Introduction
Bolt is a light-weight library for deep learning.
Bolt, as a universal deployment tool for all kinds of neural networks, aims to automate the deployment pipeline and achieve extreme acceleration.
Bolt has been widely deployed and used in many departments of HUAWEI company, such as 2012 Laboratory, CBG and HUAWEI Product Lines.
If you have questions or suggestions, you can submit issue. QQ群: 833345709
Why Bolt is what you need?
- High Performance: 15%+ faster than existing open source acceleration libraries.
- Rich Model Conversion: support Caffe, ONNX, TFLite, Tensorflow.
- Various Inference Precision: support FP32, FP16, INT8, 1-BIT.
- Multiple platforms: ARM CPU(v7, v8, v8.2+, v9), X86 CPU(AVX2, AVX512), GPU(Mali, Qualcomm, Intel, AMD)
- Bolt is the first to support NLP and also supports common CV applications.
- Minimize ROM/RAM
- Rich Graph Optimization
- Efficient Thread Affinity Setting
- Auto Algorithm Tuning
- Time-Series Data Acceleration
See more excellent features and details here
Building Status
There are some common used platform for inference. More targets can be seen from scripts/target.sh. Please make a suitable choice depending on your environment.
If you want to build on-device training module, you can add –train option.
If you want to use multi-threads parallel, you can add –openmp option.
If you want to build for cortex-M or cortex-A7 with restricted ROM/RAM(Sensor, MCU), you can see docs/LITE.md.
Bolt defaultly link static library, This may cause some problem on some platforms. You can use --shared option to link shared library.
| target platform |
precision |
build command |
Linux |
Windows |
MacOS |
| Android(armv7) |
fp32,int8 |
./install.sh --target=android-armv7 |
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| Android(armv8) |
fp32,int8 |
./install.sh --target=android-aarch64 --fp16=off |
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| Android(armv8.2+) |
fp32,fp16,int8,bnn |
./install.sh --target=android-aarch64 |
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|
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| Android(armv9) |
fp32,fp16,bf16,int8,bnn |
./install.sh --target=android-aarch64_v9 |
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|
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| Android(gpu) |
fp16 |
./install.sh --target=android-aarch64 --gpu |
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| Android(x86_64) |
fp32,int8 |
./install.sh --target=android-x86_64 |
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 |
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| iOS(armv7) |
fp32,int8 |
./install.sh --target=ios-armv7 |
/ |
/ |
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| iOS(armv8) |
fp32,int8 |
./install.sh --target=ios-aarch64 --fp16=off |
/ |
/ |
 |
| iOS(armv8.2+) |
fp32,fp16,int8,bnn |
./install.sh --target=ios-aarch64 |
/ |
/ |
|
| Linux(armv7) |
fp32,int8 |
./install.sh --target=linux-armv7_blank |
 |
/ |
/ |
| Linux(armv8) |
fp32,int8 |
./install.sh --target=linux-aarch64_blank --fp16=off |
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/ |
/ |
| Linux(armv8.2+) |
fp32,fp16,int8,bnn |
./install.sh --target=linux-aarch64_blank |
|
/ |
/ |
| Linux(x86_64) |
fp32,int8 |
./install.sh --target=linux-x86_64 |
|
/ |
/ |
| Linux(x86_64_avx2) |
fp32 |
./install.sh --target=linux-x86_64_avx2 |
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/ |
/ |
| Linux(x86_64_avx512) |
fp32,int8 |
./install.sh --target=linux-x86_64_avx512 |
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/ |
/ |
| Windows(x86_64) |
fp32,int8 |
./install.sh --target=windows-x86_64 |
/ |
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/ |
| Windows(x86_64_avx2) |
fp32 |
./install.sh --target=windows-x86_64_avx2 |
/ |
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/ |
| Windows(gpu) |
fp16 |
./install.sh --target=windows-x86_64_avx2 --gpu --fp16=on |
/ |
|
/ |
| Windows(x86_64_avx512) |
fp32,int8 |
./install.sh --target=windows-x86_64_avx512 |
/ |
|
/ |
| Windows(armv8.2+) |
fp32,fp16,int8,bnn |
./install.sh --target=windows-aarch64 |
/ |
/ |
 |
| MacOS(x86_64) |
fp32,int8 |
./install.sh --target=macos-x86_64 |
/ |
/ |
|
| MacOS(x86_64_avx2) |
fp32 |
./install.sh --target=macos-x86_64_avx2 |
/ |
/ |
 |
| MacOS(x86_64_avx512) |
fp32,int8 |
./install.sh --target=macos-x86_64_avx512 |
/ |
/ |
|
| MacOS(armv8.2+) |
fp32,fp16,int8,bnn |
./install.sh --target=macos-aarch64 |
/ |
/ |
|
Quick Start
Two steps to get started with bolt.
-
Conversion: use X2bolt to convert your model from caffe, onnx, tflite or tensorflow to .bolt file;
-
Inference: run benchmark with .bolt and data to get the inference result.
For more details about the usage of X2bolt and benchmark tools, see docs/USER_HANDBOOK.md.
DL Applications in Bolt
Here we show some interesting and useful applications in bolt.
Verified Networks
Bolt has shown its high performance in the inference of common CV, NLP and Recommendation neural networks. Some of the representative networks that we have verified are listed below. You can find detailed benchmark information in docs/BENCHMARK.md.
|
Application
|
Models
|
|
CV
|
Resnet50, Shufflenet, Squeezenet, Densenet, Efficientnet, Mobilenet_v1, Mobilenet_v2, Mobilenet_v3,
BiRealNet,
ReActNet,
Ghostnet,
unet,
LCNet, Pointnet,
hair-segmentation,
duc,
fcn,
retinanet,
SSD,
Faster-RCNN,
Mask-RCNN,
Yolov2,
Yolov3,
Yolov4,
Yolov5,
ViT, TNT, RepVGG, VitAE, CMT, EfficientFormer ...
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NLP
|
Bert, Albert, Tinybert, Neural Machine Translation, Text To Speech(Tactron,Tactron2,FastSpeech+hifigan,melgan), Automatic Speech Recognition, DFSMN, Conformer,
Tdnn,
FRILL,
T5,
GPT-2,
Roberta, Wenet ...
|
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Recommendation
|
NFM, AFM, ONN, wide&deep, DeepFM, MMOE
|
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More DL Tasks
|
...
|
More models than these mentioned above are supported, users are encouraged to further explore.
On-Device Training
On-Device Training has come, it’s a beta vesion which supports Lenet, Mobilenet_v1 and Resnet18 for training on the embedded devices and servers. Want more details of on-device training in bolt? Get with the official training tutorial.
Documentations
Everything you want to know about bolt is recorded in the detailed documentations stored in docs.
Articles
教程
Acknowledgement
Bolt refers to the following projects: caffe, onnx, tensorflow, ncnn, mnn, dabnn.
License
The MIT License(MIT)
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