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phi-1_5
In this directory, you will find examples on how you could use BigDL-LLM optimize_model API to accelerate phi-1_5 models on Intel GPUs. For illustration purposes, we utilize the microsoft/phi-1_5 as a reference phi-1_5 model.
Requirements
To run these examples with BigDL-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 phi-1_5 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 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[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
pip install einops # additional package required for phi-1_5 to conduct generation
2. Configures OneAPI environment variables
source /opt/intel/oneapi/setvars.sh
3. 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 --prompt 'What is AI?'
Arguments info:
--repo-id-or-model-path REPO_ID_OR_MODEL_PATH: argument defining the huggingface repo id for the phi-1_5 model (e.g.microsoft/phi-1_5) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be'microsoft/phi-1_5'.--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.
Sample Output
microsoft/phi-1_5
Inference time: xxxx s
-------------------- Output --------------------
Question: What is AI?
Answer: AI stands for Artificial Intelligence, which refers to the development of computer systems that can perform tasks that typically require human intelligence, such as visual perception, speech recognition,