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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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<li class="toctree-l1"><a class="reference internal" href="notes/cpu_threading_torchscript_inference.html">CPU threading and TorchScript inference</a></li>
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<li><p><a class="reference internal" href="#torchscript-language-reference" id="id7">TorchScript Language Reference</a></p>
<ul>
<li><p><a class="reference internal" href="#supported-type" id="id8">Types</a></p></li>
<li><p><a class="reference internal" href="#expressions" id="id9">Expressions</a></p></li>
<li><p><a class="reference internal" href="#statements" id="id10">Statements</a></p></li>
<li><p><a class="reference internal" href="#variable-resolution" id="id11">Variable Resolution</a></p></li>
<li><p><a class="reference internal" href="#use-of-python-values" id="id12">Use of Python Values</a></p></li>
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<div class="section" id="torchscript-language-reference">
<span id="language-reference"></span><h1><a class="toc-backref" href="#id7">TorchScript Language Reference</a><a class="headerlink" href="#torchscript-language-reference" title="Permalink to this headline">¶</a></h1>
<p>TorchScript is a statically typed subset of Python that can either be written directly (using
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> decorator) or generated automatically from Python code via
tracing. When using tracing, code is automatically converted into this subset of
Python by recording only the actual operators on tensors and simply executing and
discarding the other surrounding Python code.</p>
<p>When writing TorchScript directly using <code class="docutils literal notranslate"><span class="pre">@torch.jit.script</span></code> decorator, the programmer must
only use the subset of Python supported in TorchScript. This section documents
what is supported in TorchScript as if it were a language reference for a stand
alone language. Any features of Python not mentioned in this reference are not
part of TorchScript. See <cite>Builtin Functions</cite> for a complete reference of available
Pytorch tensor methods, modules, and functions.</p>
<p>As a subset of Python, any valid TorchScript function is also a valid Python
function. This makes it possible to <cite>disable TorchScript</cite> and debug the
function using standard Python tools like <code class="docutils literal notranslate"><span class="pre">pdb</span></code>. The reverse is not true: there
are many valid Python programs that are not valid TorchScript programs.
Instead, TorchScript focuses specifically on the features of Python that are
needed to represent neural network models in PyTorch.</p>
<div class="section" id="supported-type">
<span id="types"></span><span id="id1"></span><h2><a class="toc-backref" href="#id8">Types</a><a class="headerlink" href="#supported-type" title="Permalink to this headline">¶</a></h2>
<p>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. In particular, TorchScript supports:</p>
<table class="docutils colwidths-auto align-default">
<thead>
<tr class="row-odd"><th class="head"><p>Type</p></th>
<th class="head"><p>Description</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">Tensor</span></code></p></td>
<td><p>A PyTorch tensor of any dtype, dimension, or backend</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">Tuple[T0,</span> <span class="pre">T1,</span> <span class="pre">...,</span> <span class="pre">TN]</span></code></p></td>
<td><p>A tuple containing subtypes <code class="docutils literal notranslate"><span class="pre">T0</span></code>, <code class="docutils literal notranslate"><span class="pre">T1</span></code>, etc. (e.g. <code class="docutils literal notranslate"><span class="pre">Tuple[Tensor,</span> <span class="pre">Tensor]</span></code>)</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">bool</span></code></p></td>
<td><p>A boolean value</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">int</span></code></p></td>
<td><p>A scalar integer</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">float</span></code></p></td>
<td><p>A scalar floating point number</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">str</span></code></p></td>
<td><p>A string</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">List[T]</span></code></p></td>
<td><p>A list of which all members are type <code class="docutils literal notranslate"><span class="pre">T</span></code></p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">Optional[T]</span></code></p></td>
<td><p>A value which is either None or type <code class="docutils literal notranslate"><span class="pre">T</span></code></p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">Dict[K,</span> <span class="pre">V]</span></code></p></td>
<td><p>A dict with key type <code class="docutils literal notranslate"><span class="pre">K</span></code> and value type <code class="docutils literal notranslate"><span class="pre">V</span></code>. Only <code class="docutils literal notranslate"><span class="pre">str</span></code>, <code class="docutils literal notranslate"><span class="pre">int</span></code>, and <code class="docutils literal notranslate"><span class="pre">float</span></code> are allowed as key types.</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">T</span></code></p></td>
<td><p>A <a class="reference internal" href="#torchscript-class">TorchScript Class</a></p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">E</span></code></p></td>
<td><p>A <a class="reference internal" href="#torchscript-enum">TorchScript Enum</a></p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">NamedTuple[T0,</span> <span class="pre">T1,</span> <span class="pre">...]</span></code></p></td>
<td><p>A <a class="reference external" href="https://docs.python.org/3/library/collections.html#collections.namedtuple" title="(in Python v3.10)"><code class="xref py py-func docutils literal notranslate"><span class="pre">collections.namedtuple</span></code></a> tuple type</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">Union[T0,</span> <span class="pre">T1,</span> <span class="pre">...]</span></code></p></td>
<td><p>One of the subtypes <code class="docutils literal notranslate"><span class="pre">T0</span></code>, <code class="docutils literal notranslate"><span class="pre">T1</span></code>, etc.</p></td>
</tr>
</tbody>
</table>
<p>Unlike Python, each variable in TorchScript function must have a single static type.
This makes it easier to optimize TorchScript functions.</p>
<p>Example (a type mismatch)</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="nd">@torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span>
<span class="k">def</span> <span class="nf">an_error</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">if</span> <span class="n">x</span><span class="p">:</span>
<span class="n">r</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">r</span> <span class="o">=</span> <span class="mi">4</span>
<span class="k">return</span> <span class="n">r</span>
</pre></div>
</div>
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>Traceback (most recent call last):
...
RuntimeError: ...
Type mismatch: r is set to type Tensor in the true branch and type int in the false branch:
@torch.jit.script
def an_error(x):
if x:
~~~~~
r = torch.rand(1)
~~~~~~~~~~~~~~~~~
else:
~~~~~
r = 4
~~~~~ <--- HERE
return r
and was used here:
else:
r = 4
return r
~ <--- HERE...
</pre></div>
</div>
<div class="section" id="unsupported-typing-constructs">
<h3>Unsupported Typing Constructs<a class="headerlink" href="#unsupported-typing-constructs" title="Permalink to this headline">¶</a></h3>
<p>TorchScript does not support all features and types of the <a class="reference external" href="https://docs.python.org/3/library/typing.html#module-typing" title="(in Python v3.10)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">typing</span></code></a> module. Some of these
are more fundamental things that are unlikely to be added in the future while others
may be added if there is enough user demand to make it a priority.</p>
<p>These types and features from the <a class="reference external" href="https://docs.python.org/3/library/typing.html#module-typing" title="(in Python v3.10)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">typing</span></code></a> module are unavailable in TorchScript.</p>
<table class="docutils colwidths-auto align-default">
<thead>
<tr class="row-odd"><th class="head"><p>Item</p></th>
<th class="head"><p>Description</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.10)"><code class="xref any docutils literal notranslate"><span class="pre">typing.Any</span></code></a></p></td>
<td><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.10)"><code class="xref any docutils literal notranslate"><span class="pre">typing.Any</span></code></a> is currently in development but not yet released</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.NoReturn" title="(in Python v3.10)"><code class="xref any docutils literal notranslate"><span class="pre">typing.NoReturn</span></code></a></p></td>
<td><p>Not implemented</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Sequence" title="(in Python v3.10)"><code class="xref any docutils literal notranslate"><span class="pre">typing.Sequence</span></code></a></p></td>
<td><p>Not implemented</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.10)"><code class="xref any docutils literal notranslate"><span class="pre">typing.Callable</span></code></a></p></td>
<td><p>Not implemented</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Literal" title="(in Python v3.10)"><code class="xref any docutils literal notranslate"><span class="pre">typing.Literal</span></code></a></p></td>
<td><p>Not implemented</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.ClassVar" title="(in Python v3.10)"><code class="xref any docutils literal notranslate"><span class="pre">typing.ClassVar</span></code></a></p></td>
<td><p>Not implemented</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Final" title="(in Python v3.10)"><code class="xref any docutils literal notranslate"><span class="pre">typing.Final</span></code></a></p></td>
<td><p>This is supported for <a class="reference internal" href="#module-attributes"><span class="std std-ref">module attributes</span></a> class attribute annotations but not for functions</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.AnyStr" title="(in Python v3.10)"><code class="xref any docutils literal notranslate"><span class="pre">typing.AnyStr</span></code></a></p></td>
<td><p>TorchScript does not support <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bytes" title="(in Python v3.10)"><code class="xref any docutils literal notranslate"><span class="pre">bytes</span></code></a> so this type is not used</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.overload" title="(in Python v3.10)"><code class="xref any docutils literal notranslate"><span class="pre">typing.overload</span></code></a></p></td>
<td><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.overload" title="(in Python v3.10)"><code class="xref any docutils literal notranslate"><span class="pre">typing.overload</span></code></a> is currently in development but not yet released</p></td>
</tr>
<tr class="row-odd"><td><p>Type aliases</p></td>
<td><p>Not implemented</p></td>
</tr>
<tr class="row-even"><td><p>Nominal vs structural subtyping</p></td>
<td><p>Nominal typing is in development, but structural typing is not</p></td>
</tr>
<tr class="row-odd"><td><p>NewType</p></td>
<td><p>Unlikely to be implemented</p></td>
</tr>
<tr class="row-even"><td><p>Generics</p></td>
<td><p>Unlikely to be implemented</p></td>
</tr>
</tbody>
</table>
<p>Any other functionality from the <a class="reference external" href="https://docs.python.org/3/library/typing.html#module-typing" title="(in Python v3.10)"><code class="xref any docutils literal notranslate"><span class="pre">typing</span></code></a> module not explicitly listed in this documentation is unsupported.</p>
</div>
<div class="section" id="default-types">
<h3>Default Types<a class="headerlink" href="#default-types" title="Permalink to this headline">¶</a></h3>
<p>By default, all parameters to a TorchScript function are assumed to be Tensor.
To specify that an argument to a TorchScript function is another type, it is possible to use
MyPy-style type annotations using the types listed above.</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="nd">@torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span>
<span class="k">def</span> <span class="nf">foo</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">tup</span><span class="p">):</span>
<span class="c1"># type: (int, Tuple[Tensor, Tensor]) -> Tensor</span>
<span class="n">t0</span><span class="p">,</span> <span class="n">t1</span> <span class="o">=</span> <span class="n">tup</span>
<span class="k">return</span> <span class="n">t0</span> <span class="o">+</span> <span class="n">t1</span> <span class="o">+</span> <span class="n">x</span>
<span class="nb">print</span><span class="p">(</span><span class="n">foo</span><span class="p">(</span><span class="mi">3</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>
</pre></div>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>It is also possible to annotate types with Python 3 type hints from the
<code class="docutils literal notranslate"><span class="pre">typing</span></code> module.</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Tuple</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="nb">int</span><span class="p">,</span> <span class="n">tup</span><span class="p">:</span> <span class="n">Tuple</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">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">])</span> <span class="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
<span class="n">t0</span><span class="p">,</span> <span class="n">t1</span> <span class="o">=</span> <span class="n">tup</span>
<span class="k">return</span> <span class="n">t0</span> <span class="o">+</span> <span class="n">t1</span> <span class="o">+</span> <span class="n">x</span>
<span class="nb">print</span><span class="p">(</span><span class="n">foo</span><span class="p">(</span><span class="mi">3</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>
</pre></div>
</div>
</div>
<p>An empty list is assumed to be <code class="docutils literal notranslate"><span class="pre">List[Tensor]</span></code> and empty dicts
<code class="docutils literal notranslate"><span class="pre">Dict[str,</span> <span class="pre">Tensor]</span></code>. To instantiate an empty list or dict of other types,
use <cite>Python 3 type hints</cite>.</p>
<p>Example (type annotations for Python 3):</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Tuple</span>
<span class="k">class</span> <span class="nc">EmptyDataStructures</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">EmptyDataStructures</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="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="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-></span> <span class="n">Tuple</span><span class="p">[</span><span class="n">List</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">float</span><span class="p">]],</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">int</span><span class="p">]]:</span>
<span class="c1"># This annotates the list to be a `List[Tuple[int, float]]`</span>
<span class="n">my_list</span><span class="p">:</span> <span class="n">List</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">float</span><span class="p">]]</span> <span class="o">=</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">10</span><span class="p">):</span>
<span class="n">my_list</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">item</span><span class="p">()))</span>
<span class="n">my_dict</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">return</span> <span class="n">my_list</span><span class="p">,</span> <span class="n">my_dict</span>
<span class="n">x</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">EmptyDataStructures</span><span class="p">())</span>
</pre></div>
</div>
</div>
<div class="section" id="optional-type-refinement">
<h3>Optional Type Refinement<a class="headerlink" href="#optional-type-refinement" title="Permalink to this headline">¶</a></h3>
<p>TorchScript will refine the type of a variable of type <code class="docutils literal notranslate"><span class="pre">Optional[T]</span></code> when
a comparison to <code class="docutils literal notranslate"><span class="pre">None</span></code> is made inside the conditional of an if-statement or checked in an <code class="docutils literal notranslate"><span class="pre">assert</span></code>.
The compiler can reason about multiple <code class="docutils literal notranslate"><span class="pre">None</span></code> checks that are combined with
<code class="docutils literal notranslate"><span class="pre">and</span></code>, <code class="docutils literal notranslate"><span class="pre">or</span></code>, and <code class="docutils literal notranslate"><span class="pre">not</span></code>. Refinement will also occur for else blocks of if-statements
that are not explicitly written.</p>
<p>The <code class="docutils literal notranslate"><span class="pre">None</span></code> check must be within the if-statement’s condition; assigning
a <code class="docutils literal notranslate"><span class="pre">None</span></code> check to a variable and using it in the if-statement’s condition will
not refine the types of variables in the check.
Only local variables will be refined, an attribute like <code class="docutils literal notranslate"><span class="pre">self.x</span></code> will not and must assigned to
a local variable to be refined.</p>
<p>Example (refining types on parameters and locals):</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Optional</span>
<span class="k">class</span> <span class="nc">M</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="n">z</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</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">z</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">M</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="c1"># If `z` is None, its type cannot be inferred, so it must</span>
<span class="c1"># be specified (above)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">z</span> <span class="o">=</span> <span class="n">z</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="n">y</span><span class="p">,</span> <span class="n">z</span><span class="p">):</span>
<span class="c1"># type: (Optional[int], Optional[int], Optional[int]) -> int</span>
<span class="k">if</span> <span class="n">x</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">x</span> <span class="o">=</span> <span class="mi">1</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="mi">1</span>
<span class="c1"># Refinement for an attribute by assigning it to a local</span>
<span class="n">z</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">z</span>
<span class="k">if</span> <span class="n">y</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">z</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">y</span> <span class="o">+</span> <span class="n">z</span>
<span class="c1"># Refinement via an `assert`</span>
<span class="k">assert</span> <span class="n">z</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="n">x</span> <span class="o">+=</span> <span class="n">z</span>
<span class="k">return</span> <span class="n">x</span>
<span class="n">module</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">M</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span>
<span class="n">module</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">M</span><span class="p">(</span><span class="kc">None</span><span class="p">))</span>
</pre></div>
</div>
</div>
<div class="section" id="id2">
<span id="torchscript-classes"></span><span id="torchscript-class"></span><span id="id3"></span><h3>TorchScript Classes<a class="headerlink" href="#id2" title="Permalink to this headline">¶</a></h3>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>TorchScript class support is experimental. Currently it is best suited
for simple record-like types (think a <code class="docutils literal notranslate"><span class="pre">NamedTuple</span></code> with methods
attached).</p>
</div>
<p>Python classes can be used in TorchScript if they are annotated with <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>,
similar to how you would declare a TorchScript function:</p>
<div class="highlight-python3 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">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="n">x</span><span class="p">,</span> <span class="n">y</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">aug_add_x</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="bp">self</span><span class="o">.</span><span class="n">x</span> <span class="o">+=</span> <span class="n">inc</span>
</pre></div>
</div>
<p>This subset is restricted:</p>
<ul>
<li><p>All functions must be valid TorchScript functions (including <code class="docutils literal notranslate"><span class="pre">__init__()</span></code>).</p></li>
<li><p>Classes must be new-style classes, as we use <code class="docutils literal notranslate"><span class="pre">__new__()</span></code> to construct them with pybind11.</p></li>
<li><p>TorchScript classes are statically typed. Members can only be declared by assigning to
self in the <code class="docutils literal notranslate"><span class="pre">__init__()</span></code> method.</p>
<blockquote>
<div><p>For example, assigning to <code class="docutils literal notranslate"><span class="pre">self</span></code> outside of the <code class="docutils literal notranslate"><span class="pre">__init__()</span></code> method:</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">Foo</span><span class="p">:</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>Will result in:</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>
</div></blockquote>
</li>
<li><p>No expressions except method definitions are allowed in the body of the class.</p></li>
<li><p>No support for inheritance or any other polymorphism strategy, except for inheriting
from <code class="docutils literal notranslate"><span class="pre">object</span></code> to specify a new-style class.</p></li>
</ul>
<p>After a class is defined, it can be used in both TorchScript and Python interchangeably
like any other TorchScript type:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Declare a TorchScript class</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">Pair</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">first</span><span class="p">,</span> <span class="n">second</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">first</span> <span class="o">=</span> <span class="n">first</span>
<span class="bp">self</span><span class="o">.</span><span class="n">second</span> <span class="o">=</span> <span class="n">second</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">sum_pair</span><span class="p">(</span><span class="n">p</span><span class="p">):</span>
<span class="c1"># type: (Pair) -> Tensor</span>
<span class="k">return</span> <span class="n">p</span><span class="o">.</span><span class="n">first</span> <span class="o">+</span> <span class="n">p</span><span class="o">.</span><span class="n">second</span>
<span class="n">p</span> <span class="o">=</span> <span class="n">Pair</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="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="nb">print</span><span class="p">(</span><span class="n">sum_pair</span><span class="p">(</span><span class="n">p</span><span class="p">))</span>
</pre></div>
</div>
</div>
<div class="section" id="id4">
<span id="torchscript-enums"></span><span id="torchscript-enum"></span><span id="id5"></span><h3>TorchScript Enums<a class="headerlink" href="#id4" title="Permalink to this headline">¶</a></h3>
<p>Python enums can be used in TorchScript without any extra annotation or code:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></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="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">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>
</pre></div>
</div>
<p>After an enum is defined, it can be used in both TorchScript and Python interchangeably
like any other TorchScript type. The type of the values of an enum must 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>. All values must be of the same type; heterogenous types for enum
values are not supported.</p>
</div>
<div class="section" id="named-tuples">
<h3>Named Tuples<a class="headerlink" href="#named-tuples" title="Permalink to this headline">¶</a></h3>
<p>Types produced by <a class="reference external" href="https://docs.python.org/3/library/collections.html#collections.namedtuple" title="(in Python v3.10)"><code class="xref py py-func docutils literal notranslate"><span class="pre">collections.namedtuple</span></code></a> can be used in 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="kn">import</span> <span class="nn">collections</span>
<span class="n">Point</span> <span class="o">=</span> <span class="n">collections</span><span class="o">.</span><span class="n">namedtuple</span><span class="p">(</span><span class="s1">'Point'</span><span class="p">,</span> <span class="p">[</span><span class="s1">'x'</span><span class="p">,</span> <span class="s1">'y'</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">total</span><span class="p">(</span><span class="n">point</span><span class="p">):</span>
<span class="c1"># type: (Point) -> Tensor</span>
<span class="k">return</span> <span class="n">point</span><span class="o">.</span><span class="n">x</span> <span class="o">+</span> <span class="n">point</span><span class="o">.</span><span class="n">y</span>
<span class="n">p</span> <span class="o">=</span> <span class="n">Point</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">rand</span><span class="p">(</span><span class="mi">3</span><span class="p">),</span> <span class="n">y</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="nb">print</span><span class="p">(</span><span class="n">total</span><span class="p">(</span><span class="n">p</span><span class="p">))</span>
</pre></div>
</div>
</div>
<div class="section" id="iterables">
<span id="jit-iterables"></span><h3>Iterables<a class="headerlink" href="#iterables" title="Permalink to this headline">¶</a></h3>
<p>Some functions (for example, <a class="reference external" href="https://docs.python.org/3/library/functions.html#zip" title="(in Python v3.10)"><code class="xref any docutils literal notranslate"><span class="pre">zip</span></code></a> and <a class="reference external" href="https://docs.python.org/3/library/functions.html#enumerate" title="(in Python v3.10)"><code class="xref any docutils literal notranslate"><span class="pre">enumerate</span></code></a>) can only operate on iterable types.
Iterable types in TorchScript include <code class="docutils literal notranslate"><span class="pre">Tensor</span></code>s, lists, tuples, dictionaries, strings,
<a class="reference internal" href="generated/torch.nn.ModuleList.html#torch.nn.ModuleList" title="torch.nn.ModuleList"><code class="xref any py py-class docutils literal notranslate"><span class="pre">torch.nn.ModuleList</span></code></a> and <a class="reference internal" href="generated/torch.nn.ModuleDict.html#torch.nn.ModuleDict" title="torch.nn.ModuleDict"><code class="xref any py py-class docutils literal notranslate"><span class="pre">torch.nn.ModuleDict</span></code></a>.</p>
</div>
</div>
<div class="section" id="expressions">
<h2><a class="toc-backref" href="#id9">Expressions</a><a class="headerlink" href="#expressions" title="Permalink to this headline">¶</a></h2>
<p>The following Python Expressions are supported.</p>
<div class="section" id="literals">
<h3>Literals<a class="headerlink" href="#literals" title="Permalink to this headline">¶</a></h3>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kc">True</span>
<span class="kc">False</span>
<span class="kc">None</span>
<span class="s1">'string literals'</span>
<span class="s2">"string literals"</span>
<span class="mi">3</span> <span class="c1"># interpreted as int</span>
<span class="mf">3.4</span> <span class="c1"># interpreted as a float</span>
</pre></div>
</div>
<div class="section" id="list-construction">
<h4>List Construction<a class="headerlink" href="#list-construction" title="Permalink to this headline">¶</a></h4>
<p>An empty list is assumed have type <code class="docutils literal notranslate"><span class="pre">List[Tensor]</span></code>.
The types of other list literals are derived from the type of the members.
