598f7fd662
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.
One Docker layer per Lean step, each long one under `timeout`:
the gigabyte cache fetch earns a layer that a later failure never
invalidates, and a stalled step fails with a message instead of
sitting silent for an hour. There is deliberately no `lake build`
of our own library -- verification runs `lake env lean` against
Mathlib's oleans and needs nothing else, and building the library
was pure cost. 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 build runs with --progress=plain, because BuildKit's
default renderer collapses a long step into one rewritten line
and a stall then looks exactly like progress.
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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