296 lines
9.7 KiB
Python
296 lines
9.7 KiB
Python
import os
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import json
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import gzip
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import numpy as np
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import itertools
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from typing import *
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from tqdm.auto import tqdm
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from collections import defaultdict
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from .data import stream_jsonl
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from .execution import check_correctness
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IMPORT_HELPER = {
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"python": [
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"import math",
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"import re",
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"import sys",
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"import copy",
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"import datetime",
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"import itertools",
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"import collections",
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"import heapq",
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"import functools",
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"import hashlib",
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"import numpy",
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"import numpy as np",
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"import string",
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"from typing import *",
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"from collections import *",
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],
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"go" : [
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"math",
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"strings",
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"fmt",
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"strconv",
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"time",
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"bytes",
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"regexp",
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"sort",
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"math/rand",
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"crypto/md5",
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],
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"cpp" : [
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"#include<stdlib.h>",
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"#include<algorithm>",
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"#include<math.h>",
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"#include<stdio.h>",
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"#include<vector>",
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"#include<string>",
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"#include<climits>",
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"#include<cstring>",
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"#include<iostream>",
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"#include<cassert>"
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],
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"cs": ["using System.Numerics;", "using System.Diagnostics;", "using System.Collections.Generic;", "using System.Linq;", "using System.Text;", "using System.Security.Cryptography;", "using System.Collections.Generic;"]
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}
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LANGUAGE_NAME = {
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"cpp" : "CPP",
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"go" : "Go",
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"java" : "Java",
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"js" : "JavaScript",
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"python": "Python",
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}
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def read_dataset(
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data_file: str = None,
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dataset_type: str = "humaneval",
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num_shot=None,
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) -> Dict:
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"""
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Reads a dataset and returns a dictionary of tasks.
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"""
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if num_shot is not None:
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print(f"{num_shot}-shot setting...")
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if "humaneval" in dataset_type.lower():
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if data_file is None:
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current_path = os.path.dirname(os.path.abspath(__file__))
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data_file = os.path.join(current_path, "..", "humaneval-x", "python", "data", "humaneval_python.jsonl.gz")
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dataset = {task["task_id"]: task for task in stream_jsonl(data_file)}
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else:
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raise f"Dataset: {dataset_type} not supported."
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return dataset
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def estimate_pass_at_k(
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num_samples: Union[int, List[int], np.ndarray],
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num_correct: Union[List[int], np.ndarray],
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k: int
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) -> np.ndarray:
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"""
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Estimates pass@k of each problem and returns them in an array.
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"""
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def estimator(n: int, c: int, k: int) -> float:
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"""
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Calculates 1 - comb(n - c, k) / comb(n, k).
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"""
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if n - c < k:
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return 1.0
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return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1, n + 1))
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if isinstance(num_samples, int):
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num_samples_it = itertools.repeat(num_samples, len(num_correct))
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else:
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assert len(num_samples) == len(num_correct)
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num_samples_it = iter(num_samples)
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return np.array([estimator(int(n), int(c), k) for n, c in zip(num_samples_it, num_correct)])
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def process_humaneval_test(sample, problems, example_test=False, is_mbpp=False, language="python"):
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"""
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Processes a sample for evaluation.
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"""
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task_id = sample["task_id"]
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if is_mbpp:
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return sample["generation"] + "\n" + "\n".join(problems[task_id]["test"])
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#prompt = sample["prompt"]
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if example_test and "example_test" in problems[task_id] and problems[task_id]["example_test"] != "":
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test = problems[task_id]["example_test"]
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else:
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test = problems[task_id]["test"]
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test = "\n".join(test)
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code = sample["generation"]
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# Pre-process for different languages
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if language == "python":
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test_setup = "\n".join(IMPORT_HELPER["python"]) + "\n"
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test_string = test_setup + code + "\n" + test + "\n"
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elif language == "cpp":
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test_set_up = ""
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for s in IMPORT_HELPER["cpp"]:
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if s not in prompt:
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test_set_up += s + "\n"
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test_string = test_set_up + "\n" + code + "\n" + test
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elif language == "java":
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test_string = code + "\n" + test
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elif language == "cs":
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test_set_up = ""
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for s in IMPORT_HELPER["cs"]:
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test_set_up += s + "\n"
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test_string = test_set_up + "\n" + code + "\n" + test
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elif language in ["js", "javascript", "ts", "sh", "go"]:
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test_string = code + "\n" + test
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elif language == "go232":
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import_string = problems[task_id]["import"]
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prompt = prompt.replace(import_string, "")
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if example_test and "example_test" in problems[task_id]:
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test = problems[task_id]["example_test"]
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else:
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test = problems[task_id]["test"]
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test_setup = problems[task_id]["test_setup"]
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other_pkgs = []
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for pkg in IMPORT_HELPER["go"]:
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if pkg not in test_setup:
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p = pkg.split("/")[-1]
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if p + "." in code:
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other_pkgs.append(f"\"{pkg}\"")
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if other_pkgs:
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import_other_pkgs = "import (\n" + " ".join([p + "\n" for p in other_pkgs]) + ")"
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test_string = test_setup + "\n" + import_other_pkgs + "\n" + prompt + code + "\n" + test
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else:
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test_string = test_setup + "\n" + prompt + code + "\n" + test
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elif language == "rust":
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main = "\nfn main(){ \n } \n"
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declaration = problems[task_id]["declaration"]
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test_string = main + declaration + prompt + code + test
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elif language == "php":
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if code[:5] != "<?php":
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code = "<?php\n" + code
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test_string = code + "\n" + test + "?>"
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return test_string
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def stream_jsonl_all(filename: str) -> Iterable[Dict]:
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"""
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Streams a JSONL file.
