Close BC8 decode curve counterfactual
This commit is contained in:
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fleet-artifacts/
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fleet-artifacts/
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fleet-state/
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fleet-state/
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remote-outputs/
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remote-outputs/
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replay/
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152
runs/frontier-decode-batch-grid-v0/analyze_bc8_replay.py
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152
runs/frontier-decode-batch-grid-v0/analyze_bc8_replay.py
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@@ -0,0 +1,152 @@
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#!/usr/bin/env python3
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"""Compare original and whole-layer-curve Frontier against the BC-8 real pilot."""
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from __future__ import annotations
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import json
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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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ROOT = Path(__file__).resolve().parent
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REPO = ROOT.parents[1]
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CONFIGS = ("tp1_mns16", "tp2_mns16", "tp4_mns16", "tp4_mns32")
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REPLAY = ROOT / "replay/bc8"
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ORIGINAL = REPO / "runs/frontier-knee-sweep-v0/raw/fixed/rho0p02"
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REAL = REPO / "runs/frontier-pilot-v0/results/pilot-surface.json"
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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} under {root}, got {matches}")
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return matches[0]
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def summarize_sim(root: Path) -> dict:
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metrics = json.loads(find_one(root, "system_metrics.json").read_text())
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ledger = find_one(root, "frontier_stage_batch_ledger.jsonl")
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histogram: Counter[int] = Counter()
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service_ms: dict[int, set[float]] = {}
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for line in ledger.read_text().splitlines():
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row = json.loads(line)
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tokens = row["request_num_tokens"]
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if tokens and all(int(value) == 1 for value in tokens):
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batch = len(tokens)
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histogram[batch] += 1
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service_ms.setdefault(batch, set()).add(
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float(row["execution_time"]["model_time_ms"])
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)
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return {
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"tpot_mean_ms": metrics["tpot_statistics"]["mean"],
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"ttft_mean_ms": metrics["ttft_statistics"]["mean"],
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"e2e_mean_ms": metrics["request_e2e_time_statistics"]["mean"],
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"decode_batch_histogram": dict(sorted(histogram.items())),
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"decode_service_ms": {
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str(batch): sorted(values) for batch, values in sorted(service_ms.items())
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},
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"decode_b_gt_1_fraction": sum(
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count for batch, count in histogram.items() if batch > 1
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)
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/ sum(histogram.values()),
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}
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def main() -> None:
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real_rows = [
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row
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for row in json.loads(REAL.read_text())
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if row["load"] == "p4" and row["canonical"]
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]
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points = []
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for config in CONFIGS:
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real = [row for row in real_rows if row["config"] == config]
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original = summarize_sim(ORIGINAL / config)
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corrected = summarize_sim(REPLAY / "raw" / config)
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usage = json.loads((REPLAY / "raw" / config / "usage.json").read_text())
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points.append(
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{
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"config": config,
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"tp": int(config[2]),
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"mns": int(config.split("mns")[1]),
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"real_tpot_mean_ms": statistics.fmean(
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row["tpot_mean_ms"] for row in real
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),
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"original": original,
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"whole_curve": corrected,
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"original_tpot_residual_ms": (
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original["tpot_mean_ms"]
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- statistics.fmean(row["tpot_mean_ms"] for row in real)
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),
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"whole_curve_tpot_residual_ms": (
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corrected["tpot_mean_ms"]
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- statistics.fmean(row["tpot_mean_ms"] for row in real)
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),
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"usage": usage,
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}
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)
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def ranking(key):
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return [
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point["config"]
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for point in sorted(points, key=lambda point: (key(point), point["config"]))
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if point["mns"] == 16
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]
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real_ranking = ranking(lambda point: point["real_tpot_mean_ms"])
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original_ranking = ranking(lambda point: point["original"]["tpot_mean_ms"])
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corrected_ranking = ranking(lambda point: point["whole_curve"]["tpot_mean_ms"])
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payload = {
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"schema": "frontier-decode-grid-bc8-verdict.v1",
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"points": points,
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"mns16_rankings_fast_to_slow": {
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"real": real_ranking,
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"original": original_ranking,
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"whole_curve": corrected_ranking,
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},
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"whole_curve_restores_real_mns16_ranking": corrected_ranking == real_ranking,
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"all_corrected_batches_within_measured_support": all(
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max(map(int, point["whole_curve"]["decode_batch_histogram"])) <= 8
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for point in points
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),
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"decision": (
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"STATIC_WHOLE_CURVE_FIXES_BC8"
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if corrected_ranking == real_ranking
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else "ESCALATE_EVENT_LEVEL_STATE"
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),
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}
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output = ROOT / "results/bc8-replay-verdict.json"
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output.write_text(json.dumps(payload, indent=2, sort_keys=True) + "\n")
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lines = [
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"# BC-8 whole-layer decode-curve replay",
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"",
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"| Config | Real TPOT | Original sim | Whole-curve sim | Residual before -> after | b>1 |",
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"|---|---:|---:|---:|---:|---:|",
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]
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for point in points:
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lines.append(
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f"| {point['config']} | {point['real_tpot_mean_ms']:.3f} | "
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f"{point['original']['tpot_mean_ms']:.3f} | "
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f"{point['whole_curve']['tpot_mean_ms']:.3f} | "
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f"{point['original_tpot_residual_ms']:+.3f} -> "
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f"{point['whole_curve_tpot_residual_ms']:+.3f} | "
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f"{point['whole_curve']['decode_b_gt_1_fraction']:.2%} |"
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)
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lines.extend(
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[
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"",
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f"- Real MNS16: `{' < '.join(real_ranking)}`",
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f"- Original: `{' < '.join(original_ranking)}`",
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f"- Whole curve: `{' < '.join(corrected_ranking)}`",
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f"- Decision: **{payload['decision']}**.",
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"",
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]
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)
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(ROOT / "results/bc8-replay-verdict.md").write_text("\n".join(lines))
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print(json.dumps({"output": str(output), "decision": payload["decision"]}))
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if __name__ == "__main__":
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main()
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@@ -1,6 +1,6 @@
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# 实验 EXP-DECODE-BATCH-GRID：BC-8 是否可由稳定 whole-layer b2--b8 curve 修复
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# 实验 EXP-DECODE-BATCH-GRID：BC-8 是否可由稳定 whole-layer b2--b8 curve 修复
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> **状态：** TP4/b2 stability gate PASS，扩展 full grid
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> **状态：** COMPLETED；BC-8 排序工程修复成立，global curve merge 暂停
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>
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>
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> Parent campaign：[`../frontier-simulator-gap-campaign-v0/README.md`](../frontier-simulator-gap-campaign-v0/README.md)
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> Parent campaign：[`../frontier-simulator-gap-campaign-v0/README.md`](../frontier-simulator-gap-campaign-v0/README.md)
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@@ -54,9 +54,10 @@
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## 复现信息
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## 复现信息
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- **Frontier baseline：** `deadc4a321f0baaa534c6ebd17f974123733cdc2`；
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- **Frontier baseline：** `deadc4a321f0baaa534c6ebd17f974123733cdc2`；
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joint serving-path curves + structured-attention analysis branch。
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CPU replay 使用既有 joint serving-path curves，未合入 EXP-1 rejected patch。
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- **Remote：** 复用 dash1 的 clean detached experiment worktree；canonical
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- **Remote：** dash1--dash4 共享 clean detached experiment worktree
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checkout 的用户 dirty changes 不动。
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`ecc559938132851d61bf22daa55cea9658807dfa`；canonical checkout 的用户
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dirty changes 未改动。
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- **Known limits：** 固定 2048→128 state-matched workload；本实验只支持
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- **Known limits：** 固定 2048→128 state-matched workload；本实验只支持
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decode curve 与 BC-8，不外推到 chat prefill shape 或其它模型。
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decode curve 与 BC-8，不外推到 chat prefill shape 或其它模型。
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@@ -68,6 +69,34 @@
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`4.6263 ms` 和 `4.6201 ms`，跨 repeat CV=`0.067%`，稳定性 gate
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`4.6263 ms` 和 `4.6201 ms`，跨 repeat CV=`0.067%`，稳定性 gate
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PASS，无需 repeat 3。median=`4.6232 ms`，比旧样本 `5.044 ms` 低
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PASS，无需 repeat 3。median=`4.6232 ms`，比旧样本 `5.044 ms` 低
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`0.421 ms`；方向支持 H1，但未达到事前 `>=0.5 ms` 的强证据阈值。
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`0.421 ms`；方向支持 H1，但未达到事前 `>=0.5 ms` 的强证据阈值。
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- **观察事实：** 旧 TP4/b2 的高方差主要是测量不稳定；是否足以修复
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- **Full grid：**
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BC-8 仍须 full b2--b8 curve 与 trace replay。
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- **Decision：** 待 GPU。
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| Cell | fresh-process execute mean (ms) | median (ms) | initial CV |
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|---|---:|---:|---:|
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| TP2/b2 | 4.761 / 4.730 | 4.746 | 0.33% |
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| TP2/b4 | 5.294 / 5.607 | 5.451 | 2.87% |
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| TP2/b6 | 6.571 / 6.938 | 6.754 | 2.72% |
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| TP2/b8 | 5.967 / 6.309 | 6.138 | 2.78% |
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| TP4/b2 | 4.626 / 4.620 | 4.623 | 0.07% |
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| TP4/b4 | 4.516 / 4.727 | 4.622 | 2.27% |
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| TP4/b6 | 5.433 / 10.157 / 8.634 | 8.634 | 30.30% |
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| TP4/b8 | 4.898 / 5.047 | 4.972 | 1.50% |
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- **TP4/b6 instability：** r2/r3 的 MoE 与 attention 基本不变，均值波动由
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同步 collective 长尾造成；三个 run 的 step p50 仍约 `5.18--5.49 ms`。
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依预注册规则追加 r3 并取三次中位数，但该 cell 不能视为稳定 deterministic
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service constant。
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- **BC-8 exact replay：** replay 仅访问 b1/b2，因此没有使用 b6 或任何
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插值/外推。TP4/b2 whole-layer override 将 TP4 sim TPOT
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`5.864→5.367 ms`，残差 `+1.414→+0.918 ms`（MNS16）和
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`+1.498→+1.001 ms`（MNS32）；MNS16 完整排序从
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`TP2<TP1<TP4` 恢复为真实的 `TP2<TP4<TP1`。sim b>1 stage fraction
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`48.15%→37.47%`。
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- **7-cell support audit：** 最新 1h cells 的 decode batch 最大到
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`b6--b15`，而本实验 measured support 只到 b8；TP4/b6 又未通过
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deterministic-mean 稳定性。为避免重新引入 b8 constant extrapolation，
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不执行全局 7-cell injection，也不把整条 b2--b8 curve 合入默认 predictor。
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- **Decision：** H1 对 BC-8 成立，排序 gap 属于可工程修复的 TP4/b2
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whole-layer service residual；但绝对残差、MNS tie 和高 batch collective
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tail 仍需 event/distribution-aware 模型。建议先合入受作用域保护的 TP4/b2
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correction，再补 b9--b16 与 collective-tail telemetry 后开启 global merge。
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@@ -0,0 +1,222 @@
