//===----------------------------------------------------------------------===//
//
// 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
// Also available under a BSD-style license. See LICENSE.
//
//===----------------------------------------------------------------------===//
#define DEBUG_TYPE "torch-mlir-torch-dialect"
#include "torch-mlir/Dialect/Torch/IR/TorchOps.h"
#include "torch-mlir/Dialect/Torch/Utils/Utils.h"
#include "llvm/Support/Debug.h"

#include "mlir/Dialect/Func/IR/FuncOps.h"
#include "mlir/IR/Builders.h"
#include "mlir/IR/BuiltinOps.h"
#include "mlir/IR/PatternMatch.h"
#include "mlir/IR/TypeUtilities.h"
#include "mlir/Support/LLVM.h"
#include "torch-mlir/Dialect/Torch/Utils/Utils.h"
#include "llvm/ADT/BitVector.h"
#include "llvm/ADT/StringMap.h"
#include "llvm/Support/Casting.h"

using namespace mlir;
using namespace mlir::torch;
using namespace mlir::torch::Torch;

//===----------------------------------------------------------------------===//
// Utilities
//===----------------------------------------------------------------------===//

OpFoldResult genericViewLikeFold(Attribute self, Type resultType) {
  auto selfAttr = dyn_cast_or_null<DenseElementsAttr>(self);
  if (!selfAttr)
    return nullptr;

  auto resultTy = dyn_cast_or_null<ValueTensorType>(resultType);
  if (!resultTy || !resultTy.areAllSizesKnown())
    return nullptr;

  if (selfAttr.isSplat()) {
    return SplatElementsAttr::get(resultTy.toBuiltinTensor(),
                                  selfAttr.getSplatValue<Attribute>());
  }
  return DenseElementsAttr::get(
      resultTy.toBuiltinTensor(),
      llvm::to_vector(selfAttr.getValues<Attribute>()));
}

Value mlir::torch::Torch::adjustStaticInformation(OpBuilder &builder,
                                                  Location loc, Value value,
                                                  Type desiredType,
                                                  bool userAllowsRefinement) {
  Type type = value.getType();

  // If the value is already of the desired type, we're done.
  if (type == desiredType)
    return value;

  // If the type is a tensor, then adjust the static information.
  if ((isa<ValueTensorType>(type) && isa<ValueTensorType>(desiredType)) ||
      (isa<NonValueTensorType>(type) && isa<NonValueTensorType>(desiredType))) {
    Value adjusted = builder.create<TensorStaticInfoCastOp>(value.getLoc(),
                                                            desiredType, value);
    return adjusted;
  }

  // If the type is a subtype of desiredType, then we need to derefine it to
  // desiredType, unless the user allows refinement.
  if (isValidSubtype(type, desiredType)) {
    if (!userAllowsRefinement) {
      Value adjusted =
          builder.create<DerefineOp>(value.getLoc(), desiredType, value);
      return adjusted;
    } else {
      return value;
    }
  }

  // If the desiredType is subtype of type, then we assume that the desiredType
  // is dynamically valid, so we do an unchecked cast.
  if (isValidSubtype(desiredType, type)) {
    Value adjusted =
        builder.create<PrimUncheckedCastOp>(value.getLoc(), desiredType, value);
    return adjusted;
  }

  // No known adjustment.
  return Value();
}

Value mlir::torch::Torch::copyTensorToType(OpBuilder &builder, Location loc,
                                           BaseTensorType newType,
                                           Value tensor) {
  auto originalType = cast<BaseTensorType>(tensor.getType());
  // Adjust the static information in the type to match between the original and
  // new types.
  if (!originalType.hasSameSizesAndDtype(newType)) {
    tensor = builder.create<TensorStaticInfoCastOp>(
        loc, originalType.getWithSizesAndDtypeFrom(newType), tensor);
  }

  // Unless both the original and new types are both value tensors, we end
  // up creating one op that converts between the value and non-value tensor
  // domains. If both the original and new types are both non-value tensors,
  // then we do the copy by going to a value tensor and back.
  if (isa<NonValueTensorType>(tensor.getType()))
    tensor = builder.create<CopyToValueTensorOp>(loc, tensor);
  if (isa<NonValueTensorType>(newType))
    tensor = builder.create<CopyToNonValueTensorOp>(loc, tensor);

  return tensor;
}

bool mlir::torch::Torch::isListPotentiallyMutated(Value list) {
  assert(isa<Torch::ListType>(list.getType()));
  return llvm::any_of(list.getUsers(), potentiallyMutatesListOperands);
}

bool mlir::torch::Torch::potentiallyMutatesListOperands(Operation *op) {
  // TODO: Find a better place to put this assertion.
  assert((!op->hasTrait<Torch::OpTrait::HasValueSemantics>() ||
          op->hasTrait<OpTrait::ReadOnly>()) &&
         "HasValueSemantics should imply ReadOnly!");
  // ReadOnly ops trivially do not mutate any list operands.
  if (op->hasTrait<Torch::OpTrait::ReadOnly>())
    return false;

  // Ops with no MemoryEffectOpInterface effects also do not mutate any list
  // operands.
  if (auto effects = dyn_cast<MemoryEffectOpInterface>(op)) {
    if (effects.hasNoEffect())
      return false;
  }

  // Conservatively assume that an op might mutate any list operands.
  return true;
}

static IntegerAttr getI64IntegerAttr(MLIRContext *context, int64_t value) {
  return IntegerAttr::get(IntegerType::get(context, 64), value);
}

static FloatAttr getF64FloatAttr(MLIRContext *context, double value) {
  return FloatAttr::get(Float64Type::get(context), value);
}

static DenseElementsAttr reshapeDenseElementsAttr(DenseElementsAttr attr,
                                                  ShapedType newType) {
  // TODO: DenseElementsAttr::reshape is broken for bool splats.
  // Once that ticket is fixed, we can remove this conditional.
  if (attr.isSplat() && newType.getElementType().isInteger(/*width=*/1)) {
    auto splatValue = attr.getValues<bool>()[0];
    return DenseElementsAttr::get(newType, {splatValue});
  }
  return attr.reshape(newType);
}

static Value getScalarIntValue(Value input, Location loc,
                               PatternRewriter &rewriter) {
  auto inputType = input.getType();
  if (isa<Torch::IntType>(inputType)) {
    return input;
  }

  auto inputTensorType = dyn_cast<BaseTensorType>(inputType);
  if (!inputTensorType)
    return nullptr;

  Type inputDtype = inputTensorType.getOptionalDtype();
  if (!inputDtype || !(inputDtype.isInteger(64) || inputDtype.isInteger(1)))
    return nullptr;

  std::optional<unsigned> inputRank = getTensorRank(input);
  if (!inputRank || *inputRank != 0)
    return nullptr;

  if (auto valueTensorLiteralOp = input.getDefiningOp<ValueTensorLiteralOp>()) {
    if (inputDtype.isInteger(64)) {
      auto val = cast<DenseIntElementsAttr>(valueTensorLiteralOp.getValue())
                     .getSplatValue<int64_t>();
      return rewriter.create<Torch::ConstantIntOp>(
          loc, rewriter.getI64IntegerAttr(val));
    } else {
      auto val = cast<DenseIntElementsAttr>(valueTensorLiteralOp.getValue())
                     .getSplatValue<bool>();
      return rewriter.create<Torch::ConstantIntOp>(
          loc, rewriter.getI64IntegerAttr(val));
    }
  } else if (auto primNumToTensorScalarOp =
                 input.getDefiningOp<PrimNumToTensorScalarOp>()) {
    return primNumToTensorScalarOp.getA();
  } else if (auto tensorIntOp = input.getDefiningOp<AtenTensorIntOp>()) {
    return tensorIntOp.getT();
  }
  return nullptr;
}

static Value getScalarFloatValue(Value input, Location loc,
                                 PatternRewriter &rewriter) {
  auto inputType = input.getType();
  if (isa<Torch::FloatType>(inputType)) {
    return input;
  }

  auto inputTensorType = dyn_cast<BaseTensorType>(inputType);
  if (!inputTensorType)
    return nullptr;

  Type inputDtype = inputTensorType.getOptionalDtype();
  if (!inputDtype ||
      (!inputDtype.isF16() && !inputDtype.isF32() && !inputDtype.isF64()))
    return nullptr;

  std::optional<unsigned> inputRank = getTensorRank(input);
  if (!inputRank || *inputRank != 0)
    return nullptr;

  if (auto valueTensorLiteralOp = input.getDefiningOp<ValueTensorLiteralOp>()) {
    auto val = cast<DenseFPElementsAttr>(valueTensorLiteralOp.getValue())
                   .getSplatValue<FloatAttr>()
                   .getValueAsDouble();
    return rewriter.create<Torch::ConstantFloatOp>(
        loc, rewriter.getF64FloatAttr(val));
  } else if (auto primNumToTensorScalarOp =
                 input.getDefiningOp<PrimNumToTensorScalarOp>()) {
    return primNumToTensorScalarOp.getA();
  } else if (auto tensorFloatOp = input.getDefiningOp<AtenTensorFloatOp>()) {
    return tensorFloatOp.getT();
  }
  return nullptr;
}

//===----------------------------------------------------------------------===//
// MethodOp
//===----------------------------------------------------------------------===//

LogicalResult MethodOp::verifySymbolUses(SymbolTableCollection &symbolTable) {
  auto func = symbolTable.lookupNearestSymbolFrom<func::FuncOp>(
      *this, getFunctionAttr());
  if (!func)
    return emitError() << "'@" << getFunction()
                       << "' does not reference a valid function";
  if (func.getVisibility() != SymbolTable::Visibility::Private)
    return emitError() << "'@" << getFunction()
                       << "' must reference a private function";
  if (func.isDeclaration())
    return emitError() << "'@" << getFunction()
                       << "' must reference a function that is defined (not "
                          "merely declared)";
  auto expectedReceiverArgType = NnModuleType::get(
      getContext(), getOperation()->getParentOfType<ClassTypeOp>().getName());
  if (func.getFunctionType().getNumInputs() == 0 ||
      func.getFunctionType().getInput(0) != expectedReceiverArgType) {
    return emitError() << "the referenced function '" << getFunction()
                       << "' must have a first argument of type "
                       << expectedReceiverArgType;
  }
  return success();
}

//===----------------------------------------------------------------------===//
// NnModuleOp
//===----------------------------------------------------------------------===//

LogicalResult NnModuleOp::verify() {
  for (Operation &child : *getBody())
    if (!isa<SlotOp, NnModuleTerminatorOp>(&child))
      return child.emitOpError() << "is not allowed inside 'torch.nn_module'";
  return success();
}

LogicalResult NnModuleOp::verifySymbolUses(SymbolTableCollection &symbolTable) {
  auto classType = symbolTable.lookupNearestSymbolFrom<ClassTypeOp>(
      *this, SymbolRefAttr::get(getContext(), getClassName()));
  if (!classType)
    return emitError() << "'" << getClassName()
                       << "' does not reference a valid class type";

  auto attrs = llvm::to_vector<6>(getBody()->getOps<SlotOp>());
  auto attrDefs = llvm::to_vector<6>(classType.getBody()->getOps<AttrOp>());
  if (attrs.size() != attrDefs.size())
    return emitError() << "number of 'torch.slot's in a 'torch.nn_module' must "
                          "match number of 'torch.attr's in "
                          "the corresponding 'torch.class_type'";
  for (int i = 0, e = attrs.size(); i != e; i++) {
    SlotOp attr = attrs[i];
    AttrOp attrDef = attrDefs[i];
    if (!isValidSubtype(attr.getValue().getType(), attrDef.getType()) ||
        attr.getName() != attrDef.getName()) {
      return attr.emitOpError()
          .append("is expected to match type and name of '",
                  attrDef.getOperation(), "'")
          .attachNote(attrDef.getLoc())
          .append("see torch.attr at corresponding index ", i, " here");
    }
  }

  return success();
}

//===----------------------------------------------------------------------===//
// PrimListConstructOp
//===----------------------------------------------------------------------===//

LogicalResult PrimListConstructOp::verify() {
  auto resultType = getResult().getType();
  auto resultElementType = dyn_cast<ListType>(resultType).getContainedType();
  auto matchResultElementType = [&](Type type) {
    return isValidSubtype(type, resultElementType);
  };
  if (!llvm::all_of(getOperandTypes(), matchResultElementType)) {
    return emitError() << "operand types should have the same type as the "
                          "list contained type";
  }

  return success();
}

//===----------------------------------------------------------------------===//
// PrimDictConstructOp
//===----------------------------------------------------------------------===//

LogicalResult PrimDictConstructOp::verify() {
  auto isValidSubTypeOf = [](Type expectedType) {
    return [=](Type type) { return isValidSubtype(type, expectedType); };
  };

  if (!llvm::all_of(getKeys().getTypes(), isValidSubTypeOf(getKeyType())))
    return emitError() << "keys should be of Dict key type";

  if (!llvm::all_of(getValues().getTypes(), isValidSubTypeOf(getValueType())))
    return emitError() << "values  should be of Dict value type";

  return success();
}

//===----------------------------------------------------------------------===//
// ClassTypeOp
//===----------------------------------------------------------------------===//

LogicalResult ClassTypeOp::verify() {
  llvm::StringMap<Operation *> namesToOps;
  for (Operation &child : getBody()->without_terminator()) {
    if (!isa<AttrOp, MethodOp>(&child))
      return child.emitOpError() << "is not allowed inside `torch.class_type`";
    StringRef name;
    if (auto attr = dyn_cast<AttrOp>(child))
      name = attr.getName();
    else
      name = cast<MethodOp>(child).getName();
    auto itAndWasInserted = namesToOps.insert({name, &child});
    auto it = itAndWasInserted.first;
    bool wasInserted = itAndWasInserted.second;
    if (!wasInserted) {
      auto diag = emitOpError().append("has duplicate attr/method with name '",
                                       name, "'");
      diag.attachNote(it->second->getLoc())
          .append("see first conflicting attr/method here");
      diag.attachNote(child.getLoc())
          .append("see second conflicting attr/method here");
      return failure();
    }
  }

  return success();
}

//===----------------------------------------------------------------------===//
// PrimLoopOp
//===----------------------------------------------------------------------===//

OperandRange PrimLoopOp::getEntrySuccessorOperands(RegionBranchPoint point) {
  assert(point == getRegion());
  return getIterArgsInit();
}

void PrimLoopOp::getSuccessorRegions(
    RegionBranchPoint point, SmallVectorImpl<RegionSuccessor> &regions) {
  Region &region = getRegion();
  if (!point.getRegionOrNull()) {
    regions.emplace_back(&region, region.getArguments().slice(1));
    return;
  }
  assert(point == region);
  regions.emplace_back(&region, region.getArguments().slice(1));
  regions.emplace_back(getResults());
}

bool PrimLoopOp::isForLike() {
  bool b;
  return matchPattern(getInitialCondition(), m_TorchConstantBool(&b)) && b;
}

//===----------------------------------------------------------------------===//
// PrimLoopConditionOp
//===----------------------------------------------------------------------===//

MutableOperandRange
PrimLoopConditionOp::getMutableSuccessorOperands(RegionBranchPoint point) {
  // Pass all operands except the condition to the successor which is the
  // parent loop op.
  return getIterArgsMutable();
}

//===----------------------------------------------------------------------===//
// PrimIfOp
//===----------------------------------------------------------------------===//

ParseResult PrimIfOp::parse(OpAsmParser &parser, OperationState &result) {
  // Create the regions.
  result.regions.reserve(2);
  Region *thenRegion = result.addRegion();
  Region *elseRegion = result.addRegion();

  auto &builder = parser.getBuilder();
  OpAsmParser::UnresolvedOperand cond;
  Type boolType = builder.getType<Torch::BoolType>();
  if (parser.parseOperand(cond) ||
      parser.resolveOperand(cond, boolType, result.operands))
    return failure();
  // Parse results type list.
  if (parser.parseArrowTypeList(result.types))
    return failure();
  // Parse the 'then' region.
  if (parser.parseRegion(*thenRegion, /*arguments=*/{}, /*argTypes=*/{}))
    return failure();
  // Parse the 'else' region.
  if (parser.parseKeyword("else"))
    return failure();
  if (parser.parseRegion(*elseRegion, /*arguments=*/{}, /*argTypes=*/{}))
    return failure();
  // Parse the optional attribute list.
  if (parser.parseOptionalAttrDict(result.attributes))
    return failure();
  return success();
}

void PrimIfOp::print(OpAsmPrinter &p) {
  p << " " << getCondition();
  p << " -> (" << getResultTypes() << ") ";
  p.printRegion(getThenRegion(), /*printEntryBlockArgs=*/false);
  p << " else ";
  p.printRegion(getElseRegion(), /*printEntryBlockArgs=*/false);

  p.printOptionalAttrDict((*this)->getAttrs());
}

void PrimIfOp::getSuccessorRegions(RegionBranchPoint point,
                                   SmallVectorImpl<RegionSuccessor> &regions) {
  // The `then` and the `else` region branch back to the parent operation.
  if (point.getRegionOrNull()) {
    regions.push_back(RegionSuccessor(getResults()));
    return;
  }

  // If the condition is constant, we can give a more precise answer.
  bool condition;
  if (matchPattern(getCondition(), m_TorchConstantBool(&condition))) {
    Region *executedRegion = condition ? &getThenRegion() : &getElseRegion();
    regions.push_back(RegionSuccessor(executedRegion));
    return;
  }

  // If the condition isn't constant, both regions may be executed.
  regions.push_back(RegionSuccessor(&getThenRegion()));
  regions.push_back(RegionSuccessor(&getElseRegion()));
  return;
}

/// Replaces the given op with the contents of the given single-block region,
/// using the operands of the block terminator to replace operation results.
static void replaceOpWithRegion(PatternRewriter &rewriter, Operation *op,
                                Region &region, ValueRange blockArgs = {}) {
  assert(llvm::hasSingleElement(region) && "expected single-region block");
  Block *block = &region.front();
  Operation *terminator = block->getTerminator();
  ValueRange results = terminator->getOperands();
  rewriter.inlineBlockBefore(block, op, blockArgs);
  rewriter.replaceOp(op, results);
  rewriter.eraseOp(terminator);
}

void PrimIfOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                           MLIRContext *context) {
  // If the condition is constant, delete the dead branch and inline the live
  // branch.
  patterns.add(+[](PrimIfOp op, PatternRewriter &rewriter) {
    auto constantBool =
        op.getCondition().getDefiningOp<Torch::ConstantBoolOp>();
    if (!constantBool)
      return rewriter.notifyMatchFailure(op, "non-constant condition");
    replaceOpWithRegion(rewriter, op,
                        constantBool.getValue() ? op.getThenRegion()
                                                : op.getElseRegion());
    return success();
  });
  // If the thenRegion and elseRegion yield the same Value's, then use those
  // directly.
  patterns.add(+[](PrimIfOp op, PatternRewriter &rewriter) {
    auto trueTerminator = op.getThenRegion().front().getTerminator();
    auto falseTerminator = op.getElseRegion().front().getTerminator();
    bool madeChange = false;
    SmallVector<int> resultsToErase;
    for (auto t : llvm::zip(trueTerminator->getOperands(),
                            falseTerminator->getOperands(), op->getResults())) {
      auto trueVal = std::get<0>(t);
      auto falseVal = std::get<1>(t);
      auto resultToBeReplaced = std::get<2>(t);
      if (trueVal == falseVal) {
        madeChange |= !resultToBeReplaced.use_empty();
        resultToBeReplaced.replaceAllUsesWith(trueVal);
      }
    }
    // We leave it up to a separate pattern (not yet implemented) to erase the
    // results that are now dead. That transformation is independently useful,
    // and also pretty tricky to implement because it changes the number of
    // results.
    return success(madeChange);
  });
  // Erase any dead results.
  patterns.add(+[](PrimIfOp op, PatternRewriter &rewriter) {
    llvm::BitVector resultsToErase(op.getNumResults());
    for (auto result : llvm::enumerate(op->getResults())) {
      if (result.value().use_empty())
        resultsToErase.set(result.index());
    }

    // If no results have uses and there are no side effects, just erase the op.
    // Approximate the body having no side effects by checking if it is just a
    // terminator.
    // Note: We don't want to make this logic too fancy, because in general,
    // checking for recursive side effects can result in a quadratic amount of
    // work (N nested If's each resulting in O(N) work). It should probably be
    // split into its own pattern if we want to make it fancier.
    if (resultsToErase.all() &&
        llvm::hasSingleElement(op.getThenRegion().front()) &&
        llvm::hasSingleElement(op.getElseRegion().front())) {
      rewriter.eraseOp(op);
      return success();
    }

    // If there are no results to erase, we're done.
    if (!resultsToErase.any())
      return failure();

    SmallVector<Type> newResultTypes;
    for (int i = 0, e = op->getNumResults(); i < e; ++i) {
      if (resultsToErase[i])
        continue;
      newResultTypes.push_back(op->getResult(i).getType());
    }
    auto newIf = rewriter.create<PrimIfOp>(op->getLoc(), newResultTypes,
                                           op.getCondition());
    rewriter.inlineRegionBefore(op.getThenRegion(), newIf.getThenRegion(),
                                newIf.getThenRegion().end());
    rewriter.inlineRegionBefore(op.getElseRegion(), newIf.getElseRegion(),
                                newIf.getElseRegion().end());
    newIf.getThenRegion().front().getTerminator()->eraseOperands(
        resultsToErase);
    newIf.getElseRegion().front().getTerminator()->eraseOperands(
        resultsToErase);
    SmallVector<Value> replacementValues;
    for (int i = 0, e = op->getNumResults(), nextNewValue = 0; i < e; ++i) {
      if (resultsToErase[i])
        replacementValues.push_back(nullptr);
      else
        replacementValues.push_back(newIf->getResult(nextNewValue++));
    }
    rewriter.replaceOp(op, replacementValues);

    return success();
  });
}

//===----------------------------------------------------------------------===//
// AtenDotOp
//===----------------------------------------------------------------------===//

void AtenDotOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                            MLIRContext *context) {
  patterns.add(+[](AtenDotOp op, PatternRewriter &rewriter) {
    auto ty = dyn_cast<ValueTensorType>(op.getResult().getType());
    if (!ty || !ty.hasSizes() || !ty.hasDtype()) {
      return failure();
    }

    rewriter.replaceOpWithNewOp<AtenMatmulOp>(op, op.getResult().getType(),
                                              op.getSelf(), op.getTensor());
    return success();
  });
}

//===----------------------------------------------------------------------===//
// RuntimeAssertOp
//===----------------------------------------------------------------------===//

void RuntimeAssertOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                                  MLIRContext *context) {
  patterns.add(+[](RuntimeAssertOp op, PatternRewriter &rewriter) {
    bool value;
    if (!matchPattern(op.getCondition(), m_TorchConstantBool(&value)))
      return failure();

    if (value) {
      rewriter.eraseOp(op);
      return success();
    }
    // Even if the condition is statically false, the assert might never be
    // executed.
    return failure();
  });
}

//===----------------------------------------------------------------------===//
// DerefineOp
//===----------------------------------------------------------------------===//

bool DerefineOp::areCastCompatible(mlir::TypeRange inputs,
                                   mlir::TypeRange outputs) {
  return isValidSubtype(inputs[0], outputs[0]);
}

OpFoldResult DerefineOp::fold(FoldAdaptor adaptor) {
  auto uncheckedCast = getOperand().getDefiningOp<PrimUncheckedCastOp>();
  if (!uncheckedCast)
    return nullptr;
  if (uncheckedCast.getOperand().getType() == getType())
    return uncheckedCast.getOperand();
  return nullptr;
}

void DerefineOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                             MLIRContext *context) {
  patterns.add(+[](DerefineOp op, PatternRewriter &rewriter) {
    bool madeChange = false;
    for (OpOperand &use : llvm::make_early_inc_range(op->getUses())) {
      if (use.getOwner()->hasTrait<OpTrait::AllowsTypeRefinement>()) {
        use.set(op.getOperand());
        madeChange = true;
      }
    }
    return success(madeChange);
  });
}

static OpFoldResult atenIsOrIsNotFoldHelper(Operation *op, bool equalIsTrue) {
  Value lhs = op->getOperand(0);
  Value rhs = op->getOperand(1);
  // Look through DerefineOp's to get more refined static information.
  if (auto derefine = lhs.getDefiningOp<DerefineOp>())
    lhs = derefine.getOperand();
  if (auto derefine = rhs.getDefiningOp<DerefineOp>())
    rhs = derefine.getOperand();
  Type lhsType = lhs.getType();
  Type rhsType = rhs.getType();

  // If either type is a NoneType, make it be the lhsType.
  if (isa<Torch::NoneType>(rhsType)) {
    std::swap(lhsType, rhsType);
    std::swap(lhs, rhs);
  }

  // For now, check a few specific cases.

