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<p class="caption"><span class="caption-text">Notes</span></p>
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<li class="toctree-l1"><a class="reference internal" href="notes/autograd.html">Autograd mechanics</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/broadcasting.html">Broadcasting semantics</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/cpu_threading_torchscript_inference.html">CPU threading and TorchScript inference</a></li>
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<li class="toctree-l1"><a class="reference internal" href="notes/distributed_autograd.html">Distributed Autograd Design</a></li>
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<li class="toctree-l1"><a class="reference internal" href="notes/large_scale_deployments.html">Features for large-scale deployments</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/multiprocessing.html">Multiprocessing best practices</a></li>
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<div class="section" id="torchscript">
<h1>TorchScript<a class="headerlink" href="#torchscript" title="Permalink to this headline">¶</a></h1>
<div class="toctree-wrapper compound">
</div>
<div class="contents local topic" id="contents">
<ul class="simple">
<li><p><a class="reference internal" href="#creating-torchscript-code" id="id10">Creating TorchScript Code</a></p></li>
<li><p><a class="reference internal" href="#mixing-tracing-and-scripting" id="id11">Mixing Tracing and Scripting</a></p></li>
<li><p><a class="reference internal" href="#migrating-to-pytorch-1-2-recursive-scripting-api" id="id12">Migrating to PyTorch 1.2 Recursive Scripting API</a></p>
<ul>
<li><p><a class="reference internal" href="#modules" id="id13">Modules</a></p></li>
<li><p><a class="reference internal" href="#functions" id="id14">Functions</a></p></li>
<li><p><a class="reference internal" href="#torchscript-classes" id="id15">TorchScript Classes</a></p></li>
<li><p><a class="reference internal" href="#attributes" id="id16">Attributes</a></p>
<ul>
<li><p><a class="reference internal" href="#python-2" id="id17">Python 2</a></p></li>
</ul>
</li>
<li><p><a class="reference internal" href="#constants" id="id18">Constants</a></p></li>
<li><p><a class="reference internal" href="#variables" id="id19">Variables</a></p></li>
</ul>
</li>
<li><p><a class="reference internal" href="#torchscript-language-reference" id="id20">TorchScript Language Reference</a></p>
<ul>
<li><p><a class="reference internal" href="#supported-type" id="id21">Types</a></p>
<ul>
<li><p><a class="reference internal" href="#default-types" id="id22">Default Types</a></p></li>
<li><p><a class="reference internal" href="#optional-type-refinement" id="id23">Optional Type Refinement</a></p></li>
<li><p><a class="reference internal" href="#id3" id="id24">TorchScript Classes</a></p></li>
<li><p><a class="reference internal" href="#named-tuples" id="id25">Named Tuples</a></p></li>
</ul>
</li>
<li><p><a class="reference internal" href="#expressions" id="id26">Expressions</a></p>
<ul>
<li><p><a class="reference internal" href="#literals" id="id27">Literals</a></p>
<ul>
<li><p><a class="reference internal" href="#list-construction" id="id28">List Construction</a></p></li>
<li><p><a class="reference internal" href="#tuple-construction" id="id29">Tuple Construction</a></p></li>
<li><p><a class="reference internal" href="#dict-construction" id="id30">Dict Construction</a></p></li>
</ul>
</li>
<li><p><a class="reference internal" href="#id5" id="id31">Variables</a></p></li>
<li><p><a class="reference internal" href="#arithmetic-operators" id="id32">Arithmetic Operators</a></p></li>
<li><p><a class="reference internal" href="#comparison-operators" id="id33">Comparison Operators</a></p></li>
<li><p><a class="reference internal" href="#logical-operators" id="id34">Logical Operators</a></p></li>
<li><p><a class="reference internal" href="#subscripts-and-slicing" id="id35">Subscripts and Slicing</a></p></li>
<li><p><a class="reference internal" href="#function-calls" id="id36">Function Calls</a></p></li>
<li><p><a class="reference internal" href="#method-calls" id="id37">Method Calls</a></p></li>
<li><p><a class="reference internal" href="#ternary-expressions" id="id38">Ternary Expressions</a></p></li>
<li><p><a class="reference internal" href="#casts" id="id39">Casts</a></p></li>
<li><p><a class="reference internal" href="#accessing-module-parameters" id="id40">Accessing Module Parameters</a></p></li>
</ul>
</li>
<li><p><a class="reference internal" href="#statements" id="id41">Statements</a></p>
<ul>
<li><p><a class="reference internal" href="#simple-assignments" id="id42">Simple Assignments</a></p></li>
<li><p><a class="reference internal" href="#pattern-matching-assignments" id="id43">Pattern Matching Assignments</a></p></li>
<li><p><a class="reference internal" href="#print-statements" id="id44">Print Statements</a></p></li>
<li><p><a class="reference internal" href="#if-statements" id="id45">If Statements</a></p></li>
<li><p><a class="reference internal" href="#while-loops" id="id46">While Loops</a></p></li>
<li><p><a class="reference internal" href="#for-loops-with-range" id="id47">For loops with range</a></p></li>
<li><p><a class="reference internal" href="#for-loops-over-tuples" id="id48">For loops over tuples</a></p></li>
<li><p><a class="reference internal" href="#for-loops-over-constant-nn-modulelist" id="id49">For loops over constant nn.ModuleList</a></p></li>
<li><p><a class="reference internal" href="#break-and-continue" id="id50">Break and Continue</a></p></li>
<li><p><a class="reference internal" href="#return" id="id51">Return</a></p></li>
</ul>
</li>
<li><p><a class="reference internal" href="#variable-resolution" id="id52">Variable Resolution</a></p></li>
<li><p><a class="reference internal" href="#use-of-python-values" id="id53">Use of Python Values</a></p>
<ul>
<li><p><a class="reference internal" href="#id6" id="id54">Functions</a></p></li>
<li><p><a class="reference internal" href="#attribute-lookup-on-python-modules" id="id55">Attribute Lookup On Python Modules</a></p></li>
<li><p><a class="reference internal" href="#python-defined-constants" id="id56">Python-defined Constants</a></p></li>
<li><p><a class="reference internal" href="#module-attributes" id="id57">Module Attributes</a></p></li>
</ul>
</li>
<li><p><a class="reference internal" href="#debugging" id="id58">Debugging</a></p>
<ul>
<li><p><a class="reference internal" href="#disable-jit-for-debugging" id="id59">Disable JIT for Debugging</a></p></li>
<li><p><a class="reference internal" href="#inspecting-code" id="id60">Inspecting Code</a></p></li>
<li><p><a class="reference internal" href="#interpreting-graphs" id="id61">Interpreting Graphs</a></p></li>
<li><p><a class="reference internal" href="#tracing-edge-cases" id="id62">Tracing Edge Cases</a></p></li>
<li><p><a class="reference internal" href="#automatic-trace-checking" id="id63">Automatic Trace Checking</a></p></li>
<li><p><a class="reference internal" href="#tracer-warnings" id="id64">Tracer Warnings</a></p></li>
</ul>
</li>
<li><p><a class="reference internal" href="#builtin-functions" id="id65">Builtin Functions</a></p></li>
</ul>
</li>
<li><p><a class="reference internal" href="#frequently-asked-questions" id="id66">Frequently Asked Questions</a></p></li>
</ul>
</div>
<span class="target" id="module-torch.jit"></span><p>TorchScript is a way to create serializable and optimizable models from PyTorch code.
Any TorchScript program can be saved from a Python
process and loaded in a process where there is no Python dependency.</p>
<p>We provide tools to incrementally transition a model from a pure Python program
to a TorchScript program that can be run independently from Python, such as in a standalone C++ program.
This makes it possible to train models in PyTorch using familiar tools in Python and then export
the model via TorchScript to a production environment where Python programs may be disadvantageous
for performance and multi-threading reasons.</p>
<p>For a gentle introduction to TorchScript, see the <a class="reference external" href="https://pytorch.org/tutorials/beginner/Intro_to_TorchScript_tutorial.html">Introduction to TorchScript</a> tutorial.</p>
<p>For an end-to-end example of converting a PyTorch model to TorchScript and running it in C++, see the
<a class="reference external" href="https://pytorch.org/tutorials/advanced/cpp_export.html">Loading a PyTorch Model in C++</a> tutorial.</p>
<div class="section" id="creating-torchscript-code">
<h2><a class="toc-backref" href="#id10">Creating TorchScript Code</a><a class="headerlink" href="#creating-torchscript-code" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torch.jit.ScriptModule">
<em class="property">class </em><code class="sig-prename descclassname">torch.jit.</code><code class="sig-name descname">ScriptModule</code><a class="reference internal" href="_modules/torch/jit.html#ScriptModule"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.jit.ScriptModule" title="Permalink to this definition">¶</a></dt>
<dd><dl class="method">
<dt id="torch.jit.ScriptModule.code">
<em class="property">property </em><code class="sig-name descname">code</code><a class="headerlink" href="#torch.jit.ScriptModule.code" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a pretty-printed representation (as valid Python syntax) of
the internal graph for the <code class="docutils literal notranslate"><span class="pre">forward</span></code> method. See <a class="reference internal" href="#inspecting-code">Inspecting Code</a>
for details.</p>
</dd></dl>
<dl class="method">
<dt id="torch.jit.ScriptModule.graph">
<em class="property">property </em><code class="sig-name descname">graph</code><a class="headerlink" href="#torch.jit.ScriptModule.graph" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a string representation of the internal graph for the
<code class="docutils literal notranslate"><span class="pre">forward</span></code> method. See <a class="reference internal" href="#interpreting-graphs">Interpreting Graphs</a> for details.</p>
</dd></dl>
<dl class="method">
<dt id="torch.jit.ScriptModule.save">
<code class="sig-name descname">save</code><span class="sig-paren">(</span><em class="sig-param">f</em>, <em class="sig-param">_extra_files=ExtraFilesMap{}</em><span class="sig-paren">)</span><a class="headerlink" href="#torch.jit.ScriptModule.save" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="#torch.jit.save" title="torch.jit.save"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.jit.save</span></code></a> for details.</p>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="torch.jit.ScriptFunction">
<em class="property">class </em><code class="sig-prename descclassname">torch.jit.</code><code class="sig-name descname">ScriptFunction</code><a class="headerlink" href="#torch.jit.ScriptFunction" title="Permalink to this definition">¶</a></dt>
<dd><p>Functionally equivalent to a <a class="reference internal" href="#torch.jit.ScriptModule" title="torch.jit.ScriptModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptModule</span></code></a>, but represents a single
function and does not have any attributes or Parameters.</p>
</dd></dl>
<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><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/jit.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" 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" 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="#torchscript-language-reference">TorchScript Language Reference</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="#torchscript-class">TorchScript Classes</a> and functions.</p>
<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" 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" 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="#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" title="torch.jit.ignore"><code class="xref py py-func docutils literal notranslate"><span class="pre">@torch.jit.ignore</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>
</div>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torch.jit.trace">
<code class="sig-prename descclassname">torch.jit.</code><code class="sig-name descname">trace</code><span class="sig-paren">(</span><em class="sig-param">func</em>, <em class="sig-param">example_inputs</em>, <em class="sig-param">optimize=None</em>, <em class="sig-param">check_trace=True</em>, <em class="sig-param">check_inputs=None</em>, <em class="sig-param">check_tolerance=1e-5</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/jit.html#trace"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.jit.trace" title="Permalink to this definition">¶</a></dt>
<dd><p>Trace a function and return an executable or <a class="reference internal" href="#torch.jit.ScriptFunction" title="torch.jit.ScriptFunction"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptFunction</span></code></a>
that will be optimized using just-in-time compilation. Tracing is ideal for
code that operates only on <code class="docutils literal notranslate"><span class="pre">Tensor</span></code>s and lists, dictionaries, and tuples of <code class="docutils literal notranslate"><span class="pre">Tensor</span></code>s.</p>
<p>Using <code class="docutils literal notranslate"><span class="pre">torch.jit.trace</span></code> and <a class="reference internal" href="#torch.jit.trace_module" title="torch.jit.trace_module"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.jit.trace_module</span></code></a>, you can turn an existing module or Python
function into a TorchScript <a class="reference internal" href="#torch.jit.ScriptFunction" title="torch.jit.ScriptFunction"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptFunction</span></code></a> or <a class="reference internal" href="#torch.jit.ScriptModule" title="torch.jit.ScriptModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptModule</span></code></a>. You must provide example inputs,
and we run the function, recording the operations performed on all the tensors.</p>
<ul class="simple">
<li><p>The resulting recording of a standalone function produces <a class="reference internal" href="#torch.jit.ScriptFunction" title="torch.jit.ScriptFunction"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptFunction</span></code></a>.</p></li>
<li><p>The resulting recording of <code class="docutils literal notranslate"><span class="pre">forward</span></code> function of <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> or <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> produces <a class="reference internal" href="#torch.jit.ScriptModule" title="torch.jit.ScriptModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptModule</span></code></a>.</p></li>
</ul>
<p>This module also contains any parameters that the original
module had as well.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>Tracing only correctly records functions and modules which are not data
dependent (e.g., do not have conditionals on data in tensors) and do not have
any untracked external dependencies (e.g., perform input/output or
access global variables). Tracing only records operations done when the given
function is run on the given
tensors. Therefore, the returned <a class="reference internal" href="#torch.jit.ScriptModule" title="torch.jit.ScriptModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptModule</span></code></a> will always run the same traced
graph on any input. This has some important implications when your module is
expected to run different sets of operations, depending on the input and/or the
module state. For example,</p>
<ul class="simple">
<li><p>Tracing will not record any control-flow like if-statements or loops.
