e32d13d78c
GPU drivers are already upstreamed to Kernel 6.2+. Remove the out-of-tree driver (intel-i915-dkms) for 6.2-6.5. https://dgpu-docs.intel.com/driver/kernel-driver-types.html#gpu-driver-support * Remove intel-i915-dkms intel-fw-gpu (only for kernel 5.19)
326 lines
13 KiB
Markdown
326 lines
13 KiB
Markdown
# Install IPEX-LLM on Linux with Intel GPU
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This guide demonstrates how to install IPEX-LLM on Linux with Intel GPUs. It applies to Intel Data Center GPU Flex Series and Max Series, as well as Intel Arc Series GPU.
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IPEX-LLM currently supports the Ubuntu 20.04 operating system and later, and supports PyTorch 2.0 and PyTorch 2.1 on Linux. This page demonstrates IPEX-LLM with PyTorch 2.1. Check the [Installation](../Overview/install_gpu.md#linux) page for more details.
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## Table of Contents
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- [Install Prerequisites](./install_linux_gpu.md#install-prerequisites)
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- [Install ipex-llm](./install_linux_gpu.md#install-ipex-llm)
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- [Verify Installation](./install_linux_gpu.md#verify-installation)
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- [Runtime Configurations](./install_linux_gpu.md#runtime-configurations)
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- [A Quick Example](./install_linux_gpu.md#a-quick-example)
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- [Tips & Troubleshooting](./install_linux_gpu.md#tips--troubleshooting)
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## Install Prerequisites
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### Install GPU Driver
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#### For Linux kernel 6.2
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* Install wget, gpg-agent
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```bash
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sudo apt-get install -y gpg-agent wget
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wget -qO - https://repositories.intel.com/gpu/intel-graphics.key | \
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sudo gpg --dearmor --output /usr/share/keyrings/intel-graphics.gpg
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echo "deb [arch=amd64,i386 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/gpu/ubuntu jammy client" | \
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sudo tee /etc/apt/sources.list.d/intel-gpu-jammy.list
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```
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<img src="https://llm-assets.readthedocs.io/en/latest/_images/wget.png" width=100%; />
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* Install drivers
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```bash
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sudo apt-get update
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sudo apt-get -y install \
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gawk \
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dkms \
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linux-headers-$(uname -r) \
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libc6-dev
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sudo apt-get install -y gawk libc6-dev udev\
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intel-opencl-icd intel-level-zero-gpu level-zero \
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intel-media-va-driver-non-free libmfx1 libmfxgen1 libvpl2 \
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libegl-mesa0 libegl1-mesa libegl1-mesa-dev libgbm1 libgl1-mesa-dev libgl1-mesa-dri \
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libglapi-mesa libgles2-mesa-dev libglx-mesa0 libigdgmm12 libxatracker2 mesa-va-drivers \
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mesa-vdpau-drivers mesa-vulkan-drivers va-driver-all vainfo
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sudo reboot
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```
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<img src="https://llm-assets.readthedocs.io/en/latest/_images/i915.png" width=100%; />
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<img src="https://llm-assets.readthedocs.io/en/latest/_images/gawk.png" width=100%; />
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* Configure permissions
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```bash
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sudo gpasswd -a ${USER} render
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newgrp render
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# Verify the device is working with i915 driver
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sudo apt-get install -y hwinfo
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hwinfo --display
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```
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#### For Linux kernel 6.5
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* Install wget, gpg-agent
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```bash
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sudo apt-get install -y gpg-agent wget
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wget -qO - https://repositories.intel.com/gpu/intel-graphics.key | \
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sudo gpg --dearmor --output /usr/share/keyrings/intel-graphics.gpg
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echo "deb [arch=amd64,i386 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/gpu/ubuntu jammy client" | \
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sudo tee /etc/apt/sources.list.d/intel-gpu-jammy.list
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```
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<img src="https://llm-assets.readthedocs.io/en/latest/_images/wget.png" width=100%; />
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* Install drivers
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```bash
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sudo apt-get update
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sudo apt-get -y install \
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gawk \
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dkms \
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linux-headers-$(uname -r) \
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libc6-dev
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sudo apt-get install -y gawk libc6-dev udev\
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intel-opencl-icd intel-level-zero-gpu level-zero \
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intel-media-va-driver-non-free libmfx1 libmfxgen1 libvpl2 \
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libegl-mesa0 libegl1-mesa libegl1-mesa-dev libgbm1 libgl1-mesa-dev libgl1-mesa-dri \
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libglapi-mesa libgles2-mesa-dev libglx-mesa0 libigdgmm12 libxatracker2 mesa-va-drivers \
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mesa-vdpau-drivers mesa-vulkan-drivers va-driver-all vainfo
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sudo reboot
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```
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<img src="https://llm-assets.readthedocs.io/en/latest/_images/gawk.png" width=100%; />
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* Configure permissions
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```bash
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sudo gpasswd -a ${USER} render
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newgrp render
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# Verify the device is working with i915 driver
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sudo apt-get install -y hwinfo
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hwinfo --display
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```
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#### (Optional) Update Level Zero on Intel Core™ Ultra iGPU
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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 behind `[ext_oneapi_level_zero:gpu]`.
