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<li class="toctree-l1"><a class="reference internal" href="notes/amp_examples.html">CUDA Automatic Mixed Precision examples</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/autograd.html">Autograd mechanics</a></li>
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<div class="section" id="module-torch.nested">
<span id="torch-nested"></span><h1>torch.nested<a class="headerlink" href="#module-torch.nested" title="Permalink to this heading">¶</a></h1>
<div class="section" id="introduction">
<h2>Introduction<a class="headerlink" href="#introduction" title="Permalink to this heading">¶</a></h2>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>The PyTorch API of nested tensors is in prototype stage and will change in the near future.</p>
</div>
<p>NestedTensor allows the user to pack a list of Tensors into a single, efficient datastructure.</p>
<p>The only constraint on the input Tensors is that their dimension must match.</p>
<p>This enables more efficient metadata representations and access to purpose built kernels.</p>
<p>One application of NestedTensors is to express sequential data in various domains.
While the conventional approach is to pad variable length sequences, NestedTensor
enables users to bypass padding. The API for calling operations on a nested tensor is no different
from that of a regular <code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code>, which should allow seamless integration with existing models,
with the main difference being <a class="reference internal" href="#construction"><span class="std std-ref">construction of the inputs</span></a>.</p>
<p>As this is a prototype feature, the <a class="reference internal" href="#supported-operations"><span class="std std-ref">operations supported</span></a> are still
limited. However, we welcome issues, feature requests and contributions. More information on contributing can be found
<a class="reference external" href="https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/nested/README.md">in this Readme</a>.</p>
</div>
<div class="section" id="construction">
<span id="id1"></span><h2>Construction<a class="headerlink" href="#construction" title="Permalink to this heading">¶</a></h2>
<p>Construction is straightforward and involves passing a list of Tensors to the <code class="docutils literal notranslate"><span class="pre">torch.nested.nested_tensor</span></code>
constructor.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span><span class="p">,</span> <span class="n">b</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">3</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span> <span class="o">+</span> <span class="mi">3</span>
<span class="gp">>>> </span><span class="n">a</span>
<span class="go">tensor([0, 1, 2])</span>
<span class="gp">>>> </span><span class="n">b</span>
<span class="go">tensor([3, 4, 5, 6, 7])</span>
<span class="gp">>>> </span><span class="n">nt</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nested</span><span class="o">.</span><span class="n">nested_tensor</span><span class="p">([</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">nt</span>
<span class="go">nested_tensor([</span>
<span class="go"> tensor([0, 1, 2]),</span>
<span class="go"> tensor([3, 4, 5, 6, 7])</span>
<span class="go"> ])</span>
</pre></div>
</div>
<p>Data type, device and whether gradients are required can be chosen via the usual keyword arguments.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">nt</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nested</span><span class="o">.</span><span class="n">nested_tensor</span><span class="p">([</span><span class="n">a</span><span class="p">,</span> <span class="n">b</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">float32</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s2">"cuda"</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">nt</span>
<span class="go">nested_tensor([</span>
<span class="go"> tensor([0., 1., 2.], device='cuda:0', requires_grad=True),</span>
<span class="go"> tensor([3., 4., 5., 6., 7.], device='cuda:0', requires_grad=True)</span>
<span class="go">], device='cuda:0', requires_grad=True)</span>
</pre></div>
</div>
<p>In the vein of <code class="docutils literal notranslate"><span class="pre">torch.as_tensor</span></code>, <code class="docutils literal notranslate"><span class="pre">torch.nested.as_nested_tensor</span></code> can be used to preserve autograd
history from the tensors passed to the constructor. For more information, refer to the section on
<a class="reference internal" href="#constructor-functions"><span class="std std-ref">Nested tensor constructor and conversion functions</span></a>.</p>
<p>In order to form a valid NestedTensor all the passed Tensors need to match in dimension, but none of the other attributes need to.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</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">50</span><span class="p">,</span> <span class="mi">70</span><span class="p">)</span> <span class="c1"># image 1</span>
