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<div class="section" id="torch-utils-cpp-extension">
<h1>torch.utils.cpp_extension<a class="headerlink" href="#torch-utils-cpp-extension" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="torch.utils.cpp_extension.CppExtension">
<code class="sig-prename descclassname">torch.utils.cpp_extension.</code><code class="sig-name descname">CppExtension</code><span class="sig-paren">(</span><em class="sig-param">name</em>, <em class="sig-param">sources</em>, <em class="sig-param">*args</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#CppExtension"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.cpp_extension.CppExtension" title="Permalink to this definition">¶</a></dt>
<dd><p>Creates a <code class="xref py py-class docutils literal notranslate"><span class="pre">setuptools.Extension</span></code> for C++.</p>
<p>Convenience method that creates a <code class="xref py py-class docutils literal notranslate"><span class="pre">setuptools.Extension</span></code> with the
bare minimum (but often sufficient) arguments to build a C++ extension.</p>
<p>All arguments are forwarded to the <code class="xref py py-class docutils literal notranslate"><span class="pre">setuptools.Extension</span></code>
constructor.</p>
<p class="rubric">Example</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">setuptools</span> <span class="k">import</span> <span class="n">setup</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">torch.utils.cpp_extension</span> <span class="k">import</span> <span class="n">BuildExtension</span><span class="p">,</span> <span class="n">CppExtension</span>
<span class="gp">>>> </span><span class="n">setup</span><span class="p">(</span>
<span class="go"> name='extension',</span>
<span class="go"> ext_modules=[</span>
<span class="go"> CppExtension(</span>
<span class="go"> name='extension',</span>
<span class="go"> sources=['extension.cpp'],</span>
<span class="go"> extra_compile_args=['-g']),</span>
<span class="go"> ],</span>
<span class="go"> cmdclass={</span>
<span class="go"> 'build_ext': BuildExtension</span>
<span class="go"> })</span>
</pre></div>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.utils.cpp_extension.CUDAExtension">
<code class="sig-prename descclassname">torch.utils.cpp_extension.</code><code class="sig-name descname">CUDAExtension</code><span class="sig-paren">(</span><em class="sig-param">name</em>, <em class="sig-param">sources</em>, <em class="sig-param">*args</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#CUDAExtension"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.cpp_extension.CUDAExtension" title="Permalink to this definition">¶</a></dt>
<dd><p>Creates a <code class="xref py py-class docutils literal notranslate"><span class="pre">setuptools.Extension</span></code> for CUDA/C++.</p>
<p>Convenience method that creates a <code class="xref py py-class docutils literal notranslate"><span class="pre">setuptools.Extension</span></code> with the
bare minimum (but often sufficient) arguments to build a CUDA/C++
extension. This includes the CUDA include path, library path and runtime
library.</p>
<p>All arguments are forwarded to the <code class="xref py py-class docutils literal notranslate"><span class="pre">setuptools.Extension</span></code>
constructor.</p>
<p class="rubric">Example</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">setuptools</span> <span class="k">import</span> <span class="n">setup</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">torch.utils.cpp_extension</span> <span class="k">import</span> <span class="n">BuildExtension</span><span class="p">,</span> <span class="n">CUDAExtension</span>
<span class="gp">>>> </span><span class="n">setup</span><span class="p">(</span>
<span class="go"> name='cuda_extension',</span>
<span class="go"> ext_modules=[</span>
<span class="go"> CUDAExtension(</span>
<span class="go"> name='cuda_extension',</span>
<span class="go"> sources=['extension.cpp', 'extension_kernel.cu'],</span>
<span class="go"> extra_compile_args={'cxx': ['-g'],</span>
<span class="go"> 'nvcc': ['-O2']})</span>
<span class="go"> ],</span>
<span class="go"> cmdclass={</span>
<span class="go"> 'build_ext': BuildExtension</span>
<span class="go"> })</span>
</pre></div>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.utils.cpp_extension.BuildExtension">
<code class="sig-prename descclassname">torch.utils.cpp_extension.</code><code class="sig-name descname">BuildExtension</code><span class="sig-paren">(</span><em class="sig-param">*args</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#BuildExtension"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.cpp_extension.BuildExtension" title="Permalink to this definition">¶</a></dt>
<dd><p>A custom <code class="xref py py-mod docutils literal notranslate"><span class="pre">setuptools</span></code> build extension .</p>
<p>This <code class="xref py py-class docutils literal notranslate"><span class="pre">setuptools.build_ext</span></code> subclass takes care of passing the
minimum required compiler flags (e.g. <code class="docutils literal notranslate"><span class="pre">-std=c++11</span></code>) as well as mixed
C++/CUDA compilation (and support for CUDA files in general).</p>
<p>When using <a class="reference internal" href="#torch.utils.cpp_extension.BuildExtension" title="torch.utils.cpp_extension.BuildExtension"><code class="xref py py-class docutils literal notranslate"><span class="pre">BuildExtension</span></code></a>, it is allowed to supply a dictionary
for <code class="docutils literal notranslate"><span class="pre">extra_compile_args</span></code> (rather than the usual list) that maps from
languages (<code class="docutils literal notranslate"><span class="pre">cxx</span></code> or <code class="docutils literal notranslate"><span class="pre">nvcc</span></code>) to a list of additional compiler flags to
supply to the compiler. This makes it possible to supply different flags to
the C++ and CUDA compiler during mixed compilation.</p>
</dd></dl>
<dl class="function">
<dt id="torch.utils.cpp_extension.load">
<code class="sig-prename descclassname">torch.utils.cpp_extension.</code><code class="sig-name descname">load</code><span class="sig-paren">(</span><em class="sig-param">name</em>, <em class="sig-param">sources</em>, <em class="sig-param">extra_cflags=None</em>, <em class="sig-param">extra_cuda_cflags=None</em>, <em class="sig-param">extra_ldflags=None</em>, <em class="sig-param">extra_include_paths=None</em>, <em class="sig-param">build_directory=None</em>, <em class="sig-param">verbose=False</em>, <em class="sig-param">with_cuda=None</em>, <em class="sig-param">is_python_module=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#load"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.cpp_extension.load" title="Permalink to this definition">¶</a></dt>
<dd><p>Loads a PyTorch C++ extension just-in-time (JIT).</p>
<p>To load an extension, a Ninja build file is emitted, which is used to
compile the given sources into a dynamic library. This library is
subsequently loaded into the current Python process as a module and
returned from this function, ready for use.</p>
<p>By default, the directory to which the build file is emitted and the
resulting library compiled to is <code class="docutils literal notranslate"><span class="pre"><tmp>/torch_extensions/<name></span></code>, where
<code class="docutils literal notranslate"><span class="pre"><tmp></span></code> is the temporary folder on the current platform and <code class="docutils literal notranslate"><span class="pre"><name></span></code>
the name of the extension. This location can be overridden in two ways.
First, if the <code class="docutils literal notranslate"><span class="pre">TORCH_EXTENSIONS_DIR</span></code> environment variable is set, it
replaces <code class="docutils literal notranslate"><span class="pre"><tmp>/torch_extensions</span></code> and all extensions will be compiled
into subfolders of this directory. Second, if the <code class="docutils literal notranslate"><span class="pre">build_directory</span></code>
argument to this function is supplied, it overrides the entire path, i.e.
the library will be compiled into that folder directly.</p>
<p>To compile the sources, the default system compiler (<code class="docutils literal notranslate"><span class="pre">c++</span></code>) is used,
which can be overridden by setting the <code class="docutils literal notranslate"><span class="pre">CXX</span></code> environment variable. To pass
additional arguments to the compilation process, <code class="docutils literal notranslate"><span class="pre">extra_cflags</span></code> or
<code class="docutils literal notranslate"><span class="pre">extra_ldflags</span></code> can be provided. For example, to compile your extension
with optimizations, pass <code class="docutils literal notranslate"><span class="pre">extra_cflags=['-O3']</span></code>. You can also use
<code class="docutils literal notranslate"><span class="pre">extra_cflags</span></code> to pass further include directories.</p>
<p>CUDA support with mixed compilation is provided. Simply pass CUDA source
files (<code class="docutils literal notranslate"><span class="pre">.cu</span></code> or <code class="docutils literal notranslate"><span class="pre">.cuh</span></code>) along with other sources. Such files will be
detected and compiled with nvcc rather than the C++ compiler. This includes
passing the CUDA lib64 directory as a library directory, and linking
<code class="docutils literal notranslate"><span class="pre">cudart</span></code>. You can pass additional flags to nvcc via
<code class="docutils literal notranslate"><span class="pre">extra_cuda_cflags</span></code>, just like with <code class="docutils literal notranslate"><span class="pre">extra_cflags</span></code> for C++. Various
heuristics for finding the CUDA install directory are used, which usually
work fine. If not, setting the <code class="docutils literal notranslate"><span class="pre">CUDA_HOME</span></code> environment variable is the
safest option.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>name</strong> – The name of the extension to build. This MUST be the same as the
name of the pybind11 module!</p></li>
<li><p><strong>sources</strong> – A list of relative or absolute paths to C++ source files.</p></li>
<li><p><strong>extra_cflags</strong> – optional list of compiler flags to forward to the build.</p></li>
<li><p><strong>extra_cuda_cflags</strong> – optional list of compiler flags to forward to nvcc
when building CUDA sources.</p></li>
<li><p><strong>extra_ldflags</strong> – optional list of linker flags to forward to the build.</p></li>
<li><p><strong>extra_include_paths</strong> – optional list of include directories to forward
to the build.</p></li>
<li><p><strong>build_directory</strong> – optional path to use as build workspace.</p></li>
