diff --git a/flash_attn/models/bert.py b/flash_attn/models/bert.py
index 1c013d0..88ac4d9 100644
--- a/flash_attn/models/bert.py
+++ b/flash_attn/models/bert.py
@@ -295,7 +295,7 @@ class BertModel(BertPreTrainedModel):
             config.vocab_size += (self.pad_vocab_size_multiple
                                   - (config.vocab_size % self.pad_vocab_size_multiple))
         self.fused_dropout_add_ln = getattr(config, 'fused_dropout_add_ln', False)
-        if self.fused_dropout_add_ln and dropout_add_layer_norm is None:
+        if self.fused_dropout_add_ln and layer_norm is None:
             raise ImportError('dropout_add_layer_norm is not installed')
         assert config.position_embedding_type == 'absolute'
         assert config.hidden_act in ['gelu', 'gelu_new', 'gelu_fast']
@@ -320,14 +320,13 @@ class BertModel(BertPreTrainedModel):
         hidden_states = self.embeddings(input_ids, position_ids=position_ids,
                                         token_type_ids=token_type_ids)
         # TD [2022-12:18]: Don't need to force residual in fp32
+        # BERT puts embedding LayerNorm before embedding dropout.
         if not self.fused_dropout_add_ln:
-            hidden_states = self.emb_drop(hidden_states)
             hidden_states = self.emb_ln(hidden_states)
         else:
-            hidden_states = dropout_add_layer_norm(
-                hidden_states, None, self.emb_ln.weight, self.emb_ln.bias,
-                self.emb_drop.p if self.training else 0.0, self.emb_ln.eps, prenorm=False,
-            )
+            hidden_states = layer_norm(hidden_states, self.emb_ln.weight, self.emb_ln.bias,
+                                       self.emb_ln.eps)
+        hidden_states = self.emb_drop(hidden_states)
 
         if masked_tokens_mask is not None:
             batch_size, seqlen = input_ids.shape[:2]
diff --git a/flash_attn/models/gpt.py b/flash_attn/models/gpt.py
index e746fc7..417fac9 100644
--- a/flash_attn/models/gpt.py
+++ b/flash_attn/models/gpt.py
@@ -220,6 +220,9 @@ class GPTModel(GPTPreTrainedModel):
 
         self.apply(partial(_init_weights, n_layer=config.num_hidden_layers,
                            initializer_range=config.initializer_range))
+        self.tie_weights()
+
+    def tie_weights(self):
         if self.process_group is not None:
             sync_sequence_parallel_params(self, self.process_group)
 
@@ -266,11 +269,11 @@ class GPTLMHeadModel(GPTPreTrainedModel, GenerationMixin):
         self.apply(partial(_init_weights, n_layer=config.num_hidden_layers,
                            initializer_range=config.initializer_range))
         self.tie_weights()
-        if self.process_group is not None:
-            sync_sequence_parallel_params(self, self.process_group)
 
     def tie_weights(self):
         self.lm_head.weight = self.transformer.embeddings.word_embeddings.weight
+        if self.process_group is not None:
+            sync_sequence_parallel_params(self, self.process_group)
 
     def forward(self, input_ids, position_ids=None, inference_params=None):
         """