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<div class="section" id="module-torch.library">
<span id="torch-library"></span><span id="torch-library-docs"></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 testing custom operators, creating new
custom operators, and 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
Please see <a class="reference internal" href="notes/custom_operators.html#custom-ops-landing-page"><span class="std std-ref">PyTorch Custom Operators Landing Page</span></a>
for more details on how to effectively use these APIs.</p>
<div class="section" id="testing-custom-ops">
<h2>Testing custom ops<a class="headerlink" href="#testing-custom-ops" title="Permalink to this heading">¶</a></h2>
<p>Use <a class="reference internal" href="#torch.library.opcheck" title="torch.library.opcheck"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.library.opcheck()</span></code></a> to test custom ops for incorrect usage of the
Python torch.library and/or C++ TORCH_LIBRARY APIs. Also, if your operator supports
training, use <a class="reference internal" href="autograd.html#module-torch.autograd.gradcheck" title="torch.autograd.gradcheck"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.autograd.gradcheck()</span></code></a> to test that the gradients are
mathematically correct.</p>
<dl class="py function">
<dt class="sig sig-object py" id="torch.library.opcheck">
<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">opcheck</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</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">test_utils</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">('test_schema',</span> <span class="pre">'test_autograd_registration',</span> <span class="pre">'test_faketensor',</span> <span class="pre">'test_aot_dispatch_dynamic')</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">raise_exception</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#opcheck"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.opcheck" title="Permalink to this definition">¶</a></dt>
<dd><p>Given an operator and some sample arguments, tests if the operator is
registered correctly.</p>
<p>That is, when you use the torch.library/TORCH_LIBRARY APIs to create a
custom op, you specified metadata (e.g. mutability info) about the custom op
and these APIs require that the functions you pass them satisfy certain
properties (e.g. no data pointer access in the fake/meta/abstract kernel)
<code class="docutils literal notranslate"><span class="pre">opcheck</span></code> tests these metadata and properties.</p>
<p>Concretely, we test the following:</p>
<ul class="simple">
<li><p>test_schema: If the schema matches the implementation of
the operator. For example: if the schema specifies a Tensor is mutated,
then we check the implementation mutates the Tensor. If the schema
specifies that we return a new Tensor, then we check that the
implementation returns a new Tensor (instead of an existing one or
a view of an existing one).</p></li>
<li><p>test_autograd_registration: If the operator supports training
(autograd): we check that its autograd formula is registered via
torch.library.register_autograd or a manual registration to one
or more DispatchKey::Autograd keys. Any other DispatchKey-based
registrations may lead to undefined behavior.</p></li>
<li><p>test_faketensor: If the operator has a FakeTensor kernel
(and if it is correct). The FakeTensor kernel is necessary (
but not sufficient) for the operator to work with PyTorch compilation
APIs (torch.compile/export/FX). We check that a FakeTensor kernel
(also sometimes known as a meta kernel) was registered for the
operator and that it is correct. This test takes the result of
running the operator on real tensors and the result of running
the operator on FakeTensors and checks that they have the same
Tensor metadata (sizes/strides/dtype/device/etc).</p></li>
<li><p>test_aot_dispatch_dynamic: If the operator has correct behavior
with PyTorch compilation APIs (torch.compile/export/FX).
This checks that the outputs (and gradients, if applicable) are the
same under eager-mode PyTorch and torch.compile.
