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<p class="caption"><span class="caption-text">Notes</span></p>
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<li class="toctree-l1"><a class="reference internal" href="notes/amp_examples.html">Automatic Mixed Precision examples</a></li>
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<div class="section" id="named-tensors">
<span id="named-tensors-doc"></span><h1>Named Tensors<a class="headerlink" href="#named-tensors" title="Permalink to this headline">¶</a></h1>
<p>Named Tensors allow users to give explicit names to tensor dimensions.
In most cases, operations that take dimension parameters will accept
dimension names, avoiding the need to track dimensions by position.
In addition, named tensors use names to automatically check that APIs
are being used correctly at runtime, providing extra safety. Names can
also be used to rearrange dimensions, for example, to support
“broadcasting by name” rather than “broadcasting by position”.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>The named tensor API is a prototype feature and subject to change.</p>
</div>
<div class="section" id="creating-named-tensors">
<h2>Creating named tensors<a class="headerlink" href="#creating-named-tensors" title="Permalink to this headline">¶</a></h2>
<p>Factory functions now take a new <code class="xref py py-attr docutils literal notranslate"><span class="pre">names</span></code> argument that associates a name
with each dimension.</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">zeros</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">names</span><span class="o">=</span><span class="p">(</span><span class="s1">'N'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">))</span>
<span class="go">tensor([[0., 0., 0.],</span>
<span class="go"> [0., 0., 0.]], names=('N', 'C'))</span>
</pre></div>
</div>
<p>Named dimensions, like regular Tensor dimensions, are ordered.
<code class="docutils literal notranslate"><span class="pre">tensor.names[i]</span></code> is the name of dimension <code class="docutils literal notranslate"><span class="pre">i</span></code> of <code class="docutils literal notranslate"><span class="pre">tensor</span></code>.</p>
<p>The following factory functions support named tensors:</p>
<ul class="simple">
<li><p><a class="reference internal" href="generated/torch.empty.html#torch.empty" title="torch.empty"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.empty()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.rand.html#torch.rand" title="torch.rand"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.rand()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.randn.html#torch.randn" title="torch.randn"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.randn()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.ones.html#torch.ones" title="torch.ones"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.ones()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.tensor.html#torch.tensor" title="torch.tensor"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.tensor()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.zeros.html#torch.zeros" title="torch.zeros"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.zeros()</span></code></a></p></li>
</ul>
</div>
<div class="section" id="named-dimensions">
<h2>Named dimensions<a class="headerlink" href="#named-dimensions" title="Permalink to this headline">¶</a></h2>
<p>See <a class="reference internal" href="#torch.Tensor.names" title="torch.Tensor.names"><code class="xref py py-attr docutils literal notranslate"><span class="pre">names</span></code></a> for restrictions on tensor names.</p>
<p>Use <a class="reference internal" href="#torch.Tensor.names" title="torch.Tensor.names"><code class="xref py py-attr docutils literal notranslate"><span class="pre">names</span></code></a> to access the dimension names of a tensor and
<a class="reference internal" href="#torch.Tensor.rename" title="torch.Tensor.rename"><code class="xref py py-meth docutils literal notranslate"><span class="pre">rename()</span></code></a> to rename named dimensions.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">imgs</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">1</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="mi">3</span> <span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">(</span><span class="s1">'N'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">,</span> <span class="s1">'H'</span><span class="p">,</span> <span class="s1">'W'</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">imgs</span><span class="o">.</span><span class="n">names</span>
<span class="go">('N', 'C', 'H', 'W')</span>
<span class="gp">>>> </span><span class="n">renamed_imgs</span> <span class="o">=</span> <span class="n">imgs</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="n">H</span><span class="o">=</span><span class="s1">'height'</span><span class="p">,</span> <span class="n">W</span><span class="o">=</span><span class="s1">'width'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">renamed_imgs</span><span class="o">.</span><span class="n">names</span>
<span class="go">('N', 'C', 'height', 'width)</span>
</pre></div>
</div>
<p>Named tensors can coexist with unnamed tensors; named tensors are instances of
<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a>. Unnamed tensors have <code class="docutils literal notranslate"><span class="pre">None</span></code>-named dimensions. Named
tensors do not require all dimensions to be named.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">imgs</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">1</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="mi">3</span> <span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">,</span> <span class="s1">'H'</span><span class="p">,</span> <span class="s1">'W'</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">imgs</span><span class="o">.</span><span class="n">names</span>
<span class="go">(None, 'C', 'H', 'W')</span>
</pre></div>
</div>
</div>
<div class="section" id="name-propagation-semantics">
<h2>Name propagation semantics<a class="headerlink" href="#name-propagation-semantics" title="Permalink to this headline">¶</a></h2>
<p>Named tensors use names to automatically check that APIs are being called
correctly at runtime. This occurs in a process called <em>name inference</em>.
