[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>

docs/source/user_guide/feature_guide/eplb_swift_balancer.md

@@ -26,17 +26,17 @@ W8A8-dynamic
 
 ### Dynamic EPLB
 
-We need to add environment variable `export DYNAMIC_EPLB="true"` to enable vllm eplb. Enable dynamic balancing with auto-tuned parameters. Adjust num_iterations_eplb_update and num_wait_worker_iterations based on workload patterns.
+We need to add environment variable `export DYNAMIC_EPLB="true"` to enable vllm eplb. Enable dynamic balancing with auto-tuned parameters. Adjust expert_heat_collection_interval and algorithm_execution_interval based on workload patterns.
 
 ```shell
 vllm serve Qwen/Qwen3-235B-A22 \
   --tensor-parallel-size 16 \
   --enable-expert-parallel \
-  --additional-config '{
+  --additional-config '{ "eplb_config": {
     "dynamic_eplb": true,
-    "num_iterations_eplb_update": 400,
-    "num_wait_worker_iterations": 30
-  }'
+    "expert_heat_collection_interval": 400,
+    "algorithm_execution_interval": 30
+    }}'
 ```
 
 ### Static EPLB
@@ -49,12 +49,12 @@ We need to add environment variable `export EXPERT_MAP_RECORD="true"` to record
 vllm serve Qwen/Qwen3-235B-A22 \
   --tensor-parallel-size 16 \
   --enable-expert-parallel \
-  --additional-config '{
+  --additional-config '{ "eplb_config": {
     "expert_map_record_path": "/path/to/eplb.json",
-    "init_redundancy_expert": 16,
-    "num_iterations_eplb_update": 400,
-    "num_wait_worker_iterations": 30
-  }'
+    "num_redundant_experts": 16,
+    "expert_heat_collection_interval": 400,
+    "algorithm_execution_interval": 30
+  }}'
 ```
 
 #### Subsequent Deployments (Use Recorded Map)
@@ -73,9 +73,9 @@ vllm serve Qwen/Qwen3-235B-A22 \
 ## Critical Considerations
 
 1. Parameter Tuning:
-   - num_iterations_eplb_update: Higher values (e.g., 400+) for stable workloads; lower values (e.g., 100-200) for fluctuating traffic.
-   - num_wait_worker_iterations: Should be ≥ 30 to avoid premature balancing during startup.
-   - init_redundancy_expert: Must match tensor-parallel size (e.g., 16 for 16 GPUs) to ensure sufficient redundancy.
+   - expert_heat_collection_interval: Higher values (e.g., 400+) for stable workloads; lower values (e.g., 100-200) for fluctuating traffic.
+   - algorithm_execution_interval: Should be ≥ 30 to avoid premature balancing during startup.
+   - num_redundant_experts: Must match tensor-parallel size (e.g., 16 for 16 GPUs) to ensure sufficient redundancy.
 
 2. Hardware Requirements:
    - Ensure that all GPUs have identical memory capacity and compute capabilities.
@@ -85,20 +85,16 @@ vllm serve Qwen/Qwen3-235B-A22 \
    - Only MoE models with explicit expert parallelism support (e.g., Qwen3 MoE models) are compatible.
    - Verify model architecture supports dynamic expert routing through --enable-expert-parallel.
 
-4. Gating Configuration:
-   - When gate_eplb=true, validate that the gating mechanism can handle expert movement without routing errors.
-   - Test with synthetic workloads before production deployment.
-
-5. Monitoring & Validation:
+4. Monitoring & Validation:
    - Track metrics: expert_load_balance_ratio, ttft_p99, tpot_avg, and gpu_utilization.
    - Use vllm monitor to detect imbalances during runtime.
    - Always verify expert map JSON structure before loading (validate with jq or similar tools).
 
-6. Startup Behavior:
+5. Startup Behavior:
    - Initial requests may experience higher latency during the first balancing cycle (typically 1-2 minutes).
    - Avoid sudden traffic spikes during the warm-up phase.
 
-7. Common Pitfalls:
+6. Common Pitfalls:
    - Incorrect tensor-parallel-size vs. actual GPU count → causes resource underutilization.
    - Using expert_map_path without generating the map first → runtime errors.
-   - Setting init_redundancy_expert > available GPUs → system failure.
+   - Setting num_redundant_experts > available GPUs → system failure.