onnx-mlir

Representation and Reference Lowering of ONNX Models in MLIR Compiler Infrastructure

View the Project on GitHub onnx/onnx-mlir

How-tos

Using PyRuntime
Perform Inference Using ONNX-MLIR Runtime API

References

ONNX Dialect
OMTensor C99 Runtime API
OMTensorList C99 Runtime API
Generate ONNX Dialect
About Documentation

Discussions

Testing Guidelines

Tools

debug.py - Debug Numerical Errors
DocCheck - Handling Necessary Code Duplication

This project is maintained by onnx

Hosted on GitHub Pages — Theme by orderedlist

ONNX MLIR

The Open Neural Network Exchange implementation in MLIR.

CircleCI

Prerequisites

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

Installation on UNIX

MLIR

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 496fb70b141ccbfaba9761294f3b4b97717096a3 && cd ..
mkdir llvm-project/build
cd llvm-project/build
cmake -G Ninja ../llvm \
   -DLLVM_ENABLE_PROJECTS=mlir \
   -DLLVM_BUILD_EXAMPLES=ON \
   -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

ONNX-MLIR (this project)

Two environment variables need to be set:

To build ONNX-MLIR, use the following command:

same-as-file: <> ({“ref”: “utils/install-onnx-mlir.sh”, “skip-doc”: 2})

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

# Export environment variables pointing to LLVM-Projects.
export LLVM_PROJ_SRC=$(pwd)/llvm-project/
export LLVM_PROJ_BUILD=$(pwd)/llvm-project/build

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.

Installation on Windows

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”.

Protobuf

Build protobuf as a static library.

set root_dir=%cd%
git clone --recurse-submodules https://github.com/protocolbuffers/protobuf.git
cd protobuf
cd cmake
cmake -G "Visual Studio 16 2019" -A x64 -T host=x64 -DCMAKE_BUILD_TYPE=Release -Dprotobuf_MSVC_STATIC_RUNTIME=OFF -Dprotobuf_BUILD_TESTS=OFF -Dprotobuf_BUILD_EXAMPLES=OFF -Dprotobuf_WITH_ZLIB=OFF -DCMAKE_INSTALL_PREFIX="%root_dir%\protobuf\install"
call msbuild protobuf.sln /m /p:Configuration=Release
call msbuild INSTALL.vcxproj /p:Configuration=Release

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%

PDCurses

Build a local version of the curses library, used by various commandline tools in onnx-mlir. These instructions assume you use Public Domain Curses.

Run this from a Visual Studio developer command prompt since you will need access to the appropriate version of Visual Studio’s nmake tool.

set root_dir=%cd%
git clone https://github.com/wmcbrine/PDCurses.git
set PDCURSES_SRCDIR=%root_dir%/PDCurses
cd PDCurses
call nmake -f wincon/Makefile.vc

MLIR

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 496fb70b141ccbfaba9761294f3b4b97717096a3 && cd ..
set root_dir=%cd%
md llvm-project\build
cd llvm-project\build
call cmake -G "Visual Studio 16 2019" -A x64 -T host=x64 ..\llvm ^
   -DCMAKE_INSTALL_PREFIX="%root_dir%\llvm-project\build\install" ^
   -DLLVM_ENABLE_PROJECTS=mlir ^
   -DLLVM_BUILD_EXAMPLES=ON ^
   -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 --target -- /m
call cmake --build . --config Release --target install
call cmake --build . --config Release --target check-mlir

ONNX-MLIR (this project)

The following environment variables need to be set before building onnx-mlir:

This project uses lit (LLVM’s Integrated Tester) for unit tests. When running CMake, we will also specify the path to the lit tool from LLVM using the LLVM_EXTERNAL_LIT define.

To build ONNX MLIR, use the following command:

same-as-file: <> ({“ref”: “utils/install-onnx-mlir.cmd”, “skip-doc”: 2})

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

REM Export environment variables pointing to LLVM-Projects.
set root_dir=%cd%
set CURSES_LIB_PATH=%root_dir%/PDCurses
set LLVM_PROJ_BUILD=%root_dir%/llvm-project/build
set LLVM_PROJ_SRC=%root_dir%/llvm-project

md onnx-mlir\build
cd onnx-mlir\build
call cmake -G "Visual Studio 16 2019" -A x64 -T host=x64 -DLLVM_EXTERNAL_LIT="%root_dir%\llvm-project\build\Release\bin\llvm-lit.py" -DCMAKE_BUILD_TYPE=Release ..
call cmake --build . --config Release --target onnx-mlir -- /m

REM Run FileCheck tests
set LIT_OPTS=-v
call cmake --build . --config Release --target check-onnx-lit

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

Using ONNX-MLIR

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.

Example

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>
  }
}

Troubleshooting

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.