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<section id="complex-numbers">
<span id="complex-numbers-doc"></span><h1>Complex Numbers<a class="headerlink" href="#complex-numbers" title="Permalink to this heading">¶</a></h1>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>When using complex numbers, use Pytorch with CUDA 11.6 downloaded via pip wheel as described in
<a class="reference external" href="https://pytorch.org/get-started/locally/">Get Started</a> and select the CUDA 11.6 pip package.</p>
</div>
<p>Complex numbers are numbers that can be expressed in the form <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>a</mi><mo>+</mo><mi>b</mi><mi>j</mi></mrow><annotation encoding="application/x-tex">a + bj</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6667em;vertical-align:-0.0833em;"></span><span class="mord mathnormal">a</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.8889em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.05724em;">bj</span></span></span></span></span>, where a and b are real numbers,
and <em>j</em> is called the imaginary unit, which satisfies the equation <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msup><mi>j</mi><mn>2</mn></msup><mo>=</mo><mo>−</mo><mn>1</mn></mrow><annotation encoding="application/x-tex">j^2 = -1</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.0085em;vertical-align:-0.1944em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.05724em;">j</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8141em;"><span style="top:-3.063em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">2</span></span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.7278em;vertical-align:-0.0833em;"></span><span class="mord">−</span><span class="mord">1</span></span></span></span></span>. Complex numbers frequently occur in mathematics and
engineering, especially in topics like signal processing. Traditionally many users and libraries (e.g., TorchAudio) have
handled complex numbers by representing the data in float tensors with shape <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi mathvariant="normal">.</mi><mi mathvariant="normal">.</mi><mi mathvariant="normal">.</mi><mo separator="true">,</mo><mn>2</mn><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(..., 2)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord">...</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord">2</span><span class="mclose">)</span></span></span></span></span> where the last
dimension contains the real and imaginary values.</p>
<p>Tensors of complex dtypes provide a more natural user experience while working with complex numbers. Operations on
complex tensors (e.g., <a class="reference internal" href="generated/torch.mv.html#torch.mv" title="torch.mv"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.mv()</span></code></a>, <a class="reference internal" href="generated/torch.matmul.html#torch.matmul" title="torch.matmul"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.matmul()</span></code></a>) are likely to be faster and more memory efficient
than operations on float tensors mimicking them. Operations involving complex numbers in PyTorch are optimized
to use vectorized assembly instructions and specialized kernels (e.g. LAPACK, cuBlas).</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Spectral operations in the <a class="reference external" href="https://pytorch.org/docs/stable/fft.html#torch-fft">torch.fft module</a> support
native complex tensors.</p>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>Complex tensors is a beta feature and subject to change.</p>
</div>
<section id="creating-complex-tensors">
<h2>Creating Complex Tensors<a class="headerlink" href="#creating-complex-tensors" title="Permalink to this heading">¶</a></h2>
<p>We support two complex dtypes: <cite>torch.cfloat</cite> and <cite>torch.cdouble</cite></p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></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">2</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">cfloat</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">x</span>
<span class="go">tensor([[-0.4621-0.0303j, -0.2438-0.5874j],</span>
<span class="go"> [ 0.7706+0.1421j, 1.2110+0.1918j]])</span>
</pre></div>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The default dtype for complex tensors is determined by the default floating point dtype.
If the default floating point dtype is <cite>torch.float64</cite> then complex numbers are inferred to
have a dtype of <cite>torch.complex128</cite>, otherwise they are assumed to have a dtype of <cite>torch.complex64</cite>.</p>
</div>
<p>All factory functions apart from <a class="reference internal" href="generated/torch.linspace.html#torch.linspace" title="torch.linspace"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.linspace()</span></code></a>, <a class="reference internal" href="generated/torch.logspace.html#torch.logspace" title="torch.logspace"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.logspace()</span></code></a>, and <a class="reference internal" href="generated/torch.arange.html#torch.arange" title="torch.arange"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.arange()</span></code></a> are
supported for complex tensors.</p>
</section>
<section id="transition-from-the-old-representation">
<h2>Transition from the old representation<a class="headerlink" href="#transition-from-the-old-representation" title="Permalink to this heading">¶</a></h2>
<p>Users who currently worked around the lack of complex tensors with real tensors of shape <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi mathvariant="normal">.</mi><mi mathvariant="normal">.</mi><mi mathvariant="normal">.</mi><mo separator="true">,</mo><mn>2</mn><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(..., 2)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord">...</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord">2</span><span class="mclose">)</span></span></span></span></span>
can easily to switch using the complex tensors in their code using <a class="reference internal" href="generated/torch.view_as_complex.html#torch.view_as_complex" title="torch.view_as_complex"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.view_as_complex()</span></code></a>