When this control-flow is constant across your module, this is fine and it often
inlines the control-flow decisions. But sometimes the control-flow is actually part
of the model itself. For instance, a recurrent network is a loop over
the (possibly dynamic) length of an input sequence.</p></li>
<li><p>In the returned <a class="reference internal" href="#torch.jit.ScriptModule" title="torch.jit.ScriptModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptModule</span></code></a>, operations that have different
behaviors in <code class="docutils literal notranslate"><span class="pre">training</span></code> and <code class="docutils literal notranslate"><span class="pre">eval</span></code> modes will always behave as if it
is in the mode it was in during tracing, no matter which mode the
<a class="reference internal" href="#torch.jit.ScriptModule" title="torch.jit.ScriptModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptModule</span></code></a> is in.</p></li>
</ul>
<p>In cases like these, tracing would not be appropriate and <a class="reference internal" href="#torch.jit.script" title="torch.jit.script"><code class="xref py py-func docutils literal notranslate"><span class="pre">scripting</span></code></a> is a better
choice. If you trace such models, you may silently get
incorrect results on subsequent invocations of the model. The tracer
will try to emit warnings when doing something that may cause an
incorrect trace to be produced.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>func</strong> (<em>callable</em><em> or </em><a class="reference internal" href="nn.html#torch.nn.Module" title="torch.nn.Module"><em>torch.nn.Module</em></a>) – A Python function or <code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code>
that will be run with <code class="docutils literal notranslate"><span class="pre">example_inputs</span></code>.
arguments and returns to <code class="docutils literal notranslate"><span class="pre">func</span></code> must be tensors
or (possibly nested) tuples that
contain tensors. When a module is passed to
<a class="reference internal" href="#torch.jit.trace" title="torch.jit.trace"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.jit.trace</span></code></a>, only the
<code class="docutils literal notranslate"><span class="pre">forward</span></code> method is run and traced
(see <a class="reference internal" href="#torch.jit.trace_module" title="torch.jit.trace_module"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.jit.trace</span></code></a> for details).</p></li>
<li><p><strong>example_inputs</strong> (<em>tuple</em>) – A tuple of example inputs that will be passed to the function
while tracing. The resulting trace can be run with
inputs of different types and shapes assuming the traced operations
support those types and shapes. <code class="docutils literal notranslate"><span class="pre">example_inputs</span></code> may also be a single
Tensor in which case it is automatically wrapped in a tuple.</p></li>
</ul>
</dd>
<dt class="field-even">Keyword Arguments</dt>
<dd class="field-even"><ul class="simple">
<li><p><strong>check_trace</strong> (<code class="docutils literal notranslate"><span class="pre">bool</span></code>, optional) – Check if the same inputs run through
traced code produce the same outputs. Default: <code class="docutils literal notranslate"><span class="pre">True</span></code>. You might want
to disable this if, for example, your network contains non-
deterministic ops or if you are sure that the network is correct despite
a checker failure.</p></li>
<li><p><strong>check_inputs</strong> (<em>list of tuples</em><em>, </em><em>optional</em>) – A list of tuples of input arguments that should be used
to check the trace against what is expected. Each tuple
is equivalent to a set of input arguments that would
be specified in <code class="docutils literal notranslate"><span class="pre">example_inputs</span></code>. For best results, pass in a
set of checking inputs representative of the space of
shapes and types of inputs you expect the network to see.
If not specified, the original <code class="docutils literal notranslate"><span class="pre">example_inputs</span></code> are used for checking</p></li>
<li><p><strong>check_tolerance</strong> (<em>python:float</em><em>, </em><em>optional</em>) – Floating-point comparison tolerance to use in the checker procedure.
This can be used to relax the checker strictness in the event that
results diverge numerically for a known reason, such as operator fusion.</p></li>
</ul>
</dd>
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>If <code class="docutils literal notranslate"><span class="pre">callable</span></code> is <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> or <code class="docutils literal notranslate"><span class="pre">forward</span></code> of <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code>, <code class="docutils literal notranslate"><span class="pre">trace</span></code> returns
a <a class="reference internal" href="#torch.jit.ScriptModule" title="torch.jit.ScriptModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptModule</span></code></a> object with a single <code class="docutils literal notranslate"><span class="pre">forward</span></code> method containing the traced code.
