IntRangeOptimizations.cpp

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//===- IntRangeOptimizations.cpp - Optimizations based on integer ranges --===//
//
// 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 <utility>
 
#include "mlir/Analysis/DataFlow/ConstantPropagationAnalysis.h"
#include "mlir/Analysis/DataFlow/Utils.h"
#include "mlir/Analysis/DataFlowFramework.h"
#include "mlir/Dialect/Arith/Transforms/Passes.h"
 
#include "mlir/Analysis/DataFlow/DeadCodeAnalysis.h"
#include "mlir/Analysis/DataFlow/IntegerRangeAnalysis.h"
#include "mlir/Dialect/Arith/IR/Arith.h"
#include "mlir/Dialect/Utils/StaticValueUtils.h"
#include "mlir/IR/IRMapping.h"
#include "mlir/IR/Matchers.h"
#include "mlir/IR/PatternMatch.h"
#include "mlir/IR/TypeUtilities.h"
#include "mlir/Interfaces/LoopLikeInterface.h"
#include "mlir/Interfaces/SideEffectInterfaces.h"
#include "mlir/Transforms/FoldUtils.h"
#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
 
namespace mlir::arith {
#define GEN_PASS_DEF_ARITHINTRANGEOPTS
#include "mlir/Dialect/Arith/Transforms/Passes.h.inc"
 
#define GEN_PASS_DEF_ARITHINTRANGENARROWING
#include "mlir/Dialect/Arith/Transforms/Passes.h.inc"
} // namespace mlir::arith
 
using namespace mlir;
using namespace mlir::arith;
using namespace mlir::dataflow;
 
static std::optional<APInt> getMaybeConstantValue(DataFlowSolver &solver,
                                                  Value value) {
  auto *maybeInferredRange =
      solver.lookupState<IntegerValueRangeLattice>(value);
  if (!maybeInferredRange || maybeInferredRange->getValue().isUninitialized())
    return std::nullopt;
  const ConstantIntRanges &inferredRange =
      maybeInferredRange->getValue().getValue();
  return inferredRange.getConstantValue();
}
 
static void copyIntegerRange(DataFlowSolver &solver, Value oldVal,
                             Value newVal) {
  auto *oldState = solver.lookupState<IntegerValueRangeLattice>(oldVal);
  if (!oldState)
    return;
  (void)solver.getOrCreateState<IntegerValueRangeLattice>(newVal)->join(
      *oldState);
}
 
namespace mlir::dataflow {
/// Patterned after SCCP
LogicalResult maybeReplaceWithConstant(DataFlowSolver &solver,
                                       RewriterBase &rewriter, Value value) {
  if (value.use_empty())
    return failure();
  std::optional<APInt> maybeConstValue = getMaybeConstantValue(solver, value);
  if (!maybeConstValue.has_value())
    return failure();
 
  Type type = value.getType();
  Location loc = value.getLoc();
  Operation *maybeDefiningOp = value.getDefiningOp();
  Dialect *valueDialect =
      maybeDefiningOp ? maybeDefiningOp->getDialect()
                      : value.getParentRegion()->getParentOp()->getDialect();
 
  Attribute constAttr;
  if (auto shaped = dyn_cast<ShapedType>(type)) {
    constAttr = mlir::DenseIntElementsAttr::get(shaped, *maybeConstValue);
  } else {
    constAttr = rewriter.getIntegerAttr(type, *maybeConstValue);
  }
  Operation *constOp =
      valueDialect->materializeConstant(rewriter, constAttr, type, loc);
  // Fall back to arith.constant if the dialect materializer doesn't know what
  // to do with an integer constant.
  if (!constOp)
    constOp = rewriter.getContext()
                  ->getLoadedDialect<ArithDialect>()
                  ->materializeConstant(rewriter, constAttr, type, loc);
  if (!constOp)
    return failure();
 
  OpResult res = constOp->getResult(0);
  if (solver.lookupState<dataflow::IntegerValueRangeLattice>(res))
    solver.eraseState(res);
  copyIntegerRange(solver, value, res);
  rewriter.replaceAllUsesWith(value, res);
  return success();
}
} // namespace mlir::dataflow
 
namespace {
class DataFlowListener : public RewriterBase::Listener {
public:
  DataFlowListener(DataFlowSolver &s) : s(s) {}
 
protected:
  void notifyOperationErased(Operation *op) override {
    s.eraseState(s.getProgramPointAfter(op));
    for (Value res : op->getResults())
      s.eraseState(res);
  }
 
