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<div class="section" id="exportdb">
<span id="torch-export-db"></span><h1>ExportDB<a class="headerlink" href="#exportdb" title="Permalink to this heading">¶</a></h1>
<p>ExportDB is a centralized dataset of supported and unsupported export cases.
It is targeted towards users who want to understand specifically what types of
code are supported, the subtleties of export, and how to modify their existing
code to be compatible with export. Note that this is not an exhaustive set of
everything that is supported by exportdb, but it covers the
most common and confusing use cases that users will run into.</p>
<p>If you have a feature that you think needs a stronger guarantee from us to
support in export please create an issue in the pytorch/pytorch repo with a module:export tag.</p>
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<div class="section" id="supported">
<h2>Supported<a class="headerlink" href="#supported" title="Permalink to this heading">¶</a></h2>
<div class="section" id="assume-constant-result">
<h3>assume_constant_result<a class="headerlink" href="#assume-constant-result" title="Permalink to this heading">¶</a></h3>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Tags: <a class="reference internal" href="torch.escape-hatch.html"><span class="doc">torch.escape-hatch</span></a></p>
<p>Support Level: SUPPORTED</p>
</div>
<p>Original source code:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># mypy: allow-untyped-defs</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch._dynamo</span> <span class="k">as</span> <span class="nn">torchdynamo</span>
<span class="k">class</span> <span class="nc">AssumeConstantResult</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="w"> </span><span class="sd">"""</span>
<span class="sd"> Applying `assume_constant_result` decorator to burn make non-tracable code as constant.</span>
<span class="sd"> """</span>
<span class="nd">@torchdynamo</span><span class="o">.</span><span class="n">assume_constant_result</span>
<span class="k">def</span> <span class="nf">get_item</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="k">return</span> <span class="n">y</span><span class="o">.</span><span class="n">int</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="k">return</span> <span class="n">x</span><span class="p">[:</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">y</span><span class="p">)]</span>
<span class="n">example_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">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="mi">4</span><span class="p">))</span>
<span class="n">tags</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"torch.escape-hatch"</span><span class="p">}</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">AssumeConstantResult</span><span class="p">()</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">model</span><span class="p">,</span> <span class="n">example_args</span><span class="p">)</span>
</pre></div>
</div>
<p>Result:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ExportedProgram</span><span class="p">:</span>
<span class="k">class</span> <span class="nc">GraphModule</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="s2">"f32[3, 2]"</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="s2">"i64[]"</span><span class="p">):</span>
<span class="n">slice_1</span><span class="p">:</span> <span class="s2">"f32[3, 2]"</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">aten</span><span class="o">.</span><span class="n">slice</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">);</span> <span class="n">x</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">return</span> <span class="p">(</span><span class="n">slice_1</span><span class="p">,)</span>
<span class="n">Graph</span> <span class="n">signature</span><span class="p">:</span> <span class="n">ExportGraphSignature</span><span class="p">(</span><span class="n">input_specs</span><span class="o">=</span><span class="p">[</span><span class="n">InputSpec</span><span class="p">(</span><span class="n">kind</span><span class="o">=<</span><span class="n">InputKind</span><span class="o">.</span><span class="n">USER_INPUT</span><span class="p">:</span> <span class="mi">1</span><span class="o">></span><span class="p">,</span> <span class="n">arg</span><span class="o">=</span><span class="n">TensorArgument</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'x'</span><span class="p">),</span> <span class="n">target</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">persistent</span><span class="o">=</span><span class="kc">None</span><span class="p">),</span> <span class="n">InputSpec</span><span class="p">(</span><span class="n">kind</span><span class="o">=<</span><span class="n">InputKind</span><span class="o">.</span><span class="n">USER_INPUT</span><span class="p">:</span> <span class="mi">1</span><span class="o">></span><span class="p">,</span> <span class="n">arg</span><span class="o">=</span><span class="n">TensorArgument</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'y'</span><span class="p">),</span> <span class="n">target</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">persistent</span><span class="o">=</span><span class="kc">None</span><span class="p">)],</span> <span class="n">output_specs</span><span class="o">=</span><span class="p">[</span><span class="n">OutputSpec</span><span class="p">(</span><span class="n">kind</span><span class="o">=<</span><span class="n">OutputKind</span><span class="o">.</span><span class="n">USER_OUTPUT</span><span class="p">:</span> <span class="mi">1</span><span class="o">></span><span class="p">,</span> <span class="n">arg</span><span class="o">=</span><span class="n">TensorArgument</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'slice_1'</span><span class="p">),</span> <span class="n">target</span><span class="o">=</span><span class="kc">None</span><span class="p">)])</span>
<span class="n">Range</span> <span class="n">constraints</span><span class="p">:</span> <span class="p">{}</span>
</pre></div>
</div>
</div>
<div class="section" id="autograd-function">
<h3>autograd_function<a class="headerlink" href="#autograd-function" title="Permalink to this heading">¶</a></h3>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Tags:</p>
<p>Support Level: SUPPORTED</p>
</div>
<p>Original source code:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># mypy: allow-untyped-defs</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="k">class</span> <span class="nc">MyAutogradFunction</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">Function</span><span class="p">):</span>
<span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
<span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">grad_output</span><span class="p">):</span>
<span class="k">return</span> <span class="n">grad_output</span> <span class="o">+</span> <span class="mi">1</span>
<span class="k">class</span> <span class="nc">AutogradFunction</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="w"> </span><span class="sd">"""</span>
<span class="sd"> TorchDynamo does not keep track of backward() on autograd functions. We recommend to</span>
<span class="sd"> use `allow_in_graph` to mitigate this problem.</span>
<span class="sd"> """</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="k">return</span> <span class="n">MyAutogradFunction</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">example_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">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">),)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">AutogradFunction</span><span class="p">()</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">model</span><span class="p">,</span> <span class="n">example_args</span><span class="p">)</span>
</pre></div>
</div>
<p>Result:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ExportedProgram</span><span class="p">:</span>
<span class="k">class</span> <span class="nc">GraphModule</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="s2">"f32[3, 2]"</span><span class="p">):</span>
<span class="n">clone</span><span class="p">:</span> <span class="s2">"f32[3, 2]"</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">aten</span><span class="o">.</span><span class="n">clone</span><span class="o">.</span><span class="n">default</span><span class="p">(</span><span class="n">x</span><span class="p">);</span> <span class="n">x</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">return</span> <span class="p">(</span><span class="n">clone</span><span class="p">,)</span>
<span class="n">Graph</span> <span class="n">signature</span><span class="p">:</span> <span class="n">ExportGraphSignature</span><span class="p">(</span><span class="n">input_specs</span><span class="o">=</span><span class="p">[</span><span class="n">InputSpec</span><span class="p">(</span><span class="n">kind</span><span class="o">=<</span><span class="n">InputKind</span><span class="o">.</span><span class="n">USER_INPUT</span><span class="p">:</span> <span class="mi">1</span><span class="o">></span><span class="p">,</span> <span class="n">arg</span><span class="o">=</span><span class="n">TensorArgument</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'x'</span><span class="p">),</span> <span class="n">target</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">persistent</span><span class="o">=</span><span class="kc">None</span><span class="p">)],</span> <span class="n">output_specs</span><span class="o">=</span><span class="p">[</span><span class="n">OutputSpec</span><span class="p">(</span><span class="n">kind</span><span class="o">=<</span><span class="n">OutputKind</span><span class="o">.</span><span class="n">USER_OUTPUT</span><span class="p">:</span> <span class="mi">1</span><span class="o">></span><span class="p">,</span> <span class="n">arg</span><span class="o">=</span><span class="n">TensorArgument</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'clone'</span><span class="p">),</span> <span class="n">target</span><span class="o">=</span><span class="kc">None</span><span class="p">)])</span>
<span class="n">Range</span> <span class="n">constraints</span><span class="p">:</span> <span class="p">{}</span>
</pre></div>
</div>
</div>
<div class="section" id="class-method">
<h3>class_method<a class="headerlink" href="#class-method" title="Permalink to this heading">¶</a></h3>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Tags:</p>
<p>Support Level: SUPPORTED</p>
</div>
<p>Original source code:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># mypy: allow-untyped-defs</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="k">class</span> <span class="nc">ClassMethod</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="w"> </span><span class="sd">"""</span>
<span class="sd"> Class methods are inlined during tracing.</span>
<span class="sd"> """</span>
<span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">method</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="n">x</span> <span class="o">+</span> <span class="mi">1</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">4</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">x</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="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">method</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="n">method</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">*</span> <span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">method</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">example_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">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">),)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">ClassMethod</span><span class="p">()</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">model</span><span class="p">,</span> <span class="n">example_args</span><span class="p">)</span>
</pre></div>
</div>
<p>Result:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ExportedProgram</span><span class="p">:</span>
<span class="k">class</span> <span class="nc">GraphModule</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">p_linear_weight</span><span class="p">:</span> <span class="s2">"f32[2, 4]"</span><span class="p">,</span> <span class="n">p_linear_bias</span><span class="p">:</span> <span class="s2">"f32[2]"</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="s2">"f32[3, 4]"</span><span class="p">):</span>
<span class="n">linear</span><span class="p">:</span> <span class="s2">"f32[3, 2]"</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">aten</span><span class="o">.</span><span class="n">linear</span><span class="o">.</span><span class="n">default</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">p_linear_weight</span><span class="p">,</span> <span class="n">p_linear_bias</span><span class="p">);</span> <span class="n">x</span> <span class="o">=</span> <span class="n">p_linear_weight</span> <span class="o">=</span> <span class="n">p_linear_bias</span> <span class="o">=</span> <span class="kc">None</span>
<span class="n">add</span><span class="p">:</span> <span class="s2">"f32[3, 2]"</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">aten</span><span class="o">.</span><span class="n">add</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">linear</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">add_1</span><span class="p">:</span> <span class="s2">"f32[3, 2]"</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">aten</span><span class="o">.</span><span class="n">add</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">linear</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">mul</span><span class="p">:</span> <span class="s2">"f32[3, 2]"</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">aten</span><span class="o">.</span><span class="n">mul</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">add</span><span class="p">,</span> <span class="n">add_1</span><span class="p">);</span> <span class="n">add</span> <span class="o">=</span> <span class="n">add_1</span> <span class="o">=</span> <span class="kc">None</span>
<span class="n">add_2</span><span class="p">:</span> <span class="s2">"f32[3, 2]"</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">aten</span><span class="o">.</span><span class="n">add</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">linear</span><span class="p">,</span> <span class="mi">1</span><span class="p">);</span> <span class="n">linear</span> <span class="o">=</span> <span class="kc">None</span>
<span class="n">mul_1</span><span class="p">:</span> <span class="s2">"f32[3, 2]"</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">aten</span><span class="o">.</span><span class="n">mul</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">mul</span><span class="p">,</span> <span class="n">add_2</span><span class="p">);</span> <span class="n">mul</span> <span class="o">=</span> <span class="n">add_2</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">return</span> <span class="p">(</span><span class="n">mul_1</span><span class="p">,)</span>
<span class="n">Graph</span> <span class="n">signature</span><span class="p">:</span> <span class="n">ExportGraphSignature</span><span class="p">(</span><span class="n">input_specs</span><span class="o">=</span><span class="p">[</span><span class="n">InputSpec</span><span class="p">(</span><span class="n">kind</span><span class="o">=<</span><span class="n">InputKind</span><span class="o">.</span><span class="n">PARAMETER</span><span class="p">:</span> <span class="mi">2</span><span class="o">></span><span class="p">,</span> <span class="n">arg</span><span class="o">=</span><span class="n">TensorArgument</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'p_linear_weight'</span><span class="p">),</span> <span class="n">target</span><span class="o">=</span><span class="s1">'linear.weight'</span><span class="p">,</span> <span class="n">persistent</span><span class="o">=</span><span class="kc">None</span><span class="p">),</span> <span class="n">InputSpec</span><span class="p">(</span><span class="n">kind</span><span class="o">=<</span><span class="n">InputKind</span><span class="o">.</span><span class="n">PARAMETER</span><span class="p">:</span> <span class="mi">2</span><span class="o">></span><span class="p">,</span> <span class="n">arg</span><span class="o">=</span><span class="n">TensorArgument</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'p_linear_bias'</span><span class="p">),</span> <span class="n">target</span><span class="o">=</span><span class="s1">'linear.bias'</span><span class="p">,</span> <span class="n">persistent</span><span class="o">=</span><span class="kc">None</span><span class="p">),</span> <span class="n">InputSpec</span><span class="p">(</span><span class="n">kind</span><span class="o">=<</span><span class="n">InputKind</span><span class="o">.</span><span class="n">USER_INPUT</span><span class="p">:</span> <span class="mi">1</span><span class="o">></span><span class="p">,</span> <span class="n">arg</span><span class="o">=</span><span class="n">TensorArgument</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'x'</span><span class="p">),</span> <span class="n">target</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">persistent</span><span class="o">=</span><span class="kc">None</span><span class="p">)],</span> <span class="n">output_specs</span><span class="o">=</span><span class="p">[</span><span class="n">OutputSpec</span><span class="p">(</span><span class="n">kind</span><span class="o">=<</span><span class="n">OutputKind</span><span class="o">.</span><span class="n">USER_OUTPUT</span><span class="p">:</span> <span class="mi">1</span><span class="o">></span><span class="p">,</span> <span class="n">arg</span><span class="o">=</span><span class="n">TensorArgument</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'mul_1'</span><span class="p">),</span> <span class="n">target</span><span class="o">=</span><span class="kc">None</span><span class="p">)])</span>
