# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """This is an implementation of the `CodeEval` metric that uses `RestrictedPython` to exectue the untrusted code returned by the model. Lightly adapted and mostly copied verbatim from the implementation in `evaluate`. """ import contextlib import faulthandler import itertools import io import multiprocessing import os import platform import signal import tempfile from collections import Counter, defaultdict from concurrent.futures import ThreadPoolExecutor, as_completed import evaluate # from evaluate.metrics import code_eval import datasets import numpy as np from RestrictedPython import compile_restricted, safe_builtins, limited_builtins, utility_builtins # TODO: Add BibTeX citation _CITATION = """\ @InProceedings{huggingface:module, title = {A great new module}, authors={huggingface, Inc.}, year={2020} } """ # TODO: Add description of the module here _DESCRIPTION = """\ This module tries to extend the built in `code_eval` module to use restricted python. """ # TODO: Add description of the arguments of the module here _KWARGS_DESCRIPTION = """ Calculates how good are predictions given some references, using certain scores Args: predictions: list of candidates to evaluate. Each candidates should be a list of strings with several code candidates to solve the problem. references: a list with a test for each prediction. Each test should evaluate the correctness of a code candidate. k: number of code candidates to consider in the evaluation (Default: [1, 10, 100]) num_workers: number of workers used to evaluate the canidate programs (Default: 4). timeout: use_safe_builtins: a bool indicating whether to use the `RestrictedPython.safe_builtins` use_limited_builtins: a bool indicating whether to use the `RestrictedPython.limited_builtins` use_utility_builtins: a bool indicating whether to use the `RestrictedPython.utility_builtins` Returns: pass_at_k: dict with pass rates for each k results: dict with granular results of each unittest Examples: >>> code_eval = evaluate.load("code_eval") >>> test_cases = ["assert add(2,3)==5"] >>> candidates = [["def add(a,b): return a*b", "def add(a, b): return a+b"]] >>> pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1, 2]) >>> print(pass_at_k) {'pass@1': 0.5, 'pass@2': 1.0} """ _WARNING = """ ################################################################################ !!!WARNING!!! ################################################################################ The "code_eval" metric executes untrusted model-generated code in Python. Although it is highly unlikely that model-generated code will do something overtly malicious in response to this test suite, model-generated code may act destructively due to a lack of model capability or alignment. Users are strongly encouraged to sandbox this evaluation suite so that it does not perform destructive actions on their host or network. For more information on how OpenAI sandboxes its code, see the paper "Evaluating Large Language Models Trained on Code" (https://arxiv.org/abs/2107.03374). Once you have read this disclaimer and taken appropriate precautions, set the environment variable HF_ALLOW_CODE_EVAL="1". Within Python you can to this with: >>> import os >>> os.environ["HF_ALLOW_CODE_EVAL"] = "1" ################################################################################\ """ # TODO: who has the copyright? _LICENSE = """The MIT License Copyright (c) OpenAI (https://openai.com) Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.""" @evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION) class RestrictedPythonCodeEval(evaluate.Metric): """Exactly the same as the built in `code_eval` module, but using restricted python""" def _info(self): # TODO: Specifies the evaluate.EvaluationModuleInfo object return evaluate.MetricInfo( # This is the description that will appear on the modules page. module_type="metric", description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, # This defines the format of each prediction and reference features=datasets.Features({ 'predictions': datasets.Sequence(datasets.Value("string")), 'references': datasets.Value('string'), }), # Homepage of the module for documentation homepage="http://module.homepage", # Additional links to the codebase or references codebase_urls=["http://github.com/path/to/codebase/of/new_module"], reference_urls=["http://path.to.reference.url/new_module"] ) def _compute(self, predictions, references, k=[1, 10, 100], num_workers=4, timeout=3.0, use_safe_builtins: bool = True, use_limited_builtins: bool = True, use_utility_builtins: bool = True): """Returns the scores""" if os.getenv("HF_ALLOW_CODE_EVAL", 0) != "1": raise ValueError(_WARNING) if os.name == "nt": raise NotImplementedError("This metric is currently not supported on Windows.") with ThreadPoolExecutor(max_workers=num_workers) as executor: futures = [] completion_id = Counter() n_samples = 0 results = defaultdict(list) for task_id, (candidates, test_case) in enumerate(zip(predictions, references)): for candidate in candidates: test_program = candidate + "\n" + test_case args = (test_program, timeout, task_id, completion_id[task_id], use_safe_builtins, use_limited_builtins, use_utility_builtins) future = executor.submit(_check_correctness, *args) futures.append(future) completion_id[task_id] += 1 n_samples += 1 for future in as_completed(futures): result = future.result() results[result["task_id"]].append((result["completion_id"], result)) total, correct = [], [] for result in results.values(): result.sort() passed = [r[1]["passed"] for r in result] total.append(len(passed)) correct.append(sum(passed)) total = np.array(total) correct = np.array(correct) ks = k pass_at_k = {f"pass@{k}": estimate_pass_at_k(total, correct, k).mean() for k in ks if (total >= k).all()} return pass_at_k, results def estimate_pass_at_k(num_samples, num_correct, k): """Estimates pass@k of each problem and returns them in an array.""" def estimator(n: int, c: int, k: int) -> float: """Calculates 1 - comb(n - c, k) / comb(n, k).""" if n - c < k: return 1.0 return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1, n + 1)) # type: ignore if isinstance(num_samples, int): num_samples_it = itertools.repeat(num_samples, len(num_correct)) else: assert len(num_samples) == len(num_correct) num_samples_it = iter(num_samples) return np.array([estimator(int(n), int(c), k) for n, c in zip(num_samples_it, num_correct)]) def _check_correctness(check_program, timeout, task_id, completion_id, use_safe_builtins: bool = True, use_limited_builtins: bool = True, use_utility_builtins: bool = True): """ Evaluates the functional correctness of a completion by running the test suite provided in the problem. :param completion_id: an optional completion ID so we can match the results later even if execution finishes asynchronously. """ manager = multiprocessing.Manager() result = manager.list() p = multiprocessing.Process(target=_unsafe_execute, args=(check_program, result, timeout, use_safe_builtins, use_limited_builtins, use_utility_builtins)) p.start() p.join(timeout=timeout + 1) if p.is_alive(): p.kill() if not result: result.append("timed out") return dict( task_id=task_id, passed=result[0] == "passed", result=result[0], completion_id=completion_id, ) def _unsafe_execute(check_program, result, timeout, use_safe_builtins: bool = True, use_limited_builtins: bool = True, use_utility_builtins: bool = True): with create_tempdir(): # These system calls are needed when cleaning up tempdir. import os import shutil rmtree = shutil.rmtree rmdir = os.rmdir chdir = os.chdir # Disable functionalities that can make destructive changes to the test. reliability_guard() # Run program. try: builtins = {} if use_safe_builtins: builtins.update(safe_builtins) if use_limited_builtins: builtins.update(limited_builtins) if use_utility_builtins: builtins.update(utility_builtins) exec_globals = {'__builtins__': builtins} with swallow_io(): with time_limit(timeout): byte_code = compile_restricted(check_program, filename="", mode="exec") exec(byte_code, exec_globals, None) result.append("passed") except TimeoutException: result.append("timed out") except BaseException as e: result.append(f"failed: {e}") # Needed for cleaning up. shutil.rmtree = rmtree os.rmdir = rmdir os.chdir = chdir @contextlib.contextmanager def time_limit(seconds): def signal_handler(signum, frame): raise TimeoutException("Timed out!") signal.setitimer(signal.ITIMER_REAL, seconds) signal.signal(signal.SIGALRM, signal_handler) try: yield finally: signal.setitimer(signal.ITIMER_REAL, 0) @contextlib.contextmanager def swallow_io(): stream = WriteOnlyStringIO() with contextlib.redirect_stdout(stream): with contextlib.redirect_stderr(stream): with redirect_stdin(stream): yield @contextlib.contextmanager def create_tempdir(): with tempfile.TemporaryDirectory() as dirname: with chdir(dirname): yield dirname class TimeoutException(Exception): pass class WriteOnlyStringIO(io.StringIO): """StringIO that throws an exception when it's read from""" def read(self, *args, **kwargs): raise OSError def readline(self, *args, **kwargs): raise OSError def readlines(self, *args, **kwargs): raise OSError def readable(self, *args, **kwargs): """Returns True if the IO object can be read.""" return False class redirect_stdin(contextlib._RedirectStream): # type: ignore _stream = "stdin" @contextlib.contextmanager def chdir(root): if root == ".": yield return cwd = os.getcwd() os.chdir(root) try: yield except BaseException as exc: raise exc finally: os.chdir(cwd) def reliability_guard(maximum_memory_bytes=None): """ This disables various destructive functions and prevents the generated code from interfering with the test (e.g. fork bomb, killing other processes, removing filesystem files, etc.) WARNING This function is NOT a security sandbox. Untrusted code, including, model- generated code, should not be blindly executed outside of one. See the Codex paper for more information about OpenAI's code sandbox, and proceed with caution. """ if maximum_memory_bytes is not None: import resource resource.setrlimit(resource.RLIMIT_AS, (maximum_memory_bytes, maximum_memory_bytes)) resource.setrlimit(resource.RLIMIT_DATA, (maximum_memory_bytes, maximum_memory_bytes)) if not platform.uname().system == "Darwin": resource.setrlimit(resource.RLIMIT_STACK, (maximum_memory_bytes, maximum_memory_bytes)) faulthandler.disable() import builtins builtins.exit = None builtins.quit = None import os os.environ["OMP_NUM_THREADS"] = "1" os.kill = None os.system = None os.putenv = None os.remove = None os.removedirs = None os.rmdir = None os.fchdir = None os.setuid = None os.fork = None os.forkpty = None os.killpg = None os.rename = None os.renames = None os.truncate = None os.replace = None os.unlink = None os.fchmod = None os.fchown = None os.chmod = None os.chown = None os.chroot = None os.fchdir = None os.lchflags = None os.lchmod = None os.lchown = None os.getcwd = None os.chdir = None import shutil shutil.rmtree = None shutil.move = None shutil.chown = None import subprocess subprocess.Popen = None # type: ignore __builtins__["help"] = None import sys sys.modules["ipdb"] = None # type: ignore sys.modules["joblib"] = None # type: ignore sys.modules["resource"] = None # type: ignore sys.modules["psutil"] = None # type: ignore sys.modules["tkinter"] = None # type: ignore