<li><p><strong>verbose</strong> – If <code class="docutils literal notranslate"><span class="pre">True</span></code>, turns on verbose logging of load steps.</p></li>
<li><p><strong>with_cuda</strong> – Determines whether CUDA headers and libraries are added to
the build. If set to <code class="docutils literal notranslate"><span class="pre">None</span></code> (default), this value is
automatically determined based on the existence of <code class="docutils literal notranslate"><span class="pre">.cu</span></code> or
<code class="docutils literal notranslate"><span class="pre">.cuh</span></code> in <code class="docutils literal notranslate"><span class="pre">sources</span></code>. Set it to <cite>True`</cite> to force CUDA headers
and libraries to be included.</p></li>
<li><p><strong>is_python_module</strong> – If <code class="docutils literal notranslate"><span class="pre">True</span></code> (default), imports the produced shared
library as a Python module. If <code class="docutils literal notranslate"><span class="pre">False</span></code>, loads it into the process
as a plain dynamic library.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>If <code class="docutils literal notranslate"><span class="pre">is_python_module</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code>, returns the loaded PyTorch
extension as a Python module. If <code class="docutils literal notranslate"><span class="pre">is_python_module</span></code> is <code class="docutils literal notranslate"><span class="pre">False</span></code>
returns nothing (the shared library is loaded into the process as a side
effect).</p>
</dd>
</dl>
<p class="rubric">Example</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">torch.utils.cpp_extension</span> <span class="k">import</span> <span class="n">load</span>
<span class="gp">>>> </span><span class="n">module</span> <span class="o">=</span> <span class="n">load</span><span class="p">(</span>
<span class="go"> name='extension',</span>
<span class="go"> sources=['extension.cpp', 'extension_kernel.cu'],</span>
<span class="go"> extra_cflags=['-O2'],</span>
<span class="go"> verbose=True)</span>
</pre></div>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.utils.cpp_extension.load_inline">
<code class="sig-prename descclassname">torch.utils.cpp_extension.</code><code class="sig-name descname">load_inline</code><span class="sig-paren">(</span><em class="sig-param">name</em>, <em class="sig-param">cpp_sources</em>, <em class="sig-param">cuda_sources=None</em>, <em class="sig-param">functions=None</em>, <em class="sig-param">extra_cflags=None</em>, <em class="sig-param">extra_cuda_cflags=None</em>, <em class="sig-param">extra_ldflags=None</em>, <em class="sig-param">extra_include_paths=None</em>, <em class="sig-param">build_directory=None</em>, <em class="sig-param">verbose=False</em>, <em class="sig-param">with_cuda=None</em>, <em class="sig-param">is_python_module=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#load_inline"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.cpp_extension.load_inline" title="Permalink to this definition">¶</a></dt>
<dd><p>Loads a PyTorch C++ extension just-in-time (JIT) from string sources.</p>
<p>This function behaves exactly like <a class="reference internal" href="#torch.utils.cpp_extension.load" title="torch.utils.cpp_extension.load"><code class="xref py py-func docutils literal notranslate"><span class="pre">load()</span></code></a>, but takes its sources as
strings rather than filenames. These strings are stored to files in the
build directory, after which the behavior of <a class="reference internal" href="#torch.utils.cpp_extension.load_inline" title="torch.utils.cpp_extension.load_inline"><code class="xref py py-func docutils literal notranslate"><span class="pre">load_inline()</span></code></a> is
identical to <a class="reference internal" href="#torch.utils.cpp_extension.load" title="torch.utils.cpp_extension.load"><code class="xref py py-func docutils literal notranslate"><span class="pre">load()</span></code></a>.</p>
<p>See <a class="reference external" href="https://github.com/pytorch/pytorch/blob/master/test/test_cpp_extensions.py">the
tests</a>
for good examples of using this function.</p>
<p>Sources may omit two required parts of a typical non-inline C++ extension:
the necessary header includes, as well as the (pybind11) binding code. More
precisely, strings passed to <code class="docutils literal notranslate"><span class="pre">cpp_sources</span></code> are first concatenated into a
single <code class="docutils literal notranslate"><span class="pre">.cpp</span></code> file. This file is then prepended with <code class="docutils literal notranslate"><span class="pre">#include</span>
<span class="pre"><torch/extension.h></span></code>.</p>
<p>Furthermore, if the <code class="docutils literal notranslate"><span class="pre">functions</span></code> argument is supplied, bindings will be
automatically generated for each function specified. <code class="docutils literal notranslate"><span class="pre">functions</span></code> can
either be a list of function names, or a dictionary mapping from function
names to docstrings. If a list is given, the name of each function is used
as its docstring.</p>
<p>The sources in <code class="docutils literal notranslate"><span class="pre">cuda_sources</span></code> are concatenated into a separate <code class="docutils literal notranslate"><span class="pre">.cu</span></code>
