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Record code-trace canary fidelity gates

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This commit is contained in:
Gahow Wang
2026-07-24 02:42:29 +08:00
parent e1f2557a0c
commit e0811e95b4
8 changed files with 1526 additions and 26 deletions
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20
runs/frontier-code-trace-v0/README.md
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@@ -1,9 +1,9 @@
# Frontier code-trace campaign handoff
Phase A code prefill+decode 已开始执行。data/profile/max-length gate 和
Frontier rho calibration 已完成；TP2/TP4 paired 10min real canary 正在
运行。61min full paired inputs 与 code prefill-only calibration cache
均已物化，尚未越过 canary gate 启动正式 1h real matrix。
Phase A code prefill+decode 已进入 61min real matrix。data/profile、
max-length、Frontier rho calibration 和 TP2/TP4 paired canary 均已完成；
第一批 TP4 三个 load 与 TP2 low-rho diagnostic 正在 dash1–dash4 并行
运行。code prefill-only 的独立 sim calibration 也已完成。
完整设计与 gate 见 [`experiment-card.md`](experiment-card.md)。
@@ -16,11 +16,15 @@ Frontier rho calibration 已完成；TP2/TP4 paired 10min real canary 正在
- full paired inputs（CPFS）：
`runs/frontier-code-trace-v0/inputs/full-r0p{0002,0004,0008,0016}-v1/`；
- compact provenance：`results/calibration-summary.json`、
`results/prefill-only-calibration-summary.json`、
`results/canary-analysis-tp{2,4}-v*.json` 和
`results/paired-input-manifests/`；
- 下一步：完成 paired canary 分析；若 real 零失败、digest/hit ratio
对齐且 queue 不发散，启动 TP4
`rho={0.0002,0.0008,0.0016}` 与 TP2
`rho={0.0002,0.0004,0.0008}` 的 1h jobs。
- 当前 A4 wave 1：TP4 `rho={0.0002,0.0008,0.0016}` trial 1，以及
TP2 `rho=0.0002` trial 1 diagnostic；
- TP4 canary 的 TTFT/E2E、prefix hit 与 decode batch 通过；TP2
TTFT p90 低估 32.1%，因此 TP2 其余 cell 暂不扩展；
- prefill-only：TP2 已冻结 `rho={0.0004,0.0008,0.0016}`；
TP4 到 `0.0032` 仍亚临界，需追加更高 rho 后冻结 near-knee。
source trace 的远端位置是：

112
runs/frontier-code-trace-v0/analyze_canary.py
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@@ -16,7 +16,10 @@ from typing import Any
METRICS = ("ttft", "tpot", "e2e")
TPOT_MIN_OUTPUT_TOKENS = (2, 8, 32)
SLO_TARGET_PASS_RATE = 0.95
PROM_COUNTERS = ("vllm:prefix_cache_queries_total", "vllm:prefix_cache_hits_total")
csv.field_size_limit(16 * 1024 * 1024)
ITERATION_RE = re.compile(
r"Iteration.*?:\s+"
r"(?P<context_requests>\d+) context requests, "
@@ -165,8 +168,35 @@ def load_sim(root: Path, trace: list[dict[str, str]], trace_sha: str) -> dict[st
tail_index = max(range(len(completions)), key=completions.__getitem__)
last_arrival = max(float(row["arrived_at"]) for row in trace)
summary = json.loads((root / "summary.json").read_text())
slo_pass = []
for trace_row, metric_row in zip(trace, rows):
input_tokens = int(trace_row["num_prefill_tokens"])
ttft_threshold_ms = 1000 + 1000 * input_tokens / 8000
tpot = (
float(metric_row["tpot"])
if metric_row["tpot"].strip()
else None
)
slo_pass.append(
float(metric_row["ttft"]) <= ttft_threshold_ms
and (tpot is None or tpot <= 150)
)
return {
"values": values,
"tpot_by_min_output_tokens": {
str(threshold): [
float(metric_row["tpot"])
for trace_row, metric_row in zip(trace, rows)
if int(trace_row["num_decode_tokens"]) >= threshold
and metric_row["tpot"].strip()
]
for threshold in TPOT_MIN_OUTPUT_TOKENS
},
"slo": {
"passed": sum(slo_pass),
"pass_rate": sum(slo_pass) / len(slo_pass),
"feasible": sum(slo_pass) / len(slo_pass) >= SLO_TARGET_PASS_RATE,
},
"summary": summary,
"drain": {
"last_arrival_s": last_arrival,
@@ -224,6 +254,23 @@ def load_real(
"root": str(root),
"result_sha256": sha256(result_path),
"values": values,
"tpot_by_min_output_tokens": {
str(threshold): [
float(request["tpot_ms"])
for request in requests
if int(request["requested_output_tokens"]) >= threshold
and request.get("tpot_ms") is not None
]
for threshold in TPOT_MIN_OUTPUT_TOKENS
},
"slo": {
"passed": sum(bool(request["slo_pass"]) for request in requests),
"pass_rate": sum(bool(request["slo_pass"]) for request in requests)
/ len(requests),
"feasible": sum(bool(request["slo_pass"]) for request in requests)
/ len(requests)
>= SLO_TARGET_PASS_RATE,
},
"summary": result["summary"],
"prefix_cache": prefix_cache_delta(root),
"decode_batch": real_decode_batch(root),
@@ -262,19 +309,60 @@ def main() -> None:
latency = {}
for metric in METRICS:
real_dist = distribution(pooled[metric])
real_per_trial = [
distribution(real["values"][metric]) for real in reals
]
real_reference = {
statistic: statistics.fmean(
float(trial[statistic]) for trial in real_per_trial
)
for statistic in ("mean", "p50", "p90", "p95", "p99")
}
sim_dist = distribution(sim["values"][metric])
latency[metric] = {
"real": real_dist,
"real_trial_statistic_mean": real_reference,
"sim": sim_dist,
"relative_bias_percent": {
statistic: 100
* (float(sim_dist[statistic]) - float(real_dist[statistic]))
/ float(real_dist[statistic])
* (float(sim_dist[statistic]) - real_reference[statistic])
/ real_reference[statistic]
for statistic in ("mean", "p50", "p90", "p95", "p99")
},
"real_per_trial": real_per_trial,
}
tpot_sensitivity = {}
for threshold in TPOT_MIN_OUTPUT_TOKENS:
key = str(threshold)
real_per_trial = [
distribution(real["tpot_by_min_output_tokens"][key])
for real in reals
]
real_values = [
value
for real in reals
for value in real["tpot_by_min_output_tokens"][key]
]
sim_values = sim["tpot_by_min_output_tokens"][key]
real_dist = distribution(real_values)
real_reference = {
statistic: statistics.fmean(
float(trial[statistic]) for trial in real_per_trial
)
for statistic in ("mean", "p50", "p90", "p95", "p99")
}
sim_dist = distribution(sim_values)
tpot_sensitivity[key] = {
"real": real_dist,
"real_trial_statistic_mean": real_reference,
"real_per_trial": real_per_trial,
"sim": sim_dist,
"relative_bias_percent": {
statistic: 100
* (float(sim_dist[statistic]) - real_reference[statistic])
/ real_reference[statistic]
for statistic in ("mean", "p50", "p90", "p95", "p99")
},
"real_per_trial": [
distribution(real["values"][metric]) for real in reals
],
}
payload = {
"schema": "frontier-code-trace-canary-analysis-v1",
@@ -287,6 +375,20 @@ def main() -> None:
"frontier_csv_sha256": trace_sha,
},
"latency_ms": latency,
"tpot_by_min_output_tokens": tpot_sensitivity,
"slo": {
"definition": {
"ttft_ms": "1000 + 1000 * input_tokens / 8000",
"tpot_ms": 150,
"target_pass_rate": SLO_TARGET_PASS_RATE,
},
"real_per_trial": [real["slo"] for real in reals],
"sim": sim["slo"],
"feasibility_flip": any(
real["slo"]["feasible"] != sim["slo"]["feasible"]
for real in reals
),
},
"prefix_cache": {
"real_per_trial": [real["prefix_cache"] for real in reals],
"real_hit_ratio_mean": statistics.fmean(

56
runs/frontier-code-trace-v0/experiment-card.md
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@@ -1,8 +1,8 @@
# EXP-CODE-TRACE：从 chat 1h trace 扩展到 code 与 phase-separated replay
> **状态：RUNNING（Phase A code P+D）。** A0 数据/profile 与 A1 TP4
> max-length smoke 已完成；A2 paired canary 正在运行；A3 sim calibration
> 已冻结 topology-specific rho。只使用 `dash1`--`dash4`，禁止使用 `dash0`。
> **状态：RUNNING（Phase A code P+D）。** A0 数据/profile、A1
> max-length smoke、A2 paired canary 与 A3 sim calibration 已完成；
> A4 第一批 61min real jobs 正在 `dash1`--`dash4` 运行。禁止使用 `dash0`。
## 目标与成功定义
@@ -17,10 +17,10 @@
| 项目 | 当前结论 | 下一 gate |
|---|---|---|
| code P+D 数据 | 61min development window、long-context profile-v6、四个 paired full 输入已冻结 | paired canary real-vs-sim |
| TP4 负载 | low/mid/near-knee=`rho 0.0002/0.0008/0.0016`；三点均亚临界 | canary 通过后启动 1h |
| TP2 负载 | low/mid/near-knee=`rho 0.0002/0.0004/0.0008`；`0.0016` 明确过载 | TP2 canary/KV gate |
| code prefill-only | `rho<=0.0032` 的 paired remap cache 已生成，OSL 全为 1 | 独立 sim rho calibration |
| code P+D 数据 | 61min development window、long-context profile-v6、四个 paired full 输入已冻结 | 第一批 1h real 运行中 |
| TP4 负载 | low/mid/near-knee=`rho 0.0002/0.0008/0.0016`；paired canary 的 TTFT/E2E、KV、batch 通过 | 三个 load 的 trial 1 运行中 |
| TP2 负载 | `rho 0.0004` canary 暴露 TTFT p90 `-32.1%` bad case | 只跑 `rho 0.0002` 1h diagnostic，暂不铺满 |
| code prefill-only | TP2 已冻结 `rho 0.0004/0.0008/0.0016`，`0.0032` 过载；TP4 到 `0.0032` 仍亚临界 | TP4 追加更高 rho 边界 |
| strict decode-only | vLLM 0.20.0 有 `DecodeBenchConnector`；Frontier trace generator 尚不能注入 initial computed tokens | C0 contract canary，未进入正式结果 |
## 三种 workload mode 的冻结定义
@@ -173,9 +173,10 @@ tail driver 分解共同决定，不能只用一个 drain 秒数。
若某 topology 的 near-knee 过载，像现有 chat TP2/ρ0.01 一样排除，不为凑齐矩阵强跑。
当前状态：A0 完成；A1 的 TP4 完成、TP2 由 paired canary 同时验证；
A2 运行中；A3 完成；A4 的 paired input 已物化但尚未在 canary gate
前启动。
当前状态：A0–A3 完成。A4 第一批为 TP4
`rho={0.0002,0.0008,0.0016}` trial 1，以及 TP2 `rho=0.0002`
trial 1 diagnostic；其余 TP2 cell 等该 diagnostic 验证 canary bad case
后再决定是否扩展。
### Phase B：chat/code prefill-only
@@ -308,3 +309,38 @@ mode-specific：
FlashInfer JIT 的 apparatus cost。它发生在 readiness 前，不进入 TTFT；
runner commit `d5bb974` 改为长上下文默认使用 host-local vLLM cache，
并按 topology 复用 FlashInfer workspace。
- 第一轮 paired real canary 的 3 次旧 client 运行都只在同一个
`106709+197` 请求失败，根因是 `return_token_ids` 把 100k+ prompt
vector 放进单条 SSE event，超过 aiohttp 默认 512KiB line limit。
commit `e1f2557` 把 exact client read buffer 提到 8MiB；700KiB
单-event runtime 对照和随后 TP4×2、TP2×1 的 53/53 replay 均通过。
