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The Torch-MLIR project aims to provide first class support from the PyTorch ecosystem to the MLIR ecosystem.
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C++ 49.4%
Python 22.3%
MLIR 19.4%
Jupyter Notebook 7.1%
CMake 0.6%
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Sean Silva fec2ee0072 Avoid introducing DimOp's in LowerBroadcastToToLoops. 
This makes sure we stay resonably canonically using the shape machinery.
(In fact, DimOp should probably be in the shape dialect since it hides a
`shape.shape_of` call)
 		2020-05-11 13:12:16 -07:00
include/npcomp Initial TCF/TCP E2E seed. 2020-05-08 20:20:41 -07:00
lib Avoid introducing DimOp's in LowerBroadcastToToLoops. 2020-05-11 13:12:16 -07:00
python Add implicit constant capture. 2020-05-08 17:55:02 -07:00
python_native Add implicit constant capture. 2020-05-08 17:55:02 -07:00
test Initial TCF/TCP E2E seed. 2020-05-08 20:20:41 -07:00
tools Add -DCMAKE_EXPORT_COMPILE_COMMANDS=TRUE 2020-05-11 12:58:42 -07:00
.clang-format Add stub numpy dialect. 2020-04-26 17:20:58 -07:00
.gitignore Add MLIRContext.dense_elements_attr to create an attribute from a python buffer/array. 2020-05-08 17:36:07 -07:00
CMakeLists.txt Move python native library to python_native/_npcomp...so. 2020-05-06 22:44:12 -07:00
LICENSE Add license 2020-04-26 20:10:24 -07:00
README.md Bump llvm-project to 3af85fa8f06220b43f03f26de216a67be4568fe7. 2020-05-08 20:42:40 -07:00
contributing.md Add contributing.md 2020-04-26 20:17:16 -07:00

README.md

npcomp - An aspirational MLIR based numpy compiler

This is a research prototype of MLIR dialects for representing numpy programs, and a set of reference tracing/compiler tools. The primary purpose at this point is to establish a solid modeling of restricted Python programs and Numpy based computations in MLIR. While this project will provide some reference implementations to prove the design, the intention is to align this with the broader set of tools that exist at this level of abstraction.

Design Notes

As I work through things, I've been jotting down some design notes:

Quick start

LLVM_VERSION=10
export CC=clang-$LLVM_VERSION
export CXX=clang++-$LLVM_VERSION
export LDFLAGS=-fuse-ld=$(which ld.lld-$LLVM_VERSION)
export LLVM_SRC_DIR=/path/to/llvm-project

# Check out last known good commit.
(cd $LLVM_SRC_DIR && git checkout 3af85fa8f06220b43f03f26de216a67be4568fe7)

./tools/install_mlir.sh
./tools/cmake_configure.sh


# ./tools/test_all.sh runs all of these commands.
cd build
ninja
ninja check-npcomp-opt
# Note: currently, python tests run separately
./python/run_tests.py

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 should be sufficient to run any interactive tooling (python3, Jupyter/Colab, etc).

export PYTHONPATH="$(realpath build/python):$(realpath build/python_native)"

The run_tests.py script is special in that it sets up the PYTHONPATH correctly when run.

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.

Things to look at:

  • python/npcomp/tracing/mlir_trace_test.py : Simple test case of tracing a function to an MLIR module.

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.