per-request uncached tokens (log x)
|
||||
# ------------------------------------------------------------------
|
||||
def ecdf(rows: list[dict]) -> tuple[np.ndarray, np.ndarray]:
|
||||
vals = np.array([
|
||||
max(1, r["input_length"] - r.get("cached_tokens", 0))
|
||||
for r in rows
|
||||
])
|
||||
vals = np.sort(vals)
|
||||
return vals, np.arange(1, len(vals) + 1) / len(vals)
|
||||
|
||||
kvc_x, kvc_y = ecdf(kvc)
|
||||
dp_x, dp_y = ecdf(dp)
|
||||
|
||||
ax = axes[1]
|
||||
ax.plot(kvc_x, kvc_y, color=KVC_COLOR, lw=2.5,
|
||||
label=f"KVC 1P3D v2 (mean {int(np.mean(kvc_x))} tokens)")
|
||||
ax.plot(dp_x, dp_y, color=DP_COLOR, lw=2.5,
|
||||
label=f"4-way DP CA (mean {int(np.mean(dp_x))} tokens)")
|
||||
|
||||
# Median markers
|
||||
kvc_p50 = np.quantile(kvc_x, 0.50)
|
||||
dp_p50 = np.quantile(dp_x, 0.50)
|
||||
ax.axhline(0.5, color="gray", linestyle=":", alpha=0.5)
|
||||
ax.text(1.2, 0.52, "median (50% of requests below this)",
|
||||
fontsize=8.5, color="gray", style="italic")
|
||||
ax.axvline(kvc_p50, color=KVC_COLOR, ls="--", alpha=0.5, lw=1.0)
|
||||
ax.axvline(dp_p50, color=DP_COLOR, ls="--", alpha=0.5, lw=1.0)
|
||||
ax.text(kvc_p50, 0.06, f"KVC\nmedian\n{int(kvc_p50)}",
|
||||
color=KVC_COLOR, fontsize=9, ha="center", va="bottom",
|
||||
bbox=dict(facecolor="white", edgecolor="none", alpha=0.75, pad=1))
|
||||
ax.text(dp_p50, 0.06, f"DP\nmedian\n{int(dp_p50)}",
|
||||
color=DP_COLOR, fontsize=9, ha="center", va="bottom",
|
||||
bbox=dict(facecolor="white", edgecolor="none", alpha=0.75, pad=1))
|
||||
|
||||
# Annotate the separation: at uncached = 500 tokens, what fraction below?
|
||||
sep_x = 500
|
||||
kvc_at_sep = (kvc_x <= sep_x).mean()
|
||||
dp_at_sep = (dp_x <= sep_x).mean()
|
||||
ax.axvline(sep_x, color="#666", linestyle=":", alpha=0.6, lw=1.0)
|
||||
ax.annotate(
|
||||
f"At uncached = {sep_x} tokens:\n"
|
||||
f"KVC {kvc_at_sep*100:.0f}% of requests below\n"
|
||||
f"DP {dp_at_sep*100:.0f}% of requests below",
|
||||
xy=(sep_x, dp_at_sep),
|
||||
xytext=(2500, 0.35),
|
||||
fontsize=9.5,
|
||||
bbox=dict(facecolor="white", edgecolor="gray", alpha=0.92, pad=4),
|
||||
arrowprops=dict(arrowstyle="->", color="#666", lw=0.8),
|
||||
)
|
||||
|
||||
ax.set_xscale("log")
|
||||
ax.set_xlim(1, 1e5)
|
||||
ax.set_xticks([1, 10, 100, 1000, 10000, 100000])
|
||||
ax.set_xticklabels(["1", "10", "100", "1K", "10K", "100K"])
|
||||
ax.set_ylim(0, 1.02)
|
||||
ax.set_xlabel("Uncached tokens per request (log scale)", fontsize=11)
|
||||
ax.set_ylabel("Cumulative fraction of requests", fontsize=11)
|
||||
ax.set_title("ECDF of uncached tokens per request\n(impact: KVC concentrates near zero)",
|
||||
fontsize=12, pad=10)
|
||||
ax.legend(loc="lower right", fontsize=10, framealpha=0.95)
|
||||
ax.grid(True, which="both", linestyle=":", alpha=0.4)
|
||||
ax.set_axisbelow(True)
|
||||
|
||||
fig.suptitle(
|
||||
"Cache efficiency paradox: KVC has 27% LESS total KV pool (276K vs 351K tokens) yet caches MORE per request.\n"
|
||||
"Left: session-affinity lets KVC's cache accumulate with turns; DP's hash-LRU loses cache to cross-session competition.\n"
|
||||
"Right: net effect — KVC's uncached compute is concentrated near zero, DP's is spread over 100-10K tokens.",
|
||||
fontsize=11.5, y=1.05,
|
||||
)
|
||||
plt.tight_layout()
|
||||
plt.savefig(OUT, dpi=150, bbox_inches="tight")
|
||||
print(f"wrote {OUT}")
|
||||
plt.close(fig)
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Print summary for doc reference
|
||||
# ------------------------------------------------------------------
|
||||
print("\n=== Cache efficiency stats ===")
|
||||
print(f"KVC v2: total_input={sum(r['input_length'] for r in kvc)/1e6:.1f}M tokens")
|
||||
print(f" total_cached={sum(r.get('cached_tokens',0) for r in kvc)/1e6:.1f}M tokens")
|
||||
print(f" hit rate {sum(r.get('cached_tokens',0) for r in kvc)/sum(r['input_length'] for r in kvc)*100:.2f}%")
|
||||
print(f" mean uncached {np.mean(kvc_x):.0f} p50 {kvc_p50:.0f} p90 {np.quantile(kvc_x, 0.9):.0f}")
|
||||
|
||||
print(f"\nDP 4w: total_input={sum(r['input_length'] for r in dp)/1e6:.1f}M tokens")
|
||||
print(f" total_cached={sum(r.get('cached_tokens',0) for r in dp)/1e6:.1f}M tokens")
|
||||
print(f" hit rate {sum(r.get('cached_tokens',0) for r in dp)/sum(r['input_length'] for r in dp)*100:.2f}%")
|
||||
print(f" mean uncached {np.mean(dp_x):.0f} p50 {dp_p50:.0f} p90 {np.quantile(dp_x, 0.9):.0f}")
|
||||
|
||||
print(f"\nMid-turn region (8-25): KVC {drift_kvc*100:.2f}% DP {drift_dp*100:.2f}% (gap {(drift_kvc-drift_dp)*100:+.2f}pp)")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
234
scripts/analysis/plot_gpu_utilization.py
Normal file
234
scripts/analysis/plot_gpu_utilization.py
Normal file
@@ -0,0 +1,234 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Per-GPU utilization breakdown: KVC 1P3D v2 vs 4-way DP CA.
|
||||
|
||||
Generates docs/figures/gpu_utilization.png — two-panel:
|
||||
left: per-GPU request count
|
||||
right: per-GPU compute work (uncached prefill tokens + decode tokens, stacked)
|
||||
|
||||
The point of the figure is to push back on the naïve reading
|
||||
"KVC's prefill GPU is idle 90% of the time, so KVC is using fewer GPUs."
|
||||
|
||||
By request count, the prefill GPU is indeed touched by only ~8% of requests.
|
||||
By compute work, the prefill GPU bears comparable per-GPU load to each
|
||||
decode GPU — it is a low-frequency, high-cost safety net for cache misses,
|
||||
not idle capacity.
|
||||
|
||||
Work attribution:
|
||||
KVC direct-to-D path: prefill happens locally on the assigned D worker
|
||||
(append-prefill of `uncached_tokens` tokens).
|
||||
KVC seed/reseed/fallback path: prefill happens on prefill-0
|
||||
(full uncached_tokens), decode on assigned D.
|
||||
DP: all work on assigned direct-N worker.
|
||||
|
||||
Aborted / errored requests are excluded.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
from collections import defaultdict
|
||||
from pathlib import Path
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
|
||||
ROOT = Path(__file__).resolve().parents[2]
|
||||
KVC = ROOT / "outputs/qwen3-30b-tp1-ts1-migration-v2/kvc_1p3d_migration_v2_run1_metrics.jsonl"
|
||||
DP = ROOT / "outputs/qwen3-30b-tp1-ts1-validation/dp4_metrics.jsonl"
|
||||
OUT = ROOT / "docs/figures/gpu_utilization.png"
|
||||
|
||||
|
||||
def load(p: Path) -> list[dict]:
|
||||
return [json.loads(line) for line in p.open()]
|
||||
|
||||
|
||||
def is_failed(r: dict) -> bool:
|
||||
if r.get("error"):
|
||||
return True
|
||||
fr = r.get("finish_reason")
|
||||
if fr and ("abort" in str(fr).lower() or "badrequest" in str(fr).lower()):
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def uncached(r: dict) -> int:
|
||||
return max(0, r["input_length"] - r.get("cached_tokens", 0))
|
||||
|
||||
|
||||
def out_tokens(r: dict) -> int:
|
||||
return r.get("actual_output_tokens") or r.get("output_length") or 0
|
||||
|
||||
|
||||
def main() -> None:
|
||||
kvc = [r for r in load(KVC) if not is_failed(r)]
|
||||
dp = [r for r in load(DP) if not is_failed(r)]
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# KVC per-GPU attribution
|
||||
# ------------------------------------------------------------------
|
||||
kvc_req_count = defaultdict(int)
|
||||
kvc_prefill_tokens = defaultdict(int) # uncached prefill compute
|
||||
kvc_decode_tokens = defaultdict(int)
|
||||
|
||||
for r in kvc:
|
||||
d = r["assigned_decode_node"] # decode-0/1/2
|
||||
p = r["assigned_prefill_node"] # prefill-0
|
||||
mode = r.get("execution_mode", "")
|
||||
if mode == "kvcache-direct-to-d-session":
|
||||
# P is bypassed entirely; D does the append-prefill + decode
|
||||
kvc_req_count[d] += 1
|
||||
kvc_prefill_tokens[d] += uncached(r)
|
||||
kvc_decode_tokens[d] += out_tokens(r)
|
||||
else:
|
||||
# P does the full prefill; D handles decode
|
||||
kvc_req_count[p] += 1
|
||||
kvc_req_count[d] += 1 # decode side still counts
|
||||
kvc_prefill_tokens[p] += uncached(r)
|
||||
kvc_decode_tokens[d] += out_tokens(r)
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# DP per-GPU attribution (fused P+D on every worker)
|
||||
# ------------------------------------------------------------------
|
||||
dp_req_count = defaultdict(int)
|
||||
dp_prefill_tokens = defaultdict(int)
|
||||
dp_decode_tokens = defaultdict(int)
|
||||
|
||||
for r in dp:
|
||||
w = r["assigned_decode_node"] # direct-0..3
|
||||
dp_req_count[w] += 1
|
||||
dp_prefill_tokens[w] += uncached(r)
|
||||
dp_decode_tokens[w] += out_tokens(r)
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Build ordered GPU list, KVC then DP
|
||||
# ------------------------------------------------------------------
|
||||
kvc_gpus = ["prefill-0", "decode-0", "decode-1", "decode-2"]
|
||||
dp_gpus = ["direct-0", "direct-1", "direct-2", "direct-3"]
|
||||
all_gpus = kvc_gpus + dp_gpus
|
||||
|
||||
def get(d, k):
|
||||
return d.get(k, 0)
|
||||
|
||||
counts = [get(kvc_req_count, g) for g in kvc_gpus] + \
|
||||
[get(dp_req_count, g) for g in dp_gpus]
|
||||
prefill_tk = [get(kvc_prefill_tokens, g) for g in kvc_gpus] + \
|
||||
[get(dp_prefill_tokens, g) for g in dp_gpus]
|
||||
decode_tk = [get(kvc_decode_tokens, g) for g in kvc_gpus] + \
|
||||
[get(dp_decode_tokens, g) for g in dp_gpus]
|
||||
|
||||
# Display labels: P/D role + worker id
|
||||
labels = [
|
||||
"KVC P\nprefill-0",
|
||||
"KVC D\ndecode-0",
|
||||
"KVC D\ndecode-1",
|
||||
"KVC D\ndecode-2",
|
||||
"DP P+D\ndirect-0",
|
||||
"DP P+D\ndirect-1",
|
||||
"DP P+D\ndirect-2",
|
||||
"DP P+D\ndirect-3",
|
||||
]
|
||||
kvc_mask = [True, True, True, True, False, False, False, False]
|
||||
|
||||
KVC_P_COLOR = "#E89D44" # orange — P GPU stands out
|
||||
KVC_D_COLOR = "#1F77B4" # blue
|
||||
DP_COLOR = "#D62728" # red
|
||||
|
||||
bar_colors = [KVC_P_COLOR, KVC_D_COLOR, KVC_D_COLOR, KVC_D_COLOR,
|
||||
DP_COLOR, DP_COLOR, DP_COLOR, DP_COLOR]
|
||||
|
||||
fig, axes = plt.subplots(1, 2, figsize=(15, 6.5))
|
||||
x = np.arange(len(all_gpus))
|
||||
|
||||
# -- Left: per-GPU request count ----------------------------------
|
||||
ax = axes[0]
|
||||
bars = ax.bar(x, counts, color=bar_colors, edgecolor="black", linewidth=0.6)
|
||||
for xi, c in zip(x, counts):
|
||||
ax.text(xi, c + max(counts) * 0.015, f"{c:,}",
|
||||
ha="center", va="bottom", fontsize=9.5)
|
||||
ax.set_xticks(x)
|
||||
ax.set_xticklabels(labels, fontsize=9.5)
|
||||
ax.set_ylabel("Number of requests touching this GPU", fontsize=11)
|
||||
ax.set_title("Per-GPU request count\n(naïve view: P seems idle)", fontsize=12, pad=10)
|
||||
ax.grid(axis="y", linestyle=":", alpha=0.4)
|
||||
ax.set_axisbelow(True)
|
||||
|
||||
# Annotate: KVC P GPU is "low frequency"
|
||||
p_idx = 0
|
||||
p_pct = counts[p_idx] / sum(counts[:4]) * 100 # vs KVC total
|
||||
ax.annotate(
|
||||
f"P GPU only sees\n"
|
||||
f"{counts[p_idx]:,} requests\n"
|
||||
f"({counts[p_idx]/len(kvc)*100:.1f}% of total)",
|
||||
xy=(p_idx, counts[p_idx]),
|
||||
xytext=(p_idx + 0.6, max(counts) * 0.55),
|
||||
fontsize=9, color=KVC_P_COLOR, fontweight="bold",
|
||||
arrowprops=dict(arrowstyle="->", color=KVC_P_COLOR, lw=1.0),
|
||||
)
|
||||
|
||||
# -- Right: per-GPU compute work (stacked prefill + decode) -------
|
||||
ax = axes[1]
|
||||
prefill_M = [t / 1e6 for t in prefill_tk]
|
||||
decode_M = [t / 1e6 for t in decode_tk]
|
||||
total_M = [p + d for p, d in zip(prefill_M, decode_M)]
|
||||
|
||||
bars_p = ax.bar(x, prefill_M, color=[c for c in bar_colors],
|
||||
edgecolor="black", linewidth=0.6, label="Uncached prefill tokens",
|
||||
alpha=0.95)
|
||||
bars_d = ax.bar(x, decode_M, bottom=prefill_M, color=[c for c in bar_colors],
|
||||
edgecolor="black", linewidth=0.6, hatch="///",
|
||||
label="Decode tokens", alpha=0.55)
|
||||
|
||||
for xi, t in zip(x, total_M):
|
||||
ax.text(xi, t + max(total_M) * 0.015, f"{t:.2f}M",
|
||||
ha="center", va="bottom", fontsize=9.5)
|
||||
|
||||
ax.set_xticks(x)
|
||||
ax.set_xticklabels(labels, fontsize=9.5)
|
||||
ax.set_ylabel("Compute tokens (millions)", fontsize=11)
|
||||
ax.set_title("Per-GPU compute work\n(work view: P is comparable to each D)",
|
||||
fontsize=12, pad=10)
|
||||
ax.grid(axis="y", linestyle=":", alpha=0.4)
|
||||
ax.set_axisbelow(True)
|
||||
ax.legend(loc="upper left", fontsize=10, framealpha=0.95)
|
||||
|
||||
# Annotate: KVC P GPU does similar work to each D
|
||||
ax.annotate(
|
||||
f"P GPU does {total_M[p_idx]:.2f}M tokens of\n"
|
||||
f"prefill — comparable per-GPU\n"
|
||||
f"load to each KVC D worker",
|
||||
xy=(p_idx, total_M[p_idx]),
|
||||
xytext=(p_idx + 0.6, max(total_M) * 0.62),
|
||||
fontsize=9, color=KVC_P_COLOR, fontweight="bold",
|
||||
arrowprops=dict(arrowstyle="->", color=KVC_P_COLOR, lw=1.0),
|
||||
)
|
||||
|
||||
# Separator + group labels
|
||||
for ax in axes:
|
||||
ax.axvline(3.5, color="gray", linestyle="--", linewidth=1.0, alpha=0.5)
|
||||
ymin, ymax = ax.get_ylim()
|
||||
ax.text(1.5, ymax * 1.05, "KVC 1P3D", ha="center", fontsize=11,
|
||||
fontweight="bold", color="#444")
|
||||
ax.text(5.5, ymax * 1.05, "DP 4-way CA", ha="center", fontsize=11,
|
||||
fontweight="bold", color="#444")
|
||||
|
||||
fig.suptitle(
|
||||
"Per-GPU utilization: \"is KVC's prefill GPU wasted?\"\n"
|
||||
"Left view says yes (only 8% of requests); right view says no (comparable work to each D).",
|
||||
fontsize=13, y=1.02,
|
||||
)
|
||||
plt.tight_layout()
|
||||
plt.savefig(OUT, dpi=150, bbox_inches="tight")
|
||||
print(f"wrote {OUT}")
|
||||
plt.close(fig)
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Print numbers for doc reference
|
||||
# ------------------------------------------------------------------
|
||||
print("\n=== Per-GPU numbers ===")
|
||||
print(f"{'GPU':<22} {'requests':>10} {'prefill(M)':>12} {'decode(M)':>12} {'total(M)':>10}")
|
||||
for lbl, n, pM, dM in zip(labels, counts, prefill_M, decode_M):
|
||||
print(f" {lbl.replace(chr(10), ' '):<20} {n:>10} {pM:>12.3f} {dM:>12.3f} {pM+dM:>10.3f}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
199
scripts/analysis/plot_ttft_pdf.py
Normal file
199
scripts/analysis/plot_ttft_pdf.py
Normal file
@@ -0,0 +1,199 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Generate TTFT probability density curves: KVC 1P3D v2 vs 4-way DP CA.
|
||||
|
||||
Inputs:
|
||||
outputs/qwen3-30b-tp1-ts1-migration-v2/kvc_1p3d_migration_v2_run1_metrics.jsonl
|
||||
outputs/qwen3-30b-tp1-ts1-validation/dp4_metrics.jsonl
|
||||
|
||||
Outputs:
|
||||
docs/figures/ttft_pdf_comparison.png -- two-panel figure:
|
||||
left panel: linear x in [0, 1.0]s zoomed on the body
|
||||
right panel: log x covering full range (0.01 -- 10 s)
|
||||
Each KDE curve uses scipy.stats.gaussian_kde with Scott's rule bandwidth.
|
||||
Aborted requests are excluded (same filter as metrics.py:_is_failed_request).
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
from pathlib import Path
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
from scipy.stats import gaussian_kde
|
||||
|
||||
ROOT = Path(__file__).resolve().parents[2]
|
||||
KVC = ROOT / "outputs/qwen3-30b-tp1-ts1-migration-v2/kvc_1p3d_migration_v2_run1_metrics.jsonl"
|
||||
DP = ROOT / "outputs/qwen3-30b-tp1-ts1-validation/dp4_metrics.jsonl"
|
||||
OUT = ROOT / "docs/figures/ttft_pdf_comparison.png"
|
||||
|
||||
|
||||
def load(p: Path) -> list[dict]:
|
||||
return [json.loads(line) for line in p.open()]
|
||||
|
||||
|
||||
def is_failed(r: dict) -> bool:
|
||||
if r.get("error"):
|
||||
return True
|
||||
fr = r.get("finish_reason")
|
||||
if fr and ("abort" in str(fr).lower() or "badrequest" in str(fr).lower()):
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def pct(vals: np.ndarray, q: float) -> float:
|
||||
return float(np.quantile(vals, q))
|
||||
|
||||
|
||||
def main() -> None:
|
||||
kvc = [r for r in load(KVC) if not is_failed(r)]
|
||||
dp = [r for r in load(DP) if not is_failed(r)]
|
||||
|
||||
kvc_ttft = np.array([r["ttft_s"] for r in kvc if r.get("ttft_s") is not None])
|
||||
dp_ttft = np.array([r["ttft_s"] for r in dp if r.get("ttft_s") is not None])
|
||||
|
||||
# Trim absurdly small zeros (rare measurement artifacts) so log KDE behaves.
|
||||
kvc_ttft = kvc_ttft[kvc_ttft > 1e-4]
|
||||
dp_ttft = dp_ttft[dp_ttft > 1e-4]
|
||||
|
||||
KVC_COLOR = "#1F77B4" # blue
|
||||
DP_COLOR = "#D62728" # red
|
||||
|
||||
fig, axes = plt.subplots(1, 2, figsize=(16, 6.5))
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Left panel: linear x ∈ [0, 0.6]s -- body of the distribution
|
||||
# ------------------------------------------------------------------
|
||||
ax = axes[0]
|
||||
x_body = np.linspace(0.0, 0.6, 600)
|
||||
|
||||
# KDE on linear ttft values, clipped to body
|
||||
kde_kvc_lin = gaussian_kde(kvc_ttft, bw_method=0.15)
|
||||
kde_dp_lin = gaussian_kde(dp_ttft, bw_method=0.15)
|
||||
|
||||
ax.plot(x_body, kde_kvc_lin(x_body),
|
||||
color=KVC_COLOR, lw=2.5, label=f"KVC 1P3D v2 (n={len(kvc_ttft)})")
|
||||
ax.fill_between(x_body, kde_kvc_lin(x_body), alpha=0.20, color=KVC_COLOR)
|
||||
ax.plot(x_body, kde_dp_lin(x_body),
|
||||
color=DP_COLOR, lw=2.5, label=f"4-way DP CA (n={len(dp_ttft)})")
|
||||
ax.fill_between(x_body, kde_dp_lin(x_body), alpha=0.20, color=DP_COLOR)
|
||||
|
||||
# Vertical lines for p50, p90
|
||||
for q, ls in [(0.50, "-"), (0.90, "--")]:
|
||||
ax.axvline(pct(kvc_ttft, q), color=KVC_COLOR, ls=ls, alpha=0.55, lw=1.1)
|
||||
ax.axvline(pct(dp_ttft, q), color=DP_COLOR, ls=ls, alpha=0.55, lw=1.1)
|
||||
ymax = ax.get_ylim()[1]
|
||||
ax.text(pct(kvc_ttft, 0.50), ymax * 0.97,
|
||||
f"KVC p50\n{pct(kvc_ttft, 0.50)*1000:.0f}ms",
|
||||
color=KVC_COLOR, fontsize=9, va="top", ha="left",
|
||||
bbox=dict(facecolor="white", edgecolor="none", alpha=0.7, pad=2))
|
||||
ax.text(pct(dp_ttft, 0.50), ymax * 0.50,
|
||||
f"DP p50\n{pct(dp_ttft, 0.50)*1000:.0f}ms",
|
||||
color=DP_COLOR, fontsize=9, va="top", ha="left",
|
||||
bbox=dict(facecolor="white", edgecolor="none", alpha=0.7, pad=2))
|
||||
ax.text(pct(kvc_ttft, 0.90), ymax * 0.30,
|
||||
f"KVC p90\n{pct(kvc_ttft, 0.90)*1000:.0f}ms",
|
||||
color=KVC_COLOR, fontsize=9, va="top", ha="left",
|
||||
bbox=dict(facecolor="white", edgecolor="none", alpha=0.7, pad=2))
|
||||
ax.text(pct(dp_ttft, 0.90), ymax * 0.18,
|
||||
f"DP p90\n{pct(dp_ttft, 0.90)*1000:.0f}ms",
|
||||
color=DP_COLOR, fontsize=9, va="top", ha="left",
|
||||
bbox=dict(facecolor="white", edgecolor="none", alpha=0.7, pad=2))
|
||||
|
||||
ax.set_xlim(0, 0.6)
|
||||
ax.set_xlabel("TTFT (seconds, linear)", fontsize=11)
|
||||
ax.set_ylabel("Probability density", fontsize=11)
|
||||
ax.set_title("Body of distribution (TTFT ≤ 0.6 s)", fontsize=12, pad=10)
|
||||
ax.legend(loc="upper right", fontsize=10, framealpha=0.95)
|
||||
ax.grid(True, linestyle=":", alpha=0.4)
|
||||
ax.set_axisbelow(True)
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Right panel: log x ∈ [0.01, 10]s -- full range incl. tail
|
||||
# PDF on log-x: we plot density vs log10(t) so the curve integrates
|
||||
# to 1 over log space (standard "log-density" presentation).
|
||||
# ------------------------------------------------------------------
|
||||
ax = axes[1]
|
||||
# KDE on log10(ttft) so the resulting curve integrates to 1 over log10 t
|
||||
kde_kvc_log = gaussian_kde(np.log10(kvc_ttft), bw_method="scott")
|
||||
kde_dp_log = gaussian_kde(np.log10(dp_ttft), bw_method="scott")
|
||||
log_x = np.linspace(np.log10(0.01), np.log10(10.0), 600)
|
||||
x_full = 10 ** log_x
|
||||
|
||||
y_kvc = kde_kvc_log(log_x)
|
||||
y_dp = kde_dp_log(log_x)
|
||||
|
||||
ax.plot(x_full, y_kvc, color=KVC_COLOR, lw=2.5, label=f"KVC 1P3D v2 (n={len(kvc_ttft)})")
|
||||
ax.fill_between(x_full, y_kvc, alpha=0.20, color=KVC_COLOR)
|
||||
ax.plot(x_full, y_dp, color=DP_COLOR, lw=2.5, label=f"4-way DP CA (n={len(dp_ttft)})")
|
||||
ax.fill_between(x_full, y_dp, alpha=0.20, color=DP_COLOR)
|
||||
|
||||
ax.set_xscale("log")
|
||||
ax.set_xlim(0.01, 10.0)
|
||||
|
||||
# Percentile markers
|
||||
quartile_styles = [(0.50, "-", "p50"), (0.90, "--", "p90"), (0.99, ":", "p99")]
|
||||
for q, ls, name in quartile_styles:
|
||||
ax.axvline(pct(kvc_ttft, q), color=KVC_COLOR, ls=ls, alpha=0.55, lw=1.1)
|
||||
ax.axvline(pct(dp_ttft, q), color=DP_COLOR, ls=ls, alpha=0.55, lw=1.1)
|
||||
|
||||
# Annotate p99 specifically since this is the key reviewer-targeted callout
|
||||
ymax = max(y_kvc.max(), y_dp.max())
|
||||
kvc_p99 = pct(kvc_ttft, 0.99)
|
||||
dp_p99 = pct(dp_ttft, 0.99)
|
||||
ax.annotate(f"KVC p99 = {kvc_p99:.2f}s\n(slow-path reseed tail)",
|
||||
xy=(kvc_p99, kde_kvc_log(np.log10(kvc_p99))[0]),
|
||||
xytext=(2.0, ymax * 0.65),
|
||||
fontsize=10, color=KVC_COLOR, fontweight="bold",
|
||||
arrowprops=dict(arrowstyle="->", color=KVC_COLOR, lw=1.0))
|
||||
ax.annotate(f"DP p99 = {dp_p99*1000:.0f}ms",
|
||||
xy=(dp_p99, kde_dp_log(np.log10(dp_p99))[0]),
|
||||
xytext=(0.025, ymax * 0.80),
|
||||
fontsize=10, color=DP_COLOR, fontweight="bold",
|
||||
arrowprops=dict(arrowstyle="->", color=DP_COLOR, lw=1.0))
|
||||
# Highlight the KVC bimodal structure
|
||||
ax.annotate("KVC fast path\n(direct-to-D, 91.6%)",
|
||||
xy=(0.05, y_kvc[np.argmin(np.abs(x_full - 0.05))]),
|
||||
xytext=(0.012, ymax * 0.45),
|
||||
fontsize=9, color=KVC_COLOR, style="italic",
|
||||
arrowprops=dict(arrowstyle="->", color=KVC_COLOR, lw=0.7, alpha=0.6))
|
||||
ax.annotate("KVC slow path\n(reseed, ~3.4%)",
|
||||
xy=(2.5, y_kvc[np.argmin(np.abs(x_full - 2.5))]),
|
||||
xytext=(3.0, ymax * 0.30),
|
||||
fontsize=9, color=KVC_COLOR, style="italic",
|
||||
arrowprops=dict(arrowstyle="->", color=KVC_COLOR, lw=0.7, alpha=0.6))
|
||||
|
||||
# Custom tick labels in seconds (instead of 10^-2, 10^-1, 10^0, 10^1)
|
||||
ax.set_xticks([0.01, 0.05, 0.1, 0.5, 1.0, 5.0, 10.0])
|
||||
ax.set_xticklabels(["10ms", "50ms", "100ms", "500ms", "1s", "5s", "10s"])
|
||||
|
||||
ax.set_xlabel("TTFT (log scale)", fontsize=11)
|
||||
ax.set_ylabel("Density (per log₁₀ s)", fontsize=11)
|
||||
ax.set_title("Full range (TTFT 10 ms – 10 s, log x)", fontsize=12, pad=10)
|
||||
ax.legend(loc="upper left", fontsize=10, framealpha=0.95)
|
||||
ax.grid(True, which="both", linestyle=":", alpha=0.4)
|
||||
ax.set_axisbelow(True)
|
||||
|
||||
fig.suptitle(
|
||||
"TTFT probability density: KVC 1P3D v2 vs 4-way DP CA\n"
|
||||
"SWE-Bench 50sess trace · ts=1 · 4× H100 80GB · aborted/error requests excluded",
|
||||
fontsize=13, y=1.02,
|
||||
)
|
||||
plt.tight_layout()
|
||||
plt.savefig(OUT, dpi=150, bbox_inches="tight")
|
||||
print(f"wrote {OUT}")
|
||||
plt.close(fig)
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Print summary stats for doc cross-reference
|
||||
# ------------------------------------------------------------------
|
||||
print(f"\n=== TTFT distribution summary ===")
|
||||
for name, arr in [("KVC v2", kvc_ttft), ("DP 4w", dp_ttft)]:
|
||||
print(f" {name} (n={len(arr)})")
|
||||
print(f" min={arr.min()*1000:.1f}ms p10={pct(arr,0.10)*1000:.1f}ms "
|
||||
f"p50={pct(arr,0.50)*1000:.1f}ms p90={pct(arr,0.90)*1000:.1f}ms "
|
||||
f"p99={pct(arr,0.99)*1000:.1f}ms max={arr.max()*1000:.1f}ms")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
223
scripts/analysis/plot_v2_path_breakdown.py
Normal file
223
scripts/analysis/plot_v2_path_breakdown.py
Normal file
@@ -0,0 +1,223 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Generate the two figures referenced by docs/V2_DEEP_ANALYSIS_ZH.md §3.1 and §3.2.