  // If both types are the singleton `!torch.none` type, then we don't even need
  // to look at the values.
  if (isa<Torch::NoneType>(lhsType) && isa<Torch::NoneType>(rhsType))
    return IntegerAttr::get(IntegerType::get(op->getContext(), 1), equalIsTrue);

  // If neither type is a subtype of the other, then the result is false.
  // TODO: Implement and use subtype infra for this.
  // For now, check a specific case.
  // If the rhs is not OptionalType, then we know it cannot be None.
  if (isa<Torch::NoneType>(lhsType) && !isa<Torch::OptionalType>(rhsType)) {
    return IntegerAttr::get(IntegerType::get(op->getContext(), 1),
                            !equalIsTrue);
  }

  return nullptr;
}

//===----------------------------------------------------------------------===//
// Aten__RangeLengthOp
//===----------------------------------------------------------------------===//

OpFoldResult Aten__RangeLengthOp::fold(FoldAdaptor adaptor) {
  auto lo = adaptor.getLo();
  auto hi = adaptor.getHi();
  auto step = adaptor.getStep();
  if (!lo || !hi || !step)
    return nullptr;
  auto loInt = dyn_cast_or_null<IntegerAttr>(lo).getValue();
  auto hiInt = dyn_cast_or_null<IntegerAttr>(hi).getValue();
  auto stepInt = dyn_cast_or_null<IntegerAttr>(step).getValue();
  // TODO: Implement folding for negative steps.
  if (stepInt.isNegative())
    return nullptr;
  // From Python language spec:
  // r[i] = lo + step*i such that i >= 0 and r[i] < hi
  // So maximize `i` such that lo + step * i < hi
  // ==> i == ceildiv(hi - lo, step)
  return IntegerAttr::get(cast<TypedAttr>(lo).getType(),
                          llvm::APIntOps::RoundingSDiv(hiInt - loInt, stepInt,
                                                       APInt::Rounding::UP));
}

//===----------------------------------------------------------------------===//
// Aten__DeriveIndexOp
//===----------------------------------------------------------------------===//

OpFoldResult Aten__DeriveIndexOp::fold(FoldAdaptor adaptor) {
  auto index = adaptor.getIndex();
  auto start = adaptor.getStart();
  auto step = adaptor.getStep();
  if (!index || !start || !step)
    return nullptr;
  auto indexInt = dyn_cast_or_null<IntegerAttr>(index).getValue();
  auto startInt = dyn_cast_or_null<IntegerAttr>(start).getValue();
  auto stepInt = dyn_cast_or_null<IntegerAttr>(step).getValue();
  return IntegerAttr::get(cast<TypedAttr>(index).getType(),
                          startInt + stepInt * indexInt);
}

//===----------------------------------------------------------------------===//
// Aten__Is__Op
//===----------------------------------------------------------------------===//

OpFoldResult Aten__Is__Op::fold(FoldAdaptor adaptor) {
  return atenIsOrIsNotFoldHelper(*this, /*equalIsTrue=*/true);
}

//===----------------------------------------------------------------------===//
// Aten__Isnot__Op
//===----------------------------------------------------------------------===//

OpFoldResult Aten__Isnot__Op::fold(FoldAdaptor adaptor) {
  return atenIsOrIsNotFoldHelper(*this, /*equalIsTrue=*/false);
}

//===----------------------------------------------------------------------===//
// Aten__Not__Op
//===----------------------------------------------------------------------===//

OpFoldResult Aten__Not__Op::fold(FoldAdaptor adaptor) {
  bool value;
  if (!matchPattern(getOperand(), m_TorchConstantBool(&value)))
    return nullptr;
  return IntegerAttr::get(IntegerType::get(getContext(), 1), !value);
}

//===----------------------------------------------------------------------===//
// Aten__Or__Op
//===----------------------------------------------------------------------===//

OpFoldResult Aten__Or__BoolOp::fold(FoldAdaptor adaptor) {
  auto valueA = dyn_cast_or_null<IntegerAttr>(adaptor.getA());
  auto valueB = dyn_cast_or_null<IntegerAttr>(adaptor.getB());
  if (!valueA && !valueB)
    return nullptr;
  if ((valueA && valueA.getValue() == 1) || (valueB && valueB.getValue() == 1))
    return IntegerAttr::get(IntegerType::get(getContext(), 1), 1);
  if (valueA && valueA.getValue() == 0)
    return getB();
  if (valueB && valueB.getValue() == 0)
    return getA();
  // unreachable
  return nullptr;
}

//===----------------------------------------------------------------------===//
// AtenNeBoolOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenNeBoolOp::fold(FoldAdaptor adaptor) {
  if (getOperand(0) == getOperand(1))
    return IntegerAttr::get(IntegerType::get(getContext(), 1), false);

  bool a, b;
  if (!matchPattern(getOperand(0), m_TorchConstantBool(&a)))
    return nullptr;
  if (!matchPattern(getOperand(1), m_TorchConstantBool(&b)))
    return nullptr;
  return IntegerAttr::get(IntegerType::get(getContext(), 1), a != b);
}

//===----------------------------------------------------------------------===//
// AtenUnsqueezeOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenUnsqueezeOp::fold(FoldAdaptor adaptor) {
  auto selfTy = dyn_cast<BaseTensorType>(getSelf().getType());
  auto rty = dyn_cast<BaseTensorType>(getType());
  if (!rty.hasDtype())
    return {};

  if (auto attr = dyn_cast_or_null<DenseElementsAttr>(adaptor.getSelf())) {
    auto aty = dyn_cast<RankedTensorType>(attr.getType());
    if (rty.hasSizes() && rty.areAllSizesKnown() && attr.isSplat()) {
      auto naty = RankedTensorType::get(rty.getSizes(), aty.getElementType());
      return DenseElementsAttr::get(naty, attr.getSplatValue<Attribute>());
    }
  }

  if (getSelf().getType() != getResult().getType())
    return nullptr;
  if (selfTy && rty) {
    if (selfTy.hasSizes() && rty.hasSizes() &&
        selfTy.getSizes().size() == rty.getSizes().size())
      return getSelf();
  }
  return nullptr;
}

//===----------------------------------------------------------------------===//
// AtenSqueezeOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenSqueezeOp::fold(FoldAdaptor adaptor) {
  auto selfTy = dyn_cast<BaseTensorType>(getSelf().getType());
  auto rty = dyn_cast<BaseTensorType>(getType());
  if (!rty.hasDtype())
    return {};

  if (auto attr = dyn_cast_or_null<DenseElementsAttr>(adaptor.getSelf())) {
    auto aty = dyn_cast<RankedTensorType>(attr.getType());
    if (rty.hasSizes() && rty.areAllSizesKnown() && attr.isSplat()) {
      auto naty = RankedTensorType::get(rty.getSizes(), aty.getElementType());
      return DenseElementsAttr::get(naty, attr.getSplatValue<Attribute>());
    }
  }

  if (getSelf().getType() != getResult().getType())
    return nullptr;
  if (selfTy && rty) {
    if (selfTy.hasSizes() && rty.hasSizes() &&
        selfTy.getSizes().size() == rty.getSizes().size())
      return getSelf();
  }
  return nullptr;
}

//===----------------------------------------------------------------------===//
// AtenSqueezeDimOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenSqueezeDimOp::fold(FoldAdaptor adaptor) {
  auto inType = dyn_cast<ValueTensorType>(getOperand(0).getType());
  auto outType = dyn_cast<ValueTensorType>(getResult().getType());
  if (!inType || !outType || !inType.areAllSizesKnown() ||
      !outType.areAllSizesKnown() || !inType.hasDtype() ||
      !outType.hasDtype()) {
    return nullptr;
  }

  if (inType == outType) {
    return getOperand(0);
  }

  DenseElementsAttr input =
      dyn_cast_or_null<DenseElementsAttr>(adaptor.getSelf());
  if (input) {
    return reshapeDenseElementsAttr(input, outType.toBuiltinTensor());
  }
  return nullptr;
}

//===----------------------------------------------------------------------===//
// AtenToDtypeOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenToDtypeOp::fold(FoldAdaptor adaptor) {
  bool nonBlocking, copyArg;
  // The non_blocking arg must be `False`.
  if (!matchPattern(getNonBlocking(), m_TorchConstantBool(&nonBlocking)) ||
      nonBlocking)
    return nullptr;
  // The copy arg must be `False`.
  if (!matchPattern(getCopy(), m_TorchConstantBool(&copyArg)) || copyArg)
    return nullptr;
  // The memory_format arg must be `none`.
  if (!isa<Torch::NoneType>(getMemoryFormat().getType()))
    return nullptr;

  auto inputType = cast<BaseTensorType>(getSelf().getType());
  auto resType = cast<BaseTensorType>(getType());
  // If the types aren't equal, then we can't fold.
  if (inputType != resType)
    return nullptr;
  // If the type does not have a statically known dtype, then we cannot fold.
  // For example, folding `tensor<*,unk>` to `tensor<*,unk>` would be wrong,
  // since the `unk` could be dynamically different for the operand and result.
  if (!inputType.hasDtype())
    return nullptr;
  // Fold when both the input tensor and result are of the same type.
  return getOperand(0);
}

//===----------------------------------------------------------------------===//
// AtenToDtypeLayoutOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenToDtypeLayoutOp::fold(FoldAdaptor adaptor) {
  // The pin_memory arg should be either constant `False` or `none`.
  if (!isa<Torch::NoneType>(getPinMemory().getType())) {
    bool pinMemory;
    if (!matchPattern(getPinMemory(), m_TorchConstantBool(&pinMemory)))
      return nullptr;
    else if (pinMemory)
      return nullptr;
  }

  // The non_blocking arg should be constant `False`.
  bool nonBlocking;
  if (!matchPattern(getNonBlocking(), m_TorchConstantBool(&nonBlocking)))
    return nullptr;
  else if (nonBlocking)
    return nullptr;

  // The copy arg should be constant `False`.
  bool copyArg;
  if (!matchPattern(getCopy(), m_TorchConstantBool(&copyArg)))
    return nullptr;
  else if (copyArg)
    return nullptr;

  // The device arg must be `none`.
  if (!isa<Torch::NoneType>(getDevice().getType()))
    return nullptr;

  // The memory_format arg must be `none`.
  if (!isa<Torch::NoneType>(getMemoryFormat().getType()))
    return nullptr;

  auto inputType = cast<BaseTensorType>(getSelf().getType());
  auto resType = cast<BaseTensorType>(getType());
  // If the types aren't equal, then we can't fold.
  if (inputType != resType)
    return nullptr;
  // If the type does not have a statically known dtype, then we cannot fold.
  // For example, folding `tensor<*,unk>` to `tensor<*,unk>` would be wrong,
  // since the `unk` could be dynamically different for the operand and result.
  if (!inputType.hasDtype())
    return nullptr;

  // The layout arg should be either `none` or `0` i.e. strided.
  if (!isa<Torch::NoneType>(getLayout().getType())) {
    int64_t tensorLayout;
    if (!matchPattern(getLayout(), m_TorchConstantInt(&tensorLayout)))
      return nullptr;
    else if (tensorLayout != torch_upstream::Layout::Strided)
      return nullptr;
  }

  // Fold when both the input tensor and result are of the same type and the
  // layout arg is strided.
  return getOperand(0);
}

void AtenToDtypeLayoutOp::getCanonicalizationPatterns(
    RewritePatternSet &patterns, MLIRContext *context) {
  // `to.dtype_layout` -> `to.device/to.dtype` if layout is none and pin memory
  // is false
  patterns.add(+[](AtenToDtypeLayoutOp op, PatternRewriter &rewriter) {
    // The pin_memory arg should be either constant `False` or `none`.
    if (!isa<Torch::NoneType>(op.getPinMemory().getType())) {
      bool pinMemory;
      if (!matchPattern(op.getPinMemory(), m_TorchConstantBool(&pinMemory)))
        return failure();
      else if (pinMemory)
        return failure();
    }

    // The layout arg should be either `none` or `0` i.e. strided.
    if (!isa<Torch::NoneType>(op.getLayout().getType())) {
      int64_t tensorLayout;
      if (!matchPattern(op.getLayout(), m_TorchConstantInt(&tensorLayout)))
        return failure();
      else if (tensorLayout != torch_upstream::Layout::Strided)
        return failure();
    }

    if (isa<Torch::NoneType>(op.getDevice().getType())) {
      // The device arg is `none`. Rewrite to to.dtype.
      AtenToDtypeOp toDtype = rewriter.create<AtenToDtypeOp>(
          op.getLoc(), op.getType(), op.getSelf(), op.getDtype(),
          op.getNonBlocking(), op.getCopy(), op.getMemoryFormat());
      rewriter.replaceOp(op, toDtype->getResults());
    } else {
      // The device arg is not `none`. Rewrite to to.device.
      AtenToDeviceOp toDevice = rewriter.create<AtenToDeviceOp>(
          op.getLoc(), op.getType(), op.getSelf(), op.getDevice(),
          op.getDtype(), op.getNonBlocking(), op.getCopy(),
          op.getMemoryFormat());
      rewriter.replaceOp(op, toDevice->getResults());
    }

    return success();
  });
}

//===----------------------------------------------------------------------===//
// AtenToOtherOp
//===----------------------------------------------------------------------===//

void AtenToOtherOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                                MLIRContext *context) {
  // Canonicalize `aten.to.other` to `aten.to.device`
  patterns.add(+[](AtenToOtherOp op, PatternRewriter &rewriter) {
    auto lhs = op.getSelf();
    auto rhs = op.getOther();
    auto getRhsDevice = rewriter.create<PrimDeviceOp>(op.getLoc(), rhs);
    auto getRhsDtype = rewriter.create<PrimDtypeOp>(op.getLoc(), rhs);
    rewriter.replaceOpWithNewOp<AtenToDeviceOp>(
        op, op.getType(), lhs, getRhsDevice.getResult(),
        getRhsDtype.getResult(), op.getNonBlocking(), op.getCopy(),
        op.getMemoryFormat());
    return success();
  });
}

//===----------------------------------------------------------------------===//
// Aten_CastFloatOp
//===----------------------------------------------------------------------===//

void Aten_CastFloatOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                                   MLIRContext *context) {
  // `aten.cast_float` -> `aten.to.dtype`
  patterns.add(+[](Aten_CastFloatOp op, PatternRewriter &rewriter) {
    auto self = op.getSelf();
    auto loc = op.getLoc();
    Value constNone = rewriter.create<ConstantNoneOp>(loc);
    Value f32Type = rewriter.create<ConstantIntOp>(
        loc, (int)torch_upstream::ScalarType::Float);
    Value constFalse = rewriter.create<ConstantBoolOp>(loc, false);
    rewriter.replaceOpWithNewOp<AtenToDtypeOp>(op, op.getType(), self, f32Type,
                                               op.getNonBlocking(), constFalse,
                                               constNone);
    return success();
  });
}

//===----------------------------------------------------------------------===//
// Aten_CastLongOp
//===----------------------------------------------------------------------===//

void Aten_CastLongOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                                  MLIRContext *context) {
  // `aten.cast_long` -> `aten.to.dtype`
  patterns.add(+[](Aten_CastLongOp op, PatternRewriter &rewriter) {
    auto self = op.getSelf();
    auto loc = op.getLoc();
    Value constNone = rewriter.create<ConstantNoneOp>(loc);
    Value longType = rewriter.create<ConstantIntOp>(
        loc, (int)torch_upstream::ScalarType::Long);
    Value constFalse = rewriter.create<ConstantBoolOp>(loc, false);
    rewriter.replaceOpWithNewOp<AtenToDtypeOp>(op, op.getType(), self, longType,
                                               op.getNonBlocking(), constFalse,
                                               constNone);
    return success();
  });
}

//===----------------------------------------------------------------------===//
// AtenViewOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenViewOp::fold(FoldAdaptor adaptor) {
  if (auto genericFold = genericViewLikeFold(adaptor.getSelf(), getType()))
    return genericFold;
  auto inputType = dyn_cast<BaseTensorType>(getOperand(0).getType());
  if (!inputType || !inputType.hasSizes() || inputType.getSizes().size() != 1)
    return nullptr;
  auto resType = dyn_cast<BaseTensorType>(getType());
  if (!resType || !resType.hasSizes() || resType.getSizes().size() != 1)
    return nullptr;
  if (inputType != resType)
    return nullptr;
  // Fold when both the input tensor and result are unity rank tensors.
  return getOperand(0);
}

//===----------------------------------------------------------------------===//
// PrimsViewOfOp
//===----------------------------------------------------------------------===//

OpFoldResult PrimsViewOfOp::fold(FoldAdaptor adaptor) {
  // Always fold the op with its only input operand.
  return getOperand();
}

//===----------------------------------------------------------------------===//
// AtenDimOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenDimOp::fold(FoldAdaptor adaptor) {
  if (auto tensorType = dyn_cast<BaseTensorType>(getOperand().getType())) {
    if (tensorType.hasSizes())
      return IntegerAttr::get(IntegerType::get(getContext(), 64),
                              tensorType.getSizes().size());
  }
  return nullptr;
}

//===----------------------------------------------------------------------===//
// AtenLenTOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenLenTOp::fold(FoldAdaptor adaptor) {
  // `len([1,1,1])` -> `3`, if it is not mutated.
  if (auto listConstruct =
          getOperand().getDefiningOp<Torch::PrimListConstructOp>()) {
    if (!isListPotentiallyMutated(listConstruct)) {
      return IntegerAttr::get(IntegerType::get(getContext(), 64),
                              listConstruct.getNumOperands());
    }
  }
  return nullptr;
}

void AtenLenTOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                             MLIRContext *context) {
  // `len(t.size())` -> `t.ndim`
  patterns.add(+[](AtenLenTOp op, PatternRewriter &rewriter) {
    auto size = op.getOperand().getDefiningOp<AtenSizeOp>();
    if (!size)
      return rewriter.notifyMatchFailure(op, "operand not AtenSizeOp");
    rewriter.replaceOpWithNewOp<AtenDimOp>(op, size.getOperand());
    return success();
  });
}

//===----------------------------------------------------------------------===//
// AtenMinOtherOp
//===----------------------------------------------------------------------===//

void AtenMinOtherOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                                 MLIRContext *context) {
  // `aten.min.other` -> `aten.minimum`
  patterns.add(+[](AtenMinOtherOp op, PatternRewriter &rewriter) {
    rewriter.replaceOpWithNewOp<AtenMinimumOp>(op, op.getType(), op.getSelf(),
                                               op.getOther());
    return success();
  });
}

//===----------------------------------------------------------------------===//
// AtenMaxOtherOp
//===----------------------------------------------------------------------===//

void AtenMaxOtherOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                                 MLIRContext *context) {
  // `aten.max.other` -> `aten.maximum`
  patterns.add(+[](AtenMaxOtherOp op, PatternRewriter &rewriter) {
    rewriter.replaceOpWithNewOp<AtenMaximumOp>(op, op.getType(), op.getSelf(),
                                               op.getOther());
    return success();
  });
}

//===----------------------------------------------------------------------===//
// AtenLenStrOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenLenStrOp::fold(FoldAdaptor adaptor) {
  if (auto stringConstruct = getS().getDefiningOp<ConstantStrOp>())
    return getI64IntegerAttr(getContext(),
                             stringConstruct.getValueAttr().getValue().size());

  return nullptr;
}

LogicalResult rewrite0DBinaryTensorOp(Operation *op,
                                      PatternRewriter &rewriter) {
  Location loc = op->getLoc();
  // This canonicalization pattern also includes aten div/mul/add/sub ops
  // between tensor and scalar, like aten.add.Scalar op
  if (op->getNumOperands() < 2) {
    return failure();
  }
  auto lhs = getScalarIntValue(op->getOperand(0), loc, rewriter);
  auto rhs = getScalarIntValue(op->getOperand(1), loc, rewriter);
  auto outType = op->getResult(0).getType();

  if (!lhs || !rhs) {
    return rewriter.notifyMatchFailure(
        op, "only int scalar lhs or rhs is supported");
  }
  if (isa<AtenSubTensorOp, AtenSubScalarOp, AtenRsubScalarOp, AtenAddTensorOp,
          AtenAddScalarOp>(op)) {
    Value alpha = getScalarIntValue(op->getOperand(2), loc, rewriter);
    if (!alpha) {
      return rewriter.notifyMatchFailure(op,
                                         "only int scalar alpha is supported");
    }
    if (isa<AtenRsubScalarOp>(op))
      lhs = rewriter.create<AtenMulIntOp>(loc, lhs, alpha);
    else
      rhs = rewriter.create<AtenMulIntOp>(loc, rhs, alpha);
  }

  if (isa<AtenDivTensorModeOp, AtenDivScalarModeOp>(op)) {
    if (isa<Torch::NoneType>(op->getOperand(2).getType())) {
      // None rounding mode
      Value quotient = rewriter.create<AtenDivOp>(loc, lhs, rhs);
      rewriter.replaceOpWithNewOp<PrimNumToTensorScalarOp>(op, outType,
                                                           quotient);
      return success();
    }
    std::string roundingMode;
    if (!matchPattern(op->getOperand(2), m_TorchConstantStr(roundingMode))) {
      return rewriter.notifyMatchFailure(
          op, "only None, 'floor' or 'trunc' rounding mode is supported");
    }
    if (roundingMode == "floor") {
      Value quotient = rewriter.create<AtenFloordivIntOp>(loc, lhs, rhs);
      rewriter.replaceOpWithNewOp<PrimNumToTensorScalarOp>(op, outType,
                                                           quotient);
      return success();
    }
    // For "trunc" rounding mode, insted of canonicalizing it into
    // aten.abs, aten.floor, aten.sign and aten.mul.int ops, which adds
    // complexity but helps little in optimization (such as constant folding),
    // we are trying to fold it.
    if (roundingMode == "trunc") {
      int64_t lhsInt;
      int64_t rhsInt;
      if (!matchPattern(lhs, m_TorchConstantInt(&lhsInt))) {
        return failure();
      }
      if (!matchPattern(rhs, m_TorchConstantInt(&rhsInt))) {
        return failure();
      }

      int64_t result = (int64_t)std::trunc((double)lhsInt / rhsInt);
      Value resultScalar = rewriter.create<ConstantIntOp>(
          loc, rewriter.getI64IntegerAttr(result));
      rewriter.replaceOpWithNewOp<PrimNumToTensorScalarOp>(op, outType,
                                                           resultScalar);
      return success();
    }

    return failure();
  }

  Value result;
  // Other Add/Sub/Mul ops
  if (isa<AtenAddTensorOp, AtenAddScalarOp>(op)) {
    result = rewriter.create<AtenAddIntOp>(loc, lhs, rhs);
  } else if (isa<AtenSubScalarOp, AtenSubTensorOp>(op)) {
    result = rewriter.create<AtenSubIntOp>(loc, lhs, rhs);
  } else if (isa<AtenRsubScalarOp>(op)) {
    result = rewriter.create<AtenSubIntOp>(loc, rhs, lhs);
  } else if (isa<AtenMulScalarOp, AtenMulTensorOp>(op)) {
    result = rewriter.create<AtenMulIntOp>(loc, lhs, rhs);
  }
  rewriter.replaceOpWithNewOp<PrimNumToTensorScalarOp>(op, outType, result);
  return success();
}

//===----------------------------------------------------------------------===//
// NAry folder helpers
//===----------------------------------------------------------------------===//

static bool checkAllSplats(llvm::ArrayRef<Attribute> attrs) {
  for (auto attr : attrs) {
    if (auto dense = dyn_cast_or_null<ElementsAttr>(attr)) {
      if (!dense.isSplat())
        return false;
    }
  }

  return true;
}

llvm::SmallVector<double> getFoldValueAtIndexFp(llvm::ArrayRef<Attribute> attrs,
                                                int64_t idx = 0) {
  llvm::SmallVector<double> splattrs;

  // Note that i1 is neither signed nor unsigned.
  // But we should trait i1 as unsigned, otherwise that
  // APInt(1,1).getSExtValue() return allOnes 64-bit integer.
  // So here only distinguish signed integer.
  auto convertAPIntToDouble = [](APInt value, bool isSigned) -> double {
    if (isSigned)
      return static_cast<double>(value.getSExtValue());
    else
      return static_cast<double>(value.getZExtValue());
  };

  for (auto attr : attrs) {
    if (auto dense = dyn_cast<DenseFPElementsAttr>(attr)) {
      if (dense.isSplat()) {
        splattrs.push_back(dense.getSplatValue<APFloat>().convertToDouble());
      } else {
        splattrs.push_back(dense.getValues<APFloat>()[idx].convertToDouble());
      }
    } else if (auto dense = dyn_cast<DenseIntElementsAttr>(attr)) {
      bool isSigned = cast<IntegerType>(dense.getElementType()).isSigned();
      if (dense.isSplat()) {
        splattrs.push_back(
            convertAPIntToDouble(dense.getSplatValue<APInt>(), isSigned));
      } else {
        splattrs.push_back(
            convertAPIntToDouble(dense.getValues<APInt>()[idx], isSigned));
      }
    } else if (auto fpattr = dyn_cast<FloatAttr>(attr)) {
      splattrs.push_back(fpattr.getValueAsDouble());
    } else if (auto intattr = dyn_cast<IntegerAttr>(attr)) {
      bool isSigned = cast<IntegerType>(intattr.getType()).isSigned();
      splattrs.push_back(convertAPIntToDouble(intattr.getValue(), isSigned));
    } else {
      return {};
    }
  }

  return splattrs;
}

llvm::SmallVector<APInt> getFoldValueAtIndexInt(llvm::ArrayRef<Attribute> attrs,
                                                int64_t bitwidth,
                                                int64_t idx = 0) {
  llvm::SmallVector<APInt> splattrs;

  for (auto attr : attrs) {
    bool isSigned = false;
    if (auto dense = dyn_cast<DenseIntElementsAttr>(attr)) {
      isSigned = cast<IntegerType>(dense.getElementType()).isSigned();
      if (dense.isSplat()) {
        splattrs.push_back(dense.getSplatValue<APInt>());
      } else {
        splattrs.push_back(dense.getValues<APInt>()[idx]);
      }
    } else if (auto intattr = dyn_cast<IntegerAttr>(attr)) {
      isSigned = cast<IntegerType>(intattr.getType()).isSigned();
      splattrs.push_back(intattr.getValue());
    } else {
      return {};
    }

    // Note that i1 is neither signed nor unsigned.
    // But we should trait i1 as unsigned, otherwise that
    // APInt(1,1).getSExtValue() return allOnes 64-bit integer.
    // So here only distinguish signed integer.
    auto &apint = splattrs.back();
    if (apint.getBitWidth() < bitwidth) {
      if (isSigned) {
        apint = apint.sextOrTrunc(bitwidth);
      } else {
        apint = apint.zextOrTrunc(bitwidth);
      }
    }
  }

  return splattrs;
}

using NAryFoldFpOperator = std::function<double(ArrayRef<double>)>;
using NAryFoldIntOperator = std::function<APInt(ArrayRef<APInt>)>;

static OpFoldResult
naryFolderHelper(ArrayRef<Attribute> operands, Type ty,
                 std::optional<NAryFoldFpOperator> fpFolder,
                 std::optional<NAryFoldIntOperator> intFolder) {
  constexpr int64_t kMaxFold = 16;
  for (auto attr : operands) {
    if (!attr)
      return nullptr;
  }

  auto resultTy = dyn_cast<ValueTensorType>(ty);
  if (!resultTy || !resultTy.hasDtype() || !resultTy.areAllSizesKnown())
    return nullptr;

  auto dty = resultTy.getDtype();
  auto resultBTy = resultTy.toBuiltinTensor();

  auto fpTy = dyn_cast<mlir::FloatType>(dty);
  auto intTy = dyn_cast<mlir::IntegerType>(dty);
  if (!fpTy && !intTy)
    return nullptr;

  bool allSplats = checkAllSplats(operands);
  if (!(allSplats || resultBTy.getNumElements() <= kMaxFold))
    return nullptr;

  // We do not support broadcasting in the non-splat case so validate same
  // shaped inputs / outputs:
  if (!allSplats) {
    auto resultShape = resultBTy.getShape();
    for (int i = 0, s = operands.size(); i < s; ++i) {
      if (auto dense = dyn_cast<DenseElementsAttr>(operands[i])) {
        if (dense.isSplat())
          continue;
        auto operandShape = cast<ShapedType>(dense.getType()).getShape();
        if (operandShape.size() != resultShape.size())
          return nullptr;
        for (int i = 0, s = operandShape.size(); i < s; ++i)
          if (operandShape[i] != resultShape[i])
            return nullptr;
      }
    }
  }

  const int64_t numValues = allSplats ? 1 : resultBTy.getNumElements();

  if (fpTy) {
    if (!fpFolder.has_value())
      return nullptr;
    auto folder = fpFolder.value();
    llvm::SmallVector<APFloat> folded;
    for (int i = 0, s = numValues; i < s; ++i) {
      auto inputs = getFoldValueAtIndexFp(operands, i);
      if (inputs.size() != operands.size())
        return nullptr;
      double fold = folder(inputs);

