MLIR-Based Ideas Landing Project (Project page).

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.

Before building, please make sure the dependencies are available on your machine.

$ git clone [email protected]:buddy-compiler/buddy-mlir.git
$ cd buddy-mlir
$ git submodule update --init

$ 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

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

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

If you want to add domain-specific framework support, please add the following cmake options:

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

Bud dialect is designed for testing and demonstrating.

DIP dialect is designed for digital image processing abstraction.

The buddy-opt is the driver for dialects and optimization in buddy-mlir project.

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.

The AutoConfig mechanism is designed to detect the target hardware and configure the toolchain automatically.

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.