<span class="n">Range</span> <span class="n">constraints</span><span class="p">:</span> <span class="p">{}</span>
</pre></div>
</div>
</div>
<div class="section" id="cond-branch-class-method">
<h3>cond_branch_class_method<a class="headerlink" href="#cond-branch-class-method" title="Permalink to this heading">¶</a></h3>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Tags: <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a>, <a class="reference internal" href="torch.cond.html"><span class="doc">torch.cond</span></a></p>
<p>Support Level: SUPPORTED</p>
</div>
<p>Original source code:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># mypy: allow-untyped-defs</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">functorch.experimental.control_flow</span> <span class="kn">import</span> <span class="n">cond</span>
<span class="k">class</span> <span class="nc">MySubModule</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">foo</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="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">cos</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="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">foo</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">CondBranchClassMethod</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="w"> </span><span class="sd">"""</span>
<span class="sd"> The branch functions (`true_fn` and `false_fn`) passed to cond() must follow these rules:</span>
<span class="sd"> - both branches must take the same args, which must also match the branch args passed to cond.</span>
<span class="sd"> - both branches must return a single tensor</span>
<span class="sd"> - returned tensor must have the same tensor metadata, e.g. shape and dtype</span>
<span class="sd"> - branch function can be free function, nested function, lambda, class methods</span>
<span class="sd"> - branch function can not have closure variables</span>
<span class="sd"> - no inplace mutations on inputs or global variables</span>
<span class="sd"> This example demonstrates using class method in cond().</span>
<span class="sd"> NOTE: If the `pred` is test on a dim with batch size < 2, it will be specialized.</span>
<span class="sd"> """</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">subm</span> <span class="o">=</span> <span class="n">MySubModule</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">bar</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="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">sin</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="k">return</span> <span class="n">cond</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o"><=</span> <span class="mi">2</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">subm</span><span class="o">.</span><span class="n">forward</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">bar</span><span class="p">,</span> <span class="p">[</span><span class="n">x</span><span class="p">])</span>
<span class="n">example_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">3</span><span class="p">),)</span>
<span class="n">tags</span> <span class="o">=</span> <span class="p">{</span>
<span class="s2">"torch.cond"</span><span class="p">,</span>
<span class="s2">"torch.dynamic-shape"</span><span class="p">,</span>
<span class="p">}</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">CondBranchClassMethod</span><span class="p">()</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">model</span><span class="p">,</span> <span class="n">example_args</span><span class="p">)</span>
</pre></div>
</div>
<p>Result:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ExportedProgram</span><span class="p">:</span>
<span class="k">class</span> <span class="nc">GraphModule</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="s2">"f32[3]"</span><span class="p">):</span>
<span class="n">sin</span><span class="p">:</span> <span class="s2">"f32[3]"</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">aten</span><span class="o">.</span><span class="n">sin</span><span class="o">.</span><span class="n">default</span><span class="p">(</span><span class="n">x</span><span class="p">);</span> <span class="n">x</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">return</span> <span class="p">(</span><span class="n">sin</span><span class="p">,)</span>
<span class="n">Graph</span> <span class="n">signature</span><span class="p">:</span> <span class="n">ExportGraphSignature</span><span class="p">(</span><span class="n">input_specs</span><span class="o">=</span><span class="p">[</span><span class="n">InputSpec</span><span class="p">(</span><span class="n">kind</span><span class="o">=<</span><span class="n">InputKind</span><span class="o">.</span><span class="n">USER_INPUT</span><span class="p">:</span> <span class="mi">1</span><span class="o">></span><span class="p">,</span> <span class="n">arg</span><span class="o">=</span><span class="n">TensorArgument</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'x'</span><span class="p">),</span> <span class="n">target</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">persistent</span><span class="o">=</span><span class="kc">None</span><span class="p">)],</span> <span class="n">output_specs</span><span class="o">=</span><span class="p">[</span><span class="n">OutputSpec</span><span class="p">(</span><span class="n">kind</span><span class="o">=<</span><span class="n">OutputKind</span><span class="o">.</span><span class="n">USER_OUTPUT</span><span class="p">:</span> <span class="mi">1</span><span class="o">></span><span class="p">,</span> <span class="n">arg</span><span class="o">=</span><span class="n">TensorArgument</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'sin'</span><span class="p">),</span> <span class="n">target</span><span class="o">=</span><span class="kc">None</span><span class="p">)])</span>
<span class="n">Range</span> <span class="n">constraints</span><span class="p">:</span> <span class="p">{}</span>
