Files
clang-p2996/mlir/test/python/python_test_ops.td
Ingo Müller ca23c933bd [mlir][python] Create all missing attribute builders.
This patch adds attribute builders for all buildable attributes from the
builtin dialect that did not previously have any. These builders can be
used to construct attributes of a particular type identified by a string
from a Python argument without knowing the details of how to pass that
Python argument to the attribute constructor. This is used, for example,
in the generated code of the Python bindings of ops.

The list of "all" attributes was produced with:

(
  grep -h "ods_ir.AttrBuilder.get" $(find ../build/ -name "*_ops_gen.py") \
    | cut -f2 -d"'"
  git grep -ho "^def [a-zA-Z0-9_]*" -- include/mlir/IR/CommonAttrConstraints.td \
    | cut -f2 -d" "
) | sort -u

Then, I only retained those that had an occurence in
`mlir/include/mlir/IR`. In particular, this drops many dialect-specific
attributes; registering those builders is something that those dialects
should do. Finally, I removed those attrbiutes that had a match in
`mlir/python/mlir/ir.py` already and implemented the remaining ones. The
only ones that still miss a builder now are the following:

* Represent more than one possible attribute type:
  - `Any.*Attr` (9x)
  - `IntNonNegative`
  - `IntPositive`
  - `IsNullAttr`
  - `ElementsAttr`
* I am not sure what "constant attributes" are:
  - `ConstBoolAttrFalse`
  - `ConstBoolAttrTrue`
  - `ConstUnitAttr`
* `Location` not exposed by Python bindings:
  - `LocationArrayAttr`
  - `LocationAttr`
* `get` function not implemented in Python bindings:
  - `StringElementsAttr`

This patch also fixes a compilation problem with
`I64SmallVectorArrayAttr`.

Reviewed By: makslevental, rkayaith

Differential Revision: https://reviews.llvm.org/D159403
2023-09-06 07:09:25 +00:00

225 lines
8.2 KiB
TableGen

//===-- python_test_ops.td - Python test Op definitions ----*- tablegen -*-===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//===----------------------------------------------------------------------===//
#ifndef PYTHON_TEST_OPS
#define PYTHON_TEST_OPS
include "mlir/IR/AttrTypeBase.td"
include "mlir/IR/OpBase.td"
include "mlir/Interfaces/InferTypeOpInterface.td"
def Python_Test_Dialect : Dialect {
let name = "python_test";
let cppNamespace = "python_test";
let useDefaultTypePrinterParser = 1;
let useDefaultAttributePrinterParser = 1;
}
class TestType<string name, string typeMnemonic>
: TypeDef<Python_Test_Dialect, name> {
let mnemonic = typeMnemonic;
}
class TestAttr<string name, string attrMnemonic>
: AttrDef<Python_Test_Dialect, name> {
let mnemonic = attrMnemonic;
}
class TestOp<string mnemonic, list<Trait> traits = []>
: Op<Python_Test_Dialect, mnemonic, traits>;
//===----------------------------------------------------------------------===//
// Type definitions.
//===----------------------------------------------------------------------===//
def TestType : TestType<"TestType", "test_type">;
//===----------------------------------------------------------------------===//
// Attribute definitions.
//===----------------------------------------------------------------------===//
def TestAttr : TestAttr<"TestAttr", "test_attr">;
//===----------------------------------------------------------------------===//
// Operation definitions.
//===----------------------------------------------------------------------===//
def AttributedOp : TestOp<"attributed_op"> {
let arguments = (ins I32Attr:$mandatory_i32,
OptionalAttr<I32Attr>:$optional_i32,
UnitAttr:$unit);
}
def AttributesOp : TestOp<"attributes_op"> {
let arguments = (ins
AffineMapArrayAttr:$x_affinemaparr,
AffineMapAttr:$x_affinemap,
ArrayAttr:$x_arr,
BoolArrayAttr:$x_boolarr,
BoolAttr:$x_bool,
DenseBoolArrayAttr:$x_dboolarr,
DenseF32ArrayAttr:$x_df32arr,
DenseF64ArrayAttr:$x_df64arr,
DenseI16ArrayAttr:$x_df16arr,
DenseI32ArrayAttr:$x_di32arr,
DenseI64ArrayAttr:$x_di64arr,
DenseI8ArrayAttr:$x_di8arr,
DictArrayAttr:$x_dictarr,
DictionaryAttr:$x_dict,
F32ArrayAttr:$x_f32arr,
F32Attr:$x_f32,
F64ArrayAttr:$x_f64arr,
F64Attr:$x_f64,
F64ElementsAttr:$x_f64elems,
FlatSymbolRefArrayAttr:$x_flatsymrefarr,
FlatSymbolRefAttr:$x_flatsymref,
I16Attr:$x_i16,
I1Attr:$x_i1,
I32ArrayAttr:$x_i32arr,
I32Attr:$x_i32,
I32ElementsAttr:$x_i32elems,
I64ArrayAttr:$x_i64arr,
I64Attr:$x_i64,
I64ElementsAttr:$x_i64elems,
I64SmallVectorArrayAttr:$x_i64svecarr,
I8Attr:$x_i8,
IndexAttr:$x_idx,
IndexElementsAttr:$x_idxelems,
IndexListArrayAttr:$x_idxlistarr,
SI16Attr:$x_si16,
SI1Attr:$x_si1,
SI32Attr:$x_si32,
SI64Attr:$x_si64,
SI8Attr:$x_si8,
StrArrayAttr:$x_strarr,
StrAttr:$x_str,
SymbolNameAttr:$x_sym,
SymbolRefArrayAttr:$x_symrefarr,
SymbolRefAttr:$x_symref,
TypeArrayAttr:$x_typearr,
TypeAttr:$x_type,
UI16Attr:$x_ui16,
UI1Attr:$x_ui1,
UI32Attr:$x_ui32,
UI64Attr:$x_ui64,
UI8Attr:$x_ui8,
UnitAttr:$x_unit
);
}
def PropertyOp : TestOp<"property_op"> {
let arguments = (ins I32Attr:$property,
I32:$idx);
}
def DummyOp : TestOp<"dummy_op"> {
}
def InferResultsOp : TestOp<"infer_results_op", [InferTypeOpInterface]> {
let arguments = (ins);
let results = (outs AnyInteger:$single, AnyInteger:$doubled);
let extraClassDeclaration = [{
static ::mlir::LogicalResult inferReturnTypes(
::mlir::MLIRContext *context, ::std::optional<::mlir::Location> location,
::mlir::ValueRange operands, ::mlir::DictionaryAttr attributes,
::mlir::OpaqueProperties,
::mlir::RegionRange regions,
::llvm::SmallVectorImpl<::mlir::Type> &inferredReturnTypes) {
::mlir::Builder b(context);
inferredReturnTypes.push_back(b.getI32Type());
inferredReturnTypes.push_back(b.getI64Type());
return ::mlir::success();
}
}];
}
def I32OrF32 : TypeConstraint<Or<[I32.predicate, F32.predicate]>,
"i32 or f32">;
def InferResultsVariadicInputsOp : TestOp<"infer_results_variadic_inputs_op",
[InferTypeOpInterface, AttrSizedOperandSegments]> {
let arguments = (ins Optional<I64>:$single, Optional<I64>:$doubled);
let results = (outs I32OrF32:$res);
let extraClassDeclaration = [{
static ::mlir::LogicalResult inferReturnTypes(
::mlir::MLIRContext *context, ::std::optional<::mlir::Location> location,
::mlir::ValueRange operands, ::mlir::DictionaryAttr attributes,
::mlir::OpaqueProperties,
::mlir::RegionRange regions,
::llvm::SmallVectorImpl<::mlir::Type> &inferredReturnTypes) {
::mlir::Builder b(context);
if (operands.size() == 1)
inferredReturnTypes.push_back(b.getI32Type());
else if (operands.size() == 2)
inferredReturnTypes.push_back(b.getF32Type());
return ::mlir::success();
}
}];
}
// If all result types are buildable, the InferTypeOpInterface is implied and is
// autogenerated by C++ ODS.
def InferResultsImpliedOp : TestOp<"infer_results_implied_op"> {
let results = (outs I32:$integer, F64:$flt, Index:$index);
}
def InferShapedTypeComponentsOp : TestOp<"infer_shaped_type_components_op",
[DeclareOpInterfaceMethods<InferShapedTypeOpInterface,
["inferReturnTypeComponents"]>]> {
let arguments = (ins AnyTensor:$operand);
let results = (outs AnyTensor:$result);
let extraClassDefinition = [{
::mlir::LogicalResult $cppClass::inferReturnTypeComponents(
::mlir::MLIRContext *context, ::std::optional<::mlir::Location> location,
::mlir::ValueShapeRange operands, ::mlir::DictionaryAttr attributes,
::mlir::OpaqueProperties properties, ::mlir::RegionRange regions,
::llvm::SmallVectorImpl<
::mlir::ShapedTypeComponents>& inferredShapedTypeComponents) {
$cppClass::Adaptor adaptor(operands, attributes, properties, regions);
auto operandType =
::llvm::cast<::mlir::ShapedType>(adaptor.getOperand().getType());
if (operandType.hasRank()) {
inferredShapedTypeComponents.emplace_back(operandType.getShape(),
operandType.getElementType());
} else {
inferredShapedTypeComponents.emplace_back(operandType.getElementType());
}
return ::mlir::success();
}
}];
}
def SameOperandAndResultTypeOp : TestOp<"same_operand_and_result_type_op",
[SameOperandsAndResultType]> {
let arguments = (ins Variadic<AnyType>);
let results = (outs AnyType:$one, AnyType:$two);
}
def FirstAttrDeriveTypeAttrOp : TestOp<"first_attr_derive_type_attr_op",
[FirstAttrDerivedResultType]> {
let arguments = (ins AnyType:$input, TypeAttr:$type);
let results = (outs AnyType:$one, AnyType:$two);
}
def FirstAttrDeriveAttrOp : TestOp<"first_attr_derive_attr_op",
[FirstAttrDerivedResultType]> {
let arguments = (ins AnyAttr:$iattr);
let results = (outs AnyType:$one, AnyType:$two, AnyType:$three);
}
def OptionalOperandOp : TestOp<"optional_operand_op"> {
let arguments = (ins Optional<AnyType>:$input);
let results = (outs I32:$result);
}
#endif // PYTHON_TEST_OPS