|
||||
|
||||
Inputs:
|
||||
outputs/qwen3-30b-tp1-ts1-migration-v2/kvc_1p3d_migration_v2_run1_metrics.jsonl
|
||||
outputs/qwen3-30b-tp1-ts1-validation/dp4_metrics.jsonl
|
||||
|
||||
Outputs:
|
||||
docs/figures/v2_execution_mode_distribution.png (for §3.1)
|
||||
docs/figures/v2_path_level_latency.png (for §3.2)
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import statistics
|
||||
from collections import Counter, defaultdict
|
||||
from pathlib import Path
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
|
||||
ROOT = Path(__file__).resolve().parents[2]
|
||||
KVC = ROOT / "outputs/qwen3-30b-tp1-ts1-migration-v2/kvc_1p3d_migration_v2_run1_metrics.jsonl"
|
||||
DP = ROOT / "outputs/qwen3-30b-tp1-ts1-validation/dp4_metrics.jsonl"
|
||||
OUT = ROOT / "docs/figures"
|
||||
OUT.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
|
||||
def load(p: Path) -> list[dict]:
|
||||
return [json.loads(line) for line in p.open()]
|
||||
|
||||
|
||||
def is_failed(r: dict) -> bool:
|
||||
if r.get("error"):
|
||||
return True
|
||||
fr = r.get("finish_reason")
|
||||
if fr and ("abort" in str(fr).lower() or "badrequest" in str(fr).lower()):
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def pct(vals: list[float], q: float) -> float:
|
||||
s = sorted(vals)
|
||||
if not s:
|
||||
return float("nan")
|
||||
return s[max(0, min(len(s) - 1, int(len(s) * q)))]
|
||||
|
||||
|
||||
def main() -> None:
|
||||
kvc = load(KVC)
|
||||
dp = load(DP)
|
||||
|
||||
kvc_ok = [r for r in kvc if not is_failed(r)]
|
||||
dp_ok = [r for r in dp if not is_failed(r)]
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Figure 1: §3.1 execution_mode distribution (horizontal bar)
|
||||
# Use ALL rows (incl. failures) so percentages match the doc's 91.6%
|
||||
# ------------------------------------------------------------------
|
||||
mode_counts = Counter(r["execution_mode"] for r in kvc)
|
||||
total_kvc = len(kvc)
|
||||
|
||||
short_label = {
|
||||
"kvcache-direct-to-d-session": "direct-to-D-session (fast path)",
|
||||
"pd-router-d-session-reseed": "d-session-reseed (mooncake reseed)",
|
||||
"pd-router-fallback-session-not-resident-session-cap":
|
||||
"fallback: session-not-resident + session-cap",
|
||||
"pd-router-fallback-session-not-resident-seed-filter-early-turn":
|
||||
"fallback: session-not-resident + seed-filter",
|
||||
"pd-router-turn1-seed": "turn1-seed (first turn of each session)",
|
||||
"pd-router-fallback-no-d-capacity": "fallback: no-d-capacity",
|
||||
"pd-router-fallback-real-large-append-session-cap":
|
||||
"fallback: real-large-append",
|
||||
"pd-router-fallback-policy-no-bypass-session-cap":
|
||||
"fallback: policy-no-bypass",
|
||||
"pd-router-d-session-reseed-after-eviction":
|
||||
"d-session-reseed-after-eviction",
|
||||
"kvcache-centric": "kvcache-centric (admit-but-then-error)",
|
||||
}
|
||||
sorted_modes = mode_counts.most_common()
|
||||
labels = [short_label.get(m, m) for m, _ in sorted_modes]
|
||||
counts = [c for _, c in sorted_modes]
|
||||
pcts = [c / total_kvc * 100 for c in counts]
|
||||
|
||||
is_fast = ["direct-to-D" in lbl for lbl in labels]
|
||||
colors = ["#2C8C2C" if f else "#D62728" for f in is_fast]
|
||||
|
||||
fig, ax = plt.subplots(figsize=(11, 5.5))
|
||||
y = np.arange(len(labels))[::-1]
|
||||
ax.barh(y, counts, color=colors, edgecolor="black", linewidth=0.5)
|
||||
ax.set_yticks(y)
|
||||
ax.set_yticklabels(labels, fontsize=10)
|
||||
ax.set_xscale("log")
|
||||
ax.set_xlabel("Request count (log scale)", fontsize=11)
|
||||
ax.set_xlim(left=1)
|
||||
|
||||
# Annotate count + percentage at end of each bar
|
||||
for yi, (c, p) in zip(y, zip(counts, pcts)):
|
||||
ax.text(c * 1.05, yi, f"{c} ({p:.1f}%)",
|
||||
va="center", fontsize=9.5)
|
||||
|
||||
ax.set_title(
|
||||
f"KVC v2 execution_mode distribution (n = {total_kvc} total requests)\n"
|
||||
"green = fast path (direct-to-D), red = slow / fallback / failure paths",
|
||||
fontsize=12, pad=12,
|
||||
)
|
||||
ax.grid(axis="x", linestyle=":", alpha=0.4)
|
||||
ax.set_axisbelow(True)
|
||||
plt.tight_layout()
|
||||
out1 = OUT / "v2_execution_mode_distribution.png"
|
||||
plt.savefig(out1, dpi=150)
|
||||
print(f"wrote {out1}")
|
||||
plt.close(fig)
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Figure 2: §3.2 path-level latency (grouped bars, log y)
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
# Group KVC paths semantically
|
||||
def kvc_group(mode: str) -> str:
|
||||
if mode == "kvcache-direct-to-d-session":
|
||||
return "KVC direct-to-D\n(fast path, 91.6%)"
|
||||
if "reseed" in mode:
|
||||
return "KVC reseed\n(slow path, 3.4%)"
|
||||
if "no-d-capacity" in mode:
|
||||
return "KVC no-d-capacity\n(fallback, 0.7%)"
|
||||
if "session-not-resident" in mode:
|
||||
return "KVC session-not-resident\n(misc, 2.3%)"
|
||||
return "KVC other\n(<2%)"
|
||||
|
||||
groups = defaultdict(list)
|
||||
for r in kvc_ok:
|
||||
groups[kvc_group(r["execution_mode"])].append(r)
|
||||
|
||||
# Order paths by intuitive progression (fast → slow)
|
||||
ordered_paths = [
|
||||
"KVC direct-to-D\n(fast path, 91.6%)",
|
||||
"KVC session-not-resident\n(misc, 2.3%)",
|
||||
"KVC reseed\n(slow path, 3.4%)",
|
||||
"KVC no-d-capacity\n(fallback, 0.7%)",
|
||||
]
|
||||
# Filter to only ones present
|
||||
ordered_paths = [p for p in ordered_paths if p in groups]
|
||||
ordered_paths.append("DP dp-colo-router\n(100%)")
|
||||
|
||||
def stats(rows: list[dict]) -> dict[str, float]:
|
||||
ttfts = [r["ttft_s"] for r in rows if r.get("ttft_s") is not None]
|
||||
lats = [r["latency_s"] for r in rows if r.get("latency_s") is not None]
|
||||
return {
|
||||
"n": len(rows),
|
||||
"ttft_p50": pct(ttfts, 0.50),
|
||||
"ttft_p99": pct(ttfts, 0.99),
|
||||
"lat_p50": pct(lats, 0.50),
|
||||
}
|
||||
|
||||
path_stats = {p: stats(groups[p]) for p in ordered_paths if "DP" not in p}
|
||||
path_stats["DP dp-colo-router\n(100%)"] = stats(dp_ok)
|
||||
|
||||
metrics = [("TTFT p50", "ttft_p50"), ("TTFT p99", "ttft_p99"), ("Latency p50", "lat_p50")]
|
||||
bar_w = 0.25
|
||||
fig, ax = plt.subplots(figsize=(12, 6))
|
||||
x = np.arange(len(ordered_paths))
|
||||
|
||||
colors_metric = ["#1F77B4", "#FF7F0E", "#9467BD"]
|
||||
for i, (label, key) in enumerate(metrics):
|
||||
vals = [path_stats[p][key] for p in ordered_paths]
|
||||
bars = ax.bar(x + (i - 1) * bar_w, vals, bar_w, label=label,
|
||||
color=colors_metric[i], edgecolor="black", linewidth=0.4)
|
||||
for xi, v in zip(x + (i - 1) * bar_w, vals):
|
||||
if v > 0 and v == v: # not nan
|
||||
fmt = f"{v*1000:.0f}ms" if v < 1 else f"{v:.2f}s"
|
||||
ax.text(xi, v * 1.10, fmt,
|
||||
ha="center", va="bottom", fontsize=8.5, rotation=0)
|
||||
|
||||
ax.set_yscale("log")
|
||||
ax.set_xticks(x)
|
||||
ax.set_xticklabels(ordered_paths, fontsize=9.5)
|
||||
ax.set_ylabel("Latency (seconds, log scale)", fontsize=11)
|
||||
ax.set_title(
|
||||
"Path-level latency: KVC v2 paths vs DP single-path baseline\n"
|
||||
"log y-axis · same SWE-Bench 50sess trace · ts=1 · 4× H100 80GB",
|
||||
fontsize=12, pad=12,
|
||||
)
|
||||
ax.legend(loc="upper left", fontsize=10, framealpha=0.95)
|
||||
ax.grid(axis="y", linestyle=":", alpha=0.4, which="both")
|
||||
ax.set_axisbelow(True)
|
||||
|
||||
# Annotate sample counts under each path label
|
||||
ymin = ax.get_ylim()[0]
|
||||
for xi, p in zip(x, ordered_paths):
|
||||
n = path_stats[p]["n"]
|
||||
ax.text(xi, ymin * 0.5, f"n={n}", ha="center", va="top",
|
||||
fontsize=8.5, color="#555")
|
||||
|
||||
plt.tight_layout()
|
||||
out2 = OUT / "v2_path_level_latency.png"
|
||||
plt.savefig(out2, dpi=150)
|
||||
print(f"wrote {out2}")
|
||||
plt.close(fig)
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Print numeric values used (for doc reference)
|
||||
# ------------------------------------------------------------------
|
||||
print("\n=== Numeric values plotted ===")
|
||||
print("\nExecution mode counts (KVC v2):")
|
||||
for label, c, p in zip(labels, counts, pcts):
|
||||
print(f" {c:>5} ({p:>5.2f}%) {label}")
|
||||
|
||||
print("\nPath-level latency:")
|
||||
for p in ordered_paths:
|
||||
s = path_stats[p]
|
||||
nl = " | ".join([
|
||||
f"n={s['n']}",
|
||||
f"TTFT p50={s['ttft_p50']*1000:.1f}ms",
|
||||
f"TTFT p99={s['ttft_p99']*1000:.1f}ms",
|
||||
f"Lat p50={s['lat_p50']:.3f}s",
|
||||
])
|
||||
print(f" {p.replace(chr(10), ' '):<55} {nl}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
105
scripts/analysis/recompute_summary.py
Normal file
105
scripts/analysis/recompute_summary.py
Normal file
@@ -0,0 +1,105 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Re-derive summary.json from existing metrics.jsonl using the fixed metrics.py.
|
||||
|
||||
Bug fixed: requests aborted by SGLang (e.g. input > max-input-len returns
|
||||
a fast 400 with latency_s ~ 0.08s) were previously counted in latency_stats
|
||||
as if successful, deflating mean/p50/p90. The fixed metrics.py excludes
|
||||
all failed requests (errors or aborts) from latency/ttft/tpot stats and
|
||||
exposes abort_count / failure_count.
|
||||
|
||||
Usage:
|
||||
python3 scripts/analysis/recompute_summary.py path/to/metrics.jsonl ...
|
||||
python3 scripts/analysis/recompute_summary.py --diff path/to/metrics.jsonl path/to/old_summary.json
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
sys.path.insert(0, str(Path(__file__).resolve().parents[2] / "src"))
|
||||
|
||||
from agentic_pd_hybrid.metrics import RequestMetrics, write_summary_json
|
||||
|
||||
|
||||
def load_rows(metrics_path: Path) -> list[RequestMetrics]:
|
||||
rows = []
|
||||
field_names = {f for f in RequestMetrics.__dataclass_fields__}
|
||||
with metrics_path.open() as handle:
|
||||
for line in handle:
|
||||
line = line.strip()
|
||||
if not line:
|
||||
continue
|
||||
raw = json.loads(line)
|
||||
kwargs = {k: raw.get(k) for k in field_names}
|
||||
rows.append(RequestMetrics(**kwargs))
|
||||
return rows
|
||||
|
||||
|
||||
def main() -> None:
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("metrics_paths", nargs="+", type=Path)
|
||||
parser.add_argument(
|
||||
"--out",
|
||||
type=Path,
|
||||
default=None,
|
||||
help="output summary path (default: alongside metrics with .recomputed_summary.json)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--diff",
|
||||
action="store_true",
|
||||
help="print before/after diff against the old <metrics>.summary.json",
|
||||
)
|
||||
args = parser.parse_args()
|
||||
|
||||
for metrics_path in args.metrics_paths:
|
||||
rows = load_rows(metrics_path)
|
||||
out_path = args.out or metrics_path.with_suffix(".recomputed_summary.json")
|
||||
write_summary_json(
|
||||
out_path,
|
||||
rows,
|
||||
trace_path=metrics_path,
|
||||
router_url=None,
|
||||
)
|
||||
new = json.load(out_path.open())
|
||||
print(f"\n=== {metrics_path} ===")
|
||||
print(f" written: {out_path}")
|
||||
print(f" total rows: {new['request_count']}")
|
||||
print(f" error_count: {new['error_count']}")
|
||||
print(f" abort_count: {new.get('abort_count', '?')}")
|
||||
print(f" failure_count: {new.get('failure_count', '?')}")
|
||||
ls = new.get("latency_stats_s", {}) or {}
|
||||
ts = new.get("ttft_stats_s", {}) or {}
|
||||
print(f" lat: n={ls.get('count')} mean={ls.get('mean'):.4f} p50={ls.get('p50'):.4f} p90={ls.get('p90'):.4f} p99={ls.get('p99'):.4f}")
|
||||
print(f" ttft: n={ts.get('count')} mean={ts.get('mean'):.4f} p50={ts.get('p50'):.4f} p90={ts.get('p90'):.4f} p99={ts.get('p99'):.4f}")
|
||||
|
||||
if args.diff:
|
||||
# find old summary (sibling file)
|
||||
candidates = [
|
||||
metrics_path.parent / f"{metrics_path.stem}.summary.json",
|
||||
metrics_path.with_suffix(".summary.json"),
|
||||
]
|
||||
old_path = next((p for p in candidates if p.exists()), None)
|
||||
if old_path:
|
||||
old = json.load(old_path.open())
|
||||
print(f" vs old {old_path}:")
|
||||
old_ls = old.get("latency_stats_s", {}) or {}
|
||||
old_ts = old.get("ttft_stats_s", {}) or {}
|
||||
for k in ("count", "mean", "p50", "p90", "p99"):
|
||||
o = old_ls.get(k)
|
||||
n = ls.get(k)
|
||||
if o is not None and n is not None:
|
||||
delta = n - o
|
||||
print(f" lat.{k}: {o:.4f} -> {n:.4f} ({delta:+.4f})")
|
||||
for k in ("count", "mean", "p50", "p90", "p99"):
|
||||
o = old_ts.get(k)
|
||||
n = ts.get(k)
|
||||
if o is not None and n is not None:
|
||||
delta = n - o
|
||||
print(f" ttft.{k}: {o:.4f} -> {n:.4f} ({delta:+.4f})")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,450 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Prepare balanced real-Ali trace samples for KVC experiments.
|
||||
|
||||
The generic sampler is duration-oriented and can be dominated by one long
|
||||
session. This script keeps real request lengths/timestamps but caps turns per
|
||||
session so live sweeps can compare policies on a repeatable multi-session
|
||||
workload.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import statistics
|
||||
from collections import defaultdict
|
||||
from dataclasses import asdict, dataclass
|
||||
from pathlib import Path
|
||||
|
||||
from agentic_pd_hybrid.trace import TraceRequest, load_trace
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class SampleSummary:
|
||||
input_trace_path: str
|
||||
output_trace_path: str
|
||||
profile: str
|
||||
request_count: int
|
||||
session_count: int
|
||||
multi_turn_session_count: int
|
||||
turn2plus_count: int
|
||||
direct_eligible_turn2plus_count: int
|
||||
direct_eligible_turn2plus_ratio: float
|
||||
missing_parent_count: int
|
||||
max_sessions: int
|
||||
max_turns_per_session: int
|
||||
start_time_s: float
|
||||
end_time_s: float
|
||||
sampled_duration_s: float
|
||||
rebased_timestamps: bool
|
||||
input_tokens: dict[str, float] | None
|
||||
output_tokens: dict[str, float] | None
|
||||
append_tokens: dict[str, float] | None
|
||||
inter_turn_gap_s: dict[str, float] | None
|
||||
overlap_ratio: dict[str, float] | None
|
||||
|
||||
|
||||
def main() -> None:
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--trace", type=Path, required=True)
|
||||
parser.add_argument("--output-root", type=Path, required=True)
|
||||
parser.add_argument("--max-sessions", type=int, default=64)
|
||||
parser.add_argument("--max-turns-per-session", type=int, default=12)
|
||||
parser.add_argument("--start-time-s", type=float, default=0.0)
|
||||
parser.add_argument(
|
||||
"--window-duration-s",
|
||||
type=float,
|
||||
default=None,
|
||||
help=(
|
||||
"If set, also write continuous-window samples that keep only requests "
|
||||
"inside [start-time, start-time + window-duration]."
|
||||
),
|
||||
)
|
||||
parser.add_argument(
|
||||
"--window-target-requests",
|
||||
type=int,
|
||||
default=None,
|
||||
help=(
|
||||
"For continuous-window samples, select whole sessions across time "
|
||||
"buckets until at least this many requests are included. This keeps "
|
||||
"the window span while making live runs tractable."
