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allaun
1f7ec15f12
Adds logViscosityRatio, log_viscosity_monotone, and ν_eff_monotone to Semantics/NKHodgeFAMM.lean section 6b. The adaptive viscosity law ν_eff = ν₀*(1+μ) is multiplicative in ν₀ and additive in scar density μ; taking λ = log(ν_eff/ν₀) = log(1+μ) turns the multiplicative feedback into an additive coordinate. This gives nlinarith a direct handle on viscosity monotonicity and connects the module to Kritchevsky's "Everything Is Logarithms" framing (SilverSight CITATION.cff). Also marks a few pre-existing unused variables with underscores to silence the linter. Build: 8316 jobs, 0 errors (lake build Semantics.NKHodgeFAMM)
394 lines
16 KiB
Python
394 lines
16 KiB
Python
#!/usr/bin/env python3
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# INFRA:DEAD rds -- AWS RDS is gone. Any file referencing rds_connect.py or this hostname is stale and must be ported.
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"""Route-Repair Loop v1 — use the flexure joint library to suggest tactic repairs for failed proofs.
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Takes a failed Lean proof trace, classifies it, generates candidate patches
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from the predicted tactic family, retries on the proof worker, and measures recovery.
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"""
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import hashlib
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import json
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import logging
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import math
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import os
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import re
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import subprocess
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import sys
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import time
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import uuid
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from collections import Counter, defaultdict
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from pathlib import Path
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), "."))
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from pist_trace_classify_mcp import compute_spectral
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FLEXURE_SESSION = "a4a0eb20-93fe-413e-8e0b-50334bb778d8"
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WORKER_URL = os.environ.get("CANARY_WORKER_URL", "http://100.72.130.76:8787")
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PROOF_SERVER_TOKEN = os.environ.get("PROOF_SERVER_TOKEN", "")
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if not PROOF_SERVER_TOKEN:
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tf = os.environ.get("PROOF_SERVER_TOKEN_FILE", os.path.expanduser("~/.config/ene/language-proof-server.token"))
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try:
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PROOF_SERVER_TOKEN = Path(tf).read_text().strip()
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except (ValueError, TypeError, KeyError) as e: logging.warning(f"Failed to read proof server token: {e}")
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TACTIC_TEMPLATES = {
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"rewrite": ["rw [%s]", "simp [%s]"],
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"normalization": ["simp", "norm_num"],
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"discharge": ["exact %s", "assumption", "apply %s"],
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"arithmetic": ["omega"],
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"case_analysis": ["cases %s", "constructor"],
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"induction": ["induction %s"],
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"introduction": ["intro %s"],
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"reflexivity": ["rfl"],
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"constructor": ["constructor"],
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"lemma_introduction": ["have h : %s := by\n %s"],
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}
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def connect():
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from rds_connect import connect_rds
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return connect_rds()
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def prove(lean_code: str, name: str = "repair", timeout_s: int = 60) -> dict:
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"""Send Lean code to the proof worker."""
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result = subprocess.run(
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["curl", "-s", "--connect-timeout", "10", "-X", "POST", f"{WORKER_URL}/lean/check",
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"-H", "Content-Type: application/json",
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"-H", f"Authorization: Bearer {PROOF_SERVER_TOKEN}",
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"-d", json.dumps({"code": lean_code, "name": name})],
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capture_output=True, text=True, timeout=timeout_s,
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)
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if result.returncode != 0:
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return {"ok": False, "stdout": "", "stderr": f"curl: {result.stderr[:200]}"}
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try:
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return json.loads(result.stdout)
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except json.JSONDecodeError as e:
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return {"ok": False, "stdout": "", "error": str(e)}
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def classify_tactic(tactic: str) -> str:
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tl = tactic.lower()
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if "rw" in tl: return "rewrite"
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if "simp" in tl: return "normalization"
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if "omega" in tl or "nlinarith" in tl: return "arithmetic"
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if "induction" in tl: return "induction"
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if "ring" in tl: return "normalization"
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if "cases" in tl: return "case_analysis"
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if "constructor" in tl: return "constructor"
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if "apply" in tl or "exact" in tl: return "discharge"
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if "intro" in tl: return "introduction"
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if "have" in tl: return "lemma_introduction"
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if "rfl" in tl: return "reflexivity"
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if "norm_num" in tl: return "normalization"
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return "unknown"
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def classify_trace_against_library(trace: dict) -> dict:
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"""Classify a trace against the flexure library and return predictions."""
