format fix

Author: mercykid

vllm_ascend/eplb/adaptor/vllm_adaptor.py

@@ -194,13 +194,12 @@ def _export_tensor_to_file(self, expert_maps, expert_map_record_path: str):
                 json.dump(record, f, indent=4)
 
     def do_update_expert_map(self, layer_id, updated_expert_map):
-        pad_len = self.expert_map_per_layer[layer_id].shape[0] - updated_expert_map.shape[0]
-        updated_expert_map_padded = torch.nn.functional.pad(
-                                    updated_expert_map,
-                                    pad=(0,pad_len),
-                                    mode='constant',
-                                    value=-1
-                                    )
+        pad_len = self.expert_map_per_layer[layer_id].shape[
+            0] - updated_expert_map.shape[0]
+        updated_expert_map_padded = torch.nn.functional.pad(updated_expert_map,
+                                                            pad=(0, pad_len),
+                                                            mode='constant',
+                                                            value=-1)
         self.expert_map_per_layer[layer_id].copy_(updated_expert_map_padded)
         self.expert_map_per_layer_cpu[layer_id].copy_(updated_expert_map)
 
@@ -214,14 +213,15 @@ def do_update_expert_weight(self, layer_id, local_expert_to_replace,
 
     def do_update_log2phy_map(self, layer_id, updated_log2phy_map):
         if self.log2phy_map_per_layer[layer_id] is not None:
-            pad_len = self.log2phy_map_per_layer[layer_id].shape[0] - updated_log2phy_map.shape[0]
+            pad_len = self.log2phy_map_per_layer[layer_id].shape[
+                0] - updated_log2phy_map.shape[0]
             updated_log2phy_map_padded = torch.nn.functional.pad(
-                                        updated_log2phy_map,
-                                        pad=(0,pad_len),
-                                        mode='constant',
-                                        value=-1
-                                        )
-            self.log2phy_map_per_layer[layer_id].copy_(updated_log2phy_map_padded)
+                updated_log2phy_map,
+                pad=(0, pad_len),
+                mode='constant',
+                value=-1)
+            self.log2phy_map_per_layer[layer_id].copy_(
+                updated_log2phy_map_padded)
 
     def global2local(self, placement: torch.Tensor,
                      E_local: int) -> torch.Tensor:

vllm_ascend/ops/moe/moe_mlp.py

@@ -105,8 +105,9 @@ def quant_apply_mlp(hidden_states: torch.Tensor,
                 group_list=group_list,
                 output_dtype=torch.int32)[0]
             # act_fn: swiglu
-            group_diff = torch.diff( group_list)
-            new_group = torch.cat( [ group_list[0].unsqueeze(0), group_diff ],dim=0)
+            group_diff = torch.diff(group_list)
+            new_group = torch.cat([group_list[0].unsqueeze(0), group_diff],
+                                  dim=0)
             hidden_states, swiglu_out_scale = torch_npu.npu_dequant_swiglu_quant(
                 x=hidden_states,
                 weight_scale=w1_scale,