[mlir][NFC] update mlir/Dialect create APIs (25/n)
#149932
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See llvm#147168 for more info.
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@llvm/pr-subscribers-mlir-tosa @llvm/pr-subscribers-mlir-ub Author: Maksim Levental (makslevental) ChangesSee #147168 for more info. Patch is 50.55 KiB, truncated to 20.00 KiB below, full version: https://github.com/llvm/llvm-project/pull/149932.diff 16 Files Affected:
diff --git a/mlir/lib/Dialect/Tosa/IR/TosaCanonicalizations.cpp b/mlir/lib/Dialect/Tosa/IR/TosaCanonicalizations.cpp
index 5758d8d5ef506..606626dfe4d2c 100644
--- a/mlir/lib/Dialect/Tosa/IR/TosaCanonicalizations.cpp
+++ b/mlir/lib/Dialect/Tosa/IR/TosaCanonicalizations.cpp
@@ -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 =
@@ -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();
diff --git a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
index f0ff430bae882..5b4a2c9d85ea1 100644
--- a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
+++ b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
@@ -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;
}
@@ -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);
}
//===----------------------------------------------------------------------===//
@@ -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] =
@@ -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;
diff --git a/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeDepthwise.cpp b/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeDepthwise.cpp
index f6caa2a985a4d..9474299a39582 100644
--- a/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeDepthwise.cpp
+++ b/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeDepthwise.cpp
@@ -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) {
@@ -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);
@@ -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.
@@ -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)
@@ -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()) {
diff --git a/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeTransposeConv.cpp b/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeTransposeConv.cpp
index df6d52615478e..dc5c51b0abad5 100644
--- a/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeTransposeConv.cpp
+++ b/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeTransposeConv.cpp
@@ -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());
@@ -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(
diff --git a/mlir/lib/Dialect/Tosa/Transforms/TosaInferShapes.cpp b/mlir/lib/Dialect/Tosa/Transforms/TosaInferShapes.cpp
index a9e98c8908e15..4d347c02ee16d 100644
--- a/mlir/lib/Dialect/Tosa/Transforms/TosaInferShapes.cpp
+++ b/mlir/lib/Dialect/Tosa/Transforms/TosaInferShapes.cpp
@@ -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);
diff --git a/mlir/lib/Dialect/Tosa/Transforms/TosaReduceTransposes.cpp b/mlir/lib/Dialect/Tosa/Transforms/TosaReduceTransposes.cpp
index db7a3c671dedc..5590927c3f774 100644
--- a/mlir/lib/Dialect/Tosa/Transforms/TosaReduceTransposes.cpp
+++ b/mlir/lib/Dialect/Tosa/Transforms/TosaReduceTransposes.cpp
@@ -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(),
@@ -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);
}
diff --git a/mlir/lib/Dialect/Tosa/Transforms/TosaTypeConverters.cpp b/mlir/lib/Dialect/Tosa/Transforms/TosaTypeConverters.cpp
index 3b697a2ee3e47..677d8e9904a67 100644
--- a/mlir/lib/Dialect/Tosa/Transforms/TosaTypeConverters.cpp
+++ b/mlir/lib/Dialect/Tosa/Transforms/TosaTypeConverters.cpp
@@ -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,
@@ -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);
});
}
diff --git a/mlir/lib/Dialect/Tosa/Utils/ConversionUtils.cpp b/mlir/lib/Dialect/Tosa/Utils/ConversionUtils.cpp
index 9844abcc34cb1..69eda03e03ab3 100644
--- a/mlir/lib/Dialect/Tosa/Utils/ConversionUtils.cpp
+++ b/mlir/lib/Dialect/Tosa/Utils/ConversionUtils.cpp
@@ -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) {
@@ -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;
@@ -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);
}
diff --git a/mlir/lib/Dialect/UB/IR/UBOps.cpp b/mlir/lib/Dialect/UB/IR/UBOps.cpp
index 5b2cfe7bf4264..ee523f9522953 100644
--- a/mlir/lib/Dialect/UB/IR/UBOps.cpp
+++ b/mlir/lib/Dialect/UB/IR/UBOps.cpp
@@ -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;
}
diff --git a/mlir/lib/Dialect/X86Vector/IR/X86VectorDialect.cpp b/mlir/lib/Dialect/X86Vector/IR/X86VectorDialect.cpp
index 7de32f7cbfb8b..0fa353abc4972 100644
--- a/mlir/lib/Dialect/X86Vector/IR/X86VectorDialect.cpp
+++ b/mlir/lib/Dialect/X86Vector/IR/X86VectorDialect.cpp
@@ -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()};
@@ -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;
@@ -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());
diff --git a/mlir/lib/Dialect/X86Vector/Transforms/AVXTranspose.cpp b/mlir/lib/Dialect/X86Vector/Transforms/AVXTranspose.cpp
index 87f7867fe1b7c..385ec5e824051 100644
--- a/mlir/lib/Dialect/X86Vector/Transforms/AVXTranspose.cpp
+++ b/mlir/lib/Dialect/X86Vector/Transforms/AVXTranspose.cpp
@@ -37,8 +37,8 @@ Value mlir::x86vector::avx2::inline_asm::mm256BlendPsAsm(
"=x,x,x"; // Careful: constraint parser is very brittle: no ws!
SmallVector<Value> asmVals{v1, v2};
auto asmStr = llvm::formatv(asmTp, llvm::format_hex(mask, /*width=*/2)).str();
- auto asmOp = b.create<LLVM::InlineAsmOp>(
- v1.getType(), /*operands=*/asmVals, /*asm_string=*/asmStr,
+ auto asmOp = LLVM::InlineAsmOp::create(
+ b, v1.getType(), /*operands=*/asmVals, /*asm_string=*/asmStr,
/*constraints=*/asmCstr, /*has_side_effects=*/false,
/*is_align_stack=*/false, LLVM::TailCallKind::None,
/*asm_dialect=*/asmDialectAttr,
@@ -48,14 +48,14 @@ Value mlir::x86vector::avx2::inline_asm::mm256BlendPsAsm(
Value mlir::x86vector::avx2::intrin::mm256UnpackLoPs(ImplicitLocOpBuilder &b,
Value v1, Value v2) {
- return b.create<vector::ShuffleOp>(
- v1, v2, ArrayRef<int64_t>{0, 8, 1, 9, 4, 12, 5, 13});
+ return vector::ShuffleOp::create(b, v1, v2,
+ ArrayRef<int64_t>{0, 8, 1, 9, 4, 12, 5, 13});
}
Value mlir::x86vector::avx2::intrin::mm256UnpackHiPs(ImplicitLocOpBuilder &b,
Value v1, Value v2) {
- return b.create<vector::ShuffleOp>(
- v1, v2, ArrayRef<int64_t>{2, 10, 3, 11, 6, 14, 7, 15});
+ return vector::ShuffleOp::create(
+ b, v1, v2, ArrayRef<int64_t>{2, 10, 3, 11, 6, 14, 7, 15});
}
/// a a b b a a b b
/// Takes an 8 bit mask, 2 bit for each position of a[0, 3) **and** b[0, 4):
@@ -68,7 +68,7 @@ Value mlir::...
[truncated]
|
kuhar
approved these changes
Jul 21, 2025
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See #147168 for more info.