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<li class="toctree-l1"><a class="reference internal" href="notes/amp_examples.html">CUDA Automatic Mixed Precision examples</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/autograd.html">Autograd mechanics</a></li>
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<section id="torch-onnx">
<h1>torch.onnx<a class="headerlink" href="#torch-onnx" title="Permalink to this heading">¶</a></h1>
<nav class="contents local" id="contents">
<ul class="simple">
<li><p><a class="reference internal" href="#example-alexnet-from-pytorch-to-onnx" id="id4">Example: AlexNet from PyTorch to ONNX</a></p></li>
<li><p><a class="reference internal" href="#tracing-vs-scripting" id="id5">Tracing vs Scripting</a></p></li>
<li><p><a class="reference internal" href="#avoiding-pitfalls" id="id6">Avoiding Pitfalls</a></p>
<ul>
<li><p><a class="reference internal" href="#avoid-numpy-and-built-in-python-types" id="id7">Avoid NumPy and built-in Python types</a></p></li>
<li><p><a class="reference internal" href="#avoid-tensor-data" id="id8">Avoid Tensor.data</a></p></li>
<li><p><a class="reference internal" href="#avoid-in-place-operations-when-using-tensor-shape-in-tracing-mode" id="id9">Avoid in-place operations when using tensor.shape in tracing mode</a></p></li>
</ul>
</li>
<li><p><a class="reference internal" href="#limitations" id="id10">Limitations</a></p>
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<li><p><a class="reference internal" href="#types" id="id11">Types</a></p></li>
<li><p><a class="reference internal" href="#differences-in-operator-implementations" id="id12">Differences in Operator Implementations</a></p></li>
<li><p><a class="reference internal" href="#unsupported-tensor-indexing-patterns" id="id13">Unsupported Tensor Indexing Patterns</a></p>
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<li><p><a class="reference internal" href="#reads-gets" id="id14">Reads / Gets</a></p></li>
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<li><p><a class="reference internal" href="#onnx-exporter-internals" id="id17">ONNX exporter internals</a></p></li>
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<li><p><a class="reference internal" href="#discovering-all-unconvertible-aten-ops-at-once" id="id25">Discovering all unconvertible ATen ops at once</a></p></li>
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<span class="target" id="module-torch.onnx"></span><p>ONNX exporter.</p>
<p><a class="reference external" href="https://onnx.ai/">Open Neural Network eXchange (ONNX)</a> is an open standard
format for representing machine learning models. The torch.onnx module can export
PyTorch models to ONNX. The model can then be consumed by any of the many
<a class="reference external" href="https://onnx.ai/supported-tools.html#deployModel">runtimes that support ONNX</a>.</p>
<section id="example-alexnet-from-pytorch-to-onnx">
<h2><a class="toc-backref" href="#id4" role="doc-backlink">Example: AlexNet from PyTorch to ONNX</a><a class="headerlink" href="#example-alexnet-from-pytorch-to-onnx" title="Permalink to this heading">¶</a></h2>
<p>Here is a simple script which exports a pretrained AlexNet to an ONNX file named <code class="docutils literal notranslate"><span class="pre">alexnet.onnx</span></code>.
The call to <code class="docutils literal notranslate"><span class="pre">torch.onnx.export</span></code> runs the model once to trace its execution and then exports the
traced model to the specified file:</p>
<div class="highlight-default notranslate"><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="s2">"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 notranslate"><span class="pre">alexnet.onnx</span></code> file contains a binary <a class="reference external" href="https://developers.google.com/protocol-buffers/">protocol buffer</a>
which contains both the network structure and parameters of the model you exported
(in this case, AlexNet). The argument <code class="docutils literal notranslate"><span class="pre">verbose=True</span></code> causes the
exporter to print out a human-readable representation of the model:</p>
<div class="highlight-default notranslate"><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 output using the <a class="reference external" href="https://github.com/onnx/onnx/">ONNX</a> library,
which you can install using <code class="docutils literal notranslate"><span class="pre">pip</span></code>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">onnx</span>
</pre></div>
</div>
<p>Then, you can run:</p>
<div class="highlight-default notranslate"><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 model 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="nb">print</span><span class="p">(</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>You can also run the exported model with one of the many
<a class="reference external" href="https://onnx.ai/supported-tools.html#deployModel">runtimes that support ONNX</a>.
