We already had the `promoteTrailingOutTensor` flag, but weren't using
it. A inplaceVariantKernelName flag needed to be added.
This change is a little dissatisfying, as the conversions done by the
RecognizeKernelsPass are currently non-orthogonal. In particular,
`kDropResultAndAliasArg0` probably won't work as intended if mixed with
these (we probably need to promote kDropResultAndAliasArg0 to not be an
arg-level thing anyway, as we have done with promoteTrailingOutTensor).
This involved adding a new op `numpy.overwrite_array`.
```
numpy.overwrite_array %arg2 overwrites %arg0 : tensor<2x3xf32>, !numpy.ndarray<[2,3]:f32>
```
This models the destructive update behavior. Note that in the above op,
we cannot simply RAUW %arg0 with a suitably conveted %arg2 (for example,
%arg0 might have uses that are not dominated by %arg2, or might have an
alias relation with some other array in the program). In general, we
need a pass analogous to "SSA-formation" which knows how to see through
these to uncover an underlying tensor program.
Also, add tanh_out_e2e.py/div_inplace_e2e.py and fix some bitrot in
refjit.py which is my running example I'm trying to get working.
* 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).
* 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.
* llvm-project: b5924a8e27536d19dd5c4d302db29fb6163d5faa
* mhlo: 848ca244d20f045b7921da55a98a04d95ef94f0e
* Multiple breakages that need to be fixed.
Fixes:
* Refactor dialect registration
* Remove all kindof methods (Casting functionality has been added upstream and is implicitly
available, see https://llvm.discourse.group/t/removing-kinds-from-attributes-and-types/1547.)
* Update dialect registration to comply with https://reviews.llvm.org/D85495.
* Remove type kinds and update some changed dialect signatures.
* Upgrade ATen dialect to match upstream needs.
* Move dialect registration to tablegen.
* Register the ListType in tablegen.
* Change dialect initialization signature.
* Use TypeSwitch in MlirIr location printer.
* Remove global registry depends from npcomp-opt.
* Change LowerToLLVM to pass an MLIRContext vs an LLVMDialect for type creation.
* Remove dep on MLIREDSCInterface that is removed upstream.
* Thread through the DialectRegistry for opt and python-like tools.
* Modernize pass registration (This was forced because the GEN_PASS_REGISTRATION code now generates inline functions vs literal pass registration statements)
Co-authored-by: Marius Brehler <marius.brehler@iml.fraunhofer.de>
* Rewrites public function signatures to operate on tensors (vs ndarray).
* Most of our backends presume immutable tensors at public function boundaries.
* This elides the very common code the compiler adds for chaining otherwise tensor-related numpy ops together.
* More aggressive canonicalizations would require more advanced analysis.
* Adds an op interface for adding CPA constraints.
* Adds a type conversion hook for handling built-in types (that we can't have adopt our interface).
* Converts tensor<> to object(!Tensor, [e:<type>]) just like NdArray.
* Implement a few numpy ops far enough to do dtype inference for simple sequences.
* Gets us passed the upstream changes that enable type interfaces.
* Adds the ARM backend due to an implicit IREE dependency sneaking in for that (https://github.com/google/iree/issues/2401)
* Adds explicit TypeStorage to type base classes per upstream change.
Sean Silva
Stella Laurenzo
Marius Brehler