File size: 9,353 Bytes
24dbd99 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 |
# coding=utf-8
# Copyright 2022 ExeBench authors
# The code required to produce and load this dataset is licensed under MIT License.
# The code samples included in this dataset keep their own licenses, which can be retrieved via their metadata.
# 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.
# Please note that the dataset release is still work in progress.
"""The ExeBench dataset."""
import json
import datasets
from pathlib import Path
_CITATION = """\
@misc{TODO
}
"""
_DESCRIPTION = """\
An ML-scale dataset of executable C functions
""" # TODO: expand
_HOMEPAGE = "https://github.com/jordiae/exebench"
_LICENSE = "Multiple: see each function license (fields 'ref' and 'path')"
_URL = "" # "https://huggingface.co/datasets/jordiae/exebench-test/resolve/main/"
_REMOVED_FEATURES = ["doc", "angha_error", "real_error", "angha_io_error", "real_io_error",
"angha_io_pairs_are_trivial", "real_io_pairs_are_trivial"]
_RENAMED_FEATURES = {"angha_deps": "synth_deps", "angha_io_pairs": "synth_io_pairs",
"angha_exe_wrapper": "synth_exe_wrapper", "angha_iospec": "synth_iospec"}
_FEATURES = datasets.Features(
{
"path": datasets.Value("string"),
"func_def": datasets.Value("string"),
"func_head": datasets.Value("string"),
"fname": datasets.Value("string"),
"signature": datasets.Sequence(datasets.Value("string")),
# "doc": datasets.Value("string"),
# "angha_error": datasets.Value("string"),
# "real_error": datasets.Value("string"),
"asm": datasets.Sequence({'target': datasets.Value("string"), 'code': datasets.Value("string")}), # unflat dict#Optional[Dict[str, Optional[FuncAsm]]] = None
"synth_deps": datasets.Value("string"),
"real_deps": datasets.Value("string"),
"synth_io_pairs": datasets.Sequence({
"input": datasets.Sequence({'var': datasets.Value("string"), 'value': datasets.Value("string")}),
"output": datasets.Sequence({'var': datasets.Value("string"), 'value': datasets.Value("string")}),
"dummy_funcs": datasets.Value("string"),
"dummy_funcs_seed": datasets.Value("int64")
}),
"real_io_pairs": datasets.Sequence({
"input": datasets.Sequence({'var': datasets.Value("string"), 'value': datasets.Value("string")}),
"output": datasets.Sequence({'var': datasets.Value("string"), 'value': datasets.Value("string")}),
"dummy_funcs": datasets.Value("string"),
"dummy_funcs_seed": datasets.Value("int64")
}),
# "angha_io_error": datasets.Value("string"),
# "real_io_error": datasets.Value("string"),
"synth_exe_wrapper": datasets.Value("string"),
"real_exe_wrapper": datasets.Value("string"),
# "angha_io_pairs_are_trivial": datasets.Value("bool"),
# "real_io_pairs_are_trivial": datasets.Value("bool"),
"ref": datasets.Value("string"),
"synth_iospec": datasets.Value("string"), # serialized, TODO: improve
"real_iospec": datasets.Value("string")
}
)
class ExeBenchConfig(datasets.BuilderConfig):
"""BuilderConfig for ExeBench."""
def __init__(self, *args, **kwargs):
"""BuilderConfig for The Pile.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super().__init__(
*args,
**kwargs,
)
class ExeBench(datasets.GeneratorBasedBuilder):
"""Semantic Textual Similarity Ca dataset."""
BUILDER_CONFIGS = [
ExeBenchConfig(
name="ExeBench",
version=datasets.Version("1.0.1"),
description="Executable C dataset"
),
]
def _info(self):
"""Give information and typings for the dataset."""
