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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 notranslate"><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 notranslate"><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 notranslate"><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 notranslate"><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="sig-prename descclassname">torch.</code><code class="sig-name 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 notranslate"><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 notranslate"><span class="pre">torch.Tensor</span></code></a>. PyTorch has twelve different data types:</p>
<table class="docutils colwidths-auto align-default">
<thead>
<tr class="row-odd"><th class="head"><p>Data type</p></th>
<th class="head"><p>dtype</p></th>
<th class="head"><p>Legacy Constructors</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p>32-bit floating point</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.float32</span></code> or <code class="docutils literal notranslate"><span class="pre">torch.float</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.*.FloatTensor</span></code></p></td>
</tr>
<tr class="row-odd"><td><p>64-bit floating point</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.float64</span></code> or <code class="docutils literal notranslate"><span class="pre">torch.double</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.*.DoubleTensor</span></code></p></td>
</tr>
<tr class="row-even"><td><p>64-bit complex</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.complex64</span></code> or <code class="docutils literal notranslate"><span class="pre">torch.cfloat</span></code></p></td>
<td></td>
</tr>
<tr class="row-odd"><td><p>128-bit complex</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.complex128</span></code> or <code class="docutils literal notranslate"><span class="pre">torch.cdouble</span></code></p></td>
<td></td>
</tr>
<tr class="row-even"><td><p>16-bit floating point <a class="footnote-reference brackets" href="#id3" id="id1">1</a></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.float16</span></code> or <code class="docutils literal notranslate"><span class="pre">torch.half</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.*.HalfTensor</span></code></p></td>
</tr>
<tr class="row-odd"><td><p>16-bit floating point <a class="footnote-reference brackets" href="#id4" id="id2">2</a></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.bfloat16</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.*.BFloat16Tensor</span></code></p></td>
</tr>
<tr class="row-even"><td><p>8-bit integer (unsigned)</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.uint8</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.*.ByteTensor</span></code></p></td>
</tr>
<tr class="row-odd"><td><p>8-bit integer (signed)</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.int8</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.*.CharTensor</span></code></p></td>
</tr>
<tr class="row-even"><td><p>16-bit integer (signed)</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.int16</span></code> or <code class="docutils literal notranslate"><span class="pre">torch.short</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.*.ShortTensor</span></code></p></td>
</tr>
<tr class="row-odd"><td><p>32-bit integer (signed)</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.int32</span></code> or <code class="docutils literal notranslate"><span class="pre">torch.int</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.*.IntTensor</span></code></p></td>
</tr>
<tr class="row-even"><td><p>64-bit integer (signed)</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.int64</span></code> or <code class="docutils literal notranslate"><span class="pre">torch.long</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.*.LongTensor</span></code></p></td>
</tr>
<tr class="row-odd"><td><p>Boolean</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.bool</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.*.BoolTensor</span></code></p></td>
</tr>
</tbody>
</table>
<dl class="footnote brackets">
<dt class="label" id="id3"><span class="brackets"><a class="fn-backref" href="#id1">1</a></span></dt>
<dd><p>Sometimes referred to as binary16: uses 1 sign, 5 exponent, and 10
significand bits. Useful when precision is important.</p>
</dd>
<dt class="label" id="id4"><span class="brackets"><a class="fn-backref" href="#id2">2</a></span></dt>
<dd><p>Sometimes referred to as Brain Floating Point: use 1 sign, 8 exponent and 7
significand bits. Useful when range is important, since it has the same
number of exponent bits as <code class="docutils literal notranslate"><span class="pre">float32</span></code></p>
</dd>
</dl>
<p>To find out if a <a class="reference internal" href="#torch.torch.dtype" title="torch.torch.dtype"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.dtype</span></code></a> is a floating point data type, the property <a class="reference internal" href="generated/torch.is_floating_point.html#torch.is_floating_point" title="torch.is_floating_point"><code class="xref py py-attr docutils literal notranslate"><span class="pre">is_floating_point</span></code></a>
can be used, which returns <code class="docutils literal notranslate"><span class="pre">True</span></code> if the data type is a floating point data type.</p>
<p>To find out if a <a class="reference internal" href="#torch.torch.dtype" title="torch.torch.dtype"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.dtype</span></code></a> is a complex data type, the property <a class="reference internal" href="generated/torch.is_complex.html#torch.is_complex" title="torch.is_complex"><code class="xref py py-attr docutils literal notranslate"><span class="pre">is_complex</span></code></a>
can be used, which returns <code class="docutils literal notranslate"><span class="pre">True</span></code> if the data type is a complex data type.</p>
<p id="type-promotion-doc">When the dtypes of inputs to an arithmetic operation (<cite>add</cite>, <cite>sub</cite>, <cite>div</cite>, <cite>mul</cite>) differ, we promote
by finding the minimum dtype that satisfies the following rules:</p>
<ul class="simple">
<li><p>If the type of a scalar operand is of a higher category than tensor operands
(where complex > floating > integral > boolean), we promote to a type with sufficient size to hold
