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<div class="section" id="serialization-semantics">
<h1><a class="toc-backref" href="#id2">Serialization semantics</a><a class="headerlink" href="#serialization-semantics" title="Permalink to this heading">¶</a></h1>
<p>This note describes how you can save and load PyTorch tensors and module states
in Python, and how to serialize Python modules so they can be loaded in C++.</p>
<div class="contents topic" id="table-of-contents">
<p class="topic-title">Table of Contents</p>
<ul class="simple">
<li><p><a class="reference internal" href="#serialization-semantics" id="id2">Serialization semantics</a></p>
<ul>
<li><p><a class="reference internal" href="#saving-and-loading-tensors" id="id3">Saving and loading tensors</a></p></li>
<li><p><a class="reference internal" href="#saving-and-loading-tensors-preserves-views" id="id4">Saving and loading tensors preserves views</a></p></li>
<li><p><a class="reference internal" href="#saving-and-loading-torch-nn-modules" id="id5">Saving and loading torch.nn.Modules</a></p></li>
<li><p><a class="reference internal" href="#serializing-torch-nn-modules-and-loading-them-in-c" id="id6">Serializing torch.nn.Modules and loading them in C++</a></p></li>
<li><p><a class="reference internal" href="#saving-and-loading-scriptmodules-across-pytorch-versions" id="id7">Saving and loading ScriptModules across PyTorch versions</a></p>
<ul>
<li><p><a class="reference internal" href="#torch-div-performing-integer-division" id="id8">torch.div performing integer division</a></p></li>
<li><p><a class="reference internal" href="#torch-full-always-inferring-a-float-dtype" id="id9">torch.full always inferring a float dtype</a></p></li>
</ul>
</li>
<li><p><a class="reference internal" href="#utility-functions" id="id10">Utility functions</a></p></li>
</ul>
</li>
</ul>
</div>
<div class="section" id="saving-and-loading-tensors">
<span id="saving-loading-tensors"></span><h2><a class="toc-backref" href="#id3">Saving and loading tensors</a><a class="headerlink" href="#saving-and-loading-tensors" title="Permalink to this heading">¶</a></h2>
<p><a class="reference internal" href="../generated/torch.save.html#torch.save" title="torch.save"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.save()</span></code></a> and <a class="reference internal" href="../generated/torch.load.html#torch.load" title="torch.load"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.load()</span></code></a> let you easily save and load tensors:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">t</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">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="s1">'tensor.pt'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">'tensor.pt'</span><span class="p">)</span>
<span class="go">tensor([1., 2.])</span>
</pre></div>
</div>
<p>By convention, PyTorch files are typically written with a ‘.pt’ or ‘.pth’ extension.</p>
<p><a class="reference internal" href="../generated/torch.save.html#torch.save" title="torch.save"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.save()</span></code></a> and <a class="reference internal" href="../generated/torch.load.html#torch.load" title="torch.load"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.load()</span></code></a> use Python’s pickle by default,
so you can also save multiple tensors as part of Python objects like tuples,
lists, and dicts:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">d</span> <span class="o">=</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">tensor</span><span class="p">([</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</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">tensor</span><span class="p">([</span><span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">])}</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="s1">'tensor_dict.pt'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">'tensor_dict.pt'</span><span class="p">)</span>
<span class="go">{'a': tensor([1., 2.]), 'b': tensor([3., 4.])}</span>
</pre></div>
</div>
<p>Custom data structures that include PyTorch tensors can also be saved if the
data structure is pickle-able.</p>
</div>
<div class="section" id="saving-and-loading-tensors-preserves-views">
<span id="preserve-storage-sharing"></span><h2><a class="toc-backref" href="#id4">Saving and loading tensors preserves views</a><a class="headerlink" href="#saving-and-loading-tensors-preserves-views" title="Permalink to this heading">¶</a></h2>
<p>Saving tensors preserves their view relationships:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">numbers</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">evens</span> <span class="o">=</span> <span class="n">numbers</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="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">([</span><span class="n">numbers</span><span class="p">,</span> <span class="n">evens</span><span class="p">],</span> <span class="s1">'tensors.pt'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">loaded_numbers</span><span class="p">,</span> <span class="n">loaded_evens</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">'tensors.pt'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">loaded_evens</span> <span class="o">*=</span> <span class="mi">2</span>
<span class="gp">>>> </span><span class="n">loaded_numbers</span>
<span class="go">tensor([ 1, 4, 3, 8, 5, 12, 7, 16, 9])</span>
</pre></div>
</div>
<p>Behind the scenes, these tensors share the same “storage.” See
<a class="reference external" href="https://pytorch.org/docs/main/tensor_view.html">Tensor Views</a> for more
on views and storage.</p>
<p>When PyTorch saves tensors it saves their storage objects and tensor
metadata separately. This is an implementation detail that may change in the
future, but it typically saves space and lets PyTorch easily
reconstruct the view relationships between the loaded tensors. In the above
snippet, for example, only a single storage is written to ‘tensors.pt’.</p>
<p>In some cases, however, saving the current storage objects may be unnecessary
and create prohibitively large files. In the following snippet a storage much
larger than the saved tensor is written to a file:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">large</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1000</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">small</span> <span class="o">=</span> <span class="n">large</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">5</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">small</span><span class="p">,</span> <span class="s1">'small.pt'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">loaded_small</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">'small.pt'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">loaded_small</span><span class="o">.</span><span class="n">storage</span><span class="p">()</span><span class="o">.</span><span class="n">size</span><span class="p">()</span>
<span class="go">999</span>
</pre></div>
</div>
<p>Instead of saving only the five values in the <cite>small</cite> tensor to ‘small.pt,’
the 999 values in the storage it shares with <cite>large</cite> were saved and loaded.</p>
<p>When saving tensors with fewer elements than their storage objects, the size of
the saved file can be reduced by first cloning the tensors. Cloning a tensor
produces a new tensor with a new storage object containing only the values
in the tensor:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">large</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1000</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">small</span> <span class="o">=</span> <span class="n">large</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">5</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">small</span><span class="o">.</span><span class="n">clone</span><span class="p">(),</span> <span class="s1">'small.pt'</span><span class="p">)</span> <span class="c1"># saves a clone of small</span>
<span class="gp">>>> </span><span class="n">loaded_small</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">'small.pt'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">loaded_small</span><span class="o">.</span><span class="n">storage</span><span class="p">()</span><span class="o">.</span><span class="n">size</span><span class="p">()</span>
<span class="go">5</span>
</pre></div>
</div>
<p>Since the cloned tensors are independent of each other, however, they have
none of the view relationships the original tensors did. If both file size and
view relationships are important when saving tensors smaller than their
storage objects, then care must be taken to construct new tensors that minimize
the size of their storage objects but still have the desired view relationships
before saving.</p>
</div>
<div class="section" id="saving-and-loading-torch-nn-modules">
<span id="saving-loading-python-modules"></span><h2><a class="toc-backref" href="#id5">Saving and loading torch.nn.Modules</a><a class="headerlink" href="#saving-and-loading-torch-nn-modules" title="Permalink to this heading">¶</a></h2>
<p>See also: <a class="reference external" href="https://pytorch.org/tutorials/beginner/saving_loading_models.html">Tutorial: Saving and loading modules</a></p>
<p>In PyTorch, a module’s state is frequently serialized using a ‘state dict.’
A module’s state dict contains all of its parameters and persistent buffers:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">bn</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">BatchNorm1d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">track_running_stats</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">list</span><span class="p">(</span><span class="n">bn</span><span class="o">.</span><span class="n">named_parameters</span><span class="p">())</span>
<span class="go">[('weight', Parameter containing: tensor([1., 1., 1.], requires_grad=True)),</span>
<span class="go"> ('bias', Parameter containing: tensor([0., 0., 0.], requires_grad=True))]</span>
<span class="gp">>>> </span><span class="nb">list</span><span class="p">(</span><span class="n">bn</span><span class="o">.</span><span class="n">named_buffers</span><span class="p">())</span>
<span class="go">[('running_mean', tensor([0., 0., 0.])),</span>
<span class="go"> ('running_var', tensor([1., 1., 1.])),</span>
<span class="go"> ('num_batches_tracked', tensor(0))]</span>
<span class="gp">>>> </span><span class="n">bn</span><span class="o">.</span><span class="n">state_dict</span><span class="p">()</span>
<span class="go">OrderedDict([('weight', tensor([1., 1., 1.])),</span>
<span class="go"> ('bias', tensor([0., 0., 0.])),</span>
<span class="go"> ('running_mean', tensor([0., 0., 0.])),</span>
<span class="go"> ('running_var', tensor([1., 1., 1.])),</span>
<span class="go"> ('num_batches_tracked', tensor(0))])</span>
</pre></div>
</div>
<p>Instead of saving a module directly, for compatibility reasons it is recommended
to instead save only its state dict. Python modules even have a function,
<a class="reference internal" href="../generated/torch.nn.Module.html#torch.nn.Module.load_state_dict" title="torch.nn.Module.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a>, to restore their states from a state dict:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">bn</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span> <span class="s1">'bn.pt'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">bn_state_dict</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">'bn.pt'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">new_bn</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">BatchNorm1d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">track_running_stats</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">new_bn</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">bn_state_dict</span><span class="p">)</span>
<span class="go"><All keys matched successfully></span>
</pre></div>
</div>
<p>Note that the state dict is first loaded from its file with <a class="reference internal" href="../generated/torch.load.html#torch.load" title="torch.load"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.load()</span></code></a>
and the state then restored with <a class="reference internal" href="../generated/torch.nn.Module.html#torch.nn.Module.load_state_dict" title="torch.nn.Module.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a>.</p>
<p>Even custom modules and modules containing other modules have state dicts and
can use this pattern:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># A module with two linear layers</span>
<span class="o">>>></span> <span class="k">class</span> <span class="nc">MyModule</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="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">l0</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">l1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
<span class="n">out0</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">l0</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
<span class="n">out0_relu</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">functional</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">out0</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">l1</span><span class="p">(</span><span class="n">out0_relu</span><span class="p">)</span>
<span class="o">>>></span> <span class="n">m</span> <span class="o">=</span> <span class="n">MyModule</span><span class="p">()</span>
<span class="o">>>></span> <span class="n">m</span><span class="o">.</span><span class="n">state_dict</span><span class="p">()</span>
<span class="n">OrderedDict</span><span class="p">([(</span><span class="s1">'l0.weight'</span><span class="p">,</span> <span class="n">tensor</span><span class="p">([[</span> <span class="mf">0.1400</span><span class="p">,</span> <span class="mf">0.4563</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.0271</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.4406</span><span class="p">],</span>
<span class="p">[</span><span class="o">-</span><span class="mf">0.3289</span><span class="p">,</span> <span class="mf">0.2827</span><span class="p">,</span> <span class="mf">0.4588</span><span class="p">,</span> <span class="mf">0.2031</span><span class="p">]])),</span>
<span class="p">(</span><span class="s1">'l0.bias'</span><span class="p">,</span> <span class="n">tensor</span><span class="p">([</span> <span class="mf">0.0300</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.1316</span><span class="p">])),</span>
<span class="p">(</span><span class="s1">'l1.weight'</span><span class="p">,</span> <span class="n">tensor</span><span class="p">([[</span><span class="mf">0.6533</span><span class="p">,</span> <span class="mf">0.3413</span><span class="p">]])),</span>
<span class="p">(</span><span class="s1">'l1.bias'</span><span class="p">,</span> <span class="n">tensor</span><span class="p">([</span><span class="o">-</span><span class="mf">0.1112</span><span class="p">]))])</span>
<span class="o">>>></span> <span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span> <span class="s1">'mymodule.pt'</span><span class="p">)</span>
<span class="o">>>></span> <span class="n">m_state_dict</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">'mymodule.pt'</span><span class="p">)</span>
<span class="o">>>></span> <span class="n">new_m</span> <span class="o">=</span> <span class="n">MyModule</span><span class="p">()</span>
<span class="o">>>></span> <span class="n">new_m</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">m_state_dict</span><span class="p">)</span>
<span class="o"><</span><span class="n">All</span> <span class="n">keys</span> <span class="n">matched</span> <span class="n">successfully</span><span class="o">></span>
</pre></div>
</div>
</div>
<div class="section" id="serializing-torch-nn-modules-and-loading-them-in-c">
<span id="serializing-python-modules"></span><h2><a class="toc-backref" href="#id6">Serializing torch.nn.Modules and loading them in C++</a><a class="headerlink" href="#serializing-torch-nn-modules-and-loading-them-in-c" title="Permalink to this heading">¶</a></h2>
<p>See also: <a class="reference external" href="https://pytorch.org/tutorials/advanced/cpp_export.html">Tutorial: Loading a TorchScript Model in C++</a></p>
<p>ScriptModules can be serialized as a TorchScript program and loaded
using <a class="reference internal" href="../generated/torch.jit.load.html#torch.jit.load" title="torch.jit.load"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.jit.load()</span></code></a>.
This serialization encodes all the modules’ methods, submodules, parameters,
and attributes, and it allows the serialized program to be loaded in C++
(i.e. without Python).</p>
<p>The distinction between <a class="reference internal" href="../generated/torch.jit.save.html#torch.jit.save" title="torch.jit.save"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.jit.save()</span></code></a> and <a class="reference internal" href="../generated/torch.save.html#torch.save" title="torch.save"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.save()</span></code></a> may not
be immediately clear. <a class="reference internal" href="../generated/torch.save.html#torch.save" title="torch.save"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.save()</span></code></a> saves Python objects with pickle.
This is especially useful for prototyping, researching, and training.
<a class="reference internal" href="../generated/torch.jit.save.html#torch.jit.save" title="torch.jit.save"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.jit.save()</span></code></a>, on the other hand, serializes ScriptModules to a format
that can be loaded in Python or C++. This is useful when saving and loading C++
modules or for running modules trained in Python with C++, a common practice
when deploying PyTorch models.</p>
<p>To script, serialize and load a module in Python:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">scripted_module</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">MyModule</span><span class="p">())</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">scripted_module</span><span class="p">,</span> <span class="s1">'mymodule.pt'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">'mymodule.pt'</span><span class="p">)</span>
<span class="go">RecursiveScriptModule( original_name=MyModule</span>
<span class="go"> (l0): RecursiveScriptModule(original_name=Linear)</span>
<span class="go"> (l1): RecursiveScriptModule(original_name=Linear) )</span>
</pre></div>
</div>
<p>Traced modules can also be saved with <a class="reference internal" href="../generated/torch.jit.save.html#torch.jit.save" title="torch.jit.save"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.jit.save()</span></code></a>, with the caveat
that only the traced code path is serialized. The following example demonstrates
this:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># A module with control flow</span>
<span class="o">>>></span> <span class="k">class</span> <span class="nc">ControlFlowModule</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">l0</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">l1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
<span class="k">if</span> <span class="nb">input</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">></span> <span class="mi">1</span><span class="p">:</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">out0</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">l0</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
<span class="n">out0_relu</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">functional</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">out0</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">l1</span><span class="p">(</span><span class="n">out0_relu</span><span class="p">)</span>
<span class="o">>>></span> <span class="n">traced_module</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="n">ControlFlowModule</span><span class="p">(),</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">4</span><span class="p">))</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">save</span><span class="p">(</span><span class="n">traced_module</span><span class="p">,</span> <span class="s1">'controlflowmodule_traced.pt'</span><span class="p">)</span>
<span class="o">>>></span> <span class="n">loaded</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">'controlflowmodule_traced.pt'</span><span class="p">)</span>
<span class="o">>>></span> <span class="n">loaded</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">)))</span>
<span class="n">tensor</span><span class="p">([[</span><span class="o">-</span><span class="mf">0.1571</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mf">0.3793</span><span class="p">]],</span> <span class="n">grad_fn</span><span class="o">=<</span><span class="n">AddBackward0</span><span class="o">></span><span class="p">)</span>
<span class="o">>>></span> <span class="n">scripted_module</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">ControlFlowModule</span><span class="p">(),</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">4</span><span class="p">))</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">save</span><span class="p">(</span><span class="n">scripted_module</span><span class="p">,</span> <span class="s1">'controlflowmodule_scripted.pt'</span><span class="p">)</span>
<span class="o">>>></span> <span class="n">loaded</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">'controlflowmodule_scripted.pt'</span><span class="p">)</span>
<span class="o">>></span> <span class="n">loaded</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
<span class="n">tensor</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
</pre></div>
</div>
<p>The above module has an if statement that is not triggered by the traced inputs,
and so is not part of the traced module and not serialized with it.
The scripted module, however, contains the if statement and is serialized with it.
See the <a class="reference external" href="https://pytorch.org/docs/stable/jit.html">TorchScript documentation</a>
for more on scripting and tracing.</p>
<p>Finally, to load the module in C++:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">torch</span><span class="p">::</span><span class="n">jit</span><span class="p">::</span><span class="n">script</span><span class="p">::</span><span class="n">Module</span> <span class="n">module</span><span class="p">;</span>
<span class="gp">>>> </span><span class="n">module</span> <span class="o">=</span> <span class="n">torch</span><span class="p">::</span><span class="n">jit</span><span class="p">::</span><span class="n">load</span><span class="p">(</span><span class="s1">'controlflowmodule_scripted.pt'</span><span class="p">);</span>
</pre></div>
</div>
<p>See the <a class="reference external" href="https://pytorch.org/cppdocs/">PyTorch C++ API documentation</a>
for details about how to use PyTorch modules in C++.</p>
</div>
<div class="section" id="saving-and-loading-scriptmodules-across-pytorch-versions">
<span id="saving-loading-across-versions"></span><h2><a class="toc-backref" href="#id7">Saving and loading ScriptModules across PyTorch versions</a><a class="headerlink" href="#saving-and-loading-scriptmodules-across-pytorch-versions" title="Permalink to this heading">¶</a></h2>
<p>The PyTorch Team recommends saving and loading modules with the same version of
PyTorch. Older versions of PyTorch may not support newer modules, and newer
versions may have removed or modified older behavior. These changes are
explicitly described in
PyTorch’s <a class="reference external" href="https://github.com/pytorch/pytorch/releases">release notes</a>,
and modules relying on functionality that has changed may need to be updated
to continue working properly. In limited cases, detailed below, PyTorch will
preserve the historic behavior of serialized ScriptModules so they do not require
an update.</p>
<div class="section" id="torch-div-performing-integer-division">
<h3><a class="toc-backref" href="#id8">torch.div performing integer division</a><a class="headerlink" href="#torch-div-performing-integer-division" title="Permalink to this heading">¶</a></h3>
<p>In PyTorch 1.5 and earlier <a class="reference internal" href="../generated/torch.div.html#torch.div" title="torch.div"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.div()</span></code></a> would perform floor division when
given two integer inputs:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># PyTorch 1.5 (and earlier)</span>
<span class="o">>>></span> <span class="n">a</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="mi">5</span><span class="p">)</span>
<span class="o">>>></span> <span class="n">b</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="mi">3</span><span class="p">)</span>
<span class="o">>>></span> <span class="n">a</span> <span class="o">/</span> <span class="n">b</span>
<span class="n">tensor</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
</pre></div>
</div>
<p>In PyTorch 1.7, however, <a class="reference internal" href="../generated/torch.div.html#torch.div" title="torch.div"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.div()</span></code></a> will always perform a true division
of its inputs, just like division in Python 3:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># PyTorch 1.7</span>
<span class="o">>>></span> <span class="n">a</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="mi">5</span><span class="p">)</span>
<span class="o">>>></span> <span class="n">b</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="mi">3</span><span class="p">)</span>
<span class="o">>>></span> <span class="n">a</span> <span class="o">/</span> <span class="n">b</span>
<span class="n">tensor</span><span class="p">(</span><span class="mf">1.6667</span><span class="p">)</span>
</pre></div>
</div>
<p>The behavior of <a class="reference internal" href="../generated/torch.div.html#torch.div" title="torch.div"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.div()</span></code></a> is preserved in serialized ScriptModules.
