The Open Neural Network Exchange implementation in MLIR (http://onnx.ai/onnx-mlir/).

System	Build Status
x86-Linux
s390-Linux
x86-Windows
x86-macOS

An easy way to get started with ONNX-MLIR is to use a prebuilt docker image. These images are created as a result of a successful merge build on the trunk. This means that the latest image represents the tip of the trunk. Currently there are both Release and Debug mode images for amd64, ppc64le and s390x saved in Docker Hub as, respectively, onnxmlirczar/onnx-mlir and onnxmlirczar/onnx-mlir-dev. To use one of these images either pull it directly from Docker Hub, launch a container and run an interactive bash shell in it, or use it as the base image in a dockerfile. The onnx-mlir image just contains the built compiler and can be used to compile models. The onnx-mlir-dev image contains the full build tree including the prerequisites and a clone of the source code. The source can be modified and onnx-mlir rebuilt from within the container, so it is possible to use it as a development environment. It is also possible to attach vscode to the running container. An example Dockerfile useful for development and vscode configuration files can be seen in the docs folder. If the workspace directory and the vscode files are not present in the directory where the Docker build is run, then the lines referencing them should be commented out or deleted. The Dockerfile is shown here.

FROM onnxmlirczar/onnx-mlir-dev
WORKDIR /workdir
ENV HOME=/workdir

# 1) Install packages.
ENV PATH=$PATH:/workdir/bin
RUN apt-get update
RUN apt-get install -y python-numpy
RUN apt-get install -y python3-pip
RUN python -m pip install --upgrade pip
RUN apt-get install -y gdb
RUN apt-get install -y lldb
RUN apt-get install -y emacs
RUN apt-get install -y vim
# 2) Instal optional packages, uncomment/add as you see fit.
# RUN apt-get install -y valgrind
# RUN apt-get install -y libeigen3-dev
# RUN apt-get install -y clang-format
# RUN python -m pip install wheel
# RUN python -m pip install numpy
# RUN python -m pip install torch==1.6.0+cpu torchvision==0.7.0+cpu -f https://download.pytorch.org/whl/torch_stable.html
# RUN git clone https://github.com/onnx/tutorials.git
# Install clang-12.
# RUN apt-get install -y lsb-release wget software-properties-common
# RUN bash -c "$(wget -O - https://apt.llvm.org/llvm.sh)"

# 3) When using vscode, copy your .vscode in the Dockerfile dir and
#    uncomment the two lines below.
# WORKDIR /workdir/.vscode
# ADD .vscode /workdir/.vscode

# 4) When using a personal workspace folder, set your workspace sub-directory
#    in the Dockerfile dir and uncomment the two lines below.
# WORKDIR /workdir/workspace
# ADD workspace /workdir/workspace

# 5) Fix git by reattaching head and making git see other branches than master.
WORKDIR /workdir/onnx-mlir
RUN git checkout master
RUN git fetch --unshallow

# 6) Set the PATH environment vars for make/debug mode. Replace Debug
#    with Release in the PATH below when using Release mode.
WORKDIR /workdir
ENV MLIR_DIR=/workdir/llvm-project/build/lib/cmake/mlir
ENV NPROC=4
ENV PATH=$PATH:/workdir/onnx-mlir/build/Debug/bin/:/workdir/onnx-mlir/build/Debug/lib:/workdir/llvm-project/build/bin

gcc >= 6.4
libprotoc >= 3.11.0
cmake >= 3.15.4
ninja >= 1.10.2

At any point in time, ONNX MLIR depends on a specific commit of the LLVM project that has been shown to work with the project. Periodically the maintainers need to move to a more recent LLVM level. Among other things, this requires that the commit string in utils/clone-mlir.sh be updated. A consequence of making this change is that the TravisCI build will fail until the Docker images that contain the prereqs are rebuilt. There is a GitHub workflow that rebuilds this image for the amd64 architecture, but currently the ppc64le and s390x images must be rebuilt manually. The Dockerfiles to accomplish that are in the repo.

