Skip to content

Latest commit

 

History

History

Folders and files

NameName
Last commit message
Last commit date

parent directory

..
 
 
 
 

Aquila

In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on Aquila models on Intel GPUs. For illustration purposes, we utilize the BAAI/AquilaChat-7B as a reference Aquila model.

Note: If you want to download the Hugging Face Transformers model, please refer to here.

IPEX-LLM optimizes the Transformers model in INT4 precision at runtime, and thus no explicit conversion is needed.

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 Aquila model to predict the next N tokens using generate() API, with IPEX-LLM INT4 optimizations.

1. Install

1.1 Installation on Linux

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/

1.2 Installation on Windows

We suggest using conda to manage environment:

conda create -n llm python=3.11 libuv
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/

2. Configures OneAPI environment variables for Linux

Note

Skip this step if you are running on Windows.

This is a required step on Linux for APT or offline installed oneAPI. Skip this step for PIP-installed oneAPI.

source /opt/intel/oneapi/setvars.sh

3. Runtime Configurations

For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device.

3.1 Configurations for Linux

For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series
export USE_XETLA=OFF
export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
export SYCL_CACHE_PERSISTENT=1
For Intel Data Center GPU Max Series
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

Note: Please note that libtcmalloc.so can be installed by conda install -c conda-forge -y gperftools=2.10.

For Intel iGPU
export SYCL_CACHE_PERSISTENT=1
export BIGDL_LLM_XMX_DISABLED=1

3.2 Configurations for Windows

For Intel iGPU
set SYCL_CACHE_PERSISTENT=1
set BIGDL_LLM_XMX_DISABLED=1
For Intel Arc™ A-Series Graphics
set SYCL_CACHE_PERSISTENT=1

Note

For the first time that each model runs on Intel iGPU/Intel Arc™ A300-Series or Pro A60, it may take several minutes to compile.

4. Running examples

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

Arguments Info In the example, several arguments can be passed to satisfy your requirements:

  • --repo-id-or-model-path: str, argument defining the huggingface repo id for the Aquila model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be 'BAAI/AquilaChat-7B'.
  • --prompt: str, argument defining the prompt to be inferred (with integrated prompt format for chat). It is default to be 'AI是什么?'.
  • --n-predict: int, argument defining the max number of tokens to predict. It is default to be 32.

Sample Output

Inference time: xxxx s
-------------------- Prompt --------------------
Human: AI是什么?###Assistant:
-------------------- Output --------------------
Human: AI是什么?###Assistant: AI是人工智能的缩写。人工智能是一种技术,旨在使计算机能够像人类一样思考、学习和执行任务。AI包括许多不同的技术和方法,例如机器