  DataFlowSolver &s;
};
 
/// Rewrite any results of `op` that were inferred to be constant integers to
/// and replace their uses with that constant. Return success() if all results
/// where thus replaced and the operation is erased. Also replace any block
/// arguments with their constant values.
struct MaterializeKnownConstantValues : public RewritePattern {
  MaterializeKnownConstantValues(MLIRContext *context, DataFlowSolver &s)
      : RewritePattern::RewritePattern(Pattern::MatchAnyOpTypeTag(),
                                       /*benefit=*/1, context),
        solver(s) {}
 
  LogicalResult matchAndRewrite(Operation *op,
                                PatternRewriter &rewriter) const override {
    if (matchPattern(op, m_Constant()))
      return failure();
 
    auto needsReplacing = [&](Value v) {
      return getMaybeConstantValue(solver, v).has_value() && !v.use_empty();
    };
    bool hasConstantResults = llvm::any_of(op->getResults(), needsReplacing);
    if (op->getNumRegions() == 0)
      if (!hasConstantResults)
        return failure();
    bool hasConstantRegionArgs = false;
    for (Region &region : op->getRegions()) {
      for (Block &block : region.getBlocks()) {
        hasConstantRegionArgs |=
            llvm::any_of(block.getArguments(), needsReplacing);
      }
    }
    if (!hasConstantResults && !hasConstantRegionArgs)
      return failure();
 
    bool replacedAll = (op->getNumResults() != 0);
    for (Value v : op->getResults())
      replacedAll &=
          (succeeded(maybeReplaceWithConstant(solver, rewriter, v)) ||
           v.use_empty());
    if (replacedAll && isOpTriviallyDead(op)) {
      rewriter.eraseOp(op);
      return success();
    }
 
    PatternRewriter::InsertionGuard guard(rewriter);
    for (Region &region : op->getRegions()) {
      for (Block &block : region.getBlocks()) {
        rewriter.setInsertionPointToStart(&block);
        for (BlockArgument &arg : block.getArguments()) {
          (void)maybeReplaceWithConstant(solver, rewriter, arg);
        }
      }
    }
 
    return success();
  }
 
private:
  DataFlowSolver &solver;
};
 
template <typename RemOp>
struct DeleteTrivialRem : public OpRewritePattern<RemOp> {
  DeleteTrivialRem(MLIRContext *context, DataFlowSolver &s)
      : OpRewritePattern<RemOp>(context), solver(s) {}
 
  LogicalResult matchAndRewrite(RemOp op,
                                PatternRewriter &rewriter) const override {
    Value lhs = op.getOperand(0);
    Value rhs = op.getOperand(1);
    auto maybeModulus = getConstantIntValue(rhs);
    if (!maybeModulus.has_value())
      return failure();
    int64_t modulus = *maybeModulus;
    if (modulus <= 0)
      return failure();
    auto *maybeLhsRange = solver.lookupState<IntegerValueRangeLattice>(lhs);
    if (!maybeLhsRange || maybeLhsRange->getValue().isUninitialized())
      return failure();
    const ConstantIntRanges &lhsRange = maybeLhsRange->getValue().getValue();
    const APInt &min = isa<RemUIOp>(op) ? lhsRange.umin() : lhsRange.smin();
    const APInt &max = isa<RemUIOp>(op) ? lhsRange.umax() : lhsRange.smax();
    // The minima and maxima here are given as closed ranges, we must be
    // strictly less than the modulus.
    if (min.isNegative() || min.uge(modulus))
      return failure();
    if (max.isNegative() || max.uge(modulus))
      return failure();
    if (!min.ule(max))
      return failure();
 
    // With all those conditions out of the way, we know thas this invocation of
    // a remainder is a noop because the input is strictly within the range
    // [0, modulus), so get rid of it.
    rewriter.replaceOp(op, ValueRange{lhs});
    return success();
  }
 
private:
  DataFlowSolver &solver;
};
 
/// Gather ranges for all the values in `values`. Appends to the existing
/// vector.
static LogicalResult collectRanges(DataFlowSolver &solver, ValueRange values,
                                   SmallVectorImpl<ConstantIntRanges> &ranges) {
  for (Value val : values) {
    auto *maybeInferredRange =
        solver.lookupState<IntegerValueRangeLattice>(val);
    if (!maybeInferredRange || maybeInferredRange->getValue().isUninitialized())
      return failure();
 
    const ConstantIntRanges &inferredRange =
        maybeInferredRange->getValue().getValue();
    ranges.push_back(inferredRange);
  }
  return success();
}
 
/// Return int type truncated to `targetBitwidth`. If `srcType` is shaped,
/// return shaped type as well.
static Type getTargetType(Type srcType, unsigned targetBitwidth) {
  auto dstType = IntegerType::get(srcType.getContext(), targetBitwidth);
  if (auto shaped = dyn_cast<ShapedType>(srcType))
    return shaped.clone(dstType);
 
  assert(srcType.isIntOrIndex() && "Invalid src type");
  return dstType;
}
 
namespace {
// Enum for tracking which type of truncation should be performed
// to narrow an operation, if any.
enum class CastKind : uint8_t { None, Signed, Unsigned, Both };
} // namespace
 
/// If the values within `range` can be represented using only `width` bits,
/// return the kind of truncation needed to preserve that property.
///
/// This check relies on the fact that the signed and unsigned ranges are both
/// always correct, but that one might be an approximation of the other,
/// so we want to use the correct truncation operation.
static CastKind checkTruncatability(const ConstantIntRanges &range,
                                    unsigned targetWidth) {
  unsigned srcWidth = range.smin().getBitWidth();
  if (srcWidth <= targetWidth)
    return CastKind::None;
  unsigned removedWidth = srcWidth - targetWidth;
  // The sign bits need to extend into the sign bit of the target width. For
  // example, if we're truncating 64 bits to 32, we need 64 - 32 + 1 = 33 sign
  // bits.
  bool canTruncateSigned =
      range.smin().getNumSignBits() >= (removedWidth + 1) &&
      range.smax().getNumSignBits() >= (removedWidth + 1);
  bool canTruncateUnsigned = range.umin().countLeadingZeros() >= removedWidth &&
                             range.umax().countLeadingZeros() >= removedWidth;
  if (canTruncateSigned && canTruncateUnsigned)
    return CastKind::Both;
  if (canTruncateSigned)
    return CastKind::Signed;
  if (canTruncateUnsigned)
    return CastKind::Unsigned;
  return CastKind::None;
}
 
static CastKind mergeCastKinds(CastKind lhs, CastKind rhs) {
  if (lhs == CastKind::None || rhs == CastKind::None)
    return CastKind::None;
  if (lhs == CastKind::Both)
    return rhs;
  if (rhs == CastKind::Both)
    return lhs;
  if (lhs == rhs)
    return lhs;
  return CastKind::None;
}
 
static Value doCast(OpBuilder &builder, Location loc, Value src, Type dstType,
                    CastKind castKind) {
  Type srcType = src.getType();
  assert(isa<VectorType>(srcType) == isa<VectorType>(dstType) &&
         "Mixing vector and non-vector types");
  assert(castKind != CastKind::None && "Can't cast when casting isn't allowed");
  Type srcElemType = getElementTypeOrSelf(srcType);
  Type dstElemType = getElementTypeOrSelf(dstType);
  assert(srcElemType.isIntOrIndex() && "Invalid src type");
  assert(dstElemType.isIntOrIndex() && "Invalid dst type");
  if (srcType == dstType)
    return src;
 
  if (isa<IndexType>(srcElemType) || isa<IndexType>(dstElemType)) {
    if (castKind == CastKind::Signed)
      return arith::IndexCastOp::create(builder, loc, dstType, src);
    return arith::IndexCastUIOp::create(builder, loc, dstType, src);
  }
 
  auto srcInt = cast<IntegerType>(srcElemType);
  auto dstInt = cast<IntegerType>(dstElemType);
  if (dstInt.getWidth() < srcInt.getWidth())
    return arith::TruncIOp::create(builder, loc, dstType, src);
 
  if (castKind == CastKind::Signed)
    return arith::ExtSIOp::create(builder, loc, dstType, src);
  return arith::ExtUIOp::create(builder, loc, dstType, src);
}
 
struct NarrowElementwise final : OpTraitRewritePattern<OpTrait::Elementwise> {
  NarrowElementwise(MLIRContext *context, DataFlowSolver &s,
                    ArrayRef<unsigned> target)
      : OpTraitRewritePattern(context), solver(s), targetBitwidths(target) {}
 
  using OpTraitRewritePattern::OpTraitRewritePattern;
  LogicalResult matchAndRewrite(Operation *op,
                                PatternRewriter &rewriter) const override {
    if (op->getNumResults() == 0)
      return rewriter.notifyMatchFailure(op, "can't narrow resultless op");
 
    SmallVector<ConstantIntRanges> ranges;
    if (failed(collectRanges(solver, op->getOperands(), ranges)))
      return rewriter.notifyMatchFailure(op, "input without specified range");
    if (failed(collectRanges(solver, op->getResults(), ranges)))
      return rewriter.notifyMatchFailure(op, "output without specified range");
 
    Type srcType = op->getResult(0).getType();
    if (!llvm::all_equal(op->getResultTypes()))
      return rewriter.notifyMatchFailure(op, "mismatched result types");
    if (op->getNumOperands() == 0 ||
        !llvm::all_of(op->getOperandTypes(),
                      [=](Type t) { return t == srcType; }))
      return rewriter.notifyMatchFailure(
          op, "no operands or operand types don't match result type");
 
    for (unsigned targetBitwidth : targetBitwidths) {
      CastKind castKind = CastKind::Both;
      for (const ConstantIntRanges &range : ranges) {
        castKind = mergeCastKinds(castKind,
                                  checkTruncatability(range, targetBitwidth));
        if (castKind == CastKind::None)
          break;
      }
      if (castKind == CastKind::None)
        continue;
      Type targetType = getTargetType(srcType, targetBitwidth);
      if (targetType == srcType)
        continue;
 
      Location loc = op->getLoc();
      IRMapping mapping;
      for (auto [arg, argRange] : llvm::zip_first(op->getOperands(), ranges)) {
        CastKind argCastKind = castKind;
        // When dealing with `index` values, preserve non-negativity in the
        // index_casts since we can't recover this in unsigned when equivalent.
        if (argCastKind == CastKind::Signed && argRange.smin().isNonNegative())
          argCastKind = CastKind::Both;
        Value newArg = doCast(rewriter, loc, arg, targetType, argCastKind);
        mapping.map(arg, newArg);
      }
 
      Operation *newOp = rewriter.clone(*op, mapping);
      rewriter.modifyOpInPlace(newOp, [&]() {
        for (OpResult res : newOp->getResults()) {
          res.setType(targetType);
        }
      });
      SmallVector<Value> newResults;
      for (auto [newRes, oldRes] :
           llvm::zip_equal(newOp->getResults(), op->getResults())) {
        Value castBack = doCast(rewriter, loc, newRes, srcType, castKind);
        copyIntegerRange(solver, oldRes, castBack);
        newResults.push_back(castBack);
      }
 
      rewriter.replaceOp(op, newResults);
      return success();
    }
    return failure();
  }
 
private:
  DataFlowSolver &solver;
  SmallVector<unsigned, 4> targetBitwidths;
};
 
struct NarrowCmpI final : OpRewritePattern<arith::CmpIOp> {
  NarrowCmpI(MLIRContext *context, DataFlowSolver &s, ArrayRef<unsigned> target)
      : OpRewritePattern(context), solver(s), targetBitwidths(target) {}
 
  LogicalResult matchAndRewrite(arith::CmpIOp op,
                                PatternRewriter &rewriter) const override {
    Value lhs = op.getLhs();
    Value rhs = op.getRhs();
 
    SmallVector<ConstantIntRanges> ranges;
    if (failed(collectRanges(solver, op.getOperands(), ranges)))
      return failure();
    const ConstantIntRanges &lhsRange = ranges[0];
    const ConstantIntRanges &rhsRange = ranges[1];
 
    Type srcType = lhs.getType();
    for (unsigned targetBitwidth : targetBitwidths) {
      CastKind lhsCastKind = checkTruncatability(lhsRange, targetBitwidth);
      CastKind rhsCastKind = checkTruncatability(rhsRange, targetBitwidth);
      CastKind castKind = mergeCastKinds(lhsCastKind, rhsCastKind);
      // Note: this includes target width > src width.
      if (castKind == CastKind::None)
        continue;
 
      Type targetType = getTargetType(srcType, targetBitwidth);
      if (targetType == srcType)
        continue;
 
      Location loc = op->getLoc();
      IRMapping mapping;
      Value lhsCast = doCast(rewriter, loc, lhs, targetType, lhsCastKind);
      Value rhsCast = doCast(rewriter, loc, rhs, targetType, rhsCastKind);
      mapping.map(lhs, lhsCast);
      mapping.map(rhs, rhsCast);
 
      Operation *newOp = rewriter.clone(*op, mapping);
      copyIntegerRange(solver, op.getResult(), newOp->getResult(0));
      rewriter.replaceOp(op, newOp->getResults());
      return success();
    }
    return failure();
  }
 
private:
  DataFlowSolver &solver;
  SmallVector<unsigned, 4> targetBitwidths;
};
 
/// Fold index_cast(index_cast(%arg: i8, index), i8) -> %arg
/// This pattern assumes all passed `targetBitwidths` are not wider than index
/// type.
template <typename CastOp>
struct FoldIndexCastChain final : OpRewritePattern<CastOp> {
  FoldIndexCastChain(MLIRContext *context, ArrayRef<unsigned> target)
      : OpRewritePattern<CastOp>(context), targetBitwidths(target) {}
 
  LogicalResult matchAndRewrite(CastOp op,
                                PatternRewriter &rewriter) const override {
    auto srcOp = op.getIn().template getDefiningOp<CastOp>();
    if (!srcOp)
      return rewriter.notifyMatchFailure(op, "doesn't come from an index cast");
 
    Value src = srcOp.getIn();
    if (src.getType() != op.getType())
      return rewriter.notifyMatchFailure(op, "outer types don't match");
 
    if (!srcOp.getType().isIndex())
      return rewriter.notifyMatchFailure(op, "intermediate type isn't index");
 
    auto intType = dyn_cast<IntegerType>(op.getType());
    if (!intType || !llvm::is_contained(targetBitwidths, intType.getWidth()))
      return failure();
 
    rewriter.replaceOp(op, src);
    return success();
  }
 
private:
  SmallVector<unsigned, 4> targetBitwidths;
};
 
struct NarrowLoopBounds final : OpInterfaceRewritePattern<LoopLikeOpInterface> {
  NarrowLoopBounds(MLIRContext *context, DataFlowSolver &s,
                   ArrayRef<unsigned> target)
      : OpInterfaceRewritePattern<LoopLikeOpInterface>(context), solver(s),
        targetBitwidths(target),
        boundsNarrowingFailedAttr(
            StringAttr::get(context, "arith.bounds_narrowing_failed")) {}
 
  LogicalResult matchAndRewrite(LoopLikeOpInterface loopLike,
                                PatternRewriter &rewriter) const override {
    // Skip ops where bounds narrowing previously failed.
    if (loopLike->hasAttr(boundsNarrowingFailedAttr))
      return rewriter.notifyMatchFailure(loopLike,
                                         "bounds narrowing previously failed");
 
    std::optional<SmallVector<Value>> inductionVars =
        loopLike.getLoopInductionVars();
    if (!inductionVars.has_value() || inductionVars->empty())
      return rewriter.notifyMatchFailure(loopLike, "no induction variables");
 
    std::optional<SmallVector<OpFoldResult>> lowerBounds =
        loopLike.getLoopLowerBounds();
    std::optional<SmallVector<OpFoldResult>> upperBounds =
        loopLike.getLoopUpperBounds();
    std::optional<SmallVector<OpFoldResult>> steps = loopLike.getLoopSteps();
 
    if (!lowerBounds.has_value() || !upperBounds.has_value() ||
        !steps.has_value())
      return rewriter.notifyMatchFailure(loopLike, "no loop bounds or steps");
 
    if (lowerBounds->size() != inductionVars->size() ||
        upperBounds->size() != inductionVars->size() ||
        steps->size() != inductionVars->size())
      return rewriter.notifyMatchFailure(loopLike,
                                         "mismatched bounds/steps count");
 
    Location loc = loopLike->getLoc();
    SmallVector<OpFoldResult> newLowerBounds(*lowerBounds);
    SmallVector<OpFoldResult> newUpperBounds(*upperBounds);
    SmallVector<OpFoldResult> newSteps(*steps);
    SmallVector<std::tuple<size_t, Type, CastKind>> narrowings;
 
    // Check each (indVar, lb, ub, step) tuple.
    for (auto [idx, indVar, lbOFR, ubOFR, stepOFR] :
         llvm::enumerate(*inductionVars, *lowerBounds, *upperBounds, *steps)) {
 