See <a class="reference internal" href="#default-types">Default Types</a> for more details.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></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="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">4</span><span class="p">)]</span>
</pre></div>
</div>
</div>
<div class="section" id="tuple-construction">
<h4>Tuple Construction<a class="headerlink" href="#tuple-construction" title="Permalink to this headline">¶</a></h4>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></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="p">(</span><span class="mi">3</span><span class="p">,)</span>
</pre></div>
</div>
</div>
<div class="section" id="dict-construction">
<h4>Dict Construction<a class="headerlink" href="#dict-construction" title="Permalink to this headline">¶</a></h4>
<p>An empty dict is assumed have type <code class="docutils literal notranslate"><span class="pre">Dict[str,</span> <span class="pre">Tensor]</span></code>.
The types of other dict literals are derived from the type of the members.
See <a class="reference internal" href="#default-types">Default Types</a> for more details.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">{</span><span class="s1">'hello'</span><span class="p">:</span> <span class="mi">3</span><span class="p">}</span>
<span class="p">{}</span>
<span class="p">{</span><span class="s1">'a'</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="s1">'b'</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>
</pre></div>
</div>
</div>
</div>
<div class="section" id="variables">
<h3>Variables<a class="headerlink" href="#variables" title="Permalink to this headline">¶</a></h3>
<p>See <a class="reference internal" href="#variable-resolution">Variable Resolution</a> for how variables are resolved.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">my_variable_name</span>
</pre></div>
</div>
</div>
<div class="section" id="arithmetic-operators">
<h3>Arithmetic Operators<a class="headerlink" href="#arithmetic-operators" title="Permalink to this headline">¶</a></h3>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">a</span> <span class="o">+</span> <span class="n">b</span>
<span class="n">a</span> <span class="o">-</span> <span class="n">b</span>
<span class="n">a</span> <span class="o">*</span> <span class="n">b</span>
<span class="n">a</span> <span class="o">/</span> <span class="n">b</span>
<span class="n">a</span> <span class="o">^</span> <span class="n">b</span>
<span class="n">a</span> <span class="o">@</span> <span class="n">b</span>
</pre></div>
</div>
</div>
<div class="section" id="comparison-operators">
<h3>Comparison Operators<a class="headerlink" href="#comparison-operators" title="Permalink to this headline">¶</a></h3>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">a</span> <span class="o">==</span> <span class="n">b</span>
<span class="n">a</span> <span class="o">!=</span> <span class="n">b</span>
<span class="n">a</span> <span class="o"><</span> <span class="n">b</span>
<span class="n">a</span> <span class="o">></span> <span class="n">b</span>
<span class="n">a</span> <span class="o"><=</span> <span class="n">b</span>
<span class="n">a</span> <span class="o">>=</span> <span class="n">b</span>
</pre></div>
</div>
</div>
<div class="section" id="logical-operators">
<h3>Logical Operators<a class="headerlink" href="#logical-operators" title="Permalink to this headline">¶</a></h3>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">a</span> <span class="ow">and</span> <span class="n">b</span>
<span class="n">a</span> <span class="ow">or</span> <span class="n">b</span>
<span class="ow">not</span> <span class="n">b</span>
</pre></div>
</div>
</div>
<div class="section" id="subscripts-and-slicing">
<h3>Subscripts and Slicing<a class="headerlink" href="#subscripts-and-slicing" title="Permalink to this headline">¶</a></h3>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">t</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">t</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="n">t</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">2</span><span class="p">]</span>
<span class="n">t</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
<span class="n">t</span><span class="p">[:</span><span class="mi">1</span><span class="p">]</span>
<span class="n">t</span><span class="p">[:]</span>
<span class="n">t</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
<span class="n">t</span><span class="p">[</span><span class="mi">0</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">t</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">:</span><span class="mi">1</span><span class="p">]</span>
<span class="n">t</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">:,</span> <span class="mi">0</span><span class="p">]</span>
<span class="n">t</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="mi">0</span><span class="p">]</span>
<span class="n">t</span><span class="p">[</span><span class="n">i</span><span class="p">:</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span>
</pre></div>
</div>
</div>
<div class="section" id="function-calls">
<h3>Function Calls<a class="headerlink" href="#function-calls" title="Permalink to this headline">¶</a></h3>
