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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/amp_examples.html">Automatic Mixed Precision examples</a></li>
<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>
<li class="toctree-l1"><a class="reference internal" href="notes/cuda.html">CUDA semantics</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/ddp.html">Distributed Data Parallel</a></li>
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<div class="section" id="torchscript-language-reference">
<span id="language-reference-v2"></span><h1>TorchScript Language Reference<a class="headerlink" href="#torchscript-language-reference" title="Permalink to this headline">¶</a></h1>
<p>This reference manual describes the syntax and core semantics of the TorchScript language.
TorchScript is a statically typed subset of the Python language. This document explains the supported features of
Python in TorchScript and also how the language diverges from regular Python. Any features of Python that are not mentioned in
this reference manual are not part of TorchScript. TorchScript focuses specifically on the features of Python that are needed to
represent neural network models in PyTorch.</p>
<div class="contents local topic" id="contents">
<ul class="simple">
<li><p><a class="reference internal" href="#terminology" id="id7">Terminology</a></p></li>
<li><p><a class="reference internal" href="#id1" id="id8">Type System</a></p></li>
<li><p><a class="reference internal" href="#type-annotation" id="id9">Type Annotation</a></p></li>
<li><p><a class="reference internal" href="#expressions" id="id10">Expressions</a></p></li>
<li><p><a class="reference internal" href="#simple-statements" id="id11">Simple Statements</a></p></li>
<li><p><a class="reference internal" href="#compound-statements" id="id12">Compound Statements</a></p></li>
<li><p><a class="reference internal" href="#python-values" id="id13">Python Values</a></p></li>
<li><p><a class="reference internal" href="#torch-apis" id="id14">torch.* APIs</a></p></li>
</ul>
</div>
<div class="section" id="terminology">
<span id="type-system"></span><h2><a class="toc-backref" href="#id7">Terminology</a><a class="headerlink" href="#terminology" title="Permalink to this headline">¶</a></h2>
<p>This document uses the following terminologies:</p>
<table class="colwidths-given docutils colwidths-auto align-default">
<colgroup>
<col style="width: 50%" />
<col style="width: 50%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>Pattern</p></th>
<th class="head"><p>Notes</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">::=</span></code></p></td>
<td><p>Indicates that the given symbol is defined as.</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">"</span> <span class="pre">"</span></code></p></td>
<td><p>Represents real keywords and delimiters that are part of the syntax.</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">A</span> <span class="pre">|</span> <span class="pre">B</span></code></p></td>
<td><p>Indicates either A or B.</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">(</span> <span class="pre">)</span></code></p></td>
<td><p>Indicates grouping.</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">[]</span></code></p></td>
<td><p>Indicates optional.</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">A+</span></code></p></td>
<td><p>Indicates a regular expression where term A is repeated at least once.</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">A*</span></code></p></td>
<td><p>Indicates a regular expression where term A is repeated zero or more times.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="id1">
<h2><a class="toc-backref" href="#id8">Type System</a><a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h2>
<p>TorchScript is a statically typed subset of Python. The largest difference between TorchScript and the full Python language is that TorchScript only supports a small set of types that are needed to express
neural net models.</p>
<div class="section" id="torchscript-types">
<h3>TorchScript Types<a class="headerlink" href="#torchscript-types" title="Permalink to this headline">¶</a></h3>
<p>The TorchScript type system consists of <code class="docutils literal notranslate"><span class="pre">TSType</span></code> and <code class="docutils literal notranslate"><span class="pre">TSModuleType</span></code> as defined below.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">TSAllType</span> <span class="p">:</span><span class="o">:=</span> <span class="n">TSType</span> <span class="o">|</span> <span class="n">TSModuleType</span>
<span class="n">TSType</span> <span class="p">:</span><span class="o">:=</span> <span class="n">TSMetaType</span> <span class="o">|</span> <span class="n">TSPrimitiveType</span> <span class="o">|</span> <span class="n">TSStructuralType</span> <span class="o">|</span> <span class="n">TSNominalType</span>
</pre></div>
</div>
<p><code class="docutils literal notranslate"><span class="pre">TSType</span></code> represents the majority of TorchScript types that are composable and that can be used in TorchScript type annotations.
<code class="docutils literal notranslate"><span class="pre">TSType</span></code> refers to any of the following:</p>
<ul class="simple">
<li><p>Meta Types, e.g., <code class="docutils literal notranslate"><span class="pre">Any</span></code></p></li>
<li><p>Primitive Types, e.g., <code class="docutils literal notranslate"><span class="pre">int</span></code>, <code class="docutils literal notranslate"><span class="pre">float</span></code>, and <code class="docutils literal notranslate"><span class="pre">str</span></code></p></li>
<li><p>Structural Types, e.g., <code class="docutils literal notranslate"><span class="pre">Optional[int]</span></code> or <code class="docutils literal notranslate"><span class="pre">List[MyClass]</span></code></p></li>
<li><p>Nominal Types (Python classes), e.g., <code class="docutils literal notranslate"><span class="pre">MyClass</span></code> (user-defined), <code class="docutils literal notranslate"><span class="pre">torch.tensor</span></code> (built-in)</p></li>
</ul>
<p><code class="docutils literal notranslate"><span class="pre">TSModuleType</span></code> represents <code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code> and its subclasses. It is treated differently from <code class="docutils literal notranslate"><span class="pre">TSType</span></code> because its type schema is inferred partly from the object instance and partly from the class definition.
As such, instances of a <code class="docutils literal notranslate"><span class="pre">TSModuleType</span></code> may not follow the same static type schema. <code class="docutils literal notranslate"><span class="pre">TSModuleType</span></code> cannot be used as a TorchScript type annotation or be composed with <code class="docutils literal notranslate"><span class="pre">TSType</span></code> for type safety considerations.</p>
</div>
<div class="section" id="meta-types">
<h3>Meta Types<a class="headerlink" href="#meta-types" title="Permalink to this headline">¶</a></h3>
<p>Meta types are so abstract that they are more like type constraints than concrete types.
Currently TorchScript defines one meta-type, <code class="docutils literal notranslate"><span class="pre">Any</span></code>, that represents any TorchScript type.</p>
<div class="section" id="any-type">
<h4><code class="docutils literal notranslate"><span class="pre">Any</span></code> Type<a class="headerlink" href="#any-type" title="Permalink to this headline">¶</a></h4>
<p>The <code class="docutils literal notranslate"><span class="pre">Any</span></code> type represents any TorchScript type. <code class="docutils literal notranslate"><span class="pre">Any</span></code> specifies no type constraints, thus there is no type-checking on <code class="docutils literal notranslate"><span class="pre">Any</span></code>.
As such it can be bound to any Python or TorchScript data types (e.g., <code class="docutils literal notranslate"><span class="pre">int</span></code>, TorchScript <code class="docutils literal notranslate"><span class="pre">tuple</span></code>, or an arbitrary Python class that is not scripted).</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">TSMetaType</span> <span class="p">:</span><span class="o">:=</span> <span class="s2">"Any"</span>
</pre></div>
</div>
<p>Where:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">Any</span></code> is the Python class name from the typing module. Therefore, to use the <code class="docutils literal notranslate"><span class="pre">Any</span></code> type, you must import it from <code class="docutils literal notranslate"><span class="pre">typing</span></code> (e.g., <code class="docutils literal notranslate"><span class="pre">from</span> <span class="pre">typing</span> <span class="pre">import</span> <span class="pre">Any</span></code>).</p></li>
<li><p>Since <code class="docutils literal notranslate"><span class="pre">Any</span></code> can represent any TorchScript type, the set of operators that are allowed to operate on values of this type on <code class="docutils literal notranslate"><span class="pre">Any</span></code> is limited.</p></li>
</ul>
</div>
<div class="section" id="operators-supported-for-any-type">
<h4>Operators Supported for <code class="docutils literal notranslate"><span class="pre">Any</span></code> Type<a class="headerlink" href="#operators-supported-for-any-type" title="Permalink to this headline">¶</a></h4>
<ul class="simple">
<li><p>Assignment to data of <code class="docutils literal notranslate"><span class="pre">Any</span></code> type.</p></li>
<li><p>Binding to parameter or return of <code class="docutils literal notranslate"><span class="pre">Any</span></code> type.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">x</span> <span class="pre">is</span></code>, <code class="docutils literal notranslate"><span class="pre">x</span> <span class="pre">is</span> <span class="pre">not</span></code> where <code class="docutils literal notranslate"><span class="pre">x</span></code> is of <code class="docutils literal notranslate"><span class="pre">Any</span></code> type.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">isinstance(x,</span> <span class="pre">Type)</span></code> where <code class="docutils literal notranslate"><span class="pre">x</span></code> is of <code class="docutils literal notranslate"><span class="pre">Any</span></code> type.</p></li>
<li><p>Data of <code class="docutils literal notranslate"><span class="pre">Any</span></code> type is printable.</p></li>
<li><p>Data of <code class="docutils literal notranslate"><span class="pre">List[Any]</span></code> type may be sortable if the data is a list of values of the same type <code class="docutils literal notranslate"><span class="pre">T</span></code> and that <code class="docutils literal notranslate"><span class="pre">T</span></code> supports comparison operators.</p></li>
</ul>
<p><strong>Compared to Python</strong></p>
<p><code class="docutils literal notranslate"><span class="pre">Any</span></code> is the least constrained type in the TorchScript type system. In that sense, it is quite similar to the
<code class="docutils literal notranslate"><span class="pre">Object</span></code> class in Python. However, <code class="docutils literal notranslate"><span class="pre">Any</span></code> only supports a subset of the operators and methods that are supported by <code class="docutils literal notranslate"><span class="pre">Object</span></code>.</p>
</div>
<div class="section" id="design-notes">
<h4>Design Notes<a class="headerlink" href="#design-notes" title="Permalink to this headline">¶</a></h4>
<p>When we script a PyTorch module, we may encounter data that is not involved in the execution of the script. Nevertheless, it has to be described
by a type schema. It is not only cumbersome to describe static types for unused data (in the context of the script), but also may lead to unnecessary
scripting failures. <code class="docutils literal notranslate"><span class="pre">Any</span></code> is introduced to describe the type of the data where precise static types are not necessary for compilation.</p>
<p><strong>Example 1</strong></p>
<p>This example illustrates how <code class="docutils literal notranslate"><span class="pre">Any</span></code> can be used to allow the second element of the tuple parameter to be of any type. This is possible
because <code class="docutils literal notranslate"><span class="pre">x[1]</span></code> is not involved in any computation that requires knowing its precise type.</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">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Tuple</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</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">inc_first_element</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="n">Any</span><span class="p">]):</span>
<span class="k">return</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="mi">1</span><span class="p">,</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
<span class="n">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">inc_first_element</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">m</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="mf">2.0</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">m</span><span class="p">((</span><span class="mi">1</span><span class="p">,(</span><span class="mi">100</span><span class="p">,</span><span class="mi">200</span><span class="p">))))</span>
</pre></div>
</div>
<p>The example above produces the following output:</p>
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>(2, 2.0)
(2, (100, 200))
</pre></div>
</div>
<p>The second element of the tuple is of <code class="docutils literal notranslate"><span class="pre">Any</span></code> type, thus can bind to multiple types.
For example, <code class="docutils literal notranslate"><span class="pre">(1,</span> <span class="pre">2.0)</span></code> binds a float type to <code class="docutils literal notranslate"><span class="pre">Any</span></code> as in <code class="docutils literal notranslate"><span class="pre">Tuple[int,</span> <span class="pre">Any]</span></code>,
whereas <code class="docutils literal notranslate"><span class="pre">(1,</span> <span class="pre">(100,</span> <span class="pre">200))</span></code> binds a tuple to <code class="docutils literal notranslate"><span class="pre">Any</span></code> in the second invocation.</p>
<p><strong>Example 2</strong></p>
<p>This example illustrates how we can use <code class="docutils literal notranslate"><span class="pre">isinstance</span></code> to dynamically check the type of the data that is annotated as <code class="docutils literal notranslate"><span class="pre">Any</span></code> type:</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">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span>
<span class="k">def</span> <span class="nf">f</span><span class="p">(</span><span class="n">a</span><span class="p">:</span><span class="n">Any</span><span class="p">):</span>
<span class="nb">print</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="k">return</span> <span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">a</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="n">ones</span> <span class="o">=</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="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">f</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">m</span><span class="p">(</span><span class="n">ones</span><span class="p">))</span>
</pre></div>
</div>
<p>The example above produces the following output:</p>
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span> 1
1
[ CPUFloatType{2} ]
True
</pre></div>
</div>
</div>
</div>
<div class="section" id="primitive-types">
<h3>Primitive Types<a class="headerlink" href="#primitive-types" title="Permalink to this headline">¶</a></h3>
<p>Primitive TorchScript types are types that represent a single type of value and go with a single pre-defined
type name.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">TSPrimitiveType</span> <span class="p">:</span><span class="o">:=</span> <span class="s2">"int"</span> <span class="o">|</span> <span class="s2">"float"</span> <span class="o">|</span> <span class="s2">"double"</span> <span class="o">|</span> <span class="s2">"complex"</span> <span class="o">|</span> <span class="s2">"bool"</span> <span class="o">|</span> <span class="s2">"str"</span> <span class="o">|</span> <span class="s2">"None"</span>
</pre></div>
</div>
</div>
<div class="section" id="structural-types">
<h3>Structural Types<a class="headerlink" href="#structural-types" title="Permalink to this headline">¶</a></h3>
<p>Structural types are types that are structurally defined without a user-defined name (unlike nominal types),
such as <code class="docutils literal notranslate"><span class="pre">Future[int]</span></code>. Structural types are composable with any <code class="docutils literal notranslate"><span class="pre">TSType</span></code>.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">TSStructuralType</span> <span class="p">:</span><span class="o">:=</span> <span class="n">TSTuple</span> <span class="o">|</span> <span class="n">TSNamedTuple</span> <span class="o">|</span> <span class="n">TSList</span> <span class="o">|</span> <span class="n">TSDict</span> <span class="o">|</span>
<span class="n">TSOptional</span> <span class="o">|</span> <span class="n">TSFuture</span> <span class="o">|</span> <span class="n">TSRRef</span>
<span class="n">TSTuple</span> <span class="p">:</span><span class="o">:=</span> <span class="s2">"Tuple"</span> <span class="s2">"["</span> <span class="p">(</span><span class="n">TSType</span> <span class="s2">","</span><span class="p">)</span><span class="o">*</span> <span class="n">TSType</span> <span class="s2">"]"</span>
<span class="n">TSNamedTuple</span> <span class="p">:</span><span class="o">:=</span> <span class="s2">"namedtuple"</span> <span class="s2">"("</span> <span class="p">(</span><span class="n">TSType</span> <span class="s2">","</span><span class="p">)</span><span class="o">*</span> <span class="n">TSType</span> <span class="s2">")"</span>
<span class="n">TSList</span> <span class="p">:</span><span class="o">:=</span> <span class="s2">"List"</span> <span class="s2">"["</span> <span class="n">TSType</span> <span class="s2">"]"</span>
<span class="n">TSOptional</span> <span class="p">:</span><span class="o">:=</span> <span class="s2">"Optional"</span> <span class="s2">"["</span> <span class="n">TSType</span> <span class="s2">"]"</span>
<span class="n">TSFuture</span> <span class="p">:</span><span class="o">:=</span> <span class="s2">"Future"</span> <span class="s2">"["</span> <span class="n">TSType</span> <span class="s2">"]"</span>
<span class="n">TSRRef</span> <span class="p">:</span><span class="o">:=</span> <span class="s2">"RRef"</span> <span class="s2">"["</span> <span class="n">TSType</span> <span class="s2">"]"</span>
<span class="n">TSDict</span> <span class="p">:</span><span class="o">:=</span> <span class="s2">"Dict"</span> <span class="s2">"["</span> <span class="n">KeyType</span> <span class="s2">","</span> <span class="n">TSType</span> <span class="s2">"]"</span>
<span class="n">KeyType</span> <span class="p">:</span><span class="o">:=</span> <span class="s2">"str"</span> <span class="o">|</span> <span class="s2">"int"</span> <span class="o">|</span> <span class="s2">"float"</span> <span class="o">|</span> <span class="s2">"bool"</span> <span class="o">|</span> <span class="n">TensorType</span> <span class="o">|</span> <span class="s2">"Any"</span>
</pre></div>
</div>
<p>Where:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">Tuple</span></code>, <code class="docutils literal notranslate"><span class="pre">List</span></code>, <code class="docutils literal notranslate"><span class="pre">Optional</span></code>, <code class="docutils literal notranslate"><span class="pre">Union</span></code>, <code class="docutils literal notranslate"><span class="pre">Future</span></code>, <code class="docutils literal notranslate"><span class="pre">Dict</span></code> represent Python type class names that are defined in the module <code class="docutils literal notranslate"><span class="pre">typing</span></code>. To use these type names, you must import them from <code class="docutils literal notranslate"><span class="pre">typing</span></code> (e.g., <code class="docutils literal notranslate"><span class="pre">from</span> <span class="pre">typing</span> <span class="pre">import</span> <span class="pre">Tuple</span></code>).</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">namedtuple</span></code> represents the Python class <code class="docutils literal notranslate"><span class="pre">collections.namedtuple</span></code> or <code class="docutils literal notranslate"><span class="pre">typing.NamedTuple</span></code>.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">Future</span></code> and <code class="docutils literal notranslate"><span class="pre">RRef</span></code> represent the Python classes <code class="docutils literal notranslate"><span class="pre">torch.futures</span></code> and <code class="docutils literal notranslate"><span class="pre">torch.distributed.rpc</span></code>.</p></li>
</ul>
<p><strong>Compared to Python</strong></p>
<p>Apart from being composable with TorchScript types, these TorchScript structural types often support a common subset of the operators and methods of their Python counterparts.</p>
<p><strong>Example 1</strong></p>
<p>This example uses <code class="docutils literal notranslate"><span class="pre">typing.NamedTuple</span></code> syntax to define a tuple:</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">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">NamedTuple</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Tuple</span>
<span class="k">class</span> <span class="nc">MyTuple</span><span class="p">(</span><span class="n">NamedTuple</span><span class="p">):</span>
<span class="n">first</span><span class="p">:</span> <span class="nb">int</span>
<span class="n">second</span><span class="p">:</span> <span class="nb">int</span>
<span class="k">def</span> <span class="nf">inc</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">MyTuple</span><span class="p">)</span> <span class="o">-></span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">]:</span>
<span class="k">return</span> <span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">first</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">second</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
<span class="n">t</span> <span class="o">=</span> <span class="n">MyTuple</span><span class="p">(</span><span class="n">first</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">second</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="n">scripted_inc</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">inc</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"TorchScript:"</span><span class="p">,</span> <span class="n">scripted_inc</span><span class="p">(</span><span class="n">t</span><span class="p">))</span>
</pre></div>
</div>
<p>The example above produces the following output:</p>
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>TorchScript: (2, 3)
</pre></div>
</div>
<p><strong>Example 2</strong></p>
<p>This example uses <code class="docutils literal notranslate"><span class="pre">collections.namedtuple</span></code> syntax to define a tuple:</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">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">NamedTuple</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Tuple</span>
<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">namedtuple</span>
<span class="n">_AnnotatedNamedTuple</span> <span class="o">=</span> <span class="n">NamedTuple</span><span class="p">(</span><span class="s1">'_NamedTupleAnnotated'</span><span class="p">,</span> <span class="p">[(</span><span class="s1">'first'</span><span class="p">,</span> <span class="nb">int</span><span class="p">),</span> <span class="p">(</span><span class="s1">'second'</span><span class="p">,</span> <span class="nb">int</span><span class="p">)])</span>
<span class="n">_UnannotatedNamedTuple</span> <span class="o">=</span> <span class="n">namedtuple</span><span class="p">(</span><span class="s1">'_NamedTupleAnnotated'</span><span class="p">,</span> <span class="p">[</span><span class="s1">'first'</span><span class="p">,</span> <span class="s1">'second'</span><span class="p">])</span>
<span class="k">def</span> <span class="nf">inc</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">_AnnotatedNamedTuple</span><span class="p">)</span> <span class="o">-></span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">]:</span>
<span class="k">return</span> <span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">first</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">second</span><span class="o">+</span><span class="mi">1</span><span class="p">)</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">inc</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">inc</span><span class="p">(</span><span class="n">_UnannotatedNamedTuple</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">)))</span>
</pre></div>
</div>
<p>The example above produces the following output:</p>
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>(2, 3)
</pre></div>
</div>
<p><strong>Example 3</strong></p>
<p>This example illustrates a common mistake of annotating structural types, i.e., not importing the composite type
classes from the <code class="docutils literal notranslate"><span class="pre">typing</span></code> 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="c1"># ERROR: Tuple not recognized because not imported from typing</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">inc</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">]):</span>
<span class="k">return</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="mi">1</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="o">+</span><span class="mi">1</span><span class="p">)</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">inc</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">m</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">)))</span>
</pre></div>
</div>
<p>Running the above code yields the following scripting error:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">File</span> <span class="s2">"test-tuple.py"</span><span class="p">,</span> <span class="n">line</span> <span class="mi">5</span><span class="p">,</span> <span class="ow">in</span> <span class="o"><</span><span class="n">module</span><span class="o">></span>
<span class="k">def</span> <span class="nf">inc</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">]):</span>
<span class="ne">NameError</span><span class="p">:</span> <span class="n">name</span> <span class="s1">'Tuple'</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">defined</span>
</pre></div>
</div>
<p>The remedy is to add the line <code class="docutils literal notranslate"><span class="pre">from</span> <span class="pre">typing</span> <span class="pre">import</span> <span class="pre">Tuple</span></code> to the beginning of the code.</p>
</div>
<div class="section" id="nominal-types">
<h3>Nominal Types<a class="headerlink" href="#nominal-types" title="Permalink to this headline">¶</a></h3>
<p>Nominal TorchScript types are Python classes. These types are called nominal because they are declared with a custom
name and are compared using class names. Nominal classes are further classified into the following categories:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">TSNominalType</span> <span class="p">:</span><span class="o">:=</span> <span class="n">TSBuiltinClasses</span> <span class="o">|</span> <span class="n">TSCustomClass</span> <span class="o">|</span> <span class="n">TSEnum</span>
</pre></div>
</div>
<p>Among them, <code class="docutils literal notranslate"><span class="pre">TSCustomClass</span></code> and <code class="docutils literal notranslate"><span class="pre">TSEnum</span></code> must be compilable to TorchScript Intermediate Representation (IR). This is enforced by the type-checker.</p>
</div>
<div class="section" id="built-in-class">
<h3>Built-in Class<a class="headerlink" href="#built-in-class" title="Permalink to this headline">¶</a></h3>
<p>Built-in nominal types are Python classes whose semantics are built into the TorchScript system (e.g., tensor types).
TorchScript defines the semantics of these built-in nominal types, and often supports only a subset of the methods or
attributes of its Python class definition.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">TSBuiltinClass</span> <span class="p">:</span><span class="o">:=</span> <span class="n">TSTensor</span> <span class="o">|</span> <span class="s2">"torch.device"</span> <span class="o">|</span> <span class="s2">"torch.Stream"</span> <span class="o">|</span> <span class="s2">"torch.dtype"</span> <span class="o">|</span>
<span class="s2">"torch.nn.ModuleList"</span> <span class="o">|</span> <span class="s2">"torch.nn.ModuleDict"</span> <span class="o">|</span> <span class="o">...</span>
<span class="n">TSTensor</span> <span class="p">:</span><span class="o">:=</span> <span class="s2">"torch.Tensor"</span> <span class="o">|</span> <span class="s2">"common.SubTensor"</span> <span class="o">|</span> <span class="s2">"common.SubWithTorchFunction"</span> <span class="o">|</span>
<span class="s2">"torch.nn.parameter.Parameter"</span> <span class="o">|</span> <span class="ow">and</span> <span class="n">subclasses</span> <span class="n">of</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span>
</pre></div>
</div>
<div class="section" id="special-note-on-torch-nn-modulelist-and-torch-nn-moduledict">
<h4>Special Note on torch.nn.ModuleList and torch.nn.ModuleDict<a class="headerlink" href="#special-note-on-torch-nn-modulelist-and-torch-nn-moduledict" title="Permalink to this headline">¶</a></h4>
<p>Although <code class="docutils literal notranslate"><span class="pre">torch.nn.ModuleList</span></code> and <code class="docutils literal notranslate"><span class="pre">torch.nn.ModuleDict</span></code> are defined as a list and dictionary in Python,
they behave more like tuples in TorchScript:</p>
<ul class="simple">
<li><p>In TorchScript, instances of <code class="docutils literal notranslate"><span class="pre">torch.nn.ModuleList</span></code> or <code class="docutils literal notranslate"><span class="pre">torch.nn.ModuleDict</span></code> are immutable.</p></li>
<li><p>Code that iterates over <code class="docutils literal notranslate"><span class="pre">torch.nn.ModuleList</span></code> or <code class="docutils literal notranslate"><span class="pre">torch.nn.ModuleDict</span></code> is completely unrolled so that elements of <code class="docutils literal notranslate"><span class="pre">torch.nn.ModuleList</span></code> or keys of <code class="docutils literal notranslate"><span class="pre">torch.nn.ModuleDict</span></code> can be of different subclasses of <code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code>.</p></li>
</ul>
<p><strong>Example</strong></p>
<p>The following example highlights the use of a few built-in Torchscript classes (<code class="docutils literal notranslate"><span class="pre">torch.*</span></code>):</p>
<div class="highlight-default 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">class</span> <span class="nc">A</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="bp">self</span><span class="o">.</span><span class="n">x</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">3</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">f</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">y</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">g</span><span class="p">():</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">A</span><span class="p">()</span>
<span class="k">return</span> <span class="n">a</span><span class="o">.</span><span class="n">f</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">"cpu"</span><span class="p">))</span>
<span class="n">script_g</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">g</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">script_g</span><span class="o">.</span><span class="n">graph</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="custom-class">
<h3>Custom Class<a class="headerlink" href="#custom-class" title="Permalink to this headline">¶</a></h3>
<p>Unlike built-in classes, semantics of custom classes are user-defined and the entire class definition must be compilable to TorchScript IR and subject to TorchScript type-checking rules.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">TSClassDef</span> <span class="p">:</span><span class="o">:=</span> <span class="p">[</span> <span class="s2">"@torch.jit.script"</span> <span class="p">]</span>
<span class="s2">"class"</span> <span class="n">ClassName</span> <span class="p">[</span> <span class="s2">"(object)"</span> <span class="p">]</span> <span class="s2">":"</span>
<span class="n">MethodDefinition</span> <span class="o">|</span>
<span class="p">[</span> <span class="s2">"@torch.jit.ignore"</span> <span class="p">]</span> <span class="o">|</span> <span class="p">[</span> <span class="s2">"@torch.jit.unused"</span> <span class="p">]</span>
<span class="n">MethodDefinition</span>
</pre></div>
</div>
<p>Where:</p>
<ul class="simple">
<li><p>Classes must be new-style classes. Python 3 supports only new-style classes. In Python 2.x, a new-style class is specified by subclassing from the object.</p></li>
<li><p>Instance data attributes are statically typed, and instance attributes must be declared by assignments inside the <code class="docutils literal notranslate"><span class="pre">__init__()</span></code> method.</p></li>
<li><p>Method overloading is not supported (i.e., you cannot have multiple methods with the same method name).</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">MethodDefinition</span></code> must be compilable to TorchScript IR and adhere to TorchScript’s type-checking rules, (i.e., all methods must be valid TorchScript functions and class attribute definitions must be valid TorchScript statements).</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">torch.jit.ignore</span></code> and <code class="docutils literal notranslate"><span class="pre">torch.jit.unused</span></code> can be used to ignore the method or function that is not fully torchscriptable or should be ignored by the compiler.</p></li>
</ul>
<p><strong>Compared to Python</strong></p>
<p>TorchScript custom classes are quite limited compared to their Python counterpart. Torchscript custom classes:</p>
<ul class="simple">
<li><p>Do not support class attributes.</p></li>
<li><p>Do not support subclassing except for subclassing an interface type or object.</p></li>
<li><p>Do not support method overloading.</p></li>
<li><p>Must initialize all its instance attributes in <code class="docutils literal notranslate"><span class="pre">__init__()</span></code>; this is because TorchScript constructs a static schema of the class by inferring attribute types in <code class="docutils literal notranslate"><span class="pre">__init__()</span></code>.</p></li>
<li><p>Must contain only methods that satisfy TorchScript type-checking rules and are compilable to TorchScript IRs.</p></li>
</ul>
<p><strong>Example 1</strong></p>
<p>Python classes can be used in TorchScript if they are annotated with <code class="docutils literal notranslate"><span class="pre">@torch.jit.script</span></code>, similar to how a TorchScript function would be declared:</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">class</span> <span class="nc">MyClass</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">x</span><span class="p">:</span> <span class="nb">int</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="n">x</span>
<span class="k">def</span> <span class="nf">inc</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">:</span> <span class="nb">int</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="n">val</span>
</pre></div>
</div>
<p><strong>Example 2</strong></p>
<p>A TorchScript custom class type must “declare” all its instance attributes by assignments in <code class="docutils literal notranslate"><span class="pre">__init__()</span></code>. If an instance attribute is not defined in <code class="docutils literal notranslate"><span class="pre">__init__()</span></code> but accessed in other methods of the class, the class cannot be compiled as a TorchScript class, as shown in the following example:</p>
<div class="highlight-default 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">class</span> <span class="nc">foo</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="bp">self</span><span class="o">.</span><span class="n">y</span> <span class="o">=</span> <span class="mi">1</span>
<span class="c1"># ERROR: self.x is not defined in __init__</span>
<span class="k">def</span> <span class="nf">assign_x</span><span class="p">(</span><span class="bp">self</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="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>
</pre></div>
</div>
<p>The class will fail to compile and issue the following error:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>RuntimeError:
Tried to set nonexistent attribute: x. Did you forget to initialize it in __init__()?:
def assign_x(self):
self.x = torch.rand(2, 3)
~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
</pre></div>
</div>
<p><strong>Example 3</strong></p>
<p>In this example, a TorchScript custom class defines a class variable name, which is not allowed:</p>
<div class="highlight-default 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">class</span> <span class="nc">MyClass</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
<span class="n">name</span> <span class="o">=</span> <span class="s2">"MyClass"</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">x</span><span class="p">:</span> <span class="nb">int</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="n">x</span>
<span class="k">def</span> <span class="nf">fn</span><span class="p">(</span><span class="n">a</span><span class="p">:</span> <span class="n">MyClass</span><span class="p">):</span>
<span class="k">return</span> <span class="n">a</span><span class="o">.</span><span class="n">name</span>
</pre></div>
</div>
<p>It leads to the following compile-time error:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>RuntimeError:
'__torch__.MyClass' object has no attribute or method 'name'. Did you forget to initialize an attribute in __init__()?:
File "test-class2.py", line 10
def fn(a: MyClass):
return a.name
~~~~~~ <--- HERE
</pre></div>
</div>
</div>
<div class="section" id="enum-type">
<h3>Enum Type<a class="headerlink" href="#enum-type" title="Permalink to this headline">¶</a></h3>
<p>Like custom classes, semantics of the enum type are user-defined and the entire class definition must be compilable to TorchScript IR and adhere to TorchScript type-checking rules.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">TSEnumDef</span> <span class="p">:</span><span class="o">:=</span> <span class="s2">"class"</span> <span class="n">Identifier</span> <span class="s2">"(enum.Enum | TSEnumType)"</span> <span class="s2">":"</span>
<span class="p">(</span> <span class="n">MemberIdentifier</span> <span class="s2">"="</span> <span class="n">Value</span> <span class="p">)</span><span class="o">+</span>
<span class="p">(</span> <span class="n">MethodDefinition</span> <span class="p">)</span><span class="o">*</span>
</pre></div>
</div>
<p>Where:</p>
<ul class="simple">
<li><p>Value must be a TorchScript literal of type <code class="docutils literal notranslate"><span class="pre">int</span></code>, <code class="docutils literal notranslate"><span class="pre">float</span></code>, or <code class="docutils literal notranslate"><span class="pre">str</span></code>, and must be of the same TorchScript type.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">TSEnumType</span></code> is the name of a TorchScript enumerated type. Similar to Python enum, TorchScript allows restricted <code class="docutils literal notranslate"><span class="pre">Enum</span></code> subclassing, that is, subclassing an enumerated is allowed only if it does not define any members.</p></li>
</ul>
<p><strong>Compared to Python</strong></p>
<ul class="simple">
<li><p>TorchScript supports only <code class="docutils literal notranslate"><span class="pre">enum.Enum</span></code>. It does not support other variations such as <code class="docutils literal notranslate"><span class="pre">enum.IntEnum</span></code>, <code class="docutils literal notranslate"><span class="pre">enum.Flag</span></code>, <code class="docutils literal notranslate"><span class="pre">enum.IntFlag</span></code>, and <code class="docutils literal notranslate"><span class="pre">enum.auto</span></code>.</p></li>
<li><p>Values of TorchScript enum members must be of the same type and can only be <code class="docutils literal notranslate"><span class="pre">int</span></code>, <code class="docutils literal notranslate"><span class="pre">float</span></code>, or <code class="docutils literal notranslate"><span class="pre">str</span></code> types, whereas Python enum members can be of any type.</p></li>
<li><p>Enums containing methods are ignored in TorchScript.</p></li>
</ul>
<p><strong>Example 1</strong></p>
<p>The following example defines the class <code class="docutils literal notranslate"><span class="pre">Color</span></code> as an <code class="docutils literal notranslate"><span class="pre">Enum</span></code> type:</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">from</span> <span class="nn">enum</span> <span class="kn">import</span> <span class="n">Enum</span>
<span class="k">class</span> <span class="nc">Color</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
<span class="n">RED</span> <span class="o">=</span> <span class="mi">1</span>
<span class="n">GREEN</span> <span class="o">=</span> <span class="mi">2</span>
<span class="k">def</span> <span class="nf">enum_fn</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">Color</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">Color</span><span class="p">)</span> <span class="o">-></span> <span class="nb">bool</span><span class="p">:</span>
<span class="k">if</span> <span class="n">x</span> <span class="o">==</span> <span class="n">Color</span><span class="o">.</span><span class="n">RED</span><span class="p">:</span>
<span class="k">return</span> <span class="kc">True</span>
<span class="k">return</span> <span class="n">x</span> <span class="o">==</span> <span class="n">y</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">enum_fn</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Eager: "</span><span class="p">,</span> <span class="n">enum_fn</span><span class="p">(</span><span class="n">Color</span><span class="o">.</span><span class="n">RED</span><span class="p">,</span> <span class="n">Color</span><span class="o">.</span><span class="n">GREEN</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"TorchScript: "</span><span class="p">,</span> <span class="n">m</span><span class="p">(</span><span class="n">Color</span><span class="o">.</span><span class="n">RED</span><span class="p">,</span> <span class="n">Color</span><span class="o">.</span><span class="n">GREEN</span><span class="p">))</span>
</pre></div>
</div>
<p><strong>Example 2</strong></p>
<p>The following example shows the case of restricted enum subclassing, where <code class="docutils literal notranslate"><span class="pre">BaseColor</span></code> does not define any member, thus can be subclassed by <code class="docutils literal notranslate"><span class="pre">Color</span></code>:</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">from</span> <span class="nn">enum</span> <span class="kn">import</span> <span class="n">Enum</span>
<span class="k">class</span> <span class="nc">BaseColor</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">foo</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">pass</span>
<span class="k">class</span> <span class="nc">Color</span><span class="p">(</span><span class="n">BaseColor</span><span class="p">):</span>
<span class="n">RED</span> <span class="o">=</span> <span class="mi">1</span>
<span class="n">GREEN</span> <span class="o">=</span> <span class="mi">2</span>
<span class="k">def</span> <span class="nf">enum_fn</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">Color</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">Color</span><span class="p">)</span> <span class="o">-></span> <span class="nb">bool</span><span class="p">:</span>
<span class="k">if</span> <span class="n">x</span> <span class="o">==</span> <span class="n">Color</span><span class="o">.</span><span class="n">RED</span><span class="p">:</span>
<span class="k">return</span> <span class="kc">True</span>
