Right now `inferTypeOpInterface.inferReturnTypes` fails because there's a cast in there to `py::sequence` which throws a `TypeError` when it tries to cast the `None`s. Note `None`s are inserted into `operands` for omitted operands passed to the generated builder:
```
operands.append(_get_op_result_or_value(start) if start is not None else None)
operands.append(_get_op_result_or_value(stop) if stop is not None else None)
operands.append(_get_op_result_or_value(step) if step is not None else None)
```
Note also that skipping appending to the list operands doesn't work either because [[ 27c37327da/mlir/lib/Bindings/Python/IRCore.cpp (L1585) | build generic ]] checks against the number of operand segments expected.
Currently the only way around is to handroll through `ir.Operation.create`.
Reviewed By: rkayaith
Differential Revision: https://reviews.llvm.org/D151409
depends on D150839
This diff uses `MlirTypeID` to register `TypeCaster`s (i.e., `[](PyType pyType) -> DerivedTy { return pyType; }`) for all concrete types (i.e., `PyConcrete<...>`) that are then queried for (by `MlirTypeID`) and called in `struct type_caster<MlirType>::cast`. The result is that anywhere an `MlirType mlirType` is returned from a python binding, that `mlirType` is automatically cast to the correct concrete type. For example:
```
c0 = arith.ConstantOp(f32, 0.0)
# CHECK: F32Type(f32)
print(repr(c0.result.type))
unranked_tensor_type = UnrankedTensorType.get(f32)
unranked_tensor = tensor.FromElementsOp(unranked_tensor_type, [c0]).result
# CHECK: UnrankedTensorType
print(type(unranked_tensor.type).__name__)
# CHECK: UnrankedTensorType(tensor<*xf32>)
print(repr(unranked_tensor.type))
```
This functionality immediately extends to typed attributes (i.e., `attr.type`).
The diff also implements similar functionality for `mlir_type_subclass`es but in a slightly different way - for such types (which have no cpp corresponding `class` or `struct`) the user must provide a type caster in python (similar to how `AttrBuilder` works) or in cpp as a `py::cpp_function`.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D150927
Add C and python bindings for InferShapedTypeOpInterface
and ShapedTypeComponents. This allows users to invoke
InferShapedTypeOpInterface for ops that implement it.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D149494
This new features enabled to dedicate custom storage inline within operations.
This storage can be used as an alternative to attributes to store data that is
specific to an operation. Attribute can also be stored inside the properties
storage if desired, but any kind of data can be present as well. This offers
a way to store and mutate data without uniquing in the Context like Attribute.
See the OpPropertiesTest.cpp for an example where a struct with a
std::vector<> is attached to an operation and mutated in-place:
struct TestProperties {
int a = -1;
float b = -1.;
std::vector<int64_t> array = {-33};
};
More complex scheme (including reference-counting) are also possible.
The only constraint to enable storing a C++ object as "properties" on an
operation is to implement three functions:
- convert from the candidate object to an Attribute
- convert from the Attribute to the candidate object
- hash the object
Optional the parsing and printing can also be customized with 2 extra
functions.
A new options is introduced to ODS to allow dialects to specify:
let usePropertiesForAttributes = 1;
When set to true, the inherent attributes for all the ops in this dialect
will be using properties instead of being stored alongside discardable
attributes.
The TestDialect showcases this feature.
Another change is that we introduce new APIs on the Operation class
to access separately the inherent attributes from the discardable ones.
We envision deprecating and removing the `getAttr()`, `getAttrsDictionary()`,
and other similar method which don't make the distinction explicit, leading
to an entirely separate namespace for discardable attributes.
Recommit d572cd1b067f after fixing python bindings build.
Differential Revision: https://reviews.llvm.org/D141742
Previously we only allowed the flattened list passed in, but the same
input provided here as to buildGeneric so flatten accordingly. We have
less info here than in buildGeneric so the error is more generic if
unpacking fails.
Differential Revision: https://reviews.llvm.org/D143240
Introduce the initial support for operation interfaces in C API and Python
bindings. Interfaces are a key component of MLIR's extensibility and should be
available in bindings to make use of full potential of MLIR.
This initial implementation exposes InferTypeOpInterface all the way to the
Python bindings since it can be later used to simplify the operation
construction methods by inferring their return types instead of requiring the
user to do so. The general infrastructure for binding interfaces is defined and
InferTypeOpInterface can be used as an example for binding other interfaces.
Reviewed By: gysit
Differential Revision: https://reviews.llvm.org/D111656
