[refactor]support gatingtopk operator generalization (#4356)

### What this PR does / why we need it?
This pr is cherry-pick from :
https://github.com/vllm-project/vllm-ascend/pull/2958 and
https://github.com/vllm-project/vllm-ascend/pull/4340

Past：
npu_moe_gating_top_k can only support 'group_count=256' pattern

Now：
1、npu_moe_gating_top_k support all size of group_count
2、the functionality of `torch_npu.npu_moe_gating_top_k_softmax` are
included in `torch_npu.npu_moe_gating_top_k`

CANN: depends on 8.3.RC1

Performance：
1. GLM4.5-w8a8, TPS improve 6%
2. Qwen3, the same as before

---------

Signed-off-by: 1092626063 <1092626063@qq.com>

tests/e2e/singlecard/ops/test_fused_moe.py

@@ -28,7 +28,8 @@ import torch
 import torch_npu
 from vllm.model_executor.layers.activation import SiluAndMul
 
-from vllm_ascend.ops.moe.experts_selector import select_experts
+from vllm_ascend.ops.moe.experts_selector import (check_npu_moe_gating_top_k,
+                                                  select_experts)
 from vllm_ascend.ops.moe.moe_mlp import unified_apply_mlp
 from vllm_ascend.ops.moe.token_dispatcher import TokenDispatcherWithAllGather
 
@@ -296,7 +297,10 @@ def test_select_experts(
             e_score_correction_bias=e_score_correction_bias,
         )
 
-        if use_grouped_topk:
+        call_moe_gatingtopk = check_npu_moe_gating_top_k(
+            hidden_states, topk, topk_group, num_expert_group, scoring_func,
+            custom_routing_function)
+        if not call_moe_gatingtopk and use_grouped_topk:
             mock_native_grouped_topk.assert_called_once()
         else:
             mock_native_grouped_topk.assert_not_called()