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Merged
makslevental merged 1 commit into from
Jul 21, 2025
Merged

[mlir][NFC] update flang/Optimizer/Transforms create APIs (11/n) #149915

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28 changes: 14 additions & 14 deletions flang/lib/Optimizer/Transforms/AbstractResult.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -137,9 +137,9 @@ class CallConversion : public mlir::OpRewritePattern<Op> {
auto buffer = saveResult.getMemref();
mlir::Value arg = buffer;
if (mustEmboxResult(result.getType(), shouldBoxResult))
arg = rewriter.create<fir::EmboxOp>(
loc, argType, buffer, saveResult.getShape(), /*slice*/ mlir::Value{},
saveResult.getTypeparams());
arg = fir::EmboxOp::create(rewriter, loc, argType, buffer,
saveResult.getShape(), /*slice*/ mlir::Value{},
saveResult.getTypeparams());

llvm::SmallVector<mlir::Type> newResultTypes;
bool isResultBuiltinCPtr = fir::isa_builtin_cptr_type(result.getType());
Expand All @@ -155,8 +155,8 @@ class CallConversion : public mlir::OpRewritePattern<Op> {
if (!isResultBuiltinCPtr)
newOperands.emplace_back(arg);
newOperands.append(op.getOperands().begin(), op.getOperands().end());
newOp = rewriter.create<fir::CallOp>(loc, *op.getCallee(),
newResultTypes, newOperands);
newOp = fir::CallOp::create(rewriter, loc, *op.getCallee(),
newResultTypes, newOperands);
} else {
// Indirect calls.
llvm::SmallVector<mlir::Type> newInputTypes;
Expand All @@ -169,13 +169,13 @@ class CallConversion : public mlir::OpRewritePattern<Op> {

llvm::SmallVector<mlir::Value> newOperands;
newOperands.push_back(
rewriter.create<fir::ConvertOp>(loc, newFuncTy, op.getOperand(0)));
fir::ConvertOp::create(rewriter, loc, newFuncTy, op.getOperand(0)));
if (!isResultBuiltinCPtr)
newOperands.push_back(arg);
newOperands.append(op.getOperands().begin() + 1,
op.getOperands().end());
newOp = rewriter.create<fir::CallOp>(loc, mlir::SymbolRefAttr{},
newResultTypes, newOperands);
newOp = fir::CallOp::create(rewriter, loc, mlir::SymbolRefAttr{},
newResultTypes, newOperands);
}
}

Expand All @@ -191,8 +191,8 @@ class CallConversion : public mlir::OpRewritePattern<Op> {
passArgPos =
rewriter.getI32IntegerAttr(*op.getPassArgPos() + passArgShift);
// TODO: propagate argument and result attributes (need to be shifted).
newOp = rewriter.create<fir::DispatchOp>(
loc, newResultTypes, rewriter.getStringAttr(op.getMethod()),
newOp = fir::DispatchOp::create(
rewriter, loc, newResultTypes, rewriter.getStringAttr(op.getMethod()),
op.getOperands()[0], newOperands, passArgPos,
/*arg_attrs=*/nullptr, /*res_attrs=*/nullptr,
op.getProcedureAttrsAttr());
Expand Down Expand Up @@ -280,7 +280,7 @@ processReturnLikeOp(OpTy ret, mlir::Value newArg,
// register pass, this is possible for fir.box results, or fir.record
// with no length parameters. Simply store the result in the result
// storage. at the return point.
rewriter.create<fir::StoreOp>(loc, resultValue, newArg);
fir::StoreOp::create(rewriter, loc, resultValue, newArg);
rewriter.replaceOpWithNewOp<OpTy>(ret);
}
// Delete result old local storage if unused.
Expand Down Expand Up @@ -337,8 +337,8 @@ class AddrOfOpConversion : public mlir::OpRewritePattern<fir::AddrOfOp> {
newFuncTy = getCPtrFunctionType(oldFuncTy);
else
newFuncTy = getNewFunctionType(oldFuncTy, shouldBoxResult);
auto newAddrOf = rewriter.create<fir::AddrOfOp>(addrOf.getLoc(), newFuncTy,
addrOf.getSymbol());
auto newAddrOf = fir::AddrOfOp::create(rewriter, addrOf.getLoc(), newFuncTy,
addrOf.getSymbol());
// Rather than converting all op a function pointer might transit through
// (e.g calls, stores, loads, converts...), cast new type to the abstract
// type. A conversion will be added when calling indirect calls of abstract
Expand Down Expand Up @@ -397,7 +397,7 @@ class AbstractResultOpt
if (mustEmboxResult(resultType, shouldBoxResult)) {
auto bufferType = fir::ReferenceType::get(resultType);
rewriter.setInsertionPointToStart(&func.front());
newArg = rewriter.create<fir::BoxAddrOp>(loc, bufferType, newArg);
newArg = fir::BoxAddrOp::create(rewriter, loc, bufferType, newArg);
}
patterns.insert<ReturnOpConversion>(context, newArg);
target.addDynamicallyLegalOp<mlir::func::ReturnOp>(
Expand Down
12 changes: 7 additions & 5 deletions flang/lib/Optimizer/Transforms/AffineDemotion.cpp
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Expand Up @@ -60,9 +60,10 @@ class AffineLoadConversion
if (!maybeExpandedMap)
return failure();

