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BigDL-LLM Installation: GPU

Windows

Prerequisites

BigDL-LLM on Windows supports Intel iGPU and dGPU.

.. important::

    BigDL-LLM on Windows only supports PyTorch 2.1.

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

Install BigDL-LLM From PyPI

We recommend using miniconda to create a python 3.9 enviroment:

.. important::

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

The easiest ways to install bigdl-llm is the following commands:

conda create -n llm python=3.9 libuv
conda activate llm

pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu

Install BigDL-LLM From Wheel

If you encounter network issues when installing IPEX, you can also install BigDL-LLM dependencies for Intel XPU from source archives. First you need to download and install torch/torchvision/ipex from wheels listed below before installing bigdl-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-cp39-cp39-win_amd64.whl
wget https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_stable/xpu/torchvision-0.16.0a0%2Bcxx11.abi-cp39-cp39-win_amd64.whl
wget https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_stable/xpu/intel_extension_for_pytorch-2.1.10%2Bxpu-cp39-cp39-win_amd64.whl

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

pip install torch-2.1.0a0+cxx11.abi-cp39-cp39-win_amd64.whl
pip install torchvision-0.16.0a0+cxx11.abi-cp39-cp39-win_amd64.whl
pip install intel_extension_for_pytorch-2.1.10+xpu-cp39-cp39-win_amd64.whl

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

.. note::

   All the wheel packages mentioned here are for Python 3.9. If you would like to use Python 3.10 or 3.11, you should modify the wheel names for ``torch``, ``torchvision``, and ``intel_extension_for_pytorch`` by replacing ``cp39`` with ``cp310`` or ``cp311``, respectively.

Runtime Configuration

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

Make sure you are using CMD (Anaconda Prompt if using conda) as PowerShell is not supported:

call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat"

Please also set the following environment variable if you would like to run LLMs on:

.. tabs::
   .. tab:: Intel iGPU

      .. code-block:: cmd

         set SYCL_CACHE_PERSISTENT=1
         set BIGDL_LLM_XMX_DISABLED=1

   .. tab:: Intel Arc™ A300-Series or Pro A60

      .. code-block:: cmd

         set SYCL_CACHE_PERSISTENT=1

   .. tab:: Other Intel dGPU Series

      There is no need to set further environment variables.

.. 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=3.9 libuv

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

    conda install libuv

  • Make sure you have configured oneAPI environment variables in your Anaconda Prompt through

    call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat"

    Please note that you need to set these environment variables again once you have a new Anaconda Prompt window.

Linux

Prerequisites

BigDL-LLM for GPU supports on Linux has been verified on:

  • Intel Arc™ A-Series Graphics
  • Intel Data Center GPU Flex Series
  • Intel Data Center GPU Max Series
.. important::

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

.. important::

    We currently support the Ubuntu 20.04 operating system and later.

.. tabs::
   .. tab:: PyTorch 2.1

      To enable BigDL-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.

        .. seealso::

           Please refer to our `driver installation <https://dgpu-docs.intel.com/driver/installation.html>`_ for general purpose GPU capabilities.

           See `release page <https://dgpu-docs.intel.com/releases/index.html>`_ for latest version.

      * Step 2: Download and install `Intel® oneAPI Base Toolkit <https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit-download.html>`_ with version 2024.0. OneDNN, OneMKL and DPC++ compiler are needed, others are optional.

      Intel® oneAPI Base Toolkit 2024.0 installation methods:

      .. tabs::
         .. tab:: APT installer

            Step 1: Set up repository

            .. code-block:: bash

               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

            .. code-block:: bash

               sudo apt install -y intel-basekit

            .. note::

               You can uninstall the package by running the following command:

               .. code-block:: bash
               
                  sudo apt autoremove intel-basekit

         .. tab:: Offline installer
         
            Using the offline installer allows you to customize the installation path.

            .. code-block:: bash
            
               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:

                  .. code-block:: bash

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

   .. tab:: PyTorch 2.0

      To enable BigDL-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.

        .. seealso::

           Please refer to our `driver installation <https://dgpu-docs.intel.com/driver/installation.html>`_ for general purpose GPU capabilities.

           See `release page <https://dgpu-docs.intel.com/releases/index.html>`_ for latest version.

      * Step 2: Download and install `Intel® oneAPI Base Toolkit <https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit-download.html>`_ with version 2023.2. OneDNN, OneMKL and DPC++ compiler are needed, others are optional.

      Intel® oneAPI Base Toolkit 2023.2 installation methods:

      .. tabs::
         .. tab:: APT installer

            Step 1: Set up repository

            .. code-block:: bash

               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

            .. code-block:: bash

               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:

               .. code-block:: bash
               
                  sudo apt autoremove intel-oneapi-common-vars

         .. tab:: Offline installer
         
            Using the offline installer allows you to customize the installation path.

            .. code-block:: bash
            
               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:

               .. code-block:: bash

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

Install BigDL-LLM From PyPI

We recommend using miniconda to create a python 3.9 enviroment:

.. important::

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

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

.. tabs::
   .. tab:: PyTorch 2.1

      .. code-block:: bash

         conda create -n llm python=3.9
         conda activate llm

         pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu

      .. note::

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

         .. code-block:: bash

            pip install --pre --upgrade bigdl-llm[xpu_2.1] -f https://developer.intel.com/ipex-whl-stable-xpu
            

   .. tab:: PyTorch 2.0

      .. code-block:: bash

         conda create -n llm python=3.9
         conda activate llm

         pip install --pre --upgrade bigdl-llm[xpu_2.0] -f https://developer.intel.com/ipex-whl-stable-xpu

Install BigDL-LLM From Wheel

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

.. tabs::
   .. tab:: PyTorch 2.1

      .. code-block:: bash

         # 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-cp39-cp39-linux_x86_64.whl
         wget https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_stable/xpu/torchvision-0.16.0a0%2Bcxx11.abi-cp39-cp39-linux_x86_64.whl
         wget https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_stable/xpu/intel_extension_for_pytorch-2.1.10%2Bxpu-cp39-cp39-linux_x86_64.whl

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

      .. code-block:: bash

         # install the packages from the wheels
         pip install torch-2.1.0a0+cxx11.abi-cp39-cp39-linux_x86_64.whl
         pip install torchvision-0.16.0a0+cxx11.abi-cp39-cp39-linux_x86_64.whl
         pip install intel_extension_for_pytorch-2.1.10+xpu-cp39-cp39-linux_x86_64.whl

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

   .. tab:: PyTorch 2.0

      .. code-block:: bash

         # 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-cp39-cp39-linux_x86_64.whl
         wget https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_stable/xpu/torchvision-0.15.2a0%2Bcxx11.abi-cp39-cp39-linux_x86_64.whl
         wget https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_stable/xpu/intel_extension_for_pytorch-2.0.110%2Bxpu-cp39-cp39-linux_x86_64.whl

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

      .. code-block:: bash

         # install the packages from the wheels
         pip install torch-2.0.1a0+cxx11.abi-cp39-cp39-linux_x86_64.whl
         pip install torchvision-0.15.2a0+cxx11.abi-cp39-cp39-linux_x86_64.whl
         pip install intel_extension_for_pytorch-2.0.110+xpu-cp39-cp39-linux_x86_64.whl

         # install bigdl-llm for Intel GPU
         pip install --pre --upgrade bigdl-llm[xpu_2.0]


.. note::

   All the wheel packages mentioned here are for Python 3.9. If you would like to use Python 3.10 or 3.11, you should modify the wheel names for ``torch``, ``torchvision``, and ``intel_extension_for_pytorch`` by replacing ``cp39`` with ``cp310`` or ``cp311``, respectively.

Runtime Configuration

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

.. tabs::
   .. tab:: 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:

      .. code-block:: bash

         # Required step. Configure oneAPI environment variables
         source /opt/intel/oneapi/setvars.sh

         # Recommended Environment Variables
         export USE_XETLA=OFF
         export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1

   .. tab:: Intel Data Center GPU Max

      For Intel Data Center GPU Max Series, we recommend:

      .. code-block:: bash

         # Required step. Configure oneAPI environment variables
         source /opt/intel/oneapi/setvars.sh

         # Recommended Environment Variables
         export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so
         export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
         export ENABLE_SDP_FUSION=1

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

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 BigDL-LLM code or before importing IPEX package.

  • Step 1: Make sure you execute setvars.sh of oneAPI Base Toolkit before running BigDL-LLM code.
  • Step 2: Make sure you install matching versions of BigDL-LLM/pytorch/IPEX and oneAPI Base Toolkit. BigDL-LLM with PyTorch 2.1 should be used with oneAPI Base Toolkit version 2024.0. BigDL-LLM with PyTorch 2.0 should be used with oneAPI Base Toolkit version 2023.2.