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[mlir] Use create instead of createOrFold for ConstantOp as folding has no effect (NFC) #80129

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Merged
merged 1 commit into from
Feb 1, 2024
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[mlir] Use create instead of createOrFold for ConstantOp as folding has no effect (NFC) #80129

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4 changes: 2 additions & 2 deletions mlir/lib/Conversion/AMDGPUToROCDL/AMDGPUToROCDL.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -38,7 +38,7 @@ static Value createI32Constant(ConversionPatternRewriter &rewriter,
static Value createI1Constant(ConversionPatternRewriter &rewriter, Location loc,
bool value) {
Type llvmI1 = rewriter.getI1Type();
return rewriter.createOrFold<LLVM::ConstantOp>(loc, llvmI1, value);
return rewriter.create<LLVM::ConstantOp>(loc, llvmI1, value);
}

namespace {
Expand Down Expand Up @@ -163,7 +163,7 @@ struct RawBufferOpLowering : public ConvertOpToLLVMPattern<GpuOp> {
Value ptr = memrefDescriptor.alignedPtr(rewriter, loc);
// The stride value is always 0 for raw buffers. This also disables
// swizling.
Value stride = rewriter.createOrFold<LLVM::ConstantOp>(
Value stride = rewriter.create<LLVM::ConstantOp>(
loc, llvmI16, rewriter.getI16IntegerAttr(0));
Value numRecords;
if (memrefType.hasStaticShape()) {
Expand Down
4 changes: 2 additions & 2 deletions mlir/lib/Conversion/ArithToAMDGPU/ArithToAMDGPU.cpp
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Expand Up @@ -89,7 +89,7 @@ void ExtFOnFloat8RewritePattern::rewrite(arith::ExtFOp op,
}
VectorType inType = in.getType().cast<VectorType>();
int64_t numElements = inType.getNumElements();
Value zero = rewriter.createOrFold<arith::ConstantOp>(
Value zero = rewriter.create<arith::ConstantOp>(
loc, outElemType, rewriter.getFloatAttr(outElemType, 0.0));
Value result =
rewriter.createOrFold<vector::SplatOp>(loc, op.getOut().getType(), zero);
Expand Down Expand Up @@ -209,7 +209,7 @@ void TruncFToFloat8RewritePattern::rewrite(arith::TruncFOp op,
}
VectorType outType = op.getOut().getType().cast<VectorType>();
int64_t numElements = outType.getNumElements();
Value zero = rewriter.createOrFold<arith::ConstantOp>(
Value zero = rewriter.create<arith::ConstantOp>(
loc, outElemType, rewriter.getFloatAttr(outElemType, 0.0));
Value result = rewriter.createOrFold<vector::SplatOp>(loc, outType, zero);
if (outType.getShape().empty()) {
Expand Down
8 changes: 4 additions & 4 deletions mlir/lib/Conversion/GPUToROCDL/LowerGpuOpsToROCDLOps.cpp
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Expand Up @@ -67,8 +67,8 @@ static bool canBeCalledWithBarePointers(gpu::GPUFuncOp func) {
Value getLaneId(ConversionPatternRewriter &rewriter, Location loc,
const unsigned indexBitwidth) {
auto int32Type = IntegerType::get(rewriter.getContext(), 32);
Value zero = rewriter.createOrFold<arith::ConstantIntOp>(loc, 0, 32);
Value minus1 = rewriter.createOrFold<arith::ConstantIntOp>(loc, -1, 32);
Value zero = rewriter.create<arith::ConstantIntOp>(loc, 0, 32);
Value minus1 = rewriter.create<arith::ConstantIntOp>(loc, -1, 32);
Value mbcntLo = rewriter.create<ROCDL::MbcntLoOp>(loc, int32Type,
ValueRange{minus1, zero});
Value laneId = rewriter.create<ROCDL::MbcntHiOp>(loc, int32Type,
Expand All @@ -89,8 +89,8 @@ struct GPULaneIdOpToROCDL : ConvertOpToLLVMPattern<gpu::LaneIdOp> {
// followed by: %lid = call @llvm.amdgcn.mbcnt.hi(-1, %mlo)

Type intTy = IntegerType::get(context, 32);
Value zero = rewriter.createOrFold<arith::ConstantIntOp>(loc, 0, 32);
Value minus1 = rewriter.createOrFold<arith::ConstantIntOp>(loc, -1, 32);
Value zero = rewriter.create<arith::ConstantIntOp>(loc, 0, 32);
Value minus1 = rewriter.create<arith::ConstantIntOp>(loc, -1, 32);
Value mbcntLo =
rewriter.create<ROCDL::MbcntLoOp>(loc, intTy, ValueRange{minus1, zero});
Value laneId = rewriter.create<ROCDL::MbcntHiOp>(
Expand Down
6 changes: 3 additions & 3 deletions mlir/lib/Conversion/TosaToTensor/TosaToTensor.cpp
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Expand Up @@ -327,7 +327,7 @@ class PadConverter : public OpRewritePattern<tosa::PadOp> {
highValues.reserve(rank);

for (int i = 0; i < rank; i++) {
Value inputIndex = rewriter.createOrFold<arith::ConstantIndexOp>(loc, i);
Value inputIndex = rewriter.create<arith::ConstantIndexOp>(loc, i);
Value lowVal = rewriter.createOrFold<tensor::ExtractOp>(
loc, padding, ValueRange({inputIndex, lowIndex}));
Value highVal = rewriter.createOrFold<tensor::ExtractOp>(
Expand Down Expand Up @@ -360,8 +360,8 @@ struct ConcatConverter : public OpConversionPattern<tosa::ConcatOp> {

Location loc = op.getLoc();
int axis = op.getAxis();
Value axisValue = rewriter.createOrFold<arith::ConstantOp>(
loc, rewriter.getIndexAttr(axis));
Value axisValue =
rewriter.create<arith::ConstantOp>(loc, rewriter.getIndexAttr(axis));
int64_t rank = resultType.getRank();

SmallVector<OpFoldResult> strides(rank, rewriter.getIndexAttr(1));
Expand Down
4 changes: 2 additions & 2 deletions mlir/lib/Dialect/Tensor/Transforms/ConcatOpPatterns.cpp
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Expand Up @@ -44,8 +44,8 @@ struct DecomposeTensorConcatOp : public OpRewritePattern<ConcatOp> {
return failure();

int64_t dim = concatOp.getDim();
Value dimValue = rewriter.createOrFold<arith::ConstantOp>(
loc, rewriter.getIndexAttr(dim));
Value dimValue =
rewriter.create<arith::ConstantOp>(loc, rewriter.getIndexAttr(dim));

int64_t rank = concatOp.getResultType().getRank();
SmallVector<OpFoldResult> strides(rank, rewriter.getIndexAttr(1));
Expand Down