More formally, name inference consists of the following two steps:</p>
<ul class="simple">
<li><p><strong>Check names</strong>: an operator may perform automatic checks at runtime that
check that certain dimension names must match.</p></li>
<li><p><strong>Propagate names</strong>: name inference propagates names to output tensors.</p></li>
</ul>
<p>All operations that support named tensors propagate names.</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">3</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">(</span><span class="s1">'N'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">abs</span><span class="p">()</span><span class="o">.</span><span class="n">names</span>
<span class="go">('N', 'C')</span>
</pre></div>
</div>
<div class="section" id="match-semantics">
<span id="match-semantics-doc"></span><h3>match semantics<a class="headerlink" href="#match-semantics" title="Permalink to this headline">¶</a></h3>
<p>Two names <em>match</em> if they are equal (string equality) or if at least one is <code class="docutils literal notranslate"><span class="pre">None</span></code>.
Nones are essentially a special “wildcard” name.</p>
<p><code class="docutils literal notranslate"><span class="pre">unify(A,</span> <span class="pre">B)</span></code> determines which of the names <code class="docutils literal notranslate"><span class="pre">A</span></code> and <code class="docutils literal notranslate"><span class="pre">B</span></code> to propagate to the outputs.
It returns the more <em>specific</em> of the two names, if they match. If the names do not match,
then it errors.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>In practice, when working with named tensors, one should avoid having unnamed
dimensions because their handling can be complicated. It is recommended to lift
all unnamed dimensions to be named dimensions by using <a class="reference internal" href="#torch.Tensor.refine_names" title="torch.Tensor.refine_names"><code class="xref py py-meth docutils literal notranslate"><span class="pre">refine_names()</span></code></a>.</p>
</div>
</div>
<div class="section" id="basic-name-inference-rules">
<h3>Basic name inference rules<a class="headerlink" href="#basic-name-inference-rules" title="Permalink to this headline">¶</a></h3>
<p>Let’s see how <code class="docutils literal notranslate"><span class="pre">match</span></code> and <code class="docutils literal notranslate"><span class="pre">unify</span></code> are used in name inference in the case of
adding two one-dim tensors with no broadcasting.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></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">names</span><span class="o">=</span><span class="p">(</span><span class="s1">'X'</span><span class="p">,))</span>
<span class="n">y</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">z</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">names</span><span class="o">=</span><span class="p">(</span><span class="s1">'Z'</span><span class="p">,))</span>
</pre></div>
</div>
<p><strong>Check names</strong>: check that the names of the two tensors <em>match</em>.</p>
<p>For the following examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># x + y # match('X', None) is True</span>
<span class="gp">>>> </span><span class="c1"># x + z # match('X', 'Z') is False</span>
<span class="gp">>>> </span><span class="c1"># x + x # match('X', 'X') is True</span>
<span class="gp">>>> </span><span class="n">x</span> <span class="o">+</span> <span class="n">z</span>
<span class="go">Error when attempting to broadcast dims ['X'] and dims ['Z']: dim 'X' and dim 'Z' are at the same position from the right but do not match.</span>
</pre></div>
</div>
<p><strong>Propagate names</strong>: <em>unify</em> the names to select which one to propagate.
In the case of <code class="docutils literal notranslate"><span class="pre">x</span> <span class="pre">+</span> <span class="pre">y</span></code>, <code class="docutils literal notranslate"><span class="pre">unify('X',</span> <span class="pre">None)</span> <span class="pre">=</span> <span class="pre">'X'</span></code> because <code class="docutils literal notranslate"><span class="pre">'X'</span></code> is more
specific than <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">names</span>
<span class="go">('X',)</span>
<span class="gp">>>> </span><span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">names</span>
<span class="go">('X',)</span>
</pre></div>
</div>
<p>For a comprehensive list of name inference rules, see <a class="reference internal" href="name_inference.html#name-inference-reference-doc"><span class="std std-ref">Named Tensors operator coverage</span></a>.
Here are two common operations that may be useful to go over:</p>
<ul class="simple">
<li><p>Binary arithmetic ops: <a class="reference internal" href="name_inference.html#unifies-names-from-inputs-doc"><span class="std std-ref">Unifies names from inputs</span></a></p></li>
<li><p>Matrix multiplication ops: <a class="reference internal" href="name_inference.html#contracts-away-dims-doc"><span class="std std-ref">Contracts away dims</span></a></p></li>
</ul>
</div>
</div>
<div class="section" id="explicit-alignment-by-names">
<h2>Explicit alignment by names<a class="headerlink" href="#explicit-alignment-by-names" title="Permalink to this headline">¶</a></h2>
<p>Use <a class="reference internal" href="#torch.Tensor.align_as" title="torch.Tensor.align_as"><code class="xref py py-meth docutils literal notranslate"><span class="pre">align_as()</span></code></a> or <a class="reference internal" href="#torch.Tensor.align_to" title="torch.Tensor.align_to"><code class="xref py py-meth docutils literal notranslate"><span class="pre">align_to()</span></code></a> to align tensor dimensions
by name to a specified ordering. This is useful for performing “broadcasting by names”.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># This function is agnostic to the dimension ordering of `input`,</span>
<span class="c1"># as long as it has a `C` dimension somewhere.</span>
<span class="k">def</span> <span class="nf">scale_channels</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">scale</span><span class="p">):</span>
<span class="n">scale</span> <span class="o">=</span> <span class="n">scale</span><span class="o">.</span><span class="n">refine_names</span><span class="p">(</span><span class="s1">'C'</span><span class="p">)</span>
<span class="k">return</span> <span class="nb">input</span> <span class="o">*</span> <span class="n">scale</span><span class="o">.</span><span class="n">align_as</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
<span class="o">>>></span> <span class="n">num_channels</span> <span class="o">=</span> <span class="mi">3</span>
<span class="o">>>></span> <span class="n">scale</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="n">num_channels</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">(</span><span class="s1">'C'</span><span class="p">,))</span>
<span class="o">>>></span> <span class="n">imgs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</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="mi">3</span><span class="p">,</span> <span class="n">num_channels</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">(</span><span class="s1">'N'</span><span class="p">,</span> <span class="s1">'H'</span><span class="p">,</span> <span class="s1">'W'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">))</span>
<span class="o">>>></span> <span class="n">more_imgs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">num_channels</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="n">names</span><span class="o">=</span><span class="p">(</span><span class="s1">'N'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">,</span> <span class="s1">'H'</span><span class="p">,</span> <span class="s1">'W'</span><span class="p">))</span>
<span class="o">>>></span> <span class="n">videos</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">num_channels</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="mi">3</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">(</span><span class="s1">'N'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">,</span> <span class="s1">'H'</span><span class="p">,</span> <span class="s1">'W'</span><span class="p">,</span> <span class="s1">'D'</span><span class="p">)</span>
<span class="o">>>></span> <span class="n">scale_channels</span><span class="p">(</span><span class="n">imgs</span><span class="p">,</span> <span class="n">scale</span><span class="p">)</span>
<span class="o">>>></span> <span class="n">scale_channels</span><span class="p">(</span><span class="n">more_imgs</span><span class="p">,</span> <span class="n">scale</span><span class="p">)</span>
<span class="o">>>></span> <span class="n">scale_channels</span><span class="p">(</span><span class="n">videos</span><span class="p">,</span> <span class="n">scale</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="manipulating-dimensions">
<h2>Manipulating dimensions<a class="headerlink" href="#manipulating-dimensions" title="Permalink to this headline">¶</a></h2>
<p>Use <a class="reference internal" href="#torch.Tensor.align_to" title="torch.Tensor.align_to"><code class="xref py py-meth docutils literal notranslate"><span class="pre">align_to()</span></code></a> to permute large amounts of dimensions without
mentioning all of them as in required by <a class="reference internal" href="tensors.html#torch.Tensor.permute" title="torch.Tensor.permute"><code class="xref py py-meth docutils literal notranslate"><span class="pre">permute()</span></code></a>.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">tensor</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="mi">2</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="mi">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">named_tensor</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">refine_names</span><span class="p">(</span><span class="s1">'A'</span><span class="p">,</span> <span class="s1">'B'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">,</span> <span class="s1">'D'</span><span class="p">,</span> <span class="s1">'E'</span><span class="p">,</span> <span class="s1">'F'</span><span class="p">)</span>
<span class="go"># Move the F (dim 5) and E dimension (dim 4) to the front while keeping</span>
<span class="go"># the rest in the same order</span>
<span class="gp">>>> </span><span class="n">tensor</span><span class="o">.</span><span class="n">permute</span><span class="p">(</span><span class="mi">5</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="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="gp">>>> </span><span class="n">named_tensor</span><span class="o">.</span><span class="n">align_to</span><span class="p">(</span><span class="s1">'F'</span><span class="p">,</span> <span class="s1">'E'</span><span class="p">,</span> <span class="o">...</span><span class="p">)</span>
</pre></div>
</div>
<p>Use <a class="reference internal" href="tensors.html#torch.Tensor.flatten" title="torch.Tensor.flatten"><code class="xref py py-meth docutils literal notranslate"><span class="pre">flatten()</span></code></a> and <a class="reference internal" href="#torch.Tensor.unflatten" title="torch.Tensor.unflatten"><code class="xref py py-meth docutils literal notranslate"><span class="pre">unflatten()</span></code></a> to flatten and unflatten
dimensions, respectively. These methods are more verbose than <a class="reference internal" href="tensors.html#torch.Tensor.view" title="torch.Tensor.view"><code class="xref py py-meth docutils literal notranslate"><span class="pre">view()</span></code></a>
and <a class="reference internal" href="tensors.html#torch.Tensor.reshape" title="torch.Tensor.reshape"><code class="xref py py-meth docutils literal notranslate"><span class="pre">reshape()</span></code></a>, but have more semantic meaning to someone reading the code.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">imgs</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">32</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">named_imgs</span> <span class="o">=</span> <span class="n">imgs</span><span class="o">.</span><span class="n">refine_names</span><span class="p">(</span><span class="s1">'N'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">,</span> <span class="s1">'H'</span><span class="p">,</span> <span class="s1">'W'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">flat_imgs</span> <span class="o">=</span> <span class="n">imgs</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">named_flat_imgs</span> <span class="o">=</span> <span class="n">named_imgs</span><span class="o">.</span><span class="n">flatten</span><span class="p">([</span><span class="s1">'C'</span><span class="p">,</span> <span class="s1">'H'</span><span class="p">,</span> <span class="s1">'W'</span><span class="p">],</span> <span class="s1">'features'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">named_flat_imgs</span><span class="o">.</span><span class="n">names</span>
<span class="go">('N', 'features')</span>
<span class="gp">>>> </span><span class="n">unflattened_imgs</span> <span class="o">=</span> <span class="n">imgs</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">unflattened_named_imgs</span> <span class="o">=</span> <span class="n">named_flat_imgs</span><span class="o">.</span><span class="n">unflatten</span><span class="p">(</span>
<span class="go"> 'features', [('C', 3), ('H', 128), ('W', 128)])</span>
</pre></div>
</div>
</div>
<div class="section" id="autograd-support">
<span id="named-tensors-autograd-doc"></span><h2>Autograd support<a class="headerlink" href="#autograd-support" title="Permalink to this headline">¶</a></h2>
<p>Autograd currently supports named tensors in a limited manner: autograd ignores
names on all tensors. Gradient computation is still correct but we lose the
safety that names give us.</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="n">names</span><span class="o">=</span><span class="p">(</span><span class="s1">'D'</span><span class="p">,))</span>
<span class="gp">>>> </span><span class="n">weight</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">names</span><span class="o">=</span><span class="p">(</span><span class="s1">'D'</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">loss</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span> <span class="o">-</span> <span class="n">weight</span><span class="p">)</span><span class="o">.</span><span class="n">abs</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">grad_loss</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">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">(</span><span class="n">grad_loss</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">weight</span><span class="o">.</span><span class="n">grad</span> <span class="c1"># Unnamed for now. Will be named in the future</span>
<span class="go">tensor([-1.8107, -0.6357, 0.0783])</span>
<span class="gp">>>> </span><span class="n">weight</span><span class="o">.</span><span class="n">grad</span><span class="o">.</span><span class="n">zero_</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">grad_loss</span> <span class="o">=</span> <span class="n">grad_loss</span><span class="o">.</span><span class="n">refine_names</span><span class="p">(</span><span class="s1">'C'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">loss</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span> <span class="o">-</span> <span class="n">weight</span><span class="p">)</span><span class="o">.</span><span class="n">abs</span><span class="p">()</span>
<span class="go"># Ideally we'd check that the names of loss and grad_loss match but we don't yet.</span>
<span class="gp">>>> </span><span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">(</span><span class="n">grad_loss</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">weight</span><span class="o">.</span><span class="n">grad</span>
<span class="go">tensor([-1.8107, -0.6357, 0.0783])</span>
</pre></div>
</div>
</div>
<div class="section" id="currently-supported-operations-and-subsystems">
<h2>Currently supported operations and subsystems<a class="headerlink" href="#currently-supported-operations-and-subsystems" title="Permalink to this headline">¶</a></h2>
<div class="section" id="operators">
<h3>Operators<a class="headerlink" href="#operators" title="Permalink to this headline">¶</a></h3>
<p>See <a class="reference internal" href="name_inference.html#name-inference-reference-doc"><span class="std std-ref">Named Tensors operator coverage</span></a> for a full list of the supported torch and
tensor operations. We do not yet support the following that is not covered by the link:</p>
<ul class="simple">
<li><p>indexing, advanced indexing.</p></li>
</ul>
<p>For <code class="docutils literal notranslate"><span class="pre">torch.nn.functional</span></code> operators, we support the following:</p>
<ul class="simple">
<li><p><a class="reference internal" href="nn.functional.html#torch.nn.functional.relu" title="torch.nn.functional.relu"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.functional.relu()</span></code></a></p></li>
<li><p><a class="reference internal" href="nn.functional.html#torch.nn.functional.softmax" title="torch.nn.functional.softmax"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.functional.softmax()</span></code></a></p></li>
<li><p><a class="reference internal" href="nn.functional.html#torch.nn.functional.log_softmax" title="torch.nn.functional.log_softmax"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.functional.log_softmax()</span></code></a></p></li>
<li><p><a class="reference internal" href="nn.functional.html#torch.nn.functional.tanh" title="torch.nn.functional.tanh"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.functional.tanh()</span></code></a></p></li>
<li><p><a class="reference internal" href="nn.functional.html#torch.nn.functional.sigmoid" title="torch.nn.functional.sigmoid"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.functional.sigmoid()</span></code></a></p></li>
<li><p><a class="reference internal" href="nn.functional.html#torch.nn.functional.dropout" title="torch.nn.functional.dropout"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.functional.dropout()</span></code></a></p></li>
</ul>
</div>
<div class="section" id="subsystems">
<h3>Subsystems<a class="headerlink" href="#subsystems" title="Permalink to this headline">¶</a></h3>
<p>Autograd is supported, see <a class="reference internal" href="#named-tensors-autograd-doc"><span class="std std-ref">Autograd support</span></a>.
Because gradients are currently unnamed, optimizers may work but are untested.</p>
<p>NN modules are currently unsupported. This can lead to the following when calling
modules with named tensor inputs:</p>
<ul class="simple">
<li><p>NN module parameters are unnamed, so outputs may be partially named.</p></li>
<li><p>NN module forward passes have code that don’t support named tensors and will
error out appropriately.</p></li>
</ul>
<p>We also do not support the following subsystems, though some may work out
of the box:</p>
<ul class="simple">
<li><p>distributions</p></li>
<li><p>serialization (<a class="reference internal" href="generated/torch.load.html#torch.load" title="torch.load"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.load()</span></code></a>, <a class="reference internal" href="generated/torch.save.html#torch.save" title="torch.save"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.save()</span></code></a>)</p></li>
<li><p>multiprocessing</p></li>
<li><p>JIT</p></li>
<li><p>distributed</p></li>
<li><p>ONNX</p></li>
</ul>
<p>If any of these would help your use case, please
<a class="reference external" href="https://github.com/pytorch/pytorch/issues?q=is%3Aopen+is%3Aissue+label%3A%22module%3A+named+tensor%22">search if an issue has already been filed</a>
and if not, <a class="reference external" href="https://github.com/pytorch/pytorch/issues/new/choose">file one</a>.</p>
</div>
</div>
<div class="section" id="named-tensor-api-reference">
<h2>Named tensor API reference<a class="headerlink" href="#named-tensor-api-reference" title="Permalink to this headline">¶</a></h2>
<p>In this section please find the documentation for named tensor specific APIs.
For a comprehensive reference for how names are propagated through other PyTorch
operators, see <a class="reference internal" href="name_inference.html#name-inference-reference-doc"><span class="std std-ref">Named Tensors operator coverage</span></a>.</p>
<dl class="class">
<dt>
<em class="property">class </em><code class="sig-prename descclassname">torch.</code><code class="sig-name descname">Tensor</code></dt>
<dd><dl class="attribute">
<dt id="torch.Tensor.names">
<code class="sig-name descname">names</code><a class="headerlink" href="#torch.Tensor.names" title="Permalink to this definition">¶</a></dt>
<dd><p>Stores names for each of this tensor’s dimensions.</p>
<p><code class="docutils literal notranslate"><span class="pre">names[idx]</span></code> corresponds to the name of tensor dimension <code class="docutils literal notranslate"><span class="pre">idx</span></code>.
Names are either a string if the dimension is named or <code class="docutils literal notranslate"><span class="pre">None</span></code> if the
dimension is unnamed.</p>
<p>Dimension names may contain characters or underscore. Furthermore, a dimension
name must be a valid Python variable name (i.e., does not start with underscore).</p>
<p>Tensors may not have two named dimensions with the same name.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>The named tensor API is experimental and subject to change.</p>
</div>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.rename">
<code class="sig-name descname">rename</code><span class="sig-paren">(</span><em class="sig-param">*names</em>, <em class="sig-param">**rename_map</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/tensor.html#Tensor.rename"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.Tensor.rename" title="Permalink to this definition">¶</a></dt>
<dd><p>Renames dimension names of <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code>.</p>
<p>There are two main usages:</p>
<p><code class="docutils literal notranslate"><span class="pre">self.rename(**rename_map)</span></code> returns a view on tensor that has dims
renamed as specified in the mapping <code class="xref py py-attr docutils literal notranslate"><span class="pre">rename_map</span></code>.</p>
<p><code class="docutils literal notranslate"><span class="pre">self.rename(*names)</span></code> returns a view on tensor, renaming all
dimensions positionally using <a class="reference internal" href="#torch.Tensor.names" title="torch.Tensor.names"><code class="xref py py-attr docutils literal notranslate"><span class="pre">names</span></code></a>.
Use <code class="docutils literal notranslate"><span class="pre">self.rename(None)</span></code> to drop names on a tensor.</p>
<p>One cannot specify both positional args <a class="reference internal" href="#torch.Tensor.names" title="torch.Tensor.names"><code class="xref py py-attr docutils literal notranslate"><span class="pre">names</span></code></a> and keyword args
<code class="xref py py-attr docutils literal notranslate"><span class="pre">rename_map</span></code>.</p>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">imgs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</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">5</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">(</span><span class="s1">'N'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">,</span> <span class="s1">'H'</span><span class="p">,</span> <span class="s1">'W'</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">renamed_imgs</span> <span class="o">=</span> <span class="n">imgs</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="n">N</span><span class="o">=</span><span class="s1">'batch'</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="s1">'channels'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">renamed_imgs</span><span class="o">.</span><span class="n">names</span>
<span class="go">('batch', 'channels', 'H', 'W')</span>
<span class="gp">>>> </span><span class="n">renamed_imgs</span> <span class="o">=</span> <span class="n">imgs</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">renamed_imgs</span><span class="o">.</span><span class="n">names</span>
<span class="go">(None,)</span>
<span class="gp">>>> </span><span class="n">renamed_imgs</span> <span class="o">=</span> <span class="n">imgs</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="s1">'batch'</span><span class="p">,</span> <span class="s1">'channel'</span><span class="p">,</span> <span class="s1">'height'</span><span class="p">,</span> <span class="s1">'width'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">renamed_imgs</span><span class="o">.</span><span class="n">names</span>
<span class="go">('batch', 'channel', 'height', 'width')</span>
</pre></div>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>The named tensor API is experimental and subject to change.</p>
</div>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.rename_">
<code class="sig-name descname">rename_</code><span class="sig-paren">(</span><em class="sig-param">*names</em>, <em class="sig-param">**rename_map</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/tensor.html#Tensor.rename_"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.Tensor.rename_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.rename" title="torch.Tensor.rename"><code class="xref py py-meth docutils literal notranslate"><span class="pre">rename()</span></code></a>.</p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.refine_names">
<code class="sig-name descname">refine_names</code><span class="sig-paren">(</span><em class="sig-param">*names</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/tensor.html#Tensor.refine_names"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.Tensor.refine_names" title="Permalink to this definition">¶</a></dt>
<dd><p>Refines the dimension names of <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> according to <a class="reference internal" href="#torch.Tensor.names" title="torch.Tensor.names"><code class="xref py py-attr docutils literal notranslate"><span class="pre">names</span></code></a>.</p>
<p>Refining is a special case of renaming that “lifts” unnamed dimensions.