For example after installing <a class="reference external" href="https://www.onnxruntime.ai">ONNX Runtime</a>, you can
load and run the model:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">onnxruntime</span> <span class="k">as</span> <span class="nn">ort</span>
<span class="n">ort_session</span> <span class="o">=</span> <span class="n">ort</span><span class="o">.</span><span class="n">InferenceSession</span><span class="p">(</span><span class="s2">"alexnet.onnx"</span><span class="p">)</span>
<span class="n">outputs</span> <span class="o">=</span> <span class="n">ort_session</span><span class="o">.</span><span class="n">run</span><span class="p">(</span>
<span class="kc">None</span><span class="p">,</span>
<span class="p">{</span><span class="s2">"actual_input_1"</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="p">)</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>Here is a more involved <a class="reference external" href="https://pytorch.org/tutorials/advanced/super_resolution_with_onnxruntime.html">tutorial on exporting a model and running it with ONNX Runtime</a>.</p>
</section>
<section id="tracing-vs-scripting">
<span id="id2"></span><h2><a class="toc-backref" href="#id5" role="doc-backlink">Tracing vs Scripting</a><a class="headerlink" href="#tracing-vs-scripting" title="Permalink to this heading">¶</a></h2>
<p>Internally, <a class="reference internal" href="#torch.onnx.export" title="torch.onnx.export"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.onnx.export()</span></code></a> requires a <a class="reference internal" href="generated/torch.jit.ScriptModule.html#torch.jit.ScriptModule" title="torch.jit.ScriptModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.jit.ScriptModule</span></code></a> rather than
a <a class="reference internal" href="generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></a>. If the passed-in model is not already a <code class="docutils literal notranslate"><span class="pre">ScriptModule</span></code>,
<code class="docutils literal notranslate"><span class="pre">export()</span></code> will use <em>tracing</em> to convert it to one:</p>
<ul class="simple">
<li><p><strong>Tracing</strong>: If <code class="docutils literal notranslate"><span class="pre">torch.onnx.export()</span></code> is called with a Module that is not already a
<code class="docutils literal notranslate"><span class="pre">ScriptModule</span></code>, it first does the equivalent of <a class="reference internal" href="generated/torch.jit.trace.html#torch.jit.trace" title="torch.jit.trace"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.jit.trace()</span></code></a>, which executes the model
once with the given <code class="docutils literal notranslate"><span class="pre">args</span></code> and records all operations that happen during that execution. This
means that if your model is dynamic, e.g., changes behavior depending on input data, the exported
model will <em>not</em> capture this dynamic behavior.
We recommend examining the exported model and making sure the operators look
reasonable. Tracing will unroll loops and if statements, exporting a static graph that is exactly
the same as the traced run. If you want to export your model with dynamic control flow, you will
need to use <em>scripting</em>.</p></li>
<li><p><strong>Scripting</strong>: Compiling a model via scripting preserves dynamic control flow and is valid for inputs
of different sizes. To use scripting:</p>
<ul>
<li><p>Use <a class="reference internal" href="generated/torch.jit.script.html#torch.jit.script" title="torch.jit.script"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.jit.script()</span></code></a> to produce a <code class="docutils literal notranslate"><span class="pre">ScriptModule</span></code>.</p></li>
<li><p>Call <code class="docutils literal notranslate"><span class="pre">torch.onnx.export()</span></code> with the <code class="docutils literal notranslate"><span class="pre">ScriptModule</span></code> as the model. The <code class="docutils literal notranslate"><span class="pre">args</span></code> are still required,
but they will be used internally only to produce example outputs, so that the types and shapes of the
outputs can be captured. No tracing will be performed.</p></li>
</ul>
</li>
</ul>
<p>See <a class="reference external" href="https://pytorch.org/tutorials/beginner/Intro_to_TorchScript_tutorial.html">Introduction to TorchScript</a>
and <a class="reference external" href="jit.html">TorchScript</a> for more details, including how to compose tracing and scripting to suit the
particular requirements of different models.</p>
</section>
<section id="avoiding-pitfalls">
<h2><a class="toc-backref" href="#id6" role="doc-backlink">Avoiding Pitfalls</a><a class="headerlink" href="#avoiding-pitfalls" title="Permalink to this heading">¶</a></h2>
<section id="avoid-numpy-and-built-in-python-types">
<h3><a class="toc-backref" href="#id7" role="doc-backlink">Avoid NumPy and built-in Python types</a><a class="headerlink" href="#avoid-numpy-and-built-in-python-types" title="Permalink to this heading">¶</a></h3>
<p>PyTorch models can be written using NumPy or Python types and functions, but
during <a class="reference internal" href="#tracing-vs-scripting"><span class="std std-ref">tracing</span></a>, any variables of NumPy or Python
types (rather than torch.Tensor) are converted to constants, which will produce
the wrong result if those values should change depending on the inputs.</p>
<p>For example, rather than using numpy functions on numpy.ndarrays:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Bad! Will be replaced with constants during tracing.</span>
<span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</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">rand</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
</pre></div>
</div>
<p>Use torch operators on torch.Tensors:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Good! Tensor operations will be captured during tracing.</span>
<span class="n">x</span><span class="p">,</span> <span class="n">y</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">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">),</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
</pre></div>
</div>
<p>And rather than use <a class="reference internal" href="generated/torch.Tensor.item.html#torch.Tensor.item" title="torch.Tensor.item"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.Tensor.item()</span></code></a> (which converts a Tensor to a Python
built-in number):</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Bad! y.item() will be replaced with a constant during tracing.</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">item</span><span class="p">(),</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
</pre></div>
</div>
<p>Use torch’s support for implicit casting of single-element tensors:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Good! y will be preserved as a variable during tracing.</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
</pre></div>
</div>
</section>
<section id="avoid-tensor-data">
<h3><a class="toc-backref" href="#id8" role="doc-backlink">Avoid Tensor.data</a><a class="headerlink" href="#avoid-tensor-data" title="Permalink to this heading">¶</a></h3>
<p>Using the Tensor.data field can produce an incorrect trace and therefore an incorrect ONNX graph.