<p>Calls to <cite>builtin functions</cite></p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int</span><span class="p">)</span>
</pre></div>
</div>
<p>Calls to other script functions:</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="nd">@torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span>
<span class="k">def</span> <span class="nf">foo</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="n">x</span> <span class="o">+</span> <span class="mi">1</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">foo</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="method-calls">
<h3>Method Calls<a class="headerlink" href="#method-calls" title="Permalink to this headline">¶</a></h3>
<p>Calls to methods of builtin types like tensor: <code class="docutils literal notranslate"><span class="pre">x.mm(y)</span></code></p>
<p>On modules, methods must be compiled before they can be called. The TorchScript
compiler recursively compiles methods it sees when compiling other methods. By default,
compilation starts on the <code class="docutils literal notranslate"><span class="pre">forward</span></code> method. Any methods called by <code class="docutils literal notranslate"><span class="pre">forward</span></code> will
be compiled, and any methods called by those methods, and so on. To start compilation at
a method other than <code class="docutils literal notranslate"><span class="pre">forward</span></code>, use the <a class="reference internal" href="jit.html#torch.jit.export" title="torch.jit.export"><code class="xref py py-func docutils literal notranslate"><span class="pre">@torch.jit.export</span></code></a> decorator
(<code class="docutils literal notranslate"><span class="pre">forward</span></code> implicitly is marked <code class="docutils literal notranslate"><span class="pre">@torch.jit.export</span></code>).</p>
<p>Calling a submodule directly (e.g. <code class="docutils literal notranslate"><span class="pre">self.resnet(input)</span></code>) is equivalent to
calling its <code class="docutils literal notranslate"><span class="pre">forward</span></code> method (e.g. <code class="docutils literal notranslate"><span class="pre">self.resnet.forward(input)</span></code>).</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="kn">import</span> <span class="nn">torchvision</span>
<span class="k">class</span> <span class="nc">MyModule</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">MyModule</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="n">means</span> <span class="o">=</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="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">means</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="n">resnet</span> <span class="o">=</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="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">resnet</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">helper</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="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">helper</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
<span class="c1"># Since nothing in the model calls `top_level_method`, the compiler</span>
<span class="c1"># must be explicitly told to compile this method</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">top_level_method</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">other_helper</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">other_helper</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
<span class="k">return</span> <span class="nb">input</span> <span class="o">+</span> <span class="mi">10</span>
<span class="c1"># `my_script_module` will have the compiled methods `forward`, `helper`,</span>
<span class="c1"># `top_level_method`, and `other_helper`</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">MyModule</span><span class="p">())</span>
</pre></div>
</div>
</div>
<div class="section" id="ternary-expressions">
<h3>Ternary Expressions<a class="headerlink" href="#ternary-expressions" title="Permalink to this headline">¶</a></h3>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="k">if</span> <span class="n">x</span> <span class="o">></span> <span class="n">y</span> <span class="k">else</span> <span class="n">y</span>
</pre></div>
</div>
</div>
<div class="section" id="casts">
<h3>Casts<a class="headerlink" href="#casts" title="Permalink to this headline">¶</a></h3>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>float(ten)
int(3.5)
bool(ten)
str(2)``
</pre></div>
</div>
</div>
<div class="section" id="accessing-module-parameters">
<h3>Accessing Module Parameters<a class="headerlink" href="#accessing-module-parameters" title="Permalink to this headline">¶</a></h3>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="bp">self</span><span class="o">.</span><span class="n">my_parameter</span>
<span class="bp">self</span><span class="o">.</span><span class="n">my_submodule</span><span class="o">.</span><span class="n">my_parameter</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="statements">
<h2><a class="toc-backref" href="#id10">Statements</a><a class="headerlink" href="#statements" title="Permalink to this headline">¶</a></h2>
<p>TorchScript supports the following types of statements:</p>