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<div class="section" id="module-torch.onnx">
<span id="torch-onnx"></span><h1>torch.onnx<a class="headerlink" href="#module-torch.onnx" title="Permalink to this headline">¶</a></h1>
<div class="section" id="example-end-to-end-alexnet-from-pytorch-to-caffe2">
<h2>Example: End-to-end AlexNet from PyTorch to Caffe2<a class="headerlink" href="#example-end-to-end-alexnet-from-pytorch-to-caffe2" title="Permalink to this headline">¶</a></h2>
<p>Here is a simple script which exports a pretrained AlexNet as defined in
torchvision into ONNX. It runs a single round of inference and then
saves the resulting traced model to <code class="docutils literal"><span class="pre">alexnet.onnx</span></code>:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torchvision</span>
<span class="n">dummy_input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">'cuda'</span><span class="p">)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">alexnet</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
<span class="c1"># Providing input and output names sets the display names for values</span>
<span class="c1"># within the model's graph. Setting these does not change the semantics</span>
<span class="c1"># of the graph; it is only for readability.</span>
<span class="c1">#</span>
<span class="c1"># The inputs to the network consist of the flat list of inputs (i.e.</span>
<span class="c1"># the values you would pass to the forward() method) followed by the</span>
<span class="c1"># flat list of parameters. You can partially specify names, i.e. provide</span>
<span class="c1"># a list here shorter than the number of inputs to the model, and we will</span>
<span class="c1"># only set that subset of names, starting from the beginning.</span>
<span class="n">input_names</span> <span class="o">=</span> <span class="p">[</span> <span class="s2">"actual_input_1"</span> <span class="p">]</span> <span class="o">+</span> <span class="p">[</span> <span class="s2">"learned_</span><span class="si">%d</span><span class="s2">"</span> <span class="o">%</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">16</span><span class="p">)</span> <span class="p">]</span>
<span class="n">output_names</span> <span class="o">=</span> <span class="p">[</span> <span class="s2">"output1"</span> <span class="p">]</span>
<span class="n">torch</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">export</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">dummy_input</span><span class="p">,</span> <span class="s2">"alexnet.onnx"</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">input_names</span><span class="o">=</span><span class="n">input_names</span><span class="p">,</span> <span class="n">output_names</span><span class="o">=</span><span class="n">output_names</span><span class="p">)</span>
</pre></div>
</div>
<p>The resulting <code class="docutils literal"><span class="pre">alexnet.onnx</span></code> is a binary protobuf file which contains both
the network structure and parameters of the model you exported
(in this case, AlexNet). The keyword argument <code class="docutils literal"><span class="pre">verbose=True</span></code> causes the
exporter to print out a human-readable representation of the network:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="c1"># These are the inputs and parameters to the network, which have taken on</span>
<span class="c1"># the names we specified earlier.</span>
<span class="n">graph</span><span class="p">(</span><span class="o">%</span><span class="n">actual_input_1</span> <span class="p">:</span> <span class="n">Float</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">)</span>
<span class="o">%</span><span class="n">learned_0</span> <span class="p">:</span> <span class="n">Float</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">11</span><span class="p">,</span> <span class="mi">11</span><span class="p">)</span>
<span class="o">%</span><span class="n">learned_1</span> <span class="p">:</span> <span class="n">Float</span><span class="p">(</span><span class="mi">64</span><span class="p">)</span>
<span class="o">%</span><span class="n">learned_2</span> <span class="p">:</span> <span class="n">Float</span><span class="p">(</span><span class="mi">192</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="o">%</span><span class="n">learned_3</span> <span class="p">:</span> <span class="n">Float</span><span class="p">(</span><span class="mi">192</span><span class="p">)</span>
<span class="c1"># ---- omitted for brevity ----</span>
<span class="o">%</span><span class="n">learned_14</span> <span class="p">:</span> <span class="n">Float</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="mi">4096</span><span class="p">)</span>
<span class="o">%</span><span class="n">learned_15</span> <span class="p">:</span> <span class="n">Float</span><span class="p">(</span><span class="mi">1000</span><span class="p">))</span> <span class="p">{</span>
<span class="c1"># Every statement consists of some output tensors (and their types),</span>
<span class="c1"># the operator to be run (with its attributes, e.g., kernels, strides,</span>