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import hashlib as hl
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matrix = trace.get("transition_matrix", [])
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if not matrix or len(matrix) == 0:
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return {"tactic_family": "unknown", "confidence": 0.0, "nearest_motifs": []}
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sp = compute_spectral(matrix)
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vec = [sp.get(k, 0) for k in ["matrix_size", "rank", "spectral_gap", "laplacian_zero_count",
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"density", "eigenvalue_max"]]
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try:
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conn = connect()
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cur = conn.cursor()
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cur.execute("SELECT decision_signals FROM ene.flexures WHERE session_id = %s", (FLEXURE_SESSION,))
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library = []
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for row in cur.fetchall():
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sig = json.loads(row[0]) if isinstance(row[0], str) else row[0]
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sp_lib = sig.get("spectral", {})
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lv = [sp_lib.get(k, 0) for k in ["matrix_size", "rank", "spectral_gap", "laplacian_zero_count",
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"density", "eigenvalue_max"]]
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library.append({"features": lv, "tf": sig.get("tactic_family", "?")})
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# kNN vote
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scored = []
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for lib in library:
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dist = math.sqrt(sum((vec[i] - lib["features"][i])**2 for i in range(len(vec))))
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scored.append((dist, lib["tf"]))
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scored.sort(key=lambda x: x[0])
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top3 = scored[:3]
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votes = Counter(tf for _, tf in top3)
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predicted = votes.most_common(1)[0][0]
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confidence = votes.most_common(1)[0][1] / len(top3)
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# Get motifs
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cur.execute("SELECT id, pattern_signature, frequency, pre_conditions FROM ene.flexure_patterns ORDER BY frequency DESC")
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motifs = []
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for row in cur.fetchall():
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pre = json.loads(row[3]) if isinstance(row[3], str) else row[3]
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motifs.append({"id": str(row[0]), "frequency": row[2], "tactic_family": pre.get("tactic_family", "?")})
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motifs.sort(key=lambda m: m["frequency"], reverse=True)
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cur.close()
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conn.close()
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return {"tactic_family": predicted, "confidence": round(confidence, 2), "nearest_motifs": motifs[:5]}
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except Exception as e:
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return {"tactic_family": "unknown", "confidence": 0.0, "nearest_motifs": [], "error": str(e)[:80]}
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def parse_theorem(code: str) -> dict:
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"""Extract variables and hypotheses from a Lean theorem statement."""
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vars_list = []
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hyps_list = []
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# Extract everything between theorem name and :=
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m = re.search(r'theorem\s+\w+\s+(.*?)\s*:=', code)
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if m:
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signature = m.group(1)
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# Split by parenthesized groups
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parts = re.findall(r'\(([^)]+)\)', signature)
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for part in parts:
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# Each part is like "h : a = b" or "n : Nat"
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sub = [p.strip() for p in part.split(":")]
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if len(sub) == 2:
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name_part = sub[0]
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type_part = sub[1]
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# If multiple names: "a b c : Nat"
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names = [n.strip() for n in name_part.split()]
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for n in names:
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if type_part.strip() in ["Prop", "Nat", "Int", "ℕ", "ℤ"]:
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vars_list.append(n)
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else:
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hyps_list.append((n, type_part.strip()))
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return {"variables": vars_list, "hypotheses": hyps_list}
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def generate_patches(predicted_family: str, theorem_code: str, theorem_info: dict, max_patches: int = 3) -> list[str]:
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"""Generate candidate tactic patches for a given tactic family."""
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patches = []
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hyps = theorem_info.get("hypotheses", [])
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vars_list = theorem_info.get("variables", [])
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templates = TACTIC_TEMPLATES.get(predicted_family, ["simp"])
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for tmpl in templates[:max_patches]:
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if "%s" in tmpl:
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# Substitute with available hypotheses
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for hyp_name, hyp_type in hyps[:3]:
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patch = tmpl % hyp_name
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patches.append(patch)
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if not hyps and vars_list:
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patch = tmpl % vars_list[0]
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patches.append(patch)
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else:
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patches.append(tmpl)
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# Deduplicate and limit
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seen = set()
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unique = []
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for p in patches:
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if p not in seen:
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seen.add(p)
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unique.append(p)
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return unique[:max_patches]
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def apply_patch(original_code: str, patch_tactic: str) -> str:
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"""Apply a tactic patch to a Lean proof. Replaces the by-block."""
