In this directory, you will find examples on how you could apply BigDL-LLM INT4 optimizations on Vicuna models. For illustration purposes, we utilize the lmsys/vicuna-13b-v1.3 and eachadea/vicuna-7b-1.1 as reference Vicuna models.
To run these examples with BigDL-LLM, we have some recommended requirements for your machine, please refer to here for more information.
In the example generate.py, we show a basic use case for a Vicuna model to predict the next N tokens using generate()
API, with BigDL-LLM INT4 optimizations.
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 bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
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 bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
source /opt/intel/oneapi/setvars.sh
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.
For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device.
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 byconda install -c conda-forge -y gperftools=2.10
.
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.
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 Vicuna model (e.g.lmsys/vicuna-13b-v1.3
andeachadea/vicuna-7b-1.1
) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be'lmsys/vicuna-13b-v1.3'
.--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 be32
.
Note: When loading the model in 4-bit, BigDL-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 Vicuna model based on the capabilities of your machine.
Inference time: xxxx s
-------------------- Prompt --------------------
### Human:
What is AI?
### Assistant:
-------------------- Output --------------------
### Human:
What is AI?
### Assistant:
AI, or Artificial Intelligence, refers to the development of computer systems that can perform tasks that typically require human intelligence, such as visual perception,
Inference time: xxxx s
-------------------- Prompt --------------------
### Human:
What is AI?
### Assistant:
-------------------- Output --------------------
### Human:
What is AI?
### Assistant:
AI, or artificial intelligence, refers to the ability of a machine or computer program to mimic human intelligence and perform tasks that would normally require human intelligence to