From 39766141fb9dae2dd4ef6a6e6f56568a36b4047c Mon Sep 17 00:00:00 2001 From: Gahow Wang Date: Mon, 20 Jul 2026 12:05:20 +0800 Subject: [PATCH] Decompose good/bad selection split across frozen surfaces Co-Authored-By: Claude Fable 5 --- .../frontier-split-rootcause-s0-20260720.md | 60 + .research/ongoing.md | 13 +- .../analyze_split_decomposition.py | 1444 ++++ .../q235-ablation-a1-prov/aituner.commit | 1 + .../q235-ablation-a1-prov/artifacts.sha256 | 180 + .../q235-ablation-a1-prov/frontier.commit | 1 + .../q235-ablation-a1-prov/input.sha256 | 4 + .../q235-ablation-a1/comparison.json | 799 ++ .../q235-ablation-a1/comparison.md | 24 + .../q235-fourcase-a0/comparison.json | 952 +++ .../q235-fourcase-a0/comparison.md | 24 + .../state-diagnosis-exact.json | 1587 ++++ .../q235-state-diag/state-diagnosis-exact.md | 54 + .../state-diagnosis-optrace.json | 1405 ++++ .../state-diagnosis-optrace.md | 48 + .../q235-state-diag/state-diagnosis.json | 1361 +++ .../q235-state-diag/state-diagnosis.md 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runs/frontier-split-rootcause-v0/frozen-inputs/q30-trace-pd/summary.json create mode 100644 runs/frontier-split-rootcause-v0/results/decision-pair-axis.png create mode 100644 runs/frontier-split-rootcause-v0/results/decomposition.json create mode 100644 runs/frontier-split-rootcause-v0/results/decomposition.md create mode 100644 runs/frontier-split-rootcause-v0/results/margin-vs-residual.png diff --git a/.research/experiments/frontier-split-rootcause-s0-20260720.md b/.research/experiments/frontier-split-rootcause-s0-20260720.md new file mode 100644 index 0000000..10db9e3 --- /dev/null +++ b/.research/experiments/frontier-split-rootcause-s0-20260720.md @@ -0,0 +1,60 @@ +# 实验 S0：good/bad case 分裂的统一分解（margin vs differential residual） + +> **状态：** 已完成（2026-07-20；含 S0b 方向化修正与一轮 strict review 修复） +> +> 用户指令：核心要务是分析为什么部分 case 下 Frontier work、部分不 work 的 system 根因；本 card 是该诊断 campaign 的第一个 slice，仅使用 frozen artifacts，零 GPU 成本。人工 review 由用户的直接指令（"只有做好这个分析我们才能推进下一步"）满足。 + +## Claim 与决策 + +- **Parent claim：** ongoing.md H2——误差机制是 action-conditioned residual；本实验把它细化为"分裂从哪来"。 +- **现象（已冻结）：** 同一 best-effort Frontier 栈上，Q30/Q235 的 Trace-PD 与多数 PO 面 selection 近优，而 Fixed-PD 面 14–58% regret；失败 objective 随负载档切换（Q30 低压 TPOT/E2E 全反、高压 TTFT 56–58%）；A1 collective profile 修复了 Q235 Trace-PO p90 21.2%→0.3% 但对 Fixed-PD 33% 完全无效（本 card frozen-inputs/q235-ablation-a1）。 +- **Competing hypotheses：** + - **H-SCALE：** 分裂完全由「config-differential residual vs 真机 decision margin」的关系解释：good case 的 sim/real 比值跨 config 近似均匀（乘性偏移，argmin 不变），bad case 的比值跨 config 分散且超过 margin。workload shape 本身不需要出现在解释里。 + - **H-THRESH：** 绝对 service-time 高估近似均匀，但与离散机制（MNS admission cap、MoE token-bucket、graph bucket）交互后被转换为 config-differential 误差；fixed uniform workload 把所有请求同步到同一 state 轨迹，使阈值交叉对整个 cell 相干生效；trace 的长度/到达异质性把阈值效应摊平。 + - **H-STATE：** 失败由 simulator 闭环 batch state 分布漂移主导（Q235：sim decode batch 13.5 vs real 3.9 + B4→B5 profile cliff 正反馈）；即使打破 workload 同步性，闭环漂移仍可翻转排序。 + - 三者关系：H-SCALE 是现象层（必要条件），H-THRESH/H-STATE 是 differential residual 的两种产生机制，可共存但可判别（见事前预测）。 +- **事前预测：** + - H-SCALE 成立 ⟺ 对每个 case×objective，failure 恰好发生在「top 邻域 log-ratio spread > log1p(真机相对 margin)」处（两侧同为 log-space 尺度），无反例。 + - H-THRESH 独有：bad case 的 differential 误差集中于阈值语义分量（first-scheduling wait、bucket 跳变段），且 sim-only 反事实（去阈值/加 jitter）恢复排序——Q30 高压 TTFT 的 admission 反事实已支持一例。 + - H-STATE 独有：差异化误差在去掉阈值分量后仍在 execution 项内（Q235 Fixed-PD 的 own-composition −20.07 vs exact-state +10.90 已支持一例）。 +- **判定规则：** S0 只裁决 H-SCALE 与「分量定位」（queue vs execution）；H-THRESH/H-STATE 的干预判别属 S1+（sim-only 反事实）与 GPU 实验（需另行 review）。若 H-SCALE 出现反例（good case 有 spread>margin 仍选对，或 bad case spread5%；H-SCALE 检验=failure ⟺ 邻域 spread>log1p(margin)（review 修正：初版直接以 ln 差比较普通 relative margin，尺度不一致；修正后 70 行 verdict 不变）。 +- **分量定位：** q30-admission-diag 提供 TTFT=first-scheduling wait+prefill execution 分解；q235-state-diag 提供 own-composition vs exact-state contrast；把这些已知分量证据合并进统一表。 +- **交叉核对：** 重算的 regret 必须与各 frozen comparison.md 表一致（抽查 58.0%、33.0%、0.0%）；A0 vs A1 的 Q235 对比必须复现 trace-po p90 21.2%→0.3%、fixed-pd 四项不变。 + +## 预期产物与 review + +- runs/frontier-split-rootcause-v0/analyze_split_decomposition.py（只读 frozen-inputs，确定性输出） +- runs/frontier-split-rootcause-v0/results/decomposition.{json,md}：统一表，每行 case×objective，列出 winner、regret、scale、spread（全/邻域）、margin、H-SCALE verdict、已知分量归因 +- runs/frontier-split-rootcause-v0/results/margin-vs-residual.png：x=真机 margin，y=邻域 differential residual，点色=selection 对错；H-SCALE 成立则对错点被对角线分离 +- 人工验收：编排者亲自重跑脚本、抽查交叉核对数字、亲自查看渲染图 + +## 复现信息 + +- **Code：** AITuner branch feature/sim，自 HEAD 18c0b25 起；worker/reviewer job-id 见下方「Review 与 provenance 补记」；脚本与产物随本 card 同一 commit 入库（含 frozen-inputs 本地拷贝）。 +- **Environment：** 本地 workstation，CPU-only，python3+matplotlib；不访问远端。 +- **已知 deviation：** frozen-inputs 是 cpfs 原件的本地拷贝（scp，2026-07-20）；q30-expansion-lo 的低压 Fixed-PD 面已被高压面取代为 primary，本分析将两档并列为独立观测，不混合。 + +## 结果 + +- **观察事实：** + - 70 行（14 个 case surface）全部算出，无数据缺口；四组硬性交叉核对通过；连续运行产物 SHA 一致。 + - **H-SCALE 判为必要非充分**：23 个 material failure（regret>5%）全部满足「top-3 邻域 log spread > log1p(margin)」，无一例失败发生在 residual 小于 margin 处；但另有 40/70 行同样满足该条件却均非 material failure（其中仅 16 行 exact winner match，其余 24 行是小 regret 的 winner 错位——14 个 MNS 精确 tie 与 10 个 strict reversal）——无方向 spread 不携带决策信息。 + - **S0b 方向化后的机制普查**：23 个 material failure 的 winner-deciding pair 分布为 tp-axis 11、mixed 10、mns-axis 2（Q235 A0/A1 Fixed-PD 的 8 个 TPOT/E2E failure 全为 tp-axis；Q30 Fixed-PD 高低压为 tp/mixed）；仅有的 2 个 mns-axis failure 是 Q235 A0/A1 Trace-PD E2E p90（regret 6.2%，勉强越过 5% 门槛）。trace 面严格反序中 tp-axis 为 0（q30 trace-pd 三轴全 0）。 + - **A1 对照的轴分解**：serving-matched collective profile 把 Q235 两个 PO 面的 tp-axis 反序从 4/4 清零（trace-po p90 regret 21.2%→0.3%），但 Fixed-PD 仅 5→4、四项 regret 一位小数不动——prefill 路径的 TP-differential 误差源=collective profile（可修），decode 耦合的 TP-differential 误差另有来源。 + - **MNS 不敏感缺陷**：14 个 winner-label mismatch 是 simulator 逐位相等的 tie，全部 mns-axis（如 q30 fixed-po 的 MNS16↔32、q235 fixed-pd 的 MNS64↔128）；tie 计入后 MNS 边界误差 31 与 TP 严格反序 32 相当，但 MNS 侧 regret 小。 + - **成功的鲁棒性**：23 个 exact-winner success 中 17 个 margin-robust（margin≥1%），6 个 fragile（含 q30 trace-pd E2E p90 的 0.1% margin 与 q235 A1 fixed-po 四项）。 + - **面级 scale 对照**：prefill-only 面 geomean scale 0.96–1.37×（绝对预测基本准确），含 decode 的面 4.3–130×——绝对误差灾难集中于 decode。 +- **异常：** 无数据异常。strict review（FAIL：3 Major/1 Minor）指出 H-SCALE 尺度混用（log spread vs relative margin）、tie 轴普查缺失、card 状态过期、Q235 一致性表述过强；全部修复，修复后 70 行 verdict 逐行不变。 +- **含义：** 分裂的现象层解释是「margin 保护 + config-differential 误差」。机制层上，21/23 个 material failure 由 TP/mixed pair 决定，且所有大 regret（≥13%）failure 都发生在含 decode 的面上：其中 Q235 Fixed-PD 有 state-drift 直接证据、Q30 高压 TTFT 有 admission 反事实证据，而 **Q30 低压 TPOT/E2E 反转的机制尚未诊断**（S1 目标）。「decode 耦合的 TP-differential 误差是主要载体」是当前最强归纳，不是对全部 failure 的已证机制归因；2 个 mns-axis 边缘 failure（6.2%）在该归纳之外。trace 面成功伴随「TP 反序为零 + TP margin 宽」，但「误差小」与「margin 宽」谁是主因仍未判——这正是 H-THRESH vs H-STATE 的判别缺口。轴标签与机制不一一对应（Q30 admission 是 MNS 阈值机制但 deciding pair 为 tp/mixed，因 TP 改变到达压力）。 +- **Claim update：** H2（action-conditioned residual）supported 且被细化：residual 的决策相关分量集中在 TP 轴、由 decode 状态耦合产生；H-SCALE 降级为必要条件；H-THRESH/H-STATE 保持 competing，待 S1 判别。 +- **下一步：** S1（sim-only 反事实：Q30 低压 Fixed-PD TPOT 反转的分量定位——这是唯一无机制解释的 material failure；fixed workload jitter 判别 H-THRESH vs H-STATE）；GPU 判别实验（加压 Trace-PD、jittered Fixed-PD 真机面，dash1–4）另行出 card 供 review。 + +## Review 与 provenance 补记 + +- S0 worker：codex `task-mrsn1s9c-z6ha0w`；S0b：`task-mrsnjzn6-k18x4n`（resume）；strict reviewer（fresh 只读）：`task-mrsocmlb-386btc`（verdict FAIL）；修复轮：`task-mrsop06n-pwxo0b`（fresh writable）。编排者独立验收：脚本重跑、SHA 比对、两图目视检查。 +- 产物 SHA（修复后）：decomposition.json `81ea56b2…`、decomposition.md `4d19af54…`、margin-vs-residual.png `df2110c4…`、decision-pair-axis.png `2e787301…`。 diff --git a/.research/ongoing.md b/.research/ongoing.md index c97e5ce..dcc6521 100644 --- a/.research/ongoing.md +++ b/.research/ongoing.md @@ -3,14 +3,16 @@ > 2026-07-17｜写给未参与项目的读者，可直接作为 presentation 讲稿。历史过程与复现信息见 `../runs/*/` 各 experiment card、`../docs/` 各 campaign 文档。 > > **2026-07-19 update：** Qwen235 Fixed-PD 的错误排序在 exact real state composition 下已经翻正，主因是 simulator closed-loop batch state，而不是 collective。Qwen30 Fixed-PD 的 56--58% TTFT regret 也已定位：Frontier 将 decode service time 高估 4--8×，使 TP4 的 modeled concurrency 越过 MNS admission cap并产生虚假排队；去掉该等待后 Frontier 与真机都判定 TP4 topology 更快。详见 [`experiments/qwen30-fixed-pd-ttft-admission-diagnosis-20260719.md`](experiments/qwen30-fixed-pd-ttft-admission-diagnosis-20260719.md)。 +> +> **2026-07-20 update：** 对全部 14 个 frozen case surface（70 个 case×objective）做了统一的 margin-vs-residual 分解与方向化机制普查（[`experiments/frontier-split-rootcause-s0-20260720.md`](experiments/frontier-split-rootcause-s0-20260720.md)）。三个要点：(1) 「residual 超过 margin」是失败的必要条件但远非充分——good/bad 分裂不能用无方向误差量解释；(2) 23 个 material failure 的 winner-deciding pair 中 21 个落在 TP 轴或 mixed（其余 2 个是 6.2% regret 的边缘 mns-axis case），trace 面的 TP 反序为零；A1 measured collective 把 Qwen235 两个 prefill-only 面的 TP 反序清零（trace-PO p90 regret 21.2%→0.3%）却对 Fixed-PD 完全无效——prefill 路径的 TP 差异化误差源是 collective profile（可修），decode 耦合的 TP 差异化误差是当前所有 material failure 的载体；(3) 「Fixed-PD 失败因为高压」被否证：失败 Fixed-PD 的真机 in-flight（14.05）低于全对的 Trace-PD（38.69），且低压 Fixed-PD 同样失败、失败 objective 随负载切换。另有次要缺陷：14 个 winner 错位来自 simulator 对 MNS 逐位不敏感的精确 tie。 ## 一眼看懂 - **Topic / problem：** LLM serving 的自动、低成本配置调优（AITuner）。当前主线问题：用 simulator 给部署配置（并行度、批量上限等）排序，什么时候可信？需要补多少真机证据？算上这些成本还划算吗？ - **Central claim：** simulator 要能帮助配置调优，必须先满足 scheduler transition 的 liveness/coverage，再满足「配置相关残差小于真机 decision margin」；前者决定 capacity 是否有定义，后者决定排序是否正确。(ID: C0) - **当前结论：** 早先 35 个 trace stall 不是 Frontier scheduler liveness failure：adapter 为不满 16-token 的 prefix block 错误生成了 cache identity，Frontier 又没有 fail-fast。修正为完整 block、使用真实 graph buckets/KV blocks 和 `piecewise`/`KERNEL_ONLY` profile 后，Qwen30 Trace-PD 的全部 12 个 cell 完成 129/129 request，Frontier 对 TTFT/TPOT/E2E 的 6 个 argmin 均与三次 fresh-server 真机一致；但绝对 latency 仍高估 4--511×。这只证明一个 MoE Trace-PD surface 的 selection fidelity，不能外推到 prefill-only、fixed workload 或 235B。 -- **最大 uncertainty / risk：** 这个正确选择是否依赖 Trace-PD 的 queue/margin 偶然性；Fixed-PD、Trace-P、Fixed-P 与大型 FP8 MoE 是否仍保留正确 config ranking。Q235 还缺少同栈 profile/runtime contract，不能复用旧 vLLM 0.10.2 数据。 -- **下一项 critical action：** 完成 Qwen30 其余三项 fixed/trace × PD/P surface。Fixed case 已先启动无请求 vLLM runtime-state preflight；Trace-P 的 graph-aligned Frontier 12-cell CPU surface 正在运行。Q235 先做 vLLM 0.20 TP4/TP8 compatibility gate，随后才允许新 profile。 +- **最大 uncertainty / risk：** trace 面成功的原因未判——是 decode 耦合的 TP 差异化误差在 trace 状态分布下真的变小，还是只是被宽 TP margin 掩盖。这决定「sim 剪枝可信域」的边界怎么画，也决定加压 trace 是否会失败。 +- **下一项 critical action：** 判别两个失败机制假设（离散阈值转换 vs 闭环 state 漂移）：先做零 GPU 的 sim-only jitter 反事实与 Q30 低压 Fixed-PD TPOT 反转分量定位，再按结果决定 dash1–4 上的加压 Trace-PD / jittered Fixed-PD 真机判别面。 - **停止条件：** T1 出 verdict 且成本账本建立后：pass 且摊销论证成立 → 转向「sim 剪枝 + 真机终选」的 hybrid 机制设计；fail → 转入失败机制归因；两条路都无 insight 增量 → 收敛写作。 ## 核心概念 @@ -34,9 +36,10 @@ - **Supporting：** 235B prefill-only regret=0；235B fixed-shape mixed 的 top set 全中；30B 加 per-TP 校准后 regret 0.76%（但这是外部端到端 scale 给出的上界，不是原生 profile 保真度）。 - **Counterevidence：** 修正 prefix trace contract 后的 TP2/MNS16 `none`-graph run 完成但 p50 TPOT 约 96 ms，真机为约 14 ms；然而该比较尚未对齐 real vLLM 的 `FULL_AND_PIECEWISE` graph path。 - **下一项 discriminative experiment：** 补齐 `KERNEL_ONLY` graph family并以 `piecewise` 重跑相同 trace；若 full surface 仍错，graph omission 不再是可用解释。 - - **Hypothesis（机制，active）：** 误差机制是 action-conditioned residual——执行状态的转移（并行拓扑、kernel family、graph mode、batch 组成）使按算子 profile 的组合预测跨配置不可复合；残差大于 margin 时排序失败。(ID: H2；supported) + - **Hypothesis（机制，active）：** 误差机制是 action-conditioned residual——执行状态的转移（并行拓扑、kernel family、graph mode、batch 组成）使按算子 profile 的组合预测跨配置不可复合；残差大于 margin 时排序失败。(ID: H2；supported，已细化) - **Supporting：** 三个 TP 档的端到端校准系数为 0.72/0.47/0.35，残差确实随配置剧烈变化；235B 的批量上限交互预测错误但被 2× margin 容忍；30B prefill-only 在低负载近似对齐、饱和后按 TP 反向放大，最终 τ-b=−1。 - - **下一步：** 按 collective 通信 → batch 组成 → 调度器逐步轨迹的单变量顺序定位组合性失效点（对应 experiment card 中 A1/A2/A3 消融阶梯）。 + - **细化（2026-07-20 统一普查）：** 决策相关的残差分量集中在 TP 轴且由 decode 状态耦合产生——prefill-only 面的绝对 scale 仅 0.96–1.37× 且 measured collective 即可清除其 TP 反序，而含 decode 的面 scale 4.3–130×、全部 material failure 都由 TP/mixed pair 决定。「residual>margin」只是必要条件；失败还需要残差对准 winner-deciding pair。 + - **下一步：** 判别两个 competing 机制假设——离散阈值转换（admission cap/profile cliff 把均匀高估转成差异化误差；打破 fixed workload 的同步性应恢复排序）vs 闭环 state 漂移（打破同步性也救不了）。最便宜路径：sim-only jitter 反事实 + Q30 低压 Fixed-PD TPOT 反转的分量定位（唯一无机制解释的 material failure）。 - **Subclaim：** 成本论证只有在摊销前提下成立。(ID: C3) - **Hypothesis（active）：** 每个 model×硬件×runtime 的一次性对齐成本，摊销到大配置面、频繁重调（引擎版本 churn 的频率证据见 claim map）或禁止在线实验的场景后，低于重复真机调优。(ID: H3；untested——分母已实测，分子未入账) - **下一步：** 建 cost ledger（见「下一步」）。 @@ -52,7 +55,7 @@ ## Key evidence（最多 3 条） - **E1（否证「prefill-only 是充分 easy condition」，支持 H2）：** 30B BF16、去掉 decode/prefix/混合 batch 后，真机最优是 TP4（8 vs 7 req/s/GPU），simulator 却把 TP4 排最差（6 vs 8）：top set 无交集，regret 12.5%，τ-b=−1。产物：`../runs/frontier-phase-factorial-v0/results/final/`（dash0，12.07 H20-GPUh）。 -- **E2（margin 可掩盖残差，支持 C0/H2）：** 235B FP8 的 prefill-only 与 fixed-shape mixed 都选对 top set，但 simulator 漏掉并发上限（max-num-seqs）×批 token 上限（max-num-batched-tokens）的交互棋盘格，并把 10/34 个真机 anchor 误判为不可行；真机头部 margin 有 2×，残差被容忍。产物：`../runs/frontier-multicase-sufficiency-v0/best_effort/fixed_cohort_evidence/`、`../runs/frontier-multicase-sufficiency-v1/results/t0-final/`。 +- **E2（统一机制普查：material failure 全部由 decode 耦合的 TP 差异化误差决定，支持 H2 细化）：** 对 14 个 frozen surface、70 个 case×objective 的方向化分解显示：23 个 material failure 中 21 个由 TP/mixed pair 决定（仅 2 个 6.2% 边缘 mns-axis case）、trace 面 TP 反序为零；measured collective（A1）把 Qwen235 两个 prefill-only 面的 TP 反序清零（trace-PO p90 regret 21.2%→0.3%）但对 Fixed-PD 的 33% 无效；「residual>margin」仅为失败的必要条件。产物：[`../runs/frontier-split-rootcause-v0/results/`](../runs/frontier-split-rootcause-v0/results/decomposition.md)（实验 card：[`experiments/frontier-split-rootcause-s0-20260720.md`](experiments/frontier-split-rootcause-s0-20260720.md)）。 - **E3（selection 与 calibration 分开）：** 用完整 block projection、`piecewise` 和 graph-compatible KERNEL_ONLY profile 后，Qwen30 Trace-PD 的 12/12 sim cells 完成，6 个 mean/p90 latency objective 的 argmin 都与真机一致；但 sim/real latency ratio 仍为 4--511×。产物：[`../runs/frontier-fidelity-envelope-v1/graph-piecewise-experiment-card.md`](../runs/frontier-fidelity-envelope-v1/graph-piecewise-experiment-card.md)。 ## 下一步（最多 3 项） diff --git a/runs/frontier-split-rootcause-v0/analyze_split_decomposition.py b/runs/frontier-split-rootcause-v0/analyze_split_decomposition.py new file mode 100644 index 0000000..4e3d178 --- /dev/null +++ b/runs/frontier-split-rootcause-v0/analyze_split_decomposition.py @@ -0,0 +1,1444 @@ +#!/usr/bin/env python3 +"""Analyze frozen good/bad Frontier selection cases by margin and residual.""" + +from __future__ import annotations + +import argparse +import json +import math +import os +import re +from dataclasses import dataclass +from pathlib import Path +from statistics import fmean +from typing import Any + + +HERE = Path(__file__).resolve().parent +DEFAULT_INPUT_ROOT = HERE / "frozen-inputs" +DEFAULT_RESULTS_ROOT = HERE / "results" + +OBJECTIVE_ORDER = { + "ttft_mean_ms": 0, + "ttft_p90_ms": 1, + "tpot_mean_ms": 2, + "tpot_p90_ms": 3, + "e2e_mean_ms": 4, + "e2e_p90_ms": 5, +} + +CONFIG_PATTERN = re.compile( + r"^tp(?P[0-9]+)(?:_ep(?P[0-9]+))?_mns(?P[0-9]+)$" +) +INVERSION_AXES = ("tp-axis", "mns-axis", "mixed") + + +@dataclass(frozen=True) +class Surface: + case: str + objective: str + values: dict[str, tuple[float, float]] + expected_configs: tuple[str, ...] + expected_sim_winner: str + expected_real_winner: str + expected_regret: float | None + sources: tuple[str, ...] + + +def load_json(path: Path) -> dict[str, Any]: + with path.open() as handle: + value = json.load(handle) + if not isinstance(value, dict): + raise ValueError(f"expected JSON object: {path}") + return value + + +def atomic_write(path: Path, text: str) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + temporary = path.with_suffix(path.suffix + ".tmp") + temporary.write_text(text) + os.replace(temporary, path) + + +def positive_float(value: Any, context: str) -> float: + if not isinstance(value, (int, float)) or isinstance(value, bool): + raise ValueError(f"non-numeric value for {context}: {value!r}") + result = float(value) + if not math.isfinite(result) or result <= 0: + raise ValueError(f"non-positive or non-finite value for {context}: {value!r}") + return result + + +def normalized_objective(metric: str, statistic: str) -> str: + return f"{metric.removesuffix('_ms')}_{statistic}_ms" + + +def objective_key(objective: str) -> tuple[int, str]: + return OBJECTIVE_ORDER.get(objective, len(OBJECTIVE_ORDER)), objective + + +def config_dimensions(config: str) -> tuple[tuple[int, int | None], int]: + match = CONFIG_PATTERN.fullmatch(config) + if match is None: + raise ValueError(f"unrecognized config name: {config}") + ep = int(match.group("ep")) if match.group("ep") is not None else None + return (int(match.group("tp")), ep), int(match.group("mns")) + + +def pair_axis(left: str, right: str) -> str: + if left == right: + return "none" + left_plan, left_mns = config_dimensions(left) + right_plan, right_mns = config_dimensions(right) + plan_differs = left_plan != right_plan + mns_differs = left_mns != right_mns + if plan_differs and not mns_differs: + return "tp-axis" + if mns_differs and not plan_differs: + return "mns-axis" + if plan_differs and mns_differs: + return "mixed" + raise ValueError(f"distinct configs have identical dimensions: {left}, {right}") + + +def load_q30_trace_pd(input_root: Path) -> list[Surface]: + relative = Path("q30-trace-pd/comparison.json") + data = load_json(input_root / relative) + if data.get("schema") != "frontier-qwen30-piecewise-graph-comparison-v1": + raise ValueError(f"unexpected schema: {relative}") + configs = tuple(sorted(data["cells"])) + surfaces = [] + for source_objective, selection in sorted( + data["selection"].items(), + key=lambda item: objective_key( + normalized_objective(*item[0].split(":")) + ), + ): + metric, statistic = source_objective.split(":") + objective = normalized_objective(metric, statistic) + values = {} + for config in configs: + cell = data["cells"][config]["metrics"][metric] + values[config] = ( + positive_float(cell[f"sim_{statistic}_ms"], f"{relative}:{config}:sim"), + positive_float(cell[f"real_{statistic}_ms"], f"{relative}:{config}:real"), + ) + surfaces.append( + Surface( + case="q30 trace-pd", + objective=objective, + values=values, + expected_configs=configs, + expected_sim_winner=selection["sim_winner"], + expected_real_winner=selection["real_winner"], + expected_regret=None, + sources=(str(relative),), + ) + ) + return surfaces + + +def load_q30_case( + input_root: Path, + case: str, + comparison_relative: Path, + audit_relative: Path, +) -> list[Surface]: + comparison = load_json(input_root / comparison_relative) + audit = load_json(input_root / audit_relative) + if comparison.get("schema") != "qwen30-latency-case-frontier-real-comparison-v1": + raise ValueError(f"unexpected schema: {comparison_relative}") + if audit.get("schema") != "qwen30-latency-case-real-audit-v1": + raise ValueError(f"unexpected schema: {audit_relative}") + configs = tuple(sorted(set(comparison["cells"]) | set(audit["configs"]))) + surfaces = [] + for source_objective, selection in sorted( + comparison["selection"].items(), + key=lambda item: objective_key( + normalized_objective(*item[0].split(":")) + ), + ): + metric, statistic = source_objective.split(":") + objective = normalized_objective(metric, statistic) + values = {} + for config in configs: + if config not in comparison["cells"] or config not in audit["configs"]: + continue + sim = comparison["cells"][config]["metrics"].get(metric, {}).get(statistic) + real = ( + audit["configs"][config]["metrics"] + .get(metric, {}) + .get(f"pooled_{statistic}_ms") + ) + if sim is None or real is None: + continue + values[config] = ( + positive_float(sim, f"{comparison_relative}:{config}:sim"), + positive_float(real, f"{audit_relative}:{config}:real"), + ) + surfaces.append( + Surface( + case=case, + objective=objective, + values=values, + expected_configs=configs, + expected_sim_winner=selection["sim_winner"], + expected_real_winner=selection["real_winner"], + expected_regret=selection.get("selected_config_real_regret"), + sources=(str(comparison_relative), str(audit_relative)), + ) + ) + return surfaces + + +def load_q235_campaign( + input_root: Path, campaign: str, relative: Path +) -> list[Surface]: + data = load_json(input_root / relative) + if data.get("schema") != "qwen235-v020-simulator-real-comparison-v1": + raise ValueError(f"unexpected schema: {relative}") + surfaces = [] + for case_name in ("fixed-pd", "fixed-po", "trace-pd", "trace-po"): + case = data["cases"][case_name] + configs = tuple(sorted(set(case["sim"]) | set(case["real"]))) + for objective, selection in sorted( + case["comparison"].items(), key=lambda item: objective_key(item[0]) + ): + values = {} + for config in configs: + if config not in case["sim"] or config not in case["real"]: + continue + sim = case["sim"][config].get(objective) + real = case["real"][config].get(objective) + if sim is None or real is None: + continue + values[config] = ( + positive_float(sim, f"{relative}:{case_name}:{config}:sim"), + positive_float(real, f"{relative}:{case_name}:{config}:real"), + ) + surfaces.append( + Surface( + case=f"q235 {campaign} {case_name}", + objective=objective, + values=values, + expected_configs=configs, + expected_sim_winner=selection["sim_winner"], + expected_real_winner=selection["real_winner"], + expected_regret=selection.get("selected_real_regret"), + sources=(str(relative),), + ) + ) + return surfaces + + +def