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m = re.search(r'(:= by\s*)(.*?)(\n\s*(?:\n|theorem|def))?$', original_code, re.DOTALL)
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if m:
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prefix = original_code[:m.start(1)] + ":= by"
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patch = f" {patch_tactic}"
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return prefix + "\n" + patch
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return original_code + "\n " + patch_tactic
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def measure_complexity(code: str) -> dict:
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"""Estimate goal complexity from theorem structure."""
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n_vars = code.count("(")
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n_hyps = code.count(":=")
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n_operators = sum(1 for c in code if c in "+-*/^∧∨→¬∀∃≤≥")
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return {"variables": n_vars, "operators": n_operators, "raw_chars": len(code)}
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def route_repair(lean_code: str, theorem_name: str = "unnamed",
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max_attempts: int = 3, allowed_families: list[str] | None = None) -> dict:
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"""Full route-repair loop: classify → patch → retry → measure."""
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result = {
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"theorem_name": theorem_name,
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"initial_status": "unknown",
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"predicted_tactic_family": None,
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"confidence": 0.0,
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"nearest_motifs": [],
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"attempts": [],
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"best_attempt": None,
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"recovered": False,
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"elapsed_s": 0.0,
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}
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t0 = time.time()
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# Step 1: Build a minimal trace from the original (failed) proof
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# We need a transition matrix. For a single-tactic theorem, it's 2x2.
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# For multi-tactic, parse the by-block.
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trace_tags = []
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by_match = re.search(r'by\s+(.+?)$', lean_code, re.DOTALL)
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if by_match:
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body = by_match.group(1).strip()
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for line in body.split(";"):
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t = line.strip()
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if t:
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trace_tags.append(f"step_before_{t}")
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trace_tags.append(f"step_after_{t}")
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if not trace_tags:
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# Default: one-step trace
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trace_tags = ["step_0_before", "step_0_after"]
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hs = [hashlib.sha256(t.encode()).hexdigest()[:16] for t in trace_tags]
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uniq = list(dict.fromkeys(hs))
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n = len(uniq)
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mat = [[0] * n for _ in range(n)]
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hi = {h: i for i, h in enumerate(uniq)}
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for i in range(len(hs) - 1):
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if hs[i] in hi and hs[i + 1] in hi:
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mat[hi[hs[i]]][hi[hs[i + 1]]] += 1
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trace = {"transition_matrix": mat, "trace_tags": trace_tags, "theorem_name": theorem_name}
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# Step 2: Classify against the flexure library
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classification = classify_trace_against_library(trace)
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predicted_family = classification["tactic_family"]
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result["predicted_tactic_family"] = predicted_family
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result["confidence"] = classification["confidence"]
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result["nearest_motifs"] = classification.get("nearest_motifs", [])
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result["initial_status"] = "failed"
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if allowed_families and predicted_family not in allowed_families:
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predicted_family = allowed_families[0]
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# Step 3: Generate candidate patches
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theorem_info = parse_theorem(lean_code)
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patches = generate_patches(predicted_family, lean_code, theorem_info, max_patches=max_attempts)
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# Step 4: Try each patch on the proof worker
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best_complexity = float("inf")
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best_attempt = None
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for i, patch in enumerate(patches):
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patched_code = apply_patch(lean_code, patch)
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resp = prove(patched_code, f"{theorem_name}_repair_{i}")
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ok = resp.get("ok", False)
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stdout = resp.get("stdout", "") or ""
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stderr = resp.get("stderr", "") or ""
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# Measure complexity delta
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complexity_before = measure_complexity(lean_code)
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complexity_after = measure_complexity(patched_code)
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delta = (complexity_before["raw_chars"] - complexity_after["raw_chars"])
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attempt = {
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"attempt": i + 1,
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"candidate_patch": patch,
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"tactic_family": predicted_family,
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"status": "verified" if ok else "failed",
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"goal_complexity_delta": delta,
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"variables_before": complexity_before["variables"],
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"variables_after": complexity_after["variables"],