That is, ScriptModules serialized with versions of PyTorch before 1.6 will continue
to see <a class="reference internal" href="../generated/torch.div.html#torch.div" title="torch.div"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.div()</span></code></a> perform floor division when given two integer inputs
even when loaded with newer versions of PyTorch. ScriptModules using <a class="reference internal" href="../generated/torch.div.html#torch.div" title="torch.div"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.div()</span></code></a>
and serialized on PyTorch 1.6 and later cannot be loaded in earlier versions of
PyTorch, however, since those earlier versions do not understand the new behavior.</p>
</div>
<div class="section" id="torch-full-always-inferring-a-float-dtype">
<h3><a class="toc-backref" href="#id9">torch.full always inferring a float dtype</a><a class="headerlink" href="#torch-full-always-inferring-a-float-dtype" title="Permalink to this heading">¶</a></h3>
<p>In PyTorch 1.5 and earlier <a class="reference internal" href="../generated/torch.full.html#torch.full" title="torch.full"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.full()</span></code></a> always returned a float tensor,
regardless of the fill value it’s given:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># PyTorch 1.5 and earlier</span>
<span class="o">>>></span> <span class="n">torch</span><span class="o">.</span><span class="n">full</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="c1"># Note the integer fill value...</span>
<span class="n">tensor</span><span class="p">([</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">])</span> <span class="c1"># ...but float tensor!</span>
</pre></div>
</div>
<p>In PyTorch 1.7, however, <a class="reference internal" href="../generated/torch.full.html#torch.full" title="torch.full"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.full()</span></code></a> will infer the returned tensor’s
dtype from the fill value:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># PyTorch 1.7</span>
<span class="o">>>></span> <span class="n">torch</span><span class="o">.</span><span class="n">full</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="n">tensor</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
<span class="o">>>></span> <span class="n">torch</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="mi">3</span><span class="p">,),</span> <span class="kc">True</span><span class="p">)</span>
<span class="n">tensor</span><span class="p">([</span><span class="kc">True</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="kc">True</span><span class="p">])</span>
<span class="o">>>></span> <span class="n">torch</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="mi">3</span><span class="p">,),</span> <span class="mf">1.</span><span class="p">)</span>
<span class="n">tensor</span><span class="p">([</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">])</span>
<span class="o">>>></span> <span class="n">torch</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="mi">3</span><span class="p">,),</span> <span class="mi">1</span> <span class="o">+</span> <span class="mi">1</span><span class="n">j</span><span class="p">)</span>
<span class="n">tensor</span><span class="p">([</span><span class="mf">1.</span><span class="o">+</span><span class="mf">1.</span><span class="n">j</span><span class="p">,</span> <span class="mf">1.</span><span class="o">+</span><span class="mf">1.</span><span class="n">j</span><span class="p">,</span> <span class="mf">1.</span><span class="o">+</span><span class="mf">1.</span><span class="n">j</span><span class="p">])</span>
</pre></div>
</div>
<p>The behavior of <a class="reference internal" href="../generated/torch.full.html#torch.full" title="torch.full"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.full()</span></code></a> is preserved in serialized ScriptModules. That is,
ScriptModules serialized with versions of PyTorch before 1.6 will continue to see
torch.full return float tensors by default, even when given bool or
integer fill values. ScriptModules using <a class="reference internal" href="../generated/torch.full.html#torch.full" title="torch.full"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.full()</span></code></a> and serialized on PyTorch 1.6
and later cannot be loaded in earlier versions of PyTorch, however, since those
earlier versions do not understand the new behavior.</p>
</div>
</div>
<div class="section" id="utility-functions">
<span id="id1"></span><h2><a class="toc-backref" href="#id10">Utility functions</a><a class="headerlink" href="#utility-functions" title="Permalink to this heading">¶</a></h2>
