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Ahead-of-time compilation of TorchScript / PyTorch JIT for NVIDIA GPUs
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README.md

TRTorch

Ahead of Time (AOT) compiling for PyTorch JIT

Compiling TRTorch

Dependencies

  • Libtorch 1.4.0
  • CUDA 10.1
  • cuDNN 7.6
  • TensorRT 6.0.1.5

Install TensorRT, CUDA and cuDNN on the system before starting to compile.

bazel build //:libtrtorch --cxxopt="-DNDEBUG"

Debug build

bazel build //:libtrtorch --compilation_mode=dbg

A tarball with the include files and library can then be found in bazel-bin

Running TRTorch on a JIT Graph

Make sure to add LibTorch's version of CUDA 10.1 to your LD_LIBRARY_PATH export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$(pwd)/bazel-TRTorch/external/libtorch/lib

bazel run //cpp/trtorchexec -- $(realpath <PATH TO GRAPH>) <input-size>

How do I add support for a new op...

In TRTorch?

Thanks for wanting to contribute! There are two main ways to handle supporting a new op. Either you can write a converter for the op from scratch and register it in the NodeConverterRegistry or if you can map the op to a set of ops that already have converters you can write a graph rewrite pass which will replace your new op with an equivalent subgraph of supported ops. Its preferred to use graph rewriting because then we do not need to maintain a large library of op converters.

In my application?

The Node Converter Registry is not exposed currently in the public API but you can try using internal headers.

You can register a converter for your op using the NodeConverterRegistry inside your application.

Structure of the repo

Component Description
core Main JIT ingest, lowering, conversion and execution implementations
cpp C++ API for TRTorch
tests Unit test for TRTorch

License

The TRTorch license can be found in the LICENSE file.

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