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alibaba/MNN

MNN is a blazing fast, lightweight deep learning framework, battle-tested by business-critical use cases in Alibaba
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MNN

中文版本

MNN Homepage

Intro

MNN is a highly efficient and lightweight deep learning framework. It supports inference and training of deep learning models, and has industry leading performance for inference and training on-device. At present, MNN has been integrated in more than 30 apps of Alibaba Inc, such as Taobao, Tmall, Youku, Dingtalk, Xianyu and etc., covering more than 70 usage scenarios such as live broadcast, short video capture, search recommendation, product searching by image, interactive marketing, equity distribution, security risk control. In addition, MNN is also used on embedded devices, such as IoT.

architecture

Inside Alibaba, MNN works as the basic module of the compute container in the Walle System, the first end-to-end, general-purpose, and large-scale production system for device-cloud collaborative machine learning, which has been published in the top system conference OSDI’22. The key design principles of MNN and the extensive benchmark testing results (vs. TensorFlow, TensorFlow Lite, PyTorch, PyTorch Mobile, TVM) can be found in the OSDI paper. The scripts and instructions for benchmark testing are put in the path “/benchmark”. If MNN or the design of Walle helps your research or production use, please cite our OSDI paper as follows:

@inproceedings {proc:osdi22:walle,
    author = {Chengfei Lv and Chaoyue Niu and Renjie Gu and Xiaotang Jiang and Zhaode Wang and Bin Liu and Ziqi Wu and Qiulin Yao and Congyu Huang and Panos Huang and Tao Huang and Hui Shu and Jinde Song and Bin Zou and Peng Lan and Guohuan Xu and Fei Wu and Shaojie Tang and Fan Wu and Guihai Chen},
    title = {Walle: An {End-to-End}, {General-Purpose}, and {Large-Scale} Production System for {Device-Cloud} Collaborative Machine Learning},
    booktitle = {16th USENIX Symposium on Operating Systems Design and Implementation (OSDI 22)},
    year = {2022},
    isbn = {978-1-939133-28-1},
    address = {Carlsbad, CA},
    pages = {249--265},
    url = {https://www.usenix.org/conference/osdi22/presentation/lv},
    publisher = {USENIX Association},
    month = jul,
}

Documentation and Workbench

MNN's docs are in placed in Yuque docs here and Read the docs.

MNN Workbench could be downloaded from MNN's homepage, which provides pretrained models, visualized training tools, and one-click deployment of models to devices.

Key Features

Lightweight

  • Optimized for devices, no dependencies, can be easily deployed to mobile devices and a variety of embedded devices.
  • iOS platform: static library size will full option for armv7+arm64 platforms is about 12MB, size increase of linked executables is about 2M.
  • Android platform: core so size is about 800KB (armv7a - c++_shared).
  • Use MNN_BUILD_MINI can reduce package size about 25% , with limit of fix model input size
  • Support FP16 / Int8 qunatize, can reduce model size 50%-70%

Versatility

  • Supports Tensorflow, Caffe, ONNX,Torchscripts and supports common neural networks such as CNN, RNN, GAN, Transformork.
  • Supports AI model with multi-inputs or multi-outputs, every kind of dimenstion format, dynamic inputs, controlflow.
  • MNN supports approximate full OPs used for AI Model. The converter supports 178 Tensorflow OPs, 52 Caffe OPs, 163 Torchscripts OPs, 158 ONNX OPs.
  • Supports iOS 8.0+, Android 4.3+ and embedded devices with POSIX interface.
  • Supports hybrid computing on multiple devices. Currently supports CPU and GPU.

High performance

  • Implements core computing with lots of optimized assembly code to make full use of the ARM / x64 CPU.
  • Use Metal / OpenCL / Vulkan to support GPU inference on mobile.
  • Use CUDA and tensorcore to support NVIDIA GPU for better performance
  • Convolution and transposition convolution algorithms are efficient and stable. The Winograd convolution algorithm is widely used to better symmetric convolutions such as 3x3,4x4,5x5,6x6,7x7.
  • Twice speed increase for the new architecture ARM v8.2 with FP16 half-precision calculation support. 2.5 faster to use sdot for ARM v8.2 and VNNI.

Ease of use

  • Support use MNN's OP to do numerical calculating like numpy.
  • Support lightweight image process module like OpenCV, which is only 100k.
  • Support build model and train it on PC / mobile.
  • MNN Python API helps ML engineers to easily use MNN to inference, train, process image, without dipping their toes in C++ code.

The Architecture / Precision MNN supported is shown below:

  • S :Support and work well, deeply optimized, recommend to use
  • A :Support and work well, can use
  • B :Support but has bug or not optimized, no recommend to use
  • C :Not Support
Architecture / Precision Normal FP16 BF16 Int8
CPU Native B C B B
x86/x64-SSE4.1 A B B A
x86/x64-AVX2 S B B A
x86/x64-AVX512 S B B S
ARMv7a S S (ARMv8.2) S S
ARMv8 S S (ARMv8.2) S S
GPU OpenCL A S C C
Vulkan A A C C
Metal A S C C
CUDA A S C C
NPU CoreML B C C C
HIAI B C C B

Tools

Base on MNN (Tensor compute engine), we provided a series of tools for inference, train and general computation.

  • MNN-Converter: Convert other model to MNN model for inference, such as Tensorflow(lite), Caffe, ONNX, Torchscripts. And do graph optimization to reduce computation.
  • MNN-Compress: Compress model to reduce size and increase performance / speed
  • MNN-Express: Support model with controlflow, use MNN's OP to do general-purpose compute.
  • MNN-CV: An OpenCV liked library, but based on MNN and then much more lightweight.
  • MNN-Train: Support train MNN model.

How to Discuss and Get Help From MNN Community

The group discussions are predominantly Chinese. But we welcome and will help English speakers.

Dingtalk discussion groups:

Group #1 (Full): 23329087

Group #2 (Full): 23350225

Group #3: https://h5.dingtalk.com/circle/healthCheckin.html?dtaction=os&corpId=ding8989a1d6ae6ef130b177420cc0e366ea&f0c81=1b93a&cbdbhh=qwertyuiop

Historical Paper

The preliminary version of MNN, as mobile inference engine and with the focus on manual optimization, has also been published in MLSys 2020. Please cite the paper, if MNN previously helped your research:

@inproceedings{alibaba2020mnn,
  author = {Jiang, Xiaotang and Wang, Huan and Chen, Yiliu and Wu, Ziqi and Wang, Lichuan and Zou, Bin and Yang, Yafeng and Cui, Zongyang and Cai, Yu and Yu, Tianhang and Lv, Chengfei and Wu, Zhihua},
  title = {MNN: A Universal and Efficient Inference Engine},
  booktitle = {MLSys},
  year = {2020}
}

License

Apache 2.0

Acknowledgement

MNN participants: Taobao Technology Department, Search Engineering Team, DAMO Team, Youku and other Alibaba Group employees.

MNN refers to the following projects: