llvm-project/mlir/lib/Dialect/Tensor/IR/TensorOps.cpp

//===----------------------------------------------------------------------===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//===----------------------------------------------------------------------===//

#include "mlir/Dialect/StandardOps/Utils/Utils.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "mlir/Dialect/Utils/StaticValueUtils.h"
#include "mlir/IR/BlockAndValueMapping.h"
#include "mlir/IR/Builders.h"
#include "mlir/IR/Matchers.h"
#include "mlir/IR/PatternMatch.h"
#include "mlir/IR/TypeUtilities.h"
#include "llvm/ADT/STLExtras.h"

using namespace mlir;
using namespace mlir::tensor;

/// Materialize a single constant operation from a given attribute value with
/// the desired resultant type.
Operation *TensorDialect::materializeConstant(OpBuilder &builder,
                                              Attribute value, Type type,
                                              Location loc) {
  return builder.create<mlir::ConstantOp>(loc, type, value);
}

//===----------------------------------------------------------------------===//
// CastOp
//===----------------------------------------------------------------------===//

/// Determines whether tensor::CastOp casts to a more dynamic version of the
/// source tensor. This is useful to fold a tensor.cast into a consuming op and
/// implement canonicalization patterns for ops in different dialects that may
/// consume the results of tensor.cast operations. Such foldable tensor.cast
/// operations are typically inserted as `slice` ops and are canonicalized,
/// to preserve the type compatibility of their uses.
///
/// Returns true when all conditions are met:
/// 1. source and result are ranked tensors with same element type and rank.
/// 2. the tensor type has more static information than the result
///
/// Example:
/// ```mlir
///   %1 = tensor.cast %0 : tensor<8x16xf32> to tensor<?x?xf32>
///   %2 = consumer %1 ... : tensor<?x?xf32> ...
/// ```
///
/// folds into:
///
/// ```mlir
///   %2 = consumer %0 ... : tensor<8x16xf32> ...
/// ```
bool mlir::tensor::canFoldIntoConsumerOp(CastOp castOp) {
  if (!castOp)
    return false;

  RankedTensorType sourceType =
      castOp.source().getType().dyn_cast<RankedTensorType>();
  RankedTensorType resultType = castOp.getType().dyn_cast<RankedTensorType>();

  // Requires RankedTensorType.
  if (!sourceType || !resultType)
    return false;

  // Requires same elemental type.
  if (sourceType.getElementType() != resultType.getElementType())
    return false;

  // Requires same rank.
  if (sourceType.getRank() != resultType.getRank())
    return false;

  // If cast is towards more static sizes along any dimension, don't fold.
  for (auto t : llvm::zip(sourceType.getShape(), resultType.getShape())) {
    if (ShapedType::isDynamic(std::get<0>(t)) &&
        !ShapedType::isDynamic(std::get<1>(t)))
      return false;
  }

  return true;
}

/// Performs folding of any operand of `op` if it comes from a tensor::CastOp
/// that can be folded.
LogicalResult mlir::tensor::foldTensorCast(Operation *op) {
  bool folded = false;
  for (OpOperand &operand : op->getOpOperands()) {
    auto castOp = operand.get().getDefiningOp<tensor::CastOp>();
    if (castOp && tensor::canFoldIntoConsumerOp(castOp)) {
      operand.set(castOp.getOperand());
      folded = true;
    }
  }
  return success(folded);
}

bool CastOp::areCastCompatible(TypeRange inputs, TypeRange outputs) {
  if (inputs.size() != 1 || outputs.size() != 1)
    return false;
  Type a = inputs.front(), b = outputs.front();
  auto aT = a.dyn_cast<TensorType>();
  auto bT = b.dyn_cast<TensorType>();
  if (!aT || !bT)
    return false;

  if (aT.getElementType() != bT.getElementType())
    return false;

  return succeeded(verifyCompatibleShape(aT, bT));
}

/// Compute a TensorType that has the joined shape knowledge of the two
/// given TensorTypes. The element types need to match.
static TensorType joinShapes(TensorType one, TensorType two) {
  assert(one.getElementType() == two.getElementType());

  if (!one.hasRank())
    return two;
  if (!two.hasRank())
    return one;

  int64_t rank = one.getRank();
  if (rank != two.getRank())
    return {};

