Record code-trace calibration and canary gates
This commit is contained in:
@@ -1,18 +1,34 @@
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# Frontier code-trace campaign handoff
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# Frontier code-trace campaign handoff
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本目录已经准备好无 GPU 的 data preflight、512→16 参数化映射、prefill-only 转换和 `max_model_len` 显式适配。当前没有启动或探测 `dash1`–`dash4`。
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Phase A code prefill+decode 已开始执行。data/profile/max-length gate 和
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Frontier rho calibration 已完成；TP2/TP4 paired 10min real canary 正在
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运行。61min full paired inputs 与 code prefill-only calibration cache
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均已物化，尚未越过 canary gate 启动正式 1h real matrix。
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完整设计与 gate 见 [`experiment-card.md`](experiment-card.md)。
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完整设计与 gate 见 [`experiment-card.md`](experiment-card.md)。
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## 当前已知阻塞
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## 当前资产与下一步
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本机 `/home/gahow/ali-trace/trace-glm5.1-formatted/` 不存在。仓库历史记录的远端路径是：
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- development window：0513 `[3480,7140)`，61min；
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- held-out window：0529 `[2640,6240)`，只在 development 判据冻结后使用；
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- profile：`profiles/profile-v6-code-longctx/`，覆盖 TP1/2/4 和 131072
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KV context；
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- full paired inputs（CPFS）：
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`runs/frontier-code-trace-v0/inputs/full-r0p{0002,0004,0008,0016}-v1/`；
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- compact provenance：`results/calibration-summary.json`、
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`results/paired-input-manifests/`；
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- 下一步：完成 paired canary 分析；若 real 零失败、digest/hit ratio
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对齐且 queue 不发散，启动 TP4
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`rho={0.0002,0.0008,0.0016}` 与 TP2
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`rho={0.0002,0.0004,0.0008}` 的 1h jobs。
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source trace 的远端位置是：
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```text
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```text
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/home/admin/cpfs/wjh/ali-trace/trace-glm5.1-formatted/
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/home/admin/cpfs/wjh/ali-trace/trace-glm5.1-formatted/
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```
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```
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有机器后先确认用户给出的 `~/ali-trace/...` 是否解析到同一目录，再运行以下命令。
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以下命令保留为从 source 重新构建时的复现入口。
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## 1. 审计所有 1h+ code source
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## 1. 审计所有 1h+ code source
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@@ -103,11 +119,21 @@ bash runs/frontier-s3-real-v0/run_full_real.sh RHO_LABEL tp4_mns16 1 PORT
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- synthetic prompt 默认拒绝，只有在 experiment card 明确降级 claim 后才设为 `true`；
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- synthetic prompt 默认拒绝，只有在 experiment card 明确降级 claim 后才设为 `true`；
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- runner 会在启动前扫描 paired requests，若任何 `ISL+OSL` 超 cap 立即失败。
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- runner 会在启动前扫描 paired requests，若任何 `ISL+OSL` 超 cap 立即失败。
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正式 full job 前，先按 experiment card 的 G4 补 32k–128k attention profile，再做 TP4→TP2 的 p50/p99/max 单请求与 5min canary。
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长上下文 server 必须同时设置
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`VLLM_ALLOW_LONG_MAX_MODEL_LEN=1` 和
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`--hf-overrides '{"max_position_embeddings":MAX_MODEL_LEN}'`；runner 已在
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`ALLOW_LONG_CONTEXT_SERVER=true` 时自动处理。长上下文默认使用 host-local
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vLLM compile cache，并按 topology 复用 FlashInfer workspace；启动 compile
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不进入 workload latency。
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## 6. decode-only
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## 6. decode-only
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当前 materializer 故意不提供 `decode_only` 选项。严格 decode-only 需要 initial-KV state，而不是把 prompt 改短。只有 real `DecodeBenchConnector`（或等价能力）与 Frontier initial-KV contract 都通过 G7 后，才创建 decode-only jobs。
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当前 materializer 故意不提供 `decode_only` 选项。已安装 vLLM 0.20.0
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包含 `DecodeBenchConnector`，但它在首次 admission 后同步填 dummy KV；
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fill time 必须与 KV-ready arrival 分离。Frontier `Request` 支持
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`num_processed_tokens`，当前 trace generator 尚未从 CSV 注入该值。
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只有 real 首步无 prefill、sim ledger 首步为 decode 的 C0 gate 通过后，
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才创建 strict decode-only jobs。
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## 本地验证
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## 本地验证
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322
runs/frontier-code-trace-v0/analyze_canary.py
Normal file
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runs/frontier-code-trace-v0/analyze_canary.py
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@@ -0,0 +1,322 @@
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#!/usr/bin/env python3
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"""Analyze one paired 10-minute code-trace real/sim canary topology."""
