LMS - Large Model Serving

Introduction

LMS（Large Model Serving） is an open source tool that provides large model services. LMS can provide model compression, model evaluation, model deployment, model monitoring and other functions.

It includes LMS_Web and LMS_Client. In LMS_Client, you can import, compress and deploy models through the command line. LMS_Web provides basic model information and a visual interface for monitoring deployed models.

Installation and setting

Installation

You can install LMS with the following command.

pip install dc-lms

or

git clone git@github.com:DataCanvasIO/LMS.git
cd lms
python setup.py install

Start & Stop lms_web

You can start LMS_Web with the following command.

lms_web start --port {{LMS_WEB_PORT}}

You can stop LMS_Web with the following command.

lms_web stop

Setting

You should initiate client using following command which will register self into the web center and start a daemon to expose the monitoring metrics.

lms join {{LMS_WEB_HOST_NAME}}:{{LMS_WEB_PORT}}

Quick Guide

Model List

1. Add model You can add the model to LMS through the following command.

   lms import --model_path {{model_path}}

   After adding the model successfully,You can view the model in LMS_Web.

2. Delete model You can delete the model from LMS through the following command.

   lms del --model_name {{model_name}}

3. List model You can list the model of the current node from the LMS with the following command.

   lms list

Model Evaluation

1. Automatic evaluation: the preset task will be used to test the specified model. The preset task includes MMLU, CMMLU, BigBench, ARC , AGIEval, ceval and other benchmark. The model will be evaluated from multiple angles. You can automatically evaluate the model with the following instructions.

   lms eval --model_name {{model_name}} --task CMMLU,ceval,ARC --output_path {{output_path}}

2. Custom evaluation The system will use your specified task to test the specified model, and you can make a custom evaluation with the following command.

   lms eval --model_name {{model_name}} --task {{custom_task.py}} --input_path {{input_path}} --output_path {{output_path}}

3. Manual evaluation Execute the following command, and the system will test the model with the data you specify and return the model results to the specified path.

   lms eval --model_name {{model_name}} --task human --input_path {{input_path}} --output_path {{output_path}}

   After the execution is successful, the model output will be displayed on the LMS_Web corresponding model details page. You need to manually evaluate the output of the model.

After the evaluation is successful, the evaluation result will be displayed on the LMS_Web corresponding model details page.

Quantization

You can quantize the specified model with the following command.

lms quantization --model_name {{model_name}} --{{int8|int4}} --quantized_model_path {{quantized_model_path}}

See examples for more details.

After the quantization of the model is completed, a new quantized model is generated, and you can view the new model in LMS_Web.

Pruning

You can prune the specified model with the following command.

lms pruning {{sparse|structure}} --model_name {{model_name}}  --pruned_model_path {{pruned_model_path}}

After the pruning of the model is completed, a new pruned model is generated, and you can view the new model in LMS_Web.

Model Deployment

You can deploy bloom, llama, falcon, and many other models using the following commands.

1. deploy supported model

   lms deploy --model_name {{model_name}} --gpu 0,1 --load_{{fp16|int8|int4}} --infer_config infer_conf.json

   If you need to deploy unsupported model, you can use deploy custom model.

2. deploy custom model

   lms deploy --model_name {{model_name}} --gpu 0,1 --load_{{fp16|int8|int4}} --infer_py generate.py --infer_config infer_conf.json

See examples for more details on infer_config and infer_py.

If you want to deploy with specific port, use flag:--port {{port}} at above commands.

After the model is deployed successfully, you can view the deployment status and monitoring of the model in LMS_Web.

1. undeploy model

   You can undeploy the mode with the following command.

   lms undeploy --model_name {{model_name}}

2. deployment logs

   If you want to watch the log of the deployed model, you can use the following command.

   lms logs -f --model_name {{model_name}}

Supported Models

ModelName	Quantize int8	Quantize int4	Prune sparse	Prune structure	Deploy fp16	Deploy int8	Deploy int4
Alaya	Y	Y	Y	Y	Y	Y	Y
llama2	Y	Y	Y	Y	Y	Y	Y
RedPajama	Y	Y	Y	❌	Y	Y	Y
chatyuan	Y	❌	❌	❌	Y	Y	Y
bloom	Y	Y	Y	Y	Y	Y	Y
falcon	Y	Y	Y	❌	Y	Y	Y
mpt	Y	Y	Y	❌	Y	Y	Y
GPT-J	Y	❌	Y	❌	Y	Y	Y
dolly	Y	Y	Y	❌	Y	Y	Y
T5	Y	❌	❌	❌	Y	Y	Y

License

This project is released under the Apache 2.0 license.

DataCanvas

LMS is an open source project created by DataCanvas.