In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on Qwen1.5 models on Intel GPUs. For illustration purposes, we utilize the Qwen/Qwen1.5-7B-Chat as a reference InternLM 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 Qwen1.5 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 transformers==4.37.0 # install transformers which supports Qwen2
# only for Qwen1.5-MoE-A2.7B
pip install transformers==4.40.0
pip install trl==0.8.1
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 transformers==4.37.0 # install transformers which supports Qwen2
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.
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 Qwen1.5 model (e.g.Qwen/Qwen1.5-7B-Chat
) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be'Qwen/Qwen1.5-7B-Chat'
.--prompt PROMPT
: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be'AI是什么?'
.--n-predict N_PREDICT
: argument defining the max number of tokens to predict. It is default to be32
.
Inference time: xxxx s
-------------------- Prompt --------------------
<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
AI是什么?<|im_end|>
<|im_start|>assistant
-------------------- Output --------------------
<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
AI是什么?<|im_end|>
<|im_start|>assistant
人工智能(AI)是指通过计算机模拟、延伸和扩展人类智能的学科,其目标是使机器具有学习、推理、感知、理解、交流
Inference time: xxxx s
-------------------- Prompt --------------------
<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
What is AI?<|im_end|>
<|im_start|>assistant
-------------------- Output --------------------
<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
What is AI?<|im_end|>
<|im_start|>assistant
AI, or artificial intelligence, refers to the simulation of human intelligence in machines that are programmed to perform tasks that typically require human cognition, such as learning, reasoning