auto coorOp = rewriter.create<fir::CoordinateOp>(
op.getLoc(), fir::ReferenceType::get(op.getResult().getType()),
adaptor.getMemref(), *maybeExpandedMap);
auto coorOp = fir::CoordinateOp::create(
rewriter, op.getLoc(),
fir::ReferenceType::get(op.getResult().getType()), adaptor.getMemref(),
*maybeExpandedMap);

rewriter.replaceOpWithNewOp<fir::LoadOp>(op, coorOp.getResult());
return success();
Expand All @@ -83,8 +84,9 @@ class AffineStoreConversion
if (!maybeExpandedMap)
return failure();

auto coorOp = rewriter.create<fir::CoordinateOp>(
op.getLoc(), fir::ReferenceType::get(op.getValueToStore().getType()),
auto coorOp = fir::CoordinateOp::create(
rewriter, op.getLoc(),
fir::ReferenceType::get(op.getValueToStore().getType()),
adaptor.getMemref(), *maybeExpandedMap);
rewriter.replaceOpWithNewOp<fir::StoreOp>(op, adaptor.getValue(),
coorOp.getResult());
Expand Down
40 changes: 21 additions & 19 deletions flang/lib/Optimizer/Transforms/AffinePromotion.cpp
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Expand Up @@ -366,8 +366,9 @@ static mlir::Type coordinateArrayElement(fir::ArrayCoorOp op) {
static void populateIndexArgs(fir::ArrayCoorOp acoOp, fir::ShapeOp shape,
SmallVectorImpl<mlir::Value> &indexArgs,
mlir::PatternRewriter &rewriter) {
auto one = rewriter.create<mlir::arith::ConstantOp>(
acoOp.getLoc(), rewriter.getIndexType(), rewriter.getIndexAttr(1));
auto one = mlir::arith::ConstantOp::create(rewriter, acoOp.getLoc(),
rewriter.getIndexType(),
rewriter.getIndexAttr(1));
auto extents = shape.getExtents();
for (auto i = extents.begin(); i < extents.end(); i++) {
indexArgs.push_back(one);
Expand All @@ -379,8 +380,9 @@ static void populateIndexArgs(fir::ArrayCoorOp acoOp, fir::ShapeOp shape,
static void populateIndexArgs(fir::ArrayCoorOp acoOp, fir::ShapeShiftOp shape,
SmallVectorImpl<mlir::Value> &indexArgs,
mlir::PatternRewriter &rewriter) {
auto one = rewriter.create<mlir::arith::ConstantOp>(
acoOp.getLoc(), rewriter.getIndexType(), rewriter.getIndexAttr(1));
auto one = mlir::arith::ConstantOp::create(rewriter, acoOp.getLoc(),
rewriter.getIndexType(),
rewriter.getIndexAttr(1));
auto extents = shape.getPairs();
for (auto i = extents.begin(); i < extents.end();) {
indexArgs.push_back(*i++);
Expand Down Expand Up @@ -422,13 +424,13 @@ createAffineOps(mlir::Value arrayRef, mlir::PatternRewriter &rewriter) {

populateIndexArgs(acoOp, indexArgs, rewriter);

auto affineApply = rewriter.create<affine::AffineApplyOp>(
acoOp.getLoc(), affineMap, indexArgs);
auto affineApply = affine::AffineApplyOp::create(rewriter, acoOp.getLoc(),
affineMap, indexArgs);
auto arrayElementType = coordinateArrayElement(acoOp);
auto newType =
mlir::MemRefType::get({mlir::ShapedType::kDynamic}, arrayElementType);
auto arrayConvert = rewriter.create<fir::ConvertOp>(acoOp.getLoc(), newType,
acoOp.getMemref());
auto arrayConvert = fir::ConvertOp::create(rewriter, acoOp.getLoc(), newType,
acoOp.getMemref());
return std::make_pair(affineApply, arrayConvert);
}

Expand Down Expand Up @@ -495,7 +497,7 @@ class AffineLoopConversion : public mlir::OpRewritePattern<fir::DoLoopOp> {
affineFor.getRegionIterArgs());
if (!results.empty()) {
rewriter.setInsertionPointToEnd(affineFor.getBody());
rewriter.create<affine::AffineYieldOp>(resultOp->getLoc(), results);
affine::AffineYieldOp::create(rewriter, resultOp->getLoc(), results);
}
rewriter.finalizeOpModification(affineFor.getOperation());

Expand Down Expand Up @@ -525,8 +527,8 @@ class AffineLoopConversion : public mlir::OpRewritePattern<fir::DoLoopOp> {
std::pair<affine::AffineForOp, mlir::Value>
positiveConstantStep(fir::DoLoopOp op, int64_t step,
mlir::PatternRewriter &rewriter) const {
auto affineFor = rewriter.create<affine::AffineForOp>(
op.getLoc(), ValueRange(op.getLowerBound()),
auto affineFor = affine::AffineForOp::create(
rewriter, op.getLoc(), ValueRange(op.getLowerBound()),
mlir::AffineMap::get(0, 1,
mlir::getAffineSymbolExpr(0, op.getContext())),
ValueRange(op.getUpperBound()),
Expand All @@ -543,24 +545,24 @@ class AffineLoopConversion : public mlir::OpRewritePattern<fir::DoLoopOp> {
auto step = mlir::getAffineSymbolExpr(2, op.getContext());
mlir::AffineMap upperBoundMap = mlir::AffineMap::get(
0, 3, (upperBound - lowerBound + step).floorDiv(step));
auto genericUpperBound = rewriter.create<affine::AffineApplyOp>(
op.getLoc(), upperBoundMap,
auto genericUpperBound = affine::AffineApplyOp::create(
rewriter, op.getLoc(), upperBoundMap,
ValueRange({op.getLowerBound(), op.getUpperBound(), op.getStep()}));
auto actualIndexMap = mlir::AffineMap::get(
1, 2,
(lowerBound + mlir::getAffineDimExpr(0, op.getContext())) *
mlir::getAffineSymbolExpr(1, op.getContext()));

auto affineFor = rewriter.create<affine::AffineForOp>(
op.getLoc(), ValueRange(),
auto affineFor = affine::AffineForOp::create(
rewriter, op.getLoc(), ValueRange(),
AffineMap::getConstantMap(0, op.getContext()),
genericUpperBound.getResult(),
mlir::AffineMap::get(0, 1,
1 + mlir::getAffineSymbolExpr(0, op.getContext())),
1, op.getIterOperands());
rewriter.setInsertionPointToStart(affineFor.getBody());
auto actualIndex = rewriter.create<affine::AffineApplyOp>(
op.getLoc(), actualIndexMap,
auto actualIndex = affine::AffineApplyOp::create(
rewriter, op.getLoc(), actualIndexMap,
ValueRange(
{affineFor.getInductionVar(), op.getLowerBound(), op.getStep()}));
return std::make_pair(affineFor, actualIndex.getResult());
Expand Down Expand Up @@ -588,8 +590,8 @@ class AffineIfConversion : public mlir::OpRewritePattern<fir::IfOp> {
<< "AffineIfConversion: couldn't calculate affine condition\n";);
return failure();
}
auto affineIf = rewriter.create<affine::AffineIfOp>(
op.getLoc(), affineCondition.getIntegerSet(),
auto affineIf = affine::AffineIfOp::create(
rewriter, op.getLoc(), affineCondition.getIntegerSet(),
affineCondition.getAffineArgs(), !op.getElseRegion().empty());
rewriter.startOpModification(affineIf);
affineIf.getThenBlock()->getOperations().splice(
Expand Down
63 changes: 32 additions & 31 deletions flang/lib/Optimizer/Transforms/ArrayValueCopy.cpp
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Expand Up @@ -856,7 +856,7 @@ static bool getAdjustedExtents(mlir::Location loc,
auto idxTy = rewriter.getIndexType();
if (isAssumedSize(result)) {
// Use slice information to compute the extent of the column.
auto one = rewriter.create<mlir::arith::ConstantIndexOp>(loc, 1);
auto one = mlir::arith::ConstantIndexOp::create(rewriter, loc, 1);
mlir::Value size = one;
if (mlir::Value sliceArg = arrLoad.getSlice()) {
if (auto sliceOp =
Expand Down Expand Up @@ -896,14 +896,14 @@ static mlir::Value getOrReadExtentsAndShapeOp(
mlir::cast<SequenceType>(dyn_cast_ptrOrBoxEleTy(boxTy)).getDimension();
auto idxTy = rewriter.getIndexType();
for (decltype(rank) dim = 0; dim < rank; ++dim) {
auto dimVal = rewriter.create<mlir::arith::ConstantIndexOp>(loc, dim);
auto dimInfo = rewriter.create<BoxDimsOp>(loc, idxTy, idxTy, idxTy,
arrLoad.getMemref(), dimVal);
auto dimVal = mlir::arith::ConstantIndexOp::create(rewriter, loc, dim);
auto dimInfo = BoxDimsOp::create(rewriter, loc, idxTy, idxTy, idxTy,
arrLoad.getMemref(), dimVal);
result.emplace_back(dimInfo.getResult(1));
}
if (!arrLoad.getShape()) {
auto shapeType = ShapeType::get(rewriter.getContext(), rank);
return rewriter.create<ShapeOp>(loc, shapeType, result);
return ShapeOp::create(rewriter, loc, shapeType, result);
}
auto shiftOp = arrLoad.getShape().getDefiningOp<ShiftOp>();
auto shapeShiftType = ShapeShiftType::get(rewriter.getContext(), rank);
Expand All @@ -912,8 +912,8 @@ static mlir::Value getOrReadExtentsAndShapeOp(
shapeShiftOperands.push_back(lb);
shapeShiftOperands.push_back(extent);
}
return rewriter.create<ShapeShiftOp>(loc, shapeShiftType,
shapeShiftOperands);
return ShapeShiftOp::create(rewriter, loc, shapeShiftType,
shapeShiftOperands);
}
copyUsingSlice =
getAdjustedExtents(loc, rewriter, arrLoad, result, arrLoad.getShape());
Expand Down Expand Up @@ -952,13 +952,13 @@ static mlir::Value genCoorOp(mlir::PatternRewriter &rewriter,
auto module = load->getParentOfType<mlir::ModuleOp>();
FirOpBuilder builder(rewriter, module);
auto typeparams = getTypeParamsIfRawData(loc, builder, load, alloc.getType());
mlir::Value result = rewriter.create<ArrayCoorOp>(
loc, eleTy, alloc, shape, slice,
mlir::Value result = ArrayCoorOp::create(
rewriter, loc, eleTy, alloc, shape, slice,
llvm::ArrayRef<mlir::Value>{originated}.take_front(dimension),
typeparams);
if (dimension < originated.size())
result = rewriter.create<fir::CoordinateOp>(
loc, resTy, result,
result = fir::CoordinateOp::create(
rewriter, loc, resTy, result,
llvm::ArrayRef<mlir::Value>{originated}.drop_front(dimension));
return result;
}
Expand All @@ -971,13 +971,13 @@ static mlir::Value getCharacterLen(mlir::Location loc, FirOpBuilder &builder,
// The loaded array is an emboxed value. Get the CHARACTER length from
// the box value.
auto eleSzInBytes =
builder.create<BoxEleSizeOp>(loc, charLenTy, load.getMemref());
BoxEleSizeOp::create(builder, loc, charLenTy, load.getMemref());
auto kindSize =
builder.getKindMap().getCharacterBitsize(charTy.getFKind());
auto kindByteSize =
builder.createIntegerConstant(loc, charLenTy, kindSize / 8);
return builder.create<mlir::arith::DivSIOp>(loc, eleSzInBytes,
kindByteSize);
