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<div class="section" id="torchdynamo-based-onnx-exporter">
<h1>TorchDynamo-based ONNX Exporter<a class="headerlink" href="#torchdynamo-based-onnx-exporter" title="Permalink to this heading">¶</a></h1>
<div class="contents local topic" id="contents">
<ul class="simple">
<li><p><a class="reference internal" href="#overview" id="id1">Overview</a></p></li>
<li><p><a class="reference internal" href="#dependencies" id="id2">Dependencies</a></p></li>
<li><p><a class="reference internal" href="#a-simple-example" id="id3">A simple example</a></p></li>
<li><p><a class="reference internal" href="#inspecting-the-onnx-model-using-gui" id="id4">Inspecting the ONNX model using GUI</a></p></li>
<li><p><a class="reference internal" href="#when-the-conversion-fails" id="id5">When the conversion fails</a></p></li>
<li><p><a class="reference internal" href="#api-reference" id="id6">API Reference</a></p></li>
</ul>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>The ONNX exporter for TorchDynamo is a rapidly evolving beta technology.</p>
</div>
<div class="section" id="overview">
<h2><a class="toc-backref" href="#id1">Overview</a><a class="headerlink" href="#overview" title="Permalink to this heading">¶</a></h2>
<p>The ONNX exporter leverages TorchDynamo engine to hook into Python’s frame evaluation API
and dynamically rewrite its bytecode into an FX Graph.
The resulting FX Graph is then polished before it is finally translated into an ONNX graph.</p>
<p>The main advantage of this approach is that the <a class="reference external" href="https://pytorch.org/docs/stable/fx.html">FX graph</a> is captured using
bytecode analysis that preserves the dynamic nature of the model instead of using traditional static tracing techniques.</p>
<p>The exporter is designed to be modular and extensible. It is composed of the following components:</p>
<blockquote>
<div><ul class="simple">
<li><p><strong>ONNX Exporter</strong>: <code class="xref py py-class docutils literal notranslate"><span class="pre">Exporter</span></code> main class that orchestrates the export process.</p></li>
<li><p><strong>ONNX Export Options</strong>: <a class="reference internal" href="#torch.onnx.ExportOptions" title="torch.onnx.ExportOptions"><code class="xref py py-class docutils literal notranslate"><span class="pre">ExportOptions</span></code></a> has a set of options that control the export process.</p></li>
<li><p><strong>ONNX Registry</strong>: <a class="reference internal" href="#torch.onnx.OnnxRegistry" title="torch.onnx.OnnxRegistry"><code class="xref py py-class docutils literal notranslate"><span class="pre">OnnxRegistry</span></code></a> is the registry of ONNX operators and functions.</p></li>
<li><p><strong>FX Graph Extractor</strong>: <code class="xref py py-class docutils literal notranslate"><span class="pre">FXGraphExtractor</span></code> extracts the FX graph from the PyTorch model.</p></li>
<li><p><strong>Fake Mode</strong>: <code class="xref py py-class docutils literal notranslate"><span class="pre">ONNXFakeContext</span></code> is a context manager that enables fake mode for large scale models.</p></li>
<li><p><strong>ONNX Program</strong>: <a class="reference internal" href="#torch.onnx.ONNXProgram" title="torch.onnx.ONNXProgram"><code class="xref py py-class docutils literal notranslate"><span class="pre">ONNXProgram</span></code></a> is the output of the exporter that contains the exported ONNX graph and diagnostics.</p></li>
<li><p><strong>ONNX Diagnostic Options</strong>: <a class="reference internal" href="#torch.onnx.DiagnosticOptions" title="torch.onnx.DiagnosticOptions"><code class="xref py py-class docutils literal notranslate"><span class="pre">DiagnosticOptions</span></code></a> has a set of options that control the diagnostics emitted by the exporter.</p></li>
</ul>
</div></blockquote>
</div>
<div class="section" id="dependencies">
<h2><a class="toc-backref" href="#id2">Dependencies</a><a class="headerlink" href="#dependencies" title="Permalink to this heading">¶</a></h2>
<p>The ONNX exporter depends on extra Python packages:</p>
<blockquote>
<div><ul class="simple">
<li><p><a class="reference external" href="https://onnx.ai">ONNX</a></p></li>
<li><p><a class="reference external" href="https://onnxscript.ai">ONNX Script</a></p></li>
</ul>
</div></blockquote>
<p>They can be installed through <a class="reference external" href="https://pypi.org/project/pip/">pip</a>:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>--upgrade<span class="w"> </span>onnx<span class="w"> </span>onnxscript
</pre></div>
</div>
<p><a class="reference external" href="https://onnxruntime.ai">onnxruntime</a> can then be used to execute the model
on a large variety of processors.</p>
</div>
<div class="section" id="a-simple-example">
<h2><a class="toc-backref" href="#id3">A simple example</a><a class="headerlink" href="#a-simple-example" title="Permalink to this heading">¶</a></h2>
<p>See below a demonstration of exporter API in action with a simple Multilayer Perceptron (MLP) as example:</p>
<div class="highlight-python 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">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="k">class</span> <span class="nc">MLPModel</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="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">fc0</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">fc1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">fc2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">fc3</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</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">bias</span><span class="o">=</span><span class="kc">True</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">tensor_x</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">tensor_x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc0</span><span class="p">(</span><span class="n">tensor_x</span><span class="p">)</span>
