NPComp - MLIR based compiler toolkit for numerical python programs

This project is participating in the LLVM Incubator process: as such, it is not part of any official LLVM release. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project is not yet endorsed as a component of LLVM.

The NPComp project aims to provide tooling for compiling numerical python programs of various forms to take advantage of MLIR+LLVM code generation and backend runtime systems.

In addition to providing a bridge to a variety of Python based numerical programming frameworks, NPComp also directly develops components for tracing and compilation of generic Python program fragments.

Framework integrations

• PyTorch -- Experimental integration for extracting programs from PyTorch.

Python language compiler tookit

At the core of NPComp are a set of dialects and python support code for tracing (define by run) numerical programs and compiling idiomatic subsets of the Python language. As another interpretation of the name, NPComp also seeks to provide compiler-backed support for Numpy APIs.

See the features doc for a semi-curated status of what is implemented in this area.

Architecture

The compiler is separated into:

• Frontend importer: Translates from various AST levels to corresponding MLIR dialects.
• Frontend compiler: MLIR passes and conversions, mostly operating on the basicpy and numpy dialects.
• Backend compiler and runtime: Some effort has been taken to make this pluggable, but right now, only the IREE Backend exists. There is in-tree work to also build a minimal reference backend directly targeting LLVM.

Repository Layout

The project is roughly split into the following areas of code:

• User-facing Python code
• C++ include and lib trees, following LLVM/MLIR conventions
• LIT testing trees:
  • test: Lit/FileCheck tests covering core MLIR based infra
  • test/Python/Compiler: Lit test suite that drive the compiler infra from Python
  • backend_test: Lit test suites conditionally enabled for each backend
• tools: Scripts and binaries (npcomp-opt, npcomp-run-mlir, etc)

Interactive Use

The cmake configuration populates symlinks in the build/python directory mirroring the source layout. This allows edit-run without rebuilding (unless if files are added/removed).

Configuring the PYTHONPATH as above should be sufficient to run any interactive tooling (python3, Jupyter/Colab, etc).

Note that running the cmake_configure.sh script will also output a .env file in the workspace folder with the correct PYTHONPATH set. This allows tools like VSCode to work by default for debugging.

Notes:

• Python sources are symlinked to the output directory at configure time. Adding sources will require a reconfigure. Editing should not.
• It is a very common issue to have both python 2.7 (aka. "python") and python 3.x (aka. "python3") on a system at a time (and we can only hope that one day this ends). Since the native library at development time binds to a specific version, if you try to run with a different python, you will get an error about the "native" module not being found.

Compiler development

For bash users, adding the following to your .bashrc defines some aliases that are useful during compiler development, such as shortcuts for builing and running npcomp-opt.

source $WHERE_YOU_CHECKED_OUT_NPCOMP/tools/bash_helpers.sh

Build Instructions

Common prep

# From checkout directory.
git submodule init
git submodule update

# Use clang and lld to build (optional but recommended).
LLVM_VERSION=10
export CC=clang-$LLVM_VERSION
export CXX=clang++-$LLVM_VERSION
# If compiling on a new OS that defaults to the CXX11 ABI (i.e. Ubuntu >= 20.04)
# and looking to use binary installs from the PyTorch website, you must build
# without the CXX11 ABI.
export CXXFLAGS="-D_GLIBCXX_USE_CXX11_ABI=0"
export LDFLAGS=-fuse-ld=$(which ld.lld-$LLVM_VERSION)

# Build and install LLVM/MLIR into the ./install-mlir directory
./build_tools/install_mlir.sh

Vanilla - numpy-only, no pytorch

# Follow common prep above.
./build_tools/cmake_configure.sh

# Build and run tests
# ./build_tools/test_all.sh runs all of these commands.
cd build
ninja
ninja check-npcomp

# Setup PYTHONPATH for interactive use
export PYTHONPATH="$(realpath python):$(realpath build/python)"

PyTorch Frontend (with PyTorch installed via conda)

# See note above about -D_GLIBCXX_USE_CXX11_ABI=0
./build_tools/cmake_configure.sh \
  -DCMAKE_PREFIX_PATH=$CONDA_PREFIX/lib/python3.8/site-packages/torch/share/cmake/Torch
cmake --build build --target check-npcomp check-frontends-pytorch

PyTorch Frontend (via docker container)

Create docker image (or follow your own preferences):

• Mount the (host) source directory to /src/mlir-npcomp (in the container).
• Mount the /build directory (in the container) appropriately for your case.

docker build docker/pytorch-nightly --tag local/npcomp:build-pytorch-nightly
docker volume create npcomp-build

Shell into docker image:

docker run \
  --mount type=bind,source=$HOME/src/mlir-npcomp,target=/src/mlir-npcomp \
  --mount source=npcomp-build,target=/build \
  --rm -it local/npcomp:build-pytorch-nightly /bin/bash

Build/test npcomp (from within docker image):

# From within the docker image.
cd /src/mlir-npcomp
./build_tools/install_mlir.sh
./build_tools/cmake_configure.sh
cmake --build /build/npcomp --target check-npcomp check-frontends-pytorch

VSCode with a Docker Dev Image

Start a docker dev container based on our image

Assumes that mlir-npcomp is checked out locally under ~/src/mlir-npcomp. See docker_shell_funcs.sh for commands to modify if different.

# Build/start the container.
# Follow instructions here to allow running `docker` without `sudo`:
# https://docs.docker.com/engine/install/linux-postinstall/
source ./build_tools/docker_shell_funcs.sh
npcomp_docker_build  # Only needed first time/on updates to docker files.
npcomp_docker_start

# Get an interactive shell to the container and initial build.
npcomp_docker_login

# Stop the container (when done).
npcomp_docker_stop

Configure VSCode:

First, install the VSCode Docker extension and VSCode Remote - Containers extension. Follow instructions here to allow running docker without sudo, otherwise VSCode won't be able to use docker https://docs.docker.com/engine/install/linux-postinstall/ (Note that VSCode has some daemons that you will need to kill/restart for the instructions there to take effect; consider just rebooting your machine)

Attach to your running container by opening the Docker extension tab (left panel), right clicking on the container name, and selecting "Attach Visual Studio code". The container name if you are using docker_shell_funcs.sh is npcomp.

Install extensions in container:

• CMake Tools
• C/C++
• C++ Intellisense

Add workspace folders:

• mlir-npcomp source folder
• external/llvm-project source folder

Configure general settings:

Ctrl-Shift-P > Preferences: Open Settings (UI)

• For mlir-npcomp folder:
  • Cmake: Build directory: /build/npcomp
  • Uncheck Cmake: Configure On Edit and Cmake: Configure on Open
• For llvm-project folder:
  • Cmake: Build directory: /build/llvm-build
  • Uncheck Cmake: Configure On Edit and Cmake: Configure on Open

Configure Intellisense:

Ctrl-Shift-P > C/C++: Edit Configurations (UI)

• Open C/C++ config (for each project folder):
  • Under Advanced, Compile Commands:
    • set /build/npcomp/compile_commands.json for mlir-npcomp
    • set /build/llvm-build/compile_commands.json for llvm-project
• Open a C++ file, give it a few seconds and see if you get code completion (press CTRL-Space).

Make sure to save your workspace (prefer a local folder with the "Use Local" button)!