</pre></div>
</div>
</div>
<div class="section" id="cond-branch-nested-function">
<h3>cond_branch_nested_function<a class="headerlink" href="#cond-branch-nested-function" title="Permalink to this heading">¶</a></h3>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Tags: <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a>, <a class="reference internal" href="torch.cond.html"><span class="doc">torch.cond</span></a></p>
<p>Support Level: SUPPORTED</p>
</div>
<p>Original source code:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># mypy: allow-untyped-defs</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">functorch.experimental.control_flow</span> <span class="kn">import</span> <span class="n">cond</span>
<span class="k">class</span> <span class="nc">CondBranchNestedFunction</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="w"> </span><span class="sd">"""</span>
<span class="sd"> The branch functions (`true_fn` and `false_fn`) passed to cond() must follow these rules:</span>
<span class="sd"> - both branches must take the same args, which must also match the branch args passed to cond.</span>
<span class="sd"> - both branches must return a single tensor</span>
<span class="sd"> - returned tensor must have the same tensor metadata, e.g. shape and dtype</span>
<span class="sd"> - branch function can be free function, nested function, lambda, class methods</span>
<span class="sd"> - branch function can not have closure variables</span>
<span class="sd"> - no inplace mutations on inputs or global variables</span>
<span class="sd"> This example demonstrates using nested function in cond().</span>
<span class="sd"> NOTE: If the `pred` is test on a dim with batch size < 2, it will be specialized.</span>
<span class="sd"> """</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="k">def</span> <span class="nf">true_fn</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">inner_true_fn</span><span class="p">(</span><span class="n">y</span><span class="p">):</span>
<span class="k">return</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span>
<span class="k">return</span> <span class="n">inner_true_fn</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">false_fn</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">inner_false_fn</span><span class="p">(</span><span class="n">y</span><span class="p">):</span>
<span class="k">return</span> <span class="n">x</span> <span class="o">-</span> <span class="n">y</span>
<span class="k">return</span> <span class="n">inner_false_fn</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">return</span> <span class="n">cond</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o"><</span> <span class="mi">10</span><span class="p">,</span> <span class="n">true_fn</span><span class="p">,</span> <span class="n">false_fn</span><span class="p">,</span> <span class="p">[</span><span class="n">x</span><span class="p">])</span>
<span class="n">example_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">3</span><span class="p">),)</span>
<span class="n">tags</span> <span class="o">=</span> <span class="p">{</span>
<span class="s2">"torch.cond"</span><span class="p">,</span>
<span class="s2">"torch.dynamic-shape"</span><span class="p">,</span>
<span class="p">}</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">CondBranchNestedFunction</span><span class="p">()</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">model</span><span class="p">,</span> <span class="n">example_args</span><span class="p">)</span>
</pre></div>
</div>
<p>Result:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ExportedProgram</span><span class="p">:</span>
<span class="k">class</span> <span class="nc">GraphModule</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="s2">"f32[3]"</span><span class="p">):</span>
<span class="n">add</span><span class="p">:</span> <span class="s2">"f32[3]"</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">aten</span><span class="o">.</span><span class="n">add</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">x</span><span class="p">);</span> <span class="n">x</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">return</span> <span class="p">(</span><span class="n">add</span><span class="p">,)</span>
<span class="n">Graph</span> <span class="n">signature</span><span class="p">:</span> <span class="n">ExportGraphSignature</span><span class="p">(</span><span class="n">input_specs</span><span class="o">=</span><span class="p">[</span><span class="n">InputSpec</span><span class="p">(</span><span class="n">kind</span><span class="o">=<</span><span class="n">InputKind</span><span class="o">.</span><span class="n">USER_INPUT</span><span class="p">:</span> <span class="mi">1</span><span class="o">></span><span class="p">,</span> <span class="n">arg</span><span class="o">=</span><span class="n">TensorArgument</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'x'</span><span class="p">),</span> <span class="n">target</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">persistent</span><span class="o">=</span><span class="kc">None</span><span class="p">)],</span> <span class="n">output_specs</span><span class="o">=</span><span class="p">[</span><span class="n">OutputSpec</span><span class="p">(</span><span class="n">kind</span><span class="o">=<</span><span class="n">OutputKind</span><span class="o">.</span><span class="n">USER_OUTPUT</span><span class="p">:</span> <span class="mi">1</span><span class="o">></span><span class="p">,</span> <span class="n">arg</span><span class="o">=</span><span class="n">TensorArgument</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'add'</span><span class="p">),</span> <span class="n">target</span><span class="o">=</span><span class="kc">None</span><span class="p">)])</span>
<span class="n">Range</span> <span class="n">constraints</span><span class="p">:</span> <span class="p">{}</span>
</pre></div>
</div>
</div>
<div class="section" id="cond-branch-nonlocal-variables">
