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<li class="toctree-l1"><a class="reference internal" href="notes/amp_examples.html">CUDA Automatic Mixed Precision examples</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/autograd.html">Autograd mechanics</a></li>
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<div class="section" id="torchscript">
<h1>TorchScript<a class="headerlink" href="#torchscript" title="Permalink to this heading">¶</a></h1>
<div class="toctree-wrapper compound">
</div>
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<ul>
<li class="toctree-l1"><a class="reference internal" href="jit_language_reference_v2.html">TorchScript Language Reference</a></li>
</ul>
</div>
<div class="contents local topic" id="contents">
<ul class="simple">
<li><p><a class="reference internal" href="#creating-torchscript-code" id="id4">Creating TorchScript Code</a></p></li>
<li><p><a class="reference internal" href="#mixing-tracing-and-scripting" id="id5">Mixing Tracing and Scripting</a></p></li>
<li><p><a class="reference internal" href="#torchscript-language" id="id6">TorchScript Language</a></p></li>
<li><p><a class="reference internal" href="#built-in-functions-and-modules" id="id7">Built-in Functions and Modules</a></p>
<ul>
<li><p><a class="reference internal" href="#pytorch-functions-and-modules" id="id8">PyTorch Functions and Modules</a></p></li>
<li><p><a class="reference internal" href="#python-functions-and-modules" id="id9">Python Functions and Modules</a></p></li>
<li><p><a class="reference internal" href="#python-language-reference-comparison" id="id10">Python Language Reference Comparison</a></p></li>
</ul>
</li>
<li><p><a class="reference internal" href="#debugging" id="id11">Debugging</a></p>
<ul>
<li><p><a class="reference internal" href="#disable-jit-for-debugging" id="id12">Disable JIT for Debugging</a></p></li>
<li><p><a class="reference internal" href="#inspecting-code" id="id13">Inspecting Code</a></p></li>
<li><p><a class="reference internal" href="#interpreting-graphs" id="id14">Interpreting Graphs</a></p></li>
<li><p><a class="reference internal" href="#tracer" id="id15">Tracer</a></p></li>
</ul>
</li>
<li><p><a class="reference internal" href="#frequently-asked-questions" id="id16">Frequently Asked Questions</a></p></li>
<li><p><a class="reference internal" href="#known-issues" id="id17">Known Issues</a></p></li>
<li><p><a class="reference internal" href="#appendix" id="id18">Appendix</a></p>
<ul>
<li><p><a class="reference internal" href="#migrating-to-pytorch-1-2-recursive-scripting-api" id="id19">Migrating to PyTorch 1.2 Recursive Scripting API</a></p></li>
<li><p><a class="reference internal" href="#fusion-backends" id="id20">Fusion Backends</a></p></li>
<li><p><a class="reference internal" href="#references" id="id21">References</a></p></li>
</ul>
</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="#id4">Creating TorchScript Code</a><a class="headerlink" href="#creating-torchscript-code" title="Permalink to this heading">¶</a></h2>
<table class="autosummary longtable docutils colwidths-auto align-default">
<tbody>
<tr class="row-odd"><td><p><p id="torch.jit.script"/><a class="reference internal" href="generated/torch.jit.script.html#torch.jit.script" title="torch.jit.script"><code class="xref py py-obj docutils literal notranslate"><span class="pre">script</span></code></a></p></td>
<td><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="generated/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="generated/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>.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.jit.trace"/><a class="reference internal" href="generated/torch.jit.trace.html#torch.jit.trace" title="torch.jit.trace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">trace</span></code></a></p></td>
<td><p>Trace a function and return an executable or <a class="reference internal" href="generated/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> that will be optimized using just-in-time compilation.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.jit.script_if_tracing"/><a class="reference internal" href="generated/torch.jit.script_if_tracing.html#torch.jit.script_if_tracing" title="torch.jit.script_if_tracing"><code class="xref py py-obj docutils literal notranslate"><span class="pre">script_if_tracing</span></code></a></p></td>
<td><p>Compiles <code class="docutils literal notranslate"><span class="pre">fn</span></code> when it is first called during tracing.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.jit.trace_module"/><a class="reference internal" href="generated/torch.jit.trace_module.html#torch.jit.trace_module" title="torch.jit.trace_module"><code class="xref py py-obj docutils literal notranslate"><span class="pre">trace_module</span></code></a></p></td>
<td><p>Trace a module and return an executable <a class="reference internal" href="generated/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 will be optimized using just-in-time compilation.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.jit.fork"/><a class="reference internal" href="generated/torch.jit.fork.html#torch.jit.fork" title="torch.jit.fork"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fork</span></code></a></p></td>
<td><p>Creates an asynchronous task executing <cite>func</cite> and a reference to the value of the result of this execution.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.jit.wait"/><a class="reference internal" href="generated/torch.jit.wait.html#torch.jit.wait" title="torch.jit.wait"><code class="xref py py-obj docutils literal notranslate"><span class="pre">wait</span></code></a></p></td>
<td><p>Forces completion of a <cite>torch.jit.Future[T]</cite> asynchronous task, returning the result of the task.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.jit.ScriptModule"/><a class="reference internal" href="generated/torch.jit.ScriptModule.html#torch.jit.ScriptModule" title="torch.jit.ScriptModule"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ScriptModule</span></code></a></p></td>
<td><p>A wrapper around C++ <code class="docutils literal notranslate"><span class="pre">torch::jit::Module</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.jit.ScriptFunction"/><a class="reference internal" href="generated/torch.jit.ScriptFunction.html#torch.jit.ScriptFunction" title="torch.jit.ScriptFunction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ScriptFunction</span></code></a></p></td>
<td><p>Functionally equivalent to a <a class="reference internal" href="generated/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>, but represents a single function and does not have any attributes or Parameters.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.jit.freeze"/><a class="reference internal" href="generated/torch.jit.freeze.html#torch.jit.freeze" title="torch.jit.freeze"><code class="xref py py-obj docutils literal notranslate"><span class="pre">freeze</span></code></a></p></td>
<td><p>Freezing a <a class="reference internal" href="generated/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 clone it and attempt to inline the cloned module's submodules, parameters, and attributes as constants in the TorchScript IR Graph.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.jit.optimize_for_inference"/><a class="reference internal" href="generated/torch.jit.optimize_for_inference.html#torch.jit.optimize_for_inference" title="torch.jit.optimize_for_inference"><code class="xref py py-obj docutils literal notranslate"><span class="pre">optimize_for_inference</span></code></a></p></td>
<td><p>Performs a set of optimization passes to optimize a model for the purposes of inference.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.jit.enable_onednn_fusion"/><a class="reference internal" href="generated/torch.jit.enable_onednn_fusion.html#torch.jit.enable_onednn_fusion" title="torch.jit.enable_onednn_fusion"><code class="xref py py-obj docutils literal notranslate"><span class="pre">enable_onednn_fusion</span></code></a></p></td>
<td><p>Enables or disables onednn JIT fusion based on the parameter <cite>enabled</cite>.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.jit.onednn_fusion_enabled"/><a class="reference internal" href="generated/torch.jit.onednn_fusion_enabled.html#torch.jit.onednn_fusion_enabled" title="torch.jit.onednn_fusion_enabled"><code class="xref py py-obj docutils literal notranslate"><span class="pre">onednn_fusion_enabled</span></code></a></p></td>
<td><p>Returns whether onednn JIT fusion is enabled</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.jit.set_fusion_strategy"/><a class="reference internal" href="generated/torch.jit.set_fusion_strategy.html#torch.jit.set_fusion_strategy" title="torch.jit.set_fusion_strategy"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_fusion_strategy</span></code></a></p></td>
<td><p>Sets the type and number of specializations that can occur during fusion.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.jit.strict_fusion"/><a class="reference internal" href="generated/torch.jit.strict_fusion.html#torch.jit.strict_fusion" title="torch.jit.strict_fusion"><code class="xref py py-obj docutils literal notranslate"><span class="pre">strict_fusion</span></code></a></p></td>
<td><p>This class errors if not all nodes have been fused in inference, or symbolically differentiated in training.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.jit.save"/><a class="reference internal" href="generated/torch.jit.save.html#torch.jit.save" title="torch.jit.save"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save</span></code></a></p></td>
<td><p>Save an offline version of this module for use in a separate process.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.jit.load"/><a class="reference internal" href="generated/torch.jit.load.html#torch.jit.load" title="torch.jit.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a></p></td>
<td><p>Load a <a class="reference internal" href="generated/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="generated/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> previously saved with <a class="reference internal" href="generated/torch.jit.save.html#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></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.jit.ignore"/><a class="reference internal" href="generated/torch.jit.ignore.html#torch.jit.ignore" title="torch.jit.ignore"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ignore</span></code></a></p></td>
<td><p>This decorator indicates to the compiler that a function or method should be ignored and left as a Python function.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.jit.unused"/><a class="reference internal" href="generated/torch.jit.unused.html#torch.jit.unused" title="torch.jit.unused"><code class="xref py py-obj docutils literal notranslate"><span class="pre">unused</span></code></a></p></td>
<td><p>This decorator indicates to the compiler that a function or method should be ignored and replaced with the raising of an exception.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.jit.isinstance"/><a class="reference internal" href="generated/torch.jit.isinstance.html#torch.jit.isinstance" title="torch.jit.isinstance"><code class="xref py py-obj docutils literal notranslate"><span class="pre">isinstance</span></code></a></p></td>
<td><p>This function provides for container type refinement in TorchScript.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.jit.Attribute"/><a class="reference internal" href="generated/torch.jit.Attribute.html#torch.jit.Attribute" title="torch.jit.Attribute"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Attribute</span></code></a></p></td>
<td><p>This method is a pass-through function that returns <cite>value</cite>, mostly used to indicate to the TorchScript compiler that the left-hand side expression is a class instance attribute with type of <cite>type</cite>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.jit.annotate"/><a class="reference internal" href="generated/torch.jit.annotate.html#torch.jit.annotate" title="torch.jit.annotate"><code class="xref py py-obj docutils literal notranslate"><span class="pre">annotate</span></code></a></p></td>
<td><p>This method is a pass-through function that returns <cite>the_value</cite>, used to hint TorchScript compiler the type of <cite>the_value</cite>.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="mixing-tracing-and-scripting">
<h2><a class="toc-backref" href="#id5">Mixing Tracing and Scripting</a><a class="headerlink" href="#mixing-tracing-and-scripting" title="Permalink to this heading">¶</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-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="k">def</span> <span class="nf">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-python 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-python 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="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="torchscript-language">
<h2><a class="toc-backref" href="#id6">TorchScript Language</a><a class="headerlink" href="#torchscript-language" title="Permalink to this heading">¶</a></h2>
<p>TorchScript is a statically typed subset of Python, so many Python features apply
directly to TorchScript. See the full <a class="reference internal" href="jit_language_reference.html#language-reference"><span class="std std-ref">TorchScript Language Reference</span></a> for details.</p>
</div>
<div class="section" id="built-in-functions-and-modules">
<span id="builtin-functions"></span><h2><a class="toc-backref" href="#id7">Built-in Functions and Modules</a><a class="headerlink" href="#built-in-functions-and-modules" title="Permalink to this heading">¶</a></h2>
<p>TorchScript supports the use of most PyTorch functions and many Python built-ins.
See <a class="reference internal" href="jit_builtin_functions.html#builtin-functions"><span class="std std-ref">TorchScript Builtins</span></a> for a full reference of supported functions.</p>
<div class="section" id="pytorch-functions-and-modules">
<h3><a class="toc-backref" href="#id8">PyTorch Functions and Modules</a><a class="headerlink" href="#pytorch-functions-and-modules" title="Permalink to this heading">¶</a></h3>
<p>TorchScript supports a subset of the tensor and neural network
functions that PyTorch provides. Most methods on Tensor as well as functions in
the <code class="docutils literal notranslate"><span class="pre">torch</span></code> namespace, all functions in <code class="docutils literal notranslate"><span class="pre">torch.nn.functional</span></code> and
most modules from <code class="docutils literal notranslate"><span class="pre">torch.nn</span></code> are supported in TorchScript.</p>
<p>See <a class="reference internal" href="jit_unsupported.html#jit-unsupported"><span class="std std-ref">TorchScript Unsupported PyTorch Constructs</span></a> for a list of unsupported PyTorch functions and modules.</p>
</div>
<div class="section" id="python-functions-and-modules">
<h3><a class="toc-backref" href="#id9">Python Functions and Modules</a><a class="headerlink" href="#python-functions-and-modules" title="Permalink to this heading">¶</a></h3>
<p>Many of Python’s <a class="reference external" href="https://docs.python.org/3/library/functions.html">built-in functions</a> are supported in TorchScript.