      APFloat val(fold);
      bool unused;
      val.convert(fpTy.getFloatSemantics(), APFloat::rmNearestTiesToEven,
                  &unused);
      folded.push_back(val);
    }
    return DenseElementsAttr::get(resultBTy, folded);
  }

  if (intTy) {
    if (!intFolder.has_value())
      return nullptr;
    auto folder = intFolder.value();
    llvm::SmallVector<APInt> folded;
    for (int i = 0, s = numValues; i < s; ++i) {
      auto inputs =
          getFoldValueAtIndexInt(operands, dty.getIntOrFloatBitWidth(), i);
      if (inputs.size() != operands.size())
        return nullptr;
      folded.push_back(folder(inputs));
    }
    return DenseElementsAttr::get(resultBTy, folded);
  }

  return nullptr;
}

//===----------------------------------------------------------------------===//
// AtenAddTensorOp
//===----------------------------------------------------------------------===//
void AtenAddTensorOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                                  MLIRContext *context) {
  patterns.add(+[](AtenAddTensorOp op, PatternRewriter &rewriter) {
    return rewrite0DBinaryTensorOp(op, rewriter);
  });
}

OpFoldResult AtenAddTensorOp::fold(FoldAdaptor adaptor) {
  auto fpFold = [](llvm::ArrayRef<double> inputs) {
    assert(inputs.size() == 3);
    return inputs[0] + (inputs[1] * inputs[2]);
  };

  auto intFold = [](llvm::ArrayRef<APInt> inputs) {
    assert(inputs.size() == 3);
    return inputs[0] + (inputs[1] * inputs[2]);
  };

  return naryFolderHelper(adaptor.getOperands(), getType(), fpFold, intFold);
}

//===----------------------------------------------------------------------===//
// AtenAddScalarOp
//===----------------------------------------------------------------------===//
void AtenAddScalarOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                                  MLIRContext *context) {
  patterns.add(+[](AtenAddScalarOp op, PatternRewriter &rewriter) {
    return rewrite0DBinaryTensorOp(op, rewriter);
  });
}

OpFoldResult AtenAddScalarOp::fold(FoldAdaptor adaptor) {
  auto fpFold = [](llvm::ArrayRef<double> inputs) {
    assert(inputs.size() == 3);
    return inputs[0] + (inputs[1] * inputs[2]);
  };

  auto intFold = [](llvm::ArrayRef<APInt> inputs) {
    assert(inputs.size() == 3);
    int64_t bits = inputs[0].getBitWidth();
    APInt other(bits, inputs[1].getLimitedValue());
    APInt alpha(bits, inputs[2].getLimitedValue());
    return inputs[0] + (other * alpha);
  };

  return naryFolderHelper(adaptor.getOperands(), getType(), fpFold, intFold);
}

//===----------------------------------------------------------------------===//
// AtenSubTensorOp
//===----------------------------------------------------------------------===//
void AtenSubTensorOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                                  MLIRContext *context) {
  patterns.add(+[](AtenSubTensorOp op, PatternRewriter &rewriter) {
    return rewrite0DBinaryTensorOp(op, rewriter);
  });
}

OpFoldResult AtenSubTensorOp::fold(FoldAdaptor adaptor) {
  auto fpFold = [](llvm::ArrayRef<double> inputs) {
    assert(inputs.size() == 3);
    return inputs[0] - (inputs[1] * inputs[2]);
  };

  auto intFold = [](llvm::ArrayRef<APInt> inputs) {
    assert(inputs.size() == 3);
    return inputs[0] - (inputs[1] * inputs[2]);
  };

  return naryFolderHelper(adaptor.getOperands(), getType(), fpFold, intFold);
}

//===----------------------------------------------------------------------===//
// AtenSubScalarOp
//===----------------------------------------------------------------------===//
void AtenSubScalarOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                                  MLIRContext *context) {
  patterns.add(+[](AtenSubScalarOp op, PatternRewriter &rewriter) {
    return rewrite0DBinaryTensorOp(op, rewriter);
  });
}

OpFoldResult AtenSubScalarOp::fold(FoldAdaptor adaptor) {
  auto fpFold = [](llvm::ArrayRef<double> inputs) {
    assert(inputs.size() == 3);
    return inputs[0] - (inputs[1] * inputs[2]);
  };

  auto intFold = [](llvm::ArrayRef<APInt> inputs) {
    assert(inputs.size() == 3);
    int64_t bits = inputs[0].getBitWidth();
    APInt other(bits, inputs[1].getLimitedValue());
    APInt alpha(bits, inputs[2].getLimitedValue());
    return inputs[0] - (other * alpha);
  };

  return naryFolderHelper(adaptor.getOperands(), getType(), fpFold, intFold);
}

//===----------------------------------------------------------------------===//
// AtenRSubScalarOp
//===----------------------------------------------------------------------===//
void AtenRsubScalarOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                                   MLIRContext *context) {
  patterns.add(+[](AtenRsubScalarOp op, PatternRewriter &rewriter) {
    return rewrite0DBinaryTensorOp(op, rewriter);
  });
}

// ===----------------------------------------------------------------------===//
// AtenRSubScalarOp
// ===----------------------------------------------------------------------===//

OpFoldResult AtenRsubScalarOp::fold(FoldAdaptor adaptor) {
  auto fpFold = [](llvm::ArrayRef<double> inputs) {
    assert(inputs.size() == 3);
    return inputs[1] - inputs[0] * inputs[2];
  };

  auto intFold = [](llvm::ArrayRef<APInt> inputs) {
    assert(inputs.size() == 3);
    return inputs[1] - inputs[0] * inputs[2];
  };

  return naryFolderHelper(adaptor.getOperands(), getType(), fpFold, intFold);
}

//===----------------------------------------------------------------------===//
// AtenMulTensorOp
//===----------------------------------------------------------------------===//
void AtenMulTensorOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                                  MLIRContext *context) {
  patterns.add(+[](AtenMulTensorOp op, PatternRewriter &rewriter) {
    return rewrite0DBinaryTensorOp(op, rewriter);
  });
}

OpFoldResult AtenMulTensorOp::fold(FoldAdaptor adaptor) {
  auto fpFold = [](llvm::ArrayRef<double> inputs) {
    assert(inputs.size() == 2);
    return inputs[0] * inputs[1];
  };

  auto intFold = [](llvm::ArrayRef<APInt> inputs) {
    assert(inputs.size() == 2);
    int64_t bits = inputs[0].getBitWidth();
    APInt other(bits, inputs[1].getLimitedValue());
    return inputs[0] * other;
  };

  return naryFolderHelper(adaptor.getOperands(), getType(), fpFold, intFold);
}

//===----------------------------------------------------------------------===//
// AtenEqTensorOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenEqTensorOp::fold(FoldAdaptor adaptor) {
  constexpr int64_t kMaxFold = 16;
  auto ty = dyn_cast<ValueTensorType>(getType());
  if (!ty || !ty.hasDtype() || !ty.hasSizes())
    return nullptr;

  auto bty = ty.toBuiltinTensor();
  if (!bty.hasStaticShape())
    return nullptr;

  if (getSelf() == getOther())
    return DenseElementsAttr::get(bty,
                                  IntegerAttr::get(bty.getElementType(), 1));

  auto self = dyn_cast_or_null<DenseElementsAttr>(adaptor.getSelf());
  auto other = dyn_cast_or_null<DenseElementsAttr>(adaptor.getOther());
  if (!self || !other)
    return nullptr;

  auto selfTy = dyn_cast<ShapedType>(self.getType());
  auto otherTy = dyn_cast<ShapedType>(other.getType());
  if (!selfTy || !otherTy ||
      selfTy.getElementType() != otherTy.getElementType())
    return nullptr;

  // If both values are splats we can just compute the output value as a splat.
  if (self.isSplat() && other.isSplat()) {
    if (isa<mlir::FloatType>(selfTy.getElementType())) {
      APFloat lhsFp = self.getSplatValue<APFloat>();
      APFloat rhsFp = other.getSplatValue<APFloat>();
      bool eq = lhsFp.compare(rhsFp) == APFloat::cmpEqual;
      return DenseElementsAttr::get(bty, eq);
    }

    if (isa<mlir::IntegerType>(selfTy.getElementType())) {
      APInt lhsInt = self.getSplatValue<APInt>();
      APInt rhsInt = other.getSplatValue<APInt>();
      bool eq = lhsInt == rhsInt;
      return DenseElementsAttr::get(bty, eq);
    }

    return nullptr;
  }

  if (selfTy != otherTy || bty.getNumElements() > kMaxFold)
    return nullptr;

  if (isa<mlir::FloatType>(selfTy.getElementType())) {
    auto extract = [bty](DenseElementsAttr attr) {
      llvm::SmallVector<APFloat> vals;
      if (attr.isSplat()) {
        vals.resize(bty.getNumElements(), attr.getSplatValue<APFloat>());
        return vals;
      }

      for (auto fp : attr.getValues<APFloat>()) {
        vals.push_back(fp);
      }
      return vals;
    };

    llvm::SmallVector<APFloat> lhsFp = extract(self);
    llvm::SmallVector<APFloat> rhsFp = extract(other);
    llvm::SmallVector<bool> vals(bty.getNumElements());
    for (int i = 0, s = bty.getNumElements(); i < s; ++i) {
      vals[i] = lhsFp[i].compare(rhsFp[i]) == APFloat::cmpEqual;
    }

    return DenseElementsAttr::get(bty, vals);
  }

  if (isa<mlir::IntegerType>(selfTy.getElementType())) {
    auto extract = [bty](DenseElementsAttr attr) {
      llvm::SmallVector<APInt> vals;
      if (attr.isSplat()) {
        vals.resize(bty.getNumElements(), attr.getSplatValue<APInt>());
        return vals;
      }

      for (auto fp : attr.getValues<APInt>()) {
        vals.push_back(fp);
      }
      return vals;
    };

    llvm::SmallVector<APInt> lhsInt = extract(self);
    llvm::SmallVector<APInt> rhsInt = extract(other);
    llvm::SmallVector<bool> vals(bty.getNumElements());
    for (int i = 0, s = bty.getNumElements(); i < s; ++i) {
      vals[i] = lhsInt[i] == rhsInt[i];
    }

    return DenseElementsAttr::get(bty, vals);
  }

  return nullptr;
}

//===----------------------------------------------------------------------===//
// AtenLeScalarOp
//===----------------------------------------------------------------------===//

using ComparisonFoldFpOperator = std::function<bool(double, double)>;
using ComparisonFoldIntOperator = std::function<bool(APInt, APInt, bool)>;

static OpFoldResult comparisonScaleFolder(DenseElementsAttr lhs, Attribute rhs,
                                          ValueTensorType resultTy,
                                          ComparisonFoldFpOperator fpFolder,
                                          ComparisonFoldIntOperator intFolder) {
  constexpr int64_t kMaxFold = 16;
  if (!lhs || !rhs || !resultTy)
    return nullptr;
  if (!resultTy.areAllSizesKnown() || !resultTy.hasDtype())
    return nullptr;

  auto ctx = lhs.getContext();
  auto tensorETy = cast<RankedTensorType>(lhs.getType()).getElementType();
  if (lhs.isSplat()) {
    if (auto intAttr = dyn_cast<IntegerAttr>(rhs)) {
      auto unsign = cast<IntegerType>(tensorETy).isUnsigned();
      auto scalarAP = intAttr.getValue();
      auto tensorAP = lhs.getSplatValue<IntegerAttr>().getValue();
      tensorAP = APInt(
          scalarAP.getBitWidth(),
          unsign ? tensorAP.getZExtValue() : tensorAP.getSExtValue(), !unsign);
      auto resultBool = intFolder(tensorAP, scalarAP, unsign);
      auto resultAP = IntegerAttr::get(IntegerType::get(ctx, 1), resultBool);
      return DenseElementsAttr::get(resultTy.toBuiltinTensor(), resultAP);
    }

    if (auto floatAttr = dyn_cast<FloatAttr>(rhs)) {
      APFloat scalarAP = floatAttr.getValue();
      APFloat tensorAP = lhs.getSplatValue<FloatAttr>().getValue();
      auto resultBool =
          fpFolder(tensorAP.convertToDouble(), scalarAP.convertToDouble());
      auto resultAP = IntegerAttr::get(IntegerType::get(ctx, 1), resultBool);
      return DenseElementsAttr::get(resultTy.toBuiltinTensor(), resultAP);
    }
    return nullptr;
  }

  int64_t count = 1;
  for (auto size : resultTy.getSizes())
    count *= size;

  if (count > kMaxFold)
    return nullptr;

  if (auto intAttr = dyn_cast<IntegerAttr>(rhs)) {
    auto unsign = cast<IntegerType>(tensorETy).isUnsigned();
    llvm::SmallVector<bool> values;
    for (auto tensorAP : lhs.getValues<APInt>()) {
      auto scalarAP = intAttr.getValue();
      tensorAP = APInt(
          scalarAP.getBitWidth(),
          unsign ? tensorAP.getZExtValue() : tensorAP.getSExtValue(), !unsign);
      auto resultBool = intFolder(tensorAP, scalarAP, unsign);
      values.push_back(resultBool);
    }
    return DenseElementsAttr::get(resultTy.toBuiltinTensor(), values);
  }

  if (auto floatAttr = dyn_cast<FloatAttr>(rhs)) {
    llvm::SmallVector<bool> values;
    for (auto tensorAP : lhs.getValues<APFloat>()) {
      APFloat scalarAP = floatAttr.getValue();
      auto resultBool =
          fpFolder(tensorAP.convertToDouble(), scalarAP.convertToDouble());
      values.push_back(resultBool);
    }
    return DenseElementsAttr::get(resultTy.toBuiltinTensor(), values);
  }

  return nullptr;
}

OpFoldResult AtenLeScalarOp::fold(FoldAdaptor adaptor) {
  auto self = dyn_cast_or_null<DenseElementsAttr>(adaptor.getSelf());
  auto other = adaptor.getOther();
  auto resultTy = dyn_cast<ValueTensorType>(getType());

  auto fpFold = [](double lhs, double rhs) -> bool { return lhs <= rhs; };

  auto intFold = [](APInt lhs, APInt rhs, bool unsign) -> bool {
    int64_t bits = std::max(lhs.getBitWidth(), rhs.getBitWidth());
    APInt lhsWiden(bits, lhs.getLimitedValue());
    APInt rhsWiden(bits, rhs.getLimitedValue());
    return unsign ? lhsWiden.ule(rhsWiden) : lhsWiden.sle(rhsWiden);
  };

  return comparisonScaleFolder(self, other, resultTy, fpFold, intFold);
}

//===----------------------------------------------------------------------===//
// AtenLtScalarOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenLtScalarOp::fold(FoldAdaptor adaptor) {
  auto self = dyn_cast_or_null<DenseElementsAttr>(adaptor.getSelf());
  auto other = adaptor.getOther();
  auto resultTy = dyn_cast<ValueTensorType>(getType());

  auto fpFold = [](double lhs, double rhs) -> bool { return lhs < rhs; };

  auto intFold = [](APInt lhs, APInt rhs, bool unsign) -> bool {
    int64_t bits = std::max(lhs.getBitWidth(), rhs.getBitWidth());
    APInt lhsWiden(bits, lhs.getLimitedValue());
    APInt rhsWiden(bits, rhs.getLimitedValue());
    return unsign ? lhsWiden.ult(rhsWiden) : lhsWiden.slt(rhsWiden);
  };

  return comparisonScaleFolder(self, other, resultTy, fpFold, intFold);
}

//===----------------------------------------------------------------------===//
// AtenGtScalarOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenGtScalarOp::fold(FoldAdaptor adaptor) {
  auto self = dyn_cast_or_null<DenseElementsAttr>(adaptor.getSelf());
  auto other = adaptor.getOther();
  auto resultTy = dyn_cast<ValueTensorType>(getType());

  auto fpFold = [](double lhs, double rhs) -> bool { return lhs > rhs; };

  auto intFold = [](APInt lhs, APInt rhs, bool unsign) -> bool {
    int64_t bits = std::max(lhs.getBitWidth(), rhs.getBitWidth());
    APInt lhsWiden(bits, lhs.getLimitedValue());
    APInt rhsWiden(bits, rhs.getLimitedValue());
    return unsign ? lhsWiden.ugt(rhsWiden) : lhsWiden.sgt(rhsWiden);
  };

  return comparisonScaleFolder(self, other, resultTy, fpFold, intFold);
}

//===----------------------------------------------------------------------===//
// AtenGeScalarOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenGeScalarOp::fold(FoldAdaptor adaptor) {
  auto self = dyn_cast_or_null<DenseElementsAttr>(adaptor.getSelf());
  auto other = adaptor.getOther();
  auto resultTy = dyn_cast<ValueTensorType>(getType());

  auto fpFold = [](double lhs, double rhs) -> bool { return lhs >= rhs; };

  auto intFold = [](APInt lhs, APInt rhs, bool unsign) -> bool {
    int64_t bits = std::max(lhs.getBitWidth(), rhs.getBitWidth());
    APInt lhsWiden(bits, lhs.getLimitedValue());
    APInt rhsWiden(bits, rhs.getLimitedValue());
    return unsign ? lhsWiden.uge(rhsWiden) : lhsWiden.sge(rhsWiden);
  };

  return comparisonScaleFolder(self, other, resultTy, fpFold, intFold);
}

//===----------------------------------------------------------------------===//
// AtenEqScalarOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenEqScalarOp::fold(FoldAdaptor adaptor) {
  auto self = dyn_cast_or_null<DenseElementsAttr>(adaptor.getSelf());
  auto other = adaptor.getOther();
  auto resultTy = dyn_cast<ValueTensorType>(getType());

  auto fpFold = [](double lhs, double rhs) -> bool { return lhs == rhs; };

  auto intFold = [](APInt lhs, APInt rhs, bool unsign) -> bool {
    int64_t bits = std::max(lhs.getBitWidth(), rhs.getBitWidth());
    APInt lhsWiden(bits, lhs.getLimitedValue());
    APInt rhsWiden(bits, rhs.getLimitedValue());
    return lhsWiden.eq(rhsWiden);
  };

  return comparisonScaleFolder(self, other, resultTy, fpFold, intFold);
}

//===----------------------------------------------------------------------===//
// AtenNeScalarOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenNeScalarOp::fold(FoldAdaptor adaptor) {
  auto self = dyn_cast_or_null<DenseElementsAttr>(adaptor.getSelf());
  auto other = adaptor.getOther();
  auto resultTy = dyn_cast<ValueTensorType>(getType());

  auto fpFold = [](double lhs, double rhs) -> bool { return lhs != rhs; };

  auto intFold = [](APInt lhs, APInt rhs, bool unsign) -> bool {
    int64_t bits = std::max(lhs.getBitWidth(), rhs.getBitWidth());
    APInt lhsWiden(bits, lhs.getLimitedValue());
    APInt rhsWiden(bits, rhs.getLimitedValue());
    return lhsWiden.ne(rhsWiden);
  };

  return comparisonScaleFolder(self, other, resultTy, fpFold, intFold);
}

//===----------------------------------------------------------------------===//
// AtenLogOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenLogOp::fold(FoldAdaptor adaptor) {
  auto self = dyn_cast_or_null<DenseElementsAttr>(adaptor.getSelf());
  auto resultType = dyn_cast<ValueTensorType>(getType());
  if (!self || !resultType)
    return nullptr;

  auto fpFold = [](llvm::ArrayRef<double> inputs) -> double {
    assert(inputs.size() == 1);
    return std::log(inputs[0]);
  };

  return naryFolderHelper(adaptor.getOperands(), resultType, fpFold,
                          std::nullopt);
}

//===----------------------------------------------------------------------===//
// AtenFloorOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenFloorOp::fold(FoldAdaptor adaptor) {
  auto resultType = dyn_cast<ValueTensorType>(getType());
  if (resultType && resultType.hasDtype() &&
      isa<mlir::IntegerType>(resultType.getDtype())) {
    return getSelf();
  }
  return {};
}

//===----------------------------------------------------------------------===//
// AtenCeilOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenCeilOp::fold(FoldAdaptor adaptor) {
  auto resultType = dyn_cast<ValueTensorType>(getType());
  if (resultType && resultType.hasDtype() &&
      isa<mlir::IntegerType>(resultType.getDtype())) {
    return getSelf();
  }
  return {};
}

//===----------------------------------------------------------------------===//
// AtenRoundOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenRoundOp::fold(FoldAdaptor adaptor) {
  auto resultType = dyn_cast<ValueTensorType>(getType());
  if (resultType && resultType.hasDtype() &&
      isa<mlir::IntegerType>(resultType.getDtype())) {
    return getSelf();
  }
  return {};
}

//===----------------------------------------------------------------------===//
// AtenTruncOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenTruncOp::fold(FoldAdaptor adaptor) {
  auto resultType = dyn_cast<ValueTensorType>(getType());
  if (resultType && resultType.hasDtype() &&
      isa<mlir::IntegerType>(resultType.getDtype())) {
    return getSelf();
  }
  return {};
}

//===----------------------------------------------------------------------===//
// AtenSignOp
//===----------------------------------------------------------------------===//
void AtenSignOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                             MLIRContext *context) {
  patterns.add(+[](AtenSignOp op, PatternRewriter &rewriter) {
    rewriter.replaceOpWithNewOp<AtenSgnOp>(op, op.getType(), op.getSelf());
    return success();
  });
}

//===----------------------------------------------------------------------===//
// AtenMulScalarOp
//===----------------------------------------------------------------------===//
void AtenMulScalarOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                                  MLIRContext *context) {
  patterns.add(+[](AtenMulScalarOp op, PatternRewriter &rewriter) {
    return rewrite0DBinaryTensorOp(op, rewriter);
  });
}

OpFoldResult AtenMulScalarOp::fold(FoldAdaptor adaptor) {
  auto fpFold = [](llvm::ArrayRef<double> inputs) {
    assert(inputs.size() == 2);
    return inputs[0] * inputs[1];
  };

  auto intFold = [](llvm::ArrayRef<APInt> inputs) {
    assert(inputs.size() == 2);
    return inputs[0] * inputs[1];
  };

  return naryFolderHelper(adaptor.getOperands(), getType(), fpFold, intFold);
}

//===----------------------------------------------------------------------===//
// AtenDivTensorModeOp
//===----------------------------------------------------------------------===//
void AtenDivTensorModeOp::getCanonicalizationPatterns(
    RewritePatternSet &patterns, MLIRContext *context) {
  patterns.add(+[](AtenDivTensorModeOp op, PatternRewriter &rewriter) {
    return rewrite0DBinaryTensorOp(op, rewriter);
  });
}

// ===----------------------------------------------------------------------===//
// AtenDivTensorModeOp
// ===----------------------------------------------------------------------===//

OpFoldResult AtenDivTensorModeOp::fold(FoldAdaptor adaptor) {
  auto resultTy = dyn_cast_or_null<ValueTensorType>(getType());
  if (!resultTy || !resultTy.hasDtype()) {
    return nullptr;
  }
  std::function<double(ArrayRef<double>)> fpFold;
  std::function<APInt(ArrayRef<APInt>)> intFold;

  auto roundMode = dyn_cast_or_null<StringAttr>(adaptor.getRoundingMode());
  auto unsign = false;
  if (isa<mlir::IntegerType>(resultTy.getDtype())) {
    unsign = cast<IntegerType>(resultTy.getDtype()).isUnsigned();
  }

  fpFold = [roundMode](llvm::ArrayRef<double> inputs) {
    assert(inputs.size() == 2);
    if (!roundMode) {
      return (double)inputs[0] / inputs[1];
    } else if (roundMode.getValue().str() == "floor") {
      return std::floor((double)inputs[0] / inputs[1]);
    } else {
      return std::trunc((double)inputs[0] / inputs[1]);
    }
  };

  intFold = [unsign, roundMode](llvm::ArrayRef<APInt> inputs) {
    assert(inputs.size() == 2);
    auto lhs = unsign ? inputs[0].getZExtValue() : inputs[0].getSExtValue();
    auto rhs = unsign ? inputs[1].getZExtValue() : inputs[1].getSExtValue();
    int64_t bits = std::max(inputs[0].getBitWidth(), inputs[1].getBitWidth());
    int64_t res;
    if (roundMode.getValue().str() == "floor") {
      res = std::floor(lhs / rhs);
    } else {
      res = std::trunc(lhs / rhs);
    }
    return APInt(bits, res);
  };

  if (!roundMode) {
    return naryFolderHelper({adaptor.getSelf(), adaptor.getOther()}, getType(),
                            fpFold, std::nullopt);
  }

  return naryFolderHelper({adaptor.getSelf(), adaptor.getOther()}, getType(),
                          fpFold, intFold);
}

//===----------------------------------------------------------------------===//
// AtenDivScalarModeOp
//===----------------------------------------------------------------------===//
void AtenDivScalarModeOp::getCanonicalizationPatterns(
    RewritePatternSet &patterns, MLIRContext *context) {
  patterns.add(+[](AtenDivScalarModeOp op, PatternRewriter &rewriter) {
    return rewrite0DBinaryTensorOp(op, rewriter);
  });
}

//===----------------------------------------------------------------------===//
// AtenNumelOp
//===----------------------------------------------------------------------===//
void AtenNumelOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                              MLIRContext *context) {
  patterns.add(+[](AtenNumelOp op, PatternRewriter &rewriter) {
    auto inputType = dyn_cast<BaseTensorType>(op.getSelf().getType());
    if (!inputType || !inputType.areAllSizesKnown()) {
      return failure();
    }
    auto sizes = inputType.getSizes();
    int64_t numel = 1;
    for (int64_t d : sizes) {
      numel *= d;
    }
    rewriter.replaceOpWithNewOp<ConstantIntOp>(
        op, rewriter.getI64IntegerAttr(numel));
    return success();
  });
}

//===----------------------------------------------------------------------===//
// Aten__Or__TensorOp
//===----------------------------------------------------------------------===//

void Aten__Or__TensorOp::getCanonicalizationPatterns(
    RewritePatternSet &patterns, MLIRContext *context) {
  patterns.add(+[](Aten__Or__TensorOp op, PatternRewriter &rewriter) {
    rewriter.replaceOpWithNewOp<AtenBitwiseOrTensorOp>(
        op, op.getType(), op.getSelf(), op.getOther());
    return success();
  });
}

//===----------------------------------------------------------------------===//
// Aten__And__ScalarOp
//===----------------------------------------------------------------------===//
void Aten__And__ScalarOp::getCanonicalizationPatterns(
    RewritePatternSet &patterns, MLIRContext *context) {
  patterns.add(+[](Aten__And__ScalarOp op, PatternRewriter &rewriter) {
    rewriter.replaceOpWithNewOp<AtenBitwiseAndScalarOp>(
        op, op.getType(), op.getSelf(), op.getOther());
    return success();
  });
}

//===----------------------------------------------------------------------===//
// AtenScalarImplicitOp
//===----------------------------------------------------------------------===//
void AtenScalarImplicitOp::getCanonicalizationPatterns(
    RewritePatternSet &patterns, MLIRContext *context) {
  patterns.add(+[](AtenScalarImplicitOp op, PatternRewriter &rewriter) {
    Location loc = op.getLoc();
    Value a = op.getA();
    auto outType = op.getResult().getType();
    Value scalarIntValue = getScalarIntValue(a, loc, rewriter);
    if (scalarIntValue) {
      rewriter.replaceOpWithNewOp<Torch::DerefineOp>(op, outType,
                                                     scalarIntValue);
      return success();
    }
    Value scalarFloatValue = getScalarFloatValue(a, loc, rewriter);
    if (scalarFloatValue) {
      rewriter.replaceOpWithNewOp<Torch::DerefineOp>(op, outType,
                                                     scalarFloatValue);
      return success();
    }
    return failure();
  });
}

//===----------------------------------------------------------------------===//
// AtenFloatImplicitOp
//===----------------------------------------------------------------------===//
void AtenFloatImplicitOp::getCanonicalizationPatterns(
    RewritePatternSet &patterns, MLIRContext *context) {
  patterns.add(+[](AtenFloatImplicitOp op, PatternRewriter &rewriter) {
    Location loc = op.getLoc();
    Value a = op.getA();
    Value scalarValue = getScalarFloatValue(a, loc, rewriter);
    if (!scalarValue)
      return failure();
    rewriter.replaceOp(op, scalarValue);
    return success();
  });
}

//===----------------------------------------------------------------------===//
// AtenIntImplicitOp
//===----------------------------------------------------------------------===//
void AtenIntImplicitOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                                    MLIRContext *context) {
  patterns.add(+[](AtenIntImplicitOp op, PatternRewriter &rewriter) {
    Location loc = op.getLoc();
    Value a = op.getA();
    Value scalarValue = getScalarIntValue(a, loc, rewriter);
    if (!scalarValue)
      return failure();
    rewriter.replaceOp(op, scalarValue);
    return success();
  });
}

//===----------------------------------------------------------------------===//
// AtenSizeOp
//===----------------------------------------------------------------------===//

// Traces at most 6 parents of `value` to determine the tensor type with known
// dimension size or returns failure if such a type was not found.  If `dim` is
// `None`, then all dimension's sizes must be known.
static FailureOr<BaseTensorType>
traceKnownSizeTensorType(Value value, std::optional<int64_t> dim) {
  // Function to check if we found a type that contains the queried information.
  auto foundType = [](BaseTensorType tensorType, std::optional<int64_t>(dim)) {
    if (!tensorType.hasSizes())
      return false;

    if (dim == std::nullopt)
      return tensorType.areAllSizesKnown();

    // If the dimension value is negative, then convert it to a positive value.
    ArrayRef<int64_t> sizes = tensorType.getSizes();
    *dim = toPositiveDim(*dim, sizes.size());
    return isValidDim(*dim, sizes.size()) && sizes[*dim] != kUnknownSize;
  };

  // Limit the loop count to 6 to avoid indefinite compilation times from
  // unbounded IR traversals.
  for (auto idx = 0; idx < 6; ++idx) {
    if (!value || !isa<BaseTensorType>(value.getType()))
      return failure();

    auto tensorType = cast<BaseTensorType>(value.getType());
    if (foundType(tensorType, dim))
      return tensorType;

    auto op = value.getDefiningOp();
    if (!op || !isa<CopyToValueTensorOp, CopyToNonValueTensorOp,
                    TensorStaticInfoCastOp>(op))
      return failure();

    // In all ops of interest to us, the source tensor is operand #0.
    value = op->getOperand(0);
  }

  return failure();
}

void AtenSizeOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                             MLIRContext *context) {
  patterns.add(+[](AtenSizeOp op, PatternRewriter &rewriter) {
    auto type = traceKnownSizeTensorType(op.getOperand(), std::nullopt);
    if (failed(type))
      return rewriter.notifyMatchFailure(op, "all sizes not known");
    SmallVector<Value> listElements;
    for (int64_t size : type->getSizes()) {
      listElements.push_back(rewriter.create<Torch::ConstantIntOp>(
          op->getLoc(), rewriter.getI64IntegerAttr(size)));
    }
    rewriter.replaceOpWithNewOp<Torch::PrimListConstructOp>(
        op, Torch::ListType::get(rewriter.getType<Torch::IntType>()),
        listElements);
    return success();
  });
  // One-off pattern to erase if dead.
  // TODO: Use the effects infra to express the semantics of this op and enable
  // a centralized "erase if dead" canonicalization.
  // Specifically, we need to mark the op as only MemoryEffects::Allocate
  // so that `mlir::wouldOpBeTriviallyDead` does the right thing.
  patterns.add(+[](AtenSizeOp op, PatternRewriter &rewriter) {
    if (!op.use_empty())
      return failure();
    rewriter.eraseOp(op);
    return failure();
  });
}

//===----------------------------------------------------------------------===//
// AtenFlattenUsingIntsOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenFlattenUsingIntsOp::fold(FoldAdaptor adaptor) {
  return genericViewLikeFold(adaptor.getSelf(), getType());
}

//===----------------------------------------------------------------------===//
// AtenUnflattenIntOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenUnflattenIntOp::fold(FoldAdaptor adaptor) {
  return genericViewLikeFold(adaptor.getSelf(), getType());
}

void AtenUnflattenIntOp::getCanonicalizationPatterns(
    RewritePatternSet &patterns, MLIRContext *context) {
  // if there are only two sizes and one of them is statically 1, then convert
  // to an unqueeze.
  patterns.add(+[](AtenUnflattenIntOp op, PatternRewriter &rewriter) {
    SmallVector<Value> sizeValues;
    if (!getListConstructElements(op.getSizes(), sizeValues))
      return rewriter.notifyMatchFailure(op,
                                         "sizes must come from list construct");
    if (sizeValues.size() != 2)
      return failure();
    int64_t dim0, dim1;
    bool dim0Constant = matchPattern(sizeValues[0], m_TorchConstantInt(&dim0));
    bool dim1Constant = matchPattern(sizeValues[1], m_TorchConstantInt(&dim1));
    if (!dim0Constant && !dim1Constant)
      return failure();
    if (dim0 != 1 && dim1 != 1)
      return failure();
    Value unflattenDim = op.getDim();
    int64_t dimAsInt;
    bool dimWasConstant =
        matchPattern(unflattenDim, m_TorchConstantInt(&dimAsInt));
    Value self = op.getSelf();
    Value cstMOne = rewriter.create<Torch::ConstantIntOp>(op.getLoc(), -1);
    // the runtime asserts below are introduced to catch malformed unflatten ops
    // possibly generated from onnx IR.
    Value unsqueeze;
    if (dim0 == 1) {
      // unsqueeze at dim
      FailureOr<Value> maybeUnsqueeze =
          Torch::unsqueezeTensor(rewriter, op, self, unflattenDim);
      if (failed(maybeUnsqueeze))
        return rewriter.notifyMatchFailure(op, "failed to create unsqueeze op");
      unsqueeze = maybeUnsqueeze.value();
      // check if the remaining size value is either -1 or equal to original
      // size at dim
      Value selfSizeAtDim =
          rewriter.create<AtenSizeIntOp>(op.getLoc(), self, unflattenDim);
      Value isSameSize = rewriter.create<AtenEqIntOp>(
          op.getLoc(), selfSizeAtDim, sizeValues[1]);
      Value isMinusOne =
          rewriter.create<AtenEqIntOp>(op.getLoc(), cstMOne, sizeValues[1]);
      Value isMOneOrSameSize = rewriter.create<Aten__Or__BoolOp>(
          op.getLoc(), isMinusOne, isSameSize);
      rewriter.create<Torch::RuntimeAssertOp>(
          op.getLoc(), isMOneOrSameSize,
          rewriter.getStringAttr("unflatten sizes must be compatible"));
    }
    if (dim1 == 1) {
      // unsqueeze at dim + 1
      Value dimPlusOne;
      if (!dimWasConstant) {
        Value cstOne = rewriter.create<Torch::ConstantIntOp>(op.getLoc(), 1);
        dimPlusOne =
            rewriter.create<AtenAddIntOp>(op.getLoc(), unflattenDim, cstOne);
      } else {
        // If dim was constant, creating an AtenAddIntOp will make
        // Torch::unsqueezeTensor() interpret it as still not being a constant,
        // and the resultant shape would consist of only dynamic dims. To fix
        // this, emit a ConstantIntOp for (dim + 1) to avoid an assertion
        // failure, when AtenUnsqueezeOp is in a later pass converted to
        // ExpandShapeOp, which is bound to fail shape inference in MLIR if
        // output dims are dynamic.
        dimPlusOne = rewriter.create<Torch::ConstantIntOp>(
            op.getLoc(), rewriter.getI64IntegerAttr(dimAsInt + 1));
      }
      FailureOr<Value> maybeUnsqueeze =
          Torch::unsqueezeTensor(rewriter, op, self, dimPlusOne);
      if (failed(maybeUnsqueeze))
        return rewriter.notifyMatchFailure(op, "failed to create unsqueeze op");
      unsqueeze = maybeUnsqueeze.value();
      // check if the remaining size value is either -1 or equal to original
      // size at dim
      Value selfSizeAtDim =
          rewriter.create<AtenSizeIntOp>(op.getLoc(), self, unflattenDim);
      Value isSameSize = rewriter.create<AtenEqIntOp>(
          op.getLoc(), selfSizeAtDim, sizeValues[0]);
      Value isMinusOne =
          rewriter.create<AtenEqIntOp>(op.getLoc(), cstMOne, sizeValues[0]);
      Value isMOneOrSameSize = rewriter.create<Aten__Or__BoolOp>(
          op.getLoc(), isMinusOne, isSameSize);
      rewriter.create<Torch::RuntimeAssertOp>(
          op.getLoc(), isMOneOrSameSize,
          rewriter.getStringAttr("unflatten sizes must be compatible"));
    }
    rewriter.replaceOpWithNewOp<Torch::TensorStaticInfoCastOp>(op, op.getType(),
                                                               unsqueeze);
    return success();
  });
}

//===----------------------------------------------------------------------===//
// AtenReshapeOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenReshapeOp::fold(FoldAdaptor adaptor) {
  auto selfTy = dyn_cast<ValueTensorType>(getSelf().getType());
  auto opTy = dyn_cast<ValueTensorType>(getType());
  if (selfTy && selfTy == opTy && selfTy.hasSizes() &&
      selfTy.toBuiltinTensor().hasStaticShape())
    return getSelf();
  return nullptr;
}

//===----------------------------------------------------------------------===//
// AtenSelectIntOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenSelectIntOp::fold(FoldAdaptor adaptor) {
  auto self = dyn_cast_or_null<DenseElementsAttr>(adaptor.getSelf());
  auto ty = dyn_cast<ValueTensorType>(getType());
  if (!self || !ty || !ty.hasDtype() || !ty.hasSizes())
    return nullptr;

  auto selfTy = cast<ShapedType>(self.getType());
  auto bty = ty.toBuiltinTensor();
  if (!bty.hasStaticShape())
    return nullptr;

  if (self.isSplat())
    return DenseElementsAttr::get(bty, self.getSplatValue<Attribute>());

  auto dimAttr = dyn_cast_or_null<IntegerAttr>(adaptor.getDim());
  auto indexAttr = dyn_cast_or_null<IntegerAttr>(adaptor.getIndex());
  if (!dimAttr || !indexAttr || bty.getNumElements() != 1)
    return nullptr;

  auto dim = dimAttr.getInt();
  auto index = indexAttr.getInt();

  for (int i = 0, s = selfTy.getRank(); i < s; ++i) {
    if (i != dim && selfTy.getDimSize(i) != 1)
      return nullptr;
  }

  auto splattr = self.getValues<Attribute>()[index];
  return DenseElementsAttr::get(bty, splattr);
}

//===----------------------------------------------------------------------===//
// AtenSizeIntOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenSizeIntOp::fold(FoldAdaptor adaptor) {
  int64_t dim;
  if (!matchPattern(this->getDim(), m_TorchConstantInt(&dim)))
    return nullptr;
  auto type = traceKnownSizeTensorType(this->getSelf(), dim);
  if (failed(type))
    return nullptr;
  ArrayRef<int64_t> sizes = type->getSizes();
  dim = toPositiveDim(dim, sizes.size());
  if (!isValidDim(dim, sizes.size()))
    return nullptr;
  return IntegerAttr::get(IntegerType::get(getContext(), 64), sizes[dim]);
}

//===----------------------------------------------------------------------===//
// AtenGtIntOp
//===----------------------------------------------------------------------===//

static IntegerAttr getI1IntegerAttr(MLIRContext *context, bool value) {
  return IntegerAttr::get(IntegerType::get(context, 1),
                          static_cast<int64_t>(value));
}

using ConstantFloatComparator = std::function<bool(double, double)>;
template <typename OpTy>
static OpFoldResult
floatComparatorFoldHelper(OpTy op, ConstantFloatComparator comparator) {
  if (op.getOperand(0) == op.getOperand(1))
    return getI1IntegerAttr(op.getContext(), comparator(0, 0));

  double lhs, rhs;
  if (!matchPattern(op.getOperand(0), m_TorchConstantFloat(&lhs)) ||
      !matchPattern(op.getOperand(1), m_TorchConstantFloat(&rhs)))
    return nullptr;

  return getI1IntegerAttr(op.getContext(), comparator(lhs, rhs));
}

//===----------------------------------------------------------------------===//
// AtenLtFloatOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenLtFloatOp::fold(FoldAdaptor adaptor) {
  return floatComparatorFoldHelper(*this,
                                   [](double a, double b) { return a < b; });
}

//===----------------------------------------------------------------------===//
// AtenGtFloatOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenGtFloatOp::fold(FoldAdaptor adaptor) {
  return floatComparatorFoldHelper(*this,
                                   [](double a, double b) { return a > b; });
}

//===----------------------------------------------------------------------===//
// AtenGeFloatOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenGeFloatOp::fold(FoldAdaptor adaptor) {
  return floatComparatorFoldHelper(*this,
                                   [](double a, double b) { return a >= b; });
}

//===----------------------------------------------------------------------===//
// AtenEqFloatOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenEqFloatOp::fold(FoldAdaptor adaptor) {
  return floatComparatorFoldHelper(*this,
                                   [](double a, double b) { return a == b; });
}

using ConstantIntComparator = std::function<bool(int64_t, int64_t)>;
template <typename OpTy>
static OpFoldResult intComparatorFoldHelper(OpTy op,
                                            ConstantIntComparator comparator) {

  Value lhsValue = op->getOperand(0);
  Value rhsValue = op->getOperand(1);
  if (lhsValue == rhsValue)
    return getI1IntegerAttr(op.getContext(), comparator(0, 0));

  int64_t lhs, rhs;
  bool lhsIsConstant = matchPattern(lhsValue, m_TorchConstantInt(&lhs));
  bool rhsIsConstant = matchPattern(rhsValue, m_TorchConstantInt(&rhs));
  if (lhsIsConstant && rhsIsConstant)
    return getI1IntegerAttr(op.getContext(), comparator(lhs, rhs));

  // Ensure that if there is a constant, it is on the right.
  if (lhsIsConstant && !rhsIsConstant) {
    std::swap(lhs, rhs);
    std::swap(lhsValue, rhsValue);
    std::swap(lhsIsConstant, rhsIsConstant);
    auto newComparator = [comparator](int64_t lhs, int64_t rhs) {
      return comparator(rhs, lhs);
    };
    comparator = newComparator;
  }

  // Fold comparisons of AtenSizeIntOp against negative values.
  // AtenSizeIntOp is known to always be non-negative.
  if (rhsIsConstant && rhs < 0) {
    // We can return `comparator(0, -1)` here because of the property:
    // If x >= 0 && y < 0, then:
    // - cmp(x, y) == cmp(x + 1, y)
    // - cmp(x, y) == cmp(x, y - 1)
    // By induction all cases here are covered.
    if (auto size = lhsValue.getDefiningOp<AtenSizeIntOp>())
      return getI1IntegerAttr(op->getContext(), comparator(0, -1));
  }

  // Fold comparisons of AtenSizeIntOp against 0:
  // - torch.aten.size.int >= 0 ==> True.
  // - torch.aten.size.int < 0 ==> False.
  // (and the operand-swapped versions of the above)
  if (rhsIsConstant && rhs == 0) {
    if (auto size = lhsValue.getDefiningOp<AtenSizeIntOp>()) {
      // >= 0 comparison.
      if (comparator(0, 0) && comparator(1, 0))
        return getI1IntegerAttr(op->getContext(), true);
      // < 0 comparison.
      if (!comparator(0, 0) && comparator(-1, 0) && !comparator(1, 0))
        return getI1IntegerAttr(op->getContext(), false);
    }
  }

  return nullptr;
}

//===----------------------------------------------------------------------===//
// AtenDetachOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenDetachOp::fold(FoldAdaptor adaptor) {
  if (getSelf().getType() != getResult().getType())
    return {};
  return getSelf();
}

//===----------------------------------------------------------------------===//
// AtenNeIntOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenNeIntOp::fold(FoldAdaptor adaptor) {
  return intComparatorFoldHelper(*this,
                                 [](int64_t a, int64_t b) { return a != b; });
}

//===----------------------------------------------------------------------===//
// AtenEqIntOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenEqIntOp::fold(FoldAdaptor adaptor) {
  return intComparatorFoldHelper(*this,
                                 [](int64_t a, int64_t b) { return a == b; });
}

//===----------------------------------------------------------------------===//
// AtenEqStrOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenEqStrOp::fold(FoldAdaptor adaptor) {
  if (getOperand(0) == getOperand(1))
    return getI1IntegerAttr(getContext(), true);

  auto aStr = adaptor.getA();
  auto bStr = adaptor.getB();
  if (aStr && bStr)
    return getI1IntegerAttr(getContext(), aStr == bStr);
  return nullptr;
}

//===----------------------------------------------------------------------===//
// AtenNeStrOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenNeStrOp::fold(FoldAdaptor adaptor) {
  if (getOperand(0) == getOperand(1))
    return getI1IntegerAttr(getContext(), false);

  auto aStr = adaptor.getA();
  auto bStr = adaptor.getB();
  if (aStr && bStr)
    return getI1IntegerAttr(getContext(), aStr != bStr);
  return nullptr;
}

//===----------------------------------------------------------------------===//
// Aten__Contains__StrListOp
//===----------------------------------------------------------------------===//

OpFoldResult Aten__Contains__StrListOp::fold(FoldAdaptor adaptor) {
  StringAttr item = dyn_cast_or_null<StringAttr>(adaptor.getItem());
  if (!item)
    return nullptr;

  if (auto listConstruct = getL().getDefiningOp<Torch::PrimListConstructOp>()) {
    if (isListPotentiallyMutated(listConstruct))
      return nullptr;
  }
  llvm::SmallVector<std::string> strs;
  if (matchPattern(getL(), m_TorchListOfConstantStrs(strs))) {
    for (const auto &str : strs) {
      if (item.getValue().str() == str)
        return getI1IntegerAttr(getContext(), true);
    }
    return getI1IntegerAttr(getContext(), false);
  }
  return nullptr;
}

//===----------------------------------------------------------------------===//
// AtenLtIntOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenLtIntOp::fold(FoldAdaptor adaptor) {
  return intComparatorFoldHelper(*this,
                                 [](int64_t a, int64_t b) { return a < b; });
}

//===----------------------------------------------------------------------===//
// AtenLeIntOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenLeIntOp::fold(FoldAdaptor adaptor) {
  return intComparatorFoldHelper(*this,
                                 [](int64_t a, int64_t b) { return a <= b; });
}

//===----------------------------------------------------------------------===//
// AtenGtIntOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenGtIntOp::fold(FoldAdaptor adaptor) {
  return intComparatorFoldHelper(*this,
                                 [](int64_t a, int64_t b) { return a > b; });
}

//===----------------------------------------------------------------------===//
// AtenGeIntOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenGeIntOp::fold(FoldAdaptor adaptor) {
  return intComparatorFoldHelper(*this,
                                 [](int64_t a, int64_t b) { return a >= b; });
}

//===----------------------------------------------------------------------===//
// AtenBoolFloatOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenBoolFloatOp::fold(FoldAdaptor adaptor) {
  double c;
  if (matchPattern(getOperand(), m_TorchConstantFloat(&c)))
    return getI1IntegerAttr(getContext(), c != 0.0);
  return nullptr;
}

//===----------------------------------------------------------------------===//
// AtenBoolIntOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenBoolIntOp::fold(FoldAdaptor adaptor) {
  int64_t c;
  if (matchPattern(getOperand(), m_TorchConstantInt(&c)))
    return getI1IntegerAttr(getContext(), c != 0);
  return nullptr;
}

//===----------------------------------------------------------------------===//
// AtenAnyBoolOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenAnyBoolOp::fold(FoldAdaptor adaptor) {
  auto inputConstruct = getSelf().getDefiningOp<Torch::PrimListConstructOp>();
  if (!inputConstruct || isListPotentiallyMutated(inputConstruct))
    return nullptr;
  // If any operand is a constant true, return true.
  for (auto operand : inputConstruct.getOperands()) {
    bool b = false;
    if (matchPattern(operand, m_TorchConstantBool(&b)) && b) {
      return getI1IntegerAttr(getContext(), true);
    }
  }
  return nullptr;
}

//===----------------------------------------------------------------------===//
// AtenFloatScalarOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenFloatScalarOp::fold(FoldAdaptor adaptor) {
  // Constant fold int -> float conversion.
  if (auto integerAttr = dyn_cast_or_null<IntegerAttr>(adaptor.getA())) {
    return FloatAttr::get(
        mlir::Float64Type::get(getContext()),
        static_cast<double>(integerAttr.getValue().getSExtValue()));
  }
  // If the input is float type already, the op is an identity.
  if (getType() == getOperand().getType())
    return getOperand();
  return nullptr;
}

//===----------------------------------------------------------------------===//
// AtenIntFloatOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenIntFloatOp::fold(FoldAdaptor adaptor) {
  // Constant fold float -> int conversion.
  if (auto floatAttr = dyn_cast_or_null<FloatAttr>(adaptor.getA())) {
    return IntegerAttr::get(
        mlir::IntegerType::get(getContext(), 64),
        static_cast<int64_t>(floatAttr.getValue().convertToDouble()));
  }
  return nullptr;
}

//===----------------------------------------------------------------------===//
// AtenIntScalarOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenIntScalarOp::fold(FoldAdaptor adaptor) {
  // Constant fold float -> int conversion.
  if (auto floatAttr = dyn_cast_or_null<FloatAttr>(adaptor.getA())) {
    return IntegerAttr::get(
        mlir::IntegerType::get(getContext(), 64),
        static_cast<long>(floatAttr.getValue().convertToDouble()));
  }
  // If the input is int type already, the op is an identity.
  if (getType() == getOperand().getType())
    return getOperand();
  return nullptr;
}

//===----------------------------------------------------------------------===//
// AtenIntBoolOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenIntBoolOp::fold(FoldAdaptor adaptor) {
  bool b;
  if (matchPattern(getOperand(), m_TorchConstantBool(&b))) {
    return getI64IntegerAttr(getContext(), static_cast<long>(b));
  }
  return nullptr;
}

//===----------------------------------------------------------------------===//
// AtenMaskedFillTensorOp
//===----------------------------------------------------------------------===//

// Fold 0d fill tensor to scalar
void AtenMaskedFillTensorOp::getCanonicalizationPatterns(
    RewritePatternSet &patterns, MLIRContext *context) {
  patterns.add(+[](AtenMaskedFillTensorOp op, PatternRewriter &rewriter) {
    auto scalarIntVal =
        getScalarIntValue(op.getValue(), op->getLoc(), rewriter);
    auto scalarFloatVal =
        getScalarFloatValue(op.getValue(), op->getLoc(), rewriter);
    if (!scalarIntVal && !scalarFloatVal)
      return failure();
    Value scalarVal = scalarIntVal ? scalarIntVal : scalarFloatVal;
    rewriter.replaceOpWithNewOp<AtenMaskedFillScalarOp>(
        op, op.getType(), op.getSelf(), op.getMask(), scalarVal);
    return failure();
  });
}

//===----------------------------------------------------------------------===//
// AtenCloneOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenCloneOp::fold(FoldAdaptor adaptor) {
  // note: memory_format would be ignored
  if (getSelf().getType() == getResult().getType() &&
      llvm::dyn_cast<ValueTensorType>(getSelf().getType())) {
    // self should have value semantics
    return getSelf();
  }
  return {};
}

//===----------------------------------------------------------------------===//
// AtenSortIntOp
//===----------------------------------------------------------------------===//

void AtenSortIntOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                                MLIRContext *context) {
  patterns.add(+[](AtenSortIntOp op, PatternRewriter &rewriter) {
    SmallVector<int64_t> listElements;
    if (!matchPattern(op.getSelf(), m_TorchListOfConstantInts(listElements)))
      return rewriter.notifyMatchFailure(
          op, "all input list elements must be constant ints");
    bool reverse;
    if (!matchPattern(op.getReverse(), m_TorchConstantBool(&reverse)))
      return rewriter.notifyMatchFailure(
          op, "Expected reverse arg to be constant bool.");

    std::sort(listElements.begin(), listElements.end());
    if (reverse)
      std::reverse(listElements.begin(), listElements.end());

    SmallVector<Value> sortedListElements;
    for (int64_t elem : listElements)
      sortedListElements.push_back(rewriter.create<Torch::ConstantIntOp>(
          op->getLoc(), rewriter.getI64IntegerAttr(elem)));
    Value result = rewriter.create<Torch::PrimListConstructOp>(
        op->getLoc(), Torch::ListType::get(rewriter.getType<Torch::IntType>()),
        sortedListElements);

    op.getSelf().replaceAllUsesWith(result);
    rewriter.eraseOp(op);
    return success();
  });
}

//===----------------------------------------------------------------------===//
// AtenSortOp
//===----------------------------------------------------------------------===//

LogicalResult AtenSortOp::fold(FoldAdaptor adaptor,
                               SmallVectorImpl<OpFoldResult> &results) {
  auto operand = getSelf();
  auto operandType = dyn_cast<BaseTensorType>(operand.getType());
  if (!operandType || !operandType.hasSizes())
    return failure();

  // only ValueTensorType has toBuiltinTensor
  auto indicesTensorType = dyn_cast<ValueTensorType>(getResult(1).getType());
  if (!indicesTensorType)
    return failure();

  if (!indicesTensorType.hasDtype())
    return failure();
  auto indicesType = indicesTensorType.toBuiltinTensor();
  if (!indicesType || !indicesType.hasStaticShape())
    return failure();

  bool unaryDim = false;
  IntegerAttr dimAttribute = dyn_cast_if_present<IntegerAttr>(adaptor.getDim());
  if (!dimAttribute)
    return failure();
  int64_t dimInt = dimAttribute.getValue().getSExtValue();
  if (dimInt < 0)
    dimInt += operandType.getSizes().size();
  if (dimAttribute) {
    unaryDim = operandType.getSizes()[dimInt] == 1;
  }

