Shape.cpp

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//===- Shape.cpp - MLIR Shape 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
//
//===----------------------------------------------------------------------===//
 
#include "mlir/Dialect/Shape/IR/Shape.h"
 
#include "mlir/Dialect/Arith/IR/Arith.h"
#include "mlir/Dialect/Bufferization/IR/BufferizableOpInterface.h"
#include "mlir/Dialect/CommonFolders.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "mlir/Dialect/Traits.h"
#include "mlir/Dialect/UB/IR/UBOps.h"
#include "mlir/IR/Builders.h"
#include "mlir/IR/BuiltinTypes.h"
#include "mlir/IR/DialectImplementation.h"
#include "mlir/IR/Matchers.h"
#include "mlir/IR/PatternMatch.h"
#include "mlir/IR/TypeUtilities.h"
#include "mlir/Interfaces/FunctionImplementation.h"
#include "mlir/Transforms/InliningUtils.h"
#include "llvm/ADT/SetOperations.h"
#include "llvm/ADT/SmallVectorExtras.h"
#include "llvm/ADT/TypeSwitch.h"
#include "llvm/Support/raw_ostream.h"
#include <utility>
 
using namespace mlir;
using namespace mlir::shape;
 
#include "mlir/Dialect/Shape/IR/ShapeOpsDialect.cpp.inc"
 
namespace {
#include "ShapeCanonicalization.inc"
} // namespace
 
RankedTensorType shape::getExtentTensorType(MLIRContext *ctx, int64_t rank) {
  return RankedTensorType::get({rank}, IndexType::get(ctx));
}
 
bool shape::isExtentTensorType(Type type) {
  auto ranked = llvm::dyn_cast<RankedTensorType>(type);
  return ranked && ranked.getRank() == 1 && ranked.getElementType().isIndex();
}
 
LogicalResult shape::getShapeVec(Value input,
                                 SmallVectorImpl<int64_t> &shapeValues) {
  if (auto inputOp = input.getDefiningOp<ShapeOfOp>()) {
    auto type = llvm::cast<ShapedType>(inputOp.getArg().getType());
    if (!type.hasRank())
      return failure();
    llvm::append_range(shapeValues, type.getShape());
    return success();
  }
  DenseIntElementsAttr attr;
  if (matchPattern(input, m_Constant(&attr))) {
    llvm::append_range(shapeValues, attr.getValues<int64_t>());
    return success();
  }
  return failure();
}
 
static bool isErrorPropagationPossible(TypeRange operandTypes) {
  return llvm::any_of(operandTypes,
                      llvm::IsaPred<SizeType, ShapeType, ValueShapeType>);
}
 
static LogicalResult verifySizeOrIndexOp(Operation *op) {
  assert(op != nullptr && op->getNumResults() == 1);
  Type resultTy = op->getResultTypes().front();
  if (isErrorPropagationPossible(op->getOperandTypes())) {
    if (!llvm::isa<SizeType>(resultTy))
      return op->emitOpError()
             << "if at least one of the operands can hold error values then "
                "the result must be of type `size` to propagate them";
  }
  return success();
}
 
static LogicalResult verifyShapeOrExtentTensorOp(Operation *op) {
  assert(op != nullptr && op->getNumResults() == 1);
  Type resultTy = op->getResultTypes().front();
  if (isErrorPropagationPossible(op->getOperandTypes())) {
    if (!llvm::isa<ShapeType>(resultTy))
      return op->emitOpError()
             << "if at least one of the operands can hold error values then "
                "the result must be of type `shape` to propagate them";
  }
  return success();
}
 
template <typename... Ty>
static bool eachHasOnlyOneOfTypes(TypeRange typeRange) {
  return typeRange.size() == 1 && llvm::isa<Ty...>(typeRange.front());
}
 
template <typename... Ty, typename... ranges>
static bool eachHasOnlyOneOfTypes(TypeRange l, ranges... rs) {
  return eachHasOnlyOneOfTypes<Ty...>(l) && eachHasOnlyOneOfTypes<Ty...>(rs...);
}
 
//===----------------------------------------------------------------------===//
// InlinerInterface
//===----------------------------------------------------------------------===//
 
namespace {
/// This class defines the interface for inlining shape dialect ops.
struct ShapeInlinerInterface : public DialectInlinerInterface {
  using DialectInlinerInterface::DialectInlinerInterface;
 
  // Returns true if the given region 'src' can be inlined into the region
  // 'dest' that is attached to an operation registered to the current dialect.
  bool isLegalToInline(Region *dest, Region *src, bool wouldBeCloned,
                       IRMapping &) const final {
    return true;
  }
 
  // Returns true if the given operation 'op', that is registered to this
  // dialect, can be inlined into the region 'dest' that is attached to an
  // operation registered to the current dialect.
  bool isLegalToInline(Operation *op, Region *dest, bool wouldBeCloned,
                       IRMapping &) const final {
    return true;
  }
};
} // namespace
 
void ShapeDialect::initialize() {
  addOperations<
#define GET_OP_LIST
#include "mlir/Dialect/Shape/IR/ShapeOps.cpp.inc"
      >();
  addTypes<
#define GET_TYPEDEF_LIST
#include "mlir/Dialect/Shape/IR/ShapeOpsTypes.cpp.inc"
      >();
  addInterfaces<ShapeInlinerInterface>();
  // Allow unknown operations during prototyping and testing. As the dialect is
  // still evolving it makes it simple to start with an unregistered ops and
  // try different variants before actually defining the op.
  allowUnknownOperations();
  declarePromisedInterfaces<bufferization::BufferizableOpInterface, AssumingOp,
                            AssumingYieldOp>();
}
 
Operation *ShapeDialect::materializeConstant(OpBuilder &builder,
                                             Attribute value, Type type,
                                             Location loc) {
  if (auto poison = dyn_cast<ub::PoisonAttr>(value))
    return ub::PoisonOp::create(builder, loc, type, poison);
 
  if (llvm::isa<ShapeType>(type) || isExtentTensorType(type))
    return ConstShapeOp::create(builder, loc, type,
                                llvm::cast<DenseIntElementsAttr>(value));
  if (llvm::isa<SizeType>(type))
    return ConstSizeOp::create(builder, loc, type,
                               llvm::cast<IntegerAttr>(value));
  if (llvm::isa<WitnessType>(type))
    return ConstWitnessOp::create(builder, loc, type,
                                  llvm::cast<BoolAttr>(value));
 
  return arith::ConstantOp::materialize(builder, value, type, loc);
}
 
LogicalResult ShapeDialect::verifyOperationAttribute(Operation *op,
                                                     NamedAttribute attribute) {
  // Verify shape.lib attribute.
  if (attribute.getName() == "shape.lib") {
    if (!op->hasTrait<OpTrait::SymbolTable>())
      return op->emitError(
          "shape.lib attribute may only be on op implementing SymbolTable");
 
    if (auto symbolRef = llvm::dyn_cast<SymbolRefAttr>(attribute.getValue())) {
      auto *symbol = SymbolTable::lookupSymbolIn(op, symbolRef);
      if (!symbol)
        return op->emitError("shape function library ")
               << symbolRef << " not found";
      return isa<shape::FunctionLibraryOp>(symbol)
                 ? success()
                 : op->emitError()
                       << symbolRef << " required to be shape function library";
    }
 
    if (auto arr = llvm::dyn_cast<ArrayAttr>(attribute.getValue())) {
      // Verify all entries are function libraries and mappings in libraries
      // refer to unique ops.
      DenseSet<StringAttr> key;
      for (auto it : arr) {
        if (!llvm::isa<SymbolRefAttr>(it))
          return op->emitError(
              "only SymbolRefAttr allowed in shape.lib attribute array");
 
        auto shapeFnLib = dyn_cast_or_null<shape::FunctionLibraryOp>(
            SymbolTable::lookupSymbolIn(op, llvm::cast<SymbolRefAttr>(it)));
        if (!shapeFnLib)
          return op->emitError()
                 << it << " does not refer to FunctionLibraryOp";
        for (auto mapping : shapeFnLib.getMapping()) {
          if (!key.insert(mapping.getName()).second) {
            return op->emitError("only one op to shape mapping allowed, found "
                                 "multiple for `")
                   << mapping.getName() << "`";
          }
        }
      }
      return success();
    }
 
    return op->emitError("only SymbolRefAttr or array of SymbolRefAttrs "
                         "allowed as shape.lib attribute");
  }
  return success();
}
 
//===----------------------------------------------------------------------===//
// AnyOp
//===----------------------------------------------------------------------===//
 
// TODO: Canonicalization should be implemented for shapes that can be
// determined through mixtures of the known dimensions of the inputs.
OpFoldResult AnyOp::fold(FoldAdaptor adaptor) {
  // Only the last operand is checked because AnyOp is commutative.
  if (adaptor.getInputs().back())
    return adaptor.getInputs().back();
 
  return nullptr;
}
 
//===----------------------------------------------------------------------===//
// AssumingOp
//===----------------------------------------------------------------------===//
 
ParseResult AssumingOp::parse(OpAsmParser &parser, OperationState &result) {
  result.regions.reserve(1);
  Region *doRegion = result.addRegion();
 
  auto &builder = parser.getBuilder();
  OpAsmParser::UnresolvedOperand cond;
  if (parser.parseOperand(cond) ||
      parser.resolveOperand(cond, builder.getType<WitnessType>(),
                            result.operands))
    return failure();
 
  // Parse optional results type list.
  if (parser.parseOptionalArrowTypeList(result.types))
    return failure();
 
  // Parse the region and add a terminator if elided.
  if (parser.parseRegion(*doRegion, /*arguments=*/{}, /*argTypes=*/{}))
    return failure();
  AssumingOp::ensureTerminator(*doRegion, parser.getBuilder(), result.location);
 
  // Parse the optional attribute list.
  if (parser.parseOptionalAttrDict(result.attributes))
    return failure();
  return success();
}
 
void AssumingOp::print(OpAsmPrinter &p) {
  bool yieldsResults = !getResults().empty();
 
  p << " " << getWitness();
  if (yieldsResults)
    p << " -> (" << getResultTypes() << ")";
  p << ' ';
  p.printRegion(getDoRegion(),
                /*printEntryBlockArgs=*/false,
                /*printBlockTerminators=*/yieldsResults);
  p.printOptionalAttrDict((*this)->getDiscardableAttrDictionary().getValue());
}
 
namespace {
// Removes AssumingOp with a passing witness and inlines the region.
struct AssumingWithTrue : public OpRewritePattern<AssumingOp> {
  using OpRewritePattern<AssumingOp>::OpRewritePattern;
 
  LogicalResult matchAndRewrite(AssumingOp op,
                                PatternRewriter &rewriter) const override {
    auto witness = op.getWitness().getDefiningOp<ConstWitnessOp>();
    if (!witness || !witness.getPassingAttr())
      return failure();
 
    AssumingOp::inlineRegionIntoParent(op, rewriter);
    return success();
  }
};
 
struct AssumingOpRemoveUnusedResults : public OpRewritePattern<AssumingOp> {
  using OpRewritePattern<AssumingOp>::OpRewritePattern;
 
  LogicalResult matchAndRewrite(AssumingOp op,
                                PatternRewriter &rewriter) const override {
    Block *body = op.getBody();
    auto yieldOp = llvm::cast<AssumingYieldOp>(body->getTerminator());
 
    // Find used values.
    SmallVector<Value, 4> newYieldOperands;
    for (auto [opResult, yieldOperand] :
         llvm::zip(op.getResults(), yieldOp.getOperands())) {
      if (!opResult.getUses().empty()) {
        newYieldOperands.push_back(yieldOperand);
      }
    }
 
    // Rewrite only if redundant results exist.
    if (newYieldOperands.size() == yieldOp->getNumOperands())
      return failure();
 
    // Replace yield op in the old assuming op's body and move the entire region
    // to the new assuming op.
    rewriter.setInsertionPointToEnd(body);
    auto newYieldOp =
        rewriter.replaceOpWithNewOp<AssumingYieldOp>(yieldOp, newYieldOperands);
    rewriter.setInsertionPoint(op);
    auto newOp = AssumingOp::create(
        rewriter, op.getLoc(), newYieldOp->getOperandTypes(), op.getWitness());
    newOp.getDoRegion().takeBody(op.getDoRegion());
 
    // Use the new results to replace the previously used ones.
    SmallVector<Value, 4> replacementValues;
    auto src = newOp.getResults().begin();
    for (auto it : op.getResults()) {
      if (it.getUses().empty())
        replacementValues.push_back(nullptr);
      else
        replacementValues.push_back(*src++);
    }
    rewriter.replaceOp(op, replacementValues);
    return success();
  }
};
} // namespace
 
void AssumingOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                             MLIRContext *context) {
  patterns.add<AssumingOpRemoveUnusedResults, AssumingWithTrue>(context);
}
 
// See RegionBranchOpInterface in Interfaces/ControlFlowInterfaces.td
void AssumingOp::getSuccessorRegions(
    RegionBranchPoint point, SmallVectorImpl<RegionSuccessor> &regions) {
  // AssumingOp has unconditional control flow into the region and back to the
  // parent, so return the correct RegionSuccessor purely based on the index
  // being None or 0.
  if (!point.isParent()) {
    regions.push_back(RegionSuccessor(getOperation()));
    return;
  }
 
  regions.push_back(RegionSuccessor(&getDoRegion()));
}
 
ValueRange AssumingOp::getSuccessorInputs(RegionSuccessor successor) {
  return successor.isOperation() ? ValueRange(getResults()) : ValueRange();
}
 
void AssumingOp::inlineRegionIntoParent(AssumingOp &op,
                                        PatternRewriter &rewriter) {
  auto *blockBeforeAssuming = rewriter.getInsertionBlock();
  auto *assumingBlock = op.getBody();
  auto initPosition = rewriter.getInsertionPoint();
  auto *blockAfterAssuming =
      rewriter.splitBlock(blockBeforeAssuming, initPosition);
 
  // Remove the AssumingOp and AssumingYieldOp.
  auto &yieldOp = assumingBlock->back();
  rewriter.inlineRegionBefore(op.getDoRegion(), blockAfterAssuming);
  rewriter.replaceOp(op, yieldOp.getOperands());
  rewriter.eraseOp(&yieldOp);
 
  // Merge blocks together as there was no branching behavior from the
  // AssumingOp.
  rewriter.mergeBlocks(assumingBlock, blockBeforeAssuming);
  rewriter.mergeBlocks(blockAfterAssuming, blockBeforeAssuming);
}
 
void AssumingOp::build(
    OpBuilder &builder, OperationState &result, Value witness,
    function_ref<SmallVector<Value, 2>(OpBuilder &, Location)> bodyBuilder) {
  OpBuilder::InsertionGuard g(builder);
 
  result.addOperands(witness);
  Region *bodyRegion = result.addRegion();
  builder.createBlock(bodyRegion);
 
  // Build body.
  SmallVector<Value, 2> yieldValues = bodyBuilder(builder, result.location);
  AssumingYieldOp::create(builder, result.location, yieldValues);
 
  SmallVector<Type, 2> assumingTypes;
  for (Value v : yieldValues)
    assumingTypes.push_back(v.getType());
  result.addTypes(assumingTypes);
}
 
//===----------------------------------------------------------------------===//
// AddOp
//===----------------------------------------------------------------------===//
 
LogicalResult mlir::shape::AddOp::inferReturnTypes(
    MLIRContext *context, std::optional<Location> location,
    AddOp::Adaptor adaptor, SmallVectorImpl<Type> &inferredReturnTypes) {
  if (llvm::isa<SizeType>(adaptor.getLhs().getType()) ||
      llvm::isa<SizeType>(adaptor.getRhs().getType()))
    inferredReturnTypes.assign({SizeType::get(context)});
  else
    inferredReturnTypes.assign({IndexType::get(context)});
  return success();
}
 
bool mlir::shape::AddOp::isCompatibleReturnTypes(TypeRange l, TypeRange r) {
  // SizeType is compatible with IndexType.
  return eachHasOnlyOneOfTypes<SizeType, IndexType>(l, r);
}
 
OpFoldResult mlir::shape::AddOp::fold(FoldAdaptor adaptor) {
  // add(x, 0) -> x
  if (matchPattern(getRhs(), m_Zero()))
    return getLhs();
 
  return constFoldBinaryOp<IntegerAttr>(
      adaptor.getOperands(),
      [](APInt a, const APInt &b) { return std::move(a) + b; });
}
 
LogicalResult shape::AddOp::verify() { return verifySizeOrIndexOp(*this); }
 
//===----------------------------------------------------------------------===//
// AssumingAllOp
//===----------------------------------------------------------------------===//
 
namespace {
 
// Merge multiple `shape.assuming_all` operations together.
//
//   %0 = shape.assuming_all %w0, %w1
//   %1 = shape.assuming_all %w2, %0
//
// to:
//
//   %0 = shape.assuming_all %w0, %w2, %w2
struct MergeAssumingAllOps : public OpRewritePattern<AssumingAllOp> {
  using OpRewritePattern<AssumingAllOp>::OpRewritePattern;
 
  LogicalResult matchAndRewrite(AssumingAllOp op,
                                PatternRewriter &rewriter) const override {
    SmallVector<Value> operands;
 
    for (Value operand : op.getInputs()) {
      if (auto assumeAll = operand.getDefiningOp<AssumingAllOp>())
        operands.append(assumeAll.operand_begin(), assumeAll->operand_end());
      else
        operands.push_back(operand);
    }
 
    // We didn't find any other `assuming_all` ops to merge with.
    if (operands.size() == op.getNumOperands())
      return failure();
 
    // Replace with a new `assuming_all` operation with merged constraints.
    rewriter.replaceOpWithNewOp<AssumingAllOp>(op, operands);
    return success();
  }
};
 
// Eliminate `cstr_broadcastable` operands from `assuming_all` operation that
// are subsumed by others.
//
//   %0 = shape.cstr_broadcastable %shape0, %shape1
//   %1 = shape.cstr_broadcastable %shape0, %shape1, %shape2
//
//   %2 = shape.cstr_broadcastable %shape3, %shape4
//   %3 = shape.cstr_broadcastable %shape3, %shape4, %shape5
//
//   %4 = shape.assuming_all %0, %1, %2, %3
//
// to:
//
//   %0 = shape.cstr_broadcastable %shape0, %shape1, %shape2
//   %1 = shape.cstr_broadcastable %shape3, %shape4, %shape5
//   %2 = shape.assuming_all %0, %1
//
// In this example if shapes [0, 1, 2] are broadcastable, then it means that
// shapes [0, 1] are broadcastable too, and can be removed from the list of
// constraints. If shapes [0, 1, 2] are not broadcastable, then it doesn't
// matter if shapes [0, 1] are broadcastable (same for shapes [3, 4, 5]).
struct AssumingAllOfCstrBroadcastable : public OpRewritePattern<AssumingAllOp> {
  using OpRewritePattern<AssumingAllOp>::OpRewritePattern;
 
  LogicalResult matchAndRewrite(AssumingAllOp op,
                                PatternRewriter &rewriter) const override {
    // Collect all `CstrBroadcastableOp` operands first.
    SetVector<CstrBroadcastableOp> operands;
    for (Value operand : op.getInputs()) {
      // TODO: Apply this optimization if some of the witnesses are not
      // produced by the `cstr_broadcastable`.
      auto broadcastable = operand.getDefiningOp<CstrBroadcastableOp>();
      if (!broadcastable)
        return failure();
 
      operands.insert(broadcastable);
    }
 
    // Skip trivial `assuming_all` operations.
    if (operands.size() <= 1)
      return failure();
 
    // Collect shapes checked by `cstr_broadcastable` operands.
    SmallVector<std::pair<CstrBroadcastableOp, DenseSet<Value>>> shapes;
    for (auto cstr : operands) {
      DenseSet<Value> shapesSet(cstr->operand_begin(), cstr->operand_end());
      shapes.emplace_back(cstr, std::move(shapesSet));
    }
 
    // Sort by the number of shape operands (larger to smaller).
    llvm::sort(shapes, [](auto a, auto b) {
      return a.first.getNumOperands() > b.first.getNumOperands();
    });
 
    // We start from the `cst_broadcastable` operations with largest number of
    // shape operands, and remove redundant `cst_broadcastable` operations. We
    // do this until we find a set of `cst_broadcastable` operations with
    // non-overlapping constraints.
    SmallVector<CstrBroadcastableOp> markedForErase;
 
    for (unsigned i = 0; i < shapes.size(); ++i) {
      auto isSubset = [&](auto pair) {
        return llvm::set_is_subset(pair.second, shapes[i].second);
      };
 
      // Keep redundant `cstr_broadcastable` operations to be erased.
      auto *it = std::remove_if(shapes.begin() + i + 1, shapes.end(), isSubset);
      for (auto *it0 = it; it0 < shapes.end(); ++it0)
        markedForErase.push_back(it0->first);
      shapes.erase(it, shapes.end());
    }
 
    // We didn't find any operands that could be removed.
    if (markedForErase.empty())
      return failure();
 
    // Collect non-overlapping `cst_broadcastable` constraints.
    SmallVector<Value> uniqueConstraints;
    for (auto &shape : shapes)
      uniqueConstraints.push_back(shape.first.getResult());
 
    // Replace with a new `assuming_all` operation ...
    rewriter.replaceOpWithNewOp<AssumingAllOp>(op, uniqueConstraints);
 
    // ... and maybe erase `cstr_broadcastable` ops without uses.
    for (auto &op : markedForErase)
      if (op->use_empty())
        rewriter.eraseOp(op);
 
    return success();
  }
};
 
struct AssumingAllToCstrEqCanonicalization
    : public OpRewritePattern<AssumingAllOp> {
  using OpRewritePattern<AssumingAllOp>::OpRewritePattern;
 
  LogicalResult matchAndRewrite(AssumingAllOp op,
                                PatternRewriter &rewriter) const override {
    SmallVector<Value, 8> shapes;
    for (Value w : op.getInputs()) {
      auto cstrEqOp = w.getDefiningOp<CstrEqOp>();
      if (!cstrEqOp)
        return failure();
      bool disjointShapes = llvm::none_of(cstrEqOp.getShapes(), [&](Value s) {
        return llvm::is_contained(shapes, s);
      });
      if (!shapes.empty() && !cstrEqOp.getShapes().empty() && disjointShapes)
        return failure();
      shapes.append(cstrEqOp.getShapes().begin(), cstrEqOp.getShapes().end());
    }
    rewriter.replaceOpWithNewOp<CstrEqOp>(op, shapes);
    return success();
  }
};
 
template <typename OpTy>
struct RemoveDuplicateOperandsPattern : public OpRewritePattern<OpTy> {
  using OpRewritePattern<OpTy>::OpRewritePattern;
 
  LogicalResult matchAndRewrite(OpTy op,
                                PatternRewriter &rewriter) const override {
    // Find unique operands.
    SetVector<Value> unique(op.operand_begin(), op.operand_end());
 
    // Reduce op to equivalent with unique operands.
    if (unique.size() < op.getNumOperands()) {
      rewriter.replaceOpWithNewOp<OpTy>(
          op, op->getResultTypes(), unique.takeVector(), op.getProperties(),
          op->getDiscardableAttrDictionary().getValue());
      return success();
    }
 
    return failure();
  }
};
} // namespace
 
void AssumingAllOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                                MLIRContext *context) {
  patterns
      .add<MergeAssumingAllOps, AssumingAllOneOp,
           AssumingAllOfCstrBroadcastable, AssumingAllToCstrEqCanonicalization,
           RemoveDuplicateOperandsPattern<AssumingAllOp>>(context);
}
 
OpFoldResult AssumingAllOp::fold(FoldAdaptor adaptor) {
  // Iterate in reverse to first handle all constant operands. They are
  // guaranteed to be the tail of the inputs because this is commutative.
  for (int idx = adaptor.getInputs().size() - 1; idx >= 0; idx--) {
    Attribute a = adaptor.getInputs()[idx];
    // Cannot fold if any inputs are not constant;
    if (!a)
      return nullptr;
 
    // We do not need to keep statically known values after handling them in
    // this method.
    getOperation()->eraseOperand(idx);
 
    // Always false if any input is statically known false
    if (!llvm::cast<BoolAttr>(a).getValue())
      return a;
  }
  // If this is reached, all inputs were statically known passing.
  return BoolAttr::get(getContext(), true);
}
 
LogicalResult AssumingAllOp::verify() {
  // Ensure that AssumingAllOp contains at least one operand
  if (getNumOperands() == 0)
    return emitOpError("no operands specified");
 
  return success();
}
 
//===----------------------------------------------------------------------===//
// BroadcastOp
//===----------------------------------------------------------------------===//
 
OpFoldResult BroadcastOp::fold(FoldAdaptor adaptor) {
  if (getShapes().size() == 1) {
    // Otherwise, we need a cast which would be a canonicalization, not folding.
    if (getShapes().front().getType() != getType())
      return nullptr;
    return getShapes().front();
  }
 
  auto firstAttr =
      dyn_cast_or_null<DenseIntElementsAttr>(adaptor.getShapes().front());
  if (!firstAttr)
    return nullptr;
 
  SmallVector<int64_t, 6> resultShape(firstAttr.getValues<int64_t>());
 
  for (auto next : adaptor.getShapes().drop_front()) {
    auto nextAttr = dyn_cast_or_null<DenseIntElementsAttr>(next);
    if (!nextAttr)
      return nullptr;
    auto nextShape = llvm::to_vector<6>(nextAttr.getValues<int64_t>());
 
    SmallVector<int64_t, 6> tmpShape;
    // If the shapes are not compatible, we can't fold it.
    // TODO: Fold to an "error".
    if (!OpTrait::util::getBroadcastedShape(resultShape, nextShape, tmpShape))
      return nullptr;
 
    resultShape.clear();
    std::copy(tmpShape.begin(), tmpShape.end(),
              std::back_inserter(resultShape));
  }
 
  Builder builder(getContext());
  return builder.getIndexTensorAttr(resultShape);
}
 
LogicalResult BroadcastOp::verify() {
  return verifyShapeOrExtentTensorOp(*this);
}
 
namespace {
template <typename OpTy>
struct RemoveEmptyShapeOperandsPattern : public OpRewritePattern<OpTy> {
  using OpRewritePattern<OpTy>::OpRewritePattern;
 
  LogicalResult matchAndRewrite(OpTy op,
                                PatternRewriter &rewriter) const override {
    auto isPotentiallyNonEmptyShape = [](Value shape) {
      if (auto extentTensorTy =
              llvm::dyn_cast<RankedTensorType>(shape.getType())) {
        if (extentTensorTy.getDimSize(0) == 0)
          return false;
      }
      if (auto constShape = shape.getDefiningOp<ConstShapeOp>()) {
        if (constShape.getShape().empty())
          return false;
      }
      return true;
    };
    auto newOperands = llvm::filter_to_vector<8>(op->getOperands(),
                                                 isPotentiallyNonEmptyShape);
 
    // Replace the op with empty shape constant if all operants are reduced to
    // be empty.
    if (newOperands.empty()) {
      rewriter.replaceOpWithNewOp<ConstShapeOp>(
          op, op->getResultTypes().front(), rewriter.getIndexTensorAttr({}));
      return success();
    }
 
    // Reduce op to equivalent without empty shape operands.
    if (newOperands.size() < op.getNumOperands()) {
      rewriter.replaceOpWithNewOp<OpTy>(
          op, op->getResultTypes(), newOperands, op.getProperties(),
          op->getDiscardableAttrDictionary().getValue());
      return success();
    }
 
    return failure();
  }
};
 
struct BroadcastForwardSingleOperandPattern
    : public OpRewritePattern<BroadcastOp> {
  using OpRewritePattern<BroadcastOp>::OpRewritePattern;
 
  LogicalResult matchAndRewrite(BroadcastOp op,
                                PatternRewriter &rewriter) const override {
    if (op.getNumOperands() != 1)
      return failure();
    Value replacement = op.getShapes().front();
 
    // Insert cast if needed.
    if (replacement.getType() != op.getType()) {
      auto loc = op.getLoc();
      if (llvm::isa<ShapeType>(op.getType())) {
        replacement = FromExtentTensorOp::create(rewriter, loc, replacement);
      } else {
        assert(!llvm::isa<ShapeType>(op.getType()) &&
               !llvm::isa<ShapeType>(replacement.getType()) &&
               "expect extent tensor cast");
        replacement =
            tensor::CastOp::create(rewriter, loc, op.getType(), replacement);
      }
    }
 
    rewriter.replaceOp(op, replacement);
    return success();
  }
};
 
struct BroadcastFoldConstantOperandsPattern
    : public OpRewritePattern<BroadcastOp> {
  using OpRewritePattern<BroadcastOp>::OpRewritePattern;
 
  LogicalResult matchAndRewrite(BroadcastOp op,
                                PatternRewriter &rewriter) const override {
    SmallVector<int64_t, 8> foldedConstantShape;
    SmallVector<Value, 8> newShapeOperands;
    for (Value shape : op.getShapes()) {
      if (auto constShape = shape.getDefiningOp<ConstShapeOp>()) {
        SmallVector<int64_t, 8> newFoldedConstantShape;
        if (OpTrait::util::getBroadcastedShape(
                foldedConstantShape,
                llvm::to_vector<8>(constShape.getShape().getValues<int64_t>()),
                newFoldedConstantShape)) {
          foldedConstantShape = newFoldedConstantShape;
          continue;
        }
      }
      newShapeOperands.push_back(shape);
    }
 
    // Need at least two constant operands to fold anything.
    if (op.getNumOperands() - newShapeOperands.size() < 2)
      return failure();
 
    auto foldedConstantOperandsTy = RankedTensorType::get(
        {static_cast<int64_t>(foldedConstantShape.size())},
        rewriter.getIndexType());
    newShapeOperands.push_back(
        ConstShapeOp::create(rewriter, op.getLoc(), foldedConstantOperandsTy,
                             rewriter.getIndexTensorAttr(foldedConstantShape)));
    rewriter.replaceOpWithNewOp<BroadcastOp>(
        op, TypeRange{op.getType()}, newShapeOperands, op.getProperties(),
        op->getDiscardableAttrDictionary().getValue());
    return success();
  }
};
 
template <typename OpTy>
struct CanonicalizeCastExtentTensorOperandsPattern
    : public OpRewritePattern<OpTy> {
  using OpRewritePattern<OpTy>::OpRewritePattern;
 
  LogicalResult matchAndRewrite(OpTy op,
                                PatternRewriter &rewriter) const override {
    // Canonicalize operands.
    bool anyChange = false;
    auto canonicalizeOperand = [&](Value operand) -> Value {
      if (auto castOp = operand.getDefiningOp<tensor::CastOp>()) {
        // Only eliminate the cast if it holds no shape information.
        bool isInformationLoosingCast =
            llvm::cast<RankedTensorType>(castOp.getType()).isDynamicDim(0);
        if (isInformationLoosingCast) {
          anyChange = true;
          return castOp.getSource();
        }
      }
      return operand;
    };
    auto newOperands =
        llvm::map_to_vector<8>(op.getOperands(), canonicalizeOperand);
 
    // Rewrite op if any change required.
    if (!anyChange)
      return failure();
    rewriter.replaceOpWithNewOp<OpTy>(
        op, op->getResultTypes(), newOperands, op.getProperties(),
        op->getDiscardableAttrDictionary().getValue());
    return success();
  }
};
 
struct BroadcastConcretizeResultTypePattern
    : public OpRewritePattern<BroadcastOp> {
  using OpRewritePattern<BroadcastOp>::OpRewritePattern;
 
  LogicalResult matchAndRewrite(BroadcastOp op,
                                PatternRewriter &rewriter) const override {
    // Only concretize dynamic extent tensor result types.
    auto resultTy = llvm::dyn_cast<RankedTensorType>(op.getType());
    if (!resultTy || !resultTy.isDynamicDim(0))
      return failure();
 
    // Infer resulting shape rank if possible.
    int64_t maxRank = 0;
    for (Value shape : op.getShapes()) {
      if (auto extentTensorTy =
              llvm::dyn_cast<RankedTensorType>(shape.getType())) {
        // Cannot infer resulting shape rank if any operand is dynamically
        // ranked.
        if (extentTensorTy.isDynamicDim(0))
          return failure();
        maxRank = std::max(maxRank, extentTensorTy.getDimSize(0));
      }
    }
 
    auto newOp = BroadcastOp::create(rewriter, op.getLoc(),
                                     getExtentTensorType(getContext(), maxRank),
                                     op.getShapes(), /*error=*/nullptr);
    rewriter.replaceOpWithNewOp<tensor::CastOp>(op, op.getType(), newOp);
    return success();
  }
};
} // namespace
 
void BroadcastOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                              MLIRContext *context) {
  patterns.add<BroadcastConcretizeResultTypePattern,
               BroadcastFoldConstantOperandsPattern,
               BroadcastForwardSingleOperandPattern,
               CanonicalizeCastExtentTensorOperandsPattern<BroadcastOp>,
               RemoveDuplicateOperandsPattern<BroadcastOp>,
               RemoveEmptyShapeOperandsPattern<BroadcastOp>>(context);
}
 
//===----------------------------------------------------------------------===//
// ConcatOp
//===----------------------------------------------------------------------===//
 
OpFoldResult ConcatOp::fold(FoldAdaptor adaptor) {
  if (!adaptor.getLhs() || !adaptor.getRhs())
    return nullptr;
  auto lhsShape = llvm::to_vector<6>(
      llvm::cast<DenseIntElementsAttr>(adaptor.getLhs()).getValues<int64_t>());
  auto rhsShape = llvm::to_vector<6>(
      llvm::cast<DenseIntElementsAttr>(adaptor.getRhs()).getValues<int64_t>());
  SmallVector<int64_t, 6> resultShape;
  resultShape.append(lhsShape.begin(), lhsShape.end());
  resultShape.append(rhsShape.begin(), rhsShape.end());
  Builder builder(getContext());
  return builder.getIndexTensorAttr(resultShape);
}
 
//===----------------------------------------------------------------------===//
// ConstShapeOp
//===----------------------------------------------------------------------===//
 
void ConstShapeOp::print(OpAsmPrinter &p) {
  p << " ";
  p.printOptionalAttrDict((*this)->getDiscardableAttrDictionary().getValue());
  p << "[";
  interleaveComma(getShape().getValues<int64_t>(), p);
  p << "] : ";
  p.printType(getType());
}
 
ParseResult ConstShapeOp::parse(OpAsmParser &parser, OperationState &result) {
  if (parser.parseOptionalAttrDict(result.attributes))
    return failure();
  // We piggy-back on ArrayAttr parsing, though we don't internally store the
  // shape as an ArrayAttr.
  // TODO: Implement custom parser and maybe make syntax a bit more concise.
  Attribute extentsRaw;
  NamedAttrList dummy;
  if (parser.parseAttribute(extentsRaw, "dummy", dummy))
    return failure();
  auto extentsArray = llvm::dyn_cast<ArrayAttr>(extentsRaw);
  if (!extentsArray)
    return failure();
  SmallVector<int64_t, 6> ints;
  for (Attribute extent : extentsArray) {
    IntegerAttr attr = llvm::dyn_cast<IntegerAttr>(extent);
    if (!attr)
      return failure();
    ints.push_back(attr.getInt());
  }
  Builder &builder = parser.getBuilder();
  result.addAttribute("shape", builder.getIndexTensorAttr(ints));
  Type resultTy;
  if (parser.parseColonType(resultTy))
    return failure();
  result.types.push_back(resultTy);
  return success();
}
 
OpFoldResult ConstShapeOp::fold(FoldAdaptor) { return getShapeAttr(); }
 
void ConstShapeOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                               MLIRContext *context) {
  patterns.add<TensorCastConstShape>(context);
}
 
LogicalResult mlir::shape::ConstShapeOp::inferReturnTypes(
    MLIRContext *context, std::optional<Location> location,
    ConstShapeOp::Adaptor adaptor, SmallVectorImpl<Type> &inferredReturnTypes) {
  Builder b(context);
  const Properties prop = adaptor.getProperties();
  inferredReturnTypes.assign({RankedTensorType::get(
      {static_cast<int64_t>(prop.shape.size())}, b.getIndexType())});
  return success();
}
 
bool mlir::shape::ConstShapeOp::isCompatibleReturnTypes(TypeRange l,
                                                        TypeRange r) {
  if (l.size() != 1 || r.size() != 1)
    return false;
 
  Type lhs = l.front();
  Type rhs = r.front();
 
  if (llvm::isa<ShapeType>(lhs) || llvm::isa<ShapeType>(rhs))
    // Shape type is compatible with all other valid return types.
    return true;
  return lhs == rhs;
}
 
//===----------------------------------------------------------------------===//
// CstrBroadcastableOp
//===----------------------------------------------------------------------===//
 
void CstrBroadcastableOp::getCanonicalizationPatterns(
    RewritePatternSet &patterns, MLIRContext *context) {
  // Canonicalization patterns have overlap with the considerations during
  // folding in case additional shape information is inferred at some point that
  // does not result in folding.
  patterns.add<CanonicalizeCastExtentTensorOperandsPattern<CstrBroadcastableOp>,
               CstrBroadcastableEqOps,
               RemoveDuplicateOperandsPattern<CstrBroadcastableOp>,
               RemoveEmptyShapeOperandsPattern<CstrBroadcastableOp>>(context);
}
 
// Return true if there is exactly one attribute not representing a scalar
// broadcast.
static bool hasAtMostSingleNonScalar(ArrayRef<Attribute> attributes) {
  bool nonScalarSeen = false;
  for (Attribute a : attributes) {
    if (!a || llvm::cast<DenseIntElementsAttr>(a).getNumElements() != 0) {
      if (nonScalarSeen)
        return false;
      nonScalarSeen = true;
    }
  }
  return true;
}
 
OpFoldResult CstrBroadcastableOp::fold(FoldAdaptor adaptor) {
  // No broadcasting is needed if all operands but one are scalar.
  if (hasAtMostSingleNonScalar(adaptor.getShapes()))
    return BoolAttr::get(getContext(), true);
 
  if ([&] {
        SmallVector<SmallVector<int64_t, 6>, 6> extents;
        for (const auto &operand : adaptor.getShapes()) {
          if (!operand)
            return false;
          extents.push_back(llvm::to_vector<6>(
              llvm::cast<DenseIntElementsAttr>(operand).getValues<int64_t>()));
        }
        return OpTrait::util::staticallyKnownBroadcastable(extents);
      }())
    return BoolAttr::get(getContext(), true);
 
  // Lastly, see if folding can be completed based on what constraints are known
  // on the input shapes.
  if ([&] {
        SmallVector<SmallVector<int64_t, 6>, 6> extents;
        for (auto shapeValue : getShapes()) {
          extents.emplace_back();
          if (failed(getShapeVec(shapeValue, extents.back())))
            return false;
        }
        return OpTrait::util::staticallyKnownBroadcastable(extents);
      }())
    return BoolAttr::get(getContext(), true);
 
  // Because a failing witness result here represents an eventual assertion
  // failure, we do not replace it with a constant witness.
  return nullptr;
}
 
LogicalResult CstrBroadcastableOp::verify() {
  // Ensure that CstrBroadcastableOp contains at least two operands
  if (getNumOperands() < 2)
    return emitOpError("required at least 2 input shapes");
  return success();
}
 
//===----------------------------------------------------------------------===//
// CstrEqOp
//===----------------------------------------------------------------------===//
 
void CstrEqOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                           MLIRContext *context) {
  // If inputs are equal, return passing witness
  patterns.add<CstrEqEqOps>(context);
}
 