<span class="gp">>>> </span><span class="n">b</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">128</span><span class="p">,</span> <span class="mi">64</span><span class="p">)</span> <span class="c1"># image 2</span>
<span class="gp">>>> </span><span class="n">nt</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nested</span><span class="o">.</span><span class="n">nested_tensor</span><span class="p">([</span><span class="n">a</span><span class="p">,</span> <span class="n">b</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">float32</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">nt</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span>
<span class="go">4</span>
</pre></div>
</div>
<p>If one of the dimensions doesn’t match, the constructor throws an error.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</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">50</span><span class="p">,</span> <span class="mi">128</span><span class="p">)</span> <span class="c1"># text 1</span>
<span class="gp">>>> </span><span class="n">b</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">128</span><span class="p">,</span> <span class="mi">64</span><span class="p">)</span> <span class="c1"># image 2</span>
<span class="gp">>>> </span><span class="n">nt</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nested</span><span class="o">.</span><span class="n">nested_tensor</span><span class="p">([</span><span class="n">a</span><span class="p">,</span> <span class="n">b</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">float32</span><span class="p">)</span>
<span class="gt">Traceback (most recent call last):</span>
File <span class="nb">"<stdin>"</span>, line <span class="m">1</span>, in <span class="n"><module></span>
<span class="gr">RuntimeError</span>: <span class="n">All Tensors given to nested_tensor must have the same dimension. Found dimension 3 for Tensor at index 1 and dimension 2 for Tensor at index 0.</span>
</pre></div>
</div>
<p>Note that the passed Tensors are being copied into a contiguous piece of memory. The resulting
NestedTensor allocates new memory to store them and does not keep a reference.</p>
<p>At this moment we only support one level of nesting, i.e. a simple, flat list of Tensors. In the future
we can add support for multiple levels of nesting, such as a list that consists entirely of lists of Tensors.
Note that for this extension it is important to maintain an even level of nesting across entries so that the resulting NestedTensor
has a well defined dimension. If you have a need for this feature, please feel encouraged to open a feature request so that
we can track it and plan accordingly.</p>
</div>
<div class="section" id="size">
<h2>size<a class="headerlink" href="#size" title="Permalink to this heading">¶</a></h2>
<p>Even though a NestedTensor does not support <code class="docutils literal notranslate"><span class="pre">.size()</span></code> (or <code class="docutils literal notranslate"><span class="pre">.shape</span></code>), it supports <code class="docutils literal notranslate"><span class="pre">.size(i)</span></code> if dimension i is regular.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</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">50</span><span class="p">,</span> <span class="mi">128</span><span class="p">)</span> <span class="c1"># text 1</span>
<span class="gp">>>> </span><span class="n">b</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="c1"># text 2</span>
<span class="gp">>>> </span><span class="n">nt</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nested</span><span class="o">.</span><span class="n">nested_tensor</span><span class="p">([</span><span class="n">a</span><span class="p">,</span> <span class="n">b</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">float32</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">nt</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="go">2</span>
<span class="gp">>>> </span><span class="n">nt</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="gt">Traceback (most recent call last):</span>
File <span class="nb">"<stdin>"</span>, line <span class="m">1</span>, in <span class="n"><module></span>
<span class="gr">RuntimeError</span>: <span class="n">Given dimension 1 is irregular and does not have a size.</span>
<span class="gp">>>> </span><span class="n">nt</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
<span class="go">128</span>
</pre></div>
</div>
<p>If all dimensions are regular, the NestedTensor is intended to be semantically indistinguishable from a regular <code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code>.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</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">20</span><span class="p">,</span> <span class="mi">128</span><span class="p">)</span> <span class="c1"># text 1</span>