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from __future__ import annotations
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import argparse
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import csv
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import hashlib
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import json
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import math
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import re
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import statistics
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from collections import Counter
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from pathlib import Path
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from typing import Any
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METRICS = ("ttft", "tpot", "e2e")
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PROM_COUNTERS = ("vllm:prefix_cache_queries_total", "vllm:prefix_cache_hits_total")
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ITERATION_RE = re.compile(
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r"Iteration.*?:\s+"
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r"(?P<context_requests>\d+) context requests, "
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r"(?P<context_tokens>\d+) context tokens, "
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r"(?P<generation_requests>\d+) generation requests, "
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r"(?P<generation_tokens>\d+) generation tokens"
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)
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def parse_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser()
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parser.add_argument("--input-root", type=Path, required=True)
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parser.add_argument("--sim-root", type=Path, required=True)
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parser.add_argument("--real-root", type=Path, action="append", required=True)
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parser.add_argument("--topology", required=True)
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parser.add_argument("--output", type=Path, required=True)
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return parser.parse_args()
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def sha256(path: Path) -> str:
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digest = hashlib.sha256()
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with path.open("rb") as stream:
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for chunk in iter(lambda: stream.read(1024 * 1024), b""):
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digest.update(chunk)
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return digest.hexdigest()
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def percentile(values: list[float], q: float) -> float:
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ordered = sorted(values)
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position = (len(ordered) - 1) * q
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lower = math.floor(position)
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upper = math.ceil(position)
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if lower == upper:
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return ordered[lower]
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return ordered[lower] * (upper - position) + ordered[upper] * (position - lower)
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def distribution(values: list[float]) -> dict[str, float | int]:
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if not values:
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raise ValueError("empty distribution")
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return {
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"count": len(values),
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"mean": statistics.fmean(values),
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"p50": percentile(values, 0.5),
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"p90": percentile(values, 0.9),
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"p95": percentile(values, 0.95),
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"p99": percentile(values, 0.99),
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"max": max(values),
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}
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def read_csv(path: Path) -> list[dict[str, str]]:
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with path.open(newline="") as stream:
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return list(csv.DictReader(stream))
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def find_one(root: Path, name: str) -> Path:
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matches = list(root.glob(f"**/{name}"))
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if len(matches) != 1:
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raise ValueError(f"expected one {name} below {root}, found {matches}")
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return matches[0]
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def prom_counter(path: Path, name: str) -> float:
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values = []
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with path.open() as stream:
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for line in stream:
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if line.startswith(name + "{") or line.startswith(name + " "):
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values.append(float(line.rsplit(maxsplit=1)[1]))
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if not values:
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raise ValueError(f"{path}: missing Prometheus counter {name}")
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return sum(values)
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def prefix_cache_delta(root: Path) -> dict[str, float]:
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before = root / "metrics/before.prom"
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after = root / "metrics/after.prom"
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deltas = {
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name: prom_counter(after, name) - prom_counter(before, name)
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for name in PROM_COUNTERS
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}
