ipex-llm/python/llm/example/GPU/HF-Transformers-AutoModels/Model/falcon

Falcon

In this directory, you will find examples on how you could apply BigDL-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 BigDL-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 BigDL-LLM INT4 optimizations on Intel GPUs.

1. Install

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==2.0.110+xpu as default
# you can install specific ipex/torch version for your need
pip install bigdl-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 BigDL-LLM INT4 optimizations with KV cache support.

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

source /opt/intel/oneapi/setvars.sh

4. Run

For optimal performance on Arc, it is recommended to set several environment variables.

export USE_XETLA=OFF
export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1

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?