Use <a class="reference internal" href="generated/torch.Tensor.detach.html#torch.Tensor.detach" title="torch.Tensor.detach"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.Tensor.detach()</span></code></a> instead. (Work is ongoing to
<a class="reference external" href="https://github.com/pytorch/pytorch/issues/30987">remove Tensor.data entirely</a>).</p>
</section>
<section id="avoid-in-place-operations-when-using-tensor-shape-in-tracing-mode">
<h3><a class="toc-backref" href="#id9" role="doc-backlink">Avoid in-place operations when using tensor.shape in tracing mode</a><a class="headerlink" href="#avoid-in-place-operations-when-using-tensor-shape-in-tracing-mode" title="Permalink to this heading">¶</a></h3>
<p>In tracing mode, shapes obtained from <code class="docutils literal notranslate"><span class="pre">tensor.shape</span></code> are traced as tensors,
and share the same memory. This might cause a mismatch the final output values.
As a workaround, avoid the use of inplace operations in these scenarios.
For example, in the model:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Model</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">states</span><span class="p">):</span>
<span class="n">batch_size</span><span class="p">,</span> <span class="n">seq_length</span> <span class="o">=</span> <span class="n">states</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="mi">2</span><span class="p">]</span>
<span class="n">real_seq_length</span> <span class="o">=</span> <span class="n">seq_length</span>
<span class="n">real_seq_length</span> <span class="o">+=</span> <span class="mi">2</span>
<span class="k">return</span> <span class="n">real_seq_length</span> <span class="o">+</span> <span class="n">seq_length</span>
</pre></div>
</div>
<p><code class="docutils literal notranslate"><span class="pre">real_seq_length</span></code> and <code class="docutils literal notranslate"><span class="pre">seq_length</span></code> share the same memory in tracing mode.
This could be avoided by rewriting the inplace operation:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">real_seq_length</span> <span class="o">=</span> <span class="n">real_seq_length</span> <span class="o">+</span> <span class="mi">2</span>
</pre></div>
</div>
</section>
</section>
<section id="limitations">
<h2><a class="toc-backref" href="#id10" role="doc-backlink">Limitations</a><a class="headerlink" href="#limitations" title="Permalink to this heading">¶</a></h2>
<section id="types">
<h3><a class="toc-backref" href="#id11" role="doc-backlink">Types</a><a class="headerlink" href="#types" title="Permalink to this heading">¶</a></h3>
<ul class="simple">
<li><p>Only <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensors</span></code>, numeric types that can be trivially converted to torch.Tensors (e.g. float, int),
and tuples and lists of those types are supported as model inputs or outputs. Dict and str inputs and
outputs are accepted in <a class="reference internal" href="#tracing-vs-scripting"><span class="std std-ref">tracing</span></a> mode, but:</p>
<ul>
<li><p>Any computation that depends on the value of a dict or a str input <strong>will be replaced with the
constant value</strong> seen during the one traced execution.</p></li>
<li><p>Any output that is a dict will be silently replaced with a <strong>flattened sequence of its values
(keys will be removed)</strong>. E.g. <code class="docutils literal notranslate"><span class="pre">{"foo":</span> <span class="pre">1,</span> <span class="pre">"bar":</span> <span class="pre">2}</span></code> becomes <code class="docutils literal notranslate"><span class="pre">(1,</span> <span class="pre">2)</span></code>.</p></li>
<li><p>Any output that is a str will be silently removed.</p></li>
</ul>
</li>
<li><p>Certain operations involving tuples and lists are not supported in
<a class="reference internal" href="#tracing-vs-scripting"><span class="std std-ref">scripting</span></a> mode due to limited support in ONNX for nested sequences.
