refactor select_experts of moe module (#2150)

### What this PR does / why we need it?
this pr refactor select_experts of moe module
i merge implementations of quantitative and non-quantitative method in a
new class
use such as vllm like ExpertsSelector.select_experts
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
test in qwen3-moe and all ut.

- vLLM version: v0.10.0
- vLLM main:
e18859298d

Signed-off-by: yangcheng <yangcheng104@huawei.com>
Co-authored-by: yangcheng (AJ) <y00806874@china.huawei.com>

tests/ut/ops/test_fused_ops.py

@@ -25,6 +25,7 @@ from vllm.model_executor.layers.fused_moe import FusedMoEMethodBase
 from vllm_ascend.ascend_forward_context import _get_fused_moe_state
 from vllm_ascend.ops.fused_moe import (AscendFusedMoE,
                                        AscendUnquantizedFusedMoEMethod)
+from vllm_ascend.ops.layers.experts_selector import select_experts
 from vllm_ascend.utils import AscendSocVersion, adapt_patch  # noqa E402
 
 adapt_patch(True)
@@ -389,3 +390,28 @@ class TestAscendUnquantizedFusedMoEMethod:
                 assert result.shape == (16, 2)
             else:
                 assert result.shape == x.shape
+
+
+class TestExpertsSelector:
+
+    @pytest.mark.parametrize("global_num_experts", [[256], [128]])
+    def test_select_experts(self, mock_dist_env, mock_moe_env,
+                            global_num_experts):
+
+        x = torch.randn(8, 2)
+        router_logits = torch.randn(8, 2)
+        topk_weights, topk_ids = select_experts(
+            hidden_states=x,
+            router_logits=router_logits,
+            top_k=2,
+            use_grouped_topk=False,
+            renormalize=True,
+            topk_group=None,
+            num_expert_group=None,
+            custom_routing_function=None,
+            scoring_func="softmax",
+            e_score_correction_bias=None,
+            global_num_experts=global_num_experts)
+
+        assert topk_weights.shape == (8, 2)
+        assert topk_ids.shape == (8, 2)