Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/plaidml/tpp-mlir
TPP experimentation on MLIR for linear algebra
https://github.com/plaidml/tpp-mlir
compiler library llvm machine-learning micro-kernel
Last synced: about 2 months ago
JSON representation
TPP experimentation on MLIR for linear algebra
- Host: GitHub
- URL: https://github.com/plaidml/tpp-mlir
- Owner: plaidml
- License: other
- Created: 2022-06-06T14:45:43.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-05-22T15:22:06.000Z (8 months ago)
- Last Synced: 2024-05-22T16:39:21.998Z (8 months ago)
- Topics: compiler, library, llvm, machine-learning, micro-kernel
- Language: MLIR
- Homepage: https://arxiv.org/abs/2404.15204
- Size: 56 MB
- Stars: 94
- Watchers: 7
- Forks: 18
- Open Issues: 26
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.txt
- Security: SECURITY.md
Awesome Lists containing this project
README
# TPP MLIR
This is an experiment in using MLIR to automatically select the best [Tensor Processing Primitives](https://arxiv.org/abs/2104.05755) for linear algebra.
This repository contains an out-of-tree [MLIR](https://mlir.llvm.org/) 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](https://github.com/libxsmm/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](https://docs.github.com/en/repositories/creating-and-managing-repositories/renaming-a-repository) to rename the remote locally.
## Build Status
[](https://github.com/plaidml/tpp-mlir/actions/workflows/tpp-mlir.yml)
[](https://github.com/plaidml/tpp-mlir/actions/workflows/tpp-benchmark.yml)
## 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
```sh
# 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`._
```sh
# 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](lib/TPP/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):
```sh
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:
```sh
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)](https://arxiv.org/abs/1906.06440)
TPP: [Tensor Processing Primitives: A Programming Abstraction for Efficiency and Portability in Deep Learning & HPC Workloads (2021)](https://arxiv.org/abs/2104.05755)
PARLOOPER: [Harnessing Deep Learning and HPC Kernels via High-Level Loop and Tensor Abstractions on CPU Architectures (2023)](https://arxiv.org/abs/2304.12576)
TPP-MLIR: [Towards a high-performance AI compiler with upstream MLIR (2024)](https://arxiv.org/abs/2404.15204)