The <a class="reference external" href="https://docs.python.org/3/library/math.html#module-math" title="(in Python v3.12)"><code class="xref any docutils literal notranslate"><span class="pre">math</span></code></a> module is also supported (see <a class="reference internal" href="jit_builtin_functions.html#math-module"><span class="std std-ref">math Module</span></a> for details), but no other Python modules
(built-in or third party) are supported.</p>
</div>
<div class="section" id="python-language-reference-comparison">
<h3><a class="toc-backref" href="#id10">Python Language Reference Comparison</a><a class="headerlink" href="#python-language-reference-comparison" title="Permalink to this heading">¶</a></h3>
<p>For a full listing of supported Python features, see <a class="reference internal" href="jit_python_reference.html#python-language-reference"><span class="std std-ref">Python Language Reference Coverage</span></a>.</p>
</div>
</div>
<div class="section" id="debugging">
<h2><a class="toc-backref" href="#id11">Debugging</a><a class="headerlink" href="#debugging" title="Permalink to this heading">¶</a></h2>
<div class="section" id="disable-jit-for-debugging">
<span id="disable-torchscript"></span><h3><a class="toc-backref" href="#id12">Disable JIT for Debugging</a><a class="headerlink" href="#disable-jit-for-debugging" title="Permalink to this heading">¶</a></h3>
<dl class="std envvar">
<dt class="sig sig-object std" id="envvar-PYTORCH_JIT">
<span class="sig-name descname"><span class="pre">PYTORCH_JIT</span></span><a class="headerlink" href="#envvar-PYTORCH_JIT" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<p>Setting the environment variable <code class="docutils literal notranslate"><span class="pre">PYTORCH_JIT=0</span></code> will disable all script
and tracing annotations. If there is hard-to-debug error in one of your
TorchScript models, you can use this flag to force everything to run using native
Python. Since TorchScript (scripting and tracing) is disabled with this flag,
you can use tools like <code class="docutils literal notranslate"><span class="pre">pdb</span></code> to debug the model code. For example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></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">scripted_fn</span><span class="p">(</span><span class="n">x</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="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="n">x</span>
<span class="k">return</span> <span class="n">x</span>
<span class="k">def</span> <span class="nf">fn</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">neg</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="kn">import</span> <span class="nn">pdb</span><span class="p">;</span> <span class="n">pdb</span><span class="o">.</span><span class="n">set_trace</span><span class="p">()</span>
<span class="k">return</span> <span class="n">scripted_fn</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">traced_fn</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">fn</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">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),))</span>
<span class="n">traced_fn</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="mi">4</span><span class="p">))</span>
</pre></div>
</div>
<p>Debugging this script with <code class="docutils literal notranslate"><span class="pre">pdb</span></code> works except for when we invoke the
<a class="reference internal" href="generated/torch.jit.script.html#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> function. We can globally disable
JIT, so that we can call the <a class="reference internal" href="generated/torch.jit.script.html#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>
function as a normal Python function and not compile it. If the above script
is called <code class="docutils literal notranslate"><span class="pre">disable_jit_example.py</span></code>, we can invoke it like so:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ PYTORCH_JIT=0 python disable_jit_example.py
</pre></div>
</div>
<p>and we will be able to step into the <a class="reference internal" href="generated/torch.jit.script.html#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> function as a normal Python function. To disable the
TorchScript compiler for a specific function, see
<a class="reference internal" href="generated/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>.</p>
</div>
<div class="section" id="inspecting-code">
<span id="id1"></span><h3><a class="toc-backref" href="#id13">Inspecting Code</a><a class="headerlink" href="#inspecting-code" title="Permalink to this heading">¶</a></h3>
<p>TorchScript provides a code pretty-printer for all <a class="reference internal" href="generated/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> instances. This
pretty-printer gives an interpretation of the script method’s code as valid
Python syntax. For example:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></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="nb">len</span><span class="p">):</span>
<span class="c1"># type: (int) -> torch.Tensor</span>
<span class="n">rv</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">):</span>
<span class="k">if</span> <span class="n">i</span> <span class="o"><</span> <span class="mi">10</span><span class="p">:</span>
<span class="n">rv</span> <span class="o">=</span> <span class="n">rv</span> <span class="o">-</span> <span class="mf">1.0</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">rv</span> <span class="o">=</span> <span class="n">rv</span> <span class="o">+</span> <span class="mf">1.0</span>
<span class="k">return</span> <span class="n">rv</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>
</pre></div>
</div>
<p>A <a class="reference internal" href="generated/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> with a single <code class="docutils literal notranslate"><span class="pre">forward</span></code> method will have an attribute
<code class="docutils literal notranslate"><span class="pre">code</span></code>, which you can use to inspect the <a class="reference internal" href="generated/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>’s code.
If the <a class="reference internal" href="generated/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> has more than one method, you will need to access
<code class="docutils literal notranslate"><span class="pre">.code</span></code> on the method itself and not the module. We can inspect the
code of a method named <code class="docutils literal notranslate"><span class="pre">foo</span></code> on a <a class="reference internal" href="generated/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> by accessing <code class="docutils literal notranslate"><span class="pre">.foo.code</span></code>.