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{
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"all_corrected_batches_within_measured_support": true,
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"decision": "STATIC_WHOLE_CURVE_FIXES_BC8",
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"mns16_rankings_fast_to_slow": {
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"original": [
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"tp2_mns16",
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"tp1_mns16",
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"tp4_mns16"
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],
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"real": [
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"tp2_mns16",
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"tp4_mns16",
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"tp1_mns16"
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],
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"whole_curve": [
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"tp2_mns16",
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"tp4_mns16",
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"tp1_mns16"
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]
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},
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"points": [
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{
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"config": "tp1_mns16",
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"mns": 16,
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"original": {
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"decode_b_gt_1_fraction": 0.0,
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"decode_batch_histogram": {
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"1": 16383
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},
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"decode_service_ms": {
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"1": [
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5.493362394
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]
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},
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"e2e_mean_ms": 864.3525078423435,
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"tpot_mean_ms": 5.493362394020072,
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"ttft_mean_ms": 166.6954838017941
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},
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"original_tpot_residual_ms": 0.616915450202109,
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"real_tpot_mean_ms": 4.876446943817963,
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"tp": 1,
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"usage": {
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"collective:moe:tp1-b1:structural-zero": 16383,
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"moe:tp1-b1": 16383
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},
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"whole_curve": {
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"decode_b_gt_1_fraction": 0.0,
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"decode_batch_histogram": {
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|
"1": 16383
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|
},
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|
"decode_service_ms": {
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|
"1": [
|
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|
5.493362394
|
||||||
|
]
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||||||
|
},
|
||||||
|
"e2e_mean_ms": 864.3525078423435,
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"tpot_mean_ms": 5.493362394020072,
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"ttft_mean_ms": 166.6954838017941
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|
},
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"whole_curve_tpot_residual_ms": 0.616915450202109
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},
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{
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"config": "tp2_mns16",
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"mns": 16,
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"original": {
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"decode_b_gt_1_fraction": 0.0,
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"decode_batch_histogram": {
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|
"1": 16383
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||||||
|
},
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"decode_service_ms": {
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|
"1": [
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|
4.928319445
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|
]
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},
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"e2e_mean_ms": 744.4521695355745,
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"tpot_mean_ms": 4.9283194445308744,
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"ttft_mean_ms": 118.55560008015348
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},
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"original_tpot_residual_ms": 0.6235698385840074,
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"real_tpot_mean_ms": 4.304749605946867,
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"tp": 2,
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"usage": {
|
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"collective:attention:tp2-b1": 16383,
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"collective:moe:tp2-b1": 16383,
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"fused_norm_deduction:attn:tp2-b1": 16383,
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||||||
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"fused_norm_deduction:mlp:tp2-b1": 16383,
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"moe:tp2-b1": 16383
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},
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"whole_curve": {
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"decode_b_gt_1_fraction": 0.0,
|
||||||
|
"decode_batch_histogram": {
|
||||||
|
"1": 16383
|
||||||
|
},
|
||||||
|
"decode_service_ms": {
|
||||||
|
"1": [
|
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|
4.928319445
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"e2e_mean_ms": 744.4521695355745,
|
||||||
|
"tpot_mean_ms": 4.9283194445308744,
|
||||||
|
"ttft_mean_ms": 118.55560008015348
|
||||||
|
},
|
||||||
|
"whole_curve_tpot_residual_ms": 0.6235698385840074
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"config": "tp4_mns16",
|
||||||
|
"mns": 16,
|
||||||
|
"original": {
|
||||||
|
"decode_b_gt_1_fraction": 0.4814983594604448,
|
||||||
|
"decode_batch_histogram": {
|
||||||
|
"1": 5689,
|
||||||
|
"2": 5283
|
||||||
|
},
|
||||||
|
"decode_service_ms": {
|
||||||
|
"1": [
|
||||||
|
4.738406304
|
||||||
|
],
|
||||||
|
"2": [
|
||||||
|
5.411344412
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"e2e_mean_ms": 840.3052136178922,
|
||||||
|
"tpot_mean_ms": 5.863803518571768,
|
||||||
|
"ttft_mean_ms": 95.60216675927755
|
||||||
|
},
|
||||||
|
"original_tpot_residual_ms": 1.4143723087101598,
|
||||||
|
"real_tpot_mean_ms": 4.449431209861608,
|
||||||
|
"tp": 4,
|
||||||
|
"usage": {
|
||||||
|
"collective:attention:tp4-b1": 7393,
|
||||||
|
"collective:attention:tp4-b2": 4431,
|
||||||
|
"collective:moe:tp4-b1": 7393,
|
||||||
|
"collective:moe:tp4-b2": 4431,
|
||||||
|
"fused_norm_deduction:attn:tp4-b1": 7393,
|
||||||
|
"fused_norm_deduction:attn:tp4-b2": 4431,
|
||||||
|
"fused_norm_deduction:mlp:tp4-b1": 7393,
|
||||||
|
"fused_norm_deduction:mlp:tp4-b2": 4431,
|
||||||
|
"moe:tp4-b1": 7393,
|
||||||
|
"moe:tp4-b2": 4431,
|
||||||
|
"whole_decode:tp4-b2:target_ms=4.623176437": 4431
|
||||||
|
},
|
||||||
|
"whole_curve": {
|
||||||
|
"decode_b_gt_1_fraction": 0.37474627875507444,
|
||||||
|
"decode_batch_histogram": {
|
||||||
|
"1": 7393,
|
||||||
|
"2": 4431
|
||||||
|
},
|
||||||
|
"decode_service_ms": {
|
||||||
|
"1": [
|
||||||
|
4.738406304
|
||||||
|
],
|
||||||
|
"2": [
|
||||||
|
4.623176437
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"e2e_mean_ms": 777.2050553866409,
|
||||||
|
"tpot_mean_ms": 5.367470055757525,
|
||||||
|
"ttft_mean_ms": 95.5363583054353
|
||||||
|
},
|
||||||
|
"whole_curve_tpot_residual_ms": 0.918038845895917
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"config": "tp4_mns32",
|
||||||
|
"mns": 32,
|
||||||
|
"original": {
|
||||||
|
"decode_b_gt_1_fraction": 0.4814983594604448,
|
||||||
|
"decode_batch_histogram": {
|
||||||
|
"1": 5689,
|
||||||
|
"2": 5283
|
||||||
|
},
|
||||||
|
"decode_service_ms": {
|
||||||
|
"1": [
|
||||||
|
4.738406304
|
||||||
|
],
|
||||||
|
"2": [
|
||||||
|
5.411344412
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"e2e_mean_ms": 840.3052136178922,
|
||||||
|
"tpot_mean_ms": 5.863803518571768,
|
||||||
|
"ttft_mean_ms": 95.60216675927755
|
||||||
|
},
|
||||||
|
"original_tpot_residual_ms": 1.4977765516959103,
