ipex-llm/python/llm/example/CPU/PyTorch-Models/Model/bert

BERT

In this directory, you will find examples on how you could use IPEX-LLM optimize_model API to accelerate BERT models. For illustration purposes, we utilize the bert-large-uncased as reference BERT models.

Requirements

To run these examples with IPEX-LLM, we have some recommended requirements for your machine, please refer to here for more information.

Example: Extract the feature of given text

In the example extract_feature.py, we show a basic use case for a BERT model to extract the feature of given text, with IPEX-LLM INT4 optimizations.

1. Install

We suggest using conda to manage the Python environment. For more information about conda installation, please refer to here.

After installing conda, create a Python environment for IPEX-LLM:

On Linux:

conda create -n llm python=3.11 # recommend to use 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

On Windows:

conda create -n llm python=3.11
conda activate llm

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

2. Run

After setting up the Python environment, you could run the example by following steps.

2.1 Client

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

python ./extract_feature.py --text 'This is an example text for feature extraction.'

More information about arguments can be found in Arguments Info section.

2.2 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 ./extract_feature.py --text 'This is an example text for feature extraction.'

More information about arguments can be found in Arguments Info section.

2.3 Arguments Info

In the example, several arguments can be passed to satisfy your requirements:

• --repo-id-or-model-path REPO_ID_OR_MODEL_PATH: argument defining the huggingface repo id for the BERT model (e.g. bert-large-uncased) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be 'bert-large-uncased'.
• --text TEXT: argument defining the text to be extracted features. It is default to be 'This is an example text for feature extraction.'.