use npu_moe_gating_top_k_softmax (#1355)

### What this PR does / why we need it?
The optimization solution for non-deepseek select_experts is to replace
gating_topk_softmax with softmax+topk+to, which is optimized from 37us
to 14us on bf16/fp16 of qwen3-235b

- vLLM version: v0.9.2
- vLLM main:
1a4f35e2ea

---------

Signed-off-by: ttanzhiqiang <389825161@qq.com>

vllm_ascend/ops/common_fused_moe.py

@@ -22,10 +22,13 @@ from vllm.config import CompilationLevel, get_current_vllm_config
 from vllm.model_executor.layers.fused_moe.layer import \
     UnquantizedFusedMoEMethod
 
+import vllm_ascend.envs as envs_ascend
 from vllm_ascend.ops.fused_moe import (fused_experts, fused_experts_moge,
-                                       select_experts)
+                                       select_experts,
+                                       select_gating_top_k_softmax_experts)
 from vllm_ascend.utils import is_310p
 
+SELECT_GATING_TOPK_SOTFMAX_EXPERTS: bool = envs_ascend.SELECT_GATING_TOPK_SOTFMAX_EXPERTS
 original_unquantized_fused_moe_init_func = UnquantizedFusedMoEMethod.__init__
 
 
@@ -54,19 +57,27 @@ def forward_oot(
     apply_router_weight_on_input: bool = False,
     activation: str = "silu",
 ) -> torch.Tensor:
-    topk_weights, topk_ids = select_experts(
-        global_num_experts=global_num_experts,
-        hidden_states=x,
-        router_logits=router_logits,
-        top_k=top_k,
-        use_grouped_topk=use_grouped_topk,
-        renormalize=renormalize,
-        topk_group=topk_group,
-        num_expert_group=num_expert_group,
-        custom_routing_function=custom_routing_function,
-        scoring_func=scoring_func,
-        e_score_correction_bias=e_score_correction_bias,
-    )
+
+    if SELECT_GATING_TOPK_SOTFMAX_EXPERTS:
+        topk_weights, topk_ids = select_gating_top_k_softmax_experts(
+            hidden_states=x,
+            router_logits=router_logits,
+            top_k=top_k,
+            renormalize=renormalize)
+    else:
+        topk_weights, topk_ids = select_experts(
+            global_num_experts=global_num_experts,
+            hidden_states=x,
+            router_logits=router_logits,
+            top_k=top_k,
+            use_grouped_topk=use_grouped_topk,
+            renormalize=renormalize,
+            topk_group=topk_group,
+            num_expert_group=num_expert_group,
+            custom_routing_function=custom_routing_function,
+            scoring_func=scoring_func,
+            e_score_correction_bias=e_score_correction_bias,
+        )
 
     if topk_ids.shape[1] < top_k or is_310p():
         assert global_num_experts is not None