[shardformer] pre-commit check files

tests/kit/model_zoo/transformers/chatglm2_6b/modeling_chatglm.py

@@ -396,18 +396,17 @@ 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.query_key_value = nn.Linear(self.hidden_size,
-                                         self.qkv_hidden_size,
-                                         bias=config.add_bias_linear or config.add_qkv_bias,
-<<<<<<< HEAD
+
+        self.core_attention = CoreAttention(config, self.layer_number)
+
+        # Output.
         self.dense = nn.Linear(
             self.projection_size,
             config.hidden_size,
@@ -415,13 +414,6 @@ class SelfAttention(torch.nn.Module):
             device=device,
             **_config_to_kwargs(config),
         )
-=======
-        self.dense = nn.Linear(self.projection_size,
-                               config.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:
@@ -989,6 +981,7 @@ class ChatGLMModel(ChatGLMPreTrainedModel):
 
     def quantize(self, weight_bit_width: int):
         from .quantization import quantize
+
         quantize(self.encoder, weight_bit_width)
         return self