Fail torch.load(weights=True) gracefully

test/utils/test_io.py

@@ -3,8 +3,10 @@
 import shutil
 import tempfile
 
+import pytest
+
 import torch_frame
-from torch_frame import load, save
+from torch_frame import TensorFrame, load, save
 from torch_frame.config.text_embedder import TextEmbedderConfig
 from torch_frame.config.text_tokenizer import TextTokenizerConfig
 from torch_frame.datasets import FakeDataset
@@ -114,3 +116,21 @@ def test_save_load_tensor_frame():
     tf, col_stats = load(path)
     assert dataset.col_stats == col_stats
     assert dataset.tensor_frame == tf
+
+
+class UntrustedClass:
+    pass
+
+
+@pytest.mark.skipif(
+    not torch_frame.typing.WITH_PT24,
+    reason='Requres PyTorch 2.4',
+)
+def test_load_weights_only_gracefully(tmpdir):
+    save(
+        tensor_frame=TensorFrame({}, {}),
+        col_stats={'a': UntrustedClass()},
+        path=tmpdir.join('tf.pt'),
+    )
+    with pytest.warns(UserWarning, match='Weights only load failed'):
+        load(tmpdir.join('tf.pt'))

torch_frame/utils/io.py

@@ -1,5 +1,8 @@
 from __future__ import annotations
 
+import pickle
+import re
+import warnings
 from typing import Any
 
 import torch
@@ -13,7 +16,7 @@
 )
 from torch_frame.data.multi_tensor import _MultiTensor
 from torch_frame.data.stats import StatType
-from torch_frame.typing import WITH_PT24, TensorData
+from torch_frame.typing import TensorData
 
 
 def serialize_feat_dict(
@@ -96,9 +99,30 @@ def load(
         tuple: A tuple of loaded :class:`TensorFrame` object and
             optional :obj:`col_stats`.
     """
-    tf_dict, col_stats = torch.load(path, weights_only=WITH_PT24)
+    if torch_frame.typing.WITH_PT24:
+        try:
+            tf_dict, col_stats = torch.load(path, weights_only=True)
+        except pickle.UnpicklingError as e:
+            error_msg = str(e)
+            if "add_safe_globals" in error_msg:
+                warn_msg = ("Weights only load failed. Please file an issue "
+                            "to make `torch.load(weights_only=True)` "
+                            "compatible in your case.")
+                match = re.search(r'add_safe_globals\(.*?\)', error_msg)
+                if match is not None:
+                    warnings.warn(f"{warn_msg} Please use "
+                                  f"`torch.serialization.{match.group()}` to "
+                                  f"allowlist this global.")
+                else:
+                    warnings.warn(warn_msg)
+
+                tf_dict, col_stats = torch.load(path, weights_only=False)
+            else:
+                raise e
+    else:
+        tf_dict, col_stats = torch.load(path, weights_only=False)
+
     tf_dict['feat_dict'] = deserialize_feat_dict(
         tf_dict.pop('feat_serialized_dict'))
-    tensor_frame = TensorFrame(**tf_dict)
-    tensor_frame.to(device)
+    tensor_frame = TensorFrame(**tf_dict).to(device)
     return tensor_frame, col_stats