Linalg/IR/LinalgOps.cpp

//===- LinalgOps.cpp - Implementation of the linalg operations ------------===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//===----------------------------------------------------------------------===//
//
// This file implements the Linalg operations.
//
//===----------------------------------------------------------------------===//

#include "mlir/Dialect/Linalg/IR/Linalg.h"

#include "mlir/Dialect/Arithmetic/Utils/Utils.h"
#include "mlir/Dialect/SCF/SCF.h"
#include "mlir/Dialect/SparseTensor/IR/SparseTensor.h"
#include "mlir/Dialect/Utils/ReshapeOpsUtils.h"
#include "mlir/Dialect/Utils/StaticValueUtils.h"
#include "mlir/IR/AffineExprVisitor.h"
#include "mlir/IR/Matchers.h"
#include "mlir/IR/OpImplementation.h"
#include "mlir/IR/PatternMatch.h"
#include "mlir/Interfaces/InferTypeOpInterface.h"
#include "mlir/Parser/Parser.h"

#include "llvm/ADT/DenseMap.h"
#include "llvm/ADT/SetVector.h"
#include "llvm/ADT/SmallSet.h"
#include "llvm/ADT/StringSet.h"
#include "llvm/ADT/TypeSwitch.h"
#include "llvm/Support/FormatVariadic.h"
#include "llvm/Support/MathExtras.h"
#include "llvm/Support/raw_ostream.h"

using namespace mlir;
using namespace mlir::linalg;

//===----------------------------------------------------------------------===//
// Support for named Linalg ops defined in ods-gen.
//===----------------------------------------------------------------------===//

using RegionBuilderFn = llvm::function_ref<void(ImplicitLocOpBuilder &, Block &,
                                                ArrayRef<NamedAttribute>)>;

/// Fills the region of a structured operation using the provided
/// `regionBuilder`. The method is used by both named structured ops created by
/// ods-gen and by manually defined C++ ops. It is called by both builders and
/// parsers and creates a block with arguments corresponding to the elemental
/// types of `inputTypes` and `outputTypes`. All output types are asserted to be
/// ShapedType.
static void fillStructuredOpRegion(OpBuilder &opBuilder, Region &region,
                                   TypeRange inputTypes, TypeRange outputTypes,
                                   ArrayRef<NamedAttribute> attrs,
                                   RegionBuilderFn regionBuilder) {
  assert(llvm::all_of(outputTypes, [](Type t) { return t.isa<ShapedType>(); }));

  // TODO: atm all operands go through getElementTypeOrSelf,
  // reconsider when we have evidence we need to.
  SmallVector<Type, 8> argTypes;
  SmallVector<Location, 8> argLocs;
  for (auto containers : {inputTypes, outputTypes}) {
    for (auto t : containers) {
      argTypes.push_back(getElementTypeOrSelf(t));

      // TODO: Pass in a proper location here.
      argLocs.push_back(opBuilder.getUnknownLoc());
    }
  }

  // RAII.
  OpBuilder::InsertionGuard guard(opBuilder);
  Block *body =
      opBuilder.createBlock(&region, /*insertPt=*/{}, argTypes, argLocs);

  opBuilder.setInsertionPointToStart(body);
  ImplicitLocOpBuilder b(opBuilder.getUnknownLoc(), opBuilder);
  regionBuilder(b, *body, attrs);

  // indexing_maps is an auto-generated method.

  // iterator_types is an auto-generated method.
}

/// Creates a structured operation given `inputs`, `outputs`, and `attributes`.
/// The result types are derived automatically if `resultTensorTypes` is none.
/// The body of the operation is filled using `regionBuilder`. All ods-gen
/// created structured operations use the method to implement their builders.
static void buildStructuredOp(OpBuilder &b, OperationState &state,
                              llvm::Optional<TypeRange> resultTensorTypes,
                              ValueRange inputs, ValueRange outputs,
                              ArrayRef<NamedAttribute> attributes,
                              RegionBuilderFn regionBuilder) {
  // Derive the result types if needed.
  SmallVector<Type> derivedResultTypes =
      resultTensorTypes.getValueOr(TypeRange());
  if (!resultTensorTypes.hasValue())
    copy_if(outputs.getTypes(), std::back_inserter(derivedResultTypes),
            [](Type type) { return type.isa<RankedTensorType>(); });

  state.addOperands(inputs);
  state.addOperands(outputs);
  state.addTypes(derivedResultTypes);
  state.addAttributes(attributes);
  state.addAttribute(
      "operand_segment_sizes",
      b.getI32VectorAttr({static_cast<int32_t>(inputs.size()),
                          static_cast<int32_t>(outputs.size())}));

  // Create and fill the region of the structured operation.
  Region &region = *state.addRegion();
  fillStructuredOpRegion(b, region, TypeRange(inputs), TypeRange(outputs),
                         state.attributes.getAttrs(), regionBuilder);
}

/// Common parsing used for both named structured ops created by ods-gen and by
/// manually defined C++ ops. Does not handle regions.
static ParseResult
parseCommonStructuredOpParts(OpAsmParser &parser, OperationState &result,
                             SmallVectorImpl<Type> &inputTypes,
                             SmallVectorImpl<Type> &outputTypes) {
  SMLoc inputsOperandsLoc, outputsOperandsLoc;
  SmallVector<OpAsmParser::UnresolvedOperand, 4> inputsOperands,
      outputsOperands;

  parser.parseOptionalAttrDict(result.attributes);

  if (succeeded(parser.parseOptionalKeyword("ins"))) {
    if (parser.parseLParen())
      return failure();

    inputsOperandsLoc = parser.getCurrentLocation();
    if (parser.parseOperandList(inputsOperands) ||
        parser.parseColonTypeList(inputTypes) || parser.parseRParen())
      return failure();
  }

  if (succeeded(parser.parseOptionalKeyword("outs"))) {
    outputsOperandsLoc = parser.getCurrentLocation();
    if (parser.parseLParen() || parser.parseOperandList(outputsOperands) ||
        parser.parseColonTypeList(outputTypes) || parser.parseRParen())
      return failure();
  }

  if (parser.resolveOperands(inputsOperands, inputTypes, inputsOperandsLoc,
                             result.operands) ||
      parser.resolveOperands(outputsOperands, outputTypes, outputsOperandsLoc,
                             result.operands))
    return failure();

  result.addAttribute("operand_segment_sizes",
                      parser.getBuilder().getI32VectorAttr(
                          {static_cast<int32_t>(inputsOperands.size()),
                           static_cast<int32_t>(outputsOperands.size())}));
  return success();
}

static void printCommonStructuredOpParts(OpAsmPrinter &p, ValueRange inputs,
                                         ValueRange outputs) {
  if (!inputs.empty())
    p << " ins(" << inputs << " : " << inputs.getTypes() << ")";
  if (!outputs.empty())
    p << " outs(" << outputs << " : " << outputs.getTypes() << ")";
}

//===----------------------------------------------------------------------===//
// Specific parsing and printing for named structured ops created by ods-gen.
//===----------------------------------------------------------------------===//

static ParseResult parseNamedStructuredOpRegion(
    OpAsmParser &parser, Region &region, unsigned numRegionArgs,
    TypeRange inputTypes, TypeRange outputTypes, ArrayRef<NamedAttribute> attrs,
    RegionBuilderFn regionBuilder) {
  if (numRegionArgs != inputTypes.size() + outputTypes.size()) {
    return parser.emitError(
        parser.getCurrentLocation(),
        llvm::formatv("[parseNamedStructuredOpRegion] ods-gen generated "
                      "region expects {0} args, got {1}",
                      numRegionArgs, inputTypes.size() + outputTypes.size()));
  }

  OpBuilder opBuilder(parser.getContext());
  fillStructuredOpRegion(opBuilder, region, inputTypes, outputTypes, attrs,
                         regionBuilder);
  return success();
}

static ParseResult
parseNamedStructuredOpResults(OpAsmParser &parser,
                              SmallVectorImpl<Type> &resultTypes) {
  if (parser.parseOptionalArrowTypeList(resultTypes))
    return failure();
  return success();
}

static ParseResult parseNamedStructuredOp(OpAsmParser &parser,
                                          OperationState &result,
                                          unsigned numRegionArgs,
                                          RegionBuilderFn regionBuilder) {
  // TODO: Enable when ods-gen supports captures.
  SmallVector<Type, 1> inputTypes, outputTypes;
  if (parseCommonStructuredOpParts(parser, result, inputTypes, outputTypes))
    return failure();

  // TODO: consider merging results parsing into region parsing.
  // Need to wait for declarative assembly resolution to decide.
  SmallVector<Type, 1> outputTensorsTypes;
  if (parseNamedStructuredOpResults(parser, outputTensorsTypes))
    return failure();
  result.addTypes(outputTensorsTypes);

  std::unique_ptr<Region> region = std::make_unique<Region>();
  if (parseNamedStructuredOpRegion(parser, *region, numRegionArgs, inputTypes,
                                   outputTypes, result.attributes.getAttrs(),
                                   regionBuilder))
    return failure();
  result.addRegion(std::move(region));

  return success();
}

static void printNamedStructuredOpResults(OpAsmPrinter &p,
                                          TypeRange resultTypes) {
  if (resultTypes.empty())
    return;
  p.printOptionalArrowTypeList(resultTypes);
}

static void printNamedStructuredOp(OpAsmPrinter &p, Operation *op,
                                   ValueRange inputs, ValueRange outputs) {
  p.printOptionalAttrDict(
      op->getAttrs(),
      /*elidedAttrs=*/{"operand_segment_sizes",
                       // See generated code in mlir-linalg-yaml-gen.cpp
                       "linalg.memoized_indexing_maps"});

  // Printing is shared with generic ops, except for the region and
  // attributes.
  printCommonStructuredOpParts(p, inputs, outputs);

  // Results printing.
  printNamedStructuredOpResults(p, op->getResultTypes());

  // Region is elided.
}

/// This is a common class used for patterns of the form
/// ```
///    someop(memrefcast(%src)) -> someop(%src)
/// ```
/// It folds the source of the memref.cast into the root operation directly.
static LogicalResult foldMemRefCast(Operation *op) {
  bool folded = false;
  for (OpOperand &operand : op->getOpOperands()) {
    auto castOp = operand.get().getDefiningOp<memref::CastOp>();
    if (castOp && memref::CastOp::canFoldIntoConsumerOp(castOp)) {
      operand.set(castOp.getOperand());
      folded = true;
    }
  }
  return success(folded);
}

/// Helper function to find if there is atleast one dimension in an AffineMap
/// testMap that is contained in `testMapLocation` of  `maps` but not in any
/// other locations
static bool hasaUniqueDim(ArrayRef<AffineMap> maps, unsigned testMapLocation) {
  AffineMap testMap = maps[testMapLocation];
  llvm::SmallDenseSet<unsigned> dimsToCheck;
  for (auto result : testMap.getResults()) {
    auto expr = result.dyn_cast<AffineDimExpr>();
    if (expr != nullptr)
      dimsToCheck.insert(expr.getPosition());
  }
  for (const auto &it : llvm::enumerate(maps)) {
    if (it.index() == testMapLocation)
      continue;
    auto map = it.value();
    for (auto result : map.getResults()) {
      auto expr = result.dyn_cast<AffineDimExpr>();
      if (expr != nullptr) {
        dimsToCheck.erase(expr.getPosition());
      }
      if (dimsToCheck.empty())
        return false;
    }
  }
  return true;
}

//===----------------------------------------------------------------------===//
// Region builder helper.
// TODO: Move this to a utility library.
// The public methods on this class are referenced directly from generated code.
// Helper build the unary, binary, and type conversion functions defined by the
// DSL. See mlir-linalg-ods-yaml-gen.cpp for the code that uses this class.
//
// Implementations of the math functions must be polymorphic over numeric types,
// internally performing necessary casts. If the function application makes no
// sense, then the only recourse is to assert and return nullptr. This can be
// extended later if it becomes possible to fail construction of the region. The
// invariant should be enforced at a higher level.
//
// TODO: These helpers are currently type polymorphic over the class of integer
// and floating point types, but they will not internally cast within bit
// widths of a class (mixed precision such as i8->i32) or across classes
// (i.e. mixed float and integer). Many such combinations are ambiguous or need
// to be handled with care and work is being considered to extend the op
// language to make such cases explicit. In the mean-time, violating this will
// fail verification, which is deemed acceptable.
//===----------------------------------------------------------------------===//

namespace {

class RegionBuilderHelper {
public:
  RegionBuilderHelper(MLIRContext *context, Block &block)
      : context(context), block(block) {}

  // Build the unary functions defined by OpDSL.
  Value buildUnaryFn(UnaryFn unaryFn, Value arg) {
    if (!isFloatingPoint(arg))
      llvm_unreachable("unsupported non numeric type");
    OpBuilder builder = getBuilder();
    switch (unaryFn) {
    case UnaryFn::exp:
      return builder.create<math::ExpOp>(arg.getLoc(), arg);
    case UnaryFn::log:
      return builder.create<math::LogOp>(arg.getLoc(), arg);
    case UnaryFn::abs:
      return builder.create<math::AbsOp>(arg.getLoc(), arg);
    case UnaryFn::ceil:
      return builder.create<math::CeilOp>(arg.getLoc(), arg);
    case UnaryFn::floor:
      return builder.create<math::FloorOp>(arg.getLoc(), arg);
    case UnaryFn::negf:
      return builder.create<arith::NegFOp>(arg.getLoc(), arg);
    }
    llvm_unreachable("unsupported unary function");
  }

  // Build the binary functions defined by OpDSL.
  Value buildBinaryFn(BinaryFn binaryFn, Value arg0, Value arg1) {
    bool allFloatingPoint = isFloatingPoint(arg0) && isFloatingPoint(arg1);
    bool allInteger = isInteger(arg0) && isInteger(arg1);
    if (!allFloatingPoint && !allInteger)
      llvm_unreachable("unsupported non numeric type");
    OpBuilder builder = getBuilder();
    switch (binaryFn) {
    case BinaryFn::add:
      if (allFloatingPoint)
        return builder.create<arith::AddFOp>(arg0.getLoc(), arg0, arg1);
      return builder.create<arith::AddIOp>(arg0.getLoc(), arg0, arg1);
    case BinaryFn::sub:
      if (allFloatingPoint)
        return builder.create<arith::SubFOp>(arg0.getLoc(), arg0, arg1);
      return builder.create<arith::SubIOp>(arg0.getLoc(), arg0, arg1);
    case BinaryFn::mul:
      if (allFloatingPoint)
        return builder.create<arith::MulFOp>(arg0.getLoc(), arg0, arg1);
      return builder.create<arith::MulIOp>(arg0.getLoc(), arg0, arg1);
    case BinaryFn::max_signed:
      if (allFloatingPoint)
        return builder.create<arith::MaxFOp>(arg0.getLoc(), arg0, arg1);
      return builder.create<arith::MaxSIOp>(arg0.getLoc(), arg0, arg1);
    case BinaryFn::min_signed:
      if (allFloatingPoint)
        return builder.create<arith::MinFOp>(arg0.getLoc(), arg0, arg1);
      return builder.create<arith::MinSIOp>(arg0.getLoc(), arg0, arg1);
    case BinaryFn::max_unsigned:
      if (allFloatingPoint)
        return builder.create<arith::MaxFOp>(arg0.getLoc(), arg0, arg1);
      return builder.create<arith::MaxUIOp>(arg0.getLoc(), arg0, arg1);
    case BinaryFn::min_unsigned:
      if (allFloatingPoint)
        return builder.create<arith::MinFOp>(arg0.getLoc(), arg0, arg1);
      return builder.create<arith::MinUIOp>(arg0.getLoc(), arg0, arg1);
    }
    llvm_unreachable("unsupported binary function");
  }

  // Build the type functions defined by OpDSL.
  Value buildTypeFn(TypeFn typeFn, Type toType, Value operand) {
    switch (typeFn) {
    case TypeFn::cast_signed:
      return cast(toType, operand, false);
    case TypeFn::cast_unsigned:
      return cast(toType, operand, true);
    }
    llvm_unreachable("unsupported type conversion function");
  }

  void yieldOutputs(ValueRange values) {
    OpBuilder builder = getBuilder();
    Location loc = builder.getUnknownLoc();
    builder.create<YieldOp>(loc, values);
  }

  Value constant(const std::string &value) {
    OpBuilder builder = getBuilder();
    Location loc = builder.getUnknownLoc();
    Attribute valueAttr = parseAttribute(value, builder.getContext());
    return builder.create<arith::ConstantOp>(loc, valueAttr.getType(),
                                             valueAttr);
  }

  Value index(int64_t dim) {
    OpBuilder builder = getBuilder();
    return builder.create<IndexOp>(builder.getUnknownLoc(), dim);
  }

  Type getIntegerType(unsigned width) {
    return IntegerType::get(context, width);
  }

  Type getFloat32Type() { return Float32Type::get(context); }
  Type getFloat64Type() { return Float64Type::get(context); }

private:
  // Generates operations to cast the given operand to a specified type.
  // If the cast cannot be performed, a warning will be issued and the
  // operand returned as-is (which will presumably yield a verification
  // issue downstream).
  Value cast(Type toType, Value operand, bool isUnsignedCast) {
    OpBuilder builder = getBuilder();
    auto loc = operand.getLoc();

    if (operand.getType() == toType)
      return operand;
    if (auto toIntType = toType.dyn_cast<IntegerType>()) {
      // If operand is floating point, cast directly to the int type.
      if (operand.getType().isa<FloatType>()) {
        if (isUnsignedCast)
          return builder.create<arith::FPToUIOp>(loc, toType, operand);
        return builder.create<arith::FPToSIOp>(loc, toType, operand);
      }
      // Cast index operands directly to the int type.
      if (operand.getType().isIndex())
        return builder.create<arith::IndexCastOp>(loc, toType, operand);
      if (auto fromIntType = operand.getType().dyn_cast<IntegerType>()) {
        // Either extend or truncate.
        if (toIntType.getWidth() > fromIntType.getWidth()) {
          if (isUnsignedCast)
            return builder.create<arith::ExtUIOp>(loc, toType, operand);
          return builder.create<arith::ExtSIOp>(loc, toType, operand);
        }
        if (toIntType.getWidth() < fromIntType.getWidth())
          return builder.create<arith::TruncIOp>(loc, toType, operand);
      }
    } else if (auto toFloatType = toType.dyn_cast<FloatType>()) {
      // If operand is integer, cast directly to the float type.
      // Note that it is unclear how to cast from BF16<->FP16.
      if (operand.getType().isa<IntegerType>()) {
        if (isUnsignedCast)
          return builder.create<arith::UIToFPOp>(loc, toFloatType, operand);
        return builder.create<arith::SIToFPOp>(loc, toFloatType, operand);
      }
      if (auto fromFloatType = operand.getType().dyn_cast<FloatType>()) {
        if (toFloatType.getWidth() > fromFloatType.getWidth())
          return builder.create<arith::ExtFOp>(loc, toFloatType, operand);
        if (toFloatType.getWidth() < fromFloatType.getWidth())
          return builder.create<arith::TruncFOp>(loc, toFloatType, operand);
      }
    }

    emitWarning(operand.getLoc()) << "could not cast operand of type "
                                  << operand.getType() << " to " << toType;
    return operand;
  }

  bool isFloatingPoint(Value value) { return value.getType().isa<FloatType>(); }
  bool isInteger(Value value) { return value.getType().isa<IntegerType>(); }

  OpBuilder getBuilder() {
    OpBuilder builder(context);
    builder.setInsertionPointToEnd(&block);
    return builder;
  }

  MLIRContext *context;
  Block &block;
};

} // namespace

//===----------------------------------------------------------------------===//
// FillOp
//===----------------------------------------------------------------------===//

namespace {

/// Fold linalg.fill -> tensor.expand/collapse_shape chain.
///
/// For such op chains, we can create new linalg.fill ops with the result
/// type of the tensor.expand/collapse_shape op.
template <typename TensorReshapeOp>
struct FoldFillWithTensorReshape : OpRewritePattern<TensorReshapeOp> {
  using OpRewritePattern<TensorReshapeOp>::OpRewritePattern;
  LogicalResult matchAndRewrite(TensorReshapeOp reshapeOp,
                                PatternRewriter &rewriter) const override {
    auto oldFill = reshapeOp.src().template getDefiningOp<FillOp>();
    if (!oldFill)
      return failure();

    Location loc = oldFill.getLoc();
    auto newInit = rewriter.create<TensorReshapeOp>(
        loc, reshapeOp.getResultType(), oldFill.output(),
        reshapeOp.reassociation());
    rewriter.replaceOpWithNewOp<FillOp>(reshapeOp, ValueRange{oldFill.value()},
                                        ValueRange{newInit});

    return success();
  }
};

/// Fold tensor.pad(linalg.fill) into linalg.fill if the padding value and the
/// filling value are the same.
struct FoldFillWithPad final : public OpRewritePattern<tensor::PadOp> {
  using OpRewritePattern::OpRewritePattern;

  LogicalResult matchAndRewrite(tensor::PadOp padOp,
                                PatternRewriter &rewriter) const override {
    auto fillOp = padOp.source().getDefiningOp<linalg::FillOp>();
    if (!fillOp)
      return failure();

    // We can only fold if the padding value is the same as the original
    // filling value.
    Value padValue = padOp.getConstantPaddingValue();
    if (!padValue || fillOp.value() != padValue)
      return failure();

    ReifiedRankedShapedTypeDims reifiedShape;
    ReifyRankedShapedTypeOpInterface interface =
        cast<ReifyRankedShapedTypeOpInterface>(padOp.getOperation());
    if (failed(interface.reifyResultShapes(rewriter, reifiedShape)))
      return rewriter.notifyMatchFailure(
          padOp, "failed to reify tensor.pad op result shape");

    auto oldResultType = padOp.getResultType();
    SmallVector<int64_t, 4> staticShape(oldResultType.getRank(),
                                        ShapedType::kDynamicSize);
    auto newInitOp = rewriter.create<InitTensorOp>(
        padOp.getLoc(), reifiedShape.front(), staticShape,
        oldResultType.getElementType());
    auto newFillOp = rewriter.create<FillOp>(
        fillOp.getLoc(), ValueRange{padValue}, ValueRange{newInitOp});
    rewriter.replaceOpWithNewOp<tensor::CastOp>(padOp, oldResultType,
                                                newFillOp.result());

    return success();
  }
};

/// Fold tensor.insert_slice(tensor.pad(<input>), linalg.fill) into
/// tensor.insert_slice(<input>, linalg.fill) if the padding value and the
/// filling value are the same.
struct FoldInsertPadIntoFill : public OpRewritePattern<tensor::InsertSliceOp> {
  using OpRewritePattern::OpRewritePattern;

  LogicalResult matchAndRewrite(tensor::InsertSliceOp insertOp,
                                PatternRewriter &rewriter) const override {
    auto srcPadOp = insertOp.source().getDefiningOp<tensor::PadOp>();
    if (!srcPadOp)
      return failure();

    if (insertOp.getType().getRank() != insertOp.getSourceType().getRank())
      return failure();

    // Walk back the tensor.insert_slice chain and find the first destination
    // value at the start of the chain.
    Value firstDest = insertOp.dest();
    while (auto prevOp = firstDest.getDefiningOp<tensor::InsertSliceOp>()) {
      if (prevOp.getType().getRank() != prevOp.getSourceType().getRank())
        return failure();

      // Make sure the range of values accessed are disjoint. Without this, we
      // cannot fold tensor.pad away.
      bool disjoint = false;
      for (int i = 0, e = prevOp.getType().getRank(); i < e; ++i) {
        // If the dimension has dynamic offset/size, we cannot guarantee
        // disjoint. So just skip it.
        if (insertOp.isDynamicOffset(i) || insertOp.isDynamicSize(i) ||
            insertOp.isDynamicStride(i) || prevOp.isDynamicOffset(i) ||
            prevOp.isDynamicSize(i) || prevOp.isDynamicStride(i))
          continue;

        // Get the range start and end, inclusively for both.
        int64_t prevStart = prevOp.getStaticOffset(i);
        int64_t prevEnd = prevStart + (prevOp.getStaticSize(i) - 1) *
                                          prevOp.getStaticStride(i);
        int64_t nextStart = insertOp.getStaticOffset(i);
        int64_t nextEnd = nextStart + (insertOp.getStaticSize(i) - 1) *
                                          insertOp.getStaticStride(i);
        if (prevEnd < nextStart || nextEnd < prevStart) {
          disjoint = true;
          break;
        }
      }

      if (!disjoint)
        break;
      firstDest = prevOp.dest();
    }

    // Check whether the first destination is a fill op. For overlapped cases,
    // this also cannot be true.
    auto dstFillOp = firstDest.getDefiningOp<linalg::FillOp>();
    if (!dstFillOp)
      return failure();

    // We can only fold if the padding value is the same as the original
    // filling value.
    Value padValue = srcPadOp.getConstantPaddingValue();
    if (!padValue || dstFillOp.value() != padValue)
      return failure();

    SmallVector<OpFoldResult> lowPads = srcPadOp.getMixedLowPad();
    SmallVector<OpFoldResult> oldOffsets = insertOp.getMixedOffsets();

    Location loc = insertOp.getLoc();
    MLIRContext *context = getContext();

    AffineExpr sym0, sym1;
    bindSymbols(context, sym0, sym1);
    auto addMap = AffineMap::get(0, 2, {sym0 + sym1}, context);

    // Calculate the new offsets for the insert. It should be the old offsets
    // plus low padding sizes.
    SmallVector<OpFoldResult, 4> newOffsets;
    for (const auto &p : llvm::zip(lowPads, oldOffsets)) {
      Value padValue = getValueOrCreateConstantIndexOp(
          rewriter, srcPadOp.getLoc(), std::get<0>(p));
      Value offsetValue = getValueOrCreateConstantIndexOp(
          rewriter, insertOp.getLoc(), std::get<1>(p));
      newOffsets.push_back(
          applyMapToValues(rewriter, loc, addMap, {offsetValue, padValue})[0]);
    }

    SmallVector<OpFoldResult, 4> newSizes;
    for (int i = 0, e = srcPadOp.getSourceType().getRank(); i < e; ++i) {
      newSizes.push_back(
          rewriter.create<tensor::DimOp>(loc, srcPadOp.source(), i).result());
    }

    rewriter.replaceOpWithNewOp<tensor::InsertSliceOp>(
        insertOp, srcPadOp.source(), insertOp.dest(), newOffsets, newSizes,
        insertOp.getMixedStrides());
    return success();
  }
};

} // namespace

void FillOp::getCanonicalizationPatterns(RewritePatternSet &results,
                                         MLIRContext *context) {
  results
      .add<FoldFillWithPad, FoldFillWithTensorReshape<tensor::CollapseShapeOp>,
           FoldFillWithTensorReshape<tensor::ExpandShapeOp>,
           FoldInsertPadIntoFill>(context);
}

//===----------------------------------------------------------------------===//
// GenericOps
//===----------------------------------------------------------------------===//
void GenericOp::build(
    OpBuilder &builder, OperationState &result, TypeRange resultTensorTypes,
    ValueRange inputs, ValueRange outputs, ArrayRef<AffineMap> indexingMaps,
    ArrayRef<StringRef> iteratorTypes, StringRef doc, StringRef libraryCall,
    function_ref<void(OpBuilder &, Location, ValueRange)> bodyBuild,
    ArrayRef<NamedAttribute> attributes) {
  build(builder, result, resultTensorTypes, inputs, outputs,
        builder.getAffineMapArrayAttr(indexingMaps),
        builder.getStrArrayAttr(iteratorTypes),
        doc.empty() ? StringAttr() : builder.getStringAttr(doc),
        libraryCall.empty() ? StringAttr()
                            : builder.getStringAttr(libraryCall));
  result.addAttributes(attributes);
  if (!bodyBuild)
    return;

  SmallVector<Type, 4> blockArgTypes;
  SmallVector<Location, 4> blockArgLocs;
  for (ValueRange container : {inputs, outputs}) {
    for (Value v : container) {
      blockArgTypes.push_back(getElementTypeOrSelf(v));
      blockArgLocs.push_back(v.getLoc());
    }
  }

  OpBuilder::InsertionGuard guard(builder);
  auto &region = *result.regions.front();
  Block *bodyBlock =
      builder.createBlock(&region, region.end(), blockArgTypes, blockArgLocs);
  bodyBuild(builder, result.location, bodyBlock->getArguments());
}

void GenericOp::build(
    OpBuilder &builder, OperationState &result, ValueRange inputs,
    ValueRange outputs, ArrayRef<AffineMap> indexingMaps,
    ArrayRef<StringRef> iteratorTypes, StringRef doc, StringRef libraryCall,
    function_ref<void(OpBuilder &, Location, ValueRange)> bodyBuild,
    ArrayRef<NamedAttribute> attributes) {
  build(builder, result, TypeRange{}, inputs, outputs, indexingMaps,
        iteratorTypes, doc, libraryCall, bodyBuild, attributes);
}

void GenericOp::build(
    OpBuilder &builder, OperationState &result, ValueRange inputs,
    ValueRange outputs, ArrayRef<AffineMap> indexingMaps,
    ArrayRef<StringRef> iteratorTypes,
    function_ref<void(OpBuilder &, Location, ValueRange)> bodyBuild,
    ArrayRef<NamedAttribute> attributes) {
  build(builder, result, inputs, outputs, indexingMaps, iteratorTypes,
        /*doc=*/"",
        /*libraryCall=*/"", bodyBuild, attributes);
}

void GenericOp::build(
    OpBuilder &builder, OperationState &result, TypeRange resultTensorTypes,
    ValueRange inputs, ValueRange outputs, ArrayRef<AffineMap> indexingMaps,
    ArrayRef<StringRef> iteratorTypes,
    function_ref<void(OpBuilder &, Location, ValueRange)> bodyBuild,
    ArrayRef<NamedAttribute> attributes) {
  build(builder, result, resultTensorTypes, inputs, outputs, indexingMaps,
        iteratorTypes,
        /*doc=*/"",
        /*libraryCall=*/"", bodyBuild, attributes);
}

void GenericOp::print(OpAsmPrinter &p) {
  p << " ";

  // Print extra attributes.
  auto genericAttrNames = linalgTraitAttrNames();

  llvm::StringSet<> genericAttrNamesSet;
  genericAttrNamesSet.insert(genericAttrNames.begin(), genericAttrNames.end());
  SmallVector<NamedAttribute, 8> genericAttrs;
  for (auto attr : (*this)->getAttrs())
    if (genericAttrNamesSet.count(attr.getName().strref()) > 0)
      genericAttrs.push_back(attr);
  if (!genericAttrs.empty()) {
    auto genericDictAttr = DictionaryAttr::get(getContext(), genericAttrs);
    p << genericDictAttr;
  }

  // Printing is shared with named ops, except for the region and attributes
  printCommonStructuredOpParts(p, inputs(), outputs());

  genericAttrNames.push_back("operand_segment_sizes");
  genericAttrNamesSet.insert(genericAttrNames.back());

  bool hasExtraAttrs = false;
  for (NamedAttribute n : (*this)->getAttrs()) {
    if ((hasExtraAttrs = !genericAttrNamesSet.contains(n.getName().strref())))
      break;
  }
  if (hasExtraAttrs) {
    p << " attrs = ";
    p.printOptionalAttrDict((*this)->getAttrs(),
                            /*elidedAttrs=*/genericAttrNames);
  }

  // Print region.
  if (!region().empty()) {
    p << ' ';
    p.printRegion(region());
  }

  // Print results.
  printNamedStructuredOpResults(p, result_tensors().getTypes());
}

ParseResult GenericOp::parse(OpAsmParser &parser, OperationState &result) {
  DictionaryAttr dictAttr;
  // Parse the core linalg traits that must check into a dictAttr.
  // The name is unimportant as we will overwrite result.attributes.
  // The core linalg traits must contain the information necessary to pass the
  // verifier.
  if (parser.parseAttribute(dictAttr, "_", result.attributes))
    return failure();
  result.attributes.assign(dictAttr.getValue().begin(),
                           dictAttr.getValue().end());

  // Parsing is shared with named ops, except for the region.
  SmallVector<Type, 1> inputTypes, outputTypes;
  if (parseCommonStructuredOpParts(parser, result, inputTypes, outputTypes))
    return failure();

  // Optional attributes may be added.
  if (succeeded(parser.parseOptionalKeyword("attrs")))
    if (failed(parser.parseEqual()) ||
        failed(parser.parseOptionalAttrDict(result.attributes)))
      return failure();

  SmallVector<OpAsmParser::UnresolvedOperand, 8> regionOperands;
  std::unique_ptr<Region> region = std::make_unique<Region>();
  SmallVector<Type, 8> operandTypes, regionTypes;
  if (parser.parseRegion(*region, regionOperands, regionTypes))
    return failure();
  result.addRegion(std::move(region));

  // Generic ops may specify that a subset of its outputs are tensors. Such
  // outputs are specified in the result type.
  // TODO: may need to move output parsing before region parsing.
  // Need to wait for declarative assembly resolution to decide.
  SmallVector<Type, 1> outputTensorsTypes;
  if (parseNamedStructuredOpResults(parser, outputTensorsTypes))
    return failure();
  result.addTypes(outputTensorsTypes);

  return success();
}

static void getGenericEffectsImpl(
    SmallVectorImpl<SideEffects::EffectInstance<MemoryEffects::Effect>>
        &effects,
    ValueRange results, ValueRange inputBuffers, ValueRange outputs) {
  for (Value value : inputBuffers) {
    effects.emplace_back(MemoryEffects::Read::get(), value,
                         SideEffects::DefaultResource::get());
  }
  for (Value value : outputs) {
    effects.emplace_back(MemoryEffects::Read::get(), value,
                         SideEffects::DefaultResource::get());
    effects.emplace_back(MemoryEffects::Write::get(), value,
                         SideEffects::DefaultResource::get());
  }
}

void GenericOp::getEffects(
    SmallVectorImpl<SideEffects::EffectInstance<MemoryEffects::Effect>>
        &effects) {
  SmallVector<Value> inputBuffers = getInputBufferOperands();
  SmallVector<Value> outputBuffers = getOutputBufferOperands();
  getGenericEffectsImpl(effects, getOperation()->getResults(), inputBuffers,
                        outputBuffers);
}

LogicalResult GenericOp::verify() { return success(); }

namespace {
// Deduplicate redundant args of a linalg generic op.
// An arg is redundant if it has the same Value and indexing map as another.
struct DeduplicateGenericOpInputs : public OpRewritePattern<GenericOp> {
  using OpRewritePattern<GenericOp>::OpRewritePattern;

  LogicalResult matchAndRewrite(GenericOp genericOp,
                                PatternRewriter &rewriter) const override {
    // Associate each input to an equivalent "canonical" input that has the same
    // Value and indexing map.
    //
    // In the non-duplicate case, input `i` will have canonical input `i`. But
    // in the case of duplicated inputs, the canonical input could be some other
    // input `< i`. That is, a later input will have some earlier input as its
    // canonical input.
    llvm::SmallDenseMap<std::pair<Value, AffineMap>, unsigned> canonicalInput;
    // For later remapping tasks like deduplicating payload block arguments,
    // having a simple "inputIndex -> canonicalInputIndex" integer mapping is
    // convenient.
    SmallVector<unsigned> canonicalInputIndices;
    for (OpOperand *opOperand : genericOp.getInputOperands()) {
      AffineMap indexingMap = genericOp.getTiedIndexingMap(opOperand);
      // STL-like maps have a convenient behavior for our use case here. In the
      // case of duplicate keys, the insertion is rejected, and the returned
      // iterator gives access to the value already in the map.
      auto pair = canonicalInput.insert(
          {{opOperand->get(), indexingMap}, opOperand->getOperandNumber()});
      canonicalInputIndices.push_back(pair.first->second);
    }

    // If there are no duplicate args, then bail out.
    if (canonicalInput.size() == genericOp.getNumInputs())
      return failure();

    // The operands for the newly canonicalized op.
    SmallVector<Value> newInputOperands;
    for (OpOperand *opOperand : genericOp.getInputOperands())
      if (canonicalInputIndices[opOperand->getOperandNumber()] ==
          opOperand->getOperandNumber())
        newInputOperands.push_back(opOperand->get());

    // Repair the indexing maps by filtering out the ones that have been
    // eliminated.
    SmallVector<AffineMap> newIndexingMaps;
    for (OpOperand *opOperand : genericOp.getInputOperands())
      if (canonicalInputIndices[opOperand->getOperandNumber()] ==
          opOperand->getOperandNumber())
        newIndexingMaps.push_back(genericOp.getTiedIndexingMap(opOperand));
    for (OpOperand *opOperand : genericOp.getOutputOperands())
      newIndexingMaps.push_back(genericOp.getTiedIndexingMap(opOperand));

    // Clone the old op with new operands.
    SmallVector<Value> outputOperands = genericOp.getOutputOperands();
    auto newOp = rewriter.create<GenericOp>(
        genericOp.getLoc(), genericOp->getResultTypes(), newInputOperands,
        outputOperands, rewriter.getAffineMapArrayAttr(newIndexingMaps),
        genericOp.iterator_types(), genericOp.docAttr(),
        genericOp.library_callAttr());

    // Copy over unknown attributes. They might be load bearing for some flow.
    ArrayRef<StringRef> odsAttrs = genericOp.getAttributeNames();
    for (NamedAttribute kv : genericOp->getAttrs()) {
      if (!llvm::is_contained(odsAttrs, kv.getName().getValue())) {
        newOp->setAttr(kv.getName(), kv.getValue());
      }
    }

    rewriter.inlineRegionBefore(genericOp.region(), newOp.region(),
                                newOp.region().begin());

    // Repair the payload entry block by RAUW'ing redundant arguments and
    // erasing them.
    Block &payload = newOp.region().front();
    SmallVector<OpOperand *> inputOperands = genericOp.getInputOperands();
    for (OpOperand *opOperand : llvm::reverse(inputOperands)) {
      // Iterate in reverse, so that we erase later args first, preventing the
      // argument list from shifting unexpectedly and invalidating all our
      // indices.
      unsigned operandNumber = opOperand->getOperandNumber();
      if (canonicalInputIndices[operandNumber] == operandNumber)
        continue;
      payload.getArgument(operandNumber)
          .replaceAllUsesWith(
              payload.getArgument(canonicalInputIndices[operandNumber]));
      payload.eraseArgument(operandNumber);
    }

    rewriter.replaceOp(genericOp, newOp->getResults());
    return success();
  }
};

/// Remove generic operations (on tensors) that are just copying
/// the values from inputs to the results. Requirements are
/// 1) All iterator types are parallel
/// 2) The body contains just a yield operation with the yielded values being
///    the arguments corresponding to the operands.
struct EraseIdentityGenericOp : public OpRewritePattern<GenericOp> {
  using OpRewritePattern<GenericOp>::OpRewritePattern;

  LogicalResult matchAndRewrite(GenericOp genericOp,
                                PatternRewriter &rewriter) const override {
    // Check all indexing maps are identity.
    if (llvm::any_of(genericOp.getIndexingMaps(),
                     [](AffineMap map) { return !map.isIdentity(); }))
      return failure();

    // Check that the body of the linalg operation is just a linalg.yield
    // operation.
    Block &body = genericOp.region().front();
    if (!llvm::hasSingleElement(body))
      return failure();
    auto yieldOp = dyn_cast<linalg::YieldOp>(body.getTerminator());
    if (!yieldOp)
      return failure();

    // In the buffer case, we need to check exact buffer equality.
    if (genericOp.hasBufferSemantics()) {
      if (genericOp.getNumInputs() == 1 && genericOp.getNumOutputs() == 1 &&
          genericOp.getInputOperand(0)->get() ==
              genericOp.getOutputOperand(0)->get()) {
        rewriter.eraseOp(genericOp);
        return success();
      }
      return failure();
    }

    // Get the argument number of the returned values. That is the operand
    // number to use for replacing uses of this operation.
    SmallVector<Value> returnedArgs;
    for (const auto &yieldVal : llvm::enumerate(yieldOp.values())) {
      auto yieldArg = yieldVal.value().dyn_cast<BlockArgument>();
      if (!yieldArg || yieldArg.getOwner() != &body)
        return failure();
      unsigned argumentNumber = yieldArg.getArgNumber();
      Value returnedArg = genericOp->getOperand(argumentNumber);
      Type resultType = genericOp->getResult(yieldVal.index()).getType();
      // The input can have a different type than the result, e.g. a dynamic
      // input dimension can be turned into a static output dimension.
      Type returnType = returnedArg.getType();
      if (returnType != resultType) {
        // Distinguish between sparse conversion or dense tensor casting.
        // TODO: unify the two ops?
        if (sparse_tensor::getSparseTensorEncoding(returnType) ||
            sparse_tensor::getSparseTensorEncoding(resultType))
          returnedArg = rewriter.create<sparse_tensor::ConvertOp>(
              genericOp.getLoc(), resultType, returnedArg);
        else {
          if (!tensor::CastOp::areCastCompatible(returnedArg.getType(),
                                                 resultType))
            return failure();
          returnedArg = rewriter.create<tensor::CastOp>(
              genericOp.getLoc(), resultType, returnedArg);
        }
      }
      returnedArgs.push_back(returnedArg);
    }

    if (returnedArgs.size() != genericOp->getNumResults())
      return failure();
    rewriter.replaceOp(genericOp, returnedArgs);
    return success();
  }
};

/// Drop dead args of a linalg generic op.
/// An arg is dead if it has zero uses in the op region.
struct DeadArgsGenericOpInputs : public OpRewritePattern<GenericOp> {
  using OpRewritePattern<GenericOp>::OpRewritePattern;
  LogicalResult matchAndRewrite(GenericOp genericOp,
                                PatternRewriter &rewriter) const override {
    SmallVector<AffineMap> oldIndexingMaps = genericOp.getIndexingMaps();
    // Maps must be projected permutations.
    if (llvm::any_of(genericOp.getIndexingMaps(), [](AffineMap map) {
          return !map.isProjectedPermutation();
        }))
      return failure();
    Block &payload = genericOp.region().front();
    SmallVector<Value> newInputOperands;
    SmallVector<AffineMap> newIndexingMaps;
    bool deadArgFound = false;
    int inputSize = genericOp.getInputOperands().size();
    for (int i = inputSize - 1; i >= 0; i--) {
      OpOperand *opOperand = genericOp.getInputOperand(i);
      // Iterate in reverse, so that we erase later args first, preventing the
      // argument list from shifting unexpectedly and invalidating all our
      // indices.
      if (payload.getArgument(i).use_empty() &&
          !hasaUniqueDim(oldIndexingMaps, i)) {
        payload.eraseArgument(i);
        deadArgFound = true;
        // remove this indexing map out of consideration for hasaUniqueDim check
        oldIndexingMaps.erase(oldIndexingMaps.begin() + i);
      } else {
        newInputOperands.insert(newInputOperands.begin(), opOperand->get());
        newIndexingMaps.insert(newIndexingMaps.begin(),
                               genericOp.getTiedIndexingMap(opOperand));
      }
    }
    // Bail out if there are no dead args.
    if (!deadArgFound)
      return failure();
    for (OpOperand *opOperand : genericOp.getOutputOperands())
      newIndexingMaps.push_back(genericOp.getTiedIndexingMap(opOperand));
    SmallVector<Value> outputOperands = genericOp.getOutputOperands();

    auto newOp = rewriter.create<GenericOp>(
        genericOp.getLoc(), genericOp->getResultTypes(), newInputOperands,
        outputOperands, rewriter.getAffineMapArrayAttr(newIndexingMaps),
        genericOp.iterator_types(), genericOp.docAttr(),
        genericOp.library_callAttr());
    // Copy over unknown attributes. They might be load bearing for some flow.
    ArrayRef<StringRef> odsAttrs = genericOp.getAttributeNames();
    for (NamedAttribute kv : genericOp->getAttrs()) {
      if (!llvm::is_contained(odsAttrs, kv.getName().getValue())) {
        newOp->setAttr(kv.getName(), kv.getValue());
      }
    }
    rewriter.inlineRegionBefore(genericOp.region(), newOp.region(),
                                newOp.region().begin());
    rewriter.replaceOp(genericOp, newOp->getResults());
    return success();
  }
};
} // namespace

void GenericOp::getCanonicalizationPatterns(RewritePatternSet &results,
                                            MLIRContext *context) {
  results.add<DeduplicateGenericOpInputs, EraseIdentityGenericOp,
              DeadArgsGenericOpInputs>(context);
}

LogicalResult GenericOp::fold(ArrayRef<Attribute>,
                              SmallVectorImpl<OpFoldResult> &) {
  return foldMemRefCast(*this);
}

//===----------------------------------------------------------------------===//
// InitTensorOp
//===----------------------------------------------------------------------===//

void InitTensorOp::build(OpBuilder &b, OperationState &result,
                         ArrayRef<OpFoldResult> sizes, Type elementType,
                         ArrayRef<NamedAttribute> attrs) {
  SmallVector<Value, 4> dynamicSizes;
  SmallVector<int64_t, 4> staticSizes;
  dispatchIndexOpFoldResults(sizes, dynamicSizes, staticSizes,
                             ShapedType::kDynamicSize);
  auto resultType = RankedTensorType ::get(staticSizes, elementType);
  build(b, result, resultType, dynamicSizes, b.getI64ArrayAttr(staticSizes));
  result.addAttributes(attrs);
}

LogicalResult InitTensorOp::verify() {
  RankedTensorType resultType = getType();
  SmallVector<int64_t, 4> staticSizes = llvm::to_vector<4>(llvm::map_range(
      static_sizes().cast<ArrayAttr>(),
      [](Attribute a) -> int64_t { return a.cast<IntegerAttr>().getInt(); }));

  if (failed(verifyListOfOperandsOrIntegers(
          *this, "sizes", resultType.getRank(), static_sizes(), sizes(),
          ShapedType::isDynamic)))
    return failure();

  if (static_sizes().size() != static_cast<unsigned>(resultType.getRank()))
    return emitError("expected ") << resultType.getRank() << " sizes values";

  Type expectedType = InitTensorOp::inferResultType(
      staticSizes, resultType.getElementType(), resultType.getEncoding());
  if (resultType != expectedType) {
    return emitError("specified type ")
           << resultType << " does not match the inferred type "
           << expectedType;
  }
  return success();
}

Type InitTensorOp::inferResultType(ArrayRef<int64_t> staticSizes,
                                   Type elementType, Attribute encoding) {
  return RankedTensorType::get(staticSizes, elementType, encoding);
}

SmallVector<OpFoldResult> InitTensorOp::getMixedSizes() {
  SmallVector<OpFoldResult> mixedSizes;
  mixedSizes.reserve(getType().getRank());
  unsigned dynamicValIndex = 0;
  for (Attribute attr : static_sizes()) {
    auto intAttr = attr.cast<IntegerAttr>();
    if (!ShapedType::isDynamic(intAttr.getInt())) {
      mixedSizes.push_back(intAttr);
      continue;
    }
    mixedSizes.push_back(sizes()[dynamicValIndex++]);
  }
  return mixedSizes;
}

namespace {
/// Change the type of the result of a `linalg.init_tensor` by making the result
/// type statically sized along dimension that in the original operation where
/// defined as dynamic, but the size was defined using a `constant` op. For
/// example
///
///  %c5 = arith.constant 5: index
///  %0 = linalg.init_tensor [%arg0, %c5] : tensor<?x?xf32>
///
///  to
///
///  %0 = linalg.init_tensor [%arg0, 5] : tensor<?x5xf32>
struct ReplaceStaticShapeDims : OpRewritePattern<InitTensorOp> {
  using OpRewritePattern<InitTensorOp>::OpRewritePattern;