      // Only process value operands, skip attributes.
      auto maybeLb = dyn_cast<Value>(lbOFR);
      auto maybeUb = dyn_cast<Value>(ubOFR);
      auto maybeStep = dyn_cast<Value>(stepOFR);
 
      if (!maybeLb || !maybeUb || !maybeStep)
        continue;
 
      // Collect ranges for (lb, ub, step, indVar).
      SmallVector<ConstantIntRanges> ranges;
      if (failed(collectRanges(
              solver, ValueRange{maybeLb, maybeUb, maybeStep, indVar}, ranges)))
        continue;
 
      const ConstantIntRanges &stepRange = ranges[2];
      const ConstantIntRanges &indVarRange = ranges[3];
 
      Type srcType = maybeLb.getType();
 
      // Try each target bitwidth.
      for (unsigned targetBitwidth : targetBitwidths) {
        Type targetType = getTargetType(srcType, targetBitwidth);
        if (targetType == srcType)
          continue;
 
        // Check if the target type is valid for this loop's induction
        // variables.
        if (!loopLike.isValidInductionVarType(targetType))
          continue;
 
        // Check if all values in this tuple can be truncated.
        CastKind castKind = CastKind::Both;
        for (const ConstantIntRanges &range : ranges) {
          castKind = mergeCastKinds(castKind,
                                    checkTruncatability(range, targetBitwidth));
          if (castKind == CastKind::None)
            break;
        }
 
        if (castKind == CastKind::None)
          continue;
 
        // Check if indVar + step fits in the narrowed type.
        // This is critical for loop correctness: the loop computes
        // iv_next = iv_current + step in the narrowed type, then compares
        // iv_next < ub. If iv_current + step overflows, the comparison may
        // produce incorrect results and break loop termination.
        // Both signed and unsigned interpretations must fit because loop
        // semantics are unknown (integer types are signless).
        ConstantIntRanges indVarPlusStepRange(
            indVarRange.smin().sadd_sat(stepRange.smin()),
            indVarRange.smax().sadd_sat(stepRange.smax()),
            indVarRange.umin().uadd_sat(stepRange.umin()),
            indVarRange.umax().uadd_sat(stepRange.umax()));
 
        if (checkTruncatability(indVarPlusStepRange, targetBitwidth) !=
            CastKind::Both)
          continue;
 
        // Narrow the bounds and step values.
        Value newLb = doCast(rewriter, loc, maybeLb, targetType, castKind);
        Value newUb = doCast(rewriter, loc, maybeUb, targetType, castKind);
        Value newStep = doCast(rewriter, loc, maybeStep, targetType, castKind);
 
        newLowerBounds[idx] = newLb;
        newUpperBounds[idx] = newUb;
        newSteps[idx] = newStep;
        narrowings.push_back({idx, targetType, castKind});
        break;
      }
    }
 
    if (narrowings.empty())
      return rewriter.notifyMatchFailure(loopLike, "no narrowings found");
 
    // Save original types before modifying.
    SmallVector<Type> origTypes;
    for (auto [idx, targetType, castKind] : narrowings) {
      Value indVar = (*inductionVars)[idx];
      origTypes.push_back(indVar.getType());
    }
 
    // Attempt to update bounds and induction variable types.
    // If this fails, mark the op so we don't try again.
    bool updateFailed = false;
    rewriter.modifyOpInPlace(loopLike, [&]() {
      // Update the loop bounds and steps.
      if (failed(loopLike.setLoopLowerBounds(newLowerBounds)) ||
          failed(loopLike.setLoopUpperBounds(newUpperBounds)) ||
          failed(loopLike.setLoopSteps(newSteps))) {
        // Mark op to prevent future attempts. IR was modified (attribute
        // added), so we must return success() from the pattern.
        loopLike->setAttr(boundsNarrowingFailedAttr, rewriter.getUnitAttr());
        updateFailed = true;
        return;
      }
 
      // Update induction variable types.
      for (auto [idx, targetType, castKind] : narrowings) {
        Value indVar = (*inductionVars)[idx];
        auto blockArg = cast<BlockArgument>(indVar);
 
        // Change the block argument type.
        blockArg.setType(targetType);
      }
    });
 
    if (updateFailed)
      return success();
 