<span class="c1"># etc.), its input tensors (%actual_input_1, %learned_0, %learned_1)</span>
<span class="o">%</span><span class="mi">17</span> <span class="p">:</span> <span class="n">Float</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">55</span><span class="p">,</span> <span class="mi">55</span><span class="p">)</span> <span class="o">=</span> <span class="n">onnx</span><span class="p">::</span><span class="n">Conv</span><span class="p">[</span><span class="n">dilations</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">group</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">kernel_shape</span><span class="o">=</span><span class="p">[</span><span class="mi">11</span><span class="p">,</span> <span class="mi">11</span><span class="p">],</span> <span class="n">pads</span><span class="o">=</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="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">strides</span><span class="o">=</span><span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">]](</span><span class="o">%</span><span class="n">actual_input_1</span><span class="p">,</span> <span class="o">%</span><span class="n">learned_0</span><span class="p">,</span> <span class="o">%</span><span class="n">learned_1</span><span class="p">),</span> <span class="n">scope</span><span class="p">:</span> <span class="n">AlexNet</span><span class="o">/</span><span class="n">Sequential</span><span class="p">[</span><span class="n">features</span><span class="p">]</span><span class="o">/</span><span class="n">Conv2d</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="o">%</span><span class="mi">18</span> <span class="p">:</span> <span class="n">Float</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">55</span><span class="p">,</span> <span class="mi">55</span><span class="p">)</span> <span class="o">=</span> <span class="n">onnx</span><span class="p">::</span><span class="n">Relu</span><span class="p">(</span><span class="o">%</span><span class="mi">17</span><span class="p">),</span> <span class="n">scope</span><span class="p">:</span> <span class="n">AlexNet</span><span class="o">/</span><span class="n">Sequential</span><span class="p">[</span><span class="n">features</span><span class="p">]</span><span class="o">/</span><span class="n">ReLU</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="o">%</span><span class="mi">19</span> <span class="p">:</span> <span class="n">Float</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">27</span><span class="p">,</span> <span class="mi">27</span><span class="p">)</span> <span class="o">=</span> <span class="n">onnx</span><span class="p">::</span><span class="n">MaxPool</span><span class="p">[</span><span class="n">kernel_shape</span><span class="o">=</span><span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">pads</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">strides</span><span class="o">=</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="o">%</span><span class="mi">18</span><span class="p">),</span> <span class="n">scope</span><span class="p">:</span> <span class="n">AlexNet</span><span class="o">/</span><span class="n">Sequential</span><span class="p">[</span><span class="n">features</span><span class="p">]</span><span class="o">/</span><span class="n">MaxPool2d</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
<span class="c1"># ---- omitted for brevity ----</span>
<span class="o">%</span><span class="mi">29</span> <span class="p">:</span> <span class="n">Float</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">6</span><span class="p">)</span> <span class="o">=</span> <span class="n">onnx</span><span class="p">::</span><span class="n">MaxPool</span><span class="p">[</span><span class="n">kernel_shape</span><span class="o">=</span><span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">pads</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">strides</span><span class="o">=</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="o">%</span><span class="mi">28</span><span class="p">),</span> <span class="n">scope</span><span class="p">:</span> <span class="n">AlexNet</span><span class="o">/</span><span class="n">Sequential</span><span class="p">[</span><span class="n">features</span><span class="p">]</span><span class="o">/</span><span class="n">MaxPool2d</span><span class="p">[</span><span class="mi">12</span><span class="p">]</span>
<span class="c1"># Dynamic means that the shape is not known. This may be because of a</span>
<span class="c1"># limitation of our implementation (which we would like to fix in a</span>
<span class="c1"># future release) or shapes which are truly dynamic.</span>
<span class="o">%</span><span class="mi">30</span> <span class="p">:</span> <span class="n">Dynamic</span> <span class="o">=</span> <span class="n">onnx</span><span class="p">::</span><span class="n">Shape</span><span class="p">(</span><span class="o">%</span><span class="mi">29</span><span class="p">),</span> <span class="n">scope</span><span class="p">:</span> <span class="n">AlexNet</span>
