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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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<li class="toctree-l1"><a class="reference internal" href="notes/extending.html">Extending PyTorch</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/extending.func.html">Extending torch.func with autograd.Function</a></li>
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<section id="migrating-from-functorch-to-torch-func">
<h1>Migrating from functorch to torch.func<a class="headerlink" href="#migrating-from-functorch-to-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>
<p>functorch started as an out-of-tree library over at
the <a class="reference external" href="https://github.com/pytorch/functorch">pytorch/functorch</a> repository.
Our goal has always been to upstream functorch directly into PyTorch and provide
it as a core PyTorch library.</p>
<p>As the final step of the upstream, we’ve decided to migrate from being a top level package
(<code class="docutils literal notranslate"><span class="pre">functorch</span></code>) to being a part of PyTorch to reflect how the function transforms are
integrated directly into PyTorch core. As of PyTorch 2.0, we are deprecating
<code class="docutils literal notranslate"><span class="pre">import</span> <span class="pre">functorch</span></code> and ask that users migrate to the newest APIs, which we
will maintain going forward. <code class="docutils literal notranslate"><span class="pre">import</span> <span class="pre">functorch</span></code> will be kept around to maintain
backwards compatibility for a couple of releases.</p>
<section id="function-transforms">
<h2>function transforms<a class="headerlink" href="#function-transforms" title="Permalink to this heading">¶</a></h2>
<p>The following APIs are a drop-in replacement for the following
<a class="reference external" href="https://pytorch.org/functorch/1.13/functorch.html">functorch APIs</a>.
They are fully backwards compatible.</p>
<table class="docutils align-default">
<thead>
<tr class="row-odd"><th class="head"><p>functorch API</p></th>
<th class="head"><p>PyTorch API (as of PyTorch 2.0)</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p>functorch.vmap</p></td>
<td><p><a class="reference internal" href="generated/torch.vmap.html#torch.vmap" title="torch.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.vmap()</span></code></a> or <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">torch.func.vmap()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p>functorch.grad</p></td>
<td><p><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">torch.func.grad()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p>functorch.vjp</p></td>
<td><p><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">torch.func.vjp()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p>functorch.jvp</p></td>
<td><p><a class="reference internal" href="generated/torch.func.jvp.html#torch.func.jvp" title="torch.func.jvp"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.func.jvp()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p>functorch.jacrev</p></td>
<td><p><a class="reference internal" href="generated/torch.func.jacrev.html#torch.func.jacrev" title="torch.func.jacrev"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.func.jacrev()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p>functorch.jacfwd</p></td>
<td><p><a class="reference internal" href="generated/torch.func.jacfwd.html#torch.func.jacfwd" title="torch.func.jacfwd"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.func.jacfwd()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p>functorch.hessian</p></td>
<td><p><a class="reference internal" href="generated/torch.func.hessian.html#torch.func.hessian" title="torch.func.hessian"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.func.hessian()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p>functorch.functionalize</p></td>
<td><p><a class="reference internal" href="generated/torch.func.functionalize.html#torch.func.functionalize" title="torch.func.functionalize"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.func.functionalize()</span></code></a></p></td>
</tr>
</tbody>
</table>
<p>Furthermore, if you are using torch.autograd.functional APIs, please try out
the <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> equivalents instead. <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> function
transforms are more composable and more performant in many cases.</p>
<table class="docutils align-default">
<thead>
<tr class="row-odd"><th class="head"><p>torch.autograd.functional API</p></th>
<th class="head"><p>torch.func API (as of PyTorch 2.0)</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.autograd.functional.vjp.html#torch.autograd.functional.vjp" title="torch.autograd.functional.vjp"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.autograd.functional.vjp()</span></code></a></p></td>
<td><p><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">torch.func.grad()</span></code></a> or <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">torch.func.vjp()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.autograd.functional.jvp.html#torch.autograd.functional.jvp" title="torch.autograd.functional.jvp"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.autograd.functional.jvp()</span></code></a></p></td>
<td><p><a class="reference internal" href="generated/torch.func.jvp.html#torch.func.jvp" title="torch.func.jvp"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.func.jvp()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.autograd.functional.jacobian.html#torch.autograd.functional.jacobian" title="torch.autograd.functional.jacobian"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.autograd.functional.jacobian()</span></code></a></p></td>
<td><p><a class="reference internal" href="generated/torch.func.jacrev.html#torch.func.jacrev" title="torch.func.jacrev"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.func.jacrev()</span></code></a> or <a class="reference internal" href="generated/torch.func.jacfwd.html#torch.func.jacfwd" title="torch.func.jacfwd"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.func.jacfwd()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.autograd.functional.hessian.html#torch.autograd.functional.hessian" title="torch.autograd.functional.hessian"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.autograd.functional.hessian()</span></code></a></p></td>
<td><p><a class="reference internal" href="generated/torch.func.hessian.html#torch.func.hessian" title="torch.func.hessian"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.func.hessian()</span></code></a></p></td>
</tr>
</tbody>
</table>
</section>
<section id="nn-module-utilities">
<h2>NN module utilities<a class="headerlink" href="#nn-module-utilities" title="Permalink to this heading">¶</a></h2>
<p>We’ve changed the APIs to apply function transforms over NN modules to make them
fit better into the PyTorch design philosophy. The new API is different, so
please read this section carefully.</p>
<section id="functorch-make-functional">
<h3>functorch.make_functional<a class="headerlink" href="#functorch-make-functional" title="Permalink to this heading">¶</a></h3>
<p><a class="reference internal" href="generated/torch.func.functional_call.html#torch.func.functional_call" title="torch.func.functional_call"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.func.functional_call()</span></code></a> is the replacement for
<a class="reference external" href="https://pytorch.org/functorch/1.13/generated/functorch.make_functional.html#functorch.make_functional">functorch.make_functional</a>
and
<a class="reference external" href="https://pytorch.org/functorch/1.13/generated/functorch.make_functional_with_buffers.html#functorch.make_functional_with_buffers">functorch.make_functional_with_buffers</a>.
However, it is not a drop-in replacement.</p>
<p>If you’re in a hurry, you can use
<a class="reference external" href="https://gist.github.com/zou3519/7769506acc899d83ef1464e28f22e6cf">helper functions in this gist</a>
that emulate the behavior of functorch.make_functional and functorch.make_functional_with_buffers.
We recommend using <a class="reference internal" href="generated/torch.func.functional_call.html#torch.func.functional_call" title="torch.func.functional_call"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.func.functional_call()</span></code></a> directly because it is a more explicit
and flexible API.</p>
<p>Concretely, functorch.make_functional returns a functional module and parameters.
The functional module accepts parameters and inputs to the model as arguments.
<a class="reference internal" href="generated/torch.func.functional_call.html#torch.func.functional_call" title="torch.func.functional_call"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.func.functional_call()</span></code></a> allows one to call the forward pass of an existing
module using new parameters and buffers and inputs.</p>
<p>Here’s an example of how to compute gradients of parameters of a model using functorch
vs <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>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># ---------------</span>
<span class="c1"># using functorch</span>
<span class="c1"># ---------------</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">functorch</span>
<span class="n">inputs</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">64</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="n">targets</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">64</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="n">fmodel</span><span class="p">,</span> <span class="n">params</span> <span class="o">=</span> <span class="n">functorch</span><span class="o">.</span><span class="n">make_functional</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">compute_loss</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">targets</span><span class="p">):</span>
<span class="n">prediction</span> <span class="o">=</span> <span class="n">fmodel</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="n">inputs</span><span class="p">)</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">mse_loss</span><span class="p">(</span><span class="n">prediction</span><span class="p">,</span> <span class="n">targets</span><span class="p">)</span>
<span class="n">grads</span> <span class="o">=</span> <span class="n">functorch</span><span class="o">.</span><span class="n">grad</span><span class="p">(</span><span class="n">compute_loss</span><span class="p">)(</span><span class="n">params</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">targets</span><span class="p">)</span>
<span class="c1"># ------------------------------------</span>
<span class="c1"># using torch.func (as of PyTorch 2.0)</span>
