fastchat

Serving using IPEX-LLM and FastChat

FastChat is an open platform for training, serving, and evaluating large language model based chatbots. You can find the detailed information at their homepage.

IPEX-LLM can be easily integrated into FastChat so that user can use IPEX-LLM as a serving backend in the deployment.

Table of contents

• Install
• Start the service
  • Launch controller
  • Launch model worker(s) and load models
    • IPEX-LLM model worker (deprecated)
    • IPEX-LLM worker
    • IPEX-LLM vLLM worker
  • Launch Gradio web server
  • Launch RESTful API server

Install

You may install ipex-llm with FastChat as follows:

pip install --pre --upgrade ipex-llm[serving]
pip install transformers==4.36.0

# Or
pip install --pre --upgrade ipex-llm[all]

To add GPU support for FastChat, you may install ipex-llm as follows:

pip install --pre --upgrade ipex-llm[xpu,serving] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/

Start the service

Launch controller

You need first run the fastchat controller

python3 -m fastchat.serve.controller

Launch model worker(s) and load models

Using IPEX-LLM in FastChat does not impose any new limitations on model usage. Therefore, all Hugging Face Transformer models can be utilized in FastChat.

IPEX-LLM worker

To integrate IPEX-LLM with FastChat efficiently, we have provided a new model_worker implementation named ipex_llm_worker.py.

To run the ipex_llm_worker on CPU, using the following code:

source ipex-llm-init -t

# Available low_bit format including sym_int4, sym_int8, bf16 etc.
python3 -m ipex_llm.serving.fastchat.ipex_llm_worker --model-path lmsys/vicuna-7b-v1.5 --low-bit "sym_int4" --trust-remote-code --device "cpu"

For GPU example:

# Available low_bit format including sym_int4, sym_int8, fp16 etc.
python3 -m ipex_llm.serving.fastchat.ipex_llm_worker --model-path lmsys/vicuna-7b-v1.5 --low-bit "sym_int4" --trust-remote-code --device "xpu"

For self-speculative decoding example:

You can use IPEX-LLM to run self-speculative decoding example. Refer to here for more details on intel MAX GPUs. Refer to here for more details on intel CPUs.

# Available low_bit format only including bf16 on CPU.
source ipex-llm-init -t
python3 -m ipex_llm.serving.fastchat.ipex_llm_worker --model-path lmsys/vicuna-7b-v1.5 --low-bit "bf16" --trust-remote-code --device "cpu" --speculative

# Available low_bit format only including fp16 on GPU.
source /opt/intel/oneapi/setvars.sh
export ENABLE_SDP_FUSION=1
export SYCL_CACHE_PERSISTENT=1
export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
python3 -m ipex_llm.serving.fastchat.ipex_llm_worker --model-path lmsys/vicuna-7b-v1.5 --low-bit "fp16" --trust-remote-code --device "xpu" --speculative

For a full list of accepted arguments, you can refer to the main method of the ipex_llm_worker.py

IPEX-LLM vLLM worker

We also provide the vllm_worker which uses the vLLM engine for better hardware utilization.

To run using the vLLM_worker, we don't need to change model name, just simply uses the following command:

# On CPU
python3 -m ipex_llm.serving.fastchat.vllm_worker --model-path REPO_ID_OR_YOUR_MODEL_PATH --device cpu

# On GPU
python3 -m ipex_llm.serving.fastchat.vllm_worker --model-path REPO_ID_OR_YOUR_MODEL_PATH --device xpu

Launch Gradio web server

python3 -m fastchat.serve.gradio_web_server

This is the user interface that users will interact with.

By following these steps, you will be able to serve your models using the web UI with IPEX-LLM as the backend. You can open your browser and chat with a model now.

Launch RESTful API server

To start an OpenAI API server that provides compatible APIs using IPEX-LLM backend, you can launch the openai_api_server and follow this doc to use it.

python3 -m fastchat.serve.openai_api_server --host localhost --port 8000