09b2add673
New evalharness/fingerprint/ package (from evalstone fp_fusion v1.1, 2026-09-07 pruning final): probe battery -> concurrent collection -> five scoring views (verify/attribution/variant/adversarial/robustness), bundled family aliases + 27 reference fingerprints (12 fp_fusion schema). - CLI: 'evalharness fingerprint run ...' (REMAINDER passthrough, single source of arg definitions) + 'fingerprint list' for bundled references - imports rewritten package-relative; direct 'python3 run_fp_fusion.py' execution kept working via package bootstrap - offline analysis/collection scripts made path-independent (previously pinned to a /opt/evalscope path absent on this host) - shell scripts: hardcoded API key -> FP_API_KEY/OPENAI_API_KEY env vars - --reference accepts short names resolved against bundled references/ - pyproject: +httpx dependency, package-data references/*.json - tests/test_fingerprint.py: 10 offline tests (battery definitions, assembly counts, normalization, signals, verdict ladder, CLI wiring) - README: fingerprint section + architecture entry Verified on H20-1: tests 10/10, installed CLI OK, full-protocol run vs vectron GLM-5.3 reproduces baseline (score 0.9451, s_idn 0.846).
426 lines
20 KiB
Python
426 lines
20 KiB
Python
#!/usr/bin/env python3
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"""FP-Fusion scorer: 自称提取 / 信号得分 / 门控 / 五档裁决 / 红旗."""
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import json
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import os
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import re
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from pathlib import Path
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_SEVERITY_ORDER = {'HIGH': 0, 'MEDIUM': 1, 'LOW': 2}
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_CUTOFF_RE = re.compile(
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r"(?:cutoff|knowledge|training|截止|知识)[\s\w]*(?:is|was|in|until|up to|是|在)?\s*"
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r"((?:january|february|march|april|may|june|july|august|september|october|november|"
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r"december)\s+\d{4}|\d{4}[-/年]\d{1,2}|\d{4} 年? \d{1,2} 月|\d{4}年)",
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re.IGNORECASE)
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_NEGATION_RE = re.compile(
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r"(not|isn't|isn’t|am not|aren't|rather than|instead of|并非|不是|而不是|而不是)\s*"
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r"(?:an?\s+)?\w{0,12}$", re.IGNORECASE)
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_METACOG_NUM_RE = re.compile(
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r"\b(\d{1,4}(?:\.\d+)?)\s*([bmb]ill?ion|b\b|mb|m\b|亿|万亿|千亿|百亿|十亿)\b|"
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r"(\d+)\s*(?:gpus?|h800|a100|h100|v100|tpu|张\s?(?:gpu|卡))", re.IGNORECASE)
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def load_aliases(path=None):
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path = path or str(Path(__file__).parent / 'family_aliases.json')
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with open(path, encoding='utf-8') as f:
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return json.load(f)
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def _families_in_text(text, aliases):
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"""返回文本中提到的家族集合(带否定前缀过滤)."""
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low = text.lower()
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found = set()
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for family, spec in aliases.items():
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for tok in spec['tokens']:
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idx = low.find(tok.lower())
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while idx != -1:
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prefix = low[max(0, idx - 25):idx]
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if not _NEGATION_RE.search(prefix):
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found.add(family)
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break
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idx = low.find(tok.lower(), idx + 1)
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return found
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def requested_family(model_name, aliases):
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fams = _families_in_text(model_name.lower(), aliases)
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return next(iter(fams)) if len(fams) == 1 else None
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def identity_signal(i_records, aliases, req_family):
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"""自称一致分 + 中英一致性 + 离群自称."""
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parseable = consistent = 0
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outliers, zh_en_bad = [], False
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pair_fams = {}
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for r in i_records:
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text = r.get('response') or ''
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if r.get('error') or not text:
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continue
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fams = _families_in_text(text, aliases)
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meta = r.get('meta') or {}
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pair = meta.get('pair')
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lang = meta.get('lang')
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if pair and lang in ('en', 'zh'):
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pair_fams.setdefault(pair, {})[lang] = fams
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if len(fams) == 1:
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parseable += 1
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fam = next(iter(fams))
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if req_family is None or fam == req_family:
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consistent += 1
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else:
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outliers.append({'probe': r['id'], 'claimed_family': fam,
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'excerpt': text[:120]})
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elif len(fams) > 1:
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parseable += 1
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if req_family and req_family not in fams:
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outliers.append({'probe': r['id'], 'claimed_family': sorted(fams),
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'excerpt': text[:120]})
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# 中英配对一致性: 同一 pair 两侧声称家族交集非空 → 一致
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for pair, sides in pair_fams.items():
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en, zh = sides.get('en') or set(), sides.get('zh') or set()
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if en and zh and not (en & zh):
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zh_en_bad = True
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s_idn = (consistent / parseable) if parseable else 0.5
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return {'s_idn': s_idn, 'parseable': parseable, 'consistent': consistent,
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'outliers': outliers[:5], 'zh_en_consistent': not zh_en_bad}
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def meta_signal(k_records, i_records):
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"""S_meta: 截止口径唯一 + 元认知审计不编造."""
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cutoffs = set()
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for r in k_records + i_records:
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text = r.get('response')
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if text:
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for m in _CUTOFF_RE.finditer(text):
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cutoffs.add(re.sub(r'\s+', ' ', m.group(1).strip().lower()))
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metacog_fabricated = []
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for r in k_records:
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if not (r.get('meta') or {}).get('metacog'):
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continue
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text = r.get('response') or ''
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if text and _METACOG_NUM_RE.search(text) and 'refuse' not in text.lower() \
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and '无法' not in text and "don't have" not in text.lower():
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metacog_fabricated.append({'probe': r['id'], 'excerpt': text[:120]})
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cutoff_ok = 1.0 if len(cutoffs) <= 1 else 0.4
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metacog_ok = 1.0 if not metacog_fabricated else 0.4
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s_meta = (cutoff_ok + metacog_ok) / 2
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return {'s_meta': s_meta, 'cutoffs_unique': sorted(cutoffs),
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'metacog_fabricated': metacog_fabricated}
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def dist_signal(entries, mean_jsd, split_half):
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"""S_dist: 相对比 R = meanJSD / max(splitHalf, 0.02), 带绝对标尺封顶."""
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if mean_jsd is None:
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return None
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sh = split_half if split_half and split_half > 0 else 0.02
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ratio = mean_jsd / max(sh, 0.02)
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if ratio < 2:
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s = 1.0
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elif ratio > 8:
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s = 0.0
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else:
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s = 1.0 - (ratio - 2) / 6
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if mean_jsd > 0.35: # 论文绝对标尺: 不同模型档封顶
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s = min(s, 0.2)
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return {'s_dist': s, 'mean_jsd': mean_jsd, 'split_half': split_half,
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'relative_ratio': round(ratio, 2), 'comparable_cells': len(entries)}
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def refuse_gradient_pattern(c_records):
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"""拒答梯度向量(存档信号): {level: True(拒)/False(顺)}"""
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grad = {}
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for r in c_records:
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g = (r.get('meta') or {}).get('refusal_grad')
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if g:
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text = (r.get('response') or '').lower()
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grad[f'L{g}'] = any(s in text for s in
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("i can't", "i cannot", "无法", "抱歉", "sorry"))
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return grad
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def length_compliance(s_records):
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"""长度服从性(存档信号): 恰好 N 词/1 句 的服从率."""
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out = []
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for r in s_records:
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n = (r.get('meta') or {}).get('len_ctrl')
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if not n or r.get('error'):
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continue
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text = (r.get('response') or '').strip()
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if n == 3:
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words = len([w for w in re.split(r'\W+', text) if w])
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out.append({'probe': r['id'], 'target': 3, 'actual_words': words,
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'ok': words == 3})
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else:
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sents = len([x for x in re.split(r'[.!?。！？]', text) if x.strip()])
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out.append({'probe': r['id'], 'target': 1, 'actual_sents': sents,
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'ok': sents == 1})
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return out
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def verdict_from_score(score, has_reference):
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if score >= 0.85:
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v = 'VERIFIED'
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elif score >= 0.70:
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v = 'LIKELY_MATCH'
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elif score >= 0.50:
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v = 'INCONCLUSIVE'
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elif score >= 0.30:
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v = 'SUSPECTED_MISMATCH'
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else:
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v = 'MISMATCH'
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if not has_reference and v == 'VERIFIED':
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v = 'LIKELY_MATCH' # 无参考不得"验明正身"
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return v
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def build_report(records, d_norm, dist_cmp, model_name, reference_info,
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aliases, tokens_used, elapsed_s,
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attribution=None, adversarial=None, mode='verify'):
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text_records = [r for r in records if r['layer'] in ('I', 'K', 'C', 'S')]
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ok_text = [r for r in text_records if not r['error']]
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total = len(records)
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success = sum(1 for r in records if not r['error'])
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rate = success / max(total, 1)
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quality = ('SUFFICIENT' if success >= 8 and rate >= 0.8
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else ('DEGRADED' if success >= 4 and rate >= 0.5 else 'INSUFFICIENT'))
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req_family = requested_family(model_name, aliases)
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i_records = [r for r in records if r['layer'] == 'I']
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k_records = [r for r in records if r['layer'] == 'K']
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c_records = [r for r in records if r['layer'] == 'C']
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s_records = [r for r in records if r['layer'] == 'S']
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idn = identity_signal(i_records, aliases, req_family)
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meta = meta_signal(k_records, i_records)
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# 延迟: 按输出 token 归一化的"固定开销"估算, 替代固定 10s 绝对阈值.
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# decode_rate = median(text 单条延迟/completion_tokens) → 纯解码速度
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# overhead = baseline_p50 − decode_rate×基线平均completion_tokens
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# 代理/中转会给每个请求叠加固定的网络开销, 短请求(基线)上最显形;
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# 纯硬件慢(CPU)只影响 decode_rate, 不会产生 overhead → 不再冤枉慢端点。
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text_ok = [r for r in ok_text if (r.get('completion_tokens') or 0) > 0]
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per_tok = sorted(r['latency_ms'] / r['completion_tokens'] for r in text_ok)
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decode_rate = per_tok[len(per_tok) // 2] if per_tok else None
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base_records = [r for r in records if r['layer'] == 'BASE' and not r['error']]
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base_ct = [r.get('completion_tokens') or 1 for r in base_records]
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mean_base_ct = sum(base_ct) / len(base_ct) if base_ct else 1.0
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baseline = dist_cmp.get('baseline_p50') if isinstance(dist_cmp, dict) else None
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overhead_ms, overhead_ratio = None, None
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if baseline and decode_rate:
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overhead_ms = baseline - decode_rate * mean_base_ct
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# 用比值而非绝对值判定: CPU 等慢端点的 prefill 开销会随硬件慢等比放大,
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# 固定 10s 阈值会冤枉它; 真正的代理/中转会让短请求比按解码率外推贵数倍
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expected = decode_rate * mean_base_ct
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overhead_ratio = baseline / expected if expected > 0 else None
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latency_anomaly = bool(overhead_ratio is not None and overhead_ratio > 5
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and overhead_ms is not None and overhead_ms > 5_000)
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red_flags = []
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if quality != 'SUFFICIENT':
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red_flags.append({'severity': 'HIGH' if quality == 'INSUFFICIENT' else 'MEDIUM',
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'category': 'evidence',
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'description': f'{quality} evidence: {success}/{total} probes succeeded',
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'evidence': f'Success rate {rate:.0%}'})
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if idn['parseable'] and idn['consistent'] < idn['parseable']:
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red_flags.append({'severity': 'HIGH', 'category': 'identity',
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'description': f"Self-identification deviates from requested "
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f"name '{model_name}' (family={req_family})",
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'evidence': json.dumps(idn['outliers'][:3], ensure_ascii=False)})
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if not idn['zh_en_consistent']:
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red_flags.append({'severity': 'MEDIUM', 'category': 'consistency_zh_en',
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'description': 'Chinese vs English self-identification disagree',
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'evidence': 'paired identity probes'})
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if len(meta['cutoffs_unique']) > 1:
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red_flags.append({'severity': 'HIGH', 'category': 'consistency',
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'description': 'Inconsistent knowledge cutoff dates',
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'evidence': ', '.join(meta['cutoffs_unique'])})
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if meta['metacog_fabricated']:
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red_flags.append({'severity': 'LOW', 'category': 'metacog',
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'description': 'States specific parameter counts / training '
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'hardware (typical of substituted small models)',
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'evidence': json.dumps(meta['metacog_fabricated'][:2],
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ensure_ascii=False)})
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if latency_anomaly:
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red_flags.append({'severity': 'MEDIUM', 'category': 'latency',
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'description': f'Estimated fixed per-request overhead '
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f'{overhead_ms:.0f}ms (baseline p50 {baseline:.0f}ms '
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f'vs decode-rate expectation) suggests proxy/relay',
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'evidence': f'decode_rate={decode_rate:.1f}ms/tok, '
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f'base_ct={mean_base_ct:.1f}'})
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# v1.1: 单 cell 极端分化 → 实锤级信号(兄弟假冒案例中均值被数字cell稀释, 单cell达1.0)
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outlier_cells = dist_cmp.get('outlier_cells') or []
