refactor decay_parameters production into its own function (#26152)

src/transformers/trainer.py

@@ -951,6 +951,17 @@ class Trainer:
             optimizer = self.optimizer
         self.create_scheduler(num_training_steps=num_training_steps, optimizer=optimizer)
 
+    def get_decay_parameter_names(self, model) -> List[str]:
+        """
+        Get all parameter names that weight decay will be applied to
+
+        Note that some models implement their own layernorm instead of calling nn.LayerNorm, weight decay could still
+        apply to those modules since this function only filter out instance of nn.LayerNorm
+        """
+        decay_parameters = get_parameter_names(model, ALL_LAYERNORM_LAYERS)
+        decay_parameters = [name for name in decay_parameters if "bias" not in name]
+        return decay_parameters
+
     def create_optimizer(self):
         """
         Setup the optimizer.
@@ -961,8 +972,7 @@ class Trainer:
         opt_model = self.model_wrapped if is_sagemaker_mp_enabled() else self.model
 
         if self.optimizer is None:
-            decay_parameters = get_parameter_names(opt_model, ALL_LAYERNORM_LAYERS)
-            decay_parameters = [name for name in decay_parameters if "bias" not in name]
+            decay_parameters = self.get_decay_parameter_names(opt_model)
             optimizer_grouped_parameters = [
                 {
                     "params": [