<span class="o">%</span><span class="mi">31</span> <span class="p">:</span> <span class="n">Dynamic</span> <span class="o">=</span> <span class="n">onnx</span><span class="p">::</span><span class="n">Slice</span><span class="p">[</span><span class="n">axes</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ends</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">starts</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">]](</span><span class="o">%</span><span class="mi">30</span><span class="p">),</span> <span class="n">scope</span><span class="p">:</span> <span class="n">AlexNet</span>
<span class="o">%</span><span class="mi">32</span> <span class="p">:</span> <span class="n">Long</span><span class="p">()</span> <span class="o">=</span> <span class="n">onnx</span><span class="p">::</span><span class="n">Squeeze</span><span class="p">[</span><span class="n">axes</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">]](</span><span class="o">%</span><span class="mi">31</span><span class="p">),</span> <span class="n">scope</span><span class="p">:</span> <span class="n">AlexNet</span>
<span class="o">%</span><span class="mi">33</span> <span class="p">:</span> <span class="n">Long</span><span class="p">()</span> <span class="o">=</span> <span class="n">onnx</span><span class="p">::</span><span class="n">Constant</span><span class="p">[</span><span class="n">value</span><span class="o">=</span><span class="p">{</span><span class="mi">9216</span><span class="p">}](),</span> <span class="n">scope</span><span class="p">:</span> <span class="n">AlexNet</span>
<span class="c1"># ---- omitted for brevity ----</span>
<span class="o">%</span><span class="n">output1</span> <span class="p">:</span> <span class="n">Float</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">1000</span><span class="p">)</span> <span class="o">=</span> <span class="n">onnx</span><span class="p">::</span><span class="n">Gemm</span><span class="p">[</span><span class="n">alpha</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">broadcast</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">transB</span><span class="o">=</span><span class="mi">1</span><span class="p">](</span><span class="o">%</span><span class="mi">45</span><span class="p">,</span> <span class="o">%</span><span class="n">learned_14</span><span class="p">,</span> <span class="o">%</span><span class="n">learned_15</span><span class="p">),</span> <span class="n">scope</span><span class="p">:</span> <span class="n">AlexNet</span><span class="o">/</span><span class="n">Sequential</span><span class="p">[</span><span class="n">classifier</span><span class="p">]</span><span class="o">/</span><span class="n">Linear</span><span class="p">[</span><span class="mi">6</span><span class="p">]</span>
<span class="k">return</span> <span class="p">(</span><span class="o">%</span><span class="n">output1</span><span class="p">);</span>
<span class="p">}</span>
</pre></div>
</div>
<p>You can also verify the protobuf using the <a class="reference external" href="https://github.com/onnx/onnx/">onnx</a> library.
You can install <code class="docutils literal"><span class="pre">onnx</span></code> with conda:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">conda</span> <span class="n">install</span> <span class="o">-</span><span class="n">c</span> <span class="n">conda</span><span class="o">-</span><span class="n">forge</span> <span class="n">onnx</span>
</pre></div>
</div>
<p>Then, you can run:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">onnx</span>
<span class="c1"># Load the ONNX model</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">onnx</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">"alexnet.onnx"</span><span class="p">)</span>
<span class="c1"># Check that the IR is well formed</span>
<span class="n">onnx</span><span class="o">.</span><span class="n">checker</span><span class="o">.</span><span class="n">check_model</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
<span class="c1"># Print a human readable representation of the graph</span>
<span class="n">onnx</span><span class="o">.</span><span class="n">helper</span><span class="o">.</span><span class="n">printable_graph</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">graph</span><span class="p">)</span>
</pre></div>
</div>
<p>To run the exported script with <a class="reference external" href="https://caffe2.ai/">caffe2</a>, you will need to install <cite>caffe2</cite>: If you don’t have one already, Please <a class="reference external" href="https://caffe2.ai/docs/getting-started.html">follow the install instructions</a>.</p>
<p>Once these are installed, you can use the backend for Caffe2:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="c1"># ...continuing from above</span>
<span class="kn">import</span> <span class="nn">caffe2.python.onnx.backend</span> <span class="k">as</span> <span class="nn">backend</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="n">rep</span> <span class="o">=</span> <span class="n">backend</span><span class="o">.</span><span class="n">prepare</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s2">"CUDA:0"</span><span class="p">)</span> <span class="c1"># or "CPU"</span>
<span class="c1"># For the Caffe2 backend:</span>
<span class="c1"># rep.predict_net is the Caffe2 protobuf for the network</span>
<span class="c1"># rep.workspace is the Caffe2 workspace for the network</span>
<span class="c1"># (see the class caffe2.python.onnx.backend.Workspace)</span>
<span class="n">outputs</span> <span class="o">=</span> <span class="n">rep</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">))</span>
<span class="c1"># To run networks with more than one input, pass a tuple</span>
<span class="c1"># rather than a single numpy ndarray.</span>
<span class="nb">print</span><span class="p">(</span><span class="n">outputs</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
</pre></div>
</div>
<p>In the future, there will be backends for other frameworks as well.</p>
</div>
<div class="section" id="limitations">
<h2>Limitations<a class="headerlink" href="#limitations" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li>The ONNX exporter is a <em>trace-based</em> exporter, which means that it
operates by executing your model once, and exporting the operators which
were actually run during this run. This means that if your model is
dynamic, e.g., changes behavior depending on input data, the export
won’t be accurate. Similarly, a trace is likely to be valid only
for a specific input size (which is one reason why we require explicit inputs
on tracing.) We recommend examining the model trace and making sure
the traced operators look reasonable.</li>
<li>PyTorch and Caffe2 often have implementations of operators with some
numeric differences. Depending on model structure, these differences
may be negligible, but they can also cause major divergences in behavior
(especially on untrained models.) In a future release, we plan to
allow Caffe2 to call directly to Torch implementations of operators, to
help you smooth over these differences when precision is important,
and to also document these differences.</li>
</ul>
</div>
<div class="section" id="supported-operators">
<h2>Supported operators<a class="headerlink" href="#supported-operators" title="Permalink to this headline">¶</a></h2>
<p>The following operators are supported:</p>
<ul class="simple">
<li>add (nonzero alpha not supported)</li>
<li>sub (nonzero alpha not supported)</li>
<li>mul</li>
<li>div</li>
<li>cat</li>
<li>mm</li>
<li>addmm</li>
<li>neg</li>
<li>sqrt</li>
<li>tanh</li>
<li>sigmoid</li>
<li>mean</li>
<li>sum</li>
<li>prod</li>
<li>t</li>
<li>expand (only when used before a broadcasting ONNX operator; e.g., add)</li>
<li>transpose</li>
<li>view</li>
<li>split</li>
<li>squeeze</li>
<li>prelu (single weight shared among input channels not supported)</li>
<li>threshold (non-zero threshold/non-zero value not supported)</li>
<li>leaky_relu</li>
<li>glu</li>
<li>softmax (only dim=-1 supported)</li>
<li>avg_pool2d (ceil_mode not supported)</li>
<li>log_softmax</li>
<li>unfold (experimental support with ATen-Caffe2 integration)</li>
<li>elu</li>
<li>concat</li>
<li>abs</li>
<li>index_select</li>
<li>pow</li>
<li>clamp</li>
<li>max</li>
<li>min</li>
<li>eq</li>
<li>gt</li>
<li>lt</li>
<li>ge</li>
<li>le</li>
<li>exp</li>
<li>sin</li>
<li>cos</li>
<li>tan</li>
<li>asin</li>
<li>acos</li>
<li>atan</li>
<li>permute</li>
<li>Conv</li>
<li>BatchNorm</li>
<li>MaxPool1d (ceil_mode not supported)</li>
<li>MaxPool2d (ceil_mode not supported)</li>
<li>MaxPool3d (ceil_mode not supported)</li>
<li>Embedding (no optional arguments supported)</li>
<li>RNN</li>
<li>ConstantPadNd</li>
<li>Dropout</li>
<li>FeatureDropout (training mode not supported)</li>
<li>Index (constant integer and tuple indices supported)</li>
</ul>
<p>The operator set above is sufficient to export the following models:</p>
<ul class="simple">
<li>AlexNet</li>
<li>DCGAN</li>
<li>DenseNet</li>
<li>Inception (warning: this model is highly sensitive to changes in operator
implementation)</li>
<li>ResNet</li>
<li>SuperResolution</li>
<li>VGG</li>
<li><a class="reference external" href="https://github.com/pytorch/examples/tree/master/word_language_model">word_language_model</a></li>
</ul>
<p>Adding export support for operators is an <em>advance usage</em>.
To achieve this, developers need to touch the source code of PyTorch.
Please follow the <a class="reference external" href="https://github.com/pytorch/pytorch#from-source">instructions</a>
for installing PyTorch from source.
If the wanted operator is standardized in ONNX, it should be easy to add
support for exporting such operator (adding a symbolic function for the operator).
To confirm whether the operator is standardized or not, please check the
<a class="reference external" href="https://github.com/onnx/onnx/blob/master/docs/Operators.md">ONNX operator list</a>.</p>
<p>If the operator is an ATen operator, which means you can find the declaration
of the function in <code class="docutils literal"><span class="pre">torch/csrc/autograd/generated/VariableType.h</span></code>
(available in generated code in PyTorch install dir), you should add the symbolic
function in <code class="docutils literal"><span class="pre">torch/onnx/symbolic.py</span></code> and follow the instructions listed as below:</p>
<ul class="simple">
<li>Define the symbolic function in
<a class="reference external" href="https://github.com/pytorch/pytorch/blob/master/torch/onnx/symbolic.py">torch/onnx/symbolic.py</a>.
Make sure the function has the same name as the ATen operator/function
defined in <code class="docutils literal"><span class="pre">VariableType.h</span></code>.</li>
<li>The first parameter is always the exported ONNX graph.
Parameter names must EXACTLY match the names in <code class="docutils literal"><span class="pre">VariableType.h</span></code>,
because dispatch is done with keyword arguments.</li>
<li>Parameter ordering does NOT necessarily match what is in <code class="docutils literal"><span class="pre">VariableType.h</span></code>,
tensors (inputs) are always first, then non-tensor arguments.</li>
<li>In the symbolic function, if the operator is already standardized in ONNX,
we only need to create a node to represent the ONNX operator in the graph.</li>
<li>If the input argument is a tensor, but ONNX asks for a scalar, we have to
explicitly do the conversion. The helper function <code class="docutils literal"><span class="pre">_scalar</span></code> can convert a
scalar tensor into a python scalar, and <code class="docutils literal"><span class="pre">_if_scalar_type_as</span></code> can turn a
Python scalar into a PyTorch tensor.</li>
</ul>
<p>If the operator is a non-ATen operator, the symbolic function has to be
added in the corresponding PyTorch Function class. Please read the following
instructions:</p>
<ul class="simple">
<li>Create a symbolic function named <code class="docutils literal"><span class="pre">symbolic</span></code> in the corresponding Function class.</li>
<li>The first parameter is always the exported ONNX graph.</li>
<li>Parameter names except the first must EXACTLY match the names in <code class="docutils literal"><span class="pre">forward</span></code>.</li>
<li>The output tuple size must match the outputs of <code class="docutils literal"><span class="pre">forward</span></code>.</li>
<li>In the symbolic function, if the operator is already standardized in ONNX,
we just need to create a node to represent the ONNX operator in the graph.</li>
</ul>
<p>Symbolic functions should be implemented in Python. All of these functions interact
with Python methods which are implemented via C++-Python bindings,
but intuitively the interface they provide looks like this:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">operator</span><span class="o">/</span><span class="n">symbolic</span><span class="p">(</span><span class="n">g</span><span class="p">,</span> <span class="o">*</span><span class="n">inputs</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Modifies Graph (e.g., using "op"), adding the ONNX operations representing</span>
<span class="sd"> this PyTorch function, and returning a Value or tuple of Values specifying the</span>
<span class="sd"> ONNX outputs whose values correspond to the original PyTorch return values</span>
<span class="sd"> of the autograd Function (or None if an output is not supported by ONNX).</span>
<span class="sd"> Arguments:</span>
<span class="sd"> g (Graph): graph to write the ONNX representation into</span>
<span class="sd"> inputs (Value...): list of values representing the variables which contain</span>
<span class="sd"> the inputs for this function</span>
<span class="sd"> """</span>
<span class="k">class</span> <span class="nc">Value</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
<span class="sd">"""Represents an intermediate tensor value computed in ONNX."""</span>
<span class="k">def</span> <span class="nf">type</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""Returns the Type of the value."""</span>
<span class="k">class</span> <span class="nc">Type</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">sizes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""Returns a tuple of ints representing the shape of a tensor this describes."""</span>
<span class="k">class</span> <span class="nc">Graph</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">op</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">opname</span><span class="p">,</span> <span class="o">*</span><span class="n">inputs</span><span class="p">,</span> <span class="o">**</span><span class="n">attrs</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Create an ONNX operator 'opname', taking 'args' as inputs</span>
<span class="sd"> and attributes 'kwargs' and add it as a node to the current graph,</span>
<span class="sd"> returning the value representing the single output of this</span>
<span class="sd"> operator (see the `outputs` keyword argument for multi-return</span>
<span class="sd"> nodes).</span>
<span class="sd"> The set of operators and the inputs/attributes they take</span>
<span class="sd"> is documented at https://github.com/onnx/onnx/blob/master/docs/Operators.md</span>
<span class="sd"> Arguments:</span>
<span class="sd"> opname (string): The ONNX operator name, e.g., `Abs` or `Add`.</span>
<span class="sd"> args (Value...): The inputs to the operator; usually provided</span>
<span class="sd"> as arguments to the `symbolic` definition.</span>
<span class="sd"> kwargs: The attributes of the ONNX operator, with keys named</span>
<span class="sd"> according to the following convention: `alpha_f` indicates</span>
<span class="sd"> the `alpha` attribute with type `f`. The valid type specifiers are</span>
<span class="sd"> `f` (float), `i` (int), `s` (string) or `t` (Tensor). An attribute</span>
<span class="sd"> specified with type float accepts either a single float, or a</span>
<span class="sd"> list of floats (e.g., you would say `dims_i` for a `dims` attribute</span>
<span class="sd"> that takes a list of integers).</span>
<span class="sd"> outputs (int, optional): The number of outputs this operator returns;</span>
<span class="sd"> by default an operator is assumed to return a single output.</span>
<span class="sd"> If `outputs` is greater than one, this functions returns a tuple</span>
<span class="sd"> of output `Value`, representing each output of the ONNX operator</span>
<span class="sd"> in positional.</span>
<span class="sd"> """</span>
</pre></div>
</div>
<p>The ONNX graph C++ definition is in <code class="docutils literal"><span class="pre">torch/csrc/jit/ir.h</span></code>.</p>
<p>Here is an example of handling missing symbolic function for <code class="docutils literal"><span class="pre">elu</span></code> operator.
