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<div class="section" id="tensor-attributes">
<span id="tensor-attributes-doc"></span><h1>Tensor Attributes<a class="headerlink" href="#tensor-attributes" title="Permalink to this headline">¶</a></h1>
<p>Each <code class="docutils literal"><span class="pre">torch.Tensor</span></code> has a <a class="reference internal" href="#torch.torch.dtype" title="torch.torch.dtype"><code class="xref py py-class docutils literal"><span class="pre">torch.dtype</span></code></a>, <a class="reference internal" href="#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal"><span class="pre">torch.device</span></code></a>, and <a class="reference internal" href="#torch.torch.layout" title="torch.torch.layout"><code class="xref py py-class docutils literal"><span class="pre">torch.layout</span></code></a>.</p>
<div class="section" id="torch-dtype">
<span id="dtype-doc"></span><h2>torch.dtype<a class="headerlink" href="#torch-dtype" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torch.torch.dtype">
<em class="property">class </em><code class="descclassname">torch.</code><code class="descname">dtype</code><a class="headerlink" href="#torch.torch.dtype" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<p>A <a class="reference internal" href="#torch.torch.dtype" title="torch.torch.dtype"><code class="xref py py-class docutils literal"><span class="pre">torch.dtype</span></code></a> is an object that represents the data type of a
<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal"><span class="pre">torch.Tensor</span></code></a>. PyTorch has eight different data types:</p>
<table border="1" class="docutils">
<colgroup>
<col width="26%" />
<col width="46%" />
<col width="29%" />
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head">Data type</th>
<th class="head">dtype</th>
<th class="head">Tensor types</th>
</tr>
</thead>
<tbody valign="top">
<tr class="row-even"><td>32-bit floating point</td>
<td><code class="docutils literal"><span class="pre">torch.float32</span></code> or <code class="docutils literal"><span class="pre">torch.float</span></code></td>
<td><code class="docutils literal"><span class="pre">torch.*.FloatTensor</span></code></td>
</tr>
<tr class="row-odd"><td>64-bit floating point</td>
<td><code class="docutils literal"><span class="pre">torch.float64</span></code> or <code class="docutils literal"><span class="pre">torch.double</span></code></td>
<td><code class="docutils literal"><span class="pre">torch.*.DoubleTensor</span></code></td>
</tr>
<tr class="row-even"><td>16-bit floating point</td>
<td><code class="docutils literal"><span class="pre">torch.float16</span></code> or <code class="docutils literal"><span class="pre">torch.half</span></code></td>
<td><code class="docutils literal"><span class="pre">torch.*.HalfTensor</span></code></td>
</tr>
<tr class="row-odd"><td>8-bit integer (unsigned)</td>
<td><code class="docutils literal"><span class="pre">torch.uint8</span></code></td>
<td><code class="docutils literal"><span class="pre">torch.*.ByteTensor</span></code></td>
</tr>
<tr class="row-even"><td>8-bit integer (signed)</td>
<td><code class="docutils literal"><span class="pre">torch.int8</span></code></td>
<td><code class="docutils literal"><span class="pre">torch.*.CharTensor</span></code></td>
</tr>
<tr class="row-odd"><td>16-bit integer (signed)</td>
<td><code class="docutils literal"><span class="pre">torch.int16</span></code> or <code class="docutils literal"><span class="pre">torch.short</span></code></td>
<td><code class="docutils literal"><span class="pre">torch.*.ShortTensor</span></code></td>
</tr>
<tr class="row-even"><td>32-bit integer (signed)</td>
<td><code class="docutils literal"><span class="pre">torch.int32</span></code> or <code class="docutils literal"><span class="pre">torch.int</span></code></td>
<td><code class="docutils literal"><span class="pre">torch.*.IntTensor</span></code></td>
</tr>
<tr class="row-odd"><td>64-bit integer (signed)</td>
<td><code class="docutils literal"><span class="pre">torch.int64</span></code> or <code class="docutils literal"><span class="pre">torch.long</span></code></td>
<td><code class="docutils literal"><span class="pre">torch.*.LongTensor</span></code></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="torch-device">
<span id="device-doc"></span><h2>torch.device<a class="headerlink" href="#torch-device" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torch.torch.device">
<em class="property">class </em><code class="descclassname">torch.</code><code class="descname">device</code><a class="headerlink" href="#torch.torch.device" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<p>A <a class="reference internal" href="#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal"><span class="pre">torch.device</span></code></a> is an object representing the device on which a <a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal"><span class="pre">torch.Tensor</span></code></a> is
or will be allocated.</p>
<p>The <a class="reference internal" href="#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal"><span class="pre">torch.device</span></code></a> contains a device type (<code class="docutils literal"><span class="pre">'cpu'</span></code> or <code class="docutils literal"><span class="pre">'cuda'</span></code>) and optional device ordinal for the
device type. If the device ordinal is not present, this represents the current device for the device type;
e.g. a <a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal"><span class="pre">torch.Tensor</span></code></a> constructed with device <code class="docutils literal"><span class="pre">'cuda'</span></code> is equivalent to <code class="docutils literal"><span class="pre">'cuda:X'</span></code> where X is the result of
<a class="reference internal" href="cuda.html#torch.cuda.current_device" title="torch.cuda.current_device"><code class="xref py py-func docutils literal"><span class="pre">torch.cuda.current_device()</span></code></a>.</p>
