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README.md

AWQ

This example shows how to directly run 4-bit AWQ models using IPEX-LLM on Intel GPU.

Verified Models

Auto-AWQ Backend

llm-AWQ Backend

Requirements

To run these examples with IPEX-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 AWQ model to predict the next N tokens using generate() API, with IPEX-LLM INT4 optimizations.

1. Install

We suggest using conda to manage environment:

conda create -n llm python=3.11
conda activate llm
# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
pip install transformers==4.35.0
pip install autoawq==0.1.8 --no-deps
pip install accelerate==0.25.0
pip install einops

Note: For Mixtral model, please use transformers 4.36.0:

pip install transformers==4.36.0

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 AWQ model (e.g. TheBloke/Llama-2-7B-Chat-AWQ, TheBloke/Mistral-7B-Instruct-v0.1-AWQ, TheBloke/Mistral-7B-v0.1-AWQ) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be 'TheBloke/Llama-2-7B-Chat-AWQ'.
  • --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, IPEX-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-Chat-AWQ"

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

### RESPONSE:

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

### RESPONSE:

Artificial intelligence (AI) is the ability of machines to perform tasks that typically require human intelligence, such as learning, problem-solving, decision