The example above produces this output:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">foo</span><span class="p">(</span><span class="nb">len</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-></span> <span class="n">Tensor</span><span class="p">:</span>
<span class="n">rv</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">layout</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">pin_memory</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
<span class="n">rv0</span> <span class="o">=</span> <span class="n">rv</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">):</span>
<span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">lt</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="mi">10</span><span class="p">):</span>
<span class="n">rv1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sub</span><span class="p">(</span><span class="n">rv0</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">rv1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">rv0</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">rv0</span> <span class="o">=</span> <span class="n">rv1</span>
<span class="k">return</span> <span class="n">rv0</span>
</pre></div>
</div>
<p>This is TorchScript’s compilation of the code for the <code class="docutils literal notranslate"><span class="pre">forward</span></code> method.
You can use this to ensure TorchScript (tracing or scripting) has captured
your model code correctly.</p>
</div>
<div class="section" id="interpreting-graphs">
<span id="id2"></span><h3><a class="toc-backref" href="#id14">Interpreting Graphs</a><a class="headerlink" href="#interpreting-graphs" title="Permalink to this heading">¶</a></h3>
<p>TorchScript also has a representation at a lower level than the code pretty-
printer, in the form of IR graphs.</p>
<p>TorchScript uses a static single assignment (SSA) intermediate representation
(IR) to represent computation. The instructions in this format consist of
ATen (the C++ backend of PyTorch) operators and other primitive operators,
including control flow operators for loops and conditionals. As an example:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></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="nb">len</span><span class="p">):</span>
<span class="c1"># type: (int) -> torch.Tensor</span>
<span class="n">rv</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">):</span>
<span class="k">if</span> <span class="n">i</span> <span class="o"><</span> <span class="mi">10</span><span class="p">:</span>
<span class="n">rv</span> <span class="o">=</span> <span class="n">rv</span> <span class="o">-</span> <span class="mf">1.0</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">rv</span> <span class="o">=</span> <span class="n">rv</span> <span class="o">+</span> <span class="mf">1.0</span>
<span class="k">return</span> <span class="n">rv</span>
<span class="nb">print</span><span class="p">(</span><span class="n">foo</span><span class="o">.</span><span class="n">graph</span><span class="p">)</span>
</pre></div>
</div>
<p><code class="docutils literal notranslate"><span class="pre">graph</span></code> follows the same rules described in the <a class="reference internal" href="#inspecting-code"><span class="std std-ref">Inspecting Code</span></a> section
with regard to <code class="docutils literal notranslate"><span class="pre">forward</span></code> method lookup.</p>
<p>The example script above produces the graph:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>graph(%len.1 : int):
%24 : int = prim::Constant[value=1]()
%17 : bool = prim::Constant[value=1]() # test.py:10:5
%12 : bool? = prim::Constant()
%10 : Device? = prim::Constant()
%6 : int? = prim::Constant()
%1 : int = prim::Constant[value=3]() # test.py:9:22
%2 : int = prim::Constant[value=4]() # test.py:9:25
%20 : int = prim::Constant[value=10]() # test.py:11:16
%23 : float = prim::Constant[value=1]() # test.py:12:23
%4 : int[] = prim::ListConstruct(%1, %2)
%rv.1 : Tensor = aten::zeros(%4, %6, %6, %10, %12) # test.py:9:10
%rv : Tensor = prim::Loop(%len.1, %17, %rv.1) # test.py:10:5
block0(%i.1 : int, %rv.14 : Tensor):
%21 : bool = aten::lt(%i.1, %20) # test.py:11:12
%rv.13 : Tensor = prim::If(%21) # test.py:11:9
block0():
%rv.3 : Tensor = aten::sub(%rv.14, %23, %24) # test.py:12:18
-> (%rv.3)
block1():
%rv.6 : Tensor = aten::add(%rv.14, %23, %24) # test.py:14:18
-> (%rv.6)
-> (%17, %rv.13)
return (%rv)
</pre></div>
</div>
<p>Take the instruction <code class="docutils literal notranslate"><span class="pre">%rv.1</span> <span class="pre">:</span> <span class="pre">Tensor</span> <span class="pre">=</span> <span class="pre">aten::zeros(%4,</span> <span class="pre">%6,</span> <span class="pre">%6,</span> <span class="pre">%10,</span> <span class="pre">%12)</span> <span class="pre">#</span> <span class="pre">test.py:9:10</span></code> for
example.</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">%rv.1</span> <span class="pre">:</span> <span class="pre">Tensor</span></code> means we assign the output to a (unique) value named <code class="docutils literal notranslate"><span class="pre">rv.1</span></code>, that value is of <code class="docutils literal notranslate"><span class="pre">Tensor</span></code> type and that we do not know its concrete shape.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">aten::zeros</span></code> is the operator (equivalent to <code class="docutils literal notranslate"><span class="pre">torch.zeros</span></code>) and the input list <code class="docutils literal notranslate"><span class="pre">(%4,</span> <span class="pre">%6,</span> <span class="pre">%6,</span> <span class="pre">%10,</span> <span class="pre">%12)</span></code> specifies which values in scope should be passed as inputs. The schema for built-in functions like <code class="docutils literal notranslate"><span class="pre">aten::zeros</span></code> can be found at <a class="reference internal" href="#builtin-functions">Builtin Functions</a>.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">#</span> <span class="pre">test.py:9:10</span></code> is the location in the original source file that generated this instruction. In this case, it is a file named <cite>test.py</cite>, on line 9, and at character 10.</p></li>
