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<div class="section" id="module-torch.library">
<span id="torch-library"></span><h1>torch.library<a class="headerlink" href="#module-torch.library" title="Permalink to this heading">¶</a></h1>
<p>torch.library is a collection of APIs for extending PyTorch’s core library
of operators. It contains utilities for creating new custom operators as
well as extending operators defined with PyTorch’s C++ operator
registration APIs (e.g. aten operators).</p>
<p>For a detailed guide on effectively using these APIs, please see
<a class="reference external" href="https://docs.google.com/document/d/1W--T6wz8IY8fOI0Vm8BF44PdBgs283QvpelJZWieQWQ/edit">this gdoc</a></p>
<p>Use <a class="reference internal" href="#torch.library.define" title="torch.library.define"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.library.define()</span></code></a> to define new custom operators. Use the
impl methods, such as <a class="reference internal" href="#torch.library.impl" title="torch.library.impl"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.library.impl()</span></code></a> and
func:<cite>torch.library.impl_abstract</cite>, to add implementations
for any operators (they may have been created using <a class="reference internal" href="#torch.library.define" title="torch.library.define"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.library.define()</span></code></a> or
via PyTorch’s C++ operator registration APIs).</p>
<dl class="py function">
<dt class="sig sig-object py" id="torch.library.define">
<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><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">qualname</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">schema</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tags</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#define"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.define" title="Permalink to this definition">¶</a></dt>
<dt class="sig sig-object py">
<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><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">lib</span></span></em>, <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></dt>
<dd><p>Defines a new operator.</p>
<p>In PyTorch, defining an op (short for “operator”) is a two step-process:
- we need to define the op (by providing an operator name and schema)
- we need to implement behavior for how the operator interacts with
various PyTorch subsystems, like CPU/CUDA Tensors, Autograd, etc.</p>
<p>This entrypoint defines the custom operator (the first step)
you must then perform the second step by calling various
<code class="docutils literal notranslate"><span class="pre">impl_*</span></code> APIs, like <a class="reference internal" href="#torch.library.impl" title="torch.library.impl"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.library.impl()</span></code></a> or
<a class="reference internal" href="#torch.library.impl_abstract" title="torch.library.impl_abstract"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.library.impl_abstract()</span></code></a>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>qualname</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – The qualified name for the operator. Should be
a string that looks like “namespace::name”, e.g. “aten::sin”.
Operators in PyTorch need a namespace to
avoid name collisions; a given operator may only be created once.
If you are writing a Python library, we recommend the namespace to
be the name of your top-level module.</p></li>
<li><p><strong>schema</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – The schema of the operator. E.g. “(Tensor x) -> Tensor”
for an op that accepts one Tensor and returns one Tensor. It does
not contain the operator name (that is passed in <code class="docutils literal notranslate"><span class="pre">qualname</span></code>).</p></li>
<li><p><strong>lib</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="#torch.library.Library" title="torch.library.Library"><em>Library</em></a><em>]</em>) – If provided, the lifetime of this operator
will be tied to the lifetime of the Library object.</p></li>
<li><p><strong>tags</strong> (<a class="reference internal" href="torch.html#torch.Tag" title="torch.Tag"><em>Tag</em></a><em> | </em><em>Sequence</em><em>[</em><a class="reference internal" href="torch.html#torch.Tag" title="torch.Tag"><em>Tag</em></a><em>]</em>) – one or more torch.Tag to apply to this
operator. Tagging an operator changes the operator’s behavior
under various PyTorch subsystems; please read the docs for the
torch.Tag carefully before applying it.</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="kn">import</span> <span class="nn">torch</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="c1"># Define the operator</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">library</span><span class="o">.</span><span class="n">define</span><span class="p">(</span><span class="s2">"mylib::sin"</span><span class="p">,</span> <span class="s2">"(Tensor x) -> Tensor"</span><span class="p">)</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="c1"># Add implementations for the operator</span>
<span class="gp">>>> </span><span class="nd">@torch</span><span class="o">.</span><span class="n">library</span><span class="o">.</span><span class="n">impl</span><span class="p">(</span><span class="s2">"mylibrary::sin"</span><span class="p">,</span> <span class="s2">"cpu"</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">def</span> <span class="nf">f</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="gp">>>> </span> <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">numpy</span><span class="p">()))</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="c1"># Call the new operator from torch.ops.</span>
<span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">mylib</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">assert</span> <span class="n">torch</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
</pre></div>
</div>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.library.impl">
<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><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">qualname</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">types</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#impl"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.impl" title="Permalink to this definition">¶</a></dt>
<dt class="sig sig-object py">
<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><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">lib</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</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></dt>
<dd><p>Register an implementation for a device type for this operator.</p>
<p>You may pass “default” for <code class="docutils literal notranslate"><span class="pre">types</span></code> to register this implementation as the
default implementation for ALL device types.
