[Fix] Implement better wd_ban_list handling

pytorch_optimizer/optimizer/utils.py

@@ -198,23 +198,44 @@ def get_optimizer_parameters(
     weight_decay: float,
     wd_ban_list: List[str] = ('bias', 'LayerNorm.bias', 'LayerNorm.weight'),
 ) -> PARAMETERS:
-    r"""Get optimizer parameters while filtering specified modules.
-
+    r"""
+    Get optimizer parameters while filtering specified modules.
     :param model_or_parameter: Union[nn.Module, List]. model or parameters.
     :param weight_decay: float. weight_decay.
     :param wd_ban_list: List[str]. ban list not to set weight decay.
     :returns: PARAMETERS. new parameter list.
     """
+
+
+    fully_qualified_names = []
+    for module_name, module in model_or_parameter.named_modules():
+        for param_name, _param in module.named_parameters(recurse=False):
+            # Full parameter name includes module and parameter names
+            full_param_name = f'{module_name}.{param_name}' if module_name else param_name
+            # Check if any ban list substring is in the parameter name or module name
+            if (
+                any(banned in param_name for banned in wd_ban_list)
+                or any(banned in module_name for banned in wd_ban_list)
+                or any(banned in module._get_name() for banned in wd_ban_list)
+            ):
+                fully_qualified_names.append(full_param_name)
+
     if isinstance(model_or_parameter, nn.Module):
         model_or_parameter = list(model_or_parameter.named_parameters())
 
     return [
         {
-            'params': [p for n, p in model_or_parameter if p.requires_grad and not any(nd in n for nd in wd_ban_list)],
+            'params': [
+                p
+                for n, p in model_or_parameter
+                if p.requires_grad and not any(nd in n for nd in fully_qualified_names)
+            ],
             'weight_decay': weight_decay,
         },
         {
-            'params': [p for n, p in model_or_parameter if p.requires_grad and any(nd in n for nd in wd_ban_list)],
+            'params': [
+                p for n, p in model_or_parameter if p.requires_grad and any(nd in n for nd in fully_qualified_names)
+            ],
             'weight_decay': 0.0,
         },
     ]

tests/test_utils.py

@@ -98,7 +98,7 @@ def test_neuron_mean_norm():
 
 def test_get_optimizer_parameters():
     model: nn.Module = Example()
-    wd_ban_list: List[str] = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
+    wd_ban_list: List[str] = ['bias', 'LayerNorm.bias', 'LayerNorm.weight', 'LayerNorm']
 
     before_parameters = list(model.named_parameters())
     after_parameters = get_optimizer_parameters(model, weight_decay=1e-3, wd_ban_list=wd_ban_list)