b9e37952e6
Replaces the previous tree entirely. The pipeline is four separable pieces, each
testable on its own:
1. leanbench/llm.py provider-neutral OpenAI-compatible connector, stdlib only,
with retries, JSON coercion and a scripted client so the
rest of the code is testable without a network.
2. leanbench/detect.py per-page detection of formalizable mathematics. Case 1 is
mathematics asserted as fact (definition/theorem/lemma/...,
deliberately not sub-classified); case 2 is a problem left
open to the reader. Hits come back as spans -- start page
and index, end page and index -- so several per page is
normal. A hit cut off by the page end makes the detector
re-read that page together with the next, repeatedly, so an
item spread over three pages is found whole.
Offsets come from verbatim quotes resolved by
leanbench/anchors.py (exact -> whitespace-insensitive ->
fuzzy), because models quote well and count badly.
3. leanbench/formalize.py + leanbench/verify.py
each span becomes one Lean 4 declaration, judged by the real
compiler: full elaboration for case 1, statement legality
only for case 2, which has no proof to check. Lean's own
diagnostics feed back for a repair attempt. "verified" (Lean
accepted it) and "proved" (accepted with no sorry) are kept
strictly apart.
4. leanbench/db.py indexed SQLite plus JSONL export. Every item is indexed by
the types of its inputs, or by its own type when it is not a
proof, and records the source book (sha256) and the page it
starts on -- so `drop --source H --pages A-B` followed by a
re-ingest redoes exactly those pages and nothing else.
Infrastructure:
- lean/ Lake project pinned to Mathlib.
- docker/ image carrying Lean 4 and a prebuilt Mathlib. The Mathlib
revision and the toolchain are resolved against each other at
build time rather than both hardcoded, so the build does not rot.
docker/smoke.py runs the whole pipeline inside the image against
the real compiler, with the LLM scripted.
- .gitea/workflows ci (lint + tests in a venv, since the runner image's Python is
Debian-managed and refuses installs into itself), docker (build,
verify, optionally push), ingest (submit a book by hand).
The docker workflow is not a throwaway build: it leaves the image, the layer cache,
an idle `lean-bench` container and the `lean-bench-data` / `lean-bench-books`
volumes in place on the runner, and never prunes, so the environment is there to be
used afterwards. Pushing to the registry is optional and cannot fail the build --
Gitea's container registry does not accept the built-in Actions token, so that needs
a REGISTRY_TOKEN secret with write:package.
58 tests; they need neither network nor Lean.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ci5T3gWDhhmqDec3e1XtYZ
149 lines
5.0 KiB
Python
149 lines
5.0 KiB
Python
"""Step 3, second half: hand the generated Lean to the real compiler and believe it.
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Two verification modes, matching the two kinds of item:
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``full`` elaborate the declaration as written. If the model supplied a proof,
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this is a genuine proof check.
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``statement`` the item is an open problem, so there is no proof to check: we only
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ask whether the *statement* is legal Lean -- it elaborates with
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``sorry`` standing in for the proof.
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``verified`` never means "proved". ``proved`` does, and it is only true when Lean
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accepted the declaration with no ``sorry`` anywhere in it.
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"""
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from __future__ import annotations
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import os
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import re
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import shutil
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import subprocess
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import tempfile
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from dataclasses import dataclass
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from pathlib import Path
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MODE_FULL = "full"
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MODE_STATEMENT = "statement"
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_ERROR = re.compile(r"^.*?:\d+:\d+: error:", re.MULTILINE)
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_SORRY_WARNING = re.compile(r"declaration uses 'sorry'")
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_SORRY_TOKEN = re.compile(r"(?<![A-Za-z0-9_'])sorry(?![A-Za-z0-9_'])")
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@dataclass
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class VerifyResult:
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ok: bool
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mode: str
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used_sorry: bool
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diagnostics: str
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skipped: bool = False
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@property
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def proved(self) -> bool:
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return self.ok and not self.used_sorry and self.mode == MODE_FULL
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def as_dict(self) -> dict:
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return {
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"ok": self.ok,
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"mode": self.mode,
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"used_sorry": self.used_sorry,
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"proved": self.proved,
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"skipped": self.skipped,
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"diagnostics": self.diagnostics[-4000:],
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}
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def contains_sorry(code: str) -> bool:
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return bool(_SORRY_TOKEN.search(code))
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@dataclass
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class LeanChecker:
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"""Runs ``lake env lean`` (or whatever you configure) on a scratch file."""
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command: list[str]
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project: str | None = None
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timeout: float = 120.0
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header: str = "import Mathlib\n"
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@classmethod
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def from_settings(cls, settings) -> LeanChecker: # noqa: ANN001 - LeanSettings
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return cls(
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command=list(settings.command),
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project=settings.project,
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timeout=settings.timeout,
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header=settings.header,
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)
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def available(self) -> bool:
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if not self.command:
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return False
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if shutil.which(self.command[0]) is None:
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return False
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if self.project and not Path(self.project).is_dir():
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return False
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return True
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def source_for(self, code: str, imports: list[str] | None = None) -> str:
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header = self.header
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for module in imports or []:
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module = module.strip()
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if not module:
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continue
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line = module if module.startswith("import ") else f"import {module}"
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if line not in header:
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header += line + "\n"
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return header.rstrip("\n") + "\n\n" + code.strip() + "\n"
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def check(
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self,
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code: str,
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*,
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mode: str = MODE_FULL,
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imports: list[str] | None = None,
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) -> VerifyResult:
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if not self.available():
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return VerifyResult(
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ok=False,
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mode=mode,
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used_sorry=contains_sorry(code),
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diagnostics=(
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f"Lean not available: command {self.command!r}"
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+ (f", project {self.project!r}" if self.project else "")
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),
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skipped=True,
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)
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source = self.source_for(code, imports)
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workdir = self.project or os.getcwd()
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with tempfile.TemporaryDirectory(prefix="leanbench-", dir=workdir) as directory:
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path = Path(directory) / "LeanBenchCheck.lean"
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path.write_text(source, encoding="utf-8")
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try:
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completed = subprocess.run( # noqa: S603 - command is operator-supplied
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[*self.command, str(path)],
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cwd=workdir,
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capture_output=True,
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text=True,
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timeout=self.timeout,
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)
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except subprocess.TimeoutExpired:
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return VerifyResult(False, mode, contains_sorry(code),
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f"Lean timed out after {self.timeout}s")
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except FileNotFoundError as error:
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return VerifyResult(False, mode, contains_sorry(code),
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f"Lean not runnable: {error}", skipped=True)
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diagnostics = (completed.stdout or "") + (completed.stderr or "")
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ok = completed.returncode == 0 and not _ERROR.search(diagnostics)
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used_sorry = bool(_SORRY_WARNING.search(diagnostics)) or contains_sorry(code)
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return VerifyResult(ok, mode, used_sorry, diagnostics[-8000:])
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class NullChecker:
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"""Stands in when Lean is unavailable (unit tests, a machine without elan)."""
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def available(self) -> bool:
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return False
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def check(self, code: str, *, mode: str = MODE_FULL, imports=None) -> VerifyResult:
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return VerifyResult(False, mode, contains_sorry(code), "Lean checking disabled",
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skipped=True)
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