[format] applied code formatting on changed files in pull request 2997 (#3008)

Co-authored-by: github-actions <github-actions@github.com>

colossalai/nn/optimizer/README.md

@@ -2,30 +2,30 @@
 
 ## Introduction
 
-Welcome to the large-scale deep learning optimization techniques of [Colossal-AI](https://github.com/hpcaitech/ColossalAI), 
+Welcome to the large-scale deep learning optimization techniques of [Colossal-AI](https://github.com/hpcaitech/ColossalAI),
 which has been accepted as official tutorials by top conference [SC](https://sc22.supercomputing.org/), [AAAI](https://aaai.org/Conferences/AAAI-23/), [PPoPP](https://ppopp23.sigplan.org/), [CVPR](https://cvpr2023.thecvf.com/), [ISC](https://www.isc-hpc.com/), etc.
 
 
 [Colossal-AI](https://github.com/hpcaitech/ColossalAI), a unified deep learning system for the big model era, integrates
 many advanced technologies such as multi-dimensional tensor parallelism, sequence parallelism, heterogeneous memory management,
-large-scale optimization, adaptive task scheduling, etc. By using Colossal-AI, we could help users to efficiently and 
+large-scale optimization, adaptive task scheduling, etc. By using Colossal-AI, we could help users to efficiently and
 quickly deploy large AI model training and inference, reducing large AI model training budgets and scaling down the labor cost of learning and deployment.
 
 ### 🚀 Quick Links
 
 [**Colossal-AI**](https://github.com/hpcaitech/ColossalAI) |
-[**Paper**](https://arxiv.org/abs/2110.14883) | 
-[**Documentation**](https://www.colossalai.org/) | 
-[**Forum**](https://github.com/hpcaitech/ColossalAI/discussions) | 
+[**Paper**](https://arxiv.org/abs/2110.14883) |
+[**Documentation**](https://www.colossalai.org/) |
+[**Forum**](https://github.com/hpcaitech/ColossalAI/discussions) |
 [**Slack**](https://join.slack.com/t/colossalaiworkspace/shared_invite/zt-z7b26eeb-CBp7jouvu~r0~lcFzX832w)
 
 
 ## Table of Content
 
-Large transformer models display promising performance on a wide spectrum of AI applications. 
+Large transformer models display promising performance on a wide spectrum of AI applications.
 Both academia and industry are scaling DL training on larger clusters. However, degrading generalization performance, non-negligible communication overhead, and increasing model size prevent DL researchers and engineers from exploring large-scale AI models.
 
-We aim to provide a clear sketch of the optimizations for large-scale deep learning with regard to model accuracy and model efficiency. 
+We aim to provide a clear sketch of the optimizations for large-scale deep learning with regard to model accuracy and model efficiency.
 One way to achieve the goal of maintaining or improving the model accuracy in the large-scale setting while maintaining compute efficiency is to design algorithms that
 are less communication and memory hungry. Notably, they are not mutually exclusive but can
 be optimized jointly to further speed up training.
@@ -51,7 +51,7 @@ be optimized jointly to further speed up training.
     - Memory Efficiency
       - Mix-Precision Training
       - Memory-Efficient Methods, e.g. ZeRO, Gemini, etc.
-      
+
 Some of the above are still under development. **If you wish to make a contribution to this repository, please read the `Contributing` section below.**
 
 ## Discussion
@@ -63,7 +63,7 @@ If you encounter any problem while running these optimizers, you may want to rai
 
 ## Contributing
 
-This project welcomes constructive ideas and implementations from the community. 
+This project welcomes constructive ideas and implementations from the community.
 
 ### Update an Optimizer