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[mlir][NFC] update mlir/Dialect create APIs (25/n) #149932

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Jul 21, 2025
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[mlir][NFC] update mlir/Dialect create APIs (25/n) #149932

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12 changes: 6 additions & 6 deletions mlir/lib/Dialect/Tosa/IR/TosaCanonicalizations.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -845,9 +845,9 @@ struct PadSliceOptimization : public OpRewritePattern<tosa::SliceOp> {
getTosaConstShape(rewriter, sliceOp.getLoc(), newPadPaddings);
auto newPadTy =
RankedTensorType::get(newPadShape, inputTy.getElementType());
auto newPadOp = rewriter.create<tosa::PadOp>(
padOp.getLoc(), newPadTy, padOp.getInput1(), newPaddingsOp,
padOp.getPadConst());
auto newPadOp = tosa::PadOp::create(rewriter, padOp.getLoc(), newPadTy,
padOp.getInput1(), newPaddingsOp,
padOp.getPadConst());

// Update SliceOp and point to new PadOp
auto newStartOp =
Expand Down Expand Up @@ -897,9 +897,9 @@ struct SliceDynamicSizeCanonicalization
}

auto size_op = getTosaConstShape(rewriter, sliceOp.getLoc(), sliceSizes);
auto newSliceOp = rewriter.create<tosa::SliceOp>(
sliceOp.getLoc(), sliceOp.getType(), sliceOp.getInput1(),
sliceOp.getStart(), size_op);
auto newSliceOp =
tosa::SliceOp::create(rewriter, sliceOp.getLoc(), sliceOp.getType(),
sliceOp.getInput1(), sliceOp.getStart(), size_op);

rewriter.replaceOp(sliceOp, newSliceOp.getResult());
return success();
Expand Down
16 changes: 8 additions & 8 deletions mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
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Expand Up @@ -180,12 +180,12 @@ Operation *TosaDialect::materializeConstant(OpBuilder &builder, Attribute value,
// Tosa dialect constants only support ElementsAttr unlike standard dialect
// constant which supports all attributes.
if (llvm::isa<shapeType>(type) && llvm::isa<DenseIntElementsAttr>(value)) {
return builder.create<tosa::ConstShapeOp>(
loc, type, llvm::cast<DenseIntElementsAttr>(value));
return tosa::ConstShapeOp::create(builder, loc, type,
llvm::cast<DenseIntElementsAttr>(value));
}
if (llvm::isa<ElementsAttr>(value))
return builder.create<tosa::ConstOp>(loc, type,
llvm::cast<ElementsAttr>(value));
return tosa::ConstOp::create(builder, loc, type,
llvm::cast<ElementsAttr>(value));
return nullptr;
}

Expand Down Expand Up @@ -323,7 +323,7 @@ Value mlir::tosa::createPadConstTensor(OpBuilder &builder, Location loc,
builder.getFloatAttr(srcElemType, val))
: DenseElementsAttr::get(padConstEType,
builder.getIntegerAttr(srcElemType, val))};
return builder.create<tosa::ConstOp>(loc, padConstType, padConstAttr);
return tosa::ConstOp::create(builder, loc, padConstType, padConstAttr);
}

//===----------------------------------------------------------------------===//
Expand Down Expand Up @@ -2415,7 +2415,7 @@ LogicalResult TransposeOp::reifyResultShapes(
int32_t dimInInput = transposePerms[dim];
if (inputType.isDynamicDim(dimInInput))
returnedDims[dim] =
builder.create<tensor::DimOp>(getLoc(), input, dimInInput)
tensor::DimOp::create(builder, getLoc(), input, dimInInput)
.getResult();
else
returnedDims[dim] =
Expand Down Expand Up @@ -3947,12 +3947,12 @@ std::optional<Value> mlir::tosa::createZeroPointTensor(OpBuilder &builder,
if (llvm::isa<FloatType>(srcElemType)) {
auto zpAttr = DenseElementsAttr::get(
zpType, builder.getFloatAttr(srcElemType, static_cast<double>(zp)));
return builder.create<tosa::ConstOp>(loc, zpType, zpAttr);
return tosa::ConstOp::create(builder, loc, zpType, zpAttr);
}
if (llvm::isa<IntegerType>(srcElemType)) {
auto zpAttr =
DenseElementsAttr::get(zpType, builder.getIntegerAttr(srcElemType, zp));
return builder.create<tosa::ConstOp>(loc, zpType, zpAttr);
return tosa::ConstOp::create(builder, loc, zpType, zpAttr);
}
llvm::errs() << "zero point is not allowed for unsupported data types\n";
return std::nullopt;
Expand Down
22 changes: 11 additions & 11 deletions mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeDepthwise.cpp
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Expand Up @@ -90,12 +90,12 @@ struct DepthwiseConv2DIsMul : public OpRewritePattern<tosa::DepthwiseConv2DOp> {

if (inputETy != resultETy) {
inputType = inputType.clone(resultETy);
input = rewriter.create<tosa::CastOp>(op.getLoc(), inputType, input);
input = tosa::CastOp::create(rewriter, op.getLoc(), inputType, input);
}

if (weightETy != resultETy) {
weightType = weightType.clone(resultETy);
weight = rewriter.create<tosa::CastOp>(op.getLoc(), weightType, weight);
weight = tosa::CastOp::create(rewriter, op.getLoc(), weightType, weight);
}

if (iZp != 0 || wZp != 0) {
Expand All @@ -109,9 +109,9 @@ struct DepthwiseConv2DIsMul : public OpRewritePattern<tosa::DepthwiseConv2DOp> {
auto zpTy = RankedTensorType::get(shape, ety);
auto zpAttr =
DenseElementsAttr::get(zpTy, rewriter.getIntegerAttr(ety, zp));
auto zpVal = rewriter.create<tosa::ConstOp>(op.getLoc(), zpTy, zpAttr);
return rewriter.create<tosa::SubOp>(op.getLoc(), val.getType(), val,
zpVal);
auto zpVal = tosa::ConstOp::create(rewriter, op.getLoc(), zpTy, zpAttr);
return tosa::SubOp::create(rewriter, op.getLoc(), val.getType(), val,
zpVal);
};

input = applyZp(input, iZp);
Expand All @@ -138,10 +138,10 @@ struct DepthwiseConv2DIsMul : public OpRewritePattern<tosa::DepthwiseConv2DOp> {
auto padTy = RankedTensorType::get({1}, inputETy);
auto padAttr = DenseElementsAttr::get(padTy, zeroAttr);
Value padVal =
rewriter.create<tosa::ConstOp>(op->getLoc(), padTy, padAttr);
tosa::ConstOp::create(rewriter, op->getLoc(), padTy, padAttr);
inputType = RankedTensorType::get(newShape, inputETy);
input = rewriter.create<tosa::PadOp>(op->getLoc(), inputType, input,
padSizeVal, padVal);
input = tosa::PadOp::create(rewriter, op->getLoc(), inputType, input,
padSizeVal, padVal);
}

// Perform an elementwise mul over the reshaped input and weight.
Expand All @@ -161,7 +161,7 @@ struct DepthwiseConv2DIsMul : public OpRewritePattern<tosa::DepthwiseConv2DOp> {
auto shiftZeroAttr = DenseElementsAttr::get(
shiftType, rewriter.getIntegerAttr(shiftElementType, 0));
Value constZero =
rewriter.create<tosa::ConstOp>(op.getLoc(), shiftType, shiftZeroAttr);
tosa::ConstOp::create(rewriter, op.getLoc(), shiftType, shiftZeroAttr);
Value mulValue = rewriter
.create<tosa::MulOp>(op.getLoc(), mulShapeType, input,
weight, constZero)
Expand All @@ -174,8 +174,8 @@ struct DepthwiseConv2DIsMul : public OpRewritePattern<tosa::DepthwiseConv2DOp> {
dyn_cast<RankedTensorType>(input.getType()).getElementType());
auto outputShapeValue =
getTosaConstShape(rewriter, op->getLoc(), outputShape);
Value outputValue = rewriter.create<tosa::ReshapeOp>(
op.getLoc(), outputShapeType, mulValue, outputShapeValue);
Value outputValue = tosa::ReshapeOp::create(
rewriter, op.getLoc(), outputShapeType, mulValue, outputShapeValue);

