From 3a59d5df96489ab7c5061a6e9cc7a904454569f7 Mon Sep 17 00:00:00 2001 From: Gahow Wang Date: Fri, 17 Jul 2026 03:11:45 +0800 Subject: [PATCH] Evaluate Qwen30 prefill simulator fidelity --- .../qwen30-prefill-ranking.png | Bin 0 -> 108856 bytes .../analyze_qwen30_prefill_fidelity.py | 462 ++ .../experiment-card.md | 18 +- .../fleet-base-rerun.toml | 24 + .../fleet-exclusive.toml | 24 + .../fleet-refine-tp4-wave3.toml | 24 + .../fleet-refine-tp4-wave4.toml | 24 + .../fleet-refine.toml | 24 + .../jobs_base_rerun.toml | 21 + .../jobs_full.toml | 24 +- .../jobs_refine.toml | 240 + .../jobs_refine_tp4_wave3.toml | 41 + .../jobs_refine_tp4_wave4.toml | 21 + .../results/final/capacity.csv | 13 + .../results/final/comparison.json | 5860 +++++++++++++++++ .../frontier_surface_frozen.json | 461 ++ .../frontier_surface_frozen.json | 461 ++ .../frontier_surface_frozen.json | 461 ++ .../test_phase_factorial.py | 6 + simulator-fidelity.md | 99 +- 20 files changed, 8280 insertions(+), 28 deletions(-) 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+import argparse +import csv +import hashlib +import json +import math +import re +from collections import defaultdict +from datetime import datetime +from pathlib import Path +from typing import Any + + +RUN_PATTERN = re.compile(r"qwen30-prefill-real-tp(?P\d+)-mns(?P\d+)-") + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser() + parser.add_argument("--fleet-artifacts", type=Path, required=True) + parser.add_argument( + "--simulator-manifest", type=Path, action="append", required=True + ) + parser.add_argument("--output-root", type=Path, required=True) + return parser.parse_args() + + +def sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as handle: + for chunk in iter(lambda: handle.read(1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +def load_json(path: Path) -> dict[str, Any]: + return json.loads(path.read_text()) + + +def sign(value: float) -> int: + return (value > 0) - (value < 0) + + +def kendall_tau_b(real: list[float], simulated: list[float]) -> dict[str, Any]: + if len(real) != len(simulated): + raise ValueError("ranking vectors have different lengths") + concordant = discordant = real_only_ties = simulated_only_ties = both_ties = 0 + for left in range(len(real)): + for right in range(left + 1, len(real)): + real_sign = sign(real[left] - real[right]) + sim_sign = sign(simulated[left] - simulated[right]) + if real_sign == 0 and sim_sign == 0: + both_ties += 1 + elif real_sign == 0: + real_only_ties += 1 + elif sim_sign == 0: + simulated_only_ties += 1 + elif real_sign == sim_sign: + concordant += 1 + else: + discordant += 1 + denominator = math.sqrt( + (concordant + discordant + real_only_ties) + * (concordant + discordant + simulated_only_ties) + ) + tau = (concordant - discordant) / denominator if denominator else None + return { + "kendall_tau_b": tau, + "concordant": concordant, + "discordant": discordant, + "real_only_ties": real_only_ties, + "simulator_only_ties": simulated_only_ties, + "both_ties": both_ties, + } + + +def result_files_by_anchor(root: Path) -> dict[tuple[str, str], Path]: + selected: dict[tuple[str, str], Path] = {} + digests: dict[tuple[str, str], str] = {} + for path in root.glob("artifacts/**/round*/results/r*.json"): + if path.name.startswith("warmup_"): + continue + round_name = path.parent.parent.name + key = (round_name, path.name) + digest = sha256(path) + if key in digests and digests[key] != digest: + raise RuntimeError( + f"conflicting duplicate real anchor {key} under {root}" + ) + digests[key] = digest + if key not in selected or len(path.parts) < len(selected[key].parts): + selected[key] = path + return selected + + +def find_real_runs(root: Path) -> dict[str, Path]: + candidates: dict[str, list[Path]] = defaultdict(list) + for path in root.iterdir(): + if not path.is_dir(): + continue + match = RUN_PATTERN.search(path.name) + if not match: + continue + name = f"tp{int(match.group('tp'))}_mns{int(match.group('mns'))}" + candidates[name].append(path) + selected: dict[str, Path] = {} + for name, paths in candidates.items(): + complete = [ + path + for path in paths + if (path / "remote_run" / "exit_code").is_file() + and (path / "remote_run" / "exit_code").read_text().strip() == "0" + ] + measured_counts = { + path: len(result_files_by_anchor(path)) + for path in complete + } + if not measured_counts: + raise RuntimeError(f"no successful run for {name}") + maximum = max(measured_counts.values()) + richest = [path for path, count in measured_counts.items() if count == maximum] + if len(richest) != 1: + raise RuntimeError(f"ambiguous richest successful run for {name}: {richest}") + selected[name] = richest[0] + if len(selected) != 12: + raise RuntimeError(f"expected 12 real configs, got {sorted(selected)}") + return selected + + +def campaign_resources(root: Path) -> dict[str, Any]: + runs = [] + gpu_hours = 0.0 + for path in sorted(root.iterdir()): + match = RUN_PATTERN.search(path.name) + exit_code = path / "remote_run" / "exit_code" + started_at = path / "remote_run" / "started_at" + finished_at = path / "remote_run" / "finished_at" + if ( + not path.is_dir() + or not match + or not exit_code.is_file() + or exit_code.read_text().strip() != "0" + or not started_at.is_file() + or not finished_at.is_file() + ): + continue + started = datetime.fromisoformat(started_at.read_text().strip()) + finished = datetime.fromisoformat(finished_at.read_text().strip()) + duration_seconds = (finished - started).total_seconds() + tp = int(match.group("tp")) + run_gpu_hours = duration_seconds * tp / 3600.0 + gpu_hours += run_gpu_hours + runs.append( + { + "run": path.name, + "tp": tp, + "duration_seconds": duration_seconds, + "gpu_hours": run_gpu_hours, + } + ) + return { + "successful_fleet_jobs": len(runs), + "gpu_hours": gpu_hours, + "runs": runs, + } + + +def parse_real_config(name: str, run_root: Path) -> dict[str, Any]: + result_files = sorted(result_files_by_anchor(run_root).values()) + if len(result_files) not in {10, 16}: + raise RuntimeError(f"expected 10 base or 16 refined anchors for {name}, got {len(result_files)}") + by_rate: dict[float, list[dict[str, Any]]] = defaultdict(list) + for path in result_files: + payload = load_json(path) + rate = float(payload["workload"]["offered_request_rate"]) + by_rate[rate].append( + { + "path": str(path.resolve()), + "sha256": sha256(path), + "summary": payload["summary"], + } + ) + tp = int(name.split("_")[0][2:]) + base_rates = [4.0, 8.0, 16.0, 32.0, 64.0] + refined_rates = sorted({*base_rates, *(tp * value for value in (5.0, 6.0, 7.0))}) + if tuple(sorted(by_rate)) not in {tuple(base_rates), tuple(refined_rates)}: + raise RuntimeError(f"unexpected rate grid for {name}: {sorted(by_rate)}") + anchors = [] + for rate, rounds in sorted(by_rate.items()): + if len(rounds) != 2: + raise RuntimeError(f"expected two rounds for {name}@{rate}, got {len(rounds)}") + round_feasible = [bool(row["summary"]["slo"]["feasible"]) for row in rounds] + anchors.append( + { + "rate": rate, + "rounds": rounds, + "conservative_feasible": all(round_feasible), + "round_feasible": round_feasible, + "round_ttft_p95_ms": [ + float(row["summary"]["ttft_p95_ms"]) for row in rounds + ], + } + ) + feasible = [row["rate"] for row in anchors if row["conservative_feasible"]] + capacity = max(feasible, default=0.0) + return { + "name": name, + "tp": tp, + "mns": int(name.split("_mns")[1]), + "anchors": anchors, + "capacity": capacity, + "capacity_per_gpu": capacity / tp, + "source_run": str(run_root.resolve()), + } + + +def parse_simulator(manifests: list[Path]) -> tuple[dict[str, Any], list[dict[str, str]]]: + configs: dict[str, Any] = {} + sources = [] + for path in manifests: + payload = load_json(path) + if payload["status"] not in {"complete", "partial_not_decision_bearing"}: + raise RuntimeError(f"simulator manifest has invalid status: {path}") + sources.append({"path": str(path.resolve()), "sha256": sha256(path)}) + result_by_name = { + result["config"]["name"]: result for result in payload["config_results"] + } + for capacity in payload["capacity"]: + name = capacity["config"]["name"] + result = result_by_name.get(name) + if result is None: + raise RuntimeError(f"missing simulator config result {name}") + entry = configs.setdefault( + name, + { + "name": name, + "tp": int(capacity["config"]["tp"]), + "mns": int(capacity["config"]["mns"]), + "anchor_by_rate": {}, + }, + ) + for load in result["loads"]: + rate = float(load["offered_request_rate"]) + if rate in entry["anchor_by_rate"]: + raise RuntimeError(f"duplicate simulator anchor {name}@{rate}") + entry["anchor_by_rate"][rate] = { + "rate": rate, + "feasible": bool(load["score"]["feasible"]), + "pass_rate": float(load["score"]["pass_rate"]), + "ttft_p95_ms": float(load["score"]["ttft_p95_ms"]), + } + if len(configs) != 12: + raise RuntimeError(f"expected 12 simulator configs, got {sorted(configs)}") + for entry in configs.values(): + entry["anchors"] = [ + entry["anchor_by_rate"][rate] for rate in sorted(entry["anchor_by_rate"]) + ] + del entry["anchor_by_rate"] + feasible = [row["rate"] for row in entry["anchors"] if row["feasible"]] + entry["capacity"] = max(feasible, default=0.0) + entry["capacity_per_gpu"] = entry["capacity"] / entry["tp"] + return configs, sources + + +def compare(real: dict[str, Any], simulated: dict[str, Any]) -> dict[str, Any]: + names = sorted(real, key=lambda name: (real[name]["tp"], real[name]["mns"])) + if set(names) != set(simulated): + raise RuntimeError("real and simulator config sets differ") + real_scores = [real[name]["capacity_per_gpu"] for name in names] + sim_scores = [simulated[name]["capacity_per_gpu"] for name in names] + real_best = max(real_scores) + sim_best = max(sim_scores) + real_top = [name for name in names if real[name]["capacity_per_gpu"] == real_best] + sim_top = [ + name for name in names if simulated[name]["capacity_per_gpu"] == sim_best + ] + worst_sim_choice = min(real[name]["capacity_per_gpu"] for name in sim_top) + best_sim_choice = max(real[name]["capacity_per_gpu"] for name in sim_top) + tau = kendall_tau_b(real_scores, sim_scores) + + pairwise = {"all": {"comparable": 0, "correct": 0}, "within_tp": {}} + for left in range(len(names)): + for right in range(left + 1, len(names)): + real_sign = sign(real_scores[left] - real_scores[right]) + sim_sign = sign(sim_scores[left] - sim_scores[right]) + if real_sign: + pairwise["all"]["comparable"] += 1 + pairwise["all"]["correct"] += int(real_sign == sim_sign) + if real[names[left]]["tp"] == real[names[right]]["tp"] and real_sign: + key = f"tp{real[names[left]]['tp']}" + bucket = pairwise["within_tp"].setdefault( + key, {"comparable": 0, "correct": 0} + ) + bucket["comparable"] += 1 + bucket["correct"] += int(real_sign == sim_sign) + for bucket in [pairwise["all"], *pairwise["within_tp"].values()]: + bucket["accuracy"] = ( + bucket["correct"] / bucket["comparable"] + if bucket["comparable"] + else None + ) + + confusion = {"real_pass_sim_pass": 0, "real_pass_sim_fail": 0, + "real_fail_sim_pass": 0, "real_fail_sim_fail": 0} + for name in names: + real_anchors = {row["rate"]: row for row in real[name]["anchors"]} + sim_anchors = {row["rate"]: row for row in simulated[name]["anchors"]} + if set(real_anchors) != set(sim_anchors): + raise RuntimeError(f"anchor grids differ for {name}") + for rate in real_anchors: + real_pass = real_anchors[rate]["conservative_feasible"] + sim_pass = sim_anchors[rate]["feasible"] + key = f"real_{'pass' if real_pass else 'fail'}_sim_{'pass' if sim_pass else 'fail'}" + confusion[key] += 1 + return { + "config_order": names, + "real_top_set": real_top, + "simulator_top_set": sim_top, + "top_set_exact_match": real_top == sim_top, + "top_set_overlap": sorted(set(real_top) & set(sim_top)), + "top1_regret_best": (real_best - best_sim_choice) / real_best, + "top1_regret_worst": (real_best - worst_sim_choice) / real_best, + "real_best_capacity_per_gpu": real_best, + "simulator_best_capacity_per_gpu": sim_best, + "kendall": tau, + "pairwise_non_tied": pairwise, + "anchor_confusion": confusion, + } + + +def write_csv(path: Path, rows: list[dict[str, Any]]) -> None: + with path.open("w", newline="") as handle: + writer = csv.DictWriter( + handle, fieldnames=list(rows[0]), lineterminator="\n" + ) + writer.writeheader() + writer.writerows(rows) + + +def plot(path: Path, rows: list[dict[str, Any]], metrics: dict[str, Any]) -> None: + import matplotlib.pyplot as plt + import numpy as np + + labels = [f"TP{row['tp']}\nMNS{row['mns']}" for row in rows] + x = np.arange(len(rows)) + width = 0.36 + figure, axes = plt.subplots( + 1, 2, figsize=(13.5, 5.0), gridspec_kw={"width_ratios": [3.3, 1.0]} + ) + axis = axes[0] + axis.bar(x - width / 2, [row["real"] for row in rows], width, label="Real vLLM") + axis.bar( + x + width / 2, + [row["simulator"] for row in rows], + width, + label="Frontier profile-only", + ) + axis.set_xticks(x, labels, fontsize=8) + axis.set_ylabel("Max tested SLO-feasible request rate / GPU") + axis.set_title("Qwen3-30B-A3B prefill-only: config ranking") + axis.grid(axis="y", alpha=0.25) + axis.legend(frameon=False, ncols=2) + for separator in (3.5, 7.5): + axis.axvline(separator, color="0.75", linewidth=0.8) + tau = metrics["kendall"]["kendall_tau_b"] + annotation = f"worst regret={metrics['top1_regret_worst'] * 100:.1f}%" + annotation += ( + f"\nKendall τ-b={tau:.3f}" + if tau is not None + else "\nKendall τ-b=undefined" + ) + axis.text( + 0.01, + 0.98, + annotation, + transform=axis.transAxes, + va="top", + fontsize=9, + bbox={"facecolor": "white", "edgecolor": "0.8", "alpha": 0.9}, + ) + + confusion = metrics["anchor_confusion"] + matrix = np.array( + [ + [confusion["real_pass_sim_pass"], confusion["real_pass_sim_fail"]], + [confusion["real_fail_sim_pass"], confusion["real_fail_sim_fail"]], + ] + ) + image = axes[1].imshow(matrix, cmap="Blues", vmin=0) + axes[1].set_xticks([0, 1], ["Sim pass", "Sim fail"]) + axes[1].set_yticks([0, 1], ["Real pass", "Real fail"]) + axes[1].set_title(f"{int(matrix.sum())} anchor decisions") + for row in range(2): + for column in range(2): + axes[1].text(column, row, int(matrix[row, column]), ha="center", va="center") + figure.colorbar(image, ax=axes[1], fraction=0.047, pad=0.04) + figure.suptitle("ISL=2048, OSL=1, TTFT≤1256 ms, 95% pass gate; two real rounds") + figure.tight_layout() + figure.savefig(path, dpi=180, bbox_inches="tight") + plt.close(figure) + + +def main() -> None: + args = parse_args() + real_runs = find_real_runs(args.fleet_artifacts.resolve()) + real = {name: parse_real_config(name, path) for name, path in real_runs.items()} + simulated, simulator_sources = parse_simulator( + [path.resolve() for path in args.simulator_manifest] + ) + metrics = compare(real, simulated) + rows = [ + { + "config": name, + "tp": real[name]["tp"], + "mns": real[name]["mns"], + "real": real[name]["capacity_per_gpu"], + "simulator": simulated[name]["capacity_per_gpu"], + } + for name in metrics["config_order"] + ] + resources = campaign_resources(args.fleet_artifacts.resolve()) + resources["fresh_server_anchors"] = sum( + len(config["anchors"]) * 2 for config in