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Falcon

In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on Falcon models. For illustration purposes, we utilize the tiiuae/falcon-7b-instruct and tiiuae/falcon-40b-instruct as reference Falcon models.

0. Requirements

To run these examples with IPEX-LLM, 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.

1. Install

We suggest using conda to manage environment:

On Linux:

conda create -n llm python=3.11
conda activate llm

# install the latest ipex-llm nightly build with 'all' option
pip install --pre --upgrade ipex-llm[all] --extra-index-url https://download.pytorch.org/whl/cpu
pip install einops # additional package required for falcon-7b-instruct and falcon-40b-instruct to conduct generation

On Windows:

conda create -n llm python=3.11
conda activate llm

pip install --pre --upgrade ipex-llm[all]
pip install einops

2. (Optional) Download Model and Replace File

If you select the Falcon models (tiiuae/falcon-7b-instruct or tiiuae/falcon-40b-instruct), please note that their code (modelling_RW.py) does not support KV cache at the moment. To address issue, we have provided two updated files (falcon-7b-instruct/modelling_RW.py and falcon-40b-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 or tiiuae/falcon-40b-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}')

# for tiiuae/falcon-40b-instruct
model_path = snapshot_download(repo_id='tiiuae/falcon-40b-instruct',
                               revision="1e7fdcc9f45d13704f3826e99937917e007cd975",
                               cache_dir="dir/path/where/model/files/are/downloaded")
print(f'tiiuae/falcon-40b-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.

For tiiuae/falcon-40b-instruct, you should replace the modelling_RW.py with falcon-40b-instruct/modelling_RW.py.

3. Run

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 to be downloaded, or the path to the huggingface checkpoint folder. For model tiiuae/falcon-7b-instruct or tiiuae/falcon-40b-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.

Note: When loading the model in 4-bit, IPEX-LLM converts linear layers in the model into INT4 format. In theory, a XB model saved in 16-bit will requires approximately 2X GB of memory for loading, and ~0.5X GB memory for further inference.

Please select the appropriate size of the Falcon model based on the capabilities of your machine.

3.1 Client

On client Windows machine, it is recommended to run directly with full utilization of all cores:

python ./generate.py

3.2 Server

For optimal performance on server, it is recommended to set several environment variables (refer to here for more information), and run the example with all the physical cores of a single socket.

E.g. on Linux,

# set IPEX-LLM env variables
source ipex-llm-init

# e.g. for a server with 48 cores per socket
export OMP_NUM_THREADS=48
numactl -C 0-47 -m 0 python ./generate.py

3.3 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?

tiiuae/falcon-40b-instruct

Inference time: xxxx s
-------------------- Prompt --------------------
<human> What is AI? <bot>
-------------------- Output --------------------
<human> What is AI? <bot> AI stands for Artificial Intelligence. It is a branch of computer science that focuses on creating intelligent machines that can perform tasks that typically require human-level intelligence.