actually got gradient checkpointing working, again, again, maybe

toolkit/lora_special.py

@@ -46,9 +46,14 @@ class LoRAModule(ToolkitModuleMixin, torch.nn.Module):
             dropout=None,
             rank_dropout=None,
             module_dropout=None,
+            parent=None,
+            **kwargs
     ):
         """if alpha == 0 or None, alpha is rank (no scaling)."""
-        super().__init__()
+        super().__init__(
+            org_module=org_module,
+            parent=parent
+        )
         self.lora_name = lora_name
         self.scalar = torch.tensor(1.0)
 
@@ -256,6 +261,7 @@ class LoRASpecialNetwork(ToolkitNetworkMixin, LoRANetwork):
                                 dropout=dropout,
                                 rank_dropout=rank_dropout,
                                 module_dropout=module_dropout,
+                                parent=module,
                             )
                             loras.append(lora)
             return loras, skipped