[Feat][SP] Suport SP for VL MoE models (#7044)

### What this PR does / why we need it?

2nd PR for https://github.com/vllm-project/vllm-ascend/issues/5712,
extend SP to VL MoE models.


### Does this PR introduce _any_ user-facing change?
remove `sp_threshold` in additional config and reuse `sp_min_token_num`
from vLLM.


### How was this patch tested?
- Model: Qwen3-VL-30B-A3B, 
- TP4 DP2
- 100 reqs
- max concurrency 1

| Seq length | Mean TTFT (ms) main | Mean TTFT (ms) this PR |
|------------|---------------------|------------------------|
| 4k         | 429.40               | 323.3                  |
| 16k        | 1297.01              | 911.74                |

- vLLM version: v0.16.0
- vLLM main:
4034c3d32e

---------

Signed-off-by: realliujiaxu <realliujiaxu@163.com>

vllm_ascend/patch/worker/patch_distributed.py

@@ -84,7 +84,7 @@ class GroupCoordinatorPatch(GroupCoordinator):
         if use_message_queue_broadcaster and self.world_size > 1:
             self.mq_broadcaster = MessageQueue.create_from_process_group(self.cpu_group, 1 << 22, 6)
 
-        self.use_custom_op_call = False
+        self.use_custom_op_call = True
         self.use_cpu_custom_send_recv = False
 
     def all_to_all(
@@ -106,10 +106,5 @@ class GroupCoordinatorPatch(GroupCoordinator):
         assert self.device_communicator is not None, "device_communicator should be initialized when world_size > 1"
         return self.device_communicator.all_to_all(input_, scatter_dim, gather_dim, scatter_sizes, gather_sizes)
 
-    def all_reduce(self, input_):
-        if self.world_size == 1:
-            return input_
-        return torch.ops.vllm.all_reduce(input_, group_name=self.unique_name)
-
 
 vllm.distributed.parallel_state.GroupCoordinator = GroupCoordinatorPatch