<span class="gp">>>> </span><span class="n">nt</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nested</span><span class="o">.</span><span class="n">nested_tensor</span><span class="p">([</span><span class="n">a</span><span class="p">,</span> <span class="n">a</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">float32</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">nt</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="go">2</span>
<span class="gp">>>> </span><span class="n">nt</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="go">20</span>
<span class="gp">>>> </span><span class="n">nt</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
<span class="go">128</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">nt</span><span class="o">.</span><span class="n">unbind</span><span class="p">())</span><span class="o">.</span><span class="n">size</span><span class="p">()</span>
<span class="go">torch.Size([2, 20, 128])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">([</span><span class="n">a</span><span class="p">,</span> <span class="n">a</span><span class="p">])</span><span class="o">.</span><span class="n">size</span><span class="p">()</span>
<span class="go">torch.Size([2, 20, 128])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">equal</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">nt</span><span class="o">.</span><span class="n">unbind</span><span class="p">()),</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">([</span><span class="n">a</span><span class="p">,</span> <span class="n">a</span><span class="p">]))</span>
<span class="go">True</span>
</pre></div>
</div>
<p>In the future we might make it easier to detect this condition and convert seamlessly.</p>
<p>Please open a feature request if you have a need for this (or any other related feature for that matter).</p>
</div>
<div class="section" id="unbind">
<h2>unbind<a class="headerlink" href="#unbind" title="Permalink to this heading">¶</a></h2>
<p><code class="docutils literal notranslate"><span class="pre">unbind</span></code> allows you to retrieve a view of the constituents.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">torch</span>
<span class="gp">>>> </span><span class="n">a</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="gp">>>> </span><span class="n">b</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">4</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">nt</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nested</span><span class="o">.</span><span class="n">nested_tensor</span><span class="p">([</span><span class="n">a</span><span class="p">,</span> <span class="n">b</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">float32</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">nt</span>
<span class="go">nested_tensor([</span>
<span class="go"> tensor([[ 1.2286, -1.2343, -1.4842],</span>
<span class="go"> [-0.7827, 0.6745, 0.0658]]),</span>
<span class="go"> tensor([[-1.1247, -0.4078, -1.0633, 0.8083],</span>
<span class="go"> [-0.2871, -0.2980, 0.5559, 1.9885],</span>
<span class="go"> [ 0.4074, 2.4855, 0.0733, 0.8285]])</span>
<span class="go">])</span>
<span class="gp">>>> </span><span class="n">nt</span><span class="o">.</span><span class="n">unbind</span><span class="p">()</span>
<span class="go">(tensor([[ 1.2286, -1.2343, -1.4842],</span>
<span class="go"> [-0.7827, 0.6745, 0.0658]]), tensor([[-1.1247, -0.4078, -1.0633, 0.8083],</span>
<span class="go"> [-0.2871, -0.2980, 0.5559, 1.9885],</span>
<span class="go"> [ 0.4074, 2.4855, 0.0733, 0.8285]]))</span>
<span class="gp">>>> </span><span class="n">nt</span><span class="o">.</span><span class="n">unbind</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">a</span>
<span class="go">True</span>
<span class="gp">>>> </span><span class="n">nt</span><span class="o">.</span><span class="n">unbind</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">mul_</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
<span class="go">tensor([[ 3.6858, -3.7030, -4.4525],</span>
<span class="go"> [-2.3481, 2.0236, 0.1975]])</span>
<span class="gp">>>> </span><span class="n">nt</span>
<span class="go">nested_tensor([</span>
<span class="go"> tensor([[ 3.6858, -3.7030, -4.4525],</span>
<span class="go"> [-2.3481, 2.0236, 0.1975]]),</span>
<span class="go"> tensor([[-1.1247, -0.4078, -1.0633, 0.8083],</span>
<span class="go"> [-0.2871, -0.2980, 0.5559, 1.9885],</span>
<span class="go"> [ 0.4074, 2.4855, 0.0733, 0.8285]])</span>
<span class="go">])</span>
</pre></div>
</div>
<p>Note that <code class="docutils literal notranslate"><span class="pre">nt.unbind()[0]</span></code> is not a copy, but rather a slice of the underlying memory, which represents the first entry or constituent of the NestedTensor.</p>
</div>
<div class="section" id="nested-tensor-constructor-and-conversion-functions">
<span id="constructor-functions"></span><h2>Nested tensor constructor and conversion functions<a class="headerlink" href="#nested-tensor-constructor-and-conversion-functions" title="Permalink to this heading">¶</a></h2>
<p>The following functions are related to nested tensors:</p>
<dl class="py function">
<dt class="sig sig-object py" id="torch.nested.nested_tensor">
