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a6a6bb0 cb2eae0 a6a6bb0 d272538 a6a6bb0 d272538 a6a6bb0 d272538 a6a6bb0 d272538 a6a6bb0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 | #!/usr/bin/env python3
"""Merge Qwen3 retrieval-task probe shards into one result package."""
from __future__ import annotations
import argparse
import json
import shutil
from pathlib import Path
from typing import Any
from eval_qwen3_omni_retrieval_task_probes import TASK_SPECS, score_task, write_json, write_jsonl
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--run-id", required=True)
parser.add_argument("--output-dir", type=Path, required=True)
parser.add_argument("--shard-dir", type=Path, nargs="+", required=True)
return parser.parse_args()
def read_jsonl(path: Path) -> list[dict[str, Any]]:
rows: list[dict[str, Any]] = []
if not path.exists():
return rows
with path.open("r", encoding="utf-8") as handle:
for line in handle:
line = line.strip()
if line:
rows.append(json.loads(line))
return rows
def read_json(path: Path) -> dict[str, Any]:
return json.loads(path.read_text(encoding="utf-8")) if path.exists() else {}
def fake_args(run_id: str, first_metrics: dict[str, Any]) -> argparse.Namespace:
return argparse.Namespace(
run_id=run_id,
model_id=first_metrics.get("model_id"),
adapter_dir=Path(first_metrics.get("adapter_dir", "")),
dataset_jsonl=Path(first_metrics.get("dataset_jsonl", "")),
eval_split=first_metrics.get("eval_split", "test"),
candidate_count=int(first_metrics.get("candidate_count", 4) or 4),
future_frames=int(first_metrics.get("future_frames", 100) or 100),
sample_offset=0,
sample_stride=1,
)
def main() -> int:
args = parse_args()
args.output_dir.mkdir(parents=True, exist_ok=True)
task_metrics: dict[str, dict[str, Any]] = {}
first_metrics: dict[str, Any] | None = None
duplicate_predictions: list[dict[str, Any]] = []
for task_id, spec in TASK_SPECS.items():
rows_by_id: dict[str, dict[str, Any]] = {}
row_sources: dict[str, str] = {}
for shard_dir in args.shard_dir:
for row in read_jsonl(shard_dir / task_id / "predictions.jsonl"):
key = str(row.get("prediction_id") or f"{task_id}::{row.get('id')}")
if key in rows_by_id:
duplicate_predictions.append(
{
"task_id": task_id,
"prediction_id": key,
"kept_shard": row_sources.get(key),
"duplicate_shard": str(shard_dir),
"conflict": rows_by_id[key] != row,
}
)
continue
rows_by_id[key] = row
row_sources[key] = str(shard_dir)
shard_metrics = read_json(shard_dir / task_id / "metrics.json")
if shard_metrics and first_metrics is None:
first_metrics = shard_metrics
if not rows_by_id:
continue
ordered_rows = sorted(
rows_by_id.values(),
key=lambda row: (str(row.get("episode_id")), int(row.get("start_frame", 0)), str(row.get("id"))),
)
task_dir = args.output_dir / task_id
task_dir.mkdir(parents=True, exist_ok=True)
write_jsonl(task_dir / "predictions.jsonl", ordered_rows)
metrics = score_task(task_id, spec, ordered_rows, args.output_dir, fake_args(args.run_id, first_metrics or {}))
task_metrics[task_id] = metrics
for shard_dir in args.shard_dir:
if (shard_dir / "progress.jsonl").exists():
shutil.copy2(shard_dir / "progress.jsonl", args.output_dir / f"{shard_dir.name}.progress.jsonl")
summary = {
"title": "Qwen3-Omni v6 Retrieval Task Probes",
"status": "pass",
"run_id": args.run_id,
"shard_dirs": [str(path) for path in args.shard_dir],
"duplicate_prediction_count": len(duplicate_predictions),
"duplicate_prediction_conflict_count": sum(1 for row in duplicate_predictions if row["conflict"]),
"duplicate_predictions": duplicate_predictions[:50],
"tasks": {
task_id: {
"task_number": metrics["task_number"],
"task_label": metrics["task_label"],
"metric_key": metrics["metric_key"],
"primary_score": metrics["primary_score"],
"num_samples": metrics["num_samples"],
"metrics_json": str(args.output_dir / task_id / "metrics.json"),
}
for task_id, metrics in task_metrics.items()
},
}
write_json(args.output_dir / "summary.json", summary)
report = [
"# Qwen3-Omni v6 Retrieval Task Probes",
"",
f"- Run ID: `{args.run_id}`",
f"- Shards: `{len(args.shard_dir)}`",
"",
"| Task | Metric | Score | Samples |",
"| --- | --- | ---: | ---: |",
]
for metrics in task_metrics.values():
report.append(
f"| {metrics['task_label']} | {metrics['metric_key']} | {metrics['primary_score']:.6f} | {metrics['num_samples']} |"
)
(args.output_dir / "RUN_REPORT.md").write_text("\n".join(report) + "\n", encoding="utf-8")
print(json.dumps(summary, indent=2, sort_keys=True))
return 0
if __name__ == "__main__":
raise SystemExit(main())
|