Install IPEX-LLM on Linux with Intel GPU

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.

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 page for more details.

Install Prerequisites

Install GPU Driver

For Linux kernel 6.2

• Install wget, gpg-agent

  sudo apt-get install -y gpg-agent wget
  wget -qO - https://repositories.intel.com/gpu/intel-graphics.key | \
  sudo gpg --dearmor --output /usr/share/keyrings/intel-graphics.gpg
  echo "deb [arch=amd64,i386 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/gpu/ubuntu jammy client" | \
  sudo tee /etc/apt/sources.list.d/intel-gpu-jammy.list
  

  

• Install drivers

  sudo apt-get update
  sudo apt-get -y install \
      gawk \
      dkms \
      linux-headers-$(uname -r) \
      libc6-dev
  sudo apt install intel-i915-dkms intel-fw-gpu
  sudo apt-get install -y gawk libc6-dev udev\
      intel-opencl-icd intel-level-zero-gpu level-zero \
      intel-media-va-driver-non-free libmfx1 libmfxgen1 libvpl2 \
      libegl-mesa0 libegl1-mesa libegl1-mesa-dev libgbm1 libgl1-mesa-dev libgl1-mesa-dri \
      libglapi-mesa libgles2-mesa-dev libglx-mesa0 libigdgmm12 libxatracker2 mesa-va-drivers \
      mesa-vdpau-drivers mesa-vulkan-drivers va-driver-all vainfo
  
  sudo reboot
  

  

• Configure permissions

  sudo gpasswd -a ${USER} render
  newgrp render
  
  # Verify the device is working with i915 driver
  sudo apt-get install -y hwinfo
  hwinfo --display
  

For Linux kernel 6.5

• Install wget, gpg-agent

  sudo apt-get install -y gpg-agent wget
  wget -qO - https://repositories.intel.com/gpu/intel-graphics.key | \
  sudo gpg --dearmor --output /usr/share/keyrings/intel-graphics.gpg
  echo "deb [arch=amd64,i386 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/gpu/ubuntu jammy client" | \
  sudo tee /etc/apt/sources.list.d/intel-gpu-jammy.list
  

  

• Install drivers

  sudo apt-get update
  sudo apt-get -y install \
      gawk \
      dkms \
      linux-headers-$(uname -r) \
      libc6-dev
  
  sudo apt-get install -y gawk libc6-dev udev\
      intel-opencl-icd intel-level-zero-gpu level-zero \
      intel-media-va-driver-non-free libmfx1 libmfxgen1 libvpl2 \
      libegl-mesa0 libegl1-mesa libegl1-mesa-dev libgbm1 libgl1-mesa-dev libgl1-mesa-dri \
      libglapi-mesa libgles2-mesa-dev libglx-mesa0 libigdgmm12 libxatracker2 mesa-va-drivers \
      mesa-vdpau-drivers mesa-vulkan-drivers va-driver-all vainfo
  
  sudo reboot
  

  

• Configure permissions

  sudo gpasswd -a ${USER} render
  newgrp render
  
  # Verify the device is working with i915 driver
  sudo apt-get install -y hwinfo
  hwinfo --display
  

Install oneAPI

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

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

Setup Python Environment

Download and install the Miniconda as follows if you don’t have conda installed on your machine:

wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh
source ~/.bashrc

You can use conda --version to verify you conda installation.

After installation, create a new python environment llm:

conda create -n llm python=3.11

Activate the newly created environment llm:

conda activate llm

Install ipex-llm

With the llm environment active, use pip to install ipex-llm for GPU. Choose either US or CN website for extra-index-url:

USCN

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

Note

If you encounter network issues while installing IPEX, refer to this guide for troubleshooting advice.

Verify Installation

• 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:

  source /opt/intel/oneapi/setvars.sh
  
  python
  
  > from ipex_llm.transformers import AutoModel, AutoModelForCausalLM
  

Runtime Configurations

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

Intel Arc™ A-Series and Intel Data Center GPU FlexIntel Data Center GPU Max

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

See also

Please refer to this guide for more details regarding runtime configuration.

A Quick Example

Now let’s play with a real LLM. We’ll be using the 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?”.

• Step 1: Open the Anaconda Prompt and activate the Python environment llm you previously created:

  conda activate llm
  

• Step 2: Follow Runtime Configurations Section above to prepare your runtime environment.

• Step 3: Create a new file named demo.py and insert the code snippet below.

  # Copy/Paste the contents to a new file demo.py
  import torch
  from ipex_llm.transformers import AutoModelForCausalLM
  from transformers import AutoTokenizer, GenerationConfig
  generation_config = GenerationConfig(use_cache = True)
  
  tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-7b", trust_remote_code=True)
  # load Model using ipex-llm and load it to GPU
  model = AutoModelForCausalLM.from_pretrained(
      "tiiuae/falcon-7b", load_in_4bit=True, cpu_embedding=True, trust_remote_code=True)
  model = model.to('xpu')
  
  # Format the prompt
  question = "What is AI?"
  prompt = " Question:{prompt}\n\n Answer:".format(prompt=question)
  # Generate predicted tokens
  with torch.inference_mode():
      input_ids = tokenizer.encode(prompt, return_tensors="pt").to('xpu')
      # warm up one more time before the actual generation task for the first run, see details in `Tips & Troubleshooting`
      # output = model.generate(input_ids, do_sample=False, max_new_tokens=32, generation_config = generation_config)
      output = model.generate(input_ids, do_sample=False, max_new_tokens=32, generation_config = generation_config).cpu()
      output_str = tokenizer.decode(output[0], skip_special_tokens=True)
      print(output_str)
  

  Note: when running LLMs on Intel iGPUs with limited memory size, we recommend setting cpu_embedding=True in the from_pretrained function. This will allow the memory-intensive embedding layer to utilize the CPU instead of GPU.

• Step 5. Run demo.py within the activated Python environment using the following command:

  python demo.py
  

Example output

Example output on a system equipped with an 11th Gen Intel Core i7 CPU and Iris Xe Graphics iGPU:

Question:What is AI?
Answer: AI stands for Artificial Intelligence, which is the simulation of human intelligence in machines.

Tips & Troubleshooting

Warmup for optimial performance on first run

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.