In particular appending a tuple to a list is not supported. In tracing mode, the nested sequences
will be flattened automatically during the tracing.</p></li>
</ul>
</section>
<section id="differences-in-operator-implementations">
<h3><a class="toc-backref" href="#id12" role="doc-backlink">Differences in Operator Implementations</a><a class="headerlink" href="#differences-in-operator-implementations" title="Permalink to this heading">¶</a></h3>
<p>Due to differences in implementations of operators, running the exported model on different runtimes
may produce different results from each other or from PyTorch. Normally these differences are
numerically small, so this should only be a concern if your application is sensitive to these
small differences.</p>
</section>
<section id="unsupported-tensor-indexing-patterns">
<span id="tensor-indexing"></span><h3><a class="toc-backref" href="#id13" role="doc-backlink">Unsupported Tensor Indexing Patterns</a><a class="headerlink" href="#unsupported-tensor-indexing-patterns" title="Permalink to this heading">¶</a></h3>
<p>Tensor indexing patterns that cannot be exported are listed below.
If you are experiencing issues exporting a model that does not include any of
the unsupported patterns below, please double check that you are exporting with
the latest <code class="docutils literal notranslate"><span class="pre">opset_version</span></code>.</p>
<section id="reads-gets">
<h4><a class="toc-backref" href="#id14" role="doc-backlink">Reads / Gets</a><a class="headerlink" href="#reads-gets" title="Permalink to this heading">¶</a></h4>
<p>When indexing into a tensor for reading, the following patterns are not supported:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Tensor indices that includes negative values.</span>
<span class="n">data</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">3</span><span class="p">]]),</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])]</span>
<span class="c1"># Workarounds: use positive index values.</span>
</pre></div>
</div>
</section>
<section id="writes-sets">
<h4><a class="toc-backref" href="#id15" role="doc-backlink">Writes / Sets</a><a class="headerlink" href="#writes-sets" title="Permalink to this heading">¶</a></h4>
<p>When indexing into a Tensor for writing, the following patterns are not supported:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Multiple tensor indices if any has rank >= 2</span>
<span class="n">data</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]]),</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])]</span> <span class="o">=</span> <span class="n">new_data</span>
<span class="c1"># Workarounds: use single tensor index with rank >= 2,</span>
<span class="c1"># or multiple consecutive tensor indices with rank == 1.</span>
<span class="c1"># Multiple tensor indices that are not consecutive</span>
<span class="n">data</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]),</span> <span class="p">:,</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">])]</span> <span class="o">=</span> <span class="n">new_data</span>
<span class="c1"># Workarounds: transpose `data` such that tensor indices are consecutive.</span>
<span class="c1"># Tensor indices that includes negative values.</span>
<span class="n">data</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">]),</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])]</span> <span class="o">=</span> <span class="n">new_data</span>
<span class="c1"># Workarounds: use positive index values.</span>
<span class="c1"># Implicit broadcasting required for new_data.</span>
<span class="n">data</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</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="mi">1</span><span class="p">:</span><span class="mi">3</span><span class="p">]</span> <span class="o">=</span> <span class="n">new_data</span>
<span class="c1"># Workarounds: expand new_data explicitly.</span>
<span class="c1"># Example:</span>
<span class="c1"># data shape: [3, 4, 5]</span>
<span class="c1"># new_data shape: [5]</span>
<span class="c1"># expected new_data shape after broadcasting: [2, 2, 2, 5]</span>
</pre></div>
</div>
</section>
</section>
</section>
<section id="adding-support-for-operators">
<h2><a class="toc-backref" href="#id16" role="doc-backlink">Adding support for operators</a><a class="headerlink" href="#adding-support-for-operators" title="Permalink to this heading">¶</a></h2>
<p>When exporting a model that includes unsupported operators, you’ll see an error message like:</p>
<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>RuntimeError: ONNX export failed: Couldn't export operator foo
</pre></div>
</div>
<p>When that happens, there are a few things you can do:</p>
<ol class="arabic simple">
<li><p>Change the model to not use that operator.</p></li>
<li><p>Create a symbolic function to convert the operator and register it as a custom symbolic function.</p></li>
<li><p>Contribute to PyTorch to add the same symbolic function to <a class="reference internal" href="#module-torch.onnx" title="torch.onnx"><code class="xref py py-mod docutils literal notranslate"><span class="pre">torch.onnx</span></code></a> itself.</p></li>
</ol>
<p>If you decided to implement a symbolic function (we hope you will contribute it back to PyTorch!), here is how you can get started:</p>
<section id="onnx-exporter-internals">
<h3><a class="toc-backref" href="#id17" role="doc-backlink">ONNX exporter internals</a><a class="headerlink" href="#onnx-exporter-internals" title="Permalink to this heading">¶</a></h3>
<p>A “symbolic function” is a function that decomposes a PyTorch operator into a
composition of a series of ONNX operators.</p>
<p>During export, each node (which contains a PyTorch operator) in the TorchScript
graph is visited by the exporter in topological order.
