181 lines
7.6 KiB
TableGen
181 lines
7.6 KiB
TableGen
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// RUN: mlir-tblgen -gen-op-defs -I %S/../../include %s | FileCheck %s
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include "mlir/IR/OpBase.td"
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include "mlir/Interfaces/InferTypeOpInterface.td"
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def Test_Dialect : Dialect {
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let name = "test";
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let usePropertiesForAttributes = 0;
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}
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class NS_Op<string mnemonic, list<Trait> traits> :
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Op<Test_Dialect, mnemonic, traits>;
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def OpA : NS_Op<"one_normal_result_op", []> {
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let results = (outs I32:$result);
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}
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// CHECK-LABEL: void OpA::build
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// CHECK: ::mlir::TypeRange resultTypes, ::mlir::ValueRange operands
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// CHECK: assert(resultTypes.size() == 1u && "mismatched number of return types");
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// CHECK-NEXT: odsState.addTypes(resultTypes);
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def OpB : NS_Op<"same_input_output_type_op", [SameOperandsAndResultType]> {
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let arguments = (ins I32:$x);
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let results = (outs I32:$y);
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}
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// CHECK-LABEL: OpB definitions
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// CHECK: void OpB::build(::mlir::OpBuilder &odsBuilder, ::mlir::OperationState &odsState, ::mlir::Type y, ::mlir::Value x)
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// CHECK: odsState.addTypes(y);
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// CHECK: void OpB::build(::mlir::OpBuilder &odsBuilder, ::mlir::OperationState &odsState, ::mlir::Value x)
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// CHECK: ::llvm::SmallVector<::mlir::Type, 2> inferredReturnTypes;
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// CHECK: if (::mlir::succeeded(OpB::inferReturnTypes(odsBuilder.getContext(),
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// CHECK: odsState.location, odsState.operands,
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// CHECK: odsState.attributes.getDictionary(odsState.getContext()),
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// CHECK: odsState.regions, inferredReturnTypes)))
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// CHECK: odsState.addTypes(inferredReturnTypes);
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def OpC : NS_Op<"three_normal_result_op", []> {
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let results = (outs I32:$x, /*unnamed*/I32, I32:$z);
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}
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// CHECK-LABEL: OpC definitions
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// CHECK: void OpC::build(::mlir::OpBuilder &odsBuilder, ::mlir::OperationState &odsState, ::mlir::Type x, ::mlir::Type resultType1, ::mlir::Type z)
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// CHECK-NEXT: odsState.addTypes(x)
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// CHECK-NEXT: odsState.addTypes(resultType1)
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// CHECK-NEXT: odsState.addTypes(z)
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// CHECK: void OpC::build(::mlir::OpBuilder &odsBuilder, ::mlir::OperationState &odsState, ::mlir::TypeRange resultTypes) {
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// CHECK-NEXT: assert(resultTypes.size() == 3u && "mismatched number of results");
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// CHECK-NEXT: odsState.addTypes(resultTypes);
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def IntegerTypeAttr : TypeAttrBase<"IntegerType", "Integer type attribute">;
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def OpD : NS_Op<"type_attr_as_result_type", [FirstAttrDerivedResultType]> {
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let arguments = (ins I32:$x, IntegerTypeAttr:$attr, F32Attr:$f32);
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let results = (outs AnyTensor:$y);
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}
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// CHECK-LABEL: OpD definitions
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// CHECK: void OpD::build(::mlir::OpBuilder &odsBuilder, ::mlir::OperationState &odsState, ::mlir::ValueRange operands, ::llvm::ArrayRef<::mlir::NamedAttribute> attributes)
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// CHECK: odsState.addTypes({::llvm::cast<::mlir::TypeAttr>(attr.getValue()).getValue()});
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def OpE : NS_Op<"value_attr_as_result_type", [FirstAttrDerivedResultType]> {
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let arguments = (ins I32:$x, F32Attr:$attr);
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let results = (outs AnyTensor:$y);
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}
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// CHECK-LABEL: OpE definitions
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// CHECK: void OpE::build(::mlir::OpBuilder &odsBuilder, ::mlir::OperationState &odsState, ::mlir::ValueRange operands, ::llvm::ArrayRef<::mlir::NamedAttribute> attributes)
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// CHECK: odsState.addTypes({::llvm::cast<::mlir::TypedAttr>(attr.getValue()).getType()});
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def OpF : NS_Op<"one_variadic_result_op", []> {
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let results = (outs Variadic<I32>:$x);
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}
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// CHECK-LABEL: void OpF::build
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// CHECK-SAME: ::mlir::TypeRange x
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// CHECK-NOT: assert
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// CHECK: odsState.addTypes(x);
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def OpG : NS_Op<"one_normal_and_one_variadic_result_op", []> {
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let results = (outs I32:$x, Variadic<I32>:$y);
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}
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// CHECK-LABEL: OpG definitions
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// CHECK: void OpG::build(::mlir::OpBuilder &odsBuilder, ::mlir::OperationState &odsState, ::mlir::Type x, ::mlir::TypeRange y)
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// CHECK-NEXT: odsState.addTypes(x);
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// CHECK-NEXT: odsState.addTypes(y);
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// CHECK: void OpG::build
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// CHECK: ::mlir::TypeRange resultTypes
