fix tp plan registry

src/transformers/modeling_utils.py

@@ -1521,24 +1521,29 @@ def _get_tp_key_registry(model: "PreTrainedModel") -> Dict[str, Dict]:
         else:
             other_modules_tp_plan[key] = translate_to_torch_parallel_style(plan)
 
+    # This pattern is used to capture the whole model prefix before the layer in the keys of the state dict
+    pattern = rf"^({layer_prefix}[0-9]+)"
     # Create the registry. Here we use the layers as single entities to parallelize (and other individual modules if any)
     tp_key_registry = {}
     for key in model.state_dict().keys():
-        # This pattern is used to capture the whole model prefix before the layer
-        pattern = rf"^({layer_prefix}[0-9]+)"
         match = re.match(pattern, key)
         # In this case, the current key is part of a layer to parallelize as a single entity
         if match is not None:
+            # Extract the actual layer number
             layer = match.group(1)
             if layer not in tp_key_registry:
                 tp_key_registry[layer] = {"children": {key}, "plan": layer_wise_tp_plan}
             else:
                 tp_key_registry[layer]["children"].add(key)
-        elif key in other_modules_tp_plan:
-            if key not in tp_key_registry:
-                tp_key_registry[key] = {"children": {key}, "plan": other_modules_tp_plan[key]}
-            else:
-                tp_key_registry[key]["children"].add(key)
+        # The key may be part of another module to parallelize, e.g. `lm_head`
+        else:
+            for module, plan in other_modules_tp_plan.items():
+                if key.startswith(module):
+                    if key not in tp_key_registry:
+                        tp_key_registry[module] = {"children": {key}, "plan": plan}
+                    else:
+                        tp_key_registry[module]["children"].add(key)
+                    break
 
     return tp_key_registry