<p>A <a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal"><span class="pre">torch.Tensor</span></code></a>’s device can be accessed via the <a class="reference internal" href="tensors.html#torch.Tensor.device" title="torch.Tensor.device"><code class="xref py py-attr docutils literal"><span class="pre">Tensor.device</span></code></a> property.</p>
<p>A <a class="reference internal" href="#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal"><span class="pre">torch.device</span></code></a> can be constructed via a string or via a string and device ordinal</p>
<p>Via a string:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">'cuda:0'</span><span class="p">)</span>
<span class="go">device(type='cuda', index=0)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">'cpu'</span><span class="p">)</span>
<span class="go">device(type='cpu')</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">'cuda'</span><span class="p">)</span> <span class="c1"># current cuda device</span>
<span class="go">device(type='cuda')</span>
</pre></div>
</div>
<p>Via a string and device ordinal:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">'cuda'</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="go">device(type='cuda', index=0)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">'cpu'</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="go">device(type='cpu', index=0)</span>
</pre></div>
</div>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p>The <a class="reference internal" href="#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal"><span class="pre">torch.device</span></code></a> argument in functions can generally be substituted with a string.
This allows for fast prototyping of code.</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># Example of a function that takes in a torch.device</span>
<span class="gp">>>> </span><span class="n">cuda1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">'cuda:1'</span><span class="p">)</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">3</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">cuda1</span><span class="p">)</span>
</pre></div>
</div>
<div class="last highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># You can substitute the torch.device with a string</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">3</span><span class="p">),</span> <span class="s1">'cuda:1'</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p>For legacy reasons, a device can be constructed via a single device ordinal, which is treated
as a cuda device. This matches <a class="reference internal" href="tensors.html#torch.Tensor.get_device" title="torch.Tensor.get_device"><code class="xref py py-meth docutils literal"><span class="pre">Tensor.get_device()</span></code></a>, which returns an ordinal for cuda
tensors and is not supported for cpu tensors.</p>
<div class="last highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="go">device(type='cuda', index=1)</span>
</pre></div>
</div>
</div>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p>Methods which take a device will generally accept a (properly formatted) string
or (legacy) integer device ordinal, i.e. the following are all equivalent:</p>
<div class="last highlight-default"><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">2</span><span class="p">,</span><span class="mi">3</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">'cuda:1'</span><span class="p">))</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">3</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="s1">'cuda:1'</span><span class="p">)</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">3</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># legacy</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="torch-layout">
<span id="layout-doc"></span><h2>torch.layout<a class="headerlink" href="#torch-layout" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torch.torch.layout">
<em class="property">class </em><code class="descclassname">torch.</code><code class="descname">layout</code><a class="headerlink" href="#torch.torch.layout" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<p>A <a class="reference internal" href="#torch.torch.layout" title="torch.torch.layout"><code class="xref py py-class docutils literal"><span class="pre">torch.layout</span></code></a> is an object that represents the memory layout of a
<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal"><span class="pre">torch.Tensor</span></code></a>. Currently, we support <code class="docutils literal"><span class="pre">torch.strided</span></code> (dense Tensors)
and have experimental support for <code class="docutils literal"><span class="pre">torch.sparse_coo</span></code> (sparse COO Tensors).</p>
<p><code class="docutils literal"><span class="pre">torch.strided</span></code> represents dense Tensors and is the memory layout that
is most commonly used. Each strided tensor has an associated
<code class="xref py py-class docutils literal"><span class="pre">torch.Storage</span></code>, which holds its data. These tensors provide
multi-dimensional, <a class="reference external" href="https://en.wikipedia.org/wiki/Stride_of_an_array">strided</a>
view of a storage. Strides are a list of integers: the k-th stride
represents the jump in the memory necessary to go from one element to the
next one in the k-th dimension of the Tensor. This concept makes it possible
to perform many tensor operations efficiently.</p>
<p>Example:</p>
<div class="highlight-default"><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">Tensor</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">10</span><span class="p">]])</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">stride</span><span class="p">()</span>
<span class="go">(5, 1)</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">t</span><span class="p">()</span><span class="o">.</span><span class="n">stride</span><span class="p">()</span>
<span class="go">(1, 5)</span>
</pre></div>
</div>
<p>For more information on <code class="docutils literal"><span class="pre">torch.sparse_coo</span></code> tensors, see <a class="reference internal" href="sparse.html#sparse-docs"><span class="std std-ref">torch.sparse</span></a>.</p>
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