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<div class="section" id="autograd-mechanics">
<span id="id1"></span><h1>Autograd mechanics<a class="headerlink" href="#autograd-mechanics" title="Permalink to this headline">¶</a></h1>
<p>This note will present an overview of how autograd works and records the
operations. It’s not strictly necessary to understand all this, but we recommend
getting familiar with it, as it will help you write more efficient, cleaner
programs, and can aid you in debugging.</p>
<div class="section" id="excluding-subgraphs-from-backward">
<span id="excluding-subgraphs"></span><h2>Excluding subgraphs from backward<a class="headerlink" href="#excluding-subgraphs-from-backward" title="Permalink to this headline">¶</a></h2>
<p>Every Tensor has a flag: <code class="xref py py-attr docutils literal notranslate"><span class="pre">requires_grad</span></code> that allows for fine grained
exclusion of subgraphs from gradient computation and can increase efficiency.</p>
<div class="section" id="requires-grad">
<span id="excluding-requires-grad"></span><h3><code class="docutils literal notranslate"><span class="pre">requires_grad</span></code><a class="headerlink" href="#requires-grad" title="Permalink to this headline">¶</a></h3>
<p>If there’s a single input to an operation that requires gradient, its output
will also require gradient. Conversely, only if all inputs don’t require
gradient, the output also won’t require it. Backward computation is never
performed in the subgraphs, where all Tensors didn’t require gradients.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </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">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span> <span class="c1"># requires_grad=False by default</span>
<span class="gp">>>> </span><span class="n">y</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">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span> <span class="c1"># requires_grad=False by default</span>
<span class="gp">>>> </span><span class="n">z</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">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span>
<span class="gp">>>> </span><span class="n">a</span><span class="o">.</span><span class="n">requires_grad</span>
<span class="go">False</span>
<span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="n">a</span> <span class="o">+</span> <span class="n">z</span>
<span class="gp">>>> </span><span class="n">b</span><span class="o">.</span><span class="n">requires_grad</span>
<span class="go">True</span>
</pre></div>
</div>
<p>This is especially useful when you want to freeze part of your model, or you
know in advance that you’re not going to use gradients w.r.t. some parameters.
For example if you want to finetune a pretrained CNN, it’s enough to switch the
<code class="xref py py-attr docutils literal notranslate"><span class="pre">requires_grad</span></code> flags in the frozen base, and no intermediate buffers will
be saved, until the computation gets to the last layer, where the affine
transform will use weights that require gradient, and the output of the network
will also require them.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</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">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">for</span> <span class="n">param</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">():</span>
<span class="n">param</span><span class="o">.</span><span class="n">requires_grad</span> <span class="o">=</span> <span class="kc">False</span>
<span class="c1"># Replace the last fully-connected layer</span>
<span class="c1"># Parameters of newly constructed modules have requires_grad=True by default</span>
<span class="n">model</span><span class="o">.</span><span class="n">fc</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">512</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
<span class="c1"># Optimize only the classifier</span>
<span class="n">optimizer</span> <span class="o">=</span> <span class="n">optim</span><span class="o">.</span><span class="n">SGD</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">fc</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="mf">1e-2</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.9</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="how-autograd-encodes-the-history">
<span id="how-autograd-encodes-history"></span><h2>How autograd encodes the history<a class="headerlink" href="#how-autograd-encodes-the-history" title="Permalink to this headline">¶</a></h2>
<p>Autograd is reverse automatic differentiation system. Conceptually,
autograd records a graph recording all of the operations that created
the data as you execute operations, giving you a directed acyclic graph
whose leaves are the input tensors and roots are the output tensors.
By tracing this graph from roots to leaves, you can automatically
compute the gradients using the chain rule.</p>
<p>Internally, autograd represents this graph as a graph of
<code class="xref py py-class docutils literal notranslate"><span class="pre">Function</span></code> objects (really expressions), which can be
<code class="xref py py-meth docutils literal notranslate"><span class="pre">apply()</span></code> ed to compute the result of
evaluating the graph. When computing the forwards pass, autograd
simultaneously performs the requested computations and builds up a graph
representing the function that computes the gradient (the <code class="docutils literal notranslate"><span class="pre">.grad_fn</span></code>
attribute of each <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> is an entry point into this graph).
When the forwards pass is completed, we evaluate this graph in the
backwards pass to compute the gradients.</p>
<p>An important thing to note is that the graph is recreated from scratch at every
iteration, and this is exactly what allows for using arbitrary Python control
flow statements, that can change the overall shape and size of the graph at
every iteration. You don’t have to encode all possible paths before you
launch the training - what you run is what you differentiate.</p>
</div>
<div class="section" id="in-place-operations-with-autograd">
<h2>In-place operations with autograd<a class="headerlink" href="#in-place-operations-with-autograd" title="Permalink to this headline">¶</a></h2>
<p>Supporting in-place operations in autograd is a hard matter, and we discourage
their use in most cases. Autograd’s aggressive buffer freeing and reuse makes
it very efficient and there are very few occasions when in-place operations
actually lower memory usage by any significant amount. Unless you’re operating
under heavy memory pressure, you might never need to use them.</p>
<p>There are two main reasons that limit the applicability of in-place operations:</p>
<ol class="arabic simple">
<li><p>In-place operations can potentially overwrite values required to compute
gradients.</p></li>
<li><p>Every in-place operation actually requires the implementation to rewrite the
computational graph. Out-of-place versions simply allocate new objects and
keep references to the old graph, while in-place operations, require
changing the creator of all inputs to the <code class="xref py py-class docutils literal notranslate"><span class="pre">Function</span></code> representing
this operation. This can be tricky, especially if there are many Tensors
that reference the same storage (e.g. created by indexing or transposing),
and in-place functions will actually raise an error if the storage of
modified inputs is referenced by any other <code class="xref py py-class docutils literal notranslate"><span class="pre">Tensor</span></code>.</p></li>
</ol>
</div>
<div class="section" id="in-place-correctness-checks">
<h2>In-place correctness checks<a class="headerlink" href="#in-place-correctness-checks" title="Permalink to this headline">¶</a></h2>
<p>Every tensor keeps a version counter, that is incremented every time it is
marked dirty in any operation. When a Function saves any tensors for backward,
a version counter of their containing Tensor is saved as well. Once you access
<code class="docutils literal notranslate"><span class="pre">self.saved_tensors</span></code> it is checked, and if it is greater than the saved value
an error is raised. This ensures that if you’re using in-place
functions and not seeing any errors, you can be sure that the computed
gradients are correct.</p>
</div>
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