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<section id="faq">
<h1>FAQ<a class="headerlink" href="#faq" title="Link to this heading">#</a></h1>
<p>Listed below are a number of common issues users face with the various parts of
the C++ API.</p>
<section id="c-extensions">
<h2>C++ Extensions<a class="headerlink" href="#c-extensions" title="Link to this heading">#</a></h2>
<section id="undefined-symbol-errors-from-pytorch-aten">
<h3>Undefined symbol errors from PyTorch/ATen<a class="headerlink" href="#undefined-symbol-errors-from-pytorch-aten" title="Link to this heading">#</a></h3>
<p><strong>Problem</strong>: You import your extension and get an <code class="docutils literal notranslate"><span class="pre">ImportError</span></code> stating that
some C++ symbol from PyTorch or ATen is undefined. For example:</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="o">>>></span><span class="w"> </span><span class="k">import</span><span class="w"> </span><span class="n">extension</span>
<span class="n">Traceback</span><span class="w"> </span><span class="p">(</span><span class="n">most</span><span class="w"> </span><span class="n">recent</span><span class="w"> </span><span class="n">call</span><span class="w"> </span><span class="n">last</span><span class="p">)</span><span class="o">:</span>
<span class="w"> </span><span class="n">File</span><span class="w"> </span><span class="s">"<stdin>"</span><span class="p">,</span><span class="w"> </span><span class="n">line</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="n">in</span><span class="w"> </span><span class="o"><</span><span class="k">module</span><span class="o">></span>
<span class="nl">ImportError</span><span class="p">:</span><span class="w"> </span><span class="o">/</span><span class="n">home</span><span class="o">/</span><span class="n">user</span><span class="o">/</span><span class="p">.</span><span class="n">pyenv</span><span class="o">/</span><span class="n">versions</span><span class="o">/</span><span class="mf">3.7.1</span><span class="o">/</span><span class="n">lib</span><span class="o">/</span><span class="n">python3</span><span class="mf">.7</span><span class="o">/</span><span class="n">site</span><span class="o">-</span><span class="n">packages</span><span class="o">/</span><span class="n">extension</span><span class="p">.</span><span class="n">cpython</span><span class="mi">-37</span><span class="n">m</span><span class="o">-</span><span class="n">x86_64</span><span class="o">-</span><span class="n">linux</span><span class="o">-</span><span class="n">gnu</span><span class="p">.</span><span class="n">so</span><span class="o">:</span><span class="w"> </span><span class="n">undefined</span><span class="w"> </span><span class="n">symbol</span><span class="o">:</span><span class="w"> </span><span class="n">_ZN2at19UndefinedTensorImpl10_singletonE</span>
</pre></div>
</div>
<p><strong>Fix</strong>: The fix is to <code class="docutils literal notranslate"><span class="pre">import</span> <span class="pre">torch</span></code> before you import your extension. This will make
the symbols from the PyTorch dynamic (shared) library that your extension
depends on available, allowing them to be resolved once you import your extension.</p>
</section>
<section id="i-created-a-tensor-using-a-function-from-at-and-get-errors">
<h3>I created a tensor using a function from <code class="docutils literal notranslate"><span class="pre">at::</span></code> and get errors<a class="headerlink" href="#i-created-a-tensor-using-a-function-from-at-and-get-errors" title="Link to this heading">#</a></h3>
<p><strong>Problem</strong>: You created a tensor using e.g. <code class="docutils literal notranslate"><span class="pre">at::ones</span></code> or <code class="docutils literal notranslate"><span class="pre">at::randn</span></code> or
any other tensor factory from the <code class="docutils literal notranslate"><span class="pre">at::</span></code> namespace and are getting errors.</p>
<p><strong>Fix</strong>: Replace <code class="docutils literal notranslate"><span class="pre">at::</span></code> with <code class="docutils literal notranslate"><span class="pre">torch::</span></code> for factory function calls. You
should never use factory functions from the <code class="docutils literal notranslate"><span class="pre">at::</span></code> namespace, as they will
create tensors. The corresponding <code class="docutils literal notranslate"><span class="pre">torch::</span></code> functions will create variables,
and you should only ever deal with variables in your code.</p>
</section>
</section>
<section id="libtorch">
<h2>LibTorch<a class="headerlink" href="#libtorch" title="Link to this heading">#</a></h2>
<section id="how-do-i-move-a-model-to-gpu">
<h3>How do I move a model to GPU?<a class="headerlink" href="#how-do-i-move-a-model-to-gpu" title="Link to this heading">#</a></h3>
<p><strong>Problem</strong>: You want to run your model on GPU but are unsure how to move both
the model and tensors to the correct device.</p>
<p><strong>Fix</strong>: Use the <code class="docutils literal notranslate"><span class="pre">to()</span></code> method to move your model and tensors to a CUDA device:</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">torch</span><span class="o">::</span><span class="n">Device</span><span class="w"> </span><span class="nf">device</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">);</span>
<span class="n">model</span><span class="o">-></span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">);</span>
<span class="k">auto</span><span class="w"> </span><span class="n">input</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">randn</span><span class="p">({</span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">3</span><span class="p">,</span><span class="w"> </span><span class="mi">224</span><span class="p">,</span><span class="w"> </span><span class="mi">224</span><span class="p">}).</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">);</span>
<span class="k">auto</span><span class="w"> </span><span class="n">output</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">model</span><span class="o">-></span><span class="n">forward</span><span class="p">(</span><span class="n">input</span><span class="p">);</span>
</pre></div>
</div>
<p>You can also check for CUDA availability before moving:</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">torch</span><span class="o">::</span><span class="n">Device</span><span class="w"> </span><span class="nf">device</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">cuda</span><span class="o">::</span><span class="n">is_available</span><span class="p">()</span><span class="w"> </span><span class="o">?</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">kCUDA</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">kCPU</span><span class="p">);</span>
</pre></div>
</div>
<p>Make sure to compile with the TorchScript headers by including <code class="docutils literal notranslate"><span class="pre"><torch/script.h></span></code>.</p>
</section>
<section id="my-model-runs-slower-in-c-than-in-python">
<h3>My model runs slower in C++ than in Python<a class="headerlink" href="#my-model-runs-slower-in-c-than-in-python" title="Link to this heading">#</a></h3>
<p><strong>Problem</strong>: Your model inference is slower in C++ compared to Python.</p>
<p><strong>Fix</strong>: There are several common causes:</p>
<ol class="arabic simple">
<li><p><strong>Enable inference mode</strong>: Wrap your inference code with <code class="docutils literal notranslate"><span class="pre">torch::NoGradGuard</span></code>
to disable gradient computation:</p></li>
</ol>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">torch</span><span class="o">::</span><span class="n">NoGradGuard</span><span class="w"> </span><span class="n">no_grad</span><span class="p">;</span>
<span class="k">auto</span><span class="w"> </span><span class="n">output</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">model</span><span class="o">-></span><span class="n">forward</span><span class="p">(</span><span class="n">input</span><span class="p">);</span>
</pre></div>
</div>
<ol class="arabic simple" start="2">
<li><p><strong>Enable optimizations</strong>: For TorchScript models, use <code class="docutils literal notranslate"><span class="pre">optimize_for_inference</span></code>:</p></li>
</ol>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">module</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">optimize_for_inference</span><span class="p">(</span><span class="k">module</span><span class="p">);</span>
</pre></div>
</div>
<ol class="arabic simple" start="3">
<li><p><strong>Warm up the model</strong>: Run a few inference passes before benchmarking to allow
JIT compilation and memory allocation to complete.</p></li>
<li><p><strong>Check thread settings</strong>: Ensure proper thread configuration:</p></li>
</ol>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">at</span><span class="o">::</span><span class="n">set_num_threads</span><span class="p">(</span><span class="mi">4</span><span class="p">);</span><span class="w"> </span><span class="c1">// Adjust based on your hardware</span>
</pre></div>
</div>
</section>
</section>
<section id="neural-network-modules">
<h2>Neural Network Modules<a class="headerlink" href="#neural-network-modules" title="Link to this heading">#</a></h2>
<section id="how-do-i-register-submodules-in-a-custom-module">
<h3>How do I register submodules in a custom module?<a class="headerlink" href="#how-do-i-register-submodules-in-a-custom-module" title="Link to this heading">#</a></h3>
<p><strong>Problem</strong>: You created a custom module but the submodules are not being
recognized during <code class="docutils literal notranslate"><span class="pre">forward()</span></code> or when saving/loading the model.</p>
<p><strong>Fix</strong>: You must register submodules in the constructor using
<code class="docutils literal notranslate"><span class="pre">register_module()</span></code>:</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">struct</span><span class="w"> </span><span class="nc">MyModel</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">nn</span><span class="o">::</span><span class="n">Module</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="n">MyModel</span><span class="p">()</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="n">fc1</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">register_module</span><span class="p">(</span><span class="s">"fc1"</span><span class="p">,</span><span class="w"> </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">784</span><span class="p">,</span><span class="w"> </span><span class="mi">128</span><span class="p">));</span>
<span class="w"> </span><span class="n">fc2</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">register_module</span><span class="p">(</span><span class="s">"fc2"</span><span class="p">,</span><span class="w"> </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">128</span><span class="p">,</span><span class="w"> </span><span class="mi">10</span><span class="p">));</span>
<span class="w"> </span><span class="p">}</span>
<span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="n">forward</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="n">x</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="n">x</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">relu</span><span class="p">(</span><span class="n">fc1</span><span class="o">-></span><span class="n">forward</span><span class="p">(</span><span class="n">x</span><span class="p">));</span>
<span class="w"> </span><span class="k">return</span><span class="w"> </span><span class="n">fc2</span><span class="o">-></span><span class="n">forward</span><span class="p">(</span><span class="n">x</span><span class="p">);</span>
<span class="w"> </span><span class="p">}</span>
<span class="w"> </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="w"> </span><span class="n">fc1</span><span class="p">{</span><span class="nb">nullptr</span><span class="p">},</span><span class="w"> </span><span class="n">fc2</span><span class="p">{</span><span class="nb">nullptr</span><span class="p">};</span>
<span class="p">};</span>
</pre></div>
</div>
</section>
<section id="how-do-i-set-a-module-to-evaluation-mode">
<h3>How do I set a module to evaluation mode?<a class="headerlink" href="#how-do-i-set-a-module-to-evaluation-mode" title="Link to this heading">#</a></h3>
<p><strong>Problem</strong>: Layers like Dropout and BatchNorm behave differently during training
and evaluation, and you need to switch between modes.</p>
<p><strong>Fix</strong>: Use the <code class="docutils literal notranslate"><span class="pre">eval()</span></code> and <code class="docutils literal notranslate"><span class="pre">train()</span></code> methods:</p>
<div class="highlight-cpp 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="w"> </span><span class="c1">// Set to evaluation mode</span>
<span class="c1">// ... run inference ...</span>
<span class="n">model</span><span class="o">-></span><span class="n">train</span><span class="p">();</span><span class="w"> </span><span class="c1">// Set back to training mode</span>
</pre></div>
</div>
</section>
</section>
<section id="data-loading">
<h2>Data Loading<a class="headerlink" href="#data-loading" title="Link to this heading">#</a></h2>
<section id="how-do-i-create-a-custom-dataset">
<h3>How do I create a custom dataset?<a class="headerlink" href="#how-do-i-create-a-custom-dataset" title="Link to this heading">#</a></h3>
<p><strong>Problem</strong>: You want to load your own data instead of using built-in datasets.</p>
<p><strong>Fix</strong>: Create a class that inherits from <code class="docutils literal notranslate"><span class="pre">torch::data::datasets::Dataset</span></code> and
implement the <code class="docutils literal notranslate"><span class="pre">get()</span></code> and <code class="docutils literal notranslate"><span class="pre">size()</span></code> methods:</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">class</span><span class="w"> </span><span class="nc">CustomDataset</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="k">public</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">data</span><span class="o">::</span><span class="n">datasets</span><span class="o">::</span><span class="n">Dataset</span><span class="o"><</span><span class="n">CustomDataset</span><span class="o">></span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="k">public</span><span class="o">:</span>
<span class="w"> </span><span class="k">explicit</span><span class="w"> </span><span class="n">CustomDataset</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="o">&</span><span class="w"> </span><span class="n">data_path</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="c1">// Load your data here</span>
<span class="w"> </span><span class="p">}</span>
<span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">data</span><span class="o">::</span><span class="n">Example</span><span class="o"><></span><span class="w"> </span><span class="n">get</span><span class="p">(</span><span class="kt">size_t</span><span class="w"> </span><span class="n">index</span><span class="p">)</span><span class="w"> </span><span class="k">override</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="c1">// Return a single data sample</span>
<span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="n">data</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="cm">/* load data at index */</span><span class="p">;</span>
<span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="n">label</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="cm">/* load label at index */</span><span class="p">;</span>
<span class="w"> </span><span class="k">return</span><span class="w"> </span><span class="p">{</span><span class="n">data</span><span class="p">,</span><span class="w"> </span><span class="n">label</span><span class="p">};</span>