evaluate_genericify_cpp.py

# Heavily adapted from `Muennighoff/code_eval_octopack`
"""TODO: Add a description here."""
import collections
import os

from typing import Literal

import concurrent.futures
import datasets
import evaluate
import itertools
import numpy as np
from tqdm import tqdm

from .execute import check_correctness

# 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 new module is designed to solve this great ML task and is crafted with a lot of care.
"""


# 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 predictions to score. Each predictions
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
Returns:
    accuracy: description of the first score,
    another_score: description of the second score,
Examples:
    Examples should be written in doctest format, and should illustrate how
    to use the function.

    >>> my_new_module = evaluate.load("my_new_module")
    >>> results = my_new_module.compute(references=[0, 1], predictions=[0, 1])
    >>> print(results)
    {'accuracy': 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"
################################################################################\
"""

_CLANG_WARNING = """
Please provide the environment variable 'GENERICIFY_CLANG' with the path of the
clang++ compiler. Version 15+ is required. Within Python you can to this
with:
>>> import os
>>> os.environ["GENERICIFY_CLANG"] = "/path/to/clang++"
"""

# TODO: Define external resources urls if needed
BAD_WORDS_URL = "http://url/to/external/resource/bad_words.txt"


@evaluate.utils.file_utils.add_start_docstrings(
    _DESCRIPTION, _KWARGS_DESCRIPTION
)
class EvaluateGenericifyCpp(evaluate.Metric):
    """TODO: Short description of my evaluation module."""

    def _info(self):
        # TODO: Specifies the evaluate.EvaluationModuleInfo object
        return evaluate.MetricInfo(
            # This is the description that will appear on the modules 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.Features(
                        {
                            "tests": datasets.Value("string"),
                            "invalids": 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,
        cpp_type: Literal["base", "sfinae", "concepts"],
        k=[1, 10, 100],
    ):
        """Returns the scores"""
        num_workers = os.cpu_count()
        num_workers = num_workers if not num_workers else 8

        if os.getenv("HF_ALLOW_CODE_EVAL", default=0) != "1":
            raise ValueError(_WARNING)

        if os.getenv("GENERICIFY_CLANG", default=0) == 0:
            raise ValueError(_CLANG_WARNING)

        if os.name == "nt":
            raise NotImplementedError(
                "This metric is currently not supported on Windows."
            )

        total_predictions = sum(map(len, predictions))

        with concurrent.futures.ThreadPoolExecutor(
            max_workers=num_workers
        ) as executor:
            futures = []
            completion_id = collections.Counter()
            results = collections.defaultdict(list)

            for task_id, (candidates, reference) in enumerate(
                zip(predictions, references)
            ):
                for candidate in candidates:
                    args = (
                        candidate,
                        reference,
                        cpp_type,
                        task_id,
                        completion_id[task_id],
                    )
                    future = executor.submit(check_correctness, *args)
                    futures.append(future)
                    completion_id[task_id] += 1

            for future in tqdm(
                concurrent.futures.as_completed(futures),
                desc="Evaluating",
                total=total_predictions,
            ):
                result = future.result()
                results[result["task_id"]].append(
                    (result["completion_id"], result)
                )

        totals = collections.defaultdict(list)
        corrects = collections.defaultdict(list)

        keys = {
            "base": [
                "base_run_passed",
                "base_run_compiled",
            ],
            "sfinae": [
                "sfinae_run_passed",
                "sfinae_run_compiled",
                "sfinae_constrain_passed",
            ],
            "concepts": [
                "concepts_run_passed",
                "concepts_run_compiled",
                "concepts_constrain_passed",
            ],
        }[cpp_type]
        for result in results.values():
            result.sort()
            for pt in keys:
                passed = [r[1][pt] for r in result]
                totals[pt].append(len(passed))
                corrects[pt].append(sum(passed))

        totals = {k: np.array(v) for k, v in totals.items()}
        corrects = {k: np.array(v) for k, v in corrects.items()}

        ks = k
        pass_at_k = {
            f"{key}@{k}": estimate_pass_at_k(
                totals[key],
                corrects[key],
                k,
            ).mean()
            for key in totals.keys()
            for k in ks
            if (totals[key] >= k).all()
        }

        return pass_at_k, results


def estimate_pass_at_k(num_samples, num_correct, k) -> np.array:
    """Estimates pass@k of each problem and returns them in an array."""

    def estimator(n: int, c: 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)) for n, c in zip(num_samples_it, num_correct)]
    )