107 lines
3.2 KiB
Markdown
107 lines
3.2 KiB
Markdown
# ONNX MLIR
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The Open Neural Network Exchange implementation in MLIR.
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[](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
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libprotoc >= 3.11.0
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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):
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[same-as-file]: <> (utils/install-mlir.sh)
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``` bash
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git clone https://github.com/llvm/llvm-project.git
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# Check out a specific branch that is known to work with ONNX MLIR.
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cd llvm-project && git checkout 07e462526d0cbae40b320e1a4307ce11e197fb0a && cd ..
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mkdir llvm-project/build
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cd llvm-project/build
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cmake -G Ninja ../llvm \
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-DLLVM_ENABLE_PROJECTS=mlir \
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-DLLVM_BUILD_EXAMPLES=ON \
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-DLLVM_TARGETS_TO_BUILD="host" \
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-DCMAKE_BUILD_TYPE=Release \
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-DLLVM_ENABLE_ASSERTIONS=ON \
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-DLLVM_ENABLE_RTTI=ON
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cmake --build . --target -- ${MAKEFLAGS}
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cmake --build . --target check-mlir
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```
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Two environment variables need to be set:
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- LLVM_PROJ_SRC should point to the llvm-project src directory (e.g., llvm-project/).
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- LLVM_PROJ_BUILD should point to the llvm-project build directory (e.g., llvm-project/build).
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To build ONNX-MLIR, use the following command:
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[same-as-file]: <> ({"ref": "utils/install-onnx-mlir.sh", "skip-doc": 2})
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```
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git clone --recursive https://github.com/onnx/onnx-mlir.git
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# Export environment variables pointing to LLVM-Projects.
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export LLVM_PROJ_SRC=$(pwd)/llvm-project/
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export LLVM_PROJ_BUILD=$(pwd)/llvm-project/build
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mkdir onnx-mlir/build && cd onnx-mlir/build
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cmake ..
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cmake --build . --target onnx-mlir
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# Run FileCheck tests:
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export LIT_OPTS=-v
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cmake --build . --target check-onnx-lit
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```
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After the above commands succeed, an `onnx-mlir` executable should appear in the `bin` directory.
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## Using ONNX MLIR
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The usage of `onnx-mlir` is as such:
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```
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OVERVIEW: ONNX MLIR modular optimizer driver
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USAGE: onnx-mlir [options] <input file>
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OPTIONS:
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Generic Options:
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--help - Display available options (--help-hidden for more)
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--help-list - Display list of available options (--help-list-hidden for more)
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--version - Display the version of this program
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ONNX MLIR Options:
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These are frontend options.
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Choose target to emit:
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--EmitONNXIR - Ingest ONNX and emit corresponding ONNX dialect.
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--EmitMLIR - Lower model to MLIR built-in transformation dialect.
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--EmitLLVMIR - Lower model to LLVM IR (LLVM dialect).
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--EmitLLVMBC - Lower model to LLVM IR and emit (to file) LLVM bitcode for model.
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```
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## Example
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For example, to lower an ONNX model (e.g., add.onnx) to ONNX dialect, use the following command:
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```
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./onnx-mlir --EmitONNXIR add.onnx
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```
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The output should look like:
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```
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module {
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func @main_graph(%arg0: tensor<10x10x10xf32>, %arg1: tensor<10x10x10xf32>) -> tensor<10x10x10xf32> {
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%0 = "onnx.Add"(%arg0, %arg1) : (tensor<10x10x10xf32>, tensor<10x10x10xf32>) -> tensor<10x10x10xf32>
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return %0 : tensor<10x10x10xf32>
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}
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}
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```
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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).
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