ipex-llm/python/llm/example/GPU/Pipeline-Parallel-Inference

Run IPEX-LLM on Multiple Intel GPUs in pipeline parallel fashion

This example demonstrates how to run IPEX-LLM optimized low-bit model vertically partitioned on two Intel GPUs.

Requirements

To run this example with IPEX-LLM on Intel GPUs, we have some recommended requirements for your machine, please refer to here for more information. For this particular example, you will need at least two GPUs on your machine.

Example:

1.1 Install IPEX-LLM

conda create -n llm python=3.9
conda activate llm
# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
# you can install specific ipex/torch version for your need
pip install --pre --upgrade ipex-llm[xpu_2.1] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
# configures OneAPI environment variables
source /opt/intel/oneapi/setvars.sh

conda install -c conda-forge -y gperftools=2.10 # to enable tcmalloc

1.2 Build and install patched version of Intel Extension for PyTorch (IPEX)

conda activate llm
source /opt/intel/oneapi/setvars.sh
git clone https://github.com/intel/intel-extension-for-pytorch.git
cd intel-extension-for-pytorch
git checkout v2.1.10+xpu
git submodule update --init --recursive
git cherry-pick be8ea24078d8a271e53d2946ac533383f7a2aa78
export USE_AOT_DEVLIST='ats-m150,pvc'
python setup.py install

Important: IPEX 2.1.10+xpu requires Intel® oneAPI Base Toolkit's version == 2024.0. Please make sure you have installed the correct version.

2. Run tensor parallel inference on multiple GPUs

Here, we provide example usages on different models and different hardwares. Please refer to the appropriate script based on your model and device:

3. Run

For optimal performance on Arc, it is recommended to set several environment variables.

export USE_XETLA=OFF
export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1

python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT

Arguments info:

• --repo-id-or-model-path REPO_ID_OR_MODEL_PATH: argument defining the huggingface repo id for the Llama2 model (e.g. meta-llama/Llama-2-7b-chat-hf) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be 'meta-llama/Llama-2-7b-chat-hf'.
• --prompt PROMPT: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be 'What is AI?'.
• --n-predict N_PREDICT: argument defining the max number of tokens to predict. It is default to be 32.

Sample Output

meta-llama/Llama-2-7b-chat-hf

Inference time: xxxx s
-------------------- Prompt --------------------
<s>[INST] <<SYS>>

<</SYS>>

What is AI? [/INST]
-------------------- Output --------------------
[INST] <<SYS>>

<</SYS>>

What is AI? [/INST]  Artificial intelligence (AI) is the broader field of research and development aimed at creating machines that can perform tasks that typically require human intelligence,