first commit

.gitattributes

@@ -39,3 +39,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.mp3 filter=lfs diff=lfs merge=lfs -text
 *.ogg filter=lfs diff=lfs merge=lfs -text
 *.wav filter=lfs diff=lfs merge=lfs -text
+*.tar.gz filter=lfs diff=lfs merge=lfs -text

exebench.py

@@ -0,0 +1,201 @@
+# 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
+

split_merged_asm_ok_fhead.tar.gz

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+size 3892434961

test_real.tar.gz

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+size 7523671

test_synth.tar.gz

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+size 12458662

train_no_compilable.tar.gz

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+size 153970953

train_real_compilable.tar.gz

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train_real_simple_io.tar.gz

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+size 22654808

train_synth_compilable.tar.gz

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train_synth_rich_io.tar.gz

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+size 269227686

train_synth_simple_io.tar.gz

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valid_real.tar.gz

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valid_synth.tar.gz

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