Commit Graph

61 Commits (1d6e4c3d77a513060d05a278221b4f8e02b654e1)

Author SHA1 Message Date
Sean Silva 227dea7b2e Add support for ScalarType::QUInt8
I ran into this while poking around at
https://github.com/llvm/torch-mlir/issues/959
2022-06-29 15:33:28 -07:00
Ashay Rane bb52a460cb
mlir: bump llvm tag to 5380e3 (#856)
In addition to updating the llvm-project submodule, this patch also:

1. updates shape functions and tests so that `func` and `call`
   operations refer to the `func` dialect
2. avoid duplicate registration of dialects
2022-05-16 12:54:35 -07:00
Sean Silva ab5ad7af09 Add tracing suport to `torch_mlir.compile`.
This also has a fix for the adjustment of types of TupleConstruct
inputs, which I found when using this new functionality on a model.

Some scenarios in tracing create situations where the output of
TupleConstruct has a more refined type than the inputs.

This introduces a helper `adjustStaticInformationForValues` which
subsumes the `derefineValues` helper and the tensor static information
adjustment we were doing.
2022-05-03 09:08:40 -07:00
Ashay Rane 809f240f01
importer: add initial support for loading BFloat16 tensors (#761)
This patch updates the `torch_mlir::convertTensorToMlirElementsAttr()`
method to enable the creation of tensors whose base type is BFloat16.
This patch also adds a test to validate the IR generation, and it
updates the test for importing tensors of various types.
2022-04-29 09:01:49 -07:00
Sean Silva 73cc2ac152 Ensure that imported function input type and block arg types are consistent.
I wasn't able to find exactly what frontend situation created it, but
`torch.jit.trace` will sometimes create functions where the
`jit::Block`'s param node has refined tensor types. So we need to adjust
the function's formal param types to those refined types.
2022-04-27 08:01:23 -07:00
Sean Silva 140babd952 Add minimal support for Union types.
A recent PyTorch commit made ConstantPad2d call a helper function with a
`Union[int, float]` type annotated. This commit adds minimal support for
representing and dealing with that.
https://github.com/pytorch/pytorch/pull/73287

Changes:
- Adding support for `!torch.union<T1, T2, T3>`/`Torch::UnionType`,
  along with the importer and CAPI code.
- Add support in isValidSubtype for union types.
- Adding a canonicalizer for `torch.derefine` to help simplify some code
  that derefines to a UnionType (this also fixes #664).

There is still more work to do for really supporting UnionType well,
such as canonicalizing UnionType's so that they can be compared with
pointer equality.
2022-03-29 17:45:48 -07:00
Sean Silva 84a9693006 Elide `!torch.` prefix in nested dialect types.
This leads to much more succinct types in many cases:

```
!torch.list<!torch.int>
!torch.list<int>

!torch.tuple<!torch.list<!torch.int>, !torch.list<!torch.int>>
!torch.tuple<list<int>, list<int>>

!torch.optional<!torch.list<!torch.int>>
!torch.optional<list<int>>

!torch.list<list<list<tensor>>>
!torch.list<!torch.list<!torch.list<!torch.tensor>>>
```

I would like to take this further and allow omitting the `!torch.`
prefix in all cases, but that's harder -- for example, we currently use
`FuncOp` for functions, and so I don't think we can customize the
printing there. It seems like it will be a longer road to getting that
level of customization.
2022-03-15 17:24:08 -07:00
Sean Silva a5fe0cf063 Introduce new shape library design.
See the documentation in `docs/shape_lib.md` and
`docs/adding_a_shape_function.md` for an overview of the system.

This completely overhauls how we represent shape functions. In
particular, RefineTypes does not infer shapes anymore (only dtypes).
Shape functions are now written in (TorchScript'able) Python.

Recommended review order:

1. Read `docs/shape_lib.md` and `docs/adding_a_shape_function.md`.
1. Code and tests for ReifyShapeCalculations, DropShapeCalculations.
1. Code and tests for SimplifyShapeCalculations.
1. shape_lib_gen.py
1. Code and tests for new RefineTypes pass.
1. Random folders/canonicalizers in TorchOps.cpp and associated test in
   `canonicalize.mlir`.
1. New ReadOnly trait inferred from the registry.
1. Any miscellaneous remaining stuff.

Example `-print-ir-after-all` for ElementwiseUnaryModule:
[IR lowering dump](https://gist.github.com/silvasean/e4dc8cbc8d00aac7819602e3cbd8e212).

Example `-print-ir-after-all` for ElementwiseBinaryModule:
[IR lowering dump](https://gist.github.com/silvasean/daf6860ecced732af3568af6b1899113).
2022-03-15 12:41:58 -07:00
Henry Tu 73ac9a7e2e Added support for importing node prim::Constant with list type
Prior to this commit, importing a `prim::Constant` node with list type would result in an error since it was not supported. `ivalue_importer::importIValue` was modified to return the MlirValue corresponding to the root so its parent operation could be extracted.
2022-02-11 20:54:06 -05:00
Sean Silva 19e9fc4ef1 Bring some more order to the e2e error reporting situation.
- Move `run_pipeline_with_repro_report` to a more common place, and use it
  consistently
- Attach a `torch.debug_module_name` to the enclosing `builtin.module`
  op to allow for self-contained error reporting (not needing to pass
  the names around.
- Remove redundant error reporting in linalg_on_tensors_backend.py and
  tosa_backend.py (their respective backend abstract base classes now
  take care of the error reports themselves)
- Save off original value of sys.stderr, rather than always resetting to
  `sys.__stderr__`. This is just more hygienic, and allows nesting if
  desired.
2021-10-08 13:00:12 -07:00
Sean Silva 4fad753073 Move external/torch-mlir to the root of the repo. 2021-09-27 17:11:08 -07:00