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wassnameandClaudypoo
469788c418
Import name tinymfv -> moralmaps, pip name tiny-mfv -> moral-maps, GitHub URLs wassname/tinymfv -> wassname/moral-maps. HuggingFace dataset id wassname/tiny-mfv left as-is (separate namespace, published data artifact). Historical docs/spec/* and RESEARCH_JOURNAL keep their dated paths. Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
105 lines
4.6 KiB
Python
105 lines
4.6 KiB
Python
"""Diagnostic: reproduce the historical WORD-first-token MFV readout to confirm the
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0.826 top-1 in the validation table (journal 2026-05-08), and contrast it with the
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current debiased DIGIT readout (0.773).
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This does NOT touch the canonical eval. moralmaps.evaluate stays digit-only. This is a
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throwaway measurement (like the other probe_* scripts) that reuses the current
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_rollout_natural_or_forced core but gathers the first token of each foundation WORD
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(care/fair/loy/author/san/lib/social) instead of the option index digit -- the one
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structural change commit e4e0f4d made when it switched word->digit to drop the
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uneven-first-piece word prior. Everything else (prompt assembly, fwd/rev debias,
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think budget, softmax, top-1 vs human argmax) mirrors evaluate().
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If word-gather top-1 >> digit-gather top-1 (~0.83 vs 0.77), the gap is the readout
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method, and the showcase model DOES match the validation table under the table's
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own method.
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uv run python scripts/probe_word_readout.py --model Qwen/Qwen3-4B
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"""
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from __future__ import annotations
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import argparse
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import math
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import numpy as np
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import torch
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from loguru import logger
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from moralmaps.data import load_vignettes
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from moralmaps.eval import _label_dist
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from moralmaps.guided import (
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_DEFAULT_FORCED_FOUNDATIONS, _FORCED_FOUNDATION_DESCS,
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_resolve_first_token_ids, _rollout_natural_or_forced,
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)
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NUDGE = "Just answer"
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PREFILL = 'This is wrong because {"violation": "'
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def _word_schema(foundations: list[str]) -> str:
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"""The pre-e4e0f4d schema: enum key = foundation WORD, not the index digit."""
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opts = "\n".join(' "%s", # %s' % (f, _FORCED_FOUNDATION_DESCS[f]) for f in foundations)
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return ('Here is the JSON schema for the answer. This is wrong because:\n'
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' {\n "properties": {\n "violation": {\n "enum": [\n'
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+ opts + '\n ]\n }\n }\n }')
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@torch.no_grad()
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def main() -> None:
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ap = argparse.ArgumentParser()
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ap.add_argument("--model", default="Qwen/Qwen3-4B")
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ap.add_argument("--budget", type=int, default=256)
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ap.add_argument("--batch-size", type=int, default=8)
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ap.add_argument("--device", default="cuda")
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args = ap.parse_args()
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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(
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args.model, torch_dtype=torch.bfloat16).to(args.device).eval()
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foundations = list(_DEFAULT_FORCED_FOUNDATIONS)
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foundations_rev = list(reversed(foundations))
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K = len(foundations)
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# Gather the first token of each foundation WORD, same order both frames (the
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# pre-debias scheme). No reversal of the rev gather (that is the digit-mode fix).
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first_ids, _ = _resolve_first_token_ids(tok, foundations)
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schema_fwd, schema_rev = _word_schema(foundations), _word_schema(foundations_rev)
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vigs = load_vignettes("classic")
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rows = [(v, _label_dist(v, foundations)) for v in vigs]
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rows = [(v, lab) for v, lab in rows if lab is not None]
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prompts = [v["other_violate"] for v, _ in rows]
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y_pred, y_true, pmass = [], [], []
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for i in range(0, len(prompts), args.batch_size):
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chunk = prompts[i:i + args.batch_size]
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_, slots_f = _rollout_natural_or_forced(
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model, tok, chunk, schema_fwd, args.budget,
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scoring_slots=[(NUDGE, PREFILL)], gather_token_ids=first_ids,
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n_samples=1, temperature=0.0)
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_, slots_r = _rollout_natural_or_forced(
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model, tok, chunk, schema_rev, args.budget,
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scoring_slots=[(NUDGE, PREFILL)], gather_token_ids=first_ids,
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n_samples=1, temperature=0.0)
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for j in range(len(chunk)):
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lp_f = np.asarray(slots_f[j][0]["lp_gather"], dtype=float)
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lp_r = np.asarray(slots_r[j][0]["lp_gather"], dtype=float) # no reversal: word gather
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score = (lp_f + lp_r) / 2.0
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y_pred.append(int(np.argmax(score)))
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pmass.append(0.5 * (slots_f[j][0]["pmass_allowed"] + slots_r[j][0]["pmass_allowed"]))
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y_true = [int(np.argmax(lab)) for _, lab in rows]
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y_pred, y_true = np.array(y_pred), np.array(y_true)
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top1 = float((y_pred == y_true).mean())
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logger.info(f"WORD readout: top1={top1:.3f} mean_pmass={np.mean(pmass):.3f} n={len(y_true)}\n"
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f"SHOULD: ~0.83 (matches the 82.6% validation table) if the word gather is the cause "
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f"of the digit readout's 0.77. Canonical evaluate() is unchanged (digit).")
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if __name__ == "__main__":
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main()
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