<span class="c1"># ------------------------------------</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="n">inputs</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">64</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="n">targets</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">64</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="n">params</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">named_parameters</span><span class="p">())</span>
<span class="k">def</span> <span class="nf">compute_loss</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">targets</span><span class="p">):</span>
<span class="n">prediction</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">func</span><span class="o">.</span><span class="n">functional_call</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="p">(</span><span class="n">inputs</span><span class="p">,))</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">mse_loss</span><span class="p">(</span><span class="n">prediction</span><span class="p">,</span> <span class="n">targets</span><span class="p">)</span>
<span class="n">grads</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">func</span><span class="o">.</span><span class="n">grad</span><span class="p">(</span><span class="n">compute_loss</span><span class="p">)(</span><span class="n">params</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">targets</span><span class="p">)</span>
</pre></div>
</div>
<p>And here’s an example of how to compute jacobians of model parameters:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># ---------------</span>
<span class="c1"># using functorch</span>
<span class="c1"># ---------------</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">functorch</span>
<span class="n">inputs</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">64</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="n">fmodel</span><span class="p">,</span> <span class="n">params</span> <span class="o">=</span> <span class="n">functorch</span><span class="o">.</span><span class="n">make_functional</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
<span class="n">jacobians</span> <span class="o">=</span> <span class="n">functorch</span><span class="o">.</span><span class="n">jacrev</span><span class="p">(</span><span class="n">fmodel</span><span class="p">)(</span><span class="n">params</span><span class="p">,</span> <span class="n">inputs</span><span class="p">)</span>
<span class="c1"># ------------------------------------</span>
<span class="c1"># using torch.func (as of PyTorch 2.0)</span>
<span class="c1"># ------------------------------------</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">torch.func</span> <span class="kn">import</span> <span class="n">jacrev</span><span class="p">,</span> <span class="n">functional_call</span>
<span class="n">inputs</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">64</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="n">params</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">named_parameters</span><span class="p">())</span>
<span class="c1"># jacrev computes jacobians of argnums=0 by default.</span>
<span class="c1"># We set it to 1 to compute jacobians of params</span>
<span class="n">jacobians</span> <span class="o">=</span> <span class="n">jacrev</span><span class="p">(</span><span class="n">functional_call</span><span class="p">,</span> <span class="n">argnums</span><span class="o">=</span><span class="mi">1</span><span class="p">)(</span><span class="n">model</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="p">(</span><span class="n">inputs</span><span class="p">,))</span>
</pre></div>
</div>
<p>Note that it is important for memory consumption that you should only carry
around a single copy of your parameters. <code class="docutils literal notranslate"><span class="pre">model.named_parameters()</span></code> does not copy
the parameters. If in your model training you update the parameters of the model
in-place, then the <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> that is your model has the single copy of the
parameters and everything is OK.</p>
<p>However, if you want to carry your parameters around in a dictionary and update
them out-of-place, then there are two copies of parameters: the one in the
dictionary and the one in the <code class="docutils literal notranslate"><span class="pre">model</span></code>. In this case, you should change
<code class="docutils literal notranslate"><span class="pre">model</span></code> to not hold memory by converting it to the meta device via
<code class="docutils literal notranslate"><span class="pre">model.to('meta')</span></code>.</p>
</section>
<section id="functorch-combine-state-for-ensemble">
<h3>functorch.combine_state_for_ensemble<a class="headerlink" href="#functorch-combine-state-for-ensemble" title="Permalink to this heading">¶</a></h3>
<p>Please use <a class="reference internal" href="generated/torch.func.stack_module_state.html#torch.func.stack_module_state" title="torch.func.stack_module_state"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.func.stack_module_state()</span></code></a> instead of
<a class="reference external" href="https://pytorch.org/functorch/1.13/generated/functorch.combine_state_for_ensemble.html">functorch.combine_state_for_ensemble</a>
<a class="reference internal" href="generated/torch.func.stack_module_state.html#torch.func.stack_module_state" title="torch.func.stack_module_state"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.func.stack_module_state()</span></code></a> returns two dictionaries, one of stacked parameters, and
