[mlir][ods] Populate properties in generated builder

Previously this was only populated in the create method later. This
resolves some of invalid builder paths. This may also be sufficient that
type inference functions no longer have to consider whether property
conversion has happened (but haven't verified that yet).

This also makes Attributes corresponding to Properties as optional
inside the set from attributes method. Today that is in effect what
happens with Property value initialization and folks use it to define
custom C++ types whose default initialization is what they want. This is
the behavior users get if they use properties directly. Propagating
Attributes without allowing partial setting would require iterating over
the dictionary attribute considering the properties of the op type that
will be created. This could also have been an additional method
generated or optional behavior on the set method. But doing it
consistently seems better. In terms of whats lost, it doesn't seem like
anything compared to the pure Property path where Property is default
value initialized and then partially overwritten (this doesn't seem to
buy anything else verification wise).

Default valued Properties (as specified ODS side rather than C++ side)
triggered error as the containing class was not yet complete but
referenced nested class, so that we couldn't have default initializer
for them in the parent class. Added an additional forwarding builder to
avoid needing to update call sites. This could be split out to separate
change.

Inlined templated function in unit test that was only used 1x. Moved
initialization earlier where seen.

Author: jpienaar

mlir/include/mlir/Dialect/LLVMIR/LLVMOps.td

@@ -65,13 +65,13 @@ class LLVM_IntArithmeticOpWithOverflowFlag<string mnemonic, string instName,
   let builders = [
     OpBuilder<(ins "Type":$type, "Value":$lhs, "Value":$rhs,
                    "IntegerOverflowFlags":$overflowFlags), [{
-      build($_builder, $_state, type, lhs, rhs);
       $_state.getOrAddProperties<Properties>().overflowFlags = overflowFlags;
+      build($_builder, $_state, type, lhs, rhs);
     }]>,
     OpBuilder<(ins "Value":$lhs, "Value":$rhs,
                    "IntegerOverflowFlags":$overflowFlags), [{
-      build($_builder, $_state, lhs, rhs);
       $_state.getOrAddProperties<Properties>().overflowFlags = overflowFlags;
+      build($_builder, $_state, lhs, rhs);
     }]>
   ];
 

mlir/include/mlir/Dialect/MemRef/IR/MemRefOps.td

@@ -1764,9 +1764,9 @@ def MemRef_CollapseShapeOp : MemRef_ReassociativeReshapeOp<"collapse_shape", [
       "ArrayRef<ReassociationIndices>":$reassociation,
       CArg<"ArrayRef<NamedAttribute>", "{}">:$attrs),
     [{
-      build($_builder, $_state, resultType, src, attrs);
       $_state.addAttribute("reassociation",
                           getReassociationIndicesAttribute($_builder, reassociation));
+      build($_builder, $_state, resultType, src, attrs);
     }]>,
     OpBuilder<(ins "Type":$resultType, "Value":$src,
       "ArrayRef<ReassociationExprs>":$reassociation,

mlir/include/mlir/Dialect/Tensor/IR/TensorOps.td

@@ -1216,9 +1216,9 @@ def Tensor_CollapseShapeOp : Tensor_ReassociativeReshapeOp<"collapse_shape"> {
       "ArrayRef<ReassociationIndices>":$reassociation,
       CArg<"ArrayRef<NamedAttribute>", "{}">:$attrs),
     [{
-      build($_builder, $_state, resultType, src, attrs);
       $_state.addAttribute("reassociation",
           getReassociationIndicesAttribute($_builder, reassociation));
+      build($_builder, $_state, resultType, src, attrs);
     }]>,
     OpBuilder<(ins "Type":$resultType, "Value":$src,
       "ArrayRef<ReassociationExprs>":$reassociation,

mlir/include/mlir/IR/Operation.h

@@ -916,11 +916,12 @@ class alignas(8) Operation final
   /// operation. Returns an empty attribute if no properties are present.
   Attribute getPropertiesAsAttribute();
 
-  /// Set the properties from the provided  attribute.
+  /// Set the properties from the provided attribute.
   /// This is an expensive operation that can fail if the attribute is not
   /// matching the expectations of the properties for this operation. This is
   /// mostly useful for unregistered operations or used when parsing the
-  /// generic format. An optional diagnostic can be passed in for richer errors.
+  /// generic format. An optional diagnostic emitter can be passed in for richer
+  /// errors, if none is passed then behavior is undefined in error case.
   LogicalResult
   setPropertiesFromAttribute(Attribute attr,
                              function_ref<InFlightDiagnostic()> emitError);

mlir/lib/Dialect/Func/Transforms/OneToNFuncConversions.cpp

@@ -40,9 +40,9 @@ class ConvertTypesInFuncCallOp : public OneToNOpConversionPattern<CallOp> {
       return failure();
 