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Here are the sample output of `sycl-ls`:
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```bash
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[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]
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[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]
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[opencl:gpu:2] Intel(R) OpenCL Graphics, Intel(R) Arc(TM) Graphics OpenCL 3.0 NEO [24.09.28717.12]
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[ext_oneapi_level_zero:gpu:0] Intel(R) Level-Zero, Intel(R) Arc(TM) Graphics 1.3 [1.3.28717]
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```
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If you have level_zero version < 1.3.28717, you could update as follows:
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```bash
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wget https://github.com/intel/intel-graphics-compiler/releases/download/igc-1.0.16238.4/intel-igc-core_1.0.16238.4_amd64.deb
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wget https://github.com/intel/intel-graphics-compiler/releases/download/igc-1.0.16238.4/intel-igc-opencl_1.0.16238.4_amd64.deb
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wget https://github.com/intel/compute-runtime/releases/download/24.09.28717.12/intel-level-zero-gpu-dbgsym_1.3.28717.12_amd64.ddeb
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wget https://github.com/intel/compute-runtime/releases/download/24.09.28717.12/intel-level-zero-gpu_1.3.28717.12_amd64.deb
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wget https://github.com/intel/compute-runtime/releases/download/24.09.28717.12/intel-opencl-icd-dbgsym_24.09.28717.12_amd64.ddeb
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wget https://github.com/intel/compute-runtime/releases/download/24.09.28717.12/intel-opencl-icd_24.09.28717.12_amd64.deb
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wget https://github.com/intel/compute-runtime/releases/download/24.09.28717.12/libigdgmm12_22.3.17_amd64.deb
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sudo dpkg -i *.deb
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```
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### Install oneAPI
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```bash
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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
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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
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sudo apt update
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sudo apt install intel-oneapi-common-vars=2024.0.0-49406 \
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intel-oneapi-common-oneapi-vars=2024.0.0-49406 \
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intel-oneapi-diagnostics-utility=2024.0.0-49093 \
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intel-oneapi-compiler-dpcpp-cpp=2024.0.2-49895 \
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intel-oneapi-dpcpp-ct=2024.0.0-49381 \
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intel-oneapi-mkl=2024.0.0-49656 \
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intel-oneapi-mkl-devel=2024.0.0-49656 \
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intel-oneapi-mpi=2021.11.0-49493 \
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intel-oneapi-mpi-devel=2021.11.0-49493 \
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intel-oneapi-dal=2024.0.1-25 \
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intel-oneapi-dal-devel=2024.0.1-25 \
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intel-oneapi-ippcp=2021.9.1-5 \
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intel-oneapi-ippcp-devel=2021.9.1-5 \
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intel-oneapi-ipp=2021.10.1-13 \
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intel-oneapi-ipp-devel=2021.10.1-13 \
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intel-oneapi-tlt=2024.0.0-352 \
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intel-oneapi-ccl=2021.11.2-5 \
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intel-oneapi-ccl-devel=2021.11.2-5 \
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intel-oneapi-dnnl-devel=2024.0.0-49521 \
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intel-oneapi-dnnl=2024.0.0-49521 \
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intel-oneapi-tcm-1.0=1.0.0-435
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```
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<img src="https://llm-assets.readthedocs.io/en/latest/_images/oneapi.png" alt="image-20240221102252565" width=100%; />
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<img src="https://llm-assets.readthedocs.io/en/latest/_images/basekit.png" alt="image-20240221102252565" width=100%; />
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### Setup Python Environment
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Download and install the Miniforge as follows if you don't have conda installed on your machine:
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```bash
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wget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh
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bash Miniforge3-Linux-x86_64.sh
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source ~/.bashrc
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```
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You can use `conda --version` to verify you conda installation.
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After installation, create a new python environment `llm`:
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```bash
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conda create -n llm python=3.11
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```
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Activate the newly created environment `llm`:
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```bash
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conda activate llm
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```
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## Install `ipex-llm`
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With the `llm` environment active, use `pip` to install `ipex-llm` for GPU. Choose either US or CN website for `extra-index-url`:
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- For **US**:
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```bash
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pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
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```
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- For **CN**:
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```bash
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pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/cn/
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```
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> [!NOTE]
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> If you encounter network issues while installing IPEX, refer to [this guide](../Overview/install_gpu.md#install-ipex-llm-from-wheel-1) for troubleshooting advice.
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## Verify Installation
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- You can verify if `ipex-llm` is successfully installed by simply importing a few classes from the library. For example, execute the following import command in the terminal:
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```bash
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source /opt/intel/oneapi/setvars.sh
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python
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> from ipex_llm.transformers import AutoModel, AutoModelForCausalLM
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```
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## Runtime Configurations
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To use GPU acceleration on Linux, several environment variables are required or recommended before running a GPU example. Choose corresponding configurations based on your GPU device:
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- For **Intel Arc™ A-Series and Intel Data Center GPU Flex**:
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For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series, we recommend:
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```bash
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# Configure oneAPI environment variables. Required step for APT or offline installed oneAPI.
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# Skip this step for PIP-installed oneAPI since the environment has already been configured in LD_LIBRARY_PATH.
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source /opt/intel/oneapi/setvars.sh
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# Recommended Environment Variables for optimal performance
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export USE_XETLA=OFF
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export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
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export SYCL_CACHE_PERSISTENT=1
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```
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- For **Intel Data Center GPU Max**:
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For Intel Data Center GPU Max Series, we recommend:
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```bash
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# Configure oneAPI environment variables. Required step for APT or offline installed oneAPI.
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# Skip this step for PIP-installed oneAPI since the environment has already been configured in LD_LIBRARY_PATH.
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source /opt/intel/oneapi/setvars.sh
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# Recommended Environment Variables for optimal performance
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export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so
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export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
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export SYCL_CACHE_PERSISTENT=1
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export ENABLE_SDP_FUSION=1
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```
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Please note that `libtcmalloc.so` can be installed by `conda install -c conda-forge -y gperftools=2.10`
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> [!NOTE]
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> Please refer to [this guide](../Overview/install_gpu.md#runtime-configuration-1) for more details regarding runtime configuration.
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## A Quick Example
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Now let's play with a real LLM. We'll be using the [phi-1.5](https://huggingface.co/microsoft/phi-1_5) model, a 1.3 billion parameter LLM for this demostration. Follow the steps below to setup and run the model, and observe how it responds to a prompt "What is AI?".
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- Step 1: Activate the Python environment `llm` you previously created:
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```bash
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conda activate llm
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```
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- Step 2: Follow [Runtime Configurations Section](#runtime-configurations) above to prepare your runtime environment.
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- Step 3: Create a new file named `demo.py` and insert the code snippet below.
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```python
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# Copy/Paste the contents to a new file demo.py
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import torch
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from ipex_llm.transformers import AutoModelForCausalLM
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from transformers import AutoTokenizer, GenerationConfig
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generation_config = GenerationConfig(use_cache = True)
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tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-7b", trust_remote_code=True)
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# load Model using ipex-llm and load it to GPU
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model = AutoModelForCausalLM.from_pretrained(
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"tiiuae/falcon-7b", load_in_4bit=True, cpu_embedding=True, trust_remote_code=True)
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model = model.to('xpu')
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# Format the prompt
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question = "What is AI?"
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prompt = " Question:{prompt}\n\n Answer:".format(prompt=question)
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# Generate predicted tokens
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with torch.inference_mode():
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input_ids = tokenizer.encode(prompt, return_tensors="pt").to('xpu')
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# warm up one more time before the actual generation task for the first run, see details in `Tips & Troubleshooting`
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# output = model.generate(input_ids, do_sample=False, max_new_tokens=32, generation_config = generation_config)
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output = model.generate(input_ids, do_sample=False, max_new_tokens=32, generation_config = generation_config).cpu()
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output_str = tokenizer.decode(output[0], skip_special_tokens=True)
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print(output_str)
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```
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> **Note**:
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>
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> When running LLMs on Intel iGPUs with limited memory size, we recommend setting `cpu_embedding=True` in the `from_pretrained` function.
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> This will allow the memory-intensive embedding layer to utilize the CPU instead of GPU.
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- Step 5. Run `demo.py` within the activated Python environment using the following command:
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```bash
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python demo.py
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```
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### Example output
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Example output on a system equipped with an 11th Gen Intel Core i7 CPU and Iris Xe Graphics iGPU:
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```
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Question:What is AI?
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Answer: AI stands for Artificial Intelligence, which is the simulation of human intelligence in machines.
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```
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## Tips & Troubleshooting
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### Warmup for optimial performance on first run
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When running LLMs on GPU for the first time, you might notice the performance is lower than expected, with delays up to several minutes before the first token is generated. This delay occurs because the GPU kernels require compilation and initialization, which varies across different GPU types. To achieve optimal and consistent performance, we recommend a one-time warm-up by running `model.generate(...)` an additional time before starting your actual generation tasks. If you're developing an application, you can incorporate this warmup step into start-up or loading routine to enhance the user experience.
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