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<div class="section" id="how-to-use-mxnet-as-an-almost-full-function-torch-front-end">
<span id="how-to-use-mxnet-as-an-almost-full-function-torch-front-end"></span><h1>How to Use MXNet As an (Almost) Full-function Torch Front End<a class="headerlink" href="#how-to-use-mxnet-as-an-almost-full-function-torch-front-end" title="Permalink to this headline"></a></h1>
<p>This topic demonstrates how to use MXNet as a front end to two of Torch’s major functionalities:</p>
<ul class="simple">
<li>Call Torch’s tensor mathematical functions with MXNet.NDArray</li>
<li>Embed Torch’s neural network modules (layers) into MXNet’s symbolic graph</li>
</ul>
<div class="section" id="compile-with-torch">
<span id="compile-with-torch"></span><h2>Compile with Torch<a class="headerlink" href="#compile-with-torch" title="Permalink to this headline"></a></h2>
<ul class="simple">
<li>Install Torch using the <a class="reference external" href="http://torch.ch/docs/getting-started.html">official guide</a>.<ul>
<li>If you haven’t already done so, copy <code class="docutils literal"><span class="pre">make/config.mk</span></code> (Linux) or <code class="docutils literal"><span class="pre">make/osx.mk</span></code> (Mac) into the MXNet root folder as <code class="docutils literal"><span class="pre">config.mk</span></code>. In <code class="docutils literal"><span class="pre">config.mk</span></code> uncomment the lines <code class="docutils literal"><span class="pre">TORCH_PATH</span> <span class="pre">=</span> <span class="pre">$(HOME)/torch</span></code> and <code class="docutils literal"><span class="pre">MXNET_PLUGINS</span> <span class="pre">+=</span> <span class="pre">plugin/torch/torch.mk</span></code>.</li>
<li>By default, Torch should be installed in your home folder (so <code class="docutils literal"><span class="pre">TORCH_PATH</span> <span class="pre">=</span> <span class="pre">$(HOME)/torch</span></code>). Modify TORCH_PATH to point to your torch installation, if necessary.</li>
</ul>
</li>
<li>Run <code class="docutils literal"><span class="pre">make</span> <span class="pre">clean</span> <span class="pre">&amp;&amp;</span> <span class="pre">make</span></code> to build MXNet with Torch support.</li>
</ul>
</div>
<div class="section" id="tensor-mathematics">
<span id="tensor-mathematics"></span><h2>Tensor Mathematics<a class="headerlink" href="#tensor-mathematics" title="Permalink to this headline"></a></h2>
<p>The mxnet.th module supports calling Torch’s tensor mathematical functions with mxnet.nd.NDArray. See <a class="reference external" href="https://github.com/dmlc/mxnet/blob/master/example/torch/torch_function.py">complete code</a>:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span> <span class="kn">import</span> <span class="nn">mxnet</span> <span class="kn">as</span> <span class="nn">mx</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">th</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">cpu</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span>
<span class="k">print</span> <span class="n">x</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">th</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">print</span> <span class="n">y</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">th</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">cpu</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span>
<span class="k">print</span> <span class="n">x</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="n">mx</span><span class="o">.</span><span class="n">th</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span> <span class="c1"># in-place</span>
<span class="k">print</span> <span class="n">x</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
</pre></div>
</div>
<p>For help, use the <code class="docutils literal"><span class="pre">help(mx.th)</span></code> command.</p>
<p>We’ve added support for most common functions listed on <a class="reference external" href="https://github.com/torch/torch7/blob/master/doc/maths.md">Torch’s documentation page</a>.
If you find that the function you need is not supported, you can easily register it in <code class="docutils literal"><span class="pre">mxnet_root/plugin/torch/torch_function.cc</span></code> by using the existing registrations as examples.</p>
</div>
<div class="section" id="torch-modules-layers">
<span id="torch-modules-layers"></span><h2>Torch Modules (Layers)<a class="headerlink" href="#torch-modules-layers" title="Permalink to this headline"></a></h2>
<p>MXNet supports Torch’s neural network modules through the<code class="docutils literal"><span class="pre">mxnet.symbol.TorchModule</span></code> symbol.