Value bias = op.getBias();
if (EqualizeRanks(rewriter, op.getLoc(), outputValue, bias).failed()) {
Expand Down
22 changes: 12 additions & 10 deletions mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeTransposeConv.cpp
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Expand Up @@ -62,14 +62,16 @@ class TransposeConvNonStridedConverter
convPad[2] = kernelWidth - 1 + pad[2];
convPad[3] = kernelWidth - 1 + pad[3];

auto reverse1 = rewriter.create<tosa::ReverseOp>(
loc, weightTy, weight, /* axis = */ rewriter.getI32IntegerAttr(1));
auto reverse2 = rewriter.create<tosa::ReverseOp>(
loc, weightTy, reverse1, /* axis = */ rewriter.getI32IntegerAttr(2));

Value conv2d = rewriter.create<tosa::Conv2DOp>(
loc, resultTy, input, reverse2, bias, op.getInputZp(), op.getWeightZp(),
rewriter.getDenseI64ArrayAttr(convPad),
auto reverse1 =
tosa::ReverseOp::create(rewriter, loc, weightTy, weight,
/* axis = */ rewriter.getI32IntegerAttr(1));
auto reverse2 =
tosa::ReverseOp::create(rewriter, loc, weightTy, reverse1,
/* axis = */ rewriter.getI32IntegerAttr(2));

Value conv2d = tosa::Conv2DOp::create(
rewriter, loc, resultTy, input, reverse2, bias, op.getInputZp(),
op.getWeightZp(), rewriter.getDenseI64ArrayAttr(convPad),
rewriter.getDenseI64ArrayAttr(stride),
rewriter.getDenseI64ArrayAttr({1, 1}),
/* acc_type = */ op.getAccType());
Expand Down Expand Up @@ -216,8 +218,8 @@ class TransposeConvStridedConverter
inputPaddingVal, inputPadConst);

// We use a zero bias as we need to broadcast the bias.
auto zeroBias = rewriter.create<tosa::ConstOp>(
loc,
auto zeroBias = tosa::ConstOp::create(
rewriter, loc,
RankedTensorType::get({outputChannels * stride[0] * stride[1]},
biasETy),
DenseElementsAttr::get(
Expand Down
2 changes: 1 addition & 1 deletion mlir/lib/Dialect/Tosa/Transforms/TosaInferShapes.cpp
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Expand Up @@ -112,7 +112,7 @@ class TypeModificationState {
OpBuilder builder{value.getContext()};
builder.setInsertionPointAfter(value.getDefiningOp());
castValue =
builder.create<tensor::CastOp>(value.getLoc(), oldType, value);
tensor::CastOp::create(builder, value.getLoc(), oldType, value);
}

