Installation with XPU

Installation with XPU#

vLLM initially supports basic model inferencing and serving on Intel GPU platform.

Table of contents:

  1. Requirements

  2. Quick start using Dockerfile

  3. Build from source

Requirements#

  • OS: Linux

  • Supported Hardware: Intel Data Center GPU, Intel ARC GPU

  • OneAPI requirements: oneAPI 2024.2

Quick start using Dockerfile#

$ docker build -f Dockerfile.xpu -t vllm-xpu-env --shm-size=4g .
$ docker run -it \
             --rm \
             --network=host \
             --device /dev/dri \
             -v /dev/dri/by-path:/dev/dri/by-path \
             vllm-xpu-env

Build from source#

  • First, install required driver and intel OneAPI 2024.2 or later.

  • Second, install Python packages for vLLM XPU backend building:

$ source /opt/intel/oneapi/setvars.sh
$ pip install --upgrade pip
$ pip install -v -r requirements-xpu.txt

  • Finally, build and install vLLM XPU backend:

$ VLLM_TARGET_DEVICE=xpu python setup.py install

Note

  • FP16 is the default data type in the current XPU backend. The BF16 data type will be supported in the future.

Distributed inference and serving#

XPU platform supports tensor-parallel inference/serving and also supports pipeline parallel as a beta feature for online serving. We requires Ray as the distributed runtime backend. For example, a reference execution likes following:

$ python -m vllm.entrypoints.openai.api_server \
$      --model=facebook/opt-13b \
$      --dtype=bfloat16 \
$      --device=xpu \
$      --max_model_len=1024 \
$      --distributed-executor-backend=ray \
$      --pipeline-parallel-size=2 \
$      -tp=8

By default, a ray instance will be launched automatically if no existing one is detected in system, with num-gpus equals to parallel_config.world_size. We recommend properly starting a ray cluster before execution, referring to the examples/run_cluster.sh helper script.