  SmallVector<int64_t, 4> join;
  join.reserve(rank);
  for (int64_t i = 0; i < rank; ++i) {
    if (one.isDynamicDim(i)) {
      join.push_back(two.getDimSize(i));
      continue;
    }
    if (two.isDynamicDim(i)) {
      join.push_back(one.getDimSize(i));
      continue;
    }
    if (one.getDimSize(i) != two.getDimSize(i))
      return {};
    join.push_back(one.getDimSize(i));
  }
  return RankedTensorType::get(join, one.getElementType());
}

namespace {

/// Replaces chains of two tensor.cast operations by a single tensor.cast
/// operation if doing so does not remove runtime constraints.
struct ChainedTensorCast : public OpRewritePattern<CastOp> {
  using OpRewritePattern<CastOp>::OpRewritePattern;

  LogicalResult matchAndRewrite(CastOp tensorCast,
                                PatternRewriter &rewriter) const final {
    auto tensorCastOperand = tensorCast.getOperand().getDefiningOp<CastOp>();

    if (!tensorCastOperand)
      return failure();

    auto sourceType =
        tensorCastOperand.getOperand().getType().cast<TensorType>();
    auto intermediateType = tensorCastOperand.getType().cast<TensorType>();
    auto resultType = tensorCast.getType().cast<TensorType>();

    // We can remove the intermediate cast if joining all three produces the
    // same result as just joining the source and result shapes.
    auto firstJoin =
        joinShapes(joinShapes(sourceType, intermediateType), resultType);

    // The join might not exist if the cast sequence would fail at runtime.
    if (!firstJoin)
      return failure();

    // The newJoin always exists if the above join exists, it might just contain
    // less information. If so, we cannot drop the intermediate cast, as doing
    // so would remove runtime checks.
    auto newJoin = joinShapes(sourceType, resultType);
    if (firstJoin != newJoin)
      return failure();

    rewriter.replaceOpWithNewOp<CastOp>(tensorCast, resultType,
                                        tensorCastOperand.getOperand());
    return success();
  }
};

} // namespace

void CastOp::getCanonicalizationPatterns(RewritePatternSet &results,
                                         MLIRContext *context) {
  results.add<ChainedTensorCast>(context);
}

//===----------------------------------------------------------------------===//
// DimOp
//===----------------------------------------------------------------------===//

void DimOp::build(OpBuilder &builder, OperationState &result, Value source,
                  int64_t index) {
  auto loc = result.location;
  Value indexValue = builder.create<ConstantIndexOp>(loc, index);
  build(builder, result, source, indexValue);
}

void DimOp::build(OpBuilder &builder, OperationState &result, Value source,
                  Value index) {
  auto indexTy = builder.getIndexType();
  build(builder, result, indexTy, source, index);
}

Optional<int64_t> DimOp::getConstantIndex() {
  if (auto constantOp = index().getDefiningOp<ConstantOp>())
    return constantOp.getValue().cast<IntegerAttr>().getInt();
  return {};
}

static LogicalResult verify(DimOp op) {
  // Assume unknown index to be in range.
  Optional<int64_t> index = op.getConstantIndex();
  if (!index.hasValue())
    return success();

  // Check that constant index is not knowingly out of range.
  auto type = op.source().getType();
  if (auto tensorType = type.dyn_cast<RankedTensorType>()) {
    if (index.getValue() >= tensorType.getRank())
      return op.emitOpError("index is out of range");
  } else if (type.isa<UnrankedTensorType>()) {
    // Assume index to be in range.
  } else {
    llvm_unreachable("expected operand with tensor type");
  }
  return success();
}

OpFoldResult DimOp::fold(ArrayRef<Attribute> operands) {
  // All forms of folding require a known index.
  auto index = operands[1].dyn_cast_or_null<IntegerAttr>();
  if (!index)
    return {};

  // Folding for unranked types (UnrankedTensorType) is not supported.
  auto tensorType = source().getType().dyn_cast<RankedTensorType>();
  if (!tensorType)
    return {};

  // Fold if the shape extent along the given index is known.
  if (!tensorType.isDynamicDim(index.getInt())) {
    Builder builder(getContext());
    return builder.getIndexAttr(tensorType.getShape()[index.getInt()]);
  }

  Operation *definingOp = source().getDefiningOp();

  // Fold dim to the operand of tensor.generate.
  if (auto fromElements = dyn_cast_or_null<tensor::GenerateOp>(definingOp)) {
    auto resultType =
        fromElements.getResult().getType().cast<RankedTensorType>();
    // The case where the type encodes the size of the dimension is handled
    // above.
    assert(resultType.getShape()[index.getInt()] ==
           RankedTensorType::kDynamicSize);

    // Find the operand of the fromElements that corresponds to this index.
    auto dynExtents = fromElements.dynamicExtents().begin();
    for (auto dim : resultType.getShape().take_front(index.getInt()))
      if (dim == RankedTensorType::kDynamicSize)
        dynExtents++;

    return Value{*dynExtents};
  }

  // The size at the given index is now known to be a dynamic size.
  unsigned unsignedIndex = index.getValue().getZExtValue();

  if (auto sliceOp = dyn_cast_or_null<tensor::ExtractSliceOp>(definingOp)) {
    assert(sliceOp.isDynamicSize(unsignedIndex) &&
           "Expected dynamic slice size");
    return sliceOp.getDynamicSize(unsignedIndex);
  }

  // dim(cast) -> dim
  if (succeeded(foldTensorCast(*this)))
    return getResult();

  return {};
}

namespace {
/// Fold dim of a cast into the dim of the source of the tensor cast.
struct DimOfCastOp : public OpRewritePattern<DimOp> {
  using OpRewritePattern<DimOp>::OpRewritePattern;

  LogicalResult matchAndRewrite(DimOp dimOp,
                                PatternRewriter &rewriter) const override {
    auto castOp = dimOp.source().getDefiningOp<CastOp>();
    if (!castOp)
      return failure();
    Value newSource = castOp.getOperand();
    rewriter.replaceOpWithNewOp<DimOp>(dimOp, newSource, dimOp.index());
    return success();
  }
};
} // end anonymous namespace.

void DimOp::getCanonicalizationPatterns(RewritePatternSet &results,
                                        MLIRContext *context) {
  results.add<DimOfCastOp>(context);
}

//===----------------------------------------------------------------------===//
// ExtractOp
//===----------------------------------------------------------------------===//

static LogicalResult verify(ExtractOp op) {
  // Verify the # indices match if we have a ranked type.
  if (auto tensorType = op.tensor().getType().dyn_cast<RankedTensorType>())
    if (tensorType.getRank() != static_cast<int64_t>(op.indices().size()))
      return op.emitOpError("incorrect number of indices for extract_element");

  return success();
}

OpFoldResult ExtractOp::fold(ArrayRef<Attribute> operands) {
  // The tensor operand must be a known constant.
  Attribute tensor = operands.front();
  if (!tensor)
    return {};
  // If this is a splat elements attribute, simply return the value. All of the
  // elements of a splat attribute are the same.
  if (auto splatTensor = tensor.dyn_cast<SplatElementsAttr>())
    return splatTensor.getSplatValue();

  // Otherwise, collect the constant indices into the tensor.
  SmallVector<uint64_t, 8> indices;
  for (Attribute indice : llvm::drop_begin(operands, 1)) {
    if (!indice || !indice.isa<IntegerAttr>())
      return {};
    indices.push_back(indice.cast<IntegerAttr>().getInt());
  }

  // If this is an elements attribute, query the value at the given indices.
  auto elementsAttr = tensor.dyn_cast<ElementsAttr>();
  if (elementsAttr && elementsAttr.isValidIndex(indices))
    return elementsAttr.getValue(indices);
  return {};
}

//===----------------------------------------------------------------------===//
// FromElementsOp
//===----------------------------------------------------------------------===//

void FromElementsOp::build(OpBuilder &builder, OperationState &result,
                           Type elementType, ValueRange elements) {
  Type resultTy = RankedTensorType::get({static_cast<int64_t>(elements.size())},
                                        elementType);
  result.addOperands(elements);
  result.addTypes(resultTy);
}

void FromElementsOp::build(OpBuilder &builder, OperationState &result,
                           ValueRange elements) {
  assert(!elements.empty() && "expected at least one element");
  build(builder, result, elements.front().getType(), elements);
}

OpFoldResult FromElementsOp::fold(ArrayRef<Attribute> operands) {
  if (!llvm::is_contained(operands, nullptr))
    return DenseElementsAttr::get(getType(), operands);
  return {};
}

namespace {

// Canonicalizes the pattern of the form
//
// %tensor = tensor.from_elements(%element) : (i32) -> tensor<1xi32>
// %extracted_element = tensor.extract %tensor[%c0] : tensor<1xi32>
//
// to just %element.
struct ExtractElementFromTensorFromElements
    : public OpRewritePattern<tensor::ExtractOp> {
  using OpRewritePattern<tensor::ExtractOp>::OpRewritePattern;

