[mlir][NFC] update mlir create APIs (34/n)
#150660
Merged
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
|
@llvm/pr-subscribers-mlir-tosa @llvm/pr-subscribers-mlir Author: Maksim Levental (makslevental) ChangesSee #147168 for more info. Patch is 33.08 KiB, truncated to 20.00 KiB below, full version: https://github.com/llvm/llvm-project/pull/150660.diff 16 Files Affected:
diff --git a/mlir/lib/Conversion/ArithToEmitC/ArithToEmitC.cpp b/mlir/lib/Conversion/ArithToEmitC/ArithToEmitC.cpp
index 59b3fe2e4eaed..c3debf7afc865 100644
--- a/mlir/lib/Conversion/ArithToEmitC/ArithToEmitC.cpp
+++ b/mlir/lib/Conversion/ArithToEmitC/ArithToEmitC.cpp
@@ -402,7 +402,7 @@ class CastConversion : public OpConversionPattern<ArithOp> {
Value actualOp = adaptValueType(adaptor.getIn(), rewriter, castSrcType);
// Actual cast (may change bitwidth)
- auto cast = rewriter.template create<emitc::CastOp>(op.getLoc(),
+ auto cast = emitc::CastOp::create(rewriter, op.getLoc(),
castDestType, actualOp);
// Cast to the expected output type
@@ -507,7 +507,7 @@ class IntegerOpConversion final : public OpConversionPattern<ArithOp> {
Value lhs = adaptValueType(adaptor.getLhs(), rewriter, arithmeticType);
Value rhs = adaptValueType(adaptor.getRhs(), rewriter, arithmeticType);
- Value arithmeticResult = rewriter.template create<EmitCOp>(
+ Value arithmeticResult = EmitCOp::create(rewriter,
op.getLoc(), arithmeticType, lhs, rhs);
Value result = adaptValueType(arithmeticResult, rewriter, type);
@@ -547,7 +547,7 @@ class BitwiseOpConversion : public OpConversionPattern<ArithOp> {
Value lhs = adaptValueType(adaptor.getLhs(), rewriter, arithmeticType);
Value rhs = adaptValueType(adaptor.getRhs(), rewriter, arithmeticType);
- Value arithmeticResult = rewriter.template create<EmitCOp>(
+ Value arithmeticResult = EmitCOp::create(rewriter,
op.getLoc(), arithmeticType, lhs, rhs);
Value result = adaptValueType(arithmeticResult, rewriter, type);
@@ -748,7 +748,7 @@ class ItoFCastOpConversion : public OpConversionPattern<CastOp> {
}
Value fpCastOperand = adaptor.getIn();
if (actualOperandType != operandType) {
- fpCastOperand = rewriter.template create<emitc::CastOp>(
+ fpCastOperand = emitc::CastOp::create(rewriter,
castOp.getLoc(), actualOperandType, fpCastOperand);
}
rewriter.replaceOpWithNewOp<emitc::CastOp>(castOp, dstType, fpCastOperand);
diff --git a/mlir/lib/Conversion/BufferizationToMemRef/BufferizationToMemRef.cpp b/mlir/lib/Conversion/BufferizationToMemRef/BufferizationToMemRef.cpp
index 30a7170cf5c6a..d77cbfb1173c3 100644
--- a/mlir/lib/Conversion/BufferizationToMemRef/BufferizationToMemRef.cpp
+++ b/mlir/lib/Conversion/BufferizationToMemRef/BufferizationToMemRef.cpp
@@ -68,8 +68,7 @@ struct CloneOpConversion : public OpConversionPattern<bufferization::CloneOp> {
scf::YieldOp::create(rewriter, loc, acc);
};
- auto size = rewriter
- .create<scf::ForOp>(loc, zero, rank, one, ValueRange(one),
+ auto size = scf::ForOp::create(rewriter, loc, zero, rank, one, ValueRange(one),
loopBody)
.getResult(0);
diff --git a/mlir/lib/Conversion/ControlFlowToSCF/ControlFlowToSCF.cpp b/mlir/lib/Conversion/ControlFlowToSCF/ControlFlowToSCF.cpp
index c8311eb5a6433..f559fffbaf91f 100644
--- a/mlir/lib/Conversion/ControlFlowToSCF/ControlFlowToSCF.cpp
+++ b/mlir/lib/Conversion/ControlFlowToSCF/ControlFlowToSCF.cpp
@@ -144,8 +144,7 @@ ControlFlowToSCFTransformation::createUnreachableTerminator(Location loc,
return emitError(loc, "Cannot create unreachable terminator for '")
<< parentOp->getName() << "'";
- return builder
- .create<func::ReturnOp>(
+ return func::ReturnOp::create(builder,
loc, llvm::map_to_vector(funcOp.getResultTypes(),
[&](Type type) {
return getUndefValue(loc, builder, type);
diff --git a/mlir/lib/Conversion/GPUToSPIRV/GPUToSPIRV.cpp b/mlir/lib/Conversion/GPUToSPIRV/GPUToSPIRV.cpp
index a19194eb181fb..963f365cd688d 100644
--- a/mlir/lib/Conversion/GPUToSPIRV/GPUToSPIRV.cpp
+++ b/mlir/lib/Conversion/GPUToSPIRV/GPUToSPIRV.cpp
@@ -559,8 +559,7 @@ static Value createGroupReduceOpImpl(OpBuilder &builder, Location loc,
builder, loc, builder.getI32Type(),
builder.getIntegerAttr(builder.getI32Type(), *clusterSize));
- return builder
- .create<NonUniformOp>(loc, type, scope, groupOp, arg, clusterSizeValue)
+ return NonUniformOp::create(builder, loc, type, scope, groupOp, arg, clusterSizeValue)
.getResult();
}
diff --git a/mlir/lib/Conversion/LLVMCommon/Pattern.cpp b/mlir/lib/Conversion/LLVMCommon/Pattern.cpp
index ecd5b6367fba4..be58aa2e44e04 100644
--- a/mlir/lib/Conversion/LLVMCommon/Pattern.cpp
+++ b/mlir/lib/Conversion/LLVMCommon/Pattern.cpp
@@ -273,8 +273,7 @@ LogicalResult ConvertToLLVMPattern::copyUnrankedDescriptors(
// Allocate memory, copy, and free the source if necessary.
Value memory =
toDynamic
- ? builder
- .create<LLVM::CallOp>(loc, mallocFunc.value(), allocationSize)
+ ? LLVM::CallOp::create(builder, loc, mallocFunc.value(), allocationSize)
.getResult()
: LLVM::AllocaOp::create(builder, loc, getPtrType(),
IntegerType::get(getContext(), 8),
diff --git a/mlir/lib/Conversion/MPIToLLVM/MPIToLLVM.cpp b/mlir/lib/Conversion/MPIToLLVM/MPIToLLVM.cpp
index 5b68eb8188996..e5496e53ae529 100644
--- a/mlir/lib/Conversion/MPIToLLVM/MPIToLLVM.cpp
+++ b/mlir/lib/Conversion/MPIToLLVM/MPIToLLVM.cpp
@@ -35,7 +35,7 @@ static Op getOrDefineGlobal(ModuleOp &moduleOp, const Location loc,
if (!(ret = moduleOp.lookupSymbol<Op>(name))) {
ConversionPatternRewriter::InsertionGuard guard(rewriter);
rewriter.setInsertionPointToStart(moduleOp.getBody());
- ret = rewriter.template create<Op>(loc, std::forward<Args>(args)...);
+ ret = Op::create(rewriter, loc, std::forward<Args>(args)...);
}
return ret;
}
diff --git a/mlir/lib/Conversion/MemRefToLLVM/MemRefToLLVM.cpp b/mlir/lib/Conversion/MemRefToLLVM/MemRefToLLVM.cpp
index 53a19129103a3..88c08eb792737 100644
--- a/mlir/lib/Conversion/MemRefToLLVM/MemRefToLLVM.cpp
+++ b/mlir/lib/Conversion/MemRefToLLVM/MemRefToLLVM.cpp
@@ -575,8 +575,7 @@ struct DimOpLowering : public ConvertOpToLLVMPattern<memref::DimOp> {
Value sizePtr = LLVM::GEPOp::create(rewriter, loc, indexPtrTy,
getTypeConverter()->getIndexType(),
offsetPtr, idxPlusOne);
- return rewriter
- .create<LLVM::LoadOp>(loc, getTypeConverter()->getIndexType(), sizePtr)
+ return LLVM::LoadOp::create(rewriter, loc, getTypeConverter()->getIndexType(), sizePtr)
.getResult();
}
diff --git a/mlir/lib/Conversion/SPIRVToLLVM/SPIRVToLLVM.cpp b/mlir/lib/Conversion/SPIRVToLLVM/SPIRVToLLVM.cpp
index aae3271371c1f..9b6154057b806 100644
--- a/mlir/lib/Conversion/SPIRVToLLVM/SPIRVToLLVM.cpp
+++ b/mlir/lib/Conversion/SPIRVToLLVM/SPIRVToLLVM.cpp
@@ -1493,11 +1493,11 @@ class ShiftPattern : public SPIRVToLLVMConversion<SPIRVOp> {
Value extended;
if (op2TypeWidth < dstTypeWidth) {
if (isUnsignedIntegerOrVector(op2Type)) {
- extended = rewriter.template create<LLVM::ZExtOp>(
- loc, dstType, adaptor.getOperand2());
+ extended =
+ LLVM::ZExtOp::create(rewriter, loc, dstType, adaptor.getOperand2());
} else {
- extended = rewriter.template create<LLVM::SExtOp>(
- loc, dstType, adaptor.getOperand2());
+ extended =
+ LLVM::SExtOp::create(rewriter, loc, dstType, adaptor.getOperand2());
}
} else if (op2TypeWidth == dstTypeWidth) {
extended = adaptor.getOperand2();
@@ -1505,8 +1505,8 @@ class ShiftPattern : public SPIRVToLLVMConversion<SPIRVOp> {
return failure();
}
- Value result = rewriter.template create<LLVMOp>(
- loc, dstType, adaptor.getOperand1(), extended);
+ Value result =
+ LLVMOp::create(rewriter, loc, dstType, adaptor.getOperand1(), extended);
rewriter.replaceOp(op, result);
return success();
}
diff --git a/mlir/lib/Conversion/ShardToMPI/ShardToMPI.cpp b/mlir/lib/Conversion/ShardToMPI/ShardToMPI.cpp
index 8525543760d99..34773f16f02a2 100644
--- a/mlir/lib/Conversion/ShardToMPI/ShardToMPI.cpp
+++ b/mlir/lib/Conversion/ShardToMPI/ShardToMPI.cpp
@@ -177,8 +177,7 @@ struct ConvertShardingOp : public OpConversionPattern<ShardingOp> {
auto type = RankedTensorType::get({nSplits, 2}, i64);
Value resHaloSizes =
haloSizes.empty()
- ? rewriter
- .create<tensor::EmptyOp>(loc, std::array<int64_t, 2>{0, 0},
+ ? tensor::EmptyOp::create(rewriter, loc, std::array<int64_t, 2>{0, 0},
i64)
.getResult()
: tensor::FromElementsOp::create(rewriter, loc, type, haloSizes)
@@ -307,8 +306,7 @@ class ConvertProcessLinearIndexOp
Value commWorld =
mpi::CommWorldOp::create(rewriter, loc, mpi::CommType::get(ctx));
auto rank =
- rewriter
- .create<mpi::CommRankOp>(
+ mpi::CommRankOp::create(rewriter,
loc,
TypeRange{mpi::RetvalType::get(ctx), rewriter.getI32Type()},
commWorld)
@@ -704,8 +702,7 @@ struct ConvertUpdateHaloOp : public OpConversionPattern<UpdateHaloOp> {
for (auto &sz : haloSizes) {
if (auto value = dyn_cast<Value>(sz))
sz =
- rewriter
- .create<arith::IndexCastOp>(loc, rewriter.getIndexType(), value)
+ arith::IndexCastOp::create(rewriter, loc, rewriter.getIndexType(), value)
.getResult();
}
@@ -758,8 +755,7 @@ struct ConvertUpdateHaloOp : public OpConversionPattern<UpdateHaloOp> {
assert(currHaloDim >= 0 && (size_t)currHaloDim < haloSizes.size() / 2);
// Get the linearized ids of the neighbors (down and up) for the
// given split
- auto tmp = rewriter
- .create<NeighborsLinearIndicesOp>(loc, grid, myMultiIndex,
+ auto tmp = NeighborsLinearIndicesOp::create(rewriter, loc, grid, myMultiIndex,
splitAxes)
.getResults();
// MPI operates on i32...