|
||||||
|
"real_tpot_mean_ms": 4.366026966875857,
|
||||||
|
"tp": 4,
|
||||||
|
"usage": {
|
||||||
|
"collective:attention:tp4-b1": 7393,
|
||||||
|
"collective:attention:tp4-b2": 4431,
|
||||||
|
"collective:moe:tp4-b1": 7393,
|
||||||
|
"collective:moe:tp4-b2": 4431,
|
||||||
|
"fused_norm_deduction:attn:tp4-b1": 7393,
|
||||||
|
"fused_norm_deduction:attn:tp4-b2": 4431,
|
||||||
|
"fused_norm_deduction:mlp:tp4-b1": 7393,
|
||||||
|
"fused_norm_deduction:mlp:tp4-b2": 4431,
|
||||||
|
"moe:tp4-b1": 7393,
|
||||||
|
"moe:tp4-b2": 4431,
|
||||||
|
"whole_decode:tp4-b2:target_ms=4.623176437": 4431
|
||||||
|
},
|
||||||
|
"whole_curve": {
|
||||||
|
"decode_b_gt_1_fraction": 0.37474627875507444,
|
||||||
|
"decode_batch_histogram": {
|
||||||
|
"1": 7393,
|
||||||
|
"2": 4431
|
||||||
|
},
|
||||||
|
"decode_service_ms": {
|
||||||
|
"1": [
|
||||||
|
4.738406304
|
||||||
|
],
|
||||||
|
"2": [
|
||||||
|
4.623176437
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"e2e_mean_ms": 777.2050553866409,
|
||||||
|
"tpot_mean_ms": 5.367470055757525,
|
||||||
|
"ttft_mean_ms": 95.5363583054353
|
||||||
|
},
|
||||||
|
"whole_curve_tpot_residual_ms": 1.0014430888816674
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"schema": "frontier-decode-grid-bc8-verdict.v1",
|
||||||
|
"whole_curve_restores_real_mns16_ranking": true
|
||||||
|
}
|
||||||
@@ -0,0 +1,13 @@
|
|||||||
|
# BC-8 whole-layer decode-curve replay
|
||||||
|
|
||||||
|
| Config | Real TPOT | Original sim | Whole-curve sim | Residual before -> after | b>1 |
|
||||||
|
|---|---:|---:|---:|---:|---:|
|
||||||
|
| tp1_mns16 | 4.876 | 5.493 | 5.493 | +0.617 -> +0.617 | 0.00% |
|
||||||
|
| tp2_mns16 | 4.305 | 4.928 | 4.928 | +0.624 -> +0.624 | 0.00% |
|
||||||
|
| tp4_mns16 | 4.449 | 5.864 | 5.367 | +1.414 -> +0.918 | 37.47% |
|
||||||
|
| tp4_mns32 | 4.366 | 5.864 | 5.367 | +1.498 -> +1.001 | 37.47% |
|
||||||
|
|
||||||
|
- Real MNS16: `tp2_mns16 < tp4_mns16 < tp1_mns16`
|
||||||
|
- Original: `tp2_mns16 < tp1_mns16 < tp4_mns16`
|
||||||
|
- Whole curve: `tp2_mns16 < tp4_mns16 < tp1_mns16`
|
||||||
|
- Decision: **STATIC_WHOLE_CURVE_FIXES_BC8**.
|
||||||
410
runs/frontier-decode-batch-grid-v0/results/grid.json
Normal file
410
runs/frontier-decode-batch-grid-v0/results/grid.json
Normal file
@@ -0,0 +1,410 @@
|
|||||||
|
{
|
||||||
|
"all_repeat_gates_resolved": true,
|
||||||
|
"cells": [
|
||||||
|
{
|
||||||
|
"all_repeat_cv_pct": 0.33215115035261283,
|
||||||
|
"batch": 2,
|
||||||
|
"initial_repeat_cv_pct": 0.33215115035261283,
|
||||||
|
"median_component_ms": {
|
||||||
|
"attention": 0.7968654999999999,
|
||||||
|
"collective": 0.4589650625,
|
||||||
|
"linear_norm_rope": 0.9332466874999996,
|
||||||
|
"moe": 2.1423742343749987,
|
||||||
|
"other": 0.020707093750000002,
|
||||||
|
"output_head": 0.08698887500000001,
|
||||||
|
"router": 0.19049718749999994
|
||||||
|
},
|
||||||
|
"median_execute_ms": 4.74559925,
|
||||||
|
"needs_repeat_3": false,
|
||||||
|
"repeat_gate_resolved": true,
|
||||||
|
"repeats": [
|
||||||
|
{
|
||||||
|
"component_rank_mean_ms": {
|
||||||
|
"attention": 0.7962288124999999,
|
||||||
|
"collective": 0.470928375,
|
||||||
|
"linear_norm_rope": 0.9330912187499996,
|
||||||
|
"moe": 2.136206281249999,
|
||||||
|
"other": 0.02065321875,
|
||||||
|
"output_head": 0.08696040625000001,
|
||||||
|
"router": 0.19041484374999995
|
||||||
|
},
|
||||||
|
"execute_mean_ms": 4.7613618125,
|
||||||
|
"repeat": 1,
|
||||||
|
"source": "runs/frontier-decode-batch-grid-v0/results/tp2-b2-r1.json"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"component_rank_mean_ms": {
|
||||||
|
"attention": 0.7975021874999999,
|
||||||
|
"collective": 0.44700175,
|
||||||
|
"linear_norm_rope": 0.9334021562499997,
|
||||||
|
"moe": 2.1485421874999986,
|
||||||
|
"other": 0.02076096875,
|
||||||
|
"output_head": 0.08701734375,
|
||||||
|
"router": 0.19057953124999993
|
||||||
|
},
|
||||||
|
"execute_mean_ms": 4.7298366875,
|
||||||
|
"repeat": 2,
|
||||||
|
"source": "runs/frontier-decode-batch-grid-v0/results/tp2-b2-r2.json"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"tp": 2
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"all_repeat_cv_pct": 2.8664467148791393,
|
||||||
|
"batch": 4,
|
||||||
|
"initial_repeat_cv_pct": 2.8664467148791393,
|
||||||
|
"median_component_ms": {
|
||||||
|
"attention": 0.925814669047619,
|
||||||
|
"collective": 0.47613457857142855,
|
||||||
|
"linear_norm_rope": 0.926101651190476,
|
||||||
|
"moe": 2.5396719488095236,
|
||||||
|
"other": 0.021773490476190475,
|
||||||
|
"output_head": 0.08747840476190476,
|
||||||
|
"router": 0.18771020476190473
|
||||||
|
},
|
||||||
|
"median_execute_ms": 5.45066564047619,
|
||||||
|
"needs_repeat_3": false,
|
||||||
|
"repeat_gate_resolved": true,
|
||||||
|
"repeats": [
|
||||||
|
{
|
||||||
|
"component_rank_mean_ms": {
|
||||||
|
"attention": 0.9284945714285713,
|
||||||
|
"collective": 0.4770398571428571,
|
||||||
|
"linear_norm_rope": 0.9251340357142854,
|
||||||
|
"moe": 2.5554174642857146,
|
||||||
|
"other": 0.021663214285714286,
|
||||||
|
"output_head": 0.08753164285714285,
|
||||||
|
"router": 0.1898966428571428
|
||||||
|
},
|
||||||
|
"execute_mean_ms": 5.294425214285715,
|
||||||
|
"repeat": 1,
|
||||||
|
"source": "runs/frontier-decode-batch-grid-v0/results/tp2-b4-r1.json"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"component_rank_mean_ms": {
|
||||||
|
"attention": 0.9231347666666667,
|
||||||
|
"collective": 0.47522929999999997,
|
||||||
|
"linear_norm_rope": 0.9270692666666666,
|
||||||
|
"moe": 2.523926433333333,
|
||||||
|
"other": 0.021883766666666665,
|
||||||
|
"output_head": 0.08742516666666666,
|
||||||
|
"router": 0.18552376666666665
|
||||||
|
},
|
||||||
|
"execute_mean_ms": 5.606906066666666,
|
||||||
|
"repeat": 2,
|
||||||
|
"source": "runs/frontier-decode-batch-grid-v0/results/tp2-b4-r2.json"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"tp": 2
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"all_repeat_cv_pct": 2.718954157967049,
|
||||||
|
"batch": 6,
|
||||||
|
"initial_repeat_cv_pct": 2.718954157967049,
|
||||||
|
"median_component_ms": {
|
||||||
|
"attention": 1.0526737664835166,
|
||||||
|
"collective": 0.48269348763736264,
|
||||||
|
"linear_norm_rope": 0.9301222623626373,
|
||||||
|
"moe": 3.6929154436813176,
|
||||||
|
"other": 0.025437872252747254,
|
||||||
|
"output_head": 0.08800730082417582,
|
||||||
|
"router": 0.196665739010989
|
||||||
|
},
|
||||||
|
"median_execute_ms": 6.754197167582418,
|
||||||
|
"needs_repeat_3": false,
|
||||||
|
"repeat_gate_resolved": true,
|
||||||
|
"repeats": [
|
||||||
|
{
|
||||||
|
"component_rank_mean_ms": {
|
||||||
|
"attention": 1.0590365714285717,
|
||||||
|
"collective": 0.48200882142857143,
|
||||||
|
"linear_norm_rope": 0.9312401785714285,
|
||||||
|
"moe": 3.678020964285713,
|
||||||
|
"other": 0.02528782142857143,
|
||||||
|
"output_head": 0.08801067857142857,
|
||||||
|
"router": 0.1985437857142856
|
||||||
|
},
|
||||||
|
"execute_mean_ms": 6.570553642857143,
|
||||||
|
"repeat": 1,
|
||||||
|
"source": "runs/frontier-decode-batch-grid-v0/results/tp2-b6-r1.json"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"component_rank_mean_ms": {
|
||||||
|
"attention": 1.0463109615384618,
|
||||||
|
"collective": 0.48337815384615385,
|
||||||
|
"linear_norm_rope": 0.929004346153846,
|
||||||
|
"moe": 3.7078099230769217,
|
||||||
|
"other": 0.025587923076923078,
|
||||||
|
"output_head": 0.08800392307692309,
|
||||||
|
"router": 0.19478769230769236
|
||||||
|
},
|
||||||
|
"execute_mean_ms": 6.9378406923076925,
|
||||||
|
"repeat": 2,
|
||||||
|
"source": "runs/frontier-decode-batch-grid-v0/results/tp2-b6-r2.json"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"tp": 2
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"all_repeat_cv_pct": 2.7838528598972516,
|
||||||
|
"batch": 8,
|
||||||
|
"initial_repeat_cv_pct": 2.7838528598972516,
|
||||||
|
"median_component_ms": {
|
||||||
|
"attention": 1.408474403846154,
|
||||||
|
"collective": 0.5025935769230769,
|
||||||
|
"linear_norm_rope": 0.9324356730769228,
|
||||||
|
"moe": 2.6870017499999985,
|
||||||
|
"other": 0.02355823076923077,
|
||||||
|
"output_head": 0.08828742307692307,
|
||||||
|
"router": 0.19532132692307688
|
||||||
|
},
|
||||||
|
"median_execute_ms": 6.138216769230769,
|
||||||
|
"needs_repeat_3": false,
|
||||||
|
"repeat_gate_resolved": true,
|
||||||
|
"repeats": [
|
||||||
|
{
|
||||||
|
"component_rank_mean_ms": {
|
||||||
|
"attention": 1.4162915000000003,
|
||||||
|
"collective": 0.5046254615384616,
|
||||||
|
"linear_norm_rope": 0.933206192307692,
|
||||||
|
"moe": 2.681186538461537,
|
||||||
|
"other": 0.023443884615384616,
|
||||||
|
"output_head": 0.08826711538461537,
|
||||||
|
"router": 0.19981553846153838
|
||||||
|
},
|
||||||
|
"execute_mean_ms": 5.967337846153845,
|
||||||
|
"repeat": 1,
|
||||||
|
"source": "runs/frontier-decode-batch-grid-v0/results/tp2-b8-r1.json"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"component_rank_mean_ms": {
|
||||||
|
"attention": 1.400657307692308,
|
||||||
|
"collective": 0.5005616923076923,
|
||||||
|
"linear_norm_rope": 0.9316651538461536,
|
||||||
|
"moe": 2.69281696153846,
|
||||||
|
"other": 0.023672576923076925,
|
||||||
|
"output_head": 0.08830773076923076,
|
||||||
|
"router": 0.1908271153846154
|
||||||
|
},
|
||||||