</ul>
<p>Notice that operators can also have associated <code class="docutils literal notranslate"><span class="pre">blocks</span></code>, namely the
<code class="docutils literal notranslate"><span class="pre">prim::Loop</span></code> and <code class="docutils literal notranslate"><span class="pre">prim::If</span></code> operators. In the graph print-out, these
operators are formatted to reflect their equivalent source code forms
to facilitate easy debugging.</p>
<p>Graphs can be inspected as shown to confirm that the computation described
by a <a class="reference internal" href="generated/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> is correct, in both automated and manual fashion, as
described below.</p>
</div>
<div class="section" id="tracer">
<h3><a class="toc-backref" href="#id15">Tracer</a><a class="headerlink" href="#tracer" title="Permalink to this heading">¶</a></h3>
<div class="section" id="tracing-edge-cases">
<h4>Tracing Edge Cases<a class="headerlink" href="#tracing-edge-cases" title="Permalink to this heading">¶</a></h4>
<p>There are some edge cases that exist where the trace of a given Python
function/module will not be representative of the underlying code. These
cases can include:</p>
<ul class="simple">
<li><p>Tracing of control flow that is dependent on inputs (e.g. tensor shapes)</p></li>
<li><p>Tracing of in-place operations of tensor views (e.g. indexing on the left-hand side of an assignment)</p></li>
</ul>
<p>Note that these cases may in fact be traceable in the future.</p>
</div>
<div class="section" id="automatic-trace-checking">
<h4>Automatic Trace Checking<a class="headerlink" href="#automatic-trace-checking" title="Permalink to this heading">¶</a></h4>
<p>One way to automatically catch many errors in traces is by using <code class="docutils literal notranslate"><span class="pre">check_inputs</span></code>
on the <code class="docutils literal notranslate"><span class="pre">torch.jit.trace()</span></code> API. <code class="docutils literal notranslate"><span class="pre">check_inputs</span></code> takes a list of tuples
of inputs that will be used to re-trace the computation and verify the
results. For example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">loop_in_traced_fn</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)):</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">result</span> <span class="o">*</span> <span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
<span class="k">return</span> <span class="n">result</span>
<span class="n">inputs</span> <span class="o">=</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="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),)</span>
<span class="n">check_inputs</span> <span class="o">=</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">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</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">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">),)]</span>
<span class="n">traced</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">loop_in_traced_fn</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">check_inputs</span><span class="o">=</span><span class="n">check_inputs</span><span class="p">)</span>
</pre></div>
</div>
<p>Gives us the following diagnostic information:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>ERROR: Graphs differed across invocations!
Graph diff:
graph(%x : Tensor) {
%1 : int = prim::Constant[value=0]()
%2 : int = prim::Constant[value=0]()
%result.1 : Tensor = aten::select(%x, %1, %2)
%4 : int = prim::Constant[value=0]()
%5 : int = prim::Constant[value=0]()
%6 : Tensor = aten::select(%x, %4, %5)
%result.2 : Tensor = aten::mul(%result.1, %6)
%8 : int = prim::Constant[value=0]()
%9 : int = prim::Constant[value=1]()
%10 : Tensor = aten::select(%x, %8, %9)
- %result : Tensor = aten::mul(%result.2, %10)
+ %result.3 : Tensor = aten::mul(%result.2, %10)
? ++
%12 : int = prim::Constant[value=0]()
%13 : int = prim::Constant[value=2]()
%14 : Tensor = aten::select(%x, %12, %13)
+ %result : Tensor = aten::mul(%result.3, %14)
+ %16 : int = prim::Constant[value=0]()
+ %17 : int = prim::Constant[value=3]()
+ %18 : Tensor = aten::select(%x, %16, %17)
- %15 : Tensor = aten::mul(%result, %14)
? ^ ^
+ %19 : Tensor = aten::mul(%result, %18)
? ^ ^
- return (%15);
? ^
+ return (%19);
? ^
}
</pre></div>
</div>
<p>This message indicates to us that the computation differed between when
we first traced it and when we traced it with the <code class="docutils literal notranslate"><span class="pre">check_inputs</span></code>. Indeed,
the loop within the body of <code class="docutils literal notranslate"><span class="pre">loop_in_traced_fn</span></code> depends on the shape
of the input <code class="docutils literal notranslate"><span class="pre">x</span></code>, and thus when we try another <code class="docutils literal notranslate"><span class="pre">x</span></code> with a different
shape, the trace differs.</p>
<p>In this case, data-dependent control flow like this can be captured using
