In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on Mistral models on Intel GPUs. For illustration purposes, we utilize the mistralai/Mistral-7B-Instruct-v0.1 and mistralai/Mistral-7B-v0.1 as reference Mistral 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 Mistral Troubleshooting, please make sure you have installed transformers==4.34.0
to run the example.
In the example generate.py, we show a basic use case for a Mistral 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/
# Refer to https://huggingface.co/mistralai/Mistral-7B-v0.1#troubleshooting, please make sure you are using a stable version of Transformers, 4.34.0 or newer.
pip install transformers==4.34.0
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/
# Refer to https://huggingface.co/mistralai/Mistral-7B-v0.1#troubleshooting, please make sure you are using a stable version of Transformers, 4.34.0 or newer.
pip install transformers==4.34.0
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
export BIGDL_LLM_XMX_DISABLED=1
For Intel iGPU
set SYCL_CACHE_PERSISTENT=1
set BIGDL_LLM_XMX_DISABLED=1
For 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 --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 Mistral model (e.g.mistralai/Mistral-7B-Instruct-v0.1
andmistralai/Mistral-7B-v0.1
) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be'mistralai/Mistral-7B-Instruct-v0.1'
.--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 --------------------
[INST] What is AI? [/INST] AI stands for Artificial Intelligence. It is a branch of computer science that focuses on the development of intelligent machines that work, react, and even think like humans
Inference time: xxxx s
-------------------- Output --------------------
[INST] What is AI? [/INST]
[INST] Artificial Intelligence (AI) is a branch of computer science that deals with the simulation of intelligent behavior in computers. It is a broad
For platforms without sufficient GPU memory to serve Mistral on one signle GPU card, Deepspeed Tensor Parallel can help to scale the inference to multiple cards, which enables large model and increases resource utilization. Please to refer to here.