[EPLB]Fix ci.

Signed-off-by: offline0806 <z00858301@china.huawei.com>

Author: offline0806

vllm_ascend/eplb/core/eplb_utils.py

@@ -69,10 +69,9 @@ def generate_log2phy_map(expert_map):
         num_rank_holding_expert = positive_rank_idx.size(0)
 
         if num_rank_holding_expert == 0:
-            log2phy_map[:, idx] = torch.full(
-                (num_ranks,),
-                0,
-                dtype=log2phy_map.dtype)
+            log2phy_map[:, idx] = torch.full((num_ranks,),
+                                             0,
+                                             dtype=log2phy_map.dtype)
 
         if num_rank_holding_expert == 1:
             log2phy_map[negative_rank_idx, idx] = torch.full(
@@ -84,8 +83,9 @@ def generate_log2phy_map(expert_map):
                 random.choice(log2phy_map[positive_rank_idx, idx])
                 for _ in range(num_ranks - num_rank_holding_expert)
             ]
-            log2phy_map[negative_rank_idx, idx] = torch.tensor(random_list,
-                                                               dtype=log2phy_map.dtype)
+            log2phy_map[negative_rank_idx,
+            idx] = torch.tensor(random_list,
+                                dtype=log2phy_map.dtype)
 
     return log2phy_map
 

vllm_ascend/ops/fused_moe.py

@@ -443,8 +443,7 @@ def forward(self,
                           tuple) and len(e_hidden_states) == 2:
                 e_hidden_states, shared_hidden_states = e_hidden_states
 
-        if isinstance(e_hidden_states,
-                      tuple) and len(e_hidden_states) == 3:
+        if isinstance(e_hidden_states, tuple) and len(e_hidden_states) == 3:
             e_hidden_states, group_list_type, expert_tokens = e_hidden_states
 
         if self.dynamic_eplb: