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<p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
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<p class="caption" role="heading"><span class="caption-text">User Guide</span></p>
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<li class="toctree-l1 current"><a class="current reference internal" href="#">Torch-TensorRT Explained</a></li>
<li class="toctree-l1"><a class="reference internal" href="dynamic_shapes.html">Dynamic shapes with Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="saving_models.html">Saving models compiled with Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="runtime.html">Deploying Torch-TensorRT Programs</a></li>
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<ul>
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<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/engine_caching_bert_example.html">Engine Caching (BERT)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/refit_engine_example.html">Refitting Torch-TensorRT Programs with New Weights</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_export_cudagraphs.html">Torch Export with Cudagraphs</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/custom_kernel_plugins.html">Using Custom Kernels within TensorRT Engines with Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/auto_generate_converters.html">Automatically Generate a Converter for a Custom Kernel</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/auto_generate_plugins.html">Automatically Generate a Plugin for a Custom Kernel</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/mutable_torchtrt_module_example.html">Mutable Torch TensorRT Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/weight_streaming_example.html">Weight Streaming</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../dynamo/torch_compile.html">TensorRT Backend for <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code></a></li>
<li class="toctree-l1"><a class="reference internal" href="../dynamo/dynamo_export.html">Compiling Exported Programs with Torch-TensorRT</a></li>
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<ul>
<li class="toctree-l1"><a class="reference internal" href="../ts/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="../ts/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
<li class="toctree-l1"><a class="reference internal" href="../ts/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
<li class="toctree-l1"><a class="reference internal" href="../ts/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
<li class="toctree-l1"><a class="reference internal" href="../ts/getting_started_with_cpp_api.html">Using Torch-TensorRT in C++</a></li>
<li class="toctree-l1"><a class="reference internal" href="../ts/ptq.html">Post Training Quantization (PTQ)</a></li>
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<p class="caption" role="heading"><span class="caption-text">FX Frontend</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../fx/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
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<ul>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet with dynamic shapes using the <cite>torch.compile</cite> backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_transformers_example.html">Compiling BERT using the <cite>torch.compile</cite> backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_stable_diffusion.html">Compiling Stable Diffusion model using the <cite>torch.compile</cite> backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_gpt2.html">Compiling GPT2 using the Torch-TensorRT <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> frontend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_export_gpt2.html">Compiling GPT2 using the dynamo backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_export_llama2.html">Compiling Llama2 using the dynamo backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_export_sam2.html">Compiling SAM2 using the dynamo backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_export_flux_dev.html">Compiling FLUX.1-dev model using the Torch-TensorRT dynamo backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/notebooks.html">Legacy notebooks</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Python API Documentation</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../py_api/torch_tensorrt.html">torch_tensorrt</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../py_api/fx.html">torch_tensorrt.fx</a></li>
<li class="toctree-l1"><a class="reference internal" href="../py_api/ts.html">torch_tensorrt.ts</a></li>
<li class="toctree-l1"><a class="reference internal" href="../py_api/ptq.html">torch_tensorrt.ts.ptq</a></li>
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<p class="caption" role="heading"><span class="caption-text">C++ API Documentation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/torch_tensort_cpp.html">Torch-TensorRT C++ API</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__torchscript.html">Namespace torch_tensorrt::torchscript</a></li>
<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__ptq.html">Namespace torch_tensorrt::ptq</a></li>
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<p class="caption" role="heading"><span class="caption-text">CLI Documentation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../cli/torchtrtc.html">torchtrtc</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../contributors/dynamo_converters.html">Writing Dynamo Converters</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../contributors/useful_links.html">Useful Links for Torch-TensorRT Development</a></li>
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<section id="torch-tensorrt-explained">
<span id="id1"></span><h1>Torch-TensorRT Explained<a class="headerlink" href="#torch-tensorrt-explained" title="Permalink to this heading">¶</a></h1>
<p>Torch-TensorRT is a compiler for PyTorch models targeting NVIDIA GPUs
via the TensorRT Model Optimization SDK. It aims to provide better
inference performance for PyTorch models while still maintaining the
great ergonomics of PyTorch.</p>
<section id="dynamo-frontend">
<h2>Dynamo Frontend<a class="headerlink" href="#dynamo-frontend" title="Permalink to this heading">¶</a></h2>
<p>The Dynamo frontend is the default frontend for Torch-TensorRT. It utilizes the <a class="reference external" href="https://pytorch.org/docs/stable/torch.compiler_deepdive.html">dynamo compiler stack</a> from PyTorch.</p>
<section id="torch-compile-just-in-time">
<h3><code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> (Just-in-time)<a class="headerlink" href="#torch-compile-just-in-time" title="Permalink to this heading">¶</a></h3>
<p><code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> is a JIT compiler stack, as such, compilation is deferred until first use. This means that as conditions change in the graph, the graph will automatically recompile.
