Skip to content

Latest commit

 

History

History
156 lines (114 loc) · 6.26 KB

README.md

File metadata and controls

156 lines (114 loc) · 6.26 KB
Raw

TPP MLIR

This is an experiment in using MLIR to automatically select the best Tensor Processing Primitives for linear algebra.

This repository contains an out-of-tree MLIR dialect as well as an opt-like tool to operate on that dialect and a runner-like tool to execute and benchmark MLIR kernels.

It also contains the recipes to use LIBXSMM from inside MLIR and can be used by other tools to drive our passes.

This repository was previously called tpp-sandbox. If you have a checkout with the previous name, please follow these instructions to rename the remote locally.

Build Status

TPP-MLIR Base Tests

TPP-MLIR Benchmarks

How to setup the environment

In order to build LLVM and TPP-MLIR, several software development tools such as git, cmake, compilers, etc. are needed. If you're able to build LLVM, you'll be able to build our project, but we do have some additional (optional) dependencies (OneDNN, OpenMP) that can be disabled (see below). Our required dependencies (libxsmm, libxsmm-dnn) are fetched and built by our build system, so you should have no problems either.

If you're having trouble with your build, you can use Conda to create a minimal environment (see below).

How to build LLVM

# Clone
git clone https://github.com/llvm/llvm-project.git

# checking out a tpp-mlir compatible version of llvm-project
wget https://raw.githubusercontent.com/plaidml/tpp-mlir/main/build_tools/llvm_version.txt
pushd llvm-project
git checkout `cat ../llvm_version.txt`
popd
rm llvm_version.txt

# create build dir
mkdir llvm-project/build
pushd llvm-project/build

# This is important for the next step
export CUSTOM_LLVM_ROOT=`pwd`
echo $CUSTOM_LLVM_ROOT
export PATH=$CUSTOM_LLVM_ROOT/bin:$PATH

# Configure Build
cmake -G Ninja ../llvm \
   -DLLVM_ENABLE_PROJECTS="mlir" \
   -DLLVM_BUILD_EXAMPLES=ON \
   -DLLVM_INSTALL_UTILS=ON \
   -DLLVM_TARGETS_TO_BUILD="host" \
   -DCMAKE_BUILD_TYPE=RelWithDebInfo \
   -DLLVM_ENABLE_ASSERTIONS=ON \
   -DCMAKE_C_COMPILER=clang \
   -DCMAKE_CXX_COMPILER=clang++ \
   -DLLVM_USE_LINKER=lld

# Build
ninja 

popd

How to build TPP MLIR

This setup assumes that you have built LLVM and MLIR in $CUSTOM_LLVM_ROOT as above.

Note: OpenMP is a requirement to get multi-threaded performance on our code. If you don't want to build with OpenMP, disable with the CMake flag -DUSE_OpenMP=False.

Note: OneDNN is a requirement to get performance comparisons against our code. If you don't want to build with OneDNN, disable with the CMake flag -DUSE_OneDNN=False.

# Clone
git clone https://github.com/plaidml/tpp-mlir.git
mkdir tpp-mlir/build
pushd tpp-mlir/build

# Build & test
# Please, make sure to use clang to build TPP-MLIR
cmake -G Ninja .. \
   -DCMAKE_BUILD_TYPE=RelWithDebInfo \
   -DMLIR_DIR=$CUSTOM_LLVM_ROOT/lib/cmake/mlir \
   -DLLVM_EXTERNAL_LIT=$CUSTOM_LLVM_ROOT/bin/llvm-lit \
   -DCMAKE_C_COMPILER=clang \
   -DCMAKE_CXX_COMPILER=clang++ \
   -DLLVM_USE_LINKER=lld
cmake --build . --target check-tpp

popd

To enable experimental GPU support see: GPU/README.md

Conda Environment

Every modern Linux and MacOS system should be able to build our project without glitches, however, you may have an older OS or some special condisiont (cluster environment). As each operating system has its own package manager and package names, we opted for providing instructions for the user-level package manager conda. This environment has been successfully tested on top of a Fedora Server minimal installation with less than 400 system-wide packages being installed.

Initial Setup (using Conda):

export TPPMLIR_WORKSPACE_DIR=/foo
cd ${TPPMLIR_WORKSPACE_DIR}
export ARCH_NAME=$(uname -m)
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-${ARCH_NAME}.sh
bash Miniconda3-latest-Linux-${ARCH_NAME}.sh -b -p ${TPPMLIR_WORKSPACE_DIR}/miniconda3
eval "$(${TPPMLIR_WORKSPACE_DIR}/miniconda3/bin/conda shell.bash hook)"
conda activate

conda install -y cmake ninja git clang clangxx llvm lld llvm-openmp llvm-tools binutils
if [ "${ARCH_NAME}" == "aarch64" ]; then
   conda install -y gcc_linux-aarch64 gxx_linux-aarch64
elif [ "${ARCH_NAME}" == "x86_64" ]; then
   conda install -y gcc_linux-64 gxx_linux-64
fi
python -m pip install coloredlogs

Reloading the environment after conda deactivate/logout/reboot:

export TPPMLIR_WORKSPACE_DIR=/foo
cd ${TPPMLIR_WORKSPACE_DIR}
eval "$(${TPPMLIR_WORKSPACE_DIR}/miniconda3/bin/conda shell.bash hook)"
conda activate

Formatting Tools

Our project uses Python and C++ source formating tools. There are two Ninja targets to verify the formatting:

  • check-clang, which uses clang-format version 16
  • check-python, which uses Python's black or autopep8 lint checkers

Due to clang-format's instability and non-backwards compatibility, we require that the version used be 16. If you have another version of clang installed, make sure you install clang-format-16 on your system.

Please, do not submit PRs with formatting issues on other files than you're making your code changes. Also avoid PRs with too many formatting changes in the same file on unrelated code.

License

This project is made available under the Apache License 2.0 with LLVM Exceptions. See the LICENSE.txt file for more details.

References

BRGEMM: High-Performance Deep Learning via a Single Building Block (2019)

TPP: Tensor Processing Primitives: A Programming Abstraction for Efficiency and Portability in Deep Learning & HPC Workloads (2021)

PARLOOPER: Harnessing Deep Learning and HPC Kernels via High-Level Loop and Tensor Abstractions on CPU Architectures (2023)

TPP-MLIR: Towards a high-performance AI compiler with upstream MLIR (2024)