real.values() + ) + resources["measured_requests"] = resources["fresh_server_anchors"] * 64 + resources["warmup_requests"] = sum( + min(32, max(4, math.ceil(anchor["rate"] * 2.0))) * 2 + for config in real.values() + for anchor in config["anchors"] + ) + args.output_root.mkdir(parents=True, exist_ok=True) + payload = { + "schema": "qwen30-prefill-fidelity-comparison-v1", + "objective": "maximum_tested_slo_feasible_offered_request_rate_per_gpu", + "contract": { + "model": "Qwen3-30B-A3B", + "input_tokens": 2048, + "output_tokens": 1, + "prefix_caching": False, + "ttft_slo_ms": 1256.0, + "target_pass_rate": 0.95, + "real_anchor_merge": "both_fresh_server_rounds_must_pass", + }, + "metrics": metrics, + "real_campaign_resources": resources, + "real": real, + "simulator": simulated, + "simulator_sources": simulator_sources, + } + (args.output_root / "comparison.json").write_text( + json.dumps(payload, indent=2, sort_keys=True) + "\n" + ) + write_csv(args.output_root / "capacity.csv", rows) + plot(args.output_root / "qwen30-prefill-ranking.png", rows, metrics) + print(json.dumps(metrics, indent=2, sort_keys=True)) + + +if __name__ == "__main__": + main() diff --git a/runs/frontier-phase-factorial-v0/experiment-card.md b/runs/frontier-phase-factorial-v0/experiment-card.md index f291ab7..b2623c6 100644 --- a/runs/frontier-phase-factorial-v0/experiment-card.md +++ b/runs/frontier-phase-factorial-v0/experiment-card.md @@ -1,6 +1,6 @@ # 实验 EXP-SIMFID-PHASE-FACTORIAL：prefill-only 是否是 simulator ranking 的容易区间？ -> **状态：** 已批准，运行中（用户于 2026-07-17 明确要求先完成 235B mixed 与 30B prefill-only） +> **状态：** 已完成（2026-07-17） ## Claim 与决策 @@ -17,6 +17,7 @@ - **30B system context：** community vLLM 0.20.0+cu129，BF16 weights/activation/KV，TP∈{1,2,4}，MNS∈{8,16,32,64}，MBT=8192，chunked prefill on，prefix off；real 保留 runtime 默认 CUDA graph，Frontier profile-only 不做 E2E calibration。 - **30B workload：** fixed ISL=2048、OSL=1，64 个不同 token-chain prompts，uniform open-loop QPS；fresh server per `(config, rate, round)`，target-rate warmup 与 measured requests 分离。 - **30B SLO：** TTFT≤1256 ms，至少 61/64 requests 通过；primary score 为最大共同 tested feasible req/s / 实际 TP GPUs。 +- **Boundary refinement rule：** base grid `{4,8,16,32,64}` 先定位每个 TP 的 pass→fail 区间；若除以 TP 后的离散容量产生无法区分的 top tie，则在查看最终 ranking 前追加共同 per-GPU lattice `5/6/7 req/s/GPU`，即 TP1 测 `{5,6,7}`、TP2 测 `{10,12,14}`、TP4 测 `{20,24,28}`。refinement 不替换或删除 base anchors。 - **235B baseline：** 已冻结 `ISL=2048, OSL=128`、8 configs、68 个 fresh-server anchors；primary sensitivity TTFT≤1256 ms、TPOT≤150 ms。 - **Baselines：** real community vLLM；Frontier same-stack profile-only；historical Qwen30 mixed profile-only；historical frozen per-TP calibration 只作为 upper bound，不参与本 case 拟合。 - **Metrics：** top set、worst tie-break regret、Kendall τ-b、exact/non-tied pair direction、anchor confusion、absolute capacity、TTFT p50/p95、real trial variance与GPU-hour。 @@ -35,7 +36,7 @@ | Selective benchmarking | PASS for initial screen | 同时报已有 235B mixed success和内部 pairwise failure；后续 expansion 由预注册 verdict 触发 | | Simplified workload | NEEDS EVIDENCE | fixed-shape 只用于 phase isolation，不外推 trace-faithful mixed | | Calibration=evaluation | PASS | 新 case 不用 serving E2E 数据拟合 scale | -| Missing significance | NEEDS EVIDENCE until run | boundary anchors做独立 fresh-server repeat，保留 disagreement | +| Missing significance | PASS for ranking screen | 96 个 config-rate cells 均做两个独立 fresh-server rounds；两轮都 pass 才算 feasible | | Relative-only result | PASS by design | 同时报 req/s/GPU、TTFT distribution、rank/regret | ## 复现信息 @@ -43,12 +44,13 @@ - **Code：** AITuner branch `codex/fidelity-prefix-pilot-20260714`；Frontier upstream `d9cfeb6d8791fbf2f295dd9744c56a666171776e` + frozen known patches。 - **Environment：** 只使用 dash0 8×H20；Qwen30 venv `/tmp/wjh/venvs/vllm-0.20.0-cu129-profiler-v1`；model `/home/admin/cpfs/wjh/models/Qwen/Qwen3-30B-A3B`。 - **产物路径：** local/remote `runs/frontier-phase-factorial-v0/`；raw GPU artifacts 由 fleet harvest，condensed JSON/CSV 进入结果目录。 -- **已知 deviation：** 235B 为 FP8/vLLM0.10.2/FlashInfer eager，30B 为 BF16/vLLM0.20/FA3/default CUDA graph；因此跨模型只检验 hypothesis consistency，causal phase claim 最终仍需 same-model phase pair。 +- **已知 deviation：** 235B 为 FP8/vLLM0.10.2/FlashInfer eager，30B 为 BF16/vLLM0.20/FA3/default CUDA graph；因此跨模型只检验 hypothesis consistency，causal phase claim 最终仍需 same-model phase pair。初始 continuous fleet monitor 未在 fresh-server 间隙保留 controller-level GPU reservation，产生重叠 launch；该 attempt 整体移动到 `invalid-overlap-*`，不进入统计。后续一次试图在“其余 4 卡”上并行 refinement 时，探针再次命中 TP4 fresh-server 空窗，把 4 个 TP1 jobs 放到了同一 GPU set。这四个 jobs 尚未产生 measured result；但当时 TP4/MNS64 已产生的单个 anchor 也按污染处理。两者都整体移到 `invalid-overlap-20260716T1750Z`，TP4/MNS64 从空目录重跑。此后的 barrier waves 不在运行中追加 job；但 Wave 3 的 harvest monitor 在未及时返回 launch 状态时已发射 MNS16/32，紧接的 monitor retry 又在它们的启动空窗发射 MNS64。三者都未产生 measured result，整体移到 `invalid-overlap-20260716T1836Z`。最终 TP4 waves 使用只含本波 jobs 的独立 queue state，不再依赖 pending-job 探针调度。 ## 结果 -- **观察事实：** 235B fixed-shape mixed 已完成：real/sim TP4 top set exact match，worst regret=0、τ-b=0.8944；但 20 个 real non-tie pairs 只保持 16 个，10/34 anchors false-infeasible，TP8 MNS×MBT interaction 被漏掉。30B prefill-only 待运行。 -- **异常：** 无。 -- **Interpretation 与剩余 alternatives：** 强版本“mixed 必然失败”已被 235B top-set result 削弱；仍可能存在 phase-dependent error magnitude，由 topology margin 掩盖。 -- **Claim update：** unchanged，等待 30B prefill-only。 -- **下一步：** freeze 30B simulator surface → guided real anchors → joint verdict；只有判定需要时扩展 decode-heavy 235B 或 trace-shaped 30B prefill。 +- **观察事实：** 235B fixed-shape mixed 中 real/sim 的四个 TP4 top configs 完全一致，worst regret=0、τ-b=0.8944；但 20 个 real non-tie pairs 只保持 16 个，10/34 anchors false-infeasible，TP8 MNS×MBT interaction 被漏掉。30B prefill-only 中真机 capacity/GPU 为 TP1=7、TP2=7、TP4=8，Frontier 为 TP1=8、TP2=8、TP4=6；real top set 是四个 TP4 configs，simulator top set 是全部八个 TP1/TP2 configs，无交集。worst regret=12.5%、τ-b=-1.0，32 个 real non-tie pairs 中 0 个同序。96 个 anchor labels 中有 8 个 false-feasible 和 8 个 false-infeasible。 +- **实验成本：** 接受 24 个 fleet jobs、192 个 fresh-server anchors、12,288 个 measured requests 和 4,512 个 warmups，消耗 12.0744 H20-GPU-hours。 +- **异常与排除：** fleet controller 在 fresh-server 空窗期没有保留 GPU reservation，产生了三批重叠 launch。污染 attempt 不进入 accepted artifact root，未产生 measured result 的重叠 jobs 也不被计数；同一波中已产生的 TP4/MNS64 单 anchor 同样按污染丢弃，从空目录重跑。最终 TP4 refinement 使用彼此独立的 queue states。analyzer 按 `(round, filename)` 去重相同 harvest copy，如果 hash 冲突则直接报错。 +- **Interpretation 与剩余 alternatives：** `H-phase` 的强形式（prefill-only 是 fidelity 充分条件）被否证；`H-margin` 与数据更一致。235B 的真机 TP4/TP8 最优 margin 为 2×，足以掩盖内部 residual；30B 的 8-vs-7 margin 被 TP-dependent saturation residual 穿过。但这是跨 stack comparison，不能把差异因果归结为 model size。 +- **Claim update：** “prefill-only 容易，decode/mixed 困难”的强假设被否证。新的可证伪命题是：config-ranking fidelity 取决于 scheduler-state-conditioned action residual 是否大于 real decision margin。 +- **下一步：** 不继续扩展跨模型 phase cases。在 Qwen30 prefill-only 上依次做 measured-collective injection、batch-composition-conditioned pure-prefill attention/step profile、TP1@8/TP2@16/TP4@32 的 scheduler batch/queue/per-step trace 对齐，最后再测 routing/graph。 