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| <h1 class="post-title">NNPACK for Multi-Core CPU Support in MXNet</h1> |
| <h3></h3></header> |
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| <h3 id="nnpack-for-multi-core-cpu-support-in-mxnet">NNPACK for Multi-Core CPU Support in MXNet</h3> |
| |
| <p><a href="https://github.com/Maratyszcza/NNPACK">NNPACK</a> 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 <em>MXNet</em> can speed up |
| the execution on multi-core CPU computers, including laptops and mobile devices.</p> |
| |
| <p><em>MXNet</em> 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 <em>MXNet</em>.</p> |
| |
| <h3 id="conditions">Conditions</h3> |
| |
| <p>The underlying implementation of NNPACK utilizes several acceleration methods, |
| including <a href="https://arxiv.org/abs/1312.5851">fft</a> and <a href="https://arxiv.org/abs/1509.09308">winograd</a>. |
| These algorithms work better on some special <code>batch size</code>, <code>kernel size</code>, and <code>stride</code> 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, |
| <em>MXNet</em> will fall back to the default implementation automatically.</p> |
| |
| <p>NNPACK only supports Linux and OS X systems. Windows is not supported at present. |
| The following table explains under which conditions NNPACK will work.</p> |
| |
| <table><thead> |
| <tr> |
| <th style="text-align: left">operation</th> |
| <th style="text-align: left">conditions</th> |
| </tr> |
| </thead><tbody> |
| <tr> |
| <td style="text-align: left">convolution</td> |
| <td style="text-align: left">2d convolution <code>and</code> no-bias=False <code>and</code> dilate=(1,1) <code>and</code> num_group=1 <code>and</code> batch-size = 1 or batch-size > 1 && stride = (1,1);</td> |
| </tr> |
| <tr> |
| <td style="text-align: left">pooling</td> |
| <td style="text-align: left">max-pooling <code>and</code> kernel=(2,2) <code>and</code> stride=(2,2) <code>and</code> pooling_convention=full</td> |
| </tr> |
| <tr> |
| <td style="text-align: left">fully-connected</td> |
| <td style="text-align: left">without any restrictions</td> |
| </tr> |
| </tbody></table> |
| |
| <h3 id="build-install-nnpack-with-mxnet">Build/Install NNPACK with MXNet</h3> |
| |
| <p>If the trained model meets some conditions of using NNPACK, |
| you can build MXNet with NNPACK support. |
| Follow these simple steps: |
| * Build NNPACK shared library with the following commands. <em>MXNet</em> will link NNPACK dynamically.</p> |
| |
| <p>Note: The following NNPACK installation instructions have been tested on Ubuntu 14.04 and 16.04.</p> |
| <div class="highlight"><pre><code class="language-bash" data-lang="bash"><span class="c"># Install Pip</span> |
| <span class="nv">$ </span><span class="nb">sudo </span>apt-get update |
| <span class="nv">$ </span><span class="nb">sudo </span>apt-get <span class="nb">install</span> <span class="nt">-y</span> python-pip |
| <span class="nv">$ </span><span class="nb">sudo </span>pip <span class="nb">install</span> <span class="nt">--upgrade</span> pip |
| |
| <span class="c"># Install Peach</span> |
| <span class="nv">$ </span>git clone https://github.com/Maratyszcza/PeachPy.git |
| <span class="nv">$ </span><span class="nb">cd </span>PeachPy |
| <span class="nv">$ </span><span class="nb">sudo </span>pip <span class="nb">install</span> <span class="nt">--upgrade</span> <span class="nt">-r</span> requirements.txt |
| <span class="nv">$ </span>python setup.py generate |
| <span class="nv">$ </span><span class="nb">sudo </span>pip <span class="nb">install</span> <span class="nt">--upgrade</span> <span class="nb">.</span> |
| |
| <span class="c"># Install Ninja Build System</span> |
| <span class="nv">$ </span><span class="nb">sudo </span>apt-get <span class="nb">install </span>ninja-build |
| <span class="nv">$ </span>pip <span class="nb">install </span>ninja-syntax |
| |
| <span class="c"># Build NNPack shared library</span> |
| <span class="nv">$ </span><span class="nb">cd</span> ~ |
| <span class="nv">$ </span>git clone <span class="nt">--recursive</span> https://github.com/Maratyszcza/NNPACK.git |
| <span class="nv">$ </span><span class="nb">cd </span>NNPACK |
| <span class="c"># Latest NNPACK do not support building NNPACK as shared library using --enable-shared flag</span> |
| <span class="c"># Reset to commit that supports it.</span> |
| <span class="nv">$ </span>git reset <span class="nt">--hard</span> 9c6747d7b80051b40e6f92d6828e2ed997529cd2 |
| <span class="nv">$ </span>git submodule init <span class="o">&&</span> git submodule update <span class="nt">--recursive</span> |
| <span class="nv">$ </span>python ./configure.py <span class="nt">--enable-shared</span> |
| <span class="nv">$ </span>ninja |
| <span class="nv">$ </span><span class="nb">cd</span> ~ |
| |
| </code></pre></div> |
| <ul> |
| <li>Set lib path of NNPACK as the environment variable, e.g. <code>export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$YOUR_NNPACK_INSTALL_PATH/lib</code></li> |
| <li>Add the include file of NNPACK and its third-party to <code>ADD_CFLAGS</code> in config.mk, e.g. <code>ADD_CFLAGS = -I$(YOUR_NNPACK_INSTALL_PATH)/include/ -I$(YOUR_NNPACK_INSTALL_PATH)/third-party/pthreadpool/include/</code></li> |
| <li>Set <code>USE_NNPACK = 1</code> in config.mk.</li> |
| <li>Build MXNet from source following the <a href="/versions/1.8.0/get_started">install guide</a>.</li> |
| </ul> |
| |
| <h3 id="nnpack-performance">NNPACK Performance</h3> |
| |
| <p>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.</p> |
| |
| <p>To benchmark NNPACK, we use <code>example/image-classification/benchmark_score.py</code>(changed with more range of batch-size). We use CPU e5-2670, MXNET_CPU_NNPACK_NTHREADS=4.</p> |
| |
| <p>build MXNet without NNPACK, the log is: |
| <code> |
| 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 |
| </code></p> |
| |
| <p>build MXNet with NNPACK, log is:</p> |
| <div class="highlight"><pre><code class="language-" data-lang="">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 |
| </code></pre></div> |
| <p>The results show that NNPACK can confer a speedup of about 2X~7X as compared to the original <em>MXNet</em> CPU implementation.</p> |
| |
| <h3 id="tips">Tips</h3> |
| |
| <p>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 <code>MXNET_CPU_NNPACK_NTHREADS</code>. However, we found that the performance is not proportional to the number of threads, and suggest using 4~8 threads when using NNPACK.</p> |
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