and <a class="reference internal" href="generated/torch.view_as_real.html#torch.view_as_real" title="torch.view_as_real"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.view_as_real()</span></code></a>. Note that these functions don’t perform any copy and return a
view of the input tensor.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></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="mi">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">x</span>
<span class="go">tensor([[ 0.6125, -0.1681],</span>
<span class="go"> [-0.3773, 1.3487],</span>
<span class="go"> [-0.0861, -0.7981]])</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">view_as_complex</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">y</span>
<span class="go">tensor([ 0.6125-0.1681j, -0.3773+1.3487j, -0.0861-0.7981j])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">view_as_real</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
<span class="go">tensor([[ 0.6125, -0.1681],</span>
<span class="go"> [-0.3773, 1.3487],</span>
<span class="go"> [-0.0861, -0.7981]])</span>
</pre></div>
</div>
</section>
<section id="accessing-real-and-imag">
<h2>Accessing real and imag<a class="headerlink" href="#accessing-real-and-imag" title="Permalink to this heading">¶</a></h2>
<p>The real and imaginary values of a complex tensor can be accessed using the <code class="xref py py-attr docutils literal notranslate"><span class="pre">real</span></code> and
<code class="xref py py-attr docutils literal notranslate"><span class="pre">imag</span></code>.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Accessing <cite>real</cite> and <cite>imag</cite> attributes doesn’t allocate any memory, and in-place updates on the
<cite>real</cite> and <cite>imag</cite> tensors will update the original complex tensor. Also, the
returned <cite>real</cite> and <cite>imag</cite> tensors are not contiguous.</p>
</div>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">y</span><span class="o">.</span><span class="n">real</span>
<span class="go">tensor([ 0.6125, -0.3773, -0.0861])</span>
<span class="gp">>>> </span><span class="n">y</span><span class="o">.</span><span class="n">imag</span>
<span class="go">tensor([-0.1681, 1.3487, -0.7981])</span>
<span class="gp">>>> </span><span class="n">y</span><span class="o">.</span><span class="n">real</span><span class="o">.</span><span class="n">mul_</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
<span class="go">tensor([ 1.2250, -0.7546, -0.1722])</span>
<span class="gp">>>> </span><span class="n">y</span>
<span class="go">tensor([ 1.2250-0.1681j, -0.7546+1.3487j, -0.1722-0.7981j])</span>
<span class="gp">>>> </span><span class="n">y</span><span class="o">.</span><span class="n">real</span><span class="o">.</span><span class="n">stride</span><span class="p">()</span>
<span class="go">(2,)</span>
</pre></div>
</div>
</section>
<section id="angle-and-abs">
<h2>Angle and abs<a class="headerlink" href="#angle-and-abs" title="Permalink to this heading">¶</a></h2>
<p>The angle and absolute values of a complex tensor can be computed using <a class="reference internal" href="generated/torch.angle.html#torch.angle" title="torch.angle"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.angle()</span></code></a> and
<a class="reference internal" href="generated/torch.abs.html#torch.abs" title="torch.abs"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.abs()</span></code></a>.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x1</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="n">j</span><span class="p">,</span> <span class="mi">4</span><span class="o">+</span><span class="mi">4</span><span class="n">j</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">x1</span><span class="o">.</span><span class="n">abs</span><span class="p">()</span>
<span class="go">tensor([3.0000, 5.6569])</span>
<span class="gp">>>> </span><span class="n">x1</span><span class="o">.</span><span class="n">angle</span><span class="p">()</span>
<span class="go">tensor([1.5708, 0.7854])</span>
</pre></div>
</div>
</section>
<section id="linear-algebra">
<h2>Linear Algebra<a class="headerlink" href="#linear-algebra" title="Permalink to this heading">¶</a></h2>
<p>Many linear algebra operations, like <a class="reference internal" href="generated/torch.matmul.html#torch.matmul" title="torch.matmul"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.matmul()</span></code></a>, <a class="reference internal" href="generated/torch.svd.html#torch.svd" title="torch.svd"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.svd()</span></code></a>, <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.solve()</span></code> etc., support complex numbers.
If you’d like to request an operation we don’t currently support, please <a class="reference external" href="https://github.com/pytorch/pytorch/issues?q=is%3Aissue+is%3Aopen+complex">search</a>
if an issue has already been filed and if not, <a class="reference external" href="https://github.com/pytorch/pytorch/issues/new/choose">file one</a>.</p>
</section>
<section id="serialization">
<h2>Serialization<a class="headerlink" href="#serialization" title="Permalink to this heading">¶</a></h2>
<p>Complex tensors can be serialized, allowing data to be saved as complex values.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="s1">'complex_tensor.pt'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">'complex_tensor.pt'</span><span class="p">)</span>
<span class="go">tensor([ 0.6125-0.1681j, -0.3773+1.3487j, -0.0861-0.7981j])</span>
</pre></div>
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</section>
<section id="autograd">
<h2>Autograd<a class="headerlink" href="#autograd" title="Permalink to this heading">¶</a></h2>
<p>PyTorch supports autograd for complex tensors. The gradient computed is the Conjugate Wirtinger derivative,
the negative of which is precisely the direction of steepest descent used in Gradient Descent algorithm. Thus,
all the existing optimizers work out of the box with complex parameters. For more details,
check out the note <a class="reference internal" href="notes/autograd.html#complex-autograd-doc"><span class="std std-ref">Autograd for Complex Numbers</span></a>.</p>
<p>We do not fully support the following subsystems:</p>
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<li><p>JIT</p></li>
<li><p>Sparse Tensors</p></li>
<li><p>Distributed</p></li>
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