Upon visiting a node, the exporter looks for a registered symbolic functions for
that operator. Symbolic functions are implemented in Python. A symbolic function for
an op named <code class="docutils literal notranslate"><span class="pre">foo</span></code> would look something like:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">foo</span><span class="p">(</span>
<span class="n">g</span><span class="p">,</span>
<span class="n">input_0</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">Value</span><span class="p">,</span>
<span class="n">input_1</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">Value</span><span class="p">)</span> <span class="o">-></span> <span class="n">Union</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">Value</span><span class="p">,</span> <span class="n">List</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">Value</span><span class="p">]]:</span>
<span class="sd">"""</span>
<span class="sd"> Adds the ONNX operations representing this PyTorch function by updating the</span>
<span class="sd"> graph g with `g.op()` calls.</span>
<span class="sd"> Args:</span>
<span class="sd"> g (Graph): graph to write the ONNX representation into.</span>
<span class="sd"> input_0 (Value): value representing the variables which contain</span>
<span class="sd"> the first input for this operator.</span>
<span class="sd"> input_1 (Value): value representing the variables which contain</span>
<span class="sd"> the second input for this operator.</span>
<span class="sd"> Returns:</span>
<span class="sd"> A Value or List of Values specifying the ONNX nodes that compute something</span>
<span class="sd"> equivalent to the original PyTorch operator with the given inputs.</span>
<span class="sd"> None if it cannot be converted to ONNX.</span>
<span class="sd"> """</span>
<span class="o">...</span>
</pre></div>
</div>
<p>The <code class="docutils literal notranslate"><span class="pre">torch._C</span></code> types are Python wrappers around the types defined in C++ in
<a class="reference external" href="https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/ir/ir.h">ir.h</a>.</p>
<p>The process for adding a symbolic function depends on the type of operator.</p>
</section>
<section id="aten-operators">
<span id="adding-support-aten"></span><h3><a class="toc-backref" href="#id18" role="doc-backlink">ATen operators</a><a class="headerlink" href="#aten-operators" title="Permalink to this heading">¶</a></h3>
<p><a class="reference external" href="https://pytorch.org/cppdocs/#aten">ATen</a> is PyTorch’s built-in tensor library.
If the operator is an ATen operator (shows up in the TorchScript graph with the prefix
<code class="docutils literal notranslate"><span class="pre">aten::</span></code>), make sure it is not supported already.</p>
<section id="list-of-supported-operators">
<h4><a class="toc-backref" href="#id19" role="doc-backlink">List of supported operators</a><a class="headerlink" href="#list-of-supported-operators" title="Permalink to this heading">¶</a></h4>
<p>Visit the auto generated <a class="reference internal" href="onnx_supported_aten_ops.html"><span class="doc">list of supported TorchScript operators</span></a>
for details on which operator are supported in each <code class="docutils literal notranslate"><span class="pre">opset_version</span></code>.</p>
</section>
<section id="adding-support-for-an-aten-or-quantized-operator">
<h4><a class="toc-backref" href="#id20" role="doc-backlink">Adding support for an aten or quantized operator</a><a class="headerlink" href="#adding-support-for-an-aten-or-quantized-operator" title="Permalink to this heading">¶</a></h4>
<p>If the operator is not in the list above:</p>
<ul class="simple">
<li><p>Define the symbolic function in <code class="docutils literal notranslate"><span class="pre">torch/onnx/symbolic_opset<version>.py</span></code>, for example
<a class="reference external" href="https://github.com/pytorch/pytorch/blob/master/torch/onnx/symbolic_opset9.py">torch/onnx/symbolic_opset9.py</a>.
Make sure the function has the same name as the ATen function, which may be declared in
<code class="docutils literal notranslate"><span class="pre">torch/_C/_VariableFunctions.pyi</span></code> or <code class="docutils literal notranslate"><span class="pre">torch/nn/functional.pyi</span></code> (these files are generated at
build time, so will not appear in your checkout until you build PyTorch).</p></li>
<li><p>By default, the first arg is the ONNX graph.
Other arg names must EXACTLY match the names in the <code class="docutils literal notranslate"><span class="pre">.pyi</span></code> file,
because dispatch is done with keyword arguments.</p></li>
<li><p>In the symbolic function, if the operator is in the
<a class="reference external" href="https://github.com/onnx/onnx/blob/master/docs/Operators.md">ONNX standard operator set</a>,
we only need to create a node to represent the ONNX operator in the graph.
