[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/eplb/eplb_updator.py

@@ -21,16 +21,15 @@ import torch.distributed as dist
 import vllm.envs as envs
 from vllm.logger import logger
 
-from vllm_ascend.eplb.core.eplb_utils import EPLBParamUtils
 from vllm_ascend.eplb.core.eplb_worker import EplbProcess
 
 
 class EplbUpdator:
 
-    def __init__(self, ascend_config, loader, eplb_process: EplbProcess,
+    def __init__(self, eplb_config, loader, eplb_process: EplbProcess,
                  process):
-        self.ascend_config = ascend_config
-        self.init_eplb(self.ascend_config.expert_map_path, process)
+        self.eplb_config = eplb_config
+        self.init_eplb(self.eplb_config.expert_map_path, process)
         self.eplb_loader = loader
         self.eplb_process = eplb_process
         self.shared_dict = self.eplb_process.shared_dict
@@ -45,28 +44,24 @@ class EplbUpdator:
         self.rank_id = dist.get_rank()
         self.num_expert_load_gather = 10
         self.periodic_load_gather = True
-        self.num_iterations_eplb_update: torch.int64 = self.ascend_config.num_iterations_eplb_update
-        EPLBParamUtils.check_iterations(self.num_iterations_eplb_update)
+        self.expert_heat_collection_interval: torch.int64 = self.eplb_config.expert_heat_collection_interval
         self.expert_map_path = expert_map_path
-        self.expert_map_record_path = self.ascend_config.expert_map_record_path
+        self.expert_map_record_path = self.eplb_config.expert_map_record_path
 
         try:
             if not envs.VLLM_ALLOW_EXPERT_LOAD_COLLECTING:
-                self.num_expert_load_gather = self.num_iterations_eplb_update
+                self.num_expert_load_gather = self.expert_heat_collection_interval
                 self.periodic_load_gather = False
         except Exception:
-            self.num_expert_load_gather = self.num_iterations_eplb_update
+            self.num_expert_load_gather = self.expert_heat_collection_interval
             self.periodic_load_gather = False
 
-        self.gate_eplb = self.ascend_config.gate_eplb
-
         self.reqs = []
         self.update_info_all = []
 
         self.cur_iterations: torch.int64 = 0
 
-        self.num_wait_worker_iterations: torch.int64 = self.ascend_config.num_wait_worker_iterations
-        EPLBParamUtils.check_iterations(self.num_wait_worker_iterations)
+        self.algorithm_execution_interval: torch.int64 = self.eplb_config.algorithm_execution_interval
 
         self.process = process
 
@@ -75,8 +70,8 @@ class EplbUpdator:
 
     def update_iteration(self):
         self.cur_iterations += 1
-        if self.cur_iterations == (self.num_iterations_eplb_update + \
-                                   self.num_wait_worker_iterations + self.num_moe_layers):
+        if self.cur_iterations == (self.expert_heat_collection_interval + \
+                                   self.algorithm_execution_interval + self.num_moe_layers):
             logger.info("Finish expert parallel load balancing.")
             if self.expert_map_record_path is not None:
                 self.adaptor._export_tensor_to_file(
@@ -84,19 +79,20 @@ class EplbUpdator:
                     self.expert_map_record_path)
 
             self.adaptor.model.clear_all_moe_loads()
-            if not self.gate_eplb:
-                self.cur_iterations = 0
+            self.cur_iterations = 0
 
     def get_update_info_flag(self):
-        return self.cur_iterations == (self.num_iterations_eplb_update +
-                                       self.num_wait_worker_iterations - 1)
+        return self.cur_iterations == (self.expert_heat_collection_interval +
+                                       self.algorithm_execution_interval - 1)
 
     def wakeup_eplb_worker_flag(self):
-        return self.cur_iterations == (self.num_iterations_eplb_update - 1)
+        return self.cur_iterations == (self.expert_heat_collection_interval -
+                                       1)
 
     def update_expert_weight_flag(self):
         weight_update_counter = self.cur_iterations - (
-            self.num_iterations_eplb_update + self.num_wait_worker_iterations)
+            self.expert_heat_collection_interval +
+            self.algorithm_execution_interval)
         return (weight_update_counter >= 0
                 and weight_update_counter < self.num_moe_layers)