[doc] updated inference readme (#5269)

colossalai/inference/README.md

@@ -0,0 +1,87 @@
+# ⚡️ ColossalAI-Inference
+
+## 📚 Table of Contents
+
+- [⚡️ ColossalAI-Inference](#️-colossalai-inference)
+  - [📚 Table of Contents](#-table-of-contents)
+  - [📌 Introduction](#-introduction)
+  - [🛠 Design and Implementation](#-design-and-implementation)
+  - [🕹 Usage](#-usage)
+  - [🪅 Support Matrix](#-support-matrix)
+  - [🗺 Roadmap](#-roadmap)
+  - [🌟 Acknowledgement](#-acknowledgement)
+
+
+## 📌 Introduction
+
+ColossalAI-Inference is a library which offers acceleration to Transformers models, especially LLMs. In ColossalAI-Inference, we leverage high-performance kernels, KV cache, paged attention, continous batching and other techniques to accelerate the inference of LLMs. We also provide a unified interface for users to easily use our library.
+
+## 🛠 Design and Implementation
+
+To be added.
+
+## 🕹 Usage
+
+
+To be added.
+
+## 🪅 Support Matrix
+
+| Model |  KV Cache | Paged Attention | Kernels | Tensor Parallelism | Speculative Decoding |
+| - | - | - | - | - | - |
+| Llama |  ✅ | ✅ | ✅ | 🔜 | 🔜 |
+
+
+Notations:
+- ✅: supported
+- ❌: not supported
+- 🔜: still developing, will support soon
+
+## 🗺 Roadmap
+
+- [x] KV Cache
+- [x] Paged Attention
+- [x] High-Performance Kernels
+- [x] Llama Modelling
+- [ ] Tensor Parallelism
+- [ ] Speculative Decoding
+- [ ] Continuous Batching
+- [ ] Online Inference
+- [ ] Benchmarking
+- [ ] User Documentation
+
+## 🌟 Acknowledgement
+
+This project was written from scratch but we learned a lot from several other great open-source projects during development. Therefore, we wish to fully acknowledge their contribution to the open-source community. These projects include
+
+- [vLLM](https://github.com/vllm-project/vllm)
+- [LightLLM](https://github.com/ModelTC/lightllm)
+- [flash-attention](https://github.com/Dao-AILab/flash-attention)
+
+If you wish to cite relevant research papars, you can find the reference below.
+
+```bibtex
+# vllm
+@inproceedings{kwon2023efficient,
+  title={Efficient Memory Management for Large Language Model Serving with PagedAttention},
+  author={Woosuk Kwon and Zhuohan Li and Siyuan Zhuang and Ying Sheng and Lianmin Zheng and Cody Hao Yu and Joseph E. Gonzalez and Hao Zhang and Ion Stoica},
+  booktitle={Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles},
+  year={2023}
+}
+
+# flash attention v1 & v2
+@inproceedings{dao2022flashattention,
+  title={Flash{A}ttention: Fast and Memory-Efficient Exact Attention with {IO}-Awareness},
+  author={Dao, Tri and Fu, Daniel Y. and Ermon, Stefano and Rudra, Atri and R{\'e}, Christopher},
+  booktitle={Advances in Neural Information Processing Systems},
+  year={2022}
+}
+@article{dao2023flashattention2,
+  title={Flash{A}ttention-2: Faster Attention with Better Parallelism and Work Partitioning},
+  author={Dao, Tri},
+  year={2023}
+}
+
+# we do not find any research work related to lightllm
+
+```