  LogicalResult matchAndRewrite(tensor::ExtractOp extract,
                                PatternRewriter &rewriter) const final {
    if (extract.indices().size() != 1)
      return failure();

    auto tensorFromElements = extract.tensor().getDefiningOp<FromElementsOp>();
    if (tensorFromElements == nullptr)
      return failure();

    APInt index;
    if (!matchPattern(*extract.indices().begin(), m_ConstantInt(&index)))
      return failure();
    // Prevent out of bounds accesses. This can happen in invalid code that will
    // never execute.
    if (tensorFromElements->getNumOperands() <= index.getZExtValue() ||
        index.getSExtValue() < 0)
      return failure();
    rewriter.replaceOp(extract,
                       tensorFromElements.getOperand(index.getZExtValue()));
    return success();
  }
};

} // namespace

void FromElementsOp::getCanonicalizationPatterns(RewritePatternSet &results,
                                                 MLIRContext *context) {
  results.add<ExtractElementFromTensorFromElements>(context);
}

//===----------------------------------------------------------------------===//
// InsertOp
//===----------------------------------------------------------------------===//

static LogicalResult verify(InsertOp op) {
  // Verify the # indices match if we have a ranked type.
  if (auto destType = op.dest().getType().dyn_cast<RankedTensorType>())
    if (destType.getRank() != static_cast<int64_t>(op.indices().size()))
      return op.emitOpError("incorrect number of indices");
  return success();
}

OpFoldResult InsertOp::fold(ArrayRef<Attribute> operands) {
  Attribute scalar = operands[0];
  Attribute dest = operands[1];
  if (scalar && dest)
    if (auto splatDest = dest.dyn_cast<SplatElementsAttr>())
      if (scalar == splatDest.getSplatValue())
        return dest;
  return {};
}

//===----------------------------------------------------------------------===//
// GenerateOp
//===----------------------------------------------------------------------===//

static LogicalResult verify(GenerateOp op) {
  // Ensure that the tensor type has as many dynamic dimensions as are specified
  // by the operands.
  RankedTensorType resultTy = op.getType().cast<RankedTensorType>();
  if (op.getNumOperands() != resultTy.getNumDynamicDims())
    return op.emitError("must have as many index operands as dynamic extents "
                        "in the result type");

  // Ensure that region arguments span the index space.
  if (!llvm::all_of(op.body().getArgumentTypes(),
                    [](Type ty) { return ty.isIndex(); }))
    return op.emitError("all body arguments must be index");
  if (op.body().getNumArguments() != resultTy.getRank())
    return op.emitError("must have one body argument per input dimension");

  // Ensure that the region yields an element of the right type.
  auto yieldOp =
      llvm::cast<YieldOp>(op.body().getBlocks().front().getTerminator());
  if (yieldOp.value().getType() != resultTy.getElementType())
    return op.emitOpError(
        "body must be terminated with a `yield` operation of the tensor "
        "element type");

  return success();
}

void GenerateOp::build(
    OpBuilder &b, OperationState &result, Type resultTy,
    ValueRange dynamicExtents,
    function_ref<void(OpBuilder &, Location, ValueRange)> bodyBuilder) {
  build(b, result, resultTy, dynamicExtents);

  // Build and populate body.
  OpBuilder::InsertionGuard guard(b);
  Region *bodyRegion = result.regions.front().get();
  auto rank = resultTy.cast<RankedTensorType>().getRank();
  SmallVector<Type, 2> argumentTypes(rank, b.getIndexType());
  Block *bodyBlock =
      b.createBlock(bodyRegion, bodyRegion->end(), argumentTypes);
  bodyBuilder(b, result.location, bodyBlock->getArguments());
}

namespace {

/// Canonicalizes tensor.generate operations with a constant
/// operand into the equivalent operation with the operand expressed in the
/// result type, instead. We also insert a type cast to make sure that the
/// resulting IR is still well-typed.
struct StaticTensorGenerate : public OpRewritePattern<GenerateOp> {
  using OpRewritePattern<GenerateOp>::OpRewritePattern;

  LogicalResult matchAndRewrite(GenerateOp tensorFromElements,
                                PatternRewriter &rewriter) const final {
    auto resultType =
        tensorFromElements.getResult().getType().cast<RankedTensorType>();

    if (resultType.hasStaticShape())
      return failure();

