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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, please first verify your GPU driver version.

Note

The GPU driver version of your device can be checked in the "Task Manager" -> GPU 0 (or GPU 1, etc.) -> Driver version.

If you have driver version lower than 31.0.101.5122, it is required to update your GPU driver to the latest.

Install IPEX-LLM

Install IPEX-LLM From PyPI

We recommend using Miniforge 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.

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

  • For US:

    conda create -n llm python=3.11 libuv
conda activate llm

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

  • For CN:

    conda create -n llm python=3.11 libuv
conda activate llm

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

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:

  • 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.

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.

  • For PyTorch 2.1:

    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.

      Tip:

      For client GPUs, such as the Intel® Arc™ A-series, please refer to Client GPU Installation Guide. For data center GPUs, including Intel® Data Center GPU Max Series and Intel® Data Center GPU Flex Series, please refer to our Installation for Data Center GPU for general purpose GPU capabilities.

      See release page for latest version.

      Note:

      For Intel Core™ Ultra integrated GPU, please make sure level_zero version >= 1.3.28717. The level_zero version can be checked with sycl-ls, and verison will be tagged be [ext_oneapi_level_zero:gpu].

      [opencl:acc:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2  [2023.16.12.0.12_195853.xmain-hotfix]
[opencl:cpu:1] Intel(R) OpenCL, Intel(R) Core(TM) Ultra 5 125H OpenCL 3.0 (Build 0) [2023.16.12.0.12_195853.xmain-hotfix]
[opencl:gpu:2] Intel(R) OpenCL Graphics, Intel(R) Arc(TM) Graphics OpenCL 3.0 NEO  [24.09.28717.12]
[ext_oneapi_level_zero:gpu:0] Intel(R) Level-Zero, Intel(R) Arc(TM) Graphics 1.3 [1.3.28717]

      If you have level_zero version < 1.3.28717, you could update as follows:

      wget https://github.com/intel/intel-graphics-compiler/releases/download/igc-1.0.16238.4/intel-igc-core_1.0.16238.4_amd64.deb
wget https://github.com/intel/intel-graphics-compiler/releases/download/igc-1.0.16238.4/intel-igc-opencl_1.0.16238.4_amd64.deb
wget https://github.com/intel/compute-runtime/releases/download/24.09.28717.12/intel-level-zero-gpu-dbgsym_1.3.28717.12_amd64.ddeb
wget https://github.com/intel/compute-runtime/releases/download/24.09.28717.12/intel-level-zero-gpu_1.3.28717.12_amd64.deb
wget https://github.com/intel/compute-runtime/releases/download/24.09.28717.12/intel-opencl-icd-dbgsym_24.09.28717.12_amd64.ddeb
wget https://github.com/intel/compute-runtime/releases/download/24.09.28717.12/intel-opencl-icd_24.09.28717.12_amd64.deb
wget https://github.com/intel/compute-runtime/releases/download/24.09.28717.12/libigdgmm12_22.3.17_amd64.deb
sudo dpkg -i *.deb

    • 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:

      For APT installer

      • 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
      For PIP installer

      • Step 1: Install oneAPI in a user-defined folder, e.g., ~/intel/oneapi.

        export PYTHONUSERBASE=~/intel/oneapi
pip install dpcpp-cpp-rt==2024.0.2 mkl-dpcpp==2024.0.0 onednn==2024.0.0 --user

        Note:

        The oneAPI packages are visible in pip list only if PYTHONUSERBASE is properly set.

      • Step 2: Configure your working conda environment (e.g. with name llm) to append oneAPI path (e.g. ~/intel/oneapi/lib) to the environment variable LD_LIBRARY_PATH.

        conda env config vars set LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/intel/oneapi/lib -n llm

        Note:

        You can view the configured environment variables for your environment (e.g. with name llm) by running conda env config vars list -n llm. You can continue with your working conda environment and install ipex-llm as guided in the next section.

        Note:

        You are recommended not to install other pip packages in the user-defined folder for oneAPI (e.g. ~/intel/oneapi). You can uninstall the oneAPI package by simply deleting the package folder, and unsetting the configuration of your working conda environment (e.g., with name llm).

        rm -r ~/intel/oneapi
conda env config vars unset LD_LIBRARY_PATH -n llm
      For Offline installer

      Using the offline installer allows you to customize the installation path.

      wget https://registrationcenter-download.intel.com/akdlm/IRC_NAS/20f4e6a1-6b0b-4752-b8c1-e5eacba10e01/l_BaseKit_p_2024.0.0.49564_offline.sh
sudo sh ./l_BaseKit_p_2024.0.0.49564_offline.sh

      Note:

      You can also modify the installation or uninstall the package by running the following commands:

      cd /opt/intel/oneapi/installer
sudo ./installer

  • For PyTorch 2.0 (deprecated for versions ipex-llm >= 2.1.0b20240511):

    To enable IPEX-LLM for Intel GPUs with PyTorch 2.0, here're several prerequisite steps for tools installation and environment preparation:

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

      Tip:

      For client GPUs, such as the Intel® Arc™ A-series, please refer to Client GPU Installation Guide. For data center GPUs, including Intel® Data Center GPU Max Series and Intel® Data Center GPU Flex Series, please refer to our Installation for Data Center GPU for general purpose GPU capabilities.

      See release page for latest version.

