Files
Piotr Wilkin (ilintar) f3363593f4 Add more benchmarks to llama-eval (#26174)
* llama-eval : add sandboxed execution for code suites

Adds a lightweight replacement for the Docker containers usually used to run
generated code, so a benchmark can be executed without a daemon, an image or
root.

A cached venv is provisioned per suite and reused; candidate programs run in a
throwaway working directory as their own process group, under the strongest
isolation the host offers:

  bwrap    read-only root, tmpfs /tmp and $HOME, no network
  unshare  no network, via --map-current-user
  rlimit   cpu/memory/file-size/process caps only

Candidate code must not run as uid 0, which is why "unshare -r" is a
last-resort tier that warns: root bypasses file permission bits, so a test
asserting that a write to a chmod 0444 file fails would instead see it succeed.

Two details worth noting for the bwrap tier. Only paths hidden by the $HOME
tmpfs are bound back, because bwrap cannot mount onto a symlink and
version-manager alias directories usually are one. And the venv's base
interpreter tree is resolved by walking the symlink chain, since a
version-managed interpreter is commonly reached through an unversioned alias
that startup resolves through.

Suites can declare provisioning steps, which run once at build time while the
network is still up, so test time never needs it.

Assisted-by: Claude Opus 4.8 <noreply@anthropic.com>

* llama-eval : add HumanEval and ClassEval code suites

Adds two execution-graded suites alongside the existing text-graded ones. The
model's reply is parsed for a python code block, combined with the task's own
unit tests, and run in the sandbox.

  --dataset humaneval   164 function-level problems, scored pass@k
  --dataset classeval   100 class-level tasks, scored at class level (feeding
                        pass@k) and method level, matching upstream

pass@k reuses the existing chunk mechanism: --n_cases at a multiple of the
dataset size gives that many samples per problem, aggregated with the unbiased
estimator from the Codex paper.

Program construction and result interpretation are dataset-owned hooks, because
the two suites differ: one HumanEval problem is a function judged by a single
assert, whereas one ClassEval task is a class judged by many independent test
methods, and reducing that to pass/fail would throw away partial credit. Suites
also declare their own package set, provisioning steps, interpreter and timeout.

The package set is derived from the dataset's own imports rather than a
requirements file. HumanEval turns out to need no third-party packages at all;
ClassEval needs thirteen, two of which must be declared because no import
mentions them -- lxml is selected by string in ClassEval_44, and scipy is a
gensim requirement.

Notes on ClassEval specifically:

* gensim publishes no wheel past cp313, so 3.13 is preferred; on 3.14+ the two
  pure-Python APIs it uses are provided by a small shim instead.
* The three nltk corpora it needs are pre-seeded at venv build time. These are
  the modern names -- the dataset asks for the since-renamed punkt and
  averaged_perceptron_tagger.
* It defaults to a patched fork, ilintar/ClassEval, because three upstream
  tests cannot be passed by anything: a hard-coded 2024 date checked against
  datetime.now(), an assertion on the dataset author's own hostname, and an
  exact float comparison. Reference solutions go from 96/100 to 100/100 classes
  and 2182/2196 to 2196/2196 test methods. No skeleton is touched, so what the
  model is asked to write is unchanged; --dataset-source selects the original.

--exec-seed (default 0) seeds the RNG before each test, so suites whose tests
draw random inputs score reproducibly. ClassEval_58 fails its own reference
solution about 30% of the time without it.

The simulator gains both suites, so the harness can be exercised without a
model.

Assisted-by: Claude Opus 4.8 <noreply@anthropic.com>

* llama-eval : fix silent isolation downgrade, generalise the sandbox runner

The isolation probe ran the interpreter without binding it back, so whenever
this tool is itself started from a venv under $HOME -- the usual case, since it
needs `datasets` -- bwrap's tmpfs hid the interpreter, the probe failed, and the
sandbox quietly fell back to a tier that does not hide $HOME at all. Nothing in
the output looked wrong when that happened, and the existing suites have been
running that way. The probe now uses the same interpreter and the same binds the
suite will actually get.

Two additions the agentic suite needs, neither of which changes the existing
suites:

* run_argv() runs an arbitrary command in a caller-owned directory that survives
  the call. run() is now a thin wrapper over it, so HumanEval and ClassEval take
  exactly the same path as before. A multi-step agent needs its edits to persist
  across many invocations, and a suite for a language other than Python needs to
  drive a linter and a test runner rather than a single source string.

* RLIMIT_AS is now per-sandbox, defaulting to what it was. It caps reserved
  address space rather than resident memory, which is the wrong instrument for a
  runtime that reserves a large region and commits little of it: node's
  TypeScript type-stripping goes through WebAssembly, reserves gigabytes per
  instance, and aborts at 4 GiB with an out-of-memory error that reads like a
  fault in the code under test.

HumanEval stays at 164/164 and ClassEval at 100/100.

Assisted-by: Claude Opus 5 <noreply@anthropic.com>

* llama-eval : add SACB, a multi-turn agentic coding suite

`--dataset agentic` is a different shape from the other code suites. Rather than
answering a single prompt, the model is given a small but real repository, a
narrow tool set and a bug report, drives its own multi-turn conversation, and is
graded by running a test suite it was never allowed to see.

The corpus is SACB (Simple Agent Coding Benchmark), 60 tasks over three
hand-written repositories in Python and TypeScript, hosted at ilintar/SACB.
Grading needs no container: a cached venv gives ruff and mypy for Python, and
node's own test runner plus tsc for TypeScript, so the whole dependency set is
four packages.

Three constraints are deliberate:

* No shell, and no way to run the tests. The only feedback channel is `lint`. A
  syntax or type error can be driven out mechanically; a logic error has to be
  reasoned about. A harness that lets an agent iterate against the test suite
  measures something closer to search than to understanding.
* Two edit tools, one line-addressed and one content-addressed. Which one a
  model reaches for, and whether it keeps line numbers straight after its own
  earlier edit, is itself a signal.
* The tests never touch the working tree. They are held outside it and copied
  into a throwaway copy only once the agent has stopped, so they can be neither
  read nor edited. Every tool path is resolved and confinement-checked against
  the repository root, so a symlink planted inside the tree cannot reach out of
  it.

A task counts as resolved when every test that was failing passes and nothing
that already passed broke. Both sets are derived by running the tests against
the defective tree and against the reference fix, never written by hand, so a
task whose defect no test exercises cannot enter the corpus. Many tasks carry
several independent defects, so the fraction of target tests passed is reported
alongside, separating "fixed two of the three faults" from "changed nothing".

An episode that arrives with no tool call and no `finish` is nudged rather than
ended. Models routinely narrate their analysis mid-task, and treating that as
completion ends the episode with no edits and scores a reasoning failure that
never happened; about one episode in four exits that way.

Nothing here is installed unless this suite is selected, and then only for the
language being run: agentic_eval is imported lazily and each toolchain is keyed
per language.

Assisted-by: Claude Opus 5 <noreply@anthropic.com>

* llama-eval : make the agentic hooks typed rather than discovered

`ty` flagged nine unresolved attributes, of which the seven in main() predate
the agentic suite: `EvalState.dataset` is Optional and was dereferenced without
narrowing.

* `loaded_dataset` turns the Optional into a value in one place, raising rather
  than handing back None, so callers do not each have to narrow it.
* `is_agentic`, `prepare()` and `run()` are declared on BaseDataset. They were
  reached through getattr with a default at three call sites, which no checker
  can see through; `run()` now raises a clear error naming the suite instead of
  failing as a missing attribute.
* The sandbox setup is one branch on `is_agentic` rather than two tests of
  `grader_type`, which also drops a duplicated condition.

No behaviour change: HumanEval stays at 164/164, and `ty check` on
examples/llama-eval is clean.

Assisted-by: Claude Opus 5 <noreply@anthropic.com>

* feat: API key

* fix reasoning and response length for agentic runs, add option to save entire transcripts

* llama-eval : fix ty unresolved-attribute on loaded_dataset.ae

Assisted-by: AI coding agent

* llama-eval : trim comments and remove non-ASCII from README

Assisted-by: AI coding agent
2026-10-16 11:52:18 +02:00

489 lines
18 KiB
Python

#!/usr/bin/env python3
"""Lightweight sandboxed execution for code-generation evals (HumanEval, ClassEval).
Replaces the usual Docker-per-suite setup with a cached venv plus whatever
process isolation the host can give us without root:
tier 1 bubblewrap read-only root, tmpfs /tmp and $HOME, no network
tier 2 unshare -rn no network (home still visible)
tier 3 rlimits only last resort, still capped on cpu/memory/procs/files
Every tier also applies rlimits and runs the candidate program in a throwaway
working directory as its own process group, so a runaway solution is killed
along with anything it spawned.
"""
import json
import os
import re
import resource
import shutil
import signal
import subprocess
import sys
import tempfile
import time
import venv
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, List, Optional, Sequence
DEFAULT_CACHE_ROOT = Path(
os.environ.get("LLAMA_EVAL_CACHE", Path.home() / ".cache" / "llama-eval")
)
LIMIT_AS_BYTES = 4 * 1024 * 1024 * 1024
LIMIT_CPU_SECONDS_SLACK = 5
LIMIT_NPROC = 256
LIMIT_FSIZE_BYTES = 64 * 1024 * 1024
@dataclass
class ExecResult:
passed: bool
status: str
stdout: str = ""
stderr: str = ""
returncode: Optional[int] = None
duration: float = 0.0
def summary(self, max_chars: int = 400) -> str:
"""Short human-readable reason, for grader logs."""
if self.passed:
return "ok"
if self.status == "timeout":
return "timeout"
tail = (self.stderr or self.stdout).strip()
if not tail:
return f"{self.status} (rc={self.returncode})"
lines = [ln for ln in tail.splitlines() if ln.strip()]
reason = lines[-1] if lines else tail
return reason[:max_chars]
def resolve_python(spec: Optional[str]) -> Optional[str]:
"""Turn '3.13' or a path into a usable interpreter, or None if unavailable.
Suites can need an older interpreter than the one running this tool, because
a dependency has no wheel (or no working source build) for the newest
CPython. Accepts an explicit path, a bare version, or an executable name.
"""
if not spec:
return None
candidate = Path(spec).expanduser()
if candidate.exists() and candidate.is_file():
return str(candidate)
uv = shutil.which("uv")
if uv and re.fullmatch(r"\d+\.\d+(\.\d+)?", spec):
try:
r = subprocess.run([uv, "python", "find", spec],
capture_output=True, text=True, timeout=120)
path = r.stdout.strip()
if r.returncode == 0 and path and Path(path).exists():
return path
except Exception:
pass
return shutil.which(spec) or shutil.which(f"python{spec}")
def _detect_isolation(probe_python: Optional[str] = None,
ro_binds: Sequence[Path] = ()) -> str:
"""Pick the strongest isolation tier this host supports, once.
The probe must run the *same* interpreter the suite will, with the same
binds. Probing a bare sys.executable silently downgrades the tier whenever
this tool is itself run from a venv under $HOME -- which is the usual case,
since it needs `datasets`. bwrap tmpfs-mounts the home directory, the
interpreter disappears, the probe fails, and the sandbox falls back to a
tier that does not hide $HOME at all. Nothing about the results looks wrong
when that happens, so it has to be got right here.
"""
py = probe_python or sys.executable
probe = "import sys; sys.exit(0)"
if shutil.which("bwrap"):
try:
r = subprocess.run(
_bwrap_argv(Path.cwd(), [py, "-I", "-c", probe], ro_binds=ro_binds),
capture_output=True, timeout=20,
)
if r.returncode == 0:
return "bwrap"
except Exception:
pass
if shutil.which("unshare"):
for tier, argv in (
("unshare", ["unshare", "--user", "--map-current-user", "--net"]),
("unshare-root", ["unshare", "-rn"]),
):
try:
r = subprocess.run(
[*argv, py, "-I", "-c", probe],
capture_output=True, timeout=20,
)
if r.returncode == 0:
return tier
except Exception:
continue
return "rlimit"
UNSHARE_ARGV = {
"unshare": ["unshare", "--user", "--map-current-user", "--net"],
"unshare-root": ["unshare", "-rn"],
}
def _under(path: Path, parent: str) -> bool:
return bool(parent) and (str(path) == parent
or str(path).startswith(parent.rstrip("/") + os.sep))
def _bwrap_argv(workdir: Path, inner: Sequence[str],
ro_binds: Sequence[Path] = ()) -> List[str]:
home = os.path.expanduser("~")
argv = [
"bwrap",
"--ro-bind", "/", "/",
"--dev", "/dev",
"--proc", "/proc",
"--tmpfs", "/tmp",
]
if home and home != "/":
argv += ["--tmpfs", home]
for path in ro_binds:
if _under(path, home):
argv += ["--ro-bind", str(path), str(path)]
argv += [
"--bind", str(workdir), str(workdir),
"--chdir", str(workdir),
"--unshare-all",
"--die-with-parent",
"--new-session",
"--",
]
return argv + list(inner)
def _nproc_ceiling() -> int:
"""A process cap that bounds a fork bomb without tripping on existing load.
RLIMIT_NPROC is enforced per real UID across the whole system, not per
process tree, so it has to sit above whatever the user is already running.
"""
try:
current = len(os.listdir("/proc"))
except OSError:
current = 512
return max(current * 2, 1024)
def _apply_rlimits(timeout: float, with_nproc: bool,
as_bytes: Optional[int] = LIMIT_AS_BYTES):
def _preexec():
os.setsid()
cpu = int(timeout) + LIMIT_CPU_SECONDS_SLACK
limits = [
(resource.RLIMIT_CPU, (cpu, cpu)),
(resource.RLIMIT_FSIZE, (LIMIT_FSIZE_BYTES, LIMIT_FSIZE_BYTES)),
(resource.RLIMIT_CORE, (0, 0)),
]
if as_bytes:
limits.append((resource.RLIMIT_AS, (as_bytes, as_bytes)))
if with_nproc:
ceiling = _nproc_ceiling()
limits.append((resource.RLIMIT_NPROC, (ceiling, ceiling)))
for res, lim in limits:
try:
resource.setrlimit(res, lim)
except (ValueError, OSError):
pass
return _preexec
class Sandbox:
"""A cached venv plus an isolated runner for untrusted candidate programs."""
def __init__(
self,
name: str,
packages: Optional[Sequence[str]] = None,
cache_root: Optional[Path] = None,
base_python: Optional[str] = None,
isolation: Optional[str] = None,
provision: Optional[Sequence[str]] = None,
env: Optional[Dict[str, str]] = None,
address_space_limit: Optional[int] = LIMIT_AS_BYTES,
):
self.name = name
self.packages = sorted(set(packages or []))
self.provision = list(provision or [])
self.env = dict(env or {})
# None drops the RLIMIT_AS cap entirely -- see _apply_rlimits
self.address_space_limit = address_space_limit
self.cache_root = Path(cache_root or DEFAULT_CACHE_ROOT)
self.base_python = base_python or sys.executable
self.venv_dir = self.cache_root / f"{name}-venv"
self._python: Optional[Path] = None
self._isolation = isolation
self._base_prefix: List[Path] = []
@property
def python(self) -> Path:
if self._python is None:
raise RuntimeError("Sandbox.ensure() must be called first")
return self._python
@property
def isolation(self) -> str:
if self._isolation is None:
if self._python:
py = str(self._python)
roots = [self.venv_dir, *(self._base_prefix or [])]
else:
py = sys.executable
roots = self._detect_base_prefix(Path(py))
self._isolation = _detect_isolation(py, roots)
return self._isolation
def _stamp_path(self) -> Path:
