[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/_310p/quantization/methods/w8a8_dynamic.py

@@ -25,6 +25,7 @@ from vllm.distributed import get_ep_group
 from vllm_ascend._310p.fused_moe.experts_selector import select_experts
 from vllm_ascend.ascend_forward_context import _EXTRA_CTX
 from vllm_ascend.ops.fused_moe.experts_selector import zero_experts_compute
+from vllm_ascend.ops.fused_moe.moe_runtime_args import build_fused_experts_input
 from vllm_ascend.quantization.methods.base import AscendMoEScheme, QuantType
 
 from .registry import register_scheme
@@ -95,7 +96,9 @@ class AscendW8A8DynamicFusedMoEMethod310(AscendMoEScheme):
         log2phy: torch.Tensor | None = None,
         global_redundant_expert_num: int = 0,
         pertoken_scale: Any | None = None,
-        **kwargs,
+        activation: str = "silu",
+        apply_router_weight_on_input: bool = False,
+        mc2_mask: torch.Tensor | None = None,
     ) -> torch.Tensor:
         zero_expert_num = getattr(layer, "zero_expert_num", 0)
         zero_expert_type = getattr(layer, "zero_expert_type", None)
@@ -128,15 +131,19 @@ class AscendW8A8DynamicFusedMoEMethod310(AscendMoEScheme):
         moe_comm_method = _EXTRA_CTX.moe_comm_method
 
         final_hidden_states = moe_comm_method.fused_experts(
-            hidden_states=x,
-            w1=layer.w13_weight,
-            w1_scale=layer.w13_weight_scale,
-            w2=layer.w2_weight,
-            w2_scale=layer.w2_weight_scale,
-            topk_weights=topk_weights,
-            topk_ids=topk_ids,
-            expert_map=expert_map,
-            use_int8_w8a8=True,
+            fused_experts_input=build_fused_experts_input(
+                hidden_states=x,
+                topk_weights=topk_weights,
+                topk_ids=topk_ids,
+                w1=layer.w13_weight,
+                w2=layer.w2_weight,
+                quant_type=self.quant_type,
+                dynamic_eplb=False,
+                expert_map=expert_map,
+                apply_router_weight_on_input=apply_router_weight_on_input,
+                w1_scale=layer.w13_weight_scale,
+                w2_scale=layer.w2_weight_scale,
+            ),
         )
         if zero_expert_num > 0 and zero_expert_type is not None:
             final_hidden_states += zero_expert_result