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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.func.html">Extending torch.func with autograd.Function</a></li>
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<li class="toctree-l1"><a class="reference internal" href="notes/randomness.html">Reproducibility</a></li>
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<p class="caption" role="heading"><span class="caption-text">torch.compile</span></p>
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<li class="toctree-l1"><a class="reference internal" href="dynamo/index.html">TorchDynamo Overview</a></li>
<li class="toctree-l1"><a class="reference internal" href="dynamo/installation.html">Installing TorchDynamo</a></li>
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<section id="module-torch.func">
<span id="torch-func-api-reference"></span><h1>torch.func API Reference<a class="headerlink" href="#module-torch.func" title="Permalink to this heading">¶</a></h1>
<section id="function-transforms">
<h2>Function Transforms<a class="headerlink" href="#function-transforms" title="Permalink to this heading">¶</a></h2>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><p id="torch.func.vmap"/><a class="reference internal" href="generated/torch.func.vmap.html#torch.func.vmap" title="torch.func.vmap"><code class="xref py py-obj docutils literal notranslate"><span class="pre">vmap</span></code></a></p></td>
<td><p>vmap is the vectorizing map; <code class="docutils literal notranslate"><span class="pre">vmap(func)</span></code> returns a new function that maps <code class="docutils literal notranslate"><span class="pre">func</span></code> over some dimension of the inputs.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.func.grad"/><a class="reference internal" href="generated/torch.func.grad.html#torch.func.grad" title="torch.func.grad"><code class="xref py py-obj docutils literal notranslate"><span class="pre">grad</span></code></a></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">grad</span></code> operator helps computing gradients of <code class="docutils literal notranslate"><span class="pre">func</span></code> with respect to the input(s) specified by <code class="docutils literal notranslate"><span class="pre">argnums</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.func.grad_and_value"/><a class="reference internal" href="generated/torch.func.grad_and_value.html#torch.func.grad_and_value" title="torch.func.grad_and_value"><code class="xref py py-obj docutils literal notranslate"><span class="pre">grad_and_value</span></code></a></p></td>
<td><p>Returns a function to compute a tuple of the gradient and primal, or forward, computation.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.func.vjp"/><a class="reference internal" href="generated/torch.func.vjp.html#torch.func.vjp" title="torch.func.vjp"><code class="xref py py-obj docutils literal notranslate"><span class="pre">vjp</span></code></a></p></td>
<td><p>Standing for the vector-Jacobian product, returns a tuple containing the results of <code class="docutils literal notranslate"><span class="pre">func</span></code> applied to <code class="docutils literal notranslate"><span class="pre">primals</span></code> and a function that, when given <code class="docutils literal notranslate"><span class="pre">cotangents</span></code>, computes the reverse-mode Jacobian of <code class="docutils literal notranslate"><span class="pre">func</span></code> with respect to <code class="docutils literal notranslate"><span class="pre">primals</span></code> times <code class="docutils literal notranslate"><span class="pre">cotangents</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.func.jvp"/><a class="reference internal" href="generated/torch.func.jvp.html#torch.func.jvp" title="torch.func.jvp"><code class="xref py py-obj docutils literal notranslate"><span class="pre">jvp</span></code></a></p></td>
<td><p>Standing for the Jacobian-vector product, returns a tuple containing the output of <cite>func(*primals)</cite> and the "Jacobian of <code class="docutils literal notranslate"><span class="pre">func</span></code> evaluated at <code class="docutils literal notranslate"><span class="pre">primals</span></code>" times <code class="docutils literal notranslate"><span class="pre">tangents</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.func.linearize"/><a class="reference internal" href="generated/torch.func.linearize.html#torch.func.linearize" title="torch.func.linearize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">linearize</span></code></a></p></td>
<td><p>Returns the value of <code class="docutils literal notranslate"><span class="pre">func</span></code> at <code class="docutils literal notranslate"><span class="pre">primals</span></code> and linear approximation at <code class="docutils literal notranslate"><span class="pre">primals</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.func.jacrev"/><a class="reference internal" href="generated/torch.func.jacrev.html#torch.func.jacrev" title="torch.func.jacrev"><code class="xref py py-obj docutils literal notranslate"><span class="pre">jacrev</span></code></a></p></td>
<td><p>Computes the Jacobian of <code class="docutils literal notranslate"><span class="pre">func</span></code> with respect to the arg(s) at index <code class="docutils literal notranslate"><span class="pre">argnum</span></code> using reverse mode autodiff</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.func.jacfwd"/><a class="reference internal" href="generated/torch.func.jacfwd.html#torch.func.jacfwd" title="torch.func.jacfwd"><code class="xref py py-obj docutils literal notranslate"><span class="pre">jacfwd</span></code></a></p></td>