The returned <a class="reference internal" href="#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">callable</span></code> is a standalone function, <code class="docutils literal notranslate"><span class="pre">trace</span></code> returns <a class="reference internal" href="#torch.jit.ScriptFunction" title="torch.jit.ScriptFunction"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptFunction</span></code></a></p>
</dd>
</dl>
<p>Example (tracing 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="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">return</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span>
<span class="c1"># Run `foo` with the provided inputs and record the tensor operations</span>
<span class="n">traced_foo</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">foo</span><span class="p">,</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">3</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">3</span><span class="p">)))</span>
<span class="c1"># `traced_foo` can now be run with the TorchScript interpreter or saved</span>
<span class="c1"># and loaded in a Python-free environment</span>
</pre></div>
</div>
<p>Example (tracing an existing 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">Net</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">Net</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="bp">self</span><span class="o">.</span><span class="n">conv</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</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">conv</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">n</span> <span class="o">=</span> <span class="n">Net</span><span class="p">()</span>
<span class="n">example_weight</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="n">example_forward_input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="c1"># Trace a specific method and construct `ScriptModule` with</span>
<span class="c1"># a single `forward` method</span>
<span class="n">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">trace</span><span class="p">(</span><span class="n">n</span><span class="o">.</span><span class="n">forward</span><span class="p">,</span> <span class="n">example_forward_input</span><span class="p">)</span>
<span class="c1"># Trace a module (implicitly traces `forward`) and construct a</span>
<span class="c1"># `ScriptModule` with a single `forward` method</span>
<span class="n">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">trace</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">example_forward_input</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.jit.trace_module">
<code class="sig-prename descclassname">torch.jit.</code><code class="sig-name descname">trace_module</code><span class="sig-paren">(</span><em class="sig-param">mod</em>, <em class="sig-param">inputs</em>, <em class="sig-param">optimize=None</em>, <em class="sig-param">check_trace=True</em>, <em class="sig-param">check_inputs=None</em>, <em class="sig-param">check_tolerance=1e-5</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/jit.html#trace_module"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.jit.trace_module" title="Permalink to this definition">¶</a></dt>
<dd><p>Trace a module and return an executable <a class="reference internal" href="#torch.jit.ScriptModule" title="torch.jit.ScriptModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptModule</span></code></a> that will be optimized
using just-in-time compilation. When a module is passed to <a class="reference internal" href="#torch.jit.trace" title="torch.jit.trace"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.jit.trace</span></code></a>, only
the <code class="docutils literal notranslate"><span class="pre">forward</span></code> method is run and traced. With <code class="docutils literal notranslate"><span class="pre">trace_module</span></code>, you can specify a dictionary of
method names to example inputs to trace (see the <code class="docutils literal notranslate"><span class="pre">example_inputs</span></code>) argument below.</p>
<p>See <a class="reference internal" href="#torch.jit.trace" title="torch.jit.trace"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.jit.trace</span></code></a> for more information on tracing.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>mod</strong> (<a class="reference internal" href="nn.html#torch.nn.Module" title="torch.nn.Module"><em>torch.nn.Module</em></a>) – A <code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code> containing methods whose names are
specified in <code class="docutils literal notranslate"><span class="pre">example_inputs</span></code>. The given methods will be compiled
as a part of a single <cite>ScriptModule</cite>.</p></li>
<li><p><strong>example_inputs</strong> (<em>dict</em>) – A dict containing sample inputs indexed by method names in <code class="docutils literal notranslate"><span class="pre">mod</span></code>.
The inputs will be passed to methods whose names correspond to inputs’
keys while tracing.
<code class="docutils literal notranslate"><span class="pre">{</span> <span class="pre">'forward'</span> <span class="pre">:</span> <span class="pre">example_forward_input,</span> <span class="pre">'method2':</span> <span class="pre">example_method2_input}</span></code></p></li>
</ul>
</dd>
<dt class="field-even">Keyword Arguments</dt>
<dd class="field-even"><ul class="simple">
<li><p><strong>check_trace</strong> (<code class="docutils literal notranslate"><span class="pre">bool</span></code>, optional) – Check if the same inputs run through
traced code produce the same outputs. Default: <code class="docutils literal notranslate"><span class="pre">True</span></code>. You might want
to disable this if, for example, your network contains non-
deterministic ops or if you are sure that the network is correct despite
a checker failure.</p></li>
<li><p><strong>check_inputs</strong> (<em>list of dicts</em><em>, </em><em>optional</em>) – A list of dicts of input arguments that should be used
to check the trace against what is expected. Each tuple
is equivalent to a set of input arguments that would
be specified in <code class="docutils literal notranslate"><span class="pre">example_inputs</span></code>. For best results, pass in a
set of checking inputs representative of the space of
shapes and types of inputs you expect the network to see.