This test is a superset of <code class="docutils literal notranslate"><span class="pre">test_faketensor</span></code> and is an e2e test;
other things it tests are that the operator supports
functionalization and that the backward pass (if it exists) also
supports FakeTensor and functionalization.</p></li>
</ul>
<p>For best results, please call <code class="docutils literal notranslate"><span class="pre">opcheck</span></code> multiple times with a
representative set of inputs. If your operator supports
autograd, please use <code class="docutils literal notranslate"><span class="pre">opcheck</span></code> with inputs with <code class="docutils literal notranslate"><span class="pre">requires_grad</span> <span class="pre">=</span> <span class="pre">True</span></code>;
if your operator supports multiple devices (e.g. CPU and CUDA), please
use <code class="docutils literal notranslate"><span class="pre">opcheck</span></code> with inputs on all supported devices.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>op</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.13)"><em>Union</em></a><em>[</em><em>OpOverload</em><em>, </em><em>OpOverloadPacket</em><em>, </em><a class="reference internal" href="#torch._library.custom_ops.CustomOpDef" title="torch._library.custom_ops.CustomOpDef"><em>CustomOpDef</em></a><em>]</em>) – The operator. Must either be a function decorated with
<a class="reference internal" href="#torch.library.custom_op" title="torch.library.custom_op"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.library.custom_op()</span></code></a> or an OpOverload/OpOverloadPacket
found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo)</p></li>
<li><p><strong>args</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Tuple" title="(in Python v3.13)"><em>Tuple</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><em>Any</em></a><em>, </em><em>...</em><em>]</em>) – The args to the operator</p></li>
<li><p><strong>kwargs</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><em>Optional</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Dict" title="(in Python v3.13)"><em>Dict</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><em>Any</em></a><em>]</em><em>]</em>) – The kwargs to the operator</p></li>
<li><p><strong>test_utils</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.13)"><em>Union</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Sequence" title="(in Python v3.13)"><em>Sequence</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a><em>]</em><em>]</em>) – Tests that we should run. Default: all of them.
Example: (“test_schema”, “test_faketensor”)</p></li>
<li><p><strong>raise_exception</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a>) – If we should raise an exception on the first
error. If False, we will return a dict with information
on if each test passed or not.</p></li>
</ul>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Dict" title="(in Python v3.13)"><em>Dict</em></a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)">str</a>, <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)">str</a>]</p>
</dd>
</dl>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>opcheck and <a class="reference internal" href="autograd.html#module-torch.autograd.gradcheck" title="torch.autograd.gradcheck"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.autograd.gradcheck()</span></code></a> test different things;
opcheck tests if your usage of torch.library APIs is correct while
<a class="reference internal" href="autograd.html#module-torch.autograd.gradcheck" title="torch.autograd.gradcheck"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.autograd.gradcheck()</span></code></a> tests if your autograd formula is
mathematically correct. Use both to test custom ops that support
gradient computation.</p>
</div>
<p class="rubric">Example</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></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">custom_op</span><span class="p">(</span><span class="s2">"mylib::numpy_mul"</span><span class="p">,</span> <span class="n">mutates_args</span><span class="o">=</span><span class="p">())</span>
<span class="gp">>>> </span><span class="k">def</span> <span class="nf">numpy_add</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="nb">float</span><span class="p">)</span> <span class="o">-></span> <span class="n">Tensor</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="n">force</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">>>> </span> <span class="n">z_np</span> <span class="o">=</span> <span class="n">x_np</span> <span class="o">+</span> <span class="n">y</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">z_np</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="nd">@numpy_sin</span><span class="o">.</span><span class="n">register_fake</span>
<span class="gp">>>> </span><span class="k">def</span> <span class="nf">_</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="gp">>>> </span> <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty_like</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="k">def</span> <span class="nf">setup_context</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">output</span><span class="p">):</span>
<span class="gp">>>> </span> <span class="n">y</span><span class="p">,</span> <span class="o">=</span> <span class="n">inputs</span>
<span class="gp">>>> </span> <span class="n">ctx</span><span class="o">.</span><span class="n">y</span> <span class="o">=</span> <span class="n">y</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">grad</span><span class="p">):</span>
