Evaluation with LMDeploy

We now support evaluation of models accelerated by the 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.

Setup

Install OpenCompass

Please follow the instructions to install the OpenCompass and prepare the evaluation datasets.

Install LMDeploy

Install lmdeploy via pip (python 3.8+)

pip install lmdeploy

The default prebuilt package is compiled on CUDA 12. However, if CUDA 11+ is required, you can install lmdeploy by:

export LMDEPLOY_VERSION=0.6.0
export PYTHON_VERSION=310
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

Evaluation

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.

Taking internlm2-chat-7b as an example, the evaluation config is as follows:

# configure the dataset
from mmengine.config import read_base


with read_base():
    # choose a list of datasets
    from .datasets.mmlu.mmlu_gen_a484b3 import mmlu_datasets
    from .datasets.ceval.ceval_gen_5f30c7 import ceval_datasets
    from .datasets.triviaqa.triviaqa_gen_2121ce import triviaqa_datasets
    from opencompass.configs.datasets.gsm8k.gsm8k_0shot_v2_gen_a58960 import \
        gsm8k_datasets
    # and output the results in a chosen format
    from .summarizers.medium import summarizer

datasets = sum((v for k, v in locals().items() if k.endswith('_datasets')), [])

# configure lmdeploy
from opencompass.models import TurboMindModelwithChatTemplate



# configure the model
models = [
    dict(
        type=TurboMindModelwithChatTemplate,
        abbr=f'internlm2-chat-7b-lmdeploy',
        # model path, which can be the address of a model repository on the Hugging Face Hub or a local path
        path='internlm/internlm2-chat-7b',
        # inference backend of LMDeploy. It can be either 'turbomind' or 'pytorch'.
        # If the model is not supported by 'turbomind', it will fallback to
        # 'pytorch'
        backend='turbomind',
        # For the detailed engine config and generation config, please refer to
        # https://github.com/InternLM/lmdeploy/blob/main/lmdeploy/messages.py
        engine_config=dict(tp=1),
        gen_config=dict(do_sample=False),
        # the max size of the context window
        max_seq_len=7168,
        # the max number of new tokens
        max_out_len=1024,
        # the max number of prompts that LMDeploy receives
        # in `generate` function
        batch_size=5000,
        run_cfg=dict(num_gpus=1),
    )
]

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:

python run.py configs/eval_internlm2_lmdeploy.py -w outputs

You are expected to get the evaluation results after the inference and evaluation.