If not specified, the original <code class="docutils literal notranslate"><span class="pre">example_inputs</span></code> are used for checking</p></li>
<li><p><strong>check_tolerance</strong> (<em>python:float</em><em>, </em><em>optional</em>) – Floating-point comparison tolerance to use in the checker procedure.
This can be used to relax the checker strictness in the event that
results diverge numerically for a known reason, such as operator fusion.</p></li>
</ul>
</dd>
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>A <a class="reference internal" href="#torch.jit.ScriptModule" title="torch.jit.ScriptModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptModule</span></code></a> object with a single <code class="docutils literal notranslate"><span class="pre">forward</span></code> method containing the traced code.
When <code class="docutils literal notranslate"><span class="pre">func</span></code> is a <code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code>, the returned <a class="reference internal" href="#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 <code class="docutils literal notranslate"><span class="pre">func</span></code>.</p>
</dd>
</dl>
<p>Example (tracing a module with multiple methods):</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">Net</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">Net</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="bp">self</span><span class="o">.</span><span class="n">conv</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</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">conv</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">weighted_kernel_sum</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">weight</span><span class="p">):</span>
<span class="k">return</span> <span class="n">weight</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv</span><span class="o">.</span><span class="n">weight</span>
<span class="n">n</span> <span class="o">=</span> <span class="n">Net</span><span class="p">()</span>
<span class="n">example_weight</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="n">example_forward_input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="c1"># Trace a specific method and construct `ScriptModule` with</span>
<span class="c1"># a single `forward` method</span>
<span class="n">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">trace</span><span class="p">(</span><span class="n">n</span><span class="o">.</span><span class="n">forward</span><span class="p">,</span> <span class="n">example_forward_input</span><span class="p">)</span>
<span class="c1"># Trace a module (implicitly traces `forward`) and construct a</span>
<span class="c1"># `ScriptModule` with a single `forward` method</span>
<span class="n">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">trace</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">example_forward_input</span><span class="p">)</span>
<span class="c1"># Trace specific methods on a module (specified in `inputs`), constructs</span>
<span class="c1"># a `ScriptModule` with `forward` and `weighted_kernel_sum` methods</span>
<span class="n">inputs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'forward'</span> <span class="p">:</span> <span class="n">example_forward_input</span><span class="p">,</span> <span class="s1">'weighted_kernel_sum'</span> <span class="p">:</span> <span class="n">example_weight</span><span class="p">}</span>
<span class="n">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">trace_module</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">inputs</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.jit.save">
<code class="sig-prename descclassname">torch.jit.</code><code class="sig-name descname">save</code><span class="sig-paren">(</span><em class="sig-param">m</em>, <em class="sig-param">f</em>, <em class="sig-param">_extra_files=ExtraFilesMap{}</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/jit.html#save"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.jit.save" title="Permalink to this definition">¶</a></dt>
<dd><p>Save an offline version of this module for use in a separate process. The saved
module serializes all of the methods, submodules, parameters, and attributes of this
module. It can be loaded into the C++ API using <code class="docutils literal notranslate"><span class="pre">torch::jit::load(filename)</span></code> or into the Python
API with <a class="reference internal" href="#torch.jit.load" title="torch.jit.load"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.jit.load</span></code></a>.</p>
<p>To be able to save a module, it must not make any calls to native Python functions.
This means that all submodules must be subclasses of <code class="docutils literal notranslate"><span class="pre">torch.jit.ScriptModule</span></code> as well.</p>
<div class="admonition danger">
<p class="admonition-title">Danger</p>
<p>All modules, no matter their device, are always loaded onto the CPU during loading.