If not, we can compose several standard operators that have the
equivalent semantics to the ATen operator.</p></li>
</ul>
<p>Here is an example of handling missing symbolic function for the <code class="docutils literal notranslate"><span class="pre">ELU</span></code> operator.</p>
<p>If we run the following code:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span>
<span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span>
<span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">ELU</span><span class="p">(),</span> <span class="c1"># module</span>
<span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># example input</span>
<span class="p">)</span><span class="o">.</span><span class="n">graph</span>
<span class="p">)</span>
</pre></div>
</div>
<p>We see something like:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">graph</span><span class="p">(</span><span class="o">%</span><span class="bp">self</span> <span class="p">:</span> <span class="n">__torch__</span><span class="o">.</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">modules</span><span class="o">.</span><span class="n">activation</span><span class="o">.</span><span class="n">___torch_mangle_0</span><span class="o">.</span><span class="n">ELU</span><span class="p">,</span>
<span class="o">%</span><span class="nb">input</span> <span class="p">:</span> <span class="n">Float</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">requires_grad</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">cpu</span><span class="p">)):</span>
<span class="o">%</span><span class="mi">4</span> <span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="n">prim</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="mf">1.</span><span class="p">]()</span>
<span class="o">%</span><span class="mi">5</span> <span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">prim</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="mi">1</span><span class="p">]()</span>
<span class="o">%</span><span class="mi">6</span> <span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">prim</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="mi">1</span><span class="p">]()</span>
<span class="o">%</span><span class="mi">7</span> <span class="p">:</span> <span class="n">Float</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">requires_grad</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">cpu</span><span class="p">)</span> <span class="o">=</span> <span class="n">aten</span><span class="p">::</span><span class="n">elu</span><span class="p">(</span><span class="o">%</span><span class="nb">input</span><span class="p">,</span> <span class="o">%</span><span class="mi">4</span><span class="p">,</span> <span class="o">%</span><span class="mi">5</span><span class="p">,</span> <span class="o">%</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="mi">7</span><span class="p">)</span>
</pre></div>
</div>
<p>Since we see <code class="docutils literal notranslate"><span class="pre">aten::elu</span></code> in the graph, we know this is an ATen operator.</p>
<p>We check the <a class="reference external" href="https://github.com/onnx/onnx/blob/master/docs/Operators.md">ONNX operator list</a>,
and confirm that <code class="docutils literal notranslate"><span class="pre">Elu</span></code> is standardized in ONNX.</p>
<p>We find a signature for <code class="docutils literal notranslate"><span class="pre">elu</span></code> in <code class="docutils literal notranslate"><span class="pre">torch/nn/functional.pyi</span></code>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">elu</span><span class="p">(</span><span class="nb">input</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span> <span class="n">alpha</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="o">...</span><span class="p">,</span> <span class="n">inplace</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="o">...</span><span class="p">)</span> <span class="o">-></span> <span class="n">Tensor</span><span class="p">:</span> <span class="o">...</span>
</pre></div>
</div>
<p>We add the following lines to <code class="docutils literal notranslate"><span class="pre">symbolic_opset9.py</span></code>:</p>
<div class="highlight-default notranslate"><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">torch</span><span class="o">.</span><span class="n">Value</span><span class="p">,</span> <span class="n">alpha</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Value</span><span class="p">,</span> <span class="n">inplace</span><span class="p">:</span> <span class="nb">bool</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">alpha</span><span class="p">)</span>
</pre></div>
</div>
<p>Now PyTorch is able to export models containing the <code class="docutils literal notranslate"><span class="pre">aten::elu</span></code> operator!</p>
<p>See the <code class="docutils literal notranslate"><span class="pre">torch/onnx/symbolic_opset*.py</span></code> files for more examples.</p>
</section>
</section>
<section id="torch-autograd-functions">
<h3><a class="toc-backref" href="#id21" role="doc-backlink">torch.autograd.Functions</a><a class="headerlink" href="#torch-autograd-functions" title="Permalink to this heading">¶</a></h3>
<p>If the operator is a sub-class of <a class="reference internal" href="autograd.html#torch.autograd.Function" title="torch.autograd.Function"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.autograd.Function</span></code></a>, there are three ways
to export it.</p>
<section id="static-symbolic-method">
<h4><a class="toc-backref" href="#id22" role="doc-backlink">Static Symbolic Method</a><a class="headerlink" href="#static-symbolic-method" title="Permalink to this heading">¶</a></h4>
<p>You can add a static method named <code class="docutils literal notranslate"><span class="pre">symbolic</span></code> to your function class. It should return
ONNX operators that represent the function’s behavior in ONNX. For example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">MyRelu</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">Function</span><span class="p">):</span>
<span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="nb">input</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
<span class="n">ctx</span><span class="o">.</span><span class="n">save_for_backward</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
<span class="k">return</span> <span class="nb">input</span><span class="o">.</span><span class="n">clamp</span><span class="p">(</span><span class="nb">min</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">symbolic</span><span class="p">(</span><span class="n">g</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Graph</span><span class="p">,</span> <span class="nb">input</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Value</span><span class="p">)</span> <span class="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">Value</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">"Clip"</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">g</span><span class="o">.</span><span class="n">op</span><span class="p">(</span><span class="s2">"Constant"</span><span class="p">,</span> <span class="n">value_t</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">)))</span>
</pre></div>
</div>
</section>
<section id="inline-autograd-function">
<h4><a class="toc-backref" href="#id23" role="doc-backlink">Inline Autograd Function</a><a class="headerlink" href="#inline-autograd-function" title="Permalink to this heading">¶</a></h4>
<p>In cases where a static symbolic method is not provided for its subsequent <a class="reference internal" href="autograd.html#torch.autograd.Function" title="torch.autograd.Function"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.autograd.Function</span></code></a> or
where a function to register <code class="docutils literal notranslate"><span class="pre">prim::PythonOp</span></code> as custom symbolic functions is not provided,
<a class="reference internal" href="#torch.onnx.export" title="torch.onnx.export"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.onnx.export()</span></code></a> tries to inline the graph that corresponds to that <a class="reference internal" href="autograd.html#torch.autograd.Function" title="torch.autograd.Function"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.autograd.Function</span></code></a> such that
this function is broken down into individual operators that were used within the function.
