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Yuwen Hu fa69fed58f Small fixes to oneAPI link (#10339)
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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:

  • 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: Install Intel® oneAPI Base Toolkit 2024.0

Intel® oneAPI Base Toolkit 2024.0 installation methods:

.. tabs::
   .. tab:: Offline installer
      Download and install `Intel® oneAPI Base Toolkit <https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit-download.html?operatingsystem=window&distributions=offline>`_ version 2024.0 through Offline Installer.
      
      During installation, you could just continue with "Recommended Installation". If you would like to continue with "Custom Installation", please note that oneAPI Deep Neural Network Library, oneAPI Math Kernel Library, and oneAPI DPC++/C++ Compiler are required, the other components are optional.

   .. tab:: PIP installer
      Pip install oneAPI in your working conda environment.

      .. code-block:: bash

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

      .. note::
         Activating your working conda environment will automatically configure oneAPI environment variables.

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. For oneAPI installed using the Offline installer, configure oneAPI environment variables with:

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

  • For oneAPI installed using the Offline installer, 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 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::

    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:: PIP installer

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

            .. code-block:: bash

               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``.

            .. code-block:: bash

               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 ``bigdl-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``).
               
               .. code-block:: bash
               
                  rm -r ~/intel/oneapi
                  conda env config vars unset LD_LIBRARY_PATH -n llm

         .. 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:: PIP installer

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

            .. code-block:: bash

               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``.

            .. code-block:: bash

               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 ``bigdl-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``).
               
               .. code-block:: bash
               
                  rm -r ~/intel/oneapi
                  conda env config vars unset LD_LIBRARY_PATH -n llm

         .. 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

         # 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

   .. tab:: Intel Data Center GPU Max

      For Intel Data Center GPU Max Series, we recommend:

      .. code-block:: bash

         # 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 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.

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