A <code class="docutils literal notranslate"><span class="pre">None</span></code> dim can be refined to have any name; a named dim can only be
refined to have the same name.</p>
<p>Because named tensors can coexist with unnamed tensors, refining names
gives a nice way to write named-tensor-aware code that works with both
named and unnamed tensors.</p>
<p><a class="reference internal" href="#torch.Tensor.names" title="torch.Tensor.names"><code class="xref py py-attr docutils literal notranslate"><span class="pre">names</span></code></a> may contain up to one Ellipsis (<code class="docutils literal notranslate"><span class="pre">...</span></code>).
The Ellipsis is expanded greedily; it is expanded in-place to fill
<a class="reference internal" href="#torch.Tensor.names" title="torch.Tensor.names"><code class="xref py py-attr docutils literal notranslate"><span class="pre">names</span></code></a> to the same length as <code class="docutils literal notranslate"><span class="pre">self.dim()</span></code> using names from the
corresponding indices of <code class="docutils literal notranslate"><span class="pre">self.names</span></code>.</p>
<p>Python 2 does not support Ellipsis but one may use a string literal
instead (<code class="docutils literal notranslate"><span class="pre">'...'</span></code>).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>names</strong> (<em>iterable of str</em>) – The desired names of the output tensor. May
contain up to one Ellipsis.</p>
</dd>
</dl>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">imgs</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">32</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">named_imgs</span> <span class="o">=</span> <span class="n">imgs</span><span class="o">.</span><span class="n">refine_names</span><span class="p">(</span><span class="s1">'N'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">,</span> <span class="s1">'H'</span><span class="p">,</span> <span class="s1">'W'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">named_imgs</span><span class="o">.</span><span class="n">names</span>
<span class="go">('N', 'C', 'H', 'W')</span>
<span class="gp">>>> </span><span class="n">tensor</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="mi">5</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">11</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">tensor</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">refine_names</span><span class="p">(</span><span class="s1">'A'</span><span class="p">,</span> <span class="o">...</span><span class="p">,</span> <span class="s1">'B'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">tensor</span><span class="o">.</span><span class="n">names</span>
<span class="go">('A', None, None, 'B', 'C')</span>
</pre></div>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>The named tensor API is experimental and subject to change.</p>
</div>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.align_as">
<code class="sig-name descname">align_as</code><span class="sig-paren">(</span><em class="sig-param">other</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.align_as" title="Permalink to this definition">¶</a></dt>
<dd><p>Permutes the dimensions of the <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> tensor to match the dimension order
in the <code class="xref py py-attr docutils literal notranslate"><span class="pre">other</span></code> tensor, adding size-one dims for any new names.</p>
<p>This operation is useful for explicit broadcasting by names (see examples).</p>
<p>All of the dims of <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> must be named in order to use this method.
The resulting tensor is a view on the original tensor.</p>
<p>All dimension names of <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> must be present in <code class="docutils literal notranslate"><span class="pre">other.names</span></code>.
