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v2.7.0.dev0+297adef
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<p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../getting_started/installation.html">Installation</a></li>
<li class="toctree-l1"><a class="reference internal" href="../getting_started/jetpack.html">Overview</a></li>
<li class="toctree-l1"><a class="reference internal" href="../getting_started/quick_start.html">Quick Start</a></li>
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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"><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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<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_advanced_usage.html">Torch Compile Advanced Usage</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/vgg16_ptq.html">Deploy Quantized Models using Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/engine_caching_example.html">Engine Caching</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/engine_caching_bert_example.html">Engine Caching (BERT)</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../ts/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
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<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>
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<section id="system-overview">
<span id="id1"></span><h1>System Overview<a class="headerlink" href="#system-overview" title="Permalink to this heading">¶</a></h1>
<p>Torch-TensorRT is primarily a C++ Library with a Python API planned. We use Bazel as our build system and target Linux x86_64 and
Linux aarch64 (only natively) right now. The compiler we use is GCC 7.5.0 and the library is untested with compilers before that
version so there may be compilation errors if you try to use an older compiler.</p>
<p>The repository is structured into:</p>
<ul class="simple">
<li><p>core: Main compiler source code</p></li>
<li><p>cpp: C++ API</p></li>
<li><p>tests: tests of the C++ API, the core and converters</p></li>
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<p>The C++ API is unstable and subject to change until the library matures, though most work is done under the hood in the core.</p>
<p>The core has a couple major parts: The top level compiler interface which coordinates ingesting a module, lowering,
converting and generating a new module and returning it back to the user. There are the three main phases of the
compiler, the lowering phase, the conversion phase, and the execution phase.</p>
<section id="compiler-phases">
<h2>Compiler Phases<a class="headerlink" href="#compiler-phases" title="Permalink to this heading">¶</a></h2>
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<section id="lowering">
<h3>Lowering<a class="headerlink" href="#lowering" title="Permalink to this heading">¶</a></h3>
<p><a class="reference internal" href="lowering.html#lowering"><span class="std std-ref">Lowering Phase</span></a></p>
<p>The lowering is made up of a set of passes (some from PyTorch and some specific to Torch-TensorRT)
run over the graph IR to map the large PyTorch opset to a reduced opset that is easier to convert to
TensorRT.</p>
</section>
<section id="partitioning">
<h3>Partitioning<a class="headerlink" href="#partitioning" title="Permalink to this heading">¶</a></h3>
<p><a class="reference internal" href="partitioning.html#partitioning"><span class="std std-ref">Partitioning Phase</span></a></p>
<p>The phase is optional and enabled by the user. It instructs the compiler to separate nodes into ones that should run in PyTorch and ones that should run in TensorRT.
Criteria for separation include: Lack of a converter, operator is explicitly set to run in PyTorch by the user or the node has a flag which tells partitioning to
run in PyTorch by the module fallback passes.</p>
</section>
<section id="conversion">
<h3>Conversion<a class="headerlink" href="#conversion" title="Permalink to this heading">¶</a></h3>
<p><a class="reference internal" href="conversion.html#conversion"><span class="std std-ref">Conversion Phase</span></a></p>
<p>In the conversion phase we traverse the lowered graph and construct an equivalent TensorRT graph.
The conversion phase is made up of three main components, a context to manage compile time data,
a evaluator library which will execute operations that can be resolved at compile time and a converter
library which maps an op from JIT to TensorRT.</p>
</section>
<section id="compilation-and-runtime">
<h3>Compilation and Runtime<a class="headerlink" href="#compilation-and-runtime" title="Permalink to this heading">¶</a></h3>
<p><a class="reference internal" href="../user_guide/runtime.html#runtime"><span class="std std-ref">Deploying Torch-TensorRT Programs</span></a></p>
<p>The final compilation phase constructs a TorchScript program to run the converted TensorRT engine. It
takes a serialized engine and instantiates it within a engine manager, then the compiler will
build out a JIT graph that references this engine and wraps it in a module to return to the user.
When the user executes the module, the JIT program run in the JIT runtime extended by Torch-TensorRT with the data providied from the user.</p>
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