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<li class="toctree-l1"><a class="reference internal" href="../user_guide/torch_tensorrt_explained.html">Torch-TensorRT Explained</a></li>
<li class="toctree-l1"><a class="reference internal" href="../user_guide/dynamic_shapes.html">Dynamic shapes with Torch-TensorRT</a></li>
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<section id="creating-a-torchscript-module">
<span id="creating-a-ts-mod"></span><h1>Creating a TorchScript Module<a class="headerlink" href="#creating-a-torchscript-module" title="Permalink to this heading">¶</a></h1>
<p>TorchScript is a way to create serializable and optimizable models from PyTorch code.
PyTorch has detailed documentation on how to do this <a class="reference external" href="https://pytorch.org/tutorials/beginner/Intro_to_TorchScript_tutorial.html">https://pytorch.org/tutorials/beginner/Intro_to_TorchScript_tutorial.html</a> but briefly here is the
here is key background information and the process:</p>
<p>PyTorch programs are based around <code class="docutils literal notranslate"><span class="pre">Module</span></code> s which can be used to compose higher level modules. <code class="docutils literal notranslate"><span class="pre">Modules</span></code> contain a constructor to set up the modules, parameters and sub-modules
and a forward function which describes how to use the parameters and submodules when the module is invoked.</p>
<p>For example, we can define a LeNet module like this:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="linenos"> 1</span><span class="kn">import</span><span class="w"> </span><span class="nn">torch.nn</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">nn</span>
<span class="linenos"> 2</span><span class="kn">import</span><span class="w"> </span><span class="nn">torch.nn.functional</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">F</span>
<span class="linenos"> 3</span>
<span class="linenos"> 4</span>
<span class="linenos"> 5</span><span class="k">class</span><span class="w"> </span><span class="nc">LeNetFeatExtractor</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="linenos"> 6</span> <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="linenos"> 7</span> <span class="nb">super</span><span class="p">(</span><span class="n">LeNetFeatExtractor</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="linenos"> 8</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="linenos"> 9</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="linenos">10</span>
<span class="linenos">11</span> <span class="k">def</span><span class="w"> </span><span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="linenos">12</span> <span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">max_pool2d</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="n">x</span><span class="p">)),</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
<span class="linenos">13</span> <span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">max_pool2d</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv2</span><span class="p">(</span><span class="n">x</span><span class="p">)),</span> <span class="mi">2</span><span class="p">)</span>
<span class="linenos">14</span> <span class="k">return</span> <span class="n">x</span>
<span class="linenos">15</span>
<span class="linenos">16</span>
<span class="linenos">17</span><span class="k">class</span><span class="w"> </span><span class="nc">LeNetClassifier</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="linenos">18</span> <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="linenos">19</span> <span class="nb">super</span><span class="p">(</span><span class="n">LeNetClassifier</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="linenos">20</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc1</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">16</span> <span class="o">*</span> <span class="mi">6</span> <span class="o">*</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">120</span><span class="p">)</span>
<span class="linenos">21</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc2</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">120</span><span class="p">,</span> <span class="mi">84</span><span class="p">)</span>
<span class="linenos">22</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc3</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">84</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
<span class="linenos">23</span>
<span class="linenos">24</span> <span class="k">def</span><span class="w"> </span><span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="linenos">25</span> <span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="linenos">26</span> <span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fc1</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="linenos">27</span> <span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fc2</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="linenos">28</span> <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc3</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="linenos">29</span> <span class="k">return</span> <span class="n">x</span>
<span class="linenos">30</span>
<span class="linenos">31</span>
<span class="linenos">32</span><span class="k">class</span><span class="w"> </span><span class="nc">LeNet</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="linenos">33</span> <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="linenos">34</span> <span class="nb">super</span><span class="p">(</span><span class="n">LeNet</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="linenos">35</span> <span class="bp">self</span><span class="o">.</span><span class="n">feat</span> <span class="o">=</span> <span class="n">LeNetFeatExtractor</span><span class="p">()</span>
<span class="linenos">36</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">LeNetClassifier</span><span class="p">()</span>
<span class="linenos">37</span>
<span class="linenos">38</span> <span class="k">def</span><span class="w"> </span><span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="linenos">39</span> <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">feat</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="linenos">40</span> <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="linenos">41</span> <span class="k">return</span> <span class="n">x</span>
</pre></div>
</div>
<p>.</p>
<blockquote>
<div><p>Obviously you may want to consolidate such a simple model into a single module but we can see the composability of PyTorch here</p>
</div></blockquote>
<p>From here are two pathways for going from PyTorch Python code to TorchScript code: Tracing and Scripting.</p>
<p>Tracing follows the path of execution when the module is called and records what happens.
