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<div class="section" id="torch-jit-script">
<h1>torch.jit.script<a class="headerlink" href="#torch-jit-script" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="torch.jit.script">
<code class="sig-prename descclassname">torch.jit.</code><code class="sig-name descname">script</code><span class="sig-paren">(</span><em class="sig-param">obj</em>, <em class="sig-param">optimize=None</em>, <em class="sig-param">_frames_up=0</em>, <em class="sig-param">_rcb=None</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_script.html#script"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.jit.script" title="Permalink to this definition">¶</a></dt>
<dd><p>Scripting a function or <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> will inspect the source code, compile
it as TorchScript code using the TorchScript compiler, and return a <a class="reference internal" href="torch.jit.ScriptModule.html#torch.jit.ScriptModule" title="torch.jit.ScriptModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptModule</span></code></a> or
<a class="reference internal" href="torch.jit.ScriptFunction.html#torch.jit.ScriptFunction" title="torch.jit.ScriptFunction"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptFunction</span></code></a>. TorchScript itself is a subset of the Python language, so not all
features in Python work, but we provide enough functionality to compute on
tensors and do control-dependent operations. For a complete guide, see the
<a class="reference internal" href="../jit_language_reference.html#language-reference"><span class="std std-ref">TorchScript Language Reference</span></a>.</p>
<p><code class="docutils literal notranslate"><span class="pre">torch.jit.script</span></code> can be used as a function for modules and functions, and as a decorator
<code class="docutils literal notranslate"><span class="pre">@torch.jit.script</span></code> for <a class="reference internal" href="../jit_language_reference.html#id2"><span class="std std-ref">TorchScript Classes</span></a> and functions.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>obj</strong> (callable, class, or <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code>) – The <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code>, function, or class type to
compile.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>If <code class="docutils literal notranslate"><span class="pre">obj</span></code> is <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code>, <code class="docutils literal notranslate"><span class="pre">script</span></code> returns
a <a class="reference internal" href="torch.jit.ScriptModule.html#torch.jit.ScriptModule" title="torch.jit.ScriptModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptModule</span></code></a> object. The returned <a class="reference internal" href="torch.jit.ScriptModule.html#torch.jit.ScriptModule" title="torch.jit.ScriptModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptModule</span></code></a> will
have the same set of sub-modules and parameters as the
original <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code>. If <code class="docutils literal notranslate"><span class="pre">obj</span></code> is a standalone function,
a <a class="reference internal" href="torch.jit.ScriptFunction.html#torch.jit.ScriptFunction" title="torch.jit.ScriptFunction"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptFunction</span></code></a> will be returned.</p>
</dd>
</dl>
<dl>
<dt><strong>Scripting a function</strong></dt><dd><p>The <code class="docutils literal notranslate"><span class="pre">@torch.jit.script</span></code> decorator will construct a <a class="reference internal" href="torch.jit.ScriptFunction.html#torch.jit.ScriptFunction" title="torch.jit.ScriptFunction"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptFunction</span></code></a>
by compiling the body of the function.</p>
<p>Example (scripting a function):</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="nd">@torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span>
<span class="k">def</span> <span class="nf">foo</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">if</span> <span class="n">x</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">></span> <span class="n">y</span><span class="o">.</span><span class="n">max</span><span class="p">():</span>
<span class="n">r</span> <span class="o">=</span> <span class="n">x</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">r</span> <span class="o">=</span> <span class="n">y</span>
<span class="k">return</span> <span class="n">r</span>
<span class="nb">print</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">foo</span><span class="p">))</span> <span class="c1"># torch.jit.ScriptFuncion</span>
<span class="c1"># See the compiled graph as Python code</span>
<span class="nb">print</span><span class="p">(</span><span class="n">foo</span><span class="o">.</span><span class="n">code</span><span class="p">)</span>
<span class="c1"># Call the function using the TorchScript interpreter</span>
<span class="n">foo</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
</pre></div>
</div>
</dd>
<dt><strong>Scripting an nn.Module</strong></dt><dd><p>Scripting an <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> by default will compile the <code class="docutils literal notranslate"><span class="pre">forward</span></code> method and recursively
compile any methods, submodules, and functions called by <code class="docutils literal notranslate"><span class="pre">forward</span></code>. If a <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> only uses
features supported in TorchScript, no changes to the original module code should be necessary. <code class="docutils literal notranslate"><span class="pre">script</span></code>
