NNPACK is an acceleration package for neural network computations, which can run on x86-64, ARMv7, or ARM64 architecture CPUs. Using NNPACK, higher-level libraries like MXNet can speed up the execution on multi-core CPU computers, including laptops and mobile devices.
MXNet supports NNPACK for forward propagation (inference only) in convolution, max-pooling, and fully-connected layers. In this document, we give a high level overview of how to use NNPACK with MXNet.
The underlying implementation of NNPACK utilizes several acceleration methods, including fft and winograd. These algorithms work better on some special batch size, kernel size, and stride settings than on other, so depending on the context, not all convolution, max-pooling, or fully-connected layers can be powered by NNPACK. When favorable conditions for running NNPACKS are not met, MXNet will fall back to the default implementation automatically.
NNPACK only supports Linux and OS X systems. Windows is not supported at present. The following table explains under which conditions NNPACK will work.
| operation | conditions |
|---|---|
| convolution | 2d convolution and no-bias=False and dilate=(1,1) and num_group=1 and batch-size = 1 or batch-size > 1 && stride = (1,1); |
| pooling | max-pooling and kernel=(2,2) and stride=(2,2) and pooling_convention=full |
| fully-connected | without any restrictions |
If the trained model meets some conditions of using NNPACK, you can build MXNet with NNPACK support. Follow these simple steps:
Note: The following NNPACK installation instructions have been tested on Ubuntu 14.04 and 16.04.
# Install Pip $ sudo apt-get update $ sudo apt-get install -y python-pip $ sudo pip install --upgrade pip # Install Peach $ git clone https://github.com/Maratyszcza/PeachPy.git $ cd PeachPy $ sudo pip install --upgrade -r requirements.txt $ python setup.py generate $ sudo pip install --upgrade . # Install Ninja Build System $ sudo apt-get install ninja-build $ pip install ninja-syntax # Build NNPack shared library $ cd ~ $ git clone --recursive https://github.com/Maratyszcza/NNPACK.git $ cd NNPACK # Latest NNPACK do not support building NNPACK as shared library using --enable-shared flag # Reset to commit that supports it. $ git reset --hard 9c6747d7b80051b40e6f92d6828e2ed997529cd2 $ git submodule init && git submodule update --recursive $ python ./configure.py --enable-shared $ ninja $ cd ~
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$YOUR_NNPACK_INSTALL_PATH/libADD_CFLAGS in config.mk, e.g. ADD_CFLAGS = -I$(YOUR_NNPACK_INSTALL_PATH)/include/ -I$(YOUR_NNPACK_INSTALL_PATH)/third-party/pthreadpool/include/USE_NNPACK = 1 in config.mk.Though not all convolutional, pooling, and fully-connected layers can make full use of NNPACK, for some popular models it provides significant speedups. These include the most popular image recognition networks: Alexnet, VGG, and Inception-bn.
To benchmark NNPACK, we use example/image-classification/benchmark_score.py(changed with more range of batch-size). We use CPU e5-2670, MXNET_CPU_NNPACK_NTHREADS=4.
build MXNet without NNPACK, the log is:
INFO:root:network: alexnet INFO:root:device: cpu(0) INFO:root:batch size 1, image/sec: 6.389429 INFO:root:batch size 2, image/sec: 7.961457 INFO:root:batch size 4, image/sec: 8.950112 INFO:root:batch size 8, image/sec: 9.578176 INFO:root:batch size 16, image/sec: 9.701248 INFO:root:batch size 32, image/sec: 9.839940 INFO:root:batch size 64, image/sec: 10.075369 INFO:root:batch size 128, image/sec: 10.053556 INFO:root:batch size 256, image/sec: 9.972228 INFO:root:network: vgg INFO:root:device: cpu(0) INFO:root:batch size 1, image/sec: 1.223822 INFO:root:batch size 2, image/sec: 1.322814 INFO:root:batch size 4, image/sec: 1.383586 INFO:root:batch size 8, image/sec: 1.402376 INFO:root:batch size 16, image/sec: 1.415972 INFO:root:batch size 32, image/sec: 1.428377 INFO:root:batch size 64, image/sec: 1.443987 INFO:root:batch size 128, image/sec: 1.427531 INFO:root:batch size 256, image/sec: 1.435279
build MXNet with NNPACK, log is:
INFO:root:network: alexnet INFO:root:device: cpu(0) INFO:root:batch size 1, image/sec: 19.027215 INFO:root:batch size 2, image/sec: 12.879975 INFO:root:batch size 4, image/sec: 17.424076 INFO:root:batch size 8, image/sec: 21.283966 INFO:root:batch size 16, image/sec: 24.469325 INFO:root:batch size 32, image/sec: 25.910348 INFO:root:batch size 64, image/sec: 27.441672 INFO:root:batch size 128, image/sec: 28.009156 INFO:root:batch size 256, image/sec: 28.918950 INFO:root:network: vgg INFO:root:device: cpu(0) INFO:root:batch size 1, image/sec: 3.980907 INFO:root:batch size 2, image/sec: 2.392069 INFO:root:batch size 4, image/sec: 3.610553 INFO:root:batch size 8, image/sec: 4.994450 INFO:root:batch size 16, image/sec: 6.396612 INFO:root:batch size 32, image/sec: 7.614288 INFO:root:batch size 64, image/sec: 8.826084 INFO:root:batch size 128, image/sec: 9.193653 INFO:root:batch size 256, image/sec: 9.991472
The results show that NNPACK can confer a speedup of about 2X~7X as compared to the original MXNet CPU implementation.
NNPACK aims to provide high-performance implementations of some layers for multi-core CPUs, so you can easily set the thread number by changing the environmental variable MXNET_CPU_NNPACK_NTHREADS. However, we found that the performance is not proportional to the number of threads, and suggest using 4~8 threads when using NNPACK.