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# ONNF
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Open Neural Network Frontend : an ONNX frontend for MLIR.
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[![CircleCI ](https://circleci.com/gh/onnx/onnx-mlir/tree/master.svg?style=svg )](https://circleci.com/gh/onnx/onnx-mlir/tree/master)
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## Prerequisites
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```
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gcc >= 6.4
libprotoc >= 3.11.0
cmake >= 3.15.4
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```
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## Installation
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Firstly, install MLIR (as a part of LLVM-Project):
[same-as-file]: < > (utils/install-mlir.sh)
``` bash
git clone https://github.com/llvm/llvm-project.git
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# Check out a specific branch that is known to work with ONNF.
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cd llvm-project & & git checkout 076475713c236081a3247a53e9dbab9043c3eac2 & & cd ..
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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
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cmake --build . --target -- ${MAKEFLAGS}
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cmake --build . --target check-mlir
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```
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Two environment variables need to be set:
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- LLVM_PROJ_SRC should point to the llvm-project src directory (e.g., llvm-project/).
- LLVM_PROJ_BUILD should point to the llvm-project build directory (e.g., llvm-project/build).
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To build ONNF, use the following command:
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[same-as-file]: < > ({"ref": "utils/install-onnf.sh", "skip-doc": 2})
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```
git clone --recursive git@github.com:clang-ykt/ONNF.git
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# Export environment variables pointing to LLVM-Projects.
export LLVM_PROJ_SRC=$(pwd)/llvm-project/
export LLVM_PROJ_BUILD=$(pwd)/llvm-project/build
mkdir ONNF/build & & cd ONNF/build
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cmake ..
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cmake --build . --target onnf
# Run FileCheck tests:
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export LIT_OPTS=-v
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cmake --build . --target check-mlir-lit
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```
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 >
}
}
```
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## Troubleshooting
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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 ](https://github.com/clang-ykt/llvm-project ).