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The Torch-MLIR project aims to provide first class support from the PyTorch ecosystem to the MLIR ecosystem.
51 Commits
47 Branches
1195 Tags
92 MiB
C++ 49.4%
Python 22.3%
MLIR 19.4%
Jupyter Notebook 7.1%
CMake 0.6%
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Sean Silva e29aef855b Initial TCF/TCP E2E seed. 
Very much WIP.

This is enough to get tcf.add down to approximately the "linalg.generic
on buffers" level of abstraction. (but there are nuances)
 		2020-05-08 20:20:41 -07:00
include/npcomp Initial TCF/TCP E2E seed. 2020-05-08 20:20:41 -07:00
lib Initial TCF/TCP E2E seed. 2020-05-08 20:20:41 -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 Initial TCF/TCP E2E seed. 2020-05-08 20:20:41 -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 Add tools/test_all.sh 2020-05-08 18:13:19 -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 0c4aab27b3da05dd1b0c0c39472525325fda5e23)

./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.