Fix build errors after LLVM bump

llvm · Dec 12, 2022 · b9f941d · b9f941d
1 parent e54df07
commit b9f941d
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Showing 3 changed files with 10 additions and 10 deletions.
diff --git a/include/torch-mlir/Dialect/Torch/IR/TorchOps.h b/include/torch-mlir/Dialect/Torch/IR/TorchOps.h
@@ -185,7 +185,7 @@ struct torch_list_of_optional_constant_ints_op_binder {
     auto listConstruct = dyn_cast<Torch::PrimListConstructOp>(op);
     if (!listConstruct)
       return false;
-    for (Value value : listConstruct.elements()) {
+    for (Value value : listConstruct.getElements()) {
       int64_t num;
       if (matchPattern(value, m_TorchConstantInt(&num)))
         bind_values.push_back(num);

diff --git a/lib/Dialect/Torch/Transforms/ReifyAbstractInterpCalculationsUtils.cpp b/lib/Dialect/Torch/Transforms/ReifyAbstractInterpCalculationsUtils.cpp
@@ -203,13 +203,13 @@ FailureOr<Value> Torch::adjustFunctionArg(
       auto isNone = b.create<Aten__Is__Op>(loc, operand, none);
       auto primIf = b.create<PrimIfOp>(loc, desiredType, isNone);
       {
-        Region &thenRegion = primIf.thenRegion();
+        Region &thenRegion = primIf.getThenRegion();
         b.createBlock(&thenRegion, thenRegion.end());
         auto derefineNone = b.create<DerefineOp>(loc, desiredType, none);
         b.create<PrimIfYieldOp>(loc, ValueRange{derefineNone});
       }
       {
-        Region &elseRegion = primIf.elseRegion();
+        Region &elseRegion = primIf.getElseRegion();
         b.createBlock(&elseRegion, elseRegion.end());
         auto downcasted = b.create<PrimUncheckedCastOp>(
             loc, operandOptionalType.getContainedType(), operand);
@@ -258,7 +258,7 @@ FailureOr<Value> Torch::adjustFunctionArg(
     {
       OpBuilder::InsertionGuard guard(b);
       Block *body =
-          b.createBlock(&loop.region(), loop.region().begin(),
+          b.createBlock(&loop.getRegion(), loop.getRegion().begin(),
                         TypeRange({b.getType<Torch::IntType>()}), {loc});
       Value iterationNumber = body->getArgument(0);
       Value element = b.create<Aten__Getitem__TOp>(

diff --git a/lib/Dialect/Torch/Transforms/SimplifyDtypeCalculations.cpp b/lib/Dialect/Torch/Transforms/SimplifyDtypeCalculations.cpp
@@ -22,7 +22,7 @@ using namespace mlir::torch::Torch;
 static LogicalResult refineDtypeCalculateResult(DtypeCalculateOp op,
                                                 int resultNum,
                                                 PatternRewriter &rewriter) {
-  auto yieldDtypes = op.calculation().front().getTerminator();
+  auto yieldDtypes = op.getCalculation().front().getTerminator();
   auto dtype = yieldDtypes->getOperand(resultNum);
   auto result = op->getResult(resultNum);
 
@@ -85,12 +85,12 @@ class DecomposePromoteDtypesOp : public OpRewritePattern<PromoteDtypesOp> {
                                 PatternRewriter &rewriter) const override {
     SmallVector<Optional<int64_t>> ranks;
     SmallVector<int64_t> dtypes;
-    if (!matchPattern(op.ranks(), m_TorchListOfOptionalConstantInts(ranks))) {
+    if (!matchPattern(op.getRanks(), m_TorchListOfOptionalConstantInts(ranks))) {
       return rewriter.notifyMatchFailure(
           op, "Expected `ranks` to be a list of optional constant ints");
     }
 
-    if (!matchPattern(op.dtypes(), m_TorchListOfConstantInts(dtypes))) {
+    if (!matchPattern(op.getDtypes(), m_TorchListOfConstantInts(dtypes))) {
       return rewriter.notifyMatchFailure(
           op, "Expected `dtypes` to be a list of constant ints");
     }
@@ -161,19 +161,19 @@ class RefineNumToTensorScalarOpType
   using OpRewritePattern::OpRewritePattern;
   LogicalResult matchAndRewrite(PrimNumToTensorScalarOp op,
                                 PatternRewriter &rewriter) const override {
-    auto originalResultType = op.result().getType().cast<BaseTensorType>();
+    auto originalResultType = op.getResult().getType().cast<BaseTensorType>();
     if (originalResultType.hasDtype())
       return rewriter.notifyMatchFailure(
           op, "`PrimNumToTensorScalarOp` already has a dtype");
 
-    Type inputType = getBuiltInTypeForTorchScalar(op.a().getType());
+    Type inputType = getBuiltInTypeForTorchScalar(op.getA().getType());
     auto impliedTypeFromInputType =
         originalResultType.cast<BaseTensorType>()
             .getWithSizesAndDtype(originalResultType.getOptionalSizes(),
                                   inputType)
             .cast<BaseTensorType>();
 
-    op.result().setType(impliedTypeFromInputType);
+    op.getResult().setType(impliedTypeFromInputType);
     return success();
   }
 };