Ahead of Time (AOT) compiling for PyTorch JIT

TRTorch is a compiler for PyTorch/TorchScript, targeting NVIDIA GPUs via NVIDIA's TensorRT Deep Learning Optimizer and Runtime. Unlike PyTorch's Just-In-Time (JIT) compiler, TRTorch is an Ahead-of-Time (AOT) compiler, meaning that before you deploy your TorchScript code, you go through an explicit compile step to convert a standard TorchScript program into an module targeting a TensorRT engine. TRTorch operates as a PyTorch extention and compiles modules that integrate into the JIT runtime seamlessly. After compilation using the optimized graph should feel no different than running a TorchScript module. You also have access to TensorRT's suite of configurations at compile time, so you are able to specify operating precision (FP32/FP16/INT8) and other settings for your module.

More Information / System Architecture:

• GTC 2020 Talk

#include "torch/script.h"
#include "trtorch/trtorch.h"

...
auto compile_settings = trtorch::CompileSpec(dims);
// FP16 execution
compile_settings.op_precision = torch::kFloat;
// Compile module
auto trt_mod = trtorch::CompileGraph(ts_mod, compile_settings);
// Run like normal
auto results = trt_mod.forward({in_tensor});
// Save module for later
trt_mod.save("trt_torchscript_module.ts");
...

import trtorch

...
compile_settings = {
    "input_shapes": [
        {
            "min": [1, 3, 224, 224],
            "opt": [1, 3, 512, 512],
            "max": [1, 3, 1024, 1024]
        }, # For static size [1, 3, 224, 224]
    ],
    "op_precision": torch.half # Run with FP16
}

trt_ts_module = trtorch.compile(torch_script_module, compile_settings)

input_data = input_data.half()
result = trt_ts_module(input_data)
torch.jit.save(trt_ts_module, "trt_torchscript_module.ts")

Notes on running in lower precisions:

• Set precision with compile_spec.op_precision
• The module should be left in FP32 before compilation (FP16 can support half tensor models)
• In FP16 only input tensors should be converted to FP16, other precisions use FP32

Note: Refer NVIDIA NGC container(https://ngc.nvidia.com/catalog/containers/nvidia:l4t-pytorch) for PyTorch libraries on JetPack.

• Bazel 3.3.1
• Libtorch 1.5.1
• CUDA 10.2
• cuDNN 7.6.5 (by default, cuDNN 8 supported with compatable PyTorch build)
• TensorRT 7.0.0 (by default, TensorRT 7.1 supported with compatable PyTorch build)

Releases: https://github.com/NVIDIA/TRTorch/releases

If you don't have bazel installed, the easiest way is to install bazelisk using the method of you choosing https://github.com/bazelbuild/bazelisk

Otherwise you can use the following instructions to install binaries https://docs.bazel.build/versions/master/install.html

Finally if you need to compile from source (e.g. aarch64 until bazel distributes binaries for the architecture) you can use these instructions

export BAZEL_VERSION=<VERSION>
mkdir bazel
cd bazel
curl -fSsL -O https://github.com/bazelbuild/bazel/releases/download/$BAZEL_VERSION/bazel-$BAZEL_VERSION-dist.zip
unzip bazel-$BAZEL_VERSION-dist.zip
bash ./compile.sh

You need to start by having CUDA installed on the system, LibTorch will automatically be pulled for you by bazel, then you have two options.

This is recommended so as to build TRTorch hermetically and insures any bugs are not caused by version issues

Make sure when running TRTorch that these versions of the libraries are prioritized in your $LD_LIBRARY_PATH

1. You need to download the tarball distributions of TensorRT and cuDNN from the NVIDIA website.
   • https://developer.nvidia.com/cudnn
   • https://developer.nvidia.com/tensorrt
2. Place these files in a directory (the directories third_party/dist_dir/[x86_64-linux-gnu | aarch64-linux-gnu] exist for this purpose)
3. Compile using:

bazel build //:libtrtorch --compilation_mode opt --distdir third_party/dist_dir/[x86_64-linux-gnu | aarch64-linux-gnu]

If you find bugs and you compiled using this method please disclose it in the issue (an ldd dump would be nice too)

1. Install TensorRT, CUDA and cuDNN on the system before starting to compile.
2. In WORKSPACE comment out

# Downloaded distributions to use with --distdir
http_archive(
    name = "cudnn",
    urls = ["<URL>",],

    build_file = "@//third_party/cudnn/archive:BUILD",
    sha256 = "<TAR SHA256>",
    strip_prefix = "cuda"
)

http_archive(
    name = "tensorrt",
    urls = ["<URL>",],

    build_file = "@//third_party/tensorrt/archive:BUILD",
    sha256 = "<TAR SHA256>",
    strip_prefix = "TensorRT-<VERSION>"
)

and uncomment

# Locally installed dependencies
new_local_repository(
    name = "cudnn",
    path = "/usr/",
    build_file = "@//third_party/cudnn/local:BUILD"
)

new_local_repository(
   name = "tensorrt",
   path = "/usr/",
   build_file = "@//third_party/tensorrt/local:BUILD"
)

3. Compile using:

bazel build //:libtrtorch --compilation_mode opt

bazel build //:libtrtorch --compilation_mode=dbg

bazel build //:libtrtorch --distdir third_party/dist_dir/aarch64-linux-gnu

Note: Please refer installation instructions for Pre-requisites

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

Make sure to add LibTorch 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>

To compile the python package for your local machine, just run python3 setup.py install in the //py directory. To build wheel files for different python versions, first build the Dockerfile in //py then run the following command

docker run -it -v$(pwd)/..:/workspace/TRTorch build_trtorch_wheel /bin/bash /workspace/TRTorch/py/build_whl.sh

Python compilation expects using the tarball based compilation strategy from above.

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. Also do look at the various op support trackers in the issues for information on the support status of various operators.

The Node Converter Registry is not exposed in the top level API but in the internal headers shipped with the tarball.

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

Take a look at the CONTRIBUTING.md

The TRTorch license can be found in the LICENSE file. It is licensed with a BSD Style licence