<p>The following utility functions are related to serialization:</p>
<dl class="py function">
<dt class="sig sig-object py" id="torch.serialization.register_package">
<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">register_package</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">priority</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tagger</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">deserializer</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#register_package"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.register_package" title="Permalink to this definition">¶</a></dt>
<dd><p>Registers callables for tagging and deserializing storage objects with an associated priority.
Tagging associates a device with a storage object at save time while deserializing moves a
storage object to an appropriate device at load time. <code class="xref py py-attr docutils literal notranslate"><span class="pre">tagger</span></code> and <code class="xref py py-attr docutils literal notranslate"><span class="pre">deserializer</span></code>
are run in the order given by their <code class="xref py py-attr docutils literal notranslate"><span class="pre">priority</span></code> until a tagger/deserializer returns a
value that is not <cite>None</cite>.</p>
<p>To override the deserialization behavior for a device in the global registry, one can register a
tagger with a higher priority than the existing tagger.</p>
<p>This function can also be used to register a tagger and deserializer for new devices.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>priority</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Indicates the priority associated with the tagger and deserializer, where a lower
value indicates higher priority.</p></li>
<li><p><strong>tagger</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.12)"><em>Callable</em></a><em>[</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><em>Union</em></a><em>[</em><em>Storage</em><em>, </em><a class="reference internal" href="../storage.html#torch.TypedStorage" title="torch.storage.TypedStorage"><em>TypedStorage</em></a><em>, </em><a class="reference internal" href="../storage.html#torch.UntypedStorage" title="torch.storage.UntypedStorage"><em>UntypedStorage</em></a><em>]</em><em>]</em><em>, </em><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.12)"><em>Optional</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>]</em><em>]</em>) – Callable that takes in a storage object and returns its tagged device as a string
or None.</p></li>
<li><p><strong>deserializer</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.12)"><em>Callable</em></a><em>[</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><em>Union</em></a><em>[</em><em>Storage</em><em>, </em><a class="reference internal" href="../storage.html#torch.TypedStorage" title="torch.storage.TypedStorage"><em>TypedStorage</em></a><em>, </em><a class="reference internal" href="../storage.html#torch.UntypedStorage" title="torch.storage.UntypedStorage"><em>UntypedStorage</em></a><em>]</em><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>]</em><em>, </em><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.12)"><em>Optional</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><em>Union</em></a><em>[</em><em>Storage</em><em>, </em><a class="reference internal" href="../storage.html#torch.TypedStorage" title="torch.storage.TypedStorage"><em>TypedStorage</em></a><em>, </em><a class="reference internal" href="../storage.html#torch.UntypedStorage" title="torch.storage.UntypedStorage"><em>UntypedStorage</em></a><em>]</em><em>]</em><em>]</em>) – Callable that takes in storage object and a device string and returns a storage
object on the appropriate device or None.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><cite>None</cite></p>
</dd>
</dl>
<p class="rubric">Example</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">def</span> <span class="nf">ipu_tag</span><span class="p">(</span><span class="n">obj</span><span class="p">):</span>
<span class="gp">>>> </span> <span class="k">if</span> <span class="n">obj</span><span class="o">.</span><span class="n">device</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s1">'ipu'</span><span class="p">:</span>
<span class="gp">>>> </span> <span class="k">return</span> <span class="s1">'ipu'</span>
<span class="gp">>>> </span><span class="k">def</span> <span class="nf">ipu_deserialize</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="n">location</span><span class="p">):</span>