<code class="xref py py-attr docutils literal notranslate"><span class="pre">other</span></code> may contain named dimensions that are not in <code class="docutils literal notranslate"><span class="pre">self.names</span></code>;
the output tensor has a size-one dimension for each of those new names.</p>
<p>To align a tensor to a specific order, use <a class="reference internal" href="#torch.Tensor.align_to" title="torch.Tensor.align_to"><code class="xref py py-meth docutils literal notranslate"><span class="pre">align_to()</span></code></a>.</p>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Example 1: Applying a mask</span>
<span class="o">>>></span> <span class="n">mask</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="p">[</span><span class="mi">127</span><span class="p">,</span> <span class="mi">128</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">bool</span><span class="p">)</span><span class="o">.</span><span class="n">refine_names</span><span class="p">(</span><span class="s1">'W'</span><span class="p">,</span> <span class="s1">'H'</span><span class="p">)</span>
<span class="o">>>></span> <span class="n">imgs</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">32</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">127</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">(</span><span class="s1">'N'</span><span class="p">,</span> <span class="s1">'H'</span><span class="p">,</span> <span class="s1">'W'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">))</span>
<span class="o">>>></span> <span class="n">imgs</span><span class="o">.</span><span class="n">masked_fill_</span><span class="p">(</span><span class="n">mask</span><span class="o">.</span><span class="n">align_as</span><span class="p">(</span><span class="n">imgs</span><span class="p">),</span> <span class="mi">0</span><span class="p">)</span>
<span class="c1"># Example 2: Applying a per-channel-scale</span>
<span class="o">>>></span> <span class="k">def</span> <span class="nf">scale_channels</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">scale</span><span class="p">):</span>
<span class="o">>>></span> <span class="n">scale</span> <span class="o">=</span> <span class="n">scale</span><span class="o">.</span><span class="n">refine_names</span><span class="p">(</span><span class="s1">'C'</span><span class="p">)</span>
<span class="o">>>></span> <span class="k">return</span> <span class="nb">input</span> <span class="o">*</span> <span class="n">scale</span><span class="o">.</span><span class="n">align_as</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
<span class="o">>>></span> <span class="n">num_channels</span> <span class="o">=</span> <span class="mi">3</span>
<span class="o">>>></span> <span class="n">scale</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="n">num_channels</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">(</span><span class="s1">'C'</span><span class="p">,))</span>
<span class="o">>>></span> <span class="n">imgs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="n">num_channels</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">(</span><span class="s1">'N'</span><span class="p">,</span> <span class="s1">'H'</span><span class="p">,</span> <span class="s1">'W'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">))</span>
<span class="o">>>></span> <span class="n">more_imgs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="n">num_channels</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">(</span><span class="s1">'N'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">,</span> <span class="s1">'H'</span><span class="p">,</span> <span class="s1">'W'</span><span class="p">))</span>
<span class="o">>>></span> <span class="n">videos</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">num_channels</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">(</span><span class="s1">'N'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">,</span> <span class="s1">'H'</span><span class="p">,</span> <span class="s1">'W'</span><span class="p">,</span> <span class="s1">'D'</span><span class="p">))</span>
<span class="c1"># scale_channels is agnostic to the dimension order of the input</span>
<span class="o">>>></span> <span class="n">scale_channels</span><span class="p">(</span><span class="n">imgs</span><span class="p">,</span> <span class="n">scale</span><span class="p">)</span>
<span class="o">>>></span> <span class="n">scale_channels</span><span class="p">(</span><span class="n">more_imgs</span><span class="p">,</span> <span class="n">scale</span><span class="p">)</span>
<span class="o">>>></span> <span class="n">scale_channels</span><span class="p">(</span><span class="n">videos</span><span class="p">,</span> <span class="n">scale</span><span class="p">)</span>
</pre></div>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>The named tensor API is experimental and subject to change.</p>
</div>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.align_to">
<code class="sig-name descname">align_to</code><span class="sig-paren">(</span><em class="sig-param">*names</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/tensor.html#Tensor.align_to"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.Tensor.align_to" title="Permalink to this definition">¶</a></dt>
<dd><p>Permutes the dimensions of the <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> tensor to match the order
specified in <a class="reference internal" href="#torch.Tensor.names" title="torch.Tensor.names"><code class="xref py py-attr docutils literal notranslate"><span class="pre">names</span></code></a>, adding size-one dims for any new names.</p>
<p>All of the dims of <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> must be named in order to use this method.
The resulting tensor is a view on the original tensor.</p>
<p>All dimension names of <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> must be present in <a class="reference internal" href="#torch.Tensor.names" title="torch.Tensor.names"><code class="xref py py-attr docutils literal notranslate"><span class="pre">names</span></code></a>.
<a class="reference internal" href="#torch.Tensor.names" title="torch.Tensor.names"><code class="xref py py-attr docutils literal notranslate"><span class="pre">names</span></code></a> may contain additional names that are not in <code class="docutils literal notranslate"><span class="pre">self.names</span></code>;
the output tensor has a size-one dimension for each of those new names.</p>
<p><a class="reference internal" href="#torch.Tensor.names" title="torch.Tensor.names"><code class="xref py py-attr docutils literal notranslate"><span class="pre">names</span></code></a> may contain up to one Ellipsis (<code class="docutils literal notranslate"><span class="pre">...</span></code>).
