[mlir][NFC] update mlir/Dialect create APIs (32/n)

mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp

@@ -791,9 +791,8 @@ struct FoldFillWithPad final : public OpRewritePattern<tensor::PadOp> {
         tensor::EmptyOp::create(rewriter, padOp.getLoc(), reifiedShape.front(),
                                 padOp.getResultType().getElementType());
     Value replacement =
-        rewriter
-            .create<FillOp>(fillOp.getLoc(), ValueRange{padValue},
-                            ValueRange{emptyTensor})
+        FillOp::create(rewriter, fillOp.getLoc(), ValueRange{padValue},
+                       ValueRange{emptyTensor})
             .getResult(0);
     if (replacement.getType() != padOp.getResultType()) {
       replacement = tensor::CastOp::create(rewriter, fillOp.getLoc(),
@@ -2154,9 +2153,8 @@ struct SwapTransposeWithBroadcast : OpRewritePattern<linalg::TransposeOp> {
 
     // Create broadcast(transpose(input)).
     Value transposeResult =
-        rewriter
-            .create<TransposeOp>(loc, broadcastOp.getInput(), transposeInit,
-                                 resultPerms)
+        TransposeOp::create(rewriter, loc, broadcastOp.getInput(),
+                            transposeInit, resultPerms)
             ->getResult(0);
     rewriter.replaceOpWithNewOp<BroadcastOp>(
         transposeOp, transposeResult, transposeOp.getInit(), resultDimensions);

mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp

@@ -4133,9 +4133,8 @@ DiagnosedSilenceableFailure doit(RewriterBase &rewriter, OpTy target,
   Value extracted = tensor::ExtractSliceOp::create(
       rewriter, target.getLoc(), target.getDest(), target.getMixedOffsets(),
       target.getMixedSizes(), target.getMixedStrides());
-  Value copied = rewriter
-                     .create<linalg::CopyOp>(target.getLoc(),
-                                             target.getSource(), extracted)
+  Value copied = linalg::CopyOp::create(rewriter, target.getLoc(),
+                                        target.getSource(), extracted)
                      .getResult(0);
   // Reset the insertion point.
   rewriter.setInsertionPoint(target);

mlir/lib/Dialect/Linalg/Transforms/DataLayoutPropagation.cpp

@@ -1143,10 +1143,9 @@ pushDownUnPackOpThroughGenericOp(RewriterBase &rewriter, GenericOp genericOp,
 
   // Insert an unPackOp right after the packed generic.
   Value unPackOpRes =
-      rewriter
-          .create<linalg::UnPackOp>(genericOp.getLoc(), newResult,
-                                    destPack.getSource(), innerDimsPos,
-                                    mixedTiles, outerDimsPerm)
+      linalg::UnPackOp::create(rewriter, genericOp.getLoc(), newResult,
+                               destPack.getSource(), innerDimsPos, mixedTiles,
+                               outerDimsPerm)
           .getResult();
 
   return std::make_tuple(newGenericOp, unPackOpRes);

mlir/lib/Dialect/Linalg/Transforms/DropUnitDims.cpp

@@ -267,8 +267,8 @@ expandValue(RewriterBase &rewriter, Location loc, Value result, Value origDest,
   assert(rankReductionStrategy ==
              ControlDropUnitDims::RankReductionStrategy::ReassociativeReshape &&
          "unknown rank reduction strategy");
-  return rewriter
-      .create<tensor::ExpandShapeOp>(loc, origResultType, result, reassociation)
+  return tensor::ExpandShapeOp::create(rewriter, loc, origResultType, result,
+                                       reassociation)
       .getResult();
 }
 

mlir/lib/Dialect/Linalg/Transforms/ElementwiseOpFusion.cpp

@@ -1572,12 +1572,12 @@ static Value getCollapsedOpOperand(Location loc, LinalgOp op,
 