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queries = deltas[PROM_COUNTERS[0]]
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hits = deltas[PROM_COUNTERS[1]]
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if queries <= 0 or hits < 0 or hits > queries:
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raise ValueError(f"{root}: invalid prefix counter deltas {deltas}")
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return {
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"query_tokens": queries,
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"hit_tokens": hits,
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"hit_ratio": hits / queries,
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}
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def real_decode_batch(root: Path) -> dict[str, Any]:
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counts: Counter[int] = Counter()
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mixed_steps = 0
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for path in sorted(root.rglob("server.log")):
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with path.open(errors="replace") as stream:
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for line in stream:
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match = ITERATION_RE.search(line)
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if match is None:
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continue
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context_requests = int(match.group("context_requests"))
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generation_requests = int(match.group("generation_requests"))
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generation_tokens = int(match.group("generation_tokens"))
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if context_requests:
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mixed_steps += 1
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continue
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if generation_requests and generation_tokens == generation_requests:
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counts[generation_requests] += 1
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if not counts:
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return {
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"steps": 0,
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"mixed_steps_excluded": mixed_steps,
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"max": None,
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"share_gt_1": None,
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"histogram": {},
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}
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steps = sum(counts.values())
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return {
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"steps": steps,
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"mixed_steps_excluded": mixed_steps,
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"max": max(counts),
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"share_gt_1": sum(value for key, value in counts.items() if key > 1) / steps,
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"histogram": {str(key): value for key, value in sorted(counts.items())},
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}
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def load_sim(root: Path, trace: list[dict[str, str]], trace_sha: str) -> dict[str, Any]:
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manifest = json.loads((root / "manifest.json").read_text())
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if manifest["trace_sha256"] != trace_sha:
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raise ValueError(f"{root}: sim/input trace SHA mismatch")
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rows = read_csv(find_one(root / "metrics", "request_metrics.csv"))
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if len(rows) != len(trace):
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raise ValueError(f"{root}: sim/input request count mismatch")
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values = {
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"ttft": [float(row["ttft"]) for row in rows],
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"tpot": [float(row["tpot"]) for row in rows if row["tpot"].strip()],
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"e2e": [float(row["request_e2e_time"]) for row in rows],
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"waiting": [float(row["request_waiting_time_total"]) for row in rows],
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}
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completions = [
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float(trace_row["arrived_at"]) + float(metric_row["request_e2e_time"]) / 1000
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for trace_row, metric_row in zip(trace, rows)
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]
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tail_index = max(range(len(completions)), key=completions.__getitem__)
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last_arrival = max(float(row["arrived_at"]) for row in trace)
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summary = json.loads((root / "summary.json").read_text())
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return {
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"values": values,
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"summary": summary,
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"drain": {
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"last_arrival_s": last_arrival,
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"last_completion_s": completions[tail_index],
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"tail_after_last_arrival_s": completions[tail_index] - last_arrival,
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"tail_driver": {
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"request_index": tail_index,
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"arrival_s": float(trace[tail_index]["arrived_at"]),
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"arrival_before_cutoff_s": last_arrival
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- float(trace[tail_index]["arrived_at"]),
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"input_tokens": int(trace[tail_index]["num_prefill_tokens"]),
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"output_tokens": int(trace[tail_index]["num_decode_tokens"]),