For example, the following code defines a three-layer DNN for classifying MNIST digits (<a class="reference external" href="https://github.com/dmlc/mxnet/blob/master/example/torch/torch_module.py">full code</a>):</p>
<div class="highlight-python"><div class="highlight"><pre><span></span> <span class="n">data</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'data'</span><span class="p">)</span>
<span class="n">fc1</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">TorchModule</span><span class="p">(</span><span class="n">data_0</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="n">lua_string</span><span class="o">=</span><span class="s1">'nn.Linear(784, 128)'</span><span class="p">,</span> <span class="n">num_data</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">num_params</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">num_outputs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'fc1'</span><span class="p">)</span>
<span class="n">act1</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">TorchModule</span><span class="p">(</span><span class="n">data_0</span><span class="o">=</span><span class="n">fc1</span><span class="p">,</span> <span class="n">lua_string</span><span class="o">=</span><span class="s1">'nn.ReLU(false)'</span><span class="p">,</span> <span class="n">num_data</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">num_params</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">num_outputs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'relu1'</span><span class="p">)</span>
<span class="n">fc2</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">TorchModule</span><span class="p">(</span><span class="n">data_0</span><span class="o">=</span><span class="n">act1</span><span class="p">,</span> <span class="n">lua_string</span><span class="o">=</span><span class="s1">'nn.Linear(128, 64)'</span><span class="p">,</span> <span class="n">num_data</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">num_params</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">num_outputs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'fc2'</span><span class="p">)</span>
<span class="n">act2</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">TorchModule</span><span class="p">(</span><span class="n">data_0</span><span class="o">=</span><span class="n">fc2</span><span class="p">,</span> <span class="n">lua_string</span><span class="o">=</span><span class="s1">'nn.ReLU(false)'</span><span class="p">,</span> <span class="n">num_data</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">num_params</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">num_outputs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'relu2'</span><span class="p">)</span>
<span class="n">fc3</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">TorchModule</span><span class="p">(</span><span class="n">data_0</span><span class="o">=</span><span class="n">act2</span><span class="p">,</span> <span class="n">lua_string</span><span class="o">=</span><span class="s1">'nn.Linear(64, 10)'</span><span class="p">,</span> <span class="n">num_data</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">num_params</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">num_outputs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'fc3'</span><span class="p">)</span>
<span class="n">mlp</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">SoftmaxOutput</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">fc3</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'softmax'</span><span class="p">)</span>
</pre></div>
</div>
<p>Let’s break it down. First <code class="docutils literal"><span class="pre">data</span> <span class="pre">=</span> <span class="pre">mx.symbol.Variable('data')</span></code> defines a Variable as a placeholder for input.
Then, it’s fed through Torch’s nn modules with:
<code class="docutils literal"><span class="pre">fc1</span> <span class="pre">=</span> <span class="pre">mx.symbol.TorchModule(data_0=data,</span> <span class="pre">lua_string='nn.Linear(784,</span> <span class="pre">128)',</span> <span class="pre">num_data=1,</span> <span class="pre">num_params=2,</span> <span class="pre">num_outputs=1,</span> <span class="pre">name='fc1')</span></code>.
To use Torch’s criterion as loss functions, you can replace the last line with:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span> <span class="n">logsoftmax</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">TorchModule</span><span class="p">(</span><span class="n">data_0</span><span class="o">=</span><span class="n">fc3</span><span class="p">,</span> <span class="n">lua_string</span><span class="o">=</span><span class="s1">'nn.LogSoftMax()'</span><span class="p">,</span> <span class="n">num_data</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">num_params</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">num_outputs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'logsoftmax'</span><span class="p">)</span>
<span class="c1"># Torch's label starts from 1</span>
<span class="n">label</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'softmax_label'</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span>
<span class="n">mlp</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">TorchCriterion</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">logsoftmax</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">,</span> <span class="n">lua_string</span><span class="o">=</span><span class="s1">'nn.ClassNLLCriterion()'</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'softmax'</span><span class="p">)</span>
</pre></div>
</div>
<p>The input to the nn module is named data_i for i = 0 ... num_data-1. <code class="docutils literal"><span class="pre">lua_string</span></code> is a single Lua statement that creates the module object.
For Torch’s built-in module, this is simply <code class="docutils literal"><span class="pre">nn.module_name(arguments)</span></code>.
If you are using a custom module, place it in a .lua script file and load it with <code class="docutils literal"><span class="pre">require</span> <span class="pre">'module_file.lua'</span></code> if your script returns a torch.nn object, or <code class="docutils literal"><span class="pre">(require</span> <span class="pre">'module_file.lua')()</span></code> if your script returns a torch.nn class.</p>
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<li><a class="reference internal" href="#">How to Use MXNet As an (Almost) Full-function Torch Front End</a><ul>
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