* Need to have a dag of shared library deps in order to interop across python extensions (as presented in ODM).
* Introduced add_npcomp_library and friends to mirror the MLIR setup.
* Adds a libNPCOMP.so shared library.
* Redirects tools and extensions to link against libNPCOMP.so (instead of static libs).
* Moves all libraries to lib/, all binaries to bin/ and all python extensions to python/. The invariant is that the rpaths are setup to have a one level directory structure.
* Reworks the _torch_mlir extension to build like the others (still need to come up with a consolidated rule to do this instead of open coded).
* Includes an upstream version bump to pick up needed changes.
Sizes with dynamic linking (stripped, release, asserts enabled):
libNPCOMP.so: 43M (includes much of the underlying LLVM codegen deps)
libMLIR.so: 31M
_npcomp.so: 1.6M (python extension)
_torch_mlir.so: 670K (python extension)
npcomp-capi-ir-test: 6.3K
npcomp-opt: 351K
npcomp-run-mlir: 461K
mnist-playground: 530K
Still more can be done to normalize and optimize but this gets us structurally to the starting point.
* Had to stop short of modifying the function return signature because of a missing C-API upstream.
* Committing here is good enough for a test and will resolve the various TODOs about upstream APIs next.
* Adds at::Tensor -> MlirValue tracking.
* Adds conversions for tensor and scalar types to MLIR types.
* Adds npcomp C APIs for constructing custom types.
* Reworks pybind include so as to get Torch pybind helpers (needed to pass at::Tensor type from Python->C++).
* Uses the MLIR-C API since that will save us a lot of grief down the road (i.e. will give PyTorch and libMLIR/libNPCOMP the ability to skew version-wise).
* Quite a few TODOs and not yet populating the function in any way.
* Uses the new dispatcher API.
* Just prints to the console for the moment when an op is captured.
* Executes the op through the existing implementation.
* Exposes the op registry via a get_registered_ops method.
* Moves the aten dialect generation scripts in prep for integrating them with this facility.
Stella Laurenzo