|
"execute_mean_ms": 6.309095692307692,
|
||||||
|
"repeat": 2,
|
||||||
|
"source": "runs/frontier-decode-batch-grid-v0/results/tp2-b8-r2.json"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"tp": 2
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"all_repeat_cv_pct": 0.06699668597711499,
|
||||||
|
"batch": 2,
|
||||||
|
"initial_repeat_cv_pct": 0.06699668597711499,
|
||||||
|
"median_component_ms": {
|
||||||
|
"attention": 0.721186765625,
|
||||||
|
"collective": 0.5570387890625,
|
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353
runs/frontier-decode-batch-grid-v0/results/tp4-b6-r3.json
Normal file
353
runs/frontier-decode-batch-grid-v0/results/tp4-b6-r3.json
Normal file
@@ -0,0 +1,353 @@
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|
||||||
|
"p95_ms": 2.4758159999999987,
|
||||||
|
"population_std_ms": 0.04801953596369253
|
||||||
|
},
|
||||||
|
"other": {
|
||||||
|
"mean_ms": 0.02552642857142857,
|
||||||
|
"n": 14,
|
||||||
|
"p50_ms": 0.025569500000000002,
|
||||||
|
"p95_ms": 0.025981999999999998,
|
||||||
|
"population_std_ms": 0.00035502126984283196
|
||||||
|
},
|
||||||
|
"output_head": {
|
||||||
|
"mean_ms": 0.048183142857142855,
|
||||||
|
"n": 14,
|
||||||
|
"p50_ms": 0.048112,
|
||||||
|
"p95_ms": 0.049249,
|
||||||
|
"population_std_ms": 0.00048348686747460613
|
||||||
|
},
|
||||||
|
"router": {
|
||||||
|
"mean_ms": 0.19290549999999992,
|
||||||
|
"n": 14,
|
||||||
|
"p50_ms": 0.19270699999999996,
|
||||||
|
"p95_ms": 0.1958099999999999,
|
||||||
|
"population_std_ms": 0.0013893215584799379
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"execute_annotation_histogram": {
|
||||||
|
"execute_context_0(0)_generation_6(6)": 14,
|
||||||
|
"execute_context_1(2048)_generation_2(2)": 1,
|
||||||
|
"execute_context_3(6144)_generation_3(3)": 1
|
||||||
|
},
|
||||||
|
"execute_wall": {
|
||||||
|
"mean_ms": 8.221614142857144,
|
||||||
|
"n": 14,
|
||||||
|
"p50_ms": 5.1788165,
|
||||||
|
"p95_ms": 21.941601,
|
||||||
|
"population_std_ms": 5.439025316289738
|
||||||
|
},
|
||||||
|
"gpu_kernel_busy": {
|
||||||
|
"mean_ms": 4.9954754999999995,
|
||||||
|
"n": 14,
|
||||||
|
"p50_ms": 4.988138500000001,
|
||||||
|
"p95_ms": 5.080260999999998,
|
||||||
|
"population_std_ms": 0.04987686944831562
|
||||||
|
},
|
||||||
|
"non_kernel_gap": {
|
||||||
|
"mean_ms": 3.2261386428571432,
|
||||||
|
"n": 14,
|
||||||
|
"p50_ms": 0.12194000000000038,
|
||||||
|
"p95_ms": 16.981787,
|
||||||
|
"population_std_ms": 5.465271976091736
|
||||||
|
},
|
||||||
|
"selected_execute_annotation": "execute_context_0(0)_generation_6(6)",
|
||||||
|
"steps": 14,
|
||||||
|
"trace": "runs/frontier-decode-batch-grid-v0/fleet-artifacts/decode-grid-tp4-b6-r3-20260723-20260723T093751542606Z/artifacts/runs/frontier-decode-batch-grid-v0/remote-outputs/tp4-b6-r3/traces/profile/dp0_pp0_tp1_dcp0_ep1_rank1.1784799692310997248.pt.trace.json.gz"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"components": {
|
||||||
|
"attention": {
|
||||||
|
"mean_ms": 0.9339937142857143,
|
||||||
|
"n": 14,
|
||||||
|
"p50_ms": 0.9335875,
|
||||||
|
"p95_ms": 0.9370900000000002,
|
||||||
|
"population_std_ms": 0.001917785904502957
|
||||||
|
},
|
||||||
|
"collective": {
|
||||||
|
"mean_ms": 4.071160642857143,
|
||||||
|
"n": 14,
|
||||||
|
"p50_ms": 0.5595504999999998,
|
||||||
|
"p95_ms": 16.441921000000004,
|
||||||
|
"population_std_ms": 5.617545177819675
|
||||||
|
},
|
||||||
|
"linear_norm_rope": {
|
||||||
|
"mean_ms": 0.8418829285714284,
|
||||||
|
"n": 14,
|
||||||
|
"p50_ms": 0.8420469999999998,
|
||||||
|
"p95_ms": 0.8443809999999999,
|
||||||
|
"population_std_ms": 0.0015434930914874625
|
||||||
|
},
|
||||||
|
"moe": {
|
||||||
|
"mean_ms": 2.3997612142857148,
|
||||||
|
"n": 14,
|
||||||
|
"p50_ms": 2.3921425000000003,
|
||||||
|
"p95_ms": 2.4903699999999995,
|
||||||
|
"population_std_ms": 0.04851648560788174
|
||||||
|
},
|
||||||
|
"other": {
|
||||||
|
"mean_ms": 0.025574571428571426,
|
||||||
|
"n": 14,
|
||||||
|
"p50_ms": 0.025535999999999996,
|
||||||
|
"p95_ms": 0.026114000000000002,
|
||||||
|
"population_std_ms": 0.0002516030644503687
|
||||||
|
},
|
||||||
|
"output_head": {
|
||||||
|
"mean_ms": 0.04837264285714286,
|
||||||
|
"n": 14,
|
||||||
|
"p50_ms": 0.048512,
|
||||||
|
"p95_ms": 0.048960000000000004,
|
||||||
|
"population_std_ms": 0.00043459432761120634
|
||||||
|
},
|
||||||
|
"router": {
|
||||||
|
"mean_ms": 0.1906739285714285,
|
||||||
|
"n": 14,
|
||||||
|
"p50_ms": 0.19033549999999994,
|
||||||
|
"p95_ms": 0.19376099999999996,
|
||||||
|
"population_std_ms": 0.0011997104093599232
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"execute_annotation_histogram": {
|
||||||
|
"execute_context_0(0)_generation_6(6)": 14,
|
||||||
|
"execute_context_1(2048)_generation_2(2)": 1,
|
||||||
|
"execute_context_3(6144)_generation_3(3)": 1
|
||||||
|
},
|
||||||
|
"execute_wall": {
|
||||||
|
"mean_ms": 8.630474571428572,
|
||||||
|
"n": 14,
|
||||||
|
"p50_ms": 5.184212499999999,
|
||||||
|
"p95_ms": 20.985098999999998,
|
||||||
|
"population_std_ms": 5.5897660056633045
|
||||||
|
},
|
||||||
|
"gpu_kernel_busy": {
|
||||||
|
"mean_ms": 8.511419642857144,
|
||||||
|
"n": 14,
|
||||||
|
"p50_ms": 5.0612965,
|
||||||
|
"p95_ms": 20.866541000000005,
|
||||||
|
"population_std_ms": 5.589603699080663
|
||||||
|
},
|
||||||
|
"non_kernel_gap": {
|
||||||
|
"mean_ms": 0.11905492857142821,
|
||||||
|
"n": 14,
|
||||||
|
"p50_ms": 0.1185754999999995,
|
||||||
|
"p95_ms": 0.1234339999999996,
|
||||||
|
"population_std_ms": 0.0022576749881340562
|
||||||
|
},
|
||||||
|
"selected_execute_annotation": "execute_context_0(0)_generation_6(6)",
|
||||||
|
"steps": 14,
|
||||||
|
"trace": "runs/frontier-decode-batch-grid-v0/fleet-artifacts/decode-grid-tp4-b6-r3-20260723-20260723T093751542606Z/artifacts/runs/frontier-decode-batch-grid-v0/remote-outputs/tp4-b6-r3/traces/profile/dp0_pp0_tp2_dcp0_ep2_rank2.1784799692265838678.pt.trace.json.gz"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"components": {
|
||||||
|
"attention": {
|
||||||
|
"mean_ms": 0.9325942142857142,
|
||||||
|
"n": 14,
|
||||||
|
"p50_ms": 0.9325534999999999,
|
||||||
|
"p95_ms": 0.9368339999999997,
|
||||||
|
"population_std_ms": 0.0022950169367122264
|
||||||
|
},
|
||||||
|
"collective": {
|
||||||
|
"mean_ms": 4.056500428571428,
|
||||||
|
"n": 14,
|
||||||
|
"p50_ms": 0.5449905,
|
||||||
|
"p95_ms": 16.437329999999996,
|
||||||
|
"population_std_ms": 5.618098886085954
|
||||||
|
},
|
||||||
|
"linear_norm_rope": {
|
||||||
|
"mean_ms": 0.8472062857142859,
|
||||||
|
"n": 14,
|
||||||
|
"p50_ms": 0.8469400000000002,
|
||||||
|
"p95_ms": 0.8496459999999999,
|
||||||
|
"population_std_ms": 0.001113636605808041
|
||||||
|
},
|
||||||
|
"moe": {
|
||||||
|
"mean_ms": 2.4089950714285697,
|
||||||
|
"n": 14,
|
||||||
|
"p50_ms": 2.400836999999998,
|
||||||
|
"p95_ms": 2.491555999999999,
|
||||||
|
"population_std_ms": 0.04692533473274991
|
||||||
|
},
|
||||||
|
"other": {
|
||||||
|
"mean_ms": 0.025399285714285715,
|
||||||
|
"n": 14,
|
||||||
|
"p50_ms": 0.025391999999999998,
|
||||||
|
"p95_ms": 0.025988,
|
||||||
|
"population_std_ms": 0.00023926925316514007
|
||||||
|
},
|
||||||
|
"output_head": {
|
||||||
|
"mean_ms": 0.04848692857142857,
|
||||||
|
"n": 14,
|
||||||
|
"p50_ms": 0.048528,
|
||||||
|
"p95_ms": 0.04912,
|
||||||
|
"population_std_ms": 0.0004430105552250606
|
||||||
|
},
|
||||||
|
"router": {
|
||||||
|
"mean_ms": 0.20276942857142852,
|
||||||
|
"n": 14,
|
||||||
|
"p50_ms": 0.2021774999999999,
|
||||||
|
"p95_ms": 0.20793499999999998,
|
||||||
|
"population_std_ms": 0.0026834677382150296
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"execute_annotation_histogram": {
|
||||||
|
"execute_context_0(0)_generation_6(6)": 14,
|
||||||
|
"execute_context_1(2048)_generation_2(2)": 1,
|
||||||
|
"execute_context_3(6144)_generation_3(3)": 1
|
||||||
|
},
|
||||||
|
"execute_wall": {
|
||||||
|
"mean_ms": 8.633801928571428,
|
||||||
|
"n": 14,
|
||||||
|
"p50_ms": 5.179864499999999,
|
||||||
|
"p95_ms": 20.988474,
|
||||||
|
"population_std_ms": 5.589972337133016
|
||||||
|
},
|
||||||
|
"gpu_kernel_busy": {
|
||||||
|
"mean_ms": 8.521951642857141,
|
||||||
|
"n": 14,
|
||||||
|
"p50_ms": 5.071154499999998,
|
||||||
|
"p95_ms": 20.875197999999994,
|
||||||
|
"population_std_ms": 5.589281480492545
|
||||||
|
},
|
||||||
|
"non_kernel_gap": {
|
||||||
|
"mean_ms": 0.11185028571428772,
|
||||||
|
"n": 14,
|
||||||
|
"p50_ms": 0.11232300000000128,
|
||||||
|
"p95_ms": 0.11633899999999997,
|
||||||
|
"population_std_ms": 0.0023228722192460186
|
||||||
|
},
|
||||||
|
"selected_execute_annotation": "execute_context_0(0)_generation_6(6)",
|
||||||
|
"steps": 14,
|
||||||
|
"trace": "runs/frontier-decode-batch-grid-v0/fleet-artifacts/decode-grid-tp4-b6-r3-20260723-20260723T093751542606Z/artifacts/runs/frontier-decode-batch-grid-v0/remote-outputs/tp4-b6-r3/traces/profile/dp0_pp0_tp3_dcp0_ep3_rank3.1784799692262484339.pt.trace.json.gz"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"schema": "frontier-decode-batch-trace.v1"
|
||||||
|
}
|
||||||
222
runs/frontier-decode-batch-grid-v0/run_bc8_replay.py
Normal file
222
runs/frontier-decode-batch-grid-v0/run_bc8_replay.py
Normal file
@@ -0,0 +1,222 @@
|
|||||||
|
#!/usr/bin/env python3
|
||||||
|
"""Replay the exact BC-8 simulator cells with the whole-layer decode curve."""
