Add Doc for accelerator

README.md

@@ -70,6 +70,7 @@ Just like a compass guides us on our journey, OpenCompass will guide you through
 
 ## 🚀 What's New <a><img width="35" height="20" src="https://user-images.githubusercontent.com/12782558/212848161-5e783dd6-11e8-4fe0-bbba-39ffb77730be.png"></a>
 
+- **\[2024.06.20\]** OpenCompass now supports one-click switching between inference acceleration backends, enhancing the efficiency of the evaluation process. In addition to the default HuggingFace inference backend, it now also supports popular backends [LMDeploy](https://github.com/InternLM/lmdeploy) and [vLLM](https://github.com/vllm-project/vllm). This feature is available via a simple command-line switch and through deployment APIs. For detailed usage, see the [documentation](docs/en/advanced_guides/accelerator_intro.md).🔥🔥🔥.
 - **\[2024.05.08\]** We supported the evaluation of 4 MoE models: [Mixtral-8x22B-v0.1](configs/models/mixtral/hf_mixtral_8x22b_v0_1.py), [Mixtral-8x22B-Instruct-v0.1](configs/models/mixtral/hf_mixtral_8x22b_instruct_v0_1.py), [Qwen1.5-MoE-A2.7B](configs/models/qwen/hf_qwen1_5_moe_a2_7b.py), [Qwen1.5-MoE-A2.7B-Chat](configs/models/qwen/hf_qwen1_5_moe_a2_7b_chat.py). Try them out now!
 - **\[2024.04.30\]** We supported evaluating a model's compression efficiency by calculating its Bits per Character (BPC) metric on an [external corpora](configs/datasets/llm_compression/README.md) ([official paper](https://github.com/hkust-nlp/llm-compression-intelligence)). Check out the [llm-compression](configs/eval_llm_compression.py) evaluation config now! 🔥🔥🔥
 - **\[2024.04.29\]** We report the performance of several famous LLMs on the common benchmarks, welcome to [documentation](https://opencompass.readthedocs.io/en/latest/user_guides/corebench.html) for more information! 🔥🔥🔥.
@@ -150,6 +151,12 @@ After ensuring that OpenCompass is installed correctly according to the above st
 python run.py --models hf_llama_7b --datasets mmlu_ppl ceval_ppl
 ```
 
+Additionally, if you want to use an inference backend other than HuggingFace for accelerated evaluation, such as LMDeploy or vLLM, you can do so with the command below. Please ensure that you have installed the necessary packages for the chosen backend and that your model supports accelerated inference with it. For more information, see the documentation on inference acceleration backends [here](docs/en/advanced_guides/accelerator_intro.md). Below is an example using LMDeploy:
+
+```bash
+python run.py --models hf_llama_7b --datasets mmlu_ppl ceval_ppl -a lmdeploy
+```
+
 OpenCompass has predefined configurations for many models and datasets. You can list all available model and dataset configurations using the [tools](./docs/en/tools.md#list-configs).
 
 ```bash

README_zh-CN.md

@@ -69,6 +69,8 @@
 
 ## 🚀 最新进展 <a><img width="35" height="20" src="https://user-images.githubusercontent.com/12782558/212848161-5e783dd6-11e8-4fe0-bbba-39ffb77730be.png"></a>
 
+- **\[2024.06.20\]** OpenCompass 现已支持一键切换推理加速后端，助力评测过程更加高效。除了默认的HuggingFace推理后端外，还支持了常用的 [LMDeploy](https://github.com/InternLM/lmdeploy) 和 [vLLM](https://github.com/vllm-project/vllm) ，支持命令行一键切换和部署 API 加速服务两种方式，详细使用方法见[文档](docs/zh_cn/advanced_guides/accelerator_intro.md)。
+  欢迎试用！🔥🔥🔥.
 - **\[2024.05.08\]** 我们支持了以下四个MoE模型的评测配置文件: [Mixtral-8x22B-v0.1](configs/models/mixtral/hf_mixtral_8x22b_v0_1.py), [Mixtral-8x22B-Instruct-v0.1](configs/models/mixtral/hf_mixtral_8x22b_instruct_v0_1.py), [Qwen1.5-MoE-A2.7B](configs/models/qwen/hf_qwen1_5_moe_a2_7b.py), [Qwen1.5-MoE-A2.7B-Chat](configs/models/qwen/hf_qwen1_5_moe_a2_7b_chat.py) 。欢迎试用!
 - **\[2024.04.30\]** 我们支持了计算模型在给定[数据集](configs/datasets/llm_compression/README.md)上的压缩率（Bits per Character）的评测方法（[官方文献](https://github.com/hkust-nlp/llm-compression-intelligence)）。欢迎试用[llm-compression](configs/eval_llm_compression.py)评测集! 🔥🔥🔥
 - **\[2024.04.26\]** 我们报告了典型LLM在常用基准测试上的表现，欢迎访问[文档](https://opencompass.readthedocs.io/zh-cn/latest/user_guides/corebench.html)以获取更多信息！🔥🔥🔥.
@@ -151,6 +153,12 @@ unzip OpenCompassData-core-20240207.zip
 python run.py --models hf_llama_7b --datasets mmlu_ppl ceval_ppl
 ```
 
+另外，如果想使用除了 HuggingFace 外的推理后端进行加速评测，如 LMDeploy 或 vLLM，可以通过以下命令。使用前请确保您已经安装了相应后端的软件包，以及模型支持使用该后端进行加速推理，更多内容见推理加速后端[文档](docs/zh_cn/advanced_guides/accelerator_intro.md)，下面以LMDeploy为例：
+
+```bash
+python run.py --models hf_llama_7b --datasets mmlu_ppl ceval_ppl -a lmdeploy
+```
+
 OpenCompass 预定义了许多模型和数据集的配置，你可以通过 [工具](./docs/zh_cn/tools.md#ListConfigs) 列出所有可用的模型和数据集配置。
 
 ```bash