return self.venv_dir / ".llama-eval-stamp.json"
def _stamp_matches(self) -> bool:
try:
stamp = json.loads(self._stamp_path().read_text())
except Exception:
return False
return (
stamp.get("packages") == self.packages
and stamp.get("py") == self._base_version()
and stamp.get("provision") == self.provision
)
def _resolved_env(self) -> Dict[str, str]:
return {k: v.replace("{venv}", str(self.venv_dir)) for k, v in self.env.items()}
def _base_version(self) -> str:
r = subprocess.run(
[self.base_python, "-c",
"import sys;print('.'.join(map(str,sys.version_info[:3])))"],
capture_output=True, text=True,
)
return r.stdout.strip()
def ensure(self, force: bool = False, quiet: bool = False) -> Path:
"""Create/refresh the venv. Idempotent and cheap when already valid."""
py = self.venv_dir / "bin" / "python"
if force and self.venv_dir.exists():
shutil.rmtree(self.venv_dir)
if py.exists() and self._stamp_matches():
self._python = py
self._base_prefix = self._detect_base_prefix(py)
return py
if not quiet:
pkgs = ", ".join(self.packages) if self.packages else "stdlib only"
print(f"[sandbox] provisioning venv {self.venv_dir} ({pkgs})")
self.cache_root.mkdir(parents=True, exist_ok=True)
if self.venv_dir.exists():
shutil.rmtree(self.venv_dir)
uv = shutil.which("uv")
if uv:
subprocess.run(
[uv, "venv", "--python", self.base_python, str(self.venv_dir)],
check=True, capture_output=True,
)
elif Path(self.base_python).resolve() == Path(sys.executable).resolve():
venv.EnvBuilder(with_pip=bool(self.packages), clear=True).create(self.venv_dir)
else:
subprocess.run(
[self.base_python, "-m", "venv", str(self.venv_dir)],
check=True, capture_output=True,
)
if self.packages:
if uv:
cmd = [uv, "pip", "install", "--python", str(py), *self.packages]
else:
cmd = [str(py), "-m", "pip", "install", "--quiet",
"--disable-pip-version-check", *self.packages]
r = subprocess.run(cmd, capture_output=True, text=True)
if r.returncode != 0:
raise RuntimeError(
f"failed to install {len(self.packages)} package(s) into "
f"{self.venv_dir}:\n{(r.stderr or r.stdout)[-3000:]}"
)
for i, snippet in enumerate(self.provision, 1):
if not quiet:
print(f"[sandbox] provisioning step {i}/{len(self.provision)}")
prov_env = dict(os.environ)
prov_env["SANDBOX_VENV"] = str(self.venv_dir)
prov_env.update(self._resolved_env())
r = subprocess.run([str(py), "-c", snippet], env=prov_env,
capture_output=True, text=True, timeout=1800)
if r.returncode != 0:
raise RuntimeError(
f"provisioning step {i} failed:\n{(r.stderr or r.stdout)[-3000:]}"
)
self._stamp_path().write_text(json.dumps({
"packages": self.packages,
"py": self._base_version(),
"provision": self.provision,
}))
self._python = py
self._base_prefix = self._detect_base_prefix(py)
if not quiet:
print(f"[sandbox] ready ({self.isolation} isolation)")
if self.isolation == "unshare-root":
print("Warning: this host's unshare cannot keep the caller's uid, so "
"candidate code runs as root inside the namespace. Tests that "
"assert a permission denial will not see one. Install "
"bubblewrap (bwrap) for correct results.")
return py
@staticmethod
def _detect_base_prefix(py: Path) -> List[Path]:
"""Every directory that must stay visible for the venv to start.
A venv is only symlinks into its base interpreter, so the whole install
tree has to survive the tmpfs that hides $HOME -- and binding just the
resolved tree is not enough. Version-managed interpreters (uv, pyenv)
are commonly reached through an unversioned alias symlink
(cpython-3.13-... -> cpython-3.13.14-...), and startup resolves through
the alias path, so both spellings need to be mounted. Walk the chain
