eplb redundant expert bugfix (#4291)

### What this PR does / why we need it?
Redundant experts bugfix
### Does this PR introduce _any_ user-facing change?
After configuring the path for experts_map, users do not need to
configure iinit_redundancy_expert.
### How was this patch tested?
The accuracy of EPLB was tested with and without the use of redundant
experts.


- vLLM version: v0.11.0
- vLLM main:
2918c1b49c

---------

Signed-off-by: shenchuxiaofugui <1311027364@qq.com>

vllm_ascend/ops/expert_load_balancer.py

@@ -8,12 +8,14 @@ import torch.distributed as dist
 
 class ExpertLoadBalancer(object):
 
-    def __init__(self, expert_map_path, global_expert_num):
+    def __init__(self, expert_map_path, num_experts):
         self.expert_map_path = expert_map_path
-        self.global_expert_num = global_expert_num
+        self.num_experts = num_experts
         self.tensor_data = []
         self.expert_map_tensor, self.layers_num, self.ranks_num = (
             self._expert_file_to_tensor())
+        self.global_expert_num = num_experts + self.get_global_redundant_expert_num(
+        )
         self.expert_placement_map = self.generate_expert_placement_map()
 
     def _expert_file_to_tensor(self):
@@ -95,7 +97,7 @@ class ExpertLoadBalancer(object):
     def get_global_redundant_expert_num(self):
         global_redundant_expert_num = (
             len(self.expert_map_tensor[0][0]) * self.ranks_num -
-            self.global_expert_num)
+            self.num_experts)
         return global_redundant_expert_num
 
     def check_expert_map_tensor(self):