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dist_outlier = bool(dist_cmp.get('dist_outlier'))
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if dist_outlier:
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red_flags.append({'severity': 'MEDIUM', 'category': 'dist_outlier',
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'description': f'{len(outlier_cells)} cell(s) show extreme '
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f'distribution divergence (JSD>0.5, n>=15)',
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'evidence': json.dumps(outlier_cells, ensure_ascii=False)})
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# ---- 融合 ----
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has_ref = dist_cmp.get('mean_jsd') is not None
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s_fam = (attribution or {}).get('s_fam', 0.0)
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# 维度A：有参考时引入家族归因信号（0.25权重），身份/分布相应下调；
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# 无参考（自证模式）保持原权重，归因仅作辅助展示。
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if has_ref:
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final = (0.35 * dist_cmp['s_dist'] + 0.20 * idn['s_idn']
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+ 0.20 * meta['s_meta'] + 0.25 * s_fam)
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else:
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final = 0.60 * idn['s_idn'] + 0.40 * meta['s_meta']
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if quality != 'SUFFICIENT':
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final = min(final, 0.5)
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score = round(max(0.0, min(1.0, final)), 4)
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verdict = verdict_from_score(score, has_ref)
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# v1.1: dist_outlier 实锤信号 → 裁决封顶 SUSPECTED_MISMATCH(不许高于此档;
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# INCONCLUSIVE 也被视为"证据被稀释", 由离群 cell 证据直接升级)
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if dist_outlier:
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_order = ['VERIFIED', 'LIKELY_MATCH', 'INCONCLUSIVE', 'SUSPECTED_MISMATCH', 'MISMATCH']
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if _order.index(verdict) < _order.index('SUSPECTED_MISMATCH'):
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verdict = 'SUSPECTED_MISMATCH'
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# 维度C：impersonation 实锤 → 同样封顶 SUSPECTED_MISMATCH（复用裁决帽机制）
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impersonation_flag = bool(adversarial and adversarial.get('impersonation_flag'))
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if impersonation_flag:
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_order = ['VERIFIED', 'LIKELY_MATCH', 'INCONCLUSIVE', 'SUSPECTED_MISMATCH', 'MISMATCH']
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if _order.index(verdict) < _order.index('SUSPECTED_MISMATCH'):
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verdict = 'SUSPECTED_MISMATCH'
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report = {
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'benchmark': 'fp_fusion',
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'version': '1.1',
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'score': score,
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'num': total,
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'verdict': verdict,
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'mode': 'reference_verify' if has_ref else 'self_consistency',
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'model': model_name,
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'reference': reference_info,
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'gate': {'total_probes': total, 'successful_probes': success,
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'success_rate': round(rate, 3), 'quality': quality},
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'signals': {
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'dist': {k: v for k, v in (dist_cmp or {}).items() if k != 'baseline_p50'}
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if has_ref else {'enabled': False,
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'split_half_jsd': dist_cmp.get('split_half')},
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'family': (attribution if attribution is not None
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else {'enabled': False, 'note': 'reserved hook (v1 - 未启用归因)'}),
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'identity': {'s_idn': round(idn['s_idn'], 3),
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'parseable': idn['parseable'],
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'consistent': idn['consistent'],
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'zh_en_consistent': idn['zh_en_consistent'],
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'outliers': idn['outliers']},
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'meta': {'s_meta': round(meta['s_meta'], 3),
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'cutoffs_unique': meta['cutoffs_unique'],
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'refusal_gradient': refuse_gradient_pattern(c_records),
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'length_compliance': length_compliance(s_records)},
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'adversarial': adversarial if adversarial is not None else {'enabled': False},