load_surfaces(input_root: Path) -> list[Surface]: + surfaces = load_q30_trace_pd(input_root) + q30_specs = ( + ( + "q30 fixed-pd high", + Path("q30-fixed-hi/fixed-pd/comparison.json"), + Path("q30-fixed-hi/fixed-pd/real-audit.json"), + ), + ( + "q30 fixed-po high", + Path("q30-fixed-hi/fixed-po/comparison.json"), + Path("q30-fixed-hi/fixed-po/real-audit.json"), + ), + ( + "q30 fixed-pd low", + Path("q30-expansion-lo/fixed-pd-comparison.json"), + Path("q30-expansion-lo/fixed-pd-real-audit.json"), + ), + ( + "q30 fixed-po low", + Path("q30-expansion-lo/fixed-po-comparison.json"), + Path("q30-expansion-lo/fixed-po-real-audit.json"), + ), + ( + "q30 trace-po low", + Path("q30-expansion-lo/trace-po-comparison.json"), + Path("q30-expansion-lo/trace-po-real-audit.json"), + ), + ) + for case, comparison, audit in q30_specs: + surfaces.extend(load_q30_case(input_root, case, comparison, audit)) + surfaces.extend( + load_q235_campaign( + input_root, "A0", Path("q235-fourcase-a0/comparison.json") + ) + ) + surfaces.extend( + load_q235_campaign( + input_root, "A1", Path("q235-ablation-a1/comparison.json") + ) + ) + return surfaces + + +def analyze_surface(surface: Surface) -> tuple[dict[str, Any], list[str]]: + anomalies = [] + paired_configs = tuple(sorted(surface.values)) + missing_configs = sorted(set(surface.expected_configs) - set(paired_configs)) + if missing_configs: + anomalies.append( + f"{surface.case} / {surface.objective}: missing paired sim+real data for " + + ", ".join(missing_configs) + ) + if len(paired_configs) < 2: + return ( + { + "case": surface.case, + "objective": surface.objective, + "sim_winner": None, + "real_winner": None, + "regret": None, + "config_uniform_scale": None, + "log_ratio_spread_all": None, + "log_ratio_spread_real_top3": None, + "real_relative_margin_best_vs_second": None, + "sim_winner_real_relative_loss": None, + "failure": None, + "h_scale_condition_spread_gt_log_margin": None, + "h_scale_verdict": "N/A", + "winner_match": None, + "winner_deciding_axis": None, + "winner_pair_m_s_percent": None, + "winner_pair_delta_s_percent": None, + "winner_pair_strict_reversal": None, + "winner_pair_relation": "N/A", + "n_inv_tp": None, + "n_inv_mns": None, + "n_inv_mixed": None, + "n_tie_tp": None, + "n_tie_mns": None, + "n_tie_mixed": None, + "fragile_success": None, + "pairwise_vs_real_winner": [], + "paired_config_count": len(paired_configs), + "expected_config_count": len(surface.expected_configs), + "real_top3_configs": [], + "missing_configs": missing_configs, + "sources": list(surface.sources), + }, + anomalies, + ) + + sim_ranking = sorted(paired_configs, key=lambda config: (surface.values[config][0], config)) + real_ranking = sorted(paired_configs, key=lambda config: (surface.values[config][1], config)) + sim_winner = sim_ranking[0] + real_winner = real_ranking[0] + real_best = surface.values[real_winner][1] + regret = surface.values[sim_winner][1] / real_best - 1.0 + margin = surface.values[real_ranking[1]][1] / real_best - 1.0 + log_ratios = { + config: math.log(surface.values[config][0] / surface.values[config][1]) + for config in paired_configs + } + spread_all = max(log_ratios.values()) - min(log_ratios.values()) + top3 = tuple(real_ranking[:3]) + top3_ratios = [log_ratios[config] for config in top3] + spread_top3 = max(top3_ratios) - min(top3_ratios) + scale = math.exp(fmean(log_ratios.values())) + failure = regret > 0.05 + residual_exceeds_log_margin = spread_top3 > math.log1p(margin) + h_scale_holds = failure == residual_exceeds_log_margin + + pairwise = [] + inversion_counts = {axis: 0 for axis in INVERSION_AXES} + sim_at_real_winner = surface.values[real_winner][0] + for config in paired_configs: + sim_value, real_value = surface.values[config] + real_pair_log = math.log(real_value / real_best) + sim_pair_log = math.log(sim_value / sim_at_real_winner) + pair_margin_percent = 100.0 * real_pair_log + delta_percent = 100.0 * (sim_pair_log - real_pair_log) + axis = pair_axis(real_winner, config) + inversion = real_value > real_best and sim_value < sim_at_real_winner + delta_inversion = ( + pair_margin_percent > 0 + and delta_percent < -pair_margin_percent + ) + if inversion != delta_inversion: + anomalies.append( + f"{surface.case} / {surface.objective} / {config}: direct inversion " + f"test disagrees with delta < -margin" + ) + if inversion: + inversion_counts[axis] += 1 + pairwise.append( + { + "config": config, + "axis_vs_real_winner": axis, + "real_pair_margin_percent": pair_margin_percent, + "signed_pairwise_delta_percent": delta_percent, + "inversion": inversion, + } + ) + + winner_pair = next(item for item in pairwise if item["config"] == sim_winner) + winner_match = sim_winner == real_winner + if winner_match: + winner_pair_relation = "match" + elif winner_pair["inversion"]: + winner_pair_relation = "reversal" + elif surface.values[sim_winner][0] == sim_at_real_winner: + winner_pair_relation = "sim-tie" + else: + winner_pair_relation = "non-reversal" + tie_counts = {axis: 0 for axis in INVERSION_AXES} + if winner_pair_relation == "sim-tie": + tie_counts[winner_pair["axis_vs_real_winner"]] += 1 + + if sim_winner != surface.expected_sim_winner: + anomalies.append( + f"{surface.case} / {surface.objective}: recomputed sim winner " + f"{sim_winner} != frozen JSON {surface.expected_sim_winner}" + ) + if real_winner != surface.expected_real_winner: + anomalies.append( + f"{surface.case} / {surface.objective}: recomputed real winner " + f"{real_winner} != frozen JSON {surface.expected_real_winner}" + ) + if surface.expected_regret is not None and not math.isclose( + regret, float(surface.expected_regret), rel_tol=1e-12, abs_tol=1e-12 + ): + anomalies.append( + f"{surface.case} / {surface.objective}: recomputed regret " + f"{regret:.12g} != frozen JSON {float(surface.expected_regret):.12g}" + ) + + return ( + { + "case": surface.case, + "objective": surface.objective, + "sim_winner": sim_winner, + "real_winner": real_winner, + "regret": regret, + "config_uniform_scale": scale, + "log_ratio_spread_all": spread_all, + "log_ratio_spread_real_top3": spread_top3, + "real_relative_margin_best_vs_second": margin, + "sim_winner_real_relative_loss": regret, + "failure": failure, + "h_scale_condition_spread_gt_log_margin": residual_exceeds_log_margin, + "h_scale_verdict": "HOLDS" if h_scale_holds else "COUNTEREXAMPLE", + "winner_match": winner_match, + "winner_deciding_axis": pair_axis(real_winner, sim_winner), + "winner_pair_m_s_percent": winner_pair["real_pair_margin_percent"], + "winner_pair_delta_s_percent": winner_pair["signed_pairwise_delta_percent"], + "winner_pair_strict_reversal": winner_pair["inversion"], + "winner_pair_relation": winner_pair_relation, + "n_inv_tp": inversion_counts["tp-axis"], + "n_inv_mns": inversion_counts["mns-axis"], + "n_inv_mixed": inversion_counts["mixed"], + "n_tie_tp": tie_counts["tp-axis"], + "n_tie_mns": tie_counts["mns-axis"], + "n_tie_mixed": tie_counts["mixed"], + "fragile_success": winner_match and margin < 0.01, + "pairwise_vs_real_winner": pairwise, + "paired_config_count": len(paired_configs), + "expected_config_count": len(surface.expected_configs), + "real_top3_configs": list(top3), + "missing_configs": missing_configs, + "sources": list(surface.sources), + }, + anomalies, + ) + + +def row_index(rows: list[dict[str, Any]]) -> dict[tuple[str, str], dict[str, Any]]: + return {(row["case"], row["objective"]): row for row in rows} + + +def cross_checks(rows: list[dict[str, Any]]) -> tuple[list[dict[str, Any]], list[str]]: + by_key = row_index(rows) + checks = [] + anomalies = [] + + def check(name: str, observed: float, expected_percent: float) -> None: + observed_percent = observed * 100.0 + passed = round(observed_percent, 1) == expected_percent + checks.append( + { + "name": name, + "observed_percent": observed_percent, + "expected_percent": expected_percent, + "passed": passed, + } + ) + if not passed: + anomalies.append( + f"hard cross-check failed: {name}: observed {observed_percent:.6f}% " + f"vs expected {expected_percent:.1f}%" + ) + + check( + "q30 high-pressure fixed-pd TTFT mean regret", + by_key[("q30 fixed-pd high", "ttft_mean_ms")]["regret"], + 58.0, + ) + check( + "q235 A0 fixed-pd TPOT mean regret", + by_key[("q235 A0 fixed-pd", "tpot_mean_ms")]["regret"], + 33.0, + ) + check( + "q235 A1 fixed-pd TPOT mean regret", + by_key[("q235 A1 fixed-pd", "tpot_mean_ms")]["regret"], + 33.0, + ) + trace_pd_rows = [row for row in rows if row["case"] == "q30 trace-pd"] + trace_passed = len(trace_pd_rows) == 6 and all( + round(row["regret"] * 100.0, 1) == 0.0 for row in trace_pd_rows + ) + checks.append( + { + "name": "q30 trace-pd all-objective regret", + "observed_percent": [row["regret"] * 100.0 for row in trace_pd_rows], + "expected_percent": [0.0] * 6, + "passed": trace_passed, + } + ) + if not trace_passed: + anomalies.append( + "hard cross-check failed: q30 trace-pd does not have six objectives all at 0.0% regret" + ) + + def check_sequence( + name: str, observed: list[float], expected_percent: list[float] + ) -> None: + observed_percent = [value * 100.0 for value in observed] + passed = [round(value, 1) for value in observed_percent] == expected_percent + checks.append( + { + "name": name, + "observed_percent": observed_percent, + "expected_percent": expected_percent, + "passed": passed, + } + ) + if not passed: + anomalies.append( + f"hard cross-check failed: {name}: observed " + f"{[round(value, 6) for value in observed_percent]} vs expected " + f"{expected_percent}" + ) + + check_sequence( + "q235 Trace-PO TTFT p90 A0 to A1 regret", + [ + by_key[("q235 A0 trace-po", "ttft_p90_ms")]["regret"], + by_key[("q235 A1 trace-po", "ttft_p90_ms")]["regret"], + ], + [21.2, 0.3], + ) + fixed_pd_objectives = ( + "tpot_mean_ms", + "tpot_p90_ms", + "e2e_mean_ms", + "e2e_p90_ms", + ) + fixed_pd_expected = [33.0, 37.2, 30.7, 34.6] + check_sequence( + "q235 Fixed-PD TPOT/E2E regrets unchanged from A0 to A1", + [ + by_key[(f"q235 {campaign} fixed-pd", objective)]["regret"] + for campaign in ("A0", "A1") + for objective in fixed_pd_objectives + ], + fixed_pd_expected + fixed_pd_expected, + ) + return checks, anomalies + + +def known_attribution(input_root: Path) -> dict[str, Any]: + q30_relative = Path("q30-admission-diag/result.json") + q235_relative = Path("q235-state-diag/state-diagnosis-exact.json") + q30 = load_json(input_root / q30_relative) + q235 = load_json(input_root / q235_relative) + if q30.get("schema") != "qwen30-fixed-pd-ttft-admission-diagnosis-v1": + raise ValueError(f"unexpected schema: {q30_relative}") + if q235.get("schema") != "qwen235-fixed-pd-state-diagnosis-v1": + raise ValueError(f"unexpected schema: {q235_relative}") + + q30_cells = [] + for config, cell in sorted(q30["cells"].items()): + q30_cells.append( + { + "config": config, + "sim_tpot_over_real": cell["tpot_overprediction_ratio"], + "sim_required_slots": cell["simulator"]["required_slots"], + "mns": cell["mns"], + "real_required_slots_upper_bound": cell["real"]["required_slots_upper_bound"], + "sim_first_scheduling_wait_ms": cell["simulator"]["first_scheduling_delay_ms"], + "sim_queue_free_ttft_ms": cell["simulator"]["queue_free_ttft_ms"], + "sim_ttft_ms": cell["simulator"]["ttft_ms"], + "real_ttft_ms": cell["real"]["ttft_ms"], + "real_waiting_max": cell["real"]["periodic_queue"]["waiting_max"], + } + ) + + q235_configs = [] + for config in sorted(q235["simulator"]): + q235_configs.append( + { + "config": config, + "sim_own_decode_batch_mean": q235["simulator"][config]["decode_batch_size"]["mean"], + "real_exact_decode_batch_mean": q235["real_iteration_state"][config]["decode_batch_size"]["mean"], + "real_token_weighted_iteration_ms": q235["real_iteration_state"][config]["decode_token_weighted_iteration_elapsed_ms"], + "exact_state_matched_sim_ms": q235["exact_state_matched"]["configs"][config]["components_ms"]["total"], + "exact_state_coverage": q235["exact_state_matched"]["configs"][config]["coverage"], + } + ) + + component_order = ( + "total", + "moe_compute", + "attention", + "tp_dp_communication", + "ep_communication", + "moe_routing", + "dense_mlp_compute", + "pipeline_communication", + "runtime_overhead", + ) + q235_components = [] + own = q235["simulator_internal_all_step_tp8_minus_tp4_ms"] + exact = q235["exact_state_matched"]["tp8_minus_tp4_ms"] + for component in component_order: + q235_components.append( + { + "component": component, + "own_composition_tp8_minus_tp4_ms": own[component], + "exact_state_tp8_minus_tp4_ms": exact[component], + } + ) + return { + "q30_admission": { + "source": str(q30_relative), + "verdict": q30["verdict"], + "cells": q30_cells, + "contrasts": q30["contrasts"], + }, + "q235_state": { + "source": str(q235_relative), + "verdict": q235["verdict"], + "configs": q235_configs, + "components": q235_components, + "contrast_decomposition": q235["contrast_decomposition"], + "reference": q235["reference"], + }, + } + + +def directional_summary(rows: list[dict[str, Any]]) -> dict[str, Any]: + case_order = list(dict.fromkeys(row["case"] for row in rows)) + by_case = [] + for case in case_order: + case_rows = [row for row in rows if row["case"] == case] + by_case.append( + { + "case": case, + "n_inv_tp": sum(row["n_inv_tp"] for row in case_rows), + "n_inv_mns": sum(row["n_inv_mns"] for row in case_rows), + "n_inv_mixed": sum(row["n_inv_mixed"] for row in case_rows), + "n_tie_tp": sum(row["n_tie_tp"] for row in case_rows), + "n_tie_mns": sum(row["n_tie_mns"] for row in case_rows), + "n_tie_mixed": sum(row["n_tie_mixed"] for row in case_rows), + } + ) + fragile = [ + { + "case": row["case"], + "objective": row["objective"], + "real_relative_margin_percent": 100.0 + * row["real_relative_margin_best_vs_second"], + } + for row in rows + if row["fragile_success"] + ] + material_failures = [ + { + "case": row["case"], + "objective": row["objective"], + "winner_deciding_axis": row["winner_deciding_axis"], + "winner_pair_m_s_percent": row["winner_pair_m_s_percent"], + "winner_pair_delta_s_percent": row["winner_pair_delta_s_percent"], + "regret": row["regret"], + } + for row in rows + if row["failure"] + ] + boundary_winner_ties = [ + { + "case": row["case"], + "objective": row["objective"], + "real_winner": row["real_winner"], + "sim_winner": row["sim_winner"], + "winner_deciding_axis": row["winner_deciding_axis"], + } + for row in rows + if row["winner_pair_relation"] == "sim-tie" + ] + return { + "axis_inversions_total": { + "tp-axis": sum(row["n_inv_tp"] for row in rows), + "mns-axis": sum(row["n_inv_mns"] for row in rows), + "mixed": sum(row["n_inv_mixed"] for row in rows), + }, + "axis_inversions_by_case": by_case, + "axis_ties_total": { + "tp-axis": sum(row["n_tie_tp"] for row in rows), + "mns-axis": sum(row["n_tie_mns"] for row in rows), + "mixed": sum(row["n_tie_mixed"] for row in rows), + }, + "strict_winner_reversal_count": sum( + row["winner_pair_relation"] == "reversal" for row in rows + ), + "sim_tie_winner_mismatch_count": sum( + row["winner_pair_relation"] == "sim-tie" for row in rows + ), + "boundary_winner_ties": boundary_winner_ties, + "material_failures": material_failures, + "margin_robust_success_count": sum( + row["winner_match"] and not row["fragile_success"] for row in rows + ), + "fragile_success_count": len(fragile), + "fragile_successes": fragile, + } + + +def analyze(input_root: Path) -> dict[str, Any]: + surfaces = load_surfaces(input_root) + rows = [] + anomalies = [] + for surface in surfaces: + row, row_anomalies = analyze_surface(surface) + rows.append(row) + anomalies.extend(row_anomalies) + checks, check_anomalies = cross_checks(rows) + anomalies.extend(check_anomalies) + counterexamples = [ + {"case": row["case"], "objective": row["objective"]} + for row in rows + if row["h_scale_verdict"] == "COUNTEREXAMPLE" + ] + data_gaps = [ + { + "case": row["case"], + "objective": row["objective"], + "missing_configs": row["missing_configs"], + } + for row in rows + if row["missing_configs"] or row["h_scale_verdict"] == "N/A" + ] + directional = directional_summary(rows) + return { + "schema": "frontier-split-rootcause-decomposition-v2", + "definitions": { + "objective_direction": "all objectives are minimized", + "config_uniform_scale": "geomean_c(sim_c / real_c) over paired configs", + "log_ratio_spread_all": "max_c(log(sim_c / real_c)) - min_c(log(sim_c / real_c))", + "log_ratio_spread_real_top3": "log-ratio spread over the three configs with lowest real objective values", + "real_relative_margin_best_vs_second": "real_second / real_best - 1", + "regret": "real_sim_winner / real_best - 1, recomputed from per-config values", + "failure": "regret > 0.05", + "h_scale_verdict": "HOLDS iff failure equals (top-3 log-ratio spread > log1p(real relative margin))", + "signed_pairwise_delta_percent": "100 * (log(sim_c / sim_w) - log(real_c / real_w)); negative favors c relative to real", + "real_pair_margin_percent": "100 * log(real_c / real_w)", + "pair_inversion": "real_c > real_w and sim_c < sim_w, equivalently delta_c < -m_c", + "winner_pair_tie": "sim_s == sim_w with real_s > real_w; a decision-boundary winner-label mismatch, not a strict inversion", + "winner_deciding_axis": "axis difference between real winner w and sim winner s; none for a winner match", + "fragile_success": "winner match and real best-vs-second relative margin < 0.01", + }, + "summary": { + "row_count": len(rows), + "case_count": len({row["case"] for row in rows}), + "failure_count": sum(row["failure"] is True for row in rows), + "winner_mismatch_count": sum(row["winner_match"] is False for row in rows), + "h_scale_counterexample_count": len(counterexamples), + "h_scale_counterexamples": counterexamples, + "data_gap_count": len(data_gaps), + }, + "directional_summary": directional, + "rows": rows, + "cross_checks": checks, + "data_gaps": data_gaps, + "anomalies": anomalies, + "known_attribution": known_attribution(input_root), + } + + +def percent(value: float | None, digits: int = 1) -> str: + if value is None: + return "N/A" + return f"{value * 100.0:.{digits}f}%" + + +def multiple(value: float | None) -> str: + if value is None: + return "N/A" + return f"{value:.3f}x" + + +def percent_points(value: float | None, signed: bool = False) -> str: + if value is None: + return "N/A" + sign = "+" if signed else "" + return f"{value:{sign}.1f}%" + + +def markdown(result: dict[str, Any]) -> str: + summary = result["summary"] + lines = [ + "# Frontier good/bad split decomposition", + "", + "All 70 rows are recomputed from frozen per-config JSON values. Lower is better for every objective. " + "`scale` is the geometric mean of `sim/real`; spreads are shown as `100 ×` log-space width, while `real margin` is the ordinary relative difference `100 × (real_second / real_best - 1)`. " + "H-SCALE compares the log-space spread with `log1p(real margin)`, not directly with the ordinary relative margin. " + "The real top-3 neighborhood is selected independently for every row. Directional `m_s` and `delta_s` are already expressed in log-percentage points.", + "", + f"- Rows / case surfaces: {summary['row_count']} / {summary['case_count']}.", + f"- Failures (`regret > 5%`): {summary['failure_count']}.", + f"- Exact winner mismatches: {summary['winner_mismatch_count']}.", + f"- H-SCALE counterexamples: {summary['h_scale_counterexample_count']}.", + "", + "## Unified decomposition", + "", + "| case | objective | sim winner | real winner | regret | scale | spread all | spread real top-3 | real margin (relative) | sim-winner real loss | failure | H-SCALE | deciding axis | m_s | delta_s | n_inv_tp | n_inv_mns | n_inv_mixed | fragile success |", + "|---|---|---|---|---:|---:|---:|---:|---:|---:|---|---|---|---:|---:|---:|---:|---:|---|", + ] + for row in result["rows"]: + lines.append( + "| {case} | {objective} | {sim} | {real} | {regret} | {scale} | " + "{spread_all} | {spread_top3} | {margin} | {loss} | {failure} | {verdict} | " + "{axis} | {pair_margin} | {delta} | {n_tp} | {n_mns} | {n_mixed} | {fragile} |".format( + case=row["case"], + objective=row["objective"], + sim=row["sim_winner"] or "N/A", + real=row["real_winner"] or "N/A", + regret=percent(row["regret"]), + scale=multiple(row["config_uniform_scale"]), + spread_all=percent(row["log_ratio_spread_all"]), + spread_top3=percent(row["log_ratio_spread_real_top3"]), + margin=percent(row["real_relative_margin_best_vs_second"]), + loss=percent(row["sim_winner_real_relative_loss"]), + failure=( + "yes" if row["failure"] is True else "no" if row["failure"] is False else "N/A" + ), + verdict=row["h_scale_verdict"], + axis=row["winner_deciding_axis"] or "N/A", + pair_margin=percent_points(row["winner_pair_m_s_percent"]), + delta=percent_points(row["winner_pair_delta_s_percent"], signed=True), + n_tp=row["n_inv_tp"] if row["n_inv_tp"] is not None else "N/A", + n_mns=row["n_inv_mns"] if row["n_inv_mns"] is not None else "N/A", + n_mixed=( + row["n_inv_mixed"] if row["n_inv_mixed"] is not None else "N/A" + ), + fragile=( + "yes" + if row["fragile_success"] is True + else "no" + if row["fragile_success"] is False + else "N/A" + ), + ) + ) + + lines.extend( + [ + "", + "## H-SCALE verdict", + "", + ] + ) + if summary["h_scale_counterexample_count"] == 0: + lines.append( + "H-SCALE holds on every available row: failure occurs exactly when the real-top-3 differential residual exceeds `log1p` of the real best-vs-second relative margin." + ) + else: + failure_counterexamples = sum( + row["failure"] is True and row["h_scale_verdict"] == "COUNTEREXAMPLE" + for row in result["rows"] + ) + nonfailure_counterexamples = summary["h_scale_counterexample_count"] - failure_counterexamples + lines.extend( + [ + "H-SCALE is not sufficient as stated; the following rows are counterexamples to the biconditional:", + "", + f"All {nonfailure_counterexamples} observed counterexamples are non-failures with spread > `log1p(margin)`; " + f"there are {failure_counterexamples} failures with spread <= `log1p(margin)`. Thus the threshold is necessary for the observed failures but not sufficient to predict them.", + "", + "| case | objective | failure | spread real top-3 | real margin | counterexample type |", + "|---|---|---|---:|---:|---|", + ] + ) + for row in result["rows"]: + if row["h_scale_verdict"] != "COUNTEREXAMPLE": + continue + kind = ( + "failure with spread <= log1p(margin)" + if row["failure"] + else "non-failure with spread > log1p(margin)" + ) + lines.append( + f"| {row['case']} | {row['objective']} | " + f"{'yes' if row['failure'] else 'no'} | " + f"{percent(row['log_ratio_spread_real_top3'])} | " + f"{percent(row['real_relative_margin_best_vs_second'])} | {kind} |" + ) + + directional = result["directional_summary"] + lines.extend( + [ + "", + "## S0b directional conclusions", + "", + "Directional pair statistics remove the S0 range statistic's ambiguity by measuring every config against the real winner. " + "Strict inversions use the frozen-value test `real_c > real_w and sim_c < sim_w`; simulator ties are not counted as inversions.", + "", + "### (a) Which config axis decides each material failure?", + "", + "| case | objective | real winner → sim winner | deciding axis | m_s | delta_s | -delta_s | regret |", + "|---|---|---|---|---:|---:|---:|---:|", + ] + ) + for row in result["rows"]: + if not row["failure"]: + continue + lines.append( + f"| {row['case']} | {row['objective']} | {row['real_winner']} → " + f"{row['sim_winner']} | {row['winner_deciding_axis']} | " + f"{percent_points(row['winner_pair_m_s_percent'])} | " + f"{percent_points(row['winner_pair_delta_s_percent'], signed=True)} | " + f"{percent_points(-row['winner_pair_delta_s_percent'])} | " + f"{percent(row['regret'])} |" + ) + + lines.extend( + [ + "", + "### (b) Where do TP-axis and MNS-axis inversions and boundary ties concentrate?", + "", + "`n_inv_*` counts sum strict `(real winner, c)` pair inversions across all objectives in each case; a row can contain more than one inverted pair. " + "`n_tie_*` counts cover exact simulator ties only for the simulator-selected winner versus the real winner. Ties remain decision-boundary winner-label mismatches, not strict reversals.", + "", + "| case | n_inv_tp | n_inv_mns | n_inv_mixed | n_tie_tp | n_tie_mns | n_tie_mixed |", + "|---|---:|---:|---:|---:|---:|---:|", + ] + ) + for item in directional["axis_inversions_by_case"]: + lines.append( + f"| {item['case']} | {item['n_inv_tp']} | {item['n_inv_mns']} | " + f"{item['n_inv_mixed']} | {item['n_tie_tp']} | {item['n_tie_mns']} | " + f"{item['n_tie_mixed']} |" + ) + totals = directional["axis_inversions_total"] + tie_totals = directional["axis_ties_total"] + lines.append( + f"| **total** | **{totals['tp-axis']}** | **{totals['mns-axis']}** | " + f"**{totals['mixed']}** | **{tie_totals['tp-axis']}** | " + f"**{tie_totals['mns-axis']}** | **{tie_totals['mixed']}** |" + ) + + def concentrated(axis_key: str) -> str: + ranked = sorted( + ( + (item[axis_key], item["case"]) + for item in directional["axis_inversions_by_case"] + if item[axis_key] > 0 + ), + key=lambda item: (-item[0], item[1]), + ) + return ", ".join(f"{case} ({count})" for count, case in ranked[:5]) + + by_key = row_index(result["rows"]) + q30_ttft_axes = [ + by_key[("q30 fixed-pd high", objective)]["winner_deciding_axis"] + for objective in ("ttft_mean_ms", "ttft_p90_ms") + ] + q235_fixed_pd_failures = [ + row + for row in result["rows"] + if row["case"] in ("q235 A0 fixed-pd", "q235 A1 fixed-pd") + and row["failure"] + ] + q235_fixed_pd_axes = { + row["winner_deciding_axis"] for row in q235_fixed_pd_failures + } + q235_consistency = ( + "The eight Q235 A0/A1 Fixed-PD failures are all tp-axis. This does not conflict with the A0/MNS64 state-drift diagnosis, but does not constitute additional state-drift evidence for A1/MNS128." + if len(q235_fixed_pd_failures) == 8 and q235_fixed_pd_axes == {"tp-axis"} + else "The Q235 A0/A1 Fixed-PD failures do not match the expected eight tp-axis cases; no state-drift compatibility claim is made." + ) + lines.extend( + [ + "", + f"- Among strict inversions, TP-axis cases are most concentrated in: {concentrated('n_inv_tp')}.", + f"- Among strict inversions, MNS-axis cases are most concentrated in: {concentrated('n_inv_mns')}.", + f"- {q235_consistency}", + f"- Q30 admission evidence does not map one-to-one to an MNS-axis deciding pair: high-pressure Fixed-PD TTFT mean is **{q30_ttft_axes[0]}** and p90 is **{q30_ttft_axes[1]}**. " + "This is an axis/mechanism mismatch, not a direct causal contradiction: changing TP changes offered arrival pressure, while the diagnosed nonlinear event is crossing the MNS admission cap. It rules out the simpler claim that an MNS-threshold mechanism must appear as an mns-axis winner pair.", + f"- All {directional['sim_tie_winner_mismatch_count']} exact simulator winner ties are mns-axis. Including these boundary errors gives MNS **{totals['mns-axis']} + {tie_totals['mns-axis']} = {totals['mns-axis'] + tie_totals['mns-axis']}**, comparable to TP's **{totals['tp-axis']}** strict inversions.", + "", + "The exact ties below show that Frontier is completely insensitive to MNS changes within the corresponding case/objective cell:", + "", + "| case | objective | real winner ↔ simulator-selected tied winner (MNS pair) |", + "|---|---|---|", + ] + ) + for item in directional["boundary_winner_ties"]: + lines.append( + f"| {item['case']} | {item['objective']} | {item['real_winner']} ↔ " + f"{item['sim_winner']} |" + ) + + lines.extend( + [ + "", + "### (c) How many exact-winner successes are margin-robust?", + "", + f"Among exact winner matches, **{directional['margin_robust_success_count']}** are margin-robust (`margin >= 1%`) and " + f"**{directional['fragile_success_count']}** are fragile (`margin < 1%`). " + f"Separately, {directional['sim_tie_winner_mismatch_count']} winner-label mismatches come from exact simulator ties and sit on the decision boundary rather than being strict inversions.", + "", + "| fragile-success case | objective | real margin |", + "|---|---|---:|", + ] + ) + for item in directional["fragile_successes"]: + lines.append( + f"| {item['case']} | {item['objective']} | " + f"{item['real_relative_margin_percent']:.1f}% |" + ) + + lines.extend( + [ + "", + "## Cross-checks", + "", + "| check | observed | frozen expectation | result |", + "|---|---:|---:|---|", + ] + ) + for check in result["cross_checks"]: + observed = check["observed_percent"] + expected = check["expected_percent"] + if isinstance(observed, list): + observed_text = ", ".join(f"{value:.1f}%" for value in observed) + expected_text = ", ".join(f"{value:.1f}%" for value in expected) + else: + observed_text = f"{observed:.1f}%" + expected_text = f"{expected:.1f}%" + lines.append( + f"| {check['name']} | {observed_text} | {expected_text} | " + f"{'PASS' if check['passed'] else 'FAIL'} |" + ) + + lines.extend(["", "## Data gaps", ""]) + if result["data_gaps"]: + for gap in result["data_gaps"]: + missing = ", ".join(gap["missing_configs"]) or "insufficient paired configs" + lines.append(f"- {gap['case']} / {gap['objective']}: N/A because {missing}.") + else: + lines.append("No per-config sim/real pairing gaps were found in the requested surfaces.") + + lines.extend(["", "## 异常", ""]) + if result["anomalies"]: + lines.extend(f"- {item}" for item in result["anomalies"]) + else: + lines.append("- 无。重算 winner/regret 与 frozen JSON 一致，硬性交叉核对均通过。") + + lines.extend( + [ + "", + "## Figure", + "", + "`margin-vs-residual.png` uses x = `100 × log1p(real best-vs-second relative margin)` and y = `100 ×` real-top-3 log-ratio spread. " + "Both axes are logarithmic because the real margins span six orders of magnitude. Green means exact winner match; red means winner mismatch. " + "The diagonal is the H-SCALE threshold, while failure itself is defined by regret, not by exact winner match.", + "", + "`decision-pair-axis.png` is a mechanism census, not a hypothesis test: for a strict winner reversal, `-delta_s > m_s` is algebraically equivalent to the mismatch. " + "The frozen surfaces also contain deterministic winner-label mismatches caused by exact simulator ties; those points lie on `-delta_s = m_s` and are not counted as strict inversions. " + "Color identifies the winner-deciding config axis and marker area is proportional to regret.", + "", + "## A0 vs A1 对照", + "", + "A1 repairs the Q235 Trace-PO p90 ranking error but leaves the four large Fixed-PD TPOT/E2E regrets unchanged.", + "", + "| case / objective | A0 regret | A1 regret |", + "|---|---:|---:|", + ] + ) + by_key = row_index(result["rows"]) + a0_a1_rows = ( + ("trace-po / ttft_p90_ms", "trace-po", "ttft_p90_ms"), + ("fixed-pd / tpot_mean_ms", "fixed-pd", "tpot_mean_ms"), + ("fixed-pd / tpot_p90_ms", "fixed-pd", "tpot_p90_ms"), + ("fixed-pd / e2e_mean_ms", "fixed-pd", "e2e_mean_ms"), + ("fixed-pd / e2e_p90_ms", "fixed-pd", "e2e_p90_ms"), + ) + for label, case, objective in a0_a1_rows: + a0 = by_key[(f"q235 A0 {case}", objective)]["regret"] + a1 = by_key[(f"q235 A1 {case}", objective)]["regret"] + lines.append(f"| {label} | {percent(a0)} | {percent(a1)} |") + lines.extend( + [ + "", + "Thus Trace-PO TTFT p90 changes **21.2% → 0.3%**, whereas Fixed-PD remains **33.0% / 37.2% / 30.7% / 34.6%** in both A0 and A1.", + "", + "## 已知分量归因摘要", + "", + "### Q30 high-pressure Fixed-PD TTFT: first-scheduling wait vs execution-side TTFT", + "", + "`queue-free TTFT` is the execution-side TTFT after removing first-scheduling wait; in the frozen diagnostic, simulated TTFT is their sum (up to floating-point rounding).", + "", + "| config | sim TPOT / real | sim slots / MNS | real slots upper / MNS | first-scheduling wait | queue-free TTFT | sim TTFT | real TTFT | real waiting max |", + "|---|---:|---:|---:|---:|---:|---:|---:|---:|", + ] + ) + q30 = result["known_attribution"]["q30_admission"] + for cell in q30["cells"]: + lines.append( + f"| {cell['config']} | {cell['sim_tpot_over_real']:.2f}x | " + f"{cell['sim_required_slots']:.1f}/{cell['mns']} | " + f"{cell['real_required_slots_upper_bound']:.1f}/{cell['mns']} | " + f"{cell['sim_first_scheduling_wait_ms']:.1f} ms | " + f"{cell['sim_queue_free_ttft_ms']:.1f} ms | " + f"{cell['sim_ttft_ms']:.1f} ms | {cell['real_ttft_ms']:.1f} ms | " + f"{cell['real_waiting_max']} |" + ) + contrasts = q30["contrasts"] + lines.extend( + [ + "", + "For TP4/MNS32 minus TP2/MNS64, the observed simulator contrast is " + f"**{contrasts['observed_sim_ttft_tp4_minus_tp2_ms']:+.1f} ms**; removing first-scheduling wait changes it to " + f"**{contrasts['queue_free_sim_ttft_tp4_minus_tp2_ms']:+.1f} ms**, aligned with the real contrast " + f"**{contrasts['real_ttft_tp4_minus_tp2_ms']:+.1f} ms**. This localizes the Q30 high-pressure TTFT reversal to admission queueing, while the upstream service-time overprediction remains unattributed in the frozen data.", + "", + "### Q235 Fixed-PD: own-composition vs exact-state contrast", + "", + "Positive TP8−TP4 means TP8 is slower and therefore agrees with the real TP4 winner.", + "", + "| config | sim own decode batch mean | real exact decode batch mean | real token-weighted iteration | exact-state matched sim | coverage |", + "|---|---:|---:|---:|---:|---:|", + ] + ) + q235 = result["known_attribution"]["q235_state"] + for config in q235["configs"]: + lines.append( + f"| {config['config']} | {config['sim_own_decode_batch_mean']:.3f} | " + f"{config['real_exact_decode_batch_mean']:.3f} | " + f"{config['real_token_weighted_iteration_ms']:.4f} ms | " + f"{config['exact_state_matched_sim_ms']:.4f} ms | " + f"{config['exact_state_coverage'] * 100.0:.1f}% |" + ) + lines.extend( + [ + "", + "| component | own-composition TP8−TP4 | exact-state TP8−TP4 |", + "|---|---:|---:|", + ] + ) + for component in q235["components"]: + lines.append( + f"| {component['component']} | " + f"{component['own_composition_tp8_minus_tp4_ms']:+.4f} ms | " + f"{component['exact_state_tp8_minus_tp4_ms']:+.4f} ms |" + ) + decomposition = q235["contrast_decomposition"] + reference = q235["reference"] + lines.extend( + [ + "", + f"The total contrast flips from **{decomposition['simulator_internal_tp8_minus_tp4_ms']:+.4f} ms** on Frontier's own composition to " + f"**{decomposition['exact_state_matched_tp8_minus_tp4_ms']:+.4f} ms** at exact real state, a " + f"**{decomposition['state_composition_shift_ms']:+.4f} ms** state-composition shift. " + f"The exact real iteration contrast is **{reference['exact_iteration_tp8_minus_tp4_ms']:+.4f} ms** and the frozen real TPOT contrast is " + f"**{reference['observed_real_tp8_minus_tp4_ms']:+.4f} ms**. This supports closed-loop state/composition drift for Q235 Fixed-PD; it does not by itself attribute every remaining conditional execution residual.", + "", + ] + ) + return "\n".join(lines) + + +def render_plot(rows: list[dict[str, Any]], output: Path) -> None: + os.environ.setdefault( + "MPLCONFIGDIR", "/tmp/aituner-frontier-split-rootcause-matplotlib" + ) + import matplotlib + + matplotlib.use("Agg") + import matplotlib.pyplot as plt + + usable = [ + row + for row in rows + if row["real_relative_margin_best_vs_second"] is not None + and row["log_ratio_spread_real_top3"] is not None + ] + if not usable: + raise ValueError("no complete rows available for margin-vs-residual plot") + + plt.rcParams.update( + { + "font.family": "DejaVu Sans", + "font.size": 8, + "axes.titlesize": 10, + "axes.labelsize": 9, + } + ) + fig, ax = plt.subplots(figsize=(13.0, 9.0), dpi=180) + colors = {True: "#2a9d8f", False: "#d1495b"} + labels = {True: "winner match", False: "winner mismatch"} + for match in (True, False): + selected = [row for row in usable if row["winner_match"] is match] + ax.scatter( + [ + math.log1p(row["real_relative_margin_best_vs_second"]) * 100.0 + for row in selected + ], + [row["log_ratio_spread_real_top3"] * 100.0 for row in selected], + s=28, + color=colors[match], + edgecolor="white", + linewidth=0.4, + alpha=0.88, + label=labels[match], + zorder=3, + ) + + for index, row in enumerate(usable): + x = math.log1p(row["real_relative_margin_best_vs_second"]) * 100.0 + y = row["log_ratio_spread_real_top3"] * 100.0 + dx = 3 if index % 2 == 0 else -3 + dy = 3 if (index // 2) % 2 == 0 else -5 + ax.annotate( + row["case"], + (x, y), + xytext=(dx, dy), + textcoords="offset points", + ha="left" if dx > 0 else "right", + va="bottom" if dy > 0 else "top", + fontsize=4.5, + color="#303030", + alpha=0.82, + clip_on=True, + zorder=4, + ) + + max_value = max( + max( + math.log1p(row["real_relative_margin_best_vs_second"]) + for row in usable + ), + max(row["log_ratio_spread_real_top3"] for row in usable), + ) * 100.0 + min_value = min( + min( + math.log1p(row["real_relative_margin_best_vs_second"]) + for row in usable + ), + min(row["log_ratio_spread_real_top3"] for row in usable), + ) * 100.0 + lower = min_value / 5.0 + upper = max_value * 2.0 + ax.plot( + [lower, upper], + [lower, upper], + linestyle="--", + color="#555555", + linewidth=1.0, + label="spread = log1p(margin)", + zorder=2, + ) + ax.set_xscale("log") + ax.set_yscale("log") + ax.set_xlim(left=lower, right=upper) + ax.set_ylim(bottom=lower, top=upper) + ax.set_xlabel("Real best-vs-second log margin: 100 × log1p(relative margin)") + ax.set_ylabel("Top-3 differential residual: 100 × log-ratio spread") + ax.set_title("Frontier selection: real margin vs config-differential residual") + ax.grid(True, color="#d8d8d8", linewidth=0.5, alpha=0.7, zorder=1) + ax.legend(frameon=False, loc="upper left") + fig.tight_layout() + output.parent.mkdir(parents=True, exist_ok=True) + temporary = output.with_suffix(output.suffix + ".tmp") + fig.savefig( + temporary, + format="png", + dpi=180, + metadata={"Software": "analyze_split_decomposition.py"}, + ) + plt.close(fig) + os.replace(temporary, output) + + +def render_decision_pair_axis_plot( + rows: list[dict[str, Any]], output: Path +) -> None: + os.environ.setdefault( + "MPLCONFIGDIR", "/tmp/aituner-frontier-split-rootcause-matplotlib" + ) + import matplotlib + + matplotlib.use("Agg") + import matplotlib.pyplot as plt + from matplotlib.lines import Line2D + + usable = [ + row + for row in rows + if row["winner_match"] is False + and row["winner_pair_m_s_percent"] > 0 + and -row["winner_pair_delta_s_percent"] > 0 + ] + if not usable: + raise ValueError("no winner mismatches available for decision-pair-axis plot") + + plt.rcParams.update( + { + "font.family": "DejaVu Sans", + "font.size": 8, + "axes.titlesize": 10, + "axes.labelsize": 9, + } + ) + colors = { + "tp-axis": "#3572b0", + "mns-axis": "#e07a1f", + "mixed": "#7b4ab5", + } + fig, ax = plt.subplots(figsize=(11.0, 8.0), dpi=180) + for axis in INVERSION_AXES: + selected = [row for row in usable if row["winner_deciding_axis"] == axis] + ax.scatter( + [row["winner_pair_m_s_percent"] for row in selected], + [-row["winner_pair_delta_s_percent"] for row in selected], + s=[1000.0 * row["regret"] for row in selected], + color=colors[axis], + edgecolor="white", + linewidth=0.6, + alpha=0.82, + label=axis, + zorder=3, + ) + + values = [row["winner_pair_m_s_percent"] for row in usable] + [ + -row["winner_pair_delta_s_percent"] for row in usable + ] + lower = min(values) / 1.8 + upper = max(values) * 1.8 + ax.plot( + [lower, upper], + [lower, upper], + linestyle="--", + color="#555555", + linewidth=1.0, + label="reversal boundary", + zorder=2, + ) + ax.set_xscale("log") + ax.set_yscale("log") + ax.set_xlim(lower, upper) + ax.set_ylim(lower, upper) + ax.set_xlabel("Winner-pair real log margin $m_s$ (%)") + ax.set_ylabel("Simulator preference strength $-\\delta_s$ (%)") + ax.set_title("Winner-deciding pair census by config axis") + ax.grid(True, color="#d8d8d8", linewidth=0.5, alpha=0.7, zorder=1) + axis_handles = [ + Line2D( + [0], + [0], + marker="o", + linestyle="none", + markerfacecolor=colors[axis], + markeredgecolor="white", + markersize=7, + label=axis, + ) + for axis in INVERSION_AXES + ] + axis_handles.append( + Line2D( + [0], + [0], + linestyle="--", + color="#555555", + linewidth=1.0, + label="reversal boundary", + ) + ) + axis_legend = ax.legend( + handles=axis_handles, frameon=False, loc="upper left", title="deciding axis" + ) + ax.add_artist(axis_legend) + size_handles = [ + ax.scatter( + [], + [], + s=10.0 * regret_percent, + facecolor="none", + edgecolor="#555555", + linewidth=0.7, + label=f"{regret_percent}%", + ) + for regret_percent in (5, 20, 50) + ] + ax.legend( + handles=size_handles, + frameon=False, + loc="lower right", + title="regret (marker area)", + ) + fig.tight_layout() + output.parent.mkdir(parents=True, exist_ok=True) + temporary = output.with_suffix(output.suffix + ".tmp") + fig.savefig( + temporary, + format="png", + dpi=180, + metadata={"Software": "analyze_split_decomposition.py"}, + ) + plt.close(fig) + os.replace(temporary, output) + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser() + parser.add_argument("--input-root", type=Path, default=DEFAULT_INPUT_ROOT) + parser.add_argument("--results-root", type=Path, default=DEFAULT_RESULTS_ROOT) + return parser.parse_args() + + +def main() -> None: + args = parse_args() + result = analyze(args.input_root.resolve()) + results_root = args.results_root.resolve() + atomic_write( + results_root / "decomposition.json", + json.dumps(result, indent=2, sort_keys=True) + "\n", + ) + atomic_write(results_root / "decomposition.md", markdown(result)) + render_plot(result["rows"], results_root / "margin-vs-residual.png") + render_decision_pair_axis_plot( + result["rows"], results_root / "decision-pair-axis.png" + ) + print( + json.dumps( + { + "status": "PASS" if not result["anomalies"] else "ANOMALY", + "rows": result["summary"]["row_count"], + "h_scale_counterexamples": result["summary"]["h_scale_counterexample_count"], + "anomalies": len(result["anomalies"]), + }, + sort_keys=True, + ) + ) + if result["anomalies"]: + raise SystemExit("analysis anomalies found; see results/decomposition.md") + + +if __name__ == "__main__": + main() diff --git a/runs/frontier-split-rootcause-v0/frozen-inputs/q235-ablation-a1-prov/aituner.commit b/runs/frontier-split-rootcause-v0/frozen-inputs/q235-ablation-a1-prov/aituner.commit new file mode 100644 index 0000000..159620e --- /dev/null +++ b/runs/frontier-split-rootcause-v0/frozen-inputs/q235-ablation-a1-prov/aituner.commit @@ -0,0 +1 @@ +10567da5238db57c29a45e09ebd40343a7d1a543 diff --git a/runs/frontier-split-rootcause-v0/frozen-inputs/q235-ablation-a1-prov/artifacts.sha256 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trace-pd | tpot_p90_ms | tp8_ep8_mns64 | tp8_ep8_mns64 | yes | 0.0% | 66.7% | +| trace-pd | e2e_mean_ms | tp8_ep8_mns128 | tp8_ep8_mns64 | no | 0.6% | 83.3% | +| trace-pd | e2e_p90_ms | tp8_ep8_mns128 | tp8_ep8_mns64 | no | 6.2% | 83.3% | +| trace-po | ttft_mean_ms | tp8_ep8_mns64 | tp8_ep8_mns128 | no | 1.4% | 66.7% | +| trace-po | ttft_p90_ms | tp8_ep8_mns128 | tp8_ep8_mns64 | no | 0.3% | 100.0% | +| trace-po | e2e_mean_ms | tp8_ep8_mns64 | tp8_ep8_mns128 | no | 1.4% | 66.7% | +| trace-po | e2e_p90_ms | tp8_ep8_mns128 | tp8_ep8_mns64 | no | 0.3% | 100.0% | diff --git a/runs/frontier-split-rootcause-v0/frozen-inputs/q235-fourcase-a0/comparison.json b/runs/frontier-split-rootcause-v0/frozen-inputs/q235-fourcase-a0/comparison.json new file mode 100644 index 0000000..fb5ede7 --- /dev/null +++ b/runs/frontier-split-rootcause-v0/frozen-inputs/q235-fourcase-a0/comparison.json @@ -0,0 +1,952 @@ +{ + "cases": { + "fixed-pd": { + "comparison": { + "e2e_mean_ms": { + "informative_pairs": 4, + 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correct TP4 winner; the ranking error is caused by closed-loop state/composition mismatch, not a conditional stage reversal. + +| Config | Real Running proxy mean | Sim decode batch mean | Proxy coverage | +|---|---:|---:|---:| +| tp4_ep1_mns64 | 4.667 | 13.537 | 100.0% | +| tp8_ep8_mns64 | 12.238 | 18.938 | 100.0% | + +| Config | Real exact decode batch mean | Real token-weighted iteration ms | Matched simulator ms | Exact coverage | +|---|---:|---:|---:|---:| +| tp4_ep1_mns64 | 3.890 | 18.4710 | 34.2575 | 100.0% | +| tp8_ep8_mns64 | 10.921 | 27.5887 | 45.1603 | 100.0% | + +## Frontier internal component contrast over its own executed composition + +Decode-token-weighted over both pure-decode and mixed prefill/decode steps. + +| Component | TP8 - TP4 (ms/decoded token step) | +|---|---:| +| moe_compute | -32.4544 | +| total | -20.0663 | +| attention | -7.4619 | +| tp_dp_communication | -6.5711 | +| ep_communication | +6.2354 | +| moe_routing | -0.2862 | +| dense_mlp_compute | -0.1231 | +| pipeline_communication | +0.0000 | +| runtime_overhead | +0.0000 | + +## Frontier component contrast at exact real token composition + +Positive means TP8 slower; negative means Frontier gives TP8 an advantage. + +| Component | TP8 - TP4 (ms/step) | +|---|---:| +| total | +10.9028 | +| ep_communication | +6.5355 | +| tp_dp_communication | -6.4718 | +| attention | -2.1304 | +| moe_compute | -1.9774 | +| moe_routing | +0.1384 | +| dense_mlp_compute | -0.0308 | +| pipeline_communication | +0.0000 | +| runtime_overhead | +0.0000 | + +## Interpretation boundary + +- Real observed TPOT contrast: +6.9500 ms/token. +- Real exact-iteration contrast: +9.1177 ms/decode-token-weighted step. +- vLLM 10-second Running is active requests, not per-iteration decode batch; context lengths and graph buckets are unavailable in frozen logs. +- Frontier state outputs have no direct graph bucket/padding/launch-overhead field; graph effects remain folded into predictors. +- For TP8 shared-domain sync, 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+ }, + "status": "PASS", + "verdict": "State-composition mismatch is insufficient: after reweighting Frontier to measured real decode composition, it still predicts TP8 faster." +} diff --git a/runs/frontier-split-rootcause-v0/frozen-inputs/q235-state-diag/state-diagnosis-optrace.md b/runs/frontier-split-rootcause-v0/frozen-inputs/q235-state-diag/state-diagnosis-optrace.md new file mode 100644 index 0000000..a93d3bc --- /dev/null +++ b/runs/frontier-split-rootcause-v0/frozen-inputs/q235-state-diag/state-diagnosis-optrace.md @@ -0,0 +1,48 @@ +# Qwen235 Fixed-PD state diagnosis + +**Verdict:** State-composition mismatch is insufficient: after reweighting Frontier to measured real decode composition, it still predicts TP8 faster. + +| Config | Real Running proxy mean | Sim decode batch mean | Proxy coverage | +|---|---:|---:|---:| +| tp4_ep1_mns64 | 4.667 | 13.537 | 100.0% | +| tp8_ep8_mns64 | 12.238 | 18.938 | 100.0% | + +## Frontier internal component contrast over its own executed composition + +Decode-token-weighted over both pure-decode and mixed prefill/decode steps. + +| Component | TP8 - TP4 (ms/decoded token step) | +|---|---:| +| moe_compute | -32.4544 | +| total | -20.0663 | +| attention | -7.4619 | +| tp_dp_communication | -6.5711 | +| ep_communication | +6.2354 | +| moe_routing | -0.2862 | +| dense_mlp_compute | -0.1231 | +| pipeline_communication | +0.0000 | +| runtime_overhead | +0.0000 | + +## Frontier internal component contrast at real Running proxy + +Positive means TP8 slower; negative means Frontier gives TP8 an advantage. + +| Component | TP8 - TP4 (ms/step) | +|---|---:| +| moe_compute | -16.0581 | +| total | -8.5517 | +| ep_communication | +6.8248 | +| tp_dp_communication | -5.4010 | +| moe_routing | +0.3109 | +| attention | -0.2407 | +| dense_mlp_compute | +0.0018 | +| pipeline_communication | +0.0000 | +| runtime_overhead | +0.0000 | + +## Interpretation boundary + +- Real observed TPOT contrast: +6.9500 ms/token. +- vLLM 10-second Running is active requests, not per-iteration decode batch; context lengths and graph buckets are unavailable in frozen logs. +- Frontier state outputs have no direct graph bucket/padding/launch-overhead field; graph effects remain folded into predictors. +- For TP8 shared-domain sync, total is ledger stage_end-start (critical path); op categories are serialized work estimates and are not additive because overlap and lane-summed wait diagnostics are represented separately. +- Component deltas identify where Frontier creates its own TP8 advantage; without real per-stage measurements they are not yet root-cause proof. diff --git a/runs/frontier-split-rootcause-v0/frozen-inputs/q235-state-diag/state-diagnosis.json b/runs/frontier-split-rootcause-v0/frozen-inputs/q235-state-diag/state-diagnosis.json new file mode 100644 index 0000000..579aa9c --- /dev/null +++ b/runs/frontier-split-rootcause-v0/frozen-inputs/q235-state-diag/state-diagnosis.json @@ -0,0 +1,1361 @@ +{ + "proxy_matched": { + 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0.0036978849298677607, + "ep_communication": 0.082097370873993, + "moe_compute": 0.20779839324350205, + "moe_routing": 0.02642429565140599, + "pipeline_communication": 0.0, + "runtime_overhead": 0.0, + "total": 0.5496348506201855, + "tp_dp_communication": 0.082097370873993 + }, + "decode_token_weighted_mixed_step_share": 0.08077215382276942, + "mixed_prefill_decode_rows": 128, + "pure_decode_rows": 1609, + "pure_decode_token_weighted_components_ms": { + "attention": 0.07291164903783319, + "dense_mlp_compute": 0.0019404539326675046, + "ep_communication": 0.07197841798710232, + "moe_compute": 0.14992182626155828, + "moe_routing": 0.017426514274852836, + "pipeline_communication": 0.0, + "runtime_overhead": 0.0, + "total": 0.38615727948111644, + "tp_dp_communication": 0.07197841798710232 + }, + "scorer_equivalence": { + "base_metrics": { + "e2e_mean_ms": 14056.464119438264, + "e2e_p50_ms": 14650.653983203298, + "e2e_p90_ms": 14674.997135818061, + "e2e_p95_ms": 14677.859760866291, + "slos": { + "tpot_100ms": { + "feasible": true, + "pass_rate": 1.0, + "passed": 129 + }, + "tpot_150ms": { + "feasible": true, + "pass_rate": 1.0, + "passed": 129 + }, + "tpot_180ms": { + "feasible": true, + "pass_rate": 1.0, + "passed": 129 + }, + "tpot_50ms": { + "feasible": false, + "pass_rate": 0.14728682170542637, + "passed": 19 + } + }, + "tpot_mean_ms": 54.15667269956665, + "tpot_p50_ms": 56.52350191837838, + "tpot_p90_ms": 56.52350850045728, + "tpot_p95_ms": 56.523546100039816, + "ttft_mean_ms": 246.51258104876885, + "ttft_p50_ms": 246.7781280760306, + "ttft_p90_ms": 263.0087113349084, + "ttft_p95_ms": 264.77576248352364 + }, + "request_metrics_byte_identical": true, + "request_metrics_sha256": "51a848c4c12eacabbc72a0f6a8b5c1863e8b624b316b7ab55341e3fe5a3a753b" + }, + "stage_rows": 1738 + } + }, + "simulator_internal_all_step_tp8_minus_tp4_ms": { + "attention": -13.695027512244868, + "dense_mlp_compute": -0.28902726687751934, + "ep_communication": 0.082097370873993, + "moe_compute": -45.270987985301076, + "moe_routing": -1.7759354283867153, + "pipeline_communication": 0.0, + "runtime_overhead": 0.0, + "total": -73.67329253153181, + "tp_dp_communication": -12.724411709595623 + }, + "status": "PASS", + "verdict": "Active-batch-count mismatch alone is insufficient: after exact reweighting to each config's real Running histogram, Frontier still predicts TP8 faster." +} diff --git a/runs/frontier-split-rootcause-v0/frozen-inputs/q235-state-diag/state-diagnosis.md b/runs/frontier-split-rootcause-v0/frozen-inputs/q235-state-diag/state-diagnosis.md new file mode 100644 index 0000000..381adb4 --- /dev/null +++ b/runs/frontier-split-rootcause-v0/frozen-inputs/q235-state-diag/state-diagnosis.md @@ -0,0 +1,47 @@ +# Qwen235 Fixed-PD state diagnosis + +**Verdict:** Active-batch-count mismatch alone is insufficient: after exact reweighting to each config's real Running histogram, Frontier still predicts TP8 faster. + +| Config | Real Running proxy mean | Sim decode batch mean | Proxy coverage | +|---|---:|---:|---:| +| tp4_ep1_mns64 | 4.667 | 13.537 | 100.0% | +| tp8_ep8_mns64 | 12.238 | 18.938 | 100.0% | + +## Frontier internal component contrast over its own executed composition + +Decode-token-weighted over both pure-decode and mixed prefill/decode steps. + +| Component | TP8 - TP4 (ms/decoded token step) | +|---|---:| +| total | -73.6733 | +| moe_compute | -45.2710 | +| attention | -13.6950 | +| tp_dp_communication | -12.7244 | +| moe_routing | -1.7759 | +| dense_mlp_compute | -0.2890 | +| ep_communication | +0.0821 | +| pipeline_communication | +0.0000 | +| runtime_overhead | +0.0000 | + +## Frontier internal component contrast at real Running proxy + +Positive means TP8 slower; negative means Frontier gives TP8 an advantage. + +| Component | TP8 - TP4 (ms/step) | +|---|---:| +| total | -46.0760 | +| moe_compute | -26.3387 | +| tp_dp_communication | -12.1532 | +| attention | -6.2393 | +| moe_routing | -1.2405 | +| dense_mlp_compute | -0.1769 | +| ep_communication | +0.0726 | +| pipeline_communication | +0.0000 | +| runtime_overhead | +0.0000 | + +## Interpretation boundary + +- Real observed TPOT contrast: +6.9500 ms/token. +- vLLM 10-second Running is active requests, not per-iteration decode batch; context lengths and graph buckets are unavailable in frozen logs. +- Frontier ledger exposes additive execution components but no direct graph bucket/padding/launch-overhead field; graph effects remain folded into predictors. +- Component deltas identify where Frontier creates its own TP8 advantage; without real per-stage measurements they are not yet root-cause proof. diff --git a/runs/frontier-split-rootcause-v0/frozen-inputs/q30-admission-diag/result.json b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-admission-diag/result.json new file mode 100644 index 0000000..b81ebf0 --- /dev/null +++ b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-admission-diag/result.json @@ -0,0 +1,173 @@ +{ + "boundary": "This identifies where the selection error is created, but the frozen real logs do not attribute service-time overprediction to one operator.", + "cells": { + "tp2_mns64": { + "arrival_rate_requests_per_s": 2.25, + "mns": 64, + "real": { + "e2e_ms": 3354.8816545874283, + "periodic_queue": { + "running_max": 10, + "running_mean": 7.257142857142857, + "samples": 35, + "waiting_max": 0, + "waiting_mean": 0.0 + }, + "required_slots_upper_bound": 7.548483722821714, + "slot_margin_lower_bound": 56.451516277178285, + "tpot_ms": 12.393283087182931, + "ttft_ms": 194.56193759030603 + }, + "simulator": { + "first_scheduling_delay_ms": 38.22329108338011, + "queue_free_ttft_ms": 191.02937373774816, + "request_execution_ms": 25761.91467886552, + "required_slots": 57.96430802744742, + "slot_margin": 6.035691972552577, + "tpot_ms": 100.27798158873513, + "ttft_ms": 229.2526648211283, + "ttft_queue_fraction": 0.16672997503956338 + }, + "tp": 2, + "tpot_overprediction_ratio": 8.091316956395687 + }, + "tp4_mns16": { + "arrival_rate_requests_per_s": 4.5, + "mns": 16, + "real": { + "e2e_ms": 3252.7993474094965, + "periodic_queue": { + "running_max": 16, + "running_mean": 15.166666666666666, + "samples": 18, + "waiting_max": 0, + "waiting_mean": 0.0 + }, + "required_slots_upper_bound": 14.637597063342735, + "slot_margin_lower_bound": 1.362402936657265, + "tpot_ms": 12.267737219892378, + "ttft_ms": 124.49453797538061 + }, + "simulator": { + "first_scheduling_delay_ms": 64906.25631809643, + "queue_free_ttft_ms": 134.74066995065758, + "request_execution_ms": 12108.627705569606, + "required_slots": 54.488824675063235, + "slot_margin": -38.488824675063235, + "tpot_ms": 46.95641974752248, + "ttft_ms": 65040.99698804708, + "ttft_queue_fraction": 0.9979283732385681 + }, + "tp": 4, + "tpot_overprediction_ratio": 3.8276349505907024 + }, + "tp4_mns32": { + "arrival_rate_requests_per_s": 4.5, + "mns": 32, + "real": { + "e2e_ms": 3227.015463910283, + "periodic_queue": { + "running_max": 16, + "running_mean": 14.944444444444445, + "samples": 18, + "waiting_max": 0, + "waiting_mean": 0.0 + }, + "required_slots_upper_bound": 14.521569587596273, + "slot_margin_lower_bound": 17.478430412403725, + "tpot_ms": 12.171863019540417, + "ttft_ms": 123.15859724408143 + }, + "simulator": { + "first_scheduling_delay_ms": 27180.676177927482, + "queue_free_ttft_ms": 135.62147333612225, + "request_execution_ms": 14643.080360640608, + "required_slots": 65.89386162288272, + "slot_margin": -33.893861622882724, + "tpot_ms": 56.89199563649036, + "ttft_ms": 27316.2976512636, + "ttft_queue_fraction": 0.9950351444010626 + }, + "tp": 4, + "tpot_overprediction_ratio": 4.6740581573385525 + }, + "tp4_mns64": { + "arrival_rate_requests_per_s": 4.5, + "mns": 64, + "real": { + "e2e_ms": 3258.202659738964, + "periodic_queue": { + "running_max": 17, + "running_mean": 14.833333333333334, + "samples": 18, + "waiting_max": 0, + "waiting_mean": 0.0 + }, + "required_slots_upper_bound": 14.661911968825338, + "slot_margin_lower_bound": 49.338088031174664, + "tpot_ms": 12.29409478300949, + "ttft_ms": 123.17715882596677 + }, + "simulator": { + "first_scheduling_delay_ms": 6367.273779218055, + "queue_free_ttft_ms": 136.7304324624105, + "request_execution_ms": 17736.957363112386, + "required_slots": 79.81630813400574, + "slot_margin": -15.816308134005737, + "tpot_ms": 69.02049776725552, + "ttft_ms": 6504.004211680465, + "ttft_queue_fraction": 0.9789774993969319 + }, + "tp": 4, + "tpot_overprediction_ratio": 5.614117914776633 + } + }, + "contrasts": { + "observed_sim_ttft_tp4_minus_tp2_ms": 27087.044986442474, + "queue_free_sim_ttft_tp4_minus_tp2_ms": -55.40790040162591, + "real_ttft_tp4_minus_tp2_ms": -71.4033403462246 + }, + "schema": "qwen30-fixed-pd-ttft-admission-diagnosis-v1", + "selection": { + "agreement": 0.5909090909090909, + "concordant_pairs": 39, + "discordant_pairs": 27, + "informative_pairs": 66, + "real_ranking": [ + "tp4_mns32", + "tp4_mns64", + "tp4_mns16", + "tp2_mns64", + "tp2_mns8", + "tp2_mns32", + "tp2_mns16", + "tp1_mns8", + "tp1_mns32", + "tp1_mns16", + "tp1_mns64", + "tp4_mns8" + ], + "real_winner": "tp4_mns32", + "selected_config_real_regret": 0.5797674051508896, + "sim_ranking": [ + "tp2_mns64", + "tp1_mns64", + "tp4_mns64", + "tp1_mns32", + "tp2_mns32", + "tp4_mns32", + "tp4_mns16", + "tp2_mns16", + "tp1_mns16", + "tp4_mns8", + "tp2_mns8", + "tp1_mns8" + ], + "sim_winner": "tp2_mns64", + "tied_pairs": 0, + "winner_match": false + }, + "status": "PASS", + "verdict": "Frontier overpredicts decode service time, so TP4's higher global arrival rate crosses the MNS admission cap only in simulation. The resulting first-scheduling queue reverses the TP topology ranking; without that queue Frontier itself predicts TP4 faster than TP2, matching real hardware.", + "workload": "Qwen3-30B Fixed-PD 4096->256 at 1.125 req/s/GPU" +} diff --git a/runs/frontier-split-rootcause-v0/frozen-inputs/q30-admission-diag/result.md b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-admission-diag/result.md new file mode 100644 index 0000000..dd90030 --- /dev/null +++ b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-admission-diag/result.md @@ -0,0 +1,18 @@ +# Qwen30 Fixed-PD TTFT admission diagnosis + +**Verdict:** Frontier overpredicts decode service time, so TP4's higher global arrival rate crosses the MNS admission cap only in simulation. The resulting first-scheduling queue reverses the TP topology ranking; without that queue Frontier itself predicts TP4 faster than TP2, matching real hardware. + +| Config | Sim TPOT / real | Sim required slots / MNS | Real slots upper bound / MNS | Sim first-schedule wait | Real waiting max | +|---|---:|---:|---:|---:|---:| +| tp2_mns64 | 8.09x | 58.0/64 | 7.5/64 | 38.2 ms | 0 | +| tp4_mns16 | 3.83x | 54.5/16 | 14.6/16 | 64906.3 ms | 0 | +| tp4_mns32 | 4.67x | 65.9/32 | 14.5/32 | 27180.7 ms | 0 | +| tp4_mns64 | 5.61x | 79.8/64 | 14.7/64 | 6367.3 ms | 0 | + +## TP4/MNS32 minus TP2/MNS64 TTFT + +- Observed Frontier: +27087.0 ms. +- Frontier without first-scheduling queue: -55.4 ms. +- Real hardware: -71.4 ms. + +**Boundary:** This identifies where the selection error is created, but the frozen real logs do not attribute service-time overprediction to one operator. diff --git a/runs/frontier-split-rootcause-v0/frozen-inputs/q30-expansion-lo/analysis-launch.log b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-expansion-lo/analysis-launch.log new file mode 100644 index 0000000..2614c49 --- /dev/null +++ b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-expansion-lo/analysis-launch.log @@ -0,0 +1,2 @@ +Q30_ANALYSIS_ECHO source_real=/home/admin/cpfs/wjh/aituner/qwen30-latency-expansion-20260718-r2/real-r6 source_sim=/home/admin/cpfs/wjh/aituner/qwen30-latency-expansion-20260718-r2/sim cases=trace-po,fixed-pd,fixed-po requests=129 configs=12 trials=3 metrics=mean,p90(TTFT,TPOT-if-OSL-gt-1,E2E) gpu_cost=0 output=/home/admin/cpfs/wjh/aituner/qwen30-latency-expansion-20260718-r2/analysis-r6 +Q30_ANALYSIS_COMPLETE diff --git a/runs/frontier-split-rootcause-v0/frozen-inputs/q30-expansion-lo/fixed-pd-comparison.json b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-expansion-lo/fixed-pd-comparison.json new file mode 100644 index 0000000..dbad285 --- /dev/null +++ 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"tp4_mns32", + "tp4_mns64", + "tp4_mns8", + "tp2_mns16", + "tp2_mns32", + "tp2_mns64", + "tp2_mns8", + "tp1_mns16", + "tp1_mns32", + "tp1_mns64", + "tp1_mns8" + ], + "sim_winner": "tp4_mns16", + "tied_pairs": 18, + "winner_match": true + } + }, + "sim_root": "/home/admin/cpfs/wjh/aituner/qwen30-latency-expansion-20260718-r2/sim/fixed-pd" +} diff --git a/runs/frontier-split-rootcause-v0/frozen-inputs/q30-expansion-lo/fixed-pd-comparison.md b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-expansion-lo/fixed-pd-comparison.md new file mode 100644 index 0000000..94484d6 --- /dev/null +++ b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-expansion-lo/fixed-pd-comparison.md @@ -0,0 +1,10 @@ +# Qwen30 Frontier vs real latency selection + +| Objective | Frontier | Real | Match | Regret | Pairwise | +|---|---|---|---:|---:|---:| +| ttft_ms:mean | tp4_mns16 | tp4_mns16 | yes | 0.0% | 100.0% | +| ttft_ms:p90 | tp4_mns16 | tp4_mns16 | yes | 0.0% | 100.0% | +| tpot_ms:mean | tp1_mns16 | tp4_mns32 | no | 14.0% | 0.0% | +| tpot_ms:p90 | tp1_mns16 | tp4_mns8 | no | 13.4% | 0.0% | +| e2e_ms:mean | tp1_mns16 | tp4_mns32 | no | 26.9% | 0.0% | +| e2e_ms:p90 | tp1_mns16 | tp4_mns8 | no | 25.7% | 0.0% | diff --git a/runs/frontier-split-rootcause-v0/frozen-inputs/q30-expansion-lo/fixed-pd-comparison.stdout.json b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-expansion-lo/fixed-pd-comparison.stdout.json new file mode 100644 index 0000000..1aa57e0 --- /dev/null +++ b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-expansion-lo/fixed-pd-comparison.stdout.json @@ -0,0 +1 @@ +{"e2e_ms:mean": {"agreement": 0.0, "concordant_pairs": 0, "discordant_pairs": 48, "informative_pairs": 48, "real_ranking": ["tp4_mns32", "tp4_mns8", "tp4_mns64", "tp4_mns16", "tp2_mns16", "tp2_mns8", "tp2_mns32", "tp2_mns64", "tp1_mns64", "tp1_mns16", "tp1_mns32", "tp1_mns8"], "real_winner": "tp4_mns32", "selected_config_real_regret": 0.26921939465653383, "sim_ranking": ["tp1_mns16", "tp1_mns32", 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"tp4_mns8", + "tp2_mns8", + "tp2_mns64", + "tp2_mns32", + "tp2_mns16", + "tp1_mns16", + "tp1_mns32", + "tp1_mns8", + "tp1_mns64" + ], + "winner": "tp4_mns16", + "winner_value_ms": 63.03574686938565 + }, + "ttft_ms:pooled_p90_ms": { + "ranking": [ + "tp4_mns16", + "tp4_mns64", + "tp4_mns8", + "tp4_mns32", + "tp2_mns8", + "tp2_mns64", + "tp2_mns32", + "tp2_mns16", + "tp1_mns32", + "tp1_mns8", + "tp1_mns64", + "tp1_mns16" + ], + "winner": "tp4_mns16", + "winner_value_ms": 66.4233659626916 + } + } +} diff --git a/runs/frontier-split-rootcause-v0/frozen-inputs/q30-expansion-lo/fixed-pd-real-audit.md b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-expansion-lo/fixed-pd-real-audit.md new file mode 100644 index 0000000..9edf269 --- /dev/null +++ b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-expansion-lo/fixed-pd-real-audit.md @@ -0,0 +1,12 @@ +# Qwen30 real latency case audit + +Prefill-only: `False`. + +| Objective | Real winner | Value (ms) | +|---|---|---:| +| ttft_ms:pooled_mean_ms | tp4_mns16 | 63.04 | +| ttft_ms:pooled_p90_ms | tp4_mns16 | 66.42 | +| tpot_ms:pooled_mean_ms | tp4_mns32 | 4.25 | +| tpot_ms:pooled_p90_ms | tp4_mns8 | 4.27 | +| e2e_ms:pooled_mean_ms | tp4_mns32 | 604.41 | +| e2e_ms:pooled_p90_ms | tp4_mns8 | 610.01 | diff --git a/runs/frontier-split-rootcause-v0/frozen-inputs/q30-expansion-lo/fixed-pd-real-audit.stdout.json b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-expansion-lo/fixed-pd-real-audit.stdout.json new file mode 100644 index 0000000..01ed58b --- /dev/null +++ b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-expansion-lo/fixed-pd-real-audit.stdout.json @@ -0,0 +1 @@ +{"e2e_ms:pooled_mean_ms": {"ranking": ["tp4_mns32", "tp4_mns8", "tp4_mns64", "tp4_mns16", "tp2_mns16", "tp2_mns8", "tp2_mns32", "tp2_mns64", "tp1_mns64", "tp1_mns16", "tp1_mns32", "tp1_mns8"], "winner": "tp4_mns32", "winner_value_ms": 