file and prepended with <code class="docutils literal notranslate"><span class="pre">torch/types.h</span></code>, <code class="docutils literal notranslate"><span class="pre">cuda.h</span></code> and
<code class="docutils literal notranslate"><span class="pre">cuda_runtime.h</span></code> includes. The <code class="docutils literal notranslate"><span class="pre">.cpp</span></code> and <code class="docutils literal notranslate"><span class="pre">.cu</span></code> files are compiled
separately, but ultimately linked into a single library. Note that no
bindings are generated for functions in <code class="docutils literal notranslate"><span class="pre">cuda_sources</span></code> per se. To bind
to a CUDA kernel, you must create a C++ function that calls it, and either
declare or define this C++ function in one of the <code class="docutils literal notranslate"><span class="pre">cpp_sources</span></code> (and
include its name in <code class="docutils literal notranslate"><span class="pre">functions</span></code>).</p>
<p>See <a class="reference internal" href="#torch.utils.cpp_extension.load" title="torch.utils.cpp_extension.load"><code class="xref py py-func docutils literal notranslate"><span class="pre">load()</span></code></a> for a description of arguments omitted below.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>cpp_sources</strong> – A string, or list of strings, containing C++ source code.</p></li>
<li><p><strong>cuda_sources</strong> – A string, or list of strings, containing CUDA source code.</p></li>
<li><p><strong>functions</strong> – A list of function names for which to generate function
bindings. If a dictionary is given, it should map function names to
docstrings (which are otherwise just the function names).</p></li>
<li><p><strong>with_cuda</strong> – Determines whether CUDA headers and libraries are added to
the build. If set to <code class="docutils literal notranslate"><span class="pre">None</span></code> (default), this value is
automatically determined based on whether <code class="docutils literal notranslate"><span class="pre">cuda_sources</span></code> is
provided. Set it to <cite>True`</cite> to force CUDA headers
and libraries to be included.</p></li>
</ul>
</dd>
</dl>
<p class="rubric">Example</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">torch.utils.cpp_extension</span> <span class="k">import</span> <span class="n">load_inline</span>
<span class="gp">>>> </span><span class="n">source</span> <span class="o">=</span> <span class="s1">'''</span>
<span class="go">at::Tensor sin_add(at::Tensor x, at::Tensor y) {</span>
<span class="go"> return x.sin() + y.sin();</span>
<span class="go">}</span>
<span class="go">'''</span>
<span class="gp">>>> </span><span class="n">module</span> <span class="o">=</span> <span class="n">load_inline</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'inline_extension'</span><span class="p">,</span>
<span class="go"> cpp_sources=[source],</span>
<span class="go"> functions=['sin_add'])</span>
</pre></div>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.utils.cpp_extension.include_paths">
<code class="sig-prename descclassname">torch.utils.cpp_extension.</code><code class="sig-name descname">include_paths</code><span class="sig-paren">(</span><em class="sig-param">cuda=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#include_paths"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.cpp_extension.include_paths" title="Permalink to this definition">¶</a></dt>
<dd><p>Get the include paths required to build a C++ or CUDA extension.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>cuda</strong> – If <cite>True</cite>, includes CUDA-specific include paths.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A list of include path strings.</p>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torch.utils.cpp_extension.check_compiler_abi_compatibility">
<code class="sig-prename descclassname">torch.utils.cpp_extension.</code><code class="sig-name descname">check_compiler_abi_compatibility</code><span class="sig-paren">(</span><em class="sig-param">compiler</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#check_compiler_abi_compatibility"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.cpp_extension.check_compiler_abi_compatibility" title="Permalink to this definition">¶</a></dt>
<dd><p>Verifies that the given compiler is ABI-compatible with PyTorch.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>compiler</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.7)"><em>str</em></a>) – The compiler executable name to check (e.g. <code class="docutils literal notranslate"><span class="pre">g++</span></code>).
Must be executable in a shell process.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>False if the compiler is (likely) ABI-incompatible with PyTorch,
else True.</p>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torch.utils.cpp_extension.verify_ninja_availability">
<code class="sig-prename descclassname">torch.utils.cpp_extension.</code><code class="sig-name descname">verify_ninja_availability</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#verify_ninja_availability"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.cpp_extension.verify_ninja_availability" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns <code class="docutils literal notranslate"><span class="pre">True</span></code> if the <a class="reference external" href="https://ninja-build.org/">ninja</a> build system is
available on the system.</p>
</dd></dl>
</div>
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