diff --git a/runs/frontier-phase-factorial-v0/fleet-base-rerun.toml b/runs/frontier-phase-factorial-v0/fleet-base-rerun.toml new file mode 100644 index 0000000..5553f93 --- /dev/null +++ b/runs/frontier-phase-factorial-v0/fleet-base-rerun.toml @@ -0,0 +1,24 @@ +version = 1 + +[paths] +state_dir = "runs/frontier-phase-factorial-v0/fleet-state-base-rerun" +artifacts_dir = "runs/frontier-phase-factorial-v0/fleet-artifacts-exclusive" + +[ssh] +connect_timeout_sec = 10 + +[scheduler] +gpu_free_memory_mb = 95000 +gpu_free_utilization_pct = 5 +prefer_pack = true + +[sync] +mode = "scp" +local_path = "runs/frontier-phase-factorial-v0/remote-sync-marker" + +[[hosts]] +name = "dash0" +ssh_alias = "dash0" +enabled = true +sync_remote_path = "/home/admin/cpfs/wjh/aituner/phase-factorial-sync-marker" +fleet_root = "/home/admin/cpfs/wjh/aituner/gpu-fleet-phase-factorial-v0" diff --git a/runs/frontier-phase-factorial-v0/fleet-exclusive.toml b/runs/frontier-phase-factorial-v0/fleet-exclusive.toml new file mode 100644 index 0000000..7de87b2 --- /dev/null +++ b/runs/frontier-phase-factorial-v0/fleet-exclusive.toml @@ -0,0 +1,24 @@ +version = 1 + +[paths] +state_dir = "runs/frontier-phase-factorial-v0/fleet-state-exclusive" +artifacts_dir = "runs/frontier-phase-factorial-v0/fleet-artifacts-exclusive" + +[ssh] +connect_timeout_sec = 10 + +[scheduler] +gpu_free_memory_mb = 95000 +gpu_free_utilization_pct = 5 +prefer_pack = true + +[sync] +mode = "scp" +local_path = "runs/frontier-phase-factorial-v0/remote-sync-marker" + +[[hosts]] +name = "dash0" +ssh_alias = "dash0" +enabled = true +sync_remote_path = "/home/admin/cpfs/wjh/aituner/phase-factorial-sync-marker" +fleet_root = "/home/admin/cpfs/wjh/aituner/gpu-fleet-phase-factorial-v0" diff --git a/runs/frontier-phase-factorial-v0/fleet-refine-tp4-wave3.toml b/runs/frontier-phase-factorial-v0/fleet-refine-tp4-wave3.toml new file mode 100644 index 0000000..a7e6466 --- /dev/null +++ b/runs/frontier-phase-factorial-v0/fleet-refine-tp4-wave3.toml @@ -0,0 +1,24 @@ +version = 1 + +[paths] +state_dir = "runs/frontier-phase-factorial-v0/fleet-state-refine-tp4-wave3" +artifacts_dir = "runs/frontier-phase-factorial-v0/fleet-artifacts-exclusive" + +[ssh] +connect_timeout_sec = 10 + +[scheduler] +gpu_free_memory_mb = 95000 +gpu_free_utilization_pct = 5 +prefer_pack = true + +[sync] +mode = "scp" +local_path = "runs/frontier-phase-factorial-v0/remote-sync-marker" + +[[hosts]] +name = "dash0" +ssh_alias = "dash0" +enabled = true +sync_remote_path = "/home/admin/cpfs/wjh/aituner/phase-factorial-sync-marker" +fleet_root = "/home/admin/cpfs/wjh/aituner/gpu-fleet-phase-factorial-v0" diff --git a/runs/frontier-phase-factorial-v0/fleet-refine-tp4-wave4.toml b/runs/frontier-phase-factorial-v0/fleet-refine-tp4-wave4.toml new file mode 100644 index 0000000..3cba17e --- /dev/null +++ b/runs/frontier-phase-factorial-v0/fleet-refine-tp4-wave4.toml @@ -0,0 +1,24 @@ +version = 1 + +[paths] +state_dir = "runs/frontier-phase-factorial-v0/fleet-state-refine-tp4-wave4" +artifacts_dir = "runs/frontier-phase-factorial-v0/fleet-artifacts-exclusive" + +[ssh] +connect_timeout_sec = 10 + +[scheduler] +gpu_free_memory_mb = 95000 +gpu_free_utilization_pct = 5 +prefer_pack = true + +[sync] +mode = "scp" +local_path = "runs/frontier-phase-factorial-v0/remote-sync-marker" + +[[hosts]] +name = "dash0" +ssh_alias = "dash0" +enabled = true +sync_remote_path = "/home/admin/cpfs/wjh/aituner/phase-factorial-sync-marker" +fleet_root = "/home/admin/cpfs/wjh/aituner/gpu-fleet-phase-factorial-v0" diff --git a/runs/frontier-phase-factorial-v0/fleet-refine.toml b/runs/frontier-phase-factorial-v0/fleet-refine.toml new file mode 100644 index 0000000..786d090 --- /dev/null +++ b/runs/frontier-phase-factorial-v0/fleet-refine.toml @@ -0,0 +1,24 @@ +version = 1 + +[paths] +state_dir = "runs/frontier-phase-factorial-v0/fleet-state-refine" +artifacts_dir = "runs/frontier-phase-factorial-v0/fleet-artifacts-exclusive" + +[ssh] +connect_timeout_sec = 10 + +[scheduler] +gpu_free_memory_mb = 95000 +gpu_free_utilization_pct = 5 +prefer_pack = true + +[sync] +mode = "scp" +local_path = "runs/frontier-phase-factorial-v0/remote-sync-marker" + +[[hosts]] +name = "dash0" +ssh_alias = "dash0" +enabled = true +sync_remote_path = "/home/admin/cpfs/wjh/aituner/phase-factorial-sync-marker" +fleet_root = "/home/admin/cpfs/wjh/aituner/gpu-fleet-phase-factorial-v0" diff --git a/runs/frontier-phase-factorial-v0/jobs_base_rerun.toml b/runs/frontier-phase-factorial-v0/jobs_base_rerun.toml new file mode 100644 index 0000000..2eb2f2d --- /dev/null +++ b/runs/frontier-phase-factorial-v0/jobs_base_rerun.toml @@ -0,0 +1,21 @@ +version = 1 + +[[jobs]] +name = "qwen30-prefill-real-tp4-mns64-20260717-v2b-exclusive" +gpus = 4 +gpu_model = "H20" +hosts = ["dash0"] +command = "cd /home/admin/cpfs/wjh/aituner/aituner-qwen30-vllm020-profile-v1/runs/frontier-phase-factorial-v0 && timeout --signal=TERM --kill-after=30s 7200 bash run_qwen30_prefill_real_config.sh" +artifacts = ["artifacts/real-tp4-mns64-v1"] + +[jobs.env] +HOME = "/tmp/wjh" +XDG_CACHE_HOME = "/tmp/wjh/.cache" +VLLM_CACHE_ROOT = "/tmp/wjh/.cache/vllm" +TP = "4" +MNS = "64" +RATES = "4 8 16 32 64" +SERVER_PORT = "8731" +OUTPUT_ROOT = "/home/admin/cpfs/wjh/aituner/gpu-fleet-phase-factorial-v0/artifacts/real-tp4-mns64-v1" +VENV_ROOT = "/tmp/wjh/venvs/vllm-0.20.0-cu129-profiler-v1" +MODEL_ROOT = "/home/admin/cpfs/wjh/models/Qwen/Qwen3-30B-A3B" diff --git a/runs/frontier-phase-factorial-v0/jobs_full.toml b/runs/frontier-phase-factorial-v0/jobs_full.toml index c67f255..05d7621 100644 --- a/runs/frontier-phase-factorial-v0/jobs_full.toml +++ b/runs/frontier-phase-factorial-v0/jobs_full.toml @@ -1,7 +1,7 @@ version = 1 [[jobs]] -name = "qwen30-prefill-real-tp1-mns8-20260717-v1" +name = "qwen30-prefill-real-tp1-mns8-20260717-v2-exclusive" gpus = 1 gpu_model = "H20" hosts = ["dash0"] @@ -21,7 +21,7 @@ VENV_ROOT = "/tmp/wjh/venvs/vllm-0.20.0-cu129-profiler-v1" MODEL_ROOT = "/home/admin/cpfs/wjh/models/Qwen/Qwen3-30B-A3B" [[jobs]] -name = "qwen30-prefill-real-tp1-mns16-20260717-v1" +name = "qwen30-prefill-real-tp1-mns16-20260717-v2-exclusive" gpus = 1 gpu_model = "H20" hosts = ["dash0"] @@ -41,7 +41,7 @@ VENV_ROOT = "/tmp/wjh/venvs/vllm-0.20.0-cu129-profiler-v1" MODEL_ROOT = "/home/admin/cpfs/wjh/models/Qwen/Qwen3-30B-A3B" [[jobs]] -name = "qwen30-prefill-real-tp1-mns32-20260717-v1" +name = "qwen30-prefill-real-tp1-mns32-20260717-v2-exclusive" gpus = 1 gpu_model = "H20" hosts = ["dash0"] @@ -61,7 +61,7 @@ VENV_ROOT = "/tmp/wjh/venvs/vllm-0.20.0-cu129-profiler-v1" MODEL_ROOT = "/home/admin/cpfs/wjh/models/Qwen/Qwen3-30B-A3B" [[jobs]] -name = "qwen30-prefill-real-tp1-mns64-20260717-v1" +name = "qwen30-prefill-real-tp1-mns64-20260717-v2-exclusive" gpus = 1 gpu_model = "H20" hosts = ["dash0"] @@ -81,7 +81,7 @@ VENV_ROOT = "/tmp/wjh/venvs/vllm-0.20.0-cu129-profiler-v1" MODEL_ROOT = "/home/admin/cpfs/wjh/models/Qwen/Qwen3-30B-A3B" [[jobs]] -name = "qwen30-prefill-real-tp2-mns8-20260717-v1" +name = "qwen30-prefill-real-tp2-mns8-20260717-v2-exclusive" gpus = 2 gpu_model = "H20" hosts = ["dash0"] @@ -101,7 +101,7 @@ VENV_ROOT = "/tmp/wjh/venvs/vllm-0.20.0-cu129-profiler-v1" MODEL_ROOT = "/home/admin/cpfs/wjh/models/Qwen/Qwen3-30B-A3B" [[jobs]] -name = "qwen30-prefill-real-tp2-mns16-20260717-v1" +name = "qwen30-prefill-real-tp2-mns16-20260717-v2-exclusive" gpus = 2 gpu_model = "H20" hosts = ["dash0"] @@ -121,7 +121,7 @@ VENV_ROOT = "/tmp/wjh/venvs/vllm-0.20.0-cu129-profiler-v1" MODEL_ROOT = "/home/admin/cpfs/wjh/models/Qwen/Qwen3-30B-A3B" [[jobs]] -name = "qwen30-prefill-real-tp2-mns32-20260717-v1" +name = "qwen30-prefill-real-tp2-mns32-20260717-v2-exclusive" gpus = 2 gpu_model = "H20" hosts = ["dash0"] @@ -141,7 +141,7 @@ VENV_ROOT = "/tmp/wjh/venvs/vllm-0.20.0-cu129-profiler-v1" MODEL_ROOT = "/home/admin/cpfs/wjh/models/Qwen/Qwen3-30B-A3B" [[jobs]] -name = "qwen30-prefill-real-tp2-mns64-20260717-v1" +name = "qwen30-prefill-real-tp2-mns64-20260717-v2-exclusive" gpus = 2 gpu_model = "H20" hosts = ["dash0"] @@ -161,7 +161,7 @@ VENV_ROOT = "/tmp/wjh/venvs/vllm-0.20.0-cu129-profiler-v1" MODEL_ROOT = "/home/admin/cpfs/wjh/models/Qwen/Qwen3-30B-A3B" [[jobs]] -name = "qwen30-prefill-real-tp4-mns8-20260717-v1" +name = "qwen30-prefill-real-tp4-mns8-20260717-v2-exclusive" gpus = 4 gpu_model = "H20" hosts = ["dash0"] @@ -181,7 +181,7 @@ VENV_ROOT = "/tmp/wjh/venvs/vllm-0.20.0-cu129-profiler-v1" MODEL_ROOT = "/home/admin/cpfs/wjh/models/Qwen/Qwen3-30B-A3B" [[jobs]] -name = "qwen30-prefill-real-tp4-mns16-20260717-v1" +name = "qwen30-prefill-real-tp4-mns16-20260717-v2-exclusive" gpus = 4 gpu_model = "H20" hosts = ["dash0"] @@ -201,7 +201,7 @@ VENV_ROOT = "/tmp/wjh/venvs/vllm-0.20.0-cu129-profiler-v1" MODEL_ROOT = "/home/admin/cpfs/wjh/models/Qwen/Qwen3-30B-A3B" [[jobs]] -name = "qwen30-prefill-real-tp4-mns32-20260717-v1" +name = "qwen30-prefill-real-tp4-mns32-20260717-v2-exclusive" gpus = 4 gpu_model = "H20" hosts = ["dash0"] @@ -221,7 +221,7 @@ VENV_ROOT = "/tmp/wjh/venvs/vllm-0.20.0-cu129-profiler-v1" MODEL_ROOT = "/home/admin/cpfs/wjh/models/Qwen/Qwen3-30B-A3B" [[jobs]] -name = "qwen30-prefill-real-tp4-mns64-20260717-v1" +name = "qwen30-prefill-real-tp4-mns64-20260717-v2-exclusive" gpus = 4 gpu_model = "H20" hosts = ["dash0"] diff --git a/runs/frontier-phase-factorial-v0/jobs_refine.toml b/runs/frontier-phase-factorial-v0/jobs_refine.toml new file mode 100644 index 0000000..55fd01d --- /dev/null +++ b/runs/frontier-phase-factorial-v0/jobs_refine.toml @@ -0,0 +1,240 @@ +version = 1 + +[[jobs]] +name = "qwen30-prefill-real-tp1-mns8-20260717-v3-refine" +gpus = 1 +gpu_model = "H20" +hosts = ["dash0"] +command = "cd /home/admin/cpfs/wjh/aituner/aituner-qwen30-vllm020-profile-v1/runs/frontier-phase-factorial-v0 && timeout --signal=TERM --kill-after=30s 7200 bash run_qwen30_prefill_real_config.sh" +artifacts = ["artifacts/real-tp1-mns8-v1"] + +[jobs.env] +HOME = "/tmp/wjh" +XDG_CACHE_HOME = "/tmp/wjh/.cache" +VLLM_CACHE_ROOT = "/tmp/wjh/.cache/vllm" +TP = "1" +MNS = "8" +RATES = "5 6 7" +SERVER_PORT = "8720" +OUTPUT_ROOT = "/home/admin/cpfs/wjh/aituner/gpu-fleet-phase-factorial-v0/artifacts/real-tp1-mns8-v1" +VENV_ROOT = "/tmp/wjh/venvs/vllm-0.20.0-cu129-profiler-v1" +MODEL_ROOT = "/home/admin/cpfs/wjh/models/Qwen/Qwen3-30B-A3B" +[[jobs]] +name = "qwen30-prefill-real-tp1-mns16-20260717-v3-refine" +gpus = 1 +gpu_model = "H20" +hosts = ["dash0"] +command = "cd /home/admin/cpfs/wjh/aituner/aituner-qwen30-vllm020-profile-v1/runs/frontier-phase-factorial-v0 && timeout --signal=TERM --kill-after=30s 7200 bash run_qwen30_prefill_real_config.sh" +artifacts = ["artifacts/real-tp1-mns16-v1"] + +[jobs.env] +HOME = "/tmp/wjh" +XDG_CACHE_HOME = "/tmp/wjh/.cache" +VLLM_CACHE_ROOT = "/tmp/wjh/.cache/vllm" +TP = "1" +MNS = "16" +RATES = "5 6 7" +SERVER_PORT = "8721" +OUTPUT_ROOT = "/home/admin/cpfs/wjh/aituner/gpu-fleet-phase-factorial-v0/artifacts/real-tp1-mns16-v1" +VENV_ROOT = "/tmp/wjh/venvs/vllm-0.20.0-cu129-profiler-v1" +MODEL_ROOT = "/home/admin/cpfs/wjh/models/Qwen/Qwen3-30B-A3B" + +[[jobs]] +name = "qwen30-prefill-real-tp1-mns32-20260717-v3-refine" +gpus = 1 +gpu_model = "H20" +hosts = ["dash0"] +command = "cd /home/admin/cpfs/wjh/aituner/aituner-qwen30-vllm020-profile-v1/runs/frontier-phase-factorial-v0 && timeout --signal=TERM --kill-after=30s 7200 bash run_qwen30_prefill_real_config.sh" +artifacts = ["artifacts/real-tp1-mns32-v1"] + +[jobs.env] +HOME = "/tmp/wjh" +XDG_CACHE_HOME = "/tmp/wjh/.cache" +VLLM_CACHE_ROOT = "/tmp/wjh/.cache/vllm" +TP = "1" +MNS = "32" +RATES = "5 6 7" +SERVER_PORT = "8722" +OUTPUT_ROOT = "/home/admin/cpfs/wjh/aituner/gpu-fleet-phase-factorial-v0/artifacts/real-tp1-mns32-v1" +VENV_ROOT = "/tmp/wjh/venvs/vllm-0.20.0-cu129-profiler-v1" +MODEL_ROOT = "/home/admin/cpfs/wjh/models/Qwen/Qwen3-30B-A3B" + +[[jobs]] +name = "qwen30-prefill-real-tp1-mns64-20260717-v3-refine" +gpus = 1 +gpu_model = "H20" +hosts = ["dash0"] +command = "cd /home/admin/cpfs/wjh/aituner/aituner-qwen30-vllm020-profile-v1/runs/frontier-phase-factorial-v0 && timeout --signal=TERM --kill-after=30s 7200 bash run_qwen30_prefill_real_config.sh" +artifacts = ["artifacts/real-tp1-mns64-v1"] + +[jobs.env] +HOME = "/tmp/wjh" +XDG_CACHE_HOME = "/tmp/wjh/.cache" +VLLM_CACHE_ROOT = "/tmp/wjh/.cache/vllm" +TP = "1" +MNS = "64" +RATES = "5 6 7" +SERVER_PORT = "8723" +OUTPUT_ROOT = "/home/admin/cpfs/wjh/aituner/gpu-fleet-phase-factorial-v0/artifacts/real-tp1-mns64-v1" +VENV_ROOT = "/tmp/wjh/venvs/vllm-0.20.0-cu129-profiler-v1" +MODEL_ROOT = "/home/admin/cpfs/wjh/models/Qwen/Qwen3-30B-A3B" + +[[jobs]] +name = "qwen30-prefill-real-tp2-mns8-20260717-v3-refine" +gpus = 2 +gpu_model = "H20" +hosts = ["dash0"] +command = "cd /home/admin/cpfs/wjh/aituner/aituner-qwen30-vllm020-profile-v1/runs/frontier-phase-factorial-v0 && timeout --signal=TERM --kill-after=30s 7200 bash run_qwen30_prefill_real_config.sh" +artifacts = ["artifacts/real-tp2-mns8-v1"] + +[jobs.env] +HOME = "/tmp/wjh" +XDG_CACHE_HOME = "/tmp/wjh/.cache" +VLLM_CACHE_ROOT = "/tmp/wjh/.cache/vllm" +TP = "2" +MNS = "8" +RATES = "10 12 14" +SERVER_PORT = "8724" +OUTPUT_ROOT = "/home/admin/cpfs/wjh/aituner/gpu-fleet-phase-factorial-v0/artifacts/real-tp2-mns8-v1" +VENV_ROOT = "/tmp/wjh/venvs/vllm-0.20.0-cu129-profiler-v1" +MODEL_ROOT = "/home/admin/cpfs/wjh/models/Qwen/Qwen3-30B-A3B" + +[[jobs]] +name = "qwen30-prefill-real-tp2-mns16-20260717-v3-refine" +gpus = 2 +gpu_model = "H20" +hosts = ["dash0"] +command = "cd /home/admin/cpfs/wjh/aituner/aituner-qwen30-vllm020-profile-v1/runs/frontier-phase-factorial-v0 && timeout --signal=TERM --kill-after=30s 7200 bash run_qwen30_prefill_real_config.sh" +artifacts = ["artifacts/real-tp2-mns16-v1"] + +[jobs.env] +HOME = "/tmp/wjh" +XDG_CACHE_HOME = "/tmp/wjh/.cache" +VLLM_CACHE_ROOT = "/tmp/wjh/.cache/vllm" +TP = "2" +MNS = "16" +RATES = "10 12 14" +SERVER_PORT = "8725" +OUTPUT_ROOT = "/home/admin/cpfs/wjh/aituner/gpu-fleet-phase-factorial-v0/artifacts/real-tp2-mns16-v1" +VENV_ROOT = "/tmp/wjh/venvs/vllm-0.20.0-cu129-profiler-v1" +MODEL_ROOT = "/home/admin/cpfs/wjh/models/Qwen/Qwen3-30B-A3B" + +[[jobs]] +name = "qwen30-prefill-real-tp2-mns32-20260717-v3-refine" +gpus = 2 +gpu_model = "H20" +hosts = ["dash0"] +command = "cd /home/admin/cpfs/wjh/aituner/aituner-qwen30-vllm020-profile-v1/runs/frontier-phase-factorial-v0 && timeout --signal=TERM --kill-after=30s 7200 bash run_qwen30_prefill_real_config.sh" +artifacts = ["artifacts/real-tp2-mns32-v1"] + +[jobs.env] +HOME = "/tmp/wjh" +XDG_CACHE_HOME = "/tmp/wjh/.cache" +VLLM_CACHE_ROOT = "/tmp/wjh/.cache/vllm" +TP = "2" +MNS = "32" +RATES = "10 12 14" +SERVER_PORT = "8726" +OUTPUT_ROOT = "/home/admin/cpfs/wjh/aituner/gpu-fleet-phase-factorial-v0/artifacts/real-tp2-mns32-v1" +VENV_ROOT = "/tmp/wjh/venvs/vllm-0.20.0-cu129-profiler-v1" +MODEL_ROOT = "/home/admin/cpfs/wjh/models/Qwen/Qwen3-30B-A3B" + +[[jobs]] +name = "qwen30-prefill-real-tp2-mns64-20260717-v3-refine" +gpus = 2 +gpu_model = "H20" +hosts = ["dash0"] +command = "cd /home/admin/cpfs/wjh/aituner/aituner-qwen30-vllm020-profile-v1/runs/frontier-phase-factorial-v0 && timeout --signal=TERM --kill-after=30s 7200 bash run_qwen30_prefill_real_config.sh" +artifacts = ["artifacts/real-tp2-mns64-v1"] + +[jobs.env] +HOME = "/tmp/wjh" +XDG_CACHE_HOME = "/tmp/wjh/.cache" +VLLM_CACHE_ROOT = "/tmp/wjh/.cache/vllm" +TP = "2" +MNS = "64" +RATES = "10 12 14" +SERVER_PORT = "8727" +OUTPUT_ROOT = "/home/admin/cpfs/wjh/aituner/gpu-fleet-phase-factorial-v0/artifacts/real-tp2-mns64-v1" +VENV_ROOT = "/tmp/wjh/venvs/vllm-0.20.0-cu129-profiler-v1" +MODEL_ROOT = "/home/admin/cpfs/wjh/models/Qwen/Qwen3-30B-A3B" + +[[jobs]] +name = "qwen30-prefill-real-tp4-mns8-20260717-v3-refine" +gpus = 4 +gpu_model = "H20" +hosts = ["dash0"] +command = "cd /home/admin/cpfs/wjh/aituner/aituner-qwen30-vllm020-profile-v1/runs/frontier-phase-factorial-v0 && timeout --signal=TERM --kill-after=30s 7200 bash run_qwen30_prefill_real_config.sh" +artifacts = ["artifacts/real-tp4-mns8-v1"] + +[jobs.env] +HOME = "/tmp/wjh" +XDG_CACHE_HOME = "/tmp/wjh/.cache" +VLLM_CACHE_ROOT = "/tmp/wjh/.cache/vllm" +TP = "4" +MNS = "8" +RATES = "20 24 28" +SERVER_PORT = "8728" +OUTPUT_ROOT = "/home/admin/cpfs/wjh/aituner/gpu-fleet-phase-factorial-v0/artifacts/real-tp4-mns8-v1" +VENV_ROOT = "/tmp/wjh/venvs/vllm-0.20.0-cu129-profiler-v1" +MODEL_ROOT = "/home/admin/cpfs/wjh/models/Qwen/Qwen3-30B-A3B" + +[[jobs]] +name = "qwen30-prefill-real-tp4-mns16-20260717-v3-refine" +gpus = 4 +gpu_model = "H20" +hosts = ["dash0"] +command = "cd /home/admin/cpfs/wjh/aituner/aituner-qwen30-vllm020-profile-v1/runs/frontier-phase-factorial-v0 && timeout --signal=TERM --kill-after=30s 7200 bash run_qwen30_prefill_real_config.sh" +artifacts = ["artifacts/real-tp4-mns16-v1"] + +[jobs.env] +HOME = "/tmp/wjh" +XDG_CACHE_HOME = "/tmp/wjh/.cache" +VLLM_CACHE_ROOT = "/tmp/wjh/.cache/vllm" +TP = "4" +MNS = "16" +RATES = "20 24 28" +SERVER_PORT = "8729" +OUTPUT_ROOT = "/home/admin/cpfs/wjh/aituner/gpu-fleet-phase-factorial-v0/artifacts/real-tp4-mns16-v1" +VENV_ROOT = "/tmp/wjh/venvs/vllm-0.20.0-cu129-profiler-v1" +MODEL_ROOT = "/home/admin/cpfs/wjh/models/Qwen/Qwen3-30B-A3B" + +[[jobs]] +name = "qwen30-prefill-real-tp4-mns32-20260717-v3-refine" +gpus = 4 +gpu_model = "H20" +hosts = ["dash0"] +command = "cd /home/admin/cpfs/wjh/aituner/aituner-qwen30-vllm020-profile-v1/runs/frontier-phase-factorial-v0 && timeout --signal=TERM --kill-after=30s 7200 bash run_qwen30_prefill_real_config.sh" +artifacts = ["artifacts/real-tp4-mns32-v1"] + +[jobs.env] +HOME = "/tmp/wjh" +XDG_CACHE_HOME = "/tmp/wjh/.cache" +VLLM_CACHE_ROOT = "/tmp/wjh/.cache/vllm" +TP = "4" +MNS = "32" +RATES = "20 24 28" +SERVER_PORT = "8730" +OUTPUT_ROOT = "/home/admin/cpfs/wjh/aituner/gpu-fleet-phase-factorial-v0/artifacts/real-tp4-mns32-v1" +VENV_ROOT = "/tmp/wjh/venvs/vllm-0.20.0-cu129-profiler-v1" +MODEL_ROOT = "/home/admin/cpfs/wjh/models/Qwen/Qwen3-30B-A3B" + +[[jobs]] +name = "qwen30-prefill-real-tp4-mns64-20260717-v3-refine" +gpus = 4 +gpu_model = "H20" +hosts = ["dash0"] +command = "cd /home/admin/cpfs/wjh/aituner/aituner-qwen30-vllm020-profile-v1/runs/frontier-phase-factorial-v0 && timeout --signal=TERM --kill-after=30s 7200 bash run_qwen30_prefill_real_config.sh" +artifacts = ["artifacts/real-tp4-mns64-v1"] + +[jobs.env] +HOME = "/tmp/wjh" +XDG_CACHE_HOME = "/tmp/wjh/.cache" +VLLM_CACHE_ROOT = "/tmp/wjh/.cache/vllm" +TP = "4" +MNS = "64" +RATES = "20 24 28" +SERVER_PORT = "8731" +OUTPUT_ROOT = "/home/admin/cpfs/wjh/aituner/gpu-fleet-phase-factorial-v0/artifacts/real-tp4-mns64-v1" +VENV_ROOT = "/tmp/wjh/venvs/vllm-0.20.0-cu129-profiler-v1" +MODEL_ROOT = "/home/admin/cpfs/wjh/models/Qwen/Qwen3-30B-A3B" diff --git a/runs/frontier-phase-factorial-v0/jobs_refine_tp4_wave3.toml b/runs/frontier-phase-factorial-v0/jobs_refine_tp4_wave3.toml new file mode 100644 index 0000000..b830690 --- /dev/null +++ b/runs/frontier-phase-factorial-v0/jobs_refine_tp4_wave3.toml @@ -0,0 +1,41 @@ +version = 1 + +[[jobs]] +name = "qwen30-prefill-real-tp4-mns16-20260717-v4-refine" +gpus = 4 +gpu_model = "H20" +hosts = ["dash0"] +command = "cd /home/admin/cpfs/wjh/aituner/aituner-qwen30-vllm020-profile-v1/runs/frontier-phase-factorial-v0 && timeout --signal=TERM --kill-after=30s 7200 bash run_qwen30_prefill_real_config.sh" +artifacts = ["artifacts/real-tp4-mns16-v1"] + +[jobs.env] +HOME = "/tmp/wjh" +XDG_CACHE_HOME = "/tmp/wjh/.cache" +VLLM_CACHE_ROOT = "/tmp/wjh/.cache/vllm" +TP = "4" +MNS = "16" +RATES = "20 24 28" +SERVER_PORT = "8729" +OUTPUT_ROOT = "/home/admin/cpfs/wjh/aituner/gpu-fleet-phase-factorial-v0/artifacts/real-tp4-mns16-v1" +VENV_ROOT = "/tmp/wjh/venvs/vllm-0.20.0-cu129-profiler-v1" +MODEL_ROOT = "/home/admin/cpfs/wjh/models/Qwen/Qwen3-30B-A3B" + +[[jobs]] +name = "qwen30-prefill-real-tp4-mns32-20260717-v4-refine" +gpus = 4 +gpu_model = "H20" +hosts = ["dash0"] +command = "cd /home/admin/cpfs/wjh/aituner/aituner-qwen30-vllm020-profile-v1/runs/frontier-phase-factorial-v0 && timeout --signal=TERM --kill-after=30s 7200 bash run_qwen30_prefill_real_config.sh" +artifacts = ["artifacts/real-tp4-mns32-v1"] + +[jobs.env] +HOME = "/tmp/wjh" +XDG_CACHE_HOME = "/tmp/wjh/.cache" +VLLM_CACHE_ROOT = "/tmp/wjh/.cache/vllm" +TP = "4" +MNS = "32" +RATES = "20 24 28" +SERVER_PORT = "8730" +OUTPUT_ROOT = "/home/admin/cpfs/wjh/aituner/gpu-fleet-phase-factorial-v0/artifacts/real-tp4-mns32-v1" +VENV_ROOT = "/tmp/wjh/venvs/vllm-0.20.0-cu129-profiler-v1" +MODEL_ROOT = "/home/admin/cpfs/wjh/models/Qwen/Qwen3-30B-A3B" diff --git a/runs/frontier-phase-factorial-v0/jobs_refine_tp4_wave4.toml b/runs/frontier-phase-factorial-v0/jobs_refine_tp4_wave4.toml new file mode 100644 index 0000000..b69872d --- /dev/null +++ b/runs/frontier-phase-factorial-v0/jobs_refine_tp4_wave4.toml @@ -0,0 +1,21 @@ +version = 1 + +[[jobs]] +name = "qwen30-prefill-real-tp4-mns64-20260717-v4-refine" +gpus = 4 +gpu_model = "H20" +hosts = ["dash0"] +command = "cd /home/admin/cpfs/wjh/aituner/aituner-qwen30-vllm020-profile-v1/runs/frontier-phase-factorial-v0 && timeout --signal=TERM --kill-after=30s 7200 bash run_qwen30_prefill_real_config.sh" +artifacts = ["artifacts/real-tp4-mns64-v1"] + +[jobs.env] +HOME = "/tmp/wjh" +XDG_CACHE_HOME = "/tmp/wjh/.cache" +VLLM_CACHE_ROOT = 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"0e4dcba72918a1c4cf4e96ced31ee3829248a19ad54553cebef14417725808b0" + }, + "profile_id": "qwen3-30b-a3b-bf16-vllm020-h20-tp1-2-4-fused-mixed-total-conserving", + "projection_contract": { + "allreduce": "Frozen exact runtime measurements; base profile-only comparison keeps the historical Frontier CC backend fixed to isolate compute profile fidelity", + "attention": "Pure prefill/extend/decode FA3 core plus separately measured KV update; input/output reshape assumed zero; exported mean is used as median target; true mixed rows use a total-conserving compatibility projection", + "attention_true_mixed": "The directly measured fused total is preserved in diagnostics. Frontier's two targets are projected by the same-TP pure prefill/decode reference ratio, with projected prefill + decode exactly equal to the fused total; the split is a schema compatibility attribution, not an observation", + "linear": "Frontier profiler using vLLM 0.20 CUDA operators", + "moe": "Replicated gate and fused top-k plus TP-local modular expert kernel; expert measurement already includes prepare/finalize so shuffling is zero" + }, + "row_counts": { + "allreduce": 24, + "attention_frontier_compatible": 132, + "attention_true_mixed_fused_diagnostic": 30, + "linear": 36, + "moe": 72 + }, + "schema_version": "frontier_qwen30_vllm020_frozen_profile.v2" + } + }, + "root": "/home/gahow/phd/aituner/runs/frontier-qwen30-vllm020-profile-v1/frozen/profile-v2", + "sha256": { + "attention": "76dcb767cebb4ec1c4e24bd04d93ddd48b5d271986ebfb51a197ab33e1b3d87d", + "linear": "67666cb0a4901b74599d468df2e31bcaa2a11a7842cc0cefba24ffce62508e0c", + "manifest": "af40545e75aff55c6333cd2d5379ccf042a5a0b7d7fc7df4f745ce256cb290eb", + "moe": "0e4dcba72918a1c4cf4e96ced31ee3829248a19ad54553cebef14417725808b0" + } + }, + "schema": "frontier-qwen30-prefill-surface-v1", + "status": "partial_not_decision_bearing" +} diff --git a/runs/frontier-phase-factorial-v0/test_phase_factorial.py b/runs/frontier-phase-factorial-v0/test_phase_factorial.py index 6e30aa5..4172926 100644 --- a/runs/frontier-phase-factorial-v0/test_phase_factorial.py +++ b/runs/frontier-phase-factorial-v0/test_phase_factorial.py @@ -37,3 +37,9 @@ def test_grid_and_trace(tmp_path: Path) -> None: assert len(lines) == 4 assert lines[1].split(",")[:3] == ["0.000000000000", "2048", "1"] assert lines[3].split(",")[:3] == ["0.500000000000", "2048", "1"] + + +def test_kendall_tau_b() -> None: + analysis = load("analyze_qwen30_prefill_fidelity.py") + assert analysis.kendall_tau_b([1, 2, 3], [1, 2, 3])["kendall_tau_b"] == 1 + assert analysis.kendall_tau_b([1, 2, 3], [3, 2, 1])["kendall_tau_b"] == -1 diff --git a/simulator-fidelity.md b/simulator-fidelity.md index 728ce14..b646d5b 100644 --- a/simulator-fidelity.md +++ b/simulator-fidelity.md @@ -1,6 +1,6 @@ # Frontier simulator fidelity：以 config ranking 为目标的阶段性评测 -更新日期：2026-07-16。 +更新日期：2026-07-17。 统一实验平台：所有新增与重跑实验只使用 `dash0` 的 8×NVIDIA H20。Qwen30B 的真实 P1 artifacts 也来自 `dash0`；早期文档中的主机 provenance 标注错误，本版已按实验主机和远端 artifact 路径更正。 @@ -14,11 +14,12 @@ - 将 operator profile 更新为与真实 serving 一致的 community vLLM `0.20.0`、BF16、H20、TP1/2/4 栈后，新的 profile-only Frontier **没有恢复排序**：92 个真实 anchors 在 simulator 中全部 SLO-infeasible，12 个 config 因而全部并列，最坏 tie-break regret 为 `60.91%`。这否证了“旧 profile 版本不一致是主要原因”这一简单解释，并把问题收敛到 execution context、operator composition 与 mixed-state schema。 - 在 Qwen3-235B-A22B-FP8 prefill-only 上，补齐 FP8/MoE profile 与 serving semantics、但不做端到端 action calibration 后，Frontier 的最优集合与真机完全一致，Spearman ρ 为 `0.9487`，20/20 个可比较非 tie config pair 同序，选择 regret 为 `0`。 - 在 Qwen3-235B-A22B-FP8 fixed-shape mixed 上，Frontier 同样给出了与真机完全相同的 TP4 top set，Kendall τ-b=`0.8944`、worst tie-break regret=`0`。但这次成功掩盖了一个明确的机制错误：Frontier 认为同一 TP family 内四个 MNS/MBT config 完全等价，真机 TP8 capacity 却形成对角为 0.30、非对角为 0.20 req/s/GPU 的 checkerboard interaction。34 个 config-load labels 中有 10 个 false-infeasible；20 个 real non-tie pairs 中只保持 16 个方向。 -- 现有结果表明：**在已对齐的受控 case 中，绝对 capacity 有 gap、甚至 action differential 错误，都不必然妨碍 Frontier 找到当前 surface 的最优 config。** 统一 dash0 campaign 已完整覆盖 Qwen235 FP8 prefill-only 与 fixed-shape mixed，但仍不能外推到 trace-faithful mixed 或 decode-only，也不能忽略达到这种 fidelity 所需的真机 profile、KV capacity、runtime-semantic patch 或 calibration 成本。 +- 新增的 Qwen3-30B-A3B BF16 prefill-only 对照在没有 decode、prefix reuse 和 true-mixed attention 的情况下仍然失败：真机 top set 是四个 TP4 config，Frontier top set 却是全部 TP1/TP2 config，top set 无交集，worst regret=`12.5%`、Kendall τ-b=`-1.0`，32 个 real non-tie pairs 全部反向。这直接否证了“prefill-only 是 simulator ranking 充分容易条件”。 +- 现有结果更支持一个 **margin-aware fidelity** 解释：action-differential residual 可以很大，但只有当它足以穿过 real decision margin 时才会导致选错。Qwen235 mixed 中真机 TP4 相对最好 TP8 的 margin 是 `2×`，足以掩盖同 TP family 内的机制错误；Qwen30 prefill-only 中 TP scaling 差分更细，Frontier 的 residual 直接反转了 topology ordering。模型大小或 execution phase 单独都不足以解释现有结果。 因此，现阶段最准确的 research statement 不是“simulator 排序一定错误”，而是： -> Simulator 的 usefulness 取决于 compatibility envelope。问题是需要多少、什么类型的真机信息，才能使其 config ranking 可被信任；以及当 workload、runtime 或 execution topology 变化时，如何低成本判断该 envelope 是否仍成立。 +> Simulator 的 usefulness 取决于 action-differential residual 是否小于真实 decision margin。问题不是简单区分 prefill 与 mixed，而是定位哪些 scheduler-state-conditioned execution residual 会随 TP、batch composition 和负载被放大，并用最少的真机信息判断 ranking 是否足够可信。 ## 评测口径 @@ -89,6 +90,22 @@ Frontier 原生 profile schema 可以按 `num_tensor_parallel_workers` 选择 TP 该 case 是有意设计的 prefill mechanism-isolation workload：output 固定为 1，prefix cache 关闭，并使用固定 64-request cohort。它不保持原 trace 的 output-length distribution 和 prefix reuse，因此不能被称为 trace-faithful workload，也不能代表 mixed serving。 +### Case D：Qwen3-30B-A3B BF16 prefill-only + +| 项目 | 设置 | +|---|---| +| 真机 | `dash0`，8×NVIDIA H20；每个 job 按 TP 独占 GPU | +| model/runtime | `Qwen/Qwen3-30B-A3B`；community vLLM `0.20.0+cu129`（source `88d34c6409e9fb3c7b8ca0c04756f061d2099eb1`），BF16 weights/activation/KV，FA3，runtime 默认 CUDA graph | +| workload | 64 个不同 token-chain prompts；ISL=2,048、OSL=1、uniform open-loop QPS、prefix cache off | +| config surface | `TP∈{1,2,4} × MNS∈{8,16,32,64}`，MBT=8,192，chunked prefill on，共 12 cells | +| simulator | Frontier commit `d9cfeb6d8791fbf2f295dd9744c56a666171776e` + 当前 compatibility patches + vLLM 0.20 same-stack `profile-v2`；无 E2E calibration、decode graph=`none`、analytical collective | +| load lattice | base system rates `{4,8,16,32,64}` req/s；事先声明的 common refinement 为 `{5,6,7}` req/s/GPU | +| real anchor | 每个 `(config,rate,round)` fresh server；两轮独立运行且第二轮反转顺序；两轮都 pass 才算 feasible | +| SLO | 至少 61/64 requests 满足 TTFT≤1,256 ms | +| primary objective | 最大被测试可行 offered req/s / 实际 TP GPUs | + +这个 case 是对“prefill-only 是 simulator 的容易区间”的直接可证伪测试。它刻意去掉 decode、true-mixed batch、prefix reuse 和 KV-residency feedback，但保留 TP 改变下的 local shard、collective、batching 与 scheduler queue。Frontier surface 在查看真机 ranking 前冻结，没有使用本 surface 的 serving latency 拟合 scale。 + ## Workload fidelity 修正 `thinking_w20260327_1000` 的 600 秒原始窗口包含 15,479 requests，字段包括 exact prompt、arrival、`input_length`、`output_length`、session parent/turn 和 block-size=64 的 `hash_ids`。只读审计得到： @@ -126,10 +143,11 @@ trace 的 source hash block size 为 64，而 community vLLM 0.10.2 的 CUDA KV | Qwen30 mixed / old profile-only | 12 | TP2,MNS32 | TP4,MNS32/64 | miss | τ-b=0.000；exact sign=37.88% | 25.63% | 排序错误 | | Qwen30 mixed / vLLM 0.20 profile-only | 12 | TP2,MNS32 | 全部 12 configs | 不可辨识 | τ-b=0.000；exact sign=7.58% | 60.91% | 同栈 raw profile 仍不足 | | Qwen30 mixed / frozen per-TP calibration | 12 | TP2,MNS32 | TP2,MNS32/64 | hit，非 exact | τ-b=0.9668；exact sign=93.94% | 0.76% | dash0 | +| Qwen30 BF16 prefill-only / vLLM 0.20 profile-only | 12 | 全部 TP4 configs | 全部 TP1/TP2 configs | miss，无交集 | τ-b=-1.0000；real non-tie=0/32 | 12.50% | topology order 反转 | | Qwen235 FP8 prefill / best-effort | 8 | TP4,MBT16K,MNS64/128 | 与 real 完全相同 | exact match | ρ=0.9487；non-tied 20/20 | 0 | 足以选最优 config | | Qwen235 FP8 fixed-shape mixed / frozen full profile | 8 | 四个 TP4 configs | 与 real 完全相同 | exact match | τ-b=0.8944；exact sign=24/28；real non-tie=16/20 | 0 | 选对 topology；漏掉 TP8 MNS×MBT interaction | -最重要的区别是：Qwen30 的 high-fidelity 结果依赖 per-TP E2E calibration；Qwen235 的 high-fidelity 结果不依赖本 case 的 E2E calibration，但依赖同 runtime/hardware 的 operator profile、真实 KV capacity 和多处 compatibility fixes。二者都不能表述为“拿 stock Frontier 零成本预测即可”。 +最重要的区别是：Qwen30 的 high-fidelity mixed 结果依赖 per-TP E2E calibration，而新 prefill-only profile-only 实验证明“去掉 decode/mixed 状态”也不足以恢复 ranking。Qwen235 的 high-fidelity 结果不依赖本 case 的 E2E calibration，但依赖同 runtime/hardware 的 operator profile、真实 KV capacity 和多处 compatibility fixes。两个 model 的 stack 和精度不同，所以不能把差异直接归因为 model size；二者也都不能表述为“拿 stock Frontier 零成本预测即可”。 ## Case A baseline 结果：Qwen30 mixed serving @@ -311,6 +329,53 @@ MNS 128 fail pass 原预注册的 TPOT 40 ms 因最低负载已不可行而失去 capacity-ranking 可辨识性；本文已把 150 ms 明确记录为 post-pilot protocol amendment，并保留 40 ms 全部失败的 sensitivity 结果，没有把 SLO change 隐藏成预注册结论。 +## Case D 结果：Qwen30 BF16 prefill-only + +![Qwen3-30B BF16 prefill-only simulator-vs-real config ranking](docs/assets/simulator-fidelity/qwen30-prefill-ranking.png) + +结果不是“绝对 capacity 有偏差但 rank 可用”，而是完整的 topology ordering reversal： + +| TP | MNS | Real | Frontier profile-only | 差异 | +|---:|---|---:|---:|---:| +| 1 | 8/16/32/64 | 7.0 | **8.0** | +1.0 | +| 2 | 8/16/32/64 | 7.0 | **8.0** | +1.0 | +| 4 | 8/16/32/64 | **8.0** | 6.0 | -2.0 | + +单位为最大被测试 SLO-feasible req/s/GPU。四个 TP4 configs 是真机 top set；Frontier 却把全部八个 TP1/TP2 configs 判为 top set，两者无交集。部署任一 simulator top config 都只能在真机上得到 7 req/s/GPU，因此 optimistic 与 worst tie-break regret 都为 `1-7/8=12.5%`。Kendall τ-b=`-1.0`；32 个 real non-tie pairs 中 0 个同序。96 个 config-load decisions 中 80 个一致，但同时有 8 个 false-feasible 和 8 个 false-infeasible，它们刚好跨过了不同 TP family 的 capacity boundary。 + +### 错误从哪里开始放大 + +代表性 `MNS=8` anchors 表明，低负载单次 execution time 并没有数倍偏差，错误主要在接近饱和时被 queue 放大： + +| Config / system rate | Real TTFT p95（两轮） | Frontier TTFT p95 | Real / sim SLO | +|---|---:|---:|---| +| TP1 @ 4 req/s | 154.1 / 153.6 ms | 171.6 ms | pass / pass | +| TP1 @ 8 req/s | 1289.2 / 1254.4 ms | 516.4 ms | fail / pass | +| TP2 @ 8 req/s | 97.7 / 93.8 ms | 122.2 ms | pass / pass | +| TP2 @ 16 req/s | 1389.9 / 1365.0 ms | 962.8 ms | fail / pass | +| TP4 @ 4 req/s | 82.3 / 69.6 ms | 93.1 ms | pass / pass | +| TP4 @ 32 req/s | 1136.6 / 1134.0 ms | 1787.9 ms | pass / fail | + +这排除了“只要给所有 kernel 乘一个 global scale 就能修好”的解释：相同 stack 在低负载下接近，但在不同 TP 的饱和边界上向相反方向偏移。更符合数据的抽象是：小的 per-step composition residual 改变 service rate，再通过 scheduler queue 的非线性反馈放大成 TTFT 和 capacity 边界错误。 + +当前证据可以排除一些原因，但还不能唯一定位根因： + +1. **Decode/true-mixed schema 不是必要条件。** 这个 workload 没有 decode、FULL decode CUDA graph、fused true-mixed attention 或 initial-KV state，排序仍然失败。这些机制可能在 mixed/decode 中进一步增大误差，但不是本次失败的前提。 +2. **Communication 可能解释部分 TP scaling，但不能单独解释全部误差。** `profile-v2/allreduce.json` 已有 24 个 TP2/TP4 实测 rows，但 base Frontier 为了保持历史对照使用 analytical 600-Gbps/1-µs collective，没有注入这些数据。下一步必须单独做 measured-collective ablation；但 TP1 无 collective 仍在 8 req/s 出现 773 ms 的 p95 低估，所以 collective 不会是唯一根因。 +3. **Profile 覆盖了 token count，却未必覆盖 scheduler 实际产生的 batch composition。** linear/MoE rows 覆盖到 8,192 tokens，但 exact-2,048 pure-prefill attention 在每个 TP 下只有 batch=1 的直接样本；实际 MBT=8,192 可以产生多个 2,048-token sequence 的组合。这是一个需要补测的 coverage hypothesis，不是已证明根因。 +4. **Routing、step composition 和 scheduler batch state 仍然是联合候选。** pure-prefill 只排除了 phase mixing，没有排除 MoE routing、TP-specific local shape、launch/fusion 和持续负载下 batch/queue trajectory 的耦合。 + +### 为什么 235B 能选对，30B 却选错 + +现有数据不支持“235B 大所以 simulator 更容易”这种模型大小因果。两个 case 的 precision、vLLM 版本、attention backend、graph mode、EP 和 profile closure 都不同。能被数据直接支持的区别是 **decision margin**： + +- 235B mixed 真机中 TP4 最优 capacity 为 0.60 req/s/GPU，最好 TP8 为 0.30，有 `2×` margin；Frontier 虽然漏掉 TP8 的 MNS×MBT interaction，仍预测 TP4/TP8 为 0.40/0.15，所以 residual 没有穿过全局拓扑边界。 +- 30B prefill-only 真机 TP4 相对 TP1/TP2 只有 8 vs. 7 req/s/GPU 的 margin。Frontier 预测为 6 vs. 8，TP-dependent residual 大于真实 margin，因而把最优 topology 完整反转。 + +因此，“prefill-only 容易，decode/mixed 困难”的强假设已被否证。phase 仍可能改变 residual 的幅度，但它不是 fidelity 的充分条件。按预注册 decision rule，此时不应继续无区分度地扩展更多 phase cases，而应在 Qwen30 同一 model/workload 上按单变量顺序做：`measured collective injection` → `batch-composition-conditioned pure-prefill attention/step profile` → 在 TP1@8、TP2@16、TP4@32 对齐 real/sim scheduler batch、queue 与 per-step critical path → 最后再测 routing/graph。 + +本 case 接受的 ground truth 共 24 个 fleet jobs、192 个 fresh-server anchors、12,288 个 measured requests 和 4,512 个 warmup requests，消耗 12.07 H20-GPU-hours。运行中曾因 fleet controller 在 fresh-server 空窗误判 GPU 为空闲而产生重叠 launch；这些 attempts 未被合并到 accepted artifact root，已整体隔离并用独立 queue state 重跑。最终 comparison SHA256 为 `c9a9cac9f60c7be804d1cb9466c455f8fe9e3a8dc60b9cec3329bde6a8c19334`。 + ## 尚不能纳入 simulator ranking 的 case ### 原 internal-runtime Qwen235 prefill-only @@ -328,11 +393,12 @@ MNS 128 fail pass 当前证据的结论是： 1. **绝对 gap 不是否决 simulator 的理由。** Qwen235 中 11%--50% 的 capacity 低估仍可保持 zero-regret config selection。 -2. **但 rank 成功不能证明模型机制正确。** Qwen235 prefill 的 TP8 MBT differential 错误，fixed-shape mixed 又漏掉 TP8 的 MNS×MBT 非加性交互；Qwen30 还有 28/92 anchor SLO labels 错误。只是当前 decision margin 足以容忍这些 residual。 -3. **alignment 是结果的一部分。** Qwen30 需要 per-TP E2E calibration 才能恢复排序；Qwen235 不需要本 case E2E calibration，但需要同栈 FP8 profiles、真机 KV capacity 和 simulator patches。论文必须报告这部分真机成本，不能把它隐藏在“offline profiles”中。 -4. **目前不能建立“simulator 全局选优普遍失败”的 premise。** 受控 prefill、fixed-shape mixed 和历史 mixed surface 上，经过 alignment 的 Frontier 已经能选到最优或 0.76% 内的近最优 config。更有证据的 research premise 是：aggregate selection success 会掩盖 action interaction 与 state-transition model 的错误；若要声称全局选优失败，仍必须在 dash0 的 trace-faithful mixed、decode-only、prefix cache 或跨 topology case 上得到稳定反例。 +2. **rank 成功不能证明模型机制正确，rank 失败则表明 residual 穿过了 decision margin。** Qwen235 prefill 的 TP8 MBT differential 错误，fixed-shape mixed 又漏掉 TP8 的 MNS×MBT 非加性交互，但大 topology margin 保住了 top set；Qwen30 prefill-only 的较小 margin 被 TP-dependent saturation residual 穿过，导致 τ-b=-1 和 12.5% regret。 +3. **prefill-only 不是 fidelity 的充分容易条件。** Qwen30 在没有 decode、prefix reuse、initial KV 和 true-mixed batch 时仍选错 topology。phase 可以改变 residual，但不能单独作为 compatibility-envelope 边界。 +4. **alignment 是结果的一部分。** Qwen30 mixed 需要 per-TP E2E calibration 才能恢复排序，同栈 operator profile 或去掉 mixed phase 都不足；Qwen235 不需要本 case E2E calibration，但需要同栈 FP8 profiles、真机 KV capacity 和 simulator patches。论文必须报告这部分真机成本，不能把它隐藏在“offline profiles”中。 +5. **现在有了可复现的全局选优反例，但尚不足以声称 simulator 普遍失败。** Qwen30 BF16 prefill-only 是一个无 top-set overlap 的稳定反例；Qwen235 两个 case 又证明 Frontier 在某些 envelope 内足以选优。更有研究价值的 premise 是：**什么 state-conditioned action residual 决定 ranking 是否能跨过 real margin，以及如何在不做全 surface 真机 sweep 的情况下检测这个风险。** -后续每个 case 建议使用同一 gate：worst selected-config regret ≤5%、tie-aware rank correlation ≥0.8、足够数量的 informative pairs、ground-truth bracket 不足以反转最优决策，并单独报告达到该结果所需的 real-GPU profiling/calibration cost。 +后续每个 case 建议使用同一 gate：worst selected-config regret ≤5%、tie-aware rank correlation ≥0.8、足够数量的 informative pairs、ground-truth bracket 不足以反转最优决策，并单独报告达到该结果所需的 real-GPU profiling/calibration cost。当前下一 gate 是在 Qwen30 prefill-only 上做同模型单变量 context ablation，而不是继续扩展跨模型 phase 矩阵。 ## 数据与复现 @@ -340,6 +406,10 @@ MNS 128 fail pass - 画图脚本：[plot_simulator_fidelity.py](scripts/plot_simulator_fidelity.py) - Qwen30 audit：[report.md](runs/frontier-multicase-sufficiency-v0/results/qwen30-baseline/report.md) - Qwen30 aligned metrics：[metrics.json](runs/frontier-slo-alignment-v0/results/metrics.json) +- Qwen30 prefill-only experiment card：[experiment-card.md](runs/frontier-phase-factorial-v0/experiment-card.md) +- Qwen30 prefill-only comparison：[comparison.json](runs/frontier-phase-factorial-v0/results/final/comparison.json) +- Qwen30 prefill-only capacity table：[capacity.csv](runs/frontier-phase-factorial-v0/results/final/capacity.csv) +- Qwen30 prefill-only analyzer：[analyze_qwen30_prefill_fidelity.py](runs/frontier-phase-factorial-v0/analyze_qwen30_prefill_fidelity.py) - Qwen235 fixed-cohort comparison：[v2_refined_comparison.json](runs/frontier-multicase-sufficiency-v0/best_effort/fixed_cohort_evidence/v2_refined_comparison.json) - Qwen235 full report：[report.md](runs/frontier-multicase-sufficiency-v0/best_effort/fixed_cohort_evidence/report.md) - Fixed-shape mixed comparison：[comparison.json](runs/frontier-multicase-sufficiency-v1/results/t0-final/comparison.json) @@ -357,4 +427,17 @@ MNS 128 fail pass ```bash python3 scripts/plot_simulator_fidelity.py + +python3 runs/frontier-phase-factorial-v0/analyze_qwen30_prefill_fidelity.py \ + --fleet-artifacts runs/frontier-phase-factorial-v0/fleet-artifacts-exclusive \ + --simulator-manifest runs/frontier-phase-factorial-v0/simulator-tp1/frontier_surface_frozen.json \ + --simulator-manifest runs/frontier-phase-factorial-v0/simulator-tp2/frontier_surface_frozen.json \ + --simulator-manifest runs/frontier-phase-factorial-v0/simulator-tp4/frontier_surface_frozen.json \ + --simulator-manifest runs/frontier-phase-factorial-v0/simulator-refine-tp1/frontier_surface_frozen.json \ + --simulator-manifest runs/frontier-phase-factorial-v0/simulator-refine-tp2/frontier_surface_frozen.json \ + --simulator-manifest runs/frontier-phase-factorial-v0/simulator-refine-tp4/frontier_surface_frozen.json \ + --output-root runs/frontier-phase-factorial-v0/results/final + +cp runs/frontier-phase-factorial-v0/results/final/qwen30-prefill-ranking.png \ + docs/assets/simulator-fidelity/qwen30-prefill-ranking.png ```