<td><p>Computes the Jacobian of <code class="docutils literal notranslate"><span class="pre">func</span></code> with respect to the arg(s) at index <code class="docutils literal notranslate"><span class="pre">argnum</span></code> using forward-mode autodiff</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.func.hessian"/><a class="reference internal" href="generated/torch.func.hessian.html#torch.func.hessian" title="torch.func.hessian"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hessian</span></code></a></p></td>
<td><p>Computes the Hessian of <code class="docutils literal notranslate"><span class="pre">func</span></code> with respect to the arg(s) at index <code class="docutils literal notranslate"><span class="pre">argnum</span></code> via a forward-over-reverse strategy.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.func.functionalize"/><a class="reference internal" href="generated/torch.func.functionalize.html#torch.func.functionalize" title="torch.func.functionalize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">functionalize</span></code></a></p></td>
<td><p>functionalize is a transform that can be used to remove (intermediate) mutations and aliasing from a function, while preserving the function's semantics.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="utilities-for-working-with-torch-nn-modules">
<h2>Utilities for working with torch.nn.Modules<a class="headerlink" href="#utilities-for-working-with-torch-nn-modules" title="Permalink to this heading">¶</a></h2>
<p>In general, you can transform over a function that calls a <code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code>.
For example, the following is an example of computing a jacobian of a function
that takes three values and returns three values:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></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="k">def</span> <span class="nf">f</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="n">model</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">x</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">3</span><span class="p">)</span>
<span class="n">jacobian</span> <span class="o">=</span> <span class="n">jacrev</span><span class="p">(</span><span class="n">f</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
<span class="k">assert</span> <span class="n">jacobian</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
</pre></div>
</div>
<p>However, if you want to do something like compute a jacobian over the parameters
of the model, then there needs to be a way to construct a function where the
parameters are the inputs to the function.
That’s what <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">functional_call()</span></code></a> is for:
it accepts an nn.Module, the transformed <code class="docutils literal notranslate"><span class="pre">parameters</span></code>, and the inputs to the
Module’s forward pass. It returns the value of running the Module’s forward pass
with the replaced parameters.</p>
<p>Here’s how we would compute the Jacobian over the parameters</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></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="k">def</span> <span class="nf">f</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="n">x</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">model</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
<span class="n">x</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">3</span><span class="p">)</span>
<span class="n">jacobian</span> <span class="o">=</span> <span class="n">jacrev</span><span class="p">(</span><span class="n">f</span><span class="p">)(</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="n">x</span><span class="p">)</span>
</pre></div>
</div>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><p id="torch.func.functional_call"/><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-obj docutils literal notranslate"><span class="pre">functional_call</span></code></a></p></td>
<td><p>Performs a functional call on the module by replacing the module parameters and buffers with the provided ones.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.func.stack_module_state"/><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-obj docutils literal notranslate"><span class="pre">stack_module_state</span></code></a></p></td>
<td><p>Prepares a list of torch.nn.Modules for ensembling with <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>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.func.replace_all_batch_norm_modules_"/><a class="reference internal" href="generated/torch.func.replace_all_batch_norm_modules_.html#torch.func.replace_all_batch_norm_modules_" title="torch.func.replace_all_batch_norm_modules_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">replace_all_batch_norm_modules_</span></code></a></p></td>
<td><p>In place updates <code class="xref py py-attr docutils literal notranslate"><span class="pre">root</span></code> by setting the <code class="docutils literal notranslate"><span class="pre">running_mean</span></code> and <code class="docutils literal notranslate"><span class="pre">running_var</span></code> to be None and setting track_running_stats to be False for any nn.BatchNorm module in <code class="xref py py-attr docutils literal notranslate"><span class="pre">root</span></code></p></td>
</tr>
</tbody>
</table>
<p>If you’re looking for information on fixing Batch Norm modules, please follow the
guidance here</p>
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