File size: 13,647 Bytes
4949db9 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 | """Convert Claude CoT eval results to ms-swift JSONL training data.
Reads Claude eval JSON files (produced with cotnosubq.yaml), extracts
reasoning + scores, and builds TrainingSample records.
Usage:
python -m judge_training.data.build_from_claude_cot convert \
--prompt-config cotnosubq.yaml \
--eval-dir data/scores/claude \
--pattern 'eval_claude_cot_*.json'
"""
from __future__ import annotations
import argparse
import glob
import json
import logging
import sys
from pathlib import Path
from typing import Any
from evals.physics_criteria import get_criteria_text
from evals.prompts import GENERAL_DIMS, PromptConfig
from judge_training.data.sample import TrainingSample
from judge_training.data.build_records_from_db import build_records
from judge_training.data.build_swift_data import (
add_common_convert_args,
append_val_command_args,
run_convert,
validate_cli,
write_test_splits,
)
from judge_training.data.naming import (
prompt_config_stem,
swift_train_path,
swift_val_path,
)
logger = logging.getLogger(__name__)
MODULE = "judge_training.data.build_from_claude_cot"
def _parse_rationale(raw: Any) -> dict[str, str]:
"""Parse a rationale field that may be a JSON string or dict."""
if isinstance(raw, dict):
return raw
if isinstance(raw, str):
try:
return json.loads(raw)
except json.JSONDecodeError:
return {}
return {}
def _load_eval_results(eval_dir: str, pattern: str) -> list[dict[str, Any]]:
"""Load all eval result JSONs matching pattern, dedup by video stem."""
files = sorted(glob.glob(str(Path(eval_dir) / pattern)))
if not files:
raise FileNotFoundError(f"No files matching {pattern} in {eval_dir}")
seen: dict[str, dict[str, Any]] = {}
for fpath in files:
with open(fpath) as f:
data = json.load(f)
for result in data.get("results", []):
video = result.get("video", "")
if video:
seen[video] = result
logger.info("Loaded %d unique videos from %d files", len(seen), len(files))
return list(seen.values())
def _build_human_score_lookup(
db_path: str,
) -> dict[tuple[str, str], int]:
"""Build a lookup from (video_path, dim_or_law) -> human score."""
records = build_records(db_path, active_only=False)
lookup: dict[tuple[str, str], int] = {}
for rec in records:
vp = rec["video_path"]
for dim, score in rec["general_scores"].items():
lookup[(vp, dim)] = score
for law, score in rec["physical_scores"].items():
lookup[(vp, law)] = score
return lookup
def _extract_cot_samples(
results: list[dict[str, Any]],
prompt_cfg: PromptConfig,
model_prefix: str,
base_dir: str,
human_scores: dict[tuple[str, str], int],
) -> list[TrainingSample]:
"""Extract CoT training samples, keeping only where Claude score == human score."""
samples: list[TrainingSample] = []
skipped_no_reasoning = 0
skipped_score_mismatch = 0
skipped_no_human = 0
for result in results:
video_stem = result.get("video", "")
prompt_text = result.get("prompt", "")
if not video_stem or not prompt_text:
continue
video_path = f"data/videos/{model_prefix}/{video_stem}.mp4"
rationale_general = _parse_rationale(result.get("rationale_general", "{}"))
rationale_physical = _parse_rationale(result.get("rationale_physical", "{}"))
for dim in GENERAL_DIMS:
score = result.get(dim)
if score is None or not isinstance(score, (int, float)):
continue
score = int(score)
reasoning = rationale_general.get(dim, "")
if not reasoning:
skipped_no_reasoning += 1
continue
human = human_scores.get((video_path, dim))
if human is None:
skipped_no_human += 1
continue
if score != human:
skipped_score_mismatch += 1
continue
try:
samples.append(
TrainingSample.cot(
system=prompt_cfg.system_prompt,
user=prompt_cfg.build_training_prompt(prompt_text, dim),
video_path=video_path,
key=dim,
score=score,
reasoning=reasoning,
base_dir=base_dir,
)
)
except ValueError as e:
logger.warning("Skip %s/%s: %s", video_stem, dim, e)
physical_laws = result.get("physical_laws", [])
physical_data = result.get("physical", {})
laws_dict = physical_data.get("laws", {}) if isinstance(physical_data, dict) else {}
for law in physical_laws:
law_info = laws_dict.get(law, {})
score = law_info.get("score")
if score is None or not isinstance(score, (int, float)):
continue
score = int(score)
reasoning = rationale_physical.get(law, "")
if not reasoning:
skipped_no_reasoning += 1
continue
human = human_scores.get((video_path, law))
if human is None:
skipped_no_human += 1
continue
if score != human:
skipped_score_mismatch += 1
continue
criteria_text = get_criteria_text(law)
if not criteria_text:
continue
try:
samples.append(
TrainingSample.cot(
system=prompt_cfg.system_prompt,
user=prompt_cfg.build_physical_prompt(
prompt_text, law, criteria_text,
),
video_path=video_path,
key=law,
score=score,
reasoning=reasoning,
base_dir=base_dir,
)
)
except ValueError as e:
logger.warning("Skip %s/%s: %s", video_stem, law, e)
if skipped_no_reasoning:
logger.info("Skipped %d samples with empty reasoning", skipped_no_reasoning)
if skipped_no_human:
logger.info("Skipped %d samples with no human score", skipped_no_human)
if skipped_score_mismatch:
logger.info("Skipped %d samples where Claude != human score", skipped_score_mismatch)
return samples
def _build_eval_records(
eval_dir: str,
pattern: str,
model_prefixes: list[str],
) -> list[dict[str, Any]]:
"""Load eval results into prompt/model records for split assignment."""
