[refactor] replace scattered business kwargs with typed request objects and explicit stage boundaries (#7024)

### What this PR does / why we need it?
Refactor `vllm_ascend/ops/fused_moe` to replace scattered MoE business
`**kwargs` with typed request objects and explicit stage boundaries.

- Prepare, dispatch, MLP, and quant stages now have clearer ownership.
- Main MoE path no longer depends on business `kwargs.get(...)` lookups.
- Comm and dispatcher interfaces are request-only on the main path.
- UTs can assert stage-level fields directly instead of inferring
behavior indirectly.

### Does this PR introduce _any_ user-facing change?
No.

### How was this patch tested?
CI passed.

---------

Signed-off-by: linfeng-yuan <1102311262@qq.com>

vllm_ascend/quantization/method_adapters.py

@@ -255,28 +255,34 @@ class AscendFusedMoEMethod(FusedMoEMethodBase):
         enable_force_load_balance: bool = False,
         log2phy: torch.Tensor | None = None,
         global_redundant_expert_num=0,
-        **kwargs,
+        pertoken_scale: torch.Tensor | None = None,
+        activation: str = "silu",
+        apply_router_weight_on_input: bool = False,
+        mc2_mask: torch.Tensor | None = None,
     ) -> torch.Tensor:
         return self.quant_method.apply(
-            layer,
-            x,
-            router_logits,
-            top_k,
-            renormalize,
-            use_grouped_topk,
-            global_num_experts,
-            expert_map,
-            topk_group,
-            num_expert_group,
-            custom_routing_function,
-            scoring_func,
-            routed_scaling_factor,
-            e_score_correction_bias,
-            is_prefill,
-            enable_force_load_balance,
-            log2phy,
-            global_redundant_expert_num,
-            **kwargs,
+            layer=layer,
+            x=x,
+            router_logits=router_logits,
+            top_k=top_k,
+            renormalize=renormalize,
+            use_grouped_topk=use_grouped_topk,
+            global_num_experts=global_num_experts,
+            expert_map=expert_map,
+            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,
+            is_prefill=is_prefill,
+            enable_force_load_balance=enable_force_load_balance,
+            log2phy=log2phy,
+            global_redundant_expert_num=global_redundant_expert_num,
+            pertoken_scale=pertoken_scale,
+            activation=activation,
+            apply_router_weight_on_input=apply_router_weight_on_input,
+            mc2_mask=mc2_mask,
         )
 
     def process_weights_after_loading(self, layer: torch.nn.Module) -> None: