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* integrate lmdeploy's pipeline api * fix linting * update user guide * rename * update * update * update * rollback class name * update * remove unused code * update * update * fix ci check * compatibility * remove concurrency * Update configs/models/hf_internlm/lmdeploy_internlm2_chat_7b.py * Update docs/zh_cn/advanced_guides/evaluation_lmdeploy.md * [Bug] fix lint --------- Co-authored-by: Songyang Zhang <tonysy@users.noreply.github.com> Co-authored-by: tonysy <sy.zhangbuaa@gmail.com>
89 lines
3.4 KiB
Markdown
89 lines
3.4 KiB
Markdown
# Evaluation with LMDeploy
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We now support evaluation of models accelerated by the [LMDeploy](https://github.com/InternLM/lmdeploy). LMDeploy is a toolkit designed for compressing, deploying, and serving LLM. It has a remarkable inference performance. We now illustrate how to evaluate a model with the support of LMDeploy in OpenCompass.
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## Setup
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### Install OpenCompass
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Please follow the [instructions](https://opencompass.readthedocs.io/en/latest/get_started/installation.html) to install the OpenCompass and prepare the evaluation datasets.
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### Install LMDeploy
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Install lmdeploy via pip (python 3.8+)
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```shell
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pip install lmdeploy
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```
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The default prebuilt package is compiled on CUDA 12. However, if CUDA 11+ is required, you can install lmdeploy by:
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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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## Evaluation
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When evaluating a model, it is necessary to prepare an evaluation configuration that specifies information such as the evaluation dataset, the model, and inference parameters.
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Taking [internlm2-chat-7b](https://huggingface.co/internlm/internlm2-chat-7b) as an example, the evaluation config is as follows:
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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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Place the aforementioned configuration in a file, such as "configs/eval_internlm2_lmdeploy.py". Then, in the home folder of OpenCompass, start evaluation by the following command:
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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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You are expected to get the evaluation results after the inference and evaluation.
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