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"stdout_preview": stdout[:200],
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"stderr_preview": stderr[:200],
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}
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result["attempts"].append(attempt)
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if ok:
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result["recovered"] = True
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result["best_attempt"] = attempt
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break
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# Track best by complexity reduction
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if delta < best_complexity:
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best_complexity = delta
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best_attempt = attempt
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if not result["recovered"] and best_attempt:
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result["best_attempt"] = best_attempt
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result["elapsed_s"] = round(time.time() - t0, 2)
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return result
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FAILED_THEOREMS = [
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("simp_add_comm", 'theorem t (a b : Nat) : a + b = b + a := by\n simp [add_comm]'),
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("simp_add_assoc", 'theorem t (a b c : Nat) : (a + b) + c = a + (b + c) := by\n simp [add_assoc]'),
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("rw_mul_comm", 'theorem t (a b : Nat) : a * b = b * a := by\n rw [mul_comm]'),
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("rw_add_comm", 'theorem t (a b : Nat) : a + b = b + a := by\n rw [add_comm a b]'),
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("induct_add_zero", 'theorem t (n : Nat) : n + 0 = n := by\n induction n with k IH; rfl; simp [add_succ, IH]'),
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("omega_chain_unsat", 'theorem t (x : Nat) : x < x := by\n omega'),
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("fail_unsat", 'theorem t (x : Nat) (h : x > 0) : x = 0 := by\n omega'),
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("fail_type_mismatch", 'theorem t (n : Nat) : n = ("hello" : String) := by\n rfl'),
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("logic_or", 'theorem t (A B : Prop) : A ∨ B → B ∨ A := by\n intro h; cases h; right; exact h; left; exact h'),
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]
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def main():
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print("Route-Repair Loop v1 benchmark\n", flush=True)
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results = []
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recovered = 0
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total_attempts = 0
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for name, code in FAILED_THEOREMS:
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print(f" [{name:30s}] ", end="", flush=True)
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r = route_repair(code, name, max_attempts=3)
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best = r["best_attempt"] or {}
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status = "RECOVERED" if r["recovered"] else "still_broken"
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print(f"{status:15s} pred={r['predicted_tactic_family']:15s} "
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f"conf={r['confidence']:.2f} patches={len(r['attempts'])} "
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f"elapsed={r['elapsed_s']:.1f}s", flush=True)
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results.append(r)
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if r["recovered"]:
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recovered += 1
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total_attempts += len(r["attempts"])
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# Summary
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print(f"\n{'='*60}", flush=True)
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print("ROUTE-REPAIR BENCHMARK SUMMARY", flush=True)
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print(f"{'='*60}", flush=True)
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print(f"Total theorems: {len(results)}", flush=True)
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print(f"Recovered: {recovered}/{len(results)} = {recovered/len(results):.0%}", flush=True)
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print(f"Total patches tried: {total_attempts}", flush=True)
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# Per-family stats
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by_family = defaultdict(lambda: {"trials": 0, "recovered": 0})
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for r in results:
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fam = r["predicted_tactic_family"]
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by_family[fam]["trials"] += 1
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if r["recovered"]:
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by_family[fam]["recovered"] += 1
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for att in r["attempts"]:
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pass
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print(f"\nPer-family recovery:", flush=True)
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for fam, stats in sorted(by_family.items(), key=lambda x: -x[1]["trials"]):
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rec_rate = stats["recovered"] / max(stats["trials"], 1)
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print(f" {fam:20s}: {stats['recovered']:2d}/{stats['trials']:2d} = {rec_rate:.0%}", flush=True)
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# Mean patches per theorem
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avg_patches = total_attempts / max(len(results), 1)
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print(f"\nAvg patches per theorem: {avg_patches:.1f}", flush=True)
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print(f"Avg elapsed per theorem: {sum(r['elapsed_s'] for r in results)/len(results):.1f}s", flush=True)
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# Save
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report_path = os.path.join(os.path.dirname(__file__), "../..",
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"shared-data/pist_route_repair_benchmark.json")
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with open(report_path, "w") as f:
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json.dump({"n_theorems": len(results), "recovered": recovered,
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"total_attempts": total_attempts, "results": results,
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"summary": {"recovery_rate": round(recovered / max(len(results), 1), 4),
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"per_family": {f: round(s["recovered"] / max(s["trials"], 1), 3)
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for f, s in by_family.items()}}}, f, indent=2)
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print(f"\nReport: {report_path}", flush=True)
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if __name__ == "__main__":
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main()
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