<span class="k">return</span> <span class="n">x</span> <span class="o">==</span> <span class="n">y</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">enum_fn</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"TorchScript: "</span><span class="p">,</span> <span class="n">m</span><span class="p">(</span><span class="n">Color</span><span class="o">.</span><span class="n">RED</span><span class="p">,</span> <span class="n">Color</span><span class="o">.</span><span class="n">GREEN</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Eager: "</span><span class="p">,</span> <span class="n">enum_fn</span><span class="p">(</span><span class="n">Color</span><span class="o">.</span><span class="n">RED</span><span class="p">,</span> <span class="n">Color</span><span class="o">.</span><span class="n">GREEN</span><span class="p">))</span>
</pre></div>
</div>
</div>
<div class="section" id="torchscript-module-class">
<h3>TorchScript Module Class<a class="headerlink" href="#torchscript-module-class" title="Permalink to this headline">¶</a></h3>
<p><code class="docutils literal notranslate"><span class="pre">TSModuleType</span></code> is a special class type that is inferred from object instances that are created outside TorchScript. <code class="docutils literal notranslate"><span class="pre">TSModuleType</span></code> is named by the Python class of the object instance. The <code class="docutils literal notranslate"><span class="pre">__init__()</span></code> method of the Python class is not considered a TorchScript method, so it does not have to comply with TorchScript’s type-checking rules.</p>
<p>The type schema of a module instance class is constructed directly from an instance object (created outside the scope of TorchScript) rather than inferred from <code class="docutils literal notranslate"><span class="pre">__init__()</span></code> like custom classes. It is possible that two objects of the same instance class type follow two different type schemas.</p>
<p>In this sense, <code class="docutils literal notranslate"><span class="pre">TSModuleType</span></code> is not really a static type. Therefore, for type safety considerations, <code class="docutils literal notranslate"><span class="pre">TSModuleType</span></code> cannot be used in a TorchScript type annotation or be composed with <code class="docutils literal notranslate"><span class="pre">TSType</span></code>.</p>
</div>
<div class="section" id="module-instance-class">
<h3>Module Instance Class<a class="headerlink" href="#module-instance-class" title="Permalink to this headline">¶</a></h3>
<p>TorchScript module type represents the type schema of a user-defined PyTorch module instance. When scripting a PyTorch module, the module object is always created outside TorchScript (i.e., passed in as parameter to <code class="docutils literal notranslate"><span class="pre">forward</span></code>). The Python module class is treated as a module instance class, so the <code class="docutils literal notranslate"><span class="pre">__init__()</span></code> method of the Python module class is not subject to the type-checking rules of TorchScript.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">TSModuleType</span> <span class="p">:</span><span class="o">:=</span> <span class="s2">"class"</span> <span class="n">Identifier</span> <span class="s2">"(torch.nn.Module)"</span> <span class="s2">":"</span>
<span class="n">ClassBodyDefinition</span>
</pre></div>
</div>
<p>Where:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">forward()</span></code> and other methods decorated with <code class="docutils literal notranslate"><span class="pre">@torch.jit.export</span></code> must be compilable to TorchScript IR and subject to TorchScript’s type-checking rules.</p></li>
</ul>
<p>Unlike custom classes, only the forward method and other methods decorated with <code class="docutils literal notranslate"><span class="pre">@torch.jit.export</span></code> of the module type need to be compilable. Most notably, <code class="docutils literal notranslate"><span class="pre">__init__()</span></code> is not considered a TorchScript method. Consequently, module type constructors cannot be invoked within the scope of TorchScript. Instead, TorchScript module objects are always constructed outside and passed into <code class="docutils literal notranslate"><span class="pre">torch.jit.script(ModuleObj)</span></code>.</p>
<p><strong>Example 1</strong></p>
<p>This example illustrates a few features of module types:</p>
<ul class="simple">
<li><p>The <code class="docutils literal notranslate"><span class="pre">TestModule</span></code> instance is created outside the scope of TorchScript (i.e., before invoking <code class="docutils literal notranslate"><span class="pre">torch.jit.script</span></code>).</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">__init__()</span></code> is not considered a TorchScript method, therefore, it does not have to be annotated and can contain arbitrary Python code. In addition, the <code class="docutils literal notranslate"><span class="pre">__init__()</span></code> method of an instance class cannot be invoked in TorchScript code. Because <code class="docutils literal notranslate"><span class="pre">TestModule</span></code> instances are instantiated in Python, in this example, <code class="docutils literal notranslate"><span class="pre">TestModule(2.0)</span></code> and <code class="docutils literal notranslate"><span class="pre">TestModule(2)</span></code> create two instances with different types for its data attributes. <code class="docutils literal notranslate"><span class="pre">self.x</span></code> is of type <code class="docutils literal notranslate"><span class="pre">float</span></code> for <code class="docutils literal notranslate"><span class="pre">TestModule(2.0)</span></code>, whereas <code class="docutils literal notranslate"><span class="pre">self.y</span></code> is of type <code class="docutils literal notranslate"><span class="pre">int</span></code> for <code class="docutils literal notranslate"><span class="pre">TestModule(2.0)</span></code>.</p></li>
<li><p>TorchScript automatically compiles other methods (e.g., <code class="docutils literal notranslate"><span class="pre">mul()</span></code>) invoked by methods annotated via <code class="docutils literal notranslate"><span class="pre">@torch.jit.export</span></code> or <code class="docutils literal notranslate"><span class="pre">forward()</span></code> methods.</p></li>
<li><p>Entry-points to a TorchScript program are either <code class="docutils literal notranslate"><span class="pre">forward()</span></code> of a module type, functions annotated as <code class="docutils literal notranslate"><span class="pre">torch.jit.script</span></code>, or methods annotated as <code class="docutils literal notranslate"><span class="pre">torch.jit.export</span></code>.</p></li>
</ul>
<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">TestModule</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">v</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="n">v</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">inc</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">x</span> <span class="o">+</span> <span class="n">inc</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">TestModule</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"First instance: </span><span class="si">{</span><span class="n">m</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</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">TestModule</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">5</span><span class="p">])))</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Second instance: </span><span class="si">{</span><span class="n">m</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
</pre></div>
</div>
<p>The example above produces the following output:</p>
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>First instance: 4
Second instance: tensor([4., 4., 4., 4., 4.])
</pre></div>
</div>
<p><strong>Example 2</strong></p>
<p>The following example shows an incorrect usage of module type. Specifically, this example invokes the constructor of <code class="docutils literal notranslate"><span class="pre">TestModule</span></code> inside the scope of TorchScript:</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">TestModule</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">v</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="n">v</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="nb">int</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">x</span> <span class="o">+</span> <span class="n">x</span>
<span class="k">class</span> <span class="nc">MyModel</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">v</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">val</span> <span class="o">=</span> <span class="n">v</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">doSomething</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-></span> <span class="nb">int</span><span class="p">:</span>
<span class="c1"># error: should not invoke the constructor of module type</span>
<span class="n">myModel</span> <span class="o">=</span> <span class="n">TestModule</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">val</span><span class="p">)</span>
<span class="k">return</span> <span class="n">myModel</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
<span class="c1"># m = torch.jit.script(MyModel(2)) # Results in below RuntimeError</span>
<span class="c1"># RuntimeError: Could not get name of python class object</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="type-annotation">
<span id="id2"></span><h2><a class="toc-backref" href="#id9">Type Annotation</a><a class="headerlink" href="#type-annotation" title="Permalink to this headline">¶</a></h2>
<p>Since TorchScript is statically typed, programmers need to annotate types at <em>strategic points</em> of TorchScript code so that every local variable or
instance data attribute has a static type, and every function and method has a statically typed signature.</p>
<div class="section" id="when-to-annotate-types">
<h3>When to Annotate Types<a class="headerlink" href="#when-to-annotate-types" title="Permalink to this headline">¶</a></h3>
<p>In general, type annotations are only needed in places where static types cannot be automatically inferred (e.g., parameters or sometimes return types to
methods or functions). Types of local variables and data attributes are often automatically inferred from their assignment statements. Sometimes an inferred type
may be too restrictive, e.g., <code class="docutils literal notranslate"><span class="pre">x</span></code> being inferred as <code class="docutils literal notranslate"><span class="pre">NoneType</span></code> through assignment <code class="docutils literal notranslate"><span class="pre">x</span> <span class="pre">=</span> <span class="pre">None</span></code>, whereas <code class="docutils literal notranslate"><span class="pre">x</span></code> is actually used as an <code class="docutils literal notranslate"><span class="pre">Optional</span></code>. In such
cases, type annotations may be needed to overwrite auto inference, e.g., <code class="docutils literal notranslate"><span class="pre">x:</span> <span class="pre">Optional[int]</span> <span class="pre">=</span> <span class="pre">None</span></code>. Note that it is always safe to type annotate a local variable
or data attribute even if its type can be automatically inferred. The annotated type must be congruent with TorchScript’s type-checking.</p>
<p>When a parameter, local variable, or data attribute is not type annotated and its type cannot be automatically inferred, TorchScript assumes it to be a
default type of <code class="docutils literal notranslate"><span class="pre">TensorType</span></code>, <code class="docutils literal notranslate"><span class="pre">List[TensorType]</span></code>, or <code class="docutils literal notranslate"><span class="pre">Dict[str,</span> <span class="pre">TensorType]</span></code>.</p>
</div>
<div class="section" id="annotate-function-signature">
<h3>Annotate Function Signature<a class="headerlink" href="#annotate-function-signature" title="Permalink to this headline">¶</a></h3>
<p>Since a parameter may not be automatically inferred from the body of the function (including both functions and methods), they need to be type annotated. Otherwise, they assume the default type <code class="docutils literal notranslate"><span class="pre">TensorType</span></code>.</p>
<p>TorchScript supports two styles for method and function signature type annotation:</p>
<ul class="simple">
<li><p><strong>Python3-style</strong> annotates types directly on the signature. As such, it allows individual parameters to be left unannotated (whose type will be the default type of <code class="docutils literal notranslate"><span class="pre">TensorType</span></code>), or allows the return type to be left unannotated (whose type will be automatically inferred).</p></li>
</ul>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Python3Annotation</span> <span class="p">:</span><span class="o">:=</span> <span class="s2">"def"</span> <span class="n">Identifier</span> <span class="p">[</span> <span class="s2">"("</span> <span class="n">ParamAnnot</span><span class="o">*</span> <span class="s2">")"</span> <span class="p">]</span> <span class="p">[</span><span class="n">ReturnAnnot</span><span class="p">]</span> <span class="s2">":"</span>
<span class="n">FuncOrMethodBody</span>
<span class="n">ParamAnnot</span> <span class="p">:</span><span class="o">:=</span> <span class="n">Identifier</span> <span class="p">[</span> <span class="s2">":"</span> <span class="n">TSType</span> <span class="p">]</span> <span class="s2">","</span>
<span class="n">ReturnAnnot</span> <span class="p">:</span><span class="o">:=</span> <span class="s2">"->"</span> <span class="n">TSType</span>
</pre></div>
</div>
<p>Note that when using Python3 style, the type <code class="docutils literal notranslate"><span class="pre">self</span></code> is automatically inferred and should not be annotated.</p>
<ul class="simple">
<li><p><strong>Mypy style</strong> annotates types as a comment right below the function/method declaration. In the Mypy style, since parameter names do not appear in the annotation, all parameters have to be annotated.</p></li>
</ul>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">MyPyAnnotation</span> <span class="p">:</span><span class="o">:=</span> <span class="s2">"# type:"</span> <span class="s2">"("</span> <span class="n">ParamAnnot</span><span class="o">*</span> <span class="s2">")"</span> <span class="p">[</span> <span class="n">ReturnAnnot</span> <span class="p">]</span>
<span class="n">ParamAnnot</span> <span class="p">:</span><span class="o">:=</span> <span class="n">TSType</span> <span class="s2">","</span>
<span class="n">ReturnAnnot</span> <span class="p">:</span><span class="o">:=</span> <span class="s2">"->"</span> <span class="n">TSType</span>
</pre></div>
</div>
<p><strong>Example 1</strong></p>
<p>In this example:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">a</span></code> is not annotated and assumes the default type of <code class="docutils literal notranslate"><span class="pre">TensorType</span></code>.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">b</span></code> is annotated as type <code class="docutils literal notranslate"><span class="pre">int</span></code>.</p></li>
<li><p>The return type is not annotated and is automatically inferred as type <code class="docutils literal notranslate"><span class="pre">TensorType</span></code> (based on the type of the value being returned).</p></li>
</ul>
<div class="highlight-default 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">f</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
<span class="k">return</span> <span class="n">a</span><span class="o">+</span><span class="n">b</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">f</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"TorchScript:"</span><span class="p">,</span> <span class="n">m</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">6</span><span class="p">]),</span> <span class="mi">100</span><span class="p">))</span>
</pre></div>
</div>
<p><strong>Example 2</strong></p>
<p>The following example uses Mypy style annotation. Note that parameters or return values must be annotated even if some of
them assume the default type.</p>
<div class="highlight-default 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">f</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">):</span>
<span class="c1"># type: (torch.Tensor, int) → torch.Tensor</span>
<span class="k">return</span> <span class="n">a</span><span class="o">+</span><span class="n">b</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">f</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"TorchScript:"</span><span class="p">,</span> <span class="n">m</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">6</span><span class="p">]),</span> <span class="mi">100</span><span class="p">))</span>
</pre></div>
</div>
</div>
<div class="section" id="annotate-variables-and-data-attributes">
<h3>Annotate Variables and Data Attributes<a class="headerlink" href="#annotate-variables-and-data-attributes" title="Permalink to this headline">¶</a></h3>
<p>In general, types of data attributes (including class and instance data attributes) and local variables can be automatically inferred from assignment statements.
Sometimes, however, if a variable or attribute is associated with values of different types (e.g., as <code class="docutils literal notranslate"><span class="pre">None</span></code> or <code class="docutils literal notranslate"><span class="pre">TensorType</span></code>), then they may need to be explicitly
type annotated as a <em>wider</em> type such as <code class="docutils literal notranslate"><span class="pre">Optional[int]</span></code> or <code class="docutils literal notranslate"><span class="pre">Any</span></code>.</p>
<div class="section" id="local-variables">