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<div class="section" id="tensor-views">
<span id="tensor-view-doc"></span><h1>Tensor Views<a class="headerlink" href="#tensor-views" title="Permalink to this headline">¶</a></h1>
<p>PyTorch allows a tensor to be a <code class="docutils literal notranslate"><span class="pre">View</span></code> of an existing tensor. View tensor shares the same underlying data
with its base tensor. Supporting <code class="docutils literal notranslate"><span class="pre">View</span></code> avoids explicit data copy, thus allows us to do fast and memory efficient
reshaping, slicing and element-wise operations.</p>
<p>For example, to get a view of an existing tensor <code class="docutils literal notranslate"><span class="pre">t</span></code>, you can call <code class="docutils literal notranslate"><span class="pre">t.view(...)</span></code>.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">t</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="n">t</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">8</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">t</span><span class="o">.</span><span class="n">storage</span><span class="p">()</span><span class="o">.</span><span class="n">data_ptr</span><span class="p">()</span> <span class="o">==</span> <span class="n">b</span><span class="o">.</span><span class="n">storage</span><span class="p">()</span><span class="o">.</span><span class="n">data_ptr</span><span class="p">()</span> <span class="c1"># `t` and `b` share the same underlying data.</span>
<span class="go">True</span>
<span class="go"># Modifying view tensor changes base tensor as well.</span>
<span class="gp">>>> </span><span class="n">b</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mf">3.14</span>
<span class="gp">>>> </span><span class="n">t</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
<span class="go">tensor(3.14)</span>
</pre></div>
</div>
<p>Since views share underlying data with its base tensor, if you edit the data
in the view, it will be reflected in the base tensor as well.</p>
<p>Typically a PyTorch op returns a new tensor as output, e.g. <a class="reference internal" href="tensors.html#torch.Tensor.add" title="torch.Tensor.add"><code class="xref py py-meth docutils literal notranslate"><span class="pre">add()</span></code></a>.
But in case of view ops, outputs are views of input tensors to avoid unncessary data copy.
No data movement occurs when creating a view, view tensor just changes the way
it interprets the same data. Taking a view of contiguous tensor could potentially produce a non-contiguous tensor.
Users should be pay additional attention as contiguity might have implicit performance impact.
<a class="reference internal" href="tensors.html#torch.Tensor.transpose" title="torch.Tensor.transpose"><code class="xref py py-meth docutils literal notranslate"><span class="pre">transpose()</span></code></a> is a common example.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">base</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">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]])</span>
<span class="gp">>>> </span><span class="n">base</span><span class="o">.</span><span class="n">is_contiguous</span><span class="p">()</span>
<span class="go">True</span>
<span class="gp">>>> </span><span class="n">t</span> <span class="o">=</span> <span class="n">base</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="c1"># `t` is a view of `base`. No data movement happened here.</span>
<span class="go"># View tensors might be non-contiguous.</span>
<span class="gp">>>> </span><span class="n">t</span><span class="o">.</span><span class="n">is_contiguous</span><span class="p">()</span>
<span class="go">False</span>
<span class="go"># To get a contiguous tensor, call `.contiguous()` to enforce</span>
<span class="go"># copying data when `t` is not contiguous.</span>
<span class="gp">>>> </span><span class="n">c</span> <span class="o">=</span> <span class="n">t</span><span class="o">.</span><span class="n">contiguous</span><span class="p">()</span>
</pre></div>
</div>
<p>For reference, here’s a full list of view ops in PyTorch:</p>
<ul class="simple">
<li><p>Basic slicing and indexing op, e.g. <code class="docutils literal notranslate"><span class="pre">tensor[0,</span> <span class="pre">2:,</span> <span class="pre">1:7:2]</span></code> returns a view of base <code class="docutils literal notranslate"><span class="pre">tensor</span></code>, see note below.</p></li>
<li><p><a class="reference internal" href="tensors.html#torch.Tensor.as_strided" title="torch.Tensor.as_strided"><code class="xref py py-meth docutils literal notranslate"><span class="pre">as_strided()</span></code></a></p></li>
<li><p><a class="reference internal" href="autograd.html#torch.Tensor.detach" title="torch.Tensor.detach"><code class="xref py py-meth docutils literal notranslate"><span class="pre">detach()</span></code></a></p></li>
<li><p><a class="reference internal" href="tensors.html#torch.Tensor.diagonal" title="torch.Tensor.diagonal"><code class="xref py py-meth docutils literal notranslate"><span class="pre">diagonal()</span></code></a></p></li>
<li><p><a class="reference internal" href="tensors.html#torch.Tensor.expand" title="torch.Tensor.expand"><code class="xref py py-meth docutils literal notranslate"><span class="pre">expand()</span></code></a></p></li>