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"waiting_ms": values["waiting"][tail_index],
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"e2e_ms": values["e2e"][tail_index],
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},
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},
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}
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def load_real(
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root: Path,
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input_manifest: dict[str, Any],
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trace: list[dict[str, str]],
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) -> dict[str, Any]:
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result_path = root / "results/result.json"
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result = json.loads(result_path.read_text())
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if result["contract"]["row_vector_sha256"] != input_manifest["paired_row_vector_sha256"]:
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raise ValueError(f"{root}: real/input row digest mismatch")
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requests = result["requests"]
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if len(requests) != len(trace) or not all(row["success"] for row in requests):
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raise ValueError(f"{root}: incomplete or failed real request vector")
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for index, (request, trace_row) in enumerate(zip(requests, trace)):
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observed = (int(request["input_tokens"]), int(request["requested_output_tokens"]))
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expected = (
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int(trace_row["num_prefill_tokens"]),
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int(trace_row["num_decode_tokens"]),
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)
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if observed != expected:
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raise ValueError(f"{root}: request {index} shape {observed} != {expected}")
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values = {
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metric: [
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float(request[f"{metric}_ms"])
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for request in requests
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if request.get(f"{metric}_ms") is not None
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]
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for metric in METRICS
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}
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completions = [
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float(request["admitted_s"]) + float(request["e2e_ms"]) / 1000
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for request in requests
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]
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tail_index = max(range(len(completions)), key=completions.__getitem__)
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last_arrival = max(float(request["scheduled_s"]) for request in requests)
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return {
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"root": str(root),
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"result_sha256": sha256(result_path),
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"values": values,
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"summary": result["summary"],
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"prefix_cache": prefix_cache_delta(root),
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"decode_batch": real_decode_batch(root),
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"drain": {
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"last_arrival_s": last_arrival,
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"last_completion_s": completions[tail_index],
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"tail_after_last_arrival_s": completions[tail_index] - last_arrival,
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"tail_driver": {
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"request_index": tail_index,
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"arrival_s": float(requests[tail_index]["scheduled_s"]),
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"arrival_before_cutoff_s": last_arrival
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- float(requests[tail_index]["scheduled_s"]),
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"input_tokens": int(requests[tail_index]["input_tokens"]),
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"output_tokens": int(requests[tail_index]["requested_output_tokens"]),
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"admission_lag_ms": float(requests[tail_index]["admission_lag_ms"]),
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"e2e_ms": float(requests[tail_index]["e2e_ms"]),
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},
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},
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}
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def main() -> None:
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args = parse_args()
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input_manifest = json.loads((args.input_root / "manifest.json").read_text())
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trace_path = args.input_root / "frontier.csv"
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trace = read_csv(trace_path)
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if len(trace) != input_manifest["requests"]:
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raise ValueError("input manifest/trace request count mismatch")
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trace_sha = sha256(trace_path)
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sim = load_sim(args.sim_root, trace, trace_sha)
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reals = [load_real(root, input_manifest, trace) for root in args.real_root]
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pooled = {
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metric: [value for real in reals for value in real["values"][metric]]
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for metric in METRICS
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}
|
||||||
|
latency = {}