Firstly, install MLIR (as a part of LLVM-Project):

git clone https://github.com/llvm/llvm-project.git
# Check out a specific branch that is known to work with ONNX MLIR.
cd llvm-project && git checkout ebe408ad8003c946ef871b955ab18e64e82697cb && cd ..

mkdir llvm-project/build
cd llvm-project/build
cmake -G Ninja ../llvm \
   -DLLVM_ENABLE_PROJECTS=mlir \
   -DLLVM_TARGETS_TO_BUILD="host" \
   -DCMAKE_BUILD_TYPE=Release \
   -DLLVM_ENABLE_ASSERTIONS=ON \
   -DLLVM_ENABLE_RTTI=ON

cmake --build . --target -- ${MAKEFLAGS}
cmake --build . --target check-mlir

The following environment variables can be set before building onnx-mlir (or alternatively, they need to be passed as CMake variables):

• MLIR_DIR should point to the mlir cmake module inside an llvm-project build or install directory (e.g., llvm-project/build/lib/cmake/mlir).

This project uses lit (LLVM's Integrated Tester) for unit tests. When running CMake, we can also specify the path to the lit tool from LLVM using the LLVM_EXTERNAL_LIT define but it is not required as long as MLIR_DIR points to a build directory of llvm-project. If MLIR_DIR points to an install directory of llvm-project, LLVM_EXTERNAL_LIT is required.

To build ONNX-MLIR, use the following commands:

git clone --recursive https://github.com/onnx/onnx-mlir.git

# Export environment variables pointing to LLVM-Projects.
export MLIR_DIR=$(pwd)/llvm-project/build/lib/cmake/mlir

mkdir onnx-mlir/build && cd onnx-mlir/build
cmake ..
cmake --build .

# Run FileCheck tests:
export LIT_OPTS=-v
cmake --build . --target check-onnx-lit

If you are running on OSX Big Sur, you need to add -DCMAKE_CXX_COMPILER=/usr/bin/c++ to the cmake .. command due to changes in the compilers. After the above commands succeed, an onnx-mlir executable should appear in the bin directory.

Building onnx-mlir on Windows requires building some additional prerequisites that are not available by default.

Note that the instructions in this file assume you are using Visual Studio 2019 Community Edition. It is recommended that you have the Desktop development with C++ and Linux development with C++ workloads installed. This ensures you have all toolchains and libraries needed to compile this project and its dependencies on Windows.

Run all the commands from a shell started from "Developer Command Prompt for VS 2019".

Build protobuf as a static library.

git clone --recurse-submodules https://github.com/protocolbuffers/protobuf.git
REM Check out a specific branch that is known to work with ONNX MLIR.
REM This corresponds to the v3.11.4 tag
cd protobuf && git checkout d0bfd5221182da1a7cc280f3337b5e41a89539cf && cd ..

set root_dir=%cd%
md protobuf_build
cd protobuf_build
call cmake %root_dir%\protobuf\cmake -G "Visual Studio 16 2019" -A x64 -T host=x64 ^
   -DCMAKE_INSTALL_PREFIX="%root_dir%\protobuf_install" ^
   -DCMAKE_BUILD_TYPE=Release ^
   -Dprotobuf_BUILD_EXAMPLES=OFF ^
   -Dprotobuf_BUILD_SHARED_LIBS=OFF ^
   -Dprotobuf_BUILD_TESTS=OFF ^
   -Dprotobuf_MSVC_STATIC_RUNTIME=OFF ^
   -Dprotobuf_WITH_ZLIB=OFF

call cmake --build . --config Release -- /m
call cmake --build . --config Release --target install -- /m

Before running CMake for onnx-mlir, ensure that the bin directory to this protobuf is before any others in your PATH:

set PATH=%root_dir%\protobuf_install\bin;%PATH%

Install MLIR (as a part of LLVM-Project):

git clone https://github.com/llvm/llvm-project.git
# Check out a specific branch that is known to work with ONNX MLIR.
cd llvm-project && git checkout ebe408ad8003c946ef871b955ab18e64e82697cb && cd ..

set root_dir=%cd%
md llvm-project\build
cd llvm-project\build
call cmake %root_dir%\llvm-project\llvm -G "Visual Studio 16 2019" -A x64 -T host=x64 ^
   -DCMAKE_INSTALL_PREFIX="%root_dir%\llvm-project\build\install" ^
   -DLLVM_ENABLE_PROJECTS=mlir ^
   -DLLVM_TARGETS_TO_BUILD="host" ^
   -DCMAKE_BUILD_TYPE=Release ^
   -DLLVM_ENABLE_ASSERTIONS=ON ^
   -DLLVM_ENABLE_RTTI=ON ^
   -DLLVM_ENABLE_ZLIB=OFF

call cmake --build . --config Release -- /m
call cmake --build . --config Release --target install -- /m
call cmake --build . --config Release --target check-mlir -- /m

The following environment variables can be set before building onnx-mlir (or alternatively, they need to be passed as CMake variables):

• MLIR_DIR should point to the mlir cmake module inside an llvm-project build or install directory (e.g., c:/repos/llvm-project/build/lib/cmake/mlir).