<span class="gp">>>> </span> <span class="k">return</span> <span class="n">grad</span> <span class="o">*</span> <span class="n">ctx</span><span class="o">.</span><span class="n">y</span><span class="p">,</span> <span class="kc">None</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="n">numpy_sin</span><span class="o">.</span><span class="n">register_autograd</span><span class="p">(</span><span class="n">backward</span><span class="p">,</span> <span class="n">setup_context</span><span class="o">=</span><span class="n">setup_context</span><span class="p">)</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="n">sample_inputs</span> <span class="o">=</span> <span class="p">[</span>
<span class="gp">>>> </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">3</span><span class="p">),</span> <span class="mf">3.14</span><span class="p">),</span>
<span class="gp">>>> </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">3</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">'cuda'</span><span class="p">),</span> <span class="mf">2.718</span><span class="p">),</span>
<span class="gp">>>> </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">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span> <span class="mf">1.234</span><span class="p">),</span>
<span class="gp">>>> </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">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">'cuda'</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span> <span class="mf">90.18</span><span class="p">),</span>
<span class="gp">>>> </span><span class="p">]</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">args</span> <span class="ow">in</span> <span class="n">sample_inputs</span><span class="p">:</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">opcheck</span><span class="p">(</span><span class="n">foo</span><span class="p">,</span> <span class="n">args</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
</div>
<div class="section" id="creating-new-custom-ops-in-python">
<h2>Creating new custom ops in Python<a class="headerlink" href="#creating-new-custom-ops-in-python" title="Permalink to this heading">¶</a></h2>
<p>Use <a class="reference internal" href="#torch.library.custom_op" title="torch.library.custom_op"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.library.custom_op()</span></code></a> to create new custom ops.</p>
<dl class="py function">
<dt class="sig sig-object py" id="torch.library.custom_op">
<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">custom_op</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fn</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="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mutates_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_types</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">schema</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="headerlink" href="#torch.library.custom_op" title="Permalink to this definition">¶</a></dt>
<dd><p>Wraps a function into custom operator.</p>
<p>Reasons why you may want to create a custom op include:
- Wrapping a third-party library or custom kernel to work with PyTorch
subsystems like Autograd.
- Preventing torch.compile/export/FX tracing from peeking inside your function.</p>
<p>This API is used as a decorator around a function (please see examples).
The provided function must have type hints; these are needed to interface
with PyTorch’s various subsystems.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a>) – A name for the custom op that looks like “{namespace}::{name}”,
e.g. “mylib::my_linear”. The name is used as the op’s stable identifier
in PyTorch subsystems (e.g. torch.export, FX graphs).
To avoid name collisions, please use your project name as the namespace;
e.g. all custom ops in pytorch/fbgemm use “fbgemm” as the namespace.</p></li>
<li><p><strong>mutates_args</strong> (<em>Iterable</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a><em>] or </em><em>"unknown"</em>) – The names of args that the function mutates.
This MUST be accurate, otherwise, the behavior is undefined. If “unknown”,
it pessimistically assumes that all inputs to the operator are being mutated.</p></li>
<li><p><strong>device_types</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><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 v3.13)"><em>str</em></a><em>]</em>) – The device type(s) the function
is valid for. If no device type is provided, then the function
is used as the default implementation for all device types.
Examples: “cpu”, “cuda”.
When registering a device-specific implementation for an operator that accepts no Tensors,
we require the operator to have a “device: torch.device argument”.</p></li>
<li><p><strong>schema</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a>) – A schema string for the operator. If None
(recommended) we’ll infer a schema for the operator from its type
annotations. We recommend letting us infer a schema unless you
have a specific reason not to.
Example: “(Tensor x, int y) -> (Tensor, Tensor)”.</p></li>
</ul>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.13)"><em>Callable</em></a></p>
</dd>
</dl>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>We recommend not passing in a <code class="docutils literal notranslate"><span class="pre">schema</span></code> arg and instead letting us infer
it from the type annotations. It is error-prone to write your own schema.
You may wish to provide your own schema if our interpretation of
the type annotation is not what you want.
For more info on how to write a schema string, see
<a class="reference external" href="https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/README.md#func">here</a></p>
</div>
<dl>
<dt>Examples::</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">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">Tensor</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">torch.library</span> <span class="kn">import</span> <span class="n">custom_op</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="nd">@custom_op</span><span class="p">(</span><span class="s2">"mylib::numpy_sin"</span><span class="p">,</span> <span class="n">mutates_args</span><span class="o">=</span><span class="p">())</span>
<span class="gp">>>> </span><span class="k">def</span> <span class="nf">numpy_sin</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">)</span> <span class="o">-></span> <span class="n">Tensor</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">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
<span class="gp">>>> </span> <span class="n">y_np</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x_np</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">y_np</span><span class="p">)</span><span class="o">.</span><span class="n">to</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>
<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">numpy_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>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="c1"># Example of a custom op that only works for one device type.</span>
<span class="gp">>>> </span><span class="nd">@custom_op</span><span class="p">(</span><span class="s2">"mylib::numpy_sin_cpu"</span><span class="p">,</span> <span class="n">mutates_args</span><span class="o">=</span><span class="p">(),</span> <span class="n">device_types</span><span class="o">=</span><span class="s2">"cpu"</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">def</span> <span class="nf">numpy_sin_cpu</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">)</span> <span class="o">-></span> <span class="n">Tensor</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">y_np</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x_np</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">y_np</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">numpy_sin_cpu</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>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="c1"># Example of a custom op that mutates an input</span>
<span class="gp">>>> </span><span class="nd">@custom_op</span><span class="p">(</span><span class="s2">"mylib::numpy_sin_inplace"</span><span class="p">,</span> <span class="n">mutates_args</span><span class="o">=</span><span class="p">{</span><span class="s2">"x"</span><span class="p">},</span> <span class="n">device_types</span><span class="o">=</span><span class="s2">"cpu"</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">def</span> <span class="nf">numpy_sin_inplace</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</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">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x_np</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">x_np</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">expected</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">sin</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">numpy_sin_inplace</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">x</span><span class="p">,</span> <span class="n">expected</span><span class="p">)</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="c1"># Example of a factory function</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">custom_op</span><span class="p">(</span><span class="s2">"mylib::bar"</span><span class="p">,</span> <span class="n">mutates_args</span><span class="o">=</span><span class="p">{},</span> <span class="n">device_types</span><span class="o">=</span><span class="s2">"cpu"</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">def</span> <span class="nf">bar</span><span class="p">(</span><span class="n">device</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> <span class="o">-></span> <span class="n">Tensor</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">ones</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="n">bar</span><span class="p">(</span><span class="s2">"cpu"</span><span class="p">)</span>
</pre></div>
</div>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="extending-custom-ops-created-from-python-or-c">
<h2>Extending custom ops (created from Python or C++)<a class="headerlink" href="#extending-custom-ops-created-from-python-or-c" title="Permalink to this heading">¶</a></h2>
<p>Use the register.* methods, such as <a class="reference internal" href="#torch.library.register_kernel" title="torch.library.register_kernel"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.library.register_kernel()</span></code></a> and
func:<cite>torch.library.register_fake</cite>, to add implementations
for any operators (they may have been created using <a class="reference internal" href="#torch.library.custom_op" title="torch.library.custom_op"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.library.custom_op()</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.register_kernel">
<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">register_kernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_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="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#register_kernel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.register_kernel" title="Permalink to this definition">¶</a></dt>
<dd><p>Register an implementation for a device type for this operator.</p>
<p>Some valid device_types are: “cpu”, “cuda”, “xla”, “mps”, “ipu”, “xpu”.
This API may be used as a decorator.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>fn</strong> (<em>Callable</em>) – The function to register as the implementation for
the given device types.</p></li>
<li><p><strong>device_types</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><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 v3.13)"><em>str</em></a><em>]</em>) – The device_types to register an impl to.
If None, we will register to all device types – please only use
this option if your implementation is truly device-type-agnostic.</p></li>
</ul>
</dd>
</dl>
<dl>
<dt>Examples::</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">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">Tensor</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">torch.library</span> <span class="kn">import</span> <span class="n">custom_op</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"># Create a custom op that works on cpu</span>
<span class="gp">>>> </span><span class="nd">@custom_op</span><span class="p">(</span><span class="s2">"mylib::numpy_sin"</span><span class="p">,</span> <span class="n">mutates_args</span><span class="o">=</span><span class="p">(),</span> <span class="n">device_types</span><span class="o">=</span><span class="s2">"cpu"</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">def</span> <span class="nf">numpy_sin</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">)</span> <span class="o">-></span> <span class="n">Tensor</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">y_np</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x_np</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">y_np</span><span class="p">)</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="c1"># Add implementations for the cuda 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">register_kernel</span><span class="p">(</span><span class="s2">"mylib::numpy_sin"</span><span class="p">,</span> <span class="s2">"cuda"</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">def</span> <span class="nf">_</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">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
<span class="gp">>>> </span> <span class="n">y_np</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x_np</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">y_np</span><span class="p">)</span><span class="o">.</span><span class="n">to</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>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="n">x_cpu</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">x_cuda</span> <span class="o">=</span> <span class="n">x_cpu</span><span class="o">.</span><span class="n">cuda</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">numpy_sin</span><span class="p">(</span><span class="n">x_cpu</span><span class="p">),</span> <span class="n">x_cpu</span><span class="o">.</span><span class="n">sin</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">numpy_sin</span><span class="p">(</span><span class="n">x_cuda</span><span class="p">),</span> <span class="n">x_cuda</span><span class="o">.</span><span class="n">sin</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.register_autograd">
<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">register_autograd</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backward</span></span></em>, <em class="sig-param"><span class="o"><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">setup_context</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">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#register_autograd"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.register_autograd" title="Permalink to this definition">¶</a></dt>
<dd><p>Register a backward formula for this custom op.</p>
<p>In order for an operator to work with autograd, you need to register
a backward formula:
1. You must tell us how to compute gradients during the backward pass
by providing us a “backward” function.
2. If you need any values from the forward to compute gradients, you can
use <cite>setup_context</cite> to save values for backward.</p>
<p><code class="docutils literal notranslate"><span class="pre">backward</span></code> runs during the backward pass. It accepts <code class="docutils literal notranslate"><span class="pre">(ctx,</span> <span class="pre">*grads)</span></code>:
- <code class="docutils literal notranslate"><span class="pre">grads</span></code> is one or more gradients. The number of gradients matches
the number of outputs of the operator.
The <code class="docutils literal notranslate"><span class="pre">ctx</span></code> object is <a class="reference external" href="context_method_mixins">the same ctx object</a> used by
<a class="reference internal" href="autograd.html#torch.autograd.Function" title="torch.autograd.Function"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.autograd.Function</span></code></a>. The semantics of <code class="docutils literal notranslate"><span class="pre">backward_fn</span></code> are the
same as <a class="reference internal" href="generated/torch.autograd.Function.backward.html#torch.autograd.Function.backward" title="torch.autograd.Function.backward"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.autograd.Function.backward()</span></code></a>.</p>
<p><code class="docutils literal notranslate"><span class="pre">setup_context(ctx,</span> <span class="pre">inputs,</span> <span class="pre">output)</span></code> runs during the forward pass.
Please save quantities needed for backward onto the <code class="docutils literal notranslate"><span class="pre">ctx</span></code> object via
either <a class="reference internal" href="generated/torch.autograd.function.FunctionCtx.save_for_backward.html#torch.autograd.function.FunctionCtx.save_for_backward" title="torch.autograd.function.FunctionCtx.save_for_backward"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.autograd.function.FunctionCtx.save_for_backward()</span></code></a>
or assigning them as attributes of <code class="docutils literal notranslate"><span class="pre">ctx</span></code>. If your custom op has
kwarg-only arguments, we expect the signature of <code class="docutils literal notranslate"><span class="pre">setup_context</span></code>
to be <code class="docutils literal notranslate"><span class="pre">setup_context(ctx,</span> <span class="pre">inputs,</span> <span class="pre">keyword_only_inputs,</span> <span class="pre">output)</span></code>.</p>
<p>Both <code class="docutils literal notranslate"><span class="pre">setup_context_fn</span></code> and <code class="docutils literal notranslate"><span class="pre">backward_fn</span></code> must be traceable. That is,
they may not directly access <a class="reference internal" href="generated/torch.Tensor.data_ptr.html#torch.Tensor.data_ptr" title="torch.Tensor.data_ptr"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.data_ptr()</span></code></a> and they must
not depend on or mutate global state. If you need a non-traceable backward,
