|
| 1 | +import gzip |
| 2 | +import json |
| 3 | +import os |
| 4 | +import os.path as osp |
| 5 | +import re |
| 6 | +import shutil |
| 7 | +import subprocess |
| 8 | +import tempfile |
| 9 | +import time |
| 10 | + |
| 11 | +import numpy as np |
| 12 | +from datasets import Dataset |
| 13 | + |
| 14 | +from opencompass.openicl.icl_evaluator import BaseEvaluator |
| 15 | +from opencompass.registry import LOAD_DATASET |
| 16 | + |
| 17 | +from .base import BaseDataset |
| 18 | + |
| 19 | +# currently supporting 19 languages |
| 20 | +_LANGUAGE_NAME_DICT = { |
| 21 | + 'cpp': 'CPP', |
| 22 | + 'cs': 'C#', |
| 23 | + 'd': 'D', |
| 24 | + 'go': 'Go', |
| 25 | + 'java': 'Java', |
| 26 | + 'jl': 'Julia', |
| 27 | + 'js': 'JavaScript', |
| 28 | + 'lua': 'Lua', |
| 29 | + 'php': 'PHP', |
| 30 | + 'pl': 'Perl', |
| 31 | + 'py': 'Python', |
| 32 | + 'r': 'R', |
| 33 | + 'rb': 'Ruby', |
| 34 | + 'rkt': 'Racket', |
| 35 | + 'rs': 'Rust', |
| 36 | + 'scala': 'Scala', |
| 37 | + 'sh': 'Shell', |
| 38 | + 'swift': 'Swift', |
| 39 | + 'ts': 'TypeScript', |
| 40 | +} |
| 41 | + |
| 42 | + |
| 43 | +@LOAD_DATASET.register_module() |
| 44 | +class HumanevalMultiDataset(BaseDataset): |
| 45 | + |
| 46 | + @staticmethod |
| 47 | + def load(path, language, version, num_repeats: int = 1, **kwargs): |
| 48 | + """Load humaneval dataset for pass k mode. |
| 49 | +
|
| 50 | + Note that you can use num_repeats > 1 when your model does not support |
| 51 | + `num_return_sequence` in generation, otherwise use the raw |
| 52 | + humaneval dataset and set `num_return_sequence` in model config to |
| 53 | + generate multiple responses for testing pass@k>1. |
| 54 | +
|
| 55 | + It better to change your dataset abbr correspondingly if you want to |
| 56 | + change num_repeats>1, otherwise the number in |
| 57 | + `.cache/dataset_size.json` might be inconsistent. |
| 58 | +
|
| 59 | + Args: |
| 60 | + num_repeats(int): Number of repetition for this dataset to get |
| 61 | + multiple responses in special cases. |
| 62 | + """ |
| 63 | + assert language in _LANGUAGE_NAME_DICT.keys(), ( |
| 64 | + f'language must be in {list(_LANGUAGE_NAME_DICT.keys())}') |
| 65 | + assert version in [ |
| 66 | + 'keep', 'transform', 'reworded', 'remove' |
| 67 | + ], ('version must be in ["keep", "transform", "reworded", "remove"]') |
| 68 | + file_path = osp.join(path, f'humaneval-{language}-{version}.jsonl') |
| 69 | + dataset = [] |
| 70 | + with open(file_path, 'r', encoding='utf-8') as f: |
| 71 | + for line in f: |
| 72 | + dataset.extend( |
| 73 | + [json.loads(line.strip()) for _ in range(num_repeats)]) |
| 74 | + return Dataset.from_list(dataset) |
| 75 | + |
| 76 | + |
| 77 | +class HumanevalMultiEvaluator(BaseEvaluator): |
| 78 | + |
| 79 | + def __init__(self, |
| 80 | + language, |
| 81 | + ip_address='localhost', |
| 82 | + port=5000, |
| 83 | + retry=2, |
| 84 | + timeout=600) -> None: |
| 85 | + self.language = language |
| 86 | + self.ip_address = ip_address |
| 87 | + self.port = port |
| 88 | + self.retry = retry |
| 89 | + self.timeout = timeout |
| 90 | + super().__init__() |
| 91 | + |
| 92 | + def stop_at_stop_token(self, decoded_string, stop_tokens): |
| 93 | + """Produces the prefix of decoded_string that ends at the first |
| 94 | + occurrence of a stop_token. |
| 95 | +
|
| 96 | + WARNING: the decoded_string *must not* include the prompt, |
| 97 | + which may have stop tokens itself. |
| 98 | + """ |
| 99 | + min_stop_index = len(decoded_string) |
| 100 | + for stop_token in stop_tokens: |
| 101 | + stop_index = decoded_string.find(stop_token) |
| 102 | + if stop_index != -1 and stop_index < min_stop_index: |
| 103 | + min_stop_index = stop_index |
| 104 | + return decoded_string[:min_stop_index] |
| 105 | + |
| 106 | + def _code_eval_service(self, file_path): |
| 107 | + exec_result = subprocess.run([ |
| 108 | + 'curl', '-X', 'POST', '-F', f'file=@{file_path}', '-F', |
| 109 | + f'dataset=multipl-e/{self.language}', |
| 110 | + f'{self.ip_address}:{self.port}/evaluate' |