- TP4 canary 的 real-vs-sim prefix hit ratio =
`0.239908/0.239973`；pure-decode batch max 都为 4，
`share(b>1)=15.87%/15.69%`（real 两 trial）vs `16.35%`（sim）。
TTFT mean/p50/p90/p95/p99 bias =
`-8.3/-4.1/-8.8/-13.1/-6.3%`，E2E =
`+2.2/+6.1/+11.0/+1.4/-1.3%`。长 drain 的同一
`61976+21361` 请求 real=92.61/92.34s、sim=91.37s，不是 backlog。
- TP4 若把 OSL=4 请求纳入 TPOT，mean/p99 bias 会被单个
`~213ms/token` 样本放大到 `-29.9%/-71.5%`；OSL≥8 后
mean/p50/p90/p95/p99 bias =
`+8.7/+8.2/+0.6/+13.4/+3.7%`。因此 raw TPOT 仍保留，但正式报告必须
同时给 OSL threshold sensitivity，不能把短输出的三段 inter-token
interval 当作稳定 decode service。
- TP4 canary 两 trial 的 real SLO pass rate 都是 `50/53=94.34%`，
sim 为 `52/53=98.11%`，在 95% feasibility threshold 上发生翻转；
这由两个临界 TTFT 请求和上述 OSL=4 请求共同造成，作为明确 bad case
进入 1h 检验，不能被总体 latency gap 掩盖。
- TP2 canary 的 cache/batch/drain 仍对齐，但 TTFT p90 bias=`-32.1%`，
E2E p90/p95=`-18.4%/-23.8%`。因此先只启动 low-rho 1h diagnostic，
不直接铺满 TP2 六个正式 jobs。
- code prefill-only 已完成 10-cell Frontier calibration。TP2
`rho=0.0032` drain=1384.59s、waiting p50=751.01s，明确过载；
`0.0004/0.0008/0.0016` 冻结为 low/mid/near-knee。TP4 到
`rho=0.0032` 仍只有 9.09s drain，暂称 highest-tested，追加更高 rho
后才冻结 near-knee。compact table 在
`results/prefill-only-calibration-summary.json`。
- 2026-07-23 18:37 UTC 启动 A4 wave 1：dash1=`TP4/rho0.0002/t1`、
dash3=`TP4/rho0.0008/t1`、dash4=`TP4/rho0.0016/t1`、
dash2=`TP2/rho0.0002/t1 diagnostic`；四台启动前再次确认 8×H20
memory/utilization=0、无 compute process、uncorrected ECC=0。

4
runs/frontier-code-trace-v0/results/calibration-summary.json
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@@ -1,5 +1,6 @@
{
"attention_profile_sha256": "fbcf7e1f95789a6f6d771e24d1fc60958b7daf04eb0db260d27869a19d71d550",
"cell_version": "v3",
"cells": [
{
"decode_batch": {
@@ -628,5 +629,6 @@
}
],
"schema": "frontier-code-trace-calibration-summary-v1",
"subcritical_rule": "drain tail <= 10% of the 3660s arrival window"
"subcritical_rule": "drain tail <= 10% of the 3660s arrival window",
"workload_mode": "prefill_decode"
}

319
runs/frontier-code-trace-v0/results/canary-analysis-tp2-v1.json Normal file
View File

@@ -0,0 +1,319 @@
{
"drain": {
"interpretation": "Report the max-completion request explicitly; a response that arrived well before the cutoff can create a long drain tail without implying queue accumulation.",
"real_per_trial": [
{
"last_arrival_s": 597.0069999999832,
"last_completion_s": 714.3421455998905,
"tail_after_last_arrival_s": 117.33514559990726,
"tail_driver": {
"admission_lag_ms": 12.391218915581703,
"arrival_before_cutoff_s": 62.03099999995902,
"arrival_s": 534.9760000000242,
"e2e_ms": 179353.7543809507,
"input_tokens": 61976,
"output_tokens": 21361,
"request_index": 40
}
}
],
"sim": {
"last_arrival_s": 597.007,
"last_completion_s": 713.4565521329489,
"tail_after_last_arrival_s": 116.44955213294895,
"tail_driver": {
"arrival_before_cutoff_s": 62.03099999999995,
"arrival_s": 534.976,
"e2e_ms": 178480.5521329489,
"input_tokens": 61976,
"output_tokens": 21361,
"request_index": 40,
"waiting_ms": 0.0
}
}
},
"input": {
