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* integrate turbomind python api * update * update user guide * update * fix according to reviewer's comments * fix error * fix linting * update user guide * remove debug log --------- Co-authored-by: Songyang Zhang <tonysy@users.noreply.github.com>
80 lines
3.1 KiB
Markdown
80 lines
3.1 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. **TurboMind** is an efficient inference engine proposed by LMDeploy. OpenCompass is compatible with TurboMind. We now illustrate how to evaluate a model with the support of TurboMind 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.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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## Evaluation
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OpenCompass integrates both turbomind's python API and gRPC API for evaluation. And the former is highly recommended.
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We take the InternLM-20B as example. Please download it from huggingface and convert it to turbomind's model format:
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```shell
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# 1. Download InternLM model(or use the cached model's checkpoint)
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# Make sure you have git-lfs installed (https://git-lfs.com)
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git lfs install
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git clone https://huggingface.co/internlm/internlm-20b /path/to/internlm-20b
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# 2. Convert InternLM model to turbomind's format, and save it in the home folder of opencompass
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lmdeploy convert internlm /path/to/internlm-20b \
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--dst-path {/home/folder/of/opencompass}/turbomind
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```
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**Note**:
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If evaluating the InternLM Chat model, make sure to pass `internlm-chat` as the model name instead of `internlm` when converting the model format. The specific command is:
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```shell
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lmdeploy convert internlm-chat /path/to/internlm-20b-chat \
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--dst-path {/home/folder/of/opencompass}/turbomind
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```
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### Evaluation with Turbomind Python API (recommended)
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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_internlm_turbomind.py -w outputs/turbomind/internlm-20b
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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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**Note**:
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- If you evaluate theInternLM Chat model, please use configuration file `eval_internlm_chat_turbomind.py`
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- If you evaluate the InternLM 7B model, please modify `eval_internlm_turbomind.py` or `eval_internlm_chat_turbomind.py` by commenting out the configuration for the 20B model and enabling the configuration for the 7B model.
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### Evaluation with Turbomind gPRC API (optional)
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In the home folder of OpenCompass, launch the Triton Inference Server:
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```shell
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bash turbomind/service_docker_up.sh
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```
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And start evaluation by the following command:
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```shell
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python run.py configs/eval_internlm_turbomind_tis.py -w outputs/turbomind-tis/internlm-20b
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```
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\*\*Note: \*\*
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- If the InternLM Chat model is requested to be evaluated, please use config file `eval_internlm_chat_turbomind_tis.py`
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- In `eval_internlm_turbomind_tis.py`, the configured Triton Inference Server (TIS) address is `tis_addr='0.0.0.0:33337'`. Please modify `tis_addr` to the IP address of the machine where the server is launched.
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- If evaluating the InternLM 7B model, please modify the config file, commenting out the configuration for the 20B model and enabling the configuration for the 7B model
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