    // Insert casts back to original type for uses.
    for (auto [narrowingIdx, narrowingInfo] : llvm::enumerate(narrowings)) {
      auto [idx, targetType, castKind] = narrowingInfo;
      Value indVar = (*inductionVars)[idx];
      auto blockArg = cast<BlockArgument>(indVar);
      Type origType = origTypes[narrowingIdx];
 
      OpBuilder::InsertionGuard guard(rewriter);
      rewriter.setInsertionPointToStart(blockArg.getOwner());
      Value casted = doCast(rewriter, loc, blockArg, origType, castKind);
      copyIntegerRange(solver, blockArg, casted);
 
      // Replace all uses of the narrowed indVar with the casted value.
      rewriter.replaceAllUsesExcept(blockArg, casted, casted.getDefiningOp());
    }
 
    return success();
  }
 
private:
  DataFlowSolver &solver;
  SmallVector<unsigned, 4> targetBitwidths;
  StringAttr boundsNarrowingFailedAttr;
};
 
struct IntRangeOptimizationsPass final
    : arith::impl::ArithIntRangeOptsBase<IntRangeOptimizationsPass> {
 
  void runOnOperation() override {
    Operation *op = getOperation();
    MLIRContext *ctx = op->getContext();
    DataFlowSolver solver;
    loadBaselineAnalyses(solver);
    solver.load<IntegerRangeAnalysis>();
    if (failed(solver.initializeAndRun(op)))
      return signalPassFailure();
 
    DataFlowListener listener(solver);
 
    RewritePatternSet patterns(ctx);
    populateIntRangeOptimizationsPatterns(patterns, solver);
 
    if (failed(applyPatternsGreedily(
            op, std::move(patterns),
            GreedyRewriteConfig().setListener(&listener))))
      signalPassFailure();
  }
};
 
struct IntRangeNarrowingPass final
    : arith::impl::ArithIntRangeNarrowingBase<IntRangeNarrowingPass> {
  using ArithIntRangeNarrowingBase::ArithIntRangeNarrowingBase;
 
  void runOnOperation() override {
    Operation *op = getOperation();
    MLIRContext *ctx = op->getContext();
    DataFlowSolver solver;
    loadBaselineAnalyses(solver);
    solver.load<IntegerRangeAnalysis>();
    if (failed(solver.initializeAndRun(op)))
      return signalPassFailure();
 
    DataFlowListener listener(solver);
 
    RewritePatternSet patterns(ctx);
    populateIntRangeNarrowingPatterns(patterns, solver, bitwidthsSupported);
    populateControlFlowValuesNarrowingPatterns(patterns, solver,
                                               bitwidthsSupported);
 
    // We specifically need bottom-up traversal as cmpi pattern needs range
    // data, attached to its original argument values.
    if (failed(applyPatternsGreedily(
            op, std::move(patterns),
            GreedyRewriteConfig().setUseTopDownTraversal(false).setListener(
                &listener))))
      signalPassFailure();
  }
};
} // namespace
 
void mlir::arith::populateIntRangeOptimizationsPatterns(
    RewritePatternSet &patterns, DataFlowSolver &solver) {
  patterns.add<MaterializeKnownConstantValues, DeleteTrivialRem<RemSIOp>,
               DeleteTrivialRem<RemUIOp>>(patterns.getContext(), solver);
}
 
void mlir::arith::populateIntRangeNarrowingPatterns(
    RewritePatternSet &patterns, DataFlowSolver &solver,
    ArrayRef<unsigned> bitwidthsSupported) {
  patterns.add<NarrowElementwise, NarrowCmpI>(patterns.getContext(), solver,
                                              bitwidthsSupported);
  patterns.add<FoldIndexCastChain<arith::IndexCastUIOp>,
               FoldIndexCastChain<arith::IndexCastOp>>(patterns.getContext(),
                                                       bitwidthsSupported);
}
 
void mlir::arith::populateControlFlowValuesNarrowingPatterns(
    RewritePatternSet &patterns, DataFlowSolver &solver,
    ArrayRef<unsigned> bitwidthsSupported) {
  patterns.add<NarrowLoopBounds>(patterns.getContext(), solver,
                                 bitwidthsSupported);
}
 
std::unique_ptr<Pass> mlir::arith::createIntRangeOptimizationsPass() {
  return std::make_unique<IntRangeOptimizationsPass>();
}