   // Insert a reshape to collapse the dimensions.
   if (isa<MemRefType>(operand.getType())) {
-    return builder
-        .create<memref::CollapseShapeOp>(loc, operand, operandReassociation)
+    return memref::CollapseShapeOp::create(builder, loc, operand,
+                                           operandReassociation)
         .getResult();
   }
-  return builder
-      .create<tensor::CollapseShapeOp>(loc, operand, operandReassociation)
+  return tensor::CollapseShapeOp::create(builder, loc, operand,
+                                         operandReassociation)
       .getResult();
 }
 

mlir/lib/Dialect/Linalg/Transforms/PackAndUnpackPatterns.cpp

@@ -81,9 +81,8 @@ struct SimplifyPackToExpandShape : public OpRewritePattern<PackOp> {
                ArrayRef<ReassociationIndices> reassociation) const {
     if (operand.getType() == newOperandType)
       return operand;
-    return rewriter
-        .create<tensor::ExpandShapeOp>(loc, newOperandType, operand,
-                                       reassociation)
+    return tensor::ExpandShapeOp::create(rewriter, loc, newOperandType, operand,
+                                         reassociation)
         .getResult();
   }
 

mlir/lib/Dialect/Linalg/Transforms/Padding.cpp

@@ -333,17 +333,16 @@ linalg::rewriteAsPaddedOp(RewriterBase &rewriter, LinalgOp opToPad,
   for (auto it :
        llvm::zip(paddedSubtensorResults, opToPad.getDpsInitsMutable())) {
     if (options.copyBackOp == LinalgPaddingOptions::CopyBackOp::LinalgCopy) {
-      replacements.push_back(rewriter
-                                 .create<linalg::CopyOp>(loc, std::get<0>(it),
-                                                         std::get<1>(it).get())
+      replacements.push_back(linalg::CopyOp::create(rewriter, loc,
+                                                    std::get<0>(it),
+                                                    std::get<1>(it).get())
                                  .getResult(0));
     } else if (options.copyBackOp ==
                LinalgPaddingOptions::CopyBackOp::
                    BufferizationMaterializeInDestination) {
       replacements.push_back(
-          rewriter
-              .create<bufferization::MaterializeInDestinationOp>(
-                  loc, std::get<0>(it), std::get<1>(it).get())
+          bufferization::MaterializeInDestinationOp::create(
+              rewriter, loc, std::get<0>(it), std::get<1>(it).get())
               ->getResult(0));
     } else {
       llvm_unreachable("unsupported copy back op");

mlir/lib/Dialect/Linalg/Transforms/Transforms.cpp

@@ -947,9 +947,9 @@ DecomposePadOpPattern::matchAndRewrite(tensor::PadOp padOp,
   auto getIdxValue = [&](OpFoldResult ofr) {
     if (auto val = llvm::dyn_cast_if_present<Value>(ofr))
       return val;
-    return rewriter
-        .create<arith::ConstantIndexOp>(
-            padOp.getLoc(), cast<IntegerAttr>(cast<Attribute>(ofr)).getInt())
+    return arith::ConstantIndexOp::create(
+               rewriter, padOp.getLoc(),
+               cast<IntegerAttr>(cast<Attribute>(ofr)).getInt())
         .getResult();
   };
 

mlir/lib/Dialect/Linalg/Transforms/TransposeConv2D.cpp

@@ -70,9 +70,8 @@ FailureOr<Operation *> transposeConv2DHelper(RewriterBase &rewriter,
     input = tensor::EmptyOp::create(rewriter, loc, newFilterShape, elementTy)
                 .getResult();
   } else {
-    input = rewriter
-                .create<memref::AllocOp>(
-                    loc, MemRefType::get(newFilterShape, elementTy))
+    input = memref::AllocOp::create(rewriter, loc,
+                                    MemRefType::get(newFilterShape, elementTy))
                 .getResult();
   }
 

mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp

@@ -3714,8 +3714,8 @@ struct Conv1DGenerator
     }
     }
 
-    return rewriter
-        .create<vector::TransferWriteOp>(loc, res, resShaped, resPadding)
+    return vector::TransferWriteOp::create(rewriter, loc, res, resShaped,
+                                           resPadding)
         .getOperation();
   }
 

