[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/fused_moe/moe_mlp.py

@@ -18,6 +18,8 @@
 import torch
 import torch_npu
 
+from vllm_ascend.ops.fused_moe.moe_runtime_args import MoEMlpComputeInput
+
 
 def quant_apply_mlp(
     hidden_states: torch.Tensor,
@@ -66,17 +68,20 @@ def unquant_apply_mlp(
     return hidden_states
 
 
-def unified_apply_mlp(
-    hidden_states: torch.Tensor,
-    w1: torch.Tensor,
-    w2: torch.Tensor,
-    group_list: torch.Tensor,
-    w1_scale: torch.Tensor | None = None,
-    w2_scale: torch.Tensor | None = None,
-    group_list_type: int = 1,
-    with_quant: bool = False,
-) -> torch.Tensor:
-    if with_quant:
+def unified_apply_mlp(*, mlp_compute_input: MoEMlpComputeInput) -> torch.Tensor:
+    hidden_states = mlp_compute_input.hidden_states
+    w1 = mlp_compute_input.weights.w1
+    w2 = mlp_compute_input.weights.w2
+    w1_scale = mlp_compute_input.weights.w1_scale
+    w2_scale = mlp_compute_input.weights.w2_scale
+    group_list = mlp_compute_input.group_list
+    group_list_type = mlp_compute_input.group_list_type
+    assert isinstance(w1, torch.Tensor)
+    assert isinstance(w2, torch.Tensor)
+
+    if mlp_compute_input.quant.is_quant:
+        assert isinstance(w1_scale, torch.Tensor)
+        assert isinstance(w2_scale, torch.Tensor)
         assert w1_scale is not None and w2_scale is not None
         return quant_apply_mlp(
             hidden_states=hidden_states,
@@ -87,7 +92,11 @@ def unified_apply_mlp(
             group_list=group_list,
             group_list_type=group_list_type,
         )
-    else:
-        return unquant_apply_mlp(
-            hidden_states=hidden_states, w1=w1, w2=w2, group_list=group_list, group_list_type=group_list_type
-        )
+
+    return unquant_apply_mlp(
+        hidden_states=hidden_states,
+        w1=w1,
+        w2=w2,
+        group_list=group_list,
+        group_list_type=group_list_type,
+    )