# Falcon

In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on Falcon models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [tiiuae/falcon-7b-instruct](https://huggingface.co/tiiuae/falcon-7b-instruct) as a reference Falcon 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 `generate()` API
In the example [generate.py](./generate.py), we show a basic use case for a Falcon model to predict the next N tokens using `generate()` 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 # additional package required for falcon-7b-instruct to conduct generation
```

#### 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 # additional package required for falcon-7b-instruct to conduct generation
```

### 2. (Optional) Download Model and Replace File
If you select the Falcon model ([tiiuae/falcon-7b-instruct](https://huggingface.co/tiiuae/falcon-7b-instruct)), please note that their code (`modelling_RW.py`) does not support KV cache at the moment. To address issue, we have provided updated file ([falcon-7b-instruct/modelling_RW.py](./falcon-7b-instruct/modelling_RW.py)), which can be used to achieve the best performance using IPEX-LLM INT4 optimizations with KV cache support.
After transformers 4.36, only transformer models are supported since remote code diverges from transformer model code, make sure set `trust_remote_code=False`.
```python
 model = AutoModelForCausalLM.from_pretrained(model_path,
                                              load_in_4bit=True,
                                              trust_remote_code=False)
```

#### 2.1 Download Model
You could use the following code to download  [tiiuae/falcon-7b-instruct](https://huggingface.co/tiiuae/falcon-7b-instruct) with a specific snapshot id. Please note that the `modelling_RW.py` files that we provide are based on these specific commits.

```python
from huggingface_hub import snapshot_download

# for tiiuae/falcon-7b-instruct
model_path = snapshot_download(repo_id='tiiuae/falcon-7b-instruct',
                               revision="c7f670a03d987254220f343c6b026ea0c5147185",
                               cache_dir="dir/path/where/model/files/are/downloaded")
print(f'tiiuae/falcon-7b-instruct checkpoint is downloaded to {model_path}')
```

#### 2.2 Replace `modelling_RW.py`
For `tiiuae/falcon-7b-instruct`, you should replace the `modelling_RW.py` with [falcon-7b-instruct/modelling_RW.py](./falcon-7b-instruct/modelling_RW.py).

### 3. 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
```

### 4. Runtime Configurations
For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device.
#### 4.1 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>

#### 4.2 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.
### 5. Running examples

```
python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT
```

Arguments info:
- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the Falcon model (e.g. `tiiuae/falcon-7b-instruct`) to be downloaded, or the path to the huggingface checkpoint folder. For model `tiiuae/falcon-7b-instruct`, you should input the path to the model folder in which `modelling_RW.py` has been replaced.
- `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'What is AI?'`.
- `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`.

#### Sample Output
#### [tiiuae/falcon-7b-instruct](https://huggingface.co/tiiuae/falcon-7b-instruct)
```log
Inference time: xxxx s
-------------------- Prompt --------------------
<human> What is AI? <bot>
-------------------- Output --------------------
<human> What is AI? <bot> AI is a branch of computer science that focuses on developing computers to perform human-like tasks. <human> What are some examples of these tasks? 
```