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"""
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results = []
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if filename.endswith(".gz"):
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fp = gzip.open(open(filename, "rb"), "rt")
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else:
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fp = open(filename, "r")
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for line in fp:
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if any(not x.isspace() for x in line):
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results.append(json.loads(line))
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fp.close()
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return results
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def evaluate_functional_correctness(
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input_file: str = None,
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tmp_dir: str = "./",
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n_workers: int = 32,
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timeout: float = 10.0,
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problem_file: str = "../data/humaneval_python.jsonl.gz",
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out_dir: str = None,
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k: List[int] = [1, 10, 100],
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test_groundtruth: bool = False,
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example_test: bool = False,
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is_mbpp: bool = False,
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language: str = "python",
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):
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"""
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Evaluates the functional correctness of a model.
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"""
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if example_test:
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print("Example test...")
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problems = read_dataset(problem_file,
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dataset_type="humaneval")
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sample_jsonl = stream_jsonl_all(input_file)
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with ThreadPoolExecutor(max_workers=n_workers) as executor:
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futures = []
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completion_id = Counter()
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n_samples = 0
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results = defaultdict(list)
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if test_groundtruth:
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print("Testing ground truth...")
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for sample in tqdm(problems.values()):
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task_id = sample["task_id"]
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lang = task_id.split("/")[0].lower()
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if lang == "javascript":
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lang = "js"
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tmp_dir_ = os.path.join(tmp_dir, lang, "evaluation")
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sample["generation"] = sample["canonical_solution"]
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sample["test_code"] = process_humaneval_test(sample, problems, example_test, language)
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if sample["test_code"] is None:
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continue
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args = (task_id, sample, lang, timeout, tmp_dir_, completion_id[task_id])
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future = executor.submit(check_correctness, *args)
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futures.append(future)
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completion_id[task_id] += 1
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n_samples += 1
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else:
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print("Reading samples...")
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for sample in tqdm(sample_jsonl):
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task_id = sample["task_id"]
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if not is_mbpp:
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lang = language
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if not is_mbpp and lang == "javascript":
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lang = "js"
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if is_mbpp:
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lang = "python"
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tmp_dir_ = os.path.join(tmp_dir, lang, "evaluation")
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sample["task_id"] = task_id
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sample["test_code"] = process_humaneval_test(sample, problems, example_test, is_mbpp, language)
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if sample["test_code"] is None:
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continue
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if "completion_id" in sample:
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completion_id_ = sample["completion_id"]
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else:
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completion_id_ = completion_id[task_id]
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args = (task_id, sample, lang, timeout, tmp_dir_, completion_id_)
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future = executor.submit(check_correctness, *args)
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futures.append(future)
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completion_id[task_id] += 1
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n_samples += 1
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if len(completion_id) == len(problems):
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evaluate_pass_at_k = True
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else:
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evaluate_pass_at_k = False
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print("Running test suites...")
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for future in tqdm(as_completed(futures), total=len(futures)):
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result = future.result()
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results[result["task_id"]].append((result["completion_id"], result))
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# Calculate pass@k.
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total, correct = [], []
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for result in results.values():
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passed = [r[1]["passed"] for r in result]
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total.append(len(passed))
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correct.append(sum(passed))
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total = np.array(total)
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correct = np.array(correct)
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if evaluate_pass_at_k:
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ks = k
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pass_at_k = {f"pass@{k}": estimate_pass_at_k(total, correct, k).mean()
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for k in ks if (total >= k).all()}
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print(pass_at_k)
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else:
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print("Total:", np.sum(total))
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print("Correct:", np.sum(correct))
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return pass_at_k
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