  OpBuilder builder(getContext());
  if (unaryDim || llvm::all_of(operandType.getSizes(),
                               [](int64_t dim) { return dim == 1; })) {
    results.push_back(operand);
    results.push_back(DenseElementsAttr::get(
        indicesType, builder.getZeroAttr(indicesType.getElementType())));
    return success();
  }

  return failure();
}

//===----------------------------------------------------------------------===//
// NonValueTensorLiteralOp
//===----------------------------------------------------------------------===//

LogicalResult NonValueTensorLiteralOp::inferReturnTypes(
    MLIRContext *context, std::optional<Location> location, ValueRange operands,
    DictionaryAttr attributes, OpaqueProperties properties, RegionRange regions,
    SmallVectorImpl<Type> &inferredReturnTypes) {
  auto attr =
      dyn_cast_or_null<ElementsAttr>(properties.as<Properties *>()->getValue());
  if (!attr)
    return failure();
  RankedTensorType tensorType = cast<RankedTensorType>(attr.getType());
  NonValueTensorType returnType =
      NonValueTensorType::get(tensorType.getContext(), tensorType.getShape(),
                              tensorType.getElementType());
  inferredReturnTypes.push_back(returnType);
  return success();
}

static bool areSizesAndDtypesCompatible(BaseTensorType a, BaseTensorType b) {
  if (a.hasSizes() && b.hasSizes()) {
    if (failed(verifyCompatibleShape(makeShapeLLVMCompatible(a.getSizes()),
                                     makeShapeLLVMCompatible(b.getSizes()))))
      return false;
  }
  if (a.hasDtype() && b.hasDtype()) {
    if (a.getDtype() != b.getDtype())
      return false;
  }
  return true;
}

bool NonValueTensorLiteralOp::isCompatibleReturnTypes(TypeRange inferred,
                                                      TypeRange actual) {
  if (!isa<BaseTensorType>(actual[0]))
    return false;
  return areSizesAndDtypesCompatible(cast<BaseTensorType>(inferred[0]),
                                     cast<BaseTensorType>(actual[0]));
}

//===----------------------------------------------------------------------===//
// ValueTensorLiteralOp
//===----------------------------------------------------------------------===//

LogicalResult ValueTensorLiteralOp::inferReturnTypes(
    MLIRContext *context, std::optional<Location> location, ValueRange operands,
    DictionaryAttr attributes, OpaqueProperties properties, RegionRange regions,
    SmallVectorImpl<Type> &inferredReturnTypes) {
  auto attr =
      dyn_cast_or_null<ElementsAttr>(properties.as<Properties *>()->getValue());
  if (!attr)
    return failure();
  RankedTensorType tensorType = cast<RankedTensorType>(attr.getType());
  ValueTensorType returnType =
      ValueTensorType::get(tensorType.getContext(), tensorType.getShape(),
                           tensorType.getElementType());
  inferredReturnTypes.push_back(returnType);
  return success();
}

OpFoldResult ValueTensorLiteralOp::fold(FoldAdaptor adaptor) {
  return getValueAttr();
}

//----------------------------------------------------------------------------//
// TensorStaticInfoCast
//----------------------------------------------------------------------------//

bool TensorStaticInfoCastOp::areCastCompatible(mlir::TypeRange inputs,
                                               mlir::TypeRange outputs) {
  return areSizesAndDtypesCompatible(cast<BaseTensorType>(inputs[0]),
                                     cast<BaseTensorType>(outputs[0]));
}

void TensorStaticInfoCastOp::getCanonicalizationPatterns(
    RewritePatternSet &patterns, MLIRContext *context) {
  patterns.add(+[](TensorStaticInfoCastOp op, PatternRewriter &rewriter) {
    auto reverseCast =
        op.getOperand().getDefiningOp<Torch::TensorStaticInfoCastOp>();
    if (!reverseCast || reverseCast.getOperand().getType() != op.getType())
      return failure();

    rewriter.replaceOp(op, reverseCast.getOperand());
    return success();
  });
  patterns.add(+[](TensorStaticInfoCastOp op, PatternRewriter &rewriter) {
    if (isValidSubtype(op.getOperand().getType(), op.getType())) {
      SmallVector<std::reference_wrapper<OpOperand>> usesToChange(
          llvm::make_filter_range(op->getUses(), [](OpOperand &operand) {
            return operand.getOwner()
                ->hasTrait<mlir::torch::Torch::OpTrait::AllowsTypeRefinement>();
          }));

      if (usesToChange.empty())
        return failure();

      for (OpOperand &use : usesToChange) {
        Operation *user = use.getOwner();
        user->setOperand(use.getOperandNumber(), op.getOperand());
      }

      return success();
    }
    return failure();
  });
}

//===----------------------------------------------------------------------===//
// CopyToNonValueTensorOp
//===----------------------------------------------------------------------===//

LogicalResult CopyToNonValueTensorOp::verify() {
  auto resultType = cast<BaseTensorType>(getResult().getType());
  auto operandType = cast<BaseTensorType>(getOperand().getType());
  if (!resultType.hasSameSizesAndDtype(operandType))
    return emitError() << "operand and result must have same sizes and dtype";
  return success();
}

LogicalResult CopyToNonValueTensorOp::inferReturnTypes(
    MLIRContext *context, std::optional<Location> location, ValueRange operands,
    DictionaryAttr attributes, OpaqueProperties properties, RegionRange regions,
    SmallVectorImpl<Type> &inferredReturnTypes) {
  auto resultType = cast<ValueTensorType>(operands[0].getType());
  inferredReturnTypes.push_back(resultType.getWithoutValueSemantics());
  return success();
}

void CopyToNonValueTensorOp::getEffects(
    SmallVectorImpl<SideEffects::EffectInstance<MemoryEffects::Effect>>
        &effects) {
  effects.emplace_back(MemoryEffects::Allocate::get(),
                       getOperation()->getOpResult(0));
}

//===----------------------------------------------------------------------===//
// CopyToValueTensorOp
//===----------------------------------------------------------------------===//

LogicalResult CopyToValueTensorOp::verify() {
  auto resultType = cast<BaseTensorType>(getResult().getType());
  auto operandType = cast<BaseTensorType>(getOperand().getType());
  if (!resultType.hasSameSizesAndDtype(operandType))
    return emitError() << "operand and result must have same sizes and dtype";
  return success();
}

LogicalResult CopyToValueTensorOp::inferReturnTypes(
    MLIRContext *context, std::optional<Location> location, ValueRange operands,
    DictionaryAttr attributes, OpaqueProperties properties, RegionRange regions,
    SmallVectorImpl<Type> &inferredReturnTypes) {
  auto resultType = cast<NonValueTensorType>(operands[0].getType());
  inferredReturnTypes.push_back(resultType.getWithValueSemantics());
  return success();
}

void CopyToValueTensorOp::getEffects(
    SmallVectorImpl<SideEffects::EffectInstance<MemoryEffects::Effect>>
        &effects) {
  effects.emplace_back(MemoryEffects::Read::get(),
                       &getOperation()->getOpOperand(0));
}

//===----------------------------------------------------------------------===//
// ConstantNoneOp
//===----------------------------------------------------------------------===//

OpFoldResult ConstantNoneOp::fold(FoldAdaptor adaptor) {
  return TypeAttr::get(Torch::NoneType::get(getContext()));
}

void ConstantNoneOp::getAsmResultNames(
    function_ref<void(Value, StringRef)> setNameFn) {
  setNameFn(getResult(), "none");
}

//===----------------------------------------------------------------------===//
// ConstantStrOp
//===----------------------------------------------------------------------===//

OpFoldResult ConstantStrOp::fold(FoldAdaptor adaptor) { return getValueAttr(); }

void ConstantStrOp::getAsmResultNames(
    function_ref<void(Value, StringRef)> setNameFn) {
  setNameFn(getResult(), "str");
}

//===----------------------------------------------------------------------===//
// ConstantDeviceOp
//===----------------------------------------------------------------------===//

void ConstantDeviceOp::getAsmResultNames(
    function_ref<void(Value, StringRef)> setNameFn) {
  setNameFn(getResult(), getValue());
}

//===----------------------------------------------------------------------===//
// ConstantIntOp
//===----------------------------------------------------------------------===//

ParseResult ConstantIntOp::parse(OpAsmParser &parser, OperationState &result) {
  Builder builder(result.getContext());
  result.addTypes(builder.getType<Torch::IntType>());
  int64_t value;
  if (parser.parseInteger(value))
    return failure();
  if (parser.parseOptionalAttrDict(result.attributes))
    return failure();
  result.addAttribute("value", builder.getI64IntegerAttr(value));
  return success();
}

void ConstantIntOp::print(OpAsmPrinter &p) {
  p << " ";
  p << getValueAttr().getInt();
  p.printOptionalAttrDict((*this)->getAttrs(), {"value"});
}

OpFoldResult Torch::ConstantIntOp::fold(FoldAdaptor adaptor) {
  return getValueAttr();
}

void Torch::ConstantIntOp::getAsmResultNames(
    function_ref<void(Value, StringRef)> setNameFn) {
  SmallVector<char> buf;
  llvm::raw_svector_ostream os(buf);
  os << "int" << getValueAttr().getInt();
  setNameFn(getResult(), os.str());
}

//===----------------------------------------------------------------------===//
// ConstantFloatOp
//===----------------------------------------------------------------------===//

OpFoldResult Torch::ConstantFloatOp::fold(FoldAdaptor adaptor) {
  return getValueAttr();
}

void Torch::ConstantFloatOp::getAsmResultNames(
    function_ref<void(Value, StringRef)> setNameFn) {
  // Calculate a stringified version of the number, compatible with MLIR
  // identifier syntax. (in practice, this just removes the '+' from 'e+' in
  // float string representation).
  SmallVector<char> buf;
  getValue().toString(buf, /*FormatPrecision=*/6, /*FormatMaxPadding=*/0,
                      /*TruncateZero=*/false);
  auto isValidMLIRIdentifierChar = [](char c) {
    return isalpha(c) || isdigit(c) || c == '_' || c == '$' || c == '.' ||
           c == '-';
  };
  auto numberStr = llvm::to_vector<16>(
      llvm::make_filter_range(buf, isValidMLIRIdentifierChar));

  // Construct the identifier string.
  buf.clear();
  llvm::append_range(buf, StringRef("float"));
  llvm::append_range(buf, numberStr);
  setNameFn(getResult(), StringRef(buf.data(), buf.size()));
}

//===----------------------------------------------------------------------===//
// ConstantNumberOp
//===----------------------------------------------------------------------===//

OpFoldResult Torch::ConstantNumberOp::fold(FoldAdaptor adaptor) {
  return getValueAttr();
}

void Torch::ConstantNumberOp::getCanonicalizationPatterns(
    RewritePatternSet &patterns, MLIRContext *context) {
  patterns.add(+[](Torch::ConstantNumberOp op, PatternRewriter &rewriter) {
    Location loc = op->getLoc();

    Value constValue;
    Attribute value = op.getValueAttr();
    if (auto floatValue = dyn_cast<mlir::FloatAttr>(value)) {
      constValue = rewriter.create<Torch::ConstantFloatOp>(loc, floatValue);
    } else if (auto intValue = dyn_cast<mlir::IntegerAttr>(value)) {
      constValue = rewriter.create<Torch::ConstantIntOp>(loc, intValue);
    } else {
      return failure();
    }
    rewriter.replaceOpWithNewOp<Torch::DerefineOp>(op, op.getType(),
                                                   constValue);
    return success();
  });
}

//===----------------------------------------------------------------------===//
// ConstantBoolOp
//===----------------------------------------------------------------------===//

OpFoldResult Torch::ConstantBoolOp::fold(FoldAdaptor adaptor) {
  return getValueAttr();
}

void Torch::ConstantBoolOp::getAsmResultNames(
    function_ref<void(Value, StringRef)> setNameFn) {
  setNameFn(getResult(), getValue() ? "true" : "false");
}

//===----------------------------------------------------------------------===//
// PrimUncheckedCastOp
//===----------------------------------------------------------------------===//

bool PrimUncheckedCastOp::areCastCompatible(mlir::TypeRange inputs,
                                            mlir::TypeRange outputs) {
  return isValidSubtype(outputs[0], inputs[0]);
}

OpFoldResult PrimUncheckedCastOp::fold(FoldAdaptor adaptor) {
  if (auto derefineOp = getX().getDefiningOp<Torch::DerefineOp>()) {
    if (derefineOp.getOperand().getType() == getType())
      return derefineOp.getOperand();
  }
  return nullptr;
}

//===----------------------------------------------------------------------===//
// Aten__Getitem__TOp
//===----------------------------------------------------------------------===//

void Aten__Getitem__TOp::getCanonicalizationPatterns(
    RewritePatternSet &patterns, MLIRContext *context) {
  patterns.add(+[](Aten__Getitem__TOp op, PatternRewriter &rewriter) {
    auto torchList = op.getOperand(0);
    if (isListPotentiallyMutated(torchList))
      return failure();

    auto listConstruct = torchList.getDefiningOp<Torch::PrimListConstructOp>();
    if (!listConstruct)
      return failure();

    // Get the index, but be careful because it might be statically invalid.
    std::optional<int64_t> indexOpt = matchLegalConstantIndexIntoListOfSize(
        op.getOperand(1), listConstruct.getNumOperands());
    if (!indexOpt)
      return rewriter.notifyMatchFailure(op, "statically invalid index");

    rewriter.replaceOp(op, {listConstruct.getOperand(*indexOpt)});
    return success();
  });
  patterns.add(+[](Aten__Getitem__TOp op, PatternRewriter &rewriter) {
    auto sizeOp = op.getList().getDefiningOp<AtenSizeOp>();
    if (!sizeOp)
      return failure();
    // This assumes tht the size doesn't change between the
    // AtenSizeOp and the Aten__Getitem__TOp.
    // `t_` is the only op I can find that changes the shape in-place. It seems
    // like otherwise we can treat the size of a tensor as having value
    // semantics. The other view-like ops don't have in-place variants --
    // they always return a new SSA value that is aliased to the input.
    // Can we have a pass to normalize the `t_` case and then elsewhere in the
    // compiler treat the size as having value semantics?
    // There's a small number of such ops, and they are marked as `inplace_view`
    // in PyTorch's `native_functions.yaml` file.
    rewriter.replaceOpWithNewOp<AtenSizeIntOp>(op, sizeOp.getSelf(),
                                               op.getIdx());
    return success();
  });
}

//===----------------------------------------------------------------------===//
// AtenMeshgridOp
//===----------------------------------------------------------------------===//
void AtenMeshgridOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                                 MLIRContext *context) {
  patterns.add(+[](AtenMeshgridOp op, PatternRewriter &rewriter) {
    Value constIndexing = rewriter.create<Torch::ConstantStrOp>(
        op->getLoc(), rewriter.getStringAttr("ij"));
    rewriter.replaceOpWithNewOp<AtenMeshgridIndexingOp>(
        op, op->getResultTypes(), op.getTensors(), constIndexing);
    return success();
  });
}

//===----------------------------------------------------------------------===//
// AtenSplitSizesOp
//===----------------------------------------------------------------------===//

void AtenSplitSizesOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                                   MLIRContext *context) {
  patterns.add(+[](AtenSplitSizesOp op, PatternRewriter &rewriter) {
    rewriter.replaceOpWithNewOp<AtenSplitWithSizesOp>(
        op, op->getResultTypes(), op.getSelf(), op.getSplitSize(), op.getDim());
    return success();
  });
}

//===----------------------------------------------------------------------===//
// AtenIsFloatingPointOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenIsFloatingPointOp::fold(FoldAdaptor adaptor) {
  auto operandType = dyn_cast<BaseTensorType>(getSelf().getType());
  if (!operandType)
    return nullptr;
  if (operandType.hasDtype()) {
    bool isFloatType = isa<mlir::FloatType>(operandType.getDtype());
    return IntegerAttr::get(IntegerType::get(getContext(), 1), isFloatType);
  }
  // doesn't has dtype
  return nullptr;
}

//===----------------------------------------------------------------------===//
// AtenAddTOp
//===----------------------------------------------------------------------===//

void AtenAddTOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                             MLIRContext *context) {
  patterns.add(+[](AtenAddTOp op, PatternRewriter &rewriter) {
    auto lhsListConstruct =
        op.getA().getDefiningOp<Torch::PrimListConstructOp>();
    if (!lhsListConstruct || isListPotentiallyMutated(lhsListConstruct))
      return failure();

    auto rhsListConstruct =
        op.getB().getDefiningOp<Torch::PrimListConstructOp>();
    if (!rhsListConstruct || isListPotentiallyMutated(rhsListConstruct))
      return failure();

    SmallVector<Value> concatenatedList;
    for (auto a : lhsListConstruct.getOperands()) {
      concatenatedList.push_back(a);
    }
    for (auto b : rhsListConstruct.getOperands()) {
      concatenatedList.push_back(b);
    }

    rewriter.replaceOpWithNewOp<Torch::PrimListConstructOp>(op, op.getType(),
                                                            concatenatedList);
    return success();
  });
}

//===----------------------------------------------------------------------===//
// AtenSliceTOp
//===----------------------------------------------------------------------===//

void AtenSliceTOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                               MLIRContext *context) {
  patterns.add(+[](AtenSliceTOp op, PatternRewriter &rewriter) {
    auto valueList = op.getL();
    auto listConstructOp = valueList.getDefiningOp<PrimListConstructOp>();
    if (!listConstructOp || isListPotentiallyMutated(listConstructOp)) {
      return failure();
    }

    SmallVector<Value> listElements =
        llvm::to_vector<4>(listConstructOp.getElements());
    int64_t size = static_cast<int64_t>(listElements.size());

    int64_t start;
    int64_t end;
    int64_t step;
    if (isa<Torch::NoneType>(op.getStart().getType())) {
      start = 0;
    } else if (!matchPattern(op.getStart(), m_TorchConstantInt(&start))) {
      return failure();
    }
    if (isa<Torch::NoneType>(op.getEnd().getType())) {
      end = listElements.size();
    } else if (!matchPattern(op.getEnd(), m_TorchConstantInt(&end))) {
      return failure();
    }
    if (!matchPattern(op.getStep(), m_TorchConstantInt(&step))) {
      return failure();
    }

    start = start >= 0 ? start : start + size;
    start = start >= 0 ? start : 0;
    end = end >= 0 ? end : end + size;
    end = end < size ? end : size;
    SmallVector<Value> newListElements;

    for (int64_t i = start; i < end; i += step) {
      newListElements.push_back(listElements[i]);
    }

    rewriter.replaceOpWithNewOp<PrimListConstructOp>(
        op, Torch::ListType::get(listElements[0].getType()), newListElements);
    return success();
  });
}

//===----------------------------------------------------------------------===//
// AtenEqIntListOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenEqIntListOp::fold(FoldAdaptor adaptor) {
  auto lhsLiteral = getA().getDefiningOp<Torch::PrimListConstructOp>();
  if (!lhsLiteral)
    return nullptr;
  auto rhsLiteral = getB().getDefiningOp<Torch::PrimListConstructOp>();
  if (!rhsLiteral)
    return nullptr;

  // If the sizes don't match, then we know the lists aren't equal.
  if (lhsLiteral.getNumOperands() != rhsLiteral.getNumOperands())
    return getI1IntegerAttr(getContext(), false);

  // If the sizes match and all corresponding list elements are the same Value,
  // then we know the lists are equal.
  // Note that we can't prove that the lists are not-equal with this method,
  // since two different Value's might dynamically be equal.
  if (llvm::all_of(
          llvm::zip(lhsLiteral.getOperands(), rhsLiteral.getOperands()),
          [](const auto &pair) {
            return std::get<0>(pair) == std::get<1>(pair);
          }))
    return getI1IntegerAttr(getContext(), true);
  return nullptr;
}

//===----------------------------------------------------------------------===//
// PrimTupleConstructOp
//===----------------------------------------------------------------------===//

LogicalResult PrimTupleConstructOp::verify() {
  if (!(isValidSubtype(
          Torch::TupleType::get(getContext(),
                                llvm::to_vector<6>(getElements().getType())),
          getResult().getType())))
    return emitOpError(
        "failed to verify that contained types correspond to operand types");
  return success();
}

//===----------------------------------------------------------------------===//
// PrimTupleIndexOp
//===----------------------------------------------------------------------===//

void PrimTupleIndexOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                                   MLIRContext *context) {
  patterns.add(+[](PrimTupleIndexOp op, PatternRewriter &rewriter) {
    auto tupleConstruct =
        op.getTup().getDefiningOp<Torch::PrimTupleConstructOp>();
    if (!tupleConstruct)
      return failure();

    int64_t i;
    if (!matchPattern(op.getI(), m_TorchConstantInt(&i)))
      return failure();

    if (i >= (int64_t)tupleConstruct.getElements().size())
      return failure();

    // TODO: We should have a clear picture of whether we want to consistently
    // allow refinement, and where. It seems desirable to require precise
    // type equality for TupleConstruct / TupleIndex, but that might break
    // things.
    Value replacement = tupleConstruct.getElements()[i];
    if (replacement.getType() != op.getType()) {
      if (isa<BaseTensorType>(op.getType())) {
        replacement = rewriter.create<Torch::TensorStaticInfoCastOp>(
            op.getLoc(), op.getType(), replacement);
      } else {
        return failure();
      }
    }
    rewriter.replaceOp(op, replacement);
    return success();
  });
}

//===----------------------------------------------------------------------===//
// PrimUninitializedOp
//===----------------------------------------------------------------------===//

void PrimUninitializedOp::getCanonicalizationPatterns(
    RewritePatternSet &patterns, MLIRContext *context) {
  patterns.add(+[](PrimUninitializedOp op, PatternRewriter &rewriter) {
    if (!op.use_empty())
      return failure();
    rewriter.eraseOp(op);
    return success();
  });
}

//===----------------------------------------------------------------------===//
// PrimTupleUnpackOp
//===----------------------------------------------------------------------===//

void PrimTupleUnpackOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                                    MLIRContext *context) {
  patterns.add(+[](PrimTupleUnpackOp op, PatternRewriter &rewriter) {
    auto tupleConstruct =
        op.getTup().getDefiningOp<Torch::PrimTupleConstructOp>();
    if (!tupleConstruct)
      return failure();

    llvm::SmallVector<Value> derefinedElements;
    // The result types may be supertypes of the tuple element types.
    // Ensure we maintain the exact type, with identity `derefine`s being
    // folded.
    for (auto [type, element] :
         llvm::zip(op.getResultTypes(), tupleConstruct.getElements())) {
      derefinedElements.push_back(
          rewriter.createOrFold<DerefineOp>(op.getLoc(), type, element));
    }
    rewriter.replaceOp(op, derefinedElements);
    return success();
  });
}

//===----------------------------------------------------------------------===//
// PrimListUnpackOp
//===----------------------------------------------------------------------===//

void PrimListUnpackOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                                   MLIRContext *context) {
  patterns.add(+[](PrimListUnpackOp op, PatternRewriter &rewriter) {
    auto torchList = op.getOperand();
    if (isListPotentiallyMutated(torchList)) {
      return failure();
    }

    auto listConstruct = torchList.getDefiningOp<Torch::PrimListConstructOp>();
    if (!listConstruct)
      return failure();

    if (op->getNumResults() != listConstruct.getElements().size())
      return failure();

    SmallVector<Value> unpacked;
    for (int i = 0, s = op->getNumResults(); i < s; ++i) {
      auto element = listConstruct.getElements()[i];
      if (element.getType() != op->getResult(i).getType()) {
        element = rewriter.create<TensorStaticInfoCastOp>(
            op.getLoc(), op->getResult(i).getType(), element);
      }

      unpacked.push_back(element);
    }

    rewriter.replaceOp(op, unpacked);
    return success();
  });
}

static PrimDictConstructOp getDictConstructIfNotModified(Value torchDict) {
  if (!llvm::all_of(torchDict.getUsers(), [](Operation *op) {
        return isa<Aten__Getitem__DictStrOp, Aten__Contains__StrOp,
                   AtenKeysStrOp, AtenGetDefaultStrOp, PrimDictConstructOp>(op);
      }))
    return nullptr;

  return torchDict.getDefiningOp<Torch::PrimDictConstructOp>();
}

//===----------------------------------------------------------------------===//
// Aten__Getitem__DictStrOp
//===----------------------------------------------------------------------===//

OpFoldResult Aten__Getitem__DictStrOp::fold(FoldAdaptor adaptor) {
  auto dictConstruct = getDictConstructIfNotModified(getSelf());
  if (!dictConstruct)
    return nullptr;

  auto targetKey = getKey();
  for (auto i : llvm::zip(dictConstruct.getKeys(), dictConstruct.getValues())) {
    auto k = std::get<0>(i);
    if (k == targetKey)
      return std::get<1>(i);
  }
  return nullptr;
}

//===----------------------------------------------------------------------===//
// Aten__Contains__StrOp
//===----------------------------------------------------------------------===//

OpFoldResult Aten__Contains__StrOp::fold(FoldAdaptor adaptor) {
  auto dictConstruct = getDictConstructIfNotModified(getDict());
  if (!dictConstruct)
    return nullptr;

  auto targetKey = getKey();
  for (auto key : dictConstruct.getKeys()) {
    if (key == targetKey)
      return getI1IntegerAttr(getContext(), true);
  }
  return nullptr;
}

//===----------------------------------------------------------------------===//
// Aten__Contains__IntListOp
//===----------------------------------------------------------------------===//

static bool isListConstructNotModified(Value torchList) {
  return llvm::all_of(torchList.getUsers(), [](Operation *op) {
    return isa<Aten__Contains__IntListOp>(op);
  });
}

OpFoldResult Aten__Contains__IntListOp::fold(FoldAdaptor adaptor) {
  auto itemConstruct = getItem();
  if (!isListConstructNotModified(getL()))
    return nullptr;

  int64_t item;
  SmallVector<int64_t> list;

  if (!matchPattern(itemConstruct, m_TorchConstantInt(&item)))
    return nullptr;

  if (!matchPattern(getL(), m_TorchListOfConstantInts(list)))
    return nullptr;

  for (auto elem : list) {
    if (elem == item)
      return getI1IntegerAttr(getContext(), true);
  }
  return getI1IntegerAttr(getContext(), false);
}

using BinaryIntOperatorFn = std::function<int64_t(int64_t, int64_t)>;
static OpFoldResult
atenBinaryIntOperatorFoldHelper(ArrayRef<Attribute> operands,
                                BinaryIntOperatorFn f) {
  auto intLhs = dyn_cast_or_null<IntegerAttr>(operands[0]);
  auto intRhs = dyn_cast_or_null<IntegerAttr>(operands[1]);
  if (!intLhs || !intRhs) {
    return nullptr;
  }
  return IntegerAttr::get(
      intLhs.getType(),
      f(intLhs.getValue().getSExtValue(), intRhs.getValue().getSExtValue()));
}

using BinaryFloatOperatorFn = std::function<double(double, double)>;
static OpFoldResult
atenBinaryFloatOperatorFoldHelper(ArrayRef<Attribute> operands,
                                  BinaryFloatOperatorFn f) {
  double lhs, rhs;
  auto parseDoubleAttribute = [](Attribute attr, double &value) -> bool {
    if (auto intLhs = dyn_cast_or_null<IntegerAttr>(attr)) {
      value = static_cast<double>(intLhs.getValue().getSExtValue());
    } else if (auto floatLhs = dyn_cast_or_null<FloatAttr>(attr)) {
      value = floatLhs.getValue().convertToDouble();
    } else {
      return false;
    }
    return true;
  };
  if (!parseDoubleAttribute(operands[0], lhs) ||
      !parseDoubleAttribute(operands[1], rhs)) {
    return nullptr;
  }
  return getF64FloatAttr(operands[0].getContext(), f(lhs, rhs));
}

