[main] adapt usage of npu_moe_gating_top_k_softmax and remove envs.SELECT_GATING_TOPK_SOTFMAX_EXPERTS (#2112)

backport of v0.9.1-dev:
https://github.com/vllm-project/vllm-ascend/pull/1902

origin main npu_moe_gating_top_k_softmax:
https://github.com/vllm-project/vllm-ascend/pull/1355

- vLLM version: v0.10.0
- vLLM main:
055bd3978e

Signed-off-by: huangxialu <huangxialu1@huawei.com>

vllm_ascend/ops/common_fused_moe.py

@@ -22,13 +22,10 @@ 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_gating_top_k_softmax_experts)
+                                       select_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__
 
 
@@ -61,26 +58,19 @@ def forward_oot(
         logical_to_physical_map: Optional[torch.Tensor] = None,
         logical_replica_count: Optional[torch.Tensor] = None) -> torch.Tensor:
 
-    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,
-        )
+    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