build: update llvm tag to 9acc2f37 (#1828)

This commit makes the following changes: - Update dialects to use fold API `kEmitFoldAdaptorFolder` and update signature of `fold` methods (see PSA https://discourse.llvm.org/t/psa-new-improved-fold-method-signature-has-landed-please-update-your-downstream-projects/67618) - Replace `makeArrayRef` with `ArrayRef` (see https://reviews.llvm.org/D140896) - Remove `TypeRange{}` arg from `b.create<scf::IfOp>` since builder no longer takes that argument - Make `func`s in `Torch/invalid.mlir` private, since symbol declarations cannot be public. (see https://discourse.llvm.org/t/rfc-symbol-definition-declaration-x-visibility-checks/2140)
2023-01-24 17:29:42 -08:00 · 2023-01-24 17:29:42 -08:00 · 6c86bec04f
parent 8ce2fffca5
commit 6c86bec04f
17 changed files with 147 additions and 145 deletions
--- a/externals/llvm-external-projects/torch-mlir-dialects/include/torch-mlir-dialects/Dialect/TMTensor/IR/TMTensorBase.td
+++ b/externals/llvm-external-projects/torch-mlir-dialects/include/torch-mlir-dialects/Dialect/TMTensor/IR/TMTensorBase.td
@ -43,6 +43,7 @@ def TMTensor_Dialect : Dialect {
    to.
  }];
  let hasCanonicalizer = 1;
+  let useFoldAPI = kEmitFoldAdaptorFolder;
 }

 //===----------------------------------------------------------------------===//
--- a/externals/llvm-external-projects/torch-mlir-dialects/lib/Dialect/TMTensor/IR/TMTensorOps.cpp
+++ b/externals/llvm-external-projects/torch-mlir-dialects/lib/Dialect/TMTensor/IR/TMTensorOps.cpp
@ -204,7 +204,7 @@ LogicalResult ScanOp::generateScalarImplementation(OpBuilder &b, Location loc,
  }

  auto scfIf = b.create<scf::IfOp>(
-      loc, TypeRange{}, cond,
+      loc, cond,
      [&](OpBuilder &b, Location loc) {
        if (isInclusive) {
          auto value = b.create<memref::LoadOp>(loc, input(), indices);
@ -266,7 +266,7 @@ static LogicalResult foldMemRefCast(Operation *op) {
  return success(folded);
 }

-LogicalResult ScanOp::fold(ArrayRef<Attribute>,
+LogicalResult ScanOp::fold(FoldAdaptor adaptor,
                           SmallVectorImpl<OpFoldResult> &) {
  return foldMemRefCast(*this);
 }
--- a/externals/llvm-project
+++ b/externals/llvm-project
@ -1 +1 @@
-Subproject commit d23516e9ad477527a9db4d06b1fa9566680ac67c
+Subproject commit 9acc2f37bdfce08ca0c2faec03392db10d1bb7a9
--- a/externals/mlir-hlo
+++ b/externals/mlir-hlo
@ -1 +1 @@
-Subproject commit 81e87a95b8683f1c3c33caf9e933897e0fc4a2b7
+Subproject commit 4a173356bb1291b97046545429d7851cbc771d88
--- a/include/torch-mlir/Dialect/Torch/IR/TorchBase.td
+++ b/include/torch-mlir/Dialect/Torch/IR/TorchBase.td
@ -37,6 +37,7 @@ def Torch_Dialect : Dialect {
  let hasRegionArgAttrVerify = 1;
  let hasConstantMaterializer = 1;
  let useDefaultTypePrinterParser = 0;
+  let useFoldAPI = kEmitFoldAdaptorFolder;

  let extraClassDeclaration = [{
    /// Parse a type registered to this dialect.
--- a/include/torch-mlir/Dialect/TorchConversion/IR/TorchConversionBase.td
+++ b/include/torch-mlir/Dialect/TorchConversion/IR/TorchConversionBase.td
@ -27,6 +27,7 @@ def TorchConversion_Dialect : Dialect {
  }];

  let hasConstantMaterializer = 1;
+  let useFoldAPI = kEmitFoldAdaptorFolder;
 }

 #endif // TORCHCONVERSION_BASE
--- a/lib/Conversion/TorchToLinalg/DataMovement.cpp
+++ b/lib/Conversion/TorchToLinalg/DataMovement.cpp
@ -463,8 +463,8 @@ public:
    }

    SmallVector<Value> inputSize = getTensorSizes(rewriter, loc, input);
-    ArrayRef<Value> outputShapeInt = llvm::makeArrayRef(outputSizeInt);
-    ArrayRef<Value> inputShapeInt = llvm::makeArrayRef(inputSize);
+    ArrayRef<Value> outputShapeInt = llvm::ArrayRef(outputSizeInt);
+    ArrayRef<Value> inputShapeInt = llvm::ArrayRef(inputSize);

    // Association indices for expand/collapse ops. These two vectors
    // are populated such that two entries at the same index corresponds
@ -1136,7 +1136,7 @@ public:

    Value dimIndex = rewriter.createOrFold<arith::ConstantOp>(
        loc, rewriter.getIndexAttr(dim));
-    for (auto tensor : makeArrayRef(tensors).drop_front()) {
+    for (auto tensor : ArrayRef(tensors).drop_front()) {
      auto size = rewriter.createOrFold<tensor::DimOp>(loc, tensor, dimIndex);
      resultDimSize =
          rewriter.createOrFold<arith::AddIOp>(loc, resultDimSize, size);
@ -1270,7 +1270,7 @@ public:
                           /*resultType=*/selfType,
                           /*inputs=*/broadcastedSrc,
                           /*outputs=*/self,
-                           /*indexingMaps=*/llvm::makeArrayRef({id, id}),
+                           /*indexingMaps=*/llvm::ArrayRef({id, id}),
                           /*iteratorTypes=*/iteratorTypes,
                           [](OpBuilder &b, Location loc, ValueRange args) {
                             Value result = args[0];
--- a/lib/Conversion/TorchToMhlo/Basic.cpp
+++ b/lib/Conversion/TorchToMhlo/Basic.cpp
@ -1086,7 +1086,7 @@ LogicalResult ConvertAtenOp<AtenNativeLayerNormOp>::matchAndRewrite(
  // Reshape input
  auto mhloInput = rewriter.create<mhlo::DynamicReshapeOp>(
      op->getLoc(), mhloBatchNormOutTy, input,
-      mhlo::getConstTensor(rewriter, op, llvm::makeArrayRef(inputFlattenShape),
+      mhlo::getConstTensor(rewriter, op, llvm::ArrayRef(inputFlattenShape),
                           {static_cast<int64_t>(inputFlattenShape.size())})
          .value());

--- a/lib/Conversion/TorchToTMTensor/TorchToTMTensor.cpp
+++ b/lib/Conversion/TorchToTMTensor/TorchToTMTensor.cpp
@ -142,7 +142,7 @@ public:
    // Finding the maximum value in the input tensor.
    SmallVector<int64_t> maxTensorSizes;
    ValueTensorType maxTensorType = ValueTensorType::get(
-        context, llvm::makeArrayRef(maxTensorSizes),
+        context, llvm::ArrayRef(maxTensorSizes),
        torchTypeInput.getType().cast<ValueTensorType>().getDtype());
    Value maxTensor =
        rewriter.create<AtenMaxOp>(loc, maxTensorType, torchTypeInput);
@ -165,7 +165,7 @@ public:
    SmallVector<int64_t> expandedInputSizes{
        makeShapeTorchCompatible(inputType.getShape())[0], 1};
    ValueTensorType expandInputType = ValueTensorType::get(
-        context, llvm::makeArrayRef(expandedInputSizes),
+        context, llvm::ArrayRef(expandedInputSizes),
        torchTypeInput.getType().cast<ValueTensorType>().getDtype());
    Value torchCstOne = rewriter.create<Torch::ConstantIntOp>(
        loc, rewriter.getI64IntegerAttr(1));
@ -286,9 +286,9 @@ public:
    auto indexTensorType = indexTensor.getType().cast<BaseTensorType>();
    int64_t indexTensorSize = indexTensorType.getSizes()[0];
    SmallVector<int64_t> expandedIndexTensorSizes{indexTensorSize, 1};
-    ValueTensorType expandedIndexTensorType = ValueTensorType::get(
-        context, llvm::makeArrayRef(expandedIndexTensorSizes),
-        indexTensorType.getDtype());
+    ValueTensorType expandedIndexTensorType =
+        ValueTensorType::get(context, llvm::ArrayRef(expandedIndexTensorSizes),
+                             indexTensorType.getDtype());
    Value torchCstOne = rewriter.create<Torch::ConstantIntOp>(
        loc, rewriter.getI64IntegerAttr(1));
    Value expandedIndexTensor = rewriter.create<AtenUnsqueezeOp>(
--- a/lib/Conversion/TorchToTosa/TorchToTosa.cpp
+++ b/lib/Conversion/TorchToTosa/TorchToTosa.cpp
@ -718,8 +718,8 @@ class ConvertAtenMultipleDimsReductionOp
                                         "non-const dim parameter unsupported");
    int64_t N = reduceDims.size();
    auto reduceDimsType = RankedTensorType::get({N}, rewriter.getI64Type());
-    reduceDimsAttr = DenseIntElementsAttr::get(reduceDimsType,
-                                               llvm::makeArrayRef(reduceDims));
+    reduceDimsAttr =
+        DenseIntElementsAttr::get(reduceDimsType, llvm::ArrayRef(reduceDims));

    keepDims = false;
    if (!matchPattern(op.getKeepdim(), m_TorchConstantBool(&keepDims)))
@ -748,8 +748,8 @@ class ConvertAtenOneDimReductionOp
      return rewriter.notifyMatchFailure(op,
                                         "non-const dim parameter unsupported");
    auto reduceDimsType = RankedTensorType::get({1}, rewriter.getI64Type());
-    reduceDimsAttr = DenseIntElementsAttr::get(reduceDimsType,
-                                               llvm::makeArrayRef({reduceDim}));
+    reduceDimsAttr =
+        DenseIntElementsAttr::get(reduceDimsType, llvm::ArrayRef({reduceDim}));

    keepDims = false;
    if (!matchPattern(op.getKeepdim(), m_TorchConstantBool(&keepDims)))
@ -782,8 +782,8 @@ public:
      reduceDims.push_back(i);
    int64_t N = selfTy.getRank();
    auto reduceDimsType = RankedTensorType::get({N}, rewriter.getI64Type());
-    reduceDimsAttr = DenseIntElementsAttr::get(reduceDimsType,
-                                               llvm::makeArrayRef(reduceDims));
+    reduceDimsAttr =
+        DenseIntElementsAttr::get(reduceDimsType, llvm::ArrayRef(reduceDims));
    keepDims = false;

    return success();
--- a/lib/Dialect/Torch/IR/TorchOps.cpp
+++ b/lib/Dialect/Torch/IR/TorchOps.cpp
@ -507,7 +507,7 @@ bool DerefineOp::areCastCompatible(mlir::TypeRange inputs,
  return isValidSubtype(inputs[0], outputs[0]);
 }

-OpFoldResult DerefineOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult DerefineOp::fold(FoldAdaptor adaptor) {
  auto uncheckedCast = getOperand().getDefiningOp<PrimUncheckedCastOp>();
  if (!uncheckedCast)
    return nullptr;
@ -570,10 +570,10 @@ static OpFoldResult atenIsOrIsNotFoldHelper(Operation *op, bool equalIsTrue) {
 // Aten__RangeLengthOp
 //===----------------------------------------------------------------------===//

-OpFoldResult Aten__RangeLengthOp::fold(ArrayRef<Attribute> operands) {
-  auto lo = operands[0];
-  auto hi = operands[1];
-  auto step = operands[2];
+OpFoldResult Aten__RangeLengthOp::fold(FoldAdaptor adaptor) {
+  auto lo = adaptor.getLo();
+  auto hi = adaptor.getHi();
+  auto step = adaptor.getStep();
  if (!lo || !hi || !step)
    return nullptr;
  auto loInt = lo.dyn_cast_or_null<IntegerAttr>().getValue();
@ -595,10 +595,10 @@ OpFoldResult Aten__RangeLengthOp::fold(ArrayRef<Attribute> operands) {
 // Aten__DeriveIndexOp
 //===----------------------------------------------------------------------===//

-OpFoldResult Aten__DeriveIndexOp::fold(ArrayRef<Attribute> operands) {
-  auto index = operands[0];
-  auto start = operands[1];
-  auto step = operands[2];
+OpFoldResult Aten__DeriveIndexOp::fold(FoldAdaptor adaptor) {
+  auto index = adaptor.getIndex();
+  auto start = adaptor.getStart();
+  auto step = adaptor.getStep();
  if (!index || !start || !step)
    return nullptr;
  auto indexInt = index.dyn_cast_or_null<IntegerAttr>().getValue();
@ -612,7 +612,7 @@ OpFoldResult Aten__DeriveIndexOp::fold(ArrayRef<Attribute> operands) {
 // Aten__Is__Op
 //===----------------------------------------------------------------------===//

-OpFoldResult Aten__Is__Op::fold(ArrayRef<Attribute> operands) {
+OpFoldResult Aten__Is__Op::fold(FoldAdaptor adaptor) {
  return atenIsOrIsNotFoldHelper(*this, /*equalIsTrue=*/true);
 }

@ -620,7 +620,7 @@ OpFoldResult Aten__Is__Op::fold(ArrayRef<Attribute> operands) {
 // Aten__Isnot__Op
 //===----------------------------------------------------------------------===//