|
||||||
|
|
||||||
|
from __future__ import annotations
|
||||||
|
|
||||||
|
import argparse
|
||||||
|
import hashlib
|
||||||
|
import json
|
||||||
|
import os
|
||||||
|
import subprocess
|
||||||
|
import sys
|
||||||
|
import time
|
||||||
|
from concurrent.futures import ThreadPoolExecutor, as_completed
|
||||||
|
from pathlib import Path
|
||||||
|
|
||||||
|
|
||||||
|
ROOT = Path(__file__).resolve().parent
|
||||||
|
REPO = ROOT.parents[1]
|
||||||
|
EXPECTED_FRONTIER_COMMIT = "deadc4a321f0baaa534c6ebd17f974123733cdc2"
|
||||||
|
CONFIGS = ("tp1_mns16", "tp2_mns16", "tp4_mns16", "tp4_mns32")
|
||||||
|
SOURCE_MANIFEST = (
|
||||||
|
REPO / "runs/frontier-collective-joint-v0/counterfactual/joint-r2/manifest.json"
|
||||||
|
)
|
||||||
|
JOINT_INPUTS = REPO / "runs/frontier-knee-sweep-v0/inputs"
|
||||||
|
GRID = ROOT / "results/grid.json"
|
||||||
|
WRAPPER = ROOT / "run_frontier_with_whole_decode_curve.py"
|
||||||
|
CACHE_ROOT = REPO / "runs/frontier-knee-sweep-v0/cache"
|
||||||
|
LOCAL_DEPENDENCY_ROOTS = (
|
||||||
|
REPO / "runs/frontier-collective-joint-v0/python-deps",
|
||||||
|
Path("/home/gahow/.cache/uv/archive-v0/-_kzErLcPO5nASZFX8b9k"),
|
||||||
|
Path("/home/gahow/.cache/uv/archive-v0/FbaBs_QJ9QKEbQ9V_4aIR"),
|
||||||
|
Path("/home/gahow/.cache/uv/archive-v0/fuHsGXD0Lv_UjFC8yI4-7"),
|
||||||
|
Path("/home/gahow/.cache/uv/archive-v0/jFGdqQLpB1eopfm9VxT3j"),
|
||||||
|
Path("/home/gahow/.cache/uv/archive-v0/YWW6ExSJuPVvv4-qYQTin"),
|
||||||
|
Path("/home/gahow/.cache/uv/archive-v0/3_qxZ5Ll-EpVAGZfbksfe"),
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
def parse_args() -> argparse.Namespace:
|
||||||
|
parser = argparse.ArgumentParser()
|
||||||
|
parser.add_argument("--frontier-checkout", type=Path, required=True)
|
||||||
|
parser.add_argument("--output-root", type=Path, default=ROOT / "replay/bc8")
|
||||||
|
parser.add_argument("--jobs", type=int, default=2)
|
||||||
|
return parser.parse_args()
|
||||||
|
|
||||||
|
|
||||||
|
def sha256(path: Path) -> str:
|
||||||
|
return hashlib.sha256(path.read_bytes()).hexdigest()
|
||||||
|
|
||||||
|
|
||||||
|
def write_json(path: Path, payload) -> None:
|
||||||
|
path.parent.mkdir(parents=True, exist_ok=True)
|
||||||
|
path.write_text(json.dumps(payload, indent=2, sort_keys=True) + "\n")
|
||||||
|
|
||||||
|
|
||||||
|
def replace_flag(argv: list[str], flag: str, value: str) -> None:
|
||||||
|
index = argv.index(flag)
|
||||||
|
argv[index + 1] = value
|
||||||
|
|
||||||
|
|
||||||
|
def validate(frontier: Path) -> None:
|
||||||
|
commit = subprocess.check_output(
|
||||||
|
["git", "-C", str(frontier), "rev-parse", "HEAD"], text=True
|
||||||
|
).strip()
|
||||||
|
status = subprocess.check_output(
|
||||||
|
["git", "-C", str(frontier), "status", "--porcelain"], text=True
|
||||||
|
).strip()
|
||||||
|
if commit != EXPECTED_FRONTIER_COMMIT or status:
|
||||||
|
raise ValueError(
|
||||||
|
f"Frontier must be clean at {EXPECTED_FRONTIER_COMMIT}, "
|
||||||
|
f"got commit={commit}, dirty={bool(status)}"
|
||||||
|
)
|
||||||
|
required = (
|
||||||
|
SOURCE_MANIFEST,
|
||||||
|
GRID,
|
||||||
|
WRAPPER,
|
||||||
|
CACHE_ROOT,
|
||||||
|
*LOCAL_DEPENDENCY_ROOTS,
|
||||||
|
)
|
||||||
|
missing = [str(path) for path in required if not path.exists()]
|
||||||
|
if missing:
|
||||||
|
raise ValueError(f"missing replay inputs: {missing}")
|
||||||
|
|
||||||
|
|
||||||
|
def trace_for(config: str) -> Path:
|
||||||
|
tp = int(config[2])
|
||||||
|
return (
|
||||||
|
JOINT_INPUTS
|
||||||
|
/ f"traces-per-gpu-low/tp{tp}/w0-short-fixed-uniform-none/"
|
||||||
|
"rho0p02/public/frontier.csv"
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
def run_one(
|
||||||
|
config: str,
|
||||||
|
*,
|
||||||
|
frontier: Path,
|
||||||
|
output_root: Path,
|
||||||
|
templates: dict,
|
||||||
|
) -> dict:
|
||||||
|
point = output_root / "raw" / config
|
||||||
|
metrics_root = point / "metrics"
|
||||||
|
expected = list(metrics_root.glob("**/system_metrics.json"))
|
||||||
|
if len(expected) == 1 and (point / "usage.json").is_file():
|
||||||
|
return {"config": config, "status": "skipped_complete", "elapsed_s": 0.0}
|
||||||
|
|
||||||
|
argv = list(templates[config]["argv"])
|
||||||
|
argv[0] = sys.executable
|
||||||
|
argv[1] = str(WRAPPER.resolve())
|
||||||
|
replace_flag(argv, "--trace_request_generator_config_trace_file", str(trace_for(config)))
|
||||||
|
replace_flag(argv, "--metrics_config_output_dir", str(metrics_root))
|
||||||
|
replace_flag(argv, "--metrics_config_run_id", f"decode_grid_bc8_{config}")
|
||||||
|
replace_flag(argv, "--metrics_config_cache_dir", str(CACHE_ROOT / "model"))
|
||||||
|
replace_flag(argv, "--vidur_cc_backend_config_cache_dir", str(CACHE_ROOT / "cc"))
|
||||||
|
env = os.environ.copy()
|
||||||
|
env.update(
|
||||||
|
{
|
||||||
|
"CUDA_VISIBLE_DEVICES": "",
|
||||||
|
"PYTHONDONTWRITEBYTECODE": "1",
|
||||||
|
"PYTHONPATH": os.pathsep.join(
|
||||||
|
[str(frontier), *(str(path) for path in LOCAL_DEPENDENCY_ROOTS)]
|
||||||
|
),
|
||||||
|
"FRONTIER_COLLECTIVE_CURVE": str(
|
||||||
|
(JOINT_INPUTS / "collective-curve-b4-extrapolated.json").resolve()
|
||||||
|
),
|
||||||
|
"FRONTIER_COLLECTIVE_CURVE_VARIANT": "drop_mean",
|
||||||
|
"FRONTIER_FUSED_MOE_CURVE": str(
|
||||||
|
(JOINT_INPUTS / "fused-moe-curve-b4-extrapolated.json").resolve()
|
||||||
|
),
|
||||||
|
"FRONTIER_WHOLE_DECODE_GRID": str(GRID.resolve()),
|
||||||
|
"FRONTIER_CURVE_USAGE": str((point / "usage.json").resolve()),
|
||||||
|
}
|
||||||
|
)
|
||||||
|
point.mkdir(parents=True, exist_ok=True)
|
||||||
|
write_json(point / "command.json", argv)
|
||||||
|
started = time.monotonic()
|
||||||
|
with (point / "run.log").open("w") as output:
|
||||||
|
completed = subprocess.run(
|
||||||
|
argv,
|
||||||
|
cwd=frontier,
|
||||||
|
env=env,
|
||||||
|
stdout=output,
|
||||||
|
stderr=subprocess.STDOUT,
|
||||||
|
check=False,
|
||||||
|
)
|
||||||
|
elapsed = time.monotonic() - started
|
||||||
|
metrics = list(metrics_root.glob("**/system_metrics.json"))
|
||||||
|
status = (
|
||||||
|
"completed"
|
||||||
|
if completed.returncode == 0
|
||||||
|
and len(metrics) == 1
|
||||||
|
and (point / "usage.json").is_file()
|
||||||
|
else "failed"
|
||||||
|
)
|
||||||
|
record = {
|
||||||
|
"config": config,
|
||||||
|
"status": status,
|
||||||
|
"returncode": completed.returncode,
|
||||||
|
"elapsed_s": elapsed,
|
||||||
|
}
|
||||||
|
write_json(point / "run-status.json", record)
|
||||||
|
return record
|
||||||
|
|
||||||
|
|
||||||
|
def main() -> None:
|
||||||
|
args = parse_args()
|
||||||
|
if args.jobs < 1:
|
||||||
|
raise ValueError("--jobs must be positive")
|
||||||
|
frontier = args.frontier_checkout.resolve()
|
||||||
|
output_root = args.output_root.resolve()
|
||||||
|
validate(frontier)
|
||||||
|
source = json.loads(SOURCE_MANIFEST.read_text())
|
||||||
|
manifest = {
|
||||||
|
"schema": "frontier-decode-grid-bc8-replay.v1",
|
||||||
|
"frontier_checkout": str(frontier),
|
||||||
|
"frontier_commit": EXPECTED_FRONTIER_COMMIT,
|
||||||
|
"configs": list(CONFIGS),
|
||||||
|
"rho_per_gpu": 0.02,
|
||||||
|
"wrapper": str(WRAPPER.resolve()),
|
||||||
|
"wrapper_sha256": sha256(WRAPPER),
|
||||||
|
"whole_decode_grid": str(GRID.resolve()),
|
||||||
|
"whole_decode_grid_sha256": sha256(GRID),
|
||||||
|
"collective_curve_sha256": sha256(
|
||||||
|
JOINT_INPUTS / "collective-curve-b4-extrapolated.json"
|
||||||
|
),
|
||||||
|
"moe_curve_sha256": sha256(
|
||||||
|
JOINT_INPUTS / "fused-moe-curve-b4-extrapolated.json"
|
||||||
|
),
|
||||||
|
"traces": {
|
||||||
|
config: {
|
||||||
|
"path": str(trace_for(config).resolve()),
|
||||||
|
"sha256": sha256(trace_for(config)),
|
||||||
|
}
|
||||||
|
for config in CONFIGS
|
||||||
|
},
|
||||||
|
}
|
||||||
|
write_json(output_root / "manifest.json", manifest)
|
||||||
|
results = []
|
||||||
|
with ThreadPoolExecutor(max_workers=args.jobs) as pool:
|
||||||
|
futures = {
|
||||||
|
pool.submit(
|
||||||
|
run_one,
|
||||||
|
config,
|
||||||
|
frontier=frontier,
|
||||||
|
output_root=output_root,
|
||||||
|
templates=source["cells"],
|
||||||
|
): config
|
||||||
|
for config in CONFIGS
|
||||||
|
}
|
||||||
|
for future in as_completed(futures):
|
||||||
|
result = future.result()
|
||||||
|
results.append(result)
|
||||||
|
print(json.dumps(result, sort_keys=True), flush=True)
|
||||||
|
results.sort(key=lambda row: CONFIGS.index(row["config"]))
|
||||||
|
write_json(output_root / "run-summary.json", results)
|
||||||
|
failures = [row for row in results if row["status"] == "failed"]
|
||||||
|
if failures:
|
||||||
|
raise SystemExit(f"failed replay cells: {failures}")
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
||||||
@@ -0,0 +1,114 @@
|
|||||||
|
#!/usr/bin/env python3
|
||||||
|
"""Run Frontier with the existing joint repair plus a whole-layer decode curve."""
