layernorm_decay_fix (huggingface#35927)

* layernorm_decay_fix

* W293 fix

* ruff format fix

* black format

* ruff format

* erase last layer

* add test_get_parameter_names_rmsnorm

* rmsnorm fix

docs/source/en/perf_train_gpu_one.md

@@ -298,8 +298,7 @@ from transformers.trainer_pt_utils import get_parameter_names
 
 training_args = TrainingArguments(per_device_train_batch_size=4, **default_args)
 
-decay_parameters = get_parameter_names(model, [nn.LayerNorm])
-decay_parameters = [name for name in decay_parameters if "bias" not in name]
+decay_parameters = get_parameter_names(model, [nn.LayerNorm], ["bias", "layernorm", "rmsnorm"])
 optimizer_grouped_parameters = [
     {
         "params": [p for n, p in model.named_parameters() if n in decay_parameters],

docs/source/ja/perf_train_gpu_one.md

@@ -237,8 +237,7 @@ from transformers.trainer_pt_utils import get_parameter_names
 
 training_args = TrainingArguments(per_device_train_batch_size=4, **default_args)
 
-decay_parameters = get_parameter_names(model, [nn.LayerNorm])
-decay_parameters = [name for name in decay_parameters if "bias" not in name]
+decay_parameters = get_parameter_names(model, [nn.LayerNorm], ["bias", "layernorm", "rmsnorm"])
 optimizer_grouped_parameters = [
     {
         "params": [p for n, p in model.named_parameters() if n in decay_parameters],

examples/research_projects/robust-speech-event/run_speech_recognition_ctc_bnb.py

@@ -680,8 +680,7 @@ def compute_metrics(pred):
     # Instantiate custom data collator
     data_collator = DataCollatorCTCWithPadding(processor=processor)
 
-    decay_parameters = get_parameter_names(model, [torch.nn.LayerNorm])
-    decay_parameters = [name for name in decay_parameters if "bias" not in name]
+    decay_parameters = get_parameter_names(model, [torch.nn.LayerNorm], ["bias", "layernorm", "rmsnorm"])
     optimizer_grouped_parameters = [
         {
             "params": [p for n, p in model.named_parameters() if n in decay_parameters],

src/transformers/trainer.py

@@ -1177,13 +1177,13 @@ def create_optimizer_and_scheduler(self, num_training_steps: int):
 
     def get_decay_parameter_names(self, model) -> List[str]:
         """
-        Get all parameter names that weight decay will be applied to
+        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
+        This function filters out parameters in two ways:
+        1. By layer type (instances of layers specified in ALL_LAYERNORM_LAYERS)
+        2. By parameter name patterns (containing 'bias', 'layernorm', or 'rmsnorm')
         """
-        decay_parameters = get_parameter_names(model, ALL_LAYERNORM_LAYERS)
-        decay_parameters = [name for name in decay_parameters if "bias" not in name]
+        decay_parameters = get_parameter_names(model, ALL_LAYERNORM_LAYERS, ["bias", "layernorm", "rmsnorm"])
         return decay_parameters
 
     def create_optimizer(self):

src/transformers/trainer_pt_utils.py

@@ -1120,19 +1120,25 @@ def numel(p):
     return sum(numel(p) for p in model.parameters() if not trainable_only or p.requires_grad)
 
 
-def get_parameter_names(model, forbidden_layer_types):
+def get_parameter_names(model, forbidden_layer_types, forbidden_layer_names=None):
     """
     Returns the names of the model parameters that are not inside a forbidden layer.
     """
+    if forbidden_layer_names is None:
+        forbidden_layer_names = []
     result = []
     for name, child in model.named_children():
+        child_params = get_parameter_names(child, forbidden_layer_types, forbidden_layer_names)
         result += [
             f"{name}.{n}"
-            for n in get_parameter_names(child, forbidden_layer_types)
+            for n in child_params
             if not isinstance(child, tuple(forbidden_layer_types))
+            and not any(forbidden in f"{name}.{n}".lower() for forbidden in forbidden_layer_names)
         ]
-    # Add model specific parameters (defined with nn.Parameter) since they are not in any child.
-    result += list(model._parameters.keys())
+    # Add model specific parameters that are not in any child
+    result += [
+        k for k in model._parameters.keys() if not any(forbidden in k.lower() for forbidden in forbidden_layer_names)
+    ]
     return result
 
 

tests/trainer/test_trainer_utils.py

@@ -244,6 +244,33 @@ def test_get_parameter_names(self):
         )
         # fmt: on
 
+    def test_get_parameter_names_rmsnorm(self):
+        class RMSNorm(nn.Module):
+            def __init__(self, hidden_size):
+                super().__init__()
+                self.weight = nn.Parameter(torch.ones(hidden_size))
+                self.bias = nn.Parameter(torch.zeros(hidden_size))
+
+        class ModelWithRMSNorm(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.linear = nn.Linear(128, 128)
+                self.rmsnorm = RMSNorm(128)
+                self.bias = nn.Parameter(torch.zeros(128))
+
+        model = ModelWithRMSNorm()
+        # Test both type-based and name-based filtering
+        decay_parameters = get_parameter_names(model, [], ["bias", "rmsnorm"])
+
+        # Parameters that should be in weight decay
+        self.assertIn("linear.weight", decay_parameters)
+
+        # Parameters that should NOT be in weight decay
+        self.assertNotIn("linear.bias", decay_parameters)
+        self.assertNotIn("rmsnorm.weight", decay_parameters)
+        self.assertNotIn("rmsnorm.bias", decay_parameters)
+        self.assertNotIn("bias", decay_parameters)
+
     def test_distributed_sampler_with_loop(self):
         batch_size = 16
         for length in [23, 64, 123]: