[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/methods/w4a8.py

@@ -28,6 +28,7 @@ from vllm_ascend.ascend_config import get_ascend_config
 from vllm_ascend.ascend_forward_context import _EXTRA_CTX
 from vllm_ascend.distributed.parallel_state import get_mc2_group
 from vllm_ascend.ops.fused_moe.experts_selector import select_experts
+from vllm_ascend.ops.fused_moe.moe_runtime_args import build_fused_experts_input
 from vllm_ascend.utils import COMPRESSED_TENSORS_METHOD, maybe_trans_nz
 
 from .base import AscendLinearScheme, AscendMoEScheme, QuantType
@@ -343,7 +344,10 @@ class AscendW4A8DynamicFusedMoEMethod(AscendMoEScheme):
         enable_force_load_balance: bool = False,
         log2phy: torch.Tensor | None = None,
         global_redundant_expert_num: int = 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:
         assert router_logits.shape[1] == global_num_experts - global_redundant_expert_num, (
             "Number of global experts mismatch (excluding redundancy)"
@@ -377,20 +381,26 @@ class AscendW4A8DynamicFusedMoEMethod(AscendMoEScheme):
 
         moe_comm_method = _EXTRA_CTX.moe_comm_method
         return moe_comm_method.fused_experts(
-            hidden_states=x,
-            w1=[layer.w13_weight],
-            w2=[layer.w2_weight],
-            w1_scale=[layer.w13_weight_scale],
-            w2_scale=[layer.w2_weight_scale],
-            w1_scale_bias=layer.w13_scale_bias if hasattr(layer, "w13_scale_bias") else None,
-            w2_scale_bias=layer.w2_scale_bias if hasattr(layer, "w2_scale_bias") else None,
-            topk_weights=topk_weights,
-            topk_ids=topk_ids,
-            use_int4_w4a8=True,
-            expert_map=expert_map,
-            log2phy=log2phy,
-            dynamic_eplb=self.dynamic_eplb,
-            mc2_mask=kwargs.get("mc2_mask"),
+            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=self.dynamic_eplb,
+                expert_map=expert_map,
+                global_redundant_expert_num=global_redundant_expert_num,
+                mc2_mask=mc2_mask,
+                apply_router_weight_on_input=apply_router_weight_on_input,
+                log2phy=log2phy,
+                pertoken_scale=pertoken_scale,
+                activation=activation,
+                w1_scale=[layer.w13_weight_scale],
+                w2_scale=[layer.w2_weight_scale],
+                w1_scale_bias=layer.w13_scale_bias if hasattr(layer, "w13_scale_bias") else None,
+                w2_scale_bias=layer.w2_scale_bias if hasattr(layer, "w2_scale_bias") else None,
+            )
         )
 
     def process_scale(self, weight: torch.Tensor, scale, per_group_scale):