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'latency': {'baseline_p50_ms': baseline,
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'decode_rate_ms_per_tok': round(decode_rate, 1) if decode_rate else None,
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'estimated_overhead_ms': round(overhead_ms, 1) if overhead_ms is not None else None,
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'overhead_ratio': round(overhead_ratio, 2) if overhead_ratio else None,
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'anomaly': latency_anomaly},
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},
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'red_flags': sorted(red_flags, key=lambda f: _SEVERITY_ORDER.get(f['severity'], 3)),
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'tokens_used': tokens_used,
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'elapsed_s': round(elapsed_s, 1),
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}
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if mode != 'verify':
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report['mode_detail'] = mode
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return report
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def robustness_signal(records, temperature_sweep=None):
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"""维度 D：鲁棒性正交信号。
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三个轴：
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temp_axis : 文本层同探针在不同温度下的回答一致性（归一化到 [0,1]，1=完全一致）
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lang_axis : 文本层 en/zh 同探针（pair 配对）回答一致性率
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paraphrase_axis : D 层同 cell 不同 prompt_var 的回答分布 JSD（0=最稳，1=最有漂移）
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输出块（写入 report['signals']['robustness']）。
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"""
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text_ok = [r for r in records
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if r.get('layer') in ('I', 'K', 'C', 'S', 'V') and not r.get('error')]
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# ---- 温度轴：同一探针 id 在多个温度下的回答一致性 ----
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temp_axis = {'enabled': bool(temperature_sweep and len(temperature_sweep) > 1),
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'temperatures': temperature_sweep or [],
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'probes_covered': 0, 'mean_consistency': None}
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if temp_axis['enabled']:
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by_probe = {}
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for r in text_ok:
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by_probe.setdefault(r['id'], []).append(r)
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consist = []
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for pid, rs in by_probe.items():
|
||
temps = {r.get('temperature') for r in rs}
|
||
if len(temps) < 2:
|
||
continue
|
||
temp_axis['probes_covered'] += 1
|
||
# 两两回答归一化比较
|
||
normed = [(r.get('response') or '').strip().lower() for r in rs]
|
||
same = 0
|
||
pairs = 0
|
||
for i in range(len(normed)):
|
||
for j in range(i + 1, len(normed)):
|
||
pairs += 1
|
||
if normed[i] and normed[i] == normed[j]:
|
||
same += 1
|
||
consist.append(same / pairs if pairs else 0)
|
||
if consist:
|
||
temp_axis['mean_consistency'] = round(sum(consist) / len(consist), 3)
|
||
|
||
# ---- 语言轴：pair 配对的 en/zh 回答一致率 ----
|
||
lang_axis = {'enabled': False, 'probes_covered': 0, 'mean_consistency': None}
|
||
pair_probes = {}
|
||
for r in text_ok:
|
||
m = r.get('meta') or {}
|
||
if m.get('pair') and m.get('lang') in ('en', 'zh'):
|
||
pair_probes.setdefault(m['pair'], {})[m['lang']] = \
|
||
(r.get('response') or '').strip().lower()
|
||
if pair_probes:
|
||
lang_axis['enabled'] = True
|
||
matches = 0
|
||
checks = 0
|
||
for p, sides in pair_probes.items():
|
||
if sides.get('en') and sides.get('zh'):
|
||
checks += 1
|
||
if sides['en'] and sides['en'] == sides['zh']:
|
||
matches += 1
|
||
lang_axis['probes_covered'] = checks
|
||
lang_axis['mean_consistency'] = \
|
||
round(matches / checks, 3) if checks else None
|
||
|
||
# ---- 改写轴：D 层同 cell 不同 prompt_var 的分布 JSD ----
|
||
para_axis = {'enabled': False, 'cells_covered': 0, 'mean_jsd': None}
|
||
d_ok = [r for r in records if r.get('layer') == 'D'
|
||
and not r.get('error') and r.get('prompt_var')]
|
||
if d_ok:
|
||
from .engine import jsd_bits
|
||
by_cell_var = {}
|
||
for r in d_ok:
|
||
cell = r['cell']
|
||
key = (cell, r.get('prompt_var'))
|
||
norm = r.get('norm') if 'norm' in r else None
|
||
by_cell_var.setdefault(cell, {})
|
||
cnt = by_cell_var[cell]
|
||
cnt_key = key
|
||
cnt.setdefault(cnt_key, {})
|
||
by_cell_var[cell][cnt_key] = cnt[cnt_key]
|
||
# 用 response first-token 简化做分布（避免引入 build_d_normalized 循环依赖）
|
||
tok = (r.get('response') or '').strip().lower().split()[0] \
|
||
if (r.get('response') or '').strip() else '__empty__'
|
||
cnt[cnt_key][tok] = cnt[cnt_key].get(tok, 0) + 1
|
||
jsds = []
|
||
cells_covered = 0
|
||
for cell, var_map in by_cell_var.items():
|
||
if len(var_map) < 2:
|
||
continue
|
||
cells_covered += 1
|
||
keys = list(var_map.keys())
|
||
for i in range(len(keys)):
|
||
for j in range(i + 1, len(keys)):
|
||
jsds.append(jsd_bits(var_map[keys[i]], var_map[keys[j]]))
|
||
if jsds:
|
||
para_axis['enabled'] = True
|
||
para_axis['cells_covered'] = cells_covered
|
||
para_axis['mean_jsd'] = round(sum(jsds) / len(jsds), 3)
|
||
|
||
return {'temp_axis': temp_axis, 'lang_axis': lang_axis,
|
||
'paraphrase_axis': para_axis}
|