     // Create new CallOp.
-    auto newOp = rewriter.create<CallOp>(loc, resultMapping.getConvertedTypes(),
-                                         adaptor.getFlatOperands());
-    newOp->setAttrs(op->getAttrs());
+    auto newOp =
+        rewriter.create<CallOp>(loc, resultMapping.getConvertedTypes(),
+                                adaptor.getFlatOperands(), op->getAttrs());
 
     rewriter.replaceOp(op, newOp->getResults(), resultMapping);
     return success();

mlir/lib/Dialect/MemRef/IR/MemRefOps.cpp

@@ -1808,11 +1808,11 @@ void ReinterpretCastOp::build(OpBuilder &b, OperationState &result,
   dispatchIndexOpFoldResults(offset, dynamicOffsets, staticOffsets);
   dispatchIndexOpFoldResults(sizes, dynamicSizes, staticSizes);
   dispatchIndexOpFoldResults(strides, dynamicStrides, staticStrides);
+  result.addAttributes(attrs);
   build(b, result, resultType, source, dynamicOffsets, dynamicSizes,
         dynamicStrides, b.getDenseI64ArrayAttr(staticOffsets),
         b.getDenseI64ArrayAttr(staticSizes),
         b.getDenseI64ArrayAttr(staticStrides));
-  result.addAttributes(attrs);
 }
 
 void ReinterpretCastOp::build(OpBuilder &b, OperationState &result,
@@ -2486,9 +2486,9 @@ void CollapseShapeOp::build(OpBuilder &b, OperationState &result, Value src,
   auto srcType = llvm::cast<MemRefType>(src.getType());
   MemRefType resultType =
       CollapseShapeOp::computeCollapsedType(srcType, reassociation);
-  build(b, result, resultType, src, attrs);
   result.addAttribute(::mlir::getReassociationAttrName(),
                       getReassociationIndicesAttribute(b, reassociation));
+  build(b, result, resultType, src, attrs);
 }
 
 LogicalResult CollapseShapeOp::verify() {
@@ -2784,11 +2784,11 @@ void SubViewOp::build(OpBuilder &b, OperationState &result,
     resultType = llvm::cast<MemRefType>(SubViewOp::inferResultType(
         sourceMemRefType, staticOffsets, staticSizes, staticStrides));
   }
+  result.addAttributes(attrs);
   build(b, result, resultType, source, dynamicOffsets, dynamicSizes,
         dynamicStrides, b.getDenseI64ArrayAttr(staticOffsets),
         b.getDenseI64ArrayAttr(staticSizes),
         b.getDenseI64ArrayAttr(staticStrides));
-  result.addAttributes(attrs);
 }
 
 // Build a SubViewOp with mixed static and dynamic entries and inferred result
@@ -3323,8 +3323,8 @@ void TransposeOp::build(OpBuilder &b, OperationState &result, Value in,
   // Compute result type.
   MemRefType resultType = inferTransposeResultType(memRefType, permutationMap);
 
-  build(b, result, resultType, in, attrs);
   result.addAttribute(TransposeOp::getPermutationAttrStrName(), permutation);
+  build(b, result, resultType, in, attrs);
 }
 
 // transpose $in $permutation attr-dict : type($in) `to` type(results)

mlir/lib/Dialect/Tensor/IR/TensorOps.cpp

@@ -1743,9 +1743,9 @@ void CollapseShapeOp::build(OpBuilder &b, OperationState &result, Value src,
       llvm::cast<RankedTensorType>(src.getType()),
       getSymbolLessAffineMaps(
           convertReassociationIndicesToExprs(b.getContext(), reassociation)));
-  build(b, result, resultType, src, attrs);
   result.addAttribute(getReassociationAttrStrName(),
                       getReassociationIndicesAttribute(b, reassociation));
+  build(b, result, resultType, src, attrs);
 }
 
 template <typename TensorReshapeOp, bool isExpansion = std::is_same<
@@ -2099,11 +2099,11 @@ void ExtractSliceOp::build(OpBuilder &b, OperationState &result,
     resultType = llvm::cast<RankedTensorType>(ExtractSliceOp::inferResultType(
         sourceRankedTensorType, staticOffsets, staticSizes, staticStrides));
   }
+  result.addAttributes(attrs);
   build(b, result, resultType, source, dynamicOffsets, dynamicSizes,
         dynamicStrides, b.getDenseI64ArrayAttr(staticOffsets),
         b.getDenseI64ArrayAttr(staticSizes),
         b.getDenseI64ArrayAttr(staticStrides));
-  result.addAttributes(attrs);
 }
 
 /// Build an ExtractSliceOp with mixed static and dynamic entries and inferred
@@ -2498,11 +2498,11 @@ void InsertSliceOp::build(OpBuilder &b, OperationState &result, Value source,
   dispatchIndexOpFoldResults(offsets, dynamicOffsets, staticOffsets);
   dispatchIndexOpFoldResults(sizes, dynamicSizes, staticSizes);
   dispatchIndexOpFoldResults(strides, dynamicStrides, staticStrides);
+  result.addAttributes(attrs);
   build(b, result, dest.getType(), source, dest, dynamicOffsets, dynamicSizes,
         dynamicStrides, b.getDenseI64ArrayAttr(staticOffsets),
         b.getDenseI64ArrayAttr(staticSizes),
         b.getDenseI64ArrayAttr(staticStrides));
-  result.addAttributes(attrs);
 }
 
 /// Build an InsertSliceOp with mixed static and dynamic entries packed into a
@@ -2943,10 +2943,10 @@ void PadOp::build(OpBuilder &b, OperationState &result, Type resultType,
   auto sourceType = llvm::cast<RankedTensorType>(source.getType());
   if (!resultType)
     resultType = inferResultType(sourceType, staticLow, staticHigh);
+  result.addAttributes(attrs);
   build(b, result, resultType, source, low, high,
         b.getDenseI64ArrayAttr(staticLow), b.getDenseI64ArrayAttr(staticHigh),
         nofold ? b.getUnitAttr() : UnitAttr());
-  result.addAttributes(attrs);
 }
 
 void PadOp::build(OpBuilder &b, OperationState &result, Type resultType,
@@ -2976,10 +2976,10 @@ void PadOp::build(OpBuilder &b, OperationState &result, Type resultType,
     resultType = PadOp::inferResultType(sourceType, staticLow, staticHigh);
   }
   assert(llvm::isa<RankedTensorType>(resultType));
+  result.addAttributes(attrs);
   build(b, result, resultType, source, dynamicLow, dynamicHigh,
         b.getDenseI64ArrayAttr(staticLow), b.getDenseI64ArrayAttr(staticHigh),
         nofold ? b.getUnitAttr() : UnitAttr());
-  result.addAttributes(attrs);
 }
 
 void PadOp::build(OpBuilder &b, OperationState &result, Type resultType,
@@ -3423,11 +3423,11 @@ void ParallelInsertSliceOp::build(OpBuilder &b, OperationState &result,
   dispatchIndexOpFoldResults(offsets, dynamicOffsets, staticOffsets);
   dispatchIndexOpFoldResults(sizes, dynamicSizes, staticSizes);
   dispatchIndexOpFoldResults(strides, dynamicStrides, staticStrides);
+  result.addAttributes(attrs);
   build(b, result, {}, source, dest, dynamicOffsets, dynamicSizes,
         dynamicStrides, b.getDenseI64ArrayAttr(staticOffsets),
         b.getDenseI64ArrayAttr(staticSizes),
         b.getDenseI64ArrayAttr(staticStrides));
-  result.addAttributes(attrs);
 }
 
 /// Build an ParallelInsertSliceOp with mixed static and dynamic entries

mlir/test/Dialect/OpenMP/invalid.mlir

@@ -2113,23 +2113,23 @@ func.func @omp_distribute_allocate(%data_var : memref<i32>) -> () {
 
 func.func @omp_distribute_wrapper() -> () {
   // expected-error @below {{op must be a loop wrapper}}
-  "omp.distribute"() ({
+  omp.distribute {
       %0 = arith.constant 0 : i32
       "omp.terminator"() : () -> ()
-    }) : () -> ()
+  }
 }
 
 // -----
 
 func.func @omp_distribute_nested_wrapper(%data_var : memref<i32>) -> () {
   // expected-error @below {{only supported nested wrappers are 'omp.parallel' and 'omp.simd'}}
-  "omp.distribute"() ({
-      "omp.wsloop"() ({
-        %0 = arith.constant 0 : i32
-        "omp.terminator"() : () -> ()
-      }) : () -> ()
+  omp.distribute {
+    "omp.wsloop"() ({
+      %0 = arith.constant 0 : i32
       "omp.terminator"() : () -> ()
     }) : () -> ()
+    "omp.terminator"() : () -> ()
+  }
 }
 
 // -----

mlir/test/Dialect/OpenMP/ops.mlir

@@ -521,13 +521,13 @@ func.func @omp_wsloop_pretty(%lb : index, %ub : index, %step : index, %data_var
 // CHECK-LABEL: omp_simd
 func.func @omp_simd(%lb : index, %ub : index, %step : index) -> () {
   // CHECK: omp.simd
-  "omp.simd" () ({
+  omp.simd {
     "omp.loop_nest" (%lb, %ub, %step) ({
     ^bb1(%iv2: index):
       "omp.yield"() : () -> ()
     }) : (index, index, index) -> ()
     "omp.terminator"() : () -> ()
-  }) : () -> ()
+  }
 
   return
 }

mlir/test/lib/Dialect/Test/TestOps.td

@@ -2401,6 +2401,13 @@ def TableGenBuildOp5 : TableGenBuildInferReturnTypeBaseOp<
   let regions = (region AnyRegion:$body);
 }
 
+// Two variadic args, non variadic results, with AttrSizedOperandSegments
+// Test build method generation for property conversion & type inference.
+def TableGenBuildOp6 : TEST_Op<"tblgen_build_6", [AttrSizedOperandSegments]> {
+  let arguments = (ins Variadic<AnyType>:$a, Variadic<AnyType>:$b);
+  let results = (outs F32:$result);
+}
+
 //===----------------------------------------------------------------------===//
 // Test BufferPlacement
 //===----------------------------------------------------------------------===//