Duplicate from evaluate-metric/code_eval

Co-authored-by: Mamta Narang <[email protected]>

.gitattributes

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README.md

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+---
+title: Code Eval
+emoji: 🤗
+colorFrom: blue
+colorTo: red
+sdk: gradio
+sdk_version: 3.19.1
+app_file: app.py
+pinned: false
+tags:
+- evaluate
+- metric
+description: >-
+  This metric implements the evaluation harness for the HumanEval problem
+  solving dataset described in the paper "Evaluating Large Language Models
+  Trained on Code" (https://arxiv.org/abs/2107.03374).
+duplicated_from: evaluate-metric/code_eval
+---
+
+# Metric Card for Code Eval
+
+## Metric description
+
+The CodeEval metric estimates the pass@k metric for code synthesis. 
+
+It implements the evaluation harness for the HumanEval problem solving dataset described in the paper ["Evaluating Large Language Models Trained on Code"](https://arxiv.org/abs/2107.03374).
+
+
+## How to use 
+
+The Code Eval metric calculates how good are predictions given a set of references. Its arguments are:
+
+`predictions`: a list of candidates to evaluate. Each candidate 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. The default value is `[1, 10, 100]`.
+
+`num_workers`: the number of workers used to evaluate the candidate programs (The default value is `4`).
+
+`timeout`: The maximum time taken to produce a prediction before it is considered a "timeout". The default value is `3.0` (i.e. 3 seconds).
+
+```python
+from evaluate import load
+code_eval = 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])
+```
+
+N.B.
+This metric exists to run untrusted model-generated code. Users are strongly encouraged not to do so outside of a robust security sandbox. Before running this metric and once you've taken the necessary precautions, you will need to set the `HF_ALLOW_CODE_EVAL` environment variable. Use it at your own risk:
+```python
+import os
+os.environ["HF_ALLOW_CODE_EVAL"] = "1"` 
+```
+
+## Output values
+
+The Code Eval metric outputs two things:
+
+`pass_at_k`: a dictionary with the pass rates for each k value defined in the arguments.
+
+`results`: a dictionary with granular results of each unit test.
+
+### Values from popular papers
+The [original CODEX paper](https://arxiv.org/pdf/2107.03374.pdf) reported that the CODEX-12B model had a pass@k score of 28.8% at `k=1`, 46.8% at `k=10` and 72.3% at `k=100`. However, since the CODEX model is not open source, it is hard to verify these numbers. 
+
+
+
+## Examples 
+
+Full match at `k=1`:
+
+```python
+from evaluate import load
+code_eval = load("code_eval")
+test_cases = ["assert add(2,3)==5"]
+candidates = [["def add(a, b): return a+b"]]
+pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1])
+print(pass_at_k)
+{'pass@1': 1.0}
+```
+
+No match for k = 1:
+
+```python
+from evaluate import load
+code_eval = load("code_eval")
+test_cases = ["assert add(2,3)==5"]
+candidates = [["def add(a,b): return a*b"]]
+pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1])
+print(pass_at_k)
+{'pass@1': 0.0}
+```
+
+Partial match at k=1, full match at k=2:
+
+```python
+from evaluate import load
+code_eval = 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}
+```
+
+## Limitations and bias
+
+As per the warning included in the metric code itself:
+> This program exists to execute untrusted model-generated code. 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 accompanying paper. Once you have read this disclaimer and taken appropriate precautions, uncomment the following line and proceed at your own risk:
+
+More information about the limitations of the code can be found on the [Human Eval Github repository](https://github.com/openai/human-eval).
+
+## Citation
+
+```bibtex
+@misc{chen2021evaluating,
+      title={Evaluating Large Language Models Trained on Code},
+      author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan \
+and Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards \
+and Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray \
+and Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf \
+and Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray \
+and Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser \
+and Mohammad Bavarian and Clemens Winter and Philippe Tillet \
+and Felipe Petroski Such and Dave Cummings and Matthias Plappert \
+and Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss \
+and William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak \
+and Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain \
+and William Saunders and Christopher Hesse and Andrew N. Carr \
+and Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa \
+and Alec Radford and Matthew Knight and Miles Brundage and Mira Murati \
+and Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei \
+and Sam McCandlish and Ilya Sutskever and Wojciech Zaremba},
+      year={2021},
+      eprint={2107.03374},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG}
+}
+```
+    
+## Further References 
+
+- [Human Eval Github repository](https://github.com/openai/human-eval)
+- [OpenAI Codex website](https://openai.com/blog/openai-codex/)

app.py

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+import evaluate
+from evaluate.utils import launch_gradio_widget
+
+
+module = evaluate.load("code_eval")
+launch_gradio_widget(module)

code_eval.py

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+# 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.
+"""The CodeEval metric estimates the pass@k metric for code synthesis.
+This is an evaluation harness for the HumanEval problem solving dataset
+described in the paper "Evaluating Large Language Models Trained on Code"
+(https://arxiv.org/abs/2107.03374)."""
+
+import itertools
+import os
+from collections import Counter, defaultdict
+from concurrent.futures import ThreadPoolExecutor, as_completed
+
+import datasets
+import numpy as np
+
+import evaluate
+
+from .execute import check_correctness
+
+
+_CITATION = """\
+@misc{chen2021evaluating,
+      title={Evaluating Large Language Models Trained on Code},
+      author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan \
+and Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards \
+and Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray \
+and Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf \
+and Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray \
+and Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser \
+and Mohammad Bavarian and Clemens Winter and Philippe Tillet \
+and Felipe Petroski Such and Dave Cummings and Matthias Plappert \
+and Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss \
+and William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak \
+and Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain \
+and William Saunders and Christopher Hesse and Andrew N. Carr \
+and Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa \
+and Alec Radford and Matthew Knight and Miles Brundage and Mira Murati \
+and Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei \
+and Sam McCandlish and Ilya Sutskever and Wojciech Zaremba},
+      year={2021},
+      eprint={2107.03374},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG}
+}
+"""
+
+_DESCRIPTION = """\
+This metric implements the evaluation harness for the HumanEval problem solving dataset
+described in the paper "Evaluating Large Language Models Trained on Code"
+(https://arxiv.org/abs/2107.03374).
+"""
+
+
+_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:
+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"
+
+################################################################################\
+"""
+
+_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 CodeEval(evaluate.Metric):
+    def _info(self):
+        return evaluate.MetricInfo(
+            # This is the description that will appear on the metrics page.
+            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="https://github.com/openai/human-eval",
+            codebase_urls=["https://github.com/openai/human-eval"],
+            reference_urls=["https://github.com/openai/human-eval"],
+            license=_LICENSE,
+        )
+
+    def _compute(self, predictions, references, k=[1, 10, 100], num_workers=4, timeout=3.0):
+        """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])
+                    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))
+
+    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)])

execute.py

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+# 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 code is adapted from OpenAI's release
+# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
+
+import contextlib
+import faulthandler
+import io
+import multiprocessing
+import os
+import platform
+import signal
+import tempfile
+
+
+def check_correctness(check_program, timeout, task_id, completion_id):
+    """
+    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))
+    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):
+
+    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:
+            exec_globals = {}
+            with swallow_io():
+                with time_limit(timeout):
+                    exec(check_program, exec_globals)
+            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
+    sys.modules["joblib"] = None
+    sys.modules["resource"] = None
+    sys.modules["psutil"] = None
+    sys.modules["tkinter"] = None

requirements.txt

@@ -0,0 +1 @@
+git+https://github.com/huggingface/evaluate@af3c30561d840b83e54fc5f7150ea58046d6af69