all scalar operands of that category.</p></li>
<li><p>If a zero-dimension tensor operand has a higher category than dimensioned operands,
we promote to a type with sufficient size and category to hold all zero-dim tensor operands of
that category.</p></li>
<li><p>If there are no higher-category zero-dim operands, we promote to a type with sufficient size
and category to hold all dimensioned operands.</p></li>
</ul>
<p>A floating point scalar operand has dtype <cite>torch.get_default_dtype()</cite> and an integral
non-boolean scalar operand has dtype <cite>torch.int64</cite>. Unlike numpy, we do not inspect
values when determining the minimum <cite>dtypes</cite> of an operand. Quantized and complex types
are not yet supported.</p>
<p>Promotion Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">float_tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">1</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">float</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">double_tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">1</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">double</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">complex_float_tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">1</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">complex64</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">complex_double_tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">1</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">complex128</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">int_tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">1</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">int</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">long_tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">1</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">long</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">uint_tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">1</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">uint8</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">double_tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">1</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">double</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">bool_tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">1</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="go"># zero-dim tensors</span>
<span class="gp">>>> </span><span class="n">long_zerodim</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="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">int_zerodim</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="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">dtype</span>
<span class="go">torch.int64</span>
<span class="go"># 5 is an int64, but does not have higher category than int_tensor so is not considered.</span>
<span class="gp">>>> </span><span class="p">(</span><span class="n">int_tensor</span> <span class="o">+</span> <span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">dtype</span>
<span class="go">torch.int32</span>
<span class="gp">>>> </span><span class="p">(</span><span class="n">int_tensor</span> <span class="o">+</span> <span class="n">long_zerodim</span><span class="p">)</span><span class="o">.</span><span class="n">dtype</span>
<span class="go">torch.int32</span>
<span class="gp">>>> </span><span class="p">(</span><span class="n">long_tensor</span> <span class="o">+</span> <span class="n">int_tensor</span><span class="p">)</span><span class="o">.</span><span class="n">dtype</span>
<span class="go">torch.int64</span>
<span class="gp">>>> </span><span class="p">(</span><span class="n">bool_tensor</span> <span class="o">+</span> <span class="n">long_tensor</span><span class="p">)</span><span class="o">.</span><span class="n">dtype</span>
<span class="go">torch.int64</span>
<span class="gp">>>> </span><span class="p">(</span><span class="n">bool_tensor</span> <span class="o">+</span> <span class="n">uint_tensor</span><span class="p">)</span><span class="o">.</span><span class="n">dtype</span>
<span class="go">torch.uint8</span>
<span class="gp">>>> </span><span class="p">(</span><span class="n">float_tensor</span> <span class="o">+</span> <span class="n">double_tensor</span><span class="p">)</span><span class="o">.</span><span class="n">dtype</span>
<span class="go">torch.float64</span>
<span class="gp">>>> </span><span class="p">(</span><span class="n">complex_float_tensor</span> <span class="o">+</span> <span class="n">complex_double_tensor</span><span class="p">)</span><span class="o">.</span><span class="n">dtype</span>
<span class="go">torch.complex128</span>
<span class="gp">>>> </span><span class="p">(</span><span class="n">bool_tensor</span> <span class="o">+</span> <span class="n">int_tensor</span><span class="p">)</span><span class="o">.</span><span class="n">dtype</span>
<span class="go">torch.int32</span>
<span class="go"># Since long is a different kind than float, result dtype only needs to be large enough</span>
<span class="go"># to hold the float.</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">long_tensor</span><span class="p">,</span> <span class="n">float_tensor</span><span class="p">)</span><span class="o">.</span><span class="n">dtype</span>
<span class="go">torch.float32</span>
</pre></div>
</div>
<dl class="simple">
<dt>When the output tensor of an arithmetic operation is specified, we allow casting to its <cite>dtype</cite> except that:</dt><dd><ul class="simple">
<li><p>An integral output tensor cannot accept a floating point tensor.</p></li>
<li><p>A boolean output tensor cannot accept a non-boolean tensor.</p></li>
<li><p>A non-complex output tensor cannot accept a complex tensor</p></li>
</ul>
</dd>
</dl>
<p>Casting Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># allowed:</span>
<span class="o">>>></span> <span class="n">float_tensor</span> <span class="o">*=</span> <span class="n">double_tensor</span>
<span class="o">>>></span> <span class="n">float_tensor</span> <span class="o">*=</span> <span class="n">int_tensor</span>
<span class="o">>>></span> <span class="n">float_tensor</span> <span class="o">*=</span> <span class="n">uint_tensor</span>
<span class="o">>>></span> <span class="n">float_tensor</span> <span class="o">*=</span> <span class="n">bool_tensor</span>
<span class="o">>>></span> <span class="n">float_tensor</span> <span class="o">*=</span> <span class="n">double_tensor</span>