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=_FEATURES,
# If there's a common (input, target) tuple from the features,
# specify them here. They'll be used if as_supervised=True in
# builder.as_dataset.
supervised_keys=None,
# Homepage of the dataset for documentation
homepage=_HOMEPAGE,
# License for the dataset if available
license=_LICENSE,
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
urls_to_download = {
# "train_not_compilable": f"{_URL}train_not_compilable.tar.gz",
#"train_synth_compilable": f"{_URL}train_synth_compilable.tar.gz",
# "train_real_compilable": f"{_URL}train_real_compilable.tar.gz",
#"train_synth_simple_io": f"{_URL}train_synth_simple_io.tar.gz",
# "train_real_simple_io": f"{_URL}train_real_simple_io.tar.gz",
#"train_synth_rich_io": f"{_URL}train_synth_rich_io.tar.gz",
#"valid_synth": f"{_URL}valid_synth.tar.gz",
# "valid_real": f"{_URL}valid_real.tar.gz",
"test_synth": f"{_URL}test_synth.tar.gz",
"test_real": f"{_URL}test_real.tar.gz",
}
downloaded_files = dl_manager.download_and_extract(urls_to_download)
return [
#datasets.SplitGenerator(name='train_not_compilable',
# gen_kwargs={"files": downloaded_files["train_not_compilable"]}),
#datasets.SplitGenerator(name='train_synth_compilable',
# gen_kwargs={"files": downloaded_files["train_synth_compilable"]}),
#datasets.SplitGenerator(name='train_real_compilable',
# gen_kwargs={"files": downloaded_files["train_real_compilable"]}),
#datasets.SplitGenerator(name='train_synth_simple_io',
# gen_kwargs={"files": downloaded_files["train_synth_simple_io"]}),
#datasets.SplitGenerator(name='train_real_simple_io',
# gen_kwargs={"files": downloaded_files["train_real_simple_io"]}),
#datasets.SplitGenerator(name='train_synth_rich_io',
# gen_kwargs={"files": downloaded_files["train_synth_rich_io"]}),
#datasets.SplitGenerator(name='valid_synth',
# gen_kwargs={"files": downloaded_files["valid_synth"]}),
#datasets.SplitGenerator(name='valid_real',
# gen_kwargs={"files": downloaded_files["valid_real"]}),
datasets.SplitGenerator(name='test_synth',
gen_kwargs={"files": downloaded_files["test_synth"]}),
datasets.SplitGenerator(name='test_real',
gen_kwargs={"files": downloaded_files["test_real"]}),
]
def _generate_examples(self, files):
"""Yield examples as (key, example) tuples."""
key = 0
import zstandard as zstd
for path in Path(files).rglob('*.jsonl.zst'):
with zstd.open(open(path, "rb"), "rt", encoding="utf-8") as f:
for row in f:
data = json.loads(row)
data = data['text']
data = self._fixes(data)
for io_pairs_kind in ('synth_io_pairs', 'real_io_pairs'):
if data[io_pairs_kind]:
new_io_pairs = []
for e in data[io_pairs_kind]:
new_e = {}
new_e['input'] = [{'var': var, 'value': json.dumps(value)} for (var, value) in e['input'].items()] if e['input'] else []
new_e['output'] = [{'var': var, 'value': json.dumps(value)} for (var, value) in e['output'].items()] if e['output'] else []
new_e['dummy_funcs'] = e['dummy_funcs']
new_e['dummy_funcs_seed'] = e['dummy_funcs_seed']
new_io_pairs.append(new_e)
data[io_pairs_kind] = new_io_pairs
data['synth_iospec'] = json.dumps(data['synth_iospec'])
data['real_iospec'] = json.dumps(data['real_iospec'])
yield key, data
key += 1
def _fixes(self, row):
row['asm'] = [{'target': target, 'code': code['func_asm'] if code else None} for (target, code) in
row['asm'].items()] # TODO: pre_asm etc
for removed_key in _REMOVED_FEATURES:
if removed_key in row:
del row[removed_key]
for original_key, new_key in _RENAMED_FEATURES.items():
row[new_key] = row[original_key]
del row[original_key]
return row
|