| 111 | + ], |
| 112 | + timeout=self.timeout, |
| 113 | + capture_output=True) |
| 114 | + |
| 115 | + if exec_result.returncode == 0 and re.match( |
| 116 | + "\"{.*:.*}\"", exec_result.stdout.decode('utf-8')): |
| 117 | + return True, json.loads(exec_result.stdout.decode('utf-8')) |
| 118 | + else: |
| 119 | + if exec_result.stderr: |
| 120 | + try: |
| 121 | + err = exec_result.stderr.decode() |
| 122 | + except Exception: |
| 123 | + err = exec_result.stderr |
| 124 | + else: |
| 125 | + try: |
| 126 | + err = exec_result.stdout.decode() |
| 127 | + except Exception: |
| 128 | + err = exec_result.stdout |
| 129 | + return False, err |
| 130 | + |
| 131 | + def estimator(self, n: int, c: int, k: int) -> float: |
| 132 | + """ |
| 133 | + Calculates 1 - comb(n - c, k) / comb(n, k). |
| 134 | + """ |
| 135 | + if n - c < k: |
| 136 | + return 1.0 |
| 137 | + return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1, n + 1)) |
| 138 | + |
| 139 | + def for_file(self, path): |
| 140 | + |
| 141 | + try: |
| 142 | + with gzip.open(path, 'rt') as f: |
| 143 | + data = json.load(f) |
| 144 | + except Exception: |
| 145 | + return None |
| 146 | + |
| 147 | + n = len(data['results']) |
| 148 | + c = len([ |
| 149 | + True for r in data['results'] |
| 150 | + if r['status'] == 'OK' and r['exit_code'] == 0 |
| 151 | + ]) |
| 152 | + return { |
| 153 | + 'pass@1': self.estimator(n, c, 1), |
| 154 | + 'pass@10': self.estimator(n, c, 10), |
| 155 | + 'pass@100': self.estimator(n, c, 100), |
| 156 | + 'n': n, |
| 157 | + 'c': c, |
| 158 | + } |
| 159 | + |
| 160 | + def score(self, predictions, references, test_set): |
| 161 | + |
| 162 | + stop_tokens = test_set['stop_tokens'][0] |
| 163 | + print(stop_tokens) |
| 164 | + |
| 165 | + # convert to original version |
| 166 | + test_set = test_set.to_pandas() |
| 167 | + test_set_origin = test_set.drop_duplicates(subset='name') |
| 168 | + num_repeats = int(len(test_set) / len(test_set_origin)) |
| 169 | + print(num_repeats) |
| 170 | + |
| 171 | + # Create a temporary directory using the tempfile module |
| 172 | + with tempfile.TemporaryDirectory() as tmpdir: |
| 173 | + for i in range(len(test_set_origin)): |
| 174 | + completions = predictions[i * num_repeats:(i + 1) * |
| 175 | + num_repeats] |
| 176 | + processed_completions = [] |
| 177 | + for comp in completions: |
| 178 | + comp = self.stop_at_stop_token(comp, stop_tokens) |
| 179 | + processed_completions.append(comp) |
| 180 | + |
| 181 | + result_dict = { |
| 182 | + 'name': test_set_origin.iloc[i]['name'], |
| 183 | + 'language': test_set_origin.iloc[i]['language'], |
| 184 | + 'prompt': test_set_origin.iloc[i]['prompt'], |
| 185 | + 'tests': test_set_origin.iloc[i]['tests'], |
| 186 | + 'completions': processed_completions |
| 187 | + } |
| 188 | + |
| 189 | + json_str = json.dumps(result_dict) |
| 190 | + json_bytes = json_str.encode('utf-8') |
| 191 | + |
| 192 | + with gzip.GzipFile( |
| 193 | + os.path.join(tmpdir, f'{result_dict["name"]}.json.gz'), |
| 194 | + 'w') as f: |
| 195 | + f.write(json_bytes) |
| 196 | + |
| 197 | + # create a zip file containing all the generated .json.gz files |
| 198 | + zipname = os.path.join(tmpdir, 'archive') |
| 199 | + shutil.make_archive(zipname, 'zip', tmpdir) |
| 200 | + zipfile_path = f'{zipname}.zip' |
| 201 | + |
| 202 | + num_retry = 0 |
| 203 | + while num_retry < self.retry: |
| 204 | + succeed, output = self._code_eval_service( |
| 205 | + file_path=zipfile_path) |
| 206 | + if not succeed and '(56) Recv failure' in output: |
| 207 | + # only retry when connection failed |
| 208 | + num_retry += 1 |
| 209 | + # wait a min in case the service load is too high |
| 210 | + time.sleep(60) |
| 211 | + else: |
| 212 | + break |
| 213 | + |
| 214 | + if succeed: |
| 215 | + if isinstance(output, str): |
| 216 | + return json.loads(output) |
| 217 | + elif isinstance(output, dict): |
| 218 | + return output |
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