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<div class="section" id="the-c-frontend">
<h1>The C++ Frontend<a class="headerlink" href="#the-c-frontend" title="Permalink to this heading">¶</a></h1>
<p>The PyTorch C++ frontend is a C++14 library for CPU and GPU
tensor computation, with automatic differentiation and high level building
blocks for state of the art machine learning applications.</p>
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<h2>Description<a class="headerlink" href="#description" title="Permalink to this heading">¶</a></h2>
<p>The PyTorch C++ frontend can be thought of as a C++ version of the
PyTorch Python frontend, providing automatic differentiation and various higher
level abstractions for machine learning and neural networks. Specifically,
it consists of the following components:</p>
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</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>Component</p></th>
<th class="head"><p>Description</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">torch::Tensor</span></code></p></td>
<td><p>Automatically differentiable, efficient CPU and GPU enabled tensors</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">torch::nn</span></code></p></td>
<td><p>A collection of composable modules for neural network modeling</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">torch::optim</span></code></p></td>
<td><p>Optimization algorithms like SGD, Adam or RMSprop to train your models</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">torch::data</span></code></p></td>
<td><p>Datasets, data pipelines and multi-threaded, asynchronous data loader</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">torch::serialize</span></code></p></td>
<td><p>A serialization API for storing and loading model checkpoints</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">torch::python</span></code></p></td>
<td><p>Glue to bind your C++ models into Python</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">torch::jit</span></code></p></td>
<td><p>Pure C++ access to the TorchScript JIT compiler</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="end-to-end-example">
<h2>End-to-end example<a class="headerlink" href="#end-to-end-example" title="Permalink to this heading">¶</a></h2>
<p>Here is a simple, end-to-end example of defining and training a simple
neural network on the MNIST dataset:</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf"><torch/torch.h></span>
<span class="c1">// Define a new Module.</span>
<span class="k">struct</span><span class="w"> </span><span class="nc">Net</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">Net</span><span class="p">()</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="c1">// Construct and register two Linear submodules.</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">64</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">64</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">));</span>
<span class="w"> </span><span class="n">fc3</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">"fc3"</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">32</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="c1">// Implement the Net's algorithm.</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="c1">// Use one of many tensor manipulation functions.</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="n">reshape</span><span class="p">({</span><span class="n">x</span><span class="p">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span><span class="w"> </span><span class="mi">784</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">dropout</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="w"> </span><span class="cm">/*p=*/</span><span class="mf">0.5</span><span class="p">,</span><span class="w"> </span><span class="cm">/*train=*/</span><span class="n">is_training</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">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="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">log_softmax</span><span class="p">(</span><span class="n">fc3</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="cm">/*dim=*/</span><span class="mi">1</span><span class="p">);</span>
<span class="w"> </span><span class="k">return</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="c1">// Use one of many "standard library" modules.</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="k">nullptr</span><span class="p">},</span><span class="w"> </span><span class="n">fc2</span><span class="p">{</span><span class="k">nullptr</span><span class="p">},</span><span class="w"> </span><span class="n">fc3</span><span class="p">{</span><span class="k">nullptr</span><span class="p">};</span>
<span class="p">};</span>
<span class="kt">int</span><span class="w"> </span><span class="nf">main</span><span class="p">()</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="c1">// Create a new Net.</span>
<span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="n">net</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">make_shared</span><span class="o"><</span><span class="n">Net</span><span class="o">></span><span class="p">();</span>
<span class="w"> </span><span class="c1">// Create a multi-threaded data loader for the MNIST dataset.</span>
<span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="n">data_loader</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">data</span><span class="o">::</span><span class="n">make_data_loader</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">datasets</span><span class="o">::</span><span class="n">MNIST</span><span class="p">(</span><span class="s">"./data"</span><span class="p">).</span><span class="n">map</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">transforms</span><span class="o">::</span><span class="n">Stack</span><span class="o"><></span><span class="p">()),</span>
<span class="w"> </span><span class="cm">/*batch_size=*/</span><span class="mi">64</span><span class="p">);</span>
<span class="w"> </span><span class="c1">// Instantiate an SGD optimization algorithm to update our Net's parameters.</span>
<span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">optim</span><span class="o">::</span><span class="n">SGD</span><span class="w"> </span><span class="n">optimizer</span><span class="p">(</span><span class="n">net</span><span class="o">-></span><span class="n">parameters</span><span class="p">(),</span><span class="w"> </span><span class="cm">/*lr=*/</span><span class="mf">0.01</span><span class="p">);</span>
<span class="w"> </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="kt">size_t</span><span class="w"> </span><span class="n">epoch</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1</span><span class="p">;</span><span class="w"> </span><span class="n">epoch</span><span class="w"> </span><span class="o"><=</span><span class="w"> </span><span class="mi">10</span><span class="p">;</span><span class="w"> </span><span class="o">++</span><span class="n">epoch</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="kt">size_t</span><span class="w"> </span><span class="n">batch_index</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span>
