[CI] Fix CI Break: upstream adds routed_scaling_factor in forward_oot interface (#2675)

### What this PR does / why we need it?
Fix CI Break: upstream adds routed_scaling_factor in forward_oot
interface, vllm-ascend needs to adapt

### Does this PR introduce _any_ user-facing change?
NA

### How was this patch tested?
E2E and UT

- vLLM version: v0.10.1.1
- vLLM main:
3e330fcb21

Signed-off-by: leo-pony <nengjunma@outlook.com>

vllm_ascend/ops/common_fused_moe.py

@@ -35,7 +35,7 @@ from vllm_ascend.distributed.parallel_state import get_mc2_group
 from vllm_ascend.ops.layers.experts_selector import select_experts
 from vllm_ascend.ops.moe_dispatcher.token_dispatcher import \
     setup_token_dispatchers
-from vllm_ascend.utils import ACL_FORMAT_FRACTAL_NZ, is_310p
+from vllm_ascend.utils import ACL_FORMAT_FRACTAL_NZ, is_310p, vllm_version_is
 
 original_unquantized_fused_moe_init_func = UnquantizedFusedMoEMethod.__init__
 
@@ -246,7 +246,7 @@ def unquantized_fused_moe_init_func(self, *args, **kwargs):
                              and not vllm_config.model_config.enforce_eager)
 
 
-def forward_oot(
+def forward_oot_v01011(
         self,
         layer: torch.nn.Module,
         x: torch.Tensor,
@@ -278,6 +278,69 @@ def forward_oot(
         num_expert_group=num_expert_group,
         custom_routing_function=custom_routing_function,
         scoring_func=scoring_func,
+        routed_scaling_factor=1.0,
+        e_score_correction_bias=e_score_correction_bias,
+        global_num_experts=global_num_experts)
+
+    if topk_ids.shape[1] < top_k or is_310p():
+        assert global_num_experts is not None
+        return fused_experts_moge(
+            hidden_states=x,
+            w1=layer.w13_weight,
+            w2=layer.w2_weight,
+            moe_parallel_config=self.moe.moe_parallel_config,
+            topk_weights=topk_weights,
+            topk_ids=topk_ids,
+            top_k=top_k,
+            global_num_experts=global_num_experts,
+            expert_map=expert_map,
+            apply_router_weight_on_input=apply_router_weight_on_input)
+
+    return fused_experts(
+        hidden_states=x,
+        w1=layer.w13_weight,
+        w2=layer.w2_weight,
+        topk_weights=topk_weights,
+        topk_ids=topk_ids,
+        global_num_experts=global_num_experts,
+        expert_map=expert_map,
+    )
+
+
+def forward_oot(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        use_grouped_topk: bool,
+        top_k: int,
+        router_logits: torch.Tensor,
+        renormalize: bool,
+        topk_group: Optional[int] = None,
+        num_expert_group: Optional[int] = None,
+        custom_routing_function: Optional[Callable] = None,
+        scoring_func: str = "softmax",
+        routed_scaling_factor: float = 1.0,
+        e_score_correction_bias: Optional[torch.Tensor] = None,
+        global_num_experts: int = -1,
+        expert_map: Optional[torch.Tensor] = None,
+        apply_router_weight_on_input: bool = False,
+        activation: str = "silu",
+        enable_eplb: bool = False,
+        expert_load_view: Optional[torch.Tensor] = None,
+        logical_to_physical_map: Optional[torch.Tensor] = None,
+        logical_replica_count: Optional[torch.Tensor] = None) -> torch.Tensor:
+
+    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,
+        routed_scaling_factor=routed_scaling_factor,
         e_score_correction_bias=e_score_correction_bias,
         global_num_experts=global_num_experts)
 
@@ -441,4 +504,8 @@ class AscendFusedMoE(FusedMoE):
 
 UnquantizedFusedMoEMethod.__init__ = unquantized_fused_moe_init_func
 UnquantizedFusedMoEMethod.process_weights_after_loading = process_weights_after_loading
-UnquantizedFusedMoEMethod.forward_oot = forward_oot
+
+if vllm_version_is("0.10.1.1") or vllm_version_is("0.10.1"):
+    UnquantizedFusedMoEMethod.forward_oot = forward_oot_v01011
+else:
+    UnquantizedFusedMoEMethod.forward_oot = forward_oot

vllm_ascend/ops/layers/experts_selector.py

@@ -40,6 +40,7 @@ def select_experts(hidden_states: torch.Tensor,
                    num_expert_group: Optional[int] = None,
                    custom_routing_function: Optional[Callable] = None,
                    scoring_func: str = "softmax",
+                   routed_scaling_factor=1.0,
                    e_score_correction_bias: Optional[torch.Tensor] = None,
                    indices_type: Optional[torch.dtype] = None,
                    is_unquantized: bool = False,
@@ -78,6 +79,7 @@ def select_experts(hidden_states: torch.Tensor,
         num_expert_group=num_expert_group,
         custom_routing_function=custom_routing_function,
         scoring_func=scoring_func,
+        routed_scaling_factor=routed_scaling_factor,
         global_num_experts=global_num_experts,
         is_unquantized=is_unquantized)
 
@@ -180,6 +182,7 @@ def _select_experts_with_fusion_ops(
         num_expert_group: Optional[int],
         custom_routing_function: Optional[Callable] = None,
         scoring_func: str = "softmax",
+        routed_scaling_factor=1.0,
         global_num_experts: int = -1,
         is_unquantized: bool = False):