144 lines
5.4 KiB
Markdown
144 lines
5.4 KiB
Markdown
# XPU
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The document addresses how to set up the [SGLang](https://github.com/sgl-project/sglang) environment and run LLM inference on Intel GPU, [see more context about Intel GPU support within PyTorch ecosystem](https://docs.pytorch.org/docs/stable/notes/get_start_xpu.html).
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Specifically, SGLang is optimized for [Intel® Arc™ Pro B-Series Graphics](https://www.intel.com/content/www/us/en/ark/products/series/242616/intel-arc-pro-b-series-graphics.html) and [
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Intel® Arc™ B-Series Graphics](https://www.intel.com/content/www/us/en/ark/products/series/240391/intel-arc-b-series-graphics.html).
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## Optimized Model List
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A list of LLMs have been optimized on Intel GPU, and more are on the way:
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| Model Name | BF16 |
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|:---:|:---:|
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| Llama-3.2-3B | [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) |
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| Llama-3.1-8B | [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) |
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| Qwen2.5-1.5B | [Qwen/Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B) |
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**Note:** The model identifiers listed in the table above
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have been verified on [Intel® Arc™ B580 Graphics](https://www.intel.com/content/www/us/en/products/sku/241598/intel-arc-b580-graphics/specifications.html).
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## Installation
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### Install From Source
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Currently SGLang XPU only supports installation from source. Please refer to ["Getting Started on Intel GPU"](https://docs.pytorch.org/docs/stable/notes/get_start_xpu.html) to install XPU dependency.
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```bash
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# Create and activate a conda environment
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conda create -n sgl-xpu python=3.12 -y
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conda activate sgl-xpu
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# Set PyTorch XPU as primary pip install channel to avoid installing the larger CUDA-enabled version and prevent potential runtime issues.
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pip3 install torch==2.11.0+xpu torchao torchvision torchaudio==2.11.0+xpu --index-url https://download.pytorch.org/whl/xpu
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pip3 install xgrammar --no-deps # xgrammar will introduce CUDA-enabled triton which might conflict with XPU
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# Clone the SGLang code
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git clone https://github.com/sgl-project/sglang.git
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cd sglang
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git checkout <YOUR-DESIRED-VERSION>
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# Use dedicated toml file
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cd python
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cp pyproject_xpu.toml pyproject.toml
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# Install SGLang dependent libs, and build SGLang main package
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pip install --upgrade pip setuptools
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pip install -v . --extra-index-url https://download.pytorch.org/whl/xpu
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```
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### Install Using Docker
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The docker for XPU is under active development. Please stay tuned.
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## Launch of the Serving Engine
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Example command to launch SGLang serving:
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```bash
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python -m sglang.launch_server \
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--model <MODEL_ID_OR_PATH> \
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--trust-remote-code \
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--disable-overlap-schedule \
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--device xpu \
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--host 0.0.0.0 \
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--tp 2 \ # using multi GPUs
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--attention-backend intel_xpu \ # using intel optimized XPU attention backend
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--page-size \ # intel_xpu attention backend supports [32, 64, 128]
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```
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## Benchmarking with Requests
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You can benchmark the performance via the `bench_serving` script.
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Run the command in another terminal.
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```bash
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python -m sglang.bench_serving \
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--dataset-name random \
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--random-input-len 1024 \
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--random-output-len 1024 \
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--num-prompts 1 \
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--request-rate inf \
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--random-range-ratio 1.0
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```
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The detail explanations of the parameters can be looked up by the command:
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```bash
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python -m sglang.bench_serving -h
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```
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Additionally, the requests can be formed with
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[OpenAI Completions API](https://docs.sglang.io/basic_usage/openai_api_completions.html)
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and sent via the command line (e.g. using `curl`) or via your own script.
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## Prefill-Decode (P/D) Disaggregation on Intel XPU [Experimental]
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SGLang supports prefill-decode disaggregation on Intel XPU using the [NIXL](https://github.com/ai-dynamo/nixl) KV-transfer backend.
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**Tested models:**
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| Model | Notes |
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|:---:|:---:|
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| [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) | Used in integration tests; verified on Intel XPU with homogeneous P/D (XPU prefill + XPU decode) |
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| [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) | Verified on Intel XPU with homogeneous P/D (XPU prefill + XPU decode) |
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**Prerequisites:** `pip install nixl sglang-router`
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**Start the prefill server (GPU 0):**
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```bash
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ZE_AFFINITY_MASK=0 UCX_POSIX_USE_PROC_LINK=n python -m sglang.launch_server \
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--model-path Qwen/Qwen3-0.6B --trust-remote-code --device xpu \
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--disaggregation-mode prefill --disaggregation-transfer-backend nixl \
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--disaggregation-bootstrap-port 12335 --host 0.0.0.0 --port 30000
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```
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**Start the decode server (GPU 1):**
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```bash
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ZE_AFFINITY_MASK=1 UCX_POSIX_USE_PROC_LINK=n python -m sglang.launch_server \
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--model-path Qwen/Qwen3-0.6B --trust-remote-code --device xpu \
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--disaggregation-mode decode --disaggregation-transfer-backend nixl \
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--disaggregation-bootstrap-port 12335 --host 0.0.0.0 --port 30001
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```
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**Start the router:**
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```bash
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python -m sglang_router.launch_router \
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--pd-disaggregation \
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--prefill http://127.0.0.1:30000 \
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--decode http://127.0.0.1:30001 \
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--host 0.0.0.0 --port 8000
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```
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**Send a request:**
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```bash
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curl http://127.0.0.1:8000/v1/completions \
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-H "Content-Type: application/json" \
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-d '{"model": "Qwen/Qwen3-0.6B", "prompt": "The capital of France is", "max_tokens": 32}'
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```
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> **Note:** `UCX_POSIX_USE_PROC_LINK=n` is required on Intel XPU to avoid UCX shared-memory transport issues.
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