# 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](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Overview/install_gpu.html#linux) page for more details.

## Install Prerequisites

### Install GPU Driver

#### For Linux kernel 6.2

* Install wget, gpg-agent
    ```bash
    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
    ```

    <img src="https://llm-assets.readthedocs.io/en/latest/_images/wget.png" width=100%; />

* Install drivers

    ```bash
    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
    ```

    <img src="https://llm-assets.readthedocs.io/en/latest/_images/i915.png" width=100%; />

    <img src="https://llm-assets.readthedocs.io/en/latest/_images/gawk.png" width=100%; />


* Configure permissions
    ```bash
    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
    ```bash
    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
    ```

    <img src="https://llm-assets.readthedocs.io/en/latest/_images/wget.png" width=100%; />

* Install drivers

    ```bash
    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
    ```

    <img src="https://llm-assets.readthedocs.io/en/latest/_images/gawk.png" width=100%; />


* Configure permissions
    ```bash
    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
  ```
  <img src="https://llm-assets.readthedocs.io/en/latest/_images/oneapi.png" alt="image-20240221102252565" width=100%; />

  <img src="https://llm-assets.readthedocs.io/en/latest/_images/basekit.png" alt="image-20240221102252565" width=100%; />

### Setup Python Environment
 
Download and install the Miniconda as follows if you don't have conda installed on your machine:
  ```bash
  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`:
```cmd
conda create -n llm python=3.11
```
Activate the newly created environment `llm`:
```cmd
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`:

```eval_rst
.. tabs::
   .. tab:: US

      .. code-block:: cmd

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

   .. tab:: CN

      .. code-block:: cmd

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

```eval_rst
.. note::

  If you encounter network issues while installing IPEX, refer to `this guide <https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Overview/install_gpu.html#id3>`_ 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:
  ```bash
  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.

```eval_rst
.. 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
         export SYCL_CACHE_PERSISTENT=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 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``

```

  ```eval_rst
  .. seealso::

     Please refer to `this guide <../Overview/install_gpu.html#id5>`_ 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](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?". 

* Step 1: Open the **Anaconda Prompt** and activate the Python environment `llm` you previously created: 
   ```bash
   conda activate llm
   ```
* Step 2: Follow [Runtime Configurations Section](#runtime-configurations) above to prepare your runtime environment.  
* Step 3: Create a new file named `demo.py` and insert the code snippet below.
   ```python
   # 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:
  ```bash
  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.