[Refactor][MoE] Reuse vLLM's all_reduce logic (#5189)

### What this PR does / why we need it?
Move all_reduce logic to AscendFusedMoE.forward, reuse vLLM's logic.

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?
e2e & ut
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c

Signed-off-by: weichen <calvin_zhu0210@outlook.com>
Co-authored-by: weijinqian0 <1184188277@qq.com>

vllm_ascend/ops/fused_moe/prepare_finalize.py

@@ -22,7 +22,6 @@ import torch
 import torch.distributed as dist
 import torch.nn as nn
 import torch_npu
-from vllm.distributed import tensor_model_parallel_all_reduce
 from vllm.distributed.parallel_state import (
     get_dp_group, get_pcp_group, get_tensor_model_parallel_rank,
     get_tensor_model_parallel_world_size)
@@ -470,8 +469,4 @@ class PrepareAndFinalizeWithAllGather(PrepareAndFinalize):
         if prefill_context_parallel_enable() and self.moe_config.pcp_size > 1:
             hidden_states = get_pcp_group().reduce_scatter(hidden_states,
                                                            dim=0)
-        if reduce_results and (self.moe_config.tp_size > 1
-                               or self.moe_config.ep_size > 1):
-            hidden_states = tensor_model_parallel_all_reduce(hidden_states)
-
         return hidden_states