  LogicalResult matchAndRewrite(InitTensorOp op,
                                PatternRewriter &rewriter) const override {
    SmallVector<Value, 4> dynamicSizes;
    SmallVector<int64_t, 4> staticSizes;
    for (unsigned i = 0, e = op.getType().getRank(); i != e; ++i) {
      // If the size is already static, nothing to do.
      if (!op.isDynamicSize(i)) {
        staticSizes.push_back(op.getStaticSize(i));
        continue;
      }

      // If the size is dynamic but defined using a `constant` op, get the
      // constant value to find the static size to use.
      unsigned operandNum = op.getIndexOfDynamicSize(i);
      Value sizeOperand = op.getOperand(operandNum);
      if (auto constantIndexOp =
              sizeOperand.getDefiningOp<arith::ConstantIndexOp>()) {
        staticSizes.push_back(constantIndexOp.value());
        continue;
      }

      // Fallback case. Keep the size dynamic.
      dynamicSizes.push_back(sizeOperand);
      staticSizes.push_back(ShapedType::kDynamicSize);
    }
    RankedTensorType newType =
        RankedTensorType::get(staticSizes, op.getType().getElementType());
    if (newType == op.getType())
      return failure();
    auto newOp =
        rewriter.create<InitTensorOp>(op.getLoc(), newType, dynamicSizes,
                                      rewriter.getI64ArrayAttr(staticSizes));
    rewriter.replaceOpWithNewOp<tensor::CastOp>(op, op.getType(), newOp);
    return success();
  }
};
} // namespace

namespace {
/// Since `init_tensor` operation creates a tensor needed only for its shape, a
/// slice of this is also needed only for its shape. The result can be
/// replaced by a new init_tensor operation of the same size as the extract
/// slice op.
struct FoldInitTensorWithExtractSliceOp
    : public OpRewritePattern<tensor::ExtractSliceOp> {
  using OpRewritePattern<tensor::ExtractSliceOp>::OpRewritePattern;

  LogicalResult matchAndRewrite(tensor::ExtractSliceOp sliceOp,
                                PatternRewriter &rewriter) const override {
    if (!sliceOp.source().getDefiningOp<linalg::InitTensorOp>())
      return failure();
    // ExtractSliceOp may be rank-reducing; its dynamic sizes must be preserved
    // as well as its result type.
    rewriter.replaceOpWithNewOp<linalg::InitTensorOp>(
        sliceOp, sliceOp.sizes(),
        sliceOp.result().getType().cast<RankedTensorType>().getShape(),
        sliceOp.getSourceType().getElementType());
    return success();
  }
};

template <typename TensorReshapeOp>
struct FoldInitTensorWithTensorReshapeOp
    : public OpRewritePattern<TensorReshapeOp> {
  using OpRewritePattern<TensorReshapeOp>::OpRewritePattern;

  LogicalResult matchAndRewrite(TensorReshapeOp reshapeOp,
                                PatternRewriter &rewriter) const override {
    if (!reshapeOp.src().template getDefiningOp<InitTensorOp>())
      return failure();
    Location loc = reshapeOp.getLoc();
    ReifiedRankedShapedTypeDims resultShapes;
    ReifyRankedShapedTypeOpInterface reifyShapedTypeInterface =
        cast<ReifyRankedShapedTypeOpInterface>(reshapeOp.getOperation());
    if (failed(reifyShapedTypeInterface.reifyResultShapes(rewriter,
                                                          resultShapes)) ||
        !llvm::hasSingleElement(resultShapes))
      return failure();
    Value initTensor = rewriter.create<InitTensorOp>(
        loc, getAsOpFoldResult(resultShapes[0]),
        reshapeOp.getResultType().getElementType());
    if (initTensor.getType() != reshapeOp.getResultType()) {
      rewriter.replaceOpWithNewOp<tensor::CastOp>(
          reshapeOp, reshapeOp.getResultType(), initTensor);
    } else {
      rewriter.replaceOp(reshapeOp, initTensor);
    }
    return success();
  }
};

struct FoldInitTensorWithDimOp : public OpRewritePattern<tensor::DimOp> {
  using OpRewritePattern<tensor::DimOp>::OpRewritePattern;

  LogicalResult matchAndRewrite(tensor::DimOp dimOp,
                                PatternRewriter &rewriter) const override {
    Optional<int64_t> maybeConstantIndex = dimOp.getConstantIndex();
    auto initTensorOp = dimOp.source().getDefiningOp<linalg::InitTensorOp>();
    if (!initTensorOp || !maybeConstantIndex)
      return failure();
    if (!initTensorOp.isDynamicSize(*maybeConstantIndex))
      return failure();
    rewriter.replaceOp(dimOp, initTensorOp.getDynamicSize(*maybeConstantIndex));
    return success();
  }
};

/// Canonicalize
///
/// ```mlir
///   %0 = linalg.init_tensor [%d0, %d1] : tensor<?x?xf32>
///   %1 = tensor.cast %0 : tensor<?x?xf32> to tensor<4x?xf32>
/// ```
///
/// into
///
/// ```mlir
///   %0 = linalg.init_tensor [4, %d1] : tensor<4x?xf32>
/// ```
///
/// This assumes the input program is correct in terms of its shape. So it
/// is safe to assume that `%d0` is in fact 4. If that was not the case, the
/// input program is wrong to begin with, so its undefined behavior anyway (i.e.
/// this optimization can still triggering without violating program semantics).
struct FoldInitTensorWithTensorCastOp
    : public OpRewritePattern<tensor::CastOp> {
  using OpRewritePattern<tensor::CastOp>::OpRewritePattern;

  LogicalResult matchAndRewrite(tensor::CastOp castOp,
                                PatternRewriter &rewriter) const override {
    if (!canFoldIntoProducerOp(castOp))
      return failure();
    auto producer = castOp.source().getDefiningOp<InitTensorOp>();
    if (!producer)
      return failure();

    auto resultType = castOp->getResult(0).getType().cast<RankedTensorType>();
    ArrayRef<int64_t> resultShape = resultType.getShape();
    SmallVector<OpFoldResult> currMixedSizes = producer.getMixedSizes();
    SmallVector<OpFoldResult> newMixedSizes;
    newMixedSizes.reserve(currMixedSizes.size());
    assert(resultShape.size() == currMixedSizes.size() &&
           "mismatch in result shape and sizes of init_tensor op");
    for (auto it : llvm::zip(resultShape, currMixedSizes)) {
      int64_t newDim = std::get<0>(it);
      OpFoldResult currDim = std::get<1>(it);
      // Case 1: The init tensor dim is static. Check that the tensor cast
      // result dim matches.
      if (auto attr = currDim.dyn_cast<Attribute>()) {
        if (ShapedType::isDynamic(newDim) ||
            newDim != attr.cast<IntegerAttr>().getInt()) {
          // Something is off, the cast result shape cannot be more dynamic than
          // the init tensor result shape (enforced by `canFoldIntoProducer`).
          // Abort for now.
          return rewriter.notifyMatchFailure(
              producer, "mismatch in static value of shape of init "
                        "tensor result and cast result");
        }
        newMixedSizes.push_back(attr);
        continue;
      }

      // Case 2 : The tensor cast shape is static, but init tensor result shape
      // is dynamic.
      if (!ShapedType::isDynamic(newDim)) {
        newMixedSizes.push_back(rewriter.getIndexAttr(newDim));
        continue;
      }

      // Case 3 : The tensor cast shape is dynamic and init tensor result shape
      // is dynamic. Use the dynamic value from the init tensor op.
      newMixedSizes.push_back(currDim);
    }

    rewriter.replaceOpWithNewOp<InitTensorOp>(castOp, newMixedSizes,
                                              resultType.getElementType());
    return success();
  }
};

} // namespace

void InitTensorOp::getCanonicalizationPatterns(RewritePatternSet &results,
                                               MLIRContext *context) {
  results.add<FoldInitTensorWithTensorCastOp, FoldInitTensorWithDimOp,
              FoldInitTensorWithExtractSliceOp,
              FoldInitTensorWithTensorReshapeOp<tensor::ExpandShapeOp>,
              FoldInitTensorWithTensorReshapeOp<tensor::CollapseShapeOp>,
              ReplaceStaticShapeDims>(context);
}

LogicalResult InitTensorOp::reifyResultShapes(
    OpBuilder &builder, ReifiedRankedShapedTypeDims &reifiedReturnShapes) {
  auto shapes = llvm::to_vector<4>(llvm::map_range(
      llvm::seq<int64_t>(0, getType().getRank()), [&](int64_t dim) -> Value {
        if (isDynamicSize(dim))
          return getDynamicSize(dim);
        return builder.create<arith::ConstantIndexOp>(getLoc(),
                                                      getStaticSize(dim));
      }));
  reifiedReturnShapes.emplace_back(std::move(shapes));
  return success();
}

//===----------------------------------------------------------------------===//
// YieldOp
//===----------------------------------------------------------------------===//

void linalg::YieldOp::print(OpAsmPrinter &p) {
  if (getNumOperands() > 0)
    p << ' ' << getOperands();
  p.printOptionalAttrDict((*this)->getAttrs());
  if (getNumOperands() > 0)
    p << " : " << getOperandTypes();
}

ParseResult YieldOp::parse(OpAsmParser &parser, OperationState &result) {
  SmallVector<OpAsmParser::UnresolvedOperand, 2> opInfo;
  SmallVector<Type, 2> types;
  SMLoc loc = parser.getCurrentLocation();
  return failure(parser.parseOperandList(opInfo) ||
                 parser.parseOptionalAttrDict(result.attributes) ||
                 (!opInfo.empty() && parser.parseColonTypeList(types)) ||
                 parser.resolveOperands(opInfo, types, loc, result.operands));
}

// Check the operand number and types must match the element types of the
// LinalgOp interface's shaped operands.
static LogicalResult verifyYield(linalg::YieldOp op, LinalgOp linalgOp) {
  if (op.getNumOperands() != linalgOp.getNumOutputs())
    return op.emitOpError("expected number of yield values (")
           << linalgOp.getNumOutputs()
           << ") to match the number of operands of the enclosing "
           << "LinalgOp (" << op.getNumOperands() << ")";

  for (OpOperand &opOperand : op->getOpOperands()) {
    OpOperand *outputOperand =
        linalgOp.getOutputOperand(opOperand.getOperandNumber());
    Type elementType = getElementTypeOrSelf(outputOperand->get().getType());
    if (opOperand.get().getType() != elementType)
      return op.emitOpError("type of yield operand ")
             << (opOperand.getOperandNumber() + 1) << " ("
             << opOperand.get().getType() << ") doesn't match "
             << "the element type of the enclosing linalg.generic op ("
             << elementType << ")";
  }
  return success();
}

LogicalResult linalg::YieldOp::verify() {
  auto *parentOp = (*this)->getParentOp();
  if (parentOp->getNumRegions() != 1 || parentOp->getRegion(0).empty())
    return emitOpError("expected single non-empty parent region");

  if (auto linalgOp = dyn_cast<LinalgOp>(parentOp))
    return verifyYield(*this, linalgOp);

  return emitOpError("expected parent op with LinalgOp interface");
}

//===----------------------------------------------------------------------===//
// IndexOp
//===----------------------------------------------------------------------===//

LogicalResult IndexOp::verify() {
  auto linalgOp = dyn_cast<LinalgOp>((*this)->getParentOp());
  if (!linalgOp)
    return emitOpError("expected parent op with LinalgOp interface");
  if (linalgOp.getNumLoops() <= dim())
    return emitOpError("expected dim (")
           << dim() << ") to be lower than the number of loops ("
           << linalgOp.getNumLoops() << ") of the enclosing LinalgOp";
  return success();
}

/////// Operations corresponding to library calls defined with Tablegen ////////

#include "mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yamlgen.cpp.inc"

#define GET_OP_CLASSES
#include "mlir/Dialect/Linalg/IR/LinalgOps.cpp.inc"

#define GET_OP_CLASSES
#include "mlir/Dialect/Linalg/IR/LinalgStructuredOps.cpp.inc"

/// Return the dims that are `iteratorTypeName` loops in the LinalgOp `op`.
/// Assumes `op` is a LinalgOp.
void mlir::linalg::getDimsOfType(Operation *op, StringRef iteratorTypeName,
                                 SmallVectorImpl<unsigned> &res) {
  if (!cast<LinalgOp>(op).iterator_types())
    return;

  unsigned dim = 0;
  for (auto tn :
       cast<LinalgOp>(op).iterator_types().getAsValueRange<StringAttr>()) {
    if (tn == iteratorTypeName)
      res.push_back(dim);
    ++dim;
  }
}

AffineMap mlir::linalg::extractOrIdentityMap(Optional<AffineMap> maybeMap,
                                             unsigned rank,
                                             MLIRContext *context) {
  if (maybeMap)
    return maybeMap.getValue();
  if (rank == 0)
    return AffineMap::get(context);
  return AffineMap::getMultiDimIdentityMap(rank, context);
}

SmallVector<AffineExpr, 4>
mlir::linalg::makeAffineDimExprs(unsigned num, unsigned &startIdx,
                                 MLIRContext *context) {
  SmallVector<AffineExpr, 4> res;
  res.reserve(num);
  for (unsigned i = 0; i < num; ++i)
    res.push_back(getAffineDimExpr(startIdx++, context));
  return res;
}

SmallVector<AffineExpr, 4> mlir::linalg::concat(ArrayRef<AffineExpr> a,
                                                ArrayRef<AffineExpr> b) {
  auto rangeA = llvm::make_range(a.begin(), a.end());
  auto rangeB = llvm::make_range(b.begin(), b.end());
  auto concatRanges = llvm::concat<const AffineExpr>(rangeA, rangeB);
  return llvm::to_vector<4>(concatRanges);
}

static void appendMangledType(llvm::raw_string_ostream &ss, Type t) {
  if (auto memref = t.dyn_cast<MemRefType>()) {
    ss << "view";
    for (auto size : memref.getShape())
      if (size < 0)
        ss << "sx";
      else
        ss << size << "x";
    appendMangledType(ss, memref.getElementType());
  } else if (auto vec = t.dyn_cast<VectorType>()) {
    ss << "vector";
    llvm::interleave(
        vec.getShape(), [&](int64_t i) { ss << i; }, [&]() { ss << "x"; });
    appendMangledType(ss, vec.getElementType());
  } else if (t.isSignlessIntOrIndexOrFloat()) {
    ss << t;
  } else {
    llvm_unreachable("Invalid type for linalg library name mangling");
  }
}

std::string mlir::linalg::generateLibraryCallName(Operation *op) {
  assert(isa<LinalgOp>(op));
  std::string name(op->getName().getStringRef().str());
  name.reserve(128);
  std::replace(name.begin(), name.end(), '.', '_');
  llvm::raw_string_ostream ss(name);
  ss << "_";
  auto types = op->getOperandTypes();
  llvm::interleave(
      types.begin(), types.end(), [&](Type t) { appendMangledType(ss, t); },
      [&]() { ss << "_"; });
  return ss.str();
}

//===----------------------------------------------------------------------===//
// Canonicalizers and Folders.
//===----------------------------------------------------------------------===//

namespace {
struct EraseDeadLinalgOp : public OpInterfaceRewritePattern<LinalgOp> {
  using OpInterfaceRewritePattern<LinalgOp>::OpInterfaceRewritePattern;

  LogicalResult matchAndRewrite(LinalgOp op,
                                PatternRewriter &rewriter) const override {
    for (OpOperand *opOperand : op.getInputAndOutputOperands()) {
      // Linalg "inputs" may be either tensor or memref type.
      // tensor<0xelt_type> is a convention that may not always mean
      // "0 iterations". Only erase in cases we see memref<...x0x...>.
      auto mt = opOperand->get().getType().dyn_cast<MemRefType>();
      if (!mt)
        continue;
      if (llvm::is_contained(op.getShape(opOperand), 0)) {
        rewriter.eraseOp(op);
        return success();
      }
    }
    return failure();
  }
};

struct FoldTensorCastProducerOp : public OpInterfaceRewritePattern<LinalgOp> {
  using OpInterfaceRewritePattern<LinalgOp>::OpInterfaceRewritePattern;

  LogicalResult matchAndRewrite(LinalgOp op,
                                PatternRewriter &rewriter) const override {
    // If no operand comes from a tensor::CastOp and can be folded then fail.
    bool hasTensorCastOperand =
        llvm::any_of(op.getInputAndOutputOperands(), [&](OpOperand *opOperand) {
          if (opOperand->get().isa<BlockArgument>())
            return false;
          auto castOp = opOperand->get().getDefiningOp<tensor::CastOp>();
          return castOp && canFoldIntoConsumerOp(castOp);
        });
    if (!hasTensorCastOperand)
      return failure();

    SmallVector<Type, 4> newResultTypes;
    newResultTypes.reserve(op->getNumResults());
    SmallVector<Value, 4> newOperands;
    newOperands.reserve(op->getNumOperands());
    // Inputs may fold.
    for (OpOperand *opOperand : op.getInputOperands()) {
      auto tensorCastOp = opOperand->get().getDefiningOp<tensor::CastOp>();
      newOperands.push_back(canFoldIntoConsumerOp(tensorCastOp)
                                ? tensorCastOp.source()
                                : opOperand->get());
    }
    // Init tensors may fold, in which case the resultType must also change.
    for (OpOperand *opOperand : op.getOutputOperands()) {
      auto tensorCastOp = opOperand->get().getDefiningOp<tensor::CastOp>();
      bool fold = canFoldIntoConsumerOp(tensorCastOp);
      newOperands.push_back(fold ? tensorCastOp.getOperand()
                                 : opOperand->get());
      newResultTypes.push_back(newOperands.back().getType());
    }
    // Clone op.
    Operation *newOp =
        op.clone(rewriter, op->getLoc(), newResultTypes, newOperands);
    SmallVector<Value, 4> replacements;
    replacements.reserve(newOp->getNumResults());
    for (auto result : llvm::zip(op->getResults(), newOp->getResults())) {
      Value oldResult = std::get<0>(result);
      Value newResult = std::get<1>(result);
      if (newResult.getType() != oldResult.getType()) {
        replacements.push_back(rewriter.create<tensor::CastOp>(
            op->getLoc(), oldResult.getType(), newResult));
      } else {
        replacements.push_back(newResult);
      }
    }
    rewriter.replaceOp(op, replacements);

    return success();
  }
};

/// Fold LinalgOps with `tensor.cast` consumer if the `tensor.cast` has
/// result that is more static than the linalg op.
struct FoldTensorCastConsumerOp : public OpRewritePattern<tensor::CastOp> {
  using OpRewritePattern<tensor::CastOp>::OpRewritePattern;

  LogicalResult matchAndRewrite(tensor::CastOp castOp,
                                PatternRewriter &rewriter) const override {
    if (!tensor::canFoldIntoProducerOp(castOp))
      return failure();
    auto linalgOp = castOp.source().getDefiningOp<LinalgOp>();
    if (!linalgOp)
      return failure();

    OpBuilder::InsertionGuard guard(rewriter);
    rewriter.setInsertionPoint(linalgOp);

    Location loc = linalgOp.getLoc();
    OpResult resultValue = castOp.source().cast<OpResult>();
    unsigned resultNumber = resultValue.getResultNumber();
    auto resultType = castOp->getResult(0).getType().cast<RankedTensorType>();
    // Replace the `outs` for the result with a `tensor.cast`. This cast is now
    // going from a more dynamic shape to a less dynamic shape. If the producer
    // for this cast, i.e. producer of the out operand, is also an operation
    // that folds with tensor.cast consumer (like this pattern), the cast will
    // continue to propagate as far up the stack as it can go.
    OpOperand *outOperand = linalgOp.getOutputOperand(resultNumber);
    Value newOperand =
        rewriter.create<tensor::CastOp>(loc, resultType, outOperand->get());
    SmallVector<Value> newOperands = linalgOp.getInputOperands();
    SmallVector<Value> outputOperands = linalgOp.getOutputOperands();
    outputOperands[resultNumber] = newOperand;
    newOperands.append(outputOperands.begin(), outputOperands.end());

    SmallVector<Type> resultTypes(linalgOp->result_type_begin(),
                                  linalgOp->result_type_end());
    resultTypes[resultNumber] = resultType;
    Operation *newOp = linalgOp.clone(rewriter, loc, resultTypes, newOperands);

    // Create a tensor.cast operation back to the original type.
    Value castBack = rewriter.create<tensor::CastOp>(
        loc, resultValue.getType(), newOp->getResult(resultNumber));

    SmallVector<Value> results(newOp->result_begin(), newOp->result_end());
    results[resultNumber] = castBack;
    rewriter.replaceOp(linalgOp, results);
    rewriter.replaceOp(castOp, newOp->getResult(resultNumber));
    return success();
  }
};

/// For each of the operand in `operands` this function maps the static sizes of
/// dimensions to their affine dim expressions.
static void populateMap(LinalgOp linalgOp, ArrayRef<OpOperand *> operands,
                        llvm::DenseMap<AffineExpr, int64_t> &affineExprToSize) {
  for (OpOperand *opOperand : operands) {
    if (linalgOp.isScalar(opOperand))
      continue;
    Value src = opOperand->get();
    auto sourceType = src.getType().cast<RankedTensorType>();
    auto sourceMap = linalgOp.getTiedIndexingMap(opOperand);

    // Get the `sourceShape` of the `sourceType`. If the operand is a result of
    // `tensor.cast` operation and source of the cast operation has a static
    // shape, then assign it to the `sourceShape`.
    auto *parentOp = src.getDefiningOp();
    ArrayRef<int64_t> sourceShape = sourceType.getShape();
    if (parentOp) {
      if (auto castOp = dyn_cast<tensor::CastOp>(parentOp)) {
        Value castSource = castOp.source();
        auto castSourceType = castSource.getType().cast<RankedTensorType>();
        if (castSourceType.hasStaticShape())
          sourceShape = castSourceType.getShape();
      }
    }

    // If the source shape's dimension has a static shape, map the affine dim
    // expression to the known static size.
    for (unsigned i = 0; i < sourceShape.size(); i++) {
      if (sourceType.isDynamicDim(i))
        continue;
      if (auto affineDimExpr = sourceMap.getResult(i).dyn_cast<AffineDimExpr>())
        affineExprToSize.try_emplace(affineDimExpr, sourceShape[i]);
    }
  }
}

/// Creates new operand w.r.t 'opOperand' of `linalgOp` with static sizes
/// mapped in `affineExprToSize`. New operands are created in `newOperands` and
/// their result types is stored in `resultTypes`. If `opOperand` requires no
/// change then `changeNeeded` is false and same operand is added in the
/// `newOperands` list.
static void createNewOperandWithStaticSizes(
    Location loc, PatternRewriter &rewriter, OpOperand *opOperand,
    llvm::DenseMap<AffineExpr, int64_t> &affineExprToSize, LinalgOp linalgOp,
    SmallVector<Value> &newOperands, SmallVector<Type> &resultTypes,
    bool &changeNeeded) {
  Value src = opOperand->get();
  newOperands.push_back(src);
  if (linalgOp.isScalar(opOperand))
    return;
  auto sourceType = src.getType().cast<RankedTensorType>();
  Type resultType = sourceType;
  if (sourceType.hasStaticShape() && linalgOp.isOutputTensor(opOperand)) {
    resultTypes.push_back(resultType);
    return;
  }
  ArrayRef<int64_t> sourceShape = sourceType.getShape();
  AffineMap sourceMap = linalgOp.getTiedIndexingMap(opOperand);
  SmallVector<int64_t> newShape;
  // If operand is updated with new shape, `newOperandNeeded` will be
  // true.
  bool newOperandNeeded = false;
  for (unsigned i = 0; i < sourceShape.size(); i++) {
    int64_t dimShape = sourceShape[i];
    AffineExpr dimExpr = sourceMap.getResult(i);
    if (affineExprToSize.find(dimExpr) == affineExprToSize.end() ||
        !sourceType.isDynamicDim(i)) {
      newShape.push_back(dimShape);
      continue;
    }
    // Dimension has a dynamic shape and corresponding affine dim
    // expression is present in the map. So assign the size for the
    // given affine dim expression to the dimension.
    newShape.push_back(affineExprToSize[dimExpr]);
    newOperandNeeded = true;
  }
  resultType = RankedTensorType::get(newShape, sourceType.getElementType());
  if (newOperandNeeded) {
    changeNeeded = true;
    // Get the new operand value given its size and element type by
    // casting it.
    Value newOperand = rewriter.create<tensor::CastOp>(loc, resultType, src);
    unsigned index = opOperand->getOperandNumber();
    newOperands[index] = newOperand;
  }
  if (linalgOp.isOutputTensor(opOperand))
    resultTypes.push_back(resultType);
}

/// Static shapes for the operands can be inferred if any one of the operands
/// have a static shape. This can be done by referring to the affine dim
/// expressions for the operand.
struct InferStaticShapeOfOperands : public OpInterfaceRewritePattern<LinalgOp> {
  using OpInterfaceRewritePattern<LinalgOp>::OpInterfaceRewritePattern;

  LogicalResult matchAndRewrite(LinalgOp linalgOp,
                                PatternRewriter &rewriter) const override {
    if (!linalgOp.hasTensorSemantics())
      return failure();

    // Maps must be projected permutations.
    if (llvm::any_of(linalgOp.getIndexingMaps(), [](AffineMap map) {
          return !map.isProjectedPermutation();
        }))
      return failure();

    // Maps affine dim expressions to the static size of that dimension.
    llvm::DenseMap<AffineExpr, int64_t> affineExprToSize;
    Location loc = linalgOp.getLoc();

    // For each of the affine dim expression, check if the size is known. If
    // known add that in the map.
    populateMap(linalgOp, linalgOp.getInputAndOutputOperands(),
                affineExprToSize);

    SmallVector<Value> newOperands;
    SmallVector<Type> resultTypes;

    // `changeNeeded` is `false` if the operands of `linalgOp` require no
    // change in their types.
    bool changeNeeded = false;
    newOperands.reserve(linalgOp.getNumInputsAndOutputs());
    resultTypes.reserve(linalgOp.getNumOutputs());

    // Iterate over all the operands and update the static sizes.
    for (OpOperand *opOperand : linalgOp.getInputAndOutputOperands()) {
      createNewOperandWithStaticSizes(loc, rewriter, opOperand,
                                      affineExprToSize, linalgOp, newOperands,
                                      resultTypes, changeNeeded);
    }

    // If the generic op has all the required static information, no
    // canonicalization needed.
    if (!changeNeeded)
      return failure();

    // Clone op.
    Operation *newOp =
        linalgOp.clone(rewriter, linalgOp->getLoc(), resultTypes, newOperands);
    SmallVector<Value> replacements;
    replacements.reserve(newOp->getNumResults());
    for (auto it : llvm::zip(linalgOp->getResults(), newOp->getResults())) {
      Value newResult = std::get<1>(it);
      Value oldResult = std::get<0>(it);
      Type newType = newResult.getType();
      Type oldType = oldResult.getType();
      replacements.push_back(
          (newType != oldType)
              ? rewriter.create<tensor::CastOp>(loc, oldType, newResult)
              : newResult);
    }
    rewriter.replaceOp(linalgOp, replacements);
    return success();
  }
};

} // namespace

// All named ops canonicalizers and folders are auto-generated in the
// .cpp.inc.

//===----------------------------------------------------------------------===//
// LinalgDialect
//===----------------------------------------------------------------------===//

void LinalgDialect::getCanonicalizationPatterns(
    RewritePatternSet &results) const {
  results.add<EraseDeadLinalgOp, FoldTensorCastConsumerOp,
              FoldTensorCastProducerOp, InferStaticShapeOfOperands>(
      getContext());
}

Operation *LinalgDialect::materializeConstant(OpBuilder &builder,
                                              Attribute value, Type type,
                                              Location loc) {
  return builder.create<arith::ConstantOp>(loc, type, value);
}