<a class="reference internal" href="generated/torch.jit.script.html#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> instead:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">fn</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)):</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">result</span> <span class="o">*</span> <span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
<span class="k">return</span> <span class="n">result</span>
<span class="n">inputs</span> <span class="o">=</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="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),)</span>
<span class="n">check_inputs</span> <span class="o">=</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">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</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">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">),)]</span>
<span class="n">scripted_fn</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">fn</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">scripted_fn</span><span class="o">.</span><span class="n">graph</span><span class="p">)</span>
<span class="c1">#print(str(scripted_fn.graph).strip())</span>
<span class="k">for</span> <span class="n">input_tuple</span> <span class="ow">in</span> <span class="p">[</span><span class="n">inputs</span><span class="p">]</span> <span class="o">+</span> <span class="n">check_inputs</span><span class="p">:</span>
<span class="n">torch</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_close</span><span class="p">(</span><span class="n">fn</span><span class="p">(</span><span class="o">*</span><span class="n">input_tuple</span><span class="p">),</span> <span class="n">scripted_fn</span><span class="p">(</span><span class="o">*</span><span class="n">input_tuple</span><span class="p">))</span>
</pre></div>
</div>
<p>Which produces:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">graph</span><span class="p">(</span><span class="o">%</span><span class="n">x</span> <span class="p">:</span> <span class="n">Tensor</span><span class="p">)</span> <span class="p">{</span>
<span class="o">%</span><span class="mi">5</span> <span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">prim</span><span class="p">::</span><span class="n">Constant</span><span class="p">[</span><span class="n">value</span><span class="o">=</span><span class="mi">1</span><span class="p">]()</span>
<span class="o">%</span><span class="mi">1</span> <span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">prim</span><span class="p">::</span><span class="n">Constant</span><span class="p">[</span><span class="n">value</span><span class="o">=</span><span class="mi">0</span><span class="p">]()</span>
<span class="o">%</span><span class="n">result</span><span class="mf">.1</span> <span class="p">:</span> <span class="n">Tensor</span> <span class="o">=</span> <span class="n">aten</span><span class="p">::</span><span class="n">select</span><span class="p">(</span><span class="o">%</span><span class="n">x</span><span class="p">,</span> <span class="o">%</span><span class="mi">1</span><span class="p">,</span> <span class="o">%</span><span class="mi">1</span><span class="p">)</span>
<span class="o">%</span><span class="mi">4</span> <span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">aten</span><span class="p">::</span><span class="n">size</span><span class="p">(</span><span class="o">%</span><span class="n">x</span><span class="p">,</span> <span class="o">%</span><span class="mi">1</span><span class="p">)</span>
<span class="o">%</span><span class="n">result</span> <span class="p">:</span> <span class="n">Tensor</span> <span class="o">=</span> <span class="n">prim</span><span class="p">::</span><span class="n">Loop</span><span class="p">(</span><span class="o">%</span><span class="mi">4</span><span class="p">,</span> <span class="o">%</span><span class="mi">5</span><span class="p">,</span> <span class="o">%</span><span class="n">result</span><span class="mf">.1</span><span class="p">)</span>
<span class="n">block0</span><span class="p">(</span><span class="o">%</span><span class="n">i</span> <span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="o">%</span><span class="mi">7</span> <span class="p">:</span> <span class="n">Tensor</span><span class="p">)</span> <span class="p">{</span>
<span class="o">%</span><span class="mi">10</span> <span class="p">:</span> <span class="n">Tensor</span> <span class="o">=</span> <span class="n">aten</span><span class="p">::</span><span class="n">select</span><span class="p">(</span><span class="o">%</span><span class="n">x</span><span class="p">,</span> <span class="o">%</span><span class="mi">1</span><span class="p">,</span> <span class="o">%</span><span class="n">i</span><span class="p">)</span>
<span class="o">%</span><span class="n">result</span><span class="mf">.2</span> <span class="p">:</span> <span class="n">Tensor</span> <span class="o">=</span> <span class="n">aten</span><span class="p">::</span><span class="n">mul</span><span class="p">(</span><span class="o">%</span><span class="mi">7</span><span class="p">,</span> <span class="o">%</span><span class="mi">10</span><span class="p">)</span>
<span class="o">-></span> <span class="p">(</span><span class="o">%</span><span class="mi">5</span><span class="p">,</span> <span class="o">%</span><span class="n">result</span><span class="mf">.2</span><span class="p">)</span>
<span class="p">}</span>
<span class="k">return</span> <span class="p">(</span><span class="o">%</span><span class="n">result</span><span class="p">);</span>
<span class="p">}</span>
</pre></div>
</div>
</div>
<div class="section" id="tracer-warnings">
<h4>Tracer Warnings<a class="headerlink" href="#tracer-warnings" title="Permalink to this heading">¶</a></h4>
