# GLM-Edge
In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on GLM-Edge models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [THUDM/glm-edge-1.5b-chat](https://huggingface.co/THUDM/glm-edge-1.5b-chat) and [THUDM/glm-edge-4b-chat](https://huggingface.co/THUDM/glm-edge-4b-chat) (or [ZhipuAI/glm-edge-1.5b-chat](https://www.modelscope.cn/models/ZhipuAI/glm-edge-1.5b-chat) and [ZhipuAI/glm-edge-4b-chat](https://www.modelscope.cn/models/ZhipuAI/glm-edge-4b-chat) for ModelScope) as reference GLM-Edge models.

## 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.

## 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/

# install packages required for GLM-Edge
pip install transformers==4.47.0
pip install accelerate==0.33.0
pip install "trl<0.12.0" 

# [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/

# install packages required for GLM-Edge
pip install transformers==4.47.0
pip install accelerate==0.33.0
pip install "trl<0.12.0"

# [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

### Example 1: Predict Tokens using `generate()` API
In the example [generate.py](./generate.py), we show a basic use case for a GLM-Edge model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations on Intel GPUs.

```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

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

Arguments info:
- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the GLM-Edge model (e.g. `THUDM/glm-edge-1.5b-chat` or `THUDM/glm-edge-4b-chat`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'THUDM/glm-edge-4b-chat'` for **Hugging Face** or `'ZhipuAI/glm-edge-4b-chat'` for **ModelScope**.
- `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'AI是什么？'`.
- `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`.
- `--modelscope`: using **ModelScope** as model hub instead of **Hugging Face**.

#### Sample Output
#### [THUDM/glm-edge-1.5b-chat](https://huggingface.co/THUDM/glm-edge-1.5b-chat)
```log
Inference time: xxxx s
-------------------- Prompt --------------------
AI是什么？
-------------------- Output --------------------
AI，即人工智能，指的是由人制造出来的系统或机器能够执行通常需要人类智能才能完成的任务。人工智能可以执行多种任务，包括视觉识别、语言
```

```log
Inference time: xxxx s
-------------------- Prompt --------------------
What is AI?
-------------------- Output --------------------
Artificial Intelligence, often abbreviated as AI, refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic
```

#### [THUDM/glm-edge-4b-chat](https://huggingface.co/THUDM/glm-edge-4b-chat)
```log
Inference time: xxxx s
-------------------- Prompt --------------------
AI是什么？
-------------------- Output --------------------
AI，即人工智能（Artificial Intelligence），是计算机科学的一个分支，旨在开发出一种智能系统，使其能够执行通常需要人类智能才能完成的任务，如视觉
```

```log
Inference time: xxxx s
-------------------- Prompt --------------------
What is AI?
-------------------- Output --------------------
Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. AI systems can
```