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<li class="toctree-l1"><a class="reference internal" href="notes/amp_examples.html">Automatic Mixed Precision examples</a></li>
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<div class="section" id="torch-onnx">
<h1>torch.onnx<a class="headerlink" href="#torch-onnx" title="Permalink to this headline">¶</a></h1>
<div class="contents local topic" id="contents">
<ul class="simple">
<li><p><a class="reference internal" href="#example-end-to-end-alexnet-from-pytorch-to-onnx" id="id3">Example: End-to-end AlexNet from PyTorch to ONNX</a></p></li>
<li><p><a class="reference internal" href="#tracing-vs-scripting" id="id4">Tracing vs Scripting</a></p></li>
<li><p><a class="reference internal" href="#type-annotations" id="id5">Type Annotations</a></p></li>
<li><p><a class="reference internal" href="#write-pytorch-model-in-torch-way" id="id6">Write PyTorch model in Torch way</a></p>
<ul>
<li><p><a class="reference internal" href="#avoid-using-numpy" id="id7">Avoid using numpy</a></p></li>
<li><p><a class="reference internal" href="#avoid-using-data-field" id="id8">Avoid using .data field</a></p></li>
</ul>
</li>
<li><p><a class="reference internal" href="#using-dictionaries-to-handle-named-arguments-as-model-inputs" id="id9">Using dictionaries to handle Named Arguments as model inputs</a></p></li>
<li><p><a class="reference internal" href="#indexing" id="id10">Indexing</a></p>
<ul>
<li><p><a class="reference internal" href="#getter" id="id11">Getter</a></p></li>
<li><p><a class="reference internal" href="#setter" id="id12">Setter</a></p></li>
</ul>
</li>
<li><p><a class="reference internal" href="#torchvision-support" id="id13">TorchVision support</a></p></li>
<li><p><a class="reference internal" href="#limitations" id="id14">Limitations</a></p></li>
<li><p><a class="reference internal" href="#supported-operators" id="id15">Supported operators</a></p></li>
<li><p><a class="reference internal" href="#adding-support-for-operators" id="id16">Adding support for operators</a></p>
<ul>
<li><p><a class="reference internal" href="#aten-operators" id="id17">ATen operators</a></p></li>
<li><p><a class="reference internal" href="#non-aten-operators" id="id18">Non-ATen operators</a></p></li>
<li><p><a class="reference internal" href="#custom-operators" id="id19">Custom operators</a></p></li>
</ul>
</li>
<li><p><a class="reference internal" href="#operator-export-type" id="id20">Operator Export Type</a></p>
<ul>
<li><p><a class="reference internal" href="#id2" id="id21">ONNX</a></p></li>
<li><p><a class="reference internal" href="#onnx-aten" id="id22">ONNX_ATEN</a></p></li>
<li><p><a class="reference internal" href="#onnx-aten-fallback" id="id23">ONNX_ATEN_FALLBACK</a></p></li>
<li><p><a class="reference internal" href="#raw" id="id24">RAW</a></p></li>
<li><p><a class="reference internal" href="#onnx-fallthrough" id="id25">ONNX_FALLTHROUGH</a></p></li>
</ul>
</li>
<li><p><a class="reference internal" href="#frequently-asked-questions" id="id26">Frequently Asked Questions</a></p></li>
<li><p><a class="reference internal" href="#use-external-data-format" id="id27">Use external data format</a></p></li>
<li><p><a class="reference internal" href="#training" id="id28">Training</a></p></li>
<li><p><a class="reference internal" href="#functions" id="id29">Functions</a></p></li>
</ul>
</div>
<span class="target" id="module-torch.onnx"></span><div class="section" id="example-end-to-end-alexnet-from-pytorch-to-onnx">
<h2><a class="toc-backref" href="#id3">Example: End-to-end AlexNet from PyTorch to ONNX</a><a class="headerlink" href="#example-end-to-end-alexnet-from-pytorch-to-onnx" 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 notranslate"><span class="pre">alexnet.onnx</span></code>:</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="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 notranslate"><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 notranslate"><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 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 protobuf using the <a class="reference external" href="https://github.com/onnx/onnx/">ONNX</a> library.
