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<section id="patching-batch-norm">
<h1>Patching Batch Norm<a class="headerlink" href="#patching-batch-norm" title="Permalink to this heading">¶</a></h1>
<section id="what-s-happening">
<h2>What’s happening?<a class="headerlink" href="#what-s-happening" title="Permalink to this heading">¶</a></h2>
<p>Batch Norm requires in-place updates to running_mean and running_var of the same size as the input.
Functorch does not support inplace update to a regular tensor that takes in a batched tensor (i.e.
<code class="docutils literal notranslate"><span class="pre">regular.add_(batched)</span></code> is not allowed). So when vmapping over a batch of inputs to a single module,
we end up with this error</p>
</section>
<section id="how-to-fix">
<h2>How to fix<a class="headerlink" href="#how-to-fix" title="Permalink to this heading">¶</a></h2>
<p>One of the best supported ways is to switch BatchNorm for GroupNorm. Options 1 and 2 support this</p>
<p>All of these options assume that you don’t need running stats. If you’re using a module this means
that it’s assumed you won’t use batch norm in evaluation mode. If you have a use case that involves
running batch norm with vmap in evaluation mode, please file an issue</p>
<section id="option-1-change-the-batchnorm">
<h3>Option 1: Change the BatchNorm<a class="headerlink" href="#option-1-change-the-batchnorm" title="Permalink to this heading">¶</a></h3>
<p>If you want to change for GroupNorm, anywhere that you have BatchNorm, replace it with:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">C</span><span class="p">,</span> <span class="n">G</span><span class="p">,</span> <span class="n">track_running_stats</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
</pre></div>
</div>
<p>Here <code class="docutils literal notranslate"><span class="pre">C</span></code> is the same <code class="docutils literal notranslate"><span class="pre">C</span></code> as in the original BatchNorm. <code class="docutils literal notranslate"><span class="pre">G</span></code> is the number of groups to
break <code class="docutils literal notranslate"><span class="pre">C</span></code> into. As such, <code class="docutils literal notranslate"><span class="pre">C</span> <span class="pre">%</span> <span class="pre">G</span> <span class="pre">==</span> <span class="pre">0</span></code> and as a fallback, you can set <code class="docutils literal notranslate"><span class="pre">C</span> <span class="pre">==</span> <span class="pre">G</span></code>, meaning
each channel will be treated separately.</p>
<p>If you must use BatchNorm and you’ve built the module yourself, you can change the module to
not use running stats. In other words, anywhere that there’s a BatchNorm module, set the
<code class="docutils literal notranslate"><span class="pre">track_running_stats</span></code> flag to be False</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="n">track_running_stats</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
</pre></div>
</div>
</section>
<section id="option-2-torchvision-parameter">
<h3>Option 2: torchvision parameter<a class="headerlink" href="#option-2-torchvision-parameter" title="Permalink to this heading">¶</a></h3>
<p>Some torchvision models, like resnet and regnet, can take in a <code class="docutils literal notranslate"><span class="pre">norm_layer</span></code> parameter. These are
often defaulted to be BatchNorm2d if they’ve been defaulted.</p>
<p>Instead you can set it to be GroupNorm.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torchvision</span>
<span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">partial</span>
<span class="n">torchvision</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">resnet18</span><span class="p">(</span><span class="n">norm_layer</span><span class="o">=</span><span class="k">lambda</span> <span class="n">c</span><span class="p">:</span> <span class="n">GroupNorm</span><span class="p">(</span><span class="n">num_groups</span><span class="o">=</span><span class="n">g</span><span class="p">,</span> <span class="n">c</span><span class="p">))</span>
</pre></div>
</div>
<p>Here, once again, <code class="docutils literal notranslate"><span class="pre">c</span> <span class="pre">%</span> <span class="pre">g</span> <span class="pre">==</span> <span class="pre">0</span></code> so as a fallback, set <code class="docutils literal notranslate"><span class="pre">g</span> <span class="pre">=</span> <span class="pre">c</span></code>.</p>
<p>If you are attached to BatchNorm, be sure to use a version that doesn’t use running stats</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torchvision</span>
<span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">partial</span>
<span class="n">torchvision</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">resnet18</span><span class="p">(</span><span class="n">norm_layer</span><span class="o">=</span><span class="n">partial</span><span class="p">(</span><span class="n">BatchNorm2d</span><span class="p">,</span> <span class="n">track_running_stats</span><span class="o">=</span><span class="kc">False</span><span class="p">))</span>
</pre></div>
</div>
</section>
<section id="option-3-functorch-s-patching">
<h3>Option 3: functorch’s patching<a class="headerlink" href="#option-3-functorch-s-patching" title="Permalink to this heading">¶</a></h3>
<p>functorch has added some functionality to allow for quick, in-place patching of the module to not
use running stats. Changing the norm layer is more fragile, so we have not offered that. If you
have a net where you want the BatchNorm to not use running stats, you can run
<code class="docutils literal notranslate"><span class="pre">replace_all_batch_norm_modules_</span></code> to update the module in-place to not use running stats</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.func</span> <span class="kn">import</span> <span class="n">replace_all_batch_norm_modules_</span>
<span class="n">replace_all_batch_norm_modules_</span><span class="p">(</span><span class="n">net</span><span class="p">)</span>
</pre></div>
</div>
</section>
<section id="option-4-eval-mode">
<h3>Option 4: eval mode<a class="headerlink" href="#option-4-eval-mode" title="Permalink to this heading">¶</a></h3>
<p>When run under eval mode, the running_mean and running_var will not be updated. Therefore, vmap can support this mode</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
<span class="n">vmap</span><span class="p">(</span><span class="n">model</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
<span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
</pre></div>
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