|
||||||
|
|
||||||
|
from __future__ import annotations
|
||||||
|
|
||||||
|
import json
|
||||||
|
import os
|
||||||
|
import runpy
|
||||||
|
from pathlib import Path
|
||||||
|
|
||||||
|
|
||||||
|
ROOT = Path(__file__).resolve().parent
|
||||||
|
REPO = ROOT.parents[1]
|
||||||
|
GRID = json.loads(Path(os.environ["FRONTIER_WHOLE_DECODE_GRID"]).read_text())
|
||||||
|
WHOLE_CURVE = {
|
||||||
|
str(tp): {
|
||||||
|
str(cell["batch"]): float(cell["median_execute_ms"])
|
||||||
|
for cell in GRID["cells"]
|
||||||
|
if int(cell["tp"]) == tp
|
||||||
|
}
|
||||||
|
for tp in (2, 4)
|
||||||
|
}
|
||||||
|
|
||||||
|
# Apply the existing serving-path collective/MoE correction first.
|
||||||
|
joint = runpy.run_path(
|
||||||
|
str(REPO / "runs/frontier-collective-joint-v0/run_frontier_with_curves.py")
|
||||||
|
)
|
||||||
|
USAGE = joint["USAGE"]
|
||||||
|
_pure_decode_point = joint["_pure_decode_point"]
|
||||||
|
|
||||||
|
from frontier.entities import ExecutionTime # noqa: E402
|
||||||
|
from frontier.execution_time_predictor.sklearn_moe_execution_time_predictor import ( # noqa: E402
|
||||||
|
SklearnMoEExecutionTimePredictor,
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
_JOINT_STAGE_PREDICT = SklearnMoEExecutionTimePredictor.predict_stage_execution_time
|
||||||
|
|
||||||
|
|
||||||
|
def _whole_decode_stage_time(
|
||||||
|
self,
|
||||||
|
batch,
|
||||||
|
stage_id,
|
||||||
|
cluster_type,
|
||||||
|
num_layers=1,
|
||||||
|
layer_id=0,
|
||||||
|
):
|
||||||
|
base = _JOINT_STAGE_PREDICT(
|
||||||
|
self,
|
||||||
|
batch,
|
||||||
|
stage_id,
|
||||||
|
cluster_type,
|
||||||
|
num_layers=num_layers,
|
||||||
|
layer_id=layer_id,
|
||||||
|
)
|
||||||
|
point = _pure_decode_point(self, batch)
|
||||||
|
if point is None or point[0] == "1" or point[1] == "1":
|
||||||
|
return base
|
||||||
|
tp, decode_batch = point
|
||||||
|
if tp not in WHOLE_CURVE or decode_batch not in WHOLE_CURVE[tp]:
|
||||||
|
raise ValueError(
|
||||||
|
"Whole-layer curve has no exact pure-decode point for "
|
||||||
|
f"TP={tp}, batch={decode_batch}; refusing to extrapolate"
|
||||||
|
)
|
||||||
|
layers = int(base.num_layers)
|
||||||
|
if layers <= 0:
|
||||||
|
raise ValueError(f"invalid stage layer count: {layers}")
|
||||||
|
target_ms = WHOLE_CURVE[tp][decode_batch]
|
||||||
|
USAGE[f"whole_decode:tp{tp}-b{decode_batch}:target_ms={target_ms:.9f}"] += 1
|
||||||
|
return ExecutionTime(
|
||||||
|
num_layers_per_pipeline_stage=layers,
|
||||||
|
attention_rope_execution_time=0.0,
|
||||||
|
attention_kv_cache_save_execution_time=0.0,
|
||||||
|
attention_decode_execution_time=0.0,
|
||||||
|
attention_prefill_execution_time=0.0,
|
||||||
|
attention_layer_pre_proj_execution_time=0.0,
|
||||||
|
attention_layer_post_proj_execution_time=0.0,
|
||||||
|
attn_norm_time=0.0,
|
||||||
|
mlp_norm_time=0.0,
|
||||||
|
add_time=0.0,
|
||||||
|
tensor_parallel_communication_time=0.0,
|
||||||
|
pipeline_parallel_communication_time=0.0,
|
||||||
|
expert_parallel_communication_time=0.0,
|
||||||
|
moe_gating_time=0.0,
|
||||||
|
moe_shuffling_time=0.0,
|
||||||
|
schedule_time=base._schedule_time,
|
||||||
|
sampler_e2e_time=base._sampler_e2e_time,
|
||||||
|
prepare_inputs_e2e_time=base._prepare_inputs_e2e_time,
|
||||||
|
process_model_outputs_time=base._process_model_outputs_time,
|
||||||
|
ray_comm_time=base._ray_comm_time,
|
||||||
|
is_moe=True,
|
||||||
|
moe_grouped_gemm_time=target_ms / layers,
|
||||||
|
pp_producer_send_path_runtime_time=(
|
||||||
|
base._pp_producer_send_path_runtime_time
|
||||||
|
),
|
||||||
|
pp_receiver_head_runtime_time=base._pp_receiver_head_runtime_time,
|
||||||
|
pp_prefill_consumer_active_runtime_time=(
|
||||||
|
base._pp_prefill_consumer_active_runtime_time
|
||||||
|
),
|
||||||
|
pp_stage_boundary_residual_runtime_time=(
|
||||||
|
base._pp_stage_boundary_residual_runtime_time
|
||||||
|
),
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
SklearnMoEExecutionTimePredictor.predict_stage_execution_time = (
|
||||||
|
_whole_decode_stage_time
|
||||||
|
)
|
||||||
|
|
||||||
|
from frontier.main import main # noqa: E402
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
||||||
97
runs/frontier-simulator-gap-campaign-v0/README.md
Normal file
97
runs/frontier-simulator-gap-campaign-v0/README.md
Normal file
@@ -0,0 +1,97 @@
|
|||||||
|
# Frontier simulator residual-gap campaign
|
||||||
|
|
||||||
|
目标：在已经完成的 1h+ production chat-trace fidelity matrix 上，按信息增益和工程可修性依次关闭三个残余误差源。三个实验严格串行；前一项的产物是后一项的 baseline，不允许同时改多个 profile/component。
|
||||||
|
|
||||||
|
## 冻结基线
|
||||||
|
|
||||||
|
- Frontier：`deadc4a321f0baaa534c6ebd17f974123733cdc2`
|
||||||
|
- Profile：`runs/frontier-prefill-kvgrowth-fix-v0/profiles/profile-v5-kvgrowth`
|
||||||
|
- Workload：与 `docs/assets/frontier-fidelity/full-matrix.csv` 相同的 7 个 TP×load cells
|
||||||
|
- Real：复用现有每 cell 两次 60-min trial；不重新采集，不做逐 cell E2E calibration
|
||||||
|
- 指标：TTFT/TPOT/E2E 的 mean/p50/p90/p99；real trial 分别与同一 sim request ID 配对后汇总，不再把两次 real trial pool 成一个分布
|
||||||
|
- 有效域：TP1 两个 cell 的 simulated waiting p99 已超过原 1 s subcritical gate，必须标成 `gate-fail diagnostic`，不能继续称为 subcritical evidence
|
||||||
|
|
||||||
|
## 串行任务列表
|
||||||
|
|
||||||
|
### EXP-1：structured attention-prefill predictor
|
||||||
|
|
||||||
|
- [x] 锁定 Frontier/profile/trace provenance
|
||||||
|
- [x] 审计训练与运行时 feature contract
|
||||||
|
- [x] 冻结 experiment card、事前判据和 mock figure
|
||||||
|
- [x] 实现最小 predictor patch：
|
||||||
|
- standard prefill 只训练 `batch_size=1`；多请求继续走既有 mixed predictor
|
||||||
|
- `base(q)` 使用单请求 `KV=0` profile 的单调分段插值
|
||||||
|
- KV growth 使用非负 `KV + q×KV` 项
|
||||||
|
- [x] 单元测试：exact anchors、q/KV 单调、非负、pickle round-trip
|
||||||
|
- [x] 离线 ablation：RF-all / RF-single / structured-single；held-out context MAPE
|
||||||
|
- [x] 7-cell CPU replay
|
||||||
|
- [x] trial-aware paired verdict 与旧 v5 baseline 对照
|
||||||
|
|
||||||
|
Decision：**profile gate PASS，trace/merge gate FAIL**。TP1/TP2 TTFT mean/p99
|
||||||
|
绝对误差改善约 7--10 pp；TP4 三个 cell 的 TTFT mean 绝对误差稳定恶化
|
||||||
|
5.3--5.8 pp。该 patch 保留为机制 ablation，不作为全局默认 predictor。