<span class="n">tensor_x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span><span class="n">tensor_x</span><span class="p">)</span>
<span class="n">tensor_x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc1</span><span class="p">(</span><span class="n">tensor_x</span><span class="p">)</span>
<span class="n">tensor_x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span><span class="n">tensor_x</span><span class="p">)</span>
<span class="n">tensor_x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc2</span><span class="p">(</span><span class="n">tensor_x</span><span class="p">)</span>
<span class="n">tensor_x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span><span class="n">tensor_x</span><span class="p">)</span>
<span class="n">output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc3</span><span class="p">(</span><span class="n">tensor_x</span><span class="p">)</span>
<span class="k">return</span> <span class="n">output</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">MLPModel</span><span class="p">()</span>
<span class="n">tensor_x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">((</span><span class="mi">97</span><span class="p">,</span> <span class="mi">8</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">float32</span><span class="p">)</span>
<span class="n">onnx_program</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">export</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="p">(</span><span class="n">tensor_x</span><span class="p">,),</span> <span class="n">dynamo</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>
</div>
<p>As the code above shows, all you need is to provide <a class="reference internal" href="onnx_torchscript.html#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> with an instance of the model and its input.
The exporter will then return an instance of <a class="reference internal" href="#torch.onnx.ONNXProgram" title="torch.onnx.ONNXProgram"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.onnx.ONNXProgram</span></code></a> that contains the exported ONNX graph along with extra information.</p>
<p>The in-memory model available through <code class="docutils literal notranslate"><span class="pre">onnx_program.model_proto</span></code> is an <code class="docutils literal notranslate"><span class="pre">onnx.ModelProto</span></code> object in compliance with the <a class="reference external" href="https://github.com/onnx/onnx/blob/main/docs/IR.md">ONNX IR spec</a>.
The ONNX model may then be serialized into a <a class="reference external" href="https://protobuf.dev/">Protobuf file</a> using the <a class="reference internal" href="#torch.onnx.ONNXProgram.save" title="torch.onnx.ONNXProgram.save"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.onnx.ONNXProgram.save()</span></code></a> API.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">onnx_program</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">"mlp.onnx"</span><span class="p">)</span>
</pre></div>
</div>
<p>Two functions exist to export the model to ONNX based on TorchDynamo engine.
They slightly differ in the way they produce the <code class="xref py py-class docutils literal notranslate"><span class="pre">ExportedProgram</span></code>.
<a class="reference internal" href="#torch.onnx.dynamo_export" title="torch.onnx.dynamo_export"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.onnx.dynamo_export()</span></code></a> was introduced with PyTorch 2.1 and
<a class="reference internal" href="onnx_torchscript.html#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> was extended with PyTorch 2.5 to easily switch
from TorchScript to TorchDynamo. To call the former function,
the last line of the previous example can be replaced by the following one.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">onnx_program</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">dynamo_export</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">tensor_x</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="inspecting-the-onnx-model-using-gui">
<h2><a class="toc-backref" href="#id4">Inspecting the ONNX model using GUI</a><a class="headerlink" href="#inspecting-the-onnx-model-using-gui" title="Permalink to this heading">¶</a></h2>
<p>You can view the exported model using <a class="reference external" href="https://netron.app/">Netron</a>.</p>
<a class="reference internal image-reference" href="_images/onnx_dynamo_mlp_model.png"><img alt="MLP model as viewed using Netron" src="_images/onnx_dynamo_mlp_model.png" style="width: 40%;" /></a>
<p>Note that each layer is represented in a rectangular box with a <em>f</em> icon in the top right corner.</p>
<a class="reference internal image-reference" href="_images/onnx_dynamo_mlp_model_function_highlight.png"><img alt="ONNX function highlighted on MLP model" src="_images/onnx_dynamo_mlp_model_function_highlight.png" style="width: 40%;" /></a>
<p>By expanding it, the function body is shown.</p>
<a class="reference internal image-reference" href="_images/onnx_dynamo_mlp_model_function_body.png"><img alt="ONNX function body" src="_images/onnx_dynamo_mlp_model_function_body.png" style="width: 50%;" /></a>
<p>The function body is a sequence of ONNX operators or other functions.</p>
</div>
<div class="section" id="when-the-conversion-fails">
<h2><a class="toc-backref" href="#id5">When the conversion fails</a><a class="headerlink" href="#when-the-conversion-fails" title="Permalink to this heading">¶</a></h2>
<p>Function <a class="reference internal" href="onnx_torchscript.html#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> should called a second time with
parameter <code class="docutils literal notranslate"><span class="pre">report=True</span></code>. A markdown report is generated to help the user
to resolve the issue.</p>
<p>Function <a class="reference internal" href="#torch.onnx.dynamo_export" title="torch.onnx.dynamo_export"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.onnx.dynamo_export()</span></code></a> generates a report using ‘SARIF’ format.
