In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on Google Gemma2 models on Intel GPUs. For illustration purposes, we utilize the google/gemma-2-9b-it and google/gemma-2-2b-it as reference Gemma2 models.
To run these examples with IPEX-LLM on Intel GPUs, we have some recommended requirements for your machine, please refer to here for more information.
Important: According to Gemma2's requirement, please make sure you have installed transformers==4.43.1 and trl to run the example.
In the example generate.py, we show a basic use case for a Gemma2 model to predict the next N tokens using generate() API, with IPEX-LLM INT4 optimizations on Intel GPUs.
We suggest using conda to manage environment:
conda create -n llm python=3.11
conda activate llm
# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
# According to Gemma2's requirement, please make sure you are using a stable version of Transformers, 4.43.1 or newer.
pip install "transformers>=4.43.1"
pip install trlWe suggest using conda to manage environment:
conda create -n llm python=3.11 libuv
conda activate llm
# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
# According to Gemma2's requirement, please make sure you are using a stable version of Transformers, 4.43.1 or newer.
pip install "transformers>=4.43.1"
pip install trlNote
Skip this step if you are running on Windows.
This is a required step on Linux for APT or offline installed oneAPI. Skip this step for PIP-installed oneAPI.
source /opt/intel/oneapi/setvars.shFor 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
export SYCL_CACHE_PERSISTENT=1For 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 SYCL_CACHE_PERSISTENT=1
export ENABLE_SDP_FUSION=1Note: Please note that
libtcmalloc.socan be installed byconda install -c conda-forge -y gperftools=2.10.
For Intel iGPU
export SYCL_CACHE_PERSISTENT=1
export BIGDL_LLM_XMX_DISABLED=1For Intel iGPU
set SYCL_CACHE_PERSISTENT=1
set BIGDL_LLM_XMX_DISABLED=1For Intel Arc™ A-Series Graphics
set SYCL_CACHE_PERSISTENT=1Note
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 --prompt 'What is AI?'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 Gemma model (e.g.google/gemma-2-9b-itandgoogle/gemma-2-2b-it) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be'google/gemma-2-9b-it'.--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.
Inference time: xxxx s
-------------------- Output --------------------
user
What is AI?
model
Artificial intelligence (AI) is a broad field of computer science focused on creating intelligent agents, which are systems that can reason, learn, and act autonomously.
Inference time: xxxx s
-------------------- Output --------------------
user
What is AI?
model
AI, or Artificial Intelligence, is a broad field of computer science focused on creating intelligent agents, which are systems that can reason, learn, and act like humans