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update linux quickstart and formats of migration (#10530)

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* update linux quickstart and formats of migration

* update quickstart

* update format
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Shengsheng Huang 2024-03-25 15:38:02 +08:00 committed by GitHub
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36
docs/readthedocs/source/doc/LLM/Quickstart/bigdl_llm_migration.md
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@ -1,11 +1,36 @@
# `bigdl-llm` Migration Guide
This guide helps you migrate your `bigdl-llm` application to use `ipex-llm`.
## Upgrade `bigdl-llm` package to `ipex-llm`
First uninstall `bigdl-llm` and install `ipex-llm`.
```eval_rst
.. note::
This step assumes you have already installed `bigdl-llm`.
```
You need to uninstall `bigdl-llm` and install `ipex-llm`With your `bigdl-llm` conda envionment activated, exeucte the folloiwng command according to your device type and location:
### For CPU
```bash
pip uninstall -y bigdl-llm
pip install --pre --upgrade ipex-llm[all] # for cpu
pip install --pre --upgrade ipex-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu # for xpu
```
### For GPU
```eval_rst
.. tabs::
.. tab:: US
.. code-block:: cmd
pip uninstall -y bigdl-llm
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 uninstall -y bigdl-llm
pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/cn/
```
## Migrate `bigdl-llm` code to `ipex-llm`
There are two options to migrate `bigdl-llm` code to `ipex-llm`.
@ -13,8 +38,8 @@ There are two options to migrate `bigdl-llm` code to `ipex-llm`.
To upgrade `bigdl-llm` code to `ipex-llm`, simply replace all `bigdl.llm` with `ipex_llm`:
```python
#from bigdl.llm.transformers import AutoModelForCausalLM
from ipex_llm.transformers import AutoModelForCausalLM
#from bigdl.llm.transformers import AutoModelForCausalLM # Original line
from ipex_llm.transformers import AutoModelForCausalLM #Updated line
model = AutoModelForCausalLM.from_pretrained(model_path,
load_in_4bit=True,
trust_remote_code=True)
@ -24,8 +49,7 @@ model = AutoModelForCausalLM.from_pretrained(model_path,
To run in the compatible mode, simply add `import ipex_llm` at the beginning of the existing `bigdl-llm` code:
```python
# need to add the below line before "import bigdl.llm"
import ipex_llm
import ipex_llm # Add this line before any bigdl.llm imports
from bigdl.llm.transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(model_path,
load_in_4bit=True,

95
docs/readthedocs/source/doc/LLM/Quickstart/install_linux_gpu.md
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@ -4,10 +4,11 @@ This guide demonstrates how to install IPEX-LLM on Linux with Intel GPUs. It app
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 Intel GPU Driver
### Install GPU Driver
### For Linux kernel 6.2
#### For Linux kernel 6.2
* Install arc driver
```bash
@ -24,15 +25,12 @@ IPEX-LLM currently supports the Ubuntu 20.04 operating system and later, and sup
```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 \
@ -59,24 +57,7 @@ IPEX-LLM currently supports the Ubuntu 20.04 operating system and later, and sup
```
## Setup Python Environment
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
# Verify the installation
conda --version
# rm Miniconda3-latest-Linux-x86_64.sh # if you don't need this file any longer
```
> <img src="https://llm-assets.readthedocs.io/en/latest/_images/install_conda.png" alt="image-20240221102252569" width=100%; />
## Install oneAPI
### 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
@ -90,23 +71,36 @@ Install the Miniconda as follows if you don't have conda installed on your machi
> <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=3.9 libuv
```
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:
```
conda create -n llm python=3.9
conda activate llm
pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://developer.intel.com/ipex-whl-stable-xpu
```
> <img src="https://llm-assets.readthedocs.io/en/latest/_images/create_conda_env.png" alt="image-20240221102252564" width=100%; />
> <img src="https://llm-assets.readthedocs.io/en/latest/_images/create_conda_env.png" alt="image-20240221102252564" width=100%; />
* 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:
## 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
@ -115,15 +109,18 @@ Install the Miniconda as follows if you don't have conda installed on your machi
> from ipex_llm.transformers import AutoModel, AutoModelForCausalLM
```
> <img src="https://llm-assets.readthedocs.io/en/latest/_images/verify_ipex_import.png" alt="image-20240221102252562" width=100%; />
## Runtime Configurations
To use GPU acceleration on Linux, several environment variables are required or recommended before running a GPU example.
* For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series, we recommend:
```bash
```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
@ -131,11 +128,13 @@ To use GPU acceleration on Linux, several environment variables are required or
# 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
* For Intel Data Center GPU Max Series, we recommend:
```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
@ -144,9 +143,10 @@ To use GPU acceleration on Linux, several environment variables are required or
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```.
Please note that ``libtcmalloc.so`` can be installed by ``conda install -c conda-forge -y gperftools=2.10``
```
## A Quick Example
@ -156,16 +156,7 @@ Now let's play with a real LLM. We'll be using the [phi-1.5](https://huggingface
```bash
conda activate llm
```
* Step 2: If you're running on iGPU, set some environment variables by running below commands:
> For more details about runtime configurations, refer to [this guide](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Overview/install_gpu.html#runtime-configuration):
```bash
# 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
```
* 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

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docs/readthedocs/source/doc/LLM/Quickstart/install_windows_gpu.md
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@ -93,18 +93,18 @@ Choose either US or CN website for `extra-index-url`:
```
## Verify Installation
You can verify if `ipex-llm` is successfully installed by simply running a few lines of code:
You can verify if `ipex-llm` is successfully installed following below steps.
* Step 1: Open the **Anaconda Prompt** and activate the Python environment `llm` you previously created:
### Step 1: Runtime Configurations
* Open the **Anaconda Prompt** and activate the Python environment `llm` you previously created:
```cmd
conda activate llm
```
* Step 2: Configure oneAPI variables by running the following command:
* Configure oneAPI variables by running the following command:
```cmd
call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat"
```
* Step 3:
Please also set the following environment variable according to your device:
* Set the following environment variables according to your device:
```eval_rst
.. tabs::
@ -125,9 +125,12 @@ You can verify if `ipex-llm` is successfully installed by simply running a few l
For other Intel dGPU Series, please refer to `this guide <https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Overview/install_gpu.html#runtime-configuration>`_ for more details regarding runtime configuration.
```
* Step 4: Launch the Python interactive shell by typing `python` in the Anaconda prompt window and then press Enter.
* Step 5: Copy following code to Anaconda prompt **line by line** and press Enter **after copying each line**.
### Step 2: Run Python Code
* Launch the Python interactive shell by typing `python` in the Anaconda prompt window and then press Enter.
* Copy following code to Anaconda prompt **line by line** and press Enter **after copying each line**.
```python
import torch
from ipex_llm.transformers import AutoModel,AutoModelForCausalLM
@ -156,41 +159,12 @@ To monitor your GPU's performance and status (e.g. memory consumption, utilizati
Now let's play with a real LLM. We'll be using the [Qwen-1.8B-Chat](https://huggingface.co/Qwen/Qwen-1_8B-Chat) model, a 1.8 billion parameter LLM for this demonstration. 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:
```cmd
conda activate llm
```
* Step 2: Configure oneAPI variables by running the following command:
```cmd
call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat"
```
* Step 3:
Please also set the following environment variable according to your device:
```eval_rst
.. tabs::
.. tab:: Intel iGPU
.. code-block:: cmd
set SYCL_CACHE_PERSISTENT=1
set BIGDL_LLM_XMX_DISABLED=1
.. tab:: Intel Arc™ A770
There is no need to set further environment variables.
```
```eval_rst
.. seealso::
For other Intel dGPU Series, please refer to `this guide <https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Overview/install_gpu.html#runtime-configuration>`_ for more details regarding runtime configuration.
```
* Step 4: Install additional package required for Qwen-1.8B-Chat to conduct:
* Step 1: Follow [Runtime Configurations Section](#step-1-runtime-configurations) above to prepare your runtime environment.
* Step 2: Install additional package required for Qwen-1.8B-Chat to conduct:
```cmd
pip install tiktoken transformers_stream_generator einops
```
* Step 5: Create code file. IPEX-LLM supports loading model from Hugging Face or ModelScope. Please choose according to your requirements.
* Step 3: Create code file. IPEX-LLM supports loading model from Hugging Face or ModelScope. Please choose according to your requirements.
```eval_rst
.. tabs::
.. tab:: Hugging Face
@ -322,7 +296,7 @@ Now let's play with a real LLM. We'll be using the [Qwen-1.8B-Chat](https://hugg
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:
* Step 4. Run `demo.py` within the activated Python environment using the following command:
```cmd
python demo.py
```