you can make it a separate custom_op that you call inside <code class="docutils literal notranslate"><span class="pre">backward_fn</span></code>.</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="nd">@torch</span><span class="o">.</span><span class="n">library</span><span class="o">.</span><span class="n">custom_op</span><span class="p">(</span><span class="s2">"mylib::numpy_sin"</span><span class="p">,</span> <span class="n">mutates_args</span><span class="o">=</span><span class="p">())</span>
<span class="gp">>>> </span><span class="k">def</span> <span class="nf">numpy_sin</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">)</span> <span class="o">-></span> <span class="n">Tensor</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">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
<span class="gp">>>> </span> <span class="n">y_np</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x_np</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">y_np</span><span class="p">)</span><span class="o">.</span><span class="n">to</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>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="k">def</span> <span class="nf">setup_context</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">output</span><span class="p">)</span> <span class="o">-></span> <span class="n">Tensor</span><span class="p">:</span>
<span class="gp">>>> </span> <span class="n">x</span><span class="p">,</span> <span class="o">=</span> <span class="n">inputs</span>
<span class="gp">>>> </span> <span class="n">ctx</span><span class="o">.</span><span class="n">save_for_backward</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">grad</span><span class="p">):</span>
<span class="gp">>>> </span> <span class="n">x</span><span class="p">,</span> <span class="o">=</span> <span class="n">ctx</span><span class="o">.</span><span class="n">saved_tensors</span>
<span class="gp">>>> </span> <span class="k">return</span> <span class="n">grad</span> <span class="o">*</span> <span class="n">x</span><span class="o">.</span><span class="n">cos</span><span class="p">()</span>
<span class="go">>>></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">register_autograd</span><span class="p">(</span>
<span class="gp">... </span> <span class="s2">"mylib::numpy_sin"</span><span class="p">,</span> <span class="n">backward</span><span class="p">,</span> <span class="n">setup_context</span><span class="o">=</span><span class="n">setup_context</span>
<span class="gp">... </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="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">numpy_sin</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="gp">>>> </span><span class="p">(</span><span class="n">grad_x</span><span class="p">,)</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">grad</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">y</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">grad_x</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">cos</span><span class="p">())</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="c1"># Example with a keyword-only arg</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">custom_op</span><span class="p">(</span><span class="s2">"mylib::numpy_mul"</span><span class="p">,</span> <span class="n">mutates_args</span><span class="o">=</span><span class="p">())</span>
<span class="gp">>>> </span><span class="k">def</span> <span class="nf">numpy_mul</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">val</span><span class="p">:</span> <span class="nb">float</span><span class="p">)</span> <span class="o">-></span> <span class="n">Tensor</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">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
<span class="gp">>>> </span> <span class="n">y_np</span> <span class="o">=</span> <span class="n">x_np</span> <span class="o">*</span> <span class="n">val</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">y_np</span><span class="p">)</span><span class="o">.</span><span class="n">to</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>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="k">def</span> <span class="nf">setup_context</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">keyword_only_inputs</span><span class="p">,</span> <span class="n">output</span><span class="p">)</span> <span class="o">-></span> <span class="n">Tensor</span><span class="p">:</span>
<span class="gp">>>> </span> <span class="n">ctx</span><span class="o">.</span><span class="n">val</span> <span class="o">=</span> <span class="n">keyword_only_inputs</span><span class="p">[</span><span class="s2">"val"</span><span class="p">]</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">grad</span><span class="p">):</span>
<span class="gp">>>> </span> <span class="k">return</span> <span class="n">grad</span> <span class="o">*</span> <span class="n">ctx</span><span class="o">.</span><span class="n">val</span>
<span class="go">>>></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">register_autograd</span><span class="p">(</span>
<span class="gp">... </span> <span class="s2">"mylib::numpy_mul"</span><span class="p">,</span> <span class="n">backward</span><span class="p">,</span> <span class="n">setup_context</span><span class="o">=</span><span class="n">setup_context</span>
<span class="gp">... </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="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">numpy_mul</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">val</span><span class="o">=</span><span class="mf">3.14</span><span class="p">)</span>
<span class="gp">>>> </span><span class="p">(</span><span class="n">grad_x</span><span class="p">,)</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">grad</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">y</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">grad_x</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">full_like</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mf">3.14</span><span class="p">))</span>
</pre></div>
</div>
<dl class="field-list simple">
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.library.register_fake">
<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">register_fake</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</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="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#register_fake"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.register_fake" title="Permalink to this definition">¶</a></dt>
<dd><p>Register a FakeTensor implementation (“fake impl”) for this operator.</p>
<p>Also sometimes known as a “meta kernel”, “abstract impl”.</p>
<p>An “FakeTensor implementation” specifies the behavior of this operator on
Tensors that carry no data (“FakeTensor”). 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 FakeTensor implementation has the same signature as the operator.
It is run for both FakeTensors and meta tensors. To write a FakeTensor
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 FakeTensor 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://pytorch.org/tutorials/advanced/custom_ops_landing_page.html">https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html</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="nd">@torch</span><span class="o">.</span><span class="n">library</span><span class="o">.</span><span class="n">custom_op</span><span class="p">(</span><span class="s2">"mylib::custom_linear"</span><span class="p">,</span> <span class="n">mutates_args</span><span class="o">=</span><span class="p">())</span>
<span class="gp">>>> </span><span class="k">def</span> <span class="nf">custom_linear</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span> <span class="n">weight</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span> <span class="n">bias</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">)</span> <span class="o">-></span> <span class="n">Tensor</span><span class="p">:</span>
<span class="gp">>>> </span> <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">"Implementation goes here"</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">register_fake</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">_</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="n">bias</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="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">_subclasses</span><span class="o">.</span><span class="n">fake_tensor</span><span class="o">.</span><span class="n">FakeTensorMode</span><span class="p">():</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">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="gp">>>> </span> <span class="n">w</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="mi">3</span><span class="p">)</span>
<span class="gp">>>> </span> <span class="n">b</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">custom_linear</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="k">assert</span> <span class="n">y</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="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="nd">@torch</span><span class="o">.</span><span class="n">library</span><span class="o">.</span><span class="n">custom_op</span><span class="p">(</span><span class="s2">"mylib::custom_nonzero"</span><span class="p">,</span> <span class="n">mutates_args</span><span class="o">=</span><span class="p">())</span>
<span class="gp">>>> </span><span class="k">def</span> <span class="nf">custom_nonzero</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">)</span> <span class="o">-></span> <span class="n">Tensor</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="n">force</span><span class="o">=</span><span class="kc">True</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>
<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">register_fake</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">_</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 fake 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="kn">from</span> <span class="nn">torch.fx.experimental.proxy_tensor</span> <span class="kn">import</span> <span class="n">make_fx</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">tensor</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">trace</span> <span class="o">=</span> <span class="n">make_fx</span><span class="p">(</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">custom_nonzero</span><span class="p">,</span> <span class="n">tracing_mode</span><span class="o">=</span><span class="s2">"symbolic"</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">trace</span><span class="o">.</span><span class="n">print_readable</span><span class="p">()</span>
<span class="go">>>></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">trace</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">mylib</span><span class="o">.</span><span class="n">custom_nonzero</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
</pre></div>
</div>
<dl class="field-list simple">
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.library.register_vmap">
<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">register_vmap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</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="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#register_vmap"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.register_vmap" title="Permalink to this definition">¶</a></dt>
<dd><p>Register a vmap implementation to support <a class="reference internal" href="generated/torch.vmap.html#torch.vmap" title="torch.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.vmap()</span></code></a> for this custom op.</p>
<p>This API may be used as a decorator (see examples).</p>
<p>In order for an operator to work with <a class="reference internal" href="generated/torch.vmap.html#torch.vmap" title="torch.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.vmap()</span></code></a>, you may need to register a
vmap implementation in the following signature:</p>
<blockquote>
<div><p><code class="docutils literal notranslate"><span class="pre">vmap_func(info,</span> <span class="pre">in_dims:</span> <span class="pre">Tuple[Optional[int]],</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code>,</p>
</div></blockquote>
<p>where <code class="docutils literal notranslate"><span class="pre">*args</span></code> and <code class="docutils literal notranslate"><span class="pre">**kwargs</span></code> are the arguments and kwargs for <code class="docutils literal notranslate"><span class="pre">op</span></code>.