-OpFoldResult Aten__Isnot__Op::fold(ArrayRef<Attribute> operands) {
+OpFoldResult Aten__Isnot__Op::fold(FoldAdaptor adaptor) {
  return atenIsOrIsNotFoldHelper(*this, /*equalIsTrue=*/false);
 }

@ -628,7 +628,7 @@ OpFoldResult Aten__Isnot__Op::fold(ArrayRef<Attribute> operands) {
 // Aten__Not__Op
 //===----------------------------------------------------------------------===//

-OpFoldResult Aten__Not__Op::fold(ArrayRef<Attribute> operands) {
+OpFoldResult Aten__Not__Op::fold(FoldAdaptor adaptor) {
  bool value;
  if (!matchPattern(getOperand(), m_TorchConstantBool(&value)))
    return nullptr;
@ -639,7 +639,7 @@ OpFoldResult Aten__Not__Op::fold(ArrayRef<Attribute> operands) {
 // AtenNeBoolOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenNeBoolOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult AtenNeBoolOp::fold(FoldAdaptor adaptor) {
  if (getOperand(0) == getOperand(1))
    return IntegerAttr::get(IntegerType::get(getContext(), 1), false);

@ -655,7 +655,7 @@ OpFoldResult AtenNeBoolOp::fold(ArrayRef<Attribute> operands) {
 // AtenSqueezeOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenSqueezeOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult AtenSqueezeOp::fold(FoldAdaptor adaptor) {
  if (auto tensorType = getOperand().getType().dyn_cast<BaseTensorType>()) {
    if (tensorType.hasSizes() && tensorType.getSizes().size() == 0)
      return getOperand();
@ -667,7 +667,7 @@ OpFoldResult AtenSqueezeOp::fold(ArrayRef<Attribute> operands) {
 // AtenSqueezeDimOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenSqueezeDimOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult AtenSqueezeDimOp::fold(FoldAdaptor adaptor) {
  if (auto tensorType = getOperand(0).getType().dyn_cast<BaseTensorType>()) {
    if (tensorType.hasSizes() && tensorType.getSizes().size() == 0)
      return getOperand(0);
@ -679,7 +679,7 @@ OpFoldResult AtenSqueezeDimOp::fold(ArrayRef<Attribute> operands) {
 // AtenRoundOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenRoundOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult AtenRoundOp::fold(FoldAdaptor adaptor) {
  if (auto selfType = getSelf().getType().dyn_cast<BaseTensorType>()) {
    if (selfType.hasDtype() && selfType.getDtype().isa<mlir::IntegerType>())
      return getSelf();
@ -691,7 +691,7 @@ OpFoldResult AtenRoundOp::fold(ArrayRef<Attribute> operands) {
 // AtenTypeAsOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenTypeAsOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult AtenTypeAsOp::fold(FoldAdaptor adaptor) {
  Type inType = getSelf().getType();
  Type newType = getOther().getType();

@ -705,7 +705,7 @@ OpFoldResult AtenTypeAsOp::fold(ArrayRef<Attribute> operands) {
 // AtenToDtypeOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenToDtypeOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult AtenToDtypeOp::fold(FoldAdaptor adaptor) {
  bool nonBlocking, copyArg;
  // The non_blocking arg must be `False`.
  if (!matchPattern(getNonBlocking(), m_TorchConstantBool(&nonBlocking)) ||
@ -736,7 +736,7 @@ OpFoldResult AtenToDtypeOp::fold(ArrayRef<Attribute> operands) {
 // AtenToDtypeLayoutOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenToDtypeLayoutOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult AtenToDtypeLayoutOp::fold(FoldAdaptor adaptor) {
  // The pin_memory arg should be either constant `False` or `none`.
  if (!getPinMemory().getType().isa<Torch::NoneType>()) {
    bool pinMemory;
@ -797,7 +797,7 @@ OpFoldResult AtenToDtypeLayoutOp::fold(ArrayRef<Attribute> operands) {
 // AtenViewOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenViewOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult AtenViewOp::fold(FoldAdaptor adaptor) {
  auto inputType = getOperand(0).getType().dyn_cast<BaseTensorType>();
  if (!inputType || !inputType.hasSizes() || inputType.getSizes().size() != 1)
    return nullptr;
@ -812,7 +812,7 @@ OpFoldResult AtenViewOp::fold(ArrayRef<Attribute> operands) {
 // AtenDimOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenDimOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult AtenDimOp::fold(FoldAdaptor adaptor) {
  if (auto tensorType = getOperand().getType().dyn_cast<BaseTensorType>()) {
    if (tensorType.hasSizes())
      return IntegerAttr::get(IntegerType::get(getContext(), 64),
@ -825,7 +825,7 @@ OpFoldResult AtenDimOp::fold(ArrayRef<Attribute> operands) {
 // AtenLenTOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenLenTOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult AtenLenTOp::fold(FoldAdaptor adaptor) {
  // `len([1,1,1])` -> `3`, if it is not mutated.
  if (auto listConstruct =
          getOperand().getDefiningOp<Torch::PrimListConstructOp>()) {
@ -853,7 +853,7 @@ void AtenLenTOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
 // AtenLenStrOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenLenStrOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult AtenLenStrOp::fold(FoldAdaptor adaptor) {
  if (auto stringConstruct = getS().getDefiningOp<ConstantStrOp>())
    return getI64IntegerAttr(getContext(),
                             stringConstruct.getValueAttr().getValue().size());
@ -1092,7 +1092,7 @@ void AtenSizeOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
 // AtenSizeIntOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenSizeIntOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult AtenSizeIntOp::fold(FoldAdaptor adaptor) {
  int64_t dim;
  if (!matchPattern(this->getDim(), m_TorchConstantInt(&dim)))
    return nullptr;
@ -1132,7 +1132,7 @@ floatComparatorFoldHelper(OpTy op, ConstantFloatComparator comparator) {
 // AtenLtFloatOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenLtFloatOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult AtenLtFloatOp::fold(FoldAdaptor adaptor) {
  return floatComparatorFoldHelper(*this,
                                   [](double a, double b) { return a < b; });
 }
@ -1141,7 +1141,7 @@ OpFoldResult AtenLtFloatOp::fold(ArrayRef<Attribute> operands) {
 // AtenGtFloatOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenGtFloatOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult AtenGtFloatOp::fold(FoldAdaptor adaptor) {
  return floatComparatorFoldHelper(*this,
                                   [](double a, double b) { return a > b; });
 }
@ -1150,7 +1150,7 @@ OpFoldResult AtenGtFloatOp::fold(ArrayRef<Attribute> operands) {
 // AtenGeFloatOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenGeFloatOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult AtenGeFloatOp::fold(FoldAdaptor adaptor) {
  return floatComparatorFoldHelper(*this,
                                   [](double a, double b) { return a >= b; });
 }
@ -1159,7 +1159,7 @@ OpFoldResult AtenGeFloatOp::fold(ArrayRef<Attribute> operands) {
 // AtenEqFloatOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenEqFloatOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult AtenEqFloatOp::fold(FoldAdaptor adaptor) {
  return floatComparatorFoldHelper(*this,
                                   [](double a, double b) { return a == b; });
 }
@ -1225,7 +1225,7 @@ static OpFoldResult intComparatorFoldHelper(OpTy op,
 // AtenNeIntOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenNeIntOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult AtenNeIntOp::fold(FoldAdaptor adaptor) {
  return intComparatorFoldHelper(*this,
                                 [](int64_t a, int64_t b) { return a != b; });
 }
@ -1234,7 +1234,7 @@ OpFoldResult AtenNeIntOp::fold(ArrayRef<Attribute> operands) {
 // AtenEqIntOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenEqIntOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult AtenEqIntOp::fold(FoldAdaptor adaptor) {
  return intComparatorFoldHelper(*this,
                                 [](int64_t a, int64_t b) { return a == b; });
 }
@ -1243,7 +1243,7 @@ OpFoldResult AtenEqIntOp::fold(ArrayRef<Attribute> operands) {
 // AtenEqStrOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenEqStrOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult AtenEqStrOp::fold(FoldAdaptor adaptor) {
  if (getOperand(0) == getOperand(1))
    return getI1IntegerAttr(getContext(), true);