Please only use this if the implementation truly supports all device types;
for example, this is true if it is a composition of built-in PyTorch operators.</p>
<p>Some valid types are: “cpu”, “cuda”, “xla”, “mps”, “ipu”, “xpu”.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>qualname</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Should be a string that looks like “namespace::operator_name”.</p></li>
<li><p><strong>types</strong> (<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>Sequence</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>) – The device types to register an impl to.</p></li>
<li><p><strong>lib</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="#torch.library.Library" title="torch.library.Library"><em>Library</em></a><em>]</em>) – If provided, the lifetime of this registration
will be tied to the lifetime of the Library object.</p></li>
</ul>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">torch</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="c1"># Define the operator</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">library</span><span class="o">.</span><span class="n">define</span><span class="p">(</span><span class="s2">"mylibrary::sin"</span><span class="p">,</span> <span class="s2">"(Tensor x) -> Tensor"</span><span class="p">)</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="c1"># Add implementations for the cpu device</span>
<span class="gp">>>> </span><span class="nd">@torch</span><span class="o">.</span><span class="n">library</span><span class="o">.</span><span class="n">impl</span><span class="p">(</span><span class="s2">"mylibrary::sin"</span><span class="p">,</span> <span class="s2">"cpu"</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">def</span> <span class="nf">f</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="gp">>>> </span> <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">numpy</span><span class="p">()))</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">mylibrary</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">assert</span> <span class="n">torch</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">sin</span><span class="p">())</span>
</pre></div>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.library.impl_abstract">
<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">impl_abstract</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">qualname</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_stacklevel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#impl_abstract"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.impl_abstract" title="Permalink to this definition">¶</a></dt>
<dd><p>Register an abstract implementation for this operator.</p>
<p>An “abstract implementation” specifies the behavior of this operator on
Tensors that carry no data. Given some input Tensors with certain properties
(sizes/strides/storage_offset/device), it specifies what the properties of
the output Tensors are.</p>
<p>The abstract implementation has the same signature as the operator.
It is run for both FakeTensors and meta tensors. To write an abstract
implementation, assume that all Tensor inputs to the operator are
regular CPU/CUDA/Meta tensors, but they do not have storage, and
you are trying to return regular CPU/CUDA/Meta tensor(s) as output.
The abstract implementation must consist of only PyTorch operations
(and may not directly access the storage or data of any input or
intermediate Tensors).</p>
<p>This API may be used as a decorator (see examples).</p>
<p>For a detailed guide on custom ops, please see
<a class="reference external" href="https://docs.google.com/document/d/1W--T6wz8IY8fOI0Vm8BF44PdBgs283QvpelJZWieQWQ/edit">https://docs.google.com/document/d/1W–T6wz8IY8fOI0Vm8BF44PdBgs283QvpelJZWieQWQ/edit</a></p>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">torch</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">Tensor</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="c1"># Example 1: an operator without data-dependent output shape</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">library</span><span class="o">.</span><span class="n">define</span><span class="p">(</span>
<span class="gp">>>> </span> <span class="s2">"mylib::custom_linear"</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="s2">"(Tensor x, Tensor weight, Tensor bias) -> Tensor"</span><span class="p">)</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="nd">@torch</span><span class="o">.</span><span class="n">library</span><span class="o">.</span><span class="n">impl_abstract</span><span class="p">(</span><span class="s2">"mylib::custom_linear"</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">def</span> <span class="nf">custom_linear_abstract</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">weight</span><span class="p">):</span>
<span class="gp">>>> </span> <span class="k">assert</span> <span class="n">x</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">==</span> <span class="mi">2</span>
<span class="gp">>>> </span> <span class="k">assert</span> <span class="n">weight</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">==</span> <span class="mi">2</span>
<span class="gp">>>> </span> <span class="k">assert</span> <span class="n">bias</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="gp">>>> </span> <span class="k">assert</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">weight</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="gp">>>> </span> <span class="k">assert</span> <span class="n">weight</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">bias</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="gp">>>> </span> <span class="k">assert</span> <span class="n">x</span><span class="o">.</span><span class="n">device</span> <span class="o">==</span> <span class="n">weight</span><span class="o">.</span><span class="n">device</span>
<span class="go">>>></span>
<span class="gp">>>> </span> <span class="k">return</span> <span class="p">(</span><span class="n">x</span> <span class="o">@</span> <span class="n">weight</span><span class="o">.</span><span class="n">t</span><span class="p">())</span> <span class="o">+</span> <span class="n">bias</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="c1"># Example 2: an operator with data-dependent output shape</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">library</span><span class="o">.</span><span class="n">define</span><span class="p">(</span><span class="s2">"mylib::custom_nonzero"</span><span class="p">,</span> <span class="s2">"(Tensor x) -> Tensor"</span><span class="p">)</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="nd">@torch</span><span class="o">.</span><span class="n">library</span><span class="o">.</span><span class="n">impl_abstract</span><span class="p">(</span><span class="s2">"mylib::custom_nonzero"</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">def</span> <span class="nf">custom_nonzero_abstract</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="gp">>>> </span> <span class="c1"># Number of nonzero-elements is data-dependent.</span>
