* Now the parts of the MLIR API are directly exported under the npcomp module (i.e. `npcomp.ir`, etc).
* Has required fixes for https://reviews.llvm.org/D108489
* Deletes npcomp.tracing vs fixing it because it was a very early experiment that will not be carried forward.
* This makes the npcomp python distribution completely standalone and separate from an mlir installation.
* Makes most of npcomp itself relocatable for future use as a library.
* Most things are a namespace package now. In the future we can s/torch_mlir/npcomp.frontends.torch/ and have it layer properly.
- Make consistent with MLIR Core
- Use `//` or `///` comments.
- Use `bool` type for booleans
- No duplicated comments in .cpp files
- Split types into separate files `{Basicpy,Numpy,Torch}Types.h`
- Add dialect prefix consistently to C API symbols. We have lots of
similarly named types (e.g. "list" type in basicpy and torch).
* Most updates are mechanical except:
* python/npcomp/__init__.py and python/NpcompModule.cpp: New init/registration bits to replace some automatic things being done in the old bindings. Also an annoying linkage hack that I'll need to triage next.
* NpcompModule.cpp: New python helpers for custom types and other hard to reach items (for the new bindings).
* PybindUtils.h: Extended type casting so that the local extension can directly exchange Mlir* C types.
* python/npcomp/dialects/*: Build support and ODS bindings for local dialects.
* mlir_utils.py: Defines an ImportContext to replace the old/bad "Helper" class that tracked locations, and insertion points. This has a number of methods on it that would be good candidates to think about better ways to do them upstream.
* Also hoisted a few stand-alone samples to dedicated unit tests as they covered important things.
* More cleanup can be done, but keeping this patch as mechanical as possible to stay in NFC land (this is big enough).
* IREE doesn't have proper install support, so there is some temporary hoaky hacking in our CMakeLists.txt to shuttle some symlinks around.
* Reworked the original numpy e2e with IREE test to pipe through iree-translate.
* Removed all of the C++-level dependencies.
* Will generalize and apply to the PyTorch backend in a followup.
* A bit gross because I took the chance to upgrade all of the backend bits to the new MLIR Python bindings and we still co-mingle the old and new for now.
* Since the Python created PassManagers are configured for explicit nesting, I had to upgrade some of the pass pipelines to be explicit.
* The demo in mul_maximum_e2e.py now compiles, runs through PyTorch and through the JIT, prints and asserts the same results.
* I am not claiming that this is the prettiest API in this patch: consider that this is just directly using low-level APIs and there should be an intervening high level API.
* 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/.
Stella Laurenzo
Sean Silva