The Open Neural Network Exchange implementation in MLIR.

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

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

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:
      --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).
      --EmitLLVMBC - Lower model to LLVM IR and emit (to file) LLVM bitcode for model.

Current onnx-mlir supports operations of onnx v1.8.1. The dialect defined in onnx-mlir is able to handle some operation in previous onnx version if the operation in current version is inclusive. Otherwise, onnx version converter should be called explicitly outside of onnx-mlir, or inside onnx-mlir with option --invokeOnnxVersionConverter.

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.