Fix precommit formatting

Signed-off-by: Po-Han Huang <[email protected]>

Author: nvpohanh

vllm/model_executor/layers/fused_moe/layer.py

@@ -874,15 +874,12 @@ def _load_per_tensor_weight_scale(self, shard_id: str,
         elif shard_id == "w2":
             param_data[expert_id] = loaded_weight
 
-    def _load_w13_weight_scale(self,
-                            shard_dim: int,
-                            loaded_weight: torch.Tensor,
-                            param: torch.Tensor,
-                            tp_rank: int):
+    def _load_w13_weight_scale(self, shard_dim: int,
+                               loaded_weight: torch.Tensor,
+                               param: torch.Tensor, tp_rank: int):
         shard_size = param.shape[shard_dim]
-        loaded_weight = loaded_weight.narrow(shard_dim,
-                                            shard_size * tp_rank,
-                                            shard_size)
+        loaded_weight = loaded_weight.narrow(shard_dim, shard_size * tp_rank,
+                                             shard_size)
         param.copy_(loaded_weight)
 
     def _load_model_weight_or_group_weight_scale(self,
@@ -1135,12 +1132,10 @@ def weight_loader(self,
                 "weight_scale" in weight_name) or "input_scale" in weight_name
 
             if "w13_weight_scale" in weight_name:
-                self._load_w13_weight_scale(
-                    shard_dim=shard_dim,
-                    loaded_weight=loaded_weight,
-                    param=param,
-                    tp_rank=self.tp_rank
-                )
+                self._load_w13_weight_scale(shard_dim=shard_dim,
+                                            loaded_weight=loaded_weight,
+                                            param=param,
+                                            tp_rank=self.tp_rank)
             elif per_tensor_conditions:
                 self._load_per_tensor_weight_scale(
                     shard_id=shard_id,

vllm/model_executor/models/llama4.py

@@ -585,18 +585,15 @@ def permute_qk_weight_for_rotary(
         # Helper function to permute the weight's channels
         def permute(w: torch.Tensor, n_heads: int):
             head_dim = w.shape[0] // n_heads
-            return (
-                w.view(n_heads, head_dim // 2, 2, w.shape[1])
-                .transpose(1, 2)
-                .reshape(w.shape[0], w.shape[1])
-            )
+            return (w.view(n_heads, head_dim // 2, 2, w.shape[1]).transpose(
+                1, 2).reshape(w.shape[0], w.shape[1]))
 
         modules = name.split(".")
 
         # Permute Q/K weights and weight block scales for rotary embedding
         is_weight = modules[-1] == "weight"
-        is_nvfp4_weight_scale = (modules[-1] == "weight_scale"
-            and loaded_weight.dtype == torch.float8_e4m3fn)
+        is_nvfp4_weight_scale = (modules[-1] == "weight_scale" and
+                                 loaded_weight.dtype == torch.float8_e4m3fn)
 
         if is_weight or is_nvfp4_weight_scale:
             if ("wk" in modules or "k_proj" in modules):