Switch from warning to ValueError when inconsistent loss_average is used

curvlinops/kfac.py

@@ -171,6 +171,8 @@ def __init__(
             ValueError: If the loss average is not supported.
             ValueError: If the loss average is ``None`` and the loss function's
                 reduction is not ``'sum'``.
+            ValueError: If the loss average is not ``None`` and the loss function's
+                reduction is ``'sum'``.
             ValueError: If ``fisher_type != 'mc'`` and ``mc_samples != 1``.
             NotImplementedError: If a parameter is in an unsupported layer.
         """
@@ -189,13 +191,10 @@ def __init__(
                 f"Must be 'batch' or 'batch+sequence' if loss_func.reduction != 'sum'."
             )
         if loss_func.reduction == "sum" and loss_average is not None:
-            # reduction used in loss function will overwrite loss_average
-            warnings.warn(
+            raise ValueError(
                 f"Loss function uses reduction='sum', but loss_average={loss_average}."
-                " loss_average is set to None.",
-                stacklevel=2,
+                " Set loss_average to None if you want to use reduction='sum'."
             )
-            loss_average = None
         if fisher_type != "mc" and mc_samples != 1:
             raise ValueError(
                 f"Invalid mc_samples: {mc_samples}. "