In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on MiniCPM3 models on Intel GPUs. For illustration purposes, we utilize the openbmb/MiniCPM3-4B (or OpenBMB/MiniCPM3-4B for ModelScope) as a reference MiniCPM3 model.
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.
In the example generate.py, we show a basic use case for a MiniCPM3 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/
pip install jsonschema datamodel_code_generator
# [optional] only needed if you would like to use ModelScope as model hub
pip install modelscope==1.11.0
We 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/
pip install jsonschema datamodel_code_generator
# [optional] only needed if you would like to use ModelScope as model hub
pip install modelscope==1.11.0
Note
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.sh
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
export SYCL_CACHE_PERSISTENT=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 SYCL_CACHE_PERSISTENT=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
export SYCL_CACHE_PERSISTENT=1
For Intel iGPU and Intel Arc™ A-Series Graphics
set SYCL_CACHE_PERSISTENT=1
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.
# for Hugging Face model hub
python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT
# for ModelScope model hub
python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT --modelscope
Arguments info:
--repo-id-or-model-path REPO_ID_OR_MODEL_PATH
: argument defining the Hugging Face or ModelScope repo id for the MiniCPM3 model (e.g.openbmb/MiniCPM3-4B
) to be downloaded, or the path to the checkpoint folder. It is default to be'openbmb/MiniCPM3-4B'
for Hugging Face or'OpenBMB/MiniCPM3-4B'
for ModelScope.--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
.--modelscope
: using ModelScope as model hub instead of Hugging Face.
Inference time: xxxx s
-------------------- Prompt --------------------
<|im_start|>user
AI是什么?<|im_end|>
<|im_start|>assistant
-------------------- Output --------------------
<s><|im_start|> user
AI是什么?<|im_end|>
<|im_start|> assistant
AI,即人工智能(Artificial Intelligence),是指由人类创造的、能够模拟人类智能的相关理论和实践的一门新兴技术。它使计算机 或其他
Inference time: xxxx s
-------------------- Prompt --------------------
<|im_start|>user
What is AI?<|im_end|>
<|im_start|>assistant
-------------------- Output --------------------
<s><|im_start|> user
What is AI?<|im_end|>
<|im_start|> assistant
AI, or Artificial Intelligence, is a field of computer science that emphasizes the creation of intelligent machines capable of performing tasks that typically require human intelligence. These tasks include