"frontier_csv_sha256": "624bc983d3c9fba58cbfa3bb5126304b99b19883da4c78f63cd8622b856dbc1f",
"manifest": "runs/frontier-code-trace-v0/results/canary-input-r0p0004-v1/manifest.json",
"paired_row_vector_sha256": "72b09903851f5684838da23498a068efc409e6481d1e84efeeefddd8e431c3e7"
},
"latency_ms": {
"e2e": {
"real": {
"count": 53,
"max": 179353.7543809507,
"mean": 15936.508002247274,
"p50": 3162.7415760885924,
"p90": 31124.16882226244,
"p95": 62989.56617997021,
"p99": 165236.24419869846
},
"real_per_trial": [
{
"count": 53,
"max": 179353.7543809507,
"mean": 15936.508002247274,
"p50": 3162.7415760885924,
"p90": 31124.16882226244,
"p95": 62989.56617997021,
"p99": 165236.24419869846
}
],
"relative_bias_percent": {
"mean": -8.269543527339733,
"p50": -8.321761801888824,
"p90": -18.368943324171262,
"p95": -23.79834859601531,
"p99": -2.75020118151507
},
"sim": {
"count": 53,
"max": 178480.5521329489,
"mean": 14618.631536263456,
"p50": 2899.5457557171953,
"p90": 25406.98789118167,
"p95": 47999.08964134313,
"p99": 160691.91505845473
}
},
"tpot": {
"real": {
"count": 53,
"max": 641.5267530052612,
"mean": 27.41422879865018,
"p50": 8.106854443332919,
"p90": 23.880988038291846,
"p95": 92.03784805296183,
"p99": 397.1900480028069
},
"real_per_trial": [
{
"count": 53,
"max": 641.5267530052612,
"mean": 27.41422879865018,
"p50": 8.106854443332919,
"p90": 23.880988038291846,
"p95": 92.03784805296183,
"p99": 397.1900480028069
}
],
"relative_bias_percent": {
"mean": -9.878641617364694,
"p50": -2.3180300975258694,
"p90": 26.54184469239794,
"p95": -24.72214442719661,
"p99": -13.722225303935645
},
"sim": {
"count": 53,
"max": 556.599034356792,
"mean": 24.706075383467144,
"p50": 7.918935117373849,
"p90": 30.219442794425397,
"p95": 69.28411832962485,
"p99": 342.6867347310516
}
},
"ttft": {
"real": {
"count": 53,
"max": 23546.024559997022,
"mean": 3438.4982450513767,
"p50": 918.7854011543095,
"p90": 10432.362293498598,
"p95": 13642.840061988678,
"p99": 22228.129854062565
},
"real_per_trial": [
{
"count": 53,
"max": 23546.024559997022,
"mean": 3438.4982450513767,
"p50": 918.7854011543095,
"p90": 10432.362293498598,
"p95": 13642.840061988678,
"p99": 22228.129854062565
}
],
"relative_bias_percent": {
"mean": -21.116133763518285,
"p50": 13.780393777591794,
"p90": -32.09252788469729,
"p95": -13.833140262007847,
"p99": -14.006169043239403
},
"sim": {
"count": 53,
"max": 19564.258977976864,
"mean": 2712.4203561700992,
"p50": 1045.3976474043998,
"p90": 7084.353515424915,
"p95": 11755.606860492386,
"p99": 19114.8204115518
}
}
},
"prefix_cache": {
"real_hit_ratio_mean": 0.2399084848137731,
"real_per_trial": [
{
"hit_ratio": 0.2399084848137731,
"hit_tokens": 309968.0,
"query_tokens": 1292026.0
}
],
"sim": {
"block_size_tokens": 16,
"hit_ratio": 0.23997274866840085,
"mean_cached_prefill_tokens": 5848.452830188679,
"requests": 53,
"requests_with_hits": 9,
"total_cached_prefill_tokens": 309968,
"total_hit_blocks": 19373,
"total_query_blocks": 80730
}
},
"real_artifacts": [
{
"result_sha256": "54ebf30bb66fd845d0d6bbdb15674784989070ec12aa8b785f1dd19886707269",
