Force contiguous searchsorted (#396)

* force contiguous tensors in torch searchsorted

* improve warning message for torch backend

* run linter

* undo using workers in workflow (subject to a different issue)

Author: LarsKue

bayesflow/__init__.py

@@ -40,8 +40,12 @@ def setup():
         torch.autograd.set_grad_enabled(False)
 
         logging.warning(
+            "\n"
             "When using torch backend, we need to disable autograd by default to avoid excessive memory usage. Use\n"
+            "\n"
             "with torch.enable_grad():\n"
+            "    ...\n"
+            "\n"
             "in contexts where you need gradients (e.g. custom training loops)."
         )
 

bayesflow/utils/tensor_utils.py

@@ -202,7 +202,9 @@ def searchsorted(sorted_sequence: Tensor, values: Tensor, side: str = "left") ->
 
             out_int32 = len(sorted_sequence) <= np.iinfo(np.int32).max
 
-            indices = torch.searchsorted(sorted_sequence, values, side=side, out_int32=out_int32)
+            indices = torch.searchsorted(
+                sorted_sequence.contiguous(), values.contiguous(), side=side, out_int32=out_int32
+            )
 
             return indices
         case _: