[Flax] Example scripts - correct weight decay

examples/flax/language-modeling/run_clm_flax.py

@@ -477,9 +477,15 @@ def group_texts(examples):
     # to bias and LayerNorm scale parameters. decay_mask_fn returns a
     # mask boolean with the same structure as the parameters.
     # The mask is True for parameters that should be decayed.
+    # Note that this mask is specifically adapted for FlaxGPT2.
+    # For other models, one should correct the layer norm parameter naming
+    # accordingly.
     def decay_mask_fn(params):
         flat_params = traverse_util.flatten_dict(params)
-        flat_mask = {path: (path[-1] != "bias" and path[-2:] != ("LayerNorm", "scale")) for path in flat_params}
+        flat_mask = {
+            path: (path[-1] != "bias" and path[-2:] not in [("ln_1", "scale"), ("ln_2", "scale"), ("ln_f", "scale")])
+            for path in flat_params
+        }
         return traverse_util.unflatten_dict(flat_mask)
 
     # create adam optimizer

examples/flax/language-modeling/run_mlm_flax.py

@@ -508,6 +508,9 @@ def group_texts(examples):
     # to bias and LayerNorm scale parameters. decay_mask_fn returns a
     # mask boolean with the same structure as the parameters.
     # The mask is True for parameters that should be decayed.
+    # Note that this mask is specifically adapted for FlaxBERT-like models.
+    # For other models, one should correct the layer norm parameter naming
+    # accordingly.
     def decay_mask_fn(params):
         flat_params = traverse_util.flatten_dict(params)
         flat_mask = {path: (path[-1] != "bias" and path[-2:] != ("LayerNorm", "scale")) for path in flat_params}

examples/flax/language-modeling/run_t5_mlm_flax.py

@@ -626,7 +626,10 @@ def group_texts(examples):
     # The mask is True for parameters that should be decayed.
     def decay_mask_fn(params):
         flat_params = traverse_util.flatten_dict(params)
-        flat_mask = {path: (path[-1] != "bias" and path[-2:] != ("LayerNorm", "scale")) for path in flat_params}
+        flat_mask = {
+            path: (path[-1] != "bias" and path[-2:] not in [("layer_norm", "scale"), ("final_layer_norm", "scale")])
+            for path in flat_params
+        }
         return traverse_util.unflatten_dict(flat_mask)
 
     # create adam optimizer

examples/flax/summarization/run_summarization_flax.py

@@ -578,9 +578,15 @@ def compute_metrics(preds, labels):
     # to bias and LayerNorm scale parameters. decay_mask_fn returns a
     # mask boolean with the same structure as the parameters.
     # The mask is True for parameters that should be decayed.
+    # Note that this mask is specifically adapted for FlaxBart.
+    # For FlaxT5, one should correct the layer norm parameter naming
+    # accordingly - see `run_t5_mlm_flax.py` e.g.
     def decay_mask_fn(params):
         flat_params = traverse_util.flatten_dict(params)
-        flat_mask = {path: (path[-1] != "bias" and path[-2:] != ("LayerNorm", "scale")) for path in flat_params}
+        layer_norm_params = [
+            (name, "scale") for name in ["self_attn_layer_norm", "layernorm_embedding", "final_layer_norm"]
+        ]
+        flat_mask = {path: (path[-1] != "bias" and path[-2:] not in layer_norm_params) for path in flat_params}
         return traverse_util.unflatten_dict(flat_mask)
 
     # create adam optimizer