mlir/lib/Dialect/Linalg/Transforms/WinogradConv2D.cpp

@@ -398,10 +398,9 @@ Value filterTransform(RewriterBase &rewriter, Location loc, Value filter,
 
       retRows = GMatrix.rows;
       auto matmulType = RankedTensorType::get({retRows, filterW}, elementType);
-      auto empty =
-          builder
-              .create<tensor::EmptyOp>(loc, matmulType.getShape(), elementType)
-              .getResult();
+      auto empty = tensor::EmptyOp::create(builder, loc, matmulType.getShape(),
+                                           elementType)
+                       .getResult();
       auto init =
           linalg::FillOp::create(builder, loc, zero, empty).getResult(0);
 
@@ -422,10 +421,9 @@ Value filterTransform(RewriterBase &rewriter, Location loc, Value filter,
 
       auto matmulType =
           RankedTensorType::get({retRows, GTMatrix.cols}, elementType);
-      auto empty =
-          builder
-              .create<tensor::EmptyOp>(loc, matmulType.getShape(), elementType)
-              .getResult();
+      auto empty = tensor::EmptyOp::create(builder, loc, matmulType.getShape(),
+                                           elementType)
+                       .getResult();
       auto init =
           linalg::FillOp::create(builder, loc, zero, empty).getResult(0);
 
@@ -547,10 +545,9 @@ Value inputTransform(RewriterBase &rewriter, Location loc, Value input,
 
       retRows = BTMatrix.rows;
       auto matmulType = RankedTensorType::get({retRows, alphaW}, elementType);
-      auto empty =
-          builder
-              .create<tensor::EmptyOp>(loc, matmulType.getShape(), elementType)
-              .getResult();
+      auto empty = tensor::EmptyOp::create(builder, loc, matmulType.getShape(),
+                                           elementType)
+                       .getResult();
       auto init =
           linalg::FillOp::create(builder, loc, zero, empty).getResult(0);
 
@@ -572,10 +569,9 @@ Value inputTransform(RewriterBase &rewriter, Location loc, Value input,
 
       retCols = BMatrix.cols;
       auto matmulType = RankedTensorType::get({retRows, retCols}, elementType);
-      auto empty =
-          builder
-              .create<tensor::EmptyOp>(loc, matmulType.getShape(), elementType)
-              .getResult();
+      auto empty = tensor::EmptyOp::create(builder, loc, matmulType.getShape(),
+                                           elementType)
+                       .getResult();
       auto init =
           linalg::FillOp::create(builder, loc, zero, empty).getResult(0);
       Value B =
@@ -661,9 +657,8 @@ static Value matrixMultiply(RewriterBase &rewriter, Location loc,
       {inputShape[0] * inputShape[1],
        inputShape[2] * inputShape[3] * inputShape[4], filterShape[3]},
       outputElementType);
-  Value empty = rewriter
-                    .create<tensor::EmptyOp>(loc, matmulType.getShape(),
-                                             outputElementType)
+  Value empty = tensor::EmptyOp::create(rewriter, loc, matmulType.getShape(),
+                                        outputElementType)
                     .getResult();
   Value zero = arith::ConstantOp::create(
       rewriter, loc, rewriter.getZeroAttr(outputElementType));
@@ -782,9 +777,8 @@ Value outputTransform(RewriterBase &rewriter, Location loc, Value value,
       auto matmulType = RankedTensorType::get({retRows, valueW}, elementType);
       Value init = outInitVal;
       if (rightTransform || scalarFactor != 1) {
-        auto empty = builder
-                         .create<tensor::EmptyOp>(loc, matmulType.getShape(),
-                                                  elementType)
+        auto empty = tensor::EmptyOp::create(builder, loc,
+                                             matmulType.getShape(), elementType)
                          .getResult();
         init = linalg::FillOp::create(builder, loc, zero, empty).getResult(0);
       }
@@ -802,9 +796,8 @@ Value outputTransform(RewriterBase &rewriter, Location loc, Value value,
           RankedTensorType::get({retRows, AMatrix.cols}, elementType);
       Value init = outInitVal;
       if (scalarFactor != 1) {
-        auto empty = builder
-                         .create<tensor::EmptyOp>(loc, matmulType.getShape(),
-                                                  elementType)
+        auto empty = tensor::EmptyOp::create(builder, loc,
+                                             matmulType.getShape(), elementType)
                          .getResult();
         init = linalg::FillOp::create(builder, loc, zero, empty).getResult(0);
       }
@@ -827,23 +820,21 @@ Value outputTransform(RewriterBase &rewriter, Location loc, Value value,
           AffineMap::get(2, 0, context), identityAffineMap, identityAffineMap};
 