The export should be successful as long as these individual operators are supported. For example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">MyLogExp</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">Function</span><span class="p">):</span>
<span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="nb">input</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
<span class="n">ctx</span><span class="o">.</span><span class="n">save_for_backward</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
<span class="n">h</span> <span class="o">=</span> <span class="nb">input</span><span class="o">.</span><span class="n">exp</span><span class="p">()</span>
<span class="k">return</span> <span class="n">h</span><span class="o">.</span><span class="n">log</span><span class="p">()</span><span class="o">.</span><span class="n">log</span><span class="p">()</span>
</pre></div>
</div>
<p>There is no static symbolic method present for this model, yet it is exported as follows:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">graph</span><span class="p">(</span><span class="o">%</span><span class="nb">input</span> <span class="p">:</span> <span class="n">Float</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">requires_grad</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">cpu</span><span class="p">)):</span>
<span class="o">%</span><span class="mi">1</span> <span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="n">onnx</span><span class="p">::</span><span class="n">Exp</span><span class="p">[](</span><span class="o">%</span><span class="nb">input</span><span class="p">)</span>
<span class="o">%</span><span class="mi">2</span> <span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="n">onnx</span><span class="p">::</span><span class="n">Log</span><span class="p">[](</span><span class="o">%</span><span class="mi">1</span><span class="p">)</span>
<span class="o">%</span><span class="mi">3</span> <span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="n">onnx</span><span class="p">::</span><span class="n">Log</span><span class="p">[](</span><span class="o">%</span><span class="mi">2</span><span class="p">)</span>
<span class="k">return</span> <span class="p">(</span><span class="o">%</span><span class="mi">3</span><span class="p">)</span>
</pre></div>
</div>
<p>If you need to avoid inlining of <a class="reference internal" href="autograd.html#torch.autograd.Function" title="torch.autograd.Function"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.autograd.Function</span></code></a>, you should export models with
<code class="docutils literal notranslate"><span class="pre">operator_export_type</span></code> set to <code class="docutils literal notranslate"><span class="pre">ONNX_FALLTHROUGH</span></code> or <code class="docutils literal notranslate"><span class="pre">ONNX_ATEN_FALLBACK</span></code>.</p>
</section>
</section>
<section id="custom-operators">
<h3><a class="toc-backref" href="#id24" role="doc-backlink">Custom operators</a><a class="headerlink" href="#custom-operators" title="Permalink to this heading">¶</a></h3>
<p>If a model uses a custom operator implemented in C++ as described in
<a class="reference external" href="https://pytorch.org/tutorials/advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a>,
you can export it by following this example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.onnx</span> <span class="kn">import</span> <span class="n">symbolic_helper</span>
<span class="c1"># Define custom symbolic function</span>
<span class="nd">@symbolic_helper</span><span class="o">.</span><span class="n">parse_args</span><span class="p">(</span><span class="s2">"v"</span><span class="p">,</span> <span class="s2">"v"</span><span class="p">,</span> <span class="s2">"f"</span><span class="p">,</span> <span class="s2">"i"</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">symbolic_foo_forward</span><span class="p">(</span><span class="n">g</span><span class="p">,</span> <span class="n">input1</span><span class="p">,</span> <span class="n">input2</span><span class="p">,</span> <span class="n">attr1</span><span class="p">,</span> <span class="n">attr2</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">"custom_domain::Foo"</span><span class="p">,</span> <span class="n">input1</span><span class="p">,</span> <span class="n">input2</span><span class="p">,</span> <span class="n">attr1_f</span><span class="o">=</span><span class="n">attr1</span><span class="p">,</span> <span class="n">attr2_i</span><span class="o">=</span><span class="n">attr2</span><span class="p">)</span>
<span class="c1"># Register custom symbolic function</span>
<span class="n">torch</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">register_custom_op_symbolic</span><span class="p">(</span><span class="s2">"custom_ops::foo_forward"</span><span class="p">,</span> <span class="n">symbolic_foo_forward</span><span class="p">,</span> <span class="mi">9</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">FooModel</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">attr1</span><span class="p">,</span> <span class="n">attr2</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">attr1</span> <span class="o">=</span> <span class="n">attr1</span>
