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>

vllm_ascend/quantization/w8a8_dynamic.py

@@ -27,7 +27,7 @@ import vllm_ascend.envs as envs_ascend
 from vllm_ascend.ascend_config import get_ascend_config
 from vllm_ascend.ascend_forward_context import FusedMoEState
 from vllm_ascend.distributed.parallel_state import get_mc2_group
-from vllm_ascend.ops.fused_moe import select_experts
+from vllm_ascend.ops.layers.experts_selector import select_experts
 from vllm_ascend.torchair.utils import npu_stream_switch, npu_wait_tensor
 from vllm_ascend.utils import (ACL_FORMAT_FRACTAL_NZ, AscendSocVersion,
                                dispose_tensor, get_ascend_soc_version)
@@ -903,36 +903,18 @@ class AscendW8A8DynamicFusedMoEMethod:
         assert router_logits.shape[
             1] == global_num_experts, "Number of global experts mismatch"
 
-        is_deepseek_v3_r1 = global_num_experts == 256
-
-        # NOTE: now npu_moe_gating_top_k can only support `group_count=256` pattern
-        if is_deepseek_v3_r1:
-            topk_weights, topk_ids, _ = torch_npu.npu_moe_gating_top_k(
-                router_logits,
-                k=top_k,  # topk当前写8
-                bias=e_score_correction_bias,
-                k_group=topk_group,  # fix: 4
-                group_count=num_expert_group,  # fix 8
-                group_select_mode=1,  # 0: group中的最大; 1: topk2.sum(fix)
-                renorm=0,  # 0: softmax->topk(fix); 1: topk->softmax
-                norm_type=1,  # 0: softmax; 1: sigmoid(fix)
-                # out_flag=False, # todo new api; 第三个输出是否输出
-                # y2_flag=False, # old api; 第三个输出是否输出
-                routed_scaling_factor=1,
-                eps=float(1e-20))
-        else:
-            topk_weights, topk_ids = select_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(
+            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,
+            global_num_experts=global_num_experts)
 
         fused_moe_state = get_forward_context().fused_moe_state
         shared_gate_up, shared_dequant_scale = None, None
@@ -995,7 +977,7 @@ class AscendW8A8DynamicFusedMoEMethod:
                                  expert_map=expert_map)
         else:
             # The current implementation of deepseek moe splits hidden_states
-            # according to tp_size before they are feed into fused_moe module.
+            # according to tp_size before they are feed into layers module.
             # Therefore, all2all is needed no matter how dp/tp is set so as to
             # dispatch/combine tokens.
             return fused_experts_with_all2all(