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87 lines
3.2 KiB
Markdown
87 lines
3.2 KiB
Markdown
# 使用 LMDeploy 加速评测
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我们支持在评测大语言模型时，使用 [LMDeploy](https://github.com/InternLM/lmdeploy) 作为推理加速引擎。LMDeploy 是涵盖了 LLM 和 VLM 任务的全套轻量化、部署和服务解决方案，拥有卓越的推理性能。本教程将介绍如何使用 LMDeploy 加速对模型的评测。
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## 环境配置
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### 安装 OpenCompass
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请根据 OpenCompass [安装指南](https://opencompass.readthedocs.io/en/latest/get_started/installation.html) 来安装算法库和准备数据集。
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### 安装 LMDeploy
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使用 pip 安装 LMDeploy (python 3.8+)：
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```shell
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pip install lmdeploy
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```
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LMDeploy 预编译包默认基于 CUDA 12 编译。如果需要在 CUDA 11+ 下安装 LMDeploy，请执行以下命令：
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```shell
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export LMDEPLOY_VERSION=0.6.0
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export PYTHON_VERSION=310
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pip install https://github.com/InternLM/lmdeploy/releases/download/v${LMDEPLOY_VERSION}/lmdeploy-${LMDEPLOY_VERSION}+cu118-cp${PYTHON_VERSION}-cp${PYTHON_VERSION}-manylinux2014_x86_64.whl --extra-index-url https://download.pytorch.org/whl/cu118
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```
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## 评测
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在评测一个模型时，需要准备一份评测配置，指明评测集、模型和推理参数等信息。
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以 [internlm2-chat-7b](https://huggingface.co/internlm/internlm2-chat-7b) 模型为例，相关的配置信息如下：
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```python
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# configure the dataset
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from mmengine.config import read_base
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with read_base():
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# choose a list of datasets
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from .datasets.mmlu.mmlu_gen_a484b3 import mmlu_datasets
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from .datasets.ceval.ceval_gen_5f30c7 import ceval_datasets
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from .datasets.triviaqa.triviaqa_gen_2121ce import triviaqa_datasets
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from opencompass.configs.datasets.gsm8k.gsm8k_0shot_v2_gen_a58960 import \
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gsm8k_datasets
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# and output the results in a chosen format
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from .summarizers.medium import summarizer
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datasets = sum((v for k, v in locals().items() if k.endswith('_datasets')), [])
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# configure lmdeploy
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from opencompass.models import TurboMindModelwithChatTemplate
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# configure the model
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models = [
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dict(
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type=TurboMindModelwithChatTemplate,
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abbr=f'internlm2-chat-7b-lmdeploy',
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# model path, which can be the address of a model repository on the Hugging Face Hub or a local path
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path='internlm/internlm2-chat-7b',
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# inference backend of LMDeploy. It can be either 'turbomind' or 'pytorch'.
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# If the model is not supported by 'turbomind', it will fallback to
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# 'pytorch'
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backend='turbomind',
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# For the detailed engine config and generation config, please refer to
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# https://github.com/InternLM/lmdeploy/blob/main/lmdeploy/messages.py
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engine_config=dict(tp=1),
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gen_config=dict(do_sample=False),
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# the max size of the context window
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max_seq_len=7168,
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# the max number of new tokens
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max_out_len=1024,
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# the max number of prompts that LMDeploy receives
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# in `generate` function
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batch_size=5000,
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run_cfg=dict(num_gpus=1),
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)
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]
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```
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把上述配置放在文件中，比如 "configs/eval_internlm2_lmdeploy.py"。然后，在 OpenCompass 的项目目录下，执行如下命令可得到评测结果：
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```shell
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python run.py configs/eval_internlm2_lmdeploy.py -w outputs
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```
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