diff --git a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
index 5c7c027382977..b5792406b296f 100644
--- a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
+++ b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
@@ -569,8 +569,7 @@ static Value createLinalgBodyCalculationForElementwiseOp(
// to UIToFP.
if (srcTy.isUnsignedInteger() && isa<FloatType>(dstTy)) {
auto unrealizedCast =
- rewriter
- .create<UnrealizedConversionCastOp>(
+ UnrealizedConversionCastOp::create(rewriter,
loc, rewriter.getIntegerType(srcTy.getIntOrFloatBitWidth()),
args[0])
.getResult(0);
@@ -868,8 +867,7 @@ static Value broadcastDynamicDimension(PatternRewriter &rewriter, Location loc,
// Emit 'linalg.generic' op
auto resultTensor =
- opBuilder
- .create<linalg::GenericOp>(
+ linalg::GenericOp::create(opBuilder,
loc, outputTensor.getType(), operand, outputTensor, affineMaps,
getNParallelLoopsAttrs(rank),
[&](OpBuilder &opBuilder, Location loc, ValueRange blockArgs) {
@@ -1156,8 +1154,7 @@ static LogicalResult reduceMatchAndRewriteHelper(OpTy op, uint64_t axis,
// First fill the output buffer with the init value.
auto emptyTensor =
- rewriter
- .create<tensor::EmptyOp>(loc, reduceShape, resultTy.getElementType(),
+ tensor::EmptyOp::create(rewriter, loc, reduceShape, resultTy.getElementType(),
dynDims)
.getResult();
@@ -1167,8 +1164,7 @@ static LogicalResult reduceMatchAndRewriteHelper(OpTy op, uint64_t axis,
op, "No initial value found for reduction operation");
auto fillValue = arith::ConstantOp::create(rewriter, loc, fillValueAttr);
- auto filledTensor = rewriter
- .create<linalg::FillOp>(loc, ValueRange{fillValue},
+ auto filledTensor = linalg::FillOp::create(rewriter, loc, ValueRange{fillValue},
ValueRange{emptyTensor})
.result();
outputs.push_back(filledTensor);
@@ -1186,13 +1182,11 @@ static LogicalResult reduceMatchAndRewriteHelper(OpTy op, uint64_t axis,
auto trueAttr = rewriter.getBoolAttr(true);
auto trueValue = arith::ConstantOp::create(rewriter, loc, trueAttr);
auto emptyBoolTensor =
- rewriter
- .create<tensor::EmptyOp>(loc, reduceShape, trueValue.getType(),
+ tensor::EmptyOp::create(rewriter, loc, reduceShape, trueValue.getType(),
dynDims)
.getResult();
auto allResultsNaNTensor =
- rewriter
- .create<linalg::FillOp>(loc, ValueRange{trueValue},
+ linalg::FillOp::create(rewriter, loc, ValueRange{trueValue},
ValueRange{emptyBoolTensor})
.result();
// Note that because the linalg::ReduceOp has two variadic arguments
@@ -1261,21 +1255,18 @@ static LogicalResult reduceMatchAndRewriteHelper(OpTy op, uint64_t axis,
APFloat::getNaN(cast<FloatType>(elementTy).getFloatSemantics(), false));
auto nanValue = arith::ConstantOp::create(rewriter, loc, nanValueAttr);
auto emptyNanTensor =
- rewriter
- .create<tensor::EmptyOp>(loc, reduceShape,
+ tensor::EmptyOp::create(rewriter, loc, reduceShape,
resultTy.getElementType(), dynDims)
.getResult();
auto nanFilledTensor =
- rewriter
- .create<linalg::FillOp>(loc, ValueRange{nanValue},
+ linalg::FillOp::create(rewriter, loc, ValueRange{nanValue},
ValueRange{emptyNanTensor})
.result();
// Create an empty tensor, non need to fill this since it will be
// overwritten by the select.
auto finalEmptyTensor =
- rewriter
- .create<tensor::EmptyOp>(loc, reduceShape,
+ tensor::EmptyOp::create(rewriter, loc, reduceShape,
resultTy.getElementType(), dynDims)
.getResult();
@@ -1503,8 +1494,7 @@ class RescaleConverter : public OpRewritePattern<tosa::RescaleOp> {
Value shift = shiftConstant ? shiftConstant : blockArgs[shiftArg];
if (valueTy.isUnsignedInteger()) {
- value = nestedBuilder
- .create<UnrealizedConversionCastOp>(
+ value = UnrealizedConversionCastOp::create(nestedBuilder,
nestedLoc,
nestedBuilder.getIntegerType(
valueTy.getIntOrFloatBitWidth()),
@@ -1557,8 +1547,7 @@ class RescaleConverter : public OpRewritePattern<tosa::RescaleOp> {
}
if (outIntType.isUnsignedInteger()) {
- value = nestedBuilder
- .create<UnrealizedConversionCastOp>(nestedLoc,
+ value = UnrealizedConversionCastOp::create(nestedBuilder, nestedLoc,
outIntType, value)
.getResult(0);
}
@@ -2095,8 +2084,7 @@ class ReverseConverter : public OpRewritePattern<tosa::ReverseOp> {
Value axisDimSize = tensor::DimOp::create(rewriter, loc, input, axis);
// First fill the output buffer with the init value.
- auto emptyTensor = rewriter
- .create<tensor::EmptyOp>(loc, inputTy.getShape(),
+ auto emptyTensor = tensor::EmptyOp::create(rewriter, loc, inputTy.getShape(),
inputTy.getElementType(),
ArrayRef<Value>({dynDims}))
.getResult();
@@ -2241,21 +2229,18 @@ class ArgMaxConverter : public OpRewritePattern<tosa::ArgMaxOp> {
}
// First fill the output buffer for the index.
- auto emptyTensorIdx = rewriter
- .create<tensor::EmptyOp>(loc, resultTy.getShape(),
+ auto emptyTensorIdx = tensor::EmptyOp::create(rewriter, loc, resultTy.getShape(),
outElementTy, dynDims)
.getResult();
auto fillValueIdx = arith::ConstantOp::create(
rewriter, loc, rewriter.getIntegerAttr(outElementTy, 0));
auto filledTensorIdx =
- rewriter
- .create<linalg::FillOp>(loc, ValueRange{fillValueIdx},
+ linalg::FillOp::create(rewriter, loc, ValueRange{fillValueIdx},
ValueRange{emptyTensorIdx})
.result();
// Second fill the output buffer for the running max.
- auto emptyTensorMax = rewriter
- .create<tensor::EmptyOp>(loc, resultTy.getShape(),
+ auto emptyTensorMax = tensor::EmptyOp::create(rewriter, loc, resultTy.getShape(),
inElementTy, dynDims)
.getResult();
auto fillValueMaxAttr =
@@ -2268,8 +2253,7 @@ class ArgMaxConverter : public OpRewritePattern<tosa::ArgMaxOp> {
auto fillValueMax =
arith::ConstantOp::create(rewriter, loc, fillValueMaxAttr);
auto filledTensorMax =
- rewriter
- .create<linalg::FillOp>(loc, ValueRange{fillValueMax},
+ linalg::FillOp::create(rewriter, loc, ValueRange{fillValueMax},
ValueRange{emptyTensorMax})
.result();
@@ -2371,8 +2355,7 @@ class GatherConverter : public OpConversionPattern<tosa::GatherOp> {
auto loc = op.getLoc();
auto emptyTensor =
- rewriter
- .create<tensor::EmptyOp>(loc, resultTy.getShape(), resultElementTy,
+ tensor::EmptyOp::create(rewriter, loc, resultTy.getShape(), resultElementTy,
dynamicDims)
.getResult();
@@ -2448,8 +2431,7 @@ class TableConverter : public OpRewritePattern<tosa::TableOp> {
}
}
- auto emptyTensor = rewriter
- .create<tensor::EmptyOp>(loc, resultTy.getShape(),
+ auto emptyTensor = tensor::EmptyOp::create(rewriter, loc, resultTy.getShape(),
resultElementTy, dynDims)
.getResult();
@@ -2585,8 +2567,7 @@ struct RFFT2dConverter final : public OpRewritePattern<RFFT2dOp> {
tensor::EmptyOp::create(rewriter, loc, type, dynamicSizes);
auto fillValueAttr = rewriter.getZeroAttr(type.getElementType());
auto fillValue = arith::ConstantOp::create(rewriter, loc, fillValueAttr);
- auto filledTensor = rewriter
- .create<linalg::FillOp>(loc, ValueRange{fillValue},
+ auto filledTensor = linalg::FillOp::create(rewriter, loc, ValueRange{fillValue},
ValueRange{emptyTensor})
.result();
return filledTensor;
diff --git a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
index 3a205246ddd9e..3d7d4e8f6afa4 100644
--- a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
+++ b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
@@ -64,8 +64,7 @@ linalgIntBroadcastExtSIAdd(PatternRewriter &rewriter, Location loc, Value bias,
Value conv, Value result,
ArrayRef<AffineMap> indexingMaps) {
ShapedType resultTy = cast<ShapedType>(conv.getType());
- return rewriter
- .create<linalg::GenericOp>(
+ return linalg::GenericOp::create(rewriter,
loc, resultTy, ValueRange({bias, conv}), result, indexingMaps,
getNParallelLoopsAttrs(resultTy.getRank()),
[](OpBuilder &builder, Location loc, ValueRange args) {
@@ -124,8 +123,7 @@ static mlir::Value linalgBroadcastAndMaybeExt(PatternRewriter &rewriter,
indexingMaps.push_back(rewriter.getMultiDimIdentityMap(resultRank));
// Build the broadcast-like operation ...