|
||||||
|
for metric in METRICS:
|
||||||
|
real_dist = distribution(pooled[metric])
|
||||||
|
sim_dist = distribution(sim["values"][metric])
|
||||||
|
latency[metric] = {
|
||||||
|
"real": real_dist,
|
||||||
|
"sim": sim_dist,
|
||||||
|
"relative_bias_percent": {
|
||||||
|
statistic: 100
|
||||||
|
* (float(sim_dist[statistic]) - float(real_dist[statistic]))
|
||||||
|
/ float(real_dist[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",
|
||||||
|
"topology": args.topology,
|
||||||
|
"requests_per_trial": len(trace),
|
||||||
|
"trials": len(reals),
|
||||||
|
"input": {
|
||||||
|
"manifest": str(args.input_root / "manifest.json"),
|
||||||
|
"paired_row_vector_sha256": input_manifest["paired_row_vector_sha256"],
|
||||||
|
"frontier_csv_sha256": trace_sha,
|
||||||
|
},
|
||||||
|
"latency_ms": latency,
|
||||||
|
"prefix_cache": {
|
||||||
|
"real_per_trial": [real["prefix_cache"] for real in reals],
|
||||||
|
"real_hit_ratio_mean": statistics.fmean(
|
||||||
|
real["prefix_cache"]["hit_ratio"] for real in reals
|
||||||
|
),
|
||||||
|
"sim": sim["summary"]["prefix_cache"],
|
||||||
|
},
|
||||||
|
"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": [real["drain"] for real in reals],
|
||||||
|
"sim": sim["drain"],
|
||||||
|
},
|
||||||
|
"sim_decode_batch": sim["summary"]["decode_batch"],
|
||||||
|
"real_decode_batch_per_trial": [real["decode_batch"] for real in reals],
|
||||||
|
"real_artifacts": [
|
||||||
|
{
|
||||||
|
"root": real["root"],
|
||||||
|
"result_sha256": real["result_sha256"],
|
||||||
|
}
|
||||||
|
for real in reals
|
||||||
|
],
|
||||||
|
}
|
||||||
|
args.output.parent.mkdir(parents=True, exist_ok=True)
|
||||||
|
args.output.write_text(json.dumps(payload, indent=2, sort_keys=True) + "\n")
|
||||||
|
print(json.dumps({"output": str(args.output), "topology": args.topology}))
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
||||||
@@ -1,6 +1,8 @@
|
|||||||
# EXP-CODE-TRACE：从 chat 1h trace 扩展到 code 与 phase-separated replay
|
# EXP-CODE-TRACE：从 chat 1h trace 扩展到 code 与 phase-separated replay
|
||||||
|
|
||||||
> **状态：READY_FOR_DATA PREFLIGHT，未启动 GPU。** 当前只完成本地适配与实验冻结；`dash1`--`dash4` 有整机空闲后按本文 gate 顺序推进。禁止使用 `dash0`。
|
> **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`。
|
||||||
|
|
||||||
## 目标与成功定义
|
## 目标与成功定义
|
||||||
|
|
||||||
@@ -11,6 +13,16 @@
|
|||||||
|
|
||||||
本轮不是只看“能否跑完”。每个正式 cell 必须满足：同一 request vector、同一 arrival、同一 token shape、同一 prefix/initial-KV 合约、real 零失败、无持续 backlog，并同时报告 TTFT/TPOT/E2E、queue/batch、KV/prefix state 与 5min 分窗漂移。
|
本轮不是只看“能否跑完”。每个正式 cell 必须满足：同一 request vector、同一 arrival、同一 token shape、同一 prefix/initial-KV 合约、real 零失败、无持续 backlog，并同时报告 TTFT/TPOT/E2E、queue/batch、KV/prefix state 与 5min 分窗漂移。
|
||||||
|
|
||||||
|
## 当前决策快照
|
||||||
|
|
||||||
|
| 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 |
|
||||||
|
| 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
|
## 三种 workload mode 的冻结定义
|
||||||
|
|
||||||
| Mode | 保留 | 改写 | 主指标 | 明确不声称 |
|
| Mode | 保留 | 改写 | 主指标 | 明确不声称 |
|
||||||
@@ -112,6 +124,14 @@ source-window audit 先用 `40960/65536/98304/131072/262144` 给出完整
|
|||||||
|
|
||||||
`max_model_len` 变大不等于每个请求都预占最大 KV，但会改变启动合法性与可表达的单请求上界；实际 KV 压力仍由并发 token state 决定。
|
`max_model_len` 变大不等于每个请求都预占最大 KV，但会改变启动合法性与可表达的单请求上界；实际 KV 压力仍由并发 token state 决定。
|
||||||
|
|
||||||
|
对本模型，`VLLM_ALLOW_LONG_MAX_MODEL_LEN=1` 只放宽 scheduler/config
|
||||||
|
校验，不会扩展模型内部 RoPE cache；server 还必须显式传
|
||||||
|
`--hf-overrides '{"max_position_embeddings":147456}'`。runner 对
|
||||||
|
`MAX_MODEL_LEN>40960` 自动同时设置这两层。长上下文 job 默认使用
|
||||||
|
host-local vLLM compile cache；FlashInfer workspace 按 topology 复用，
|
||||||
|
避免每个 rho/trial 重编译同一组 fused-MoE kernels。两者只影响启动，
|
||||||
|
不进入 replay latency。
|
||||||
|
|
||||||
### G6：每种 mode 独立标定 rho
|
### G6：每种 mode 独立标定 rho
|
||||||
|
|
||||||
不能复用 P+D rho：
|
不能复用 P+D rho：
|
||||||
@@ -122,6 +142,12 @@ source-window audit 先用 `40960/65536/98304/131072/262144` 给出完整
|
|||||||
|
|
||||||
每种 workload×mode 选择 `low/mid/near-knee` 三点；正式点必须亚临界：全请求完成、无持续 backlog、drain tail≤10%、waiting p99 不单调随时间增长。跨 knee 点若运行，只作为 overload boundary，不支持“不发散”结论。
|
每种 workload×mode 选择 `low/mid/near-knee` 三点；正式点必须亚临界：全请求完成、无持续 backlog、drain tail≤10%、waiting p99 不单调随时间增长。跨 knee 点若运行，只作为 overload boundary，不支持“不发散”结论。
|
||||||
|
|
||||||
|
`drain tail` 必须同时列出最后完成请求的 arrival、ISL、OSL、waiting
|
||||||
|
和 E2E。早于 cutoff 到达但 OSL 很长的请求可以在最后 arrival 后继续
|
||||||
|
decode；这属于 intrinsic response tail，不等价于 arrival cutoff 时仍有
|
||||||
|
持续增长的 queue backlog。亚临界判断以 queue/waiting trajectory 和
|
||||||
|
tail driver 分解共同决定，不能只用一个 drain 秒数。
|
||||||
|
|
||||||
### G7：strict decode-only capability gate
|
### G7：strict decode-only capability gate
|
||||||
|
|
||||||
先在 10min synthetic trace 上验证：
|
先在 10min synthetic trace 上验证：
|
||||||
@@ -147,6 +173,10 @@ source-window audit 先用 `40960/65536/98304/131072/262144` 给出完整
|
|||||||
|
|
||||||
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。
|
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。
|
||||||
|
|
||||||
### Phase B：chat/code prefill-only
|
### Phase B：chat/code prefill-only
|
||||||
|
|
||||||
- 复用各自已物化 window，只把 OSL 改为 1；
|
- 复用各自已物化 window，只把 OSL 改为 1；
|
||||||
@@ -165,6 +195,20 @@ source-window audit 先用 `40960/65536/98304/131072/262144` 给出完整
|
|||||||
|
|
||||||
decode profile/serving anchors 至少覆盖实际 batch p99。当前 whole-layer grid 只对少数 b≤8 有证据，且 b6 有长尾；在 MNS128 case 前必须补 b{1,2,4,8,16,32,64,128} 或实际访问 bucket，不能把 b8 常数外推到 b128。
|
decode profile/serving anchors 至少覆盖实际 batch p99。当前 whole-layer grid 只对少数 b≤8 有证据，且 b6 有长尾；在 MNS128 case 前必须补 b{1,2,4,8,16,32,64,128} 或实际访问 bucket，不能把 b8 常数外推到 b128。
|
||||||
|
|
||||||
|
已安装 vLLM 0.20.0 的 `DecodeBenchConnector` 会在首次 schedule 时把
|
||||||
|
`request.num_tokens-num_computed_tokens-1` 个 token 标为 external，
|
||||||
|
同步向已分配的每层 KV blocks 写 dummy non-zero values，再从最后一个
|
||||||
|
prompt token 开始 forward。因此它适合测大 context 下的 decode
|
||||||
|
compute/scheduling，但 connector fill 发生在 client admission 之后：
|
||||||
|
fill time 必须单独记账并从 KV-ready arrival/TPOT 口径中排除。
|
||||||
|
dummy KV 也不提供真实 prompt-content 或 MoE-routing fidelity。
|
||||||
|
|
||||||
|
Frontier commit `deadc4a3` 的 `Request` 已支持构造
|
||||||
|
`num_processed_tokens`，但 `TraceReplayRequestGenerator` 不读取该列；
|
||||||
|
因此 sim 侧仍需一个显式、可测试的 `initial_computed_tokens` trace
|
||||||
|
contract。C0 必须同时证明 real 首个 model step 是 decode、Frontier
|
||||||
|
首个 ledger stage 是 decode，之后才能解除 strict decode-only 的 BLOCKED。
|
||||||
|
|
||||||
## 指标与判据
|
## 指标与判据
|
||||||
|
|
||||||
共同口径：
|
共同口径：
|
||||||
@@ -238,3 +282,29 @@ mode-specific：
|
|||||||
`VLLM_ALLOW_LONG_MAX_MODEL_LEN=1` 下成功；该 override 只支持
|
`VLLM_ALLOW_LONG_MAX_MODEL_LEN=1` 下成功；该 override 只支持
|
||||||
performance/shape fidelity，不形成生成质量或模型长上下文正确性 claim，
|
