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<li class="toctree-l1"><a class="reference internal" href="notes/autograd.html">Autograd mechanics</a></li>
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<div class="section" id="torch-utils-dlpack">
<h1>torch.utils.dlpack<a class="headerlink" href="#torch-utils-dlpack" title="Permalink to this heading">¶</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="torch.utils.dlpack.from_dlpack">
<span class="sig-prename descclassname"><span class="pre">torch.utils.dlpack.</span></span><span class="sig-name descname"><span class="pre">from_dlpack</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ext_tensor</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="reference internal" href="_modules/torch/utils/dlpack.html#from_dlpack"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.dlpack.from_dlpack" title="Permalink to this definition">¶</a></dt>
<dd><p>Converts a tensor from an external library into a <code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code>.</p>
<p>The returned PyTorch tensor will share the memory with the input tensor
(which may have come from another library). Note that in-place operations
will therefore also affect the data of the input tensor. This may lead to
unexpected issues (e.g., other libraries may have read-only flags or
immutable data structures), so the user should only do this if they know
for sure that this is fine.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>ext_tensor</strong> (object with <code class="docutils literal notranslate"><span class="pre">__dlpack__</span></code> attribute, or a DLPack capsule) – <p>The tensor or DLPack capsule to convert.</p>
<p>If <code class="docutils literal notranslate"><span class="pre">ext_tensor</span></code> is a tensor (or ndarray) object, it must support
the <code class="docutils literal notranslate"><span class="pre">__dlpack__</span></code> protocol (i.e., have a <code class="docutils literal notranslate"><span class="pre">ext_tensor.__dlpack__</span></code>
method). Otherwise <code class="docutils literal notranslate"><span class="pre">ext_tensor</span></code> may be a DLPack capsule, which is
an opaque <code class="docutils literal notranslate"><span class="pre">PyCapsule</span></code> instance, typically produced by a
<code class="docutils literal notranslate"><span class="pre">to_dlpack</span></code> function or method.</p>
</p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
</dd>
</dl>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">torch.utils.dlpack</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">arange</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
<span class="go"># Convert a tensor directly (supported in PyTorch >= 1.10)</span>
<span class="gp">>>> </span><span class="n">t2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_dlpack</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">t2</span><span class="p">[:</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span> <span class="c1"># show that memory is shared</span>
<span class="gp">>>> </span><span class="n">t2</span>
<span class="go">tensor([-1, -1, 2, 3])</span>
<span class="gp">>>> </span><span class="n">t</span>
<span class="go">tensor([-1, -1, 2, 3])</span>
<span class="go"># The old-style DLPack usage, with an intermediate capsule object</span>
<span class="gp">>>> </span><span class="n">capsule</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">dlpack</span><span class="o">.</span><span class="n">to_dlpack</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">capsule</span>
<span class="go"><capsule object "dltensor" at ...></span>
<span class="gp">>>> </span><span class="n">t3</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_dlpack</span><span class="p">(</span><span class="n">capsule</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">t3</span>
<span class="go">tensor([-1, -1, 2, 3])</span>
<span class="gp">>>> </span><span class="n">t3</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">9</span> <span class="c1"># now we're sharing memory between 3 tensors</span>
<span class="gp">>>> </span><span class="n">t3</span>
<span class="go">tensor([-9, -1, 2, 3])</span>
<span class="gp">>>> </span><span class="n">t2</span>
<span class="go">tensor([-9, -1, 2, 3])</span>
<span class="gp">>>> </span><span class="n">t</span>
<span class="go">tensor([-9, -1, 2, 3])</span>
</pre></div>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.utils.dlpack.to_dlpack">
<span class="sig-prename descclassname"><span class="pre">torch.utils.dlpack.</span></span><span class="sig-name descname"><span class="pre">to_dlpack</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">PyCapsule</span></span></span><a class="headerlink" href="#torch.utils.dlpack.to_dlpack" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns an opaque object (a “DLPack capsule”) representing the tensor.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p><code class="docutils literal notranslate"><span class="pre">to_dlpack</span></code> is a legacy DLPack interface. The capsule it returns
cannot be used for anything in Python other than use it as input to
<code class="docutils literal notranslate"><span class="pre">from_dlpack</span></code>. The more idiomatic use of DLPack is to call
<code class="docutils literal notranslate"><span class="pre">from_dlpack</span></code> directly on the tensor object - this works when that
object has a <code class="docutils literal notranslate"><span class="pre">__dlpack__</span></code> method, which PyTorch and most other
libraries indeed have now.</p>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>Only call <code class="docutils literal notranslate"><span class="pre">from_dlpack</span></code> once per capsule produced with <code class="docutils literal notranslate"><span class="pre">to_dlpack</span></code>.
Behavior when a capsule is consumed multiple times is undefined.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>tensor</strong> – a tensor to be exported</p>
</dd>
</dl>
<p>The DLPack capsule shares the tensor’s memory.</p>
</dd></dl>
</div>
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