* This has been anticipated for a long time in that it is quite hard to keep C++ binary compatibility across a system landscape as diverse as PyTorch, LLVM, and this project. This is why we based the PyTorch extension on the MLIR and NPCOMP C APIs only: that is the only sane linkage story for the entire matrix.
* Removes the few LLVM'isms in torch_mlir that had snuck in, using either STL or PyTorch support utilities. The new rule here is that LLVM C++ includes are forbidden at this level and (as stated in the design), torch_mlir should use the PyTorch runtime and support libraries (not introduce an incidental C++ dependency on LLVM).
* Also deletes mnist-playground as it was proving impossible to keep the grid of PyTorch vs system ABI divisions functioning. I am open to a less drastic course here (optional/disabled by default?)
* This gets us pretty close to just using PyTorch's extension builder API, which will be nice for distribution (i.e. it integrates well with the PyTorch ecosystem for deployment). I ended up just simplifying the in-tree CMake support for now.
* Fixes#138
* In most situations, this eliminates the need to explicitly set a path to the Torch cmake files.
* Also upgrades to new Python3 find package. (should eliminate 2.x mismatches)
* Since PyTorch is located by asking Python where it is, this eliminates a lot of causes of mismatch. (one source of truth)
* Enables -gsplit-dwarf for both LLVM and NPCOMP, reducing the occurrence of the ~GB scale binaries.
* CMake shared linking seems incompatible with this, so shared objects are still "too big" but there are few of them.
* Reduces disk thrash on clean/install of everything.
* Now gets far enough to capture batch_norm.
* Has some issues still with in-place ops.
* Can materialize constants.
* Includes an upgrade to PyTorch nightly, which has important bug fixes for fallback and boxed kernel dispatch.
* Fixes#78, #79, #80.
* Will do more testing in a follow-up once further bugs are fixed that facilitate getting at the other features.
* Forks a subset of my shell functions into docker_shell_funcs.sh, specifically needed to create docker images that run as yourself.
* Extends the readme with the three command bootstrap to get a dev container running.
* Step by step instructions for configuring VSCode for Intellisense in either npcomp or LLVM.
* Changes LLVM config options to enable tests. This setup is now suitable for upstream changes as well without rebuilding.
* Includes pybind11 directly (for some reason using the pytorch helper header for this depends on a source file not in the image).
* Installs nnpack into the image.
* Installs new-clang and LLD and configures environment to use it (otherwise, link time is terrible).
* Fixes a gcc compile error (in the off chance you build with default gcc compiler).
* Tests are failing based on some dialect registration stuff that must not have been factored correctly. Will followup with a fix.
* These were separated originally for layering reasons that no longer apply.
* Most of the python extension code is under lib/ with just the module setup in python/.
* Updates the LICENSE to the same verbiage as used in the circt project.
* Adds the incubator disclaimer to the README.
* Reworks the introduction of the README to more accurately reflect the eventual scope.
* There is a fair amount of further rework of the repo that needs to take place. This is just the minimal cosmetic changes now that it is part of LLVM.
This script (make sure to `source` it!) defines a number of handy
aliases which make compiler development more convenient.
Please feel free to add anything you find useful here.
We should look into having a `ninja check-npcomp` that runs everything
with lit so that we get decent test multithreading.
We can look to how LLVM does its gtest tests ("unittests") for
inspiration.
Stella Laurenzo
Sean Silva
Phoenix Meadowlark
Stella Laurenzo