This provides users the most runtime flexibility, however limits options regarding serialization.</p>
<p>Under the hood, <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.compile.html#torch.compile">torch.compile</a> delegates subgraphs it believes can be lowered to Torch-TensorRT. Torch-TensorRT further lowers these graphs into ops consisting of solely <a class="reference external" href="https://pytorch.org/executorch/stable/ir-ops-set-definition.html">Core ATen Operators</a>
or select “High-level Ops” amenable to TensorRT acceleration. Subgraphs are further partitioned into components that will run in PyTorch and ones to be further compiled to TensorRT based
on support for operators. TensorRT engines then replace supported blocks and a hybrid subgraph is returned to <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> to be run on call.</p>
<section id="accepted-formats">
<h4>Accepted Formats<a class="headerlink" href="#accepted-formats" title="Permalink to this heading">¶</a></h4>
<ul class="simple">
<li><p>torch.fx GraphModule (<code class="docutils literal notranslate"><span class="pre">torch.fx.GraphModule</span></code>)</p></li>
<li><p>PyTorch Module (<code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code>)</p></li>
</ul>
</section>
<section id="returns">
<h4>Returns<a class="headerlink" href="#returns" title="Permalink to this heading">¶</a></h4>
<ul class="simple">
<li><p>Boxed-function that triggers compilation on first call</p></li>
</ul>
</section>
</section>
<section id="torch-tensorrt-dynamo-compile-ahead-of-time">
<h3><code class="docutils literal notranslate"><span class="pre">torch_tensorrt.dynamo.compile</span></code> (Ahead-of-time)<a class="headerlink" href="#torch-tensorrt-dynamo-compile-ahead-of-time" title="Permalink to this heading">¶</a></h3>
<p><code class="docutils literal notranslate"><span class="pre">torch_tensorrt.dynamo.compile</span></code> is an AOT compiler, models are compiled in an explicit compilation phase. These compilation artifacts can then be serialized and reloaded at a later date.
Graphs go through the <code class="docutils literal notranslate"><span class="pre">torch.export.trace</span></code> system to be lowered into a graph consisting of <a class="reference external" href="https://pytorch.org/executorch/stable/ir-ops-set-definition.html">Core ATen Operators</a> or select “High-level Ops” amenable to TensoRT acceleration.
Subgraphs are further partitioned into components that will run in PyTorch and ones to be further compiled to TensorRT based on support for operators. TensorRT engines then replace supported blocks
and a hybrid subgraph is packed into an <a class="reference external" href="https://pytorch.org/docs/stable/export.ir_spec.html#exportedprogram">ExportedProgram</a> which can be serialized and reloaded.</p>
<section id="id3">
<h4>Accepted Formats<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h4>
<ul class="simple">
<li><p>torch.export.ExportedProgram (<code class="docutils literal notranslate"><span class="pre">torch.export.ExportedProgram</span></code>)</p></li>
<li><p>torch.fx GraphModule (<code class="docutils literal notranslate"><span class="pre">torch.fx.GraphModule</span></code>) (via <code class="docutils literal notranslate"><span class="pre">torch.export.export</span></code>)</p></li>
<li><p>PyTorch Module (<code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code>) (via <code class="docutils literal notranslate"><span class="pre">torch.export.export</span></code>)</p></li>
</ul>
</section>
<section id="id4">
<h4>Returns<a class="headerlink" href="#id4" title="Permalink to this heading">¶</a></h4>
<ul class="simple">
<li><p>torch.fx.GraphModule (serializable with <code class="docutils literal notranslate"><span class="pre">torch.export.ExportedProgram</span></code>)</p></li>
</ul>
</section>
</section>
</section>
<section id="legacy-frontends">
<h2>Legacy Frontends<a class="headerlink" href="#legacy-frontends" title="Permalink to this heading">¶</a></h2>
<p>As there has been a number of compiler technologies in the PyTorch ecosystem over the years
Torch-TensorRT has some legacy features targeting them.</p>
<section id="torchscript-torch-tensorrt-ts-compile">
<h3>TorchScript (<cite>torch_tensorrt.ts.compile</cite>)<a class="headerlink" href="#torchscript-torch-tensorrt-ts-compile" title="Permalink to this heading">¶</a></h3>
<p>The TorchScript frontend was the original default frontend for Torch-TensorRT and targets models in the TorchScript format. The graph provided will be partitioned into supported and unsupported
blocks. Supported blocks will be lowered to TensorRT and unsupported blocks will remain to run with LibTorch. The resultant graph is returned back to the user as a <code class="docutils literal notranslate"><span class="pre">ScriptModule</span></code> that can be loaded and saved
with the Torch-TensorRT PyTorch runtime extension.</p>
<section id="id5">
<h4>Accepted Formats<a class="headerlink" href="#id5" title="Permalink to this heading">¶</a></h4>
<ul class="simple">
<li><p>TorchScript Module (<code class="docutils literal notranslate"><span class="pre">torch.jit.ScriptModule</span></code>)</p></li>