performance/shape fidelity，不形成生成质量或模型长上下文正确性 claim，
|
||||||
并作为 provenance 中的显式实验变量。
|
并作为 provenance 中的显式实验变量。
|
||||||
|
- profile-v6-code-longctx 覆盖 TP1/2/4、KV context 到 131072，
|
||||||
|
33 个 long-context rows；两次 fresh-process repeat 的最大相对差
|
||||||
|
4.648%，旧 anchor drift 最大 1.7%。attention profile SHA256 =
|
||||||
|
`fbcf7e1f95789a6f6d771e24d1fc60958b7daf04eb0db260d27869a19d71d550`。
|
||||||
|
- TP4 `max_model_len=147456` smoke 已在 dash4 通过。server 日志同时确认
|
||||||
|
`max_model_len=147456` 与 `hf_overrides.max_position_embeddings=147456`；
|
||||||
|
20,051+78、119,702+68、136,774+242 三个 shape 均成功。对应
|
||||||
|
TTFT=853.29/12,699.04/3,820.55ms，TPOT=16.65/7.21/8.13ms。
|
||||||
|
最长请求的非单调 TTFT 来自 cold compile/cache state，因此这里只作为
|
||||||
|
runtime support gate，不作为 profile accuracy 数据。
|
||||||
|
- calibration 全部使用同一 profile-v6 SHA。TP4 的 `rho=0.0016`
|
||||||
|
decode batch max=16、drain=21.07s，仍通过 10% 亚临界 gate；TP2 的
|
||||||
|
`rho=0.0016` waiting p50=332.97s、drain=976.86s，明确过载并排除。
|
||||||
|
完整 compact table 在 `results/calibration-summary.json`。
|
||||||
|
- TP2/TP4 的 `rho={0.0002,0.0004,0.0008,0.0016}` 61min full paired
|
||||||
|
inputs 已在 CPFS 物化；每个 paired row digest 和 Frontier CSV SHA
|
||||||
|
均与 calibration input 逐项一致。manifest 副本在
|
||||||
|
`results/paired-input-manifests/`。最大一个目录约 901MiB，不把大型
|
||||||
|
token arrays 提交进 Git。
|
||||||
|
- code prefill-only 的最大 calibration cache 已物化：
|
||||||
|
3477 requests、总 prefill 115,828,371 tokens、OSL 全为 1，
|
||||||
|
paired digest=`40865068e02414612ba1cd4595894e20e85e01fd34d73f8660185552d531ecea`。
|
||||||
|
- 多 host 并发 server startup 暴露出 shared CPFS AOT cache 和每-job
|
||||||
|
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。
|
||||||
|
|||||||
632
runs/frontier-code-trace-v0/results/calibration-summary.json
Normal file
632
runs/frontier-code-trace-v0/results/calibration-summary.json
Normal file
@@ -0,0 +1,632 @@
|
|||||||
|
{
|
||||||
|
"attention_profile_sha256": "fbcf7e1f95789a6f6d771e24d1fc60958b7daf04eb0db260d27869a19d71d550",
|
||||||
|
"cells": [
|
||||||
|
{
|
||||||
|
"decode_batch": {
|
||||||
|
"histogram": {
|
||||||
|
"1": 5525
|
||||||
|
},
|
||||||
|
"max": 1,
|
||||||
|
"share_gt_1": 0.0,
|
||||||
|
"share_gt_4": 0.0,
|
||||||
|
"stages": 5525
|
||||||
|
},
|
||||||
|
"drain_fraction": 0.00044126232317770095,
|
||||||
|
"drain_tail_s": 1.6150201028303854,
|
||||||
|
"offered_load": {
|
||||||
|
"decode_tokens_per_s": 1.5183060109289617,
|
||||||
|
"prefill_tokens_per_s_after_prefix": 189.91639344262296,
|
||||||
|
"prefill_tokens_per_s_raw": 201.38743169398907,
|
||||||
|
"requests_per_s": 0.008743169398907104
|
||||||
|
},
|
||||||
|
"prefix_cache_hit_ratio": 0.05698154180238871,
|
||||||
|
"requests": 32,
|
||||||
|
"rho": 5e-05,
|
||||||
|
"subcritical_gate": true,
|
||||||
|
"topology": "tp4_mns16",
|
||||||
|
"tpot_ms": {
|
||||||
|
"count": 32,
|
||||||
|
"max": 6.06516500761245,
|
||||||
|
"mean": 5.267661756941212,
|
||||||
|
"min": 4.642776716764274,
|
||||||
|
"p50": 5.27961358181982,
|
||||||
|
"p90": 6.065165007589712,
|
||||||
|
"p95": 6.06516500761245,
|
||||||
|
"p99": 6.06516500761245
|
||||||
|
},
|
||||||
|
"trace_sha256": "bdc54edafacf3e3f353de7a54d5e43a7e4323eb1e8b3dc55ce333a5e0b347ce5",
|
||||||
|
"ttft_ms": {
|
||||||
|
"count": 32,
|
||||||
|
"max": 9026.404118319988,
|
||||||
|
"mean": 1316.2539772416199,
|
||||||
|
"min": 76.98844366404956,
|
||||||
|
"p50": 401.828087941567,
|
||||||
|
"p90": 3363.622890573971,
|
||||||
|
"p95": 6721.082190563591,
|
||||||
|
"p99": 8741.382807755395
|
||||||
|
},
|
||||||
|
"waiting_ms": {
|
||||||
|
"count": 32,
|
||||||
|
"max": 0.0,
|
||||||
|
"mean": 0.0,
|
||||||
|
"min": 0.0,
|
||||||
|
"p50": 0.0,
|
||||||
|
"p90": 0.0,
|
||||||
|
"p95": 0.0,
|
||||||
|
"p99": 0.0
|
||||||
|
}
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"decode_batch": {
|
||||||
|
"histogram": {
|
||||||
|
"1": 33249,
|
||||||
|
"2": 294,
|
||||||
|
"3": 76
|
||||||
|
},
|
||||||
|
"max": 3,
|
||||||
|
"share_gt_1": 0.011005681311163331,
|
||||||
|
"share_gt_4": 0.0,
|
||||||
|
"stages": 33619
|
||||||
|
},
|
||||||
|
"drain_fraction": 0.0006573023445132039,
|
||||||
|
"drain_tail_s": 2.4057265809183264,
|
||||||
|
"offered_load": {
|
||||||
|
"decode_tokens_per_s": 9.343989071038251,
|
||||||
|
"prefill_tokens_per_s_after_prefix": 491.8571038251366,
|
||||||
|
"prefill_tokens_per_s_raw": 1477.1773224043716,
|
||||||
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||||||
|
"upstream_remap_manifest_sha256": "8649b6acf10007e16a6b0eccebe95ee25a33481d6c3c0381ec8caf6dad05f12d",
|
||||||
|
"window": {
|
||||||
|
"duration_s": null,
|
||||||
|
"start_timestamp": null
|
||||||
|
},
|
||||||
|
"workload_mode": "prefill_decode"
|
||||||
|
}
|
||||||
@@ -0,0 +1,69 @@
|
|||||||
|
{
|
||||||
|
"block_contract": {
|
||||||
|
"input_length_mismatches": 0,
|
||||||
|
"runtime_identity_collisions": 0,
|
||||||
|
"runtime_to_source_relation_conflicts": 0,
|
||||||
|
"source_hash_count_mismatches": 0,
|
||||||
|
"source_runtime_granularity": "one 512-token source hash to 32 consecutive 16-token identities; final partial source block may have fewer",
|
||||||
|
"source_to_runtime_relation_conflicts": 0,
|
||||||
|
"unique_runtime_identities": 2613467,
|
||||||
|
"unique_runtime_relations": 0,
|
||||||
|
"unique_source_hash_relations": 0
|
||||||
|
},
|
||||||
|
"excluded_over_max_total_tokens": 0,
|
||||||
|
"frontier_csv": "/home/admin/cpfs/wjh/aituner/aituner-code-trace-fbaa909/runs/frontier-code-trace-v0/inputs/full-r0p0016-v1/frontier.csv",
|
||||||
|
"frontier_csv_sha256": "d785665c86a562c9d9a7492ad710e6393bb883bf8c3ad552a34ff18f22925043",
|
||||||
|
"mapping": "real prompt tokens: BLAKE2b-128(parent runtime identity, exact 16-token block); missing prompt fallback: source hashes first define deterministic 16-token content blocks, then the same parent-sensitive BLAKE2b-128 runtime identity contract is applied",
|
||||||
|
"max_total_tokens_filter": null,
|
||||||
|
"paired_row_vector_sha256": "d3d232ea41eadb507780b4830ce3e944ac88cd997d0a4f418f86ca71de1377bf",
|
||||||
|
"parent_validation": {
|
||||||
|
"enabled": false,
|
||||||
|
"links_checked": 0,
|
||||||
|
"links_outside_selected_stream": 0,
|
||||||
|
"mean_tail_nonreused_per_link": 0.0,
|
||||||
|
"parent_prefix_common_blocks": 0,
|
||||||
|
"parent_tail_nonreused_blocks": 0,
|
||||||
|
"rule": "contiguous common prefix required; mismatch tolerated only within parent trailing partial 512-token block (<= 33 runtime blocks)"
|
||||||
|
},
|
||||||
|
"prefix_cache_blocks": "complete 16-token blocks only; final partial block excluded",
|
||||||
|
"prompt_contract": {
|
||||||
|
"input_is_pre_remapped": true,
|
||||||
|
"real_prompt_requests": 0,
|
||||||
|
"synthetic_fallback_requests": 1718,
|
||||||
|