To trace an instance of our LeNet module, we can call <code class="docutils literal notranslate"><span class="pre">torch.jit.trace</span></code> with an example input.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">torch</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">LeNet</span><span class="p">()</span>
<span class="n">input_data</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">])</span>
<span class="n">traced_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">input_data</span><span class="p">)</span>
</pre></div>
</div>
<p>Scripting actually inspects your code with a compiler and generates an equivalent TorchScript program. The difference is that since tracing
is following the execution of your module, it cannot pick up control flow for instance. By working from the Python code, the compiler can
include these components. We can run the script compiler on our LeNet module by calling <code class="docutils literal notranslate"><span class="pre">torch.jit.script</span></code></p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">torch</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">LeNet</span><span class="p">()</span>
<span class="n">script_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
</pre></div>
</div>
<p>There are reasons to use one path or another, the PyTorch documentation has information on how to choose. From a Torch-TensorRT perspective, there is
better support (i.e your module is more likely to compile) for traced modules because it doesn’t include all the complexities of a complete
programming language, though both paths supported.</p>
<p>After scripting or tracing your module, you are given back a TorchScript Module. This contains the code and parameters used to run the module stored
in a intermediate representation that Torch-TensorRT can consume.</p>
<p>Here is what the LeNet traced module IR looks like:</p>
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>graph(%self.1 : __torch__.___torch_mangle_10.LeNet,
%input.1 : Float(1, 1, 32, 32)):
%129 : __torch__.___torch_mangle_9.LeNetClassifier = prim::GetAttr[name="classifier"](%self.1)
%119 : __torch__.___torch_mangle_5.LeNetFeatExtractor = prim::GetAttr[name="feat"](%self.1)
%137 : Tensor = prim::CallMethod[name="forward"](%119, %input.1)
%138 : Tensor = prim::CallMethod[name="forward"](%129, %137)
return (%138)
</pre></div>
</div>
<p>and the LeNet scripted module IR:</p>
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>graph(%self : __torch__.LeNet,
%x.1 : Tensor):
%2 : __torch__.LeNetFeatExtractor = prim::GetAttr[name="feat"](%self)
%x.3 : Tensor = prim::CallMethod[name="forward"](%2, %x.1) # x.py:38:12
%5 : __torch__.LeNetClassifier = prim::GetAttr[name="classifier"](%self)
%x.5 : Tensor = prim::CallMethod[name="forward"](%5, %x.3) # x.py:39:12
return (%x.5)
</pre></div>
</div>
<p>You can see that the IR preserves the module structure we have in our python code.</p>
</section>
<section id="working-with-torchscript-in-python">
<span id="ts-in-py"></span><h1>Working with TorchScript in Python<a class="headerlink" href="#working-with-torchscript-in-python" title="Permalink to this heading">¶</a></h1>
<p>TorchScript Modules are run the same way you run normal PyTorch modules. You can run the forward pass using the
<code class="docutils literal notranslate"><span class="pre">forward</span></code> method or just calling the module <code class="docutils literal notranslate"><span class="pre">torch_script_module(in_tensor)</span></code> The JIT compiler will compile
and optimize the module on the fly and then returns the results.</p>
</section>
<section id="saving-torchscript-module-to-disk">
<h1>Saving TorchScript Module to Disk<a class="headerlink" href="#saving-torchscript-module-to-disk" title="Permalink to this heading">¶</a></h1>
<p>For either traced or scripted modules, you can save the module to disk with the following command</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">torch</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">LeNet</span><span class="p">()</span>
<span class="n">script_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
<span class="n">script_model</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">"lenet_scripted.ts"</span><span class="p">)</span>
</pre></div>
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