<span class="sig-prename descclassname"><span class="pre">torch.nested.</span></span><span class="sig-name descname"><span class="pre">nested_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor_list</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pin_memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="headerlink" href="#torch.nested.nested_tensor" title="Permalink to this definition">¶</a></dt>
<dd><p>Constructs a nested tensor with no autograd history (also known as a “leaf tensor”, see
<a class="reference internal" href="notes/autograd.html#autograd-mechanics"><span class="std std-ref">Autograd mechanics</span></a>) from <code class="xref py py-attr docutils literal notranslate"><span class="pre">tensor_list</span></code> a list of tensors.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tensor_list</strong> (<em>List</em><em>[</em><em>array_like</em><em>]</em>) – a list of tensors, or anything that can be passed to torch.tensor,</p></li>
<li><p><strong>dimensionality.</strong> (<em>where each element</em><em> of </em><em>the list has the same</em>) – </p></li>
</ul>
</dd>
<dt class="field-even">Keyword Arguments</dt>
<dd class="field-even"><ul class="simple">
<li><p><strong>dtype</strong> (<a class="reference internal" href="tensor_attributes.html#torch.dtype" title="torch.dtype"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.dtype</span></code></a>, optional) – the desired type of returned nested tensor.
Default: if None, same <a class="reference internal" href="tensor_attributes.html#torch.dtype" title="torch.dtype"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.dtype</span></code></a> as leftmost tensor in the list.</p></li>
<li><p><strong>device</strong> (<a class="reference internal" href="tensor_attributes.html#torch.device" title="torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a>, optional) – the desired device of returned nested tensor.
Default: if None, same <a class="reference internal" href="tensor_attributes.html#torch.device" title="torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a> as leftmost tensor in the list</p></li>
<li><p><strong>requires_grad</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>optional</em>) – If autograd should record operations on the
returned nested tensor. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
<li><p><strong>pin_memory</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>optional</em>) – If set, returned nested tensor would be allocated in
the pinned memory. Works only for CPU tensors. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
</ul>
</dd>
</dl>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">3</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="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">5</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="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">nt</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nested</span><span class="o">.</span><span class="n">nested_tensor</span><span class="p">([</span><span class="n">a</span><span class="p">,</span> <span class="n">b</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">nt</span><span class="o">.</span><span class="n">is_leaf</span>
<span class="go">True</span>
</pre></div>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.nested.as_nested_tensor">
<span class="sig-prename descclassname"><span class="pre">torch.nested.</span></span><span class="sig-name descname"><span class="pre">as_nested_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nested.html#as_nested_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nested.as_nested_tensor" title="Permalink to this definition">¶</a></dt>
<dd><p>Constructs a nested tensor preserving autograd history from <code class="xref py py-attr docutils literal notranslate"><span class="pre">tensor_list</span></code> a list of tensors.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Tensors within the list are always copied by this function due to current nested tensor semantics.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>tensor_list</strong> (<em>List</em><em>[</em><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>]</em>) – a list of tensors with the same ndim</p>
</dd>
<dt class="field-even">Keyword Arguments</dt>
<dd class="field-even"><ul class="simple">
<li><p><strong>dtype</strong> (<a class="reference internal" href="tensor_attributes.html#torch.dtype" title="torch.dtype"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.dtype</span></code></a>, optional) – the desired type of returned nested tensor.
Default: if None, same <a class="reference internal" href="tensor_attributes.html#torch.dtype" title="torch.dtype"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.dtype</span></code></a> as leftmost tensor in the list.</p></li>
<li><p><strong>device</strong> (<a class="reference internal" href="tensor_attributes.html#torch.device" title="torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a>, optional) – the desired device of returned nested tensor.