return mlir::arith::DivSIOp::create(builder, loc, eleSzInBytes,
kindByteSize);
}
// The loaded array is a (set of) unboxed values. If the CHARACTER's
// length is not a constant, it must be provided as a type parameter to
Expand All @@ -1003,25 +1003,25 @@ void genArrayCopy(mlir::Location loc, mlir::PatternRewriter &rewriter,
auto idxTy = rewriter.getIndexType();
// Build loop nest from column to row.
for (auto sh : llvm::reverse(extents)) {
auto ubi = rewriter.create<ConvertOp>(loc, idxTy, sh);
auto zero = rewriter.create<mlir::arith::ConstantIndexOp>(loc, 0);
auto one = rewriter.create<mlir::arith::ConstantIndexOp>(loc, 1);
auto ub = rewriter.create<mlir::arith::SubIOp>(loc, idxTy, ubi, one);
auto loop = rewriter.create<DoLoopOp>(loc, zero, ub, one);
auto ubi = ConvertOp::create(rewriter, loc, idxTy, sh);
auto zero = mlir::arith::ConstantIndexOp::create(rewriter, loc, 0);
auto one = mlir::arith::ConstantIndexOp::create(rewriter, loc, 1);
auto ub = mlir::arith::SubIOp::create(rewriter, loc, idxTy, ubi, one);
auto loop = DoLoopOp::create(rewriter, loc, zero, ub, one);
rewriter.setInsertionPointToStart(loop.getBody());
indices.push_back(loop.getInductionVar());
}
// Reverse the indices so they are in column-major order.
std::reverse(indices.begin(), indices.end());
auto module = arrLoad->getParentOfType<mlir::ModuleOp>();
FirOpBuilder builder(rewriter, module);
auto fromAddr = rewriter.create<ArrayCoorOp>(
loc, getEleTy(src.getType()), src, shapeOp,
auto fromAddr = ArrayCoorOp::create(
rewriter, loc, getEleTy(src.getType()), src, shapeOp,
CopyIn && copyUsingSlice ? sliceOp : mlir::Value{},
factory::originateIndices(loc, rewriter, src.getType(), shapeOp, indices),
getTypeParamsIfRawData(loc, builder, arrLoad, src.getType()));
auto toAddr = rewriter.create<ArrayCoorOp>(
loc, getEleTy(dst.getType()), dst, shapeOp,
auto toAddr = ArrayCoorOp::create(
rewriter, loc, getEleTy(dst.getType()), dst, shapeOp,
!CopyIn && copyUsingSlice ? sliceOp : mlir::Value{},
factory::originateIndices(loc, rewriter, dst.getType(), shapeOp, indices),
getTypeParamsIfRawData(loc, builder, arrLoad, dst.getType()));
Expand Down Expand Up @@ -1093,15 +1093,16 @@ allocateArrayTemp(mlir::Location loc, mlir::PatternRewriter &rewriter,
findNonconstantExtents(baseType, extents);
llvm::SmallVector<mlir::Value> typeParams =
genArrayLoadTypeParameters(loc, rewriter, load);
mlir::Value allocmem = rewriter.create<AllocMemOp>(
loc, dyn_cast_ptrOrBoxEleTy(baseType), typeParams, nonconstantExtents);
mlir::Value allocmem =
AllocMemOp::create(rewriter, loc, dyn_cast_ptrOrBoxEleTy(baseType),
typeParams, nonconstantExtents);
mlir::Type eleType =
fir::unwrapSequenceType(fir::unwrapPassByRefType(baseType));
if (fir::isRecordWithAllocatableMember(eleType)) {
// The allocatable component descriptors need to be set to a clean
// deallocated status before anything is done with them.
mlir::Value box = rewriter.create<fir::EmboxOp>(
loc, fir::BoxType::get(allocmem.getType()), allocmem, shape,
mlir::Value box = fir::EmboxOp::create(
rewriter, loc, fir::BoxType::get(allocmem.getType()), allocmem, shape,
/*slice=*/mlir::Value{}, typeParams);
auto module = load->getParentOfType<mlir::ModuleOp>();
FirOpBuilder builder(rewriter, module);
Expand All @@ -1111,12 +1112,12 @@ allocateArrayTemp(mlir::Location loc, mlir::PatternRewriter &rewriter,
auto cleanup = [=](mlir::PatternRewriter &r) {
FirOpBuilder builder(r, module);
runtime::genDerivedTypeDestroy(builder, loc, box);
r.create<FreeMemOp>(loc, allocmem);
FreeMemOp::create(r, loc, allocmem);
};
return {allocmem, cleanup};
}
auto cleanup = [=](mlir::PatternRewriter &r) {
r.create<FreeMemOp>(loc, allocmem);
FreeMemOp::create(r, loc, allocmem);
};
return {allocmem, cleanup};
}
Expand Down Expand Up @@ -1257,7 +1258,7 @@ class ArrayUpdateConversion : public ArrayUpdateConversionBase<ArrayUpdateOp> {
if (auto inEleTy = dyn_cast_ptrEleTy(input.getType())) {
emitFatalError(loc, "array_update on references not supported");
} else {
rewriter.create<fir::StoreOp>(loc, input, coor);
fir::StoreOp::create(rewriter, loc, input, coor);
}
};
auto lhsEltRefType = toRefType(update.getMerge().getType());
Expand Down Expand Up @@ -1368,7 +1369,7 @@ class ArrayAmendConversion : public mlir::OpRewritePattern<ArrayAmendOp> {
auto *op = amend.getOperation();
rewriter.setInsertionPoint(op);
auto loc = amend.getLoc();
auto undef = rewriter.create<UndefOp>(loc, amend.getType());
auto undef = UndefOp::create(rewriter, loc, amend.getType());
rewriter.replaceOp(amend, undef.getResult());
return mlir::success();
}
Expand Down
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