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<div class="section" id="python-closure">
<h1>python.closure<a class="headerlink" href="#python-closure" title="Permalink to this heading">¶</a></h1>
<div class="section" id="cond-closed-over-variable">
<h2>cond_closed_over_variable<a class="headerlink" href="#cond-closed-over-variable" title="Permalink to this heading">¶</a></h2>
<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="#"><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="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>
</pre></div>
</div>
<p>Result:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, l_pred_: "b8[]", l_x_: "f32[3, 2]"):
true_graph_0 = self.true_graph_0
false_graph_0 = self.false_graph_0
conditional = torch.ops.higher_order.cond(l_pred_, true_graph_0, false_graph_0, [l_x_]); l_pred_ = true_graph_0 = false_graph_0 = l_x_ = None
getitem: "f32[3, 2]" = conditional[0]; conditional = None
return (getitem,)
class <lambda>(torch.nn.Module):
def forward(self, arg0_1: "f32[3, 2]"):
mul: "f32[3, 2]" = torch.ops.aten.mul.Tensor(arg0_1, 2); arg0_1 = None
return (mul,)
class <lambda>(torch.nn.Module):
def forward(self, arg0_1: "f32[3, 2]"):
sub: "f32[3, 2]" = torch.ops.aten.sub.Tensor(arg0_1, 2); arg0_1 = None
return (sub,)
Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='l_pred_'), target=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='l_x_'), target=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='getitem'), target=None)])
Range constraints: {}
Equality constraints: []
</pre></div>
</div>
</div>
<div class="section" id="nested-function">
<h2>nested_function<a class="headerlink" href="#nested-function" title="Permalink to this heading">¶</a></h2>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Tags: <a class="reference internal" href="#"><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="kn">import</span> <span class="nn">torch</span>
<span class="k">def</span> <span class="nf">nested_function</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Nested functions are traced through. Side effects on global captures</span>
<span class="sd"> are not supported though.</span>
<span class="sd"> """</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span>
<span class="n">z</span> <span class="o">=</span> <span class="n">a</span> <span class="o">-</span> <span class="n">b</span>
<span class="k">def</span> <span class="nf">closure</span><span class="p">(</span><span class="n">y</span><span class="p">):</span>
<span class="k">nonlocal</span> <span class="n">x</span>
<span class="n">x</span> <span class="o">+=</span> <span class="mi">1</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">closure</span><span class="p">(</span><span class="n">x</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">l_a_</span><span class="p">:</span> <span class="s2">"f32[3, 2]"</span><span class="p">,</span> <span class="n">l_b_</span><span class="p">:</span> <span class="s2">"f32[2]"</span><span class="p">):</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">l_a_</span><span class="p">,</span> <span class="n">l_b_</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">l_a_</span><span class="p">,</span> <span class="n">l_b_</span><span class="p">);</span> <span class="n">l_a_</span> <span class="o">=</span> <span class="n">l_b_</span> <span class="o">=</span> <span class="kc">None</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">add</span><span class="p">,</span> <span class="mi">1</span><span class="p">);</span> <span class="n">add</span> <span class="o">=</span> <span class="kc">None</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_1</span><span class="p">,</span> <span class="n">add_1</span><span class="p">);</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">mul</span><span class="p">,</span> <span class="n">sub</span><span class="p">);</span> <span class="n">mul</span> <span class="o">=</span> <span class="n">sub</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">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">'l_a_'</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">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">'l_b_'</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">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>
<span class="n">Equality</span> <span class="n">constraints</span><span class="p">:</span> <span class="p">[]</span>
</pre></div>
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