The Ellipsis is expanded to be equal to all dimension names of <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code>
that are not mentioned in <a class="reference internal" href="#torch.Tensor.names" title="torch.Tensor.names"><code class="xref py py-attr docutils literal notranslate"><span class="pre">names</span></code></a>, in the order that they appear
in <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code>.</p>
<p>Python 2 does not support Ellipsis but one may use a string literal
instead (<code class="docutils literal notranslate"><span class="pre">'...'</span></code>).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>names</strong> (<em>iterable of str</em>) – The desired dimension ordering of the
output tensor. May contain up to one Ellipsis that is expanded
to all unmentioned dim names of <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code>.</p>
</dd>
</dl>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">tensor</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="mi">2</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="mi">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">named_tensor</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">refine_names</span><span class="p">(</span><span class="s1">'A'</span><span class="p">,</span> <span class="s1">'B'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">,</span> <span class="s1">'D'</span><span class="p">,</span> <span class="s1">'E'</span><span class="p">,</span> <span class="s1">'F'</span><span class="p">)</span>
<span class="go"># Move the F and E dims to the front while keeping the rest in order</span>
<span class="gp">>>> </span><span class="n">named_tensor</span><span class="o">.</span><span class="n">align_to</span><span class="p">(</span><span class="s1">'F'</span><span class="p">,</span> <span class="s1">'E'</span><span class="p">,</span> <span class="o">...</span><span class="p">)</span>
</pre></div>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>The named tensor API is experimental and subject to change.</p>
</div>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.unflatten">
<code class="sig-name descname">unflatten</code><span class="sig-paren">(</span><em class="sig-param">dim</em>, <em class="sig-param">sizes</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/tensor.html#Tensor.unflatten"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.Tensor.unflatten" title="Permalink to this definition">¶</a></dt>
<dd><p>Expands the dimension <a class="reference internal" href="tensors.html#torch.Tensor.dim" title="torch.Tensor.dim"><code class="xref py py-attr docutils literal notranslate"><span class="pre">dim</span></code></a> of the <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> tensor over multiple dimensions
of sizes given by <code class="xref py py-attr docutils literal notranslate"><span class="pre">sizes</span></code>.</p>
<ul class="simple">
<li><p><code class="xref py py-attr docutils literal notranslate"><span class="pre">sizes</span></code> is the new shape of the unflattened dimension and it can be a <cite>Tuple[int]</cite> as well
as <cite>torch.Size</cite> if <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> is a <cite>Tensor</cite>, or <cite>namedshape</cite> (Tuple[(name: str, size: int)])
if <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> is a <cite>NamedTensor</cite>. The total number of elements in sizes must match the number
of elements in the original dim being unflattened.</p></li>
</ul>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>dim</strong> (<em>Union</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.9)"><em>str</em></a><em>]</em>) – Dimension to unflatten</p></li>
<li><p><strong>sizes</strong> (<em>Union</em><em>[</em><em>Tuple</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em>] or </em><em>torch.Size</em><em>, </em><em>Tuple</em><em>[</em><em>Tuple</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.9)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em>]</em><em>]</em><em>]</em>) – New shape of the unflattened dimension</p></li>
</ul>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="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">4</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">unflatten</span><span class="p">(</span><span class="mi">1</span><span class="p">,</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="o">.</span><span class="n">shape</span>
<span class="go">torch.Size([3, 2, 2, 1])</span>
<span class="gp">>>> </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">4</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">(</span><span class="s1">'A'</span><span class="p">,</span> <span class="s1">'B'</span><span class="p">))</span><span class="o">.</span><span class="n">unflatten</span><span class="p">(</span><span class="s1">'B'</span><span class="p">,</span> <span class="p">((</span><span class="s1">'B1'</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="p">(</span><span class="s1">'B2'</span><span class="p">,</span> <span class="mi">2</span><span class="p">)))</span>
<span class="go">tensor([[[-1.1772, 0.0180],</span>
<span class="go"> [ 0.2412, 0.1431]],</span>
</pre></div>
</div>
<blockquote>
<div><p>[[-1.1819, -0.8899],
[ 1.5813, 0.2274]]], names=(‘A’, ‘B1’, ‘B2’))</p>
</div></blockquote>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>The named tensor API is experimental and subject to change.</p>
</div>
</dd></dl>
<dl class="method">
<dt>
<code class="sig-name descname">flatten</code><span class="sig-paren">(</span><em class="sig-param">dims</em>, <em class="sig-param">out_dim</em><span class="sig-paren">)</span> → Tensor</dt>
<dd><p>Flattens <code class="xref py py-attr docutils literal notranslate"><span class="pre">dims</span></code> into a single dimension with name <code class="xref py py-attr docutils literal notranslate"><span class="pre">out_dim</span></code>.</p>
<p>All of <cite>dims</cite> must be consecutive in order in the <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> tensor,
but not necessary contiguous in memory.</p>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">imgs</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">32</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">(</span><span class="s1">'N'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">,</span> <span class="s1">'H'</span><span class="p">,</span> <span class="s1">'W'</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">flat_imgs</span> <span class="o">=</span> <span class="n">imgs</span><span class="o">.</span><span class="n">flatten</span><span class="p">([</span><span class="s1">'C'</span><span class="p">,</span> <span class="s1">'H'</span><span class="p">,</span> <span class="s1">'W'</span><span class="p">],</span> <span class="s1">'features'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">flat_imgs</span><span class="o">.</span><span class="n">names</span><span class="p">,</span> <span class="n">flat_imgs</span><span class="o">.</span><span class="n">shape</span>
<span class="go">(('N', 'features'), torch.Size([32, 49152]))</span>
</pre></div>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>The named tensor API is experimental and subject to change.</p>
</div>
</dd></dl>
</dd></dl>
</div>
</div>
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