reformat code

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

Author: Liccol

tests/ut/quantization/test_w8a8.py

@@ -731,11 +731,11 @@ def test_softmax_scoring(self, mock_topk):
                                                                0).contiguous())
 
         weights, ids = select_experts(hidden_states=self.hidden_states,
-                                         router_logits=self.router_logits,
-                                         top_k=self.top_k,
-                                         use_grouped_topk=False,
-                                         renormalize=False,
-                                         scoring_func="softmax")
+                                      router_logits=self.router_logits,
+                                      top_k=self.top_k,
+                                      use_grouped_topk=False,
+                                      renormalize=False,
+                                      scoring_func="softmax")
 
         self.assertEqual(weights.shape, (self.num_tokens, self.top_k))
         self.assertEqual(ids.shape, (self.num_tokens, self.top_k))
@@ -744,11 +744,11 @@ def test_sigmoid_scoring(self):
         """Test sigmoid scoring function"""
 
         weights, ids = select_experts(hidden_states=self.hidden_states,
-                                         router_logits=self.router_logits,
-                                         top_k=self.top_k,
-                                         use_grouped_topk=False,
-                                         renormalize=False,
-                                         scoring_func="sigmoid")
+                                      router_logits=self.router_logits,
+                                      top_k=self.top_k,
+                                      use_grouped_topk=False,
+                                      renormalize=False,
+                                      scoring_func="sigmoid")
 
         self.assertEqual(weights.shape, (self.num_tokens, self.top_k))
         self.assertEqual(ids.shape, (self.num_tokens, self.top_k))
@@ -772,12 +772,12 @@ def test_grouped_topk(self, mock_topk):
                                               dtype=torch.long))
 
         weights, ids = select_experts(hidden_states=self.hidden_states,
-                                         router_logits=self.router_logits,
-                                         top_k=self.top_k,
-                                         use_grouped_topk=True,
-                                         renormalize=False,
-                                         topk_group=4,
-                                         num_expert_group=2)
+                                      router_logits=self.router_logits,
+                                      top_k=self.top_k,
+                                      use_grouped_topk=True,
+                                      renormalize=False,
+                                      topk_group=4,
+                                      num_expert_group=2)
 
         mock_topk.assert_called()
         self.assertEqual(weights.shape, (self.num_tokens, self.top_k))

vllm_ascend/ops/moe/experts_selector.py

@@ -176,7 +176,8 @@ def _select_experts_with_fusion_ops(
         norm_type = 1
     if e_score_correction_bias is not None and \
         e_score_correction_bias.dtype != router_logits.dtype:
-        e_score_correction_bias = e_score_correction_bias.to(router_logits.dtype)
+        e_score_correction_bias = e_score_correction_bias.to(
+            router_logits.dtype)
     topk_weights, topk_ids, _ = torch_npu.npu_moe_gating_top_k(
         router_logits,
         k=top_k,
@@ -267,4 +268,3 @@ def _native_select_experts(
     topk_weights = _renormalize_topk_weights(topk_weights, renormalize)
 
     return topk_weights, topk_ids
-