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<section id="torch-func">
<h1>torch.func<a class="headerlink" href="#torch-func" title="Permalink to this heading">¶</a></h1>
<p>torch.func, previously known as “functorch”, is
<a class="reference external" href="https://github.com/google/jax">JAX-like</a> composable function transforms for PyTorch.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This library is currently in <a class="reference external" href="https://pytorch.org/blog/pytorch-feature-classification-changes/#beta">beta</a>.
What this means is that the features generally work (unless otherwise documented)
and we (the PyTorch team) are committed to bringing this library forward. However, the APIs
may change under user feedback and we don’t have full coverage over PyTorch operations.</p>
<p>If you have suggestions on the API or use-cases you’d like to be covered, please
open an GitHub issue or reach out. We’d love to hear about how you’re using the library.</p>
</div>
<section id="what-are-composable-function-transforms">
<h2>What are composable function transforms?<a class="headerlink" href="#what-are-composable-function-transforms" title="Permalink to this heading">¶</a></h2>
<ul class="simple">
<li><p>A “function transform” is a higher-order function that accepts a numerical function
and returns a new function that computes a different quantity.</p></li>
<li><p><a class="reference internal" href="func.api.html#module-torch.func" title="torch.func"><code class="xref py py-mod docutils literal notranslate"><span class="pre">torch.func</span></code></a> has auto-differentiation transforms (<code class="docutils literal notranslate"><span class="pre">grad(f)</span></code> returns a function that
computes the gradient of <code class="docutils literal notranslate"><span class="pre">f</span></code>), a vectorization/batching transform (<code class="docutils literal notranslate"><span class="pre">vmap(f)</span></code>
returns a function that computes <code class="docutils literal notranslate"><span class="pre">f</span></code> over batches of inputs), and others.</p></li>
<li><p>These function transforms can compose with each other arbitrarily. For example,
composing <code class="docutils literal notranslate"><span class="pre">vmap(grad(f))</span></code> computes a quantity called per-sample-gradients that
stock PyTorch cannot efficiently compute today.</p></li>
</ul>
</section>
<section id="why-composable-function-transforms">
<h2>Why composable function transforms?<a class="headerlink" href="#why-composable-function-transforms" title="Permalink to this heading">¶</a></h2>
<p>There are a number of use cases that are tricky to do in PyTorch today:</p>
<ul class="simple">
<li><p>computing per-sample-gradients (or other per-sample quantities)</p></li>
<li><p>running ensembles of models on a single machine</p></li>
<li><p>efficiently batching together tasks in the inner-loop of MAML</p></li>
<li><p>efficiently computing Jacobians and Hessians</p></li>
<li><p>efficiently computing batched Jacobians and Hessians</p></li>
</ul>
<p>Composing <a class="reference internal" href="generated/torch.func.vmap.html#torch.func.vmap" title="torch.func.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">vmap()</span></code></a>, <a class="reference internal" href="generated/torch.func.grad.html#torch.func.grad" title="torch.func.grad"><code class="xref py py-func docutils literal notranslate"><span class="pre">grad()</span></code></a>, and <a class="reference internal" href="generated/torch.func.vjp.html#torch.func.vjp" title="torch.func.vjp"><code class="xref py py-func docutils literal notranslate"><span class="pre">vjp()</span></code></a> transforms allows us to express the above without designing a separate subsystem for each.
This idea of composable function transforms comes from the <a class="reference external" href="https://github.com/google/jax">JAX framework</a>.</p>
</section>
<section id="read-more">
<h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this heading">¶</a></h2>
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