<li><p>PyTorch Module (<code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code>) (via <code class="docutils literal notranslate"><span class="pre">torch.jit.script</span></code> or <code class="docutils literal notranslate"><span class="pre">torch.jit.trace</span></code>)</p></li>
</ul>
</section>
<section id="id6">
<h4>Returns<a class="headerlink" href="#id6" title="Permalink to this heading">¶</a></h4>
<ul class="simple">
<li><p>TorchScript Module (<code class="docutils literal notranslate"><span class="pre">torch.jit.ScriptModule</span></code>)</p></li>
</ul>
</section>
</section>
<section id="fx-graph-modules-torch-tensorrt-fx-compile">
<h3>FX Graph Modules (<cite>torch_tensorrt.fx.compile</cite>)<a class="headerlink" href="#fx-graph-modules-torch-tensorrt-fx-compile" title="Permalink to this heading">¶</a></h3>
<p>This frontend has almost entirely been replaced by the Dynamo frontend which is a superset of the
features available though the FX frontend. The original FX frontend remains in the codebase for
backwards compatibility reasons.</p>
<section id="id7">
<h4>Accepted Formats<a class="headerlink" href="#id7" title="Permalink to this heading">¶</a></h4>
<ul class="simple">
<li><p>torch.fx GraphModule (<code class="docutils literal notranslate"><span class="pre">torch.fx.GraphModule</span></code>)</p></li>
<li><p>PyTorch Module (<code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code>) (via <code class="docutils literal notranslate"><span class="pre">torch.fx.trace</span></code>)</p></li>
</ul>
</section>
<section id="id8">
<h4>Returns<a class="headerlink" href="#id8" title="Permalink to this heading">¶</a></h4>
<ul class="simple">
<li><p>torch.fx GraphModule (<code class="docutils literal notranslate"><span class="pre">torch.fx.GraphModule</span></code>)</p></li>
</ul>
</section>
</section>
</section>
<section id="torch-tensorrt-compile">
<h2><code class="docutils literal notranslate"><span class="pre">torch_tensorrt.compile</span></code><a class="headerlink" href="#torch-tensorrt-compile" title="Permalink to this heading">¶</a></h2>
<p>As there are many different frontends and supported formats, we provide a convenience layer called <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.compile</span></code> which lets users access
all the different compiler options. You can specify to <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.compile</span></code> what compiler path to use by setting the <code class="docutils literal notranslate"><span class="pre">ir</span></code> option, telling
Torch-TensorRT to try to lower the provided model through a specific intermediate representation.</p>
<section id="ir-options">
<h3><code class="docutils literal notranslate"><span class="pre">ir</span></code> Options<a class="headerlink" href="#ir-options" title="Permalink to this heading">¶</a></h3>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">torch_compile</span></code>: Use the <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> system. Immediately returns a boxed-function that will compile on first call</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">dynamo</span></code>: Run the graph through the <code class="docutils literal notranslate"><span class="pre">torch.export</span></code>/ torchdynamo stack. If the input module is a <code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code>, it must be “export-traceable” as the module will be traced with <code class="docutils literal notranslate"><span class="pre">torch.export.export</span></code>. Returns a <code class="docutils literal notranslate"><span class="pre">torch.fx.GraphModule</span></code> which can be run immediately or saved via <code class="docutils literal notranslate"><span class="pre">torch.export.export</span></code> or <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.save</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">torchscript</span></code> or <code class="docutils literal notranslate"><span class="pre">ts</span></code>: Run graph through the TorchScript stack. If the input module is a <code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code>, it must be “scriptable” as the module will be compiled with <code class="docutils literal notranslate"><span class="pre">torch.jit.script</span></code>. Returns a <code class="docutils literal notranslate"><span class="pre">torch.jit.ScriptModule</span></code> which can be run immediately or saved via <code class="docutils literal notranslate"><span class="pre">torch.save</span></code> or <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.save</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">fx</span></code>: Run graph through the <code class="docutils literal notranslate"><span class="pre">torch.fx</span></code> stack. If the input module is a <code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code>, it will be traced with <code class="docutils literal notranslate"><span class="pre">torch.fx.trace</span></code> and subject to its limitations.</p></li>
</ul>
</section>
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