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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="#diagnosing-issues-with-sarif" id="id5">Diagnosing issues with SARIF</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 Program Serializer</strong>: <a class="reference internal" href="#torch.onnx.ONNXProgramSerializer" title="torch.onnx.ONNXProgramSerializer"><code class="xref py py-class docutils literal notranslate"><span class="pre">ONNXProgramSerializer</span></code></a> serializes the exported model to a file.</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>
</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">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>
<p>As the code above shows, all you need is to provide <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> 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>
</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="diagnosing-issues-with-sarif">
<h2><a class="toc-backref" href="#id5">Diagnosing issues with SARIF</a><a class="headerlink" href="#diagnosing-issues-with-sarif" title="Permalink to this heading">¶</a></h2>
<p>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="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 external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><em>Union</em></a><em>[</em><a class="reference internal" href="generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.modules.module.Module"><em>Module</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.12)"><em>Callable</em></a><em>, </em><a class="reference internal" href="export.html#torch.export.ExportedProgram" title="torch.export.exported_program.ExportedProgram"><em>ExportedProgram</em></a><em>]</em>) – 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 external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.12)"><em>Optional</em></a><em>[</em><a class="reference internal" href="#torch.onnx.ExportOptions" title="torch.onnx.ExportOptions"><em>ExportOptions</em></a><em>]</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"><em>ONNXProgram</em></a></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.</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>
<p>By printing input dynamic dimensions we can see the input shape is no longer (2,2,2)</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">onnx_program</span><span class="o">.</span><span class="n">model_proto</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">input</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="go">name: "arg0"</span>
<span class="go">type {</span>
<span class="go"> tensor_type {</span>
<span class="go"> elem_type: 1</span>
<span class="go"> shape {</span>
<span class="go"> dim {</span>
<span class="go"> dim_param: "arg0_dim_0"</span>
<span class="go"> }</span>
<span class="go"> dim {</span>
<span class="go"> dim_param: "arg0_dim_1"</span>
<span class="go"> }</span>
<span class="go"> dim {</span>
<span class="go"> dim_param: "arg0_dim_2"</span>
<span class="go"> }</span>
<span class="go"> }</span>
<span class="go"> }</span>
<span class="go">}</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">op_level_debug</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> (<em>Optional</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>]</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>op_level_debug</strong> (<em>Optional</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>]</em>) – Whether to export the model with op-level debug information</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>Optional</em><em>[</em><em>ONNXFakeContext</em><em>]</em>) – The fake context used for symbolic tracing.</p></li>
<li><p><strong>onnx_registry</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="#torch.onnx.OnnxRegistry" title="torch.onnx.OnnxRegistry"><em>OnnxRegistry</em></a><em>]</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 that must be passed to <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">dynamo_export()</span></code></a>
through the <code class="xref py py-attr docutils literal notranslate"><span class="pre">ExportOptions.fake_context</span></code> argument.</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">dynamo_export</span><span class="p">(</span>
<span class="o">...</span> <span class="n">my_nn_module</span><span class="p">,</span>
<span class="o">...</span> <span class="n">arg1</span><span class="p">,</span>
<span class="o">...</span> <span class="n">export_options</span><span class="o">=</span><span class="n">export_options</span>
<span class="o">...</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="s2">"my_model_without_initializers.onnx"</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> <span class="n">model_state</span><span class="o">=</span><span class="n">MyModel</span><span class="p">()</span><span class="o">.</span><span class="n">state_dict</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_signature</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>Optional</em><em>[</em><em>ONNXFakeContext</em><em>]</em>) – The fake context used for symbolic tracing.</p></li>
<li><p><strong>export_exception</strong> (<em>Optional</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#Exception" title="(in Python v3.12)"><em>Exception</em></a><em>]</em>) – The exception that occurred during export, if any.</p></li>
<li><p><strong>model_signature</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="export.html#torch.export.ExportGraphSignature" title="torch.export.ExportGraphSignature"><em>torch.export.ExportGraphSignature</em></a><em>]</em>) – The model signature for the exported ONNX graph.</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.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 external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.12)"><em>Optional</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><em>Union</em></a><em>[</em><a class="reference internal" href="generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.modules.module.Module"><em>Module</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.12)"><em>Callable</em></a><em>, </em><a class="reference internal" href="export.html#torch.export.ExportedProgram" title="torch.export.exported_program.ExportedProgram"><em>ExportedProgram</em></a><em>]</em><em>]</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><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Sequence" title="(in Python v3.12)"><em>Sequence</em></a>[<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><em>Union</em></a>[<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>, <a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a>, <a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a>, <a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">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="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.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> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><em>Any</em></a>) – The PyTorch model outputs.</p></li>
<li><p><strong>model_with_state_dict</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.12)"><em>Optional</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><em>Union</em></a><em>[</em><a class="reference internal" href="generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.modules.module.Module"><em>Module</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.12)"><em>Callable</em></a><em>, </em><a class="reference internal" href="export.html#torch.export.ExportedProgram" title="torch.export.exported_program.ExportedProgram"><em>ExportedProgram</em></a><em>]</em><em>]</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><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Sequence" title="(in Python v3.12)"><em>Sequence</em></a>[<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><em>Union</em></a>[<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>, <a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a>, <a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a>, <a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">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 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><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.12)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><span class="pre">ONNXFakeContext</span><span class="p"><span class="pre">]</span></span></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 property">
<dt class="sig sig-object py" id="torch.onnx.ONNXProgram.model_signature">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">model_signature</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.12)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="export.html#torch.export.graph_signature.ExportGraphSignature" title="torch.export.graph_signature.ExportGraphSignature"><span class="pre">ExportGraphSignature</span></a><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#torch.onnx.ONNXProgram.model_signature" title="Permalink to this definition">¶</a></dt>
<dd><p>The model signature for the exported ONNX graph.</p>
<p>This information is relevant because ONNX specification often differs from PyTorch’s, resulting
in a ONNX graph with input and output schema different from the actual PyTorch model implementation.
