handle clean code

Signed-off-by: weijinqian_v1 <weijinqian@huawei.com>

Author: weijinqian

vllm_ascend/ops/fused_moe.py

@@ -517,8 +517,8 @@ def fused_experts_with_all2all_buffer(
         dtype=expert_idx_buffer_scatter.dtype,
         device=expert_idx_buffer_scatter.device,
     )
-    non_pad_len = torch.sum((expert_idx_buffer_scatter
-                             != global_num_experts).to(torch.int32))
+    non_pad_len = torch.sum(
+        (expert_idx_buffer_scatter != global_num_experts).to(torch.int32))
     hidden_states_pad_idx[expert_idx_buffer_scatter != global_num_experts] = (
         torch.arange(
             non_pad_len,
@@ -580,8 +580,8 @@ def fused_experts_with_all2all_buffer(
     dist.all_to_all_single(hidden_states_gatter,
                            hidden_states_scatter,
                            group=ep_group.device_group)
-    hidden_states_gatter = hidden_states_gatter[expert_idx_buffer_scatter !=
-                                                global_num_experts]
+    hidden_states_gatter = hidden_states_gatter[
+        expert_idx_buffer_scatter != global_num_experts]
     if hidden_states_gatter.shape[0] != row_idx_len:
         hidden_states = torch.zeros(
             (row_idx_len, hidden_states.shape[1]),
@@ -776,10 +776,9 @@ def fused_experts(
         # This created multiple NaN and index_add_ will mix them up which harms accuracy
         # remove this mask and filter after it being fixed
         num_valid_tokens = mask.sum()
-        valid_token_mask = (torch.arange(0,
-                                         sorted_token_indices.shape[0],
-                                         device=device).unsqueeze(1)
-                            < num_valid_tokens)
+        valid_token_mask = (torch.arange(
+            0, sorted_token_indices.shape[0], device=device).unsqueeze(1) <
+                            num_valid_tokens)
         valid_output = torch.where(
             valid_token_mask, weighted_down_out,
             torch.zeros_like(weighted_down_out)).to(dtype)