ipex-llm/python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/GPTQ

GPTQ

This example shows how to directly run 4-bit GPTQ models using BigDL-LLM on Intel GPU. For illustration purposes, we utilize the "TheBloke/Llama-2-7B-GPTQ" as a reference.

0. Requirements

To run these examples with BigDL-LLM, we have some recommended requirements for your machine, please refer to here for more information.

Example: Predict Tokens using generate() API

In the example generate.py, we show a basic use case for a Llama2 model to predict the next N tokens using generate() API, with BigDL-LLM INT4 optimizations.

1. Install

We suggest using conda to manage environment:

conda create -n llm python=3.9
conda activate llm

pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
pip install transformers==4.34.0
BUILD_CUDA_EXT=0 pip install git+https://github.com/PanQiWei/AutoGPTQ.git@1de9ab6
pip install optimum==0.14.0

2. Configures OneAPI environment variables

source /opt/intel/oneapi/setvars.sh

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-gptq model (e.g. TheBloke/Llama-2-7B-GPTQ) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be 'TheBloke/Llama-2-7B-GPTQ'.
• --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.

Note

: When loading the model in 4-bit, BigDL-LLM converts linear layers in the model into INT4 format. In theory, a XB model saved in 16-bit will requires approximately 2X GB of memory for loading, and ~0.5X GB memory for further inference.

Please select the appropriate size of the Llama2 model based on the capabilities of your machine.

2.3 Sample Output

TheBloke/Llama-2-7B-GPTQ

Inference time: xxxx s
-------------------- Prompt --------------------
### HUMAN:
What is AI?

### RESPONSE:

-------------------- Output --------------------
### HUMAN:
What is AI?

### RESPONSE:

> AI is a branch of computer science that aims to create intelligent machines that think and act like humans.

### HUMAN