This is different from <a class="reference internal" href="#torch.jit.load" title="torch.jit.load"><code class="xref py py-func docutils literal notranslate"><span class="pre">load</span></code></a>’s semantics and may change in the future.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>m</strong> – A ScriptModule to save.</p></li>
<li><p><strong>f</strong> – A file-like object (has to implement write and flush) or a string
containing a file name.</p></li>
<li><p><strong>_extra_files</strong> – Map from filename to contents which will be stored as part of ‘f’.</p></li>
</ul>
</dd>
</dl>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>If you are using Python 2, <code class="docutils literal notranslate"><span class="pre">torch.jit.save</span></code> does NOT support <code class="docutils literal notranslate"><span class="pre">StringIO.StringIO</span></code>
as a valid file-like object. This is because the write method should return
the number of bytes written; <code class="docutils literal notranslate"><span class="pre">StringIO.write()</span></code> does not do this.</p>
<p>Please use something like <code class="docutils literal notranslate"><span class="pre">io.BytesIO</span></code> instead.</p>
</div>
<p>Example:</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">io</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="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">x</span> <span class="o">+</span> <span class="mi">10</span>
<span class="n">m</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="c1"># Save to file</span>
<span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="s1">'scriptmodule.pt'</span><span class="p">)</span>
<span class="c1"># This line is equivalent to the previous</span>
<span class="n">m</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">"scriptmodule.pt"</span><span class="p">)</span>
<span class="c1"># Save to io.BytesIO buffer</span>
<span class="n">buffer</span> <span class="o">=</span> <span class="n">io</span><span class="o">.</span><span class="n">BytesIO</span><span class="p">()</span>
<span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">buffer</span><span class="p">)</span>
<span class="c1"># Save with extra files</span>
<span class="n">extra_files</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">ExtraFilesMap</span><span class="p">()</span>
<span class="n">extra_files</span><span class="p">[</span><span class="s1">'foo.txt'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'bar'</span>
<span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="s1">'scriptmodule.pt'</span><span class="p">,</span> <span class="n">_extra_files</span><span class="o">=</span><span class="n">extra_files</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.jit.load">
<code class="sig-prename descclassname">torch.jit.</code><code class="sig-name descname">load</code><span class="sig-paren">(</span><em class="sig-param">f</em>, <em class="sig-param">map_location=None</em>, <em class="sig-param">_extra_files=ExtraFilesMap{}</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/jit.html#load"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.jit.load" title="Permalink to this definition">¶</a></dt>
<dd><p>Load a <a class="reference internal" href="#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" title="torch.jit.ScriptFunction"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptFunction</span></code></a> previously
saved with <a class="reference internal" href="#torch.jit.save" title="torch.jit.save"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.jit.save</span></code></a></p>
<p>All previously saved modules, no matter their device, are first loaded onto CPU,
and then are moved to the devices they were saved from. If this fails (e.g. because
the run time system doesn’t have certain devices), an exception is raised.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>f</strong> – a file-like object (has to implement read, readline, tell, and seek),
or a string containing a file name</p></li>
<li><p><strong>map_location</strong> (<em>string</em><em> or </em><a class="reference internal" href="tensor_attributes.html#torch.torch.device" title="torch.torch.device"><em>torch.device</em></a>) – A simplified version of <code class="docutils literal notranslate"><span class="pre">map_location</span></code> in
<code class="docutils literal notranslate"><span class="pre">torch.save</span></code> used to dynamically remap storages to an alternative set of devices.</p></li>
<li><p><strong>_extra_files</strong> (<em>dictionary of filename to content</em>) – The extra
filenames given in the map would be loaded and their content
would be stored in the provided map.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A <a class="reference internal" href="#torch.jit.ScriptModule" title="torch.jit.ScriptModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptModule</span></code></a> object.</p>
</dd>
</dl>
<p>Example:</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">io</span>
<span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">'scriptmodule.pt'</span><span class="p">)</span>
<span class="c1"># Load ScriptModule from io.BytesIO object</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">'scriptmodule.pt'</span><span class="p">,</span> <span class="s1">'rb'</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="n">buffer</span> <span class="o">=</span> <span class="n">io</span><span class="o">.</span><span class="n">BytesIO</span><span class="p">(</span><span class="n">f</span><span class="o">.</span><span class="n">read</span><span class="p">())</span>
<span class="c1"># Load all tensors to the original device</span>
<span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">buffer</span><span class="p">)</span>
<span class="c1"># Load all tensors onto CPU, using a device</span>
<span class="n">buffer</span><span class="o">.</span><span class="n">seek</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">buffer</span><span class="p">,</span> <span class="n">map_location</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">'cpu'</span><span class="p">))</span>
<span class="c1"># Load all tensors onto CPU, using a string</span>
<span class="n">buffer</span><span class="o">.</span><span class="n">seek</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">buffer</span><span class="p">,</span> <span class="n">map_location</span><span class="o">=</span><span class="s1">'cpu'</span><span class="p">)</span>
<span class="c1"># Load with extra files.</span>
<span class="n">extra_files</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">ExtraFilesMap</span><span class="p">()</span>
<span class="n">extra_files</span><span class="p">[</span><span class="s1">'foo.txt'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'bar'</span>
<span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">'scriptmodule.pt'</span><span class="p">,</span> <span class="n">_extra_files</span><span class="o">=</span><span class="n">extra_files</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">extra_files</span><span class="p">[</span><span class="s1">'foo.txt'</span><span class="p">])</span>
</pre></div>
</div>
</dd></dl>
</div>
<div class="section" id="mixing-tracing-and-scripting">
<h2><a class="toc-backref" href="#id11">Mixing Tracing and Scripting</a><a class="headerlink" href="#mixing-tracing-and-scripting" title="Permalink to this headline">¶</a></h2>
<p>In many cases either tracing or scripting is an easier approach for converting a model to TorchScript.