"tokenizer": null
|
||||||
|
},
|
||||||
|
"prompt_window": null,
|
||||||
|
"prompt_window_sha256": null,
|
||||||
|
"real_requests": "/home/admin/cpfs/wjh/aituner/aituner-code-trace-fbaa909/runs/frontier-code-trace-v0/inputs/full-r0p0016-v1/real_requests.jsonl",
|
||||||
|
"real_requests_sha256": "3261a15be3e1f7d5ecf8a86c03a9e0ff9ec69a3502e4fbc8e8b82b18b0854e1f",
|
||||||
|
"requests": 1718,
|
||||||
|
"rho": 0.0016,
|
||||||
|
"sampling_rule": "keep iff sampling_u <= rho (SimFid convention; rho=1 keeps all rows)",
|
||||||
|
"schema": "frontier-s3-real-remap-v2",
|
||||||
|
"selected_remapped": "/home/admin/cpfs/wjh/aituner/aituner-code-trace-fbaa909/runs/frontier-code-trace-v0/inputs/full-r0p0016-v1/selected-remapped.jsonl",
|
||||||
|
"selected_remapped_sha256": "4d5c66e5cad4efa4c500009630c6ca6069fdd71c9e1a7cd446c8869d84dc70f7",
|
||||||
|
"source": "/home/admin/cpfs/wjh/aituner/aituner-code-trace-fbaa909/runs/frontier-code-trace-v0/inputs/remap-cache-r0p0032-v2/selected-remapped.jsonl",
|
||||||
|
"source_block_size": 512,
|
||||||
|
"source_blocks": 104582,
|
||||||
|
"source_rows_scanned": 3477,
|
||||||
|
"source_sha256": "f46c7d8ddbd146bafe00fbc173bf3f29039d8b96aa611f1ae4e2a0b9eac189f1",
|
||||||
|
"synthetic_fallback_contract": {
|
||||||
|
"different_16_block_hash_different_tokens": true,
|
||||||
|
"encoding": "injective base-(vocab_size-token_offset), 16 little-endian digits",
|
||||||
|
"runtime_identity_is_parent_sensitive": true,
|
||||||
|
"same_16_block_hash_same_tokens": true,
|
||||||
|
"token_offset": 1024,
|
||||||
|
"vocab_size": 151936
|
||||||
|
},
|
||||||
|
"target_16_blocks": 3318510,
|
||||||
|
"target_block_size": 16,
|
||||||
|
"total_decode_tokens": 813493,
|
||||||
|
"total_prefill_tokens": 53108885,
|
||||||
|
"upstream_remap_manifest": "/home/admin/cpfs/wjh/aituner/aituner-code-trace-fbaa909/runs/frontier-code-trace-v0/inputs/remap-cache-r0p0032-v2/manifest.json",
|
||||||
|
"upstream_remap_manifest_sha256": "8649b6acf10007e16a6b0eccebe95ee25a33481d6c3c0381ec8caf6dad05f12d",
|
||||||
|
"window": {
|
||||||
|
"duration_s": null,
|
||||||
|
"start_timestamp": null
|
||||||
|
},
|
||||||
|
"workload_mode": "prefill_decode"
|
||||||
|
}
|
||||||
@@ -0,0 +1,69 @@
|
|||||||
|
{
|
||||||
|
"block_contract": {
|
||||||
|
"input_length_mismatches": 0,
|
||||||
|
"runtime_identity_collisions": 0,
|
||||||
|
"runtime_to_source_relation_conflicts": 0,
|
||||||
|
"source_hash_count_mismatches": 0,
|
||||||
|
"source_runtime_granularity": "one 512-token source hash to 32 consecutive 16-token identities; final partial source block may have fewer",
|
||||||
|
"source_to_runtime_relation_conflicts": 0,
|
||||||
|
"unique_runtime_identities": 2613467,
|
||||||
|
"unique_runtime_relations": 0,
|
||||||
|
"unique_source_hash_relations": 0
|
||||||
|
},
|
||||||
|
"excluded_over_max_total_tokens": 0,
|
||||||
|
"frontier_csv": "/home/admin/cpfs/wjh/aituner/aituner-code-trace-fbaa909/runs/frontier-code-trace-v0/inputs/prefill-only-r0p0032-v1/frontier.csv",
|
||||||
|
"frontier_csv_sha256": "8eeea3f44626e30e6da6ab58950c9a7f52e6cf6eb29c6bd2e35e1c25e4a9c5ce",
|
||||||
|
"mapping": "real prompt tokens: BLAKE2b-128(parent runtime identity, exact 16-token block); missing prompt fallback: source hashes first define deterministic 16-token content blocks, then the same parent-sensitive BLAKE2b-128 runtime identity contract is applied",
|
||||||
|
"max_total_tokens_filter": null,
|
||||||
|
"paired_row_vector_sha256": "40865068e02414612ba1cd4595894e20e85e01fd34d73f8660185552d531ecea",
|
||||||
|
"parent_validation": {
|
||||||
|
"enabled": false,
|
||||||
|
"links_checked": 0,
|
||||||
|
"links_outside_selected_stream": 0,
|
||||||
|
"mean_tail_nonreused_per_link": 0.0,
|
||||||
|
"parent_prefix_common_blocks": 0,
|
||||||
|
"parent_tail_nonreused_blocks": 0,
|
||||||
|
"rule": "contiguous common prefix required; mismatch tolerated only within parent trailing partial 512-token block (<= 33 runtime blocks)"
|
||||||
|
},
|
||||||
|
"prefix_cache_blocks": "complete 16-token blocks only; final partial block excluded",
|
||||||
|
"prompt_contract": {
|
||||||
|
"input_is_pre_remapped": true,
|
||||||
|
"real_prompt_requests": 0,
|
||||||
|
"synthetic_fallback_requests": 3477,
|
||||||
|
"tokenizer": null
|
||||||
|
},
|
||||||
|
"prompt_window": null,
|
||||||
|
"prompt_window_sha256": null,
|
||||||
|
"real_requests": "/home/admin/cpfs/wjh/aituner/aituner-code-trace-fbaa909/runs/frontier-code-trace-v0/inputs/prefill-only-r0p0032-v1/real_requests.jsonl",
|
||||||
|
"real_requests_sha256": "48a9a6f5f73fe4daebff5a2fba9aec732a89219d19a00d6811cf14e0cd5afa2c",
|
||||||
|
"requests": 3477,
|
||||||
|
"rho": 0.0032,
|
||||||
|
"sampling_rule": "keep iff sampling_u <= rho (SimFid convention; rho=1 keeps all rows)",
|
||||||
|
"schema": "frontier-s3-real-remap-v2",
|
||||||
|
"selected_remapped": "/home/admin/cpfs/wjh/aituner/aituner-code-trace-fbaa909/runs/frontier-code-trace-v0/inputs/prefill-only-r0p0032-v1/selected-remapped.jsonl",
|
||||||
|
"selected_remapped_sha256": "a9c31bb600ac6b637ded1c1aa4c84df5613bc3fbdc986bfe935f4ebe9aee0aff",
|
||||||
|
"source": "/home/admin/cpfs/wjh/aituner/aituner-code-trace-fbaa909/runs/frontier-code-trace-v0/inputs/remap-cache-r0p0032-v2/selected-remapped.jsonl",
|
||||||
|
"source_block_size": 512,
|
||||||
|
"source_blocks": 227960,
|
||||||
|
"source_rows_scanned": 3477,
|
||||||
|
"source_sha256": "f46c7d8ddbd146bafe00fbc173bf3f29039d8b96aa611f1ae4e2a0b9eac189f1",
|
||||||
|
"synthetic_fallback_contract": {
|
||||||
|
"different_16_block_hash_different_tokens": true,
|
||||||
|
"encoding": "injective base-(vocab_size-token_offset), 16 little-endian digits",
|
||||||
|
"runtime_identity_is_parent_sensitive": true,
|
||||||
|
"same_16_block_hash_same_tokens": true,
|
||||||
|
"token_offset": 1024,
|
||||||
|
"vocab_size": 151936
|
||||||
|
},
|
||||||
|
"target_16_blocks": 7237642,
|
||||||
|
"target_block_size": 16,
|
||||||
|
"total_decode_tokens": 3477,
|
||||||
|
"total_prefill_tokens": 115828371,
|
||||||
|
"upstream_remap_manifest": "/home/admin/cpfs/wjh/aituner/aituner-code-trace-fbaa909/runs/frontier-code-trace-v0/inputs/remap-cache-r0p0032-v2/manifest.json",
|
||||||
|
"upstream_remap_manifest_sha256": "8649b6acf10007e16a6b0eccebe95ee25a33481d6c3c0381ec8caf6dad05f12d",
|
||||||
|
"window": {
|
||||||
|
"duration_s": null,
|
||||||
|
"start_timestamp": null
|
||||||
|
},
|
||||||
|
"workload_mode": "prefill_only"
|
||||||
|
}
|
||||||
88
runs/frontier-code-trace-v0/summarize_calibration.py
Normal file
88
runs/frontier-code-trace-v0/summarize_calibration.py
Normal file
@@ -0,0 +1,88 @@
|
|||||||
|
#!/usr/bin/env python3
|
||||||
|
"""Compact code-trace Frontier rho calibration into a reviewable artifact."""
