Replace BertLayerNorm with LayerNorm (#14385)

Running Movement pruning experiments with the newest HuggingFace would crash due to non-existing BertLayerNorm.

examples/research_projects/movement-pruning/emmental/modeling_bert_masked.py

@@ -30,7 +30,7 @@ from emmental import MaskedBertConfig
 from emmental.modules import MaskedLinear
 from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
 from transformers.modeling_utils import PreTrainedModel, prune_linear_layer
-from transformers.models.bert.modeling_bert import ACT2FN, BertLayerNorm, load_tf_weights_in_bert
+from transformers.models.bert.modeling_bert import ACT2FN, load_tf_weights_in_bert
 
 
 logger = logging.getLogger(__name__)
@@ -47,7 +47,7 @@ class BertEmbeddings(nn.Module):
 
         # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
         # any TensorFlow checkpoint file
-        self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
         self.dropout = nn.Dropout(config.hidden_dropout_prob)
 
     def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None):
@@ -182,7 +182,7 @@ class BertSelfOutput(nn.Module):
             mask_init=config.mask_init,
             mask_scale=config.mask_scale,
         )
-        self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
         self.dropout = nn.Dropout(config.hidden_dropout_prob)
 
     def forward(self, hidden_states, input_tensor, threshold):
@@ -275,7 +275,7 @@ class BertOutput(nn.Module):
             mask_init=config.mask_init,
             mask_scale=config.mask_scale,
         )
-        self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
         self.dropout = nn.Dropout(config.hidden_dropout_prob)
 
     def forward(self, hidden_states, input_tensor, threshold):
@@ -398,7 +398,7 @@ class MaskedBertPreTrainedModel(PreTrainedModel):
             # Slightly different from the TF version which uses truncated_normal for initialization
             # cf https://github.com/pytorch/pytorch/pull/5617
             module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-        elif isinstance(module, BertLayerNorm):
+        elif isinstance(module, nn.LayerNorm):
             module.bias.data.zero_()
             module.weight.data.fill_(1.0)
         if isinstance(module, nn.Linear) and module.bias is not None: