[EPLB]Eplb Config Renaming (#5533)

### What this PR does / why we need it?
1. Rename num_iterations_eplb_update to expert_heat_collection_interval.
2. Rename num_wait_worker_iterations to algorithm_execution_interval.
3. Rename init_redundancy_expert to num_redundant_experts because the
variable with the same meaning in vLLM is named this way.
4. Delete gate_eplb because we don't need this feature.
5. Move eplb config into a dict in additional config.
6. Depend on pr5817

### Does this PR introduce _any_ user-facing change?

before this pr：
`--additional-config '{"dynamic_eplb":true,
"num_iterations_eplb_update": 4000, "num_wait_worker_iterations": 150,
"init_redundancy_expert": 16, "expert_map_path": "xxx.json"}'`

after this pr: 
`--additional-config
'{"eplb_config":{"dynamic_eplb":true,"expert_heat_collection_interval":4000,
"algorithm_execution_interval":150,"num_redundant_experts": 16,
"expert_map_path": "xxx.json"}}'`

### How was this patch tested?

#### test qwen3-235b eplb num_redundant_experts=16

without pr5817
| dataset | version | metric | mode | vllm-api-general-chat |
|----- | ----- | ----- | ----- | -----|
| aime2024 | 604a78 | accuracy | gen | 83.33 |

with pr5817
| dataset | version | metric | mode | vllm-api-general-chat |
|----- | ----- | ----- | ----- | -----|
| aime2024 | 604a78 | accuracy | gen | 86.67 |

- vLLM version: v0.13.0
- vLLM main:
45c1ca1ca1

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

vllm_ascend/ops/fused_moe/fused_moe.py

@@ -55,7 +55,7 @@ class AscendUnquantizedFusedMoEMethod(UnquantizedFusedMoEMethod):
     def __init__(self, moe: FusedMoEConfig = None):
 
         super().__init__(moe=moe)
-        self.dynamic_eplb = get_ascend_config().dynamic_eplb
+        self.dynamic_eplb = get_ascend_config().eplb_config.dynamic_eplb
 
     def process_weights_after_loading(self, layer):
         super(UnquantizedFusedMoEMethod,
@@ -187,14 +187,14 @@ class AscendFusedMoE(FusedMoE):
                 dtype=vllm_config.model_config.dtype)
 
         # init moe
+        eplb_config = ascend_config.eplb_config
         self.global_expert_map, self.log2phy, self.global_redundant_expert_num = init_eplb_config(
-            ascend_config, self.moe_instance_id, self.moe_config)
+            eplb_config, self.moe_instance_id, self.moe_config)
         if self.global_expert_map is not None:
             self._expert_map = self.global_expert_map[self.ep_rank].npu()
         self.global_num_experts = num_experts + self.global_redundant_expert_num
-        self.dynamic_eplb = (ascend_config.dynamic_eplb
-                             or ascend_config.expert_map_record_path) and (
-                                 self.log2phy is not None)
+        self.dynamic_eplb = eplb_config.dynamic_eplb and (self.log2phy
+                                                          is not None)
         self.local_num_experts = (torch.sum(
             self._expert_map != -1).item() if self._expert_map is not None else
                                   self.global_num_experts)