<span class="gp">>>> </span> <span class="c1"># Since we cannot peek at the data in an abstract impl,</span>
<span class="gp">>>> </span> <span class="c1"># we use the ctx object to construct a new symint that</span>
<span class="gp">>>> </span> <span class="c1"># represents the data-dependent size.</span>
<span class="gp">>>> </span> <span class="n">ctx</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">library</span><span class="o">.</span><span class="n">get_ctx</span><span class="p">()</span>
<span class="gp">>>> </span> <span class="n">nnz</span> <span class="o">=</span> <span class="n">ctx</span><span class="o">.</span><span class="n">new_dynamic_size</span><span class="p">()</span>
<span class="gp">>>> </span> <span class="n">shape</span> <span class="o">=</span> <span class="p">[</span><span class="n">nnz</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">dim</span><span class="p">()]</span>
<span class="gp">>>> </span> <span class="n">result</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">new_empty</span><span class="p">(</span><span class="n">shape</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">int64</span><span class="p">)</span>
<span class="gp">>>> </span> <span class="k">return</span> <span class="n">result</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="nd">@torch</span><span class="o">.</span><span class="n">library</span><span class="o">.</span><span class="n">impl</span><span class="p">(</span><span class="s2">"mylib::custom_nonzero"</span><span class="p">,</span> <span class="s2">"cpu"</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">def</span> <span class="nf">custom_nonzero_cpu</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="gp">>>> </span> <span class="n">x_np</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
<span class="gp">>>> </span> <span class="n">res</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nonzero</span><span class="p">(</span><span class="n">x_np</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="gp">>>> </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="n">res</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">x</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.library.get_ctx">
<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">get_ctx</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#get_ctx"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.get_ctx" title="Permalink to this definition">¶</a></dt>
<dd><p>get_ctx() returns the current AbstractImplCtx object.</p>
<p>Calling <code class="docutils literal notranslate"><span class="pre">get_ctx()</span></code> is only valid inside of an abstract impl
(see <a class="reference internal" href="#torch.library.impl_abstract" title="torch.library.impl_abstract"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.library.impl_abstract()</span></code></a> for more usage details.</p>
<dl class="field-list simple">
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><em>AbstractImplCtx</em></p>
</dd>
</dl>
</dd></dl>
<div class="section" id="low-level-apis">
<h2>Low-level APIs<a class="headerlink" href="#low-level-apis" title="Permalink to this heading">¶</a></h2>
<p>The following APIs are direct bindings to PyTorch’s C++ low-level
operator registration APIs.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>The low-level operator registration APIs and the PyTorch Dispatcher are a
complicated PyTorch concept. We recommend you use the higher level APIs above
(that do not require a torch.library.Library object) when possible.
This blog post <<a class="reference external" href="http://blog.ezyang.com/2020/09/lets-talk-about-the-pytorch-dispatcher/">http://blog.ezyang.com/2020/09/lets-talk-about-the-pytorch-dispatcher/</a>>`_
is a good starting point to learn about the PyTorch Dispatcher.</p>
</div>
<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>
<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”.
To create a fragment of a possibly existing library to register operators (and bypass
the limitation that there is only one library for a given namespace), set the kind to
“FRAGMENT”.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>ns</strong> – library name</p></li>
<li><p><strong>kind</strong> – “DEF”, “IMPL” (default: “IMPL”), “FRAGMENT”</p></li>
<li><p><strong>dispatch_key</strong> – PyTorch dispatch key (default: “”)</p></li>
</ul>
</dd>
</dl>
<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>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tags</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</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>
<li><p><strong>tags</strong> (<a class="reference internal" href="torch.html#torch.Tag" title="torch.Tag"><em>Tag</em></a><em> | </em><em>Sequence</em><em>[</em><a class="reference internal" href="torch.html#torch.Tag" title="torch.Tag"><em>Tag</em></a><em>]</em>) – one or more torch.Tag to apply to this
operator. Tagging an operator changes the operator’s behavior
under various PyTorch subsystems; please read the docs for the
torch.Tag carefully before applying it.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</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</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 or <a class="reference internal" href="#torch.library.fallthrough_kernel" title="torch.library.fallthrough_kernel"><code class="xref py py-func docutils literal notranslate"><span class="pre">fallthrough_kernel()</span></code></a>
to register a fallthrough.</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="n">div_cpu</span><span class="p">,</span> <span class="s2">"CPU"</span><span class="p">)</span>
</pre></div>
</div>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.library.fallthrough_kernel">
<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">fallthrough_kernel</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#fallthrough_kernel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.fallthrough_kernel" title="Permalink to this definition">¶</a></dt>
<dd><p>A dummy function to pass to <code class="docutils literal notranslate"><span class="pre">Library.impl</span></code> in order to register a fallthrough.</p>
</dd></dl>
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