# Running Lightweight Serving using IPEX-LLM on one Intel GPU
## Requirements
To run this example with IPEX-LLM on one Intel GPU, we have some recommended requirements for your machine, please refer to [here](../README.md#recommended-requirements) for more information.
## Example
### 1. Install
#### 1.1 Installation on Linux
We suggest using conda to manage environment:
```bash
conda create -n llm python=3.11
conda activate llm
# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
pip install fastapi uvicorn openai
pip install gradio # for gradio web UI
conda install -c conda-forge -y gperftools=2.10 # to enable tcmalloc
# for internlm-xcomposer2-vl-7b
pip install transformers==4.31.0
pip install accelerate timm==0.4.12 sentencepiece==0.1.99 gradio==3.44.4 markdown2==2.4.10 xlsxwriter==3.1.2 einops
```
#### 1.2 Installation on Windows
We suggest using conda to manage environment:
```bash
conda create -n llm python=3.11 libuv
conda activate llm
# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
pip install fastapi uvicorn openai
pip install gradio # for gradio web UI
conda install -c conda-forge -y gperftools=2.10 # to enable tcmalloc
```
### 2. Configures OneAPI environment variables for Linux
> [!NOTE]
> Skip this step if you are running on Windows.
This is a required step on Linux for APT or offline installed oneAPI. Skip this step for PIP-installed oneAPI.
```bash
source /opt/intel/oneapi/setvars.sh
```
### 3. Runtime Configurations
For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device.
#### 3.1 Configurations for Linux
For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series
```bash
export USE_XETLA=OFF
export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
export SYCL_CACHE_PERSISTENT=1
```
For Intel Data Center GPU Max Series
```bash
export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so
export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
export SYCL_CACHE_PERSISTENT=1
export ENABLE_SDP_FUSION=1
```
> Note: Please note that `libtcmalloc.so` can be installed by `conda install -c conda-forge -y gperftools=2.10`.
For Intel iGPU
```bash
export SYCL_CACHE_PERSISTENT=1
export BIGDL_LLM_XMX_DISABLED=1
```
#### 3.2 Configurations for Windows
For Intel iGPU
```cmd
set SYCL_CACHE_PERSISTENT=1
set BIGDL_LLM_XMX_DISABLED=1
```
For Intel Arc™ A-Series Graphics
```cmd
set SYCL_CACHE_PERSISTENT=1
```
> [!NOTE]
> For the first time that each model runs on Intel iGPU/Intel Arc™ A300-Series or Pro A60, it may take several minutes to compile.
### 4. Running example
```
python ./lightweight_serving.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --low-bit LOW_BIT --port PORT
```
Arguments info:
- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the model (e.g. `meta-llama/Llama-2-7b-chat-hf` and `meta-llama/Llama-2-13b-chat-hf`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'meta-llama/Llama-2-7b-chat-hf'`.
- `--low-bit LOW_BIT`: Sets the low bit optimizations (such as 'sym_int4', 'fp16', 'fp8' and 'fp6') for the model. It is default to be `sym_int4`.
- `--port PORT`: The serving access port. It is default to be `8000`.
### 5. Sample Input and Output
We can use `curl` to test serving api. And need to set no_proxy to ensure that requests are not forwarded by a proxy. `export no_proxy=localhost,127.0.0.1`
#### /generate
```bash
curl -X POST -H "Content-Type: application/json" -d '{
"inputs": "What is AI?",
"parameters": {
"max_new_tokens": 32,
"min_new_tokens": 32,
"repetition_penalty": 1.0,
"temperature": 1.0,
"do_sample": false,
"top_k": 5,
"tok_p": 1.0
},
"stream": false
}' http://localhost:8000/generate
```
#### /generate_stream
```bash
curl -X POST -H "Content-Type: application/json" -d '{
"inputs": "What is AI?",
"parameters": {
"max_new_tokens": 32,
"min_new_tokens": 32,
"repetition_penalty": 1.0,
"temperature": 1.0,
"do_sample": false,
"top_k": 5,
"tok_p": 1.0
},
"stream": false
}' http://localhost:8000/generate_stream
```
#### /v1/chat/completions
```bash
curl http://localhost:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Llama-2-7b-chat-hf",
"messages": [{"role": "user", "content": "Hello! What is your name?"}],
"stream": false
}'
```
##### Image input
image input only supports [internlm-xcomposer2-vl-7b](https://huggingface.co/internlm/internlm-xcomposer2-vl-7b) now, and it must install transformers==4.31.0 to run.
```bash
wget -O ./test.jpg http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg
curl http://localhost:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "internlm-xcomposer2-vl-7b",
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "What'\''s in this image?"
},
{
"type": "image_url",
"image_url": {
"url": "./test.jpg"
}
}
]
}
],
"max_tokens": 128
}'
```
#### /v1/completions
```bash
curl http://localhost:8000/v1/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Llama-2-7b-chat-hf",
"prompt": "Once upon a time",
"max_tokens": 32,
"stream": false
}'
```
### 6. Benchmark with wrk
Please refer to [here](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/Pipeline-Parallel-Serving#4-benchmark-with-wrk) for more details
## 7. Using the `benchmark.py` Script
Please refer to [here](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/Pipeline-Parallel-Serving#5-using-the-benchmarkpy-script) for more details
## 8. Gradio Web UI
Please refer to [here](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/Pipeline-Parallel-Serving#6-gradio-web-ui) for more details