This project uses lit (LLVM's Integrated Tester) for unit tests. When running CMake, we can also specify the path to the lit tool from LLVM using the LLVM_EXTERNAL_LIT define but it is not required as long as MLIR_DIR points to a build directory of llvm-project. If MLIR_DIR points to an install directory of llvm-project, LLVM_EXTERNAL_LIT is required.

To build ONNX MLIR, use the following commands:

git clone --recursive https://github.com/onnx/onnx-mlir.git

set root_dir=%cd%

md onnx-mlir\build
cd onnx-mlir\build
call cmake %root_dir%\onnx-mlir -G "Visual Studio 16 2019" -A x64 -T host=x64 ^
   -DCMAKE_BUILD_TYPE=Release ^
   -DCMAKE_PREFIX_PATH=%root_dir%\protobuf_install ^
   -DLLVM_LIT_ARGS=-v ^
   -DMLIR_DIR=%root_dir%\llvm-project\build\lib\cmake\mlir

call cmake --build . --config Release --target onnx-mlir -- /m

To test ONNX MLIR, use the following command:

call cmake --build . --config Release --target check-onnx-lit -- /m

After the above commands succeed, an onnx-mlir executable should appear in the bin directory.

The usage of onnx-mlir is as such:

OVERVIEW: ONNX MLIR modular optimizer driver

USAGE: onnx-mlir [options] <input file>

OPTIONS:

Generic Options:

  --help        - Display available options (--help-hidden for more)
  --help-list   - Display list of available options (--help-list-hidden for more)
  --version     - Display the version of this program

ONNX MLIR Options:
These are frontend options.

  Choose target to emit:
      --EmitONNXBasic - Ingest ONNX and emit the basic ONNX operations without inferred shapes.
      --EmitONNXIR    - Ingest ONNX and emit corresponding ONNX dialect.
      --EmitMLIR      - Lower model to MLIR built-in transformation dialect.
      --EmitLLVMIR    - Lower model to LLVM IR (LLVM dialect).
      --EmitLib       - Lower model to LLVM IR, emit (to file) LLVM bitcode for model, compile and link it to a shared library.

For example, to lower an ONNX model (e.g., add.onnx) to ONNX dialect, use the following command:

./onnx-mlir --EmitONNXIR add.onnx

The output should look like:

module {
  func @main_graph(%arg0: tensor<10x10x10xf32>, %arg1: tensor<10x10x10xf32>) -> tensor<10x10x10xf32> {
    %0 = "onnx.Add"(%arg0, %arg1) : (tensor<10x10x10xf32>, tensor<10x10x10xf32>) -> tensor<10x10x10xf32>
    return %0 : tensor<10x10x10xf32>
  }
}

If the latest LLVM project fails to work due to the latest changes to the MLIR subproject please consider using a slightly older version of LLVM. One such version, which we use, can be found here.

Backend tests are triggered by make check-onnx-backend in the build directory and require a few preliminary steps to run successfully. Similarily, rebuilding the ONNX operations in ONNX-MLIR from their ONNX descriptions is triggered by make OMONNXOpsIncTranslation.

You will need to install python 3.x if its not default in your environment, and possibly set the cmake PYTHON_EXECUTABLE varialbe in your top cmake file.

You will also need pybind11 which may need to be installed (mac: brew install pybind for example) and you may need to indicate where to find the software (Mac, POWER, possibly other platforms: export pybind11_DIR=<your path to pybind>). Then install the third_party/onnx software (Mac: pip install -e third_party/onnx) typed in the top directory.

On Macs/POWER and possibly other platforms, there is currently an issue that arises when installing ONNX. If you get an error during the build, try a fix where you edit the top CMakefile as reported in this PR: https://github.com/onnx/onnx/pull/2482/files.

We have a slack channel established under the Linux Foundation AI and Data Workspace, named #onnx-mlir-discussion. THis channel can be used for asking quick questions related to this project. A direct link is here.