<li><p><a class="reference internal" href="tensors.html#torch.Tensor.expand_as" title="torch.Tensor.expand_as"><code class="xref py py-meth docutils literal notranslate"><span class="pre">expand_as()</span></code></a></p></li>
<li><p><a class="reference internal" href="tensors.html#torch.Tensor.narrow" title="torch.Tensor.narrow"><code class="xref py py-meth docutils literal notranslate"><span class="pre">narrow()</span></code></a></p></li>
<li><p><a class="reference internal" href="tensors.html#torch.Tensor.permute" title="torch.Tensor.permute"><code class="xref py py-meth docutils literal notranslate"><span class="pre">permute()</span></code></a></p></li>
<li><p><a class="reference internal" href="tensors.html#torch.Tensor.select" title="torch.Tensor.select"><code class="xref py py-meth docutils literal notranslate"><span class="pre">select()</span></code></a></p></li>
<li><p><a class="reference internal" href="tensors.html#torch.Tensor.squeeze" title="torch.Tensor.squeeze"><code class="xref py py-meth docutils literal notranslate"><span class="pre">squeeze()</span></code></a></p></li>
<li><p><a class="reference internal" href="tensors.html#torch.Tensor.transpose" title="torch.Tensor.transpose"><code class="xref py py-meth docutils literal notranslate"><span class="pre">transpose()</span></code></a></p></li>
<li><p><a class="reference internal" href="tensors.html#torch.Tensor.t" title="torch.Tensor.t"><code class="xref py py-meth docutils literal notranslate"><span class="pre">t()</span></code></a></p></li>
<li><p><a class="reference internal" href="tensors.html#torch.Tensor.T" title="torch.Tensor.T"><code class="xref py py-attr docutils literal notranslate"><span class="pre">T</span></code></a></p></li>
<li><p><a class="reference internal" href="tensors.html#torch.Tensor.unfold" title="torch.Tensor.unfold"><code class="xref py py-meth docutils literal notranslate"><span class="pre">unfold()</span></code></a></p></li>
<li><p><a class="reference internal" href="tensors.html#torch.Tensor.unsqueeze" title="torch.Tensor.unsqueeze"><code class="xref py py-meth docutils literal notranslate"><span class="pre">unsqueeze()</span></code></a></p></li>
<li><p><a class="reference internal" href="tensors.html#torch.Tensor.view" title="torch.Tensor.view"><code class="xref py py-meth docutils literal notranslate"><span class="pre">view()</span></code></a></p></li>
<li><p><a class="reference internal" href="tensors.html#torch.Tensor.view_as" title="torch.Tensor.view_as"><code class="xref py py-meth docutils literal notranslate"><span class="pre">view_as()</span></code></a></p></li>
<li><p><a class="reference internal" href="tensors.html#torch.Tensor.unbind" title="torch.Tensor.unbind"><code class="xref py py-meth docutils literal notranslate"><span class="pre">unbind()</span></code></a></p></li>
<li><p><a class="reference internal" href="tensors.html#torch.Tensor.split" title="torch.Tensor.split"><code class="xref py py-meth docutils literal notranslate"><span class="pre">split()</span></code></a></p></li>
<li><p><a class="reference internal" href="tensors.html#torch.Tensor.chunk" title="torch.Tensor.chunk"><code class="xref py py-meth docutils literal notranslate"><span class="pre">chunk()</span></code></a></p></li>
<li><p><a class="reference internal" href="tensors.html#torch.Tensor.indices" title="torch.Tensor.indices"><code class="xref py py-meth docutils literal notranslate"><span class="pre">indices()</span></code></a> (sparse tensor only)</p></li>
<li><p><a class="reference internal" href="tensors.html#torch.Tensor.values" title="torch.Tensor.values"><code class="xref py py-meth docutils literal notranslate"><span class="pre">values()</span></code></a> (sparse tensor only)</p></li>
</ul>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>When accessing the contents of a tensor via indexing, PyTorch follows Numpy behaviors
that basic indexing returns views, while advanced indexing returns a copy.
Assignment via either basic or advanced indexing is in-place. See more examples in
<a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html">Numpy indexing documentation</a>.</p>
</div>
<p>It’s also worth mentioning a few ops with special behaviors:</p>
<ul class="simple">
<li><p><a class="reference internal" href="tensors.html#torch.Tensor.reshape" title="torch.Tensor.reshape"><code class="xref py py-meth docutils literal notranslate"><span class="pre">reshape()</span></code></a> and <a class="reference internal" href="tensors.html#torch.Tensor.reshape_as" title="torch.Tensor.reshape_as"><code class="xref py py-meth docutils literal notranslate"><span class="pre">reshape_as()</span></code></a> can return either a view or new tensor, user code shouldn’t rely on whether it’s view or not.</p></li>
<li><p><a class="reference internal" href="tensors.html#torch.Tensor.contiguous" title="torch.Tensor.contiguous"><code class="xref py py-meth docutils literal notranslate"><span class="pre">contiguous()</span></code></a> returns <strong>itself</strong> if input tensor is already contiguous, otherwise it returns a new contiguous tensor by copying data.</p></li>
</ul>
<p>For a more detailed walk-through of PyTorch internal implementation,
please refer to <a class="reference external" href="http://blog.ezyang.com/2019/05/pytorch-internals/">ezyang’s blogpost about PyTorch Internals</a>.</p>
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