BUDDY MLIR
An MLIR-based compiler framework designed for a co-design ecosystem from DSL (domain-specific languages) to DSA (domain-specific architectures). (Project page).
Getting Started
The default build system uses LLVM/MLIR as an external library. We also provide a one-step build strategy for users who only want to use our tools. Please make sure the dependencies are available on your machine.
LLVM/MLIR Dependencies
Before building, please make sure the dependencies are available on your machine.
Clone and Initialize
$ git clone git@github.com:buddy-compiler/buddy-mlir.git
$ cd buddy-mlir
$ git submodule update --init
Build and Test LLVM/MLIR/CLANG
$ cd buddy-mlir
$ mkdir llvm/build
$ cd llvm/build
$ cmake -G Ninja ../llvm \
-DLLVM_ENABLE_PROJECTS="mlir;clang" \
-DLLVM_TARGETS_TO_BUILD="host;RISCV" \
-DLLVM_ENABLE_ASSERTIONS=ON \
-DCMAKE_BUILD_TYPE=RELEASE
$ ninja check-mlir check-clang
If your target machine includes a Nvidia GPU, you can use the following configuration:
$ cmake -G Ninja ../llvm \
-DLLVM_ENABLE_PROJECTS="mlir;clang" \
-DLLVM_TARGETS_TO_BUILD="host;RISCV;NVPTX" \
-DMLIR_ENABLE_CUDA_RUNNER=ON \
-DLLVM_ENABLE_ASSERTIONS=ON \
-DCMAKE_BUILD_TYPE=RELEASE
To enable MLIR Python bindings, please use the following configuration:
$ cmake -G Ninja ../llvm \
-DLLVM_ENABLE_PROJECTS="mlir;clang" \
-DLLVM_TARGETS_TO_BUILD="host;RISCV" \
-DLLVM_ENABLE_ASSERTIONS=ON \
-DCMAKE_BUILD_TYPE=RELEASE \
-DMLIR_ENABLE_BINDINGS_PYTHON=ON \
-DPython3_EXECUTABLE=$(which python3)
If your target machine has lld installed, you can use the following configuration:
$ cmake -G Ninja ../llvm \
-DLLVM_ENABLE_PROJECTS="mlir;clang" \
-DLLVM_TARGETS_TO_BUILD="host;RISCV" \
-DLLVM_USE_LINKER=lld \
-DLLVM_ENABLE_ASSERTIONS=ON \
-DCMAKE_BUILD_TYPE=RELEASE
Build buddy-mlir
If you have previously built the llvm-project, you can replace the $PWD with the path to the directory where you have successfully built the llvm-project.
$ cd buddy-mlir
$ mkdir build
$ cd build
$ cmake -G Ninja .. \
-DMLIR_DIR=$PWD/../llvm/build/lib/cmake/mlir \
-DLLVM_DIR=$PWD/../llvm/build/lib/cmake/llvm \
-DLLVM_ENABLE_ASSERTIONS=ON \
-DCMAKE_BUILD_TYPE=RELEASE
$ ninja
$ ninja check-buddy
To utilize the Buddy Compiler Python package, please ensure that the MLIR Python bindings are enabled and use the following configuration:
$ cmake -G Ninja .. \
-DMLIR_DIR=$PWD/../llvm/build/lib/cmake/mlir \
-DLLVM_DIR=$PWD/../llvm/build/lib/cmake/llvm \
-DLLVM_ENABLE_ASSERTIONS=ON \
-DCMAKE_BUILD_TYPE=RELEASE \
-DBUDDY_MLIR_ENABLE_PYTHON_PACKAGES=ON \
-DPython3_EXECUTABLE=$(which python3)
If you want to add domain-specific framework support, please add the following cmake options:
Framework | Enable Option | Other Options |
---|---|---|
OpenCV | -DBUDDY_ENABLE_OPENCV=ON |
Add -DOpenCV_DIR=</PATH/TO/OPENCV/BUILD/> or install OpenCV release version on your local device. |
One-step building strategy
If you only want to use our tools and integrate them more easily into your projects, you can choose to use the one-step build strategy.
$ cmake -G Ninja -Bbuild \
-DCMAKE_BUILD_TYPE=Release \
-DLLVM_ENABLE_PROJECTS="mlir;clang" \
-DLLVM_TARGETS_TO_BUILD="host;RISCV" \
-DLLVM_EXTERNAL_PROJECTS="buddy-mlir" \
-DLLVM_EXTERNAL_BUDDY_MLIR_SOURCE_DIR="$PWD" \
-DLLVM_ENABLE_ASSERTIONS=ON \
-DCMAKE_BUILD_TYPE=RELEASE \
llvm/llvm
$ cd build
$ ninja check-mlir check-clang
$ ninja
$ ninja check-buddy
Dialects
Bud Dialect
Bud dialect is designed for testing and demonstrating.
DIP Dialect
DIP dialect is designed for digital image processing abstraction.
Tools
buddy-opt
The buddy-opt is the driver for dialects and optimization in buddy-mlir project.
buddy-lsp-server
This program should be a drop-in replacement for mlir-lsp-server
, supporting new dialects defined in buddy-mlir. To use it, please directly modify mlir LSP server path in VSCode settings (or similar settings for other editors) to:
{
"mlir.server_path": "YOUR_BUDDY_MLIR_BUILD/bin/buddy-lsp-server",
}
After modification, your editor should have correct completion and error prompts for new dialects such as rvv
and gemmini
.
Examples
The purpose of the examples is to give users a better understanding of how to use the passes and the interfaces in buddy-mlir. Currently, we provide three types of examples.
- IR level conversion and transformation examples.
- Domain-specific application level examples.
- Testing and demonstrating examples.
For more details, please see the documentation of the examples.
How to Cite
If you find our project and research useful or refer to it in your own work, please cite our paper as follows:
@article{zhang2023compiler,
title={Compiler Technologies in Deep Learning Co-Design: A Survey},
author={Zhang, Hongbin and Xing, Mingjie and Wu, Yanjun and Zhao, Chen},
journal={Intelligent Computing},
year={2023},
publisher={AAAS}
}
For direct access to the paper, please visit Compiler Technologies in Deep Learning Co-Design: A Survey.