[mlir] tosa: build error when building with clang

[lipracer]

Summary

The change of https://reviews.llvm.org/D121513#3411651
has caused a build error when building with clang:

/mnt/vss/_work/1/llvm-project/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp:599:26: error: extra ';' outside of a function is incompatible with C++98 [-Werror,-Wc++98-compat-extra-semi]
ReduceFolder(ReduceAllOp);

[hpmorgan]

LGTM. Thanks!

[lipracer]

@hpmorgan Could you please review it, It's very strange why we get normal clang-format results when we change the macro definition to uppercase.

[Mogball]

I'll land this one

mlir/lib/Dialect/Tosa/IR/TosaOps.cpp

OpFoldResult CastOp::fold(ArrayRef<Attribute> operands) {
   return {};
 }
 
 OpFoldResult ConstOp::fold(ArrayRef<Attribute> operands) {
   assert(operands.empty() && "constant has no operands");
   return valueAttr();
 }
 
-#define ReduceFolder(OP)                                                       \
+#define REDUCE_FOLDER(OP)                                                      \
   OpFoldResult OP::fold(ArrayRef<Attribute> operands) {                        \
     ShapedType inputTy = input().getType().cast<ShapedType>();                 \
     if (!inputTy.hasRank())                                                    \
       return {};                                                               \
     if (inputTy.getDimSize(axis()) == 1)                                       \
       return input();                                                          \
     return {};                                                                 \
   }
 
-ReduceFolder(ReduceAllOp);
-ReduceFolder(ReduceAnyOp);
-ReduceFolder(ReduceMaxOp);
-ReduceFolder(ReduceMinOp);
-ReduceFolder(ReduceProdOp);
-ReduceFolder(ReduceSumOp);
-#undef ReduceFolder
+REDUCE_FOLDER(ReduceAllOp)
+REDUCE_FOLDER(ReduceAnyOp)
+REDUCE_FOLDER(ReduceMaxOp)
+REDUCE_FOLDER(ReduceMinOp)
+REDUCE_FOLDER(ReduceProdOp)
+REDUCE_FOLDER(ReduceSumOp)
+#undef REDUCE_FOLDER
 
 OpFoldResult ReshapeOp::fold(ArrayRef<Attribute> operands) {
   auto inputTy = input1().getType().dyn_cast<RankedTensorType>();
   auto outputTy = getType().dyn_cast<RankedTensorType>();
 
   if (!inputTy || !outputTy || inputTy != outputTy)
     return {};
   return input1();