Default: if None, same <a class="reference internal" href="tensor_attributes.html#torch.device" title="torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a> as leftmost tensor in the list</p></li>
</ul>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
</dd>
</dl>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">3</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="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">5</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="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">nt</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nested</span><span class="o">.</span><span class="n">as_nested_tensor</span><span class="p">([</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">nt</span><span class="o">.</span><span class="n">is_leaf</span>
<span class="go">False</span>
<span class="gp">>>> </span><span class="n">fake_grad</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nested</span><span class="o">.</span><span class="n">nested_tensor</span><span class="p">([</span><span class="n">torch</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">a</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">b</span><span class="p">)])</span>
<span class="gp">>>> </span><span class="n">nt</span><span class="o">.</span><span class="n">backward</span><span class="p">(</span><span class="n">fake_grad</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">a</span><span class="o">.</span><span class="n">grad</span>
<span class="go">tensor([1., 1., 1.])</span>
<span class="gp">>>> </span><span class="n">b</span><span class="o">.</span><span class="n">grad</span>
<span class="go">tensor([0., 0., 0., 0., 0.])</span>
</pre></div>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.nested.to_padded_tensor">
<span class="sig-prename descclassname"><span class="pre">torch.nested.</span></span><span class="sig-name descname"><span class="pre">to_padded_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="headerlink" href="#torch.nested.to_padded_tensor" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a new (non-nested) Tensor by padding the <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> nested tensor.
The leading entries will be filled with the nested data,
while the trailing entries will be padded.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p><a class="reference internal" href="#torch.nested.to_padded_tensor" title="torch.nested.to_padded_tensor"><code class="xref py py-func docutils literal notranslate"><span class="pre">to_padded_tensor()</span></code></a> always copies the underlying data,
since the nested and the non-nested tensors differ in memory layout.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>padding</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – The padding value for the trailing entries.</p>
</dd>
<dt class="field-even">Keyword Arguments</dt>
<dd class="field-even"><ul class="simple">
<li><p><strong>output_size</strong> (<em>Tuple</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>]</em>) – The size of the output tensor.
If given, it must be large enough to contain all nested data;
else, will infer by taking the max size of each nested sub-tensor along each dimension.</p></li>
<li><p><strong>out</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>, </em><em>optional</em>) – the output tensor.</p></li>
</ul>
</dd>
</dl>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">nt</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nested</span><span class="o">.</span><span class="n">nested_tensor</span><span class="p">([</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">5</span><span class="p">)),</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="go">nested_tensor([</span>
<span class="go"> tensor([[ 1.6862, -1.1282, 1.1031, 0.0464, -1.3276],</span>
<span class="go"> [-1.9967, -1.0054, 1.8972, 0.9174, -1.4995]]),</span>
<span class="go"> tensor([[-1.8546, -0.7194, -0.2918, -0.1846],</span>
<span class="go"> [ 0.2773, 0.8793, -0.5183, -0.6447],</span>
<span class="go"> [ 1.8009, 1.8468, -0.9832, -1.5272]])</span>
<span class="go">])</span>
<span class="gp">>>> </span><span class="n">pt_infer</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nested</span><span class="o">.</span><span class="n">to_padded_tensor</span><span class="p">(</span><span class="n">nt</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">)</span>
<span class="go">tensor([[[ 1.6862, -1.1282, 1.1031, 0.0464, -1.3276],</span>
<span class="go"> [-1.9967, -1.0054, 1.8972, 0.9174, -1.4995],</span>
<span class="go"> [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000]],</span>
<span class="go"> [[-1.8546, -0.7194, -0.2918, -0.1846, 0.0000],</span>
<span class="go"> [ 0.2773, 0.8793, -0.5183, -0.6447, 0.0000],</span>
<span class="go"> [ 1.8009, 1.8468, -0.9832, -1.5272, 0.0000]]])</span>
<span class="gp">>>> </span><span class="n">pt_large</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nested</span><span class="o">.</span><span class="n">to_padded_tensor</span><span class="p">(</span><span class="n">nt</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</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="mi">6</span><span class="p">))</span>
