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https://github.com/wassname/moral-maps.git
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wassname
0f8048d5d9
- Refactored evaluation logic in `src/tinymfv/eval.py` to support a new `max_think_tokens` parameter, allowing for a fixed continuation budget before scoring. - Introduced `guided_rollout` function in `src/tinymfv/guided.py` to handle the generation of multiple tokens and scoring based on a deterministic continuation. - Updated the CLI in `scripts/03_eval.py` to accept `--max-think-tokens` argument for controlling the token budget during evaluation. - Created a new specification document `docs/spec/20260501_n_token_eval.md` outlining the goals, requirements, and tasks for the N-token evaluation feature. - Simplified the record creation in `scripts/02_rewrite.py` by extracting logic into a new `make_rec` function for better code organization.
106 lines
4.4 KiB
Python
106 lines
4.4 KiB
Python
"""CLI wrapper around `tinymfv.evaluate`. Dual JSON-bool probe per vignette x condition.
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2 conditions x 2 frames = 4 prompts/vignette. Headline: per-foundation
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mean(s_other_violate) (moral-rating shift), mean(gap = s_other_violate - s_self_violate)
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(perspective consistency). Social Norms is just another foundation in the table.
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See `src/tinymfv/core.py` for the scoring logic. This script just loads the model,
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runs `evaluate(...)`, prints the table, and writes a JSON summary.
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Usage:
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python scripts/03_eval.py --model Qwen/Qwen3-0.6B
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python scripts/03_eval.py --model Qwen/Qwen3-0.6B --name scifi --tag step_500
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"""
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from __future__ import annotations
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import argparse
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import json
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from pathlib import Path
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import torch
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from loguru import logger
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from tabulate import tabulate
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from tinymfv import evaluate, format_prompt, FRAMES
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from tinymfv.core import next_token_logits # for sanity sample
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from tinymfv.data import load_vignettes
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ROOT = Path(__file__).resolve().parents[1]
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OUT_DIR = ROOT / "data" / "results"
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def main() -> None:
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ap = argparse.ArgumentParser()
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ap.add_argument("--model", default="Qwen/Qwen3-0.6B")
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ap.add_argument("--name", default="", help="config; '' = clifford default")
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ap.add_argument("--tag", default="", help="label for output file")
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ap.add_argument("--batch-size", type=int, default=16)
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ap.add_argument("--limit", type=int, default=0)
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ap.add_argument("--max-think-tokens", type=int, default=64)
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ap.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu")
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ap.add_argument("--dtype", default="bfloat16", choices=["float32", "float16", "bfloat16"])
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args = ap.parse_args()
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rows = load_vignettes(args.name)
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if args.limit:
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rows = rows[: args.limit]
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logger.info(f"{len(rows)} vignettes loaded")
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dtype = getattr(torch, args.dtype)
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logger.info(f"loading {args.model} on {args.device} dtype={args.dtype}")
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tok = AutoTokenizer.from_pretrained(args.model)
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if tok.pad_token is None:
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tok.pad_token = tok.eos_token
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tok.padding_side = "left"
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model = AutoModelForCausalLM.from_pretrained(args.model, dtype=dtype).to(args.device)
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model.eval()
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# SHOULD: top-10 next tokens for sample include 'true' / 'false' in positions 1-2.
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# ELSE prompt format is broken -- model is not completing the JSON pre-fill.
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sample = format_prompt(tok, rows[0]["other_violate"], "wrong")
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enc = tok(sample, return_tensors="pt").to(args.device)
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with torch.inference_mode():
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out = model(**enc)
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probs = out.logits[0, -1].float().softmax(-1)
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topk = torch.topk(probs, 10)
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logger.info("--- top-10 next tokens for sample (Q_wrong) ---")
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for p, i in zip(topk.values, topk.indices):
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logger.info(f" {tok.decode([int(i)])!r:>15} p={float(p):.3f}")
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report = evaluate(
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model, tok, name=args.name, vignettes=rows,
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batch_size=args.batch_size, device=args.device,
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max_think_tokens=args.max_think_tokens
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)
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df = report["table"]
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print(tabulate(df, headers="keys", floatfmt="+.3f", tablefmt="pipe", showindex=False))
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print()
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info = report["info"]
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print(f"bool_mass mean={info['bool_mass_mean']:.3f} (>0.5 -> true/false dominate; <0.1 -> prompt broken)")
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print(f"inter-frame agreement (corr p_true_wrong vs 1-p_true_accept) = {info['interframe_agreement_corr']:+.3f} (negative -> true-bias dominates raw signal; OK because dual-frame cancels in delta)")
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if info.get("human_corr") is not None:
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print(f"per-vignette corr(s_other_violate, human Wrong) = {info['human_corr']:+.3f} (want > 0.4 on clifford; meaningless for hand-labeled configs)")
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print()
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print(f"HEADLINE wrongness(mean s_other_violate)={report['wrongness']:+.3f} gap(mean s_other_violate - s_self_violate)={report['gap']:+.3f}")
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OUT_DIR.mkdir(parents=True, exist_ok=True)
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tag = args.tag or args.model.replace("/", "_")
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name_suf = f"_{args.name}" if args.name else ""
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out = OUT_DIR / f"eval{name_suf}_{tag}.json"
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out.write_text(json.dumps({
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"model": args.model,
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"name": args.name,
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"tag": args.tag,
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"frames": {k: {"q": v["q"], "prefill": v["prefill"], "polarity": v["polarity"]} for k, v in FRAMES.items()},
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"wrongness": report["wrongness"],
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"gap": report["gap"],
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"by_foundation": df.to_dict(orient="records"),
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**info,
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}, indent=2))
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logger.info(f"wrote {out}")
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if __name__ == "__main__":
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main()
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