[truncated]
|
|
@llvm/pr-subscribers-mlir-llvm Author: Maksim Levental (makslevental) ChangesSee #147168 for more info. Patch is 33.08 KiB, truncated to 20.00 KiB below, full version: https://github.com/llvm/llvm-project/pull/150660.diff 16 Files Affected:
diff --git a/mlir/lib/Conversion/ArithToEmitC/ArithToEmitC.cpp b/mlir/lib/Conversion/ArithToEmitC/ArithToEmitC.cpp
index 59b3fe2e4eaed..c3debf7afc865 100644
--- a/mlir/lib/Conversion/ArithToEmitC/ArithToEmitC.cpp
+++ b/mlir/lib/Conversion/ArithToEmitC/ArithToEmitC.cpp
@@ -402,7 +402,7 @@ class CastConversion : public OpConversionPattern<ArithOp> {
Value actualOp = adaptValueType(adaptor.getIn(), rewriter, castSrcType);
// Actual cast (may change bitwidth)
- auto cast = rewriter.template create<emitc::CastOp>(op.getLoc(),
+ auto cast = emitc::CastOp::create(rewriter, op.getLoc(),
castDestType, actualOp);
// Cast to the expected output type
@@ -507,7 +507,7 @@ class IntegerOpConversion final : public OpConversionPattern<ArithOp> {
Value lhs = adaptValueType(adaptor.getLhs(), rewriter, arithmeticType);
Value rhs = adaptValueType(adaptor.getRhs(), rewriter, arithmeticType);
- Value arithmeticResult = rewriter.template create<EmitCOp>(
+ Value arithmeticResult = EmitCOp::create(rewriter,
op.getLoc(), arithmeticType, lhs, rhs);
Value result = adaptValueType(arithmeticResult, rewriter, type);
@@ -547,7 +547,7 @@ class BitwiseOpConversion : public OpConversionPattern<ArithOp> {
Value lhs = adaptValueType(adaptor.getLhs(), rewriter, arithmeticType);
Value rhs = adaptValueType(adaptor.getRhs(), rewriter, arithmeticType);
- Value arithmeticResult = rewriter.template create<EmitCOp>(
+ Value arithmeticResult = EmitCOp::create(rewriter,
op.getLoc(), arithmeticType, lhs, rhs);
Value result = adaptValueType(arithmeticResult, rewriter, type);
@@ -748,7 +748,7 @@ class ItoFCastOpConversion : public OpConversionPattern<CastOp> {
}
Value fpCastOperand = adaptor.getIn();
if (actualOperandType != operandType) {
- fpCastOperand = rewriter.template create<emitc::CastOp>(
+ fpCastOperand = emitc::CastOp::create(rewriter,
castOp.getLoc(), actualOperandType, fpCastOperand);
}
rewriter.replaceOpWithNewOp<emitc::CastOp>(castOp, dstType, fpCastOperand);
diff --git a/mlir/lib/Conversion/BufferizationToMemRef/BufferizationToMemRef.cpp b/mlir/lib/Conversion/BufferizationToMemRef/BufferizationToMemRef.cpp
index 30a7170cf5c6a..d77cbfb1173c3 100644
--- a/mlir/lib/Conversion/BufferizationToMemRef/BufferizationToMemRef.cpp
+++ b/mlir/lib/Conversion/BufferizationToMemRef/BufferizationToMemRef.cpp
@@ -68,8 +68,7 @@ struct CloneOpConversion : public OpConversionPattern<bufferization::CloneOp> {
scf::YieldOp::create(rewriter, loc, acc);
};
- auto size = rewriter
- .create<scf::ForOp>(loc, zero, rank, one, ValueRange(one),
+ auto size = scf::ForOp::create(rewriter, loc, zero, rank, one, ValueRange(one),
loopBody)
.getResult(0);
diff --git a/mlir/lib/Conversion/ControlFlowToSCF/ControlFlowToSCF.cpp b/mlir/lib/Conversion/ControlFlowToSCF/ControlFlowToSCF.cpp
index c8311eb5a6433..f559fffbaf91f 100644
--- a/mlir/lib/Conversion/ControlFlowToSCF/ControlFlowToSCF.cpp
+++ b/mlir/lib/Conversion/ControlFlowToSCF/ControlFlowToSCF.cpp
@@ -144,8 +144,7 @@ ControlFlowToSCFTransformation::createUnreachableTerminator(Location loc,
return emitError(loc, "Cannot create unreachable terminator for '")
<< parentOp->getName() << "'";
- return builder
- .create<func::ReturnOp>(
+ return func::ReturnOp::create(builder,
loc, llvm::map_to_vector(funcOp.getResultTypes(),
[&](Type type) {
return getUndefValue(loc, builder, type);
diff --git a/mlir/lib/Conversion/GPUToSPIRV/GPUToSPIRV.cpp b/mlir/lib/Conversion/GPUToSPIRV/GPUToSPIRV.cpp
index a19194eb181fb..963f365cd688d 100644
--- a/mlir/lib/Conversion/GPUToSPIRV/GPUToSPIRV.cpp
+++ b/mlir/lib/Conversion/GPUToSPIRV/GPUToSPIRV.cpp
@@ -559,8 +559,7 @@ static Value createGroupReduceOpImpl(OpBuilder &builder, Location loc,
builder, loc, builder.getI32Type(),
builder.getIntegerAttr(builder.getI32Type(), *clusterSize));
- return builder
- .create<NonUniformOp>(loc, type, scope, groupOp, arg, clusterSizeValue)
+ return NonUniformOp::create(builder, loc, type, scope, groupOp, arg, clusterSizeValue)
.getResult();
}
diff --git a/mlir/lib/Conversion/LLVMCommon/Pattern.cpp b/mlir/lib/Conversion/LLVMCommon/Pattern.cpp
index ecd5b6367fba4..be58aa2e44e04 100644
--- a/mlir/lib/Conversion/LLVMCommon/Pattern.cpp
+++ b/mlir/lib/Conversion/LLVMCommon/Pattern.cpp
@@ -273,8 +273,7 @@ LogicalResult ConvertToLLVMPattern::copyUnrankedDescriptors(
// Allocate memory, copy, and free the source if necessary.
Value memory =
toDynamic
- ? builder
- .create<LLVM::CallOp>(loc, mallocFunc.value(), allocationSize)
+ ? LLVM::CallOp::create(builder, loc, mallocFunc.value(), allocationSize)
.getResult()
: LLVM::AllocaOp::create(builder, loc, getPtrType(),
IntegerType::get(getContext(), 8),
diff --git a/mlir/lib/Conversion/MPIToLLVM/MPIToLLVM.cpp b/mlir/lib/Conversion/MPIToLLVM/MPIToLLVM.cpp
index 5b68eb8188996..e5496e53ae529 100644
--- a/mlir/lib/Conversion/MPIToLLVM/MPIToLLVM.cpp
+++ b/mlir/lib/Conversion/MPIToLLVM/MPIToLLVM.cpp
@@ -35,7 +35,7 @@ static Op getOrDefineGlobal(ModuleOp &moduleOp, const Location loc,
if (!(ret = moduleOp.lookupSymbol<Op>(name))) {
ConversionPatternRewriter::InsertionGuard guard(rewriter);
rewriter.setInsertionPointToStart(moduleOp.getBody());
- ret = rewriter.template create<Op>(loc, std::forward<Args>(args)...);
+ ret = Op::create(rewriter, loc, std::forward<Args>(args)...);
}
return ret;
}
diff --git a/mlir/lib/Conversion/MemRefToLLVM/MemRefToLLVM.cpp b/mlir/lib/Conversion/MemRefToLLVM/MemRefToLLVM.cpp
index 53a19129103a3..88c08eb792737 100644
--- a/mlir/lib/Conversion/MemRefToLLVM/MemRefToLLVM.cpp
+++ b/mlir/lib/Conversion/MemRefToLLVM/MemRefToLLVM.cpp
@@ -575,8 +575,7 @@ struct DimOpLowering : public ConvertOpToLLVMPattern<memref::DimOp> {
Value sizePtr = LLVM::GEPOp::create(rewriter, loc, indexPtrTy,
getTypeConverter()->getIndexType(),
offsetPtr, idxPlusOne);
- return rewriter
- .create<LLVM::LoadOp>(loc, getTypeConverter()->getIndexType(), sizePtr)
+ return LLVM::LoadOp::create(rewriter, loc, getTypeConverter()->getIndexType(), sizePtr)
.getResult();
}
diff --git a/mlir/lib/Conversion/SPIRVToLLVM/SPIRVToLLVM.cpp b/mlir/lib/Conversion/SPIRVToLLVM/SPIRVToLLVM.cpp
index aae3271371c1f..9b6154057b806 100644
--- a/mlir/lib/Conversion/SPIRVToLLVM/SPIRVToLLVM.cpp
+++ b/mlir/lib/Conversion/SPIRVToLLVM/SPIRVToLLVM.cpp
@@ -1493,11 +1493,11 @@ class ShiftPattern : public SPIRVToLLVMConversion<SPIRVOp> {
Value extended;
if (op2TypeWidth < dstTypeWidth) {
if (isUnsignedIntegerOrVector(op2Type)) {
- extended = rewriter.template create<LLVM::ZExtOp>(
- loc, dstType, adaptor.getOperand2());
+ extended =
+ LLVM::ZExtOp::create(rewriter, loc, dstType, adaptor.getOperand2());
} else {
- extended = rewriter.template create<LLVM::SExtOp>(
- loc, dstType, adaptor.getOperand2());
+ extended =
+ LLVM::SExtOp::create(rewriter, loc, dstType, adaptor.getOperand2());
}
} else if (op2TypeWidth == dstTypeWidth) {
extended = adaptor.getOperand2();
@@ -1505,8 +1505,8 @@ class ShiftPattern : public SPIRVToLLVMConversion<SPIRVOp> {
return failure();
}
- Value result = rewriter.template create<LLVMOp>(
- loc, dstType, adaptor.getOperand1(), extended);
+ Value result =
+ LLVMOp::create(rewriter, loc, dstType, adaptor.getOperand1(), extended);
rewriter.replaceOp(op, result);
return success();
}
diff --git a/mlir/lib/Conversion/ShardToMPI/ShardToMPI.cpp b/mlir/lib/Conversion/ShardToMPI/ShardToMPI.cpp
index 8525543760d99..34773f16f02a2 100644
--- a/mlir/lib/Conversion/ShardToMPI/ShardToMPI.cpp
+++ b/mlir/lib/Conversion/ShardToMPI/ShardToMPI.cpp
@@ -177,8 +177,7 @@ struct ConvertShardingOp : public OpConversionPattern<ShardingOp> {
auto type = RankedTensorType::get({nSplits, 2}, i64);
Value resHaloSizes =
haloSizes.empty()
- ? rewriter
- .create<tensor::EmptyOp>(loc, std::array<int64_t, 2>{0, 0},
+ ? tensor::EmptyOp::create(rewriter, loc, std::array<int64_t, 2>{0, 0},
i64)
.getResult()
: tensor::FromElementsOp::create(rewriter, loc, type, haloSizes)
@@ -307,8 +306,7 @@ class ConvertProcessLinearIndexOp
Value commWorld =
mpi::CommWorldOp::create(rewriter, loc, mpi::CommType::get(ctx));
auto rank =
- rewriter
- .create<mpi::CommRankOp>(
+ mpi::CommRankOp::create(rewriter,
loc,
TypeRange{mpi::RetvalType::get(ctx), rewriter.getI32Type()},
commWorld)
@@ -704,8 +702,7 @@ struct ConvertUpdateHaloOp : public OpConversionPattern<UpdateHaloOp> {
for (auto &sz : haloSizes) {
if (auto value = dyn_cast<Value>(sz))
sz =
- rewriter
- .create<arith::IndexCastOp>(loc, rewriter.getIndexType(), value)
+ arith::IndexCastOp::create(rewriter, loc, rewriter.getIndexType(), value)
.getResult();
}
@@ -758,8 +755,7 @@ struct ConvertUpdateHaloOp : public OpConversionPattern<UpdateHaloOp> {
assert(currHaloDim >= 0 && (size_t)currHaloDim < haloSizes.size() / 2);
// Get the linearized ids of the neighbors (down and up) for the
// given split
- auto tmp = rewriter
- .create<NeighborsLinearIndicesOp>(loc, grid, myMultiIndex,
+ auto tmp = NeighborsLinearIndicesOp::create(rewriter, loc, grid, myMultiIndex,
splitAxes)
.getResults();
// MPI operates on i32...
diff --git a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
index 5c7c027382977..b5792406b296f 100644
--- a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
+++ b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
@@ -569,8 +569,7 @@ static Value createLinalgBodyCalculationForElementwiseOp(
// to UIToFP.
if (srcTy.isUnsignedInteger() && isa<FloatType>(dstTy)) {
auto unrealizedCast =
- rewriter
- .create<UnrealizedConversionCastOp>(
+ UnrealizedConversionCastOp::create(rewriter,
loc, rewriter.getIntegerType(srcTy.getIntOrFloatBitWidth()),
args[0])
.getResult(0);
@@ -868,8 +867,7 @@ static Value broadcastDynamicDimension(PatternRewriter &rewriter, Location loc,
// Emit 'linalg.generic' op
auto resultTensor =
- opBuilder
- .create<linalg::GenericOp>(
+ linalg::GenericOp::create(opBuilder,
loc, outputTensor.getType(), operand, outputTensor, affineMaps,
getNParallelLoopsAttrs(rank),
[&](OpBuilder &opBuilder, Location loc, ValueRange blockArgs) {
@@ -1156,8 +1154,7 @@ static LogicalResult reduceMatchAndRewriteHelper(OpTy op, uint64_t axis,
// First fill the output buffer with the init value.
auto emptyTensor =
- rewriter
- .create<tensor::EmptyOp>(loc, reduceShape, resultTy.getElementType(),
+ tensor::EmptyOp::create(rewriter, loc, reduceShape, resultTy.getElementType(),
dynDims)
.getResult();
@@ -1167,8 +1164,7 @@ static LogicalResult reduceMatchAndRewriteHelper(OpTy op, uint64_t axis,
op, "No initial value found for reduction operation");
auto fillValue = arith::ConstantOp::create(rewriter, loc, fillValueAttr);
- auto filledTensor = rewriter
- .create<linalg::FillOp>(loc, ValueRange{fillValue},
+ auto filledTensor = linalg::FillOp::create(rewriter, loc, ValueRange{fillValue},
ValueRange{emptyTensor})
.result();
outputs.push_back(filledTensor);
@@ -1186,13 +1182,11 @@ static LogicalResult reduceMatchAndRewriteHelper(OpTy op, uint64_t axis,
auto trueAttr = rewriter.getBoolAttr(true);
auto trueValue = arith::ConstantOp::create(rewriter, loc, trueAttr);
auto emptyBoolTensor =
- rewriter
- .create<tensor::EmptyOp>(loc, reduceShape, trueValue.getType(),
+ tensor::EmptyOp::create(rewriter, loc, reduceShape, trueValue.getType(),
dynDims)
.getResult();
auto allResultsNaNTensor =
- rewriter
- .create<linalg::FillOp>(loc, ValueRange{trueValue},
+ linalg::FillOp::create(rewriter, loc, ValueRange{trueValue},
ValueRange{emptyBoolTensor})
.result();
// Note that because the linalg::ReduceOp has two variadic arguments
@@ -1261,21 +1255,18 @@ static LogicalResult reduceMatchAndRewriteHelper(OpTy op, uint64_t axis,
APFloat::getNaN(cast<FloatType>(elementTy).getFloatSemantics(), false));
auto nanValue = arith::ConstantOp::create(rewriter, loc, nanValueAttr);
auto emptyNanTensor =
- rewriter
- .create<tensor::EmptyOp>(loc, reduceShape,
+ tensor::EmptyOp::create(rewriter, loc, reduceShape,
resultTy.getElementType(), dynDims)
.getResult();
auto nanFilledTensor =
- rewriter
- .create<linalg::FillOp>(loc, ValueRange{nanValue},
+ linalg::FillOp::create(rewriter, loc, ValueRange{nanValue},
ValueRange{emptyNanTensor})
.result();
// Create an empty tensor, non need to fill this since it will be
// overwritten by the select.
auto finalEmptyTensor =
- rewriter
- .create<tensor::EmptyOp>(loc, reduceShape,
+ tensor::EmptyOp::create(rewriter, loc, reduceShape,
resultTy.getElementType(), dynDims)
.getResult();
@@ -1503,8 +1494,7 @@ class RescaleConverter : public OpRewritePattern<tosa::RescaleOp> {
Value shift = shiftConstant ? shiftConstant : blockArgs[shiftArg];
if (valueTy.isUnsignedInteger()) {
- value = nestedBuilder
- .create<UnrealizedConversionCastOp>(
+ value = UnrealizedConversionCastOp::create(nestedBuilder,
nestedLoc,
nestedBuilder.getIntegerType(
valueTy.getIntOrFloatBitWidth()),
@@ -1557,8 +1547,7 @@ class RescaleConverter : public OpRewritePattern<tosa::RescaleOp> {
}
if (outIntType.isUnsignedInteger()) {
- value = nestedBuilder
- .create<UnrealizedConversionCastOp>(nestedLoc,
+ value = UnrealizedConversionCastOp::create(nestedBuilder, nestedLoc,
outIntType, value)
.getResult(0);
}
@@ -2095,8 +2084,7 @@ class ReverseConverter : public OpRewritePattern<tosa::ReverseOp> {
Value axisDimSize = tensor::DimOp::create(rewriter, loc, input, axis);
// First fill the output buffer with the init value.
- auto emptyTensor = rewriter
- .create<tensor::EmptyOp>(loc, inputTy.getShape(),
+ auto emptyTensor = tensor::EmptyOp::create(rewriter, loc, inputTy.getShape(),
inputTy.getElementType(),
ArrayRef<Value>({dynDims}))
.getResult();
@@ -2241,21 +2229,18 @@ class ArgMaxConverter : public OpRewritePattern<tosa::ArgMaxOp> {
}
// First fill the output buffer for the index.
- auto emptyTensorIdx = rewriter
- .create<tensor::EmptyOp>(loc, resultTy.getShape(),
+ auto emptyTensorIdx = tensor::EmptyOp::create(rewriter, loc, resultTy.getShape(),
outElementTy, dynDims)
.getResult();
auto fillValueIdx = arith::ConstantOp::create(
rewriter, loc, rewriter.getIntegerAttr(outElementTy, 0));
auto filledTensorIdx =
- rewriter
- .create<linalg::FillOp>(loc, ValueRange{fillValueIdx},
+ linalg::FillOp::create(rewriter, loc, ValueRange{fillValueIdx},
ValueRange{emptyTensorIdx})
.result();
// Second fill the output buffer for the running max.
- auto emptyTensorMax = rewriter
- .create<tensor::EmptyOp>(loc, resultTy.getShape(),
+ auto emptyTensorMax = tensor::EmptyOp::create(rewriter, loc, resultTy.getShape(),
inElementTy, dynDims)
.getResult();
auto fillValueMaxAttr =
@@ -2268,8 +2253,7 @@ class ArgMaxConverter : public OpRewritePattern<tosa::ArgMaxOp> {
auto fillValueMax =
arith::ConstantOp::create(rewriter, loc, fillValueMaxAttr);
auto filledTensorMax =
- rewriter
- .create<linalg::FillOp>(loc, ValueRange{fillValueMax},
+ linalg::FillOp::create(rewriter, loc, ValueRange{fillValueMax},
ValueRange{emptyTensorMax})
.result();
@@ -2371,8 +2355,7 @@ class GatherConverter : public OpConversionPattern<tosa::GatherOp> {
auto loc = op.getLoc();
auto emptyTensor =
- rewriter
- .create<tensor::EmptyOp>(loc, resultTy.getShape(), resultElementTy,
+ tensor::EmptyOp::create(rewriter, loc, resultTy.getShape(), resultElementTy,
dynamicDims)
.getResult();
@@ -2448,8 +2431,7 @@ class TableConverter : public OpRewritePattern<tosa::TableOp> {
}
}
- auto emptyTensor = rewriter
- .create<tensor::EmptyOp>(loc, resultTy.getShape(),
+ auto emptyTensor = tensor::EmptyOp::create(rewriter, loc, resultTy.getShape(),
resultElementTy, dynDims)
.getResult();
@@ -2585,8 +2567,7 @@ struct RFFT2dConverter final : public OpRewritePattern<RFFT2dOp> {
tensor::EmptyOp::create(rewriter, loc, type, dynamicSizes);
auto fillValueAttr = rewriter.getZeroAttr(type.getElementType());
auto fillValue = arith::ConstantOp::create(rewriter, loc, fillValueAttr);
- auto filledTensor = rewriter
- .create<linalg::FillOp>(loc, ValueRange{fillValue},
+ auto filledTensor = linalg::FillOp::create(rewriter, loc, ValueRange{fillValue},
ValueRange{emptyTensor})
.result();
return filledTensor;
diff --git a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
index 3a205246ddd9e..3d7d4e8f6afa4 100644
--- a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
+++ b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
@@ -64,8 +64,7 @@ linalgIntBroadcastExtSIAdd(PatternRewriter &rewriter, Location loc, Value bias,
Value conv, Value result,
ArrayRef<AffineMap> indexingMaps) {
ShapedType resultTy = cast<ShapedType>(conv.getType());
- return rewriter
- .create<linalg::GenericOp>(
+ return linalg::GenericOp::create(rewriter,
loc, resultTy, ValueRange({bias, conv}), result, indexingMaps,
getNParallelLoopsAttrs(resultTy.getRank()),
[](OpBuilder &builder, Location loc, ValueRange args) {
@@ -124,8 +123,7 @@ static mlir::Value linalgBroadcastAndMaybeExt(PatternRewriter &rewriter,
indexingMaps.push_back(rewriter.getMultiDimIdentityMap(resultRank));
// Build the broadcast-like operation ...
[truncated]
|
|
@llvm/pr-subscribers-mlir-vector Author: Maksim Levental (makslevental) ChangesSee #147168 for more info. Patch is 33.08 KiB, truncated to 20.00 KiB below, full version: https://github.com/llvm/llvm-project/pull/150660.diff 16 Files Affected:
diff --git a/mlir/lib/Conversion/ArithToEmitC/ArithToEmitC.cpp b/mlir/lib/Conversion/ArithToEmitC/ArithToEmitC.cpp
index 59b3fe2e4eaed..c3debf7afc865 100644
--- a/mlir/lib/Conversion/ArithToEmitC/ArithToEmitC.cpp
+++ b/mlir/lib/Conversion/ArithToEmitC/ArithToEmitC.cpp
@@ -402,7 +402,7 @@ class CastConversion : public OpConversionPattern<ArithOp> {
Value actualOp = adaptValueType(adaptor.getIn(), rewriter, castSrcType);
// Actual cast (may change bitwidth)
- auto cast = rewriter.template create<emitc::CastOp>(op.getLoc(),
+ auto cast = emitc::CastOp::create(rewriter, op.getLoc(),
castDestType, actualOp);
// Cast to the expected output type
@@ -507,7 +507,7 @@ class IntegerOpConversion final : public OpConversionPattern<ArithOp> {
Value lhs = adaptValueType(adaptor.getLhs(), rewriter, arithmeticType);
Value rhs = adaptValueType(adaptor.getRhs(), rewriter, arithmeticType);
- Value arithmeticResult = rewriter.template create<EmitCOp>(
+ Value arithmeticResult = EmitCOp::create(rewriter,
op.getLoc(), arithmeticType, lhs, rhs);
Value result = adaptValueType(arithmeticResult, rewriter, type);
@@ -547,7 +547,7 @@ class BitwiseOpConversion : public OpConversionPattern<ArithOp> {
Value lhs = adaptValueType(adaptor.getLhs(), rewriter, arithmeticType);
Value rhs = adaptValueType(adaptor.getRhs(), rewriter, arithmeticType);
- Value arithmeticResult = rewriter.template create<EmitCOp>(
+ Value arithmeticResult = EmitCOp::create(rewriter,
op.getLoc(), arithmeticType, lhs, rhs);
Value result = adaptValueType(arithmeticResult, rewriter, type);
@@ -748,7 +748,7 @@ class ItoFCastOpConversion : public OpConversionPattern<CastOp> {
}
Value fpCastOperand = adaptor.getIn();
if (actualOperandType != operandType) {
- fpCastOperand = rewriter.template create<emitc::CastOp>(
+ fpCastOperand = emitc::CastOp::create(rewriter,
castOp.getLoc(), actualOperandType, fpCastOperand);
}
rewriter.replaceOpWithNewOp<emitc::CastOp>(castOp, dstType, fpCastOperand);
diff --git a/mlir/lib/Conversion/BufferizationToMemRef/BufferizationToMemRef.cpp b/mlir/lib/Conversion/BufferizationToMemRef/BufferizationToMemRef.cpp
index 30a7170cf5c6a..d77cbfb1173c3 100644
--- a/mlir/lib/Conversion/BufferizationToMemRef/BufferizationToMemRef.cpp
+++ b/mlir/lib/Conversion/BufferizationToMemRef/BufferizationToMemRef.cpp
@@ -68,8 +68,7 @@ struct CloneOpConversion : public OpConversionPattern<bufferization::CloneOp> {
scf::YieldOp::create(rewriter, loc, acc);
};
- auto size = rewriter
- .create<scf::ForOp>(loc, zero, rank, one, ValueRange(one),
+ auto size = scf::ForOp::create(rewriter, loc, zero, rank, one, ValueRange(one),
loopBody)
.getResult(0);
diff --git a/mlir/lib/Conversion/ControlFlowToSCF/ControlFlowToSCF.cpp b/mlir/lib/Conversion/ControlFlowToSCF/ControlFlowToSCF.cpp
index c8311eb5a6433..f559fffbaf91f 100644
--- a/mlir/lib/Conversion/ControlFlowToSCF/ControlFlowToSCF.cpp
+++ b/mlir/lib/Conversion/ControlFlowToSCF/ControlFlowToSCF.cpp
@@ -144,8 +144,7 @@ ControlFlowToSCFTransformation::createUnreachableTerminator(Location loc,
return emitError(loc, "Cannot create unreachable terminator for '")
<< parentOp->getName() << "'";
- return builder
- .create<func::ReturnOp>(
+ return func::ReturnOp::create(builder,
loc, llvm::map_to_vector(funcOp.getResultTypes(),
[&](Type type) {
return getUndefValue(loc, builder, type);
diff --git a/mlir/lib/Conversion/GPUToSPIRV/GPUToSPIRV.cpp b/mlir/lib/Conversion/GPUToSPIRV/GPUToSPIRV.cpp
index a19194eb181fb..963f365cd688d 100644
--- a/mlir/lib/Conversion/GPUToSPIRV/GPUToSPIRV.cpp
+++ b/mlir/lib/Conversion/GPUToSPIRV/GPUToSPIRV.cpp
@@ -559,8 +559,7 @@ static Value createGroupReduceOpImpl(OpBuilder &builder, Location loc,
builder, loc, builder.getI32Type(),
builder.getIntegerAttr(builder.getI32Type(), *clusterSize));
- return builder
- .create<NonUniformOp>(loc, type, scope, groupOp, arg, clusterSizeValue)
+ return NonUniformOp::create(builder, loc, type, scope, groupOp, arg, clusterSizeValue)
.getResult();
}
diff --git a/mlir/lib/Conversion/LLVMCommon/Pattern.cpp b/mlir/lib/Conversion/LLVMCommon/Pattern.cpp
index ecd5b6367fba4..be58aa2e44e04 100644
--- a/mlir/lib/Conversion/LLVMCommon/Pattern.cpp
+++ b/mlir/lib/Conversion/LLVMCommon/Pattern.cpp
@@ -273,8 +273,7 @@ LogicalResult ConvertToLLVMPattern::copyUnrankedDescriptors(
// Allocate memory, copy, and free the source if necessary.
Value memory =
toDynamic
- ? builder
- .create<LLVM::CallOp>(loc, mallocFunc.value(), allocationSize)
+ ? LLVM::CallOp::create(builder, loc, mallocFunc.value(), allocationSize)
.getResult()
: LLVM::AllocaOp::create(builder, loc, getPtrType(),
IntegerType::get(getContext(), 8),
diff --git a/mlir/lib/Conversion/MPIToLLVM/MPIToLLVM.cpp b/mlir/lib/Conversion/MPIToLLVM/MPIToLLVM.cpp
index 5b68eb8188996..e5496e53ae529 100644
--- a/mlir/lib/Conversion/MPIToLLVM/MPIToLLVM.cpp
+++ b/mlir/lib/Conversion/MPIToLLVM/MPIToLLVM.cpp
@@ -35,7 +35,7 @@ static Op getOrDefineGlobal(ModuleOp &moduleOp, const Location loc,
if (!(ret = moduleOp.lookupSymbol<Op>(name))) {
ConversionPatternRewriter::InsertionGuard guard(rewriter);
rewriter.setInsertionPointToStart(moduleOp.getBody());
- ret = rewriter.template create<Op>(loc, std::forward<Args>(args)...);
+ ret = Op::create(rewriter, loc, std::forward<Args>(args)...);
}
return ret;
}
diff --git a/mlir/lib/Conversion/MemRefToLLVM/MemRefToLLVM.cpp b/mlir/lib/Conversion/MemRefToLLVM/MemRefToLLVM.cpp
index 53a19129103a3..88c08eb792737 100644
--- a/mlir/lib/Conversion/MemRefToLLVM/MemRefToLLVM.cpp
+++ b/mlir/lib/Conversion/MemRefToLLVM/MemRefToLLVM.cpp
@@ -575,8 +575,7 @@ struct DimOpLowering : public ConvertOpToLLVMPattern<memref::DimOp> {
Value sizePtr = LLVM::GEPOp::create(rewriter, loc, indexPtrTy,
getTypeConverter()->getIndexType(),
offsetPtr, idxPlusOne);
- return rewriter
- .create<LLVM::LoadOp>(loc, getTypeConverter()->getIndexType(), sizePtr)
+ return LLVM::LoadOp::create(rewriter, loc, getTypeConverter()->getIndexType(), sizePtr)
.getResult();
}
diff --git a/mlir/lib/Conversion/SPIRVToLLVM/SPIRVToLLVM.cpp b/mlir/lib/Conversion/SPIRVToLLVM/SPIRVToLLVM.cpp
index aae3271371c1f..9b6154057b806 100644
--- a/mlir/lib/Conversion/SPIRVToLLVM/SPIRVToLLVM.cpp
+++ b/mlir/lib/Conversion/SPIRVToLLVM/SPIRVToLLVM.cpp
@@ -1493,11 +1493,11 @@ class ShiftPattern : public SPIRVToLLVMConversion<SPIRVOp> {
Value extended;
if (op2TypeWidth < dstTypeWidth) {
if (isUnsignedIntegerOrVector(op2Type)) {
- extended = rewriter.template create<LLVM::ZExtOp>(
- loc, dstType, adaptor.getOperand2());
+ extended =
+ LLVM::ZExtOp::create(rewriter, loc, dstType, adaptor.getOperand2());
} else {
- extended = rewriter.template create<LLVM::SExtOp>(
- loc, dstType, adaptor.getOperand2());
+ extended =
+ LLVM::SExtOp::create(rewriter, loc, dstType, adaptor.getOperand2());
}
} else if (op2TypeWidth == dstTypeWidth) {
extended = adaptor.getOperand2();
@@ -1505,8 +1505,8 @@ class ShiftPattern : public SPIRVToLLVMConversion<SPIRVOp> {
return failure();
}
- Value result = rewriter.template create<LLVMOp>(
- loc, dstType, adaptor.getOperand1(), extended);
+ Value result =
+ LLVMOp::create(rewriter, loc, dstType, adaptor.getOperand1(), extended);
rewriter.replaceOp(op, result);
return success();
}
diff --git a/mlir/lib/Conversion/ShardToMPI/ShardToMPI.cpp b/mlir/lib/Conversion/ShardToMPI/ShardToMPI.cpp
index 8525543760d99..34773f16f02a2 100644
--- a/mlir/lib/Conversion/ShardToMPI/ShardToMPI.cpp
+++ b/mlir/lib/Conversion/ShardToMPI/ShardToMPI.cpp
@@ -177,8 +177,7 @@ struct ConvertShardingOp : public OpConversionPattern<ShardingOp> {
auto type = RankedTensorType::get({nSplits, 2}, i64);
Value resHaloSizes =
haloSizes.empty()
- ? rewriter
- .create<tensor::EmptyOp>(loc, std::array<int64_t, 2>{0, 0},
+ ? tensor::EmptyOp::create(rewriter, loc, std::array<int64_t, 2>{0, 0},
i64)
.getResult()
: tensor::FromElementsOp::create(rewriter, loc, type, haloSizes)
@@ -307,8 +306,7 @@ class ConvertProcessLinearIndexOp
Value commWorld =
mpi::CommWorldOp::create(rewriter, loc, mpi::CommType::get(ctx));
auto rank =
- rewriter
- .create<mpi::CommRankOp>(
+ mpi::CommRankOp::create(rewriter,
loc,
TypeRange{mpi::RetvalType::get(ctx), rewriter.getI32Type()},
commWorld)
@@ -704,8 +702,7 @@ struct ConvertUpdateHaloOp : public OpConversionPattern<UpdateHaloOp> {
for (auto &sz : haloSizes) {
if (auto value = dyn_cast<Value>(sz))
sz =
- rewriter
- .create<arith::IndexCastOp>(loc, rewriter.getIndexType(), value)
+ arith::IndexCastOp::create(rewriter, loc, rewriter.getIndexType(), value)
.getResult();
}
@@ -758,8 +755,7 @@ struct ConvertUpdateHaloOp : public OpConversionPattern<UpdateHaloOp> {
assert(currHaloDim >= 0 && (size_t)currHaloDim < haloSizes.size() / 2);
// Get the linearized ids of the neighbors (down and up) for the
// given split
- auto tmp = rewriter
- .create<NeighborsLinearIndicesOp>(loc, grid, myMultiIndex,
+ auto tmp = NeighborsLinearIndicesOp::create(rewriter, loc, grid, myMultiIndex,
splitAxes)
.getResults();
// MPI operates on i32...
diff --git a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
index 5c7c027382977..b5792406b296f 100644
--- a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
+++ b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
@@ -569,8 +569,7 @@ static Value createLinalgBodyCalculationForElementwiseOp(
// to UIToFP.
if (srcTy.isUnsignedInteger() && isa<FloatType>(dstTy)) {
auto unrealizedCast =
- rewriter
- .create<UnrealizedConversionCastOp>(
+ UnrealizedConversionCastOp::create(rewriter,
loc, rewriter.getIntegerType(srcTy.getIntOrFloatBitWidth()),
args[0])
.getResult(0);
@@ -868,8 +867,7 @@ static Value broadcastDynamicDimension(PatternRewriter &rewriter, Location loc,
// Emit 'linalg.generic' op
auto resultTensor =
- opBuilder
- .create<linalg::GenericOp>(
+ linalg::GenericOp::create(opBuilder,
loc, outputTensor.getType(), operand, outputTensor, affineMaps,
getNParallelLoopsAttrs(rank),
[&](OpBuilder &opBuilder, Location loc, ValueRange blockArgs) {
@@ -1156,8 +1154,7 @@ static LogicalResult reduceMatchAndRewriteHelper(OpTy op, uint64_t axis,
// First fill the output buffer with the init value.
auto emptyTensor =
- rewriter
- .create<tensor::EmptyOp>(loc, reduceShape, resultTy.getElementType(),
+ tensor::EmptyOp::create(rewriter, loc, reduceShape, resultTy.getElementType(),
dynDims)
.getResult();
@@ -1167,8 +1164,7 @@ static LogicalResult reduceMatchAndRewriteHelper(OpTy op, uint64_t axis,
op, "No initial value found for reduction operation");
auto fillValue = arith::ConstantOp::create(rewriter, loc, fillValueAttr);
- auto filledTensor = rewriter
- .create<linalg::FillOp>(loc, ValueRange{fillValue},
+ auto filledTensor = linalg::FillOp::create(rewriter, loc, ValueRange{fillValue},
ValueRange{emptyTensor})
.result();
outputs.push_back(filledTensor);
@@ -1186,13 +1182,11 @@ static LogicalResult reduceMatchAndRewriteHelper(OpTy op, uint64_t axis,
auto trueAttr = rewriter.getBoolAttr(true);
auto trueValue = arith::ConstantOp::create(rewriter, loc, trueAttr);
auto emptyBoolTensor =
- rewriter
- .create<tensor::EmptyOp>(loc, reduceShape, trueValue.getType(),
+ tensor::EmptyOp::create(rewriter, loc, reduceShape, trueValue.getType(),
dynDims)
.getResult();
auto allResultsNaNTensor =
- rewriter
- .create<linalg::FillOp>(loc, ValueRange{trueValue},
+ linalg::FillOp::create(rewriter, loc, ValueRange{trueValue},
ValueRange{emptyBoolTensor})
.result();
// Note that because the linalg::ReduceOp has two variadic arguments
@@ -1261,21 +1255,18 @@ static LogicalResult reduceMatchAndRewriteHelper(OpTy op, uint64_t axis,
APFloat::getNaN(cast<FloatType>(elementTy).getFloatSemantics(), false));
auto nanValue = arith::ConstantOp::create(rewriter, loc, nanValueAttr);
auto emptyNanTensor =
- rewriter
- .create<tensor::EmptyOp>(loc, reduceShape,
+ tensor::EmptyOp::create(rewriter, loc, reduceShape,
resultTy.getElementType(), dynDims)
.getResult();
auto nanFilledTensor =
- rewriter
- .create<linalg::FillOp>(loc, ValueRange{nanValue},
+ linalg::FillOp::create(rewriter, loc, ValueRange{nanValue},
ValueRange{emptyNanTensor})
.result();
// Create an empty tensor, non need to fill this since it will be
// overwritten by the select.
auto finalEmptyTensor =
- rewriter
- .create<tensor::EmptyOp>(loc, reduceShape,
+ tensor::EmptyOp::create(rewriter, loc, reduceShape,
resultTy.getElementType(), dynDims)
.getResult();
@@ -1503,8 +1494,7 @@ class RescaleConverter : public OpRewritePattern<tosa::RescaleOp> {
Value shift = shiftConstant ? shiftConstant : blockArgs[shiftArg];
if (valueTy.isUnsignedInteger()) {
- value = nestedBuilder
- .create<UnrealizedConversionCastOp>(
+ value = UnrealizedConversionCastOp::create(nestedBuilder,
nestedLoc,
nestedBuilder.getIntegerType(
valueTy.getIntOrFloatBitWidth()),
@@ -1557,8 +1547,7 @@ class RescaleConverter : public OpRewritePattern<tosa::RescaleOp> {
}
if (outIntType.isUnsignedInteger()) {
- value = nestedBuilder
- .create<UnrealizedConversionCastOp>(nestedLoc,
+ value = UnrealizedConversionCastOp::create(nestedBuilder, nestedLoc,
outIntType, value)
.getResult(0);
}
@@ -2095,8 +2084,7 @@ class ReverseConverter : public OpRewritePattern<tosa::ReverseOp> {
Value axisDimSize = tensor::DimOp::create(rewriter, loc, input, axis);
// First fill the output buffer with the init value.
- auto emptyTensor = rewriter
- .create<tensor::EmptyOp>(loc, inputTy.getShape(),
+ auto emptyTensor = tensor::EmptyOp::create(rewriter, loc, inputTy.getShape(),
inputTy.getElementType(),
ArrayRef<Value>({dynDims}))
.getResult();
@@ -2241,21 +2229,18 @@ class ArgMaxConverter : public OpRewritePattern<tosa::ArgMaxOp> {
}
// First fill the output buffer for the index.
- auto emptyTensorIdx = rewriter
- .create<tensor::EmptyOp>(loc, resultTy.getShape(),
+ auto emptyTensorIdx = tensor::EmptyOp::create(rewriter, loc, resultTy.getShape(),
outElementTy, dynDims)
.getResult();
auto fillValueIdx = arith::ConstantOp::create(
rewriter, loc, rewriter.getIntegerAttr(outElementTy, 0));
auto filledTensorIdx =
- rewriter
- .create<linalg::FillOp>(loc, ValueRange{fillValueIdx},
+ linalg::FillOp::create(rewriter, loc, ValueRange{fillValueIdx},
ValueRange{emptyTensorIdx})
.result();
// Second fill the output buffer for the running max.
- auto emptyTensorMax = rewriter
- .create<tensor::EmptyOp>(loc, resultTy.getShape(),
+ auto emptyTensorMax = tensor::EmptyOp::create(rewriter, loc, resultTy.getShape(),
inElementTy, dynDims)
.getResult();
auto fillValueMaxAttr =
@@ -2268,8 +2253,7 @@ class ArgMaxConverter : public OpRewritePattern<tosa::ArgMaxOp> {
auto fillValueMax =
arith::ConstantOp::create(rewriter, loc, fillValueMaxAttr);
auto filledTensorMax =
- rewriter
- .create<linalg::FillOp>(loc, ValueRange{fillValueMax},
+ linalg::FillOp::create(rewriter, loc, ValueRange{fillValueMax},
ValueRange{emptyTensorMax})
.result();
@@ -2371,8 +2355,7 @@ class GatherConverter : public OpConversionPattern<tosa::GatherOp> {
auto loc = op.getLoc();
auto emptyTensor =
- rewriter
- .create<tensor::EmptyOp>(loc, resultTy.getShape(), resultElementTy,
+ tensor::EmptyOp::create(rewriter, loc, resultTy.getShape(), resultElementTy,
dynamicDims)
.getResult();
@@ -2448,8 +2431,7 @@ class TableConverter : public OpRewritePattern<tosa::TableOp> {
}
}
- auto emptyTensor = rewriter
- .create<tensor::EmptyOp>(loc, resultTy.getShape(),
+ auto emptyTensor = tensor::EmptyOp::create(rewriter, loc, resultTy.getShape(),
resultElementTy, dynDims)
.getResult();
@@ -2585,8 +2567,7 @@ struct RFFT2dConverter final : public OpRewritePattern<RFFT2dOp> {
tensor::EmptyOp::create(rewriter, loc, type, dynamicSizes);
auto fillValueAttr = rewriter.getZeroAttr(type.getElementType());
auto fillValue = arith::ConstantOp::create(rewriter, loc, fillValueAttr);
- auto filledTensor = rewriter
- .create<linalg::FillOp>(loc, ValueRange{fillValue},
+ auto filledTensor = linalg::FillOp::create(rewriter, loc, ValueRange{fillValue},
ValueRange{emptyTensor})
.result();
return filledTensor;
diff --git a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
index 3a205246ddd9e..3d7d4e8f6afa4 100644
--- a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
+++ b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
@@ -64,8 +64,7 @@ linalgIntBroadcastExtSIAdd(PatternRewriter &rewriter, Location loc, Value bias,
Value conv, Value result,
ArrayRef<AffineMap> indexingMaps) {
ShapedType resultTy = cast<ShapedType>(conv.getType());
- return rewriter
- .create<linalg::GenericOp>(
+ return linalg::GenericOp::create(rewriter,
loc, resultTy, ValueRange({bias, conv}), result, indexingMaps,
getNParallelLoopsAttrs(resultTy.getRank()),
[](OpBuilder &builder, Location loc, ValueRange args) {
@@ -124,8 +123,7 @@ static mlir::Value linalgBroadcastAndMaybeExt(PatternRewriter &rewriter,
indexingMaps.push_back(rewriter.getMultiDimIdentityMap(resultRank));
// Build the broadcast-like operation ...
[truncated]
|
|
@llvm/pr-subscribers-mlir-gpu Author: Maksim Levental (makslevental) ChangesSee #147168 for more info. Patch is 33.08 KiB, truncated to 20.00 KiB below, full version: https://github.com/llvm/llvm-project/pull/150660.diff 16 Files Affected:
diff --git a/mlir/lib/Conversion/ArithToEmitC/ArithToEmitC.cpp b/mlir/lib/Conversion/ArithToEmitC/ArithToEmitC.cpp
index 59b3fe2e4eaed..c3debf7afc865 100644
--- a/mlir/lib/Conversion/ArithToEmitC/ArithToEmitC.cpp
+++ b/mlir/lib/Conversion/ArithToEmitC/ArithToEmitC.cpp
@@ -402,7 +402,7 @@ class CastConversion : public OpConversionPattern<ArithOp> {
Value actualOp = adaptValueType(adaptor.getIn(), rewriter, castSrcType);
// Actual cast (may change bitwidth)
- auto cast = rewriter.template create<emitc::CastOp>(op.getLoc(),
+ auto cast = emitc::CastOp::create(rewriter, op.getLoc(),
castDestType, actualOp);
// Cast to the expected output type
@@ -507,7 +507,7 @@ class IntegerOpConversion final : public OpConversionPattern<ArithOp> {
Value lhs = adaptValueType(adaptor.getLhs(), rewriter, arithmeticType);
Value rhs = adaptValueType(adaptor.getRhs(), rewriter, arithmeticType);
- Value arithmeticResult = rewriter.template create<EmitCOp>(
+ Value arithmeticResult = EmitCOp::create(rewriter,
op.getLoc(), arithmeticType, lhs, rhs);
Value result = adaptValueType(arithmeticResult, rewriter, type);
@@ -547,7 +547,7 @@ class BitwiseOpConversion : public OpConversionPattern<ArithOp> {
Value lhs = adaptValueType(adaptor.getLhs(), rewriter, arithmeticType);
Value rhs = adaptValueType(adaptor.getRhs(), rewriter, arithmeticType);
- Value arithmeticResult = rewriter.template create<EmitCOp>(
+ Value arithmeticResult = EmitCOp::create(rewriter,
op.getLoc(), arithmeticType, lhs, rhs);
Value result = adaptValueType(arithmeticResult, rewriter, type);
@@ -748,7 +748,7 @@ class ItoFCastOpConversion : public OpConversionPattern<CastOp> {
}
Value fpCastOperand = adaptor.getIn();
if (actualOperandType != operandType) {
- fpCastOperand = rewriter.template create<emitc::CastOp>(
+ fpCastOperand = emitc::CastOp::create(rewriter,
castOp.getLoc(), actualOperandType, fpCastOperand);
}
rewriter.replaceOpWithNewOp<emitc::CastOp>(castOp, dstType, fpCastOperand);
diff --git a/mlir/lib/Conversion/BufferizationToMemRef/BufferizationToMemRef.cpp b/mlir/lib/Conversion/BufferizationToMemRef/BufferizationToMemRef.cpp
index 30a7170cf5c6a..d77cbfb1173c3 100644
--- a/mlir/lib/Conversion/BufferizationToMemRef/BufferizationToMemRef.cpp
+++ b/mlir/lib/Conversion/BufferizationToMemRef/BufferizationToMemRef.cpp
@@ -68,8 +68,7 @@ struct CloneOpConversion : public OpConversionPattern<bufferization::CloneOp> {
scf::YieldOp::create(rewriter, loc, acc);
};
- auto size = rewriter
- .create<scf::ForOp>(loc, zero, rank, one, ValueRange(one),
+ auto size = scf::ForOp::create(rewriter, loc, zero, rank, one, ValueRange(one),
loopBody)
.getResult(0);
diff --git a/mlir/lib/Conversion/ControlFlowToSCF/ControlFlowToSCF.cpp b/mlir/lib/Conversion/ControlFlowToSCF/ControlFlowToSCF.cpp
index c8311eb5a6433..f559fffbaf91f 100644
--- a/mlir/lib/Conversion/ControlFlowToSCF/ControlFlowToSCF.cpp
+++ b/mlir/lib/Conversion/ControlFlowToSCF/ControlFlowToSCF.cpp
@@ -144,8 +144,7 @@ ControlFlowToSCFTransformation::createUnreachableTerminator(Location loc,
return emitError(loc, "Cannot create unreachable terminator for '")
<< parentOp->getName() << "'";
- return builder
- .create<func::ReturnOp>(
+ return func::ReturnOp::create(builder,
loc, llvm::map_to_vector(funcOp.getResultTypes(),
[&](Type type) {
return getUndefValue(loc, builder, type);
diff --git a/mlir/lib/Conversion/GPUToSPIRV/GPUToSPIRV.cpp b/mlir/lib/Conversion/GPUToSPIRV/GPUToSPIRV.cpp
index a19194eb181fb..963f365cd688d 100644
--- a/mlir/lib/Conversion/GPUToSPIRV/GPUToSPIRV.cpp
+++ b/mlir/lib/Conversion/GPUToSPIRV/GPUToSPIRV.cpp
@@ -559,8 +559,7 @@ static Value createGroupReduceOpImpl(OpBuilder &builder, Location loc,
builder, loc, builder.getI32Type(),
builder.getIntegerAttr(builder.getI32Type(), *clusterSize));
- return builder
- .create<NonUniformOp>(loc, type, scope, groupOp, arg, clusterSizeValue)
+ return NonUniformOp::create(builder, loc, type, scope, groupOp, arg, clusterSizeValue)
.getResult();
}
diff --git a/mlir/lib/Conversion/LLVMCommon/Pattern.cpp b/mlir/lib/Conversion/LLVMCommon/Pattern.cpp
index ecd5b6367fba4..be58aa2e44e04 100644
--- a/mlir/lib/Conversion/LLVMCommon/Pattern.cpp
+++ b/mlir/lib/Conversion/LLVMCommon/Pattern.cpp
@@ -273,8 +273,7 @@ LogicalResult ConvertToLLVMPattern::copyUnrankedDescriptors(
// Allocate memory, copy, and free the source if necessary.
Value memory =
toDynamic
- ? builder
- .create<LLVM::CallOp>(loc, mallocFunc.value(), allocationSize)
+ ? LLVM::CallOp::create(builder, loc, mallocFunc.value(), allocationSize)
.getResult()
: LLVM::AllocaOp::create(builder, loc, getPtrType(),
IntegerType::get(getContext(), 8),
diff --git a/mlir/lib/Conversion/MPIToLLVM/MPIToLLVM.cpp b/mlir/lib/Conversion/MPIToLLVM/MPIToLLVM.cpp
index 5b68eb8188996..e5496e53ae529 100644
--- a/mlir/lib/Conversion/MPIToLLVM/MPIToLLVM.cpp
+++ b/mlir/lib/Conversion/MPIToLLVM/MPIToLLVM.cpp
@@ -35,7 +35,7 @@ static Op getOrDefineGlobal(ModuleOp &moduleOp, const Location loc,
if (!(ret = moduleOp.lookupSymbol<Op>(name))) {
ConversionPatternRewriter::InsertionGuard guard(rewriter);
rewriter.setInsertionPointToStart(moduleOp.getBody());
- ret = rewriter.template create<Op>(loc, std::forward<Args>(args)...);
+ ret = Op::create(rewriter, loc, std::forward<Args>(args)...);
}
return ret;
}
diff --git a/mlir/lib/Conversion/MemRefToLLVM/MemRefToLLVM.cpp b/mlir/lib/Conversion/MemRefToLLVM/MemRefToLLVM.cpp
index 53a19129103a3..88c08eb792737 100644
--- a/mlir/lib/Conversion/MemRefToLLVM/MemRefToLLVM.cpp
+++ b/mlir/lib/Conversion/MemRefToLLVM/MemRefToLLVM.cpp
@@ -575,8 +575,7 @@ struct DimOpLowering : public ConvertOpToLLVMPattern<memref::DimOp> {
Value sizePtr = LLVM::GEPOp::create(rewriter, loc, indexPtrTy,
getTypeConverter()->getIndexType(),
offsetPtr, idxPlusOne);
- return rewriter
- .create<LLVM::LoadOp>(loc, getTypeConverter()->getIndexType(), sizePtr)
+ return LLVM::LoadOp::create(rewriter, loc, getTypeConverter()->getIndexType(), sizePtr)
.getResult();
}
diff --git a/mlir/lib/Conversion/SPIRVToLLVM/SPIRVToLLVM.cpp b/mlir/lib/Conversion/SPIRVToLLVM/SPIRVToLLVM.cpp
index aae3271371c1f..9b6154057b806 100644
--- a/mlir/lib/Conversion/SPIRVToLLVM/SPIRVToLLVM.cpp
+++ b/mlir/lib/Conversion/SPIRVToLLVM/SPIRVToLLVM.cpp
@@ -1493,11 +1493,11 @@ class ShiftPattern : public SPIRVToLLVMConversion<SPIRVOp> {
Value extended;
if (op2TypeWidth < dstTypeWidth) {
if (isUnsignedIntegerOrVector(op2Type)) {
- extended = rewriter.template create<LLVM::ZExtOp>(
- loc, dstType, adaptor.getOperand2());
+ extended =
+ LLVM::ZExtOp::create(rewriter, loc, dstType, adaptor.getOperand2());
} else {
- extended = rewriter.template create<LLVM::SExtOp>(
- loc, dstType, adaptor.getOperand2());
+ extended =
+ LLVM::SExtOp::create(rewriter, loc, dstType, adaptor.getOperand2());
}
} else if (op2TypeWidth == dstTypeWidth) {
extended = adaptor.getOperand2();
@@ -1505,8 +1505,8 @@ class ShiftPattern : public SPIRVToLLVMConversion<SPIRVOp> {
return failure();
}
- Value result = rewriter.template create<LLVMOp>(
- loc, dstType, adaptor.getOperand1(), extended);
+ Value result =
+ LLVMOp::create(rewriter, loc, dstType, adaptor.getOperand1(), extended);
rewriter.replaceOp(op, result);
return success();
}
diff --git a/mlir/lib/Conversion/ShardToMPI/ShardToMPI.cpp b/mlir/lib/Conversion/ShardToMPI/ShardToMPI.cpp
index 8525543760d99..34773f16f02a2 100644
--- a/mlir/lib/Conversion/ShardToMPI/ShardToMPI.cpp
+++ b/mlir/lib/Conversion/ShardToMPI/ShardToMPI.cpp
@@ -177,8 +177,7 @@ struct ConvertShardingOp : public OpConversionPattern<ShardingOp> {
auto type = RankedTensorType::get({nSplits, 2}, i64);
Value resHaloSizes =
haloSizes.empty()
- ? rewriter
- .create<tensor::EmptyOp>(loc, std::array<int64_t, 2>{0, 0},
+ ? tensor::EmptyOp::create(rewriter, loc, std::array<int64_t, 2>{0, 0},
i64)
.getResult()
: tensor::FromElementsOp::create(rewriter, loc, type, haloSizes)
@@ -307,8 +306,7 @@ class ConvertProcessLinearIndexOp
Value commWorld =
mpi::CommWorldOp::create(rewriter, loc, mpi::CommType::get(ctx));
auto rank =
- rewriter
- .create<mpi::CommRankOp>(
+ mpi::CommRankOp::create(rewriter,
loc,
TypeRange{mpi::RetvalType::get(ctx), rewriter.getI32Type()},
commWorld)
@@ -704,8 +702,7 @@ struct ConvertUpdateHaloOp : public OpConversionPattern<UpdateHaloOp> {
for (auto &sz : haloSizes) {
if (auto value = dyn_cast<Value>(sz))
sz =
- rewriter
- .create<arith::IndexCastOp>(loc, rewriter.getIndexType(), value)
+ arith::IndexCastOp::create(rewriter, loc, rewriter.getIndexType(), value)
.getResult();
}
@@ -758,8 +755,7 @@ struct ConvertUpdateHaloOp : public OpConversionPattern<UpdateHaloOp> {
assert(currHaloDim >= 0 && (size_t)currHaloDim < haloSizes.size() / 2);
// Get the linearized ids of the neighbors (down and up) for the
// given split
- auto tmp = rewriter
- .create<NeighborsLinearIndicesOp>(loc, grid, myMultiIndex,
+ auto tmp = NeighborsLinearIndicesOp::create(rewriter, loc, grid, myMultiIndex,
splitAxes)
.getResults();
// MPI operates on i32...
diff --git a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
index 5c7c027382977..b5792406b296f 100644
--- a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
+++ b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
@@ -569,8 +569,7 @@ static Value createLinalgBodyCalculationForElementwiseOp(
// to UIToFP.
if (srcTy.isUnsignedInteger() && isa<FloatType>(dstTy)) {
auto unrealizedCast =
- rewriter
- .create<UnrealizedConversionCastOp>(
+ UnrealizedConversionCastOp::create(rewriter,
loc, rewriter.getIntegerType(srcTy.getIntOrFloatBitWidth()),
args[0])
.getResult(0);
@@ -868,8 +867,7 @@ static Value broadcastDynamicDimension(PatternRewriter &rewriter, Location loc,
// Emit 'linalg.generic' op
auto resultTensor =
- opBuilder
- .create<linalg::GenericOp>(
+ linalg::GenericOp::create(opBuilder,
loc, outputTensor.getType(), operand, outputTensor, affineMaps,
getNParallelLoopsAttrs(rank),
[&](OpBuilder &opBuilder, Location loc, ValueRange blockArgs) {
@@ -1156,8 +1154,7 @@ static LogicalResult reduceMatchAndRewriteHelper(OpTy op, uint64_t axis,
// First fill the output buffer with the init value.
auto emptyTensor =
- rewriter
- .create<tensor::EmptyOp>(loc, reduceShape, resultTy.getElementType(),
+ tensor::EmptyOp::create(rewriter, loc, reduceShape, resultTy.getElementType(),
dynDims)
.getResult();
@@ -1167,8 +1164,7 @@ static LogicalResult reduceMatchAndRewriteHelper(OpTy op, uint64_t axis,
op, "No initial value found for reduction operation");
auto fillValue = arith::ConstantOp::create(rewriter, loc, fillValueAttr);
- auto filledTensor = rewriter
- .create<linalg::FillOp>(loc, ValueRange{fillValue},
+ auto filledTensor = linalg::FillOp::create(rewriter, loc, ValueRange{fillValue},
ValueRange{emptyTensor})
.result();
outputs.push_back(filledTensor);
@@ -1186,13 +1182,11 @@ static LogicalResult reduceMatchAndRewriteHelper(OpTy op, uint64_t axis,
auto trueAttr = rewriter.getBoolAttr(true);
auto trueValue = arith::ConstantOp::create(rewriter, loc, trueAttr);
auto emptyBoolTensor =
- rewriter
- .create<tensor::EmptyOp>(loc, reduceShape, trueValue.getType(),
+ tensor::EmptyOp::create(rewriter, loc, reduceShape, trueValue.getType(),
dynDims)
.getResult();
auto allResultsNaNTensor =
- rewriter
- .create<linalg::FillOp>(loc, ValueRange{trueValue},
+ linalg::FillOp::create(rewriter, loc, ValueRange{trueValue},
ValueRange{emptyBoolTensor})
.result();
// Note that because the linalg::ReduceOp has two variadic arguments
@@ -1261,21 +1255,18 @@ static LogicalResult reduceMatchAndRewriteHelper(OpTy op, uint64_t axis,
APFloat::getNaN(cast<FloatType>(elementTy).getFloatSemantics(), false));
auto nanValue = arith::ConstantOp::create(rewriter, loc, nanValueAttr);
auto emptyNanTensor =
- rewriter
- .create<tensor::EmptyOp>(loc, reduceShape,
+ tensor::EmptyOp::create(rewriter, loc, reduceShape,
resultTy.getElementType(), dynDims)
.getResult();
auto nanFilledTensor =
- rewriter
- .create<linalg::FillOp>(loc, ValueRange{nanValue},
+ linalg::FillOp::create(rewriter, loc, ValueRange{nanValue},
ValueRange{emptyNanTensor})
.result();
// Create an empty tensor, non need to fill this since it will be
// overwritten by the select.
auto finalEmptyTensor =
- rewriter
- .create<tensor::EmptyOp>(loc, reduceShape,
+ tensor::EmptyOp::create(rewriter, loc, reduceShape,
resultTy.getElementType(), dynDims)
.getResult();
@@ -1503,8 +1494,7 @@ class RescaleConverter : public OpRewritePattern<tosa::RescaleOp> {
Value shift = shiftConstant ? shiftConstant : blockArgs[shiftArg];
if (valueTy.isUnsignedInteger()) {
- value = nestedBuilder
- .create<UnrealizedConversionCastOp>(
+ value = UnrealizedConversionCastOp::create(nestedBuilder,
nestedLoc,
nestedBuilder.getIntegerType(
valueTy.getIntOrFloatBitWidth()),
@@ -1557,8 +1547,7 @@ class RescaleConverter : public OpRewritePattern<tosa::RescaleOp> {
}
if (outIntType.isUnsignedInteger()) {
- value = nestedBuilder
- .create<UnrealizedConversionCastOp>(nestedLoc,
+ value = UnrealizedConversionCastOp::create(nestedBuilder, nestedLoc,
outIntType, value)
.getResult(0);
}
@@ -2095,8 +2084,7 @@ class ReverseConverter : public OpRewritePattern<tosa::ReverseOp> {
Value axisDimSize = tensor::DimOp::create(rewriter, loc, input, axis);
// First fill the output buffer with the init value.
- auto emptyTensor = rewriter
- .create<tensor::EmptyOp>(loc, inputTy.getShape(),
+ auto emptyTensor = tensor::EmptyOp::create(rewriter, loc, inputTy.getShape(),
inputTy.getElementType(),
ArrayRef<Value>({dynDims}))
.getResult();
@@ -2241,21 +2229,18 @@ class ArgMaxConverter : public OpRewritePattern<tosa::ArgMaxOp> {
}
// First fill the output buffer for the index.
- auto emptyTensorIdx = rewriter
- .create<tensor::EmptyOp>(loc, resultTy.getShape(),
+ auto emptyTensorIdx = tensor::EmptyOp::create(rewriter, loc, resultTy.getShape(),
outElementTy, dynDims)
.getResult();
auto fillValueIdx = arith::ConstantOp::create(
rewriter, loc, rewriter.getIntegerAttr(outElementTy, 0));
auto filledTensorIdx =
- rewriter
- .create<linalg::FillOp>(loc, ValueRange{fillValueIdx},
+ linalg::FillOp::create(rewriter, loc, ValueRange{fillValueIdx},
ValueRange{emptyTensorIdx})
.result();
// Second fill the output buffer for the running max.
- auto emptyTensorMax = rewriter
- .create<tensor::EmptyOp>(loc, resultTy.getShape(),
+ auto emptyTensorMax = tensor::EmptyOp::create(rewriter, loc, resultTy.getShape(),
inElementTy, dynDims)
.getResult();
auto fillValueMaxAttr =
@@ -2268,8 +2253,7 @@ class ArgMaxConverter : public OpRewritePattern<tosa::ArgMaxOp> {
auto fillValueMax =
arith::ConstantOp::create(rewriter, loc, fillValueMaxAttr);
auto filledTensorMax =
- rewriter
- .create<linalg::FillOp>(loc, ValueRange{fillValueMax},
+ linalg::FillOp::create(rewriter, loc, ValueRange{fillValueMax},
ValueRange{emptyTensorMax})
.result();
@@ -2371,8 +2355,7 @@ class GatherConverter : public OpConversionPattern<tosa::GatherOp> {
auto loc = op.getLoc();
auto emptyTensor =
- rewriter
- .create<tensor::EmptyOp>(loc, resultTy.getShape(), resultElementTy,
+ tensor::EmptyOp::create(rewriter, loc, resultTy.getShape(), resultElementTy,
dynamicDims)
.getResult();
@@ -2448,8 +2431,7 @@ class TableConverter : public OpRewritePattern<tosa::TableOp> {
}
}
- auto emptyTensor = rewriter
- .create<tensor::EmptyOp>(loc, resultTy.getShape(),
+ auto emptyTensor = tensor::EmptyOp::create(rewriter, loc, resultTy.getShape(),
resultElementTy, dynDims)
.getResult();
@@ -2585,8 +2567,7 @@ struct RFFT2dConverter final : public OpRewritePattern<RFFT2dOp> {
tensor::EmptyOp::create(rewriter, loc, type, dynamicSizes);
auto fillValueAttr = rewriter.getZeroAttr(type.getElementType());
auto fillValue = arith::ConstantOp::create(rewriter, loc, fillValueAttr);
- auto filledTensor = rewriter
- .create<linalg::FillOp>(loc, ValueRange{fillValue},
+ auto filledTensor = linalg::FillOp::create(rewriter, loc, ValueRange{fillValue},
ValueRange{emptyTensor})
.result();
return filledTensor;
diff --git a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
index 3a205246ddd9e..3d7d4e8f6afa4 100644
--- a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
+++ b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
@@ -64,8 +64,7 @@ linalgIntBroadcastExtSIAdd(PatternRewriter &rewriter, Location loc, Value bias,
Value conv, Value result,
ArrayRef<AffineMap> indexingMaps) {
ShapedType resultTy = cast<ShapedType>(conv.getType());
- return rewriter
- .create<linalg::GenericOp>(
+ return linalg::GenericOp::create(rewriter,
loc, resultTy, ValueRange({bias, conv}), result, indexingMaps,
getNParallelLoopsAttrs(resultTy.getRank()),
[](OpBuilder &builder, Location loc, ValueRange args) {
@@ -124,8 +123,7 @@ static mlir::Value linalgBroadcastAndMaybeExt(PatternRewriter &rewriter,
indexingMaps.push_back(rewriter.getMultiDimIdentityMap(resultRank));
// Build the broadcast-like operation ...
[truncated]
|
kuhar
approved these changes
Jul 25, 2025
|
✅ With the latest revision this PR passed the C/C++ code formatter. |
See llvm#147168 for more info.
makslevental
force-pushed
the
makslevental/update-create-34n
branch
from
74298fd to
673ae83
Compare
kuhar
approved these changes
Jul 25, 2025
jpienaar
added a commit
that referenced
this pull request
Jul 26, 2025
Taken from git history: 9e7834c Maksim Levental [mlir][NFC] update `mlir/lib` create APIs (35/n) (#150708) 284a5c2 Maksim Levental [mlir][NFC] update `mlir/examples` create APIs (31/n) (#150652) c090ed5 Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (33/n) (#150659) fcbcfe4 Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (32/n) (#150657) 258daf5 Maksim Levental [mlir][NFC] update `mlir` create APIs (34/n) (#150660) c610b24 Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (27/n) (#150638) b58ad36 Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (30/n) (#150643) 258d04c Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (28/n) (#150641) a6bf40d Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (29/n) (#150642) dcfc853 Maksim Levental [mlir][NFC] update `flang/lib` create APIs (12/n) (#149914) 3f74334 Maksim Levental [mlir][NFC] update `flang` create APIs (13/n) (#149913) a636b7b Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (18/n) (#149925) 75aa706 Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (17/n) (#149924) 2f53125 Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (15/n) (#149921) 967626b Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (14/n) (#149920) 588845d Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (20/n) (#149927) b043492 Maksim Levental [mlir][NFC] update `Conversion` create APIs (4/n) (#149879) 8fff238 Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (23/n) (#149930) 38976a0 Maksim Levental [mlir][NFC] update `Conversion` create APIs (7/n) (#149889) eaa67a3 Maksim Levental [mlir][NFC] update `Conversion` create APIs (5/n) (#149887) b0312be Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (19/n) (#149926) 2736fbd Maksim Levental [mlir][NFC] update `mlir/lib` create APIs (26/n) (#149933) 4ae9fdc Maksim Levental [mlir][NFC] update `Conversion` create APIs (6/n) (#149888) f904cdd Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (24/n) (#149931) 972ac59 Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (21/n) (#149928) 7b78796 Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (25/n) (#149932) c3823af Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (22/n) (#149929) dce6679 Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (16/n) (#149922) 9844ba6 Maksim Levental [mlir][NFC] update `flang/Optimizer/Builder` create APIs (9/n) (#149917) 5547c6c Maksim Levental [mlir][NFC] update `flang/Optimizer/Builder/Runtime` create APIs (10/n) (#149916) a3a007a Maksim Levental [mlir][NFC] update `flang/Lower` create APIs (8/n) (#149912) 46f6df0 Maksim Levental [mlir][NFC] update `flang/Optimizer/Transforms` create APIs (11/n) (#149915) b7e332d Maksim Levental [mlir][NFC] update `include` create APIs (3/n) (#149687) 6056f94 Maksim Levental [mlir][NFC] update LLVM create APIs (2/n) (#149667) 906295b Maksim Levental [mlir] update affine+arith create APIs (1/n) (#149656)
jpienaar
added a commit
that referenced
this pull request
Jul 26, 2025
The update is most likely not what someone wants when looking at the blame for one of these lines. Taken from git history: ``` 9e7834c Maksim Levental [mlir][NFC] update `mlir/lib` create APIs (35/n) (#150708) 284a5c2 Maksim Levental [mlir][NFC] update `mlir/examples` create APIs (31/n) (#150652) c090ed5 Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (33/n) (#150659) fcbcfe4 Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (32/n) (#150657) 258daf5 Maksim Levental [mlir][NFC] update `mlir` create APIs (34/n) (#150660) c610b24 Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (27/n) (#150638) b58ad36 Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (30/n) (#150643) 258d04c Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (28/n) (#150641) a6bf40d Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (29/n) (#150642) dcfc853 Maksim Levental [mlir][NFC] update `flang/lib` create APIs (12/n) (#149914) 3f74334 Maksim Levental [mlir][NFC] update `flang` create APIs (13/n) (#149913) a636b7b Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (18/n) (#149925) 75aa706 Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (17/n) (#149924) 2f53125 Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (15/n) (#149921) 967626b Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (14/n) (#149920) 588845d Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (20/n) (#149927) b043492 Maksim Levental [mlir][NFC] update `Conversion` create APIs (4/n) (#149879) 8fff238 Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (23/n) (#149930) 38976a0 Maksim Levental [mlir][NFC] update `Conversion` create APIs (7/n) (#149889) eaa67a3 Maksim Levental [mlir][NFC] update `Conversion` create APIs (5/n) (#149887) b0312be Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (19/n) (#149926) 2736fbd Maksim Levental [mlir][NFC] update `mlir/lib` create APIs (26/n) (#149933) 4ae9fdc Maksim Levental [mlir][NFC] update `Conversion` create APIs (6/n) (#149888) f904cdd Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (24/n) (#149931) 972ac59 Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (21/n) (#149928) 7b78796 Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (25/n) (#149932) c3823af Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (22/n) (#149929) dce6679 Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (16/n) (#149922) 9844ba6 Maksim Levental [mlir][NFC] update `flang/Optimizer/Builder` create APIs (9/n) (#149917) 5547c6c Maksim Levental [mlir][NFC] update `flang/Optimizer/Builder/Runtime` create APIs (10/n) (#149916) a3a007a Maksim Levental [mlir][NFC] update `flang/Lower` create APIs (8/n) (#149912) 46f6df0 Maksim Levental [mlir][NFC] update `flang/Optimizer/Transforms` create APIs (11/n) (#149915) b7e332d Maksim Levental [mlir][NFC] update `include` create APIs (3/n) (#149687) 6056f94 Maksim Levental [mlir][NFC] update LLVM create APIs (2/n) (#149667) 906295b Maksim Levental [mlir] update affine+arith create APIs (1/n) (#149656) ```
mahesh-attarde
pushed a commit
to mahesh-attarde/llvm-project
that referenced
this pull request
Jul 28, 2025
See llvm#147168 for more info.
mahesh-attarde
pushed a commit
to mahesh-attarde/llvm-project
that referenced
this pull request
Jul 28, 2025
The update is most likely not what someone wants when looking at the blame for one of these lines. Taken from git history: ``` 9e7834c Maksim Levental [mlir][NFC] update `mlir/lib` create APIs (35/n) (llvm#150708) 284a5c2 Maksim Levental [mlir][NFC] update `mlir/examples` create APIs (31/n) (llvm#150652) c090ed5 Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (33/n) (llvm#150659) fcbcfe4 Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (32/n) (llvm#150657) 258daf5 Maksim Levental [mlir][NFC] update `mlir` create APIs (34/n) (llvm#150660) c610b24 Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (27/n) (llvm#150638) b58ad36 Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (30/n) (llvm#150643) 258d04c Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (28/n) (llvm#150641) a6bf40d Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (29/n) (llvm#150642) dcfc853 Maksim Levental [mlir][NFC] update `flang/lib` create APIs (12/n) (llvm#149914) 3f74334 Maksim Levental [mlir][NFC] update `flang` create APIs (13/n) (llvm#149913) a636b7b Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (18/n) (llvm#149925) 75aa706 Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (17/n) (llvm#149924) 2f53125 Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (15/n) (llvm#149921) 967626b Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (14/n) (llvm#149920) 588845d Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (20/n) (llvm#149927) b043492 Maksim Levental [mlir][NFC] update `Conversion` create APIs (4/n) (llvm#149879) 8fff238 Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (23/n) (llvm#149930) 38976a0 Maksim Levental [mlir][NFC] update `Conversion` create APIs (7/n) (llvm#149889) eaa67a3 Maksim Levental [mlir][NFC] update `Conversion` create APIs (5/n) (llvm#149887) b0312be Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (19/n) (llvm#149926) 2736fbd Maksim Levental [mlir][NFC] update `mlir/lib` create APIs (26/n) (llvm#149933) 4ae9fdc Maksim Levental [mlir][NFC] update `Conversion` create APIs (6/n) (llvm#149888) f904cdd Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (24/n) (llvm#149931) 972ac59 Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (21/n) (llvm#149928) 7b78796 Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (25/n) (llvm#149932) c3823af Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (22/n) (llvm#149929) dce6679 Maksim Levental [mlir][NFC] update `mlir/Dialect` create APIs (16/n) (llvm#149922) 9844ba6 Maksim Levental [mlir][NFC] update `flang/Optimizer/Builder` create APIs (9/n) (llvm#149917) 5547c6c Maksim Levental [mlir][NFC] update `flang/Optimizer/Builder/Runtime` create APIs (10/n) (llvm#149916) a3a007a Maksim Levental [mlir][NFC] update `flang/Lower` create APIs (8/n) (llvm#149912) 46f6df0 Maksim Levental [mlir][NFC] update `flang/Optimizer/Transforms` create APIs (11/n) (llvm#149915) b7e332d Maksim Levental [mlir][NFC] update `include` create APIs (3/n) (llvm#149687) 6056f94 Maksim Levental [mlir][NFC] update LLVM create APIs (2/n) (llvm#149667) 906295b Maksim Levental [mlir] update affine+arith create APIs (1/n) (llvm#149656) ```
ajaden-codes
pushed a commit
to Jaddyen/llvm-project
that referenced
this pull request
Jul 28, 2025
See llvm#147168 for more info.
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
See #147168 for more info.