Merge branch 'main' into fix-training-on-mps

Author: manujosephv

src/pytorch_tabular/tabular_datamodule.py

@@ -756,7 +756,14 @@ def _load_dataset_from_cache(self, tag: str = "train"):
                 raise AttributeError(f"{tag}_dataset not found in memory. Please provide the data for {tag} dataloader")
         elif self.cache_mode is self.CACHE_MODES.DISK:
             try:
-                dataset = torch.load(self.cache_dir / f"{tag}_dataset", weights_only=False)
+                # get the torch version
+                torch_version = torch.__version__
+                if torch_version < "2.6":
+                    dataset = torch.load(
+                        self.cache_dir / f"{tag}_dataset"
+                    )  # fix for torch version change of torch.load
+                elif torch_version >= "2.6":
+                    dataset = torch.load(self.cache_dir / f"{tag}_dataset", weights_only=False)
             except FileNotFoundError:
                 raise FileNotFoundError(
                     f"{tag}_dataset not found in {self.cache_dir}. Please provide the data for {tag} dataloader"

src/pytorch_tabular/utils/python_utils.py

@@ -74,15 +74,24 @@ def pl_load(
     """
     if not isinstance(path_or_url, (str, Path)):
         # any sort of BytesIO or similar
-        return torch.load(path_or_url, map_location=map_location, weights_only=False)
+        # get the torch version
+        torch_version = torch.__version__
+        if torch_version < "2.6":
+            return torch.load(path_or_url, map_location=map_location)  # for torch version < 2.6
+        elif torch_version >= "2.6":
+            return torch.load(path_or_url, map_location=map_location, weights_only=False)
     if str(path_or_url).startswith("http"):
         return torch.hub.load_state_dict_from_url(
             str(path_or_url),
             map_location=map_location,  # type: ignore[arg-type] # upstream annotation is not correct
         )
     fs = get_filesystem(path_or_url)
     with fs.open(path_or_url, "rb") as f:
-        return torch.load(f, map_location=map_location, weights_only=False)
+        torch_version = torch.__version__
+        if torch_version < "2.6":
+            return torch.load(f, map_location=map_location)  # for torch version < 2.6
+        elif torch_version >= "2.6":
+            return torch.load(f, map_location=map_location, weights_only=False)
 
 
 def check_numpy(x):