and keep every install root it passes through.
"""
roots: List[Path] = []
def add(path: Path):
if path.exists() and path not in roots:
roots.append(path)
cur, hops = py, 0
while hops < 16:
hops += 1
add(cur.parent.parent)
if cur.is_symlink():
target = Path(os.readlink(cur))
cur = target if target.is_absolute() else (cur.parent / target)
else:
break
try:
r = subprocess.run([str(py), "-c", "import sys; print(sys.base_prefix)"],
capture_output=True, text=True, timeout=60)
if r.returncode == 0 and r.stdout.strip():
add(Path(r.stdout.strip()))
except Exception:
pass
return [p for p in roots if str(p) not in ("/", "/usr", "/usr/local")]
def _isolate(self, argv: Sequence[str], workdir: Path,
ro_binds: Sequence[Path] = ()) -> List[str]:
"""Wrap a command in the strongest isolation tier this host supports."""
if self.isolation == "bwrap":
return _bwrap_argv(
workdir, argv,
ro_binds=[self.venv_dir, *(self._base_prefix or []), *ro_binds],
)
if self.isolation in UNSHARE_ARGV:
return [*UNSHARE_ARGV[self.isolation], *argv]
return list(argv)
def _base_env(self, workdir: Path) -> Dict[str, str]:
env = {
"PATH": "/usr/bin:/bin",
"HOME": str(workdir),
"TMPDIR": str(workdir),
"LC_ALL": "C.UTF-8",
"PYTHONHASHSEED": "0",
"PYTHONDONTWRITEBYTECODE": "1",
"OPENBLAS_NUM_THREADS": "1",
"OMP_NUM_THREADS": "1",
}
env.update(self._resolved_env())
return env
def run_argv(self, argv: Sequence[str], workdir: Path,
timeout: float = 15.0, env: Optional[Dict[str, str]] = None,
ro_binds: Sequence[Path] = ()) -> ExecResult:
"""Run an arbitrary command in `workdir`, which stays writable.
Unlike run(), the caller owns the directory and it survives the call.
That is what a multi-step agent needs: its edits have to persist across
many tool invocations before anything is graded. Taking an argv rather
than a source string is what lets a suite drive a linter or a test
runner for a language other than Python.
"""
run_env = self._base_env(workdir)
if env:
run_env.update(env)
started = time.monotonic()
try:
proc = subprocess.Popen(
self._isolate(argv, workdir, ro_binds),
cwd=str(workdir),
stdin=subprocess.DEVNULL,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
text=True,
errors="replace",
env=run_env,
preexec_fn=_apply_rlimits(timeout,
with_nproc=self.isolation == "rlimit",
as_bytes=self.address_space_limit),
)
except Exception as e:
return ExecResult(False, "error", "", f"{type(e).__name__}: {e}")
try:
out, err = proc.communicate(timeout=timeout)
except subprocess.TimeoutExpired:
_kill_tree(proc)
out, err = proc.communicate()
return ExecResult(False, "timeout", out or "", err or "",
None, time.monotonic() - started)
duration = time.monotonic() - started
if proc.returncode == 0:
return ExecResult(True, "ok", out, err, 0, duration)
return ExecResult(False, "failed", out, err, proc.returncode, duration)
def run(self, code: str, timeout: float = 15.0,
env: Optional[Dict[str, str]] = None) -> ExecResult:
"""Run `code` to completion in the sandbox. Exit 0 == passed."""
workdir = Path(tempfile.mkdtemp(prefix=f"{self.name}-", dir="/tmp"))
try:
prog = workdir / "candidate.py"
prog.write_text(code)
return self.run_argv([str(self.python), "-I", "-B", str(prog)],
workdir, timeout=timeout, env=env)
except Exception as e:
return ExecResult(False, "error", "", f"{type(e).__name__}: {e}")
finally:
shutil.rmtree(workdir, ignore_errors=True)
def _kill_tree(proc: subprocess.Popen):
"""Kill the candidate and everything it spawned."""
for sig in (signal.SIGKILL,):
try:
os.killpg(os.getpgid(proc.pid), sig)
except (ProcessLookupError, PermissionError):
try:
proc.kill()
except ProcessLookupError:
pass