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  <div class="section" id="meta-device">
<h1>Meta device<a class="headerlink" href="#meta-device" title="Permalink to this heading">¶</a></h1>
<p>The “meta” device is an abstract device which denotes a tensor which records
only metadata, but no actual data.  Meta tensors have two primary use cases:</p>
<ul class="simple">
<li><p>Models can be loaded on the meta device, allowing you to load a
representation of the model without actually loading the actual parameters
into memory.  This can be helpful if you need to make transformations on
the model before you load the actual data.</p></li>
<li><p>Most operations can be performed on meta tensors, producing new meta
tensors that describe what the result would have been if you performed
the operation on a real tensor.  You can use this to perform abstract
analysis without needing to spend time on compute or space to represent
the actual tensors.  Because meta tensors do not have real data, you cannot
perform data-dependent operations like <a class="reference internal" href="generated/torch.nonzero.html#torch.nonzero" title="torch.nonzero"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nonzero()</span></code></a> or
<a class="reference internal" href="generated/torch.Tensor.item.html#torch.Tensor.item" title="torch.Tensor.item"><code class="xref py py-meth docutils literal notranslate"><span class="pre">item()</span></code></a>.  In some cases, not all device types (e.g., CPU
and CUDA) have exactly the same output metadata for an operation; we
typically prefer representing the CUDA behavior faithfully in this
situation.</p></li>
</ul>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>Although in principle meta tensor computation should always be faster than
an equivalent CPU/CUDA computation, many meta tensor implementations are
implemented in Python and have not been ported to C++ for speed, so you
may find that you get lower absolute framework latency with small CPU tensors.</p>
</div>
<div class="section" id="idioms-for-working-with-meta-tensors">
<h2>Idioms for working with meta tensors<a class="headerlink" href="#idioms-for-working-with-meta-tensors" title="Permalink to this heading">¶</a></h2>
<p>An object can be loaded with <a class="reference internal" href="generated/torch.load.html#torch.load" title="torch.load"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.load()</span></code></a> onto meta device by specifying
<code class="docutils literal notranslate"><span class="pre">map_location='meta'</span></code>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">torch</span><span class="o">.</span><span class="n">save</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="s1">&#39;foo.pt&#39;</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">&#39;foo.pt&#39;</span><span class="p">,</span> <span class="n">map_location</span><span class="o">=</span><span class="s1">&#39;meta&#39;</span><span class="p">)</span>
<span class="go">tensor(..., device=&#39;meta&#39;, size=(2,))</span>
</pre></div>
</div>
<p>If you have some arbitrary code which performs some tensor construction without
explicitly specifying a device, you can override it to instead construct on meta device by using
the <a class="reference internal" href="tensor_attributes.html#torch.device" title="torch.device"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.device()</span></code></a> context manager:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">&#39;meta&#39;</span><span class="p">):</span>
<span class="gp">... </span>    <span class="nb">print</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">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">))</span>
<span class="gp">...</span>
<span class="go">tensor(..., device=&#39;meta&#39;, size=(30, 30))</span>
</pre></div>
</div>
<p>This is especially helpful NN module construction, where you often are not
able to explicitly pass in a device for initialization:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">torch.nn.modules</span> <span class="kn">import</span> <span class="n">Linear</span>
<span class="gp">&gt;&gt;&gt; </span><span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">&#39;meta&#39;</span><span class="p">):</span>
<span class="gp">... </span>    <span class="nb">print</span><span class="p">(</span><span class="n">Linear</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">30</span><span class="p">))</span>
<span class="gp">...</span>
<span class="go">Linear(in_features=20, out_features=30, bias=True)</span>
</pre></div>
</div>
<p>You cannot convert a meta tensor directly to a CPU/CUDA tensor, because the
meta tensor stores no data and we do not know what the correct data values for
your new tensor are:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;meta&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cpu&quot;</span><span class="p">)</span>
<span class="gt">Traceback (most recent call last):</span>
  File <span class="nb">&quot;&lt;stdin&gt;&quot;</span>, line <span class="m">1</span>, in <span class="n">&lt;module&gt;</span>
<span class="gr">NotImplementedError</span>: <span class="n">Cannot copy out of meta tensor; no data!</span>
</pre></div>
</div>
<p>Use a factory function like <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> to explicitly specify how
you would like the missing data to be filled in.</p>
<p>NN modules have a convenience method <a class="reference internal" href="generated/torch.nn.Module.html#torch.nn.Module.to_empty" title="torch.nn.Module.to_empty"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.nn.Module.to_empty()</span></code></a> that
allow you to the module to another device, leaving all parameters
uninitialized.  You are expected to explicitly reinitialize the parameters
manually:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">torch.nn.modules</span> <span class="kn">import</span> <span class="n">Linear</span>
<span class="gp">&gt;&gt;&gt; </span><span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">&#39;meta&#39;</span><span class="p">):</span>
<span class="gp">... </span>    <span class="n">m</span> <span class="o">=</span> <span class="n">Linear</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">30</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">m</span><span class="o">.</span><span class="n">to_empty</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="s2">&quot;cpu&quot;</span><span class="p">)</span>
<span class="go">Linear(in_features=20, out_features=30, bias=True)</span>
</pre></div>
</div>
<p><code class="xref py py-mod docutils literal notranslate"><span class="pre">torch._subclasses.meta_utils</span></code> contains undocumented utilities for taking
an arbitrary Tensor and constructing an equivalent meta Tensor with high
fidelity.  These APIs are experimental and may be changed in a BC breaking way
at any time.</p>
</div>
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