You can install <code class="docutils literal notranslate"><span class="pre">ONNX</span></code> with conda:</p>
<div class="highlight-default notranslate"><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 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 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 notranslate"><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>You can also run the exported model with <a class="reference external" href="https://github.com/microsoft/onnxruntime">ONNX Runtime</a>,
you will need to install <cite>ONNX Runtime</cite>: please <a class="reference external" href="https://github.com/microsoft/onnxruntime#installation">follow these instructions</a>.</p>
<p>Once these are installed, you can use the backend for ONNX Runtime:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># ...continuing from above</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="s1">'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="s1">'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="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 another <a class="reference external" href="https://pytorch.org/tutorials/advanced/super_resolution_with_onnxruntime.html">tutorial of exporting the SuperResolution model to ONNX.</a>.</p>
<p>In the future, there will be backends for other frameworks as well.</p>
</div>
<div class="section" id="tracing-vs-scripting">
<h2><a class="toc-backref" href="#id4">Tracing vs Scripting</a><a class="headerlink" href="#tracing-vs-scripting" title="Permalink to this headline">¶</a></h2>
<p>The ONNX exporter can be both <em>trace-based</em> and <em>script-based</em> exporter.</p>
<ul class="simple">
<li><p><em>trace-based</em> 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. If your model contains control flows like
for loops and if conditions, <em>trace-based</em> exporter will unroll the loops and if conditions,
exporting a static graph that is exactly the same as this run. If you want
to export your model with dynamic control flows, you will need to use the <em>script-based</em> exporter.</p></li>
<li><p><em>script-based</em> means that the model you are trying to export is a <a class="reference external" href="jit.html">ScriptModule</a>.
<cite>ScriptModule</cite> is the core data structure in <cite>TorchScript</cite>, and <cite>TorchScript</cite> is a subset of Python language,
that creates serializable and optimizable models from PyTorch code.</p></li>
</ul>
<p>We allow mixing tracing and scripting. You can compose tracing and scripting to suit the particular requirements
of a part of a model. Checkout this example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="c1"># Trace-based only</span>
<span class="k">class</span> <span class="nc">LoopModel</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">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</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="n">y</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="n">i</span>
<span class="k">return</span> <span class="n">x</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">LoopModel</span><span class="p">()</span>
<span class="n">dummy_input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</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">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">)</span>
<span class="n">loop_count</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">5</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">long</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">dummy_input</span><span class="p">,</span> <span class="n">loop_count</span><span class="p">),</span> <span class="s1">'loop.onnx'</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>
</div>
<p>With <em>trace-based</em> exporter, we get the result ONNX graph which unrolls the for loop:</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="mi">0</span> <span class="p">:</span> <span class="n">Long</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="mi">1</span> <span class="p">:</span> <span class="n">Long</span><span class="p">()):</span>
<span class="o">%</span><span class="mi">2</span> <span class="p">:</span> <span class="n">Tensor</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">1</span><span class="p">}]()</span>
<span class="o">%</span><span class="mi">3</span> <span class="p">:</span> <span class="n">Tensor</span> <span class="o">=</span> <span class="n">onnx</span><span class="p">::</span><span class="n">Add</span><span class="p">(</span><span class="o">%</span><span class="mi">0</span><span class="p">,</span> <span class="o">%</span><span class="mi">2</span><span class="p">)</span>
<span class="o">%</span><span class="mi">4</span> <span class="p">:</span> <span class="n">Tensor</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">2</span><span class="p">}]()</span>