//===----------------------------------------------------------------------===//
// AtenAliasOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenAliasOp::fold(FoldAdaptor adaptor) {
  if (getOperand().getType() != getResult().getType())
    return {};
  return getOperand();
}

//===----------------------------------------------------------------------===//
// AtenFloordivIntOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenFloordivIntOp::fold(FoldAdaptor adaptor) {
  return atenBinaryIntOperatorFoldHelper(
      adaptor.getOperands(),
      [](int64_t a, int64_t b) { return std::floor(a / (double)b); });
}

void AtenFloordivIntOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                                    MLIRContext *context) {
  patterns.add(+[](AtenFloordivIntOp op, PatternRewriter &rewriter) {
    int64_t lhs, rhs;
    bool lConstant = matchPattern(op.getA(), m_TorchConstantInt(&lhs));
    bool rConstant = matchPattern(op.getB(), m_TorchConstantInt(&rhs));
    if (lConstant && rConstant)
      return failure();
    if (lConstant || rConstant) {
      int64_t firstConstant = lConstant ? lhs : rhs;
      Value firstOperand = lConstant ? op.getB() : op.getA();
      if (firstOperand.getDefiningOp() &&
          firstOperand.getDefiningOp<AtenMulIntOp>()) {
        auto prevMulIntOp = firstOperand.getDefiningOp<AtenMulIntOp>();
        int64_t prevLhs, prevRhs;
        bool prevLConstant =
            matchPattern(prevMulIntOp.getA(), m_TorchConstantInt(&prevLhs));
        bool prevRConstant =
            matchPattern(prevMulIntOp.getB(), m_TorchConstantInt(&prevRhs));
        if (prevLConstant && prevRConstant)
          return failure();
        if ((prevLConstant || prevRConstant) &&
            prevMulIntOp->hasOneUse() == 1) {
          int64_t secondConstant = prevLConstant ? prevLhs : prevRhs;
          if (secondConstant == firstConstant) {
            rewriter.replaceAllUsesWith(
                op.getResult(), prevMulIntOp.getOperand(prevLConstant ? 1 : 0));
            rewriter.eraseOp(op);
            rewriter.eraseOp(prevMulIntOp);
            return success();
          }
        }
      }
    }
    return failure();
  });
}

//===----------------------------------------------------------------------===//
// AtenRemainderIntOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenRemainderIntOp::fold(FoldAdaptor adaptor) {
  return atenBinaryIntOperatorFoldHelper(
      adaptor.getOperands(), [](int64_t a, int64_t b) { return a % b; });
}

// ===----------------------------------------------------------------------===//
// AtenRemainderScalarOp
// ===----------------------------------------------------------------------===//

OpFoldResult AtenRemainderScalarOp::fold(FoldAdaptor adaptor) {
  auto resultTy = dyn_cast_or_null<ValueTensorType>(getType());
  if (!resultTy || !resultTy.hasDtype()) {
    return nullptr;
  }

  auto unsign = false;
  if (isa<mlir::IntegerType>(resultTy.getDtype())) {
    unsign = cast<IntegerType>(resultTy.getDtype()).isUnsigned();
  }
  auto fpFold = [](llvm::ArrayRef<double> inputs) {
    assert(inputs.size() == 2);
    return std::fmod(inputs[0], inputs[1]);
  };

  auto intFold = [unsign](llvm::ArrayRef<APInt> inputs) {
    assert(inputs.size() == 2);
    auto ret = unsign ? inputs[0].urem(inputs[1]) : inputs[0].srem(inputs[1]);
    return ret;
  };

  return naryFolderHelper(adaptor.getOperands(), getType(), fpFold, intFold);
}

//===----------------------------------------------------------------------===//
// AtenAddIntOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenAddIntOp::fold(FoldAdaptor adaptor) {
  auto intLhs = dyn_cast_or_null<IntegerAttr>(adaptor.getA());
  auto intRhs = dyn_cast_or_null<IntegerAttr>(adaptor.getB());
  if (intRhs && intRhs.getValue().getSExtValue() == 0)
    return getA();
  if (intLhs && intLhs.getValue().getSExtValue() == 0)
    return getB();
  return atenBinaryIntOperatorFoldHelper(
      adaptor.getOperands(), [](int64_t a, int64_t b) { return a + b; });
}

//===----------------------------------------------------------------------===//
// AtenSubIntOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenSubIntOp::fold(FoldAdaptor adaptor) {
  if (getA() == getB())
    return IntegerAttr::get(
        IntegerType::get(getContext(), 64, IntegerType::Signless), 0);
  return atenBinaryIntOperatorFoldHelper(
      adaptor.getOperands(), [](int64_t a, int64_t b) { return a - b; });
}

//===----------------------------------------------------------------------===//
// AtenTransposeIntOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenTransposeIntOp::fold(FoldAdaptor adaptor) {
  // first check for no-op
  IntegerAttr dim0 = dyn_cast_or_null<IntegerAttr>(adaptor.getDim0());
  IntegerAttr dim1 = dyn_cast_or_null<IntegerAttr>(adaptor.getDim1());
  if (!dim0 || !dim1)
    return nullptr;
  int64_t _dim0 = dim0.getValue().getSExtValue();
  int64_t _dim1 = dim1.getValue().getSExtValue();
  auto selfTy = dyn_cast<ValueTensorType>(getSelf().getType());
  if (!selfTy || !selfTy.hasSizes())
    return nullptr;
  int64_t rank = selfTy.getSizes().size();
  _dim0 = toPositiveDim(_dim0, rank);
  _dim1 = toPositiveDim(_dim1, rank);
  if (!isValidDim(_dim0, rank) || !isValidDim(_dim1, rank))
    return nullptr;
  // if dims are the same, return self
  if (_dim0 == _dim1)
    return getSelf();

  // We set a maximum folding size of 16. This is a reasonable upper limit
  // for shape computations.
  constexpr int64_t kMaxFoldSize = 16;
  auto self = dyn_cast_or_null<DenseElementsAttr>(adaptor.getSelf());
  if (!self || self.getNumElements() > kMaxFoldSize)
    return nullptr;
  auto resultTy = dyn_cast<ValueTensorType>(getType());
  if (!selfTy || !resultTy || !selfTy.areAllSizesKnown())
    return nullptr;
  if (self.isSplat())
    return SplatElementsAttr::get(resultTy.toBuiltinTensor(),
                                  self.getSplatValue<Attribute>());

  // TODO: add support for rank != 2
  if (rank != 2)
    return nullptr;

  ArrayRef<int64_t> sizes = selfTy.getSizes();
  auto values = llvm::to_vector(self.getValues<Attribute>());
  // reordered[i] = Trans[i//sizes[0], i % sizes[0]] = Self[i % sizes[0],
  // i//sizes[0]] = values[(i % sizes[0])*sizes[1] + (i//sizes[0])].
  // e.g., Self size = [4,2]; Trans size = [2,4].
  // reindex(i) = (i % 4)*2 + (i // 4) .
  // i = 0 -> Trans[0,0] -> Self[0,0] -> 0 .
  // i = 1 -> Trans[0,1] -> Self[1,0] -> 2 .
  // i = 2 -> Trans[0,2] -> Self[2,0] -> 4 .
  // i = 3 -> Trans[0,3] -> Self[3,0] -> 6 .
  // i = 4 -> Trans[1,0] -> Self[0,1] -> 1 .
  // i = 5 -> Trans[1,1] -> Self[1,1] -> 3 .
  auto reindex = [&](int64_t i) {
    return (i % sizes[0]) * sizes[1] + (i / sizes[0]);
  };
  SmallVector<Attribute> reordered;
  for (int64_t i = 0; i < self.getNumElements(); i++) {
    reordered.push_back(values[reindex(i)]);
  }
  return DenseElementsAttr::get(resultTy.toBuiltinTensor(), reordered);
}

//===----------------------------------------------------------------------===//
// AtenCatOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenCatOp::fold(FoldAdaptor adaptor) {
  // We set a maximum folding size of 16. This is a reasonable upper limit
  // for shape computations.
  constexpr int64_t kMaxFoldSize = 16;
  auto list = getOperand(0).getDefiningOp<PrimListConstructOp>();
  if (!list)
    return nullptr;

  auto elements = list.getElements();
  if (elements.size() == 1 && elements[0].getType() == getResult().getType())
    return list.getElements()[0];

  auto resultTy = dyn_cast<ValueTensorType>(getType());
  if (!resultTy || !resultTy.hasSizes() || !resultTy.hasDtype())
    return nullptr;

  auto bResultTy = resultTy.toBuiltinTensor();
  if (!bResultTy.hasStaticShape() || bResultTy.getNumElements() > kMaxFoldSize)
    return nullptr;

  auto dimAttr = dyn_cast_or_null<IntegerAttr>(adaptor.getDim());
  if (!dimAttr)
    return nullptr;
  auto dim = dimAttr.getValue().getSExtValue();
  dim += dim < 0 ? bResultTy.getRank() : 0;

  for (int i = 0, s = bResultTy.getRank(); i < s; ++i) {
    if (i == dim)
      continue;
    if (bResultTy.getDimSize(i) != 1)
      return nullptr;
  }

  llvm::SmallVector<Attribute> values;
  for (auto operand : list.getOperands()) {
    DenseElementsAttr dattr;
    if (!matchPattern(operand, m_Constant(&dattr)))
      return nullptr;

    auto oty = dyn_cast<RankedTensorType>(dattr.getType());
    if (!oty)
      return nullptr;

    if (dattr.isSplat()) {
      for (int i = 0, s = oty.getDimSize(dim); i < s; ++i)
        values.push_back(dattr.getSplatValue<Attribute>());
    } else {
      auto evals = dattr.getValues<Attribute>();
      for (int i = 0, s = oty.getDimSize(dim); i < s; ++i)
        values.push_back(evals[i]);
    }
  }

  return DenseElementsAttr::get(bResultTy.clone(resultTy.getDtype()), values);
}

void AtenCatOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                            MLIRContext *context) {
  patterns.add(+[](AtenCatOp op, PatternRewriter &rewriter) {
    auto list = op.getTensors().getDefiningOp<PrimListConstructOp>();
    auto resultTy = dyn_cast<BaseTensorType>(op.getType());
    if (!list || !resultTy)
      return failure();

    int64_t dim;
    if (!matchPattern(op.getDim(), m_TorchConstantInt(&dim)))
      return failure();

    llvm::SmallVector<Value> filtered;
    for (auto operand : list.getOperands()) {
      auto operandTy = dyn_cast<BaseTensorType>(operand.getType());
      if (!operandTy || !operandTy.hasSizes())
        return failure();
      int64_t adim = dim < 0 ? dim + operandTy.getSizes().size() : dim;
      if (operandTy.getSizes()[adim] != 0)
        filtered.push_back(operand);
    }

    if (filtered.size() == list.getNumOperands())
      return failure();

    auto newlist = rewriter.create<PrimListConstructOp>(
        op.getLoc(), list.getType(), filtered);
    rewriter.replaceOpWithNewOp<AtenCatOp>(op, op.getType(), newlist,
                                           op.getDim());
    return success();
  });
}

//===----------------------------------------------------------------------===//
// AtenBroadcastToOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenBroadcastToOp::fold(FoldAdaptor adaptor) {
  auto inType = dyn_cast<BaseTensorType>(getOperand(0).getType());
  auto outType = dyn_cast<BaseTensorType>(getResult().getType());
  if (!inType || !outType || !inType.hasSizes() || !outType.hasSizes() ||
      !outType.hasDtype())
    return nullptr;

  if (!inType.areAllSizesKnown() || !outType.areAllSizesKnown())
    return nullptr;

  auto inSizes = inType.getSizes();
  auto outSizes = outType.getSizes();
  if (inSizes.size() == outSizes.size()) {
    bool sameSizes = true;
    for (int i = 0, s = inSizes.size(); i < s; ++i)
      sameSizes &= inSizes[i] == outSizes[i];

    if (sameSizes)
      return getOperand(0);
  }

  auto selfAttr = dyn_cast_or_null<DenseElementsAttr>(adaptor.getSelf());
  if (!selfAttr)
    return nullptr;
  if (!selfAttr.isSplat())
    return nullptr;

  auto attrty = RankedTensorType::get(outType.getSizes(), outType.getDtype());
  return DenseElementsAttr::get(attrty, selfAttr.getSplatValue<Attribute>());
}

//===----------------------------------------------------------------------===//
// AtenSliceTensorOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenSliceTensorOp::fold(FoldAdaptor adaptor) {
  DenseElementsAttr input =
      dyn_cast_or_null<DenseElementsAttr>(adaptor.getSelf());
  IntegerAttr start = dyn_cast_or_null<IntegerAttr>(adaptor.getStart());
  IntegerAttr end = dyn_cast_or_null<IntegerAttr>(adaptor.getEnd());
  IntegerAttr step = dyn_cast_or_null<IntegerAttr>(adaptor.getStep());
  IntegerAttr dim = dyn_cast_or_null<IntegerAttr>(adaptor.getDim());
  auto inType = dyn_cast<ValueTensorType>(getOperand(0).getType());
  auto outType = dyn_cast<ValueTensorType>(getResult().getType());

  if (!inType || !outType || !inType.hasSizes() || !outType.hasSizes() ||
      !inType.hasDtype() || !outType.hasDtype() ||
      inType.getDtype() != outType.getDtype())
    return nullptr;

  if (start && end && step && step.getValue().getSExtValue() == 1 &&
      start.getValue().getSExtValue() == 0 &&
      end.getValue().getSExtValue() == std::numeric_limits<int64_t>::max() &&
      inType == outType)
    return getOperand(0);

  if (inType.getSizes().size() != outType.getSizes().size() ||
      !inType.areAllSizesKnown() || !outType.areAllSizesKnown())
    return nullptr;

  if (input && input.isSplat())
    return DenseElementsAttr::get(outType.toBuiltinTensor(),
                                  input.getSplatValue<Attribute>());

  int64_t count = 1;
  for (auto dim : outType.getSizes())
    count = count * dim;
  if (count == 0)
    return nullptr;

  if (!dim)
    return nullptr;
  int64_t dimInt = dim.getValue().getSExtValue();
  if (dimInt < 0)
    dimInt += inType.getSizes().size();

  // Fold the slice if the output tensor is relatively small, currently
  // coded to 16:
  constexpr int64_t kMaxFold = 16;
  if (input && start && step && dim && end && count <= kMaxFold) {
    int64_t begin = start.getValue().getSExtValue();
    int64_t limit = end.getValue().getSExtValue();
    int64_t stride = step.getValue().getSExtValue();
    begin = begin < 0 ? begin + inType.getSizes()[dimInt] : begin;
    limit = limit < 0 ? limit + inType.getSizes()[dimInt] : limit;
    limit = limit < 0 ? -1 : limit;
    limit = std::min(limit, inType.getSizes()[dimInt]);
    assert((stride > 0 && begin < limit) ||
           (stride < 0 && begin > limit) &&
               "aten.slice.Tensor iteration args are statically invalid.");

    int64_t inputRank = inType.getSizes().size();
    llvm::SmallVector<int64_t> inputStrides(inputRank, 1);
    for (int64_t i = inputRank - 2; i >= 0; i--) {
      inputStrides[i] = inputStrides[i + 1] * inType.getSizes()[i + 1];
    }

    llvm::SmallVector<Attribute> values;
    values.reserve(count);
    auto recursiveIter = [&](auto &self, int64_t currDim, int64_t currOffset) {
      if (currDim >= inputRank)
        return;
      int64_t _stride = (currDim == dimInt) ? stride : 1;
      int64_t _begin = (currDim == dimInt) ? begin : 0;
      int64_t _limit = (currDim == dimInt) ? limit : inType.getSizes()[currDim];
      // ensure that the limit is reached exactly (even with negative strides)
      // E.g., with begin = 0, limit = 10, stride = 3, we modify limit to be 11
      // = 10 + (10-0) % 3 .
      // E.g., with begin = 8, limit = -1, stride = -2, limit becomes -2 = -1 +
      // (-1-8) % (-2) - stride = -1 + 1 - 2 = -2 .
      // Note: cpp uses true math remainder "n % d = least positive int, x, such
      // that d divides (n - x)"
      int64_t limit_rem = (_limit - _begin) % _stride;
      limit_rem =
          (_stride > 0 || limit_rem == 0) ? limit_rem : limit_rem - _stride;
      _limit += limit_rem;
      for (int64_t i = _begin; std::abs(_limit - i) > 0; i += _stride) {
        if (currDim == inputRank - 1) {
          values.push_back(input.getValues<Attribute>()[currOffset + i]);
        }
        self(self, currDim + 1, currOffset + inputStrides[currDim] * i);
      }
    };
    recursiveIter(recursiveIter, 0, 0);
    return DenseElementsAttr::get(outType.toBuiltinTensor(), values);
  }

  // If the input and output shapes are the same & step == 1 we can fold:
  if (!step || step.getValue().getSExtValue() != 1)
    return nullptr;
  for (size_t i = 0; i < inType.getSizes().size(); ++i) {
    if (inType.getSizes()[i] != outType.getSizes()[i])
      return nullptr;
  }
  return getOperand(0);
}

//===----------------------------------------------------------------------===//
// AtenMulIntOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenMulIntOp::fold(FoldAdaptor adaptor) {
  int64_t lhs, rhs;
  bool lConstant = matchPattern(getOperand(0), m_TorchConstantInt(&lhs));
  bool rConstant = matchPattern(getOperand(1), m_TorchConstantInt(&rhs));
  if ((lConstant && lhs == 0) || (rConstant && rhs == 0))
    return getI64IntegerAttr(getContext(), 0);
  if (lConstant && rConstant)
    return getI64IntegerAttr(getContext(), lhs * rhs);
  return nullptr;
}

void AtenMulIntOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                               MLIRContext *context) {
  patterns.add(+[](AtenMulIntOp op, PatternRewriter &rewriter) {
    int64_t lhs, rhs;
    bool lConstant = matchPattern(op.getA(), m_TorchConstantInt(&lhs));
    bool rConstant = matchPattern(op.getB(), m_TorchConstantInt(&rhs));
    if (lConstant && rConstant)
      return failure();
    if (lConstant || rConstant) {
      int64_t firstConstant = lConstant ? lhs : rhs;
      Value firstOperand = lConstant ? op.getB() : op.getA();
      if (firstOperand.getDefiningOp() &&
          firstOperand.getDefiningOp<AtenMulIntOp>()) {
        auto prevMulIntOp = firstOperand.getDefiningOp<AtenMulIntOp>();
        int64_t prevLhs, prevRhs;
        bool prevLConstant =
            matchPattern(prevMulIntOp.getA(), m_TorchConstantInt(&prevLhs));
        bool prevRConstant =
            matchPattern(prevMulIntOp.getB(), m_TorchConstantInt(&prevRhs));
        if (prevLConstant && prevRConstant)
          return failure();
        if ((prevLConstant || prevRConstant) &&
            prevMulIntOp->hasOneUse() == 1) {
          auto newConstant = rewriter.create<Torch::ConstantIntOp>(
              op.getLoc(), rewriter.getI64IntegerAttr(
                               prevLConstant ? prevLhs * firstConstant
                                             : prevRhs * firstConstant));
          rewriter.replaceOpWithNewOp<AtenMulIntOp>(
              op, op.getType(), prevMulIntOp.getOperand(prevLConstant ? 1 : 0),
              newConstant);
          rewriter.eraseOp(prevMulIntOp);
          return success();
        }
      }
    }
    return failure();
  });
}

//===----------------------------------------------------------------------===//
// AtenMulFloatOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenMulFloatOp::fold(FoldAdaptor adaptor) {
  return atenBinaryFloatOperatorFoldHelper(
      adaptor.getOperands(), [](double a, double b) { return a * b; });
}

//===----------------------------------------------------------------------===//
// AtenSubFloatOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenSubFloatOp::fold(FoldAdaptor adaptor) {
  return atenBinaryFloatOperatorFoldHelper(
      adaptor.getOperands(), [](double a, double b) { return a - b; });
}

//===----------------------------------------------------------------------===//
// AtenAddOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenAddOp::fold(FoldAdaptor adaptor) {
  if (!adaptor.getA() || !adaptor.getB()) {
    return nullptr;
  }

  if (isa<IntegerAttr>(adaptor.getA()) && isa<IntegerAttr>(adaptor.getB())) {
    return atenBinaryIntOperatorFoldHelper(
        adaptor.getOperands(),
        [](int64_t a, int64_t b) -> int64_t { return a + b; });
  }
  return atenBinaryFloatOperatorFoldHelper(
      adaptor.getOperands(),
      [](double a, double b) -> double { return a + b; });
}

//===----------------------------------------------------------------------===//
// AtenMulOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenMulOp::fold(FoldAdaptor adaptor) {
  if (!adaptor.getA() || !adaptor.getB()) {
    return nullptr;
  }

  if (isa<IntegerAttr>(adaptor.getA()) && isa<IntegerAttr>(adaptor.getB())) {
    return atenBinaryIntOperatorFoldHelper(
        adaptor.getOperands(),
        [](int64_t a, int64_t b) -> int64_t { return a * b; });
  }
  return atenBinaryFloatOperatorFoldHelper(
      adaptor.getOperands(),
      [](double a, double b) -> double { return a * b; });
}

//===----------------------------------------------------------------------===//
// AtenSubOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenSubOp::fold(FoldAdaptor adaptor) {
  if (!adaptor.getA() || !adaptor.getB()) {
    return nullptr;
  }

  if (isa<IntegerAttr>(adaptor.getA()) && isa<IntegerAttr>(adaptor.getB())) {
    return atenBinaryIntOperatorFoldHelper(
        adaptor.getOperands(),
        [](int64_t a, int64_t b) -> int64_t { return a - b; });
  }
  return atenBinaryFloatOperatorFoldHelper(
      adaptor.getOperands(),
      [](double a, double b) -> double { return a - b; });
}

//===----------------------------------------------------------------------===//
// AtenDivOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenDivOp::fold(FoldAdaptor adaptor) {
  if (!adaptor.getA() || !adaptor.getB()) {
    return nullptr;
  }
  // Since AtenDivOp always returns float value, we don't need to deal with the
  // case where the operands are both integers separately.
  return atenBinaryFloatOperatorFoldHelper(
      adaptor.getOperands(),
      [](double a, double b) -> double { return a / b; });
}

//===----------------------------------------------------------------------===//
// AtenAddFloatIntOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenAddFloatIntOp::fold(FoldAdaptor adaptor) {
  if (!adaptor.getA() || !adaptor.getB()) {
    return nullptr;
  }
  return atenBinaryFloatOperatorFoldHelper(
      adaptor.getOperands(), [](double a, double b) { return a + b; });
}

//===----------------------------------------------------------------------===//
// AtenPowIntFloatOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenPowIntFloatOp::fold(FoldAdaptor adaptor) {
  if (!adaptor.getA() || !adaptor.getB()) {
    return nullptr;
  }
  return atenBinaryFloatOperatorFoldHelper(
      adaptor.getOperands(), [](double a, double b) { return std::pow(a, b); });
}

//===----------------------------------------------------------------------===//
// AtenCeilScalarOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenCeilScalarOp::fold(FoldAdaptor adaptor) {
  if (!adaptor.getA()) {
    return nullptr;
  }
  auto floatValue = dyn_cast_or_null<FloatAttr>(adaptor.getA());
  if (!floatValue) {
    return nullptr;
  }
  return getI64IntegerAttr(
      getContext(),
      static_cast<int64_t>(std::ceil(floatValue.getValue().convertToDouble())));
}

//===----------------------------------------------------------------------===//
// AtenNegIntOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenNegIntOp::fold(FoldAdaptor adaptor) {
  int64_t c;
  if (matchPattern(getOperand(), m_TorchConstantInt(&c)))
    return getI64IntegerAttr(getContext(), -c);
  return nullptr;
}

//===----------------------------------------------------------------------===//
// AtenNegFloatOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenNegFloatOp::fold(FoldAdaptor adaptor) {
  if (!adaptor.getA()) {
    return nullptr;
  }
  auto value = dyn_cast_or_null<FloatAttr>(adaptor.getA());
  if (!value) {
    return nullptr;
  }
  return getF64FloatAttr(getContext(), -value.getValue().convertToDouble());
}

//===----------------------------------------------------------------------===//
// AtenSqrtIntOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenSqrtIntOp::fold(FoldAdaptor adaptor) {
  int64_t c;
  if (matchPattern(getOperand(), m_TorchConstantInt(&c)))
    return getF64FloatAttr(getContext(), std::sqrt(c));
  return nullptr;
}

//===----------------------------------------------------------------------===//
// PrimDtypeOp
//===----------------------------------------------------------------------===//

OpFoldResult PrimDtypeOp::fold(FoldAdaptor adaptor) {
  BaseTensorType tensorType = cast<BaseTensorType>(getA().getType());
  if (tensorType.hasDtype()) {
    torch_upstream::ScalarType scalarType =
        Torch::getScalarTypeForType(tensorType.getDtype());
    return getI64IntegerAttr(getContext(), static_cast<int64_t>(scalarType));
  }
  return nullptr;
}

//===----------------------------------------------------------------------===//
// PrimDeviceOp
//===----------------------------------------------------------------------===//

void PrimDeviceOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                               MLIRContext *context) {
  patterns.add(+[](PrimDeviceOp op, PatternRewriter &rewriter) {
    // Device information isn't relevant to torch-mlir, just replace it with
    // "cpu".
    rewriter.replaceOpWithNewOp<Torch::ConstantDeviceOp>(op, "cpu");
    return success();
  });
}

//===----------------------------------------------------------------------===//
// AtenCudaOp
//===----------------------------------------------------------------------===//

void AtenCudaOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                             MLIRContext *context) {
  patterns.add(+[](AtenCudaOp op, PatternRewriter &rewriter) {
    // Device information isn't relevant to torch-mlir
    auto inputTensor = op.getSelf();
    rewriter.replaceOp(op, inputTensor);
    return success();
  });
}

