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<li class="toctree-l1"><a class="reference internal" href="../getting_started/quick_start.html">Quick Start</a></li>
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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>
<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>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
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<section id="compiler-phases">
<h1>Compiler Phases<a class="headerlink" href="#compiler-phases" title="Permalink to this heading">¶</a></h1>
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<section id="lowering">
<h2>Lowering<a class="headerlink" href="#lowering" title="Permalink to this heading">¶</a></h2>
<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>
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<section id="partitioning">
<h2>Partitioning<a class="headerlink" href="#partitioning" title="Permalink to this heading">¶</a></h2>
<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>
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<section id="conversion">
<h2>Conversion<a class="headerlink" href="#conversion" title="Permalink to this heading">¶</a></h2>
<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>
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<section id="compilation-and-runtime">
<h2>Compilation and Runtime<a class="headerlink" href="#compilation-and-runtime" title="Permalink to this heading">¶</a></h2>
<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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$(this).closest("a").addClass("has-code");
});
})
</script>
</body>
</html>