Open Neural Network Frontend : an ONNX frontend for MLIR.
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 -- ${MAKEFLAGS}
cmake --build . --target check-mlirTwo environment variables need to be set:
- LLVM_PROJ_SRC should point to the llvm-project src directory (e.g., llvm-project/).
- LLVM_PROJ_BUILD should point to the llvm-project build directory (e.g., llvm-project/build).
To build ONNF, use the following command:
git clone --recursive [email protected]:clang-ykt/ONNF.git
# Export environment variables pointing to LLVM-Projects.
export LLVM_PROJ_SRC=$(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:
export LIT_OPTS=-v
cmake --build . --target check-mlir-lit
After the above commands succeed, an onnf executable should appear in the bin directory.
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.
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>
}
}
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