//===----------------------------------------------------------------------===//
// AtenDeviceWithIndexOp
//===----------------------------------------------------------------------===//

void AtenDeviceWithIndexOp::getCanonicalizationPatterns(
    RewritePatternSet &patterns, MLIRContext *context) {
  patterns.add(+[](AtenDeviceWithIndexOp op, PatternRewriter &rewriter) {
    std::string type;
    int64_t index;
    if (!matchPattern(op.getType(), m_TorchConstantStr(type))) {
      return rewriter.notifyMatchFailure(
          op, "unimplemented: type must be a constant string");
    }
    if (!matchPattern(op.getIndex(), m_TorchConstantInt(&index))) {
      return rewriter.notifyMatchFailure(
          op, "unimplemented: index must be a constant integer");
    }
    rewriter.replaceOpWithNewOp<Torch::ConstantDeviceOp>(
        op, type + ":" + std::to_string(index));
    return success();
  });
}

//===----------------------------------------------------------------------===//
// AtenTensorOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenTensorOp::fold(FoldAdaptor adaptor) {
  // If a torch.aten.tensor op is initialized by a list with a constant, single
  // element, fold it into a torch.vtensor.literal
  auto resultTy = dyn_cast<ValueTensorType>(getType());
  if (!resultTy || !resultTy.hasSizes() || !resultTy.hasDtype())
    return nullptr;
  Type eTy = resultTy.getDtype();
  ShapedType shapedTy = resultTy.toBuiltinTensor();

  SmallVector<int64_t> data;
  if (matchPattern(getData(), m_TorchListOfConstantInts(data)) &&
      data.size() == 1) {
    Attribute attribute = IntegerAttr::get(eTy, data[0]);
    return DenseElementsAttr::get(shapedTy, attribute);
  }

  return nullptr;
}

//===----------------------------------------------------------------------===//
// AtenTensorIntOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenTensorIntOp::fold(FoldAdaptor adaptor) {
  auto resultTy = dyn_cast<ValueTensorType>(getType());
  if (!resultTy || !resultTy.hasSizes() || !resultTy.hasDtype())
    return nullptr;
  Type eTy = resultTy.getDtype();
  ShapedType shapedTy = resultTy.toBuiltinTensor();

  int64_t data;
  if (matchPattern(getT(), m_TorchConstantInt(&data))) {
    Attribute attribute = IntegerAttr::get(eTy, data);
    return DenseElementsAttr::get(shapedTy, attribute);
  }

  return nullptr;
}

//===----------------------------------------------------------------------===//
// AtenTensorFloatOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenTensorFloatOp::fold(FoldAdaptor adaptor) {
  auto resultTy = dyn_cast<ValueTensorType>(getType());
  if (!resultTy || !resultTy.hasSizes() || !resultTy.hasDtype())
    return nullptr;
  Type eTy = resultTy.getDtype();
  ShapedType shapedTy = resultTy.toBuiltinTensor();

  double data;
  if (matchPattern(getT(), m_TorchConstantFloat(&data))) {
    Attribute attribute = FloatAttr::get(eTy, data);
    return DenseElementsAttr::get(shapedTy, attribute);
  }

  return nullptr;
}

//===----------------------------------------------------------------------===//
// Aten_ShapeAsTensorOp
//===----------------------------------------------------------------------===//

OpFoldResult Aten_ShapeAsTensorOp::fold(FoldAdaptor adaptor) {
  auto selfTy = dyn_cast<BaseTensorType>(getSelf().getType());
  auto resultTy = dyn_cast<BaseTensorType>(getType());
  if (!selfTy || !resultTy || !selfTy.hasSizes() || !resultTy.hasDtype() ||
      !resultTy.hasSizes())
    return {};

  llvm::SmallVector<int64_t> values(selfTy.getSizes());
  if (llvm::any_of(values, [](int64_t d) { return d == Torch::kUnknownSize; }))
    return {};

  auto dty = dyn_cast<IntegerType>(resultTy.getDtype());
  if (!dty)
    return {};

  llvm::SmallVector<Attribute> attrs;
  for (auto val : values) {
    attrs.push_back(IntegerAttr::get(dty, val));
  }

  auto attrty = RankedTensorType::get(resultTy.getSizes(), dty);
  return DenseElementsAttr::get(attrty, attrs);
}

//===----------------------------------------------------------------------===//
// AtenIntTensorOp
//===----------------------------------------------------------------------===//

void AtenIntTensorOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                                  MLIRContext *context) {
  patterns.add(+[](AtenIntTensorOp op, PatternRewriter &rewriter) {
    Value scalarInt = getScalarIntValue(op.getA(), op.getLoc(), rewriter);
    if (!scalarInt)
      return failure();
    rewriter.replaceOp(op, scalarInt);
    return success();
  });
}

//===----------------------------------------------------------------------===//
// AtenIntTensorOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenIntTensorOp::fold(FoldAdaptor adaptor) {
  auto value = adaptor.getA();
  auto dense = dyn_cast_or_null<DenseElementsAttr>(value);
  if (!dense || !dense.isSplat()) {
    return nullptr;
  }

  auto splat = dense.getSplatValue<Attribute>();
  if (auto intAttr = dyn_cast<IntegerAttr>(splat)) {
    auto type = getType();
    if (!isa<mlir::IntegerType>(type)) {
      return nullptr;
    }

    if (type.isSignlessInteger()) {
      return getI64IntegerAttr(getContext(), intAttr.getInt());
    } else if (type.isSignedInteger()) {
      return getI64IntegerAttr(getContext(), intAttr.getSInt());
    } else {
      return getI64IntegerAttr(getContext(), intAttr.getUInt());
    }
  }

  if (auto floatAttr = dyn_cast<FloatAttr>(splat)) {
    return getI64IntegerAttr(
        getContext(),
        static_cast<long>(floatAttr.getValue().convertToDouble()));
  }

  return nullptr;
}

//===----------------------------------------------------------------------===//
// AtenFloatTensorOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenFloatTensorOp::fold(FoldAdaptor adaptor) {
  // If a scalar number is converted to a 0-d tensor and passed on to
  // aten.Float.Tensor, fold to the scalar number.
  if (auto numToTensorScalar = getA().getDefiningOp<PrimNumToTensorScalarOp>())
    return numToTensorScalar.getA();
  return nullptr;
}

//===----------------------------------------------------------------------===//
// AtenDivFloatOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenDivFloatOp::fold(FoldAdaptor adaptor) {
  double lhs, rhs;
  bool lConstant = matchPattern(getOperand(0), m_TorchConstantFloat(&lhs));
  bool rConstant = matchPattern(getOperand(1), m_TorchConstantFloat(&rhs));
  if (lConstant && lhs == 0.0)
    return getF64FloatAttr(getContext(), 0.0);
  if (lConstant && rConstant && rhs == 1.0)
    return getF64FloatAttr(getContext(), lhs);
  if (lConstant && rConstant)
    return getF64FloatAttr(getContext(), lhs / rhs);
  return nullptr;
}

//===----------------------------------------------------------------------===//
// AtenDivIntOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenDivIntOp::fold(FoldAdaptor adaptor) {
  int64_t lhs, rhs;
  bool lConstant = matchPattern(getOperand(0), m_TorchConstantInt(&lhs));
  bool rConstant = matchPattern(getOperand(1), m_TorchConstantInt(&rhs));
  if (lConstant && rConstant)
    return getF64FloatAttr(getContext(), double(lhs) / rhs);
  return nullptr;
}

//===----------------------------------------------------------------------===//
// AtenIndexSelectOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenIndexSelectOp::fold(FoldAdaptor adaptor) {
  auto self = getSelf();
  auto index = getIndex();
  auto selfTy = dyn_cast<ValueTensorType>(self.getType());
  auto indexTy = dyn_cast<ValueTensorType>(index.getType());
  auto resultTy = dyn_cast<ValueTensorType>(getType());
  if (!selfTy || !indexTy || !resultTy || !selfTy.hasSizes() ||
      !indexTy.hasSizes() || !resultTy.hasSizes() || !selfTy.hasDtype() ||
      !indexTy.hasDtype() || !resultTy.hasDtype())
    return nullptr;

  auto selfSizes = selfTy.getSizes();
  auto indexSizes = indexTy.getSizes();
  auto resultSizes = resultTy.getSizes();

  if (selfTy.getDtype() != resultTy.getDtype() ||
      selfSizes.size() != resultSizes.size() || indexSizes.size() != 1)
    return nullptr;

  // If the selection results in a tensor of the same dimensions as the
  // input, the selection must have specified every index of the input,
  // so the result is exactly the same as the input.

  bool fullTensor = true;
  for (int i = 0, s = selfSizes.size(); i < s; ++i) {
    fullTensor &= selfSizes[i] == resultSizes[i];
    fullTensor &= selfSizes[i] != Torch::kUnknownSize;
    fullTensor &= resultSizes[i] != Torch::kUnknownSize;
  }

  if (fullTensor && indexSizes[0] == 1)
    return self;

  // If the input tensor, index dimension, or indexes are non-constant,
  // can't fold.

  auto selfAttr = dyn_cast_or_null<DenseElementsAttr>(adaptor.getSelf());
  auto dimAttr = dyn_cast_or_null<IntegerAttr>(adaptor.getDim());
  auto indexAttr = dyn_cast_or_null<DenseElementsAttr>(adaptor.getIndex());

  if (!selfAttr || !dimAttr || !indexAttr)
    return {};

  // If the input's dimensions are all 1 except for one dimension, and if
  // there is a single index in the index list (as detected by the result
  // dimension being 1), then fold to a <1x1x...x1> tensor literal containing
  // a single element.  Handles float and int types.

  int64_t dimInt = dimAttr.getInt();
  // If the selected dim is negative, count backwards from the last dim
  if (dimInt < 0)
    dimInt = selfSizes.size() + dimInt;
  assert(uint64_t(dimInt) < selfSizes.size() &&
         "Selected dim > number of dims");

  for (int i = 0, s = selfSizes.size(); i < s; ++i) {
    if ((selfSizes[i] != 1 && i != dimInt) || resultSizes[i] != 1)
      return nullptr;
  }

  // Get the single index value for the selected dimension
  auto splatValue = indexAttr.getSplatValue<IntegerAttr>();
  int64_t indexInt = getIntAttrAsSigned(splatValue);
  indexInt = indexInt < 0 && selfSizes[dimInt] ? indexInt + selfSizes[dimInt]
                                               : indexInt;

  // Extract the single constant value from the input tensor and turn the
  // extracted value into a single-element tensor of the output shape and dtype
  Attribute splattr = selfAttr.isSplat()
                          ? selfAttr.getSplatValue<Attribute>()
                          : selfAttr.getValues<Attribute>()[indexInt];

  auto dty = resultTy.getDtype();
  auto attrTy = resultTy.toBuiltinTensor();
  if (auto floatAttr = dyn_cast<FloatAttr>(splattr))
    return DenseElementsAttr::get(
        attrTy, FloatAttr::get(dty, floatAttr.getValueAsDouble()));

  if (auto intAttr = dyn_cast<IntegerAttr>(splattr)) {
    return DenseElementsAttr::get(attrTy,
                                  IntegerAttr::get(dty, intAttr.getValue()));
  }
  return nullptr;
}

//===----------------------------------------------------------------------===//
// AtenItemOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenItemOp::fold(FoldAdaptor adaptor) {
  // see if we have a constant tensor
  DenseElementsAttr attr;
  if (matchPattern(getOperand(), m_Constant(&attr))) {
    auto splat = attr.getSplatValue<Attribute>();
    if (auto intAttr = dyn_cast<IntegerAttr>(splat)) {
      return intAttr.getType().isUnsignedInteger()
                 ? getI64IntegerAttr(getContext(), intAttr.getUInt())
                 : getI64IntegerAttr(getContext(),
                                     intAttr.getValue().getSExtValue());
    }
    if (auto floatAttr = dyn_cast<FloatAttr>(splat)) {
      return getF64FloatAttr(getContext(), floatAttr.getValueAsDouble());
    }
    return nullptr;
  }

  if (auto full = getOperand().getDefiningOp<Torch::AtenFullOp>()) {
    return full.getFillValue();
  }

  if (auto numToTensor =
          getOperand().getDefiningOp<Torch::PrimNumToTensorScalarOp>()) {
    return numToTensor.getA();
  }

  return nullptr;
}

//===----------------------------------------------------------------------===//
// AtenOnesOp, AtenZerosOp, AtenFullOp
//===----------------------------------------------------------------------===//
OpFoldResult AtenOnesOp::fold(FoldAdaptor adaptor) {
  SmallVector<int64_t> sizes;
  if (!matchPattern(getSize(), m_TorchListOfConstantInts(sizes))) {
    return nullptr;
  }

  Type resultType = getResult().getType();
  BaseTensorType resultTensorType = dyn_cast<BaseTensorType>(resultType);
  if (!resultTensorType || !resultTensorType.hasDtype() ||
      !resultTensorType.hasSizes()) {
    return nullptr;
  }

  for (auto sz : sizes)
    if (sz == Torch::kUnknownSize || sz < 0)
      return nullptr;

  for (auto sz : resultTensorType.getSizes())
    if (sz == Torch::kUnknownSize || sz < 0)
      return nullptr;

  ShapedType shapedty =
      mlir::RankedTensorType::get( // convert Torch type to builtin ShapedType
          sizes, resultTensorType.getDtype());
  if (!shapedty) {
    return nullptr;
  }
  auto elementType = shapedty.getElementType();
  if (isa<IntegerType>(elementType)) {
    Attribute attribute = IntegerAttr::get(elementType, 1);
    return DenseElementsAttr::get(shapedty, attribute);
  }
  if (isa<FloatType>(elementType)) {
    Attribute attribute = FloatAttr::get(elementType, 1.0);
    return DenseElementsAttr::get(shapedty, attribute);
  }
  return nullptr;
}

OpFoldResult AtenZerosOp::fold(FoldAdaptor adaptor) {
  SmallVector<int64_t> sizes;
  if (!matchPattern(getSize(), m_TorchListOfConstantInts(sizes))) {
    return nullptr;
  }

  Type resultType = getResult().getType();
  BaseTensorType resultTensorType = dyn_cast<BaseTensorType>(resultType);
  if (!resultTensorType || !resultTensorType.hasDtype() ||
      !resultTensorType.hasSizes()) {
    return nullptr;
  }

  for (auto sz : sizes)
    if (sz == Torch::kUnknownSize || sz < 0)
      return nullptr;

  for (auto sz : resultTensorType.getSizes())
    if (sz == Torch::kUnknownSize || sz < 0)
      return nullptr;

  ShapedType shapedty =
      mlir::RankedTensorType::get( // convert Torch type to builtin ShapedType
          sizes, resultTensorType.getDtype());
  if (!shapedty) {
    return nullptr;
  }

  auto elementType = shapedty.getElementType();
  if (isa<IntegerType>(elementType)) {
    Attribute attribute = IntegerAttr::get(elementType, 0);
    return DenseElementsAttr::get(shapedty, attribute);
  }
  if (isa<FloatType>(elementType)) {
    Attribute attribute = FloatAttr::get(elementType, 0.0);
    return DenseElementsAttr::get(shapedty, attribute);
  }

  return nullptr;
}

OpFoldResult AtenFullOp::fold(FoldAdaptor adaptor) {
  SmallVector<int64_t> sizes;
  if (!matchPattern(getSize(), m_TorchListOfConstantInts(sizes))) {
    return nullptr;
  }

  Type resultType = getResult().getType();
  BaseTensorType resultTensorType = dyn_cast<BaseTensorType>(resultType);
  if (!resultTensorType || !resultTensorType.hasDtype() ||
      !resultTensorType.hasSizes()) {
    return nullptr;
  }

  for (auto sz : sizes)
    if (sz == Torch::kUnknownSize || sz < 0)
      return nullptr;

  for (auto sz : resultTensorType.getSizes())
    if (sz == Torch::kUnknownSize || sz < 0)
      return nullptr;

  ShapedType shapedty = mlir::RankedTensorType::get(
      resultTensorType.getSizes(), resultTensorType.getDtype());

  auto elementType = shapedty.getElementType();
  if (isa<IntegerType>(elementType)) {
    int64_t value = 0;
    if (matchPattern(getFillValue(), m_TorchConstantInt(&value))) {
      Attribute attribute = IntegerAttr::get(elementType, value);
      return DenseElementsAttr::get(shapedty, attribute);
    }
  }
  if (isa<FloatType>(elementType)) {
    double value = 0.0;
    if (matchPattern(getFillValue(), m_TorchConstantFloat(&value))) {
      Attribute attribute = FloatAttr::get(elementType, value);
      return DenseElementsAttr::get(shapedty, attribute);
    }
  }
  return nullptr;
}
//===----------------------------------------------------------------------===//
// AtenCeilFloatOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenCeilFloatOp::fold(FoldAdaptor adaptor) {
  double c;
  if (matchPattern(getOperand(), m_TorchConstantFloat(&c)))
    return getI64IntegerAttr(getContext(), std::ceil(c));
  return nullptr;
}

//===----------------------------------------------------------------------===//
// AtenWhereSelfOp
//===----------------------------------------------------------------------===//

static Attribute getBroadcastedAttr(Attribute attr, ValueTensorType ty) {
  if (!attr || !ty.hasDtype() || !ty.hasSizes())
    return nullptr;

  auto dty = ty.getDtype();

  if (auto valueDense = dyn_cast<DenseElementsAttr>(attr)) {
    if (!valueDense.isSplat())
      return nullptr;
    auto splattr = valueDense.getSplatValue<Attribute>();
    auto attrty = ty.toBuiltinTensor();
    return DenseElementsAttr::get(attrty, splattr);
  }

  if (auto intAttr = dyn_cast_or_null<IntegerAttr>(attr)) {
    if (!isa<mlir::IntegerType>(dty))
      return nullptr;
    int64_t intval = intAttr.getInt();
    auto attrty = ty.toBuiltinTensor();
    return DenseElementsAttr::get(attrty, IntegerAttr::get(dty, intval));
  }

  if (auto fpAttr = dyn_cast_or_null<FloatAttr>(attr)) {
    if (!isa<mlir::FloatType>(dty))
      return nullptr;
    double dblval = fpAttr.getValueAsDouble();
    auto attrty = ty.toBuiltinTensor();
    return DenseElementsAttr::get(attrty, FloatAttr::get(dty, dblval));
  }

  return nullptr;
}

OpFoldResult AtenWhereSelfOp::fold(FoldAdaptor adaptor) {
  if (getSelf() == getOther())
    return getSelf();

  auto dense = dyn_cast_or_null<DenseElementsAttr>(adaptor.getCondition());
  auto resultTy = dyn_cast<ValueTensorType>(getType());
  if (!resultTy || !resultTy.hasDtype() || !resultTy.hasSizes() || !dense ||
      !dense.isSplat())
    return nullptr;

  auto condattr = dense.getSplatValue<APInt>();
  auto value = getSelf();
  auto valueAttr = adaptor.getSelf();
  if (condattr.isZero()) {
    value = getOther();
    valueAttr = adaptor.getOther();
  }

  auto valueTy = dyn_cast<ValueTensorType>(value.getType());
  if (valueTy && valueTy.hasSizes() && valueTy.hasDtype() &&
      valueTy == resultTy)
    return value;

  return getBroadcastedAttr(valueAttr, resultTy);
}

//===----------------------------------------------------------------------===//
// AtenWhereScalarOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenWhereScalarOp::fold(FoldAdaptor adaptor) {
  auto dense = dyn_cast_or_null<DenseElementsAttr>(adaptor.getCondition());
  auto resultTy = dyn_cast<ValueTensorType>(getType());
  if (!resultTy || !resultTy.hasDtype() || !resultTy.hasSizes() || !dense ||
      !dense.isSplat())
    return nullptr;

  auto condattr = dense.getSplatValue<APInt>();
  auto valueAttr = adaptor.getSelf();
  if (condattr.isZero()) {
    valueAttr = adaptor.getOther();
  }

  return getBroadcastedAttr(valueAttr, resultTy);
}

void AtenWhereScalarOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
                                                    MLIRContext *context) {

  patterns.add(+[](AtenWhereScalarOp op, PatternRewriter &rewriter) {
    auto cond = op.getCondition();
    auto self = op.getSelf();
    auto other = op.getOther();

    if (self != other)
      return rewriter.notifyMatchFailure(op, "differing output");

    auto condTy = dyn_cast<BaseTensorType>(cond.getType());
    if (!condTy || !condTy.hasSizes())
      return rewriter.notifyMatchFailure(op, "output size unknown");

    SmallVector<Value> dims;
    auto torchIntTy = rewriter.getType<Torch::IntType>();
    for (int i = 0, s = condTy.getSizes().size(); i < s; ++i) {
      Value iv = rewriter.create<Torch::ConstantIntOp>(
          op.getLoc(), torchIntTy, rewriter.getI64IntegerAttr(i));
      dims.push_back(rewriter.create<Torch::AtenSizeIntOp>(
          op.getLoc(), torchIntTy, cond, iv));
    }

    Value dimsList = rewriter.create<Torch::PrimListConstructOp>(
        op.getLoc(), Torch::ListType::get(torchIntTy), dims);

    Value none = rewriter.create<Torch::ConstantNoneOp>(op.getLoc());
    rewriter.replaceOpWithNewOp<Torch::AtenFullOp>(
        op, op.getType(), dimsList, self, none, none, none, none);
    return success();
  });
}

//===----------------------------------------------------------------------===//
// AtenWhereScalarOtherOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenWhereScalarOtherOp::fold(FoldAdaptor adaptor) {
  auto dense = dyn_cast_or_null<DenseElementsAttr>(adaptor.getCondition());
  auto resultTy = dyn_cast<ValueTensorType>(getType());
  if (!resultTy || !resultTy.hasDtype() || !resultTy.hasSizes() || !dense ||
      !dense.isSplat())
    return nullptr;

  auto condattr = dense.getSplatValue<APInt>();
  auto valueAttr = adaptor.getSelf();
  if (condattr.isZero()) {
    valueAttr = adaptor.getOther();
  }

  return getBroadcastedAttr(valueAttr, resultTy);
}

//===----------------------------------------------------------------------===//
// AtenWhereScalarSelfOp
//===----------------------------------------------------------------------===//

OpFoldResult AtenWhereScalarSelfOp::fold(FoldAdaptor adaptor) {
  auto dense = dyn_cast_or_null<DenseElementsAttr>(adaptor.getCondition());
  auto resultTy = dyn_cast<ValueTensorType>(getType());
  if (!resultTy || !resultTy.hasDtype() || !resultTy.hasSizes() || !dense ||
      !dense.isSplat())
    return nullptr;

  auto condattr = dense.getSplatValue<APInt>();
  auto valueAttr = adaptor.getSelf();
  if (condattr.isZero()) {
    valueAttr = adaptor.getOther();
  }

  return getBroadcastedAttr(valueAttr, resultTy);
}

//===----------------------------------------------------------------------===//
// PrimMaxIntOp
//===----------------------------------------------------------------------===//

OpFoldResult PrimMaxIntOp::fold(FoldAdaptor adaptor) {
  // If both operands are the same, then the operation is an identity.
  if (getA() == getB())
    return getA();

  auto lhs = dyn_cast_or_null<IntegerAttr>(adaptor.getA());
  auto rhs = dyn_cast_or_null<IntegerAttr>(adaptor.getB());
  if (!lhs || !rhs)
    return nullptr;
  // Torch semantics are that !torch.int is 64-bit signed.
  return IntegerAttr::get(
      lhs.getType(),
      std::max(lhs.getValue().getSExtValue(), rhs.getValue().getSExtValue()));
}

//===----------------------------------------------------------------------===//
// PrimNumToTensorScalarOp
//===----------------------------------------------------------------------===//

OpFoldResult PrimNumToTensorScalarOp::fold(FoldAdaptor adaptor) {
  Attribute a = adaptor.getA();
  auto resultTy = dyn_cast<ValueTensorType>(getType());
  if (!a)
    return {};
  if (!resultTy || !resultTy.hasDtype() || !resultTy.hasSizes())
    return {};

  auto dty = resultTy.getDtype();
  if (auto iattr = dyn_cast<IntegerAttr>(a)) {
    a = IntegerAttr::get(dty, iattr.getInt());
  } else if (auto fattr = dyn_cast<FloatAttr>(a)) {
    a = FloatAttr::get(dty, fattr.getValueAsDouble());
  } else {
    // doesn't handle other types, like complex type
    return {};
  }

  auto mlirTensorType =
      RankedTensorType::get(resultTy.getSizes(), resultTy.getDtype());
  return SplatElementsAttr::get(mlirTensorType, a);
}

//===----------------------------------------------------------------------===//
// PrimMinSelfIntOp
//===----------------------------------------------------------------------===//

OpFoldResult PrimMinSelfIntOp::fold(FoldAdaptor adaptor) {
  auto list = getOperand().getDefiningOp<PrimListConstructOp>();
  if (!list)
    return nullptr;
  // TODO: What does it return for an empty list?
  if (list->getNumOperands() == 0)
    return nullptr;

  SmallVector<int64_t> values;
  for (auto operand : list->getOperands()) {
    int64_t value;
    if (!matchPattern(operand, m_TorchConstantInt(&value)))
      return nullptr;
    values.push_back(value);
  }
  return getI64IntegerAttr(getContext(),
                           *std::min_element(values.begin(), values.end()));
}

//===----------------------------------------------------------------------===//
// PrimMinIntOp
//===----------------------------------------------------------------------===//

OpFoldResult PrimMinIntOp::fold(FoldAdaptor adaptor) {
  // If both operands are the same, then the operation is an identity.
  if (getA() == getB())
    return getA();

  auto lhs = dyn_cast_or_null<IntegerAttr>(adaptor.getA());
  auto rhs = dyn_cast_or_null<IntegerAttr>(adaptor.getB());
  if (!lhs || !rhs)
    return nullptr;
  // Torch semantics are that !torch.int is 64-bit signed.
  return IntegerAttr::get(
      lhs.getType(),
      std::min(lhs.getValue().getSExtValue(), rhs.getValue().getSExtValue()));
}

//===----------------------------------------------------------------------===//
// ShapeCalculateOp
//===----------------------------------------------------------------------===//

template <typename CalculateOp>
static void
getSuccessorRegionsForCalculateOp(CalculateOp op, RegionBranchPoint point,
                                  SmallVectorImpl<RegionSuccessor> &regions) {
  if (!point.getRegionOrNull()) {
    // First thing the op does is branch into the calculation.
    regions.emplace_back(&op.getCalculation());
    return;
  }
  if (point == op.getBody()) {
    // Body returns control to the outer op, passing through results.
    regions.emplace_back(op.getResults());
    return;
  }
  assert(point == op.getCalculation());
  // Calculation branches to the body.
  regions.emplace_back(&op.getBody());
}

void ShapeCalculateOp::getSuccessorRegions(
    RegionBranchPoint point, SmallVectorImpl<RegionSuccessor> &regions) {
  getSuccessorRegionsForCalculateOp(*this, point, regions);
}