will construct <a class="reference internal" href="torch.jit.ScriptModule.html#torch.jit.ScriptModule" title="torch.jit.ScriptModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptModule</span></code></a> that has copies of the attributes, parameters, and methods of
the original module.</p>
<p>Example (scripting a simple module with a Parameter):</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="k">class</span> <span class="nc">MyModule</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">M</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">MyModule</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="c1"># This parameter will be copied to the new ScriptModule</span>
<span class="bp">self</span><span class="o">.</span><span class="n">weight</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">N</span><span class="p">,</span> <span class="n">M</span><span class="p">))</span>
<span class="c1"># When this submodule is used, it will be compiled</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="n">N</span><span class="p">,</span> <span class="n">M</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="nb">input</span><span class="p">):</span>
<span class="n">output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">mv</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
<span class="c1"># This calls the `forward` method of the `nn.Linear` module, which will</span>
<span class="c1"># cause the `self.linear` submodule to be compiled to a `ScriptModule` here</span>
<span class="n">output</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">output</span><span class="p">)</span>
<span class="k">return</span> <span class="n">output</span>
<span class="n">scripted_module</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">MyModule</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
</pre></div>
</div>
<p>Example (scripting a module with traced submodules):</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span>
<span class="k">class</span> <span class="nc">MyModule</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">MyModule</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="c1"># torch.jit.trace produces a ScriptModule's conv1 and conv2</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">16</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">16</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="nb">input</span><span class="p">):</span>
<span class="nb">input</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="nb">input</span><span class="p">))</span>
<span class="nb">input</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv2</span><span class="p">(</span><span class="nb">input</span><span class="p">))</span>
<span class="k">return</span> <span class="nb">input</span>
<span class="n">scripted_module</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">MyModule</span><span class="p">())</span>
</pre></div>
</div>
<p>To compile a method other than <code class="docutils literal notranslate"><span class="pre">forward</span></code> (and recursively compile anything it calls), add
the <a class="reference internal" href="../jit.html#torch.jit.export" title="torch.jit.export"><code class="xref py py-func docutils literal notranslate"><span class="pre">@torch.jit.export</span></code></a> decorator to the method. To opt out of compilation
use <a class="reference internal" href="torch.jit.ignore.html#torch.jit.ignore" title="torch.jit.ignore"><code class="xref py py-func docutils literal notranslate"><span class="pre">@torch.jit.ignore</span></code></a> or <a class="reference internal" href="torch.jit.unused.html#torch.jit.unused" title="torch.jit.unused"><code class="xref py py-func docutils literal notranslate"><span class="pre">@torch.jit.unused</span></code></a>.</p>
<p>Example (an exported and ignored method in a module):</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="k">class</span> <span class="nc">MyModule</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">MyModule</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="nd">@torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">export</span>
<span class="k">def</span> <span class="nf">some_entry_point</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
<span class="k">return</span> <span class="nb">input</span> <span class="o">+</span> <span class="mi">10</span>
<span class="nd">@torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">ignore</span>
<span class="k">def</span> <span class="nf">python_only_fn</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
<span class="c1"># This function won't be compiled, so any</span>
<span class="c1"># Python APIs can be used</span>
<span class="kn">import</span> <span class="nn">pdb</span>
<span class="n">pdb</span><span class="o">.</span><span class="n">set_trace</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="nb">input</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">training</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">python_only_fn</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
<span class="k">return</span> <span class="nb">input</span> <span class="o">*</span> <span class="mi">99</span>
<span class="n">scripted_module</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">MyModule</span><span class="p">())</span>
<span class="nb">print</span><span class="p">(</span><span class="n">scripted_module</span><span class="o">.</span><span class="n">some_entry_point</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">scripted_module</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)))</span>
</pre></div>
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