       matmulRetValue =
-          rewriter
-              .create<linalg::GenericOp>(
-                  loc, matmulType,
-                  ValueRange{scalarFactorValue, matmulRetValue},
-                  ValueRange{outInitVal}, affineMaps,
-                  llvm::ArrayRef<utils::IteratorType>{
-                      utils::IteratorType::parallel,
-                      utils::IteratorType::parallel},
-                  [&](OpBuilder &nestedBuilder, Location nestedLoc,
-                      ValueRange args) {
-                    auto mulf = arith::MulFOp::create(nestedBuilder, nestedLoc,
-                                                      args[0], args[1]);
-                    auto addf = arith::AddFOp::create(
-                        nestedBuilder, nestedLoc, mulf.getResult(), args[2]);
-                    linalg::YieldOp::create(nestedBuilder, nestedLoc,
-                                            addf.getResult());
-                  })
+          linalg::GenericOp::create(
+              rewriter, loc, matmulType,
+              ValueRange{scalarFactorValue, matmulRetValue},
+              ValueRange{outInitVal}, affineMaps,
+              llvm::ArrayRef<utils::IteratorType>{
+                  utils::IteratorType::parallel, utils::IteratorType::parallel},
+              [&](OpBuilder &nestedBuilder, Location nestedLoc,
+                  ValueRange args) {
+                auto mulf = arith::MulFOp::create(nestedBuilder, nestedLoc,
+                                                  args[0], args[1]);
+                auto addf = arith::AddFOp::create(nestedBuilder, nestedLoc,
+                                                  mulf.getResult(), args[2]);
+                linalg::YieldOp::create(nestedBuilder, nestedLoc,
+                                        addf.getResult());
+              })
               .getResult(0);
     }
 

mlir/lib/Dialect/Vector/IR/VectorOps.cpp

@@ -372,9 +372,8 @@ SmallVector<Value> vector::getAsValues(OpBuilder &builder, Location loc,
   llvm::transform(foldResults, std::back_inserter(values),
                   [&](OpFoldResult foldResult) {
                     if (auto attr = dyn_cast<Attribute>(foldResult))
-                      return builder
-                          .create<arith::ConstantIndexOp>(
-                              loc, cast<IntegerAttr>(attr).getInt())
+                      return arith::ConstantIndexOp::create(
+                                 builder, loc, cast<IntegerAttr>(attr).getInt())
                           .getResult();
 
                     return cast<Value>(foldResult);

mlir/lib/Dialect/Vector/Transforms/LowerVectorGather.cpp

@@ -248,11 +248,10 @@ struct Gather1DToConditionalLoads : OpRewritePattern<vector::GatherOp> {
         scf::YieldOp::create(b, loc, result);
       };
 
-      result =
-          rewriter
-              .create<scf::IfOp>(loc, condition, /*thenBuilder=*/loadBuilder,
+      result = scf::IfOp::create(rewriter, loc, condition,
+                                 /*thenBuilder=*/loadBuilder,
                                  /*elseBuilder=*/passThruBuilder)
-              .getResult(0);
+                   .getResult(0);
     }
 
     rewriter.replaceOp(op, result);

mlir/lib/Dialect/Vector/Transforms/LowerVectorTransfer.cpp

@@ -142,8 +142,8 @@ struct TransferReadPermutationLowering
 
     // Transpose result of transfer_read.
     SmallVector<int64_t> transposePerm(permutation.begin(), permutation.end());
-    return rewriter
-        .create<vector::TransposeOp>(op.getLoc(), newRead, transposePerm)
+    return vector::TransposeOp::create(rewriter, op.getLoc(), newRead,
+                                       transposePerm)
         .getResult();
   }
 };
@@ -371,8 +371,8 @@ struct TransferOpReduceRank
         rewriter, op.getLoc(), newReadType, op.getBase(), op.getIndices(),
         AffineMapAttr::get(newMap), op.getPadding(), op.getMask(),
         newInBoundsAttr);
-    return rewriter
-        .create<vector::BroadcastOp>(op.getLoc(), originalVecType, newRead)
+    return vector::BroadcastOp::create(rewriter, op.getLoc(), originalVecType,
+                                       newRead)
         .getVector();
   }
 };

mlir/lib/Dialect/Vector/Transforms/VectorDistribute.cpp

@@ -451,10 +451,9 @@ struct WarpOpTransferWrite : public WarpDistributionPattern {
     }
     SmallVector<Value> delinearized;
     if (map.getNumResults() > 1) {
-      delinearized = rewriter
-                         .create<mlir::affine::AffineDelinearizeIndexOp>(
-                             newWarpOp.getLoc(), newWarpOp.getLaneid(),
-                             delinearizedIdSizes)
+      delinearized = mlir::affine::AffineDelinearizeIndexOp::create(
+                         rewriter, newWarpOp.getLoc(), newWarpOp.getLaneid(),
+                         delinearizedIdSizes)
                          .getResults();
     } else {
       // If there is only one map result, we can elide the delinearization
@@ -1538,19 +1537,18 @@ struct WarpOpInsertScalar : public WarpDistributionPattern {
         arith::CmpIOp::create(rewriter, loc, arith::CmpIPredicate::eq,
                               newWarpOp.getLaneid(), insertingLane);
     Value newResult =
-        rewriter
-            .create<scf::IfOp>(
-                loc, isInsertingLane,
-                /*thenBuilder=*/
-                [&](OpBuilder &builder, Location loc) {
-                  Value newInsert = vector::InsertOp::create(
-                      builder, loc, newSource, distributedVec, newPos);
-                  scf::YieldOp::create(builder, loc, newInsert);
-                },
-                /*elseBuilder=*/
-                [&](OpBuilder &builder, Location loc) {
-                  scf::YieldOp::create(builder, loc, distributedVec);
-                })
+        scf::IfOp::create(
+            rewriter, loc, isInsertingLane,
+            /*thenBuilder=*/
+            [&](OpBuilder &builder, Location loc) {
+              Value newInsert = vector::InsertOp::create(
+                  builder, loc, newSource, distributedVec, newPos);
+              scf::YieldOp::create(builder, loc, newInsert);
+            },
+            /*elseBuilder=*/
+            [&](OpBuilder &builder, Location loc) {
+              scf::YieldOp::create(builder, loc, distributedVec);
+            })
             .getResult(0);
     rewriter.replaceAllUsesWith(newWarpOp->getResult(operandNumber), newResult);
     return success();
@@ -1661,10 +1659,9 @@ struct WarpOpInsert : public WarpDistributionPattern {
       auto nonInsertingBuilder = [&](OpBuilder &builder, Location loc) {
         scf::YieldOp::create(builder, loc, distributedDest);
       };
-      newResult = rewriter
-                      .create<scf::IfOp>(loc, isInsertingLane,
-                                         /*thenBuilder=*/insertingBuilder,
-                                         /*elseBuilder=*/nonInsertingBuilder)
+      newResult = scf::IfOp::create(rewriter, loc, isInsertingLane,
+                                    /*thenBuilder=*/insertingBuilder,
+                                    /*elseBuilder=*/nonInsertingBuilder)
                       .getResult(0);
     }