<h3>cond_branch_nonlocal_variables<a class="headerlink" href="#cond-branch-nonlocal-variables" title="Permalink to this heading">¶</a></h3>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Tags: <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a>, <a class="reference internal" href="torch.cond.html"><span class="doc">torch.cond</span></a></p>
<p>Support Level: SUPPORTED</p>
</div>
<p>Original source code:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># mypy: allow-untyped-defs</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">functorch.experimental.control_flow</span> <span class="kn">import</span> <span class="n">cond</span>
<span class="k">class</span> <span class="nc">CondBranchNonlocalVariables</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="w"> </span><span class="sd">"""</span>
<span class="sd"> The branch functions (`true_fn` and `false_fn`) passed to cond() must follow these rules:</span>
<span class="sd"> - both branches must take the same args, which must also match the branch args passed to cond.</span>
<span class="sd"> - both branches must return a single tensor</span>
<span class="sd"> - returned tensor must have the same tensor metadata, e.g. shape and dtype</span>
<span class="sd"> - branch function can be free function, nested function, lambda, class methods</span>
<span class="sd"> - branch function can not have closure variables</span>
<span class="sd"> - no inplace mutations on inputs or global variables</span>
<span class="sd"> This example demonstrates how to rewrite code to avoid capturing closure variables in branch functions.</span>
<span class="sd"> The code below will not work because capturing closure variables is not supported.</span>
<span class="sd"> ```</span>
<span class="sd"> my_tensor_var = x + 100</span>
<span class="sd"> my_primitive_var = 3.14</span>
<span class="sd"> def true_fn(y):</span>
<span class="sd"> nonlocal my_tensor_var, my_primitive_var</span>
<span class="sd"> return y + my_tensor_var + my_primitive_var</span>
<span class="sd"> def false_fn(y):</span>
<span class="sd"> nonlocal my_tensor_var, my_primitive_var</span>
<span class="sd"> return y - my_tensor_var - my_primitive_var</span>
<span class="sd"> return cond(x.shape[0] > 5, true_fn, false_fn, [x])</span>
<span class="sd"> ```</span>
<span class="sd"> NOTE: If the `pred` is test on a dim with batch size < 2, it will be specialized.</span>
<span class="sd"> """</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">my_tensor_var</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="mi">100</span>
<span class="n">my_primitive_var</span> <span class="o">=</span> <span class="mf">3.14</span>
<span class="k">def</span> <span class="nf">true_fn</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="k">return</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span> <span class="o">+</span> <span class="n">z</span>
<span class="k">def</span> <span class="nf">false_fn</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="k">return</span> <span class="n">x</span> <span class="o">-</span> <span class="n">y</span> <span class="o">-</span> <span class="n">z</span>
<span class="k">return</span> <span class="n">cond</span><span class="p">(</span>
<span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">></span> <span class="mi">5</span><span class="p">,</span>
<span class="n">true_fn</span><span class="p">,</span>
<span class="n">false_fn</span><span class="p">,</span>
<span class="p">[</span><span class="n">x</span><span class="p">,</span> <span class="n">my_tensor_var</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">my_primitive_var</span><span class="p">)],</span>
<span class="p">)</span>
<span class="n">example_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">6</span><span class="p">),)</span>
<span class="n">tags</span> <span class="o">=</span> <span class="p">{</span>
<span class="s2">"torch.cond"</span><span class="p">,</span>
<span class="s2">"torch.dynamic-shape"</span><span class="p">,</span>
<span class="p">}</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">CondBranchNonlocalVariables</span><span class="p">()</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">model</span><span class="p">,</span> <span class="n">example_args</span><span class="p">)</span>
</pre></div>
</div>
<p>Result:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ExportedProgram</span><span class="p">:</span>
<span class="k">class</span> <span class="nc">GraphModule</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c_lifted_tensor_0</span><span class="p">:</span> <span class="s2">"f32[]"</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="s2">"f32[6]"</span><span class="p">):</span>
<span class="n">add</span><span class="p">:</span> <span class="s2">"f32[6]"</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">aten</span><span class="o">.</span><span class="n">add</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
<span class="n">lift_fresh_copy</span><span class="p">:</span> <span class="s2">"f32[]"</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">aten</span><span class="o">.</span><span class="n">lift_fresh_copy</span><span class="o">.</span><span class="n">default</span><span class="p">(</span><span class="n">c_lifted_tensor_0</span><span class="p">);</span> <span class="n">c_lifted_tensor_0</span> <span class="o">=</span> <span class="kc">None</span>
<span class="n">detach</span><span class="p">:</span> <span class="s2">"f32[]"</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">aten</span><span class="o">.</span><span class="n">detach</span><span class="o">.</span><span class="n">default</span><span class="p">(</span><span class="n">lift_fresh_copy</span><span class="p">);</span> <span class="n">lift_fresh_copy</span> <span class="o">=</span> <span class="kc">None</span>
<span class="n">add_1</span><span class="p">:</span> <span class="s2">"f32[6]"</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">aten</span><span class="o">.</span><span class="n">add</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">add</span><span class="p">);</span> <span class="n">x</span> <span class="o">=</span> <span class="n">add</span> <span class="o">=</span> <span class="kc">None</span>
<span class="n">add_2</span><span class="p">:</span> <span class="s2">"f32[6]"</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">aten</span><span class="o">.</span><span class="n">add</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">add_1</span><span class="p">,</span> <span class="n">detach</span><span class="p">);</span> <span class="n">add_1</span> <span class="o">=</span> <span class="n">detach</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">return</span> <span class="p">(</span><span class="n">add_2</span><span class="p">,)</span>
<span class="n">Graph</span> <span class="n">signature</span><span class="p">:</span> <span class="n">ExportGraphSignature</span><span class="p">(</span><span class="n">input_specs</span><span class="o">=</span><span class="p">[</span><span class="n">InputSpec</span><span class="p">(</span><span class="n">kind</span><span class="o">=<</span><span class="n">InputKind</span><span class="o">.</span><span class="n">CONSTANT_TENSOR</span><span class="p">:</span> <span class="mi">4</span><span class="o">></span><span class="p">,</span> <span class="n">arg</span><span class="o">=</span><span class="n">TensorArgument</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'c_lifted_tensor_0'</span><span class="p">),</span> <span class="n">target</span><span class="o">=</span><span class="s1">'lifted_tensor_0'</span><span class="p">,</span> <span class="n">persistent</span><span class="o">=</span><span class="kc">None</span><span class="p">),</span> <span class="n">InputSpec</span><span class="p">(</span><span class="n">kind</span><span class="o">=<</span><span class="n">InputKind</span><span class="o">.</span><span class="n">USER_INPUT</span><span class="p">:</span> <span class="mi">1</span><span class="o">></span><span class="p">,</span> <span class="n">arg</span><span class="o">=</span><span class="n">TensorArgument</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'x'</span><span class="p">),</span> <span class="n">target</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">persistent</span><span class="o">=</span><span class="kc">None</span><span class="p">)],</span> <span class="n">output_specs</span><span class="o">=</span><span class="p">[</span><span class="n">OutputSpec</span><span class="p">(</span><span class="n">kind</span><span class="o">=<</span><span class="n">OutputKind</span><span class="o">.</span><span class="n">USER_OUTPUT</span><span class="p">:</span> <span class="mi">1</span><span class="o">></span><span class="p">,</span> <span class="n">arg</span><span class="o">=</span><span class="n">TensorArgument</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'add_2'</span><span class="p">),</span> <span class="n">target</span><span class="o">=</span><span class="kc">None</span><span class="p">)])</span>
<span class="n">Range</span> <span class="n">constraints</span><span class="p">:</span> <span class="p">{}</span>
</pre></div>
</div>
</div>
<div class="section" id="cond-closed-over-variable">
<h3>cond_closed_over_variable<a class="headerlink" href="#cond-closed-over-variable" title="Permalink to this heading">¶</a></h3>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Tags: <a class="reference internal" href="torch.cond.html"><span class="doc">torch.cond</span></a>, <a class="reference internal" href="python.closure.html"><span class="doc">python.closure</span></a></p>
<p>Support Level: SUPPORTED</p>
</div>
<p>Original source code:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># mypy: allow-untyped-defs</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">functorch.experimental.control_flow</span> <span class="kn">import</span> <span class="n">cond</span>
<span class="k">class</span> <span class="nc">CondClosedOverVariable</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="w"> </span><span class="sd">"""</span>
<span class="sd"> torch.cond() supports branches closed over arbitrary variables.</span>
<span class="sd"> """</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">pred</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">true_fn</span><span class="p">(</span><span class="n">val</span><span class="p">):</span>
<span class="k">return</span> <span class="n">x</span> <span class="o">*</span> <span class="mi">2</span>
<span class="k">def</span> <span class="nf">false_fn</span><span class="p">(</span><span class="n">val</span><span class="p">):</span>
<span class="k">return</span> <span class="n">x</span> <span class="o">-</span> <span class="mi">2</span>
<span class="k">return</span> <span class="n">cond</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">true_fn</span><span class="p">,</span> <span class="n">false_fn</span><span class="p">,</span> <span class="p">[</span><span class="n">x</span> <span class="o">+</span> <span class="mi">1</span><span class="p">])</span>
<span class="n">example_args</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="kc">True</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="mi">2</span><span class="p">))</span>
<span class="n">tags</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"torch.cond"</span><span class="p">,</span> <span class="s2">"python.closure"</span><span class="p">}</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">CondClosedOverVariable</span><span class="p">()</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">model</span><span class="p">,</span> <span class="n">example_args</span><span class="p">)</span>
</pre></div>
</div>
<p>Result:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ExportedProgram</span><span class="p">:</span>
<span class="k">class</span> <span class="nc">GraphModule</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pred</span><span class="p">:</span> <span class="s2">"b8[]"</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="s2">"f32[3, 2]"</span><span class="p">):</span>
<span class="n">true_graph_0</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">true_graph_0</span>
<span class="n">false_graph_0</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">false_graph_0</span>
<span class="n">cond</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">higher_order</span><span class="o">.</span><span class="n">cond</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">true_graph_0</span><span class="p">,</span> <span class="n">false_graph_0</span><span class="p">,</span> <span class="p">[</span><span class="n">x</span><span class="p">]);</span> <span class="n">pred</span> <span class="o">=</span> <span class="n">true_graph_0</span> <span class="o">=</span> <span class="n">false_graph_0</span> <span class="o">=</span> <span class="n">x</span> <span class="o">=</span> <span class="kc">None</span>
<span class="n">getitem</span><span class="p">:</span> <span class="s2">"f32[3, 2]"</span> <span class="o">=</span> <span class="n">cond</span><span class="p">[</span><span class="mi">0</span><span class="p">];</span> <span class="n">cond</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">return</span> <span class="p">(</span><span class="n">getitem</span><span class="p">,)</span>
<span class="k">class</span> <span class="nc">true_graph_0</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="s2">"f32[3, 2]"</span><span class="p">):</span>
<span class="n">mul</span><span class="p">:</span> <span class="s2">"f32[3, 2]"</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">aten</span><span class="o">.</span><span class="n">mul</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">2</span><span class="p">);</span> <span class="n">x</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">return</span> <span class="p">(</span><span class="n">mul</span><span class="p">,)</span>
<span class="k">class</span> <span class="nc">false_graph_0</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="s2">"f32[3, 2]"</span><span class="p">):</span>
<span class="n">sub</span><span class="p">:</span> <span class="s2">"f32[3, 2]"</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">aten</span><span class="o">.</span><span class="n">sub</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">2</span><span class="p">);</span> <span class="n">x</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">return</span> <span class="p">(</span><span class="n">sub</span><span class="p">,)</span>
<span class="n">Graph</span> <span class="n">signature</span><span class="p">:</span> <span class="n">ExportGraphSignature</span><span class="p">(</span><span class="n">input_specs</span><span class="o">=</span><span class="p">[</span><span class="n">InputSpec</span><span class="p">(</span><span class="n">kind</span><span class="o">=<</span><span class="n">InputKind</span><span class="o">.</span><span class="n">USER_INPUT</span><span class="p">:</span> <span class="mi">1</span><span class="o">></span><span class="p">,</span> <span class="n">arg</span><span class="o">=</span><span class="n">TensorArgument</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'pred'</span><span class="p">),</span> <span class="n">target</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">persistent</span><span class="o">=</span><span class="kc">None</span><span class="p">),</span> <span class="n">InputSpec</span><span class="p">(</span><span class="n">kind</span><span class="o">=<</span><span class="n">InputKind</span><span class="o">.</span><span class="n">USER_INPUT</span><span class="p">:</span> <span class="mi">1</span><span class="o">></span><span class="p">,</span> <span class="n">arg</span><span class="o">=</span><span class="n">TensorArgument</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'x'</span><span class="p">),</span> <span class="n">target</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">persistent</span><span class="o">=</span><span class="kc">None</span><span class="p">)],</span> <span class="n">output_specs</span><span class="o">=</span><span class="p">[</span><span class="n">OutputSpec</span><span class="p">(</span><span class="n">kind</span><span class="o">=<</span><span class="n">OutputKind</span><span class="o">.</span><span class="n">USER_OUTPUT</span><span class="p">:</span> <span class="mi">1</span><span class="o">></span><span class="p">,</span> <span class="n">arg</span><span class="o">=</span><span class="n">TensorArgument</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'getitem'</span><span class="p">),</span> <span class="n">target</span><span class="o">=</span><span class="kc">None</span><span class="p">)])</span>
<span class="n">Range</span> <span class="n">constraints</span><span class="p">:</span> <span class="p">{}</span>
</pre></div>
</div>
</div>
<div class="section" id="cond-operands">
<h3>cond_operands<a class="headerlink" href="#cond-operands" title="Permalink to this heading">¶</a></h3>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Tags: <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a>, <a class="reference internal" href="torch.cond.html"><span class="doc">torch.cond</span></a></p>
<p>Support Level: SUPPORTED</p>
</div>
<p>Original source code:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># mypy: allow-untyped-defs</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">torch.export</span> <span class="kn">import</span> <span class="n">Dim</span>
<span class="kn">from</span> <span class="nn">functorch.experimental.control_flow</span> <span class="kn">import</span> <span class="n">cond</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="mi">2</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
<span class="n">dim0_x</span> <span class="o">=</span> <span class="n">Dim</span><span class="p">(</span><span class="s2">"dim0_x"</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">CondOperands</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>