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// CHECK: assert(resultTypes.size() >= 1u && "mismatched number of return types");
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// CHECK-NEXT: odsState.addTypes(resultTypes);
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def OpI : NS_Op<"mix_variadic_and_normal_results_op", [SameVariadicResultSize]> {
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let results = (outs Variadic<AnyTensor>:$output1, AnyTensor:$output2, Variadic<AnyTensor>:$output3);
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}
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// CHECK-LABEL: ::mlir::Operation::result_range OpI::getOutput1
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// CHECK-NEXT: return getODSResults(0);
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// CHECK-LABEL: ::mlir::TypedValue<::mlir::TensorType> OpI::getOutput2
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// CHECK-NEXT: return ::llvm::cast<::mlir::TypedValue<::mlir::TensorType>>(*getODSResults(1).begin());
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// CHECK-LABEL: OpI::build
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// CHECK-NEXT: odsState.addTypes(output1);
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// CHECK-NEXT: odsState.addTypes(output2);
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// CHECK-NEXT: odsState.addTypes(output3);
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// Test that if the only operand is variadic, we access the first value in the
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// pack to set result type
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// ---
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def OpK : NS_Op<"only_input_is_variadic_with_same_value_type_op", [SameOperandsAndResultType]> {
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let arguments = (ins Variadic<AnyTensor>:$input);
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let results = (outs AnyTensor:$result);
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}
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// CHECK-LABEL: OpK::build(::mlir::OpBuilder &odsBuilder, ::mlir::OperationState &odsState, ::mlir::ValueRange operands, ::llvm::ArrayRef<::mlir::NamedAttribute> attributes)
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// CHECK: odsState.addTypes({operands[0].getType()});
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// Test with inferred shapes and interleaved with operands/attributes.
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//
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def OpL1 : NS_Op<"op_with_all_types_constraint",
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[AllTypesMatch<["a", "b"]>]> {
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let arguments = (ins I32Attr:$attr1, AnyType:$a);
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let results = (outs Res<AnyType, "output b", []>:$b);
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}
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// CHECK-LABEL: LogicalResult OpL1::inferReturnTypes
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// CHECK-NOT: }
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// CHECK: ::mlir::Type odsInferredType0 = operands[0].getType();
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// CHECK: inferredReturnTypes[0] = odsInferredType0;
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def OpL2 : NS_Op<"op_with_all_types_constraint",
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[AllTypesMatch<["c", "b"]>, AllTypesMatch<["a", "d"]>]> {
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let arguments = (ins I32Attr:$attr1, AnyType:$a, AnyType:$a2, AnyType:$c);
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let results = (outs Res<AnyType, "output b", []>:$b, AnyType:$d);
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}
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// CHECK-LABEL: LogicalResult OpL2::inferReturnTypes
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// CHECK-NOT: }
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// CHECK: ::mlir::Type odsInferredType0 = operands[2].getType();
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// CHECK: ::mlir::Type odsInferredType1 = operands[0].getType();
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// CHECK: inferredReturnTypes[0] = odsInferredType0;
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// CHECK: inferredReturnTypes[1] = odsInferredType1;
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def OpL3 : NS_Op<"op_with_all_types_constraint",
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[AllTypesMatch<["a", "b"]>]> {
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let arguments = (ins I32Attr:$a);
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let results = (outs AnyType:$b);
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}
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// CHECK-LABEL: LogicalResult OpL3::inferReturnTypes
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// CHECK-NOT: }
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// CHECK: ::mlir::Type odsInferredType0 = odsInferredTypeAttr0.getType();
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// CHECK: inferredReturnTypes[0] = odsInferredType0;
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def OpL4 : NS_Op<"two_inference_edges", [
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TypesMatchWith<"", "a", "b", "infer0($_self)">,
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TypesMatchWith<"", "b", "c", "infer1($_self)">,
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TypesMatchWith<"", "input", "a", "fromInput($_self)">]> {
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let arguments = (ins I32:$input);
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let results = (outs AnyType:$a, AnyType:$b, AnyType:$c);
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}
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// CHECK-LABEL: LogicalResult OpL4::inferReturnTypes
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// CHECK: odsInferredType0 = fromInput(operands[0].getType())
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// CHECK: odsInferredType1 = infer0(odsInferredType0)
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// CHECK: odsInferredType2 = infer1(odsInferredType1)
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// CHECK: inferredReturnTypes[0] = odsInferredType0
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// CHECK: inferredReturnTypes[1] = odsInferredType1
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// CHECK: inferredReturnTypes[2] = odsInferredType2
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def OpM : NS_Op<"mix_diff_size_variadic_and_normal_results_op", [AttrSizedResultSegments]> {
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let results = (outs Variadic<AnyTensor>:$output1, AnyTensor:$output2, Optional<AnyTensor>:$output3);
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}
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// CHECK-LABEL: OpM::build(::mlir::OpBuilder &odsBuilder, ::mlir::OperationState &odsState, ::mlir::TypeRange output1, ::mlir::Type output2, /*optional*/::mlir::Type output3)
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// CHECK: odsState.addAttribute(getResultSegmentSizesAttrName(odsState.name), odsBuilder.getDenseI32ArrayAttr({static_cast<int32_t>(output1.size()), 1, (output3 ? 1 : 0)}));
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