<span class="o">>>></span> <span class="n">int_tensor</span> <span class="o">*=</span> <span class="n">long_tensor</span>
<span class="o">>>></span> <span class="n">int_tensor</span> <span class="o">*=</span> <span class="n">uint_tensor</span>
<span class="o">>>></span> <span class="n">uint_tensor</span> <span class="o">*=</span> <span class="n">int_tensor</span>
<span class="c1"># disallowed (RuntimeError: result type can't be cast to the desired output type):</span>
<span class="o">>>></span> <span class="n">int_tensor</span> <span class="o">*=</span> <span class="n">float_tensor</span>
<span class="o">>>></span> <span class="n">bool_tensor</span> <span class="o">*=</span> <span class="n">int_tensor</span>
<span class="o">>>></span> <span class="n">bool_tensor</span> <span class="o">*=</span> <span class="n">uint_tensor</span>
<span class="o">>>></span> <span class="n">float_tensor</span> <span class="o">*=</span> <span class="n">complex_float_tensor</span>
</pre></div>
</div>
</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="sig-prename descclassname">torch.</code><code class="sig-name 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 notranslate"><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 notranslate"><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 notranslate"><span class="pre">torch.device</span></code></a> contains a device type (<code class="docutils literal notranslate"><span class="pre">'cpu'</span></code> or <code class="docutils literal notranslate"><span class="pre">'cuda'</span></code>) and optional device
ordinal for the device type. If the device ordinal is not present, this object will always represent
the current device for the device type, even after <a class="reference internal" href="cuda.html#torch.cuda.set_device" title="torch.cuda.set_device"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.cuda.set_device()</span></code></a> is called; e.g.,
a <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> constructed with device <code class="docutils literal notranslate"><span class="pre">'cuda'</span></code> is equivalent to <code class="docutils literal notranslate"><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 notranslate"><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 notranslate"><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 notranslate"><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 notranslate"><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 notranslate"><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 notranslate"><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="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 notranslate"><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="doctest highlight-default notranslate"><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="doctest highlight-default notranslate"><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="n">device</span><span class="o">=</span><span class="s1">'cuda:1'</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="admonition note">
<p class="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 notranslate"><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="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">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="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="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">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="sig-prename descclassname">torch.</code><code class="sig-name 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 notranslate"><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 notranslate"><span class="pre">torch.Tensor</span></code></a>. Currently, we support <code class="docutils literal notranslate"><span class="pre">torch.strided</span></code> (dense Tensors)
and have beta support for <code class="docutils literal notranslate"><span class="pre">torch.sparse_coo</span></code> (sparse COO Tensors).</p>
<p><code class="docutils literal notranslate"><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 notranslate"><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 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">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 notranslate"><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>
</div>
<div class="section" id="torch-memory-format">
<h2>torch.memory_format<a class="headerlink" href="#torch-memory-format" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torch.torch.memory_format">
<em class="property">class </em><code class="sig-prename descclassname">torch.</code><code class="sig-name descname">memory_format</code><a class="headerlink" href="#torch.torch.memory_format" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<p>A <a class="reference internal" href="#torch.torch.memory_format" title="torch.torch.memory_format"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.memory_format</span></code></a> is an object representing the memory format on which a <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> is
or will be allocated.</p>
<p>Possible values are:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">torch.contiguous_format</span></code>:
Tensor is or will be allocated in dense non-overlapping memory. Strides represented by values in decreasing order.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">torch.channels_last</span></code>:
Tensor is or will be allocated in dense non-overlapping memory. Strides represented by values in
<code class="docutils literal notranslate"><span class="pre">strides[0]</span> <span class="pre">></span> <span class="pre">strides[2]</span> <span class="pre">></span> <span class="pre">strides[3]</span> <span class="pre">></span> <span class="pre">strides[1]</span> <span class="pre">==</span> <span class="pre">1</span></code> aka NHWC order.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">torch.preserve_format</span></code>:
Used in functions like <cite>clone</cite> to preserve the memory format of the input tensor. If input tensor is
allocated in dense non-overlapping memory, the output tensor strides will be copied from the input.
Otherwise output strides will follow <code class="docutils literal notranslate"><span class="pre">torch.contiguous_format</span></code></p></li>
</ul>
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