<span class="w"> </span><span class="c1">// Iterate the data loader to yield batches from the dataset.</span>
<span class="w"> </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="k">auto</span><span class="o">&</span><span class="w"> </span><span class="n">batch</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="o">*</span><span class="n">data_loader</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="c1">// Reset gradients.</span>
<span class="w"> </span><span class="n">optimizer</span><span class="p">.</span><span class="n">zero_grad</span><span class="p">();</span>
<span class="w"> </span><span class="c1">// Execute the model on the input data.</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">prediction</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">net</span><span class="o">-></span><span class="n">forward</span><span class="p">(</span><span class="n">batch</span><span class="p">.</span><span class="n">data</span><span class="p">);</span>
<span class="w"> </span><span class="c1">// Compute a loss value to judge the prediction of our model.</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">loss</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">nll_loss</span><span class="p">(</span><span class="n">prediction</span><span class="p">,</span><span class="w"> </span><span class="n">batch</span><span class="p">.</span><span class="n">target</span><span class="p">);</span>
<span class="w"> </span><span class="c1">// Compute gradients of the loss w.r.t. the parameters of our model.</span>
<span class="w"> </span><span class="n">loss</span><span class="p">.</span><span class="n">backward</span><span class="p">();</span>
<span class="w"> </span><span class="c1">// Update the parameters based on the calculated gradients.</span>
<span class="w"> </span><span class="n">optimizer</span><span class="p">.</span><span class="n">step</span><span class="p">();</span>
<span class="w"> </span><span class="c1">// Output the loss and checkpoint every 100 batches.</span>
<span class="w"> </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="o">++</span><span class="n">batch_index</span><span class="w"> </span><span class="o">%</span><span class="w"> </span><span class="mi">100</span><span class="w"> </span><span class="o">==</span><span class="w"> </span><span class="mi">0</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="s">"Epoch: "</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">epoch</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="s">" | Batch: "</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">batch_index</span>
<span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="s">" | Loss: "</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">loss</span><span class="p">.</span><span class="n">item</span><span class="o"><</span><span class="kt">float</span><span class="o">></span><span class="p">()</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">endl</span><span class="p">;</span>
<span class="w"> </span><span class="c1">// Serialize your model periodically as a checkpoint.</span>
<span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">save</span><span class="p">(</span><span class="n">net</span><span class="p">,</span><span class="w"> </span><span class="s">"net.pt"</span><span class="p">);</span>
<span class="w"> </span><span class="p">}</span>
<span class="w"> </span><span class="p">}</span>
<span class="w"> </span><span class="p">}</span>
<span class="p">}</span>
</pre></div>
</div>
<p>To see more complete examples of using the PyTorch C++ frontend, see <a class="reference external" href="https://github.com/pytorch/examples/tree/master/cpp">the example repository</a>.</p>
</div>
<div class="section" id="philosophy">
<h2>Philosophy<a class="headerlink" href="#philosophy" title="Permalink to this heading">¶</a></h2>
<p>PyTorch’s C++ frontend was designed with the idea that the Python frontend is
great, and should be used when possible; but in some settings, performance and
portability requirements make the use of the Python interpreter infeasible. For
example, Python is a poor choice for low latency, high performance or
multithreaded environments, such as video games or production servers. The
goal of the C++ frontend is to address these use cases, while not sacrificing
the user experience of the Python frontend.</p>
<p>As such, the C++ frontend has been written with a few philosophical goals in mind:</p>
<ul class="simple">
<li><p><strong>Closely model the Python frontend in its design</strong>, naming, conventions and
functionality. While there may be occasional differences between the two
frontends (e.g., where we have dropped deprecated features or fixed “warts”
in the Python frontend), we guarantee that the effort in porting a Python model
to C++ should lie exclusively in <strong>translating language features</strong>,
not modifying functionality or behavior.</p></li>
<li><p><strong>Prioritize flexibility and user-friendliness over micro-optimization.</strong>
In C++, you can often get optimal code, but at the cost of an extremely
unfriendly user experience. Flexibility and dynamism is at the heart of
PyTorch, and the C++ frontend seeks to preserve this experience, in some
cases sacrificing performance (or “hiding” performance knobs) to keep APIs
simple and explicable. We want researchers who don’t write C++ for a living
to be able to use our APIs.</p></li>
</ul>
<p>A word of warning: Python is not necessarily slower than
C++! The Python frontend calls into C++ for almost anything computationally expensive
(especially any kind of numeric operation), and these operations will take up
the bulk of time spent in a program. If you would prefer to write Python,
and can afford to write Python, we recommend using the Python interface to
PyTorch. However, if you would prefer to write C++, or need to write C++
(because of multithreading, latency or deployment requirements), the
C++ frontend to PyTorch provides an API that is approximately as convenient,
flexible, friendly and intuitive as its Python counterpart. The two frontends
serve different use cases, work hand in hand, and neither is meant to
unconditionally replace the other.</p>
</div>
<div class="section" id="installation">
<h2>Installation<a class="headerlink" href="#installation" title="Permalink to this heading">¶</a></h2>
<p>Instructions on how to install the C++ frontend library distribution, including
an example for how to build a minimal application depending on LibTorch, may be
found by following <a class="reference external" href="https://pytorch.org/cppdocs/installing.html">this</a> link.</p>
</div>
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