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Multimodal

README.md

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
MiniCPM-Llama3-V-2_5 openbmb/MiniCPM-Llama3-V-2_5
MiniCPM-V-2_6 openbmb/MiniCPM-V-2_6
Bce-Embedding-Base-V1 maidalun1020/bce-embedding-base_v1
Speech_Paraformer-Large iic/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch

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 -> Browse my computer for drivers. And then manually select the unzipped driver folder to install.

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 'npu' option
pip install --pre --upgrade ipex-llm[npu]
pip install torchvision

# [optional] for MiniCPM-V-2_6
pip install timm torch==2.1.2 torchvision==0.16.2

# [optional] for Bce-Embedding-Base-V1
pip install BCEmbedding==0.1.5 transformers==4.40.0

# [optional] for Speech_Paraformer-Large
pip install funasr==1.1.14
pip install modelscope==1.20.1 torch==2.1.2 torchaudio==2.1.2

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.
  • --lowbit-path LOWBIT_MODEL_PATH: argument defining the path to save/load lowbit version of the model. If it is an empty string, the original pretrained model specified by REPO_ID_OR_MODEL_PATH will be loaded. If it is an existing path, the lowbit model in LOWBIT_MODEL_PATH will be loaded. If it is a non-existing path, the original pretrained model specified by REPO_ID_OR_MODEL_PATH will be loaded, and the converted lowbit version will be saved into LOWBIT_MODEL_PATH. It is default to be '', i.e. an empty string.
  • --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):

4. Run Optimized Models (Experimental)

The examples below show how to run the optimized HuggingFace & FunASR model implementations on Intel NPU, including

4.1 Run MiniCPM-Llama3-V-2_5 & MiniCPM-V-2_6

# to run MiniCPM-Llama3-V-2_5
python minicpm-llama3-v2.5.py --save-directory <converted_model_path>

# to run MiniCPM-V-2_6
python minicpm_v_2_6.py --save-directory <converted_model_path>

Arguments info:

  • --repo-id-or-model-path REPO_ID_OR_MODEL_PATH: argument defining the huggingface repo id for the model (i.e. openbmb/MiniCPM-Llama3-V-2_5) to be downloaded, or the path to the huggingface checkpoint folder.
  • 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.
  • --max-output-len MAX_OUTPUT_LEN: Defines the maximum sequence length for both input and output tokens. It is default to be 1024.
  • --max-prompt-len MAX_PROMPT_LEN: Defines the maximum number of tokens that the input prompt can contain. It is default to be 512.
  • --disable-transpose-value-cache: Disable the optimization of transposing value cache.
  • --save-directory SAVE_DIRECTORY: argument defining the path to save converted model. If it is a non-existing path, the original pretrained model specified by REPO_ID_OR_MODEL_PATH will be loaded, otherwise the lowbit model in SAVE_DIRECTORY will be loaded.

Sample Output

openbmb/MiniCPM-V-2_6
Inference time: xx.xx s
-------------------- Input --------------------
http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg
-------------------- Prompt --------------------
What is in this image?
-------------------- Output --------------------
The image features a young child holding and showing off a white teddy bear wearing a pink dress. The background includes some red flowers and a stone wall, suggesting an outdoor setting.

4.2 Run Speech_Paraformer-Large

# to run Speech_Paraformer-Large
python speech_paraformer-large.py --save-directory <converted_model_path>

Arguments info:

  • --repo-id-or-model-path REPO_ID_OR_MODEL_PATH: argument defining the asr repo id for the model (i.e. iic/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch) to be downloaded, or the path to the asr checkpoint folder.
  • --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.
  • --save-directory SAVE_DIRECTORY: argument defining the path to save converted model. If it is a non-existing path, the original pretrained model specified by REPO_ID_OR_MODEL_PATH will be loaded, otherwise the lowbit model in SAVE_DIRECTORY will be loaded.

Sample Output

iic/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch
# speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/example/asr_example.wav
rtf_avg: 0.090: 100%|███████████████████████████████████| 1/1 [00:01<00:00,  1.18s/it]
[{'key': 'asr_example', 'text': '正 是 因 为 存 在 绝 对 正 义 所 以 我 们 接 受 现 实 的 相 对 正 义 但 是 不 要 因 为 现 实 的 相 对 正 义 我 们 就 认 为 这 个 世 界 没 有 正 义 因 为 如 果 当 你 认 为 这 个 世 界 没 有 正 义'}]

# https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav
rtf_avg: 0.232: 100%|███████████████████████████████████| 1/1 [00:01<00:00,  1.29s/it]
[{'key': 'asr_example_zh', 'text': '欢 迎 大 家 来 体 验 达 摩 院 推 出 的 语 音 识 别 模 型'}]

4.3 Run Bce-Embedding-Base-V1

# to run Bce-Embedding-Base-V1
python bce-embedding.py --save-directory <converted_model_path>

Arguments info:

  • --repo-id-or-model-path REPO_ID_OR_MODEL_PATH: argument defining the asr repo id for the model (i.e. maidalun1020/bce-embedding-base_v1) to be downloaded, or the path to the asr checkpoint folder.
  • --save-directory SAVE_DIRECTORY: argument defining the path to save converted model. If it is a non-existing path, the original pretrained model specified by REPO_ID_OR_MODEL_PATH will be loaded, otherwise the lowbit model in SAVE_DIRECTORY will be loaded.

Sample Output

maidalun1020/bce-embedding-base_v1 |
Inference time: xxx s
[[-0.00674987 -0.01700369 -0.0028928  ... -0.05296675 -0.00352772
   0.00827096]
 [-0.04398304  0.00023038  0.00643183 ... -0.02717186  0.00483789
   0.02298774]]