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* Replace `bigdl-nano-init` with `bigdl-llm-init`. * Install `bigdl-llm` instead of `bigdl-nano`. * Remove nano in README.
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BERT
In this directory, you will find examples on how you could use BigDL-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 BigDL-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 BigDL-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 BigDL-LLM:
conda create -n llm python=3.9 # recommend to use Python 3.9
conda activate llm
pip install --pre --upgrade bigdl-llm[all] # install the latest bigdl-llm nightly build with 'all' option
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 BigDL-LLM env variables
source bigdl-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.'.