We do not support kwarg-only Tensor args.</p>
<p>It specifies how do we compute the batched version of <code class="docutils literal notranslate"><span class="pre">op</span></code> given inputs with an additional
dimension (specified by <code class="docutils literal notranslate"><span class="pre">in_dims</span></code>).</p>
<p>For each arg in <code class="docutils literal notranslate"><span class="pre">args</span></code>, <code class="docutils literal notranslate"><span class="pre">in_dims</span></code> has a corresponding <code class="docutils literal notranslate"><span class="pre">Optional[int]</span></code>. It is <code class="docutils literal notranslate"><span class="pre">None</span></code>
if the arg is not a Tensor or if the arg is not being vmapped over, otherwise, it is an integer
specifying what dimension of the Tensor is being vmapped over.</p>
<p><code class="docutils literal notranslate"><span class="pre">info</span></code> is a collection of additional metadata that may be helpful:
<code class="docutils literal notranslate"><span class="pre">info.batch_size</span></code> specifies the size of the dimension being vmapped over, while
<code class="docutils literal notranslate"><span class="pre">info.randomness</span></code> is the <code class="docutils literal notranslate"><span class="pre">randomness</span></code> option that was passed to <a class="reference internal" href="generated/torch.vmap.html#torch.vmap" title="torch.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.vmap()</span></code></a>.</p>
<p>The return of the function <code class="docutils literal notranslate"><span class="pre">func</span></code> is a tuple of <code class="docutils literal notranslate"><span class="pre">(output,</span> <span class="pre">out_dims)</span></code>. Similar to <code class="docutils literal notranslate"><span class="pre">in_dims</span></code>,
<code class="docutils literal notranslate"><span class="pre">out_dims</span></code> should be of the same structure as <code class="docutils literal notranslate"><span class="pre">output</span></code> and contain one <code class="docutils literal notranslate"><span class="pre">out_dim</span></code>
per output that specifies if the output has the vmapped dimension and what index it is in.</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="gp">>>> </span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Tuple</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="k">def</span> <span class="nf">to_numpy</span><span class="p">(</span><span class="n">tensor</span><span class="p">):</span>
<span class="gp">>>> </span> <span class="k">return</span> <span class="n">tensor</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</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">lib</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">Library</span><span class="p">(</span><span class="s2">"mylib"</span><span class="p">,</span> <span class="s2">"FRAGMENT"</span><span class="p">)</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">custom_op</span><span class="p">(</span><span class="s2">"mylib::numpy_cube"</span><span class="p">,</span> <span class="n">mutates_args</span><span class="o">=</span><span class="p">())</span>
<span class="gp">>>> </span><span class="k">def</span> <span class="nf">numpy_cube</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">)</span> <span class="o">-></span> <span class="n">Tuple</span><span class="p">[</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">Tensor</span><span class="p">]:</span>
<span class="gp">>>> </span> <span class="n">x_np</span> <span class="o">=</span> <span class="n">to_numpy</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="gp">>>> </span> <span class="n">dx</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="o">*</span> <span class="n">x_np</span> <span class="o">**</span> <span class="mi">2</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>
<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">x_np</span> <span class="o">**</span> <span class="mi">3</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> <span class="n">dx</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="k">def</span> <span class="nf">numpy_cube_vmap</span><span class="p">(</span><span class="n">info</span><span class="p">,</span> <span class="n">in_dims</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="gp">>>> </span> <span class="n">result</span> <span class="o">=</span> <span class="n">numpy_cube</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">result</span><span class="p">,</span> <span class="p">(</span><span class="n">in_dims</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">in_dims</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="go">>>></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">register_vmap</span><span class="p">(</span><span class="n">numpy_cube</span><span class="p">,</span> <span class="n">numpy_cube_vmap</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">torch</span><span class="o">.</span><span class="n">vmap</span><span class="p">(</span><span class="n">numpy_cube</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>