@ -1259,7 +1259,7 @@ OpFoldResult AtenEqStrOp::fold(ArrayRef<Attribute> operands) {
 // AtenLtIntOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenLtIntOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult AtenLtIntOp::fold(FoldAdaptor adaptor) {
  return intComparatorFoldHelper(*this,
                                 [](int64_t a, int64_t b) { return a < b; });
 }
@ -1268,7 +1268,7 @@ OpFoldResult AtenLtIntOp::fold(ArrayRef<Attribute> operands) {
 // AtenLeIntOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenLeIntOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult AtenLeIntOp::fold(FoldAdaptor adaptor) {
  return intComparatorFoldHelper(*this,
                                 [](int64_t a, int64_t b) { return a <= b; });
 }
@ -1277,7 +1277,7 @@ OpFoldResult AtenLeIntOp::fold(ArrayRef<Attribute> operands) {
 // AtenGtIntOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenGtIntOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult AtenGtIntOp::fold(FoldAdaptor adaptor) {
  return intComparatorFoldHelper(*this,
                                 [](int64_t a, int64_t b) { return a > b; });
 }
@ -1286,7 +1286,7 @@ OpFoldResult AtenGtIntOp::fold(ArrayRef<Attribute> operands) {
 // AtenGeIntOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenGeIntOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult AtenGeIntOp::fold(FoldAdaptor adaptor) {
  return intComparatorFoldHelper(*this,
                                 [](int64_t a, int64_t b) { return a >= b; });
 }
@ -1295,7 +1295,7 @@ OpFoldResult AtenGeIntOp::fold(ArrayRef<Attribute> operands) {
 // AtenBoolFloatOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenBoolFloatOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult AtenBoolFloatOp::fold(FoldAdaptor adaptor) {
  double c;
  if (matchPattern(getOperand(), m_TorchConstantFloat(&c)))
    return getI1IntegerAttr(getContext(), c != 0.0);
@ -1306,7 +1306,7 @@ OpFoldResult AtenBoolFloatOp::fold(ArrayRef<Attribute> operands) {
 // AtenBoolIntOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenBoolIntOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult AtenBoolIntOp::fold(FoldAdaptor adaptor) {
  int64_t c;
  if (matchPattern(getOperand(), m_TorchConstantInt(&c)))
    return getI1IntegerAttr(getContext(), c != 0);
@ -1317,9 +1317,9 @@ OpFoldResult AtenBoolIntOp::fold(ArrayRef<Attribute> operands) {
 // AtenFloatScalarOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenFloatScalarOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult AtenFloatScalarOp::fold(FoldAdaptor adaptor) {
  // Constant fold int -> float conversion.
-  if (auto integerAttr = operands[0].dyn_cast_or_null<IntegerAttr>()) {
+  if (auto integerAttr = adaptor.getA().dyn_cast_or_null<IntegerAttr>()) {
    return FloatAttr::get(
        mlir::Float64Type::get(getContext()),
        static_cast<double>(integerAttr.getValue().getSExtValue()));
@ -1334,9 +1334,9 @@ OpFoldResult AtenFloatScalarOp::fold(ArrayRef<Attribute> operands) {
 // AtenIntScalarOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenIntScalarOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult AtenIntScalarOp::fold(FoldAdaptor adaptor) {
  // Constant fold float -> int conversion.
-  if (auto floatAttr = operands[0].dyn_cast_or_null<FloatAttr>()) {
+  if (auto floatAttr = adaptor.getA().dyn_cast_or_null<FloatAttr>()) {
    return IntegerAttr::get(
        mlir::IntegerType::get(getContext(), 64, IntegerType::Signed),
        static_cast<long>(floatAttr.getValue().convertToDouble()));
@ -1351,7 +1351,7 @@ OpFoldResult AtenIntScalarOp::fold(ArrayRef<Attribute> operands) {
 // AtenIntBoolOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenIntBoolOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult AtenIntBoolOp::fold(FoldAdaptor adaptor) {
  bool b;
  if (matchPattern(getOperand(), m_TorchConstantBool(&b))) {
    return getI64IntegerAttr(getContext(), static_cast<long>(b));
@ -1452,7 +1452,7 @@ LogicalResult ValueTensorLiteralOp::inferReturnTypes(
  return success();
 }

-OpFoldResult ValueTensorLiteralOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult ValueTensorLiteralOp::fold(FoldAdaptor adaptor) {
  return getValueAttr();
 }

@ -1557,7 +1557,7 @@ void CopyToValueTensorOp::getEffects(
 // ConstantNoneOp
 //===----------------------------------------------------------------------===//

-OpFoldResult ConstantNoneOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult ConstantNoneOp::fold(FoldAdaptor adaptor) {
  return TypeAttr::get(Torch::NoneType::get(getContext()));
 }

@ -1570,9 +1570,7 @@ void ConstantNoneOp::getAsmResultNames(
 // ConstantStrOp
 //===----------------------------------------------------------------------===//

-OpFoldResult ConstantStrOp::fold(ArrayRef<Attribute> operands) {
-  return getValueAttr();
-}
+OpFoldResult ConstantStrOp::fold(FoldAdaptor adaptor) { return getValueAttr(); }

 void ConstantStrOp::getAsmResultNames(
    function_ref<void(Value, StringRef)> setNameFn) {
@ -1610,7 +1608,7 @@ void ConstantIntOp::print(OpAsmPrinter &p) {
  p.printOptionalAttrDict((*this)->getAttrs(), {"value"});
 }

-OpFoldResult Torch::ConstantIntOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult Torch::ConstantIntOp::fold(FoldAdaptor adaptor) {
  return getValueAttr();
 }