|
||||
),
|
||||
)
|
||||
parser.add_argument(
|
||||
"--window-buckets",
|
||||
type=int,
|
||||
default=15,
|
||||
help="Number of time buckets used with --window-target-requests.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--window-min-turns",
|
||||
type=int,
|
||||
default=1,
|
||||
help=(
|
||||
"Minimum number of in-window turns per selected session for "
|
||||
"continuous-window samples."
|
||||
),
|
||||
)
|
||||
parser.add_argument(
|
||||
"--window-output-name",
|
||||
default="ali-window.jsonl",
|
||||
help="Output filename for the continuous-window sample.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--max-sampled-duration-s",
|
||||
type=float,
|
||||
default=None,
|
||||
help=(
|
||||
"For balanced profile samples, drop requests after the first selected "
|
||||
"timestamp plus this duration. Use only for quick smoke runs; headline "
|
||||
"runs should preserve the full sampled span."
|
||||
),
|
||||
)
|
||||
parser.add_argument(
|
||||
"--profiles",
|
||||
nargs="+",
|
||||
default=["representative-mt", "kvc-fit-smallappend"],
|
||||
choices=["representative-mt", "kvc-fit-smallappend"],
|
||||
)
|
||||
parser.add_argument(
|
||||
"--no-rebase-timestamps",
|
||||
action="store_true",
|
||||
help="Keep original timestamps instead of shifting the sample to start at 0.",
|
||||
)
|
||||
args = parser.parse_args()
|
||||
|
||||
requests = load_trace(args.trace)
|
||||
sessions: dict[str, list[TraceRequest]] = defaultdict(list)
|
||||
for request in requests:
|
||||
sessions[request.session_id].append(request)
|
||||
|
||||
args.output_root.mkdir(parents=True, exist_ok=True)
|
||||
if args.window_duration_s is not None:
|
||||
if args.window_target_requests is None:
|
||||
selected = _select_window(
|
||||
requests=requests,
|
||||
start_time_s=args.start_time_s,
|
||||
window_duration_s=args.window_duration_s,
|
||||
)
|
||||
profile = "window"
|
||||
else:
|
||||
selected = _select_window_session_sample(
|
||||
sessions=sessions,
|
||||
start_time_s=args.start_time_s,
|
||||
window_duration_s=args.window_duration_s,
|
||||
target_requests=args.window_target_requests,
|
||||
bucket_count=args.window_buckets,
|
||||
min_turns=args.window_min_turns,
|
||||
)
|
||||
profile = (
|
||||
"window-session-sample"
|
||||
if args.window_min_turns <= 1
|
||||
else f"window-session-sample-min{args.window_min_turns}turns"
|
||||
)
|
||||
output_path = args.output_root / args.window_output_name
|
||||
summary = _write_sample(
|
||||
selected=selected,
|
||||
input_trace_path=args.trace,
|
||||
output_path=output_path,
|
||||
profile=profile,
|
||||
max_sessions=args.max_sessions,
|
||||
max_turns_per_session=args.max_turns_per_session,
|
||||
rebase_timestamps=not args.no_rebase_timestamps,
|
||||
)
|
||||
print(
|
||||
f"window: wrote {summary.request_count} requests from "
|
||||
f"{summary.session_count} sessions to {output_path}"
|
||||
)
|
||||
|
||||
for profile in args.profiles:
|
||||
selected = _select_profile(
|
||||
sessions=sessions,
|
||||
profile=profile,
|
||||
start_time_s=args.start_time_s,
|
||||
max_sessions=args.max_sessions,
|
||||
max_turns_per_session=args.max_turns_per_session,
|
||||
max_sampled_duration_s=args.max_sampled_duration_s,
|
||||
)
|
||||
output_path = args.output_root / f"ali-{profile}.jsonl"
|
||||
summary = _write_sample(
|
||||
selected=selected,
|
||||
input_trace_path=args.trace,
|
||||
output_path=output_path,
|
||||
profile=profile,
|
||||
max_sessions=args.max_sessions,
|
||||
max_turns_per_session=args.max_turns_per_session,
|
||||
rebase_timestamps=not args.no_rebase_timestamps,
|
||||
)
|
||||
print(
|
||||
f"{profile}: wrote {summary.request_count} requests from "
|
||||
f"{summary.session_count} sessions to {output_path}"
|
||||
)
|
||||
|
||||
|
||||
def _select_profile(
|
||||
*,
|
||||
sessions: dict[str, list[TraceRequest]],
|
||||
profile: str,
|
||||
start_time_s: float,
|
||||
max_sessions: int,
|
||||
max_turns_per_session: int,
|
||||
max_sampled_duration_s: float | None,
|
||||
) -> list[TraceRequest]:
|
||||
eligible: list[list[TraceRequest]] = []
|
||||
for session_requests in sessions.values():
|
||||
ordered = _ordered(session_requests)
|
||||
if len(ordered) < 2:
|
||||
continue
|
||||
if ordered[0].timestamp_s < start_time_s:
|
||||
continue
|
||||
if profile == "kvc-fit-smallappend" and not _is_kvc_fit_smallappend(ordered):
|
||||
continue
|
||||
eligible.append(ordered[:max_turns_per_session])
|
||||
|
||||
eligible.sort(key=lambda items: (items[0].timestamp_s, items[0].session_id))
|
||||
selected_sessions = eligible[:max_sessions]
|
||||
selected = [request for items in selected_sessions for request in items]
|
||||
selected.sort(key=lambda request: (request.timestamp_s, request.chat_id))
|
||||
if selected and max_sampled_duration_s is not None:
|
||||
first_ts = selected[0].timestamp_s
|
||||
end_ts = first_ts + max_sampled_duration_s
|
||||
selected = [
|
||||
request for request in selected if request.timestamp_s <= end_ts
|
||||
]
|
||||
return selected
|
||||
|
||||
|
||||
def _select_window(
|
||||
*,
|
||||
requests: list[TraceRequest],
|
||||
start_time_s: float,
|
||||
window_duration_s: float,
|
||||
) -> list[TraceRequest]:
|
||||
end_time_s = start_time_s + window_duration_s
|
||||
selected = [
|
||||
request
|
||||
for request in requests
|
||||
if start_time_s <= request.timestamp_s <= end_time_s
|
||||
]
|
||||
selected.sort(key=lambda request: (request.timestamp_s, request.chat_id))
|
||||
return selected
|
||||
|
||||
|
||||
def _select_window_session_sample(
|
||||
*,
|
||||
sessions: dict[str, list[TraceRequest]],
|
||||
start_time_s: float,
|
||||
window_duration_s: float,
|
||||
target_requests: int,
|
||||
bucket_count: int,
|
||||
min_turns: int,
|
||||
) -> list[TraceRequest]:
|
||||
if target_requests <= 0:
|
||||
raise ValueError("--window-target-requests must be positive")
|
||||
if bucket_count <= 0:
|
||||
raise ValueError("--window-buckets must be positive")
|
||||
if min_turns <= 0:
|
||||
raise ValueError("--window-min-turns must be positive")
|
||||
|
||||
end_time_s = start_time_s + window_duration_s
|
||||
bucket_width_s = window_duration_s / bucket_count
|
||||
buckets: list[list[list[TraceRequest]]] = [[] for _ in range(bucket_count)]
|
||||
for session_requests in sessions.values():
|
||||
ordered = _ordered(session_requests)
|
||||
if not ordered:
|
||||
continue
|
||||
first = ordered[0]
|
||||
if first.timestamp_s < start_time_s or first.timestamp_s > end_time_s:
|
||||
continue
|
||||
in_window = [
|
||||
request
|
||||
for request in ordered
|
||||
if start_time_s <= request.timestamp_s <= end_time_s
|
||||
]
|
||||
if len(in_window) < min_turns:
|
||||
continue
|
||||
bucket_index = min(
|
||||
bucket_count - 1,
|
||||
int((first.timestamp_s - start_time_s) / bucket_width_s),
|
||||
)
|
||||
buckets[bucket_index].append(in_window)
|
||||
|
||||
for bucket in buckets:
|
||||
bucket.sort(key=lambda items: (items[0].timestamp_s, items[0].session_id))
|
||||
|
||||
selected_sessions: list[list[TraceRequest]] = []
|
||||
selected_count = 0
|
||||
positions = [0 for _ in range(bucket_count)]
|
||||
while selected_count < target_requests:
|
||||
progressed = False
|
||||
for index, bucket in enumerate(buckets):
|
||||
if positions[index] >= len(bucket):
|
||||
continue
|
||||
session_requests = bucket[positions[index]]
|
||||
positions[index] += 1
|
||||
selected_sessions.append(session_requests)
|
||||
selected_count += len(session_requests)
|
||||
progressed = True
|
||||
if selected_count >= target_requests:
|
||||
break
|
||||
if not progressed:
|
||||
break
|
||||
|
||||
selected = [request for items in selected_sessions for request in items]
|
||||
selected.sort(key=lambda request: (request.timestamp_s, request.chat_id))
|
||||
if len(selected) < target_requests:
|
||||
raise ValueError(
|
||||
f"window session sample selected only {len(selected)} requests; "
|
||||
f"target was {target_requests}"
|
||||
)
|
||||
return selected
|
||||
|
||||
|
||||
def _is_kvc_fit_smallappend(session_requests: list[TraceRequest]) -> bool:
|
||||
initial = session_requests[0]
|
||||
if initial.input_length < 2048 or initial.input_length > 16000:
|
||||
return False
|
||||
for request in session_requests:
|
||||
if request.output_length > 2048:
|
||||
return False
|
||||
for previous, current in zip(session_requests, session_requests[1:], strict=False):
|
||||
append_tokens = current.input_length - (
|
||||
previous.input_length + previous.output_length
|
||||
)
|
||||
if append_tokens <= 0 or append_tokens > 2048:
|
||||
return False
|
||||
if _overlap_ratio(previous, current) < 0.75:
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def _write_sample(
|
||||
*,
|
||||
selected: list[TraceRequest],
|
||||
input_trace_path: Path,
|
||||
output_path: Path,
|
||||
profile: str,
|
||||
max_sessions: int,
|
||||
max_turns_per_session: int,
|
||||
rebase_timestamps: bool,
|
||||
) -> SampleSummary:
|
||||
if not selected:
|
||||
raise ValueError(f"profile {profile!r} selected no requests")
|
||||
|
||||
first_ts = selected[0].timestamp_s
|
||||
output_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
with output_path.open("w", encoding="utf-8") as handle:
|
||||
for request in selected:
|
||||
timestamp = request.timestamp_s - first_ts if rebase_timestamps else request.timestamp_s
|
||||
payload = {
|
||||
"chat_id": request.chat_id,
|
||||
"parent_chat_id": request.parent_chat_id,
|
||||
"timestamp": round(timestamp, 6),
|
||||
"input_length": request.input_length,
|
||||
"output_length": request.output_length,
|
||||
"type": request.request_type,
|
||||
"turn": request.turn_id,
|
||||
"hash_ids": list(request.hash_ids),
|
||||
}
|
||||
handle.write(json.dumps(payload, sort_keys=True) + "\n")
|
||||
|
||||
sessions = defaultdict(list)
|
||||
for request in selected:
|
||||
sessions[request.session_id].append(request)
|
||||
|
||||
selected_chat_ids = {request.chat_id for request in selected}
|
||||
missing_parent_count = sum(
|
||||
1
|
||||
for request in selected
|
||||
if request.parent_chat_id >= 0 and request.parent_chat_id not in selected_chat_ids
|
||||
)
|
||||
append_values: list[float] = []
|
||||
gap_values: list[float] = []
|
||||
overlap_values: list[float] = []
|
||||
direct_eligible_count = 0
|
||||
for session_requests in sessions.values():
|
||||
ordered = _ordered(session_requests)
|
||||
for previous, current in zip(ordered, ordered[1:], strict=False):
|
||||
append_tokens = current.input_length - (
|
||||
previous.input_length + previous.output_length
|
||||
)
|
||||
overlap_ratio = _overlap_ratio(previous, current)
|
||||
append_values.append(float(append_tokens))
|
||||
gap_values.append(float(current.timestamp_s - previous.timestamp_s))
|
||||
overlap_values.append(overlap_ratio)
|
||||
if append_tokens > 0 and append_tokens <= 2048 and overlap_ratio > 0:
|
||||
direct_eligible_count += 1
|
||||
|
||||
turn2plus_count = sum(max(0, len(items) - 1) for items in sessions.values())
|
||||
|
||||
start = min(request.timestamp_s for request in selected)
|
||||
end = max(request.timestamp_s for request in selected)
|
||||
summary = SampleSummary(
|
||||
input_trace_path=str(input_trace_path),
|
||||
output_trace_path=str(output_path),
|
||||
profile=profile,
|
||||
request_count=len(selected),
|
||||
session_count=len(sessions),
|
||||
multi_turn_session_count=sum(1 for items in sessions.values() if len(items) > 1),
|
||||
turn2plus_count=turn2plus_count,
|
||||
direct_eligible_turn2plus_count=direct_eligible_count,
|
||||
direct_eligible_turn2plus_ratio=(
|
||||
direct_eligible_count / turn2plus_count if turn2plus_count else 0.0
|
||||
),
|
||||
missing_parent_count=missing_parent_count,
|
||||
max_sessions=max_sessions,
|
||||
max_turns_per_session=max_turns_per_session,
|
||||
start_time_s=0.0 if rebase_timestamps else start,
|
||||
end_time_s=end - start if rebase_timestamps else end,
|
||||
sampled_duration_s=end - start,
|
||||
rebased_timestamps=rebase_timestamps,
|
||||
input_tokens=_stats([float(request.input_length) for request in selected]),
|
||||
output_tokens=_stats([float(request.output_length) for request in selected]),
|
||||
append_tokens=_stats(append_values),
|
||||
inter_turn_gap_s=_stats(gap_values),
|
||||
overlap_ratio=_stats(overlap_values),
|
||||
)
|
||||
with output_path.with_suffix(output_path.suffix + ".summary.json").open(
|
||||
"w", encoding="utf-8"
|
||||
) as handle:
|
||||
json.dump(asdict(summary), handle, indent=2, sort_keys=True)
|
||||
return summary
|
||||
|
||||
|
||||
def _ordered(session_requests: list[TraceRequest]) -> list[TraceRequest]:
|
||||
return sorted(
|
||||
session_requests,
|
||||
key=lambda request: (request.timestamp_s, request.turn_id, request.chat_id),
|
||||
)
|
||||
|
||||
|
||||
def _overlap_ratio(previous: TraceRequest, current: TraceRequest) -> float:
|
||||
if not current.hash_ids:
|
||||
return 0.0
|
||||
previous_blocks = set(previous.hash_ids)
|
||||
overlap = sum(1 for block in current.hash_ids if block in previous_blocks)
|
||||
return overlap / len(current.hash_ids)
|
||||
|
||||
|
||||
def _stats(values: list[float]) -> dict[str, float] | None:
|
||||
if not values:
|
||||
return None
|
||||
ordered = sorted(values)
|
||||
return {
|
||||
"count": float(len(ordered)),
|
||||
"mean": statistics.fmean(ordered),
|
||||
"min": ordered[0],
|
||||
"p50": _percentile(ordered, 0.50),
|
||||
"p90": _percentile(ordered, 0.90),
|
||||
"p99": _percentile(ordered, 0.99),
|
||||
"max": ordered[-1],
|
||||
}
|
||||
|
||||
|
||||
def _percentile(sorted_values: list[float], percentile: float) -> float:
|
||||
if len(sorted_values) == 1:
|
||||
return sorted_values[0]
|
||||
return sorted_values[round((len(sorted_values) - 1) * percentile)]