We try to export the model and see the error message as below:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="ne">UserWarning</span><span class="p">:</span> <span class="n">ONNX</span> <span class="n">export</span> <span class="n">failed</span> <span class="n">on</span> <span class="n">elu</span> <span class="n">because</span> <span class="n">torch</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">symbolic</span><span class="o">.</span><span class="n">elu</span> <span class="n">does</span> <span class="ow">not</span> <span class="n">exist</span>
<span class="ne">RuntimeError</span><span class="p">:</span> <span class="n">ONNX</span> <span class="n">export</span> <span class="n">failed</span><span class="p">:</span> <span class="n">Couldn</span><span class="s1">'t export operator elu</span>
</pre></div>
</div>
<p>The export fails because PyTorch does not support exporting <code class="docutils literal"><span class="pre">elu</span></code> operator.
We find <code class="docutils literal"><span class="pre">virtual</span> <span class="pre">Tensor</span> <span class="pre">elu(const</span> <span class="pre">Tensor</span> <span class="pre">&</span> <span class="pre">input,</span> <span class="pre">Scalar</span> <span class="pre">alpha,</span> <span class="pre">bool</span> <span class="pre">inplace)</span> <span class="pre">const</span> <span class="pre">override;</span></code>
in <code class="docutils literal"><span class="pre">VariableType.h</span></code>. This means <code class="docutils literal"><span class="pre">elu</span></code> is an ATen operator.
We check the <a class="reference external" href="http://https://github.com/onnx/onnx/blob/master/docs/Operators.md">ONNX operator list</a>,
and confirm that <code class="docutils literal"><span class="pre">Elu</span></code> is standardized in ONNX.
We add the following lines to <code class="docutils literal"><span class="pre">symbolic.py</span></code>:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">elu</span><span class="p">(</span><span class="n">g</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">alpha</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="k">return</span> <span class="n">g</span><span class="o">.</span><span class="n">op</span><span class="p">(</span><span class="s2">"Elu"</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">alpha_f</span><span class="o">=</span><span class="n">_scalar</span><span class="p">(</span><span class="n">alpha</span><span class="p">))</span>
</pre></div>
</div>
<p>Now PyTorch is able to export <code class="docutils literal"><span class="pre">elu</span></code> operator.</p>
<p>There are more examples in
<a class="reference external" href="https://github.com/pytorch/pytorch/blob/master/torch/onnx/symbolic.py">symbolic.py</a>,
<a class="reference external" href="https://github.com/pytorch/pytorch/blob/99037d627da68cdf53d3d0315deceddfadf03bba/torch/autograd/_functions/tensor.py#L24">tensor.py</a>,
<a class="reference external" href="https://github.com/pytorch/pytorch/blob/99037d627da68cdf53d3d0315deceddfadf03bba/torch/nn/_functions/padding.py#L8">padding.py</a>.</p>
<p>The interface for specifying operator definitions is experimental;
adventurous users should note that the APIs will probably
change in a future interface.</p>
</div>
<div class="section" id="functions">
<h2>Functions<a class="headerlink" href="#functions" title="Permalink to this headline">¶</a></h2>
<dl class="function">
<dt id="torch.onnx.export">
<code class="descclassname">torch.onnx.</code><code class="descname">export</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx.html#export"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.onnx.export" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
</div>
</div>
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