<p>The tracer produces warnings for several problematic patterns in traced
computation. As an example, take a trace of a function that contains an
in-place assignment on a slice (a view) of a Tensor:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">fill_row_zero</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</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="o">*</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">2</span><span class="p">])</span>
<span class="k">return</span> <span class="n">x</span>
<span class="n">traced</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">fill_row_zero</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="mi">4</span><span class="p">),))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">traced</span><span class="o">.</span><span class="n">graph</span><span class="p">)</span>
</pre></div>
</div>
<p>Produces several warnings and a graph which simply returns the input:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>fill_row_zero.py:4: TracerWarning: There are 2 live references to the data region being modified when tracing in-place operator copy_ (possibly due to an assignment). This might cause the trace to be incorrect, because all other views that also reference this data will not reflect this change in the trace! On the other hand, if all other views use the same memory chunk, but are disjoint (e.g. are outputs of torch.split), this might still be safe.
x[0] = torch.rand(*x.shape[1:2])
fill_row_zero.py:6: TracerWarning: Output nr 1. of the traced function does not match the corresponding output of the Python function. Detailed error:
Not within tolerance rtol=1e-05 atol=1e-05 at input[0, 1] (0.09115803241729736 vs. 0.6782537698745728) and 3 other locations (33.00%)
traced = torch.jit.trace(fill_row_zero, (torch.rand(3, 4),))
graph(%0 : Float(3, 4)) {
return (%0);
}
</pre></div>
</div>
<p>We can fix this by modifying the code to not use the in-place update, but
rather build up the result tensor out-of-place with <code class="docutils literal notranslate"><span class="pre">torch.cat</span></code>:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">fill_row_zero</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</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="o">*</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">2</span><span class="p">]),</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">2</span><span class="p">]),</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="k">return</span> <span class="n">x</span>
<span class="n">traced</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">fill_row_zero</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="mi">4</span><span class="p">),))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">traced</span><span class="o">.</span><span class="n">graph</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="section" id="frequently-asked-questions">
<h2><a class="toc-backref" href="#id16">Frequently Asked Questions</a><a class="headerlink" href="#frequently-asked-questions" title="Permalink to this heading">¶</a></h2>
<p>Q: I would like to train a model on GPU and do inference on CPU. What are the
best practices?</p>
<blockquote>
<div><p>First convert your model from GPU to CPU and then save it, like so:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">cpu_model</span> <span class="o">=</span> <span class="n">gpu_model</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
<span class="n">sample_input_cpu</span> <span class="o">=</span> <span class="n">sample_input_gpu</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
<span class="n">traced_cpu</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">cpu_model</span><span class="p">,</span> <span class="n">sample_input_cpu</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">traced_cpu</span><span class="p">,</span> <span class="s2">"cpu.pt"</span><span class="p">)</span>
<span class="n">traced_gpu</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">gpu_model</span><span class="p">,</span> <span class="n">sample_input_gpu</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">traced_gpu</span><span class="p">,</span> <span class="s2">"gpu.pt"</span><span class="p">)</span>
<span class="c1"># ... later, when using the model:</span>
<span class="k">if</span> <span class="n">use_gpu</span><span class="p">:</span>
<span class="n">model</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">load</span><span class="p">(</span><span class="s2">"gpu.pt"</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">model</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">load</span><span class="p">(</span><span class="s2">"cpu.pt"</span><span class="p">)</span>
<span class="n">model</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
</pre></div>
</div>
<p>This is recommended because the tracer may witness tensor creation on a
specific device, so casting an already-loaded model may have unexpected
effects. Casting the model <em>before</em> saving it ensures that the tracer has
the correct device information.</p>
</div></blockquote>
<p>Q: How do I store attributes on a <a class="reference internal" href="generated/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>?</p>
<blockquote>
<div><p>Say we have a model like:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="k">class</span> <span class="nc">Model</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="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">x</span> <span class="o">=</span> <span class="mi">2</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="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">x</span>