records: list[dict[str, Any]] = []
for prefix in model_prefixes:
model_pattern = pattern.replace("*", f"*{prefix}*")
try:
results = _load_eval_results(eval_dir, model_pattern)
except FileNotFoundError:
logger.warning("No files for %s, skipping", prefix)
continue
n_records = 0
for result in results:
prompt_text = result.get("prompt", "")
video_stem = result.get("video", "")
if not prompt_text or not video_stem:
continue
records.append(
{
"prompt": prompt_text,
"video_model": prefix,
"result": result,
}
)
n_records += 1
logger.info(" %s: %d eval records", prefix, n_records)
return records
def _records_to_samples(
records: list[dict[str, Any]],
prompt_cfg: PromptConfig,
base_dir: str,
human_scores: dict[tuple[str, str], int],
) -> list[TrainingSample]:
"""Convert split-assigned eval records into training samples."""
model_to_results: dict[str, list[dict[str, Any]]] = {}
for rec in records:
model_to_results.setdefault(rec["video_model"], []).append(rec["result"])
samples: list[TrainingSample] = []
for model_prefix in sorted(model_to_results):
model_results = model_to_results[model_prefix]
model_samples = _extract_cot_samples(
model_results,
prompt_cfg,
model_prefix,
base_dir,
human_scores,
)
logger.info(
" %s: %d results -> %d samples",
model_prefix,
len(model_results),
len(model_samples),
)
samples.extend(model_samples)
return samples
def _metadata(prompt_config: str) -> dict[str, object]:
return {
"prompt_config": prompt_config,
"prompt_config_source": "cli",
"label_source": "claude_cot",
"target_format": "cot",
"dims": [*GENERAL_DIMS, "physical_laws"],
"score_scale": "1-5",
}
def _build_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser(
description="Convert Claude CoT eval results to ms-swift JSONL.",
)
subparsers = parser.add_subparsers(dest="command")
convert = subparsers.add_parser("convert", help="Build JSONL from Claude CoT evals")
convert.add_argument("--output", default=None)
convert.add_argument("--eval-dir", default="data/scores/claude")
convert.add_argument("--pattern", default="eval_claude_cot_*.json")
convert.add_argument("--model-prefix", action="append", dest="model_prefixes",
help="Model prefix(es) to include (repeat for multiple). "
"Default: infer from filenames.")
convert.add_argument("--prompt-config", dest="prompt_config", required=True)
convert.add_argument("--db-path", default="evals/human_eval/human_eval_filtered.db",
help="Path to human eval DB for score filtering")
convert.add_argument(
"--holdout-prompt-ratio",
dest="holdout_prompt_ratio",
type=float,
default=0.1,
help="Fraction of prompts to hold out (default: 0.1).",
)
convert.add_argument(
"--prompt-seed",
dest="prompt_seed",
type=int,
default=42,
help="Random seed for prompt holdout sampling (default: 42)",
)
add_common_convert_args(convert)
validate = subparsers.add_parser("validate", help="Validate a JSONL file")
validate.add_argument("jsonl")
return parser
def main(argv: list[str] | None = None) -> int:
logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")
parser = _build_parser()
args = parser.parse_args(argv)
if args.command == "validate":
return validate_cli(args.jsonl)
if args.command == "convert":
stem = prompt_config_stem(args.prompt_config)
data_stem = f"{stem}_claude_cot"
if args.output is None:
args.output = swift_train_path(data_stem)
if args.val_output is None:
args.val_output = swift_val_path(data_stem)
prompt_cfg = PromptConfig.load(args.prompt_config)
all_files = sorted(glob.glob(str(Path(args.eval_dir) / args.pattern)))
if not all_files:
logger.error("No files matching %s in %s", args.pattern, args.eval_dir)
return 1
if args.model_prefixes:
prefixes = args.model_prefixes
else:
prefixes = set()
for fpath in all_files:
name = Path(fpath).stem
parts = name.split("_humaneval_set_")
if len(parts) == 2:
model = parts[1].rsplit("_", 2)[0]
prefixes.add(model)
prefixes = sorted(prefixes)
logger.info("Auto-detected model prefixes: %s", prefixes)
human_scores = _build_human_score_lookup(args.db_path)
logger.info("Loaded %d human score entries", len(human_scores))
eval_records = _build_eval_records(args.eval_dir, args.pattern, prefixes)
logger.info("Loaded %d eval records for split assignment", len(eval_records))
from judge_training.data.build_records_from_db import split_by_prompt_and_model
record_splits = split_by_prompt_and_model(
eval_records,
args.holdout_model,
args.holdout_prompt_ratio,
args.prompt_seed,
)
sample_splits = {
name: _records_to_samples(recs, prompt_cfg, args.base_dir, human_scores)
for name, recs in record_splits.items()
}
write_test_splits(sample_splits, data_stem)
train_samples = sample_splits["train"]
command_args = [
"--eval-dir", args.eval_dir,
"--pattern", args.pattern,
"--prompt-config", args.prompt_config,
"--db-path", args.db_path,
"--holdout-prompt-ratio", str(args.holdout_prompt_ratio),
"--prompt-seed", str(args.prompt_seed),
"--output", args.output,
]
if args.model_prefixes:
for prefix in args.model_prefixes:
command_args.extend(["--model-prefix", prefix])
append_val_command_args(command_args, args)
return run_convert(
samples=train_samples,
args=args,
metadata=_metadata(args.prompt_config),
module=MODULE,
command_args=command_args,
)
parser.print_help()
return 1
if __name__ == "__main__":
sys.exit(main())
|