@ -1626,7 +1624,7 @@ void Torch::ConstantIntOp::getAsmResultNames(
 // ConstantFloatOp
 //===----------------------------------------------------------------------===//

-OpFoldResult Torch::ConstantFloatOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult Torch::ConstantFloatOp::fold(FoldAdaptor adaptor) {
  return getValueAttr();
 }

@ -1656,7 +1654,7 @@ void Torch::ConstantFloatOp::getAsmResultNames(
 // ConstantNumberOp
 //===----------------------------------------------------------------------===//

-OpFoldResult Torch::ConstantNumberOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult Torch::ConstantNumberOp::fold(FoldAdaptor adaptor) {
  return getValueAttr();
 }

@ -1684,7 +1682,7 @@ void Torch::ConstantNumberOp::getCanonicalizationPatterns(
 // ConstantBoolOp
 //===----------------------------------------------------------------------===//

-OpFoldResult Torch::ConstantBoolOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult Torch::ConstantBoolOp::fold(FoldAdaptor adaptor) {
  return getValueAttr();
 }

@ -1702,7 +1700,7 @@ bool PrimUncheckedCastOp::areCastCompatible(mlir::TypeRange inputs,
  return isValidSubtype(outputs[0], inputs[0]);
 }

-OpFoldResult PrimUncheckedCastOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult PrimUncheckedCastOp::fold(FoldAdaptor adaptor) {
  if (auto derefineOp = getX().getDefiningOp<Torch::DerefineOp>()) {
    if (derefineOp.getOperand().getType() == getType())
      return derefineOp.getOperand();
@ -1836,7 +1834,7 @@ void AtenSliceTOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
 // AtenEqIntListOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenEqIntListOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult AtenEqIntListOp::fold(FoldAdaptor adaptor) {
  auto lhsLiteral = getA().getDefiningOp<Torch::PrimListConstructOp>();
  if (!lhsLiteral)
    return nullptr;
@ -1976,7 +1974,7 @@ static PrimDictConstructOp getDictConstructIfNotModified(Value torchDict) {
 // Aten__Getitem__DictStrOp
 //===----------------------------------------------------------------------===//

-OpFoldResult Aten__Getitem__DictStrOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult Aten__Getitem__DictStrOp::fold(FoldAdaptor adaptor) {
  auto dictConstruct = getDictConstructIfNotModified(getSelf());
  if (!dictConstruct)
    return nullptr;
@ -1994,7 +1992,7 @@ OpFoldResult Aten__Getitem__DictStrOp::fold(ArrayRef<Attribute> operands) {
 // Aten__Contains__StrOp
 //===----------------------------------------------------------------------===//

-OpFoldResult Aten__Contains__StrOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult Aten__Contains__StrOp::fold(FoldAdaptor adaptor) {
  auto dictConstruct = getDictConstructIfNotModified(getDict());
  if (!dictConstruct)
    return nullptr;
@ -2017,7 +2015,7 @@ static bool isListConstructNotModified(Value torchList) {
  });
 }

-OpFoldResult Aten__Contains__IntListOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult Aten__Contains__IntListOp::fold(FoldAdaptor adaptor) {
  auto itemConstruct = getItem();
  if (!isListConstructNotModified(getL()))
    return nullptr;
@ -2078,43 +2076,44 @@ atenBinaryFloatOperatorFoldHelper(ArrayRef<Attribute> operands,
 // AtenFloordivIntOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenFloordivIntOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult AtenFloordivIntOp::fold(FoldAdaptor adaptor) {
  return atenBinaryIntOperatorFoldHelper(
-    operands, [](int64_t a, int64_t b) { return std::floor(a / (double)b); });
+      adaptor.getOperands(),
+      [](int64_t a, int64_t b) { return std::floor(a / (double)b); });
 }

 //===----------------------------------------------------------------------===//
 // AtenRemainderIntOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenRemainderIntOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult AtenRemainderIntOp::fold(FoldAdaptor adaptor) {
  return atenBinaryIntOperatorFoldHelper(
-    operands, [](int64_t a, int64_t b) { return a % b; });
+      adaptor.getOperands(), [](int64_t a, int64_t b) { return a % b; });
 }

 //===----------------------------------------------------------------------===//
 // AtenAddIntOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenAddIntOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult AtenAddIntOp::fold(FoldAdaptor adaptor) {
  return atenBinaryIntOperatorFoldHelper(
-    operands, [](int64_t a, int64_t b) { return a + b; });
+      adaptor.getOperands(), [](int64_t a, int64_t b) { return a + b; });
 }

 //===----------------------------------------------------------------------===//
 // AtenSubIntOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenSubIntOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult AtenSubIntOp::fold(FoldAdaptor adaptor) {
  return atenBinaryIntOperatorFoldHelper(
-    operands, [](int64_t a, int64_t b) { return a - b; });
+      adaptor.getOperands(), [](int64_t a, int64_t b) { return a - b; });
 }

 //===----------------------------------------------------------------------===//
 // AtenCatOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenCatOp::fold(llvm::ArrayRef<mlir::Attribute> operands) {
+OpFoldResult AtenCatOp::fold(FoldAdaptor adaptor) {
  auto list = getOperand(0).getDefiningOp<PrimListConstructOp>();
  if (!list || !list->hasOneUse() || list.getElements().size() != 1)
    return nullptr;
@ -2125,7 +2124,7 @@ OpFoldResult AtenCatOp::fold(llvm::ArrayRef<mlir::Attribute> operands) {
 // AtenSliceTensorOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenSliceTensorOp::fold(llvm::ArrayRef<mlir::Attribute> operands) {
+OpFoldResult AtenSliceTensorOp::fold(FoldAdaptor adaptor) {
  auto inType = getOperand(0).getType().dyn_cast<ValueTensorType>();
  auto outType = getResult().getType().dyn_cast<ValueTensorType>();
  if (!inType || !outType || !inType.hasSizes() || !outType.hasSizes())
@ -2144,7 +2143,7 @@ OpFoldResult AtenSliceTensorOp::fold(llvm::ArrayRef<mlir::Attribute> operands) {
 // AtenMulIntOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenMulIntOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult AtenMulIntOp::fold(FoldAdaptor adaptor) {
  int64_t lhs, rhs;
  bool lConstant = matchPattern(getOperand(0), m_TorchConstantInt(&lhs));
  bool rConstant = matchPattern(getOperand(1), m_TorchConstantInt(&rhs));
@ -2159,42 +2158,45 @@ OpFoldResult AtenMulIntOp::fold(ArrayRef<Attribute> operands) {
 // AtenSubOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenSubOp::fold(ArrayRef<Attribute> operands) {
-  if (!operands[0] || !operands[1]) {
+OpFoldResult AtenSubOp::fold(FoldAdaptor adaptor) {
+  if (!adaptor.getA() || !adaptor.getB()) {
    return nullptr;
  }

-  if (operands[0].isa<IntegerAttr>() && operands[1].isa<IntegerAttr>()) {
+  if (adaptor.getA().isa<IntegerAttr>() && adaptor.getB().isa<IntegerAttr>()) {
    return atenBinaryIntOperatorFoldHelper(
-        operands, [](int64_t a, int64_t b) -> int64_t { return a - b; });
+        adaptor.getOperands(),
+        [](int64_t a, int64_t b) -> int64_t { return a - b; });
  }
  return atenBinaryFloatOperatorFoldHelper(
-      operands, [](double a, double b) -> double { return a - b; });
+      adaptor.getOperands(),
+      [](double a, double b) -> double { return a - b; });
 }

 //===----------------------------------------------------------------------===//
 // AtenDivOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenDivOp::fold(ArrayRef<Attribute> operands) {
-  if (!operands[0] || !operands[1]) {
+OpFoldResult AtenDivOp::fold(FoldAdaptor adaptor) {
+  if (!adaptor.getA() || !adaptor.getB()) {
    return nullptr;
  }
  // Since AtenDivOp always returns float value, we don't need to deal with the
  // case where the operands are both integers separately.
  return atenBinaryFloatOperatorFoldHelper(
-      operands, [](double a, double b) -> double { return a / b; });
+      adaptor.getOperands(),
+      [](double a, double b) -> double { return a / b; });
 }

 //===----------------------------------------------------------------------===//
 // AtenCeilScalarOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenCeilScalarOp::fold(ArrayRef<Attribute> operands) {
-  if (!operands[0]) {
+OpFoldResult AtenCeilScalarOp::fold(FoldAdaptor adaptor) {
+  if (!adaptor.getA()) {
    return nullptr;
  }
-  auto floatValue = operands[0].dyn_cast_or_null<FloatAttr>();
+  auto floatValue = adaptor.getA().dyn_cast_or_null<FloatAttr>();
  if (!floatValue) {
    return nullptr;
  }
@ -2207,7 +2209,7 @@ OpFoldResult AtenCeilScalarOp::fold(ArrayRef<Attribute> operands) {
 // AtenNegIntOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenNegIntOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult AtenNegIntOp::fold(FoldAdaptor adaptor) {
  int64_t c;
  if (matchPattern(getOperand(), m_TorchConstantInt(&c)))
    return getI64IntegerAttr(getContext(), -c);
@ -2218,7 +2220,7 @@ OpFoldResult AtenNegIntOp::fold(ArrayRef<Attribute> operands) {
 // AtenSqrtIntOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenSqrtIntOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult AtenSqrtIntOp::fold(FoldAdaptor adaptor) {
  int64_t c;
  if (matchPattern(getOperand(), m_TorchConstantInt(&c)))
    return getF64FloatAttr(getContext(), std::sqrt(c));
@ -2229,7 +2231,7 @@ OpFoldResult AtenSqrtIntOp::fold(ArrayRef<Attribute> operands) {
 // PrimDtypeOp
 //===----------------------------------------------------------------------===//

-OpFoldResult PrimDtypeOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult PrimDtypeOp::fold(FoldAdaptor adaptor) {
  BaseTensorType tensorType = getA().getType().cast<BaseTensorType>();
  if (tensorType.hasDtype()) {
    torch_upstream::ScalarType scalarType =
@ -2243,7 +2245,7 @@ OpFoldResult PrimDtypeOp::fold(ArrayRef<Attribute> operands) {
 // AtenIntTensorOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenIntTensorOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult AtenIntTensorOp::fold(FoldAdaptor adaptor) {
  // If a scalar number is converted to a 0-d tensor and passed on to
  // aten.Int.Tensor, fold to the scalar number.
  if (auto numToTensorScalar = getA().getDefiningOp<PrimNumToTensorScalarOp>())
@ -2255,7 +2257,7 @@ OpFoldResult AtenIntTensorOp::fold(ArrayRef<Attribute> operands) {
 // AtenFloatTensorOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenFloatTensorOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult AtenFloatTensorOp::fold(FoldAdaptor adaptor) {
  // If a scalar number is converted to a 0-d tensor and passed on to
  // aten.Float.Tensor, fold to the scalar number.
  if (auto numToTensorScalar = getA().getDefiningOp<PrimNumToTensorScalarOp>())
@ -2267,7 +2269,7 @@ OpFoldResult AtenFloatTensorOp::fold(ArrayRef<Attribute> operands) {
 // AtenDivFloatOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenDivFloatOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult AtenDivFloatOp::fold(FoldAdaptor adaptor) {
  double lhs, rhs;
  bool lConstant = matchPattern(getOperand(0), m_TorchConstantFloat(&lhs));
  bool rConstant = matchPattern(getOperand(1), m_TorchConstantFloat(&rhs));
@ -2284,7 +2286,7 @@ OpFoldResult AtenDivFloatOp::fold(ArrayRef<Attribute> operands) {
 // AtenDivIntOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenDivIntOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult AtenDivIntOp::fold(FoldAdaptor adaptor) {
  int64_t lhs, rhs;
  bool lConstant = matchPattern(getOperand(0), m_TorchConstantInt(&lhs));
  bool rConstant = matchPattern(getOperand(1), m_TorchConstantInt(&rhs));
@ -2297,7 +2299,7 @@ OpFoldResult AtenDivIntOp::fold(ArrayRef<Attribute> operands) {
 // AtenCeilFloatOp
 //===----------------------------------------------------------------------===//

-OpFoldResult AtenCeilFloatOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult AtenCeilFloatOp::fold(FoldAdaptor adaptor) {
  double c;
  if (matchPattern(getOperand(), m_TorchConstantFloat(&c)))
    return getI64IntegerAttr(getContext(), std::ceil(c));
@ -2308,13 +2310,13 @@ OpFoldResult AtenCeilFloatOp::fold(ArrayRef<Attribute> operands) {
 // PrimMaxIntOp
 //===----------------------------------------------------------------------===//

-OpFoldResult PrimMaxIntOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult PrimMaxIntOp::fold(FoldAdaptor adaptor) {
  // If both operands are the same, then the operation is an identity.
  if (getA() == getB())
    return getA();

-  auto lhs = operands[0].dyn_cast_or_null<IntegerAttr>();
-  auto rhs = operands[1].dyn_cast_or_null<IntegerAttr>();
+  auto lhs = adaptor.getA().dyn_cast_or_null<IntegerAttr>();
+  auto rhs = adaptor.getB().dyn_cast_or_null<IntegerAttr>();
  if (!lhs || !rhs)
    return nullptr;
  // Torch semantics are that !torch.int is 64-bit signed.
@ -2327,7 +2329,7 @@ OpFoldResult PrimMaxIntOp::fold(ArrayRef<Attribute> operands) {
 // PrimMinSelfIntOp
 //===----------------------------------------------------------------------===//

-OpFoldResult PrimMinSelfIntOp::fold(ArrayRef<Attribute> operands) {
+OpFoldResult PrimMinSelfIntOp::fold(FoldAdaptor adaptor) {
  auto list = getOperand().getDefiningOp<PrimListConstructOp>();
  if (!list)
    return nullptr;
--- a/lib/Dialect/Torch/IR/TorchTypes.cpp
+++ b/lib/Dialect/Torch/IR/TorchTypes.cpp
@ -463,7 +463,7 @@ Type Torch::meetTensorTypes(BaseTensorType lhs, BaseTensorType rhs) {
    }
  }

-  return lhs.getWithSizesAndDtype(makeArrayRef(newSizes), dtype);
+  return lhs.getWithSizesAndDtype(ArrayRef(newSizes), dtype);
 }

 ////===----------------------------------------------------------------------===//
@ -505,4 +505,4 @@ DictType::verify(llvm::function_ref<InFlightDiagnostic()> emitError,
    return failure();
  }
  return success();
-}
+}
--- a/lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp
+++ b/lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp
@ -72,7 +72,7 @@ static Type computeReductionType(PatternRewriter &rewriter, Operation *op,

  Type resultType = tensorType.getWithSizesAndDtype(
      sizes.size() == 0 ? std::optional<ArrayRef<int64_t>>()
-                        : llvm::makeArrayRef(sizes),
+                        : llvm::ArrayRef(sizes),
      tensorType.getOptionalDtype());
  return resultType;
 }
@ -108,7 +108,7 @@ static Value createMaxAlongDimension(PatternRewriter &rewriter, Location loc,
      valueType
          .getWithSizesAndDtype(
              !valueType.hasSizes() ? std::optional<ArrayRef<int64_t>>()
-                                    : llvm::makeArrayRef(valueType.getSizes()),
+                                    : llvm::ArrayRef(valueType.getSizes()),
              IntegerType::get(op->getContext(), 64, IntegerType::Signed))
          .cast<BaseTensorType>();
  return rewriter
@ -142,7 +142,7 @@ static Value createRank0Tensor(PatternRewriter &rewriter, Location loc,
                               BaseTensorType inputType, Value scalar) {
  SmallVector<int64_t> sizes;
  Type rank0TensorTy = inputType.getWithSizesAndDtype(
-      makeArrayRef(sizes), inputType.getOptionalDtype());
+      ArrayRef(sizes), inputType.getOptionalDtype());
  Value dimList = rewriter.create<PrimListConstructOp>(
      loc, Torch::ListType::get(Torch::IntType::get(inputType.getContext())),
      ValueRange{});
@ -940,7 +940,7 @@ public:
      SmallVector<int64_t> sizes;
      sizes.append(inputShape.begin(), inputShape.end());
      sizes[cstDim] = kUnknownSize;
-      Type sliceTy = selfTy.getWithSizesAndDtype(llvm::makeArrayRef(sizes),
+      Type sliceTy = selfTy.getWithSizesAndDtype(llvm::ArrayRef(sizes),
                                                 selfTy.getOptionalDtype());
      Value slice0 = rewriter.create<AtenSliceTensorOp>(
          loc, sliceTy, input, dim, negShift, constNone, constOne);
@ -1077,9 +1077,9 @@ public:

    Type dtype = self.getType().cast<ValueTensorType>().getOptionalDtype();
    Type unsqueezedType = ValueTensorType::get(
-        context, llvm::makeArrayRef(unsqueezedIntSizes), dtype);
-    Type expandedType = ValueTensorType::get(
-        context, llvm::makeArrayRef(expandedIntSizes), dtype);
+        context, llvm::ArrayRef(unsqueezedIntSizes), dtype);
+    Type expandedType =
+        ValueTensorType::get(context, llvm::ArrayRef(expandedIntSizes), dtype);

    auto listType = Torch::ListType::get(Torch::IntType::get(op.getContext()));
    Value unsqueezedDims =
@ -2004,7 +2004,7 @@ public:

    auto inputType = input.getType().cast<BaseTensorType>();
    SmallVector<int64_t> empty;
-    Type tensorType = inputType.getWithSizesAndDtype(llvm::makeArrayRef(empty),
+    Type tensorType = inputType.getWithSizesAndDtype(llvm::ArrayRef(empty),
                                                     rewriter.getF64Type());
    Value prob = rewriter.create<PrimNumToTensorScalarOp>(loc, tensorType, p);
    Value output;
@ -2082,8 +2082,8 @@ class DecomposeAtenLayerNormOp : public OpRewritePattern<AtenLayerNormOp> {
    std::vector<int64_t> meanVarSizes(inputRank, 1);
    for (int i = 0; i < axis; i++)
      meanVarSizes[i] = input.getSizes()[i];
-    auto meanVarType = input.getWithSizesAndDtype(
-        llvm::makeArrayRef(meanVarSizes), input.getOptionalDtype());
+    auto meanVarType = input.getWithSizesAndDtype(llvm::ArrayRef(meanVarSizes),
+                                                  input.getOptionalDtype());
    auto nativeLayerNorm = rewriter.create<AtenNativeLayerNormOp>(
        loc, op.getType(), meanVarType, meanVarType, op.getInput(),
        op.getNormalizedShape(), op.getWeight(), op.getBias(), op.getEps());
@ -2320,7 +2320,7 @@ class DecomposeAtenNativeBatchNormOp
    runningStatsShapeInt[1] = kUnknownSize;
    Type dtype = input.getType().cast<ValueTensorType>().getOptionalDtype();
    Type reshapeType = ValueTensorType::get(
-        context, llvm::makeArrayRef(runningStatsShapeInt), dtype);
+        context, llvm::ArrayRef(runningStatsShapeInt), dtype);

    runningMean = rewriter.create<AtenViewOp>(loc, reshapeType, runningMean,
                                              runningStatsSizeList);
@ -2466,8 +2466,7 @@ public:
    SmallVector<int64_t> empty;
    auto dtype =
        getTypeForTorchType(op.getContext(), op.getFillValue().getType());
-    Type tensorType =
-        outTy.getWithSizesAndDtype(llvm::makeArrayRef(empty), dtype);
+    Type tensorType = outTy.getWithSizesAndDtype(llvm::ArrayRef(empty), dtype);
    Value fillVal = rewriter.create<PrimNumToTensorScalarOp>(loc, tensorType,
                                                             op.getFillValue());
    fillVal = convertTensorToDtype(rewriter, loc, fillVal, outTy.getDtype());
@ -2503,7 +2502,7 @@ public:
    SmallVector<int64_t> transposeShape =
        llvm::to_vector(llvm::reverse(weightType.getSizes()));
    Type transposeType = weightType.getWithSizesAndDtype(
-        llvm::makeArrayRef(transposeShape), weightType.getOptionalDtype());
+        llvm::ArrayRef(transposeShape), weightType.getOptionalDtype());
    Value transposeWeight =
        rewriter.create<AtenTOp>(loc, transposeType, weight);

@ -2573,8 +2572,7 @@ public:
    SmallVector<int64_t> empty;
    auto dtype =
        getTypeForTorchType(op.getContext(), op.getFillValue().getType());
-    Type tensorType =
-        outTy.getWithSizesAndDtype(llvm::makeArrayRef(empty), dtype);
+    Type tensorType = outTy.getWithSizesAndDtype(llvm::ArrayRef(empty), dtype);
    Value fillVal = rewriter.create<PrimNumToTensorScalarOp>(
        op.getLoc(), tensorType, op.getFillValue());
    fillVal =
@ -3216,7 +3214,7 @@ public:
      sizes.resize(srcShape.size() + 1, kUnknownSize);
    }
    Type srcType = srcTensorType.getWithSizesAndDtype(
-        llvm::makeArrayRef(sizes), srcTensorType.getOptionalDtype());
+        llvm::ArrayRef(sizes), srcTensorType.getOptionalDtype());
    src = rewriter.create<AtenUnsqueezeOp>(loc, srcType, src, dim);
    rewriter.replaceOpWithNewOp<AtenSliceScatterOp>(
        op, op.getSelf().getType(), self, src, dim, start, startPlusOne,
@ -3314,7 +3312,7 @@ public:
          op, "Expected the input tensor to have sizes");
    BaseTensorType subType =
        inputType
-            .getWithSizesAndDtype(llvm::makeArrayRef(inputType.getSizes()),
+            .getWithSizesAndDtype(llvm::ArrayRef(inputType.getSizes()),
                                  resultType.getOptionalDtype())
            .cast<BaseTensorType>();

--- a/lib/Dialect/Torch/Transforms/SimplifyShapeCalculations.cpp
+++ b/lib/Dialect/Torch/Transforms/SimplifyShapeCalculations.cpp
@ -129,8 +129,7 @@ public:
    // Truncate the list of users to the number of users we're going to
    // interpret.
    allUsers.resize(numUsersToInterpret);
-    auto usersToInterpret =
-        makeArrayRef(allUsers).take_front(numUsersToInterpret);
+    auto usersToInterpret = ArrayRef(allUsers).take_front(numUsersToInterpret);

    // For each mutating op (which must be in the same block), we save the
    // current state of the list as a vector of Value's. These will then
@ -336,7 +335,7 @@ static LogicalResult refineShapeCalculateResult(ShapeCalculateOp op,
  auto originalResultType = result.getType().cast<BaseTensorType>();
  auto impliedTypesFromShape =
      originalResultType.cast<BaseTensorType>()
-          .getWithSizesAndDtype(makeArrayRef(sizes),
+          .getWithSizesAndDtype(ArrayRef(sizes),
                                originalResultType.getOptionalDtype())
          .cast<BaseTensorType>();

--- a/lib/Dialect/TorchConversion/IR/TorchConversionOps.cpp
+++ b/lib/Dialect/TorchConversion/IR/TorchConversionOps.cpp
@ -75,8 +75,8 @@ LogicalResult FromBuiltinTensorOp::verify() {
 // FromI64Op
 //===----------------------------------------------------------------------===//

-OpFoldResult FromI64Op::fold(llvm::ArrayRef<mlir::Attribute> operands) {
-  auto attr = operands[0].dyn_cast_or_null<mlir::IntegerAttr>();
+OpFoldResult FromI64Op::fold(FoldAdaptor adaptor) {
+  auto attr = adaptor.getOperand().dyn_cast_or_null<mlir::IntegerAttr>();
  if (attr) {
    return attr;
  } else {
@ -88,8 +88,8 @@ OpFoldResult FromI64Op::fold(llvm::ArrayRef<mlir::Attribute> operands) {
 // ToI64Op
 //===----------------------------------------------------------------------===//

-OpFoldResult ToI64Op::fold(llvm::ArrayRef<mlir::Attribute> operands) {
-  auto attr = operands[0].dyn_cast_or_null<mlir::IntegerAttr>();
+OpFoldResult ToI64Op::fold(FoldAdaptor adaptor) {
+  auto attr = adaptor.getOperand().dyn_cast_or_null<mlir::IntegerAttr>();
  if (attr) {
    return attr;
  } else {
@ -101,8 +101,8 @@ OpFoldResult ToI64Op::fold(llvm::ArrayRef<mlir::Attribute> operands) {
 // ToF64Op
 //===----------------------------------------------------------------------===//

-OpFoldResult ToF64Op::fold(llvm::ArrayRef<mlir::Attribute> operands) {
-  auto attr = operands[0].dyn_cast_or_null<mlir::FloatAttr>();
+OpFoldResult ToF64Op::fold(FoldAdaptor adaptor) {
+  auto attr = adaptor.getOperand().dyn_cast_or_null<mlir::FloatAttr>();
  if (attr) {
    return attr;
  } else {
@ -114,8 +114,8 @@ OpFoldResult ToF64Op::fold(llvm::ArrayRef<mlir::Attribute> operands) {
 // FromF64Op
 //===----------------------------------------------------------------------===//

-OpFoldResult FromF64Op::fold(llvm::ArrayRef<mlir::Attribute> operands) {
-  auto attr = operands[0].dyn_cast_or_null<mlir::FloatAttr>();
+OpFoldResult FromF64Op::fold(FoldAdaptor adaptor) {
+  auto attr = adaptor.getOperand().dyn_cast_or_null<mlir::FloatAttr>();
  if (attr) {
    return attr;
  } else {
--- a/lib/RefBackend/RefBackend.cpp
+++ b/lib/RefBackend/RefBackend.cpp
@ -392,7 +392,7 @@ Operation *createLinalgCopyOp(OpBuilder &b, Location loc, Value from,
      loc,
      /*inputs=*/from,
      /*outputs=*/to,
-      /*indexingMaps=*/llvm::makeArrayRef({id, id}),
+      /*indexingMaps=*/llvm::ArrayRef({id, id}),
      /*iteratorTypes=*/iteratorTypes,
      [](OpBuilder &b, Location loc, ValueRange args) {
        b.create<linalg::YieldOp>(loc, args.front());
--- a/test/Dialect/Torch/invalid.mlir
+++ b/test/Dialect/Torch/invalid.mlir
@ -101,17 +101,17 @@ torch.class_type @c {
 // -----

 // expected-error @+1 {{'torch.type_bound' must be attached to an argument of !torch.tensor/!torch.vtensor type}}
-func.func @f(%arg0: i32 {torch.type_bound = !torch.tensor<*,f32>})
+func.func private @f(%arg0: i32 {torch.type_bound = !torch.tensor<*,f32>})

 // -----

 // expected-error @+1 {{'torch.type_bound' must be TypeAttr}}
-func.func @f(%arg0: i32 {torch.type_bound = 1})
+func.func private @f(%arg0: i32 {torch.type_bound = 1})

 // -----

 // expected-error @+1 {{'torch.type_bound' must be of !torch.tensor/!torch.vtensor type}}
-func.func @f(%arg0: i32 {torch.type_bound = i32})
+func.func private @f(%arg0: i32 {torch.type_bound = i32})

 // -----