<span class="go">tensor([[[ 1.6862, -1.1282, 1.1031, 0.0464, -1.3276, 1.0000],</span>
<span class="go"> [-1.9967, -1.0054, 1.8972, 0.9174, -1.4995, 1.0000],</span>
<span class="go"> [ 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000],</span>
<span class="go"> [ 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000]],</span>
<span class="go"> [[-1.8546, -0.7194, -0.2918, -0.1846, 1.0000, 1.0000],</span>
<span class="go"> [ 0.2773, 0.8793, -0.5183, -0.6447, 1.0000, 1.0000],</span>
<span class="go"> [ 1.8009, 1.8468, -0.9832, -1.5272, 1.0000, 1.0000],</span>
<span class="go"> [ 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000]]])</span>
<span class="gp">>>> </span><span class="n">pt_small</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nested</span><span class="o">.</span><span class="n">to_padded_tensor</span><span class="p">(</span><span class="n">nt</span><span class="p">,</span> <span class="mf">2.0</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="mi">2</span><span class="p">))</span>
<span class="go">RuntimeError: Value in output_size is less than NestedTensor padded size. Truncation is not supported.</span>
</pre></div>
</div>
</dd></dl>
</div>
<div class="section" id="supported-operations">
<span id="id2"></span><h2>Supported operations<a class="headerlink" href="#supported-operations" title="Permalink to this heading">¶</a></h2>
<p>In this section, we summarize the operations that are currently supported on
NestedTensor and any constraints they have.</p>
<table class="colwidths-given docutils colwidths-auto align-default">
<colgroup>
<col style="width: 35%" />
<col style="width: 65%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>PyTorch operation</p></th>
<th class="head"><p>Constraints</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.matmul.html#torch.matmul" title="torch.matmul"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.matmul()</span></code></a></p></td>
<td><p>Supports matrix multiplication between two (>= 3d) nested tensors where
the last two dimensions are matrix dimensions and the leading (batch) dimensions have the same size
(i.e. no broadcasting support for batch dimensions yet).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.bmm.html#torch.bmm" title="torch.bmm"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.bmm()</span></code></a></p></td>
<td><p>Supports batch matrix multiplication of two 3-d nested tensors.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.nn.Linear.html#torch.nn.Linear" title="torch.nn.Linear"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.Linear()</span></code></a></p></td>
<td><p>Supports 3-d nested input and a dense 2-d weight matrix.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.nn.functional.softmax.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></td>
<td><p>Supports softmax along all dims except dim=0.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.nn.Dropout.html#torch.nn.Dropout" title="torch.nn.Dropout"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.Dropout()</span></code></a></p></td>
<td><p>Behavior is the same as on regular tensors.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.masked_fill.html#torch.Tensor.masked_fill" title="torch.Tensor.masked_fill"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.Tensor.masked_fill()</span></code></a></p></td>
<td><p>Behavior is the same as on regular tensors.</p></td>
</tr>
<tr class="row-even"><td><p><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.relu()</span></code></p></td>
<td><p>Behavior is the same as on regular tensors.</p></td>
</tr>
<tr class="row-odd"><td><p><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.gelu()</span></code></p></td>
<td><p>Behavior is the same as on regular tensors.</p></td>
</tr>
<tr class="row-even"><td><p><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.silu()</span></code></p></td>
<td><p>Behavior is the same as on regular tensors.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.abs.html#torch.abs" title="torch.abs"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.abs()</span></code></a></p></td>
<td><p>Behavior is the same as on regular tensors.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.sgn.html#torch.sgn" title="torch.sgn"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.sgn()</span></code></a></p></td>
<td><p>Behavior is the same as on regular tensors.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.logical_not.html#torch.logical_not" title="torch.logical_not"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.logical_not()</span></code></a></p></td>
<td><p>Behavior is the same as on regular tensors.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.neg.html#torch.neg" title="torch.neg"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.neg()</span></code></a></p></td>
<td><p>Behavior is the same as on regular tensors.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.sub.html#torch.sub" title="torch.sub"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.sub()</span></code></a></p></td>
<td><p>Supports elementwise subtraction of two nested tensors.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.add.html#torch.add" title="torch.add"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.add()</span></code></a></p></td>
