qwen2-audio

Qwen2-Audio

In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on Qwen2-Audio models on Intel GPUs. For illustration purposes, we utilize Qwen/Qwen2-Audio-7B-Instruct as reference model.

0. Requirements

To run these examples with IPEX-LLM on Intel GPUs, 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 Qwen2-Audio model to conduct transcription using processor API, then use the recoginzed text as the input for Qwen2-Audio model to perform an English-Chinese translation using generate() API, with IPEX-LLM INT4 optimizations on Intel GPUs.

1. Install

Note

Qwen2-Audio requires minimal transformers version of 4.35.0, which is not yet released. Currently, you can install the latest version of transformers from GitHub. When such a version is released, you can install it using pip install transformers==4.35.0.

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/

pip install librosa
pip install git+https://github.com/huggingface/transformers

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/

pip install librosa
pip install git+https://github.com/huggingface/transformers

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

Arguments info:

• --repo-id-or-model-path REPO_ID_OR_MODEL_PATH: argument defining the huggingface repo id for the Qwen2-Audio model (e.g. Qwen/Qwen2-Audio-7B-Instruct) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be 'Qwen/Qwen2-Audio-7B-Instruct'.

Sample Output

In generate.py, an audio clip is used as the input, which asks the model to translate an English sentence into Chinese. The response from the model is expected to be similar to:

['每个人都希望被赏识，所以如果你欣赏某人，不要保密。']