[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>

tests/ut/_310p/fused_moe/test_moe_mlp_310.py

@@ -19,6 +19,38 @@ import torch
 
 from tests.ut.base import TestBase
 from vllm_ascend._310p.fused_moe.moe_mlp import unified_apply_mlp
+from vllm_ascend.ops.fused_moe.moe_runtime_args import (
+    MoEMlpComputeInput,
+    MoEQuantParams,
+    MoEWeights,
+)
+from vllm_ascend.quantization.quant_type import QuantType
+
+
+def build_mlp_compute_input_fixture(
+    *,
+    hidden_states: torch.Tensor,
+    w1: torch.Tensor,
+    w2: torch.Tensor,
+    group_list: torch.Tensor,
+    with_quant: bool,
+    w1_scale: torch.Tensor | None = None,
+    w2_scale: torch.Tensor | None = None,
+    group_list_type: int = 1,
+) -> MoEMlpComputeInput:
+    return MoEMlpComputeInput(
+        hidden_states=hidden_states,
+        group_list=group_list,
+        group_list_type=group_list_type,
+        dynamic_scale=None,
+        topk_scales=None,
+        weights=MoEWeights(w1=w1, w2=w2, w1_scale=w1_scale, w2_scale=w2_scale),
+        quant=MoEQuantParams(quant_type=QuantType.W8A8 if with_quant else QuantType.NONE),
+        fusion=False,
+        activation="silu",
+        need_trans=False,
+        dynamic_eplb=False,
+    )
 
 
 class TestUnifiedApplyMLP310(TestBase):
@@ -38,14 +70,13 @@ class TestUnifiedApplyMLP310(TestBase):
         group_list = torch.tensor([2, 4, 6, 8, 10], dtype=torch.int64)
 
         result = unified_apply_mlp(
-            hidden_states=hidden_states,
-            w1=w1,
-            w1_scale=None,
-            w2=w2,
-            w2_scale=None,
-            group_list=group_list,
-            group_list_type=1,
-            with_quant=False,
+            mlp_compute_input=build_mlp_compute_input_fixture(
+                hidden_states=hidden_states,
+                w1=w1,
+                w2=w2,
+                group_list=group_list,
+                with_quant=False,
+            )
         )
 
         self.assertEqual(mock_npu_grouped_matmul.call_count, 2)
@@ -94,14 +125,15 @@ class TestUnifiedApplyMLP310(TestBase):
         group_list = torch.tensor([2, 4, 6, 8, 10], dtype=torch.int64)
 
         result = unified_apply_mlp(
-            hidden_states=hidden_states,
-            w1=w1,
-            w1_scale=w1_scale,
-            w2=w2,
-            w2_scale=w2_scale,
-            group_list=group_list,
-            group_list_type=1,
-            with_quant=True,
+            mlp_compute_input=build_mlp_compute_input_fixture(
+                hidden_states=hidden_states,
+                w1=w1,
+                w2=w2,
+                group_list=group_list,
+                with_quant=True,
+                w1_scale=w1_scale,
+                w2_scale=w2_scale,
+            )
         )
 
         mock_cumsum.assert_called_once()