604.4058500071964}, "e2e_ms:pooled_p90_ms": {"ranking": ["tp4_mns8", "tp4_mns64", "tp4_mns32", "tp4_mns16", "tp2_mns8", "tp2_mns32", "tp2_mns16", "tp2_mns64", "tp1_mns64", "tp1_mns16", "tp1_mns32", "tp1_mns8"], "winner": "tp4_mns8", "winner_value_ms": 610.0081680342555}, "tpot_ms:pooled_mean_ms": {"ranking": ["tp4_mns32", "tp4_mns8", "tp4_mns64", "tp4_mns16", "tp2_mns16", "tp2_mns32", "tp2_mns8", "tp2_mns64", "tp1_mns64", "tp1_mns32", "tp1_mns16", "tp1_mns8"], "winner": "tp4_mns32", "winner_value_ms": 4.254808547439789}, "tpot_ms:pooled_p90_ms": {"ranking": ["tp4_mns8", "tp4_mns32", "tp4_mns64", "tp4_mns16", "tp2_mns32", "tp2_mns16", "tp2_mns8", "tp2_mns64", "tp1_mns16", "tp1_mns64", "tp1_mns32", "tp1_mns8"], "winner": "tp4_mns8", "winner_value_ms": 4.268226101921182}, "ttft_ms:pooled_mean_ms": {"ranking": ["tp4_mns16", "tp4_mns64", "tp4_mns32", "tp4_mns8", "tp2_mns8", "tp2_mns64", "tp2_mns32", "tp2_mns16", "tp1_mns16", "tp1_mns32", "tp1_mns8", "tp1_mns64"], "winner": "tp4_mns16", "winner_value_ms": 63.03574686938565}, "ttft_ms:pooled_p90_ms": {"ranking": ["tp4_mns16", "tp4_mns64", "tp4_mns8", "tp4_mns32", "tp2_mns8", "tp2_mns64", "tp2_mns32", "tp2_mns16", "tp1_mns32", "tp1_mns8", "tp1_mns64", "tp1_mns16"], "winner": "tp4_mns16", "winner_value_ms": 66.4233659626916}} diff --git a/runs/frontier-split-rootcause-v0/frozen-inputs/q30-expansion-lo/fixed-po-comparison.json b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-expansion-lo/fixed-po-comparison.json new file mode 100644 index 0000000..40f961a --- /dev/null +++ b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-expansion-lo/fixed-po-comparison.json @@ -0,0 +1,326 @@ +{ + "applicable_metrics": [ + "ttft_ms", + "e2e_ms" + ], + "cells": { + "tp1_mns16": { + "metrics": { + "e2e_ms": { + "mean": 166.66991554923203, + "p90": 166.6699155492779 + }, + "ttft_ms": { + "mean": 166.66991554923203, + "p90": 166.6699155492779 + } + }, + "path": "/home/admin/cpfs/wjh/aituner/qwen30-latency-expansion-20260718-r2/sim/fixed-po/runs/tp1_mns16/tp1/result.json" + }, + "tp1_mns32": { + "metrics": { + "e2e_ms": { + "mean": 166.66991554923203, + "p90": 166.6699155492779 + }, + "ttft_ms": { + "mean": 166.66991554923203, + "p90": 166.6699155492779 + } + }, + "path": "/home/admin/cpfs/wjh/aituner/qwen30-latency-expansion-20260718-r2/sim/fixed-po/runs/tp1_mns32/tp1/result.json" + }, + "tp1_mns64": { + "metrics": { + "e2e_ms": { + "mean": 166.66991554923203, + "p90": 166.6699155492779 + }, + "ttft_ms": { + "mean": 166.66991554923203, + "p90": 166.6699155492779 + } + }, + "path": "/home/admin/cpfs/wjh/aituner/qwen30-latency-expansion-20260718-r2/sim/fixed-po/runs/tp1_mns64/tp1/result.json" + }, + "tp1_mns8": { + "metrics": { + "e2e_ms": { + "mean": 166.66991554923203, + "p90": 166.6699155492779 + }, + "ttft_ms": { + "mean": 166.66991554923203, + "p90": 166.6699155492779 + } + }, + "path": "/home/admin/cpfs/wjh/aituner/qwen30-latency-expansion-20260718-r2/sim/fixed-po/runs/tp1_mns8/tp1/result.json" + }, + "tp2_mns16": { + "metrics": { + "e2e_ms": { + "mean": 118.55592059231998, + "p90": 118.55592059232833 + }, + "ttft_ms": { + "mean": 118.55592059231998, + "p90": 118.55592059232833 + } + }, + "path": "/home/admin/cpfs/wjh/aituner/qwen30-latency-expansion-20260718-r2/sim/fixed-po/runs/tp2_mns16/tp2/result.json" + }, + "tp2_mns32": { + "metrics": { + "e2e_ms": { + "mean": 118.55592059231998, + "p90": 118.55592059232833 + }, + "ttft_ms": { + "mean": 118.55592059231998, + "p90": 118.55592059232833 + } + }, + "path": "/home/admin/cpfs/wjh/aituner/qwen30-latency-expansion-20260718-r2/sim/fixed-po/runs/tp2_mns32/tp2/result.json" + }, + "tp2_mns64": { + "metrics": { + "e2e_ms": { + "mean": 118.55592059231998, + "p90": 118.55592059232833 + }, + "ttft_ms": { + "mean": 118.55592059231998, + "p90": 118.55592059232833 + } + }, + "path": "/home/admin/cpfs/wjh/aituner/qwen30-latency-expansion-20260718-r2/sim/fixed-po/runs/tp2_mns64/tp2/result.json" + }, + "tp2_mns8": { + "metrics": { + "e2e_ms": { + "mean": 118.55592059231998, + "p90": 118.55592059232833 + }, + "ttft_ms": { + "mean": 118.55592059231998, + "p90": 118.55592059232833 + } + }, + "path": "/home/admin/cpfs/wjh/aituner/qwen30-latency-expansion-20260718-r2/sim/fixed-po/runs/tp2_mns8/tp2/result.json" + }, + "tp4_mns16": { + "metrics": { + "e2e_ms": { + "mean": 86.52545838364681, + "p90": 86.52545838364745 + }, + "ttft_ms": { + "mean": 86.52545838364681, + "p90": 86.52545838364745 + } + }, + "path": "/home/admin/cpfs/wjh/aituner/qwen30-latency-expansion-20260718-r2/sim/fixed-po/runs/tp4_mns16/tp4/result.json" + }, + "tp4_mns32": { + "metrics": { + "e2e_ms": { + "mean": 86.52545838364681, + "p90": 86.52545838364745 + }, + "ttft_ms": { + "mean": 86.52545838364681, + "p90": 86.52545838364745 + } + }, + "path": "/home/admin/cpfs/wjh/aituner/qwen30-latency-expansion-20260718-r2/sim/fixed-po/runs/tp4_mns32/tp4/result.json" + }, + "tp4_mns64": { + "metrics": { + "e2e_ms": { + "mean": 86.52545838364681, + "p90": 86.52545838364745 + }, + "ttft_ms": { + "mean": 86.52545838364681, + "p90": 86.52545838364745 + } + }, + "path": "/home/admin/cpfs/wjh/aituner/qwen30-latency-expansion-20260718-r2/sim/fixed-po/runs/tp4_mns64/tp4/result.json" + }, + "tp4_mns8": { + "metrics": { + "e2e_ms": { + "mean": 86.52545838364681, + "p90": 86.52545838364745 + }, + "ttft_ms": { + "mean": 86.52545838364681, + "p90": 86.52545838364745 + } + }, + "path": "/home/admin/cpfs/wjh/aituner/qwen30-latency-expansion-20260718-r2/sim/fixed-po/runs/tp4_mns8/tp4/result.json" + } + }, + "prefill_only": true, + "real_audit": "/home/admin/cpfs/wjh/aituner/qwen30-latency-expansion-20260718-r2/analysis-r6/fixed-po-real-audit.json", + "schema": "qwen30-latency-case-frontier-real-comparison-v1", + "selection": { + "e2e_ms:mean": { + "agreement": 1.0, + "concordant_pairs": 48, + "discordant_pairs": 0, + "informative_pairs": 48, + "real_ranking": [ + "tp4_mns8", + "tp4_mns32", + "tp4_mns64", + "tp4_mns16", + "tp2_mns64", + "tp2_mns8", + "tp2_mns32", + "tp2_mns16", + "tp1_mns32", + "tp1_mns16", + "tp1_mns8", + "tp1_mns64" + ], + "real_winner": "tp4_mns8", + "selected_config_real_regret": 0.014895639803871355, + "sim_ranking": [ + "tp4_mns16", + "tp4_mns32", + "tp4_mns64", + "tp4_mns8", + "tp2_mns16", + "tp2_mns32", + "tp2_mns64", + "tp2_mns8", + "tp1_mns16", + "tp1_mns32", + "tp1_mns64", + "tp1_mns8" + ], + "sim_winner": "tp4_mns16", + "tied_pairs": 18, + "winner_match": false + }, + "e2e_ms:p90": { + "agreement": 1.0, + "concordant_pairs": 48, + "discordant_pairs": 0, + "informative_pairs": 48, + "real_ranking": [ + "tp4_mns8", + "tp4_mns64", + "tp4_mns32", + "tp4_mns16", + "tp2_mns64", + "tp2_mns8", + "tp2_mns32", + "tp2_mns16", + "tp1_mns16", + "tp1_mns32", + "tp1_mns8", + "tp1_mns64" + ], + "real_winner": "tp4_mns8", + "selected_config_real_regret": 0.03590282591969048, + "sim_ranking": [ + "tp4_mns16", + "tp4_mns32", + "tp4_mns64", + "tp4_mns8", + "tp2_mns16", + "tp2_mns32", + "tp2_mns64", + "tp2_mns8", + "tp1_mns16", + "tp1_mns32", + "tp1_mns64", + "tp1_mns8" + ], + "sim_winner": "tp4_mns16", + "tied_pairs": 18, + "winner_match": false + }, + "ttft_ms:mean": { + "agreement": 1.0, + "concordant_pairs": 48, + "discordant_pairs": 0, + "informative_pairs": 48, + "real_ranking": [ + "tp4_mns8", + "tp4_mns32", + "tp4_mns64", + "tp4_mns16", + "tp2_mns64", + "tp2_mns8", + "tp2_mns32", + "tp2_mns16", + "tp1_mns32", + "tp1_mns16", + "tp1_mns8", + "tp1_mns64" + ], + "real_winner": "tp4_mns8", + "selected_config_real_regret": 0.014940997472395743, + "sim_ranking": [ + "tp4_mns16", + "tp4_mns32", + "tp4_mns64", + "tp4_mns8", + "tp2_mns16", + "tp2_mns32", + "tp2_mns64", + "tp2_mns8", + "tp1_mns16", + "tp1_mns32", + "tp1_mns64", + "tp1_mns8" + ], + "sim_winner": "tp4_mns16", + "tied_pairs": 18, + "winner_match": false + }, + "ttft_ms:p90": { + "agreement": 1.0, + "concordant_pairs": 48, + "discordant_pairs": 0, + "informative_pairs": 48, + "real_ranking": [ + "tp4_mns8", + "tp4_mns64", + "tp4_mns32", + "tp4_mns16", + "tp2_mns64", + "tp2_mns8", + "tp2_mns32", + "tp2_mns16", + "tp1_mns16", + "tp1_mns32", + "tp1_mns8", + "tp1_mns64" + ], + "real_winner": "tp4_mns8", + "selected_config_real_regret": 0.035908453871728634, + "sim_ranking": [ + "tp4_mns16", + "tp4_mns32", + "tp4_mns64", + "tp4_mns8", + "tp2_mns16", + "tp2_mns32", + "tp2_mns64", + "tp2_mns8", + "tp1_mns16", + "tp1_mns32", + "tp1_mns64", + "tp1_mns8" + ], + "sim_winner": "tp4_mns16", + "tied_pairs": 18, + "winner_match": false + } + }, + "sim_root": "/home/admin/cpfs/wjh/aituner/qwen30-latency-expansion-20260718-r2/sim/fixed-po" +} diff --git a/runs/frontier-split-rootcause-v0/frozen-inputs/q30-expansion-lo/fixed-po-comparison.md b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-expansion-lo/fixed-po-comparison.md new file mode 100644 index 0000000..5237196 --- /dev/null +++ b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-expansion-lo/fixed-po-comparison.md @@ -0,0 +1,8 @@ +# Qwen30 Frontier vs real latency selection + +| Objective | Frontier | Real | Match | Regret | Pairwise | +|---|---|---|---:|---:|---:| +| ttft_ms:mean | tp4_mns16 | tp4_mns8 | no | 1.5% | 100.0% | +| ttft_ms:p90 | tp4_mns16 | tp4_mns8 | no | 3.6% | 100.0% | +| e2e_ms:mean | tp4_mns16 | tp4_mns8 | no | 1.5% | 100.0% | +| e2e_ms:p90 | tp4_mns16 | tp4_mns8 | no | 3.6% | 100.0% | diff --git a/runs/frontier-split-rootcause-v0/frozen-inputs/q30-expansion-lo/fixed-po-comparison.stdout.json b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-expansion-lo/fixed-po-comparison.stdout.json new file mode 100644 index 0000000..0a76756 --- /dev/null +++ b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-expansion-lo/fixed-po-comparison.stdout.json @@ -0,0 +1 @@ +{"e2e_ms:mean": {"agreement": 1.0, "concordant_pairs": 48, "discordant_pairs": 0, "informative_pairs": 48, "real_ranking": ["tp4_mns8", "tp4_mns32", "tp4_mns64", "tp4_mns16", "tp2_mns64", "tp2_mns8", "tp2_mns32", "tp2_mns16", "tp1_mns32", "tp1_mns16", "tp1_mns8", "tp1_mns64"], "real_winner": "tp4_mns8", "selected_config_real_regret": 0.014895639803871355, "sim_ranking": ["tp4_mns16", "tp4_mns32", "tp4_mns64", "tp4_mns8", "tp2_mns16", "tp2_mns32", "tp2_mns64", "tp2_mns8", "tp1_mns16", "tp1_mns32", "tp1_mns64", "tp1_mns8"], "sim_winner": "tp4_mns16", "tied_pairs": 18, "winner_match": false}, "e2e_ms:p90": {"agreement": 1.0, "concordant_pairs": 48, "discordant_pairs": 0, "informative_pairs": 48, "real_ranking": ["tp4_mns8", "tp4_mns64", "tp4_mns32", "tp4_mns16", "tp2_mns64", "tp2_mns8", "tp2_mns32", "tp2_mns16", "tp1_mns16", "tp1_mns32", "tp1_mns8", "tp1_mns64"], "real_winner": "tp4_mns8", "selected_config_real_regret": 0.03590282591969048, "sim_ranking": ["tp4_mns16", "tp4_mns32", "tp4_mns64", "tp4_mns8", "tp2_mns16", "tp2_mns32", "tp2_mns64", "tp2_mns8", "tp1_mns16", "tp1_mns32", "tp1_mns64", "tp1_mns8"], "sim_winner": "tp4_mns16", "tied_pairs": 18, "winner_match": false}, "ttft_ms:mean": {"agreement": 1.0, "concordant_pairs": 48, "discordant_pairs": 0, "informative_pairs": 48, "real_ranking": ["tp4_mns8", 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a/runs/frontier-split-rootcause-v0/frozen-inputs/q30-expansion-lo/fixed-po-real-audit.md b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-expansion-lo/fixed-po-real-audit.md new file mode 100644 index 0000000..2ffe8bf --- /dev/null +++ b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-expansion-lo/fixed-po-real-audit.md @@ -0,0 +1,10 @@ +# Qwen30 real latency case audit + +Prefill-only: `True`. + +| Objective | Real winner | Value (ms) | +|---|---|---:| +| ttft_ms:pooled_mean_ms | tp4_mns8 | 64.28 | +| ttft_ms:pooled_p90_ms | tp4_mns8 | 67.95 | +| e2e_ms:pooled_mean_ms | tp4_mns8 | 64.33 | +| e2e_ms:pooled_p90_ms | tp4_mns8 | 67.99 | diff --git a/runs/frontier-split-rootcause-v0/frozen-inputs/q30-expansion-lo/fixed-po-real-audit.stdout.json b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-expansion-lo/fixed-po-real-audit.stdout.json new file mode 100644 index 0000000..282995c --- /dev/null +++ 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+|---|---|---|---:|---:|---:| +| ttft_ms:mean | tp4_mns16 | tp4_mns64 | no | 3.2% | 84.1% | +| ttft_ms:p90 | tp4_mns16 | tp4_mns8 | no | 0.4% | 100.0% | +| e2e_ms:mean | tp4_mns16 | tp4_mns64 | no | 3.2% | 84.1% | +| e2e_ms:p90 | tp4_mns16 | tp4_mns8 | no | 0.3% | 100.0% | diff --git a/runs/frontier-split-rootcause-v0/frozen-inputs/q30-expansion-lo/trace-po-comparison.stdout.json b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-expansion-lo/trace-po-comparison.stdout.json new file mode 100644 index 0000000..c28456a --- /dev/null +++ b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-expansion-lo/trace-po-comparison.stdout.json @@ -0,0 +1 @@ +{"e2e_ms:mean": {"agreement": 0.8412698412698413, "concordant_pairs": 53, "discordant_pairs": 10, "informative_pairs": 63, "real_ranking": ["tp4_mns64", "tp4_mns32", "tp4_mns8", "tp4_mns16", "tp2_mns64", "tp2_mns8", "tp2_mns32", "tp2_mns16", "tp1_mns16", "tp1_mns64", "tp1_mns32", "tp1_mns8"], "real_winner": "tp4_mns64", 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"/home/admin/cpfs/wjh/aituner/qwen30-latency-expansion-20260718-r2/traces/trace-po/tp4/private/real_requests.jsonl", + "private_jsonl_sha256": "c38514fa524fc215cd9097d990a2985532f83ac6314bc46ad89ebfc1bd89b4f8", + "public_csv": "/home/admin/cpfs/wjh/aituner/qwen30-latency-expansion-20260718-r2/traces/trace-po/tp4/public/frontier.csv", + "public_csv_sha256": "f4112a2b0366b559bfced6a0986a01e2b7b39899a650d030c3e680b3dc63c92e", + "requests": 129, + "row_vector_sha256": "851e796f6d3e87444b58da87e0ef4b7482a13bb6502cb6873fa80523877ab74e", + "schema": "qwen30-latency-case-v1", + "tensor_parallel_size": 4 + } + }, + "traces_root": "/home/admin/cpfs/wjh/aituner/qwen30-latency-expansion-20260718-r2/traces/trace-po", + "winners": { + "e2e_ms:pooled_mean_ms": { + "ranking": [ + "tp4_mns64", + "tp4_mns32", + "tp4_mns8", + "tp4_mns16", + "tp2_mns64", + "tp2_mns8", + "tp2_mns32", + "tp2_mns16", + "tp1_mns16", + "tp1_mns64", + "tp1_mns32", + "tp1_mns8" + ], + "winner": "tp4_mns64", + "winner_value_ms": 223.1923911500318 + }, + "e2e_ms:pooled_p90_ms": { + "ranking": [ + "tp4_mns8", + "tp4_mns64", + "tp4_mns32", + "tp4_mns16", + "tp2_mns64", + "tp2_mns8", + "tp2_mns32", + "tp2_mns16", + "tp1_mns8", + "tp1_mns16", + "tp1_mns32", + "tp1_mns64" + ], + "winner": "tp4_mns8", + "winner_value_ms": 667.0993800507858 + }, + "ttft_ms:pooled_mean_ms": { + "ranking": [ + "tp4_mns64", + "tp4_mns32", + "tp4_mns8", + "tp4_mns16", + "tp2_mns64", + "tp2_mns8", + "tp2_mns32", + "tp2_mns16", + "tp1_mns16", + "tp1_mns64", + "tp1_mns32", + "tp1_mns8" + ], + "winner": "tp4_mns64", + "winner_value_ms": 223.12445959072676 + }, + "ttft_ms:pooled_p90_ms": { + "ranking": [ + "tp4_mns8", + "tp4_mns64", + "tp4_mns32", + "tp4_mns16", + "tp2_mns64", + "tp2_mns8", + "tp2_mns32", + "tp2_mns16", + "tp1_mns8", + "tp1_mns16", + "tp1_mns32", + "tp1_mns64" + ], + "winner": "tp4_mns8", + "winner_value_ms": 666.7667380534112 + } + } +} diff --git a/runs/frontier-split-rootcause-v0/frozen-inputs/q30-expansion-lo/trace-po-real-audit.md b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-expansion-lo/trace-po-real-audit.md new file mode 100644 index 0000000..fee21bb --- /dev/null +++ b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-expansion-lo/trace-po-real-audit.md @@ -0,0 +1,10 @@ +# Qwen30 real latency case audit + +Prefill-only: `True`. + +| Objective | Real winner | Value (ms) | +|---|---|---:| +| ttft_ms:pooled_mean_ms | tp4_mns64 | 223.12 | +| ttft_ms:pooled_p90_ms | tp4_mns8 | 666.77 | +| e2e_ms:pooled_mean_ms | tp4_mns64 | 223.19 | +| e2e_ms:pooled_p90_ms | tp4_mns8 | 667.10 | diff --git a/runs/frontier-split-rootcause-v0/frozen-inputs/q30-expansion-lo/trace-po-real-audit.stdout.json b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-expansion-lo/trace-po-real-audit.stdout.json new file mode 100644 index 0000000..a5cdce0 --- /dev/null +++ b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-expansion-lo/trace-po-real-audit.stdout.json @@ -0,0 +1 @@ +{"e2e_ms:pooled_mean_ms": {"ranking": ["tp4_mns64", "tp4_mns32", "tp4_mns8", "tp4_mns16", "tp2_mns64", "tp2_mns8", "tp2_mns32", "tp2_mns16", "tp1_mns16", "tp1_mns64", "tp1_mns32", "tp1_mns8"], "winner": "tp4_mns64", "winner_value_ms": 223.1923911500318}, "e2e_ms:pooled_p90_ms": {"ranking": ["tp4_mns8", "tp4_mns64", "tp4_mns32", "tp4_mns16", "tp2_mns64", "tp2_mns8", "tp2_mns32", "tp2_mns16", "tp1_mns8", "tp1_mns16", "tp1_mns32", "tp1_mns64"], "winner": "tp4_mns8", "winner_value_ms": 667.0993800507858}, "ttft_ms:pooled_mean_ms": {"ranking": ["tp4_mns64", "tp4_mns32", "tp4_mns8", "tp4_mns16", "tp2_mns64", "tp2_mns8", "tp2_mns32", "tp2_mns16", "tp1_mns16", "tp1_mns64", "tp1_mns32", "tp1_mns8"], "winner": "tp4_mns64", "winner_value_ms": 223.12445959072676}, "ttft_ms:pooled_p90_ms": {"ranking": ["tp4_mns8", "tp4_mns64", "tp4_mns32", "tp4_mns16", "tp2_mns64", "tp2_mns8", "tp2_mns32", "tp2_mns16", "tp1_mns8", "tp1_mns16", "tp1_mns32", "tp1_mns64"], "winner": "tp4_mns8", "winner_value_ms": 666.7667380534112}} diff --git a/runs/frontier-split-rootcause-v0/frozen-inputs/q30-fixed-hi/fixed-pd/comparison.json b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-fixed-hi/fixed-pd/comparison.json new file mode 100644 index 0000000..c370177 --- /dev/null +++ b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-fixed-hi/fixed-pd/comparison.json @@ -0,0 +1,453 @@ +{ + "applicable_metrics": [ + "ttft_ms", + "tpot_ms", + "e2e_ms" + ], + "cells": { + "tp1_mns16": { + "metrics": { + "e2e_ms": { + "mean": 128201.44370110576, + "p90": 216221.8716312423 + }, + "tpot_ms": { + "mean": 106.74127671151493, + "p90": 107.53204073371177 + }, + "ttft_ms": { + "mean": 100982.41813966946, + "p90": 188801.20124415262 + } + }, + "path": "/home/admin/cpfs/wjh/aituner/qwen30-fixed-pressure-surface-20260719-r1/sim/fixed-pd/runs/tp1_mns16/tp1/result.json" + }, + "tp1_mns32": { + 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"metrics": { + "e2e_ms": { + "mean": 133968.39678804282, + "p90": 233355.7233519645 + }, + "tpot_ms": { + "mean": 37.645089303294014, + "p90": 37.75522144488961 + }, + "ttft_ms": { + "mean": 124368.89901570287, + "p90": 223728.14188352478 + } + }, + "path": "/home/admin/cpfs/wjh/aituner/qwen30-fixed-pressure-surface-20260719-r1/sim/fixed-pd/runs/tp4_mns8/tp4/result.json" + } + }, + "prefill_only": false, + "real_audit": "/home/admin/cpfs/wjh/aituner/qwen30-fixed-pressure-surface-20260719-r1/analysis/fixed-pd/real-audit.json", + "schema": "qwen30-latency-case-frontier-real-comparison-v1", + "selection": { + "e2e_ms:mean": { + "agreement": 0.5151515151515151, + "concordant_pairs": 34, + "discordant_pairs": 32, + "informative_pairs": 66, + "real_ranking": [ + "tp1_mns32", + "tp4_mns32", + "tp1_mns16", + "tp1_mns8", + "tp4_mns16", + "tp4_mns64", + "tp1_mns64", + "tp2_mns8", + "tp2_mns64", + "tp2_mns16", + "tp2_mns32", + "tp4_mns8" + ], + "real_winner": "tp1_mns32", + "selected_config_real_regret": 0.017005214686575575, + "sim_ranking": [ + "tp4_mns64", + "tp2_mns64", + "tp1_mns64", + "tp4_mns32", + "tp2_mns32", + "tp1_mns32", + "tp4_mns16", + "tp2_mns16", + "tp1_mns16", + "tp4_mns8", + "tp2_mns8", + "tp1_mns8" + ], + "sim_winner": "tp4_mns64", + "tied_pairs": 0, + "winner_match": false + }, + "e2e_ms:p90": { + "agreement": 0.5151515151515151, + "concordant_pairs": 34, + "discordant_pairs": 32, + "informative_pairs": 66, + "real_ranking": [ + "tp1_mns32", + "tp1_mns8", + "tp1_mns16", + "tp1_mns64", + "tp4_mns32", + "tp4_mns64", + "tp4_mns16", + "tp2_mns8", + "tp2_mns64", + "tp2_mns32", + "tp2_mns16", + "tp4_mns8" + ], + "real_winner": "tp1_mns32", + "selected_config_real_regret": 0.054933980018932745, + "sim_ranking": [ + "tp2_mns64", + "tp4_mns64", + "tp1_mns64", + "tp4_mns32", + "tp1_mns32", + "tp2_mns32", + "tp4_mns16", + "tp2_mns16", + "tp1_mns16", + "tp4_mns8", + "tp2_mns8", + "tp1_mns8" + ], + "sim_winner": "tp2_mns64", + "tied_pairs": 0, + "winner_match": false + }, + "tpot_ms:mean": { + "agreement": 0.48484848484848486, + "concordant_pairs": 32, + "discordant_pairs": 34, + "informative_pairs": 66, + "real_ranking": [ + "tp4_mns8", + "tp1_mns32", + "tp1_mns16", + "tp1_mns8", + "tp1_mns64", + "tp4_mns32", + "tp2_mns8", + "tp4_mns16", + "tp4_mns64", + "tp2_mns64", + "tp2_mns16", + "tp2_mns32" + ], + "real_winner": "tp4_mns8", + "selected_config_real_regret": 0.0, + "sim_ranking": [ + "tp4_mns8", + "tp4_mns16", + "tp2_mns8", + "tp4_mns32", + "tp4_mns64", + "tp2_mns16", + "tp1_mns8", + "tp2_mns32", + "tp2_mns64", + "tp1_mns16", + "tp1_mns32", + "tp1_mns64" + ], + "sim_winner": "tp4_mns8", + "tied_pairs": 0, + "winner_match": true + }, + "tpot_ms:p90": { + "agreement": 0.42424242424242425, + "concordant_pairs": 28, + "discordant_pairs": 38, + "informative_pairs": 66, + "real_ranking": [ + "tp4_mns8", + "tp1_mns32", + "tp1_mns16", + "tp1_mns8", + "tp1_mns64", + "tp2_mns8", + "tp4_mns32", + "tp2_mns64", + "tp4_mns64", + "tp4_mns16", + "tp2_mns32", + "tp2_mns16" + ], + "real_winner": "tp4_mns8", + "selected_config_real_regret": 0.0, + "sim_ranking": [ + "tp4_mns8", + "tp4_mns16", + "tp2_mns8", + "tp4_mns32", + "tp2_mns16", + "tp4_mns64", + "tp1_mns8", + "tp2_mns32", + "tp2_mns64", + "tp1_mns16", + "tp1_mns32", + "tp1_mns64" + ], + "sim_winner": "tp4_mns8", + "tied_pairs": 0, + "winner_match": true + }, + "ttft_ms:mean": { + "agreement": 0.5909090909090909, + "concordant_pairs": 39, + "discordant_pairs": 27, + "informative_pairs": 66, + "real_ranking": [ + "tp4_mns32", + "tp4_mns64", + "tp4_mns16", + "tp2_mns64", + "tp2_mns8", + "tp2_mns32", + "tp2_mns16", + "tp1_mns8", + "tp1_mns32", + "tp1_mns16", + "tp1_mns64", + "tp4_mns8" + ], + "real_winner": "tp4_mns32", + "selected_config_real_regret": 0.5797674051508896, + "sim_ranking": [ + "tp2_mns64", + "tp1_mns64", + "tp4_mns64", + "tp1_mns32", + "tp2_mns32", + "tp4_mns32", + "tp4_mns16", + "tp2_mns16", + "tp1_mns16", + "tp4_mns8", + "tp2_mns8", + 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b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-fixed-hi/fixed-pd/comparison.md new file mode 100644 index 0000000..a53acbe --- /dev/null +++ b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-fixed-hi/fixed-pd/comparison.md @@ -0,0 +1,10 @@ +# Qwen30 Frontier vs real latency selection + +| Objective | Frontier | Real | Match | Regret | Pairwise | +|---|---|---|---:|---:|---:| +| ttft_ms:mean | tp2_mns64 | tp4_mns32 | no | 58.0% | 59.1% | +| ttft_ms:p90 | tp2_mns64 | tp4_mns64 | no | 56.2% | 59.1% | +| tpot_ms:mean | tp4_mns8 | tp4_mns8 | yes | 0.0% | 48.5% | +| tpot_ms:p90 | tp4_mns8 | tp4_mns8 | yes | 0.0% | 42.4% | +| e2e_ms:mean | tp4_mns64 | tp1_mns32 | no | 1.7% | 51.5% | +| e2e_ms:p90 | tp2_mns64 | tp1_mns32 | no | 5.5% | 51.5% | diff --git a/runs/frontier-split-rootcause-v0/frozen-inputs/q30-fixed-hi/fixed-pd/real-audit.json b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-fixed-hi/fixed-pd/real-audit.json new file mode 100644 index 0000000..035bf0b --- /dev/null +++ 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"tp4_mns16", + "tp2_mns64", + "tp2_mns8", + "tp2_mns32", + "tp2_mns16", + "tp1_mns8", + "tp1_mns32", + "tp1_mns16", + "tp1_mns64", + "tp4_mns8" + ], + "winner": "tp4_mns32", + "winner_value_ms": 123.15859724408143 + }, + "ttft_ms:pooled_p90_ms": { + "ranking": [ + "tp4_mns64", + "tp4_mns32", + "tp4_mns16", + "tp2_mns64", + "tp2_mns8", + "tp2_mns16", + "tp2_mns32", + "tp1_mns8", + "tp1_mns32", + "tp1_mns16", + "tp1_mns64", + "tp4_mns8" + ], + "winner": "tp4_mns64", + "winner_value_ms": 126.84211903251708 + } + } +} diff --git a/runs/frontier-split-rootcause-v0/frozen-inputs/q30-fixed-hi/fixed-pd/real-audit.md b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-fixed-hi/fixed-pd/real-audit.md new file mode 100644 index 0000000..cd51259 --- /dev/null +++ b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-fixed-hi/fixed-pd/real-audit.md @@ -0,0 +1,12 @@ +# Qwen30 real latency case audit + +Prefill-only: `False`. + +| Objective | Real winner | Value (ms) | +|---|---|---:| +| ttft_ms:pooled_mean_ms | tp4_mns32 | 123.16 | +| ttft_ms:pooled_p90_ms | tp4_mns64 | 126.84 | +| tpot_ms:pooled_mean_ms | tp4_mns8 | 8.42 | +| tpot_ms:pooled_p90_ms | tp4_mns8 | 8.77 | +| e2e_ms:pooled_mean_ms | tp1_mns32 | 3203.72 | +| e2e_ms:pooled_p90_ms | tp1_mns32 | 3389.19 | diff --git a/runs/frontier-split-rootcause-v0/frozen-inputs/q30-fixed-hi/fixed-po/comparison.json b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-fixed-hi/fixed-po/comparison.json new file mode 100644 index 0000000..9074b19 --- /dev/null +++ b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-fixed-hi/fixed-po/comparison.json @@ -0,0 +1,326 @@ +{ + "applicable_metrics": [ + "ttft_ms", + "e2e_ms" + ], + "cells": { + "tp1_mns16": { + "metrics": { + "e2e_ms": { + "mean": 267.46878507222704, + "p90": 267.46878507223215 + }, + "ttft_ms": { + "mean": 267.46878507222704, + "p90": 267.46878507223215 + } + }, + "path": "/home/admin/cpfs/wjh/aituner/qwen30-fixed-pressure-surface-20260719-r1/sim/fixed-po/runs/tp1_mns16/tp1/result.json" + }, + "tp1_mns32": { + "metrics": { + "e2e_ms": { + "mean": 267.46878507222704, + "p90": 267.46878507223215 + }, + "ttft_ms": { + "mean": 267.46878507222704, + "p90": 267.46878507223215 + } + }, + "path": "/home/admin/cpfs/wjh/aituner/qwen30-fixed-pressure-surface-20260719-r1/sim/fixed-po/runs/tp1_mns32/tp1/result.json" + }, + "tp1_mns64": { + "metrics": { + "e2e_ms": { + "mean": 267.46878507222704, + "p90": 267.46878507223215 + }, + "ttft_ms": { + "mean": 267.46878507222704, + "p90": 267.46878507223215 + } + }, + "path": "/home/admin/cpfs/wjh/aituner/qwen30-fixed-pressure-surface-20260719-r1/sim/fixed-po/runs/tp1_mns64/tp1/result.json" + }, + "tp1_mns8": { + "metrics": { + "e2e_ms": { + "mean": 267.46878507222704, + "p90": 267.46878507223215 + }, + "ttft_ms": { + "mean": 267.46878507222704, + "p90": 267.46878507223215 + } + }, + "path": "/home/admin/cpfs/wjh/aituner/qwen30-fixed-pressure-surface-20260719-r1/sim/fixed-po/runs/tp1_mns8/tp1/result.json" + }, + "tp2_mns16": { + "metrics": { + "e2e_ms": { + "mean": 175.26431810890125, + "p90": 175.2643181089013 + }, + "ttft_ms": { + "mean": 175.26431810890125, + "p90": 175.2643181089013 + } + }, + "path": "/home/admin/cpfs/wjh/aituner/qwen30-fixed-pressure-surface-20260719-r1/sim/fixed-po/runs/tp2_mns16/tp2/result.json" + }, + "tp2_mns32": { + "metrics": { + "e2e_ms": { + "mean": 175.26431810890125, + "p90": 175.2643181089013 + }, + "ttft_ms": { + "mean": 175.26431810890125, + "p90": 175.2643181089013 + } + }, + "path": "/home/admin/cpfs/wjh/aituner/qwen30-fixed-pressure-surface-20260719-r1/sim/fixed-po/runs/tp2_mns32/tp2/result.json" + }, + "tp2_mns64": { + "metrics": { + "e2e_ms": { + "mean": 175.26431810890125, + "p90": 175.2643181089013 + }, + "ttft_ms": { + "mean": 175.26431810890125, + "p90": 175.2643181089013 + } + }, + "path": "/home/admin/cpfs/wjh/aituner/qwen30-fixed-pressure-surface-20260719-r1/sim/fixed-po/runs/tp2_mns64/tp2/result.json" + }, + "tp2_mns8": { + "metrics": { + "e2e_ms": { + "mean": 175.26431810890125, + "p90": 175.2643181089013 + }, + "ttft_ms": { + "mean": 175.26431810890125, + "p90": 175.2643181089013 + } + }, + "path": "/home/admin/cpfs/wjh/aituner/qwen30-fixed-pressure-surface-20260719-r1/sim/fixed-po/runs/tp2_mns8/tp2/result.json" + }, + "tp4_mns16": { + "metrics": { + "e2e_ms": { + "mean": 127.7919646347385, + "p90": 127.79196463473852 + }, + "ttft_ms": { + "mean": 127.7919646347385, + "p90": 127.79196463473852 + } + }, + "path": "/home/admin/cpfs/wjh/aituner/qwen30-fixed-pressure-surface-20260719-r1/sim/fixed-po/runs/tp4_mns16/tp4/result.json" + }, + "tp4_mns32": { + "metrics": { + "e2e_ms": { + "mean": 127.7919646347385, + "p90": 127.79196463473852 + }, + "ttft_ms": { + "mean": 127.7919646347385, + "p90": 127.79196463473852 + } + }, + "path": "/home/admin/cpfs/wjh/aituner/qwen30-fixed-pressure-surface-20260719-r1/sim/fixed-po/runs/tp4_mns32/tp4/result.json" + }, + "tp4_mns64": { + "metrics": { + "e2e_ms": { + "mean": 127.7919646347385, + "p90": 127.79196463473852 + }, + "ttft_ms": { + "mean": 127.7919646347385, + "p90": 127.79196463473852 + } + }, + "path": "/home/admin/cpfs/wjh/aituner/qwen30-fixed-pressure-surface-20260719-r1/sim/fixed-po/runs/tp4_mns64/tp4/result.json" + }, + "tp4_mns8": { + "metrics": { + "e2e_ms": { + "mean": 127.7919646347385, + "p90": 127.79196463473852 + }, + "ttft_ms": { + "mean": 127.7919646347385, + "p90": 127.79196463473852 + } + }, + "path": "/home/admin/cpfs/wjh/aituner/qwen30-fixed-pressure-surface-20260719-r1/sim/fixed-po/runs/tp4_mns8/tp4/result.json" + } + }, + "prefill_only": true, + "real_audit": "/home/admin/cpfs/wjh/aituner/qwen30-fixed-pressure-surface-20260719-r1/analysis/fixed-po/real-audit.json", + "schema": "qwen30-latency-case-frontier-real-comparison-v1", + "selection": { + "e2e_ms:mean": { + "agreement": 1.0, + "concordant_pairs": 48, + "discordant_pairs": 0, + "informative_pairs": 48, + "real_ranking": [ + "tp4_mns32", + "tp4_mns64", + "tp4_mns8", + "tp4_mns16", + "tp2_mns8", + "tp2_mns64", + "tp2_mns32", + "tp2_mns16", + "tp1_mns32", + "tp1_mns16", + "tp1_mns64", + "tp1_mns8" + ], + "real_winner": "tp4_mns32", + "selected_config_real_regret": 0.0034531419781375313, + "sim_ranking": [ + "tp4_mns16", + "tp4_mns32", + "tp4_mns64", + "tp4_mns8", + "tp2_mns16", + "tp2_mns32", + "tp2_mns64", + "tp2_mns8", + "tp1_mns16", + "tp1_mns32", + "tp1_mns64", + "tp1_mns8" + ], + "sim_winner": "tp4_mns16", + "tied_pairs": 18, + "winner_match": false + }, + "e2e_ms:p90": { + "agreement": 1.0, + "concordant_pairs": 48, + "discordant_pairs": 0, + "informative_pairs": 48, + "real_ranking": [ + "tp4_mns32", + "tp4_mns8", + "tp4_mns64", + "tp4_mns16", + "tp2_mns8", + "tp2_mns64", + "tp2_mns32", + "tp2_mns16", + "tp1_mns32", + "tp1_mns16", + "tp1_mns64", + "tp1_mns8" + ], + "real_winner": "tp4_mns32", + "selected_config_real_regret": 0.005277055274057908, + "sim_ranking": [ + "tp4_mns16", + "tp4_mns32", + "tp4_mns64", + "tp4_mns8", + "tp2_mns16", + "tp2_mns32", + "tp2_mns64", + "tp2_mns8", + "tp1_mns16", + "tp1_mns32", + "tp1_mns64", + "tp1_mns8" + ], + "sim_winner": "tp4_mns16", + "tied_pairs": 18, + "winner_match": false + }, + "ttft_ms:mean": { + "agreement": 1.0, + "concordant_pairs": 48, + "discordant_pairs": 0, + "informative_pairs": 48, + "real_ranking": [ + "tp4_mns32", + "tp4_mns64", + "tp4_mns8", + "tp4_mns16", + "tp2_mns8", + "tp2_mns64", + "tp2_mns32", + "tp2_mns16", + "tp1_mns32", + "tp1_mns16", + "tp1_mns64", + "tp1_mns8" + ], + "real_winner": "tp4_mns32", + "selected_config_real_regret": 0.003463041945107654, + "sim_ranking": [ + "tp4_mns16", + "tp4_mns32", + "tp4_mns64", + "tp4_mns8", + "tp2_mns16", + "tp2_mns32", + "tp2_mns64", + "tp2_mns8", + "tp1_mns16", + "tp1_mns32", + "tp1_mns64", + "tp1_mns8" + ], + "sim_winner": "tp4_mns16", + "tied_pairs": 18, + "winner_match": false + }, + "ttft_ms:p90": { + "agreement": 1.0, + "concordant_pairs": 48, + "discordant_pairs": 0, + "informative_pairs": 48, + "real_ranking": [ + "tp4_mns32", + "tp4_mns8", + "tp4_mns64", + "tp4_mns16", + "tp2_mns8", + "tp2_mns64", + "tp2_mns32", + "tp2_mns16", + "tp1_mns32", + "tp1_mns16", + "tp1_mns64", + "tp1_mns8" + ], + "real_winner": "tp4_mns32", + "selected_config_real_regret": 0.005266030780421245, + "sim_ranking": [ + "tp4_mns16", + "tp4_mns32", + "tp4_mns64", + "tp4_mns8", + "tp2_mns16", + "tp2_mns32", + "tp2_mns64", + "tp2_mns8", + "tp1_mns16", + "tp1_mns32", + "tp1_mns64", + "tp1_mns8" + ], + "sim_winner": "tp4_mns16", + "tied_pairs": 18, + "winner_match": false + } + }, + "sim_root": "/home/admin/cpfs/wjh/aituner/qwen30-fixed-pressure-surface-20260719-r1/sim/fixed-po" +} diff --git a/runs/frontier-split-rootcause-v0/frozen-inputs/q30-fixed-hi/fixed-po/comparison.md b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-fixed-hi/fixed-po/comparison.md new file mode 100644 index 0000000..d38638c --- /dev/null +++ b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-fixed-hi/fixed-po/comparison.md @@ -0,0 +1,8 @@ +# Qwen30 Frontier vs real latency selection + +| Objective | Frontier | Real | Match | Regret | Pairwise | +|---|---|---|---:|---:|---:| +| ttft_ms:mean | tp4_mns16 | tp4_mns32 | no | 0.3% | 100.0% | +| ttft_ms:p90 | tp4_mns16 | tp4_mns32 | no | 0.5% | 100.0% | +| e2e_ms:mean | tp4_mns16 | tp4_mns32 | no | 0.3% | 100.0% | +| e2e_ms:p90 | tp4_mns16 | tp4_mns32 | no | 0.5% | 100.0% | diff --git a/runs/frontier-split-rootcause-v0/frozen-inputs/q30-fixed-hi/fixed-po/real-audit.json b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-fixed-hi/fixed-po/real-audit.json new file mode 100644 index 0000000..74c5c48 --- /dev/null +++ b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-fixed-hi/fixed-po/real-audit.json @@ -0,0 +1,997 @@ +{ + "applicable_metrics": [ + "ttft_ms", + "e2e_ms" + ], + "case_root": "/home/admin/cpfs/wjh/aituner/qwen30-fixed-pressure-surface-20260719-r1/real/fixed-po", + "configs": { + "tp1_mns16": { + "metrics": { + "e2e_ms": { + "pooled_mean_ms": 311.5405924270961, + "pooled_p90_ms": 313.7628498952836, + "pooled_samples": 771, + "trial_mean_of_means_ms": 311.5405924270961, + "trial_stddev_of_means_ms": 1.3039412575877052 + }, + "ttft_ms": { + "pooled_mean_ms": 311.49589566449873, + "pooled_p90_ms": 313.7203580699861, + "pooled_samples": 771, + "trial_mean_of_means_ms": 311.49589566449873, + "trial_stddev_of_means_ms": 1.3039649961427384 + } + }, + "trials": [ + { + "metrics": { + "e2e_ms": { + "mean_ms": 310.6955070939125, + "p90_ms": 311.31368898786604, + "samples": 257 + }, + "ttft_ms": { + "mean_ms": 310.65253322356983, + "p90_ms": 311.2668279791251, + "samples": 257 + } + }, + "requests": 257, + "result_path": "/home/admin/cpfs/wjh/aituner/qwen30-fixed-pressure-surface-20260719-r1/real/fixed-po/real/tp1_mns16/trial1/results/result.json", + "trial": 1 + }, + { + "metrics": { + "e2e_ms": { + "mean_ms": 310.8839521212648, + "p90_ms": 311.4629740593955, + "samples": 257 + }, + "ttft_ms": { + "mean_ms": 310.837354891071, + "p90_ms": 311.4192739594728, + "samples": 257 + } + }, + "requests": 257, + "result_path": "/home/admin/cpfs/wjh/aituner/qwen30-fixed-pressure-surface-20260719-r1/real/fixed-po/real/tp1_mns16/trial2/results/result.json", + "trial": 2 + }, + { + "metrics": { + "e2e_ms": { + "mean_ms": 313.04231806611097, + "p90_ms": 314.1881680348888, + "samples": 257 + }, + "ttft_ms": { + "mean_ms": 312.99779887885535, + "p90_ms": 314.14560007397085, + "samples": 257 + } + }, + "requests": 257, + "result_path": "/home/admin/cpfs/wjh/aituner/qwen30-fixed-pressure-surface-20260719-r1/real/fixed-po/real/tp1_mns16/trial3/results/result.json", + "trial": 3 + } + ] + }, + "tp1_mns32": { + "metrics": 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b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-fixed-hi/fixed-po/real-audit.md @@ -0,0 +1,10 @@ +# Qwen30 real latency case audit + +Prefill-only: `True`. + +| Objective | Real winner | Value (ms) | +|---|---|---:| +| ttft_ms:pooled_mean_ms | tp4_mns32 | 113.40 | +| ttft_ms:pooled_p90_ms | tp4_mns32 | 114.69 | +| e2e_ms:pooled_mean_ms | tp4_mns32 | 113.44 | +| e2e_ms:pooled_p90_ms | tp4_mns32 | 114.74 | diff --git a/runs/frontier-split-rootcause-v0/frozen-inputs/q30-trace-pd/comparison.json b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-trace-pd/comparison.json new file mode 100644 index 0000000..f07a37a --- /dev/null +++ b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-trace-pd/comparison.json @@ -0,0 +1,726 @@ +{ + "cells": { + "tp1_mns16": { + "config": "tp1_mns16", + "elapsed_seconds": 351.88990092277527, + "metrics": { + "e2e_ms": { + "mean_ratio_sim_over_real": 13.842098628638448, + "p90_ratio_sim_over_real": 13.072241203568879, + "real_mean_ms": 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"sim_ranking": [ + "tp4_mns64", + "tp2_mns64", + "tp1_mns64", + "tp4_mns32", + "tp2_mns32", + "tp1_mns32", + "tp4_mns16", + "tp2_mns16", + "tp4_mns8", + "tp1_mns16", + "tp2_mns8", + "tp1_mns8" + ], + "sim_winner": "tp4_mns64", + "tied_pairs": 0, + "winner_match": true + }, + "ttft_ms:p90": { + "concordant_pairs": 56, + "discordant_pairs": 10, + "informative_pairs": 66, + "pairwise_agreement_fraction": 0.8484848484848485, + "real_ranking": [ + "tp4_mns64", + "tp2_mns64", + "tp1_mns32", + "tp1_mns64", + "tp2_mns32", + "tp4_mns32", + "tp1_mns16", + "tp4_mns16", + "tp2_mns16", + "tp1_mns8", + "tp2_mns8", + "tp4_mns8" + ], + "real_winner": "tp4_mns64", + "sim_ranking": [ + "tp4_mns64", + "tp2_mns64", + "tp1_mns64", + "tp4_mns32", + "tp2_mns32", + "tp1_mns32", + "tp4_mns16", + "tp2_mns16", + "tp4_mns8", + "tp1_mns16", + "tp2_mns8", + "tp1_mns8" + ], + "sim_winner": "tp4_mns64", + "tied_pairs": 0, + "winner_match": true + } + }, + "sim_root": "/home/admin/cpfs/wjh/aituner/graph-piecewise-qwen30-20260717/simulator-piecewise-surface-v2" +} diff --git a/runs/frontier-split-rootcause-v0/frozen-inputs/q30-trace-pd/comparison.md b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-trace-pd/comparison.md new file mode 100644 index 0000000..83f66a7 --- /dev/null +++ b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-trace-pd/comparison.md @@ -0,0 +1,29 @@ +# Frontier piecewise graph-profile vs. real vLLM + +Each cell uses its TP-normalized, 129-request trace. Frontier values are one deterministic simulation; real values pool three fresh-server trials (387 requests/cell). + +| Config | TTFT sim / real mean (ms) | TPOT sim / real mean (ms) | E2E sim / real mean (ms) | +|---|---:|---:|---:| +| tp1_mns8 | 1669003.8 / 88479.9 | 76.0 / 14.3 | 1930772.9 / 137996.4 | +| tp1_mns16 | 870562.2 / 17378.2 | 99.1 / 20.6 | 1209178.7 / 87355.2 | +| tp1_mns32 | 316980.6 / 620.5 | 119.6 / 23.8 | 722298.9 / 81051.2 | +| tp1_mns64 | 105003.3 / 619.8 | 137.8 / 24.1 | 549486.0 / 82253.7 | +| tp2_mns8 | 1171654.6 / 95920.1 | 51.1 / 9.2 | 1348614.2 / 128296.9 | +| tp2_mns16 | 606885.2 / 34443.0 | 64.4 / 14.1 | 828082.5 / 82936.7 | +| tp2_mns32 | 224495.4 / 1050.6 | 74.8 / 18.0 | 479340.1 / 62267.5 | +| tp2_mns64 | 30524.5 / 375.8 | 82.6 / 18.2 | 311160.1 / 62353.6 | +| tp4_mns8 | 834500.2 / 102605.9 | 35.4 / 6.8 | 957838.5 / 126405.4 | +| tp4_mns16 | 429098.7 / 35042.6 | 43.2 / 8.8 | 578209.7 / 65779.1 | +| tp4_mns32 | 166734.5 / 6296.2 | 49.4 / 12.2 | 336309.9 / 47944.5 | +| tp4_mns64 | 26688.5 / 246.0 | 53.7 / 13.2 | 209788.3 / 44985.1 | + +## Selection agreement + +| Target | Frontier winner | Real winner | Match | Pairwise agreement | +|---|---|---|---:|---:| +| ttft_ms:mean | tp4_mns64 | tp4_mns64 | yes | 84.8% (56/66) | +| ttft_ms:p90 | tp4_mns64 | tp4_mns64 | yes | 84.8% (56/66) | +| tpot_ms:mean | tp4_mns8 | tp4_mns8 | yes | 97.0% (64/66) | +| tpot_ms:p90 | tp4_mns8 | tp4_mns8 | yes | 97.0% (64/66) | +| e2e_ms:mean | tp4_mns64 | tp4_mns64 | yes | 92.4% (61/66) | +| e2e_ms:p90 | tp4_mns64 | tp4_mns64 | yes | 83.3% (55/66) | diff --git a/runs/frontier-split-rootcause-v0/frozen-inputs/q30-trace-pd/launch.log b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-trace-pd/launch.log new file mode 100644 index 0000000..4b641d8 --- /dev/null +++ b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-trace-pd/launch.log @@ -0,0 +1,2 @@ +ANALYSIS_LAUNCH_ECHO sim_root=/home/admin/cpfs/wjh/aituner/graph-piecewise-qwen30-20260717/simulator-piecewise-surface-v2 real_audit=/home/admin/cpfs/wjh/aituner/simulator-tuning-latency-q30-tp-normalized-u0p01-20260717/qwen30-tracepd-u0p01-real-audit.json outputs=/home/admin/cpfs/wjh/aituner/graph-piecewise-qwen30-20260717/simulator-piecewise-surface-v2/analysis expected=12 completed cells, rank+latency comparison +ANALYSIS_RETRY_ECHO sim_root=/home/admin/cpfs/wjh/aituner/graph-piecewise-qwen30-20260717/simulator-piecewise-surface-v2 real_audit_sha256=6e2c17bce5b0edb5f6f92aae4cc1b00f9390f692d31d059efe647158b386c8e9 output=/home/admin/cpfs/wjh/aituner/graph-piecewise-qwen30-20260717/simulator-piecewise-surface-v2/analysis diff --git a/runs/frontier-split-rootcause-v0/frozen-inputs/q30-trace-pd/summary.json b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-trace-pd/summary.json new file mode 100644 index 0000000..7ffaba6 --- /dev/null +++ b/runs/frontier-split-rootcause-v0/frozen-inputs/q30-trace-pd/summary.json @@ -0,0 +1,230 @@ +{ + "e2e_ms:mean": { + "concordant_pairs": 61, + "discordant_pairs": 5, + "informative_pairs": 66, + "pairwise_agreement_fraction": 0.9242424242424242, + "real_ranking": [ + "tp4_mns64", + "tp4_mns32", + "tp2_mns32", + "tp2_mns64", + "tp4_mns16", + "tp1_mns32", + "tp1_mns64", + "tp2_mns16", + "tp1_mns16", + "tp4_mns8", + "tp2_mns8", + "tp1_mns8" + ], + "real_winner": "tp4_mns64", + "sim_ranking": [ + "tp4_mns64", + "tp2_mns64", + "tp4_mns32", + "tp2_mns32", + "tp1_mns64", + "tp4_mns16", + "tp1_mns32", + "tp2_mns16", + "tp4_mns8", + "tp1_mns16", + "tp2_mns8", + "tp1_mns8" + ], + "sim_winner": "tp4_mns64", + "tied_pairs": 0, + "winner_match": true + }, + "e2e_ms:p90": { + "concordant_pairs": 55, + "discordant_pairs": 11, + "informative_pairs": 66, + "pairwise_agreement_fraction": 0.8333333333333334, + "real_ranking": [ + "tp4_mns64", + "tp4_mns32", + "tp4_mns16", + "tp2_mns32", + "tp2_mns64", + 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file mode 100644 index 0000000..d96b226 --- /dev/null +++ b/runs/frontier-split-rootcause-v0/results/decomposition.json @@ -0,0 +1,7410 @@ +{ + "anomalies": [], + "cross_checks": [ + { + "expected_percent": 58.0, + "name": "q30 high-pressure fixed-pd TTFT mean regret", + "observed_percent": 57.97674051508896, + "passed": true + }, + { + "expected_percent": 33.0, + "name": "q235 A0 fixed-pd TPOT mean regret", + "observed_percent": 33.025510615656906, + "passed": true + }, + { + "expected_percent": 33.0, + "name": "q235 A1 fixed-pd TPOT mean regret", + "observed_percent": 33.025510615656906, + "passed": true + }, + { + "expected_percent": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0 + ], + "name": "q30 trace-pd all-objective regret", + "observed_percent": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0 + ], + "passed": true + }, + { + "expected_percent": [ + 21.2, + 0.3 + ], + "name": "q235 Trace-PO TTFT p90 A0 to A1 regret", + "observed_percent": [ + 21.17895828808376, + 0.2607797240047338 + ], + "passed": true + }, + { + "expected_percent": [ + 33.0, + 37.2, + 30.7, + 34.6, + 33.0, + 37.2, + 30.7, + 34.6 + ], + "name": "q235 Fixed-PD TPOT/E2E regrets unchanged from A0 to A1", + "observed_percent": [ + 33.025510615656906, + 37.228890038064755, + 30.67524317507717, + 34.56375801249614, + 33.025510615656906, + 37.228890038064755, + 30.67524317507717, + 34.56375801249614 + ], + "passed": true + } + ], + "data_gaps": [], + "definitions": { + "config_uniform_scale": "geomean_c(sim_c / real_c) over paired configs", + "failure": "regret > 0.05", + "fragile_success": "winner match and real best-vs-second relative margin < 0.01", + "h_scale_verdict": "HOLDS iff failure equals (top-3 log-ratio spread > log1p(real relative margin))", + "log_ratio_spread_all": "max_c(log(sim_c / real_c)) - min_c(log(sim_c / real_c))", + "log_ratio_spread_real_top3": "log-ratio spread over the three configs with lowest real objective values", + "objective_direction": "all objectives are minimized", + "pair_inversion": "real_c > real_w and sim_c < sim_w, equivalently delta_c < -m_c", + "real_pair_margin_percent": "100 * log(real_c / real_w)", + "real_relative_margin_best_vs_second": "real_second / real_best - 1", + "regret": "real_sim_winner / real_best - 1, recomputed from per-config values", + "signed_pairwise_delta_percent": "100 * (log(sim_c / sim_w) - log(real_c / real_w)); negative favors c relative to real", + "winner_deciding_axis": "axis difference between real winner w and sim winner s; none for a winner match", + "winner_pair_tie": "sim_s == sim_w with real_s > real_w; a decision-boundary winner-label mismatch, not a strict inversion" + }, + "directional_summary": { + "axis_inversions_by_case": [ + { + "case": "q30 trace-pd", + "n_inv_mixed": 0, + "n_inv_mns": 0, + "n_inv_tp": 0, + "n_tie_mixed": 0, + "n_tie_mns": 0, + "n_tie_tp": 0 + }, + { + "case": "q30 fixed-pd high", + "n_inv_mixed": 6, + "n_inv_mns": 3, + "n_inv_tp": 7, + "n_tie_mixed": 0, + "n_tie_mns": 0, + 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"q30 fixed-po high", + "objective": "ttft_mean_ms", + "real_winner": "tp4_mns32", + "sim_winner": "tp4_mns16", + "winner_deciding_axis": "mns-axis" + }, + { + "case": "q30 fixed-po high", + "objective": "ttft_p90_ms", + "real_winner": "tp4_mns32", + "sim_winner": "tp4_mns16", + "winner_deciding_axis": "mns-axis" + }, + { + "case": "q30 fixed-po high", + "objective": "e2e_mean_ms", + "real_winner": "tp4_mns32", + "sim_winner": "tp4_mns16", + "winner_deciding_axis": "mns-axis" + }, + { + "case": "q30 fixed-po high", + "objective": "e2e_p90_ms", + "real_winner": "tp4_mns32", + "sim_winner": "tp4_mns16", + "winner_deciding_axis": "mns-axis" + }, + { + "case": "q30 fixed-po low", + "objective": "ttft_mean_ms", + "real_winner": "tp4_mns8", + "sim_winner": "tp4_mns16", + "winner_deciding_axis": "mns-axis" + }, + { + "case": "q30 fixed-po low", + "objective": "ttft_p90_ms", + "real_winner": "tp4_mns8", + "sim_winner": "tp4_mns16", + "winner_deciding_axis": "mns-axis" + }, + { + "case": "q30 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"component": "pipeline_communication", + "exact_state_tp8_minus_tp4_ms": 0.0, + "own_composition_tp8_minus_tp4_ms": 0.0 + }, + { + "component": "runtime_overhead", + "exact_state_tp8_minus_tp4_ms": 0.0, + "own_composition_tp8_minus_tp4_ms": 0.0 + } + ], + "configs": [ + { + "config": "tp4_ep1_mns64", + "exact_state_coverage": 1.0, + "exact_state_matched_sim_ms": 34.25746674314369, + "real_exact_decode_batch_mean": 3.8896771904930825, + "real_token_weighted_iteration_ms": 18.47097461620307, + "sim_own_decode_batch_mean": 13.537037037037036 + }, + { + "config": "tp8_ep8_mns64", + "exact_state_coverage": 1.0, + "exact_state_matched_sim_ms": 45.160255377181116, + "real_exact_decode_batch_mean": 10.921314741035857, + "real_token_weighted_iteration_ms": 27.588699194406445, + "sim_own_decode_batch_mean": 18.93782383419689 + } + ], + "contrast_decomposition": { + "exact_state_matched_tp8_minus_tp4_ms": 10.902788634037428, + "residual_vs_exact_iteration_ms": 1.7850640558340523, + "residual_vs_frozen_real_tpot_ms": 3.9527886340374287, + "simulator_internal_tp8_minus_tp4_ms": -20.06625468177714, + "state_composition_shift_ms": 30.969043315814567 + }, + "reference": { + "exact_iteration_tp8_minus_tp4_ms": 9.117724578203376, + "observed_real_tp8_minus_tp4_ms": 6.949999999999999, + "real_tpot_ms": { + "tp4_ep1_mns64": 21.0442, + "tp8_ep8_mns64": 27.9942 + } + }, + "source": "q235-state-diag/state-diagnosis-exact.json", + "verdict": "Exact real state composition flips Frontier to the correct TP4 winner; the ranking error is caused by closed-loop state/composition mismatch, not a conditional stage reversal." + }, + "q30_admission": { + "cells": [ + { + "config": "tp2_mns64", + "mns": 64, + "real_required_slots_upper_bound": 7.548483722821714, + "real_ttft_ms": 194.56193759030603, + "real_waiting_max": 0, + "sim_first_scheduling_wait_ms": 38.22329108338011, + "sim_queue_free_ttft_ms": 191.02937373774816, + "sim_required_slots": 57.96430802744742, + "sim_tpot_over_real": 8.091316956395687, + "sim_ttft_ms": 229.2526648211283 + }, + { + "config": "tp4_mns16", + "mns": 16, + "real_required_slots_upper_bound": 14.637597063342735, + "real_ttft_ms": 124.49453797538061, + "real_waiting_max": 0, + "sim_first_scheduling_wait_ms": 64906.25631809643, + "sim_queue_free_ttft_ms": 134.74066995065758, + "sim_required_slots": 54.488824675063235, + "sim_tpot_over_real": 3.8276349505907024, + "sim_ttft_ms": 65040.99698804708 + }, + { + "config": "tp4_mns32", + "mns": 32, + "real_required_slots_upper_bound": 14.521569587596273, + "real_ttft_ms": 123.15859724408143, + "real_waiting_max": 0, + "sim_first_scheduling_wait_ms": 27180.676177927482, + "sim_queue_free_ttft_ms": 135.62147333612225, + "sim_required_slots": 65.89386162288272, + "sim_tpot_over_real": 4.6740581573385525, + "sim_ttft_ms": 27316.2976512636 + }, + { + "config": "tp4_mns64", + "mns": 64, + "real_required_slots_upper_bound": 14.661911968825338, + "real_ttft_ms": 123.17715882596677, + 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true + }, + { + "case": "q235 A1 trace-po", + "config_uniform_scale": 0.5678289728094312, + "expected_config_count": 4, + "failure": false, + "fragile_success": false, + "h_scale_condition_spread_gt_log_margin": true, + "h_scale_verdict": "COUNTEREXAMPLE", + "log_ratio_spread_all": 0.31919899251598766, + "log_ratio_spread_real_top3": 0.31919899251598766, + "missing_configs": [], + "n_inv_mixed": 0, + "n_inv_mns": 0, + "n_inv_tp": 0, + "n_tie_mixed": 0, + "n_tie_mns": 1, + "n_tie_tp": 0, + "objective": "ttft_p90_ms", + "paired_config_count": 4, + "pairwise_vs_real_winner": [ + { + "axis_vs_real_winner": "mixed", + "config": "tp4_ep1_mns128", + "inversion": false, + "real_pair_margin_percent": 19.209826109044702, + "signed_pairwise_delta_percent": 31.659458967916663 + }, + { + "axis_vs_real_winner": "tp-axis", + "config": "tp4_ep1_mns64", + "inversion": false, + "real_pair_margin_percent": 19.400420037632387, + "signed_pairwise_delta_percent": 31.468865039328982 + }, + { + "axis_vs_real_winner": "mns-axis", + "config": "tp8_ep8_mns128", + "inversion": false, + "real_pair_margin_percent": 0.26044028368209854, + "signed_pairwise_delta_percent": -0.26044028368209854 + }, + { + "axis_vs_real_winner": "none", + "config": "tp8_ep8_mns64", + "inversion": false, + "real_pair_margin_percent": 0.0, + "signed_pairwise_delta_percent": 0.0 + } + ], + "real_relative_margin_best_vs_second": 0.002607797240047338, + "real_top3_configs": [ + "tp8_ep8_mns64", + "tp8_ep8_mns128", + "tp4_ep1_mns128" + ], + "real_winner": "tp8_ep8_mns64", + "regret": 0.002607797240047338, + "sim_winner": "tp8_ep8_mns128", + "sim_winner_real_relative_loss": 0.002607797240047338, + "sources": [ + "q235-ablation-a1/comparison.json" + ], + "winner_deciding_axis": "mns-axis", + "winner_match": false, + "winner_pair_delta_s_percent": -0.26044028368209854, + "winner_pair_m_s_percent": 0.26044028368209854, + "winner_pair_relation": "sim-tie", + "winner_pair_strict_reversal": false + }, + { + "case": "q235 A1 trace-po", + "config_uniform_scale": 0.548058912389237, + "expected_config_count": 4, + "failure": false, + "fragile_success": false, + "h_scale_condition_spread_gt_log_margin": true, + "h_scale_verdict": "COUNTEREXAMPLE", + "log_ratio_spread_all": 0.32202928326406854, + "log_ratio_spread_real_top3": 0.32202928326406854, + "missing_configs": [], + "n_inv_mixed": 0, + "n_inv_mns": 1, + "n_inv_tp": 0, + "n_tie_mixed": 0, + "n_tie_mns": 0, + "n_tie_tp": 0, + "objective": "e2e_mean_ms", + "paired_config_count": 4, + "pairwise_vs_real_winner": [ + { + "axis_vs_real_winner": "tp-axis", + "config": "tp4_ep1_mns128", + "inversion": false, + "real_pair_margin_percent": 5.1172644115479695, + "signed_pairwise_delta_percent": 30.825805751400548 + }, + { + "axis_vs_real_winner": "mixed", + "config": "tp4_ep1_mns64", + "inversion": false, + "real_pair_margin_percent": 6.800551883155552, + "signed_pairwise_delta_percent": 29.109242897486777 + }, + { + "axis_vs_real_winner": "none", + "config": "tp8_ep8_mns128", + "inversion": false, + "real_pair_margin_percent": 0.0, + "signed_pairwise_delta_percent": 0.0 + }, + { + "axis_vs_real_winner": "mns-axis", + "config": "tp8_ep8_mns64", + "inversion": true, + "real_pair_margin_percent": 1.3654615828579657, + "signed_pairwise_delta_percent": -1.3771225750063092 + } + ], + "real_relative_margin_best_vs_second": 0.0137482658615673, + "real_top3_configs": [ + "tp8_ep8_mns128", + "tp8_ep8_mns64", + "tp4_ep1_mns128" + ], + "real_winner": "tp8_ep8_mns128", + "regret": 0.0137482658615673, + "sim_winner": "tp8_ep8_mns64", + "sim_winner_real_relative_loss": 0.0137482658615673, + "sources": [ + "q235-ablation-a1/comparison.json" + ], + "winner_deciding_axis": "mns-axis", + "winner_match": false, + "winner_pair_delta_s_percent": -1.3771225750063092, + "winner_pair_m_s_percent": 1.3654615828579657, + "winner_pair_relation": "reversal", + "winner_pair_strict_reversal": true + }, + { + "case": "q235 A1 trace-po", + "config_uniform_scale": 0.5678075555479308, + "expected_config_count": 4, + "failure": false, + "fragile_success": false, + "h_scale_condition_spread_gt_log_margin": true, + "h_scale_verdict": "COUNTEREXAMPLE", + "log_ratio_spread_all": 0.3192076739943569, + "log_ratio_spread_real_top3": 0.3192076739943569, + "missing_configs": [], + "n_inv_mixed": 0, + "n_inv_mns": 0, + "n_inv_tp": 0, + "n_tie_mixed": 0, + "n_tie_mns": 1, + "n_tie_tp": 0, + "objective": "e2e_p90_ms", + "paired_config_count": 4, + "pairwise_vs_real_winner": [ + { + "axis_vs_real_winner": "mixed", + "config": "tp4_ep1_mns128", + "inversion": false, + "real_pair_margin_percent": 19.20014493069586, + "signed_pairwise_delta_percent": 31.669140146265505 + }, + { + "axis_vs_real_winner": "tp-axis", + "config": "tp4_ep1_mns64", + "inversion": false, + "real_pair_margin_percent": 19.390739890554553, + "signed_pairwise_delta_percent": 31.478545186406816 + }, + { + "axis_vs_real_winner": "mns-axis", + "config": "tp8_ep8_mns128", + "inversion": false, + "real_pair_margin_percent": 0.2516272531701875, + "signed_pairwise_delta_percent": -0.2516272531701875 + }, + { + "axis_vs_real_winner": "none", + "config": "tp8_ep8_mns64", + "inversion": false, + "real_pair_margin_percent": 0.0, + "signed_pairwise_delta_percent": 0.0 + } + ], + "real_relative_margin_best_vs_second": 0.0025194410024500513, + "real_top3_configs": [ + "tp8_ep8_mns64", + "tp8_ep8_mns128", + "tp4_ep1_mns128" + ], + "real_winner": "tp8_ep8_mns64", + "regret": 0.0025194410024500513, + "sim_winner": "tp8_ep8_mns128", + "sim_winner_real_relative_loss": 0.0025194410024500513, + "sources": [ + "q235-ablation-a1/comparison.json" + ], + "winner_deciding_axis": "mns-axis", + "winner_match": false, + "winner_pair_delta_s_percent": -0.2516272531701875, + "winner_pair_m_s_percent": 0.2516272531701875, + "winner_pair_relation": "sim-tie", + "winner_pair_strict_reversal": false + } + ], + "schema": "frontier-split-rootcause-decomposition-v2", + "summary": { + "case_count": 14, + "data_gap_count": 0, + "failure_count": 23, + "h_scale_counterexample_count": 40, + "h_scale_counterexamples": [ + { + "case": "q30 trace-pd", + "objective": "ttft_mean_ms" + }, + { + "case": "q30 trace-pd", + "objective": "ttft_p90_ms" + }, + { + "case": "q30 trace-pd", + "objective": "e2e_mean_ms" + }, + { + "case": "q30 trace-pd", + "objective": "e2e_p90_ms" + }, + { + "case": "q30 fixed-pd high", + "objective": "tpot_mean_ms" + }, + { + "case": "q30 fixed-pd high", + "objective": "tpot_p90_ms" + }, + { + "case": "q30 fixed-pd high", + "objective": "e2e_mean_ms" + }, + { + "case": "q30 fixed-po high", + "objective": "ttft_mean_ms" + }, + { + "case": "q30 fixed-po high", + "objective": "ttft_p90_ms" + }, + { + "case": "q30 fixed-po high", + "objective": "e2e_mean_ms" + }, + { + "case": "q30 fixed-po high", + "objective": "e2e_p90_ms" + }, + { + "case": "q30 fixed-pd low", + "objective": "ttft_mean_ms" + }, + { + "case": "q30 fixed-pd low", + "objective": "ttft_p90_ms" + }, + { + "case": "q30 fixed-po low", + "objective": "ttft_mean_ms" + }, + { + "case": "q30 fixed-po low", + "objective": "ttft_p90_ms" + }, + { + "case": "q30 fixed-po low", + "objective": "e2e_mean_ms" + }, + { + "case": "q30 fixed-po low", + "objective": "e2e_p90_ms" + }, + { + "case": "q30 trace-po low", + "objective": "ttft_mean_ms" + }, + { + "case": "q30 trace-po low", + "objective": "ttft_p90_ms" + }, + { + "case": "q30 trace-po low", + "objective": "e2e_mean_ms" + }, + { + "case": "q30 trace-po low", + "objective": "e2e_p90_ms" + }, + { + "case": "q235 A0 fixed-pd", + "objective": "ttft_mean_ms" + }, + { + "case": "q235 A0 fixed-pd", + "objective": "ttft_p90_ms" + }, + { + "case": "q235 A0 fixed-po", + "objective": "ttft_p90_ms" + }, + { + "case": "q235 A0 fixed-po", + "objective": "e2e_p90_ms" + }, + { + "case": "q235 A0 trace-pd", + "objective": "tpot_mean_ms" + }, + { + "case": "q235 A0 trace-pd", + "objective": "tpot_p90_ms" + }, + { + "case": "q235 A0 trace-pd", + "objective": "e2e_mean_ms" + }, + { + "case": "q235 A1 fixed-pd", + "objective": "ttft_mean_ms" + }, + { + "case": "q235 A1 fixed-pd", + "objective": "ttft_p90_ms" + }, + { + "case": "q235 A1 fixed-po", + "objective": "ttft_mean_ms" + }, + { + "case": "q235 A1 fixed-po", + "objective": "ttft_p90_ms" + }, + { + "case": "q235 A1 fixed-po", + "objective": "e2e_mean_ms" + }, + { + "case": "q235 A1 fixed-po", + "objective": "e2e_p90_ms" + }, + { + "case": "q235 A1 trace-pd", + "objective": "tpot_mean_ms" + }, + { + "case": "q235 A1 trace-pd", + "objective": "e2e_mean_ms" + }, + { + "case": "q235 A1 trace-po", + "objective": "ttft_mean_ms" + }, + { + "case": "q235 A1 trace-po", + "objective": "ttft_p90_ms" + }, + { + "case": "q235 A1 trace-po", + "objective": "e2e_mean_ms" + }, + { + "case": "q235 A1 trace-po", + "objective": "e2e_p90_ms" + } + ], + "row_count": 70, + "winner_mismatch_count": 47 + } +} diff --git a/runs/frontier-split-rootcause-v0/results/decomposition.md b/runs/frontier-split-rootcause-v0/results/decomposition.md new file mode 100644 index 0000000..bac3454 --- /dev/null +++ b/runs/frontier-split-rootcause-v0/results/decomposition.md @@ -0,0 +1,301 @@ +# Frontier good/bad split decomposition + +All 70 rows are recomputed from frozen per-config JSON values. Lower is better for every objective. `scale` is the geometric mean of `sim/real`; spreads are shown as `100 ×` log-space width, while `real margin` is the ordinary relative difference `100 × (real_second / real_best - 1)`. H-SCALE compares the log-space spread with `log1p(real margin)`, not directly with the ordinary relative margin. The real top-3 neighborhood is selected independently for every row. Directional `m_s` and `delta_s` are already expressed in log-percentage points. + +- Rows / case surfaces: 70 / 14. +- Failures (`regret > 5%`): 23. +- Exact winner mismatches: 47. +- H-SCALE counterexamples: 40. + +## Unified decomposition + +| case | objective | sim winner | real winner | regret | scale | spread all | spread real top-3 | real margin (relative) | sim-winner real loss | failure | H-SCALE | deciding axis | m_s | delta_s | n_inv_tp | n_inv_mns | n_inv_mixed | fragile success | +|---|---|---|---|---:|---:|---:|---:|---:|---:|---|---|---|---:|---:|---:|---:|---:|---| +| q30 trace-pd | ttft_mean_ms | tp4_mns64 | tp4_mns64 | 0.0% | 45.900x | 414.0% | 73.5% | 52.8% | 0.0% | no | COUNTEREXAMPLE | none | 0.0% | +0.0% | 0 | 0 | 0 | no | +| q30 trace-pd | ttft_p90_ms | tp4_mns64 | tp4_mns64 | 0.0% | 43.384x | 386.3% | 136.8% | 67.6% | 0.0% | no | COUNTEREXAMPLE | none | 0.0% | +0.0% | 0 | 0 | 0 | no | +| q30 trace-pd | tpot_mean_ms | tp4_mns8 | tp4_mns8 | 0.0% | 4.799x | 34.2% | 11.9% | 30.3% | 0.0% | no | HOLDS | none | 0.0% | +0.0% | 0 | 0 | 0 | no | +| q30 trace-pd | tpot_p90_ms | tp4_mns8 | tp4_mns8 | 0.0% | 4.291x | 34.9% | 10.9% | 32.9% | 0.0% | no | HOLDS | none | 0.0% | +0.0% | 0 | 0 | 0 | no | +| q30 trace-pd | e2e_mean_ms | tp4_mns64 | tp4_mns64 | 0.0% | 8.263x | 109.9% | 50.1% | 6.6% | 0.0% | no | COUNTEREXAMPLE | none | 0.0% | +0.0% | 0 | 0 | 0 | no | +| q30 trace-pd | e2e_p90_ms | tp4_mns64 | tp4_mns64 | 0.0% | 8.066x | 123.0% | 70.3% | 0.1% | 0.0% | no | COUNTEREXAMPLE | none | 0.0% | +0.0% | 0 | 0 | 0 | yes | +| q30 fixed-pd high | ttft_mean_ms | tp2_mns64 | tp4_mns32 | 58.0% | 80.274x | 664.7% | 229.2% | 0.0% | 58.0% | yes | HOLDS | mixed | 45.7% | -523.8% | 2 | 1 | 2 | no | +| q30 fixed-pd high | ttft_p90_ms | tp2_mns64 | tp4_mns64 | 56.2% | 130.663x | 709.3% | 222.7% | 0.1% | 56.2% | yes | HOLDS | tp-axis | 44.6% | -435.3% | 2 | 0 | 0 | no | +| q30 fixed-pd high | tpot_mean_ms | tp4_mns8 | tp4_mns8 | 0.0% | 6.313x | 118.1% | 95.7% | 34.4% | 0.0% | no | COUNTEREXAMPLE | none | 0.0% | +0.0% | 0 | 0 | 0 | no | +| q30 fixed-pd high | tpot_p90_ms | tp4_mns8 | tp4_mns8 | 0.0% | 5.885x | 122.3% | 95.1% | 37.2% | 0.0% | no | COUNTEREXAMPLE | none | 0.0% | +0.0% | 0 | 0 | 0 | no | +| q30 fixed-pd high | e2e_mean_ms | tp4_mns64 | tp1_mns32 | 1.7% | 18.867x | 226.5% | 111.5% | 0.7% | 1.7% | no | COUNTEREXAMPLE | mixed | 1.7% | -82.6% | 2 | 1 | 2 | no | +| q30 fixed-pd high | e2e_p90_ms | tp2_mns64 | tp1_mns32 | 5.5% | 24.706x | 274.2% | 172.5% | 1.0% | 5.5% | yes | HOLDS | mixed | 5.3% | -101.7% | 1 | 1 | 2 | no | +| q30 fixed-po high | ttft_mean_ms | tp4_mns16 | tp4_mns32 | 0.3% | 0.964x | 27.5% | 0.3% | 0.1% | 0.3% | no | COUNTEREXAMPLE | mns-axis | 0.3% | -0.3% | 0 | 0 | 0 | no | +| q30 fixed-po high | ttft_p90_ms | tp4_mns16 | tp4_mns32 | 0.5% | 0.956x | 26.9% | 0.3% | 0.0% | 0.5% | no | COUNTEREXAMPLE | mns-axis | 0.5% | -0.5% | 0 | 0 | 0 | no | +| q30 fixed-po high | e2e_mean_ms | tp4_mns16 | tp4_mns32 | 0.3% | 0.964x | 27.5% | 0.3% | 0.1% | 0.3% | no | COUNTEREXAMPLE | mns-axis | 0.3% | -0.3% | 0 | 0 | 0 | no | +| q30 fixed-po high | e2e_p90_ms | tp4_mns16 | tp4_mns32 | 0.5% | 0.956x | 26.9% | 0.3% | 0.0% | 0.5% | no | COUNTEREXAMPLE | mns-axis | 0.5% | -0.5% | 0 | 0 | 0 | no | +| q30 fixed-pd low | ttft_mean_ms | tp4_mns16 | tp4_mns16 | 0.0% | 1.368x | 43.8% | 1.5% | 1.3% | 0.0% | no | COUNTEREXAMPLE | none | 0.0% | +0.0% | 0 | 0 | 0 | no | +| q30 fixed-pd low | ttft_p90_ms | tp4_mns16 | tp4_mns16 | 0.0% | 1.402x | 48.7% | 2.9% | 1.8% | 0.0% | no | COUNTEREXAMPLE | none | 0.0% | +0.0% | 0 | 0 | 0 | no | +| q30 fixed-pd low | tpot_mean_ms | tp1_mns16 | tp4_mns32 | 14.0% | 4.952x | 52.7% | 0.2% | 0.0% | 14.0% | yes | HOLDS | mixed | 13.1% | -52.5% | 2 | 0 | 6 | no | +| q30 fixed-pd low | tpot_p90_ms | tp1_mns16 | tp4_mns8 | 13.4% | 4.950x | 53.2% | 0.3% | 0.2% | 13.4% | yes | HOLDS | mixed | 12.5% | -52.8% | 2 | 0 | 6 | no | +| q30 fixed-pd low | e2e_mean_ms | tp1_mns16 | tp4_mns32 | 26.9% | 4.423x | 59.8% | 0.2% | 0.1% | 26.9% | yes | HOLDS | mixed | 23.8% | -59.6% | 2 | 0 | 6 | no | +| q30 fixed-pd low | e2e_p90_ms | tp1_mns16 | tp4_mns8 | 25.7% | 4.421x | 60.0% | 0.2% | 0.1% | 25.7% | yes | HOLDS | mixed | 22.9% | -59.8% | 2 | 0 | 6 | no | +| q30 fixed-po low | ttft_mean_ms | tp4_mns16 | tp4_mns8 | 1.5% | 1.218x | 20.9% | 1.1% | 0.3% | 1.5% | no | COUNTEREXAMPLE | mns-axis | 1.5% | -1.5% | 0 | 0 | 0 | no | +| q30 fixed-po low | ttft_p90_ms | tp4_mns16 | tp4_mns8 | 3.6% | 1.184x | 16.1% | 2.3% | 0.6% | 3.6% | no | COUNTEREXAMPLE | mns-axis | 3.5% | -3.5% | 0 | 0 | 0 | no | +| q30 fixed-po low | e2e_mean_ms | tp4_mns16 | tp4_mns8 | 1.5% | 1.217x | 20.8% | 1.1% | 0.3% | 1.5% | no | COUNTEREXAMPLE | mns-axis | 1.5% | -1.5% | 0 | 0 | 0 | no | +| q30 fixed-po low | e2e_p90_ms | tp4_mns16 | tp4_mns8 | 3.6% | 1.183x | 16.0% | 2.4% | 0.7% | 3.6% | no | COUNTEREXAMPLE | mns-axis | 3.5% | -3.5% | 0 | 0 | 0 | no | +| q30 trace-po low | ttft_mean_ms | tp4_mns16 | tp4_mns64 | 3.2% | 0.657x | 18.2% | 2.6% | 1.7% | 3.2% | no | COUNTEREXAMPLE | mns-axis | 3.1% | -3.1% | 0 | 3 | 0 | no | +| q30 trace-po low | ttft_p90_ms | tp4_mns16 | tp4_mns8 | 0.4% | 0.478x | 17.7% | 0.2% | 0.2% | 0.4% | no | COUNTEREXAMPLE | mns-axis | 0.4% | -0.4% | 0 | 0 | 0 | no | +| q30 trace-po low | e2e_mean_ms | tp4_mns16 | tp4_mns64 | 3.2% | 0.657x | 18.2% | 2.6% | 1.7% | 3.2% | no | COUNTEREXAMPLE | mns-axis | 3.1% | -3.1% | 0 | 3 | 0 | no | +| q30 trace-po low | e2e_p90_ms | tp4_mns16 | tp4_mns8 | 0.3% | 0.478x | 17.7% | 0.2% | 0.1% | 0.3% | no | COUNTEREXAMPLE | mns-axis | 0.3% | -0.3% | 0 | 0 | 0 | no | +| q235 A0 fixed-pd | ttft_mean_ms | tp4_ep1_mns128 | tp8_ep8_mns128 | 4.2% | 0.991x | 6.1% | 5.0% | 0.4% | 4.2% | no | COUNTEREXAMPLE | tp-axis | 4.1% | -5.0% | 1 | 0 | 1 | no | +| q235 A0 fixed-pd | ttft_p90_ms | tp8_ep8_mns128 | tp8_ep8_mns64 | 0.2% | 1.020x | 2.4% | 2.4% | 0.2% | 0.2% | no | COUNTEREXAMPLE | mns-axis | 0.2% | -0.2% | 0 | 0 | 0 | no | +| q235 A0 fixed-pd | tpot_mean_ms | tp8_ep8_mns128 | tp4_ep1_mns128 | 33.0% | 3.025x | 64.0% | 63.9% | 0.7% | 33.0% | yes | HOLDS | tp-axis | 28.5% | -64.0% | 1 | 0 | 1 | no | +| q235 A0 fixed-pd | tpot_p90_ms | tp8_ep8_mns128 | tp4_ep1_mns128 | 37.2% | 3.062x | 62.9% | 62.8% | 0.3% | 37.2% | yes | HOLDS | tp-axis | 31.6% | -62.8% | 1 | 0 | 1 | no | +| q235 A0 fixed-pd | e2e_mean_ms | tp8_ep8_mns128 | tp4_ep1_mns128 | 30.7% | 2.913x | 61.5% | 61.5% | 0.7% | 30.7% | yes | HOLDS | tp-axis | 26.8% | -61.5% | 1 | 0 | 1 | no | +| q235 A0 fixed-pd | e2e_p90_ms | tp8_ep8_mns128 | tp4_ep1_mns128 | 34.6% | 2.955x | 60.5% | 60.4% | 0.3% | 34.6% | yes | HOLDS | tp-axis | 29.7% | -60.4% | 1 | 0 | 1 | no | +| q235 A0 fixed-po | ttft_mean_ms | tp4_ep1_mns128 | tp8_ep8_mns128 | 5.9% | 0.964x | 12.1% | 12.0% | 0.2% | 5.9% | yes | HOLDS | tp-axis | 5.7% | -12.0% | 1 | 0 | 1 | no | +| q235 A0 fixed-po | ttft_p90_ms | tp4_ep1_mns128 | tp8_ep8_mns128 | 1.7% | 0.941x | 8.3% | 8.0% | 0.3% | 1.7% | no | COUNTEREXAMPLE | tp-axis | 1.7% | -8.0% | 1 | 0 | 1 | no | +| q235 A0 fixed-po | e2e_mean_ms | tp4_ep1_mns128 | tp8_ep8_mns128 | 5.9% | 0.964x | 12.1% | 12.0% | 0.2% | 5.9% | yes | HOLDS | tp-axis | 5.7% | -12.0% | 1 | 0 | 1 | no | +| q235 A0 fixed-po | e2e_p90_ms | tp4_ep1_mns128 | tp8_ep8_mns128 | 1.7% | 0.941x | 8.3% | 8.0% | 0.3% | 1.7% | no | COUNTEREXAMPLE | tp-axis | 1.7% | -8.0% | 1 | 0 | 1 | no | +| q235 A0 trace-pd | ttft_mean_ms | tp8_ep8_mns128 | tp8_ep8_mns128 | 0.0% | 1.493x | 101.6% | 101.6% | 2220.3% | 0.0% | no | HOLDS | none | 0.0% | +0.0% | 0 | 0 | 0 | no | +| q235 A0 trace-pd | ttft_p90_ms | tp8_ep8_mns128 | tp8_ep8_mns128 | 0.0% | 1.511x | 96.5% | 96.5% | 3455.8% | 0.0% | no | HOLDS | none | 0.0% | +0.0% | 0 | 0 | 0 | no | +| q235 A0 trace-pd | tpot_mean_ms | tp8_ep8_mns64 | tp8_ep8_mns64 | 0.0% | 1.340x | 61.5% | 43.1% | 16.8% | 0.0% | no | COUNTEREXAMPLE | none | 0.0% | +0.0% | 0 | 0 | 0 | no | +| q235 A0 trace-pd | tpot_p90_ms | tp8_ep8_mns64 | tp8_ep8_mns64 | 0.0% | 1.281x | 49.4% | 29.8% | 26.3% | 0.0% | no | COUNTEREXAMPLE | none | 0.0% | +0.0% | 0 | 0 | 0 | no | +| q235 A0 trace-pd | e2e_mean_ms | tp8_ep8_mns128 | tp8_ep8_mns64 | 0.6% | 1.415x | 69.3% | 69.3% | 0.6% | 0.6% | no | COUNTEREXAMPLE | mns-axis | 0.6% | -18.1% | 0 | 1 | 0 | no | +| q235 A0 trace-pd | e2e_p90_ms | tp8_ep8_mns128 | tp8_ep8_mns64 | 6.2% | 1.473x | 71.5% | 71.5% | 6.2% | 6.2% | yes | HOLDS | mns-axis | 6.0% | -16.7% | 0 | 1 | 0 | no | +| q235 A0 trace-po | ttft_mean_ms | tp4_ep1_mns64 | tp8_ep8_mns128 | 7.0% | 0.753x | 36.3% | 34.6% | 1.4% | 7.0% | yes | HOLDS | mixed | 6.8% | -36.3% | 1 | 1 | 1 | no | +| q235 A0 trace-po | ttft_p90_ms | tp4_ep1_mns128 | tp8_ep8_mns64 | 21.2% | 0.767x | 31.8% | 31.6% | 0.3% | 21.2% | yes | HOLDS | mixed | 19.2% | -31.6% | 1 | 0 | 1 | no | +| q235 A0 trace-po | e2e_mean_ms | tp4_ep1_mns64 | tp8_ep8_mns128 | 7.0% | 0.753x | 36.3% | 34.6% | 1.4% | 7.0% | yes | HOLDS | mixed | 6.8% | -36.3% | 1 | 1 | 1 | no | +| q235 A0 trace-po | e2e_p90_ms | tp4_ep1_mns128 | tp8_ep8_mns64 | 21.2% | 0.766x | 31.8% | 31.6% | 0.3% | 21.2% | yes | HOLDS | mixed | 19.2% | -31.6% | 1 | 0 | 1 | no | +| q235 A1 fixed-pd | ttft_mean_ms | tp8_ep8_mns128 | tp8_ep8_mns128 | 0.0% | 0.805x | 26.8% | 26.8% | 0.4% | 0.0% | no | COUNTEREXAMPLE | none | 0.0% | +0.0% | 0 | 0 | 0 | yes | +| q235 A1 fixed-pd | ttft_p90_ms | tp8_ep8_mns128 | tp8_ep8_mns64 | 0.2% | 0.837x | 30.5% | 30.5% | 0.2% | 0.2% | no | COUNTEREXAMPLE | mns-axis | 0.2% | -0.2% | 0 | 0 | 0 | no | +| q235 A1 fixed-pd | tpot_mean_ms | tp8_ep8_mns128 | tp4_ep1_mns128 | 33.0% | 2.608x | 60.1% | 60.0% | 0.7% | 33.0% | yes | HOLDS | tp-axis | 28.5% | -60.1% | 1 | 0 | 1 | no | +| q235 A1 fixed-pd | tpot_p90_ms | tp8_ep8_mns128 | tp4_ep1_mns128 | 37.2% | 2.587x | 61.2% | 61.1% | 0.3% | 37.2% | yes | HOLDS | tp-axis | 31.6% | -61.1% | 1 | 0 | 1 | no | +| q235 A1 fixed-pd | e2e_mean_ms | tp8_ep8_mns128 | tp4_ep1_mns128 | 30.7% | 2.508x | 58.3% | 58.2% | 0.7% | 30.7% | yes | HOLDS | tp-axis | 26.8% | -58.3% | 1 | 0 | 1 | no | +| q235 A1 fixed-pd | e2e_p90_ms | tp8_ep8_mns128 | tp4_ep1_mns128 | 34.6% | 2.495x | 59.3% | 59.2% | 0.3% | 34.6% | yes | HOLDS | tp-axis | 29.7% | -59.2% | 1 | 0 | 1 | no | +| q235 A1 fixed-po | ttft_mean_ms | tp8_ep8_mns128 | tp8_ep8_mns128 | 0.0% | 0.776x | 24.8% | 24.8% | 0.2% | 0.0% | no | COUNTEREXAMPLE | none | 0.0% | +0.0% | 0 | 0 | 0 | yes | +| q235 A1 fixed-po | ttft_p90_ms | tp8_ep8_mns128 | tp8_ep8_mns128 | 0.0% | 0.757x | 28.9% | 28.9% | 0.3% | 0.0% | no | COUNTEREXAMPLE | none | 0.0% | +0.0% | 0 | 0 | 0 | yes | +| q235 A1 fixed-po | e2e_mean_ms | tp8_ep8_mns128 | tp8_ep8_mns128 | 0.0% | 0.776x | 24.8% | 24.8% | 0.2% | 0.0% | no | COUNTEREXAMPLE | none | 0.0% | +0.0% | 0 | 0 | 0 | yes | +| q235 A1 fixed-po | e2e_p90_ms | tp8_ep8_mns128 | tp8_ep8_mns128 | 0.0% | 0.757x | 28.9% | 28.9% | 0.3% | 0.0% | no | COUNTEREXAMPLE | none | 0.0% | +0.0% | 0 | 0 | 0 | yes | +| q235 A1 trace-pd | ttft_mean_ms | tp8_ep8_mns128 | tp8_ep8_mns128 | 0.0% | 1.217x | 154.4% | 154.4% | 2220.3% | 0.0% | no | HOLDS | none | 0.0% | +0.0% | 0 | 0 | 0 | no | +| q235 A1 trace-pd | ttft_p90_ms | tp8_ep8_mns128 | tp8_ep8_mns128 | 0.0% | 1.219x | 151.1% | 151.1% | 3455.8% | 0.0% | no | HOLDS | none | 0.0% | +0.0% | 0 | 0 | 0 | no | +| q235 A1 trace-pd | tpot_mean_ms | tp8_ep8_mns64 | tp8_ep8_mns64 | 0.0% | 1.338x | 46.7% | 28.2% | 16.8% | 0.0% | no | COUNTEREXAMPLE | none | 0.0% | +0.0% | 0 | 0 | 0 | no | +| q235 A1 trace-pd | tpot_p90_ms | tp8_ep8_mns64 | tp8_ep8_mns64 | 0.0% | 1.221x | 45.3% | 22.3% | 26.3% | 0.0% | no | HOLDS | none | 0.0% | +0.0% | 0 | 0 | 0 | no | +| q235 A1 trace-pd | e2e_mean_ms | tp8_ep8_mns128 | tp8_ep8_mns64 | 0.6% | 1.430x | 48.8% | 48.8% | 0.6% | 0.6% | no | COUNTEREXAMPLE | mns-axis | 0.6% | -15.9% | 0 | 1 | 0 | no | +| q235 A1 trace-pd | e2e_p90_ms | tp8_ep8_mns128 | tp8_ep8_mns64 | 6.2% | 1.492x | 49.7% | 49.7% | 6.2% | 6.2% | yes | HOLDS | mns-axis | 6.0% | -17.4% | 0 | 1 | 0 | no | +| q235 A1 trace-po | ttft_mean_ms | tp8_ep8_mns64 | tp8_ep8_mns128 | 1.4% | 0.548x | 32.2% | 32.2% | 1.4% | 1.4% | no | COUNTEREXAMPLE | mns-axis | 1.4% | -1.4% | 0 | 1 | 0 | no | +| q235 A1 trace-po | ttft_p90_ms | tp8_ep8_mns128 | tp8_ep8_mns64 | 0.3% | 0.568x | 31.9% | 31.9% | 0.3% | 0.3% | no | COUNTEREXAMPLE | mns-axis | 0.3% | -0.3% | 0 | 0 | 0 | no | +| q235 A1 trace-po | e2e_mean_ms | tp8_ep8_mns64 | tp8_ep8_mns128 | 1.4% | 0.548x | 32.2% | 32.2% | 1.4% | 1.4% | no | COUNTEREXAMPLE | mns-axis | 1.4% | -1.4% | 0 | 1 | 0 | no | +| q235 A1 trace-po | e2e_p90_ms | tp8_ep8_mns128 | tp8_ep8_mns64 | 0.3% | 0.568x | 31.9% | 31.9% | 0.3% | 0.3% | no | COUNTEREXAMPLE | mns-axis | 0.3% | -0.3% | 0 | 0 | 0 | no | + +## H-SCALE verdict + +H-SCALE is not sufficient as stated; the following rows are counterexamples to the biconditional: + +All 40 observed counterexamples are non-failures with spread > `log1p(margin)`; there are 0 failures with spread <= `log1p(margin)`. Thus the threshold is necessary for the observed failures but not sufficient to predict them. + +| case | objective | failure | spread real top-3 | real margin | counterexample type | +|---|---|---|---:|---:|---| +| q30 trace-pd | ttft_mean_ms | no | 73.5% | 52.8% | non-failure with spread > log1p(margin) | +| q30 trace-pd | ttft_p90_ms | no | 136.8% | 67.6% | non-failure with spread > log1p(margin) | +| q30 trace-pd | e2e_mean_ms | no | 50.1% | 6.6% | non-failure with spread > log1p(margin) | +| q30 trace-pd | e2e_p90_ms | no | 70.3% | 0.1% | non-failure with spread > log1p(margin) | +| q30 fixed-pd high | tpot_mean_ms | no | 95.7% | 34.4% | non-failure with spread > log1p(margin) | +| q30 fixed-pd high | tpot_p90_ms | no | 95.1% | 37.2% | non-failure with spread > log1p(margin) | +| q30 fixed-pd high | e2e_mean_ms | no | 111.5% | 0.7% | non-failure with spread > log1p(margin) | +| q30 fixed-po high | ttft_mean_ms | no | 0.3% | 0.1% | non-failure with spread > log1p(margin) | +| q30 fixed-po high | ttft_p90_ms | no | 0.3% | 0.0% | non-failure with spread > log1p(margin) | +| q30 fixed-po high | e2e_mean_ms | no | 0.3% | 0.1% | non-failure with spread > log1p(margin) | +| q30 fixed-po high | e2e_p90_ms | no | 0.3% | 0.0% | non-failure with spread > log1p(margin) | +| q30 fixed-pd low | ttft_mean_ms | no | 1.5% | 1.3% | non-failure with spread > log1p(margin) | +| q30 fixed-pd low | ttft_p90_ms | no | 2.9% | 1.8% | non-failure with spread > log1p(margin) | +| q30 fixed-po low | ttft_mean_ms | no | 1.1% | 0.3% | non-failure with spread > log1p(margin) | +| q30 fixed-po low | ttft_p90_ms | no | 2.3% | 0.6% | non-failure with spread > log1p(margin) | +| q30 fixed-po low | e2e_mean_ms | no | 1.1% | 0.3% | non-failure with spread > log1p(margin) | +| q30 fixed-po low | e2e_p90_ms | no | 2.4% | 0.7% | non-failure with spread > log1p(margin) | +| q30 trace-po low | ttft_mean_ms | no | 2.6% | 1.7% | non-failure with spread > log1p(margin) | +| q30 trace-po low | ttft_p90_ms | no | 0.2% | 0.2% | non-failure with spread > log1p(margin) | +| q30 trace-po low | e2e_mean_ms | no | 2.6% | 1.7% | non-failure with spread > log1p(margin) | +| q30 trace-po low | e2e_p90_ms | no | 0.2% | 0.1% | non-failure with spread > log1p(margin) | +| q235 A0 fixed-pd | ttft_mean_ms | no | 5.0% | 0.4% | non-failure with spread > log1p(margin) | +| q235 A0 fixed-pd | ttft_p90_ms | no | 2.4% | 0.2% | non-failure with spread > log1p(margin) | +| q235 A0 fixed-po | ttft_p90_ms | no | 8.0% | 0.3% | non-failure with spread > log1p(margin) | +| q235 A0 fixed-po | e2e_p90_ms | no | 8.0% | 0.3% | non-failure with spread > log1p(margin) | +| q235 A0 trace-pd | tpot_mean_ms | no | 43.1% | 16.8% | non-failure with spread > log1p(margin) | +| q235 A0 trace-pd | tpot_p90_ms | no | 29.8% | 26.3% | non-failure with spread > log1p(margin) | +| q235 A0 trace-pd | e2e_mean_ms | no | 69.3% | 0.6% | non-failure with spread > log1p(margin) | +| q235 A1 fixed-pd | ttft_mean_ms | no | 26.8% | 0.4% | non-failure with spread > log1p(margin) | +| q235 A1 fixed-pd | ttft_p90_ms | no | 30.5% | 0.2% | non-failure with spread > log1p(margin) | +| q235 A1 fixed-po | ttft_mean_ms | no | 24.8% | 0.2% | non-failure with spread > log1p(margin) | +| q235 A1 fixed-po | ttft_p90_ms | no | 28.9% | 0.3% | non-failure with spread > log1p(margin) | +| q235 A1 fixed-po | e2e_mean_ms | no | 24.8% | 0.2% | non-failure with spread > log1p(margin) | +| q235 A1 fixed-po | e2e_p90_ms | no | 28.9% | 0.3% | non-failure with spread > log1p(margin) | +| q235 A1 trace-pd | tpot_mean_ms | no | 28.2% | 16.8% | non-failure with spread > log1p(margin) | +| q235 A1 trace-pd | e2e_mean_ms | no | 48.8% | 0.6% | non-failure with spread > log1p(margin) | +| q235 A1 trace-po | ttft_mean_ms | no | 32.2% | 1.4% | non-failure with spread > log1p(margin) | +| q235 A1 trace-po | ttft_p90_ms | no | 31.9% | 0.3% | non-failure with spread > log1p(margin) | +| q235 A1 trace-po | e2e_mean_ms | no | 32.2% | 1.4% | non-failure with spread > log1p(margin) | +| q235 A1 trace-po | e2e_p90_ms | no | 31.9% | 0.3% | non-failure with spread > log1p(margin) | + +## S0b directional conclusions + +Directional pair statistics remove the S0 range statistic's ambiguity by measuring every config against the real winner. Strict inversions use the frozen-value test `real_c > real_w and sim_c < sim_w`; simulator ties are not counted as inversions. + +### (a) Which config axis decides each material failure? + +| case | objective | real winner → sim winner | deciding axis | m_s | delta_s | -delta_s | regret | +|---|---|---|---|---:|---:|---:|---:| +| q30 fixed-pd high | ttft_mean_ms | tp4_mns32 → tp2_mns64 | mixed | 45.7% | -523.8% | 523.8% | 58.0% | +| q30 fixed-pd high | ttft_p90_ms | tp4_mns64 → tp2_mns64 | tp-axis | 44.6% | -435.3% | 435.3% | 56.2% | +| q30 fixed-pd high | e2e_p90_ms | tp1_mns32 → tp2_mns64 | mixed | 5.3% | -101.7% | 101.7% | 5.5% | +| q30 fixed-pd low | tpot_mean_ms | tp4_mns32 → tp1_mns16 | mixed | 13.1% | -52.5% | 52.5% | 14.0% | +| q30 fixed-pd low | tpot_p90_ms | tp4_mns8 → tp1_mns16 | mixed | 12.5% | -52.8% | 52.8% | 13.4% | +| q30 fixed-pd low | e2e_mean_ms | tp4_mns32 → tp1_mns16 | mixed | 23.8% | -59.6% | 59.6% | 26.9% | +| q30 fixed-pd low | e2e_p90_ms | tp4_mns8 → tp1_mns16 | mixed | 22.9% | -59.8% | 59.8% | 25.7% | +| q235 A0 fixed-pd | tpot_mean_ms | tp4_ep1_mns128 → tp8_ep8_mns128 | tp-axis | 28.5% | -64.0% | 64.0% | 33.0% | +| q235 A0 fixed-pd | tpot_p90_ms | tp4_ep1_mns128 → tp8_ep8_mns128 | tp-axis | 31.6% | -62.8% | 62.8% | 37.2% | +| q235 A0 fixed-pd | e2e_mean_ms | tp4_ep1_mns128 → tp8_ep8_mns128 | tp-axis | 26.8% | -61.5% | 61.5% | 30.7% | +| q235 A0 fixed-pd | e2e_p90_ms | tp4_ep1_mns128 → tp8_ep8_mns128 | tp-axis | 29.7% | -60.4% | 60.4% | 34.6% | +| q235 A0 fixed-po | ttft_mean_ms | tp8_ep8_mns128 → tp4_ep1_mns128 | tp-axis | 5.7% | -12.0% | 12.0% | 5.9% | +| q235 A0 fixed-po | e2e_mean_ms | tp8_ep8_mns128 → tp4_ep1_mns128 | tp-axis | 5.7% | -12.0% | 12.0% | 5.9% | +| q235 A0 trace-pd | e2e_p90_ms | tp8_ep8_mns64 → tp8_ep8_mns128 | mns-axis | 6.0% | -16.7% | 16.7% | 6.2% | +| q235 A0 trace-po | ttft_mean_ms | tp8_ep8_mns128 → tp4_ep1_mns64 | mixed | 6.8% | -36.3% | 36.3% | 7.0% | +| q235 A0 trace-po | ttft_p90_ms | tp8_ep8_mns64 → tp4_ep1_mns128 | mixed | 19.2% | -31.6% | 31.6% | 21.2% | +| q235 A0 trace-po | e2e_mean_ms | tp8_ep8_mns128 → tp4_ep1_mns64 | mixed | 6.8% | -36.3% | 36.3% | 7.0% | +| q235 A0 trace-po | e2e_p90_ms | tp8_ep8_mns64 → tp4_ep1_mns128 | mixed | 19.2% | -31.6% | 31.6% | 21.2% | +| q235 A1 fixed-pd | tpot_mean_ms | tp4_ep1_mns128 → tp8_ep8_mns128 | tp-axis | 28.5% | -60.1% | 60.1% | 33.0% | +| q235 A1 fixed-pd | tpot_p90_ms | tp4_ep1_mns128 → tp8_ep8_mns128 | tp-axis | 31.6% | -61.1% | 61.1% | 37.2% | +| q235 A1 fixed-pd | e2e_mean_ms | tp4_ep1_mns128 → tp8_ep8_mns128 | tp-axis | 26.8% | -58.3% | 58.3% | 30.7% | +| q235 A1 fixed-pd | e2e_p90_ms | tp4_ep1_mns128 → tp8_ep8_mns128 | tp-axis | 29.7% | -59.2% | 59.2% | 34.6% | +| q235 A1 trace-pd | e2e_p90_ms | tp8_ep8_mns64 → tp8_ep8_mns128 | mns-axis | 6.0% | -17.4% | 17.4% | 6.2% | + +### (b) Where do TP-axis and MNS-axis inversions and boundary ties concentrate? + +`n_inv_*` counts sum strict `(real winner, c)` pair inversions across all objectives in each case; a row can contain more than one inverted pair. `n_tie_*` counts cover exact simulator ties only for the simulator-selected winner versus the real winner. Ties remain decision-boundary winner-label mismatches, not strict reversals. + +| case | n_inv_tp | n_inv_mns | n_inv_mixed | n_tie_tp | n_tie_mns | n_tie_mixed | +|---|---:|---:|---:|---:|---:|---:| +| q30 trace-pd | 0 | 0 | 0 | 0 | 0 | 0 | +| q30 fixed-pd high | 7 | 3 | 6 | 0 | 0 | 0 | +| q30 fixed-po high | 0 | 0 | 0 | 0 | 4 | 0 | +| q30 fixed-pd low | 8 | 0 | 24 | 0 | 0 | 0 | +| q30 fixed-po low | 0 | 0 | 0 | 0 | 4 | 0 | +| q30 trace-po low | 0 | 6 | 0 | 0 | 2 | 0 | +| q235 A0 fixed-pd | 5 | 0 | 5 | 0 | 1 | 0 | +| q235 A0 fixed-po | 4 | 0 | 4 | 0 | 0 | 0 | +| q235 A0 trace-pd | 0 | 2 | 0 | 0 | 0 | 0 | +| q235 A0 trace-po | 4 | 2 | 4 | 0 | 0 | 0 | +| q235 A1 fixed-pd | 4 | 0 | 4 | 0 | 1 | 0 | +| q235 A1 fixed-po | 0 | 0 | 0 | 0 | 0 | 0 | +| q235 A1 trace-pd | 0 | 2 | 0 | 0 | 0 | 0 | +| q235 A1 trace-po | 0 | 2 | 0 | 0 | 2 | 0 | +| **total** | **32** | **17** | **47** | **0** | **14** | **0** | + +- Among strict inversions, TP-axis cases are most concentrated in: q30 fixed-pd low (8), q30 fixed-pd high (7), q235 A0 fixed-pd (5), q235 A0 fixed-po (4), q235 A0 trace-po (4). +- Among strict inversions, MNS-axis cases are most concentrated in: q30 trace-po low (6), q30 fixed-pd high (3), q235 A0 trace-pd (2), q235 A0 trace-po (2), q235 A1 trace-pd (2). +- The eight Q235 A0/A1 Fixed-PD failures are all tp-axis. This does not conflict with the A0/MNS64 state-drift diagnosis, but does not constitute additional state-drift evidence for A1/MNS128. +- Q30 admission evidence does not map one-to-one to an MNS-axis deciding pair: high-pressure Fixed-PD TTFT mean is **mixed** and p90 is **tp-axis**. This is an axis/mechanism mismatch, not a direct causal contradiction: changing TP changes offered arrival pressure, while the diagnosed nonlinear event is crossing the MNS admission cap. It rules out the simpler claim that an MNS-threshold mechanism must appear as an mns-axis winner pair. +- All 14 exact simulator winner ties are mns-axis. Including these boundary errors gives MNS **17 + 14 = 31**, comparable to TP's **32** strict inversions. + +The exact ties below show that Frontier is completely insensitive to MNS changes within the corresponding case/objective cell: + +| case | objective | real winner ↔ simulator-selected tied winner (MNS pair) | +|---|---|---| +| q30 fixed-po high | ttft_mean_ms | tp4_mns32 ↔ tp4_mns16 | +| q30 fixed-po high | ttft_p90_ms | tp4_mns32 ↔ tp4_mns16 | +| q30 fixed-po high | e2e_mean_ms | tp4_mns32 ↔ tp4_mns16 | +| q30 fixed-po high | e2e_p90_ms | tp4_mns32 ↔ tp4_mns16 | +| q30 fixed-po low | ttft_mean_ms | tp4_mns8 ↔ tp4_mns16 | +| q30 fixed-po low | ttft_p90_ms | tp4_mns8 ↔ tp4_mns16 | +| q30 fixed-po low | e2e_mean_ms | tp4_mns8 ↔ tp4_mns16 | +| q30 fixed-po low | e2e_p90_ms | tp4_mns8 ↔ tp4_mns16 | +| q30 trace-po low | ttft_p90_ms | tp4_mns8 ↔ tp4_mns16 | +| q30 trace-po low | e2e_p90_ms | tp4_mns8 ↔ tp4_mns16 | +| q235 A0 fixed-pd | ttft_p90_ms | tp8_ep8_mns64 ↔ tp8_ep8_mns128 | +| q235 A1 fixed-pd | ttft_p90_ms | tp8_ep8_mns64 ↔ tp8_ep8_mns128 | +| q235 A1 trace-po | ttft_p90_ms | tp8_ep8_mns64 ↔ tp8_ep8_mns128 | +| q235 A1 trace-po | e2e_p90_ms | tp8_ep8_mns64 ↔ tp8_ep8_mns128 | + +### (c) How many exact-winner successes are margin-robust? + +Among exact winner matches, **17** are margin-robust (`margin >= 1%`) and **6** are fragile (`margin < 1%`). Separately, 14 winner-label mismatches come from exact simulator ties and sit on the decision boundary rather than being strict inversions. + +| fragile-success case | objective | real margin | +|---|---|---:| +| q30 trace-pd | e2e_p90_ms | 0.1% | +| q235 A1 fixed-pd | ttft_mean_ms | 0.4% | +| q235 A1 fixed-po | ttft_mean_ms | 0.2% | +| q235 A1 fixed-po | ttft_p90_ms | 0.3% | +| q235 A1 fixed-po | e2e_mean_ms | 0.2% | +| q235 A1 fixed-po | e2e_p90_ms | 0.3% | + +## Cross-checks + +| check | observed | frozen expectation | result | +|---|---:|---:|---| +| q30 high-pressure fixed-pd TTFT mean regret | 58.0% | 58.0% | PASS | +| q235 A0 fixed-pd TPOT mean regret | 33.0% | 33.0% | PASS | +| q235 A1 fixed-pd TPOT mean regret | 33.0% | 33.0% | PASS | +| q30 trace-pd all-objective regret | 0.0%, 0.0%, 0.0%, 0.0%, 0.0%, 0.0% | 0.0%, 0.0%, 0.0%, 0.0%, 0.0%, 0.0% | PASS | +| q235 Trace-PO TTFT p90 A0 to A1 regret | 21.2%, 0.3% | 21.2%, 0.3% | PASS | +| q235 Fixed-PD TPOT/E2E regrets unchanged from A0 to A1 | 33.0%, 37.2%, 30.7%, 34.6%, 33.0%, 37.2%, 30.7%, 34.6% | 33.0%, 37.2%, 30.7%, 34.6%, 33.0%, 37.2%, 30.7%, 34.6% | PASS | + +## Data gaps + +No per-config sim/real pairing gaps were found in the requested surfaces. + +## 异常 + +- 无。重算 winner/regret 与 frozen JSON 一致，硬性交叉核对均通过。 + +## Figure + +`margin-vs-residual.png` uses x = `100 × log1p(real best-vs-second relative margin)` and y = `100 ×` real-top-3 log-ratio spread. Both axes are logarithmic because the real margins span six orders of magnitude. Green means exact winner match; red means winner mismatch. The diagonal is the H-SCALE threshold, while failure itself is defined by regret, not by exact winner match. + +`decision-pair-axis.png` is a mechanism census, not a hypothesis test: for a strict winner reversal, `-delta_s > m_s` is algebraically equivalent to the mismatch. The frozen surfaces also contain deterministic winner-label mismatches caused by exact simulator ties; those points lie on `-delta_s = m_s` and are not counted as strict inversions. Color identifies the winner-deciding config axis and marker area is proportional to regret. + +## A0 vs A1 对照 + +A1 repairs the Q235 Trace-PO p90 ranking error but leaves the four large Fixed-PD TPOT/E2E regrets unchanged. + +| case / objective | A0 regret | A1 regret | +|---|---:|---:| +| trace-po / ttft_p90_ms | 21.2% | 0.3% | +| fixed-pd / tpot_mean_ms | 33.0% | 33.0% | +| fixed-pd / tpot_p90_ms | 37.2% | 37.2% | +| fixed-pd / e2e_mean_ms | 30.7% | 30.7% | +| fixed-pd / e2e_p90_ms | 34.6% | 34.6% | + +Thus Trace-PO TTFT p90 changes **21.2% → 0.3%**, whereas Fixed-PD remains **33.0% / 37.2% / 30.7% / 34.6%** in both A0 and A1. + +## 已知分量归因摘要 + +### Q30 high-pressure Fixed-PD TTFT: first-scheduling wait vs execution-side TTFT + +`queue-free TTFT` is the execution-side TTFT after removing first-scheduling wait; in the frozen diagnostic, simulated TTFT is their sum (up to floating-point rounding). + +| config | sim TPOT / real | sim slots / MNS | real slots upper / MNS | first-scheduling wait | queue-free TTFT | sim TTFT | real TTFT | real waiting max | +|---|---:|---:|---:|---:|---:|---:|---:|---:| +| tp2_mns64 | 8.09x | 58.0/64 | 7.5/64 | 38.2 ms | 191.0 ms | 229.3 ms | 194.6 ms | 0 | +| tp4_mns16 | 3.83x | 54.5/16 | 14.6/16 | 64906.3 ms | 134.7 ms | 65041.0 ms | 124.5 ms | 0 | +| tp4_mns32 | 4.67x | 65.9/32 | 14.5/32 | 27180.7 ms | 135.6 ms | 27316.3 ms | 123.2 ms | 0 | +| tp4_mns64 | 5.61x | 79.8/64 | 14.7/64 | 6367.3 ms | 136.7 ms | 6504.0 ms | 123.2 ms | 0 | + +For TP4/MNS32 minus TP2/MNS64, the observed simulator contrast is **+27087.0 ms**; removing first-scheduling wait changes it to **-55.4 ms**, aligned with the real contrast **-71.4 ms**. This localizes the Q30 high-pressure TTFT reversal to admission queueing, while the upstream service-time overprediction remains unattributed in the frozen data. + +### Q235 Fixed-PD: own-composition vs exact-state contrast + +Positive TP8−TP4 means TP8 is slower and therefore agrees with the real TP4 winner. + +| config | sim own decode batch mean | real exact decode batch mean | real token-weighted iteration | exact-state matched sim | coverage | +|---|---:|---:|---:|---:|---:| +| tp4_ep1_mns64 | 13.537 | 3.890 | 18.4710 ms | 34.2575 ms | 100.0% | +| tp8_ep8_mns64 | 18.938 | 10.921 | 27.5887 ms | 45.1603 ms | 100.0% | + +| component | own-composition TP8−TP4 | exact-state TP8−TP4 | +|---|---:|---:| +| total | -20.0663 ms | +10.9028 ms | +| moe_compute | -32.4544 ms | -1.9774 ms | +| attention | -7.4619 ms | -2.1304 ms | +| tp_dp_communication | -6.5711 ms | -6.4718 ms | +| ep_communication | +6.2354 ms | +6.5355 ms | +| moe_routing | -0.2862 ms | +0.1384 ms | +| dense_mlp_compute | -0.1231 ms | -0.0308 ms | +| pipeline_communication | +0.0000 ms | +0.0000 ms | +| runtime_overhead | +0.0000 ms | +0.0000 ms | + +The total contrast flips from **-20.0663 ms** on Frontier's own composition to **+10.9028 ms** at exact real state, a **+30.9690 ms** state-composition shift. The exact real iteration contrast is **+9.1177 ms** and the frozen real TPOT contrast is **+6.9500 ms**. 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