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GLM-4V

In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on GLM-4V models. For illustration purposes, we utilize the THUDM/glm-4v-9b as a reference GLM-4V model.

0. 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 GLM-4V model to predict the next N tokens using generate() API, with IPEX-LLM INT4 optimizations.

1. Install

We suggest using conda to manage environment:

On Linux:

conda create -n llm python=3.11 # recommend to use Python 3.11
conda activate llm

# install ipex-llm with 'all' option
pip install --pre --upgrade ipex-llm[all] --extra-index-url https://download.pytorch.org/whl/cpu

pip install torchvision tiktoken transformers==4.42.4 trl

On Windows:

conda create -n llm python=3.11
conda activate llm

pip install --pre --upgrade ipex-llm[all]

pip install torchvision tiktoken transformers==4.42.4 trl

2. Run

python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --image-url-or-path IMAGE_URL_OR_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 GLM-4V model (e.g. THUDM/glm-4v-9b) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be 'THUDM/glm-4v-9b'.
  • --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.

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 GLM-4V model based on the capabilities of your machine.

2.1 Client

On client Windows machines, it is recommended to run directly with full utilization of all cores:

python ./generate.py

2.2 Server

For optimal performance on server, it is recommended to set several environment variables (refer to here for more information), and run the example with all the physical cores of a single socket.

E.g. on Linux,

# set IPEX-LLM env variables
source ipex-llm-init

# e.g. for a server with 48 cores per socket
export OMP_NUM_THREADS=48
numactl -C 0-47 -m 0 python ./generate.py

2.3 Sample Output

THUDM/glm-4v-9b

Inference time: xxxx s
-------------------- Prompt --------------------
What is in the image?
-------------------- Output --------------------
The image shows a young child holding up a small white teddy bear dressed in a pink

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