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Close BC8 decode curve counterfactual

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This commit is contained in:
Gahow Wang
2026-07-23 18:08:00 +08:00
parent ecc5599381
commit cf610003ed
11 changed files with 1666 additions and 7 deletions
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1
runs/frontier-decode-batch-grid-v0/.gitignore vendored
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@@ -1,3 +1,4 @@
fleet-artifacts/
fleet-state/
remote-outputs/
replay/

152
runs/frontier-decode-batch-grid-v0/analyze_bc8_replay.py Normal file
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@@ -0,0 +1,152 @@
#!/usr/bin/env python3
"""Compare original and whole-layer-curve Frontier against the BC-8 real pilot."""
from __future__ import annotations
import json
import statistics
from collections import Counter
from pathlib import Path
ROOT = Path(__file__).resolve().parent
REPO = ROOT.parents[1]
CONFIGS = ("tp1_mns16", "tp2_mns16", "tp4_mns16", "tp4_mns32")
REPLAY = ROOT / "replay/bc8"
ORIGINAL = REPO / "runs/frontier-knee-sweep-v0/raw/fixed/rho0p02"
REAL = REPO / "runs/frontier-pilot-v0/results/pilot-surface.json"
def find_one(root: Path, name: str) -> Path:
matches = list(root.glob(f"**/{name}"))
if len(matches) != 1:
raise ValueError(f"expected one {name} under {root}, got {matches}")
return matches[0]
def summarize_sim(root: Path) -> dict:
metrics = json.loads(find_one(root, "system_metrics.json").read_text())
ledger = find_one(root, "frontier_stage_batch_ledger.jsonl")
histogram: Counter[int] = Counter()
service_ms: dict[int, set[float]] = {}
for line in ledger.read_text().splitlines():
row = json.loads(line)
tokens = row["request_num_tokens"]
if tokens and all(int(value) == 1 for value in tokens):
batch = len(tokens)
histogram[batch] += 1
service_ms.setdefault(batch, set()).add(
float(row["execution_time"]["model_time_ms"])
)
return {
"tpot_mean_ms": metrics["tpot_statistics"]["mean"],
"ttft_mean_ms": metrics["ttft_statistics"]["mean"],
"e2e_mean_ms": metrics["request_e2e_time_statistics"]["mean"],
"decode_batch_histogram": dict(sorted(histogram.items())),
"decode_service_ms": {
str(batch): sorted(values) for batch, values in sorted(service_ms.items())
},
"decode_b_gt_1_fraction": sum(
count for batch, count in histogram.items() if batch > 1
)
/ sum(histogram.values()),
}
def main() -> None:
real_rows = [
row
for row in json.loads(REAL.read_text())
if row["load"] == "p4" and row["canonical"]
]
points = []
for config in CONFIGS:
real = [row for row in real_rows if row["config"] == config]
original = summarize_sim(ORIGINAL / config)
corrected = summarize_sim(REPLAY / "raw" / config)
usage = json.loads((REPLAY / "raw" / config / "usage.json").read_text())
points.append(
{
"config": config,
"tp": int(config[2]),
"mns": int(config.split("mns")[1]),
"real_tpot_mean_ms": statistics.fmean(
row["tpot_mean_ms"] for row in real
),
"original": original,
"whole_curve": corrected,
"original_tpot_residual_ms": (
original["tpot_mean_ms"]
- statistics.fmean(row["tpot_mean_ms"] for row in real)
),
"whole_curve_tpot_residual_ms": (
corrected["tpot_mean_ms"]
- statistics.fmean(row["tpot_mean_ms"] for row in real)
),
"usage": usage,
}
)
def ranking(key):
return [
point["config"]
for point in sorted(points, key=lambda point: (key(point), point["config"]))
if point["mns"] == 16
]
real_ranking = ranking(lambda point: point["real_tpot_mean_ms"])
original_ranking = ranking(lambda point: point["original"]["tpot_mean_ms"])
corrected_ranking = ranking(lambda point: point["whole_curve"]["tpot_mean_ms"])
payload = {
"schema": "frontier-decode-grid-bc8-verdict.v1",
"points": points,
"mns16_rankings_fast_to_slow": {
"real": real_ranking,
"original": original_ranking,
"whole_curve": corrected_ranking,
},
"whole_curve_restores_real_mns16_ranking": corrected_ranking == real_ranking,
"all_corrected_batches_within_measured_support": all(
max(map(int, point["whole_curve"]["decode_batch_histogram"])) <= 8
for point in points
),
"decision": (
"STATIC_WHOLE_CURVE_FIXES_BC8"
if corrected_ranking == real_ranking
else "ESCALATE_EVENT_LEVEL_STATE"
),
}
output = ROOT / "results/bc8-replay-verdict.json"
output.write_text(json.dumps(payload, indent=2, sort_keys=True) + "\n")
lines = [
"# BC-8 whole-layer decode-curve replay",
"",
"| Config | Real TPOT | Original sim | Whole-curve sim | Residual before -> after | b>1 |",
"|---|---:|---:|---:|---:|---:|",
]
for point in points:
lines.append(
f"| {point['config']} | {point['real_tpot_mean_ms']:.3f} | "
f"{point['original']['tpot_mean_ms']:.3f} | "
f"{point['whole_curve']['tpot_mean_ms']:.3f} | "
f"{point['original_tpot_residual_ms']:+.3f} -> "
f"{point['whole_curve_tpot_residual_ms']:+.3f} | "
f"{point['whole_curve']['decode_b_gt_1_fraction']:.2%} |"
)
lines.extend(
[
"",
f"- Real MNS16: `{' < '.join(real_ranking)}`",
f"- Original: `{' < '.join(original_ranking)}`",
f"- Whole curve: `{' < '.join(corrected_ranking)}`",
f"- Decision: **{payload['decision']}**.",
"",
]
)
(ROOT / "results/bc8-replay-verdict.md").write_text("\n".join(lines))
print(json.dumps({"output": str(output), "decision": payload["decision"]}))
if __name__ == "__main__":
main()

