In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on InternLM_XComposer models. For illustration purposes, we utilize the internlm/internlm-xcomposer-vl-7b as a reference InternLM_XComposer model.
To run these examples with IPEX-LLM, we have some recommended requirements for your machine, please refer to here for more information.
In the example chat.py, we show a basic use case for an InternLM_XComposer model to start a multi-turn chat centered around an image using chat() API, with IPEX-LLM INT4 optimizations.
We suggest using conda to manage the Python environment. For more information about conda installation, please refer to here.
After installing conda, create a Python environment for IPEX-LLM:
On Linux:
conda create -n llm python=3.11 # recommend to use Python 3.11
conda activate llm
# install the latest ipex-llm nightly build with 'all' option
pip install --pre --upgrade ipex-llm[all] --extra-index-url https://download.pytorch.org/whl/cpu
pip install accelerate timm==0.4.12 sentencepiece==0.1.99 gradio==3.44.4 markdown2==2.4.10 xlsxwriter==3.1.2 einops # additional package required for InternLM_XComposer to conduct generation
On Windows:
conda create -n llm python=3.11
conda activate llm
pip install --pre --upgrade ipex-llm[all]
pip install accelerate timm==0.4.12 sentencepiece==0.1.99 gradio==3.44.4 markdown2==2.4.10 xlsxwriter==3.1.2 einops
If you select the InternLM_XComposer model (internlm/internlm-xcomposer-vl-7b), please note that their code (modeling_InternLM_XComposer.py) does not support inference on CPU. To address this issue, we have provided the updated file (internlm-xcomposer-vl-7b/modeling_InternLM_XComposer.py, which can be used to conduct inference on CPU.
You could use the following code to download internlm/internlm-xcomposer-vl-7b with a specific snapshot id. Please note that the modeling_InternLM_XComposer.py file that we provide are based on these specific commits.
from huggingface_hub import snapshot_download
# for internlm/internlm-xcomposer-vl-7b
model_path = snapshot_download(repo_id='internlm/internlm-xcomposer-vl-7b',
revision="b06eb0c11653fe1568b6c5614b6b7be407ef8660",
cache_dir="dir/path/where/model/files/are/downloaded")
print(f'internlm/internlm-xcomposer-vl-7b checkpoint is downloaded to {model_path}')
For internlm/internlm-xcomposer-vl-7b, you should replace the modeling_InternLM_XComposer.py with internlm-xcomposer-vl-7b/modeling_InternLM_XComposer.py.
After setting up the Python environment, you could run the example by following steps.
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 LLaVA model based on the capabilities of your machine.
On client Windows machines, it is recommended to run directly with full utilization of all cores:
python ./chat.py --image-path demo.jpgMore information about arguments can be found in Arguments Info section. The expected output can be found in Sample Output section.
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 ./chat.py --image-path demo.jpgMore information about arguments can be found in Arguments Info section. The expected output can be found in Sample Output section.
In the example, several arguments can be passed to satisfy your requirements:
--repo-id-or-model-path REPO_ID_OR_MODEL_PATH: argument defining the huggingface repo id for the LLaVA model (e.g.internlm/internlm-xcomposer-vl-7b) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be'internlm/internlm-xcomposer-vl-7b'.--image-path IMAGE_PATH: argument defining the input image that the chat will focus on. It is required and should be a local path (not url).--n-predict N_PREDICT: argument defining the max number of tokens to predict. It is default to be512.
User: 这是什么？
Bot: bus
User: 它可以用来干什么
Bot: transport people
The sample input image is (which is fetched from COCO dataset):