use->set(castValue);
Expand Down
8 changes: 4 additions & 4 deletions mlir/lib/Dialect/Tosa/Transforms/TosaReduceTransposes.cpp
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Expand Up @@ -419,8 +419,8 @@ std::optional<Value> TosaReduceTransposes::buildMappedToValue(
return std::nullopt;
}
ImplicitLocOpBuilder builder(reshapeOp.getLoc(), rewriter);
auto foldedReshape = rewriter.create<ReshapeOp>(
reshapeOp.getLoc(),
auto foldedReshape = ReshapeOp::create(
rewriter, reshapeOp.getLoc(),
RankedTensorType::get(applyTOSAPermutation(shape, hoistedPerms),
reshapeOutputType.getElementType()),
reshapeOp.getInput1(),
Expand All @@ -439,8 +439,8 @@ std::optional<Value> TosaReduceTransposes::buildMappedToValue(
if (!maybeNewDenseAttr.has_value())
return std::nullopt;
auto newDenseAttr = maybeNewDenseAttr.value();
auto newConstOp = rewriter.create<ConstOp>(
constOp.getLoc(), newDenseAttr.getType(), newDenseAttr);
auto newConstOp = ConstOp::create(rewriter, constOp.getLoc(),
newDenseAttr.getType(), newDenseAttr);
return newConstOp->getResult(0);
}

Expand Down
4 changes: 2 additions & 2 deletions mlir/lib/Dialect/Tosa/Transforms/TosaTypeConverters.cpp
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Expand Up @@ -37,7 +37,7 @@ void mlir::tosa::populateTosaTypeConversion(TypeConverter &converter) {
if (inputs.size() != 1)
return Value();

return builder.create<UnrealizedConversionCastOp>(loc, resultType, inputs)
return UnrealizedConversionCastOp::create(builder, loc, resultType, inputs)
.getResult(0);
});
converter.addTargetMaterialization([&](OpBuilder &builder, Type resultType,
Expand All @@ -46,7 +46,7 @@ void mlir::tosa::populateTosaTypeConversion(TypeConverter &converter) {
if (inputs.size() != 1)
return Value();

return builder.create<UnrealizedConversionCastOp>(loc, resultType, inputs)
return UnrealizedConversionCastOp::create(builder, loc, resultType, inputs)
.getResult(0);
});
}
18 changes: 9 additions & 9 deletions mlir/lib/Dialect/Tosa/Utils/ConversionUtils.cpp
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Expand Up @@ -33,18 +33,18 @@ mlir::tosa::condenseValues(const SmallVector<Value> &values) {

Value mlir::tosa::clampFloatHelper(Location loc, Value arg, Value min,
Value max, OpBuilder &rewriter) {
Value minValue = rewriter.create<arith::MinimumFOp>(loc, arg, max);
return rewriter.create<arith::MaximumFOp>(loc, minValue, min);
Value minValue = arith::MinimumFOp::create(rewriter, loc, arg, max);
return arith::MaximumFOp::create(rewriter, loc, minValue, min);
}

Value mlir::tosa::clampIntHelper(Location loc, Value arg, Value min, Value max,
OpBuilder &rewriter, bool isUnsigned) {
if (isUnsigned) {
auto minOrArg = rewriter.create<arith::MaxUIOp>(loc, min, arg);
return rewriter.create<arith::MinUIOp>(loc, max, minOrArg);
auto minOrArg = arith::MaxUIOp::create(rewriter, loc, min, arg);
return arith::MinUIOp::create(rewriter, loc, max, minOrArg);
}
auto minOrArg = rewriter.create<arith::MaxSIOp>(loc, min, arg);
return rewriter.create<arith::MinSIOp>(loc, max, minOrArg);
auto minOrArg = arith::MaxSIOp::create(rewriter, loc, min, arg);
return arith::MinSIOp::create(rewriter, loc, max, minOrArg);
}

bool mlir::tosa::validIntegerRange(IntegerType ty, int64_t value) {
Expand Down Expand Up @@ -144,8 +144,8 @@ LogicalResult mlir::tosa::EqualizeRanks(ImplicitLocOpBuilder &builder,
ArrayRef<int64_t>(reshapeOutputShape), reshapeInputType.getElementType());
auto reshapeOutputShapeValue = getTosaConstShape(builder, reshapeOutputShape);

auto reshapeLower = builder.create<tosa::ReshapeOp>(
reshapeOutputType, lowerTensorValue, reshapeOutputShapeValue);
auto reshapeLower = tosa::ReshapeOp::create(
builder, reshapeOutputType, lowerTensorValue, reshapeOutputShapeValue);

if (input1Rank > input2Rank) {
input1 = higherTensorValue;
Expand All @@ -162,7 +162,7 @@ Value mlir::tosa::getTosaConstShape(ImplicitLocOpBuilder &builder,
llvm::ArrayRef<int64_t> shape) {
auto attr = builder.getIndexTensorAttr(convertFromMlirShape(shape));
auto type = mlir::tosa::shapeType::get(builder.getContext(), shape.size());
mlir::Operation *mlir_op = builder.create<tosa::ConstShapeOp>(type, attr);
mlir::Operation *mlir_op = tosa::ConstShapeOp::create(builder, type, attr);
return mlir_op->getResult(0);
}

Expand Down
2 changes: 1 addition & 1 deletion mlir/lib/Dialect/UB/IR/UBOps.cpp
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Expand Up @@ -52,7 +52,7 @@ void UBDialect::initialize() {
Operation *UBDialect::materializeConstant(OpBuilder &builder, Attribute value,
Type type, Location loc) {
if (auto attr = dyn_cast<PoisonAttr>(value))
return builder.create<PoisonOp>(loc, type, attr);
return PoisonOp::create(builder, loc, type, attr);

return nullptr;
}
Expand Down
16 changes: 8 additions & 8 deletions mlir/lib/Dialect/X86Vector/IR/X86VectorDialect.cpp
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Expand Up @@ -60,11 +60,11 @@ SmallVector<Value> x86vector::MaskCompressOp::getIntrinsicOperands(
if (adaptor.getSrc()) {
src = adaptor.getSrc();
} else if (adaptor.getConstantSrc()) {
src = rewriter.create<LLVM::ConstantOp>(loc, opType,
adaptor.getConstantSrcAttr());
src = LLVM::ConstantOp::create(rewriter, loc, opType,
adaptor.getConstantSrcAttr());
} else {
auto zeroAttr = rewriter.getZeroAttr(opType);
src = rewriter.create<LLVM::ConstantOp>(loc, opType, zeroAttr);
src = LLVM::ConstantOp::create(rewriter, loc, opType, zeroAttr);
}

return SmallVector<Value>{adaptor.getA(), src, adaptor.getK()};
Expand All @@ -77,7 +77,7 @@ x86vector::DotOp::getIntrinsicOperands(ArrayRef<Value> operands,
SmallVector<Value> intrinsicOperands(operands);
// Dot product of all elements, broadcasted to all elements.
Value scale =
rewriter.create<LLVM::ConstantOp>(getLoc(), rewriter.getI8Type(), 0xff);
LLVM::ConstantOp::create(rewriter, getLoc(), rewriter.getI8Type(), 0xff);
intrinsicOperands.push_back(scale);

return intrinsicOperands;
Expand All @@ -90,14 +90,14 @@ SmallVector<Value> x86vector::DotInt8Op::getIntrinsicOperands(
Adaptor adaptor(operands, *this);
intrinsicOprnds.push_back(adaptor.getW());
// Bitcast `a` and `b` to i32
Value bitcast_a = rewriter.create<LLVM::BitcastOp>(
getLoc(),
Value bitcast_a = LLVM::BitcastOp::create(
rewriter, getLoc(),
VectorType::get((getA().getType().getShape()[0] / 4),
rewriter.getIntegerType(32)),
adaptor.getA());
intrinsicOprnds.push_back(bitcast_a);
Value bitcast_b = rewriter.create<LLVM::BitcastOp>(
getLoc(),
Value bitcast_b = LLVM::BitcastOp::create(
rewriter, getLoc(),
VectorType::get((getB().getType().getShape()[0] / 4),
rewriter.getIntegerType(32)),
adaptor.getB());
Expand Down
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