ONNX diagnostics goes beyond regular logs through the adoption of
<a class="reference external" href="https://docs.oasis-open.org/sarif/sarif/v2.1.0/sarif-v2.1.0.html">Static Analysis Results Interchange Format (aka SARIF)</a>
to help users debug and improve their model using a GUI, such as
Visual Studio Code’s <a class="reference external" href="https://marketplace.visualstudio.com/items?itemName=MS-SarifVSCode.sarif-viewer">SARIF Viewer</a>.</p>
<p>The main advantages are:</p>
<blockquote>
<div><ul class="simple">
<li><p>The diagnostics are emitted in machine parseable <a class="reference external" href="https://docs.oasis-open.org/sarif/sarif/v2.1.0/sarif-v2.1.0.html">Static Analysis Results Interchange Format (SARIF)</a>.</p></li>
<li><p>A new clearer, structured way to add new and keep track of diagnostic rules.</p></li>
<li><p>Serve as foundation for more future improvements consuming the diagnostics.</p></li>
</ul>
</div></blockquote>
<div class="toctree-wrapper compound">
<p class="caption" role="heading"><span class="caption-text">ONNX Diagnostic SARIF Rules</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="generated/onnx_dynamo_diagnostics_rules/FXE0007%3Afx-graph-to-onnx.html">FXE0007:fx-graph-to-onnx</a></li>
<li class="toctree-l1"><a class="reference internal" href="generated/onnx_dynamo_diagnostics_rules/FXE0008%3Afx-node-to-onnx.html">FXE0008:fx-node-to-onnx</a></li>
<li class="toctree-l1"><a class="reference internal" href="generated/onnx_dynamo_diagnostics_rules/FXE0010%3Afx-pass.html">FXE0010:fx-pass</a></li>
<li class="toctree-l1"><a class="reference internal" href="generated/onnx_dynamo_diagnostics_rules/FXE0011%3Ano-symbolic-function-for-call-function.html">FXE0011:no-symbolic-function-for-call-function</a></li>
<li class="toctree-l1"><a class="reference internal" href="generated/onnx_dynamo_diagnostics_rules/FXE0012%3Aunsupported-fx-node-analysis.html">FXE0012:unsupported-fx-node-analysis</a></li>
<li class="toctree-l1"><a class="reference internal" href="generated/onnx_dynamo_diagnostics_rules/FXE0013%3Aop-level-debugging.html">FXE0013:op-level-debugging</a></li>
<li class="toctree-l1"><a class="reference internal" href="generated/onnx_dynamo_diagnostics_rules/FXE0014%3Afind-opschema-matched-symbolic-function.html">FXE0014:find-opschema-matched-symbolic-function</a></li>
<li class="toctree-l1"><a class="reference internal" href="generated/onnx_dynamo_diagnostics_rules/FXE0015%3Afx-node-insert-type-promotion.html">FXE0015:fx-node-insert-type-promotion</a></li>
<li class="toctree-l1"><a class="reference internal" href="generated/onnx_dynamo_diagnostics_rules/FXE0016%3Afind-operator-overloads-in-onnx-registry.html">FXE0016:find-operator-overloads-in-onnx-registry</a></li>
</ul>
</div>
</div>
<div class="section" id="api-reference">
<h2><a class="toc-backref" href="#id6">API Reference</a><a class="headerlink" href="#api-reference" title="Permalink to this heading">¶</a></h2>
<dl class="py function">
<dt class="sig sig-object py" id="torch.onnx.dynamo_export">
<span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">dynamo_export</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">model_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">export_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">model_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx.html#dynamo_export"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.dynamo_export" title="Permalink to this definition">¶</a></dt>
<dd><p>Export a torch.nn.Module to an ONNX graph.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>model</strong> (<a class="reference internal" href="generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><em>torch.nn.Module</em></a><em> | </em><em>Callable</em><em> | </em><a class="reference internal" href="export.html#torch.export.ExportedProgram" title="torch.export.ExportedProgram"><em>torch.export.ExportedProgram</em></a>) – The PyTorch model to be exported to ONNX.</p></li>
<li><p><strong>model_args</strong> – Positional inputs to <code class="docutils literal notranslate"><span class="pre">model</span></code>.</p></li>
<li><p><strong>model_kwargs</strong> – Keyword inputs to <code class="docutils literal notranslate"><span class="pre">model</span></code>.</p></li>
<li><p><strong>export_options</strong> (<a class="reference internal" href="#torch.onnx.ExportOptions" title="torch.onnx.ExportOptions"><em>ExportOptions</em></a><em> | </em><em>None</em>) – Options to influence the export to ONNX.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>An in-memory representation of the exported ONNX model.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference internal" href="#torch.onnx.ONNXProgram" title="torch.onnx.ONNXProgram">ONNXProgram</a> | Any</p>
</dd>
</dl>
<p><strong>Example 1 - Simplest export</strong></p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">MyModel</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="o">-></span> <span class="kc">None</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">linear</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">Linear</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="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">bias</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">linear</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">out</span> <span class="o">+</span> <span class="n">bias</span>
<span class="k">return</span> <span class="n">out</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">MyModel</span><span class="p">()</span>
<span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"bias"</span><span class="p">:</span> <span class="mf">3.0</span><span class="p">}</span>
<span class="n">args</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">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">onnx_program</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">dynamo_export</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span><span class="o">.</span><span class="n">save</span><span class="p">(</span>