|
||||||
|
|
||||||
|
from __future__ import annotations
|
||||||
|
|
||||||
|
import argparse
|
||||||
|
import hashlib
|
||||||
|
import json
|
||||||
|
import re
|
||||||
|
from pathlib import Path
|
||||||
|
|
||||||
|
|
||||||
|
CELL_RE = re.compile(r"r(?P<rho>[0-9p]+)-tp(?P<tp>[24])-v3")
|
||||||
|
ATTENTION_FLAG = "--random_forrest_execution_time_predictor_config_atten_input_file"
|
||||||
|
|
||||||
|
|
||||||
|
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("--output", type=Path, required=True)
|
||||||
|
return parser.parse_args()
|
||||||
|
|
||||||
|
|
||||||
|
def sha256(path: Path) -> str:
|
||||||
|
digest = hashlib.sha256()
|
||||||
|
with path.open("rb") as stream:
|
||||||
|
for chunk in iter(lambda: stream.read(1024 * 1024), b""):
|
||||||
|
digest.update(chunk)
|
||||||
|
return digest.hexdigest()
|
||||||
|
|
||||||
|
|
||||||
|
def main() -> None:
|
||||||
|
args = parse_args()
|
||||||
|
profile = json.loads(args.profile_manifest.read_text())
|
||||||
|
expected_profile_sha = profile["output_sha256"]
|
||||||
|
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():
|
||||||
|
continue
|
||||||
|
manifest = json.loads((root / "manifest.json").read_text())
|
||||||
|
observed_profile_sha = manifest.get("attention_profile_sha256")
|
||||||
|
if observed_profile_sha is None:
|
||||||
|
argv = manifest["argv"]
|
||||||
|
profile_path = Path(argv[argv.index(ATTENTION_FLAG) + 1])
|
||||||
|
observed_profile_sha = sha256(profile_path)
|
||||||
|
if observed_profile_sha != expected_profile_sha:
|
||||||
|
raise ValueError(
|
||||||
|
f"{root}: attention profile {observed_profile_sha} "
|
||||||
|
f"!= profile-v6 {expected_profile_sha}"
|
||||||
|
)
|
||||||
|
summary = json.loads((root / "summary.json").read_text())
|
||||||
|
drain_fraction = (
|
||||||
|
summary["drain"]["tail_after_last_arrival_s"] / summary["duration_s"]
|
||||||
|
)
|
||||||
|
cells.append(
|
||||||
|
{
|
||||||
|
"topology": f"tp{match.group('tp')}_mns16",
|
||||||
|
"rho": float(match.group("rho").replace("p", ".")),
|
||||||
|
"requests": summary["requests"],
|
||||||
|
"offered_load": summary["offered_load"],
|
||||||
|
"waiting_ms": summary["latency_ms"]["waiting"],
|
||||||
|
"ttft_ms": summary["latency_ms"]["ttft"],
|
||||||
|
"tpot_ms": summary["latency_ms"]["tpot"],
|
||||||
|
"decode_batch": summary["decode_batch"],
|
||||||
|
"drain_tail_s": summary["drain"]["tail_after_last_arrival_s"],
|
||||||
|
"drain_fraction": drain_fraction,
|
||||||
|
"subcritical_gate": drain_fraction <= 0.1,
|
||||||
|
"prefix_cache_hit_ratio": summary["prefix_cache"]["hit_ratio"],
|
||||||
|
"trace_sha256": manifest["trace_sha256"],
|
||||||
|
}
|
||||||
|
)
|
||||||
|
if not cells:
|
||||||
|
raise ValueError(f"no completed v3 calibration cells below {args.sim_root}")
|
||||||
|
payload = {
|
||||||
|
"schema": "frontier-code-trace-calibration-summary-v1",
|
||||||
|
"attention_profile_sha256": expected_profile_sha,
|
||||||
|
"subcritical_rule": "drain tail <= 10% of the 3660s arrival window",
|
||||||
|
"cells": cells,
|
||||||
|
}
|
||||||
|
args.output.parent.mkdir(parents=True, exist_ok=True)
|
||||||
|
args.output.write_text(json.dumps(payload, indent=2, sort_keys=True) + "\n")
|
||||||
|
print(json.dumps({"cells": len(cells), "output": str(args.output)}, sort_keys=True))
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
||||||
Reference in New Issue
Block a user