[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/token_dispatcher.py

@@ -25,26 +25,27 @@
 import torch
 from vllm.distributed.parallel_state import get_ep_group
 
-from vllm_ascend.ops.fused_moe.token_dispatcher import TokenDispatcherWithAllGather, TokenDispatchResult
+from vllm_ascend.ops.fused_moe.moe_runtime_args import MoEAllGatherCombineMetadata, MoETokenDispatchInput
+from vllm_ascend.ops.fused_moe.token_dispatcher import MoETokenDispatchOutput, TokenDispatcherWithAllGather
 
 
 class TokenDispatcherWithAllGather310(TokenDispatcherWithAllGather):
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
 
-    def token_dispatch(  # type: ignore[override]
+    def token_dispatch(
         self,
-        hidden_states: torch.Tensor,
-        topk_weights: torch.Tensor,
-        topk_ids: torch.Tensor,
-        expert_map: torch.Tensor | None = None,
-        apply_router_weight_on_input: bool = False,
+        token_dispatch_input: MoETokenDispatchInput,
     ):
-        self.original_shape = hidden_states.shape
+        hidden_states = token_dispatch_input.hidden_states
+        topk_weights = token_dispatch_input.topk_weights
+        topk_ids = token_dispatch_input.topk_ids
+        expert_map = token_dispatch_input.routing.expert_map
+        apply_router_weight_on_input = token_dispatch_input.routing.apply_router_weight_on_input
+        restore_shape = hidden_states.shape
 
         num_tokens = hidden_states.shape[:-1].numel()
-        self.apply_router_weight_on_input = apply_router_weight_on_input
-        if self.apply_router_weight_on_input:
+        if apply_router_weight_on_input:
             assert topk_weights.dim() == 2, "`topk_weights` should be in shape (num_tokens, topk)"
             _, topk = topk_weights.shape
             assert topk == 1, "Only support topk=1 when `apply_router_weight_on_input` is True"
@@ -66,13 +67,16 @@ class TokenDispatcherWithAllGather310(TokenDispatcherWithAllGather):
         )
         expert_tokens = expert_tokens.to(torch.int64)
         group_list_type = 1  # `count` mode
-        context_metadata = {"topk_weights": topk_weights, "expanded_row_idx": expanded_row_idx}
 
-        return TokenDispatchResult(
+        return MoETokenDispatchOutput(
             hidden_states=sorted_hidden_states,
             group_list=expert_tokens,
             group_list_type=group_list_type,
-            context_metadata=context_metadata,
+            combine_metadata=MoEAllGatherCombineMetadata(
+                topk_weights=topk_weights,
+                expanded_row_idx=expanded_row_idx,
+                restore_shape=restore_shape,
+            ),
         )
 
     def moe_init_routing(self, x, expert_idx, active_num, active_expert_range):