IPEX-LLM Installation: GPU#

Windows#

Prerequisites#

IPEX-LLM on Windows supports Intel iGPU and dGPU.

Important

IPEX-LLM on Windows only supports PyTorch 2.1.

To apply Intel GPU acceleration, there’re several prerequisite steps for tools installation and environment preparation:

  • Step 1: Install Visual Studio 2022 Community Edition and select “Desktop development with C++” workload, like this

  • Step 2: Install or update to latest GPU driver

  • Step 3 (Recommended): Install Miniconda for Python environment management. Choose Miniconda installer for Windows.

  • Step 4: Install Intel® oneAPI Base Toolkit 2024.0:

    First, Create a Python 3.11 enviroment and activate it. In Anaconda Prompt:

    conda create -n llm python=3.11 libuv

conda activate llm

    Important

    ipex-llm is tested with Python 3.9, 3.10 and 3.11. Python 3.11 is recommended for best practices.

    Then, use pip to install the Intel oneAPI Base Toolkit 2024.0:

    pip install dpcpp-cpp-rt==2024.0.2 mkl-dpcpp==2024.0.0 onednn==2024.0.0

Install IPEX-LLM#

Install IPEX-LLM From PyPI#

The easiest ways to install ipex-llm is the following commands, choosing either US or CN website for extra-index-url:

conda activate llm

pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/

Install IPEX-LLM From Wheel#

If you encounter network issues when installing IPEX, you can also install IPEX-LLM dependencies for Intel XPU from source archives. First you need to download and install torch/torchvision/ipex from wheels listed below before installing ipex-llm.

Download the wheels on Windows system:

wget https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_stable/xpu/torch-2.1.0a0%2Bcxx11.abi-cp311-cp311-win_amd64.whl
wget https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_stable/xpu/torchvision-0.16.0a0%2Bcxx11.abi-cp311-cp311-win_amd64.whl
wget https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_stable/xpu/intel_extension_for_pytorch-2.1.10%2Bxpu-cp311-cp311-win_amd64.whl

You may install dependencies directly from the wheel archives and then install ipex-llm using following commands:

pip install torch-2.1.0a0+cxx11.abi-cp311-cp311-win_amd64.whl
pip install torchvision-0.16.0a0+cxx11.abi-cp311-cp311-win_amd64.whl
pip install intel_extension_for_pytorch-2.1.10+xpu-cp311-cp311-win_amd64.whl

pip install --pre --upgrade ipex-llm[xpu]

Note

All the wheel packages mentioned here are for Python 3.11. If you would like to use Python 3.9 or 3.10, you should modify the wheel names for torch, torchvision, and intel_extension_for_pytorch by replacing cp11 with cp39 or cp310, respectively.

Runtime Configuration#

To use GPU acceleration on Windows, several environment variables are required before running a GPU example:

set SYCL_CACHE_PERSISTENT=1
set BIGDL_LLM_XMX_DISABLED=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.

Troubleshooting#

1. Error loading intel_extension_for_pytorch#

If you met error when importing intel_extension_for_pytorch, please ensure that you have completed the following steps:

  • Ensure that you have installed Visual Studio with “Desktop development with C++” workload.

  • Make sure that the correct version of oneAPI, specifically 2024.0, is installed.

  • Ensure that libuv is installed in your conda environment. This can be done during the creation of the environment with the command:

    conda create -n llm python=3.11 libuv

    If you missed libuv, you can add it to your existing environment through

    conda install libuv

Linux#

Prerequisites#

IPEX-LLM GPU support on Linux has been verified on:

  • Intel Arc™ A-Series Graphics

  • Intel Data Center GPU Flex Series

  • Intel Data Center GPU Max Series

Important

IPEX-LLM on Linux supports PyTorch 2.0 and PyTorch 2.1.

Warning

IPEX-LLM support for Pytorch 2.0 is deprecated as of ipex-llm >= 2.1.0b20240511.

Important

We currently support the Ubuntu 20.04 operating system and later.

To enable IPEX-LLM for Intel GPUs with PyTorch 2.1, here are several prerequisite steps for tools installation and environment preparation:

  • Step 1: Install Intel GPU Driver version >= stable_775_20_20231219. We highly recommend installing the latest version of intel-i915-dkms using apt.

    See also

    Please refer to our driver installation for general purpose GPU capabilities.

    See release page for latest version.

  • Step 2: Download and install Intel® oneAPI Base Toolkit with version 2024.0. OneDNN, OneMKL and DPC++ compiler are needed, others are optional.

Intel® oneAPI Base Toolkit 2024.0 installation methods:

Step 1: Set up repository

wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | sudo tee /usr/share/keyrings/oneapi-archive-keyring.gpg > /dev/null
echo "deb [signed-by=/usr/share/keyrings/oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main" | sudo tee /etc/apt/sources.list.d/oneAPI.list
sudo apt update

Step 2: Install the package

sudo apt install intel-oneapi-common-vars=2024.0.0-49406 \
   intel-oneapi-common-oneapi-vars=2024.0.0-49406 \
   intel-oneapi-diagnostics-utility=2024.0.0-49093 \
   intel-oneapi-compiler-dpcpp-cpp=2024.0.2-49895 \
   intel-oneapi-dpcpp-ct=2024.0.0-49381 \
   intel-oneapi-mkl=2024.0.0-49656 \
   intel-oneapi-mkl-devel=2024.0.0-49656 \
   intel-oneapi-mpi=2021.11.0-49493 \
   intel-oneapi-mpi-devel=2021.11.0-49493 \
   intel-oneapi-dal=2024.0.1-25 \
   intel-oneapi-dal-devel=2024.0.1-25 \
   intel-oneapi-ippcp=2021.9.1-5 \
   intel-oneapi-ippcp-devel=2021.9.1-5 \
   intel-oneapi-ipp=2021.10.1-13 \
   intel-oneapi-ipp-devel=2021.10.1-13 \
   intel-oneapi-tlt=2024.0.0-352 \
   intel-oneapi-ccl=2021.11.2-5 \
   intel-oneapi-ccl-devel=2021.11.2-5 \
   intel-oneapi-dnnl-devel=2024.0.0-49521 \
   intel-oneapi-dnnl=2024.0.0-49521 \
   intel-oneapi-tcm-1.0=1.0.0-435