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,170 +0,0 @@
|
||||
#!/usr/bin/env bash
|
||||
# Real Ali workload sweep for KVC pd-hybrid.
|
||||
#
|
||||
# This script expects a prebuilt sample trace and replays it exactly for every
|
||||
# mechanism. It intentionally keeps pool polling disabled for performance runs.
|
||||
set -euo pipefail
|
||||
|
||||
REPO_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
|
||||
cd "$REPO_ROOT"
|
||||
|
||||
MODEL=${MODEL:-/home/admin/cpfs/wjh/models/Qwen/Qwen3-Coder-30B-A3B-Instruct}
|
||||
TRACE=${TRACE:-outputs/real-ali-kvc-iter/samples-balanced/ali-kvc-fit-smallappend.jsonl}
|
||||
OUT_ROOT=${OUT_ROOT:-outputs/real-ali-kvc-iter/runs}
|
||||
TIME_SCALE=${TIME_SCALE:-1}
|
||||
CONCURRENCY=${CONCURRENCY:-32}
|
||||
REQUEST_TIMEOUT_S=${REQUEST_TIMEOUT_S:-300}
|
||||
STACK_TIMEOUT_S=${STACK_TIMEOUT_S:-1200}
|
||||
RUNS=${RUNS:-"dp kvc_bp"}
|
||||
EXTRA_SERVER_ARGS=${EXTRA_SERVER_ARGS:-}
|
||||
PREFILL_EXTRA_SERVER_ARGS=${PREFILL_EXTRA_SERVER_ARGS:-}
|
||||
DECODE_EXTRA_SERVER_ARGS=${DECODE_EXTRA_SERVER_ARGS:-}
|
||||
KVC_SEED_MIN_TURN_ID=${KVC_SEED_MIN_TURN_ID:-1}
|
||||
KVC_SEED_ONLY_MULTITURN=${KVC_SEED_ONLY_MULTITURN:-0}
|
||||
|
||||
mkdir -p "$OUT_ROOT"
|
||||
LOG="$OUT_ROOT/sweep.log"
|
||||
|
||||
log() {
|
||||
echo "[$(date '+%F %T')] $*" | tee -a "$LOG"
|
||||
}
|
||||
|
||||
common_args=(
|
||||
--trace "$TRACE"
|
||||
--model-path "$MODEL"
|
||||
--output-root "$OUT_ROOT"
|
||||
--use-trace-as-sample
|
||||
--time-scale "$TIME_SCALE"
|
||||
--concurrency-limit "$CONCURRENCY"
|
||||
--timeout-s "$STACK_TIMEOUT_S"
|
||||
--request-timeout-s "$REQUEST_TIMEOUT_S"
|
||||
)
|
||||
if [[ -n "$EXTRA_SERVER_ARGS" ]]; then
|
||||
common_args+=(--extra-server-args "$EXTRA_SERVER_ARGS")
|
||||
fi
|
||||
if [[ -n "$PREFILL_EXTRA_SERVER_ARGS" ]]; then
|
||||
common_args+=(--prefill-extra-server-args "$PREFILL_EXTRA_SERVER_ARGS")
|
||||
fi
|
||||
if [[ -n "$DECODE_EXTRA_SERVER_ARGS" ]]; then
|
||||
common_args+=(--decode-extra-server-args "$DECODE_EXTRA_SERVER_ARGS")
|
||||
fi
|
||||
|
||||
kvc_args=(
|
||||
"${common_args[@]}"
|
||||
--mechanism kvcache-centric
|
||||
--policy kv-aware
|
||||
--prefill-workers 2
|
||||
--decode-workers 6
|
||||
--prefill-tp-size 1
|
||||
--decode-tp-size 1
|
||||
--prefill-gpu-ids 0,1
|
||||
--decode-gpu-ids 2,3,4,5,6,7
|
||||
--transfer-backend mooncake
|
||||
--gpu-budget 8
|
||||
--kvcache-admission-mode worker
|
||||
--kvcache-seed-min-turn-id "$KVC_SEED_MIN_TURN_ID"
|
||||
--kvcache-seed-max-inflight-decode -1
|
||||
--kvcache-prefill-backup-policy release-after-transfer
|
||||
--kvcache-prefill-priority-eviction
|
||||
)
|
||||
if [[ "$KVC_SEED_ONLY_MULTITURN" == "1" ]]; then
|
||||
kvc_args+=(--kvcache-seed-only-multiturn-sessions)
|
||||
fi
|
||||
|
||||
run_dp() {
|
||||
log "=== DP cache-aware baseline: 8 direct workers ==="
|
||||
uv run agentic-pd-hybrid benchmark-live \
|
||||
"${common_args[@]}" \
|
||||
--mechanism pd-colo \
|
||||
--policy kv-aware \
|
||||
--prefill-workers 0 \
|
||||
--decode-workers 0 \
|
||||
--direct-workers 8 \
|
||||
--direct-tp-size 1 \
|
||||
--direct-gpu-ids 0,1,2,3,4,5,6,7 \
|
||||
--gpu-budget 8
|
||||
}
|
||||
|
||||
run_pd_disagg() {
|
||||
log "=== PD-disaggregation baseline: 2P6D ==="
|
||||
uv run agentic-pd-hybrid benchmark-live \
|
||||
"${common_args[@]}" \
|
||||
--mechanism pd-disaggregation \
|
||||
--policy kv-aware \
|
||||
--prefill-workers 2 \
|
||||
--decode-workers 6 \
|
||||
--prefill-tp-size 1 \
|
||||
--decode-tp-size 1 \
|
||||
--prefill-gpu-ids 0,1 \
|
||||
--decode-gpu-ids 2,3,4,5,6,7 \
|
||||
--transfer-backend mooncake \
|
||||
--gpu-budget 8
|
||||
}
|
||||
|
||||
run_pd_sticky() {
|
||||
log "=== PD-disaggregation sticky baseline: 2P6D ==="
|
||||
uv run agentic-pd-hybrid benchmark-live \
|
||||
"${common_args[@]}" \
|
||||
--mechanism pd-disaggregation \
|
||||
--policy sticky \
|
||||
--prefill-workers 2 \
|
||||
--decode-workers 6 \
|
||||
--prefill-tp-size 1 \
|
||||
--decode-tp-size 1 \
|
||||
--prefill-gpu-ids 0,1 \
|
||||
--decode-gpu-ids 2,3,4,5,6,7 \
|
||||
--transfer-backend mooncake \
|
||||
--gpu-budget 8
|
||||
}
|
||||
|
||||
run_kvc() {
|
||||
log "=== KVC baseline: 2P6D worker admission, no backpressure ==="
|
||||
uv run agentic-pd-hybrid benchmark-live "${kvc_args[@]}"
|
||||
}
|
||||
|
||||
run_kvc_bp() {
|
||||
log "=== KVC candidate: 2P6D worker admission + backpressure ==="
|
||||
uv run agentic-pd-hybrid benchmark-live \
|
||||
"${kvc_args[@]}" \
|
||||
--enable-backpressure \
|
||||
--backpressure-max-pause-s 2.0
|
||||
}
|
||||
|
||||
summarize_latest() {
|
||||
log "=== Latest summaries ==="
|
||||
find "$OUT_ROOT" -maxdepth 2 -name 'request-metrics.jsonl.summary.json' -print \
|
||||
| sort \
|
||||
| while read -r summary; do
|
||||
python - "$summary" <<'PY'
|
||||
import json, sys
|
||||
p=sys.argv[1]
|
||||
d=json.load(open(p))
|
||||
lat=d.get("latency_stats_s") or {}
|
||||
tt=d.get("ttft_stats_s") or {}
|
||||
em=d.get("execution_modes") or {}
|
||||
print(p)
|
||||
print(" reqs", d.get("request_count"), "errors", d.get("error_count"), "trunc", d.get("truncated_request_count"))
|
||||
print(" lat mean/p50/p90/p99", lat.get("mean"), lat.get("p50"), lat.get("p90"), lat.get("p99"))
|
||||
print(" ttft mean/p50/p90", tt.get("mean"), tt.get("p50"), tt.get("p90"))
|
||||
print(" modes", em)
|
||||
PY
|
||||
done | tee -a "$LOG"
|
||||
}
|
||||
|
||||
log "Trace: $TRACE"
|
||||
log "Model: $MODEL"
|
||||
log "Runs: $RUNS | time-scale=$TIME_SCALE concurrency=$CONCURRENCY | kvc-seed-min-turn-id=$KVC_SEED_MIN_TURN_ID | kvc-seed-only-multiturn=$KVC_SEED_ONLY_MULTITURN"
|
||||
|
||||
for run in $RUNS; do
|
||||
case "$run" in
|
||||
dp) run_dp ;;
|
||||
pd) run_pd_disagg ;;
|
||||
pd_sticky) run_pd_sticky ;;
|
||||
kvc) run_kvc ;;
|
||||
kvc_bp) run_kvc_bp ;;
|
||||
*) log "Unknown run name: $run"; exit 2 ;;
|
||||
esac
|
||||
done
|
||||
|
||||
summarize_latest
|
||||
log "DONE"
|
||||
146
scripts/sweep_ts1_kvc_n3_plus_dp.sh
Executable file
146
scripts/sweep_ts1_kvc_n3_plus_dp.sh
Executable file
@@ -0,0 +1,146 @@
|
||||
#!/bin/bash
|
||||
# Time-scale=1 validation sweep, downscaled to 4 GPUs:
|
||||
# - KVC v5 1P3D × N=3 (new data, validates §1/§2 structural claims at real timing)
|
||||
# - 4-way DP cache-aware × 1 (sanity baseline at same scale + ts=1)
|
||||
#
|
||||
# Goal: per docs/AGENTIC_FIT_ANALYSIS_ZH.md §7 / TEAM_REPORT §2.6 — all v3-v6 KVC
|
||||
# data was at time-scale=10 (inter-turn gap p50 = 0.25s, vs real 2.5s). This run
|
||||
# tests whether the gap structurally reverses any conclusion.
|
||||
#
|
||||
# CONFIG NOTE: Original experiments used 8 GPUs (2P6D / 8-way DP). This host has
|
||||
# only 4 H100s available, so we downscale proportionally to 1P3D / 4-way DP.
|
||||
# Cross-compare against existing 2P6D ts=10 data is confounded by *both*
|
||||
# time-scale and capacity. Internal comparison (1P3D KVC vs 4DP) at ts=1 is the
|
||||
# clean signal. §5 (P-side imbalance) is NOT testable here — only 1 P.
|
||||
#
|
||||
# Capacity ratio: 3D × ~92K tok = 276K KV pool vs 52 sessions × ~50K peak input
|
||||
# working set ≈ 1.5M → ~5.4× overload (vs 2.7× in original 2P6D).
|
||||
# Pressure is HIGHER than original; partly offset by ts=1 letting D drain between turns.
|
||||
#
|
||||
# Output:
|
||||
# outputs/qwen3-30b-tp1-ts1-validation/
|
||||
# ├── kvc_1p3d_run{1,2,3}_summary.json
|
||||
# ├── kvc_1p3d_run{1,2,3}_metrics.jsonl
|
||||
# ├── dp4_summary.json
|
||||
# ├── dp4_metrics.jsonl
|
||||
# └── kvcache-centric-... / pd-colo-kv-aware-... (raw run dirs)
|
||||
#
|
||||
# Estimated GPU time: KVC ts=1 ≈ 100-180 min/run × 3 = 5-9h
|
||||
# DP ts=1 ≈ 100-120 min × 1 = ~2h
|
||||
# Total = 7-11h
|
||||
set -euo pipefail
|
||||
cd "$(dirname "$0")/.."
|
||||
|
||||
MODEL=/mnt/kzlin/workflow/pd-hybrid/simm-swe-bench/models/Qwen3-30B-A3B-Instruct-2507
|
||||
TRACE=outputs/qwen35-swebench-50sess.jsonl
|
||||
OUTPUT=outputs/qwen3-30b-tp1-ts1-validation
|
||||
VENV_PYTHON=.venv/bin/python
|
||||
RESULTS_FILE=$OUTPUT/sweep_results.txt
|
||||
|
||||
mkdir -p $OUTPUT
|
||||
|
||||
log() {
|
||||
echo "[$(date '+%Y-%m-%d %H:%M:%S')] $*" | tee -a $RESULTS_FILE
|
||||
}
|
||||
|
||||
run_kvc_1p3d() {
|
||||
local run_idx=$1
|
||||
local label="kvc_1p3d_run${run_idx}"
|
||||
log ""
|
||||
log "=== [KVC ${run_idx}/3] 1P3D KVC kv-aware Option D, time-scale=1 ==="
|
||||
PYTHONPATH=src:third_party/sglang/python \
|
||||
$VENV_PYTHON -m agentic_pd_hybrid.cli benchmark-live \
|
||||
--trace $TRACE \
|
||||
--output-root $OUTPUT \
|
||||
--mechanism kvcache-centric \
|
||||
--policy kv-aware \
|
||||
--model-path $MODEL \
|
||||
--prefill-workers 1 --decode-workers 3 \
|
||||
--prefill-tp-size 1 --decode-tp-size 1 \
|
||||
--prefill-gpu-ids 0 --decode-gpu-ids 1,2,3 \
|
||||
--transfer-backend mooncake \
|
||||
--gpu-budget 4 \
|
||||
--time-scale 1 \
|
||||
--session-sample-rate 1.0 \
|
||||
--target-duration-s 100000 \
|
||||
--concurrency-limit 32 \
|
||||
--timeout-s 900 \
|
||||
--request-timeout-s 300 \
|
||||
--kvcache-admission-mode worker \
|
||||
--kvcache-seed-min-turn-id 1 \
|
||||
--kvcache-seed-max-inflight-decode -1 \
|
||||
--kvcache-prefill-backup-policy release-after-transfer \
|
||||
--kvcache-prefill-priority-eviction
|
||||
|
||||
local run_dir=$(ls -td $OUTPUT/kvcache-centric-*/ 2>/dev/null | head -1)
|
||||
log "=== [KVC ${run_idx}/3] $label COMPLETED ==="
|
||||
if [ -f "$run_dir/request-metrics.jsonl.summary.json" ]; then
|
||||
cp "$run_dir/request-metrics.jsonl.summary.json" "$OUTPUT/${label}_summary.json"
|
||||
cp "$run_dir/request-metrics.jsonl" "$OUTPUT/${label}_metrics.jsonl"
|
||||
local errs=$($VENV_PYTHON -c "import json; d=json.load(open('$OUTPUT/${label}_summary.json')); print(d.get('error_count',0))")
|
||||
log " errors = $errs"
|
||||
cat "$run_dir/request-metrics.jsonl.summary.json" >> $RESULTS_FILE
|
||||
echo "" >> $RESULTS_FILE
|
||||
else
|
||||
log "WARNING: no summary file in $run_dir"
|
||||
fi
|
||||
}
|
||||
|
||||
run_dp4_sanity() {
|
||||
local label="dp4"
|
||||
log ""
|
||||
log "=== [DP] 4-way DP cache-aware sanity, time-scale=1 ==="
|
||||
PYTHONPATH=src:third_party/sglang/python \
|
||||
$VENV_PYTHON -m agentic_pd_hybrid.cli benchmark-live \
|
||||
--trace $TRACE \
|
||||
--output-root $OUTPUT \
|
||||
--mechanism pd-colo \
|
||||
--policy kv-aware \
|
||||
--model-path $MODEL \
|
||||
--prefill-workers 0 --decode-workers 0 \
|
||||
--direct-workers 4 --direct-tp-size 1 \
|
||||
--direct-gpu-ids 0,1,2,3 \
|
||||
--gpu-budget 4 \
|
||||
--time-scale 1 \
|
||||
--session-sample-rate 1.0 \
|
||||
--target-duration-s 100000 \
|
||||
--concurrency-limit 32 \
|
||||
--timeout-s 900 \
|
||||
--request-timeout-s 300
|
||||
|
||||
local run_dir=$(ls -td $OUTPUT/pd-colo-kv-aware-*/ 2>/dev/null | head -1)
|
||||
log "=== [DP] $label COMPLETED ==="
|
||||
if [ -f "$run_dir/request-metrics.jsonl.summary.json" ]; then
|
||||
cp "$run_dir/request-metrics.jsonl.summary.json" "$OUTPUT/${label}_summary.json"
|
||||
cp "$run_dir/request-metrics.jsonl" "$OUTPUT/${label}_metrics.jsonl"
|
||||
local errs=$($VENV_PYTHON -c "import json; d=json.load(open('$OUTPUT/${label}_summary.json')); print(d.get('error_count',0))")
|
||||
log " errors = $errs"
|
||||
cat "$run_dir/request-metrics.jsonl.summary.json" >> $RESULTS_FILE
|
||||
echo "" >> $RESULTS_FILE
|
||||
else
|
||||
log "WARNING: no summary file in $run_dir"
|
||||
fi
|
||||
}
|
||||
|
||||
log "=== TS=1 VALIDATION (4-GPU): KVC 1P3D × N=3 + 4DP × 1 ==="
|
||||
log "Model: $MODEL"
|
||||
log "Trace: $TRACE (4449 requests, 52 sessions)"
|
||||
log "Goal: validate whether ts=10 was the main distortion in v3-v6 KVC vs DP"
|
||||
|
||||
# KVC × 3 first (the new data we need); DP last (cheaper sanity at end)
|
||||
for i in 1 2 3; do
|
||||
run_kvc_1p3d $i
|
||||
done
|
||||
|
||||
run_dp4_sanity
|
||||
|
||||
log ""
|
||||
log "=== TS=1 SUMMARY ==="
|
||||
for label in kvc_1p3d_run1 kvc_1p3d_run2 kvc_1p3d_run3 dp4; do
|
||||
if [ -f "$OUTPUT/${label}_summary.json" ]; then
|
||||
e=$($VENV_PYTHON -c "import json; d=json.load(open('$OUTPUT/${label}_summary.json')); print(d.get('error_count',0))")
|
||||
p50=$($VENV_PYTHON -c "import json; d=json.load(open('$OUTPUT/${label}_summary.json')); print(d.get('latency_stats_s',{}).get('p50','n/a'))")
|
||||
log " ${label}: errors=$e lat_p50=${p50}s"
|
||||
fi
|
||||
done
|
||||
log "=== TS=1 ALL DONE ==="
|
||||
65
scripts/sweep_ts1_migration_v1.sh
Executable file
65
scripts/sweep_ts1_migration_v1.sh
Executable file
@@ -0,0 +1,65 @@
|
||||
#!/bin/bash
|
||||
# Migration v1 validation: KVC 1P3D ts=1 with --kvcache-migration-reject-threshold=3
|
||||
# Compare against baseline outputs/qwen3-30b-tp1-ts1-validation/kvc_1p3d_run{1,2,3}
|
||||
# (all of which had no migration — runs were structurally identical).
|
||||
#
|
||||
# Goal: verify §1 fix changes the categorical outcome — direct-to-D % up,
|
||||
# fallback-session-not-resident % down, lat mean down.
|
||||
#
|
||||
# ts=1 is deterministic at the categorical level, so N=1 is sufficient
|
||||
# (TEAM_REPORT §2.8 revised).
|
||||
set -euo pipefail
|
||||
cd "$(dirname "$0")/.."
