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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>
<li class="toctree-l1"><a class="reference internal" href="../user_guide/saving_models.html">Saving models compiled with Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../user_guide/runtime.html">Deploying Torch-TensorRT Programs</a></li>
<li class="toctree-l1"><a class="reference internal" href="../user_guide/using_dla.html">DLA</a></li>
<li class="toctree-l1"><a class="reference internal" href="../user_guide/mixed_precision.html">Compile Mixed Precision models with Torch-TensorRT</a></li>
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<section id="compiling-exported-programs-with-torch-tensorrt">
<span id="dynamo-export"></span><h1>Compiling Exported Programs with Torch-TensorRT<a class="headerlink" href="#compiling-exported-programs-with-torch-tensorrt" title="Permalink to this heading">¶</a></h1>
<span class="target" id="module-torch_tensorrt.dynamo"></span><p>Pytorch 2.1 introduced <code class="docutils literal notranslate"><span class="pre">torch.export</span></code> APIs which
can export graphs from Pytorch programs into <code class="docutils literal notranslate"><span class="pre">ExportedProgram</span></code> objects. Torch-TensorRT dynamo
frontend compiles these <code class="docutils literal notranslate"><span class="pre">ExportedProgram</span></code> objects and optimizes them using TensorRT. Here’s a simple
usage of the dynamo frontend</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="kn">import</span><span class="w"> </span><span class="nn">torch_tensorrt</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">MyModel</span><span class="p">()</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
<span class="n">inputs</span> <span class="o">=</span> <span class="p">[</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="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">()]</span>
<span class="n">exp_program</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">export</span><span class="o">.</span><span class="n">export</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">inputs</span><span class="p">))</span>
<span class="n">trt_gm</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">dynamo</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">exp_program</span><span class="p">,</span> <span class="n">inputs</span><span class="p">)</span> <span class="c1"># Output is a torch.fx.GraphModule</span>
<span class="n">trt_gm</span><span class="p">(</span><span class="o">*</span><span class="n">inputs</span><span class="p">)</span>
</pre></div>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p><code class="docutils literal notranslate"><span class="pre">torch_tensorrt.dynamo.compile</span></code> is the main API for users to interact with Torch-TensorRT dynamo frontend. The input type of the model should be <code class="docutils literal notranslate"><span class="pre">ExportedProgram</span></code> (ideally the output of <code class="docutils literal notranslate"><span class="pre">torch.export.export</span></code> or <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.dynamo.trace</span></code> (discussed in the section below)) and output type is a <code class="docutils literal notranslate"><span class="pre">torch.fx.GraphModule</span></code> object.</p>
</div>
<section id="customizable-settings">
<h2>Customizable Settings<a class="headerlink" href="#customizable-settings" title="Permalink to this heading">¶</a></h2>
<p>There are lot of options for users to customize their settings for optimizing with TensorRT.
Some of the frequently used options are as follows:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">inputs</span></code> - For static shapes, this can be a list of torch tensors or <cite>torch_tensorrt.Input</cite> objects. For dynamic shapes, this should be a list of <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.Input</span></code> objects.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">enabled_precisions</span></code> - Set of precisions that TensorRT builder can use during optimization.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">truncate_long_and_double</span></code> - Truncates long and double values to int and floats respectively.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">torch_executed_ops</span></code> - Operators which are forced to be executed by Torch.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">min_block_size</span></code> - Minimum number of consecutive operators required to be executed as a TensorRT segment.</p></li>
</ul>
<p>The complete list of options can be found <a class="reference external" href="https://github.com/pytorch/TensorRT/blob/123a486d6644a5bbeeec33e2f32257349acc0b8f/py/torch_tensorrt/dynamo/compile.py#L51-L77">here</a></p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>We do not support INT precision currently in Dynamo. Support for this currently exists in our Torchscript IR. We plan to implement similar support for dynamo in our next release.</p>
</div>
</section>
<section id="under-the-hood">
<h2>Under the hood<a class="headerlink" href="#under-the-hood" title="Permalink to this heading">¶</a></h2>
<p>Under the hood, <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.dynamo.compile</span></code> performs the following on the graph.</p>
<ul class="simple">
<li><p>Lowering - Applies lowering passes to add/remove operators for optimal conversion.</p></li>
<li><p>Partitioning - Partitions the graph into Pytorch and TensorRT segments based on the <code class="docutils literal notranslate"><span class="pre">min_block_size</span></code> and <code class="docutils literal notranslate"><span class="pre">torch_executed_ops</span></code> field.</p></li>
<li><p>Conversion - Pytorch ops get converted into TensorRT ops in this phase.</p></li>
<li><p>Optimization - Post conversion, we build the TensorRT engine and embed this inside the pytorch graph.</p></li>
</ul>
</section>
<section id="tracing">
<h2>Tracing<a class="headerlink" href="#tracing" title="Permalink to this heading">¶</a></h2>
<p><code class="docutils literal notranslate"><span class="pre">torch_tensorrt.dynamo.trace</span></code> can be used to trace a Pytorch graphs and produce <code class="docutils literal notranslate"><span class="pre">ExportedProgram</span></code>.
This internally performs some decompositions of operators for downstream optimization.
The <code class="docutils literal notranslate"><span class="pre">ExportedProgram</span></code> can then be used with <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.dynamo.compile</span></code> API.
If you have dynamic input shapes in your model, you can use this <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.dynamo.trace</span></code> to export
the model with dynamic shapes. Alternatively, you can use <code class="docutils literal notranslate"><span class="pre">torch.export</span></code> <a class="reference external" href="https://pytorch.org/docs/stable/export.html#expressing-dynamism">with constraints</a> directly as well.</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="kn">import</span><span class="w"> </span><span class="nn">torch_tensorrt</span>
<span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">Input</span><span class="p">(</span><span class="n">min_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">),</span>
<span class="n">opt_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">),</span>
<span class="n">max_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">),</span>
<span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)]</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">MyModel</span><span class="p">()</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
<span class="n">exp_program</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">dynamo</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">inputs</span><span class="p">)</span>
</pre></div>
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