43
runs/frontier-decode-batch-grid-v0/experiment-card.md
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@@ -1,6 +1,6 @@
# 实验 EXP-DECODE-BATCH-GRID：BC-8 是否可由稳定 whole-layer b2--b8 curve 修复
> **状态：** TP4/b2 stability gate PASS，扩展 full grid
> **状态：** COMPLETED；BC-8 排序工程修复成立，global curve merge 暂停
>
> Parent campaign：[`../frontier-simulator-gap-campaign-v0/README.md`](../frontier-simulator-gap-campaign-v0/README.md)
@@ -54,9 +54,10 @@
## 复现信息
- **Frontier baseline：** `deadc4a321f0baaa534c6ebd17f974123733cdc2`；
joint serving-path curves + structured-attention analysis branch。
- **Remote：** 复用 dash1 的 clean detached experiment worktree；canonical
checkout 的用户 dirty changes 不动。
CPU replay 使用既有 joint serving-path curves，未合入 EXP-1 rejected patch。
- **Remote：** dash1--dash4 共享 clean detached experiment worktree
`ecc559938132851d61bf22daa55cea9658807dfa`；canonical checkout 的用户
dirty changes 未改动。
- **Known limits：** 固定 2048→128 state-matched workload；本实验只支持
decode curve 与 BC-8，不外推到 chat prefill shape 或其它模型。
@@ -68,6 +69,34 @@
`4.6263 ms` 和 `4.6201 ms`，跨 repeat CV=`0.067%`，稳定性 gate
PASS，无需 repeat 3。median=`4.6232 ms`，比旧样本 `5.044 ms` 低
`0.421 ms`；方向支持 H1，但未达到事前 `>=0.5 ms` 的强证据阈值。
- **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
BC-8 仍须 full b2--b8 curve 与 trace replay。
- **Decision：** 待 GPU。
- **Full grid：**
| Cell | fresh-process execute mean (ms) | median (ms) | initial CV |
|---|---:|---:|---:|
| TP2/b2 | 4.761 / 4.730 | 4.746 | 0.33% |
| TP2/b4 | 5.294 / 5.607 | 5.451 | 2.87% |
| TP2/b6 | 6.571 / 6.938 | 6.754 | 2.72% |
| TP2/b8 | 5.967 / 6.309 | 6.138 | 2.78% |
| TP4/b2 | 4.626 / 4.620 | 4.623 | 0.07% |
| TP4/b4 | 4.516 / 4.727 | 4.622 | 2.27% |
| TP4/b6 | 5.433 / 10.157 / 8.634 | 8.634 | 30.30% |
| TP4/b8 | 4.898 / 5.047 | 4.972 | 1.50% |
- **TP4/b6 instability：** r2/r3 的 MoE 与 attention 基本不变，均值波动由
同步 collective 长尾造成；三个 run 的 step p50 仍约 `5.18--5.49 ms`。
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
service constant。
- **BC-8 exact replay：** replay 仅访问 b1/b2，因此没有使用 b6 或任何
插值/外推。TP4/b2 whole-layer override 将 TP4 sim TPOT
`5.864→5.367 ms`，残差 `+1.414→+0.918 ms`（MNS16）和
`+1.498→+1.001 ms`（MNS32）；MNS16 完整排序从
`TP2<TP1<TP4` 恢复为真实的 `TP2<TP4<TP1`。sim b>1 stage fraction
`48.15%→37.47%`。
- **7-cell support audit：** 最新 1h cells 的 decode batch 最大到
`b6--b15`，而本实验 measured support 只到 b8；TP4/b6 又未通过
deterministic-mean 稳定性。为避免重新引入 b8 constant extrapolation，
不执行全局 7-cell injection，也不把整条 b2--b8 curve 合入默认 predictor。
- **Decision：** H1 对 BC-8 成立，排序 gap 属于可工程修复的 TP4/b2
whole-layer service residual；但绝对残差、MNS tie 和高 batch collective
tail 仍需 event/distribution-aware 模型。建议先合入受作用域保护的 TP4/b2
correction，再补 b9--b16 与 collective-tail telemetry 后开启 global merge。