|
||||
|
||||
MODEL=/mnt/kzlin/workflow/pd-hybrid/simm-swe-bench/models/Qwen3-30B-A3B-Instruct-2507
|
||||
TRACE=outputs/qwen35-swebench-50sess.jsonl
|
||||
OUTPUT=outputs/qwen3-30b-tp1-ts1-migration-v1
|
||||
VENV_PYTHON=.venv/bin/python
|
||||
RESULTS_FILE=$OUTPUT/sweep_results.txt
|
||||
|
||||
mkdir -p $OUTPUT
|
||||
|
||||
log() { echo "[$(date '+%Y-%m-%d %H:%M:%S')] $*" | tee -a $RESULTS_FILE; }
|
||||
|
||||
log "=== TS=1 MIGRATION v1: KVC 1P3D --kvcache-migration-reject-threshold=3 ==="
|
||||
log "Baseline reference: outputs/qwen3-30b-tp1-ts1-validation/kvc_1p3d_run1 (errors=5, lat mean=1.574s, direct-to-D=42.8%)"
|
||||
|
||||
label=kvc_1p3d_migration_run1
|
||||
log ""
|
||||
log "=== [migration v1] starting ==="
|
||||
PYTHONPATH=src:third_party/sglang/python \
|
||||
$VENV_PYTHON -m agentic_pd_hybrid.cli benchmark-live \
|
||||
--trace $TRACE \
|
||||
--output-root $OUTPUT \
|
||||
--mechanism kvcache-centric \
|
||||
--policy kv-aware \
|
||||
--model-path $MODEL \
|
||||
--prefill-workers 1 --decode-workers 3 \
|
||||
--prefill-tp-size 1 --decode-tp-size 1 \
|
||||
--prefill-gpu-ids 0 --decode-gpu-ids 1,2,3 \
|
||||
--transfer-backend mooncake \
|
||||
--gpu-budget 4 \
|
||||
--time-scale 1 \
|
||||
--session-sample-rate 1.0 \
|
||||
--target-duration-s 100000 \
|
||||
--concurrency-limit 32 \
|
||||
--timeout-s 900 \
|
||||
--request-timeout-s 300 \
|
||||
--kvcache-admission-mode worker \
|
||||
--kvcache-seed-min-turn-id 1 \
|
||||
--kvcache-seed-max-inflight-decode -1 \
|
||||
--kvcache-prefill-backup-policy release-after-transfer \
|
||||
--kvcache-prefill-priority-eviction \
|
||||
--kvcache-migration-reject-threshold 3
|
||||
|
||||
run_dir=$(ls -td $OUTPUT/kvcache-centric-*/ 2>/dev/null | head -1)
|
||||
log "=== [migration v1] $label COMPLETED ==="
|
||||
if [ -f "$run_dir/request-metrics.jsonl.summary.json" ]; then
|
||||
cp "$run_dir/request-metrics.jsonl.summary.json" "$OUTPUT/${label}_summary.json"
|
||||
cp "$run_dir/request-metrics.jsonl" "$OUTPUT/${label}_metrics.jsonl"
|
||||
errs=$($VENV_PYTHON -c "import json; d=json.load(open('$OUTPUT/${label}_summary.json')); print(d.get('error_count',0))")
|
||||
p50=$($VENV_PYTHON -c "import json; d=json.load(open('$OUTPUT/${label}_summary.json')); print(d.get('latency_stats_s',{}).get('p50',0))")
|
||||
log " errors=$errs lat_p50=${p50}s"
|
||||
cat "$run_dir/request-metrics.jsonl.summary.json" >> $RESULTS_FILE
|
||||
fi
|
||||
log "=== migration v1 DONE ==="
|
||||
76
scripts/sweep_ts1_migration_v2.sh
Executable file
76
scripts/sweep_ts1_migration_v2.sh
Executable file
@@ -0,0 +1,76 @@
|
||||
#!/bin/bash
|
||||
# Migration v2 validation: KVC 1P3D ts=1 with BOTH:
|
||||
# (1) reset-on-success blacklist decay (replay.py code change)
|
||||
# (2) --kvcache-direct-max-uncached-tokens 8192 (was 2048 default)
|
||||
#
|
||||
# v1 results (kvc_1p3d_migration_run1) showed:
|
||||
# - lat mean WORSE +11.7%, TTFT mean WORSE +71.3% — thrashing tax
|
||||
# - direct-to-D rate UP +10.5pp (42.8 → 53.3%)
|
||||
# - Fallback breakdown surprise: 41.3% are 'real-large-append' (>2048 tok),
|
||||
# NOT 'session-not-resident' as we hypothesized
|
||||
#
|
||||
# v2 design (REFACTOR_PLAN_V1 + MIGRATION_V1_FINDINGS):
|
||||
# (1) reset-on-success: clear (sess,D) reject counter on successful direct-to-D
|
||||
# — eliminates blacklist-permanence bug → kills thrashing
|
||||
# (2) bump direct-append threshold 2048 → 8192: lets more large-append turns
|
||||
# go direct-to-D instead of fall through to seed (which often rejects)
|
||||
set -euo pipefail
|
||||
cd "$(dirname "$0")/.."
|
||||
|
||||
MODEL=/mnt/kzlin/workflow/pd-hybrid/simm-swe-bench/models/Qwen3-30B-A3B-Instruct-2507
|
||||
TRACE=outputs/qwen35-swebench-50sess.jsonl
|
||||
OUTPUT=outputs/qwen3-30b-tp1-ts1-migration-v2
|
||||
VENV_PYTHON=.venv/bin/python
|
||||
RESULTS_FILE=$OUTPUT/sweep_results.txt
|
||||
|
||||
mkdir -p $OUTPUT
|
||||
|
||||
log() { echo "[$(date '+%Y-%m-%d %H:%M:%S')] $*" | tee -a $RESULTS_FILE; }
|
||||
|
||||
log "=== TS=1 MIGRATION v2: reset-on-success + threshold=8192 ==="
|
||||
log "Baselines:"
|
||||
log " baseline (no migration): kvc_1p3d_run1 errors=5 lat_p50=0.811s ttft_p50=0.124s direct=42.8%"
|
||||
log " v1 (migration permanent): kvc_1p3d_migration_run1 errors=6 lat_p50=0.773s ttft_p50=0.057s direct=53.3% lat_mean=1.758s"
|
||||
log " 4DP ts=1: errors=0 lat_p50=0.659s ttft_p50=0.090s lat_mean=1.443s"
|
||||
log "Goal: kill thrashing tax (lat_mean ≤ 1.5s, p99 ≤ 9s) while preserving v1's direct-to-D gains."
|
||||
|
||||
label=kvc_1p3d_migration_v2_run1
|
||||
log ""
|
||||
log "=== [migration v2] starting ==="
|
||||
PYTHONPATH=src:third_party/sglang/python \
|
||||
$VENV_PYTHON -m agentic_pd_hybrid.cli benchmark-live \
|
||||
--trace $TRACE \
|
||||
--output-root $OUTPUT \
|
||||
--mechanism kvcache-centric \
|
||||
--policy kv-aware \
|
||||
--model-path $MODEL \
|
||||
--prefill-workers 1 --decode-workers 3 \
|
||||
--prefill-tp-size 1 --decode-tp-size 1 \
|
||||
--prefill-gpu-ids 0 --decode-gpu-ids 1,2,3 \
|
||||
--transfer-backend mooncake \
|
||||
--gpu-budget 4 \
|
||||
--time-scale 1 \
|
||||
--session-sample-rate 1.0 \
|
||||
--target-duration-s 100000 \
|
||||
--concurrency-limit 32 \
|
||||
--timeout-s 900 \
|
||||
--request-timeout-s 300 \
|
||||
--kvcache-admission-mode worker \
|
||||
--kvcache-seed-min-turn-id 1 \
|
||||
--kvcache-seed-max-inflight-decode -1 \
|
||||
--kvcache-prefill-backup-policy release-after-transfer \
|
||||
--kvcache-prefill-priority-eviction \
|
||||
--kvcache-migration-reject-threshold 3 \
|
||||
--kvcache-direct-max-uncached-tokens 8192
|
||||
|
||||
run_dir=$(ls -td $OUTPUT/kvcache-centric-*/ 2>/dev/null | head -1)
|
||||
log "=== [migration v2] $label COMPLETED ==="
|
||||
if [ -f "$run_dir/request-metrics.jsonl.summary.json" ]; then
|
||||
cp "$run_dir/request-metrics.jsonl.summary.json" "$OUTPUT/${label}_summary.json"
|
||||
cp "$run_dir/request-metrics.jsonl" "$OUTPUT/${label}_metrics.jsonl"
|
||||
errs=$($VENV_PYTHON -c "import json; d=json.load(open('$OUTPUT/${label}_summary.json')); print(d.get('error_count',0))")
|
||||
p50=$($VENV_PYTHON -c "import json; d=json.load(open('$OUTPUT/${label}_summary.json')); print(d.get('latency_stats_s',{}).get('p50',0))")
|
||||
log " errors=$errs lat_p50=${p50}s"
|
||||
cat "$run_dir/request-metrics.jsonl.summary.json" >> $RESULTS_FILE
|
||||
fi
|
||||
log "=== migration v2 DONE ==="
|
||||
@@ -3,20 +3,13 @@ from __future__ import annotations
|
||||
import asyncio
|
||||
import json
|
||||
import signal
|
||||
import shutil
|
||||
from collections import Counter
|
||||
from dataclasses import asdict, dataclass, replace
|
||||
from datetime import UTC, datetime
|
||||
from pathlib import Path
|
||||
|
||||
from agentic_pd_hybrid.replay import ReplayConfig, replay_trace
|
||||
from agentic_pd_hybrid.sampling import (
|
||||
SessionSampleConfig,
|
||||
SessionSampleSummary,
|
||||
sample_trace_sessions,
|
||||
)
|
||||
from agentic_pd_hybrid.sampling import SessionSampleConfig, sample_trace_sessions
|
||||
from agentic_pd_hybrid.stack import ManagedPdStack, launch_pd_stack
|
||||
from agentic_pd_hybrid.trace import load_trace
|
||||
from agentic_pd_hybrid.topology import SingleNodeTopology
|
||||
|
||||
|
||||
@@ -54,14 +47,13 @@ class BenchmarkConfig:
|
||||
pool_poll_include_sessions: bool = True
|
||||
enable_backpressure: bool = False
|
||||
backpressure_max_pause_s: float = 2.0
|
||||
progress_interval_s: float = 30.0
|
||||
kvcache_migration_reject_threshold: int = 3
|
||||
sample_profile: str = "default"
|
||||
min_initial_input_tokens: int | None = None
|
||||
max_initial_input_tokens: int | None = None
|
||||
max_append_input_tokens: int | None = None
|
||||
max_output_tokens: int | None = None
|
||||
min_overlap_ratio: float | None = None
|
||||
use_trace_as_sample: bool = False
|
||||
launch_stack: bool = True
|
||||
|
||||
|
||||
@@ -103,37 +95,22 @@ def run_live_benchmark(config: BenchmarkConfig) -> BenchmarkArtifacts:
|
||||
)
|
||||
|
||||
sampled_trace_path = run_dir / "sampled-trace.jsonl"
|
||||
if config.use_trace_as_sample:
|
||||
shutil.copyfile(config.trace_path, sampled_trace_path)
|
||||
sample_summary = _summarize_trace_sample(
|
||||
input_trace_path=config.trace_path,
|
||||
sampled_trace_path=sampled_trace_path,
|
||||
profile=config.sample_profile,
|
||||
sample_summary = sample_trace_sessions(
|
||||
SessionSampleConfig(
|
||||
trace_path=config.trace_path,
|
||||
output_path=sampled_trace_path,
|
||||
target_duration_s=config.target_duration_s,
|
||||
start_time_s=config.start_time_s,
|
||||
session_sample_rate=config.session_sample_rate,
|
||||
min_turns=config.min_turns,
|
||||
profile=config.sample_profile, # type: ignore[arg-type]
|
||||
min_initial_input_tokens=config.min_initial_input_tokens,
|
||||
max_initial_input_tokens=config.max_initial_input_tokens,
|
||||
max_append_input_tokens=config.max_append_input_tokens,
|
||||
max_output_tokens=config.max_output_tokens,
|
||||
min_overlap_ratio=config.min_overlap_ratio,
|
||||
)
|
||||
else:
|
||||
sample_summary = sample_trace_sessions(
|
||||
SessionSampleConfig(
|
||||
trace_path=config.trace_path,
|
||||
output_path=sampled_trace_path,
|
||||
target_duration_s=config.target_duration_s,
|
||||
start_time_s=config.start_time_s,
|
||||
session_sample_rate=config.session_sample_rate,
|
||||
min_turns=config.min_turns,
|
||||
profile=config.sample_profile, # type: ignore[arg-type]
|
||||
min_initial_input_tokens=config.min_initial_input_tokens,
|
||||
max_initial_input_tokens=config.max_initial_input_tokens,
|
||||
max_append_input_tokens=config.max_append_input_tokens,
|
||||
max_output_tokens=config.max_output_tokens,
|
||||
min_overlap_ratio=config.min_overlap_ratio,
|
||||
)
|
||||
)
|
||||
)
|
||||
|
||||
stack: ManagedPdStack | None = None
|
||||
previous_sigint = signal.getsignal(signal.SIGINT)
|
||||
@@ -222,7 +199,7 @@ def run_live_benchmark(config: BenchmarkConfig) -> BenchmarkArtifacts:
|
||||
pool_poll_include_sessions=config.pool_poll_include_sessions,
|
||||
enable_backpressure=config.enable_backpressure,
|
||||
backpressure_max_pause_s=config.backpressure_max_pause_s,
|
||||
progress_interval_s=config.progress_interval_s,
|
||||
kvcache_migration_reject_threshold=config.kvcache_migration_reject_threshold,
|
||||
)
|
||||
if config.request_timeout_s is not None:
|
||||
replay_config = replace(
|
||||
@@ -283,14 +260,13 @@ def run_live_benchmark(config: BenchmarkConfig) -> BenchmarkArtifacts:
|
||||
"pool_poll_include_sessions": config.pool_poll_include_sessions,
|
||||
"enable_backpressure": config.enable_backpressure,
|
||||
"backpressure_max_pause_s": config.backpressure_max_pause_s,
|
||||
"progress_interval_s": config.progress_interval_s,
|
||||
"kvcache_migration_reject_threshold": config.kvcache_migration_reject_threshold,
|
||||
"sample_profile": config.sample_profile,
|
||||
"min_initial_input_tokens": config.min_initial_input_tokens,
|
||||
"max_initial_input_tokens": config.max_initial_input_tokens,
|
||||
"max_append_input_tokens": config.max_append_input_tokens,
|
||||
"max_output_tokens": config.max_output_tokens,
|
||||
"min_overlap_ratio": config.min_overlap_ratio,
|
||||
"use_trace_as_sample": config.use_trace_as_sample,
|
||||
"sample_summary": asdict(sample_summary),
|
||||
"topology": {
|
||||
"model_path": config.topology.model_path,
|
||||
@@ -337,44 +313,3 @@ def _header_mode_for(policy_name: str) -> str:
|
||||
if policy_name == "kv-aware":
|
||||
return "target-worker"
|
||||
return "none"
|
||||
|
||||
|
||||
def _summarize_trace_sample(
|
||||
*,
|
||||
input_trace_path: Path,
|
||||
sampled_trace_path: Path,
|
||||
profile: str,
|
||||
session_sample_rate: float,
|
||||
min_turns: int,
|
||||
min_initial_input_tokens: int | None,
|
||||
max_initial_input_tokens: int | None,
|
||||
max_append_input_tokens: int | None,
|
||||
max_output_tokens: int | None,
|
||||
min_overlap_ratio: float | None,
|
||||
) -> SessionSampleSummary:
|
||||
requests = load_trace(sampled_trace_path)
|
||||
if not requests:
|
||||
raise ValueError(f"Trace sample is empty: {sampled_trace_path}")
|
||||
session_turns = Counter(request.session_id for request in requests)
|
||||
start_time_s = requests[0].timestamp_s
|
||||
end_time_s = requests[-1].timestamp_s
|
||||
return SessionSampleSummary(
|
||||
input_trace_path=str(input_trace_path),
|
||||
output_trace_path=str(sampled_trace_path),
|
||||
request_count=len(requests),
|
||||
session_count=len(session_turns),
|
||||
multi_turn_session_count=sum(1 for turns in session_turns.values() if turns > 1),
|
||||
start_time_s=start_time_s,
|
||||
end_time_s=end_time_s,
|
||||
sampled_duration_s=end_time_s - start_time_s,
|
||||
session_sample_rate=session_sample_rate,
|
||||
min_turns=min_turns,
|
||||
profile=profile,
|
||||
min_initial_input_tokens=min_initial_input_tokens,
|
||||
max_initial_input_tokens=max_initial_input_tokens,
|
||||
max_append_input_tokens=max_append_input_tokens,
|
||||
max_output_tokens=max_output_tokens,
|
||||
min_overlap_ratio=min_overlap_ratio,
|
||||
mean_append_input_tokens=None,
|
||||
mean_turn_overlap_ratio=None,
|
||||
)
|
||||
|
||||
@@ -2,7 +2,6 @@ from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import asyncio
|
||||
import shlex
|
||||
from pathlib import Path
|
||||
|
||||
from agentic_pd_hybrid.benchmark import BenchmarkConfig, run_live_benchmark
|
||||
@@ -262,12 +261,13 @@ def main() -> None:
|
||||
help="Cap on per-request backpressure sleep, regardless of D hint.",
|
||||
)
|
||||
replay.add_argument(
|
||||
"--progress-interval-s",
|
||||
type=float,
|
||||
default=30.0,
|
||||
"--kvcache-migration-reject-threshold",
|
||||
type=int,
|
||||
default=3,
|
||||
help=(
|
||||
"Write client-side replay progress to <output_dir>/replay-progress.jsonl "
|
||||
"every N seconds. 0 disables the heartbeat."