"root": "runs/frontier-code-trace-v0/results/real-canary/r0p0004-tp2-t1-v3"
}
],
"real_decode_batch_per_trial": [
{
"histogram": {
"1": 53281,
"2": 5701,
"3": 2919,
"4": 476,
"5": 110,
"6": 62
},
"max": 6,
"mixed_steps_excluded": 152,
"share_gt_1": 0.14817183328270636,
"steps": 62549
}
],
"requests_per_trial": 53,
"schema": "frontier-code-trace-canary-analysis-v1",
"sim_decode_batch": {
"histogram": {
"1": 53233,
"2": 6466,
"3": 2626,
"4": 395,
"5": 103,
"6": 29
},
"max": 6,
"share_gt_1": 0.1530420670782155,
"share_gt_4": 0.0021001718322408197,
"stages": 62852
},
"slo": {
"definition": {
"target_pass_rate": 0.95,
"tpot_ms": 150,
"ttft_ms": "1000 + 1000 * input_tokens / 8000"
},
"feasibility_flip": false,
"real_per_trial": [
{
"feasible": false,
"pass_rate": 0.7735849056603774,
"passed": 41
}
],
"sim": {
"feasible": false,
"pass_rate": 0.8679245283018868,
"passed": 46
}
},
"topology": "tp2_mns16",
"tpot_by_min_output_tokens": {
"2": {
"real": {
"count": 53,
"max": 641.5267530052612,
"mean": 27.41422879865018,
"p50": 8.106854443332919,
"p90": 23.880988038291846,
"p95": 92.03784805296183,
"p99": 397.1900480028069
},
"relative_bias_percent": {
"mean": -9.878641617364694,
"p50": -2.3180300975258694,
"p90": 26.54184469239794,
"p95": -24.72214442719661,
"p99": -13.722225303935645
},
"sim": {
"count": 53,
"max": 556.599034356792,
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}

18
runs/frontier-code-trace-v0/summarize_calibration.py
View File

@@ -10,7 +10,9 @@ import re
from pathlib import Path
CELL_RE = re.compile(r"r(?P<rho>[0-9p]+)-tp(?P<tp>[24])-v3")
CELL_RE = re.compile(
r"r(?P<rho>[0-9p]+)-tp(?P<tp>[24])-(?P<version>v[0-9]+)"
)
ATTENTION_FLAG = "--random_forrest_execution_time_predictor_config_atten_input_file"
@@ -18,6 +20,12 @@ def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument("--sim-root", type=Path, required=True)
parser.add_argument("--profile-manifest", type=Path, required=True)
parser.add_argument(
"--workload-mode",
choices=("prefill_decode", "prefill_only"),
default="prefill_decode",
)
parser.add_argument("--cell-version", default="v3")
parser.add_argument("--output", type=Path, required=True)
return parser.parse_args()
@@ -37,7 +45,11 @@ def main() -> None:
cells = []
for root in sorted(args.sim_root.iterdir()):
match = CELL_RE.fullmatch(root.name)
if match is None or not (root / "summary.json").is_file():
if (
match is None
or match.group("version") != args.cell_version
or not (root / "summary.json").is_file()
):
continue
manifest = json.loads((root / "manifest.json").read_text())
observed_profile_sha = manifest.get("attention_profile_sha256")
@@ -75,6 +87,8 @@ def main() -> None:
raise ValueError(f"no completed v3 calibration cells below {args.sim_root}")
payload = {
"schema": "frontier-code-trace-calibration-summary-v1",
"workload_mode": args.workload_mode,
"cell_version": args.cell_version,
"attention_profile_sha256": expected_profile_sha,
"subcritical_rule": "drain tail <= 10% of the 3660s arrival window",
"cells": cells,