falcon

Falcon

In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on Falcon models on Intel GPUs. For illustration purposes, we utilize the 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 for more information.

Example: Predict Tokens using generate() API

In the example 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:

conda create -n llm 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
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:

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
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), 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), 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.

 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 with a specific snapshot id. Please note that the modelling_RW.py files that we provide are based on these specific commits.

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.

3. Configures OneAPI environment variables

3.1 Configurations for Linux

source /opt/intel/oneapi/setvars.sh

3.2 Configurations for Windows

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.

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

For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series

export USE_XETLA=OFF
export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1

For Intel Data Center GPU Max Series

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.

4.2 Configurations for Windows

For Intel iGPU

set SYCL_CACHE_PERSISTENT=1
set BIGDL_LLM_XMX_DISABLED=1

For Intel Arc™ A300-Series or Pro A60

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.

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

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?