|
||||
"Per-(session, D) admission-reject count after which KvAwarePolicy "
|
||||
"skips that D for the session (forces migration). 0 disables. "
|
||||
"See REFACTOR_PLAN_V1 §6.2 / TEAM_REPORT §2.1."
|
||||
),
|
||||
)
|
||||
|
||||
@@ -512,12 +512,13 @@ def main() -> None:
|
||||
help="Cap on per-request backpressure sleep, regardless of D hint.",
|
||||
)
|
||||
benchmark.add_argument(
|
||||
"--progress-interval-s",
|
||||
type=float,
|
||||
default=30.0,
|
||||
"--kvcache-migration-reject-threshold",
|
||||
type=int,
|
||||
default=3,
|
||||
help=(
|
||||
"Write client-side replay progress to <run_dir>/replay-progress.jsonl "
|
||||
"every N seconds. 0 disables the heartbeat."
|
||||
"Per-(session, D) admission-reject count after which KvAwarePolicy "
|
||||
"skips that D for the session (forces migration). 0 disables. "
|
||||
"See REFACTOR_PLAN_V1 §6.2 / TEAM_REPORT §2.1."
|
||||
),
|
||||
)
|
||||
benchmark.add_argument(
|
||||
@@ -531,14 +532,6 @@ def main() -> None:
|
||||
benchmark.add_argument("--max-append-input-tokens", type=int, default=None)
|
||||
benchmark.add_argument("--max-output-tokens", type=int, default=None)
|
||||
benchmark.add_argument("--min-overlap-ratio", type=float, default=None)
|
||||
benchmark.add_argument(
|
||||
"--use-trace-as-sample",
|
||||
action="store_true",
|
||||
help=(
|
||||
"Replay the provided --trace exactly instead of sampling sessions into "
|
||||
"a new trace. Use this for prebuilt real-workload samples."
|
||||
),
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
@@ -613,7 +606,7 @@ def main() -> None:
|
||||
pool_poll_include_sessions=not args.pool_poll_no_sessions,
|
||||
enable_backpressure=args.enable_backpressure,
|
||||
backpressure_max_pause_s=args.backpressure_max_pause_s,
|
||||
progress_interval_s=args.progress_interval_s,
|
||||
kvcache_migration_reject_threshold=args.kvcache_migration_reject_threshold,
|
||||
)
|
||||
results = asyncio.run(replay_trace(config))
|
||||
print(
|
||||
@@ -760,14 +753,13 @@ def main() -> None:
|
||||
pool_poll_include_sessions=not args.pool_poll_no_sessions,
|
||||
enable_backpressure=args.enable_backpressure,
|
||||
backpressure_max_pause_s=args.backpressure_max_pause_s,
|
||||
progress_interval_s=args.progress_interval_s,
|
||||
kvcache_migration_reject_threshold=args.kvcache_migration_reject_threshold,
|
||||
sample_profile=args.sample_profile,
|
||||
min_initial_input_tokens=args.min_initial_input_tokens,
|
||||
max_initial_input_tokens=args.max_initial_input_tokens,
|
||||
max_append_input_tokens=args.max_append_input_tokens,
|
||||
max_output_tokens=args.max_output_tokens,
|
||||
min_overlap_ratio=args.min_overlap_ratio,
|
||||
use_trace_as_sample=args.use_trace_as_sample,
|
||||
launch_stack=True,
|
||||
)
|
||||
)
|
||||
@@ -827,26 +819,6 @@ def _add_topology_arguments(parser: argparse.ArgumentParser) -> None:
|
||||
"--no-trust-remote-code",
|
||||
action="store_true",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--extra-server-args",
|
||||
default="",
|
||||
help="Extra arguments appended to every sglang.launch_server command.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--prefill-extra-server-args",
|
||||
default="",
|
||||
help="Extra arguments appended only to prefill launch_server commands.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--decode-extra-server-args",
|
||||
default="",
|
||||
help="Extra arguments appended only to decode launch_server commands.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--direct-extra-server-args",
|
||||
default="",
|
||||
help="Extra arguments appended only to direct launch_server commands.",
|
||||
)
|
||||
|
||||
|
||||
def _topology_from_args(args: argparse.Namespace):
|
||||
@@ -876,13 +848,7 @@ def _topology_from_args(args: argparse.Namespace):
|
||||
force_rdma=args.force_rdma,
|
||||
trust_remote_code=not args.no_trust_remote_code,
|
||||
ib_device=args.ib_device,
|
||||
extra_server_args=tuple(shlex.split(args.extra_server_args)),
|
||||
prefill_extra_server_args=tuple(shlex.split(args.prefill_extra_server_args)),
|
||||
decode_extra_server_args=tuple(shlex.split(args.decode_extra_server_args)),
|
||||
direct_extra_server_args=(
|
||||
"--enable-streaming-session",
|
||||
*tuple(shlex.split(args.direct_extra_server_args)),
|
||||
),
|
||||
direct_extra_server_args=("--enable-streaming-session",),
|
||||
)
|
||||
|
||||
|
||||
|
||||
@@ -114,6 +114,16 @@ def write_metrics_jsonl(path: Path, rows: list[RequestMetrics]) -> None:
|
||||
handle.write(json.dumps(asdict(row), sort_keys=True) + "\n")
|
||||
|
||||
|
||||
def _is_failed_request(row: RequestMetrics) -> bool:
|
||||
if row.error is not None:
|
||||
return True
|
||||
if row.finish_reason is not None:
|
||||
fr = str(row.finish_reason).lower()
|
||||
if "abort" in fr or "badrequest" in fr:
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def write_summary_json(
|
||||
path: Path,
|
||||
rows: list[RequestMetrics],
|
||||
@@ -121,9 +131,10 @@ def write_summary_json(
|
||||
trace_path: Path,
|
||||
router_url: str | None,
|
||||
) -> None:
|
||||
latencies = [row.latency_s for row in rows if row.latency_s is not None]
|
||||
ttfts = [row.ttft_s for row in rows if row.ttft_s is not None]
|
||||
tpots = [row.tpot_s for row in rows if row.tpot_s is not None]
|
||||
successful = [row for row in rows if not _is_failed_request(row)]
|
||||
latencies = [row.latency_s for row in successful if row.latency_s is not None]
|
||||
ttfts = [row.ttft_s for row in successful if row.ttft_s is not None]
|
||||
tpots = [row.tpot_s for row in successful if row.tpot_s is not None]
|
||||
per_decode_load = Counter(row.assigned_decode_node for row in rows)
|
||||
per_prefill_load = Counter(row.assigned_prefill_node for row in rows)
|
||||
prefill_priorities = Counter(
|
||||
@@ -167,6 +178,17 @@ def write_summary_json(
|
||||
str(key): value for key, value in sorted(decode_priorities.items())
|
||||
},
|
||||
"error_count": sum(1 for row in rows if row.error is not None),
|
||||
"abort_count": sum(
|
||||
1
|
||||
for row in rows
|
||||
if row.error is None
|
||||
and row.finish_reason is not None
|
||||
and (
|
||||
"abort" in str(row.finish_reason).lower()
|
||||
or "badrequest" in str(row.finish_reason).lower()
|
||||
)
|
||||
),
|
||||
"failure_count": sum(1 for row in rows if _is_failed_request(row)),
|
||||
"truncated_request_count": sum(
|
||||
1
|
||||
for row in rows
|
||||
|
||||
@@ -44,6 +44,10 @@ class RoutingState:
|
||||
inflight_decode: Counter[str] = field(default_factory=Counter)
|
||||
decode_assignment_counts: Counter[str] = field(default_factory=Counter)
|
||||
decode_resident_blocks: dict[str, set[int]] = field(default_factory=dict)
|
||||
# Migration support: per-(session_id, decode_worker_id) admission reject counter.
|
||||
# KvAwarePolicy uses this to skip D's that have repeatedly rejected this session
|
||||
# (avoids the structural starvation observed in TEAM_REPORT §2.1).
|
||||
session_d_rejects: Counter[tuple[str, str]] = field(default_factory=Counter)
|
||||
|
||||
@classmethod
|
||||
def create(cls, topology: SingleNodeTopology) -> "RoutingState":
|
||||
@@ -66,6 +70,12 @@ class RoutingState:
|
||||
self.decode_cursor += 1
|
||||
return worker.worker_id
|
||||
|
||||
def record_admission_reject(self, session_id: str, decode_worker_id: str) -> int:
|
||||
"""Increment per-(session, D) rejection counter. Returns new count."""
|
||||
key = (session_id, decode_worker_id)
|
||||
self.session_d_rejects[key] += 1
|
||||
return self.session_d_rejects[key]
|
||||
|
||||
def finish(self, request: TraceRequest, decision: RoutingDecision) -> None:
|
||||
session = self.session_state.setdefault(request.session_id, SessionRouteState())
|
||||
session.last_decode_worker = decision.decode_worker_id
|
||||
@@ -146,6 +156,11 @@ class StickyDecodePolicy:
|
||||
class KvAwarePolicy:
|
||||
name: str = "kv-aware"
|
||||
sticky_bonus: int = 1
|
||||
# Session migration: when (session, D) has been rejected this many times,
|
||||
# skip D entirely for this session (force migration to another D).
|
||||
# 0 disables the mechanism. Default 3 picked empirically to allow brief
|
||||
# transient saturation without panicking, but to reroute persistent starvation.
|
||||
migration_reject_threshold: int = 3
|
||||
|
||||
def select(
|
||||
self,
|
||||
@@ -158,8 +173,19 @@ class KvAwarePolicy:
|
||||
session = state.session_state.get(request.session_id)
|
||||
|
||||
best_decode_worker_id: str | None = None
|
||||
best_score: tuple[int, int, int] | None = None
|
||||
best_score: tuple[int, int, int, int] | None = None
|
||||
candidates_considered = 0
|
||||
for worker in topology.route_workers:
|
||||
# Migration: skip workers that have rejected this session too many times.
|
||||
# If all candidates get filtered (degenerate case), fall through to
|
||||
# un-filtered selection below.
|
||||
if self.migration_reject_threshold > 0:
|
||||
rejects = state.session_d_rejects.get(
|
||||
(request.session_id, worker.worker_id), 0
|
||||
)
|
||||
if rejects >= self.migration_reject_threshold:
|
||||
continue
|
||||
candidates_considered += 1
|
||||
overlap = _overlap_blocks(request, state, worker.worker_id)
|
||||
sticky = int(session is not None and session.last_decode_worker == worker.worker_id)
|
||||
inflight_penalty = -state.inflight_decode.get(worker.worker_id, 0)
|
||||
@@ -174,6 +200,16 @@ class KvAwarePolicy:
|
||||
best_score = score
|
||||
best_decode_worker_id = worker.worker_id
|
||||
|
||||
# Degenerate fallback: every D was filtered. Pick the least-rejected D.
|
||||
if best_decode_worker_id is None:
|
||||
best_decode_worker_id = min(
|
||||
(w.worker_id for w in topology.route_workers),
|
||||
key=lambda wid: state.session_d_rejects.get(
|
||||
(request.session_id, wid), 0
|
||||
),
|
||||
)
|
||||
best_score = (0, 0, 0, 0)
|
||||
|
||||
assert best_decode_worker_id is not None
|
||||
reuse_expected = bool(best_score and best_score[0] > 0)
|
||||
return _build_decision(
|
||||
@@ -187,14 +223,14 @@ class KvAwarePolicy:
|
||||
)
|
||||
|
||||
|
||||
def create_policy(name: str) -> RoutingPolicy:
|
||||
def create_policy(name: str, *, migration_reject_threshold: int = 3) -> RoutingPolicy:
|
||||
normalized = name.strip().lower()
|
||||
if normalized == "default":
|
||||
return DefaultPolicy()
|
||||
if normalized == "sticky":
|
||||
return StickyDecodePolicy()
|
||||
if normalized in {"kv-aware", "kv_aware", "kv"}:
|
||||
return KvAwarePolicy()
|
||||
return KvAwarePolicy(migration_reject_threshold=migration_reject_threshold)
|
||||
raise ValueError(f"Unsupported policy: {name}")
|
||||
|
||||
|
||||
|
||||
@@ -106,8 +106,12 @@ class ReplayConfig:
|
||||
pool_poll_include_sessions: bool = True
|
||||
enable_backpressure: bool = False
|
||||
backpressure_max_pause_s: float = 2.0
|
||||
# Session migration via per-(sess, D) admission reject memory.
|
||||
# When a session has been admission-rejected this many times on a given D,
|
||||
# KvAwarePolicy skips that D for the session (forcing migration). Default 3.
|
||||
# Set 0 to disable. See REFACTOR_PLAN_V1 §6.2.
|
||||
kvcache_migration_reject_threshold: int = 3
|
||||
structural_log_dir: Path | None = None
|
||||
progress_interval_s: float = 30.0
|
||||
|
||||
|
||||
@dataclass
|
||||
@@ -175,62 +179,6 @@ class ExecutionResult:
|
||||
finish_reason: str | None = None
|
||||
|
||||
|
||||
@dataclass
|
||||
class ReplayProgress:
|
||||
total_requests: int
|
||||
output_path: Path
|
||||
interval_s: float
|
||||
start_time_s: float
|
||||
submitted_count: int = 0
|
||||
completed_count: int = 0
|
||||
error_count: int = 0
|
||||
truncated_count: int = 0
|
||||
last_request_id: str | None = None
|
||||
last_session_id: str | None = None
|
||||
last_trace_timestamp_s: float | None = None
|
||||
execution_modes: Counter[str] = field(default_factory=Counter)
|
||||
lock: asyncio.Lock = field(default_factory=asyncio.Lock)
|
||||
|
||||
async def record_submitted(self, request: TraceRequest) -> None:
|
||||
async with self.lock:
|
||||
self.submitted_count += 1
|
||||
self.last_request_id = request.request_id
|
||||
self.last_session_id = request.session_id
|
||||
self.last_trace_timestamp_s = request.timestamp_s
|
||||
|
||||
async def record_completed(self, row: RequestMetrics) -> None:
|
||||
async with self.lock:
|
||||
self.completed_count += 1
|
||||
if row.error is not None:
|
||||
self.error_count += 1
|
||||
if _is_truncated(row):
|
||||
self.truncated_count += 1
|
||||
self.execution_modes[row.execution_mode] += 1
|
||||
self.last_request_id = row.request_id
|
||||
self.last_session_id = row.session_id
|
||||
self.last_trace_timestamp_s = row.trace_timestamp_s
|
||||
|
||||
async def emit(self, phase: str) -> None:
|
||||
async with self.lock:
|
||||
event = {
|
||||
"phase": phase,
|
||||
"elapsed_s": round(time.perf_counter() - self.start_time_s, 3),
|
||||
"total_requests": self.total_requests,
|
||||
"submitted_count": self.submitted_count,
|
||||
"completed_count": self.completed_count,
|
||||
"inflight_count": self.submitted_count - self.completed_count,
|
||||
"error_count": self.error_count,
|
||||
"truncated_count": self.truncated_count,
|
||||
"last_request_id": self.last_request_id,
|
||||
"last_session_id": self.last_session_id,
|
||||
"last_trace_timestamp_s": self.last_trace_timestamp_s,
|
||||
"execution_modes": dict(sorted(self.execution_modes.items())),
|
||||
}
|
||||
self.output_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
with self.output_path.open("a", encoding="utf-8") as handle:
|
||||
handle.write(json.dumps(event, sort_keys=True) + "\n")
|
||||
|
||||
|
||||
async def replay_trace(config: ReplayConfig) -> list[RequestMetrics]:
|
||||
structural_dir = config.structural_log_dir
|
||||
if structural_dir is None and config.output_path is not None:
|
||||
@@ -247,7 +195,10 @@ async def replay_trace(config: ReplayConfig) -> list[RequestMetrics]:
|
||||
if turn_count > 1
|
||||
),
|
||||
)
|
||||
policy = create_policy(config.policy_name)
|
||||
policy = create_policy(
|
||||
config.policy_name,
|
||||
migration_reject_threshold=config.kvcache_migration_reject_threshold,
|
||||
)
|
||||
state = RoutingState.create(config.topology)
|
||||
state_lock = asyncio.Lock()
|
||||
semaphore = asyncio.Semaphore(config.concurrency_limit)
|
||||
@@ -256,23 +207,6 @@ async def replay_trace(config: ReplayConfig) -> list[RequestMetrics]:
|
||||
session_tail_tasks: dict[str, asyncio.Task[RequestMetrics]] = {}
|
||||
direct_sessions: dict[str, DirectSessionState] = {}
|
||||
direct_session_lock = asyncio.Lock()
|
||||
progress = (
|
||||
ReplayProgress(
|
||||
total_requests=len(requests),
|
||||
output_path=config.output_path.parent / "replay-progress.jsonl",
|
||||
interval_s=config.progress_interval_s,
|
||||
start_time_s=start_time,
|
||||
)
|
||||
if config.progress_interval_s > 0
|
||||
else None
|
||||
)
|
||||
progress_stop = asyncio.Event()
|
||||
progress_task: asyncio.Task[None] | None = None
|
||||
if progress is not None:
|
||||
await progress.emit("start")
|
||||
progress_task = asyncio.create_task(
|
||||
_progress_heartbeat(progress, progress_stop)
|
||||
)
|
||||
async with httpx.AsyncClient(timeout=config.timeout_s, trust_env=False) as client:
|
||||
decode_residency = await _discover_decode_residency(
|
||||
client=client,
|
||||
@@ -304,8 +238,6 @@ async def replay_trace(config: ReplayConfig) -> list[RequestMetrics]:
|
||||
sleep_s = target_offset - (time.perf_counter() - start_time)
|
||||
if sleep_s > 0:
|
||||
await asyncio.sleep(sleep_s)
|
||||
if progress is not None:
|
||||
await progress.record_submitted(request)
|
||||
tasks.append(
|
||||
asyncio.create_task(
|
||||
_run_request(
|
||||
@@ -320,15 +252,12 @@ async def replay_trace(config: ReplayConfig) -> list[RequestMetrics]:
|
||||
direct_session_lock=direct_session_lock,
|
||||
decode_residency=decode_residency,
|
||||
depends_on=session_tail_tasks.get(request.session_id),
|
||||
progress=progress,
|
||||
)
|
||||
)
|
||||
)
|
||||
session_tail_tasks[request.session_id] = tasks[-1]
|
||||
|
||||
results = await asyncio.gather(*tasks)
|
||||
if progress is not None:
|
||||
await progress.emit("requests-complete")
|
||||
if poll_task is not None:
|
||||
poll_task.cancel()
|
||||
try:
|
||||
@@ -364,14 +293,6 @@ async def replay_trace(config: ReplayConfig) -> list[RequestMetrics]:
|
||||
trace_path=config.trace_path,
|
||||
router_url=config.router_url,
|
||||
)
|
||||
if progress is not None:
|
||||
await progress.emit("final")
|
||||
progress_stop.set()
|
||||
if progress_task is not None:
|
||||
try:
|
||||
await progress_task
|
||||
except asyncio.CancelledError:
|
||||
pass
|
||||
_structural_close()
|
||||
return results
|
||||
|
||||
@@ -389,7 +310,6 @@ async def _run_request(
|
||||
direct_session_lock: asyncio.Lock,
|
||||
decode_residency: DecodeResidencyState,
|
||||
depends_on: asyncio.Task[RequestMetrics] | None,
|
||||
progress: ReplayProgress | None = None,
|
||||
) -> RequestMetrics:
|
||||
if depends_on is not None:
|
||||
await depends_on
|
||||
@@ -438,8 +358,24 @@ async def _run_request(
|
||||
|
||||
async with state_lock:
|
||||
state.finish(request, decision)
|
||||
# Migration feedback: if this request was forced into a fallback path
|
||||
# because the chosen D rejected admission, record the (session, D)
|
||||
# rejection so KvAwarePolicy can migrate this session next turn.
|
||||
if _is_admission_rejection_mode(execution.execution_mode):
|
||||
state.record_admission_reject(
|
||||
request.session_id,
|
||||
decision.decode_worker_id,
|
||||
)
|
||||
# Reset-on-success: a successful direct-to-D path proves D-X can
|
||||
# currently serve this session — clear the cumulative reject counter
|
||||
# so that brief past saturation doesn't permanently blacklist the D.
|
||||
# (MIGRATION_V1_FINDINGS §4.1: blacklist-permanence bug fix.)
|
||||
elif execution.execution_mode == "kvcache-direct-to-d-session":
|
||||
state.session_d_rejects[
|
||||
(request.session_id, decision.decode_worker_id)
|
||||
] = 0
|
||||
|
||||
row = RequestMetrics.from_decision(
|
||||
return RequestMetrics.from_decision(
|
||||
request,
|
||||
decision,
|
||||
mechanism_name=config.mechanism_name,
|
||||
@@ -459,29 +395,6 @@ async def _run_request(
|
||||
requested_output_tokens=execution.requested_output_tokens,
|
||||
finish_reason=execution.finish_reason,
|
||||
)
|
||||
if progress is not None:
|
||||
await progress.record_completed(row)
|
||||
return row
|
||||
|
||||
|
||||
async def _progress_heartbeat(
|
||||
progress: ReplayProgress,
|
||||
stop_event: asyncio.Event,
|
||||
) -> None:
|
||||
while not stop_event.is_set():
|
||||
try:
|
||||
await asyncio.wait_for(stop_event.wait(), timeout=progress.interval_s)
|
||||
except asyncio.TimeoutError:
|
||||
await progress.emit("heartbeat")
|
||||
|
||||
|
||||
def _is_truncated(row: RequestMetrics) -> bool:
|
||||
return (
|
||||
row.actual_output_tokens is not None
|
||||
and row.requested_output_tokens is not None
|
||||
and row.requested_output_tokens > 1
|
||||
and row.actual_output_tokens < row.requested_output_tokens * 0.5
|
||||
)
|
||||
|
||||
|
||||
async def _invoke_router(
|
||||
@@ -754,41 +667,16 @@ async def _open_streaming_session(
|
||||
request.input_length * 16,
|
||||
(request.input_length + request.output_length) * 16,
|
||||
)
|
||||
payload = {
|
||||
"capacity_of_str_len": capacity,
|
||||
"session_id": session_id,
|
||||
"streaming": True,
|
||||
}
|
||||
url = f"{server_url.rstrip('/')}/open_session"
|
||||
response = await client.post(url, json=payload, timeout=_ADMISSION_PROBE_TIMEOUT_S)
|
||||
try:
|
||||
response.raise_for_status()
|
||||
except httpx.HTTPStatusError:
|
||||
if response.status_code != 400:
|
||||
raise
|
||||
await _structural_emit(
|
||||
"session-lifecycle.jsonl",
|
||||
{
|
||||
"event": "open-session-400-retry",
|
||||
"server_url": server_url,
|
||||
"session_id": session_id,
|
||||
"request_id": request.request_id,
|
||||
"turn_id": request.turn_id,
|
||||
"response_text": response.text[:512],
|
||||
},
|
||||
)
|
||||
await _close_streaming_session(
|
||||
client=client,
|
||||
server_url=server_url,
|
||||
session_id=session_id,
|
||||
allow_missing=True,
|
||||
)
|
||||
response = await client.post(
|
||||
url,
|
||||
json=payload,
|
||||
timeout=_ADMISSION_PROBE_TIMEOUT_S,
|
||||
)
|
||||
response.raise_for_status()
|
||||
response = await client.post(
|
||||
f"{server_url.rstrip('/')}/open_session",
|
||||
json={
|
||||
"capacity_of_str_len": capacity,
|
||||
"session_id": session_id,
|
||||
"streaming": True,
|
||||
},
|
||||
timeout=_ADMISSION_PROBE_TIMEOUT_S,
|
||||
)
|
||||
response.raise_for_status()
|
||||
opened_session_id = response.json()
|
||||
if opened_session_id != session_id:
|
||||
raise ValueError(
|
||||
@@ -1485,6 +1373,49 @@ def _is_stale_decode_session_error(exc: Exception) -> bool:
|
||||
)
|
||||
|
||||
|
||||
# execution_mode substrings that signal D-side admission rejected this request.
|
||||
# Used by _run_request to update state.session_d_rejects so KvAwarePolicy can
|
||||
# migrate persistently-starved sessions to a different D next turn.
|
||||
_ADMISSION_REJECTION_SUBSTRINGS = (
|
||||
"session-cap",
|
||||
"no-d-capacity",
|
||||
"d-backpressure",
|
||||
)
|
||||
|
||||
|
||||
def _is_admission_rejection_mode(execution_mode: str) -> bool:
|
||||
return any(token in execution_mode for token in _ADMISSION_REJECTION_SUBSTRINGS)
|
||||
|
||||
|
||||
def _fallthrough_reason(
|
||||
*,
|
||||
request: TraceRequest,
|
||||
config: ReplayConfig,
|
||||
decision,
|
||||
direct_append_length: int | None,
|
||||
direct_session_reused: bool,
|
||||
direct_session_reset: bool,
|
||||
) -> str:
|
||||
"""Classify why a turn-2+ KVC request fell through to the seed/large-append branch.
|
||||
|
||||
Returns a short label suffix used in execution_mode strings to replace the
|
||||
misleading 'large-append' label (TEAM_REPORT §2.7). In particular,
|
||||
'session-not-resident' is the §1 starvation signature — direct_session_reused
|
||||
is False because the session was never opened on the policy-chosen D.
|
||||
"""
|
||||
if not direct_session_reused:
|
||||
return "session-not-resident"
|
||||
if direct_session_reset:
|
||||
return "session-was-evicted"
|
||||
if direct_append_length is None:
|
||||
return "no-direct-info"
|
||||
if direct_append_length > config.kvcache_direct_max_uncached_tokens:
|
||||
return "real-large-append"
|
||||
if not _should_bypass_prefill(request=request, config=config, decision=decision):
|
||||
return "policy-no-bypass"
|
||||
return "other-large-append"
|
||||
|
||||
|
||||
def _dynamic_decode_headroom_tokens(
|
||||
*,
|
||||
residency: DecodeResidencyState,
|
||||
@@ -2646,6 +2577,17 @@ async def _execute_request(
|
||||
decode_residency=decode_residency,
|
||||
)
|
||||
|
||||
# TEAM_REPORT §2.7: 'large-append' is misleading — most fallthroughs are
|
||||
# actually 'session-not-resident-on-pinned-D' (§1 starvation). Classify
|
||||
# the real reason and embed it in the execution_mode label.
|
||||
fallthrough = _fallthrough_reason(
|
||||
request=request,
|
||||
config=config,
|
||||
decision=decision,
|
||||
direct_append_length=direct_append_length,
|
||||
direct_session_reused=direct_session_reused,
|
||||
direct_session_reset=direct_session_reset,
|
||||
)
|
||||
seed_filter_reason = _seed_filter_reason(
|
||||
request=request,
|
||||
config=config,
|
||||
@@ -2657,7 +2599,7 @@ async def _execute_request(
|
||||
client=client,
|
||||
config=config,
|
||||
decision=decision,
|
||||
execution_mode=f"pd-router-fallback-large-append-{seed_filter_reason}",
|
||||
execution_mode=f"pd-router-fallback-{fallthrough}-{seed_filter_reason}",
|
||||
decode_residency=decode_residency,
|
||||
)
|
||||
async with direct_session_lock:
|
||||
@@ -2702,7 +2644,7 @@ async def _execute_request(
|
||||
client=client,
|
||||
config=config,
|
||||
decision=decision,
|
||||
execution_mode="pd-router-fallback-large-append-session-cap",
|
||||
execution_mode=f"pd-router-fallback-{fallthrough}-session-cap",
|
||||
decode_residency=decode_residency,
|
||||
)
|
||||
if can_seed:
|
||||
@@ -2718,23 +2660,27 @@ async def _execute_request(
|
||||
decode_residency=decode_residency,
|
||||
reserved_tokens=reserved_tokens,
|
||||
execution_mode=(
|
||||
"pd-router-large-append-reseed"
|
||||
f"pd-router-{fallthrough}-reseed"
|
||||
+ _eviction_suffix(
|
||||
evicted_sessions,
|
||||
prefill_backed_evictions,
|
||||
)
|
||||
),
|
||||
)
|
||||
# Preserve seed_reason in the label so migration feedback fires for
|
||||
# 'd-no-space' / 'd-*-backpressure' (matched via _is_admission_rejection_mode).
|
||||
if _is_decode_backpressure_reason(seed_reason):
|
||||
mode_label = f"pd-router-fallback-{fallthrough}-d-backpressure"
|
||||
elif seed_reason == "d-no-space":
|
||||
mode_label = f"pd-router-fallback-{fallthrough}-no-d-capacity"
|
||||
else:
|
||||
mode_label = f"pd-router-fallback-{fallthrough}"
|
||||
return await _invoke_plain_router(
|
||||
request=request,
|
||||
client=client,
|
||||
config=config,
|
||||
decision=decision,
|
||||
execution_mode=(
|
||||
"pd-router-fallback-d-backpressure"
|
||||
if _is_decode_backpressure_reason(seed_reason)
|
||||
else "pd-router-fallback-large-append"
|
||||
),
|
||||
execution_mode=mode_label,
|
||||
decode_residency=decode_residency,
|
||||
)
|
||||
|
||||
|
||||
Reference in New Issue
Block a user