<span class="s2">"my_simple_model.onnx"</span>
<span class="p">)</span>
</pre></div>
</div>
<p><strong>Example 2 - Exporting with dynamic shapes</strong></p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># The previous model can be exported with dynamic shapes</span>
<span class="n">export_options</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">ExportOptions</span><span class="p">(</span><span class="n">dynamic_shapes</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">onnx_program</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">dynamo_export</span><span class="p">(</span>
<span class="n">model</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span> <span class="n">export_options</span><span class="o">=</span><span class="n">export_options</span>
<span class="p">)</span>
<span class="n">onnx_program</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">"my_dynamic_model.onnx"</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="torch.onnx.ExportOptions">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">ExportOptions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_shapes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fake_context</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">onnx_registry</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diagnostic_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#torch.onnx.ExportOptions" title="Permalink to this definition">¶</a></dt>
<dd><p>Options to influence the TorchDynamo ONNX exporter.</p>
<dl class="field-list simple">
<dt class="field-odd">Variables</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>dynamic_shapes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a><em> | </em><em>None</em>) – Shape information hint for input/output tensors.
When <code class="docutils literal notranslate"><span class="pre">None</span></code>, the exporter determines the most compatible setting.
When <code class="docutils literal notranslate"><span class="pre">True</span></code>, all input shapes are considered dynamic.
When <code class="docutils literal notranslate"><span class="pre">False</span></code>, all input shapes are considered static.</p></li>
<li><p><strong>diagnostic_options</strong> (<a class="reference internal" href="#torch.onnx.DiagnosticOptions" title="torch.onnx.DiagnosticOptions"><em>DiagnosticOptions</em></a>) – The diagnostic options for the exporter.</p></li>
<li><p><strong>fake_context</strong> (<em>ONNXFakeContext</em><em> | </em><em>None</em>) – The fake context used for symbolic tracing.</p></li>
<li><p><strong>onnx_registry</strong> (<a class="reference internal" href="#torch.onnx.OnnxRegistry" title="torch.onnx.OnnxRegistry"><em>OnnxRegistry</em></a><em> | </em><em>None</em>) – The ONNX registry used to register ATen operators to ONNX functions.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.onnx.enable_fake_mode">
<span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">enable_fake_mode</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.onnx.enable_fake_mode" title="Permalink to this definition">¶</a></dt>
<dd><p>Enable fake mode for the duration of the context.</p>
<p>Internally it instantiates a <code class="xref py py-class docutils literal notranslate"><span class="pre">torch._subclasses.fake_tensor.FakeTensorMode</span></code> context manager
that converts user input and model parameters into <code class="xref py py-class docutils literal notranslate"><span class="pre">torch._subclasses.fake_tensor.FakeTensor</span></code>.</p>
<p>A <code class="xref py py-class docutils literal notranslate"><span class="pre">torch._subclasses.fake_tensor.FakeTensor</span></code>
is a <a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> with the ability to run PyTorch code without having to
actually do computation through tensors allocated on a <code class="docutils literal notranslate"><span class="pre">meta</span></code> device. Because
there is no actual data being allocated on the device, this API allows for
exporting large models without the actual memory footprint needed for executing it.</p>
<p>It is highly recommended to enable fake mode when exporting models that
are too large to fit into memory.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>A <code class="xref py py-class docutils literal notranslate"><span class="pre">ONNXFakeContext</span></code> object.</p>
</dd>
</dl>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># xdoctest: +REQUIRES(env:TORCH_DOCTEST_ONNX)</span>
<span class="o">>>></span> <span class="kn">import</span> <span class="nn">torch</span>
<span class="o">>>></span> <span class="kn">import</span> <span class="nn">torch.onnx</span>
<span class="o">>>></span> <span class="k">class</span> <span class="nc">MyModel</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="c1"># Dummy model</span>
<span class="o">...</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="o">-></span> <span class="kc">None</span><span class="p">:</span>
<span class="o">...</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="o">...</span> <span class="bp">self</span><span class="o">.</span><span class="n">linear</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">Linear</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="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="o">...</span> <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">linear</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">out</span>
<span class="o">>>></span> <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">enable_fake_mode</span><span class="p">()</span> <span class="k">as</span> <span class="n">fake_context</span><span class="p">:</span>
<span class="o">...</span> <span class="n">my_nn_module</span> <span class="o">=</span> <span class="n">MyModel</span><span class="p">()</span>
