2d930bdca8
* add argument load_in_low_bit * add docs * modify gpu doc * done --------- Co-authored-by: ivy-lv11 <lvzc@lamda.nju.edu.cn>
111 lines
4.6 KiB
Markdown
111 lines
4.6 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 BigDL-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/v0.2.1.post1).
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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. Please check the requirements at [here](https://github.com/intel-analytics/BigDL/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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To run vLLM continuous batching on Intel GPUs, install the dependencies as follows:
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```bash
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# First create an conda environment
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conda create -n bigdl-vllm python==3.9
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conda activate bigdl-vllm
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# Install dependencies
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pip3 install psutil
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pip3 install sentencepiece # Required for LLaMA tokenizer.
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pip3 install numpy
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# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
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pip install --pre --upgrade "bigdl-llm[xpu]" -f https://developer.intel.com/ipex-whl-stable-xpu
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pip3 install fastapi
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pip3 install "uvicorn[standard]"
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pip3 install "pydantic<2" # Required for OpenAI server.
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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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```bash
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#!/bin/bash
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# You may also want to adjust the `--max-num-batched-tokens` argument, it indicates the hard limit
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# of batched prompt length the server will accept
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python -m bigdl.llm.vllm.entrypoints.openai.api_server \
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--model /MODEL_PATH/Llama-2-7b-chat-hf/ --port 8000 \
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--load-format 'auto' --device xpu --dtype bfloat16 \
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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-bigdl/",
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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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### 4. (Optional) Add a new model
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Currently we have only supported LLaMA family model (including `llama`, `vicuna`, `llama-2`, etc.). To use aother model, you may need add some adaptions.
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#### 4.1 Add model code
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Create or clone the Pytorch model code to `BigDL/python/llm/src/bigdl/llm/vllm/model_executor/models`.
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#### 4.2 Rewrite the forward methods
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Refering to `BigDL/python/llm/src/bigdl/llm/vllm/model_executor/models/bigdl_llama.py`, it's necessary to maintain a `kv_cache`, which is a nested list of dictionary that maps `req_id` to a three-dimensional tensor **(the structure may vary from models)**. Before the model's actual `forward` method, you could prepare a `past_key_values` according to current `req_id`, and after that you need to update the `kv_cache` with `output.past_key_values`. The clearence will be executed when the request is finished.
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#### 4.3 Register new model
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Finally, register your `*ForCausalLM` class to the _MODEL_REGISTRY in `BigDL/python/llm/src/bigdl/llm/vllm/model_executor/model_loader.py`.
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