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<section id="torch-stable-api">
<h1>Torch Stable API<a class="headerlink" href="#torch-stable-api" title="Link to this heading">#</a></h1>
<p>The PyTorch Stable C++ API provides a binary-compatible interface for calling
tensor operations and utilities that is guaranteed to remain stable across
PyTorch versions. This enables ahead-of-time compiled extensions that don’t
need recompilation when PyTorch is updated.</p>
<p><strong>When to use the Stable API:</strong></p>
<ul class="simple">
<li><p>When building extensions that must work across multiple PyTorch versions</p></li>
<li><p>When distributing pre-compiled binaries</p></li>
<li><p>When binary compatibility is more important than access to the latest features</p></li>
<li><p>When writing custom operators for production deployment</p></li>
</ul>
<p><strong>Basic usage:</strong></p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf"><torch/csrc/stable/library.h></span>
<span class="cp">#include</span><span class="w"> </span><span class="cpf"><torch/csrc/stable/ops.h></span>
<span class="c1">// Create a tensor using stable API</span>
<span class="k">auto</span><span class="w"> </span><span class="n">tensor</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">stable</span><span class="o">::</span><span class="n">empty</span><span class="p">(</span>
<span class="w"> </span><span class="p">{</span><span class="mi">3</span><span class="p">,</span><span class="w"> </span><span class="mi">4</span><span class="p">},</span>
<span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">headeronly</span><span class="o">::</span><span class="n">ScalarType</span><span class="o">::</span><span class="n">Float</span><span class="p">,</span>
<span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">headeronly</span><span class="o">::</span><span class="n">Layout</span><span class="o">::</span><span class="n">Strided</span><span class="p">,</span>
<span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">stable</span><span class="o">::</span><span class="n">Device</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">headeronly</span><span class="o">::</span><span class="n">DeviceType</span><span class="o">::</span><span class="n">CPU</span><span class="p">),</span>
<span class="w"> </span><span class="nb">false</span><span class="p">,</span>
<span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">headeronly</span><span class="o">::</span><span class="n">MemoryFormat</span><span class="o">::</span><span class="n">Contiguous</span><span class="p">);</span>
<span class="c1">// Register operators with stable ABI</span>
<span class="n">STABLE_TORCH_LIBRARY</span><span class="p">(</span><span class="n">myops</span><span class="p">,</span><span class="w"> </span><span class="n">m</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="n">m</span><span class="p">.</span><span class="n">def</span><span class="p">(</span><span class="s">"my_op(Tensor input) -> Tensor"</span><span class="p">);</span>
<span class="p">}</span>
<span class="n">STABLE_TORCH_LIBRARY_IMPL</span><span class="p">(</span><span class="n">myops</span><span class="p">,</span><span class="w"> </span><span class="n">CPU</span><span class="p">,</span><span class="w"> </span><span class="n">m</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>