[WebDataset] Add .pth support for torch tensors

src/datasets/packaged_modules/webdataset/webdataset.py

@@ -7,6 +7,7 @@
 import pyarrow as pa
 
 import datasets
+from datasets.features.features import cast_to_python_objects
 
 
 logger = datasets.utils.logging.get_logger(__name__)
@@ -64,7 +65,10 @@ def _split_generators(self, dl_manager):
                     "The TAR archives of the dataset should be in WebDataset format, "
                     "but the files in the archive don't share the same prefix or the same types."
                 )
-            pa_tables = [pa.Table.from_pylist([example]) for example in first_examples]
+            pa_tables = [
+                pa.Table.from_pylist(cast_to_python_objects([example], only_1d_for_numpy=True))
+                for example in first_examples
+            ]
             if datasets.config.PYARROW_VERSION.major < 14:
                 inferred_arrow_schema = pa.concat_tables(pa_tables, promote=True).schema
             else:
@@ -256,16 +260,21 @@ def cbor_loads(data: bytes):
     return cbor.loads(data)
 
 
+def torch_loads(data: bytes):
+    import torch
+
+    return torch.load(io.BytesIO(data), weights_only=True)
+
+
 # Obtained by checking `decoders` in `webdataset.autodecode`
 # and removing unsafe extension decoders.
 # Removed Pickle decoders:
 # - "pyd": lambda data: pickle.loads(data)
 # - "pickle": lambda data: pickle.loads(data)
-# Removed Torch decoders:
-# - "pth": lambda data: torch_loads(data)
-# Modified NumPy decoders to fix CVE-2019-6446 (add allow_pickle=False):
+# Modified NumPy decoders to fix CVE-2019-6446 (add allow_pickle=False and weights_only=True):
 # - "npy": npy_loads,
 # - "npz": lambda data: np.load(io.BytesIO(data)),
+# - "pth": lambda data: torch_loads(data)
 DECODERS = {
     "txt": text_loads,
     "text": text_loads,
@@ -284,5 +293,6 @@ def cbor_loads(data: bytes):
     "npy": npy_loads,
     "npz": npz_loads,
     "cbor": cbor_loads,
+    "pth": torch_loads,
 }
 WebDataset.DECODERS = DECODERS

tests/fixtures/files.py

@@ -551,6 +551,16 @@ def audio_file():
     return os.path.join("tests", "features", "data", "test_audio_44100.wav")
 
 
+@pytest.fixture(scope="session")
+def tensor_file(tmp_path_factory):
+    import torch
+
+    path = tmp_path_factory.mktemp("data") / "tensor.pth"
+    with open(path, "wb") as f:
+        torch.save(torch.ones(128), f)
+    return path
+
+
 @pytest.fixture(scope="session")
 def zip_image_path(image_file, tmp_path_factory):
     path = tmp_path_factory.mktemp("data") / "dataset.img.zip"

tests/packaged_modules/test_webdataset.py

@@ -4,10 +4,10 @@
 import numpy as np
 import pytest
 
-from datasets import Audio, DownloadManager, Features, Image, Value
+from datasets import Audio, DownloadManager, Features, Image, Sequence, Value
 from datasets.packaged_modules.webdataset.webdataset import WebDataset
 
-from ..utils import require_pil, require_sndfile
+from ..utils import require_pil, require_sndfile, require_torch
 
 
 @pytest.fixture
@@ -50,6 +50,20 @@ def bad_wds_file(tmp_path, image_file, text_file):
     return str(filename)
 
 
+@pytest.fixture
+def tensor_wds_file(tmp_path, tensor_file):
+    json_file = tmp_path / "data.json"
+    filename = tmp_path / "file.tar"
+    num_examples = 3
+    with json_file.open("w", encoding="utf-8") as f:
+        f.write(json.dumps({"text": "this is a text"}))
+    with tarfile.open(str(filename), "w") as f:
+        for example_idx in range(num_examples):
+            f.add(json_file, f"{example_idx:05d}.json")
+            f.add(tensor_file, f"{example_idx:05d}.pth")
+    return str(filename)
+
+
 @require_pil
 def test_image_webdataset(image_wds_file):
     import PIL.Image
@@ -145,3 +159,34 @@ def test_webdataset_with_features(image_wds_file):
     assert isinstance(decoded["json"], dict)
     assert isinstance(decoded["json"]["caption"], str)
     assert isinstance(decoded["jpg"], PIL.Image.Image)
+
+
+@require_torch
+def test_tensor_webdataset(tensor_wds_file):
+    import torch
+
+    data_files = {"train": [tensor_wds_file]}
+    webdataset = WebDataset(data_files=data_files)
+    split_generators = webdataset._split_generators(DownloadManager())
+    assert webdataset.info.features == Features(
+        {
+            "__key__": Value("string"),
+            "__url__": Value("string"),
+            "json": {"text": Value("string")},
+            "pth": Sequence(Value("float32")),
+        }
+    )
+    assert len(split_generators) == 1
+    split_generator = split_generators[0]
+    assert split_generator.name == "train"
+    generator = webdataset._generate_examples(**split_generator.gen_kwargs)
+    _, examples = zip(*generator)
+    assert len(examples) == 3
+    assert isinstance(examples[0]["json"], dict)
+    assert isinstance(examples[0]["json"]["text"], str)
+    assert isinstance(examples[0]["pth"], torch.Tensor)  # keep encoded to avoid unecessary copies
+    encoded = webdataset.info.features.encode_example(examples[0])
+    decoded = webdataset.info.features.decode_example(encoded)
+    assert isinstance(decoded["json"], dict)
+    assert isinstance(decoded["json"]["text"], str)
+    assert isinstance(decoded["pth"], list)