# LLaVA
In this directory, you will find examples on how you could use IPEX-LLM `optimize_model` API on LLaVA models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [liuhaotian/llava-v1.5-7b](https://huggingface.co/liuhaotian/llava-v1.5-7b) as a reference LLaVA model.
## 0. Requirements
To run these examples with IPEX-LLM on Intel GPUs, we have some recommended requirements for your machine, please refer to [here](../../../README.md#requirements) for more information.
## Example: Multi-turn chat centered around an image using `generate()` API
In the example [generate.py](./generate.py), we show a basic use case for a LLaVA model to start a multi-turn chat centered around an image using `generate()` API, with IPEX-LLM INT4 optimizations on Intel GPUs.
### 1. Install
#### 1.1 Installation on Linux
We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://docs.conda.io/en/latest/miniconda.html#).
After installing conda, create a Python environment for IPEX-LLM:
```bash
conda create -n llm python=3.9 # recommend to use Python 3.9
conda activate llm
# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
pip install --pre --upgrade ipex-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
git clone -b v1.1.1 --depth=1 https://github.com/haotian-liu/LLaVA.git # clone the llava libary
pip install einops # install dependencies required by llava
cp generate.py ./LLaVA/ # copy our example to the LLaVA folder
cd LLaVA # change the working directory to the LLaVA folder
```
#### 1.2 Installation on Windows
We suggest using conda to manage environment:
```bash
conda create -n llm python=3.9 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] -f https://developer.intel.com/ipex-whl-stable-xpu
git clone -b v1.1.1 --depth=1 https://github.com/haotian-liu/LLaVA.git # clone the llava libary
pip install einops # install dependencies required by llava
cp generate.py ./LLaVA/ # copy our example to the LLaVA folder
cd LLaVA # change the working directory to the LLaVA folder
```
### 2. Configures OneAPI environment variables
#### 2.1 Configurations for Linux
```bash
source /opt/intel/oneapi/setvars.sh
```
#### 2.2 Configurations for Windows
```cmd
call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat"
```
> Note: Please make sure you are using **CMD** (**Anaconda Prompt** if using conda) to run the command as PowerShell is not supported.
### 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
```
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 ENABLE_SDP_FUSION=1
```
> Note: Please note that `libtcmalloc.so` can be installed by `conda install -c conda-forge -y gperftools=2.10`.
#### 3.2 Configurations for Windows
For Intel iGPU
```cmd
set SYCL_CACHE_PERSISTENT=1
set BIGDL_LLM_XMX_DISABLED=1
```
For Intel Arc™ A300-Series or Pro A60
```cmd
set SYCL_CACHE_PERSISTENT=1
```
For other Intel dGPU Series
There is no need to set further environment variables.
> 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 examples
```bash
python ./generate.py --image-path-or-url 'https://llava-vl.github.io/static/images/monalisa.jpg'
```
In the example, several arguments can be passed to satisfy your requirements:
- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the LLaVA model (e.g. `liuhaotian/llava-v1.5-7b` to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'liuhaotian/llava-v1.5-7b'`.
- `--image-path-or-url IMAGE_PATH_OR_URL`: argument defining the input image that the chat will focus on. It is required.
- `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `512`.
If you encounter some network error (which means your machine is unable to access huggingface.co) when running this example, refer to [Trouble Shooting](#4-trouble-shooting) section.
#### Sample Output
#### [liuhaotian/llava-v1.5-7b](https://huggingface.co/liuhaotian/llava-v1.5-7b)
```log
USER: Do you know who drew this painting?
ASSISTANT: Yes, the painting is a portrait of a woman by Leonardo da Vinci. It's a famous artwork known as the "Mona Lisa."
USER: Can you describe this painting?
ASSISTANT: The painting features a well-detailed portrait of a woman, painted in oil on a canvas. The woman appears to be a young woman staring straight ahead in a direct gaze towards the viewer. The woman's facial features are rendered sharply in the brush strokes, giving her a lifelike, yet enigmatic expression.
The background of the image mainly showcases the woman's face, with some hills visible in the lower part of the painting. The artist employs a wide range of shades, evoking a sense of depth and realism in the subject matter. The technique used in this portrait sets it apart from other artworks during the Renaissance period, making it a notable piece in art history.
```
The sample input image is:
### 5 Trouble shooting
#### 5.1 SSLError
If you encounter the following output, it means your machine has some trouble accessing huggingface.co.
```log
requests.exceptions.SSLError: (MaxRetryError("HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /openai/clip-vit-large-patch14-336/resolve/main/config.json (Caused by SSLError(SSLZeroReturnError(6, 'TLS/SSL connection has been closed (EOF) (_ssl.c:1129)')))"),
```
You can resolve this problem with the following steps:
1. Download https://huggingface.co/openai/clip-vit-large-patch14-336 on some machine that can access huggingface.co, and put it in huggingface's local cache (default to be `~/.cache/huggingface/hub`) on the machine that you are going to run this example.
2. Set the environment variable (`export TRANSFORMERS_OFFLINE=1`) before you run the example.