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runs/frontier-decode-batch-grid-v0/results/bc8-replay-verdict.json Normal file
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@@ -0,0 +1,222 @@
{
"all_corrected_batches_within_measured_support": true,
"decision": "STATIC_WHOLE_CURVE_FIXES_BC8",
"mns16_rankings_fast_to_slow": {
"original": [
"tp2_mns16",
"tp1_mns16",
"tp4_mns16"
],
"real": [
"tp2_mns16",
"tp4_mns16",
"tp1_mns16"
],
"whole_curve": [
"tp2_mns16",
"tp4_mns16",
"tp1_mns16"
]
},
"points": [
{
"config": "tp1_mns16",
"mns": 16,
"original": {
"decode_b_gt_1_fraction": 0.0,
"decode_batch_histogram": {
"1": 16383
},
"decode_service_ms": {
"1": [
5.493362394
]
},
"e2e_mean_ms": 864.3525078423435,
"tpot_mean_ms": 5.493362394020072,
"ttft_mean_ms": 166.6954838017941
},
"original_tpot_residual_ms": 0.616915450202109,
"real_tpot_mean_ms": 4.876446943817963,
"tp": 1,
"usage": {
"collective:moe:tp1-b1:structural-zero": 16383,
"moe:tp1-b1": 16383
},
"whole_curve": {
"decode_b_gt_1_fraction": 0.0,
"decode_batch_histogram": {
"1": 16383
},
"decode_service_ms": {
"1": [
5.493362394
]
},
"e2e_mean_ms": 864.3525078423435,
"tpot_mean_ms": 5.493362394020072,
"ttft_mean_ms": 166.6954838017941
},
"whole_curve_tpot_residual_ms": 0.616915450202109
},
{
"config": "tp2_mns16",
"mns": 16,
"original": {
"decode_b_gt_1_fraction": 0.0,
"decode_batch_histogram": {
"1": 16383
},
"decode_service_ms": {
"1": [
4.928319445
]
},
"e2e_mean_ms": 744.4521695355745,
"tpot_mean_ms": 4.9283194445308744,
"ttft_mean_ms": 118.55560008015348
},
"original_tpot_residual_ms": 0.6235698385840074,
"real_tpot_mean_ms": 4.304749605946867,
"tp": 2,
"usage": {
"collective:attention:tp2-b1": 16383,
"collective:moe:tp2-b1": 16383,
"fused_norm_deduction:attn:tp2-b1": 16383,
"fused_norm_deduction:mlp:tp2-b1": 16383,
"moe:tp2-b1": 16383
},
"whole_curve": {
"decode_b_gt_1_fraction": 0.0,
"decode_batch_histogram": {
"1": 16383
},
"decode_service_ms": {
"1": [
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
}

13
runs/frontier-decode-batch-grid-v0/results/bc8-replay-verdict.md Normal file
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@@ -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
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@@ -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,
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"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"
}

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runs/frontier-decode-batch-grid-v0/run_bc8_replay.py Normal file
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#!/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()

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#!/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()