    • Step 2: Download and install Intel® oneAPI Base Toolkit with version 2023.2. OneDNN, OneMKL and DPC++ compiler are needed, others are optional.

      Intel® oneAPI Base Toolkit 2023.2 installation methods:

      For APT installer

      • 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 packages

        sudo apt install -y intel-oneapi-common-vars=2023.2.0-49462 \
   intel-oneapi-compiler-cpp-eclipse-cfg=2023.2.0-49495 intel-oneapi-compiler-dpcpp-eclipse-cfg=2023.2.0-49495 \
   intel-oneapi-diagnostics-utility=2022.4.0-49091 \
   intel-oneapi-compiler-dpcpp-cpp=2023.2.0-49495 \
   intel-oneapi-mkl=2023.2.0-49495 intel-oneapi-mkl-devel=2023.2.0-49495 \
   intel-oneapi-mpi=2021.10.0-49371 intel-oneapi-mpi-devel=2021.10.0-49371 \
   intel-oneapi-tbb=2021.10.0-49541 intel-oneapi-tbb-devel=2021.10.0-49541\
   intel-oneapi-ccl=2021.10.0-49084 intel-oneapi-ccl-devel=2021.10.0-49084\
   intel-oneapi-dnnl-devel=2023.2.0-49516 intel-oneapi-dnnl=2023.2.0-49516

        Note:

        You can uninstall the package by running the following command:

        sudo apt autoremove intel-oneapi-common-vars
      For PIP installer

      • Step 1: Install oneAPI in a user-defined folder, e.g., ~/intel/oneapi

        export PYTHONUSERBASE=~/intel/oneapi
pip install dpcpp-cpp-rt==2023.2.0 mkl-dpcpp==2023.2.0 onednn-cpu-dpcpp-gpu-dpcpp==2023.2.0 --user

        Note:

        The oneAPI packages are visible in pip list only if PYTHONUSERBASE is properly set.

      • Step 2: Configure your working conda environment (e.g. with name llm) to append oneAPI path (e.g. ~/intel/oneapi/lib) to the environment variable LD_LIBRARY_PATH.

        conda env config vars set LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/intel/oneapi/lib -n llm

        Note:

        You can view the configured environment variables for your environment (e.g. with name llm) by running conda env config vars list -n llm. You can continue with your working conda environment and install ipex-llm as guided in the next section.

        Note:

        You are recommended not to install other pip packages in the user-defined folder for oneAPI (e.g. ~/intel/oneapi). You can uninstall the oneAPI package by simply deleting the package folder, and unsetting the configuration of your working conda environment (e.g., with name llm).

        rm -r ~/intel/oneapi
conda env config vars unset LD_LIBRARY_PATH -n llm
      For Offline installer

      Using the offline installer allows you to customize the installation path.

      wget https://registrationcenter-download.intel.com/akdlm/IRC_NAS/992857b9-624c-45de-9701-f6445d845359/l_BaseKit_p_2023.2.0.49397_offline.sh
sudo sh ./l_BaseKit_p_2023.2.0.49397_offline.sh

      Note:

      You can also modify the installation or uninstall the package by running the following commands:

      cd /opt/intel/oneapi/installer
sudo ./installer

Install IPEX-LLM

Install IPEX-LLM From PyPI

We recommend using Miniforge 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.

  • For PyTorch 2.1:

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

    • For US:

      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/

    • For CN:

      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/cn/

      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/cn/

  • For PyTorch 2.0 (deprecated for versions ipex-llm >= 2.1.0b20240511):

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

    • For US:

      conda create -n llm python=3.11
conda activate llm

pip install --pre --upgrade ipex-llm[xpu_2.0]==2.1.0b20240510 --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/

    • For CN:

      conda create -n llm python=3.11
conda activate llm

pip install --pre --upgrade ipex-llm[xpu_2.0]==2.1.0b20240510 --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/cn/

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.

  • For PyTorch 2.1:

    # 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]

  • For PyTorch 2.0 (deprecated for versions ipex-llm >= 2.1.0b20240511):

    # get the wheels on Linux system for IPEX 2.0.110+xpu
wget https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_stable/xpu/torch-2.0.1a0%2Bcxx11.abi-cp311-cp311-linux_x86_64.whl
wget https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_stable/xpu/torchvision-0.15.2a0%2Bcxx11.abi-cp311-cp311-linux_x86_64.whl
wget https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_stable/xpu/intel_extension_for_pytorch-2.0.110%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-2.0.1a0+cxx11.abi-cp311-cp311-linux_x86_64.whl
pip install torchvision-0.15.2a0+cxx11.abi-cp311-cp311-linux_x86_64.whl
pip install intel_extension_for_pytorch-2.0.110+xpu-cp311-cp311-linux_x86_64.whl

# install ipex-llm for Intel GPU
pip install --pre --upgrade ipex-llm[xpu_2.0]==2.1.0b20240510

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 and Intel Data Center GPU Flex:

    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

  • For Intel Data Center GPU Max:

    For Intel Data Center GPU Max 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 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

    Please note that libtcmalloc.so can be installed by conda install -c conda-forge -y gperftools=2.10

  • For Intel iGPU:

    # 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

export SYCL_CACHE_PERSISTENT=1
export 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.

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.

  • 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.

2. core dump when running with GPU

If encountered random core dump when running with GPU, please remove out of tree driver.

sudo apt purge -y  intel-i915-dkms  intel-fw-gpu