<td><p>Supports elementwise addition of two nested tensors. Supports addition of a scalar to a nested tensor.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.mul.html#torch.mul" title="torch.mul"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.mul()</span></code></a></p></td>
<td><p>Supports elementwise multiplication of two nested tensors. Supports multiplication of a nested tensor by a scalar.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.select.html#torch.select" title="torch.select"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.select()</span></code></a></p></td>
<td><p>Supports selecting along all dimensions.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.clone.html#torch.clone" title="torch.clone"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.clone()</span></code></a></p></td>
<td><p>Behavior is the same as on regular tensors.</p></td>
</tr>
<tr class="row-even"><td><p><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.detach()</span></code></p></td>
<td><p>Behavior is the same as on regular tensors.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.unbind.html#torch.unbind" title="torch.unbind"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.unbind()</span></code></a></p></td>
<td><p>Supports unbinding along <code class="docutils literal notranslate"><span class="pre">dim=0</span></code> only.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.reshape.html#torch.reshape" title="torch.reshape"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.reshape()</span></code></a></p></td>
<td><p>Supports reshaping with size of <code class="docutils literal notranslate"><span class="pre">dim=0</span></code> preserved (i.e. number of tensors nested cannot be changed).
Unlike regular tensors, a size of <code class="docutils literal notranslate"><span class="pre">-1</span></code> here means that the existing size is inherited.
In particular, the only valid size for a irregular dimension is <code class="docutils literal notranslate"><span class="pre">-1</span></code>.
Size inference is not implemented yet and hence for new dimensions the size cannot be <code class="docutils literal notranslate"><span class="pre">-1</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.reshape_as.html#torch.Tensor.reshape_as" title="torch.Tensor.reshape_as"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.Tensor.reshape_as()</span></code></a></p></td>
<td><p>Similar constraint as for <code class="docutils literal notranslate"><span class="pre">reshape</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.transpose.html#torch.transpose" title="torch.transpose"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.transpose()</span></code></a></p></td>
<td><p>Supports transposing of all dims except <code class="docutils literal notranslate"><span class="pre">dim=0</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.view.html#torch.Tensor.view" title="torch.Tensor.view"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.Tensor.view()</span></code></a></p></td>
<td><p>Rules for the new shape are similar to that of <code class="docutils literal notranslate"><span class="pre">reshape</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.empty_like.html#torch.empty_like" title="torch.empty_like"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.empty_like()</span></code></a></p></td>
<td><p>Behavior is analogous to that of regular tensors; returns a new empty nested tensor (i.e. with uninitialized values) matching the nested structure of the input.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.randn_like.html#torch.randn_like" title="torch.randn_like"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.randn_like()</span></code></a></p></td>
<td><p>Behavior is analogous to that of regular tensors; returns a new nested tensor with values randomly initialized according to a standard normal distribution matching the nested structure of the input.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.zeros_like.html#torch.zeros_like" title="torch.zeros_like"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.zeros_like()</span></code></a></p></td>
<td><p>Behavior is analogous to that of regular tensors; returns a new nested tensor with all zero values matching the nested structure of the input.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.nn.LayerNorm.html#torch.nn.LayerNorm" title="torch.nn.LayerNorm"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.LayerNorm()</span></code></a></p></td>
<td><p>The <code class="docutils literal notranslate"><span class="pre">normalized_shape</span></code> argument is restricted to not extend into the irregular dimensions of the NestedTensor.</p></td>
</tr>
</tbody>
</table>
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<li><a class="reference internal" href="#introduction">Introduction</a></li>
<li><a class="reference internal" href="#construction">Construction</a></li>
<li><a class="reference internal" href="#size">size</a></li>
<li><a class="reference internal" href="#unbind">unbind</a></li>
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