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<section id="torch-library">
<h1>torch.library<a class="headerlink" href="#torch-library" title="Permalink to this heading">¶</a></h1>
<p>Python operator registration API provides capabilities for extending PyTorch’s core library
of operators with user defined operators. Currently, this can be done in two ways:</p>
<ol class="arabic">
<li><p>Creating new libraries</p>
<ul>
<li><p>Lets you to register <strong>new operators</strong> and kernels for various backends and functionalities by specifying the appropriate dispatch keys. For example,</p>
<blockquote>
<div><ul class="simple">
<li><p>Consider registering a new operator <code class="docutils literal notranslate"><span class="pre">add</span></code> in your newly created namespace <code class="docutils literal notranslate"><span class="pre">foo</span></code>. You can access this operator using the <code class="docutils literal notranslate"><span class="pre">torch.ops</span></code> API and calling into by calling <code class="docutils literal notranslate"><span class="pre">torch.ops.foo.add</span></code>. You can also access specific registered overloads by calling <code class="docutils literal notranslate"><span class="pre">torch.ops.foo.add.{overload_name}</span></code>.</p></li>
<li><p>If you registered a new kernel for the <code class="docutils literal notranslate"><span class="pre">CUDA</span></code> dispatch key for this operator, then your custom defined function will be called for CUDA tensor inputs.</p></li>
</ul>
</div></blockquote>
</li>
<li><p>This can be done by creating Library class objects of <code class="docutils literal notranslate"><span class="pre">"DEF"</span></code> kind.</p></li>
</ul>
</li>
<li><p>Extending existing C++ libraries (e.g., aten)</p>
<ul>
<li><p>Lets you register kernels for <strong>existing operators</strong> corresponding to various backends and functionalities by specifying the appropriate dispatch keys.</p></li>
<li><p>This may come in handy to fill up spotty operator support for a feature implemented through a dispatch key. For example.,</p>
<blockquote>
<div><ul class="simple">
<li><p>You can add operator support for Meta Tensors (by registering function to the <code class="docutils literal notranslate"><span class="pre">Meta</span></code> dispatch key).</p></li>
</ul>
</div></blockquote>
</li>
<li><p>This can be done by creating Library class objects of <code class="docutils literal notranslate"><span class="pre">"IMPL"</span></code> kind.</p></li>
</ul>
</li>
</ol>
<p>A tutorial that walks you through some examples on how to use this API is available on <a class="reference external" href="https://colab.research.google.com/drive/1RRhSfk7So3Cn02itzLWE9K4Fam-8U011?usp=sharing">Google Colab</a>.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>Dispatcher is a complicated PyTorch concept and having a sound understanding of Dispatcher is crucial
to be able to do anything advanced with this API. <a class="reference external" href="http://blog.ezyang.com/2020/09/lets-talk-about-the-pytorch-dispatcher/">This blog post</a>
is a good starting point to learn about Dispatcher.</p>
</div>
<dl class="py class">
<dt class="sig sig-object py" id="torch.library.Library">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">Library</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ns</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kind</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dispatch_key</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#Library"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.Library" title="Permalink to this definition">¶</a></dt>
<dd><p>A class to create libraries that can be used to register new operators or
override operators in existing libraries from Python.
A user can optionally pass in a dispatch keyname if they only want to register
kernels corresponding to only one specific dispatch key.</p>
<p>To create a library to override operators in an existing library (with name ns), set the kind to “IMPL”.
To create a new library (with name ns) to register new operators, set the kind to “DEF”.
:param ns: library name
:param kind: “DEF”, “IMPL” (default: “IMPL”)
:param dispatch_key: PyTorch dispatch key (default: “”)</p>
<dl class="py method">
<dt class="sig sig-object py" id="torch.library.Library.define">
<span class="sig-name descname"><span class="pre">define</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">schema</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alias_analysis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#Library.define"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.Library.define" title="Permalink to this definition">¶</a></dt>
<dd><p>Defines a new operator and its semantics in the ns namespace.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>schema</strong> – function schema to define a new operator.</p></li>
<li><p><strong>alias_analysis</strong> (<em>optional</em>) – Indicates if the aliasing properties of the operator arguments can be
inferred from the schema (default behavior) or not (“CONSERVATIVE”).</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>name of the operator as inferred from the schema.</p>
</dd>
</dl>
<dl>
<dt>Example::</dt><dd><div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">my_lib</span> <span class="o">=</span> <span class="n">Library</span><span class="p">(</span><span class="s2">"foo"</span><span class="p">,</span> <span class="s2">"DEF"</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">my_lib</span><span class="o">.</span><span class="n">define</span><span class="p">(</span><span class="s2">"sum(Tensor self) -> Tensor"</span><span class="p">)</span>
</pre></div>
</div>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.library.Library.impl">
<span class="sig-name descname"><span class="pre">impl</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dispatch_key</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#Library.impl"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.Library.impl" title="Permalink to this definition">¶</a></dt>
<dd><p>Registers the function implementation for an operator defined in the library.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>op_name</strong> – operator name (along with the overload) or OpOverload object.</p></li>
<li><p><strong>fn</strong> – function that’s the operator implementation for the input dispatch key.</p></li>
<li><p><strong>dispatch_key</strong> – dispatch key that the input function should be registered for. By default, it uses
the dispatch key that the library was created with.</p></li>
</ul>
</dd>
</dl>
<dl>
<dt>Example::</dt><dd><div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">my_lib</span> <span class="o">=</span> <span class="n">Library</span><span class="p">(</span><span class="s2">"aten"</span><span class="p">,</span> <span class="s2">"IMPL"</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">def</span> <span class="nf">div_cpu</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
<span class="gp">>>> </span> <span class="k">return</span> <span class="bp">self</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="n">other</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">my_lib</span><span class="o">.</span><span class="n">impl</span><span class="p">(</span><span class="s2">"div.Tensor"</span><span class="p">,</span> <span class="s2">"CPU"</span><span class="p">)</span>
</pre></div>
</div>
</dd>
</dl>
</dd></dl>
</dd></dl>
<p>We have also added some function decorators to make it convenient to register functions for operators:</p>
<ul class="simple">
<li><p><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.library.impl()</span></code></p></li>
<li><p><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.library.define()</span></code></p></li>
</ul>
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