<span class="gp">>>> </span> <span class="k">if</span> <span class="n">location</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">'ipu'</span><span class="p">):</span>
<span class="gp">>>> </span> <span class="n">ipu</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">torch</span><span class="p">,</span> <span class="s2">"ipu"</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
<span class="gp">>>> </span> <span class="k">assert</span> <span class="n">ipu</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span> <span class="s2">"IPU device module is not loaded"</span>
<span class="gp">>>> </span> <span class="k">assert</span> <span class="n">torch</span><span class="o">.</span><span class="n">ipu</span><span class="o">.</span><span class="n">is_available</span><span class="p">(),</span> <span class="s2">"ipu is not available"</span>
<span class="gp">>>> </span> <span class="k">return</span> <span class="n">obj</span><span class="o">.</span><span class="n">ipu</span><span class="p">(</span><span class="n">location</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">serialization</span><span class="o">.</span><span class="n">register_package</span><span class="p">(</span><span class="mi">11</span><span class="p">,</span> <span class="n">ipu_tag</span><span class="p">,</span> <span class="n">ipu_deserialize</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.serialization.get_default_load_endianness">
<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">get_default_load_endianness</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#get_default_load_endianness"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.get_default_load_endianness" title="Permalink to this definition">¶</a></dt>
<dd><p>Get fallback byte order for loading files</p>
<p>If byteorder mark is not present in saved checkpoint,
this byte order is used as fallback.
By default, it’s “native” byte order.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>Optional[LoadEndianness]</p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p>default_load_endian</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.serialization.set_default_load_endianness">
<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">set_default_load_endianness</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">endianness</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#set_default_load_endianness"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.set_default_load_endianness" title="Permalink to this definition">¶</a></dt>
<dd><p>Set fallback byte order for loading files</p>
<p>If byteorder mark is not present in saved checkpoint,
this byte order is used as fallback.
By default, it’s “native” byte order.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>endianness</strong> – the new fallback byte order</p>
</dd>
</dl>
</dd></dl>
</div>
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<ul>
<li><a class="reference internal" href="#">Serialization semantics</a><ul>
<li><a class="reference internal" href="#saving-and-loading-tensors">Saving and loading tensors</a></li>
<li><a class="reference internal" href="#saving-and-loading-tensors-preserves-views">Saving and loading tensors preserves views</a></li>
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<li><a class="reference internal" href="#serializing-torch-nn-modules-and-loading-them-in-c">Serializing torch.nn.Modules and loading them in C++</a></li>
<li><a class="reference internal" href="#saving-and-loading-scriptmodules-across-pytorch-versions">Saving and loading ScriptModules across PyTorch versions</a><ul>
<li><a class="reference internal" href="#torch-div-performing-integer-division">torch.div performing integer division</a></li>
<li><a class="reference internal" href="#torch-full-always-inferring-a-float-dtype">torch.full always inferring a float dtype</a></li>
</ul>
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<li><a class="reference internal" href="#utility-functions">Utility functions</a><ul>
<li><a class="reference internal" href="#torch.serialization.register_package"><code class="docutils literal notranslate"><span class="pre">register_package()</span></code></a></li>
<li><a class="reference internal" href="#torch.serialization.get_default_load_endianness"><code class="docutils literal notranslate"><span class="pre">get_default_load_endianness()</span></code></a></li>
<li><a class="reference internal" href="#torch.serialization.set_default_load_endianness"><code class="docutils literal notranslate"><span class="pre">set_default_load_endianness()</span></code></a></li>
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