106 lines
3.8 KiB
Markdown
106 lines
3.8 KiB
Markdown
# vLLM continuous batching on Intel GPUs (experimental support)
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This example demonstrates how to serve a LLaMA2-7B model using vLLM continuous batching on Intel GPU (with IPEX-LLM low-bits optimizations).
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The code shown in the following example is ported from [vLLM](https://github.com/vllm-project/vllm/tree/v0.3.3).
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Currently, we support the following models for vLLM engine:
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- Qwen series models
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- Llama series models
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- ChatGLM series models
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- Baichuan series models
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## Example: Serving LLaMA2-7B using Intel GPU
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In this example, we will run Llama2-7b model using Arc A770 and provide `OpenAI-compatible` interface for users.
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### 0. Environment
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To use Intel GPUs for deep-learning tasks, you should install the XPU driver and the oneAPI Base Toolkit 2024.0. Please check the requirements at [here](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU#requirements).
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After install the toolkit, run the following commands in your environment before starting vLLM GPU:
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```bash
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source /opt/intel/oneapi/setvars.sh
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# sycl-ls will list all the compatible Intel GPUs in your environment
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sycl-ls
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# Example output with one Arc A770:
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[opencl:acc:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device 1.2 [2023.16.7.0.21_160000]
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[opencl:cpu:1] Intel(R) OpenCL, 13th Gen Intel(R) Core(TM) i9-13900K 3.0 [2023.16.7.0.21_160000]
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[opencl:gpu:2] Intel(R) OpenCL Graphics, Intel(R) Arc(TM) A770 Graphics 3.0 [23.17.26241.33]
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[ext_oneapi_level_zero:gpu:0] Intel(R) Level-Zero, Intel(R) Arc(TM) A770 Graphics 1.3 [1.3.26241]
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```
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### 1. Install
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Install the dependencies for vLLM as follows:
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```bash
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# This directory may change depends on where you install oneAPI-basekit
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source /opt/intel/oneapi/setvars.sh
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# First create an conda environment
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conda create -n ipex-vllm python=3.11
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conda activate ipex-vllm
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# Install dependencies
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pip install --pre --upgrade "ipex-llm[xpu]" --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
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# cd to your workdir
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git clone -b sycl_xpu https://github.com/analytics-zoo/vllm.git
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cd vllm
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pip install -r requirements-xpu.txt
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pip install --no-deps xformers
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VLLM_BUILD_XPU_OPS=1 pip install --no-build-isolation -v -e .
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pip install outlines==0.0.34 --no-deps
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pip install interegular cloudpickle diskcache joblib lark nest-asyncio numba scipy
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# For Qwen model support
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pip install transformers_stream_generator einops tiktoken
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```
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### 2. Configure recommended environment variables
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```bash
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export USE_XETLA=OFF
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export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
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```
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### 3. Offline inference/Service
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#### Offline inference
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To run offline inference using vLLM for a quick impression, use the following example:
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```bash
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#!/bin/bash
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# Please first modify the MODEL_PATH in offline_inference.py
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# Modify load_in_low_bit to use different quantization dtype
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python offline_inference.py
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```
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#### Service
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To fully utilize the continuous batching feature of the `vLLM`, you can send requests to the service using curl or other similar methods. The requests sent to the engine will be batched at token level. Queries will be executed in the same `forward` step of the LLM and be removed when they are finished instead of waiting for all sequences to be finished.
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For vLLM, you can start the service using the following command:
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```bash
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python -m ipex_llm.vllm.entrypoints.openai.api_server \
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--model /MODEL_PATH/Llama-2-7b-chat-hf/ --port 8000 \
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--device xpu --dtype float16 \
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--load-in-low-bit sym_int4 \
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--max-num-batched-tokens 4096
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```
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Then you can access the api server as follows:
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```bash
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curl http://localhost:8000/v1/completions \
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-H "Content-Type: application/json" \
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-d '{
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"model": "/MODEL_PATH/Llama-2-7b-chat-hf/",
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"prompt": "San Francisco is a",
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"max_tokens": 128,
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"temperature": 0
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}' &
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```
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