Commit Graph

12 Commits (79aade33da54e9c104f8b2024d003024aa63d68c)

Author SHA1 Message Date
Sean Silva 544cb4ef54 Bump llvm-project to 484b6648fdd4b104eaf7a2504dd07b60af2c9f8d
- add_mlir_doc arg order
- fix some dependent dialects on passes that were now causing errors
- "encoding" attribute on mlirRankedTensorTypeGetChecked
2021-04-22 18:12:55 -07:00
Aaron Arthurs 85898aaf10
Add TCF convolutional op with bias addition (#137) 2020-12-15 12:53:12 -08:00
Sean Silva b2077738ca Bump llvm-project to 444822d77a7fea28aa49edf24533c987efa1b2ee
Fixes:
- renames StandardTypes -> BuiltinTypes
- std.extract_element -> tensor.extract
2020-12-11 14:43:38 -08:00
Aaron J Arthurs 94ea6f7c92 [RefBackend] Support element-wise multiply op
Register the following for the multiply op:
- tcf.mul
- tcp.mul
- TCP->TCP lowering
- Shape transfer, broadcasted multiplicands
- Lower to standard `MulFOp` op
2020-10-27 19:41:23 -07:00
Sean Silva f9b37c55b7 [RefE2E] Add support for unary ops exp and tanh
This is fairly mechanical.
2020-09-24 18:41:30 -07:00
Sean Silva c69e9fabc5 [RefE2E] Add support for "max".
This cleans up the lowering pipeline to easily allow extending to
multiple binary ops. It looks fairly repetitive at multiple levels, but
I don't want to prematurely generalize. I think that in principle we
could derive a large swatch of TCF + TCP from a single linalg-style
specification. Another direction is to use an OpInterface (something
like "buildLinalgGenericBody"). I'm keeping my eye on it.

In a subsequent commit, I'll mechanically add a set of binary ops
modeled off of the std arithmetic ops.
2020-09-22 18:38:32 -07:00
Sean Silva 276f5b80ea [RefE2E] Add assemblyFormat for TCF and TCP ops and tidy up. 2020-09-18 15:03:53 -07:00
Sean Silva d8675f8ad2 [RefE2E] Add support for matmul.
I'm pretty happy with how this turned out. It looks pretty much like it
should -- one change at each layer. This particular op bottoms out on
linalg which takes care of the rest.

- Add tcf.matmul
- Add tcp.matmul
- Add TCF->TCP lowering
- Add tcp.matmul shape transfer function (BypassShapes.cpp)
- Add tcp.matmul -> linalg.matmul lowering (LowerShapedResultsToMemref.cpp)
- Add support to LowerShapeConstraints for lowering the new
shape.cstr_require

This matmul op is pretty limited in its capabilities. There is no
batching and no multidimensional contraction. Certainly more design work
will be needed to find the right abstractions that aren't too general
but also help to canonicalize many cases from frontends. This is mainly
to show that adding a new op needn't be very "scary" once we have the
e2e infra in place.

Also,
- this clears out some exploratory cruft from the TCF dialect now that
this is starting to become real.
2020-09-18 11:31:01 -07:00
Marius Brehler d62f8227c2
Bump LLVM to @7d1ed69 and fix namespace handling changed upstream.
* Bump LLVM to llvm/llvm-project@7d1ed69
* Bump MLIR-HLO to tensorflow/mlir-hlo@1880f87
* Adopt to MLIR's changed namespace handling
2020-09-16 15:52:15 -07:00
Stella Laurenzo 97d83f786a Bump submodule versions.
* 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>
2020-09-08 13:26:42 -07:00
Stella Laurenzo 9e4a62fc71 Allow JITModule passes to be built separately.
* Re-introduces frontent/backend split.
* Adds a (very) trivial shape refinement pass.
2020-07-10 22:57:26 -07:00
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