HeteroCL dialect is an out-of-tree MLIR dialect for accelerator design. HeteroCL dialect decouples algorithm from hardware customizations, and classifies them into compute and data customizations. The HeteroCL dialect is part of the HeteroCL compilation flow. HeteroCL provides an end-to-end flow from Python to LLVM backend or C HLS FPGA backends. With HeteroCL, designers can explore tradeoffs with hardware customizations in a systematic manner and quickly obtain high-performance design with little manual effort.
- gcc >= 9 (Please make sure you have installed the gcc that supports C 17)
- cmake >= 3.22
- python >= 3.8
- Download LLVM from llvm-project or checkout the Github branch
git clone https://github.com/llvm/llvm-project.git
cd llvm-project
git checkout tags/llvmorg-18-init
- Build
- Without Python binding
mkdir build && cd build cmake -G "Unix Makefiles" ../llvm \ -DLLVM_ENABLE_PROJECTS=mlir \ -DLLVM_BUILD_EXAMPLES=ON \ -DLLVM_TARGETS_TO_BUILD="X86" \ -DCMAKE_BUILD_TYPE=Release \ -DLLVM_ENABLE_ASSERTIONS=ON \ -DLLVM_INSTALL_UTILS=ON make -j8 # You can either use ninja to build # cmake -G Ninja # ninja
- With Python binding: Please follow the official guide to set up the environment. In the following, we set up a virtual environment called
hcl-dev
using Python venv, but we prefer you to install Anaconda3 and create an environment there. If you want to use your own Python environment, please specify the path for-DPython3_EXECUTABLE
.
# Create a virtual environment. Make sure you have installed Python3. which python3 python3 -m venv ~/.venv/hcl-dev source ~/.venv/hcl-dev/bin/activate # It is recommended to upgrade pip python3 -m pip install --upgrade pip # Install required packages. Suppose you are inside the llvm-project folder. python3 -m pip install -r mlir/python/requirements.txt # Run cmake mkdir build && cd build cmake -G "Unix Makefiles" ../llvm \ -DLLVM_ENABLE_PROJECTS=mlir \ -DLLVM_BUILD_EXAMPLES=ON \ -DLLVM_TARGETS_TO_BUILD="host" \ -DCMAKE_BUILD_TYPE=Release \ -DLLVM_ENABLE_ASSERTIONS=ON \ -DLLVM_INSTALL_UTILS=ON \ -DMLIR_ENABLE_BINDINGS_PYTHON=ON \ -DPython3_EXECUTABLE=`which python3` make -j8 # You can either use ninja to build # cmake -G Ninja # ninja # Export the LLVM build directory export LLVM_BUILD_DIR=$(pwd) # To enable better backtracing for debugging, # we suggest setting the following system path export LLVM_SYMBOLIZER_PATH=$(pwd)/bin/llvm-symbolizer
This setup assumes that you have built LLVM and MLIR in $LLVM_BUILD_DIR
. Please firstly clone our repository.
git clone --recursive [email protected]:cornell-zhang/hcl-dialect.git
cd hcl-dialect
mkdir build && cd build
NOTE: The HeteroCL dialect is a standalone system that works without a frontend. If you are using it with the HeteroCL frontend, the minimum requirement is to build with Python binding. Building with OpenSCoP extraction is optional.
- Build without Python binding
cmake -G "Unix Makefiles" .. \
-DMLIR_DIR=$LLVM_BUILD_DIR/lib/cmake/mlir \
-DLLVM_EXTERNAL_LIT=$LLVM_BUILD_DIR/bin/llvm-lit \
-DPYTHON_BINDING=OFF \
-DOPENSCOP=OFF
make -j8
- Build with Python binding
cmake -G "Unix Makefiles" .. \
-DMLIR_DIR=$LLVM_BUILD_DIR/lib/cmake/mlir \
-DLLVM_EXTERNAL_LIT=$LLVM_BUILD_DIR/bin/llvm-lit \
-DPYTHON_BINDING=ON \
-DOPENSCOP=OFF \
-DPython3_EXECUTABLE=`which python3` \
-DCMAKE_CXX_FLAGS="-Wfatal-errors -std=c 17"
make -j8
# Export the generated HCL-MLIR Python library
export PYTHONPATH=$(pwd)/tools/hcl/python_packages/hcl_core:${PYTHONPATH}
- Build with OpenSCoP extraction enabled: Set
-DOPENSCOP=ON
and export the library path.
export LD_LIBRARY_PATH=$(pwd)/openscop/lib:$LD_LIBRARY_PATH
Lastly, you can use the following integration test to see whether your built dialect works properly.
cmake --build . --target check-hcl
# perform loop transformation passes
./bin/hcl-opt -opt ../test/Transforms/compute/tiling.mlir
# generate C HLS code
./bin/hcl-opt -opt ../test/Transforms/compute/tiling.mlir | \
./bin/hcl-translate -emit-vivado-hls
# generate OpenSCoP
# An hcl.openscop file will be generated in the build folder
./bin/hcl-opt -opt ../test/Transforms/memory/buffer_add.mlir | \
./bin/hcl-translate --extract-scop-stmt
# run code on CPU
./bin/hcl-opt -opt -jit ../test/Translation/mm.mlir
Make sure you have correctly built the above HCL-MLIR dialect, and follow the instruction below.
# clone the HeteroCL repo
git clone https://github.com/cornell-zhang/heterocl.git heterocl-mlir
cd heterocl-mlir
# install dependencies
python3 -m pip install -r requirements.txt
# export the library
export HCL_HOME=$(pwd)
export PYTHONPATH=$HCL_HOME/python:${PYTHONPATH}
# run regression tests in the HeteroCL repo
cd tests && python3 -m pytest
We follow Google Style Guides and use
- clang-format for C/C
- black and pylint for Python
To install clang-format
, you can reuse the LLVM project by specifying the following CMake options:
# Inside the llvm-project folder
mkdir build-clang && cd build-clang
cmake -G Ninja ../llvm \
-DLLVM_ENABLE_PROJECTS="clang" \
-DLLVM_BUILD_EXAMPLES=ON \
-DLLVM_TARGETS_TO_BUILD="host" \
-DCMAKE_BUILD_TYPE=Release \
-DLLVM_ENABLE_ASSERTIONS=ON \
-DLLVM_INSTALL_UTILS=ON
ninja clang-format