Tracing and scripting can be composed to suit the particular requirements
of a part of a model.</p>
<p>Scripted functions can call traced functions. This is particularly useful when you need
to use control-flow around a simple feed-forward model. For instance the beam search
of a sequence to sequence model will typically be written in script but can call an
encoder module generated using tracing.</p>
<p>Example (calling a traced function in script):</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">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">return</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span>
<span class="n">traced_foo</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">foo</span><span class="p">,</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">3</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">3</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">script</span>
<span class="k">def</span> <span class="nf">bar</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="n">traced_foo</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
</pre></div>
</div>
<p>Traced functions can call script functions. This is useful when a small part of
a model requires some control-flow even though most of the model is just a feed-forward
network. Control-flow inside of a script function called by a traced function is
preserved correctly.</p>
<p>Example (calling a script function in a traced 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="k">def</span> <span class="nf">bar</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">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="o">+</span> <span class="n">z</span>
<span class="n">traced_bar</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">bar</span><span class="p">,</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">3</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">3</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">3</span><span class="p">)))</span>
</pre></div>
</div>
<p>This composition also works for <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code>s as well, where it can be used to generate
a submodule using tracing that can be called from the methods of a script module.</p>
<p>Example (using a traced module):</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">torchvision</span>
<span class="k">class</span> <span class="nc">MyScriptModule</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="nb">super</span><span class="p">(</span><span class="n">MyScriptModule</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="bp">self</span><span class="o">.</span><span class="n">means</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">103.939</span><span class="p">,</span> <span class="mf">116.779</span><span class="p">,</span> <span class="mf">123.68</span><span class="p">])</span>
<span class="o">.</span><span class="n">resize_</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">resnet</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">torchvision</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">resnet18</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">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</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">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">resnet</span><span class="p">(</span><span class="nb">input</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">means</span><span class="p">)</span>
<span class="n">my_script_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">MyScriptModule</span><span class="p">())</span>
</pre></div>
</div>
</div>
<div class="section" id="migrating-to-pytorch-1-2-recursive-scripting-api">
<h2><a class="toc-backref" href="#id12">Migrating to PyTorch 1.2 Recursive Scripting API</a><a class="headerlink" href="#migrating-to-pytorch-1-2-recursive-scripting-api" title="Permalink to this headline">¶</a></h2>
<p>This section details the changes to TorchScript in PyTorch 1.2. If you are new to TorchScript you can
skip this section. There are two main changes to the TorchScript API with PyTorch 1.2.</p>
<p>1. <a class="reference internal" href="#torch.jit.script" title="torch.jit.script"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.jit.script</span></code></a> will now attempt to recursively compile functions,
methods, and classes that it encounters. Once you call <code class="docutils literal notranslate"><span class="pre">torch.jit.script</span></code>,
compilation is “opt-out”, rather than “opt-in”.</p>
<p>2. <code class="docutils literal notranslate"><span class="pre">torch.jit.script(nn_module_instance)</span></code> is now the preferred way to create
<a class="reference internal" href="#torch.jit.ScriptModule" title="torch.jit.ScriptModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptModule</span></code></a>s, instead of inheriting from <code class="docutils literal notranslate"><span class="pre">torch.jit.ScriptModule</span></code>.
These changes combine to provide a simpler, easier-to-use API for converting
your <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code>s into <a class="reference internal" href="#torch.jit.ScriptModule" title="torch.jit.ScriptModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptModule</span></code></a>s, ready to be optimized and executed in a
non-Python environment.</p>
<p>The new usage looks like this:</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">Model</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">Model</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>