ONNF

Open Neural Network Frontend : an ONNX frontend for MLIR.

Installation

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

git clone https://github.com/llvm/llvm-project.git
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 check-mlir -- ${MAKEFLAGS}

Two environment variables need to be set:

• LLVM_PROJ_SRC should point to the llvm-project src directory (e.g., llvm-project/).
• LLVM_PROJ_BUILD should point to the llvm-project build directory (e.g., llvm-project/build).

To build ONNF, use the following command:

git clone --recursive git@github.com:clang-ykt/ONNF.git

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

mkdir ONNF/build && cd ONNF/build
cmake ..
cmake --build . --target onnf

# Run FileCheck tests:
cmake --build . --target check-mlir-lit

After the above commands succeed, an onnf executable should appear in the bin directory.

Using ONNF

The usage of onnf is as such:

OVERVIEW: ONNF MLIR modular optimizer driver

USAGE: onnf [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

ONNF 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:

./onnf --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>
  }
}