[EPLB]Fix eplbReformat eplb doc.

Signed-off-by: offline0806 <z00858301@china.huawei.com>

Author: offline0806

docs/source/user_guide/feature_guide/eplb_swift_balancer.md

@@ -1,16 +1,16 @@
 # Swift Balancer
 
 ## Overview
-Experts rebalancing of MoE models for LLM serving is a mandatory option.Changing experts dynamically would have a negative impact on TTFT and TPOT while stop-the-world. 
+Experts rebalancing of MoE models for LLM serving is a mandatory option.Changing experts dynamically would have a negative impact on TTFT and TPOT while stop-the-world.
 Asynchronously expert load balancing would be a better choice.
 We have launched SwiftBalancer to support dynamic experts load balancing with Zero-overhead experts movement.
 
-## Design 
+## Design
 
 ![img.png](images/eplb_img.png)
 
 The overall workflow involves:
-1. Record experts distribution during forward. We using expert_token_num after disptach instead of topk_ids, thus we got much smaller tensor shape to reduce cost of hbm
+1. Record experts distribution during forward. We using expert_token_num after dispatch instead of topk_ids, thus we got much smaller tensor shape to reduce cost of hbm
    recording and add-operator.
 2. Do all-gather for experts distribution. Using all-gather instead of all-reduce as less traffic volume.
 3. Wake up eplb worker process with experts distribution when num_iterations comes. Run eplb algorithm in eplb worker.