MixPrecHLS Project

MixPrecHLS: A High-Level Synthesis Framework for Mixed-Precision Computing.

It is important to note that MixPrecHLS is made based on ScaleHLS.

Setting this up

Prerequisites

• python3
• cmake
• ninja
• clang and lld

Optionally, the following packages are required for the Python binding.

• pybind11
• numpy

Clone MixPrecHLS

$ git clone --recursive [email protected]:sheenisme/MixPrecHLS.git mixprechls
$ cd mixprechls

Build MixPrecHLS

Run the following script to build MixPrecHLS. Optionally, add -p ON to enable the Python binding and -j xx to specify the number of parallel linking jobs.

$ ./build-mixprechls.sh

After the build, we suggest to export the following paths.

$ export PATH=$PATH:$PWD/build/bin:$PWD/polygeist/build/bin
$ export PYTHONPATH=$PYTHONPATH:$PWD/build/tools/scalehls/python_packages/scalehls_core

Compiling PyTorch Model

Install the pre-built Torch-MLIR front-end:

$ python -m venv mlir_venv
$ source mlir_venv/bin/activate
$ python -m pip install --upgrade pip
$ pip install --pre torch-mlir torchvision -f https://llvm.github.io/torch-mlir/package-index/ --extra-index-url https://download.pytorch.org/whl/nightly/cpu

Once Torch-MLIR is installed, you should be able to run the following test:

$ cd docs/samples/pytorch/resnet18

$ # Parse PyTorch model to LinAlg dialect (with Torch-MLIR mlir_venv activated).
$ python3 resnet18.py > resnet18.mlir

$ # Optimize the model and emit C++ code.
$ scalehls-opt resnet18.mlir \
    -hida-pytorch-pipeline="top-func=forward loop-tile-size=8 loop-unroll-factor=4" \
    | scalehls-translate -scalehls-emit-hlscpp > resnet18.cpp

Repository Layout

The project follows the conventions of typical MLIR-based projects:

• include/scalehls and lib for C++ MLIR dialects/passes.
• polygeist for the C/C++ front-end.
• docs/samples for C/C++ and PyTorch examples of ScaleHLS.
• test for holding regression tests.
• tools for command line tools.