fix lint

Signed-off-by: wangxiyuan <[email protected]>

Author: wangxiyuan

vllm_ascend/models/deepseek_v2.py

@@ -148,54 +148,47 @@ def __init__(
         self.enable_shared_expert_dp = ascend_config.enable_shared_expert_dp
 
         if self.q_lora_rank is not None:
-            self.q_a_proj = ReplicatedLinear(
-                self.hidden_size,
-                self.q_lora_rank,
-                bias=False,
-                quant_config=quant_config,
-                prefix=f"{prefix}.q_a_proj"
-            )
+            self.q_a_proj = ReplicatedLinear(self.hidden_size,
+                                             self.q_lora_rank,
+                                             bias=False,
+                                             quant_config=quant_config,
+                                             prefix=f"{prefix}.q_a_proj")
             self.q_a_layernorm = RMSNorm(self.q_lora_rank,
                                          eps=config.rms_norm_eps)
-            self.q_b_proj = ColumnParallelLinear(
-                q_lora_rank,
-                self.num_heads * self.qk_head_dim,
-                bias=False,
-                quant_config=quant_config,
-                prefix=f"{prefix}.q_b_proj"
-            )
+            self.q_b_proj = ColumnParallelLinear(q_lora_rank,
+                                                 self.num_heads *
+                                                 self.qk_head_dim,
+                                                 bias=False,
+                                                 quant_config=quant_config,
+                                                 prefix=f"{prefix}.q_b_proj")
         else:
-            self.q_proj = ColumnParallelLinear(
-                self.hidden_size,
-                self.num_heads * self.qk_head_dim,
-                bias=False,
-                quant_config=quant_config,
-                prefix=f"{prefix}.q_proj"
-            )
+            self.q_proj = ColumnParallelLinear(self.hidden_size,
+                                               self.num_heads *
+                                               self.qk_head_dim,
+                                               bias=False,
+                                               quant_config=quant_config,
+                                               prefix=f"{prefix}.q_proj")
 
         self.kv_a_proj_with_mqa = ReplicatedLinear(
             self.hidden_size,
             self.kv_lora_rank + self.qk_rope_head_dim,
             bias=False,
             quant_config=quant_config,
-            prefix=f"{prefix}.kv_a_proj_with_mqa"
-        )
+            prefix=f"{prefix}.kv_a_proj_with_mqa")
         self.kv_a_layernorm = RMSNorm(self.kv_lora_rank,
                                       eps=config.rms_norm_eps)
         self.kv_b_proj = ColumnParallelLinear(
             self.kv_lora_rank,
             self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
             bias=False,
             quant_config=quant_config,
-            prefix=f"{prefix}.kv_b_proj"
-        )
+            prefix=f"{prefix}.kv_b_proj")
         self.o_proj = CustomDeepseekV2RowParallelLinear(
             self.num_heads * self.v_head_dim,
             self.hidden_size,
             bias=False,
             quant_config=quant_config,
-            prefix=f"{prefix}.o_proj"
-        )
+            prefix=f"{prefix}.o_proj")
 
         if rope_scaling:
             rope_scaling["rope_type"] = 'deepseek_yarn'