ipex-llm/python/llm/example/NPU/HF-Transformers-AutoModels/Multimodal

Run Large Multimodal Model on Intel NPU

In this directory, you will find examples on how you could apply IPEX-LLM INT4 or INT8 optimizations on Large Multimodal Models on Intel NPUs. See the table blow for verified models.

Verified Models

Model	Model Link
Phi-3-Vision	microsoft/Phi-3-vision-128k-instruct

0. Requirements

To run these examples with IPEX-LLM on Intel NPUs, make sure to install the newest driver version of Intel NPU. Go to https://www.intel.com/content/www/us/en/download/794734/intel-npu-driver-windows.html to download and unzip the driver. Then go to Device Manager, find Neural Processors -> Intel(R) AI Boost. Right click and select Update Driver. And then manually select the folder unzipped from the driver.

Example: Predict Tokens using generate() API

In the example generate.py, we show a basic use case for a phi-3-vision model to predict the next N tokens using generate() API, with IPEX-LLM INT4 optimizations on Intel NPUs.

1. Install

1.1 Installation on Windows

We suggest using conda to manage environment:

conda create -n llm python=3.10 libuv
conda activate llm

# install ipex-llm with 'all' option
pip install --pre --upgrade ipex-llm[all]
pip install torchvision

# below command will install intel_npu_acceleration_library
pip install intel-npu-acceleration-library==1.3

pip install transformers==4.40

2. Runtime Configurations

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

2.1 Configurations for Windows

Note

For optimal performance, we recommend running code in conhost rather than Windows Terminal:

• Press Win+R and input conhost, then press Enter to launch conhost.
• Run following command to use conda in conhost. Replace <your conda install location> with your conda install location.

call <your conda install location>\Scripts\activate

Following envrionment variables are required:

set BIGDL_USE_NPU=1

3. Running examples

python ./generate.py

Arguments info:

• --repo-id-or-model-path REPO_ID_OR_MODEL_PATH: argument defining the huggingface repo id for the Phi-3-vision model (e.g. microsoft/Phi-3-vision-128k-instruct) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be 'microsoft/Phi-3-vision-128k-instruct', and more verified models please see the list in Verified Models.
• --image-url-or-path IMAGE_URL_OR_PATH: argument defining the image to be infered. It is default to be 'http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg'.
• --prompt PROMPT: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be 'What is in the image?'.
• --n-predict N_PREDICT: argument defining the max number of tokens to predict. It is default to be 32.
• --load_in_low_bit: argument defining the load_in_low_bit format used. It is default to be sym_int8, sym_int4 can also be used.

Sample Output

microsoft/Phi-3-vision-128k-instruct

Inference time: xxxx s
-------------------- Prompt --------------------
Message: [{'role': 'user', 'content': '<|image_1|>\nWhat is in the image?'}]
Image link/path: http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg
-------------------- Output --------------------


What is in the image?
 The image shows a young girl holding a white teddy bear. She is wearing a pink dress with a heart on it. The background includes a stone

The sample input image is (which is fetched from COCO dataset):