Datasets:
File size: 8,849 Bytes
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Usage:
traj-normalize --input nebius/SWE-rebench-openhands-trajectories \
--out-dir out --max-success 5000 --seed 0
traj-normalize --input /path/to/trajectories.parquet --out-dir out
"""
from __future__ import annotations
import argparse
import json
import random
import sys
import time
from pathlib import Path
import pyarrow.parquet as pq
from .core import Stats, load_tools_schema, normalize_row, row_to_record
META_COLS = ["trajectory_id", "instance_id", "repo", "resolved"]
DEFAULT_REPO = "nebius/SWE-rebench-openhands-trajectories"
DEFAULT_DATA_FILE = "trajectories.parquet"
def resolve_input(inp: str) -> tuple[Path, str]:
"""Return (parquet_path, source_desc). Downloads from the Hub if needed."""
p = Path(inp)
if p.is_file():
return p, str(p.resolve())
from huggingface_hub import hf_hub_download
path = hf_hub_download(repo_id=inp, repo_type="dataset", filename=DEFAULT_DATA_FILE)
return Path(path), inp
def resolve_tools_json(tools_arg: str | None, source: str) -> str | None:
if tools_arg:
return tools_arg
# If input came from the Hub, try to fetch tools.json from the same repo.
if not Path(source).is_file():
try:
from huggingface_hub import hf_hub_download
return hf_hub_download(repo_id=source, repo_type="dataset", filename="tools.json")
except Exception:
return None
# Local file: look for tools.json next to the parquet.
cand = Path(source).parent / "tools.json"
return str(cand) if cand.exists() else None
def pass1_select_indices(pf: pq.ParquetFile, args) -> tuple[set[int], Stats]:
"""Scan light columns; return selected row indices + populated counters."""
stats = Stats()
resolved_idx, fail_idx = [], []
offset = 0
for batch in pf.iter_batches(batch_size=args.batch_size, columns=META_COLS):
for row in batch.to_pylist():
stats.input_rows += 1
if row.get("resolved") == 1:
stats.resolved_rows += 1
resolved_idx.append(offset)
else:
fail_idx.append(offset)
offset += 1
if args.limit and stats.input_rows >= args.limit:
break
if args.limit and stats.input_rows >= args.limit:
break
rng = random.Random(args.seed)
if args.max_success and len(resolved_idx) > args.max_success:
resolved_idx = sorted(rng.sample(resolved_idx, args.max_success))
if args.include_failures:
if args.max_fail and len(fail_idx) > args.max_fail:
fail_idx = sorted(rng.sample(fail_idx, args.max_fail))
else:
fail_idx = []
selected = set(resolved_idx) | set(fail_idx)
return selected, stats
def run(args) -> int:
parquet_path, source = resolve_input(args.input)
tools_path = resolve_tools_json(args.tools_json, source)
tools_schema = load_tools_schema(tools_path)
if args.tools_json and tools_schema is None:
print(f"warning: could not load tools schema from {args.tools_json}", file=sys.stderr)
out_dir = Path(args.out_dir)
out_dir.mkdir(parents=True, exist_ok=True)
success_path = out_dir / "sft_success.jsonl"
fail_path = out_dir / "sft_fail.jsonl"
meta_path = out_dir / "meta.json"
pf = pq.ParquetFile(parquet_path)
t0 = time.time()
selected, stats = pass1_select_indices(pf, args)
print(f"scanned {stats.input_rows} rows ({stats.resolved_rows} resolved); "
f"selected {len(selected)} for extraction", file=sys.stderr)
# Pass 2: full rows for selected indices.
records = [] if args.shuffle else None
f_succ = open(success_path, "w")
f_fail = open(fail_path, "w") if args.include_failures else None
offset = 0
try:
for batch in pf.iter_batches(batch_size=args.batch_size):
for row in batch.to_pylist():
idx = offset
offset += 1
if args.limit and idx >= args.limit:
break
if idx not in selected:
continue
messages, err = normalize_row(row, stats, tools_schema, args.strict_tools)
if err:
stats.drop(err)
continue
rec = row_to_record(row, messages, emit_tools=not args.no_tools)
if records is not None:
records.append(rec)
else:
(f_succ if rec["resolved"] == 1 else f_fail).write(
json.dumps(rec, ensure_ascii=False) + "\n")
if rec["resolved"] == 1:
stats.written_success += 1
else:
stats.written_fail += 1
if args.limit and offset >= args.limit:
break
if records is not None:
rng = random.Random(args.seed)
rng.shuffle(records)
stats.written_success = stats.written_fail = 0
for rec in records:
(f_succ if rec["resolved"] == 1 else f_fail).write(
json.dumps(rec, ensure_ascii=False) + "\n")
if rec["resolved"] == 1:
stats.written_success += 1
else:
stats.written_fail += 1
finally:
f_succ.close()
if f_fail:
f_fail.close()
if not args.include_failures and fail_path.exists():
fail_path.unlink()
stats.skipped_unresolved = stats.input_rows - stats.resolved_rows
meta = {
"tool": "traj-normalize",
"source": source,
"tools_schema": tools_path if tools_schema else None,
"params": {
"limit": args.limit,
"max_success": args.max_success,
"max_fail": args.max_fail if args.include_failures else None,
"include_failures": args.include_failures,
"seed": args.seed,
"shuffle": args.shuffle,
"strict_tools": args.strict_tools,
},
"counts": {
"input_rows": stats.input_rows,
"resolved_rows": stats.resolved_rows,
"written_success": stats.written_success,
"written_fail": stats.written_fail,
"skipped_unresolved": stats.skipped_unresolved,
"dropped": sum(stats.drop_reasons.values()),
},
"drop_reasons": stats.drop_reasons,
"fixes": stats.fixes,
"unknown_tools": stats.unknown_tools,
"missing_required_params": stats.missing_required_params,
"unparseable_examples": stats.unparseable_examples,
"outputs": {
p.name: p.stat().st_size for p in (success_path, fail_path) if p.exists()
},
"elapsed_sec": round(time.time() - t0, 1),
}
meta_path.write_text(json.dumps(meta, indent=2, ensure_ascii=False))
print(json.dumps(meta["counts"], indent=2), file=sys.stderr)
print(f"wrote {success_path} (+meta {meta_path})", file=sys.stderr)
return 0
def build_parser() -> argparse.ArgumentParser:
ap = argparse.ArgumentParser(
prog="traj-normalize",
description="Deserialize Nebius OpenHands trajectories into SFT-ready message lists.",
)
ap.add_argument("--input", default=DEFAULT_REPO,
help="Local parquet path or HF dataset repo id (default: %(default)s)")
ap.add_argument("--out-dir", default="out")
ap.add_argument("--tools-json", default=None,
help="Path to upstream tools.json for soft validation (auto-fetched for Hub input)")
ap.add_argument("--limit", type=int, default=0, help="Max input rows to scan (0 = all)")
ap.add_argument("--max-success", type=int, default=0,
help="Cap on resolved trajectories written (0 = all). Random sample with --seed.")
ap.add_argument("--include-failures", action="store_true",
help="Also write unresolved trajectories to sft_fail.jsonl")
ap.add_argument("--max-fail", type=int, default=0, help="Cap on failure trajectories (0 = all)")
ap.add_argument("--seed", type=int, default=0)
ap.add_argument("--shuffle", action="store_true",
help="Shuffle output records (buffers selected rows in memory)")
ap.add_argument("--strict-tools", action="store_true",
help="Drop trajectories calling tools absent from tools.json")
ap.add_argument("--no-tools", action="store_true",
help="Do not embed the per-row tool list in each record")
ap.add_argument("--batch-size", type=int, default=32)
return ap
def main(argv=None) -> int:
args = build_parser().parse_args(argv)
return run(args)
if __name__ == "__main__":
sys.exit(main())
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