[bugfix][refactor]fix torchair_w8a8 (#2569)

### What this PR does / why we need it?
torchair w8a8 and w4a8 Separate from fused_moe due to the refactor and
change for fused_moe

### Does this PR introduce _any_ user-facing change?
NO

### How was this patch tested?
vLLM version: main
vLLM main:
ab9f2cfd19


- vLLM version: v0.10.1.1
- vLLM main:
69244e67e6

Signed-off-by: hust17yixuan <303660421@qq.com>

vllm_ascend/torchair/quantization/torchair_w4a8_dynamic.py

@@ -27,7 +27,7 @@ from vllm.forward_context import get_forward_context
 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.layers.experts_selector import select_experts
+from vllm_ascend.torchair.ops.torchair_fused_moe import torchair_select_experts
 from vllm_ascend.torchair.quantization.torchair_w8a8_dynamic import (
     torchair_fused_experts_with_all2all, torchair_fused_experts_with_mc2)
 from vllm_ascend.torchair.utils import npu_stream_switch, npu_wait_tensor
@@ -267,19 +267,34 @@ class TorchairAscendW4A8DynamicFusedMoEMethod:
         assert router_logits.shape[
             1] == global_num_experts, "Number of global experts mismatch"
 
-        # NOTE: now npu_moe_gating_top_k can only support `group_count=256` pattern
-        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)
+        if global_num_experts == 256:
+            topk_weights, topk_ids, _ = torch_npu.npu_moe_gating_top_k(
+                router_logits,
+                k=top_k,  # topk currently is 8
+                bias=e_score_correction_bias,
+                k_group=topk_group,  # fix: 4
+                group_count=num_expert_group,  # fix 8
+                group_select_mode=
+                1,  # 0: the maximum in the group; 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; should the third output be output
+                # y2_flag=False, # old api; should the third output be output
+                routed_scaling_factor=1,
+                eps=float(1e-20))
+        else:
+            topk_weights, topk_ids = torchair_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,
+            )
 
         fused_moe_state = get_forward_context().fused_moe_state
         shared_gate_up, shared_dequant_scale = None, None

vllm_ascend/torchair/quantization/torchair_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.layers.experts_selector import select_experts
+from vllm_ascend.torchair.ops.torchair_fused_moe import torchair_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)
@@ -904,18 +904,37 @@ class TorchairAscendW8A8DynamicFusedMoEMethod:
         assert router_logits.shape[
             1] == global_num_experts, "Number of global experts mismatch"
 
-        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)
+        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 currently is 8
+                bias=e_score_correction_bias,
+                k_group=topk_group,  # fix: 4
+                group_count=num_expert_group,  # fix 8
+                group_select_mode=
+                1,  # 0: the maximum in the group; 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; should the third output be output
+                # y2_flag=False, # old api; should the third output be output
+                routed_scaling_factor=1,
+                eps=float(1e-20))
+        else:
+            topk_weights, topk_ids = torchair_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,
+            )
 
         fused_moe_state = get_forward_context().fused_moe_state
         shared_gate_up, shared_dequant_scale = None, None