//===----------------------------------------------------------------------===//
// DtypeCalculateOp
//===----------------------------------------------------------------------===//

void DtypeCalculateOp::getSuccessorRegions(
    RegionBranchPoint point, SmallVectorImpl<RegionSuccessor> &regions) {
  getSuccessorRegionsForCalculateOp(*this, point, regions);
}

//===----------------------------------------------------------------------===//
// ShapeCalculateYieldShapesOp
//===----------------------------------------------------------------------===//

MutableOperandRange ShapeCalculateYieldShapesOp::getMutableSuccessorOperands(
    RegionBranchPoint point) {
  // The shape operands don't get forwarded to the body.
  // MutableOperandRange always has an owning operation, even if empty, so
  // create a 0-length range.
  return MutableOperandRange(*this, /*start=*/0, /*length=*/0);
}

LogicalResult ShapeCalculateYieldShapesOp::verify() {
  auto parent = cast<ShapeCalculateOp>(getOperation()->getParentOp());
  if (parent.getNumResults() != getNumOperands())
    return emitOpError("expected number of shapes to match number of results");
  return success();
}

//===----------------------------------------------------------------------===//
// AtenNormScalarOp
//===----------------------------------------------------------------------===//

LogicalResult AtenNormScalarOp::verify() {

  // Verificaion of input type for torch.aten.norm.Scalar.
  // Per PyTorch docs, only float and complex types are valid for norm
  // operation.

  auto inTensor = cast<BaseTensorType>(getSelf().getType());

  // If no dtype is specified, it will default to a float one.
  if (!inTensor.hasDtype()) {
    return success();
  }

  auto inTensorDtype = inTensor.getDtype();

  // Check if dtype is one of those supported by norm operation.
  // ComplexType will match any torch complex types, but each float must be
  // checked individually.
  if (!isa<mlir::ComplexType, mlir::Float16Type, mlir::Float32Type,
           mlir::Float64Type>(inTensorDtype)) {
    return emitOpError(
               "expected a float or complex type for input tensor, but got ")
           << inTensorDtype;
  }

  return success();
}

//===----------------------------------------------------------------------===//
// AtenRenormOp
//===----------------------------------------------------------------------===//

LogicalResult AtenRenormOp::verify() {

  auto selfType = cast<BaseTensorType>(getSelf().getType());

  if (!selfType.hasDtype() || !selfType.hasSizes())
    return success();

  auto inShape = selfType.getSizes();
  int64_t selfRank = inShape.size();
  auto selfDtype = selfType.getDtype();

  if (!isa<mlir::Float16Type, mlir::BFloat16Type, mlir::Float32Type,
           mlir::Float64Type, mlir::ComplexType>(selfDtype))
    return emitOpError(
               "expected a float or complex type for input tensor, but got ")
           << selfDtype;

  // According to the Pytoch documentation tensor need to be at least rank 2
  if (selfRank <= 1)
    return emitOpError("renorm: input needs at least 2 dimensions, got ")
           << selfRank << " dimensions";

  // Check if argument p is valid
  auto pType = getP().getType();

  if (isa<mlir::ComplexType>(pType))
    return emitOpError("renorm: p must be real-valued");

  // The argument 'p' can be either an integer or a floating-point number,
  // so we need to consider both options and check if 'p' is within the correct
  // range
  int64_t pInt = 1;
  double_t pDouble = 1;
  if (!matchPattern(getP(), m_TorchConstantInt(&pInt)) &&
      !matchPattern(getP(), m_TorchConstantFloat(&pDouble)))
    return success();

  if (pInt <= 0 || pDouble <= 0)
    return emitOpError("renorm: non-positive norm not supported");

  // Check if argument maxnorm is valid
  auto maxnormType = getMaxnorm().getType();
  if (isa<mlir::ComplexType>(maxnormType))
    return emitOpError("renorm: maxnorm must be real-valued");

  // The argument 'maxnorm' can be either an integer or a floating-point number,
  // so we need to consider both options and check if 'maxnorm' is within the
  // correct range
  int64_t maxnormInt = 0;
  double_t maxnormDouble = 0;
  if (!matchPattern(getMaxnorm(), m_TorchConstantInt(&maxnormInt)) &&
      !matchPattern(getMaxnorm(), m_TorchConstantFloat(&maxnormDouble)))
    return success();

  if (maxnormInt < 0 || maxnormDouble < 0)
    return emitOpError("renorm: expected maxnorm to be >= 0");

  // Get the dimension
  int64_t dim;
  if (!matchPattern(getDim(), m_TorchConstantInt(&dim)))
    return success();

  // check if is dim is in the correct range
  if (dim >= selfRank || dim < -selfRank)
    return emitOpError("Dimension out of range (expected to be in range of [")
           << -selfRank << ", " << selfRank - 1 << "], but got " << dim;

  return success();
}

//===----------------------------------------------------------------------===//
// AtenPermuteOp
//===----------------------------------------------------------------------===//

LogicalResult AtenPermuteOp::verify() {

  // Verification of the permute op for input & output dimensions with
  // statically known sizes.

  SmallVector<Value> permutation;
  auto permutationObtained = getListConstructElements(getDims(), permutation);
  if (!permutationObtained) {
    return success();
  }

  auto outType = cast<BaseTensorType>(getResult().getType());
  auto inType = cast<BaseTensorType>(getSelf().getType());

  if (!outType.hasSizes() || !inType.hasSizes()) {
    return success();
  }

  auto outShape = outType.getSizes();
  auto inShape = inType.getSizes();

  auto outRank = outShape.size();

  if (outRank != inShape.size()) {
    return emitOpError(
               "expected input and output tensors to have same rank, but ")
           << inShape.size() << " != " << outRank << '.';
  }

  if (outRank != permutation.size()) {
    return emitOpError() << "expected permutation to have size equal result "
                            "tensor rank. The permutation has "
                         << permutation.size()
                         << " elements, the output has rank " << outRank << '.';
  }

  // Initialization of the reverse permutation. -1 denotes an unknown
  // permutation index.
  SmallVector<int64_t> reversePermutation(outRank, -1);

  // In this loop:
  //  (1) check that the permutation indices are in bounds, and not duplicated.
  //  (2) populate reversePermutation (to check for duplicates).
  //  (3) check that the input and output shapes agree with the permutation. For
  //  example, if the permutation is (1,2,0) and the input shape is (2,3,5),
  //  then the output shape must be (3,5,2).

  for (uint64_t to = 0; to < outRank; ++to) {
    int64_t from;

    auto fromIsSet = matchPattern(permutation[to], m_TorchConstantInt(&from));

    if (!fromIsSet) {
      continue;
    }

    // if 'from' is the unkwown index, continue.
    if (from == -1) {
      continue;
    }

    if (!isValidDim(from, outRank)) {
      return emitError("observed invalid index in permutation (")
             << from << ") for input tensor of rank " << outRank << '.';
    }

    if (reversePermutation[from] != -1) {
      return emitOpError("has a duplicate dimension (")
             << from << ") in its permutation " << getDims() << '.';
    }
    reversePermutation[from] = to;

    auto dimSizesDefined =
        inShape[from] != kUnknownSize && outShape[to] != kUnknownSize;
    auto dimSizesDifferent = inShape[from] != outShape[to];

    if (dimSizesDefined && dimSizesDifferent) {
      return emitOpError("has a permutation which is not compatible with the "
                         "input and output shapes. ")
             << "The input shape in dimension " << from << " is "
             << inShape[from] << ", and the output shape in dimension " << to
             << " is " << outShape[to]
             << " : they should be the same with this permutation. ";
    }
  }

  return success();
}

//===----------------------------------------------------------------------===//
// PrimsConvertElementTypeOp
//===----------------------------------------------------------------------===//

OpFoldResult PrimsConvertElementTypeOp::fold(FoldAdaptor adaptor) {
  auto inputType = cast<BaseTensorType>(getA().getType());
  auto outputType = cast<BaseTensorType>(getResult().getType());
  if (inputType != outputType)
    return nullptr;
  if (!inputType.hasDtype() || !outputType.hasDtype())
    return nullptr;
  if (inputType.getDtype() != outputType.getDtype())
    return nullptr;
  return getA();
}

//===----------------------------------------------------------------------===//
// AtenMaxPoolWithIndicesOp
//===----------------------------------------------------------------------===//

namespace {

template <typename OpTy> struct MaxPoolWithoutIndices {
  using type = OpTy;
};

template <> struct MaxPoolWithoutIndices<AtenMaxPool2dWithIndicesOp> {
  using type = AtenMaxPool2dOp;
};

template <> struct MaxPoolWithoutIndices<AtenMaxPool3dWithIndicesOp> {
  using type = AtenMaxPool3dOp;
};

} // namespace

template <typename OpTy>
struct SimplifyMaxPoolWithIndices : public mlir::OpRewritePattern<OpTy> {
  SimplifyMaxPoolWithIndices(mlir::MLIRContext *context)
      : OpRewritePattern<OpTy>(context, /*benefit=*/1) {}

  LogicalResult
  matchAndRewrite(OpTy op, mlir::PatternRewriter &rewriter) const override {
    if (!op.getResult1().use_empty()) {
      return rewriter.notifyMatchFailure(
          op, "result1 of MaxPoolWithIndices should be unused");
    }

    Value result = rewriter.create<typename MaxPoolWithoutIndices<OpTy>::type>(
        op->getLoc(), op.getResult0().getType(), op.getSelf(),
        op.getKernelSize(), op.getStride(), op.getPadding(), op.getDilation(),
        op.getCeilMode());

    op.getResult0().replaceAllUsesWith(result);
    rewriter.eraseOp(op);
    return success();
  }
};

void AtenMaxPool2dWithIndicesOp::getCanonicalizationPatterns(
    RewritePatternSet &patterns, MLIRContext *context) {
  patterns.add<SimplifyMaxPoolWithIndices<AtenMaxPool2dWithIndicesOp>>(context);
}

void AtenMaxPool3dWithIndicesOp::getCanonicalizationPatterns(
    RewritePatternSet &patterns, MLIRContext *context) {
  patterns.add<SimplifyMaxPoolWithIndices<AtenMaxPool3dWithIndicesOp>>(context);
}

//===----------------------------------------------------------------------===//
// Aten_AdaptiveAvgPool2dOp
//===----------------------------------------------------------------------===//

void Aten_AdaptiveAvgPool2dOp::getCanonicalizationPatterns(
    RewritePatternSet &patterns, MLIRContext *context) {
  patterns.add(+[](Aten_AdaptiveAvgPool2dOp op, PatternRewriter &rewriter) {
    rewriter.replaceOpWithNewOp<AtenAdaptiveAvgPool2dOp>(
        op, op.getType(), op.getSelf(), op.getOutputSize());

    return success();
  });
}

//===----------------------------------------------------------------------===//
// AtenLinalgCrossOp
//===----------------------------------------------------------------------===//

LogicalResult AtenLinalgCrossOp::verify() {

  auto selfType = cast<BaseTensorType>(getSelf().getType());
  auto otherType = cast<BaseTensorType>(getOther().getType());

  if (!selfType.hasDtype() || !otherType.hasDtype() || !selfType.hasSizes() ||
      !otherType.hasSizes()) {
    return success();
  }

  Type selfDtype = selfType.getDtype();
  Type otherDtype = otherType.getDtype();

  // the operation succeeds only if both inputs have the same dtype
  if (selfDtype != otherDtype) {
    return emitOpError("input tensors must have the same dtype, but got ")
           << selfDtype << " and " << otherDtype;
  }

  // Check if any of the input tensors has torch.bool dtype.
  // The operation does not support this type.
  // The docs state that only float, double, cfloat and cdouble dtypes are
  // supported, but, when testing, it fails only for boolean dtype. Update to
  // fit the docs if necessary.
  // https://pytorch.org/docs/stable/generated/torch.linalg.cross.html
  if (selfDtype.isSignlessInteger(1) || otherDtype.isSignlessInteger(1)) {
    return emitOpError("input tensors must not have bool dtype");
  }

  ArrayRef<int64_t> selfShape = selfType.getSizes();
  ArrayRef<int64_t> otherShape = otherType.getSizes();

  int64_t selfRank = selfShape.size();
  int64_t otherRank = otherShape.size();

  // check if both input tensors have the same number of dims
  if (selfRank != otherRank) {
    return emitOpError("input tensors must have the same number of dimensions, "
                       "but got ")
           << selfRank << " and " << otherRank;
  }

  // convert dim to an integer type
  int64_t dim;
  if (!matchPattern(getDim(), m_TorchConstantInt(&dim))) {
    return success();
  }

  // check if dim is in the correct range
  if (dim >= selfRank || dim < -selfRank) {
    return emitOpError("dim expected to be in rank of [")
           << -selfRank << ", " << selfRank - 1 << "], but got " << dim;
  }

  // compensate for possible negative dim value
  if (dim < 0) {
    dim += selfRank;
  }

  // check if the size of the dimensions specified by 'dim' is equal to 3
  // (required by the operation)
  if ((selfShape[dim] != 3 && selfShape[dim] != kUnknownSize) ||
      (otherShape[dim] != 3 && otherShape[dim] != kUnknownSize)) {
    return emitOpError("inputs dimension ")
           << dim << " must have length 3, but got " << selfShape[dim]
           << " and " << otherShape[dim];
  }

  // Check if there is a disparity between dimension sizes.
  // Dimensions at the same index must either have the same size,
  // or one of them must be equal to 1.
  int32_t i = 0;
  for (auto [selfCurrent, otherCurrent] :
       llvm::zip_equal(selfShape, otherShape)) {
    if (selfCurrent != otherCurrent && selfCurrent != 1 && otherCurrent != 1) {
      return emitOpError("the size of first tensor (")
             << selfCurrent << ") must match the size of second tensor ("
             << otherCurrent << ") at dimension " << i
             << " or one of them must be 1";
    }
    ++i;
  }

  return success();
}

LogicalResult AtenKthvalueOp::verify() {

  auto selfType = cast<BaseTensorType>(getSelf().getType());

  if (!selfType.hasDtype() || !selfType.hasSizes())
    return success();

  Type selfDtype = selfType.getDtype();
  if (selfDtype.isSignlessInteger(1))
    return emitOpError("input tensors must not have bool dtype");

  int64_t dim;
  if (!matchPattern(getDim(), m_TorchConstantInt(&dim)))
    return success();

  ArrayRef<int64_t> selfShape = selfType.getSizes();
  int64_t selfRank = selfShape.size();

  dim = toPositiveDim(dim, selfRank);
  if (!isValidDim(dim, selfRank))
    return emitOpError("dim expected to be in range of [")
           << -selfRank << ", " << selfRank - 1 << "], but got " << dim;

  // convert k to an integer type
  int64_t k;
  if (!matchPattern(getK(), m_TorchConstantInt(&k)))
    return success();

  // check if k is in the correct range
  if (selfShape[dim] != kUnknownSize && (k < 1 || k > selfShape[dim]))
    return emitOpError("k expected to be in range of [")
           << 1 << ", " << selfShape[dim] << "], but got " << k;

  return success();
}

//===----------------------------------------------------------------------===//
// DtypeCalculateYieldDtypesOp
//===----------------------------------------------------------------------===//

MutableOperandRange DtypeCalculateYieldDtypesOp::getMutableSuccessorOperands(
    RegionBranchPoint point) {
  // The dtype operands don't get forwarded to the body.
  // MutableOperandRange always has an owning operation, even if empty, so
  // create a 0-length range.
  return MutableOperandRange(*this, /*start=*/0, /*length=*/0);
}

LogicalResult DtypeCalculateYieldDtypesOp::verify() {
  auto parent = cast<DtypeCalculateOp>(getOperation()->getParentOp());
  if (parent.getNumResults() != getNumOperands())
    return emitOpError("expected number of dtypes to match number of results");
  return success();
}

//===----------------------------------------------------------------------===//
// GlobalSlotModuleInitializerOp
//===----------------------------------------------------------------------===//

LogicalResult GlobalSlotModuleInitializerOp::verify() {
  // We centralize all verification of the global slots and the
  // InitializeGlobalSlotsOp into here, since it requires processing the whole
  // module.

  // TODO: We should really have a `torch.module` and have this initializer be
  // a region attached to it.

  ModuleOp module = cast<ModuleOp>(getOperation()->getParentOp());
  for (auto op : module.getOps<GlobalSlotModuleInitializerOp>()) {
    if (op.getOperation() != getOperation())
      return op.emitError("there must be only one global slot initializer");
  }

  // Collect the relevant symbol names we will verify.
  DenseSet</*StringAttr*/ Attribute> knownGlobalSlots;
  for (auto op : module.getOps<GlobalSlotOp>())
    knownGlobalSlots.insert(op.getSymNameAttr());
  DenseSet</*StringAttr*/ Attribute> initializedGlobalSlots;
  auto initialize = cast<InitializeGlobalSlotsOp>(getBody()->getTerminator());
  for (Attribute symName : initialize.getSlotSymNames()) {
    auto wasInserted = initializedGlobalSlots
                           .insert(cast<FlatSymbolRefAttr>(symName).getAttr())
                           .second;
    if (!wasInserted)
      return initialize.emitError("duplicate initialization of global slot: ")
             << symName;
  }
  auto lessThanByStringValue = [](Attribute lhs, Attribute rhs) {
    return cast<StringAttr>(lhs).getValue() < cast<StringAttr>(rhs).getValue();
  };
  auto known = llvm::to_vector(knownGlobalSlots);
  llvm::sort(known, lessThanByStringValue);
  auto initialized = llvm::to_vector(initializedGlobalSlots);
  llvm::sort(initialized, lessThanByStringValue);

  // Check that the global slots in the module are all initialized.
  SymbolTable symbolTable(module);
  if (initializedGlobalSlots != knownGlobalSlots) {
    InFlightDiagnostic diag = initialize.emitOpError(
        "must have one initializer for each global slot in the module");
    for (auto knownGlobalSlot : known) {
      auto symName = FlatSymbolRefAttr::get(cast<StringAttr>(knownGlobalSlot));
      if (!initializedGlobalSlots.count(knownGlobalSlot)) {
        diag.attachNote(
                symbolTable.lookup<GlobalSlotOp>(symName.getAttr()).getLoc())
            .append("missing global slot initializer for ", symName);
      }
    }
    for (auto initializedGlobalSlot : initialized) {
      if (!knownGlobalSlots.count(initializedGlobalSlot)) {
        diag.attachNote().append(
            "unexpected global slot initializer for non-existent global slot ",
            FlatSymbolRefAttr::get(cast<StringAttr>(initializedGlobalSlot)));
      }
    }
    return diag;
  }

  // Check that initial values satisfy type bounds.
  for (int i = 0, e = initialize.getNumOperands(); i < e; ++i) {
    auto symName = cast<FlatSymbolRefAttr>(initialize.getSlotSymNames()[i]);
    auto initialValue = initialize.getOperand(i);
    auto globalSlotOp = symbolTable.lookup<GlobalSlotOp>(symName.getValue());
    if (!isValidSubtype(initialValue.getType(), globalSlotOp.getTypeBound())) {
      return initialize.emitOpError().append(
          "initial value for global slot ", symName, " has type ",
          initialValue.getType(), " which is not within the bound ",
          globalSlotOp.getTypeBound());
    }
  }

  auto walkResult = getOperation()->walk([](Operation *op) {
    // We only permit a small set of ops in the module initializer.
    // These ops are essentially those which can be produced by the IValue
    // importer.
    if (op->hasTrait<mlir::torch::Torch::OpTrait::AllowedInModuleInitializer>())
      return WalkResult::advance();
    op->emitOpError() << "is not allowed in a module initializer";
    return WalkResult::interrupt();
  });
  if (walkResult.wasInterrupted())
    return failure();

  return success();
}

//===----------------------------------------------------------------------===//
// InitializeGlobalSlotsOp
//===----------------------------------------------------------------------===//

ParseResult InitializeGlobalSlotsOp::parse(OpAsmParser &parser,
                                           OperationState &result) {
  if (parser.parseOptionalAttrDict(result.attributes))
    return failure();
  if (parser.parseLSquare())
    return failure();
  SmallVector<Attribute> slotSymNames;
  while (!succeeded(parser.parseOptionalRSquare())) {
    NamedAttrList dummy;
    StringAttr slotSymName;
    if (parser.parseSymbolName(slotSymName, "dummy", dummy))
      return failure();
    slotSymNames.push_back(FlatSymbolRefAttr::get(slotSymName));
    if (parser.parseLParen())
      return failure();
    OpAsmParser::UnresolvedOperand initialValue;
    if (parser.parseOperand(initialValue))
      return failure();
    Type initialValueType;
    if (parser.parseColonType(initialValueType))
      return failure();
    if (parser.parseRParen())
      return failure();
    if (parser.resolveOperand(initialValue, initialValueType, result.operands))
      return failure();
  }
  result.addAttribute("slotSymNames",
                      ArrayAttr::get(parser.getContext(), slotSymNames));
  return success();
}

void InitializeGlobalSlotsOp::print(OpAsmPrinter &p) {
  p.printOptionalAttrDict(getOperation()->getAttrs(),
                          /*elidedAttrs=*/{"slotSymNames"});
  p << " [";
  p.printNewline();
  for (int i = 0, e = getNumOperands(); i < e; ++i) {
    p << "  " << getSlotSymNames()[i] << "(" << getInitialValues()[i] << " : "
      << getInitialValues()[i].getType() << ")";
    p.printNewline();
  }
  p << "]";
}

LogicalResult InitializeGlobalSlotsOp::verify() {
  if (getInitialValues().size() != getSlotSymNames().size())
    return emitOpError("expected number of operands to match number of slots");
  return success();
}

//===----------------------------------------------------------------------===//
// BindSymbolicShapeOp
//===----------------------------------------------------------------------===//

//
// torch.bind_symbolic_shape %6, [%0, %1, %2], affine_map<()[s0, s1, s2] ->
// (s0, s1 * 2 + s2, 3)> : !torch.vtensor<[?,?,3],f32>
//

ParseResult BindSymbolicShapeOp::parse(OpAsmParser &parser,
                                       OperationState &result) {
  OpAsmParser::UnresolvedOperand operand;
  SmallVector<OpAsmParser::UnresolvedOperand> shapeSymbols;
  AffineMapAttr shapeExpressions;
  Type operandType;

  if (parser.parseOperand(operand) || parser.parseComma() ||
      parser.parseLSquare() || parser.parseOperandList(shapeSymbols) ||
      parser.parseRSquare() || parser.parseComma() ||
      parser.parseAttribute(shapeExpressions, "shape_expressions",
                            result.attributes) ||
      parser.parseOptionalAttrDict(result.attributes) ||
      parser.parseColonType(operandType)) {
    return failure();
  }

  if (parser.resolveOperand(operand, operandType, result.operands) ||
      parser.resolveOperands(shapeSymbols,
                             parser.getBuilder().getType<Torch::IntType>(),
                             result.operands)) {
    return failure();
  }

  return success();
}

// Use a custom printer here to avoid the AffineMap from getting hoisted
// when printed. This makes it so the AffineMap is printed inline with the op.
void BindSymbolicShapeOp::print(OpAsmPrinter &p) {
  p << " " << getOperand() << ", [";
  llvm::interleaveComma(getShapeSymbols(), p);
  p << "], " << "affine_map<" << getShapeExpressions().getValue() << ">";
  p.printOptionalAttrDict((*this)->getAttrs(),
                          /*elidedAttrs=*/{"shape_expressions"});
  p << " : " << getOperand().getType();
}

LogicalResult BindSymbolicShapeOp::verify() {
  if (getShapeSymbols().size() !=
      getShapeExpressions().getValue().getNumSymbols())
    return emitOpError()
           << "requires equal number of shape symbol args and symbol args to "
              "the attached affine map, since they are 1:1 mapped";

  for (auto symbol : getShapeSymbols()) {
    Operation *definingOp = symbol.getDefiningOp();
    if (!isa<SymbolicIntOp>(definingOp)) {
      return emitOpError()
             << "shape symbol must be produced by a SymbolicIntOp";
    }
  }

  return success();
}
// AtenTriuIndicesOp
//===----------------------------------------------------------------------===//

LogicalResult AtenTriuIndicesOp::verify() {

  // Check if row, col and offset are constant ints
  int64_t row;
  if (!matchPattern(getRow(), m_TorchConstantInt(&row)))
    return success();

  int64_t col;
  if (!matchPattern(getCol(), m_TorchConstantInt(&col)))
    return success();

  int64_t offset;
  if (!matchPattern(getOffset(), m_TorchConstantInt(&offset)))
    return success();

  // Check if values of row, and col are valid
  if (row < 0)
    return emitOpError("row must be non-negative, got ") << row;

  if (col < 0)
    return emitOpError("col must be non-negative, got ") << col;

  // Check if dtype is valid
  int64_t dtype;
  if (!matchPattern(getDtype(), m_TorchConstantInt(&dtype)))
    return success();
  if (dtype != (int)torch_upstream::ScalarType::Int &&
      dtype != (int)torch_upstream::ScalarType::Long)
    return emitOpError(
        "'triu_indices' implemented only for torch.int32 and torch.int64");

  return success();
}

// AtenTrilIndicesOp
//===----------------------------------------------------------------------===//

LogicalResult AtenTrilIndicesOp::verify() {

  // Check if row, col and offset are constant ints
  int64_t row;
  if (!matchPattern(getRow(), m_TorchConstantInt(&row)))
    return success();

  int64_t col;
  if (!matchPattern(getCol(), m_TorchConstantInt(&col)))
    return success();

  int64_t offset;
  if (!matchPattern(getOffset(), m_TorchConstantInt(&offset)))
    return success();

  // Check if values of row, and col are valid
  if (row < 0)
    return emitOpError("row must be non-negative, got ") << row;

  if (col < 0)
    return emitOpError("col must be non-negative, got ") << col;

  // Check if dtype is valid
  int64_t dtype;
  if (!matchPattern(getDtype(), m_TorchConstantInt(&dtype)))
    return success();
  if (dtype != (int)torch_upstream::ScalarType::Int &&
      dtype != (int)torch_upstream::ScalarType::Long)
    return emitOpError(
        "'triu_indices' implemented only for torch.int32 and torch.int64");

  return success();
}

// AtenRot90Op
//===----------------------------------------------------------------------===//

LogicalResult AtenRot90Op::verify() {
  // Check rotation dimensions.
  SmallVector<Value> dims;
  if (!getListConstructElements(getDims(), dims))
    return success();

  if (dims.size() != 2)
    return emitOpError("expected total rotation dims == 2, but got dims = ")
           << dims.size();

  // Check a rank of the input tensor.
  auto selfType = cast<BaseTensorType>(getSelf().getType());
  if (!selfType.hasSizes())
    return success();

  auto selfShape = selfType.getSizes();
  int64_t selfRank = selfShape.size();

  if (selfRank < 2)
    return emitOpError("expected total dims >= 2, but got total dims = ")
           << selfRank;

  if (dims[0] == dims[1])
    return emitOpError(
               "expected rotation dims to be different, but got dim0 = ")
           << dims[0] << " and dim1 = " << dims[1];

  return success();
}