diff --git a/tests/kit/model_zoo/transformers/chatglm2_6b/modeling_chatglm.py b/tests/kit/model_zoo/transformers/chatglm2_6b/modeling_chatglm.py
index f704715e1..46078f441 100644
--- a/tests/kit/model_zoo/transformers/chatglm2_6b/modeling_chatglm.py
+++ b/tests/kit/model_zoo/transformers/chatglm2_6b/modeling_chatglm.py
@@ -396,17 +396,18 @@ class SelfAttention(torch.nn.Module):
             self.num_multi_query_groups_per_partition = config.multi_query_group_num
             self.qkv_hidden_size = (self.projection_size +
                                     2 * self.hidden_size_per_attention_head * config.multi_query_group_num)
+<<<<<<< HEAD
         self.query_key_value = nn.Linear(
             config.hidden_size,
             self.qkv_hidden_size,
-            bias=config.add_bias_linear or config.add_qkv_bias,
             device=device,
             **_config_to_kwargs(config),
         )
-
-        self.core_attention = CoreAttention(config, self.layer_number)
-
-        # Output.
+=======
+        self.query_key_value = nn.Linear(self.hidden_size,
+                                         self.qkv_hidden_size,
+                                         bias=config.add_bias_linear or config.add_qkv_bias,
+<<<<<<< HEAD
         self.dense = nn.Linear(
             self.projection_size,
             config.hidden_size,
@@ -414,6 +415,13 @@ class SelfAttention(torch.nn.Module):
             device=device,
             **_config_to_kwargs(config),
         )
+=======
+        self.dense = nn.Linear(self.projection_size,
+                               self.hidden_size,
+                               bias=config.add_bias_linear,
+                               device=device,
+                               **_config_to_kwargs(config))
+>>>>>>> [shardformer] support chatglm without layernorm
 
     def _allocate_memory(self, inference_max_sequence_len, batch_size, device=None, dtype=None):
         if self.multi_query_attention:
@@ -925,7 +933,6 @@ class ChatGLMModel(ChatGLMPreTrainedModel):
 
         if inputs_embeds is None:
             inputs_embeds = self.embedding(input_ids)
-        print(inputs_embeds)
 
         if self.pre_seq_len is not None:
             if past_key_values is None: