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<section id="torch-dynamo">
<span id="id1"></span><h1>torch._dynamo<a class="headerlink" href="#torch-dynamo" title="Permalink to this heading">¶</a></h1>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>This module is an early prototype and is subject to change.</p>
</div>
<span class="target" id="module-torch._dynamo"></span><dl class="py function">
<dt class="sig sig-object py" id="torch._dynamo.allow_in_graph">
<span class="sig-prename descclassname"><span class="pre">torch._dynamo.</span></span><span class="sig-name descname"><span class="pre">allow_in_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/_dynamo.html#allow_in_graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch._dynamo.allow_in_graph" title="Permalink to this definition">¶</a></dt>
<dd><p>Customize which functions TorchDynamo will include in the generated
graph. Similar to <cite>torch.fx.wrap()</cite>.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">torch</span><span class="o">.</span><span class="n">_dynamo</span><span class="o">.</span><span class="n">allow_in_graph</span><span class="p">(</span><span class="n">my_custom_function</span><span class="p">)</span>
<span class="nd">@torch</span><span class="o">.</span><span class="n">_dynamo</span><span class="o">.</span><span class="n">optimize</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">fn</span><span class="p">(</span><span class="n">a</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">my_custom_function</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="n">torch</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="k">return</span> <span class="n">x</span>
<span class="n">fn</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
</pre></div>
</div>
<p>Will capture a single graph containing <cite>my_custom_function()</cite>.</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch._dynamo.disallow_in_graph">
<span class="sig-prename descclassname"><span class="pre">torch._dynamo.</span></span><span class="sig-name descname"><span class="pre">disallow_in_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/_dynamo.html#disallow_in_graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch._dynamo.disallow_in_graph" title="Permalink to this definition">¶</a></dt>
<dd><p>Customize which functions TorchDynamo will exclude in the generated
graph and force a graph break on.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">torch</span><span class="o">.</span><span class="n">_dynamo</span><span class="o">.</span><span class="n">disallow_in_graph</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">sub</span><span class="p">)</span>
<span class="nd">@torch</span><span class="o">.</span><span class="n">_dynamo</span><span class="o">.</span><span class="n">optimize</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">fn</span><span class="p">(</span><span class="n">a</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sub</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="k">return</span> <span class="n">x</span>
<span class="n">fn</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
</pre></div>
</div>
<p>Will break the graph on <cite>torch.sub</cite>, and give two graphs each with a
single <cite>torch.add()</cite> op.</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch._dynamo.graph_break">
<span class="sig-prename descclassname"><span class="pre">torch._dynamo.</span></span><span class="sig-name descname"><span class="pre">graph_break</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/_dynamo.html#graph_break"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch._dynamo.graph_break" title="Permalink to this definition">¶</a></dt>
<dd><p>Force a graph break</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch._dynamo.optimize">
<span class="sig-prename descclassname"><span class="pre">torch._dynamo.</span></span><span class="sig-name descname"><span class="pre">optimize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'inductor'</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">nopython</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">guard_export_fn</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">disable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/_dynamo/eval_frame.html#optimize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch._dynamo.optimize" title="Permalink to this definition">¶</a></dt>
<dd><p>The main entrypoint of TorchDynamo. Do graph capture and call
backend() to optimize extracted graphs.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>backend</strong> – One of the two things:
- Either, a function/callable taking a torch.fx.GraphModule and
example_inputs and returning a python callable that runs the
graph faster.
One can also provide additional context for the backend, like
torch.jit.fuser(“fuser2”), by setting the backend_ctx_ctor attribute.
See AOTAutogradMemoryEfficientFusionWithContext for the usage.
- Or, a string backend name in <cite>torch._dynamo.list_backends()</cite></p></li>
<li><p><strong>nopython</strong> – If True, graph breaks will be errors and there will
be a single whole-program graph.</p></li>
<li><p><strong>disable</strong> – If True, turn this decorator into a no-op</p></li>
<li><p><strong>dynamic</strong> – If True, turn on dynamic shapes support</p></li>
</ul>
</dd>
</dl>
<p>Example Usage:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@torch</span><span class="o">.</span><span class="n">_dynamo</span><span class="o">.</span><span class="n">optimize</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">toy_example</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="o">...</span>
</pre></div>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch._dynamo.optimize_assert">
<span class="sig-prename descclassname"><span class="pre">torch._dynamo.</span></span><span class="sig-name descname"><span class="pre">optimize_assert</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend</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">guard_export_fn</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</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/_dynamo/eval_frame.html#optimize_assert"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch._dynamo.optimize_assert" title="Permalink to this definition">¶</a></dt>
<dd><p>The same as <cite>torch._dynamo.optimize(backend, nopython=True)</cite></p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch._dynamo.run">
<span class="sig-prename descclassname"><span class="pre">torch._dynamo.</span></span><span class="sig-name descname"><span class="pre">run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</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/_dynamo/eval_frame.html#run"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch._dynamo.run" title="Permalink to this definition">¶</a></dt>
<dd><p>Don’t do any dynamic compiles, just use prior optimizations</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch._dynamo.disable">
<span class="sig-prename descclassname"><span class="pre">torch._dynamo.</span></span><span class="sig-name descname"><span class="pre">disable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</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/_dynamo/eval_frame.html#disable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch._dynamo.disable" title="Permalink to this definition">¶</a></dt>
<dd><p>Decorator and context manager to disable TorchDynamo</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch._dynamo.reset">
<span class="sig-prename descclassname"><span class="pre">torch._dynamo.</span></span><span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/_dynamo.html#reset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch._dynamo.reset" title="Permalink to this definition">¶</a></dt>
<dd><p>Clear all compile caches and restore initial state</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch._dynamo.list_backends">
<span class="sig-prename descclassname"><span class="pre">torch._dynamo.</span></span><span class="sig-name descname"><span class="pre">list_backends</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/_dynamo.html#list_backends"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch._dynamo.list_backends" title="Permalink to this definition">¶</a></dt>
<dd><p>Return valid strings that can be passed to:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@torch</span><span class="o">.</span><span class="n">_dynamo</span><span class="o">.</span><span class="n">optimize</span><span class="p">(</span><span class="o"><</span><span class="n">backend</span><span class="o">></span><span class="p">)</span>
<span class="k">def</span> <span class="nf">foo</span><span class="p">(</span><span class="o">...</span><span class="p">):</span>
<span class="o">....</span>
</pre></div>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch._dynamo.skip">
<span class="sig-prename descclassname"><span class="pre">torch._dynamo.</span></span><span class="sig-name descname"><span class="pre">skip</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</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/_dynamo/eval_frame.html#skip"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch._dynamo.skip" title="Permalink to this definition">¶</a></dt>
<dd><p>Skip frames associated with the function code, but still process recursively
invoked frames</p>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="torch._dynamo.OptimizedModule">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch._dynamo.</span></span><span class="sig-name descname"><span class="pre">OptimizedModule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamo_ctx</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/_dynamo/eval_frame.html#OptimizedModule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch._dynamo.OptimizedModule" title="Permalink to this definition">¶</a></dt>
<dd><p>Wraps the original nn.Module object and later patches its
forward method to optimized self.forward method.</p>
</dd></dl>
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