|
||||||
|
EXP-2 先执行 entry audit；仅当 structured 分支上的 TP2 chunk residual 仍 ≥10%
|
||||||
|
才进入 GPU 三臂测量。
|
||||||
|
|
||||||
|
Go/no-go：
|
||||||
|
|
||||||
|
- profile gate：held-out context MAPE ≤5%，且所有 TP 的 q/KV 单调检查通过
|
||||||
|
- trace gate：TP1 TTFT mean/p99 的绝对偏差各改善 ≥5 pp；TP2/TP4 任一 TTFT/E2E quantile 不得恶化 >5 pp
|
||||||
|
- 若 profile gate 不过，不进入 trace replay；若 trace gate 不过，保留 profile diagnosis，回退 patch，不进入 EXP-2
|
||||||
|
|
||||||
|
### EXP-2：TP2 base-prefill serving-path profile
|
||||||
|
|
||||||
|
- [x] 在 EXP-1 冻结输出上重新确认 chunk #1 residual：TP2 是否仍为孤立点
|
||||||
|
- [x] probe `dash1`--`dash4`；只选择 8 张 H20 全部 idle/healthy 的主机
|
||||||
|
- [x] 三臂中的 A/C 同形状 profile：
|
||||||
|
- A：当前 standalone microbenchmark
|
||||||
|
- C：真实 serving path extract
|
||||||
|
- [x] q8k smoke 与两个 TP rank 的 component contract
|
||||||
|
- [x] 注入 q8k ratio，重放 TP2 两个 load cell；其余 profile 冻结
|
||||||
|
- [x] paired verdict
|
||||||
|
|
||||||
|
Decision：**mechanism gate PASS，global constant injection FAIL**。serving q8k
|
||||||
|
execute=`408.19 ms`（real=`410 ms`），MoE=`214.52 ms` vs sim=`171.12 ms`，
|
||||||
|
解释 `80.75%` residual。全 prefill-domain constant `1.25366×` 会过校正：
|
||||||
|
subcritical TTFT mean `−4.54%→+5.23%` 且 E2E mean
|
||||||
|
`+12.70%→+15.55%`。保留的工程方向是 TP2 token/routing-conditioned MoE
|
||||||
|
curve，不合入 constant scale。B arm 合并为该 follow-up curve 的真实 routing
|
||||||
|
采样，不再为已被 C 直接确认的机制单独占一次 GPU run。
|
||||||
|
|
||||||
|
Go/no-go：
|
||||||
|
|
||||||
|
- 若 B/C 相对 A 都没有稳定的 ≥10% shift，拒绝“tactic/warmup 或 serving composition”假设，不做 profile 注入
|
||||||
|
- 若 C 能解释 real chunk residual，TP2 TTFT mean 目标收敛至 ±10%，且 E2E/TPOT 不恶化 >5 pp
|
||||||
|
|
||||||
|
### EXP-3：decode whole-layer residual × batch curve
|
||||||
|
|
||||||
|
- [x] 在 EXP-2 冻结输出上确认 TP4 low-load TPOT/E2E 正偏仍存在
|
||||||
|
- [x] probe dash1--dash4；补 TP2/TP4 × batch 2/4/6/8 whole-layer grid，
|
||||||
|
b1 复用既有 serving anchor
|
||||||
|
- [x] 将 whole-layer time 与 component sum 对齐，定位 TP4/b6 collective tail
|
||||||
|
- [x] 稳定性 gate：TP4/b2 fresh-process CV=`0.067%`；TP4/b6 依规则加 r3
|
||||||
|
- [x] 重放历史 BC-8 knee，检查 `TP2<TP4<TP1` 排序
|
||||||
|
- [x] 审计 7-cell runtime support；因 batch 最大到 b15 且 TP4/b6
|
||||||
|
deterministic mean 不稳定，拒绝 b8 constant extrapolation，不做 global injection
|
||||||
|
- [x] 分层判决：BC-8 排序不升级 event telemetry；绝对残差/MNS/high-batch
|
||||||
|
tail 保留 event/distribution-aware follow-up
|
||||||
|
|
||||||
|
Decision：**BC-8 engineering fix PASS，global curve merge NO-GO**。稳定 TP4/b2
|
||||||
|
median=`4.623 ms`；exact b2 injection 使 TP4 P4 sim TPOT
|
||||||
|
`5.864→5.367 ms`，完整 MNS16 排序恢复为真实的 `TP2<TP4<TP1`。
|
||||||
|
绝对 residual 仍为 `+0.918--+1.001 ms`，MNS16/32 仍 tie。TP4/b6 的
|
||||||
|
5.433/10.157/8.634 ms 跨进程均值由 collective tail 主导；1h trace 又访问
|
||||||
|
b9--b15，故当前只接受 scoped TP4/b2 correction，不接受全局 b2--b8 lookup。
|
||||||
|
|
||||||
|
Go/no-go：
|
||||||
|
|
||||||
|
- low-load TPOT/E2E mean 绝对偏差各改善 ≥5 pp
|
||||||
|
- BC-8 topology order 恢复，且原 7-cell TTFT 任一 quantile 不恶化 >5 pp
|
||||||
|
|
||||||
|
## 统一输出格式
|
||||||
|
|
||||||
|
每个实验最终报告都分为：
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1. `Fact`：原始测量与 paired metric
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2. `Interpretation`：支持/反驳哪条机制假设
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3. `Decision`：merge、回退、继续下一实验或停止
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@@ -0,0 +1,46 @@
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# Frontier residual-gap 三实验判决
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## EXP-1 structured attention-prefill
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||||||
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- **Fact：** held-out context MAPE 为 TP1/2/4 `0.84/1.60/3.01%`，profile
|
||||||
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gate PASS；7-cell 中 TP1/TP2 TTFT 绝对误差改善约 7--10 pp，但 TP4
|
||||||
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TTFT mean 稳定恶化 5.3--5.8 pp。
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- **Interpretation：** structured feature contract 正确，但旧 TP4 的好结果含
|
||||||
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error cancellation；单独替换 attention 会暴露其它正偏。
|
||||||
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- **Decision：** 不合入默认 predictor，保留为机制 ablation。
|
||||||
|
|
||||||
|
## EXP-2 TP2 prefill serving-path
|
||||||
|
|
||||||
|
- **Fact：** q8k TP2 serving execute=`408.19 ms`（real anchor=`410 ms`）；
|
||||||
|
MoE=`214.52 ms` vs sim=`171.12 ms`，解释 `80.75%` residual。全域
|
||||||
|
`1.25366×` 常数注入使 subcritical TTFT mean `-4.54%→+5.23%`，
|
||||||
|
E2E mean `+12.70%→+15.55%`。
|
||||||
|
- **Interpretation：** TP2 residual 的主机制是 serving-path MoE composition，
|
||||||
|
但 correction 随 token/routing state 变化，不是常数。
|
||||||
|
- **Decision：** 机制 PASS、constant merge FAIL；工程项为 TP2
|
||||||
|
token/routing-conditioned MoE curve。
|
||||||
|
|
||||||
|
## EXP-3 decode whole-layer batch grid
|
||||||
|
|
||||||
|
- **Fact：** TP4/b2 fresh-process=`4.626/4.620 ms`，CV=`0.067%`，
|
||||||
|
median=`4.623 ms`。BC-8 exact b2 replay 将 TP4 sim TPOT
|
||||||
|
`5.864→5.367 ms`，排序恢复 `TP2<TP4<TP1`；残差仍
|
||||||
|
`+0.918--+1.001 ms`。TP4/b6 三次均值 `5.433/10.157/8.634 ms`，
|
||||||
|
差异来自 collective tail。
|
||||||
|
- **Interpretation：** BC-8 topology gap 可由稳定 TP4/b2 whole-layer
|
||||||
|
service correction 工程修复；高 batch 需要分布式 collective-tail 模型，
|
||||||
|
不能当确定性 lookup。
|
||||||
|
- **Decision：** scoped TP4/b2 correction PASS；global b2--b8 merge NO-GO。
|
||||||
|
1h trace 会访问 b9--b15，下一步补 b9--b16 与 collective-tail telemetry。
|
||||||
|
|
||||||
|
## 总结
|
||||||
|
|
||||||
|
三个明显 gap 中：
|
||||||
|
|
||||||
|
1. **可直接工程化：** BC-8 的 scoped TP4/b2 whole-layer correction。
|
||||||
|
2. **可工程化但需条件曲线：** TP2 prefill 的 token/routing-conditioned MoE。
|
||||||
|
3. **不应直接合入：** structured attention 全局替换、prefill 常数 scale、
|
||||||
|
decode 全局 deterministic b2--b8 lookup。
|
||||||
|
|
||||||
|
剩余新机制工作集中在 MNS/admission event semantics、TP4 high-batch
|
||||||
|
collective-tail distribution，以及 b9--b16 profile support。
|
||||||
Reference in New Issue
Block a user