one of stacked buffers, that can then be used with <a class="reference internal" href="generated/torch.vmap.html#torch.vmap" title="torch.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.vmap()</span></code></a> and <a class="reference internal" href="generated/torch.func.functional_call.html#torch.func.functional_call" title="torch.func.functional_call"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.func.functional_call()</span></code></a>
for ensembling.</p>
<p>For example, here is an example of how to ensemble over a very simple model:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="n">num_models</span> <span class="o">=</span> <span class="mi">5</span>
<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">64</span>
<span class="n">in_features</span><span class="p">,</span> <span class="n">out_features</span> <span class="o">=</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span>
<span class="n">models</span> <span class="o">=</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">in_features</span><span class="p">,</span> <span class="n">out_features</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_models</span><span class="p">)]</span>
<span class="n">data</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="n">batch_size</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="c1"># ---------------</span>
<span class="c1"># using functorch</span>
<span class="c1"># ---------------</span>
<span class="kn">import</span> <span class="nn">functorch</span>
<span class="n">fmodel</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">buffers</span> <span class="o">=</span> <span class="n">functorch</span><span class="o">.</span><span class="n">combine_state_for_ensemble</span><span class="p">(</span><span class="n">models</span><span class="p">)</span>
<span class="n">output</span> <span class="o">=</span> <span class="n">functorch</span><span class="o">.</span><span class="n">vmap</span><span class="p">(</span><span class="n">fmodel</span><span class="p">,</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="kc">None</span><span class="p">))(</span><span class="n">params</span><span class="p">,</span> <span class="n">buffers</span><span class="p">,</span> <span class="n">data</span><span class="p">)</span>
<span class="k">assert</span> <span class="n">output</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="p">(</span><span class="n">num_models</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">out_features</span><span class="p">)</span>
<span class="c1"># ------------------------------------</span>
<span class="c1"># using torch.func (as of PyTorch 2.0)</span>
<span class="c1"># ------------------------------------</span>
<span class="kn">import</span> <span class="nn">copy</span>
<span class="c1"># Construct a version of the model with no memory by putting the Tensors on</span>
<span class="c1"># the meta device.</span>
<span class="n">base_model</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">models</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">base_model</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s1">'meta'</span><span class="p">)</span>
<span class="n">params</span><span class="p">,</span> <span class="n">buffers</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">func</span><span class="o">.</span><span class="n">stack_module_state</span><span class="p">(</span><span class="n">models</span><span class="p">)</span>
<span class="c1"># It is possible to vmap directly over torch.func.functional_call,</span>
<span class="c1"># but wrapping it in a function makes it clearer what is going on.</span>
<span class="k">def</span> <span class="nf">call_single_model</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="n">buffers</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">func</span><span class="o">.</span><span class="n">functional_call</span><span class="p">(</span><span class="n">base_model</span><span class="p">,</span> <span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="n">buffers</span><span class="p">),</span> <span class="p">(</span><span class="n">data</span><span class="p">,))</span>
<span class="n">output</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">vmap</span><span class="p">(</span><span class="n">call_single_model</span><span class="p">,</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="kc">None</span><span class="p">))(</span><span class="n">params</span><span class="p">,</span> <span class="n">buffers</span><span class="p">,</span> <span class="n">data</span><span class="p">)</span>
<span class="k">assert</span> <span class="n">output</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="p">(</span><span class="n">num_models</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">out_features</span><span class="p">)</span>
</pre></div>
</div>
</section>
</section>
<section id="functorch-compile">
<h2>functorch.compile<a class="headerlink" href="#functorch-compile" title="Permalink to this heading">¶</a></h2>
<p>We are no longer supporting functorch.compile (also known as AOTAutograd)
as a frontend for compilation in PyTorch; we have integrated AOTAutograd
into PyTorch’s compilation story. If you are a user, please use
<a class="reference internal" href="generated/torch.compile.html#torch.compile" title="torch.compile"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code></a> instead.</p>
</section>
</section>
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