Note

You can uninstall the package by running the following command:

sudo apt autoremove intel-oneapi-common-vars

Install IPEX-LLM#

Install IPEX-LLM From PyPI#

We recommend using miniconda to create a python 3.11 enviroment:

Important

ipex-llm is tested with Python 3.9, 3.10 and 3.11. Python 3.11 is recommended for best practices.

Important

Make sure you install matching versions of ipex-llm/pytorch/IPEX and oneAPI Base Toolkit. IPEX-LLM with Pytorch 2.1 should be used with oneAPI Base Toolkit version 2024.0. IPEX-LLM with Pytorch 2.0 should be used with oneAPI Base Toolkit version 2023.2.

Choose either US or CN website for extra-index-url:

conda create -n llm python=3.11
conda activate llm

pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/

Note

The xpu option will install IPEX-LLM with PyTorch 2.1 by default, which is equivalent to

pip install --pre --upgrade ipex-llm[xpu_2.1] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/

Install IPEX-LLM From Wheel#

If you encounter network issues when installing IPEX, you can also install IPEX-LLM dependencies for Intel XPU from source archives. First you need to download and install torch/torchvision/ipex from wheels listed below before installing ipex-llm.

# get the wheels on Linux system for IPEX 2.1.10+xpu
wget https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_stable/xpu/torch-2.1.0a0%2Bcxx11.abi-cp311-cp311-linux_x86_64.whl
wget https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_stable/xpu/torchvision-0.16.0a0%2Bcxx11.abi-cp311-cp311-linux_x86_64.whl
wget https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_stable/xpu/intel_extension_for_pytorch-2.1.10%2Bxpu-cp311-cp311-linux_x86_64.whl

Then you may install directly from the wheel archives using following commands:

# install the packages from the wheels
pip install torch-2.1.0a0+cxx11.abi-cp311-cp311-linux_x86_64.whl
pip install torchvision-0.16.0a0+cxx11.abi-cp311-cp311-linux_x86_64.whl
pip install intel_extension_for_pytorch-2.1.10+xpu-cp311-cp311-linux_x86_64.whl

# install ipex-llm for Intel GPU
pip install --pre --upgrade ipex-llm[xpu]

Note

All the wheel packages mentioned here are for Python 3.11. If you would like to use Python 3.9 or 3.10, you should modify the wheel names for torch, torchvision, and intel_extension_for_pytorch by replacing cp11 with cp39 or cp310, respectively.

Runtime Configuration#

To use GPU acceleration on Linux, several environment variables are required or recommended before running a GPU example.

For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series, we recommend:

# Configure oneAPI environment variables. Required step for APT or offline installed oneAPI.
# Skip this step for PIP-installed oneAPI since the environment has already been configured in LD_LIBRARY_PATH.
source /opt/intel/oneapi/setvars.sh

# Recommended Environment Variables for optimal performance
export USE_XETLA=OFF
export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
export 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.

Known issues#

1. Potential suboptimal performance with Linux kernel 6.2.0#

For Ubuntu 22.04 and driver version < stable_775_20_20231219, the performance on Linux kernel 6.2.0 is worse than Linux kernel 5.19.0. You can use sudo apt update && sudo apt install -y intel-i915-dkms intel-fw-gpu to install the latest driver to solve this issue (need to reboot OS).

Tips: You can use sudo apt list --installed | grep intel-i915-dkms to check your intel-i915-dkms’s version, the version should be latest and >= 1.23.9.11.231003.15+i19-1.

2. Driver installation unmet dependencies error: intel-i915-dkms#

The last apt install command of the driver installation may produce the following error:

The following packages have unmet dependencies:
 intel-i915-dkms : Conflicts: intel-platform-cse-dkms
                   Conflicts: intel-platform-vsec-dkms

You can use sudo apt install -y intel-i915-dkms intel-fw-gpu to install instead. As the intel-platform-cse-dkms and intel-platform-vsec-dkms are already provided by intel-i915-dkms.

Troubleshooting#

1. Cannot open shared object file: No such file or directory#

Error where libmkl file is not found, for example,

OSError: libmkl_intel_lp64.so.2: cannot open shared object file: No such file or directory

Error: libmkl_sycl_blas.so.4: cannot open shared object file: No such file or directory

The reason for such errors is that oneAPI has not been initialized properly before running IPEX-LLM code or before importing IPEX package.

  • For oneAPI installed using APT or Offline Installer, make sure you execute setvars.sh of oneAPI Base Toolkit before running IPEX-LLM.

  • For PIP-installed oneAPI, activate your working environment and run echo $LD_LIBRARY_PATH to check if the installation path is properly configured for the environment. If the output does not contain oneAPI path (e.g. ~/intel/oneapi/lib), check Prerequisites to re-install oneAPI with PIP installer.

  • Make sure you install matching versions of ipex-llm/pytorch/IPEX and oneAPI Base Toolkit. IPEX-LLM with PyTorch 2.1 should be used with oneAPI Base Toolkit version 2024.0. IPEX-LLM with PyTorch 2.0 should be used with oneAPI Base Toolkit version 2023.2.