By using the model signature, the users can understand the inputs and outputs differences
and properly execute the model in ONNX Runtime.</p>
<p>NOTE: Model signature is only available when the ONNX graph was exported from a
<a class="reference internal" href="export.html#torch.export.ExportedProgram" title="torch.export.ExportedProgram"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.export.ExportedProgram</span></code></a> object.</p>
<p>NOTE: Any transformation done to the model that changes the model signature must be accompanied
by updates to this model signature as well through <code class="xref py py-class docutils literal notranslate"><span class="pre">InputAdaptStep</span></code> and/or <code class="xref py py-class docutils literal notranslate"><span class="pre">OutputAdaptStep</span></code>.</p>
<p class="rubric">Example</p>
<p>The following model produces different sets of inputs and outputs.
The first 4 inputs are model parameters (namely conv1.weight, conv2.weight, fc1.weight, fc2.weight),
and the next 2 inputs are registered buffers (namely my_buffer2, my_buffer1) and finally
the last 2 inputs are user inputs (namely x and b).
The first output is a buffer mutation (namely my_buffer2) and the last output is the actual model output.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">class</span> <span class="nc">CustomModule</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="gp">... </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="gp">... </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="gp">... </span> <span class="bp">self</span><span class="o">.</span><span class="n">my_parameter</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">Parameter</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">2.0</span><span class="p">))</span>
<span class="gp">... </span> <span class="bp">self</span><span class="o">.</span><span class="n">register_buffer</span><span class="p">(</span><span class="s2">"my_buffer1"</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">3.0</span><span class="p">))</span>
<span class="gp">... </span> <span class="bp">self</span><span class="o">.</span><span class="n">register_buffer</span><span class="p">(</span><span class="s2">"my_buffer2"</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">4.0</span><span class="p">))</span>
<span class="gp">... </span> <span class="bp">self</span><span class="o">.</span><span class="n">conv1</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">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="gp">... </span> <span class="bp">self</span><span class="o">.</span><span class="n">conv2</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">Conv2d</span><span class="p">(</span><span class="mi">32</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">1</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="gp">... </span> <span class="bp">self</span><span class="o">.</span><span class="n">fc1</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">9216</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="gp">... </span> <span class="bp">self</span><span class="o">.</span><span class="n">fc2</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">128</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="gp">... </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">b</span><span class="p">):</span>
<span class="gp">... </span> <span class="n">tensor_x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="gp">... </span> <span class="n">tensor_x</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">functional</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="gp">... </span> <span class="n">tensor_x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span><span class="p">(</span><span class="n">tensor_x</span><span class="p">)</span>
<span class="gp">... </span> <span class="n">tensor_x</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">functional</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="gp">... </span> <span class="n">tensor_x</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">functional</span><span class="o">.</span><span class="n">max_pool2d</span><span class="p">(</span><span class="n">tensor_x</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="gp">... </span> <span class="n">tensor_x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">tensor_x</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="gp">... </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="gp">... </span> <span class="n">tensor_x</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">functional</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="gp">... </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="gp">... </span> <span class="n">output</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">functional</span><span class="o">.</span><span class="n">log_softmax</span><span class="p">(</span><span class="n">tensor_x</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="gp">... </span> <span class="p">(</span>
<span class="gp">... </span> <span class="bp">self</span><span class="o">.</span><span class="n">my_buffer2</span><span class="o">.</span><span class="n">add_</span><span class="p">(</span><span class="mf">1.0</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">my_buffer1</span>
<span class="gp">... </span> <span class="p">)</span> <span class="c1"># Mutate buffer through in-place addition</span>
<span class="gp">... </span> <span class="k">return</span> <span class="n">output</span>
<span class="gp">>>> </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">rand</span><span class="p">((</span><span class="mi">64</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">28</span><span class="p">,</span> <span class="mi">28</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">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="gp">>>> </span><span class="n">exported_program</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">export</span><span class="o">.</span><span class="n">export</span><span class="p">(</span><span class="n">CustomModule</span><span class="p">(),</span> <span class="n">args</span><span class="o">=</span><span class="n">inputs</span><span class="p">)</span>
<span class="gp">>>> </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">exported_program</span><span class="p">,</span> <span class="o">*</span><span class="n">inputs</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">onnx_program</span><span class="o">.</span><span class="n">model_signature</span><span class="p">)</span>
<span class="go">ExportGraphSignature(</span>
<span class="go"> input_specs=[</span>
<span class="go"> InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='arg0_1'),</span>
<span class="go"> target='conv1.weight', persistent=None),</span>
<span class="go"> InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='arg1_1'),</span>
<span class="go"> target='conv2.weight', persistent=None),</span>
<span class="go"> InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='arg2_1'),</span>
<span class="go"> target='fc1.weight', persistent=None),</span>
<span class="go"> InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='arg3_1'),</span>
<span class="go"> target='fc2.weight', persistent=None),</span>
<span class="go"> InputSpec(kind=<InputKind.BUFFER: 3>, arg=TensorArgument(name='arg4_1'),</span>
<span class="go"> target='my_buffer2', persistent=True),</span>
<span class="go"> InputSpec(kind=<InputKind.BUFFER: 3>, arg=TensorArgument(name='arg5_1'),</span>
<span class="go"> target='my_buffer1', persistent=True),</span>
<span class="go"> InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg6_1'),</span>
<span class="go"> target=None, persistent=None),</span>
<span class="go"> InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg7_1'),</span>
<span class="go"> target=None, persistent=None)</span>
<span class="go"> ],</span>