<span class="o">%</span><span class="mi">5</span> <span class="p">:</span> <span class="n">Tensor</span> <span class="o">=</span> <span class="n">onnx</span><span class="p">::</span><span class="n">Add</span><span class="p">(</span><span class="o">%</span><span class="mi">3</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">6</span> <span class="p">:</span> <span class="n">Tensor</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">3</span><span class="p">}]()</span>
<span class="o">%</span><span class="mi">7</span> <span class="p">:</span> <span class="n">Tensor</span> <span class="o">=</span> <span class="n">onnx</span><span class="p">::</span><span class="n">Add</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="o">%</span><span class="mi">8</span> <span class="p">:</span> <span class="n">Tensor</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">4</span><span class="p">}]()</span>
<span class="o">%</span><span class="mi">9</span> <span class="p">:</span> <span class="n">Tensor</span> <span class="o">=</span> <span class="n">onnx</span><span class="p">::</span><span class="n">Add</span><span class="p">(</span><span class="o">%</span><span class="mi">7</span><span class="p">,</span> <span class="o">%</span><span class="mi">8</span><span class="p">)</span>
<span class="k">return</span> <span class="p">(</span><span class="o">%</span><span class="mi">9</span><span class="p">)</span>
</pre></div>
</div>
<p>To utilize <em>script-based</em> exporter for capturing the dynamic loop,
we can write the loop in script, and call it from the regular nn.Module:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Mixing tracing and scripting</span>
<span class="nd">@torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span>
<span class="k">def</span> <span class="nf">loop</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">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">y</span><span class="p">)):</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="n">i</span>
<span class="k">return</span> <span class="n">x</span>
<span class="k">class</span> <span class="nc">LoopModel2</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">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="k">return</span> <span class="n">loop</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">model</span> <span class="o">=</span> <span class="n">LoopModel2</span><span class="p">()</span>
<span class="n">dummy_input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</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">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">)</span>
<span class="n">loop_count</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">5</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">long</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">dummy_input</span><span class="p">,</span> <span class="n">loop_count</span><span class="p">),</span> <span class="s1">'loop.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="p">[</span><span class="s1">'input_data'</span><span class="p">,</span> <span class="s1">'loop_range'</span><span class="p">])</span>
</pre></div>
</div>
<p>Now the exported ONNX graph becomes:</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="n">input_data</span> <span class="p">:</span> <span class="n">Long</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">loop_range</span> <span class="p">:</span> <span class="n">Long</span><span class="p">()):</span>
<span class="o">%</span><span class="mi">2</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">1</span><span class="p">}](),</span> <span class="n">scope</span><span class="p">:</span> <span class="n">LoopModel2</span><span class="o">/</span><span class="n">loop</span>
<span class="o">%</span><span class="mi">3</span> <span class="p">:</span> <span class="n">Tensor</span> <span class="o">=</span> <span class="n">onnx</span><span class="p">::</span><span class="n">Cast</span><span class="p">[</span><span class="n">to</span><span class="o">=</span><span class="mi">9</span><span class="p">](</span><span class="o">%</span><span class="mi">2</span><span class="p">)</span>
<span class="o">%</span><span class="mi">4</span> <span class="p">:</span> <span class="n">Long</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">onnx</span><span class="p">::</span><span class="n">Loop</span><span class="p">(</span><span class="o">%</span><span class="n">loop_range</span><span class="p">,</span> <span class="o">%</span><span class="mi">3</span><span class="p">,</span> <span class="o">%</span><span class="n">input_data</span><span class="p">),</span> <span class="n">scope</span><span class="p">:</span> <span class="n">LoopModel2</span><span class="o">/</span><span class="n">loop</span>
<span class="n">block0</span><span class="p">(</span><span class="o">%</span><span class="n">i</span><span class="mf">.1</span> <span class="p">:</span> <span class="n">Long</span><span class="p">(),</span> <span class="o">%</span><span class="n">cond</span> <span class="p">:</span> <span class="nb">bool</span><span class="p">,</span> <span class="o">%</span><span class="n">x</span><span class="mf">.6</span> <span class="p">:</span> <span class="n">Long</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="mi">8</span> <span class="p">:</span> <span class="n">Long</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">onnx</span><span class="p">::</span><span class="n">Add</span><span class="p">(</span><span class="o">%</span><span class="n">x</span><span class="mf">.6</span><span class="p">,</span> <span class="o">%</span><span class="n">i</span><span class="mf">.1</span><span class="p">),</span> <span class="n">scope</span><span class="p">:</span> <span class="n">LoopModel2</span><span class="o">/</span><span class="n">loop</span>
<span class="o">%</span><span class="mi">9</span> <span class="p">:</span> <span class="n">Tensor</span> <span class="o">=</span> <span class="n">onnx</span><span class="p">::</span><span class="n">Cast</span><span class="p">[</span><span class="n">to</span><span class="o">=</span><span class="mi">9</span><span class="p">](</span><span class="o">%</span><span class="mi">2</span><span class="p">)</span>
<span class="o">-></span> <span class="p">(</span><span class="o">%</span><span class="mi">9</span><span class="p">,</span> <span class="o">%</span><span class="mi">8</span><span class="p">)</span>
<span class="k">return</span> <span class="p">(</span><span class="o">%</span><span class="mi">4</span><span class="p">)</span>
</pre></div>
</div>
<p>The dynamic control flow is captured correctly. We can verify in backends with different loop range.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></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="kn">import</span> <span class="nn">onnx</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="s1">'loop.onnx'</span><span class="p">)</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">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">dummy_input</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="mi">9</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">int64</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>
<span class="c1">#[[37 37 37]</span>
<span class="c1"># [37 37 37]]</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_sess</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="s1">'loop.onnx'</span><span class="p">)</span>
<span class="n">outputs</span> <span class="o">=</span> <span class="n">ort_sess</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="s1">'input_data'</span><span class="p">:</span> <span class="n">dummy_input</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span>
<span class="s1">'loop_range'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="mi">9</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">int64</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="c1">#[array([[37, 37, 37],</span>
<span class="c1"># [37, 37, 37]], dtype=int64)]</span>
</pre></div>
</div>
<p>To avoid exporting a variable scalar tensor as a fixed value constant as part of the ONNX model, please
avoid use of <code class="docutils literal notranslate"><span class="pre">torch.Tensor.item()</span></code>. Torch supports implicit cast of single-element tensors to numbers.
E.g.:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">LoopModel</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">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="n">res</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">arr</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</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="nb">int</span><span class="p">(</span><span class="n">y</span><span class="p">)):</span>
<span class="n">res</span> <span class="o">+=</span> <span class="p">[</span><span class="n">arr</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="kc">False</span><span class="p">)]</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">LoopModel</span><span class="p">())</span>
<span class="n">inputs</span> <span class="o">=</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">16</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">8</span><span class="p">))</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="o">*</span><span class="n">inputs</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">inputs</span><span class="p">,</span> <span class="s1">'loop_and_list.onnx'</span><span class="p">,</span> <span class="n">opset_version</span><span class="o">=</span><span class="mi">11</span><span class="p">,</span> <span class="n">example_outputs</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="type-annotations">
<h2><a class="toc-backref" href="#id5">Type Annotations</a><a class="headerlink" href="#type-annotations" title="Permalink to this headline">¶</a></h2>
<p>TorchScript only supports a subset of Python types. You can find more details about type annotation
<a class="reference external" href="https://pytorch.org/docs/stable/jit_language_reference.html#id8">here</a>.</p>
<p>Due to optimization purposes, TorchScript only supports variables with single static types for script functions.
By default, each variable is assumed to be Tensor. If an argument to a ScriptModule function is not Tensor,
its type should be specified using MyPy-style annotations.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="k">class</span> <span class="nc">Module</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">x</span><span class="p">,</span> <span class="n">tup</span><span class="p">):</span>
<span class="c1"># type: (int, Tuple[Tensor, Tensor]) -> Tensor</span>
<span class="n">t0</span><span class="p">,</span> <span class="n">t1</span> <span class="o">=</span> <span class="n">tup</span>
<span class="k">return</span> <span class="n">t0</span> <span class="o">+</span> <span class="n">t1</span> <span class="o">+</span> <span class="n">x</span>
</pre></div>
</div>
<p>If the type annotation is not specified, TorchScript compiler fails with the runtime error below.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="ne">RuntimeError</span><span class="p">:</span>
<span class="n">Tensor</span> <span class="p">(</span><span class="n">inferred</span><span class="p">)</span> <span class="n">cannot</span> <span class="n">be</span> <span class="n">used</span> <span class="k">as</span> <span class="n">a</span> <span class="nb">tuple</span><span class="p">:</span>
<span class="n">File</span> <span class="o"><</span><span class="n">filename</span><span class="o">></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">tup</span><span class="p">):</span>
<span class="n">t0</span><span class="p">,</span> <span class="n">t1</span> <span class="o">=</span> <span class="n">tup</span>
<span class="o">~~~</span> <span class="o"><---</span> <span class="n">HERE</span>
<span class="k">return</span> <span class="n">t0</span> <span class="o">+</span> <span class="n">t1</span> <span class="o">+</span> <span class="n">x</span>
</pre></div>
</div>
</div>
<div class="section" id="write-pytorch-model-in-torch-way">
<h2><a class="toc-backref" href="#id6">Write PyTorch model in Torch way</a><a class="headerlink" href="#write-pytorch-model-in-torch-way" title="Permalink to this headline">¶</a></h2>
<div class="section" id="avoid-using-numpy">
<h3><a class="toc-backref" href="#id7">Avoid using numpy</a><a class="headerlink" href="#avoid-using-numpy" title="Permalink to this headline">¶</a></h3>
<p>PyTorch models can be written using numpy manipulations, but this is not proper when we convert to the ONNX model.
For the trace-based exporter, tracing treats the numpy values as the constant node,
therefore it calculates the wrong result if we change the input.
So the PyTorch model need implement using torch operators.
For example, do not use numpy operators on numpy tensors:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></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">z</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>do not convert to numpy types:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">y</span> <span class="o">=</span> <span class="n">x</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">int</span><span class="p">)</span>
</pre></div>
</div>
<p>Always use torch tensors and torch operators: torch.concat, etc.
In addition, Dropout layer need defined in init function so that inferencing can handle it properly, i.e.,</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">MyModule</span><span class="p">(</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="bp">self</span><span class="o">.</span><span class="n">dropout</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.5</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">x</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="avoid-using-data-field">
<h3><a class="toc-backref" href="#id8">Avoid using .data field</a><a class="headerlink" href="#avoid-using-data-field" title="Permalink to this headline">¶</a></h3>
<p>The .data field is an old field that is kept for backward compatibility and should be avoided when writing models.
It’s usage is dangerous and can make computations wrong, furthermore it can produce an incorrect trace graph and
therefore an incorrect ONNX graph. A safer alternative is to use .detach() instead.</p>
</div>
</div>
<div class="section" id="using-dictionaries-to-handle-named-arguments-as-model-inputs">
<h2><a class="toc-backref" href="#id9">Using dictionaries to handle Named Arguments as model inputs</a><a class="headerlink" href="#using-dictionaries-to-handle-named-arguments-as-model-inputs" title="Permalink to this headline">¶</a></h2>
<p>There are two ways to handle models which consist of named parameters or keyword arguments as inputs:</p>
<ul class="simple">
<li><p>The first method is to pass all the inputs in the same order as required by the model and pass None
values for the keyword arguments that do not require a value to be passed</p></li>
<li><p>The second and more intuitive method is to represent the keyword arguments as key-value pairs where
the key represents the name of the argument in the model signature and the value represents the value
of the argument to be passed</p></li>
</ul>
<p>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">x</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">z</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">if</span> <span class="n">y</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">return</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span>
<span class="k">if</span> <span class="n">z</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">return</span> <span class="n">x</span> <span class="o">+</span> <span class="n">z</span>
<span class="k">return</span> <span class="n">x</span>
<span class="n">m</span> <span class="o">=</span> <span class="n">Model</span><span class="p">()</span>
<span class="n">x</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">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="n">z</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">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
</pre></div>
</div>
<p>There are two ways of exporting the model:</p>
<ul>
<li><p>Not using a dictionary for the keyword arguments and passing all the inputs in the same order
as required by the model</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>torch.onnx.export(model, (x, None, z), ‘test.onnx’)
</pre></div>
</div>
</li>
<li><p>Using a dictionary to represent the keyword arguments. This dictionary is always passed in
addition to the non-keyword arguments and is always the last argument in the args tuple.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>torch.onnx.export(model, (x, {'y': None, 'z': z}), ‘test.onnx’)
</pre></div>
</div>
</li>
</ul>
<p>For cases in which there are no keyword arguments, models can be exported with either an
empty or no dictionary. For example,</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>torch.onnx.export(model, (x, {}), ‘test.onnx’)
or
torch.onnx.export(model, (x, ), ‘test.onnx’)
</pre></div>
</div>
<p>An exception to this rule are cases in which the last input is also of a dictionary type.
In these cases it is mandatory to have an empty dictionary as the last argument in the
args tuple. For example,</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">k</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="o">...</span>
<span class="k">return</span> <span class="n">x</span>
<span class="n">m</span> <span class="o">=</span> <span class="n">Model</span><span class="p">()</span>
<span class="n">k</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">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</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="mf">1.</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">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)}</span>
</pre></div>
</div>
<p>Without the presence of the empty dictionary, the export call assumes that the
‘x’ input is intended to represent the optional dictionary consisting of named arguments.
In order to prevent this from being an issue a constraint is placed to provide an empty
dictionary as the last input in the tuple args in such cases.
The new call would look like this.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>torch.onnx.export(model, (k, x, {}), ‘test.onnx’)
</pre></div>
</div>
</div>
<div class="section" id="indexing">
<h2><a class="toc-backref" href="#id10">Indexing</a><a class="headerlink" href="#indexing" title="Permalink to this headline">¶</a></h2>
<p>Tensor indexing in PyTorch is very flexible and complicated.
There are two categories of indexing. Both are largely supported in exporting today.
If you are experiencing issues exporting indexing that belongs to the supported patterns below,
please double check that you are exporting with the latest opset (opset_version=12).</p>
<div class="section" id="getter">
<h3><a class="toc-backref" href="#id11">Getter</a><a class="headerlink" href="#getter" title="Permalink to this headline">¶</a></h3>
<p>This type of indexing occurs on the RHS. Export is supported for ONNX opset version >= 9. E.g.:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">data</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">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
<span class="n">index</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">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
<span class="c1"># RHS indexing is supported in ONNX opset >= 11.</span>
<span class="k">class</span> <span class="nc">RHSIndexing</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">data</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span>
<span class="k">return</span> <span class="n">data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">RHSIndexing</span><span class="p">()(</span><span class="n">data</span><span class="p">,</span> <span class="n">index</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">RHSIndexing</span><span class="p">(),</span> <span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">index</span><span class="p">),</span> <span class="s1">'indexing.onnx'</span><span class="p">,</span> <span class="n">opset_version</span><span class="o">=</span><span class="mi">9</span><span class="p">)</span>
<span class="c1"># onnxruntime</span>
<span class="kn">import</span> <span class="nn">onnxruntime</span>
<span class="n">sess</span> <span class="o">=</span> <span class="n">onnxruntime</span><span class="o">.</span><span class="n">InferenceSession</span><span class="p">(</span><span class="s1">'indexing.onnx'</span><span class="p">)</span>
<span class="n">out_ort</span> <span class="o">=</span> <span class="n">sess</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="n">sess</span><span class="o">.</span><span class="n">get_inputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">name</span><span class="p">:</span> <span class="n">data</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span>
<span class="n">sess</span><span class="o">.</span><span class="n">get_inputs</span><span class="p">()[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">name</span><span class="p">:</span> <span class="n">index</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span>
<span class="p">})</span>
<span class="k">assert</span> <span class="n">torch</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">eq</span><span class="p">(</span><span class="n">out</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="n">out_ort</span><span class="p">)))</span>
</pre></div>
</div>
<p>Below is the list of supported patterns for RHS indexing.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Scalar indices</span>
<span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
<span class="c1"># Slice indices</span>
<span class="n">data</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span>
<span class="c1"># Tensor indices</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">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="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="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="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="c1"># Ellipsis followed by tensor indexing</span>
<span class="c1"># Not supported in scripting</span>
<span class="c1"># i.e. torch.jit.script(model) will fail if model contains this pattern.</span>
<span class="c1"># Export is supported under tracing</span>
<span class="c1"># i.e. torch.onnx.export(model)</span>
<span class="n">data</span><span class="p">[</span><span class="o">...</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">1</span><span class="p">])]</span>
<span class="c1"># The combination of above</span>
<span class="n">data</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="o">...</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">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">]),</span> <span class="mi">2</span><span class="p">:</span><span class="mi">4</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="p">[</span><span class="mi">2</span><span class="p">]])]</span>
<span class="c1"># Boolean mask (supported for ONNX opset version >= 11)</span>
<span class="n">data</span><span class="p">[</span><span class="n">data</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">]</span>
</pre></div>
</div>
<p>And below is the list of unsupported patterns for RHS indexing.</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>
</pre></div>
</div>
</div>
<div class="section" id="setter">
<h3><a class="toc-backref" href="#id12">Setter</a><a class="headerlink" href="#setter" title="Permalink to this headline">¶</a></h3>
<p>In code, this type of indexing occurs on the LHS.
Export is supported for ONNX opset version >= 11. E.g.:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
<span class="n">new_data</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
<span class="c1"># LHS indexing is supported in ONNX opset >= 11.</span>
<span class="k">class</span> <span class="nc">LHSIndexing</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">data</span><span class="p">,</span> <span class="n">new_data</span><span class="p">):</span>
<span class="n">data</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">new_data</span>
<span class="k">return</span> <span class="n">data</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">LHSIndexing</span><span class="p">()(</span><span class="n">data</span><span class="p">,</span> <span class="n">new_data</span><span class="p">)</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
<span class="n">new_data</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</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">LHSIndexing</span><span class="p">(),</span> <span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">new_data</span><span class="p">),</span> <span class="s1">'inplace_assign.onnx'</span><span class="p">,</span> <span class="n">opset_version</span><span class="o">=</span><span class="mi">11</span><span class="p">)</span>
<span class="c1"># onnxruntime</span>
<span class="kn">import</span> <span class="nn">onnxruntime</span>
<span class="n">sess</span> <span class="o">=</span> <span class="n">onnxruntime</span><span class="o">.</span><span class="n">InferenceSession</span><span class="p">(</span><span class="s1">'inplace_assign.onnx'</span><span class="p">)</span>
<span class="n">out_ort</span> <span class="o">=</span> <span class="n">sess</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="n">sess</span><span class="o">.</span><span class="n">get_inputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">name</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span>
<span class="n">sess</span><span class="o">.</span><span class="n">get_inputs</span><span class="p">()[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">name</span><span class="p">:</span> <span class="n">new_data</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span>
<span class="p">})</span>
<span class="k">assert</span> <span class="n">torch</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">eq</span><span class="p">(</span><span class="n">out</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="n">out_ort</span><span class="p">)))</span>
</pre></div>
</div>
<p>Below is the list of supported patterns for LHS indexing.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Scalar indices</span>
<span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">new_data</span>
<span class="c1"># Slice indices</span>
<span class="n">data</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"># Tensor indices</span>
<span class="c1"># If more than one tensor are used as indices, only consecutive 1-d tensor indices are supported.</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="o">=</span> <span class="n">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">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">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"># Ellipsis followed by tensor indexing</span>
<span class="c1"># Not supported to export in script modules</span>
<span class="c1"># i.e. torch.onnx.export(torch.jit.script(model)) will fail if model contains this pattern.</span>
<span class="c1"># Export is supported under tracing</span>
<span class="c1"># i.e. torch.onnx.export(model)</span>
<span class="n">data</span><span class="p">[</span><span class="o">...</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">1</span><span class="p">])]</span> <span class="o">=</span> <span class="n">new_data</span>
<span class="c1"># The combination of above</span>
<span class="n">data</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="o">...</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">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">]),</span> <span class="mi">2</span><span class="p">:</span><span class="mi">4</span><span class="p">]</span> <span class="o">+=</span> <span class="n">update</span>
<span class="c1"># Boolean mask</span>
<span class="n">data</span><span class="p">[</span><span class="n">data</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">new_data</span>
</pre></div>
</div>
<p>And below is the list of unsupported patterns for LHS indexing.</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"># 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"># 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>
</pre></div>
</div>
<p>If you are experiencing issues exporting indexing that belongs to the above supported patterns, please double check that
you are exporting with the latest opset (opset_version=12).</p>
</div>
</div>
<div class="section" id="torchvision-support">
<h2><a class="toc-backref" href="#id13">TorchVision support</a><a class="headerlink" href="#torchvision-support" title="Permalink to this headline">¶</a></h2>
<p>All TorchVision models, except for quantized versions, are exportable to ONNX.
More details can be found in <a class="reference external" href="torchvision/models.html">TorchVision</a>.</p>
</div>
<div class="section" id="limitations">
<h2><a class="toc-backref" href="#id14">Limitations</a><a class="headerlink" href="#limitations" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li><p>Only tuples, lists and Variables are supported as JIT inputs/outputs. Dictionaries and strings are also accepted
but their usage is not recommended. Users need to verify their dict inputs carefully, and keep in mind that
dynamic lookups are not available.</p></li>
<li><p>PyTorch and ONNX backends(Caffe2, ONNX Runtime, etc) 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.) We 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.</p></li>
</ul>
</div>
<div class="section" id="supported-operators">
<h2><a class="toc-backref" href="#id15">Supported operators</a><a class="headerlink" href="#supported-operators" title="Permalink to this headline">¶</a></h2>
<p>The following operators are supported:</p>
<ul class="simple">
<li><p>BatchNorm</p></li>
<li><p>ConstantPadNd</p></li>
<li><p>Conv</p></li>
<li><p>Dropout</p></li>
<li><p>Embedding (no optional arguments supported)</p></li>
<li><p>EmbeddingBag</p></li>
<li><p>FeatureDropout (training mode not supported)</p></li>
<li><p>Index</p></li>
<li><p>MaxPool1d</p></li>
<li><p>MaxPool2d</p></li>
<li><p>MaxPool3d</p></li>
<li><p>RNN</p></li>
<li><p>abs</p></li>
<li><p>absolute</p></li>
<li><p>acos</p></li>
<li><p>adaptive_avg_pool1d</p></li>
<li><p>adaptive_avg_pool2d</p></li>
<li><p>adaptive_avg_pool3d</p></li>
<li><p>adaptive_max_pool1d</p></li>
<li><p>adaptive_max_pool2d</p></li>
<li><p>adaptive_max_pool3d</p></li>
<li><p>add (nonzero alpha not supported)</p></li>
<li><p>addmm</p></li>
<li><p>and</p></li>
<li><p>arange</p></li>
<li><p>argmax</p></li>
<li><p>argmin</p></li>
<li><p>asin</p></li>
<li><p>atan</p></li>
<li><p>avg_pool1d</p></li>
<li><p>avg_pool2d</p></li>
<li><p>avg_pool2d</p></li>
<li><p>avg_pool3d</p></li>
<li><p>as_strided</p></li>
<li><p>baddbmm</p></li>
<li><p>bitshift</p></li>
<li><p>cat</p></li>
<li><p>ceil</p></li>
<li><p>celu</p></li>
<li><p>clamp</p></li>
<li><p>clamp_max</p></li>
<li><p>clamp_min</p></li>