OpFoldResult CstrEqOp::fold(FoldAdaptor adaptor) {
  if (llvm::all_of(adaptor.getShapes(), [&](Attribute a) {
        return a && a == adaptor.getShapes().front();
      }))
    return BoolAttr::get(getContext(), true);
 
  // Because a failing witness result here represents an eventual assertion
  // failure, we do not try to replace it with a constant witness. Similarly, we
  // cannot if there are any non-const inputs.
  return nullptr;
}
 
//===----------------------------------------------------------------------===//
// ConstSizeOp
//===----------------------------------------------------------------------===//
 
void ConstSizeOp::build(OpBuilder &builder, OperationState &result,
                        int64_t value) {
  build(builder, result, builder.getIndexAttr(value));
}
 
OpFoldResult ConstSizeOp::fold(FoldAdaptor) { return getValueAttr(); }
 
void ConstSizeOp::getAsmResultNames(
    llvm::function_ref<void(Value, StringRef)> setNameFn) {
  SmallString<4> buffer;
  llvm::raw_svector_ostream os(buffer);
  os << "c" << getValue();
  setNameFn(getResult(), os.str());
}
 
//===----------------------------------------------------------------------===//
// ConstWitnessOp
//===----------------------------------------------------------------------===//
 
OpFoldResult ConstWitnessOp::fold(FoldAdaptor) { return getPassingAttr(); }
 
//===----------------------------------------------------------------------===//
// CstrRequireOp
//===----------------------------------------------------------------------===//
 
OpFoldResult CstrRequireOp::fold(FoldAdaptor adaptor) {
  return adaptor.getPred();
}
 
//===----------------------------------------------------------------------===//
// DimOp
//===----------------------------------------------------------------------===//
 
std::optional<int64_t> DimOp::getConstantIndex() {
  if (auto constSizeOp = getIndex().getDefiningOp<ConstSizeOp>())
    return constSizeOp.getValue().getLimitedValue();
  if (auto constantOp = getIndex().getDefiningOp<arith::ConstantOp>())
    return llvm::cast<IntegerAttr>(constantOp.getValue()).getInt();
  return std::nullopt;
}
 
OpFoldResult DimOp::fold(FoldAdaptor adaptor) {
  Type valType = getValue().getType();
  auto valShapedType = llvm::dyn_cast<ShapedType>(valType);
  if (!valShapedType || !valShapedType.hasRank())
    return nullptr;
  std::optional<int64_t> index = getConstantIndex();
  if (!index.has_value())
    return nullptr;
  if (index.value() < 0 || index.value() >= valShapedType.getRank())
    return nullptr;
  auto extent = valShapedType.getDimSize(*index);
  if (ShapedType::isDynamic(extent))
    return nullptr;
  return IntegerAttr::get(IndexType::get(getContext()), extent);
}
 
LogicalResult mlir::shape::DimOp::inferReturnTypes(
    MLIRContext *context, std::optional<Location> location,
    DimOp::Adaptor adaptor, SmallVectorImpl<Type> &inferredReturnTypes) {
  inferredReturnTypes.assign({adaptor.getIndex().getType()});
  return success();
}
 
bool mlir::shape::DimOp::isCompatibleReturnTypes(TypeRange l, TypeRange r) {
  return eachHasOnlyOneOfTypes<SizeType, IndexType>(l, r);
}
 
//===----------------------------------------------------------------------===//
// DivOp
//===----------------------------------------------------------------------===//
 
OpFoldResult DivOp::fold(FoldAdaptor adaptor) {
  auto lhs = llvm::dyn_cast_if_present<IntegerAttr>(adaptor.getLhs());
  if (!lhs)
    return nullptr;
  auto rhs = llvm::dyn_cast_if_present<IntegerAttr>(adaptor.getRhs());
  if (!rhs || rhs.getValue().isZero())
    return nullptr;
 
  // Division in APInt does not follow floor(lhs, rhs) when the result is
  // negative. Rather, APInt rounds toward zero.
  APInt quotient, remainder;
  APInt::sdivrem(lhs.getValue(), rhs.getValue(), quotient, remainder);
  if (quotient.isNegative() && !remainder.isZero()) {
    quotient -= 1;
  }
 
  Type indexTy = IndexType::get(getContext());
  return IntegerAttr::get(indexTy, quotient);
}
 
LogicalResult mlir::shape::DivOp::inferReturnTypes(
    MLIRContext *context, std::optional<Location> location,
    DivOp::Adaptor adaptor, SmallVectorImpl<Type> &inferredReturnTypes) {
  if (llvm::isa<SizeType>(adaptor.getLhs().getType()) ||
      llvm::isa<SizeType>(adaptor.getRhs().getType()))
    inferredReturnTypes.assign({SizeType::get(context)});
  else
    inferredReturnTypes.assign({IndexType::get(context)});
  return success();
}
 
bool mlir::shape::DivOp::isCompatibleReturnTypes(TypeRange l, TypeRange r) {
  // SizeType is compatible with IndexType.
  return eachHasOnlyOneOfTypes<SizeType, IndexType>(l, r);
}
 
LogicalResult DivOp::verify() { return verifySizeOrIndexOp(*this); }
 
//===----------------------------------------------------------------------===//
// ShapeEqOp
//===----------------------------------------------------------------------===//
 
OpFoldResult ShapeEqOp::fold(FoldAdaptor adaptor) {
  bool allSame = true;
  if (!adaptor.getShapes().empty() && !adaptor.getShapes().front())
    return {};
  for (Attribute operand : adaptor.getShapes().drop_front()) {
    if (!operand)
      return {};
    allSame = allSame && operand == adaptor.getShapes().front();
  }
  return BoolAttr::get(getContext(), allSame);
}
 
//===----------------------------------------------------------------------===//
// IndexToSizeOp
//===----------------------------------------------------------------------===//
 
OpFoldResult IndexToSizeOp::fold(FoldAdaptor adaptor) {
  // Constant values of both types, `shape.size` and `index`, are represented as
  // `IntegerAttr`s which makes constant folding simple.
  if (Attribute arg = adaptor.getArg())
    return arg;
  return {};
}
 
void IndexToSizeOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                                MLIRContext *context) {
  patterns.add<SizeToIndexToSizeCanonicalization>(context);
}
 
//===----------------------------------------------------------------------===//
// FromExtentsOp
//===----------------------------------------------------------------------===//
 
OpFoldResult FromExtentsOp::fold(FoldAdaptor adaptor) {
  SmallVector<int64_t, 6> extents;
  for (Attribute attr : adaptor.getExtents()) {
    auto intAttr = llvm::dyn_cast_if_present<IntegerAttr>(attr);
    if (!intAttr)
      return nullptr;
    extents.push_back(intAttr.getInt());
  }
  Builder builder(getContext());
  return builder.getIndexTensorAttr(extents);
}
 
//===----------------------------------------------------------------------===//
// FunctionLibraryOp
//===----------------------------------------------------------------------===//
 
void FunctionLibraryOp::build(OpBuilder &builder, OperationState &result,
                              StringRef name) {
  result.getOrAddProperties<Properties>().sym_name =
      builder.getStringAttr(name);
}
 
FuncOp FunctionLibraryOp::getShapeFunction(Operation *op) {
  auto attr = llvm::dyn_cast_or_null<FlatSymbolRefAttr>(
      getMapping().get(op->getName().getIdentifier()));
  if (!attr)
    return nullptr;
  return lookupSymbol<FuncOp>(attr);
}
 
ParseResult FunctionLibraryOp::parse(OpAsmParser &parser,
                                     OperationState &result) {
  // Parse the op name.
  StringAttr nameAttr;
  if (parser.parseSymbolName(nameAttr, getSymNameAttrName(result.name),
                             result.attributes))
    return failure();
 
  if (parser.parseOptionalAttrDictWithKeyword(result.attributes))
    return failure();
 
  auto *bodyRegion = result.addRegion();
  if (parser.parseRegion(*bodyRegion))
    return failure();
 
  if (parser.parseKeyword("mapping"))
    return failure();
 
  DictionaryAttr mappingAttr;
  if (parser.parseAttribute(mappingAttr,
                            parser.getBuilder().getType<NoneType>(), "mapping",
                            result.attributes))
    return failure();
  return success();
}
 
void FunctionLibraryOp::print(OpAsmPrinter &p) {
  p << ' ';
  p.printSymbolName(getName());
  p.printOptionalAttrDictWithKeyword(
      (*this)->getDiscardableAttrDictionary().getValue());
  p << ' ';
  p.printRegion(getRegion(), /*printEntryBlockArgs=*/false,
                /*printBlockTerminators=*/false);
  p << " mapping ";
  p.printAttributeWithoutType(getMappingAttr());
}
 
//===----------------------------------------------------------------------===//
// FuncOp
//===----------------------------------------------------------------------===//
 
FuncOp FuncOp::create(Location location, StringRef name, FunctionType type,
                      ArrayRef<NamedAttribute> attrs) {
  OpBuilder builder(location->getContext());
  OperationState state(location, getOperationName());
  FuncOp::build(builder, state, name, type, attrs);
  return cast<FuncOp>(Operation::create(state));
}
FuncOp FuncOp::create(Location location, StringRef name, FunctionType type,
                      Operation::dialect_attr_range attrs) {
  SmallVector<NamedAttribute, 8> attrRef(attrs);
  return create(location, name, type, llvm::ArrayRef(attrRef));
}
FuncOp FuncOp::create(Location location, StringRef name, FunctionType type,
                      ArrayRef<NamedAttribute> attrs,
                      ArrayRef<DictionaryAttr> argAttrs) {
  FuncOp func = create(location, name, type, attrs);
  func.setAllArgAttrs(argAttrs);
  return func;
}
 
void FuncOp::build(OpBuilder &builder, OperationState &state, StringRef name,
                   FunctionType type, ArrayRef<NamedAttribute> attrs,
                   ArrayRef<DictionaryAttr> argAttrs) {
  state.addAttribute(FuncOp::getSymNameAttrName(state.name),
                     builder.getStringAttr(name));
  state.addAttribute(FuncOp::getFunctionTypeAttrName(state.name),
                     TypeAttr::get(type));
  state.attributes.append(attrs.begin(), attrs.end());
  state.addRegion();
 
  if (argAttrs.empty())
    return;
  assert(type.getNumInputs() == argAttrs.size());
  call_interface_impl::addArgAndResultAttrs(
      builder, state, argAttrs, /*resultAttrs=*/{},
      getArgAttrsAttrName(state.name), getResAttrsAttrName(state.name));
}
 
ParseResult FuncOp::parse(OpAsmParser &parser, OperationState &result) {
  auto buildFuncType =
      [](Builder &builder, ArrayRef<Type> argTypes, ArrayRef<Type> results,
         function_interface_impl::VariadicFlag,
         std::string &) { return builder.getFunctionType(argTypes, results); };
 
  return function_interface_impl::parseFunctionOp(
      parser, result, /*allowVariadic=*/false,
      getFunctionTypeAttrName(result.name), buildFuncType,
      getArgAttrsAttrName(result.name), getResAttrsAttrName(result.name));
}
 
void FuncOp::print(OpAsmPrinter &p) {
  function_interface_impl::printFunctionOp(
      p, *this, /*isVariadic=*/false, getFunctionTypeAttrName(),
      getArgAttrsAttrName(), getResAttrsAttrName());
}
 
//===----------------------------------------------------------------------===//
// GetExtentOp
//===----------------------------------------------------------------------===//
 
std::optional<int64_t> GetExtentOp::getConstantDim() {
  if (auto constSizeOp = getDim().getDefiningOp<ConstSizeOp>())
    return constSizeOp.getValue().getLimitedValue();
  if (auto constantOp = getDim().getDefiningOp<arith::ConstantOp>())
    return llvm::cast<IntegerAttr>(constantOp.getValue()).getInt();
  return std::nullopt;
}
 
OpFoldResult GetExtentOp::fold(FoldAdaptor adaptor) {
  auto elements =
      llvm::dyn_cast_if_present<DenseIntElementsAttr>(adaptor.getShape());
  if (!elements)
    return nullptr;
  std::optional<int64_t> dim = getConstantDim();
  if (!dim.has_value())
    return nullptr;
  if (dim.value() >= elements.getNumElements())
    return nullptr;
  return elements.getValues<Attribute>()[(uint64_t)dim.value()];
}
 
void GetExtentOp::build(OpBuilder &builder, OperationState &result, Value shape,
                        int64_t dim) {
  auto loc = result.location;
  auto dimAttr = builder.getIndexAttr(dim);
  if (llvm::isa<ShapeType>(shape.getType())) {
    Value dim = ConstSizeOp::create(builder, loc, dimAttr);
    build(builder, result, builder.getType<SizeType>(), shape, dim);
  } else {
    Value dim = arith::ConstantOp::create(builder, loc, builder.getIndexType(),
                                          dimAttr);
    build(builder, result, builder.getIndexType(), shape, dim);
  }
}
 
LogicalResult mlir::shape::GetExtentOp::inferReturnTypes(
    MLIRContext *context, std::optional<Location> location,
    GetExtentOp::Adaptor adaptor, SmallVectorImpl<Type> &inferredReturnTypes) {
  inferredReturnTypes.assign({IndexType::get(context)});
  return success();
}
 
bool mlir::shape::GetExtentOp::isCompatibleReturnTypes(TypeRange l,
                                                       TypeRange r) {
  // SizeType is compatible with IndexType.
  return eachHasOnlyOneOfTypes<SizeType, IndexType>(l, r);
}
 
LogicalResult GetExtentOp::verify() { return verifySizeOrIndexOp(*this); }
 
//===----------------------------------------------------------------------===//
// IsBroadcastableOp
//===----------------------------------------------------------------------===//
 
void IsBroadcastableOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                                    MLIRContext *context) {
  patterns.add<RemoveDuplicateOperandsPattern<IsBroadcastableOp>>(context);
}
 
OpFoldResult IsBroadcastableOp::fold(FoldAdaptor adaptor) {
  // Can always broadcast fewer than two shapes.
  if (adaptor.getShapes().size() < 2) {
    return BoolAttr::get(getContext(), true);
  }
 
  return nullptr;
}
 
//===----------------------------------------------------------------------===//
// MeetOp
//===----------------------------------------------------------------------===//
 
LogicalResult mlir::shape::MeetOp::inferReturnTypes(
    MLIRContext *context, std::optional<Location> location,
    MeetOp::Adaptor adaptor, SmallVectorImpl<Type> &inferredReturnTypes) {
  if (adaptor.getOperands().empty())
    return failure();
 
  auto isShapeType = [](Type arg) {
    if (llvm::isa<ShapeType>(arg))
      return true;
    return isExtentTensorType(arg);
  };
 
  ValueRange::type_range types = adaptor.getOperands().getTypes();
  Type acc = types.front();
  for (auto t : drop_begin(types)) {
    Type l = acc, r = t;
    if (!llvm::isa<ShapeType, SizeType>(l))
      std::swap(l, r);
 
    // Handle sizes, propagate error type if present.
    if (llvm::isa<SizeType>(l)) {
      if (llvm::isa<SizeType, IndexType>(r))
        acc = l;
      else
        return emitOptionalError(location, "requires all sizes or shapes");
    } else if (llvm::isa<IndexType>(l)) {
      if (llvm::isa<IndexType>(r))
        acc = r;
      else
        return emitOptionalError(location, "requires all sizes or shapes");
    } else if (llvm::isa<ShapeType>(l)) {
      // Handle shapes, propagate error type if present.
      if (isShapeType(r))
        acc = l;
      else
        return emitOptionalError(location, "requires all sizes or shapes");
    } else if (isExtentTensorType(l)) {
      auto rank1 = llvm::cast<RankedTensorType>(l).getShape()[0];
      auto rank2 = llvm::cast<RankedTensorType>(r).getShape()[0];
      if (ShapedType::isDynamic(rank1))
        acc = l;
      else if (ShapedType::isDynamic(rank2))
        acc = r;
      else if (rank1 != rank2)
        return emitOptionalError(location, "unequal shape cardinality");
      else
        acc = l;
    }
  }
  inferredReturnTypes.assign({acc});
  return success();
}
 
bool mlir::shape::MeetOp::isCompatibleReturnTypes(TypeRange l, TypeRange r) {
  if (l.size() != 1 || r.size() != 1)
    return false;
  if (l == r)
    return true;
 
  Type lhs = l.front();
  Type rhs = r.front();
 
  if (!llvm::isa<ShapeType, SizeType>(lhs))
    std::swap(lhs, rhs);
 
  if (llvm::isa<SizeType>(lhs))
    return llvm::isa<SizeType, IndexType>(rhs);
  if (llvm::isa<ShapeType>(lhs))
    return llvm::isa<ShapeType, TensorType>(rhs);
 
  if (succeeded(verifyCompatibleShapes({lhs, rhs})))
    return true;
  return false;
}
 
//===----------------------------------------------------------------------===//
// RankOp
//===----------------------------------------------------------------------===//
 
OpFoldResult shape::RankOp::fold(FoldAdaptor adaptor) {
  auto shape =
      llvm::dyn_cast_if_present<DenseIntElementsAttr>(adaptor.getShape());
  if (!shape)
    return {};
  int64_t rank = shape.getNumElements();
  Builder builder(getContext());
  return builder.getIndexAttr(rank);
}
 
/// Evaluate the `rank` operation for shapes of ranked tensors at compile time.
/// Constant folding fails in cases where only the rank is constant, not the
/// shape itself.
/// This canonicalization matches `shape.rank(shape.shape_of(%ranked_tensor))`.
///
/// Example:
///
/// %shape = shape.shape_of %ranked_tensor : tensor<1x2x?xf32>
/// %rank = shape.rank %shape
///
/// becomes
///
/// %rank = shape.const_size 3
 
namespace {
struct RankShapeOfCanonicalizationPattern
    : public OpRewritePattern<shape::RankOp> {
  using OpRewritePattern<shape::RankOp>::OpRewritePattern;
 
  LogicalResult matchAndRewrite(shape::RankOp op,
                                PatternRewriter &rewriter) const override {
    auto shapeOfOp = op.getShape().getDefiningOp<ShapeOfOp>();
    if (!shapeOfOp)
      return failure();
    auto rankedTensorType =
        llvm::dyn_cast<RankedTensorType>(shapeOfOp.getArg().getType());
    if (!rankedTensorType)
      return failure();
    int64_t rank = rankedTensorType.getRank();
    if (llvm::isa<IndexType>(op.getType())) {
      rewriter.replaceOpWithNewOp<arith::ConstantIndexOp>(op.getOperation(),
                                                          rank);
    } else if (llvm::isa<shape::SizeType>(op.getType())) {
      rewriter.replaceOpWithNewOp<shape::ConstSizeOp>(op.getOperation(), rank);
    } else {
      return failure();
    }
    return success();
  }
};
} // namespace
 
void shape::RankOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                                MLIRContext *context) {
  patterns.add<RankShapeOfCanonicalizationPattern>(context);
}
 
LogicalResult mlir::shape::RankOp::inferReturnTypes(
    MLIRContext *context, std::optional<Location> location,
    RankOp::Adaptor adaptor, SmallVectorImpl<Type> &inferredReturnTypes) {
  if (llvm::isa<ShapeType>(adaptor.getShape().getType()))
    inferredReturnTypes.assign({SizeType::get(context)});
  else
    inferredReturnTypes.assign({IndexType::get(context)});
  return success();
}
 
bool mlir::shape::RankOp::isCompatibleReturnTypes(TypeRange l, TypeRange r) {
  // SizeType is compatible with IndexType.
  return eachHasOnlyOneOfTypes<SizeType, IndexType>(l, r);
}
 
LogicalResult shape::RankOp::verify() { return verifySizeOrIndexOp(*this); }
 
//===----------------------------------------------------------------------===//
// NumElementsOp
//===----------------------------------------------------------------------===//
 
OpFoldResult NumElementsOp::fold(FoldAdaptor adaptor) {
 
  // Fold only when argument constant.
  Attribute shape = adaptor.getShape();
  if (!shape)
    return {};
 
  APInt product(64, 1);
  for (auto value : llvm::cast<DenseIntElementsAttr>(shape))
    product *= value;
  Builder builder(getContext());
  return builder.getIndexAttr(product.getLimitedValue());
}
 
LogicalResult mlir::shape::NumElementsOp::inferReturnTypes(
    MLIRContext *context, std::optional<Location> location,
    NumElementsOp::Adaptor adaptor,
    SmallVectorImpl<Type> &inferredReturnTypes) {
  if (llvm::isa<ShapeType>(adaptor.getShape().getType()))
    inferredReturnTypes.assign({SizeType::get(context)});
  else
    inferredReturnTypes.assign({IndexType::get(context)});
  return success();
}
 
bool mlir::shape::NumElementsOp::isCompatibleReturnTypes(TypeRange l,
                                                         TypeRange r) {
  // SizeType is compatible with IndexType.
  return eachHasOnlyOneOfTypes<SizeType, IndexType>(l, r);
}
 
LogicalResult shape::NumElementsOp::verify() {
  return verifySizeOrIndexOp(*this);
}
 
//===----------------------------------------------------------------------===//
// MaxOp
//===----------------------------------------------------------------------===//
 
OpFoldResult MaxOp::fold(FoldAdaptor adaptor) {
  // If operands are equal, just propagate one.
  if (getLhs() == getRhs())
    return getLhs();
  return nullptr;
}
 
LogicalResult mlir::shape::MaxOp::inferReturnTypes(
    MLIRContext *context, std::optional<Location> location,
    MaxOp::Adaptor adaptor, SmallVectorImpl<Type> &inferredReturnTypes) {
  if (adaptor.getLhs().getType() == adaptor.getRhs().getType())
    inferredReturnTypes.assign({adaptor.getLhs().getType()});
  else
    inferredReturnTypes.assign({SizeType::get(context)});
  return success();
}
 
bool mlir::shape::MaxOp::isCompatibleReturnTypes(TypeRange l, TypeRange r) {
  if (l.size() != 1 || r.size() != 1)
    return false;
  if (llvm::isa<ShapeType>(l.front()) && llvm::isa<ShapeType>(r.front()))
    return true;
  if (llvm::isa<SizeType>(l.front()) && llvm::isa<SizeType>(r.front()))
    return true;
  return false;
}
 
//===----------------------------------------------------------------------===//
// MinOp
//===----------------------------------------------------------------------===//
 
OpFoldResult MinOp::fold(FoldAdaptor adaptor) {
  // If operands are equal, just propagate one.
  if (getLhs() == getRhs())
    return getLhs();
  return nullptr;
}
 
LogicalResult mlir::shape::MinOp::inferReturnTypes(
    MLIRContext *context, std::optional<Location> location,
    MinOp::Adaptor adaptor, SmallVectorImpl<Type> &inferredReturnTypes) {
  if (adaptor.getLhs().getType() == adaptor.getRhs().getType())
    inferredReturnTypes.assign({adaptor.getLhs().getType()});
  else
    inferredReturnTypes.assign({SizeType::get(context)});
  return success();
}
 
bool mlir::shape::MinOp::isCompatibleReturnTypes(TypeRange l, TypeRange r) {
  if (l.size() != 1 || r.size() != 1)
    return false;
  if (llvm::isa<ShapeType>(l.front()) && llvm::isa<ShapeType>(r.front()))
    return true;
  if (llvm::isa<SizeType>(l.front()) && llvm::isa<SizeType>(r.front()))
    return true;
  return false;
}
 
//===----------------------------------------------------------------------===//
// MulOp
//===----------------------------------------------------------------------===//
 
OpFoldResult MulOp::fold(FoldAdaptor adaptor) {
  auto lhs = llvm::dyn_cast_if_present<IntegerAttr>(adaptor.getLhs());
  if (!lhs)
    return nullptr;
  auto rhs = llvm::dyn_cast_if_present<IntegerAttr>(adaptor.getRhs());
  if (!rhs)
    return nullptr;
  APInt folded = lhs.getValue() * rhs.getValue();
  Type indexTy = IndexType::get(getContext());
  return IntegerAttr::get(indexTy, folded);
}
 
LogicalResult mlir::shape::MulOp::inferReturnTypes(
    MLIRContext *context, std::optional<Location> location,
    MulOp::Adaptor adaptor, SmallVectorImpl<Type> &inferredReturnTypes) {
  if (llvm::isa<SizeType>(adaptor.getLhs().getType()) ||
      llvm::isa<SizeType>(adaptor.getRhs().getType()))
    inferredReturnTypes.assign({SizeType::get(context)});
  else
    inferredReturnTypes.assign({IndexType::get(context)});
  return success();
}
 
bool mlir::shape::MulOp::isCompatibleReturnTypes(TypeRange l, TypeRange r) {
  // SizeType is compatible with IndexType.
  return eachHasOnlyOneOfTypes<SizeType, IndexType>(l, r);
}
 
LogicalResult shape::MulOp::verify() { return verifySizeOrIndexOp(*this); }
 
//===----------------------------------------------------------------------===//
// ShapeOfOp
//===----------------------------------------------------------------------===//
 
namespace {
/// Replace shape_of(x) where x has a constant shape with a const_shape op.
struct ShapeOfOpToConstShapeOp : public OpRewritePattern<shape::ShapeOfOp> {
  using OpRewritePattern<shape::ShapeOfOp>::OpRewritePattern;
 
  LogicalResult matchAndRewrite(shape::ShapeOfOp op,
                                PatternRewriter &rewriter) const override {
    auto type = llvm::dyn_cast<ShapedType>(op.getArg().getType());
    if (!type || !type.hasStaticShape())
      return failure();
 
    Type resultType = op.getResult().getType();
    Location loc = op.getLoc();
    Type constResType =
        isa<ShapeType>(resultType)
            ? resultType
            : RankedTensorType::get({type.getRank()}, rewriter.getIndexType());
    Value constShape =
        ConstShapeOp::create(rewriter, loc, constResType,
                             rewriter.getIndexTensorAttr(type.getShape()))
            .getResult();
    if (constShape.getType() != resultType)
      constShape =
          tensor::CastOp::create(rewriter, loc, resultType, constShape);
    rewriter.replaceOp(op, constShape);
    return success();
  }
};
 
// Canonicalize
//
// %0 = tensor.reshape %input(%shape) : (tensor<*xf32>, tensor<?xindex>) ->
//     tensor<*xf32>
// %1 = shape.shape_of %0 : tensor<*xf32> -> tensor<?xindex>
//
// to
//
// %0 = tensor.reshape %input(%shape) : (tensor<*xf32>, tensor<?xindex>) ->
//     tensor<*xf32>
// %1 = %shape
//
struct ShapeOfFromReshape : public OpRewritePattern<shape::ShapeOfOp> {
  using OpRewritePattern<shape::ShapeOfOp>::OpRewritePattern;
 
  LogicalResult matchAndRewrite(shape::ShapeOfOp op,
                                PatternRewriter &rewriter) const override {
    auto tensorReshapeOp = op.getArg().getDefiningOp<tensor::ReshapeOp>();
    if (!tensorReshapeOp)
      return rewriter.notifyMatchFailure(op, "producer is not tensor.reshape");
    if (!isa<TensorType>(op.getType()))
      return rewriter.notifyMatchFailure(op, "result is not a tensor");
 
    // Operand 'shape' of 'tensor.reshape' may now be used as the result of
    // 'shape.shape_of'. While its type is guaranteed to be compatible in well-
    // formed IR, it may not be identical (dynamically vs statically shaped),
    // in which case it needs to be cast first using 'tensor.cast'.
    // Additionally, it may not have identical element type (i32 vs index)
    // while it has identical shaped type (dynamic vs static), in which case it
    // needs to be cast first using 'arith.index_cast'. Note: 'shape.shape_of'
    // op result must be shape or extent tensor.
    Value shape = tensorReshapeOp.getShape();
 
    auto opTensorTy = cast<RankedTensorType>(op.getType());
    auto shapeTensorTy = cast<RankedTensorType>(shape.getType());
 
    if (opTensorTy != shapeTensorTy) {
      if (opTensorTy.getElementType() == shapeTensorTy.getElementType())
        shape =
            tensor::CastOp::create(rewriter, op.getLoc(), opTensorTy, shape);
      else if (!isExtentTensorType(shapeTensorTy))
        shape = arith::IndexCastOp::create(rewriter, op.getLoc(), opTensorTy,
                                           shape);
    }
 
    rewriter.replaceOp(op, shape);
    return success();
  }
};
 
// Canonicalize
// ```
// %0 = shape.shape_of %arg : tensor<?x?x?xf32> -> tensor<3xindex>
// %1 = tensor.cast %0 : tensor<3xindex> to tensor<?xindex>
// ```
// to
// ```
// %1 = shape.shape_of %arg : tensor<?x?x?xf32> -> tensor<?xindex>
// ```
struct ShapeOfCastExtentTensor : public OpRewritePattern<tensor::CastOp> {
  using OpRewritePattern<tensor::CastOp>::OpRewritePattern;
 
  LogicalResult matchAndRewrite(tensor::CastOp op,
                                PatternRewriter &rewriter) const override {
    auto ty = llvm::dyn_cast<RankedTensorType>(op.getType());
    if (!ty || ty.getRank() != 1)
      return failure();
 
    auto shapeOfOp = op.getSource().getDefiningOp<ShapeOfOp>();
    if (!shapeOfOp)
      return failure();
 
    // Argument type must be ranked and must not conflict.
    auto argTy = llvm::dyn_cast<RankedTensorType>(shapeOfOp.getArg().getType());
    if (!argTy || (!ty.isDynamicDim(0) && ty.getDimSize(0) != argTy.getRank()))
      return failure();
 
    rewriter.replaceOpWithNewOp<ShapeOfOp>(op, ty, shapeOfOp.getArg());
    return success();
  }
};
} // namespace
 
void ShapeOfOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                            MLIRContext *context) {
  patterns.add<ShapeOfCastExtentTensor, ShapeOfFromReshape,
               ExtractFromShapeOfExtentTensor, ShapeOfOpToConstShapeOp>(
      context);
}
 
LogicalResult mlir::shape::ShapeOfOp::inferReturnTypes(
    MLIRContext *context, std::optional<Location> location,
    ShapeOfOp::Adaptor adaptor, SmallVectorImpl<Type> &inferredReturnTypes) {
  if (llvm::isa<ValueShapeType>(adaptor.getArg().getType()))
    inferredReturnTypes.assign({ShapeType::get(context)});
  else {
    auto shapedTy = llvm::cast<ShapedType>(adaptor.getArg().getType());
    int64_t rank =
        shapedTy.hasRank() ? shapedTy.getRank() : ShapedType::kDynamic;
    Type indexTy = IndexType::get(context);
    Type extentTensorTy = RankedTensorType::get({rank}, indexTy);
    inferredReturnTypes.assign({extentTensorTy});
  }
  return success();
}
 
bool mlir::shape::ShapeOfOp::isCompatibleReturnTypes(TypeRange l, TypeRange r) {
  if (l.size() != 1 || r.size() != 1)
    return false;
  if (l == r)
    return true;
 
  Type lhs = l.front();
  Type rhs = r.front();
 
  if (!llvm::isa<ShapeType, ShapedType>(lhs) ||
      !llvm::isa<ShapeType, ShapedType>(rhs))
    return false;
 
  if (llvm::isa<ShapeType>(lhs) || llvm::isa<ShapeType>(rhs))
    // Shape type is compatible with all other valid return types.
    return true;
 
  if (succeeded(verifyCompatibleShapes({lhs, rhs})))
    return true;
  return false;
}
 
LogicalResult shape::ShapeOfOp::verify() {
  return verifyShapeOrExtentTensorOp(*this);
}
 
//===----------------------------------------------------------------------===//
// SizeToIndexOp
//===----------------------------------------------------------------------===//
 
OpFoldResult SizeToIndexOp::fold(FoldAdaptor adaptor) {
  // Constant values of both types, `shape.size` and `index`, are represented as
  // `IntegerAttr`s which makes constant folding simple.
  if (Attribute arg = adaptor.getArg())
    return arg;
  return OpFoldResult();
}
 
void SizeToIndexOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                                MLIRContext *context) {
  patterns.add<IndexToSizeToIndexCanonicalization>(context);
}
 
bool SizeToIndexOp::areCastCompatible(TypeRange inputs, TypeRange outputs) {
  if (inputs.size() != 1 || outputs.size() != 1)
    return false;
  return llvm::isa<IndexType, SizeType>(inputs[0]) &&
         llvm::isa<IndexType>(outputs[0]);
}
 
//===----------------------------------------------------------------------===//
// YieldOp
//===----------------------------------------------------------------------===//
 
LogicalResult shape::YieldOp::verify() {
  auto *parentOp = (*this)->getParentOp();
  auto results = parentOp->getResults();
  auto operands = getOperands();
 
  if (parentOp->getNumResults() != getNumOperands())
    return emitOpError() << "number of operands does not match number of "
                            "results of its parent";
  for (auto e : llvm::zip(results, operands))
    if (std::get<0>(e).getType() != std::get<1>(e).getType())
      return emitOpError() << "types mismatch between yield op and its parent";
 
  return success();
}
 
//===----------------------------------------------------------------------===//
// SplitAtOp
//===----------------------------------------------------------------------===//
 
LogicalResult SplitAtOp::fold(FoldAdaptor adaptor,
                              SmallVectorImpl<OpFoldResult> &results) {
  if (!adaptor.getOperand() || !adaptor.getIndex())
    return failure();
  auto shapeVec =
      llvm::to_vector<6>(llvm::cast<DenseIntElementsAttr>(adaptor.getOperand())
                             .getValues<int64_t>());
  auto shape = llvm::ArrayRef(shapeVec);
  auto splitPoint = llvm::cast<IntegerAttr>(adaptor.getIndex()).getInt();
  // Verify that the split point is in the correct range.
  // TODO: Constant fold to an "error".
  int64_t rank = shape.size();
  if (-rank > splitPoint || splitPoint > rank)
    return failure();
  if (splitPoint < 0)
    splitPoint += shape.size();
  Builder builder(adaptor.getOperand().getContext());
  results.push_back(builder.getIndexTensorAttr(shape.take_front(splitPoint)));
  results.push_back(builder.getIndexTensorAttr(shape.drop_front(splitPoint)));
  return success();
}
 
//===----------------------------------------------------------------------===//
// ToExtentTensorOp
//===----------------------------------------------------------------------===//
 
OpFoldResult ToExtentTensorOp::fold(FoldAdaptor adaptor) {
  if (!adaptor.getInput())
    return OpFoldResult();
  Builder builder(getContext());
  auto shape =
      llvm::to_vector<6>(llvm::cast<DenseIntElementsAttr>(adaptor.getInput())
                             .getValues<int64_t>());
  auto type = RankedTensorType::get({static_cast<int64_t>(shape.size())},
                                    builder.getIndexType());
  return DenseIntElementsAttr::get(type, shape);
}
 
bool ToExtentTensorOp::areCastCompatible(TypeRange inputs, TypeRange outputs) {
  if (inputs.size() != 1 || outputs.size() != 1)
    return false;
  if (auto inputTensor = llvm::dyn_cast<RankedTensorType>(inputs[0])) {
    if (!llvm::isa<IndexType>(inputTensor.getElementType()) ||
        inputTensor.getRank() != 1)
      return false;
  } else if (!llvm::isa<ShapeType>(inputs[0])) {
    return false;
  }
 
  TensorType outputTensor = llvm::dyn_cast<TensorType>(outputs[0]);
  return outputTensor && llvm::isa<IndexType>(outputTensor.getElementType());
}
 
//===----------------------------------------------------------------------===//
// ReduceOp
//===----------------------------------------------------------------------===//
 
void ReduceOp::build(OpBuilder &builder, OperationState &result, Value shape,
                     ValueRange initVals) {
  OpBuilder::InsertionGuard g(builder);
  result.addOperands(shape);
  result.addOperands(initVals);
 
  Region *bodyRegion = result.addRegion();
  Block *bodyBlock = builder.createBlock(
      bodyRegion, /*insertPt=*/{}, builder.getIndexType(), result.location);
 
  Type elementType;
  if (auto tensorType = llvm::dyn_cast<TensorType>(shape.getType()))
    elementType = tensorType.getElementType();
  else
    elementType = SizeType::get(builder.getContext());
  bodyBlock->addArgument(elementType, shape.getLoc());
 
  for (Value initVal : initVals) {
    bodyBlock->addArgument(initVal.getType(), initVal.getLoc());
    result.addTypes(initVal.getType());
  }
}
 
LogicalResult ReduceOp::verify() {
  // Verify block arg types.
  Block &block = getRegion().front();
 
  // The block takes index, extent, and aggregated values as arguments.
  auto blockArgsCount = getInitVals().size() + 2;
  if (block.getNumArguments() != blockArgsCount)
    return emitOpError() << "ReduceOp body is expected to have "
                         << blockArgsCount << " arguments";
 
  // The first block argument is the index and must always be of type `index`.
  if (!llvm::isa<IndexType>(block.getArgument(0).getType()))
    return emitOpError(
        "argument 0 of ReduceOp body is expected to be of IndexType");
 
  // The second block argument is the extent and must be of type `size` or
  // `index`, depending on whether the reduce operation is applied to a shape or
  // to an extent tensor.
  Type extentTy = block.getArgument(1).getType();
  if (llvm::isa<ShapeType>(getShape().getType())) {
    if (!llvm::isa<SizeType>(extentTy))
      return emitOpError("argument 1 of ReduceOp body is expected to be of "
                         "SizeType if the ReduceOp operates on a ShapeType");
  } else {
    if (!llvm::isa<IndexType>(extentTy))
      return emitOpError(
          "argument 1 of ReduceOp body is expected to be of IndexType if the "
          "ReduceOp operates on an extent tensor");
  }
 
  for (const auto &type : llvm::enumerate(getInitVals()))
    if (block.getArgument(type.index() + 2).getType() != type.value().getType())
      return emitOpError() << "type mismatch between argument "
                           << type.index() + 2
                           << " of ReduceOp body and initial value "
                           << type.index();
  return success();
}
 
ParseResult ReduceOp::parse(OpAsmParser &parser, OperationState &result) {
  // Parse operands.
  SmallVector<OpAsmParser::UnresolvedOperand, 3> operands;
  Type shapeOrExtentTensorType;
  if (parser.parseOperandList(operands, /*requiredOperandCount=*/-1,
                              OpAsmParser::Delimiter::Paren) ||
      parser.parseColonType(shapeOrExtentTensorType) ||
      parser.parseOptionalArrowTypeList(result.types))
    return failure();
 
  // Resolve operands.
  auto initVals = llvm::ArrayRef(operands).drop_front();
  if (parser.resolveOperand(operands.front(), shapeOrExtentTensorType,
                            result.operands) ||
      parser.resolveOperands(initVals, result.types, parser.getNameLoc(),
                             result.operands))
    return failure();
 
  // Parse the body.
  Region *body = result.addRegion();
  if (parser.parseRegion(*body, /*args=*/{}, /*argTypes=*/{}))
    return failure();
 
  // Parse attributes.
  if (parser.parseOptionalAttrDict(result.attributes))
    return failure();
 
  return success();
}
 
void ReduceOp::print(OpAsmPrinter &p) {
  p << '(' << getShape() << ", " << getInitVals()
    << ") : " << getShape().getType();
  p.printOptionalArrowTypeList(getResultTypes());
  p << ' ';
  p.printRegion(getRegion());
  p.printOptionalAttrDict((*this)->getDiscardableAttrDictionary().getValue());
}
 
#define GET_OP_CLASSES
#include "mlir/Dialect/Shape/IR/ShapeOps.cpp.inc"
 
#define GET_TYPEDEF_CLASSES
#include "mlir/Dialect/Shape/IR/ShapeOpsTypes.cpp.inc"