# InternVL2
In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on InternVL2 model on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [OpenGVLab/InternVL2-4B](https://huggingface.co/OpenGVLab/InternVL2-4B) (or [OpenGVLab/InternVL2-4B](https://www.modelscope.cn/models/OpenGVLab/InternVL2-4B) for ModelScope) as a reference InternVL2 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: Predict Tokens using `chat()` API
In the example [chat.py](./chat.py), we show a basic use case for an InternVL2-4B model to predict the next N tokens using `chat()` API, with IPEX-LLM INT4 optimizations on Intel GPUs.
### 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 einops timm

# [optional] only needed if you would like to use ModelScope as model hub
pip install modelscope==1.11.0

```

#### 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 einops timm

# [optional] only needed if you would like to use ModelScope as model hub
pip install modelscope==1.11.0

```

### 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
<details>

<summary>For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series</summary>

```bash
export USE_XETLA=OFF
export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
export SYCL_CACHE_PERSISTENT=1
```

</details>

<details>

<summary>For Intel Data Center GPU Max Series</summary>

```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`.
</details>

<details>

<summary>For Intel iGPU</summary>

```bash
export SYCL_CACHE_PERSISTENT=1
```

</details>

#### 3.2 Configurations for Windows
<details>

<summary>For Intel iGPU and Intel Arc™ A-Series Graphics</summary>

```cmd
set SYCL_CACHE_PERSISTENT=1
```

</details>


> [!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

- chat with specified prompt:
  ```bash
  # for Hugging Face model hub
  python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT --image-url-or-path IMAGE_URL_OR_PATH

  # for ModelScope model hub
  python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT --image-url-or-path IMAGE_URL_OR_PATH --modelscope
  ```

Arguments info:
- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the **Hugging Face** or **ModelScope** repo id for the InternVL2 (e.g. `OpenGVLab/InternVL2-4B`) to be downloaded, or the path to the checkpoint folder. It is default to be `'OpenGVLab/InternVL2-4B'`.
- `--image-url-or-path IMAGE_URL_OR_PATH`: argument defining the image to be infered. It is default to be `'https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg'`.
- `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'What is in the image?'`.
- `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `64`.
- `--modelscope`: using **ModelScope** as model hub instead of **Hugging Face**.

#### Sample Output

#### [OpenGVLab/InternVL2-4B](https://huggingface.co/OpenGVLab/InternVL2-4B)

```log
-------------------- Input Image --------------------
https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg
-------------------- Input Prompt --------------------
What is in the image?
-------------------- Chat Output --------------------
The image shows a tiger lying on the grass.
```

The sample input image is:

<a href="https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg"><img width=400px src="https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg" ></a>