<span class="bp">self</span><span class="o">.</span><span class="n">attr2</span> <span class="o">=</span> <span class="n">attr2</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input1</span><span class="p">,</span> <span class="n">input2</span><span class="p">):</span>
<span class="c1"># Calling custom op</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">custom_ops</span><span class="o">.</span><span class="n">foo_forward</span><span class="p">(</span><span class="n">input1</span><span class="p">,</span> <span class="n">input2</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">attr1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">attr2</span><span class="p">)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">FooModel</span><span class="p">(</span><span class="n">attr1</span><span class="p">,</span> <span class="n">attr2</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="p">(</span><span class="n">example_input1</span><span class="p">,</span> <span class="n">example_input1</span><span class="p">),</span>
<span class="s2">"model.onnx"</span><span class="p">,</span>
<span class="c1"># only needed if you want to specify an opset version > 1.</span>
<span class="n">custom_opsets</span><span class="o">=</span><span class="p">{</span><span class="s2">"custom_domain"</span><span class="p">:</span> <span class="mi">2</span><span class="p">}</span>
<span class="p">)</span>
</pre></div>
</div>
<p>You can export your model as one or a combination of many standard ONNX ops, or as a custom ONNX operator.</p>
<p>The example above exports it as a custom operator in the “custom_domain” opset.
When exporting a custom operator, you can specify the custom domain version using the
<code class="docutils literal notranslate"><span class="pre">custom_opsets</span></code> dictionary at export. If not specified, the custom opset version defaults to 1.</p>
<p>The runtime that consumes the model needs to support the custom op. See
<a class="reference external" href="https://caffe2.ai/docs/custom-operators.html">Caffe2 custom ops</a>,
<a class="reference external" href="https://onnxruntime.ai/docs/reference/operators/add-custom-op.html">ONNX Runtime custom ops</a>,
or your runtime of choice’s documentation.</p>
</section>
<section id="discovering-all-unconvertible-aten-ops-at-once">
<h3><a class="toc-backref" href="#id25" role="doc-backlink">Discovering all unconvertible ATen ops at once</a><a class="headerlink" href="#discovering-all-unconvertible-aten-ops-at-once" title="Permalink to this heading">¶</a></h3>
<p>When export fails due to an unconvertible ATen op, there may in fact be more
than one such op but the error message only mentions the first. To discover
all of the unconvertible ops in one go you can:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># prepare model, args, opset_version</span>
<span class="o">...</span>
<span class="n">torch_script_graph</span><span class="p">,</span> <span class="n">unconvertible_ops</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">unconvertible_ops</span><span class="p">(</span>
<span class="n">model</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">opset_version</span><span class="o">=</span><span class="n">opset_version</span>
<span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">unconvertible_ops</span><span class="p">))</span>
</pre></div>
</div>
<p>The set is approximated because some ops may be removed during the conversion
process and don’t need to be converted. Some other ops may have partial support
that will fail conversion with particular inputs, but this should give you a
general idea of what ops are not supported. Please feel free to open Github Issues
for op support requests.</p>
</section>
</section>
<section id="frequently-asked-questions">
<h2><a class="toc-backref" href="#id26" role="doc-backlink">Frequently Asked Questions</a><a class="headerlink" href="#frequently-asked-questions" title="Permalink to this heading">¶</a></h2>
<p>Q: I have exported my LSTM model, but its input size seems to be fixed?</p>
<blockquote>
<div><p>The tracer records the shapes of the example inputs. If the model should accept
inputs of dynamic shapes, set <code class="docutils literal notranslate"><span class="pre">dynamic_axes</span></code> when calling <a class="reference internal" href="#torch.onnx.export" title="torch.onnx.export"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.onnx.export()</span></code></a>.</p>
</div></blockquote>
<p>Q: How to export models containing loops?</p>
<blockquote>
<div><p>See <a class="reference internal" href="#id2">Tracing vs Scripting</a>.</p>
</div></blockquote>
<p>Q: How to export models with primitive type inputs (e.g. int, float)?</p>
<blockquote>
<div><p>Support for primitive numeric type inputs was added in PyTorch 1.9.
However, the exporter does not support models with str inputs.</p>
</div></blockquote>
<p>Q: Does ONNX support implicit scalar datatype casting?</p>
<blockquote>
<div><p>The ONNX standard does not, but the exporter will try to handle that part.
Scalars are exported as constant tensors.