<span class="o">...</span> <span class="n">arg1</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">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> <span class="c1"># positional input 1</span>
<span class="o">>>></span> <span class="n">export_options</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">ExportOptions</span><span class="p">(</span><span class="n">fake_context</span><span class="o">=</span><span class="n">fake_context</span><span class="p">)</span>
<span class="o">>>></span> <span class="n">onnx_program</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">export</span><span class="p">(</span><span class="n">my_nn_module</span><span class="p">,</span> <span class="p">(</span><span class="n">arg1</span><span class="p">,),</span> <span class="n">dynamo</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="o">>>></span> <span class="n">onnx_program</span><span class="o">.</span><span class="n">apply_weights</span><span class="p">(</span><span class="n">MyModel</span><span class="p">()</span><span class="o">.</span><span class="n">state_dict</span><span class="p">())</span>
<span class="o">>>></span> <span class="c1"># Saving model WITHOUT initializers</span>
<span class="o">>>></span> <span class="n">onnx_program</span><span class="o">.</span><span class="n">save</span><span class="p">(</span>
<span class="o">...</span> <span class="s2">"my_model_without_initializers.onnx"</span><span class="p">,</span>
<span class="o">...</span> <span class="n">include_initializers</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="o">...</span> <span class="n">keep_initializers_as_inputs</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="o">...</span> <span class="p">)</span>
<span class="o">>>></span> <span class="c1"># Saving model WITH initializers</span>
<span class="o">>>></span> <span class="n">onnx_program</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">"my_model_with_initializers.onnx"</span><span class="p">)</span>
</pre></div>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>This API is experimental and is <em>NOT</em> backward-compatible.</p>
</div>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="torch.onnx.ONNXProgram">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">ONNXProgram</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_proto</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_adapter</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_adapter</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diagnostic_context</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fake_context</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">export_exception</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_torch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#torch.onnx.ONNXProgram" title="Permalink to this definition">¶</a></dt>
<dd><p>An in-memory representation of a PyTorch model that has been exported to ONNX.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>model_proto</strong> (<em>onnx.ModelProto</em>) – The exported ONNX model as an <code class="xref py py-obj docutils literal notranslate"><span class="pre">onnx.ModelProto</span></code>.</p></li>
<li><p><strong>input_adapter</strong> (<em>io_adapter.InputAdapter</em>) – The input adapter used to convert PyTorch inputs into ONNX inputs.</p></li>
<li><p><strong>output_adapter</strong> (<em>io_adapter.OutputAdapter</em>) – The output adapter used to convert PyTorch outputs into ONNX outputs.</p></li>
<li><p><strong>diagnostic_context</strong> (<em>diagnostics.DiagnosticContext</em>) – Context object for the SARIF diagnostic system responsible for logging errors and metadata.</p></li>
<li><p><strong>fake_context</strong> (<em>ONNXFakeContext</em><em> | </em><em>None</em>) – The fake context used for symbolic tracing.</p></li>
<li><p><strong>export_exception</strong> (<a class="reference external" href="https://docs.python.org/3/library/exceptions.html#Exception" title="(in Python v3.13)"><em>Exception</em></a><em> | </em><em>None</em>) – The exception that occurred during export, if any.</p></li>
</ul>
</dd>
</dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.onnx.ONNXProgram.adapt_torch_inputs_to_onnx">
<span class="sig-name descname"><span class="pre">adapt_torch_inputs_to_onnx</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">model_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_with_state_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">model_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/_internal/_exporter_legacy.html#ONNXProgram.adapt_torch_inputs_to_onnx"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.ONNXProgram.adapt_torch_inputs_to_onnx" title="Permalink to this definition">¶</a></dt>
<dd><p>Converts the PyTorch model inputs to exported ONNX model inputs format.</p>
<p>Due to design differences, input/output format between PyTorch model and exported
ONNX model are often not the same. E.g., None is allowed for PyTorch model, but are
not supported by ONNX. Nested constructs of tensors are allowed for PyTorch model,
but only flattened tensors are supported by ONNX, etc.</p>
<p>The actual adapting steps are associated with each individual export. It
depends on the PyTorch model, the particular set of model_args and model_kwargs
used for the export, and export options.</p>
<p>This method replays the adapting steps recorded during export.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>model_args</strong> – The PyTorch model inputs.</p></li>
<li><p><strong>model_with_state_dict</strong> (<a class="reference internal" href="generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><em>torch.nn.Module</em></a><em> | </em><em>Callable</em><em> | </em><em>None</em>) – The PyTorch model to get extra state from.
If not specified, the model used during export is used.
Required when <a class="reference internal" href="#torch.onnx.enable_fake_mode" title="torch.onnx.enable_fake_mode"><code class="xref py py-func docutils literal notranslate"><span class="pre">enable_fake_mode()</span></code></a> is used to extract real initializers as needed by the ONNX graph.</p></li>
<li><p><strong>model_kwargs</strong> – The PyTorch model keyword inputs.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A sequence of tensors converted from PyTorch model inputs.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>Sequence[<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor">torch.Tensor</a> | <a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a> | <a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)">float</a> | <a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)">bool</a> | <a class="reference internal" href="tensor_attributes.html#torch.dtype" title="torch.dtype">torch.dtype</a>]</p>
</dd>
</dl>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># xdoctest: +REQUIRES(env:TORCH_DOCTEST_ONNX)</span>
<span class="o">>>></span> <span class="kn">import</span> <span class="nn">torch</span>
<span class="o">>>></span> <span class="kn">import</span> <span class="nn">torch.onnx</span>
<span class="o">>>></span> <span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">Tuple</span>
<span class="o">>>></span> <span class="k">def</span> <span class="nf">func_nested_input</span><span class="p">(</span>
<span class="o">...</span> <span class="n">x_dict</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</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">y_tuple</span><span class="p">:</span> <span class="n">Tuple</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">Tuple</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">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]]</span>
<span class="o">...</span> <span class="p">):</span>
<span class="o">...</span> <span class="k">if</span> <span class="s2">"a"</span> <span class="ow">in</span> <span class="n">x_dict</span><span class="p">:</span>
<span class="o">...</span> <span class="n">x</span> <span class="o">=</span> <span class="n">x_dict</span><span class="p">[</span><span class="s2">"a"</span><span class="p">]</span>
<span class="o">...</span> <span class="k">elif</span> <span class="s2">"b"</span> <span class="ow">in</span> <span class="n">x_dict</span><span class="p">:</span>
<span class="o">...</span> <span class="n">x</span> <span class="o">=</span> <span class="n">x_dict</span><span class="p">[</span><span class="s2">"b"</span><span class="p">]</span>
<span class="o">...</span> <span class="k">else</span><span class="p">:</span>
<span class="o">...</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">3</span><span class="p">)</span>
<span class="o">...</span>
<span class="o">...</span> <span class="n">y1</span><span class="p">,</span> <span class="p">(</span><span class="n">y2</span><span class="p">,</span> <span class="n">y3</span><span class="p">)</span> <span class="o">=</span> <span class="n">y_tuple</span>
<span class="o">...</span>
<span class="o">...</span> <span class="k">return</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y1</span> <span class="o">+</span> <span class="n">y2</span> <span class="o">+</span> <span class="n">y3</span>
<span class="o">>>></span> <span class="n">x_dict</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"a"</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="o">>>></span> <span class="n">y_tuple</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">2.</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="mf">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="mf">4.</span><span class="p">)))</span>
<span class="o">>>></span> <span class="n">onnx_program</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">dynamo_export</span><span class="p">(</span><span class="n">func_nested_input</span><span class="p">,</span> <span class="n">x_dict</span><span class="p">,</span> <span class="n">y_tuple</span><span class="p">)</span>
<span class="o">>>></span> <span class="nb">print</span><span class="p">(</span><span class="n">x_dict</span><span class="p">,</span> <span class="n">y_tuple</span><span class="p">)</span>
<span class="p">{</span><span class="s1">'a'</span><span class="p">:</span> <span class="n">tensor</span><span class="p">(</span><span class="mf">1.</span><span class="p">)}</span> <span class="p">(</span><span class="n">tensor</span><span class="p">(</span><span class="mf">2.</span><span class="p">),</span> <span class="p">(</span><span class="n">tensor</span><span class="p">(</span><span class="mf">3.</span><span class="p">),</span> <span class="n">tensor</span><span class="p">(</span><span class="mf">4.</span><span class="p">)))</span>
<span class="o">>>></span> <span class="nb">print</span><span class="p">(</span><span class="n">onnx_program</span><span class="o">.</span><span class="n">adapt_torch_inputs_to_onnx</span><span class="p">(</span><span class="n">x_dict</span><span class="p">,</span> <span class="n">y_tuple</span><span class="p">,</span> <span class="n">model_with_state_dict</span><span class="o">=</span><span class="n">func_nested_input</span><span class="p">))</span>
<span class="p">(</span><span class="n">tensor</span><span class="p">(</span><span class="mf">1.</span><span class="p">),</span> <span class="n">tensor</span><span class="p">(</span><span class="mf">2.</span><span class="p">),</span> <span class="n">tensor</span><span class="p">(</span><span class="mf">3.</span><span class="p">),</span> <span class="n">tensor</span><span class="p">(</span><span class="mf">4.</span><span class="p">))</span>
</pre></div>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>This API is experimental and is <em>NOT</em> backward-compatible.</p>
</div>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.onnx.ONNXProgram.adapt_torch_outputs_to_onnx">
<span class="sig-name descname"><span class="pre">adapt_torch_outputs_to_onnx</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_outputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_with_state_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/_internal/_exporter_legacy.html#ONNXProgram.adapt_torch_outputs_to_onnx"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.ONNXProgram.adapt_torch_outputs_to_onnx" title="Permalink to this definition">¶</a></dt>
<dd><p>Converts the PyTorch model outputs to exported ONNX model outputs format.</p>
<p>Due to design differences, input/output format between PyTorch model and exported
ONNX model are often not the same. E.g., None is allowed for PyTorch model, but are
not supported by ONNX. Nested constructs of tensors are allowed for PyTorch model,
but only flattened tensors are supported by ONNX, etc.</p>
<p>The actual adapting steps are associated with each individual export. It
depends on the PyTorch model, the particular set of model_args and model_kwargs
used for the export, and export options.</p>
<p>This method replays the adapting steps recorded during export.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>model_outputs</strong> (<em>Any</em>) – The PyTorch model outputs.</p></li>
<li><p><strong>model_with_state_dict</strong> (<a class="reference internal" href="generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><em>torch.nn.Module</em></a><em> | </em><em>Callable</em><em> | </em><em>None</em>) – The PyTorch model to get extra state from.
If not specified, the model used during export is used.
Required when <a class="reference internal" href="#torch.onnx.enable_fake_mode" title="torch.onnx.enable_fake_mode"><code class="xref py py-func docutils literal notranslate"><span class="pre">enable_fake_mode()</span></code></a> is used to extract real initializers as needed by the ONNX graph.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>PyTorch model outputs in exported ONNX model outputs format.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>Sequence[<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor">torch.Tensor</a> | <a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a> | <a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)">float</a> | <a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)">bool</a>]</p>
</dd>
</dl>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># xdoctest: +REQUIRES(env:TORCH_DOCTEST_ONNX)</span>
<span class="o">>>></span> <span class="kn">import</span> <span class="nn">torch</span>
<span class="o">>>></span> <span class="kn">import</span> <span class="nn">torch.onnx</span>
<span class="o">>>></span> <span class="k">def</span> <span class="nf">func_returning_tuples</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="o">...</span> <span class="n">x</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span>
<span class="o">...</span> <span class="n">y</span> <span class="o">=</span> <span class="n">y</span> <span class="o">+</span> <span class="n">z</span>
<span class="o">...</span> <span class="n">z</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span>
<span class="o">...</span> <span class="k">return</span> <span class="p">(</span><span class="n">x</span><span class="p">,</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="o">>>></span> <span class="n">x</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="mf">1.</span><span class="p">)</span>
<span class="o">>>></span> <span class="n">y</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="mf">2.</span><span class="p">)</span>
<span class="o">>>></span> <span class="n">z</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="mf">3.</span><span class="p">)</span>
<span class="o">>>></span> <span class="n">onnx_program</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">dynamo_export</span><span class="p">(</span><span class="n">func_returning_tuples</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="o">>>></span> <span class="n">pt_output</span> <span class="o">=</span> <span class="n">func_returning_tuples</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="o">>>></span> <span class="nb">print</span><span class="p">(</span><span class="n">pt_output</span><span class="p">)</span>
<span class="p">(</span><span class="n">tensor</span><span class="p">(</span><span class="mf">3.</span><span class="p">),</span> <span class="p">(</span><span class="n">tensor</span><span class="p">(</span><span class="mf">5.</span><span class="p">),</span> <span class="n">tensor</span><span class="p">(</span><span class="mf">8.</span><span class="p">)))</span>
<span class="o">>>></span> <span class="nb">print</span><span class="p">(</span><span class="n">onnx_program</span><span class="o">.</span><span class="n">adapt_torch_outputs_to_onnx</span><span class="p">(</span><span class="n">pt_output</span><span class="p">,</span> <span class="n">model_with_state_dict</span><span class="o">=</span><span class="n">func_returning_tuples</span><span class="p">))</span>
<span class="p">[</span><span class="n">tensor</span><span class="p">(</span><span class="mf">3.</span><span class="p">),</span> <span class="n">tensor</span><span class="p">(</span><span class="mf">5.</span><span class="p">),</span> <span class="n">tensor</span><span class="p">(</span><span class="mf">8.</span><span class="p">)]</span>
</pre></div>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>This API is experimental and is <em>NOT</em> backward-compatible.</p>
</div>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.onnx.ONNXProgram.apply_weights">
<span class="sig-name descname"><span class="pre">apply_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/_internal/_exporter_legacy.html#ONNXProgram.apply_weights"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.ONNXProgram.apply_weights" title="Permalink to this definition">¶</a></dt>
<dd><p>Apply the weights from the specified state dict to the ONNX model.
:param state_dict: The state dict containing the weights to apply to the ONNX model.</p>
<dl class="field-list simple">
</dl>
</dd></dl>
<dl class="py property">
<dt class="sig sig-object py" id="torch.onnx.ONNXProgram.diagnostic_context">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">diagnostic_context</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">diagnostics.DiagnosticContext</span></em><a class="headerlink" href="#torch.onnx.ONNXProgram.diagnostic_context" title="Permalink to this definition">¶</a></dt>
<dd><p>The diagnostic context associated with the export.</p>
</dd></dl>
<dl class="py property">
<dt class="sig sig-object py" id="torch.onnx.ONNXProgram.fake_context">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">fake_context</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">ONNXFakeContext</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></em><a class="headerlink" href="#torch.onnx.ONNXProgram.fake_context" title="Permalink to this definition">¶</a></dt>
<dd><p>The fake context associated with the export.</p>
</dd></dl>
<dl class="py property">
<dt class="sig sig-object py" id="torch.onnx.ONNXProgram.model_proto">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">model_proto</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">onnx.ModelProto</span></em><a class="headerlink" href="#torch.onnx.ONNXProgram.model_proto" title="Permalink to this definition">¶</a></dt>
<dd><p>The exported ONNX model as an <code class="xref py py-obj docutils literal notranslate"><span class="pre">onnx.ModelProto</span></code>.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.onnx.ONNXProgram.save">
<span class="sig-name descname"><span class="pre">save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">destination</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_initializers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/_internal/_exporter_legacy.html#ONNXProgram.save"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.ONNXProgram.save" title="Permalink to this definition">¶</a></dt>
<dd><p>Saves the in-memory ONNX model to <code class="docutils literal notranslate"><span class="pre">destination</span></code> using specified <code class="docutils literal notranslate"><span class="pre">serializer</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>destination</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/io.html#io.BufferedIOBase" title="(in Python v3.13)"><em>io.BufferedIOBase</em></a>) – The destination to save the ONNX model. It can be either a string or a file-like object.
When used with <code class="docutils literal notranslate"><span class="pre">model_state</span></code>, it must be a string with a full path to the destination.
If <cite>destination</cite> is a string, besides saving the ONNX model into a file, model weights are also stored
in separate files in the same directory as the ONNX model. E.g. for <cite>destination=”/path/model.onnx”</cite>,
the initializers are saved in “/path/” folder along with “onnx.model”.</p></li>
<li><p><strong>include_initializers</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a>) – Whether to include initializers in the ONNX graph as external data.
Cannot be combined with <cite>model_state_dict</cite>.</p></li>
<li><p><strong>model_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><em>dict</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a><em>, </em><em>Any</em><em>] </em><em>| </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a><em> | </em><em>None</em>) – The state_dict of the PyTorch model containing all weights on it.
It can be either a string with the path to a checkpoint or a dictionary with the actual model state.
The supported file formats are the same as those supported by <cite>torch.load</cite> and <cite>safetensors.safe_open</cite>.
Required when <a class="reference internal" href="#torch.onnx.enable_fake_mode" title="torch.onnx.enable_fake_mode"><code class="xref py py-func docutils literal notranslate"><span class="pre">enable_fake_mode()</span></code></a> is used but real initializers are needed on the ONNX graph.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.onnx.ONNXProgram.save_diagnostics">
<span class="sig-name descname"><span class="pre">save_diagnostics</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">destination</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/_internal/_exporter_legacy.html#ONNXProgram.save_diagnostics"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.ONNXProgram.save_diagnostics" title="Permalink to this definition">¶</a></dt>
<dd><p>Saves the export diagnostics as a SARIF log to the specified destination path.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>destination</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a>) – The destination to save the diagnostics SARIF log.
It must have a <cite>.sarif</cite> extension.</p>
</dd>
<dt class="field-even">Raises</dt>
<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.13)"><strong>ValueError</strong></a> – If the destination path does not end with <cite>.sarif</cite> extension.</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="torch.onnx.ONNXRuntimeOptions">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">ONNXRuntimeOptions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">session_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">execution_providers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">execution_provider_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#torch.onnx.ONNXRuntimeOptions" title="Permalink to this definition">¶</a></dt>
<dd><p>Options to influence the execution of the ONNX model through ONNX Runtime.</p>
<dl class="field-list simple">
<dt class="field-odd">Variables</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>session_options</strong> (<em>Sequence</em><em>[</em><em>onnxruntime.SessionOptions</em><em>] </em><em>| </em><em>None</em>) – ONNX Runtime session options.</p></li>
<li><p><strong>execution_providers</strong> (<em>Sequence</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><em>tuple</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><em>dict</em></a><em>[</em><em>Any</em><em>, </em><em>Any</em><em>]</em><em>]</em><em>] </em><em>| </em><em>None</em>) – ONNX Runtime execution providers to use during model execution.</p></li>
<li><p><strong>execution_provider_options</strong> (<em>Sequence</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><em>dict</em></a><em>[</em><em>Any</em><em>, </em><em>Any</em><em>]</em><em>] </em><em>| </em><em>None</em>) – ONNX Runtime execution provider options.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="torch.onnx.OnnxExporterError">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">OnnxExporterError</span></span><a class="headerlink" href="#torch.onnx.OnnxExporterError" title="Permalink to this definition">¶</a></dt>
<dd><p>Errors raised by the ONNX exporter. This is the base class for all exporter errors.</p>
</dd></dl>