    SmallVector<Value, 4> newOperands;
    SmallVector<int64_t, 4> newShape;
    auto operandsIt = tensorFromElements.dynamicExtents().begin();

    for (int64_t dim : resultType.getShape()) {
      if (dim != RankedTensorType::kDynamicSize) {
        newShape.push_back(dim);
        continue;
      }
      APInt index;
      if (!matchPattern(*operandsIt, m_ConstantInt(&index))) {
        newShape.push_back(RankedTensorType::kDynamicSize);
        newOperands.push_back(*operandsIt++);
        continue;
      }
      newShape.push_back(index.getSExtValue());
      operandsIt++;
    }

    if (newOperands.size() == tensorFromElements.dynamicExtents().size())
      return failure();

    auto loc = tensorFromElements.getLoc();
    auto newOp = rewriter.create<GenerateOp>(
        loc, RankedTensorType::get(newShape, resultType.getElementType()),
        newOperands);
    rewriter.inlineRegionBefore(tensorFromElements.body(), newOp.body(),
                                newOp.body().begin());
    rewriter.replaceOpWithNewOp<tensor::CastOp>(tensorFromElements, resultType,
                                                newOp);
    return success();
  }
};

/// Canonicalizes the pattern of the form
///
/// %tensor = tensor.generate %x {
///   ^bb0(%arg0: index):  // no predecessors
///   <computation>
///   yield %1 : index
/// } : tensor<?xindex>
/// %extracted_element = tensor.extract %tensor[%c0] : tensor<?xi32>
///
/// to just <computation> with %arg0 replaced by %c0. We only do this if the
/// tensor.generate operation has no side-effects.
struct ExtractFromTensorGenerate : public OpRewritePattern<tensor::ExtractOp> {
  using OpRewritePattern<tensor::ExtractOp>::OpRewritePattern;

  LogicalResult matchAndRewrite(tensor::ExtractOp extract,
                                PatternRewriter &rewriter) const final {
    auto tensorFromElements = extract.tensor().getDefiningOp<GenerateOp>();
    if (!tensorFromElements || !wouldOpBeTriviallyDead(tensorFromElements))
      return failure();

    BlockAndValueMapping mapping;
    Block *body = tensorFromElements.getBody();
    mapping.map(body->getArguments(), extract.indices());
    for (auto &op : body->without_terminator())
      rewriter.clone(op, mapping);

    auto yield = cast<YieldOp>(body->getTerminator());

    rewriter.replaceOp(extract, mapping.lookupOrDefault(yield.value()));
    return success();
  }
};

/// Canonicalizes the pattern of the form
///
/// %val = tensor.cast %source : : tensor<?xi32> to tensor<2xi32>
/// %extracted_element = tensor.extract %val[%c0] : tensor<2xi32>
///
/// to
///
/// %extracted_element = tensor.extract %source[%c0] : tensor<?xi32>
struct ExtractFromTensorCast : public OpRewritePattern<tensor::ExtractOp> {
  using OpRewritePattern<tensor::ExtractOp>::OpRewritePattern;

  LogicalResult matchAndRewrite(tensor::ExtractOp extract,
                                PatternRewriter &rewriter) const final {
    auto tensorCast = extract.tensor().getDefiningOp<tensor::CastOp>();
    if (!tensorCast)
      return failure();

    rewriter.replaceOpWithNewOp<tensor::ExtractOp>(extract, tensorCast.source(),
                                                   extract.indices());
    return success();
  }
};

} // namespace

void GenerateOp::getCanonicalizationPatterns(RewritePatternSet &results,
                                             MLIRContext *context) {
  // TODO: Move extract patterns to tensor::ExtractOp.
  results.add<ExtractFromTensorGenerate, ExtractFromTensorCast,
              StaticTensorGenerate>(context);
}

//===----------------------------------------------------------------------===//
// ReshapeOp
//===----------------------------------------------------------------------===//

static int64_t GetNumElements(ShapedType type) {
  int64_t numElements = 1;
  for (auto dim : type.getShape())
    numElements *= dim;
  return numElements;
}

static LogicalResult verify(ReshapeOp op) {
  TensorType operandType = op.source().getType().cast<TensorType>();
  TensorType resultType = op.result().getType().cast<TensorType>();

  if (operandType.getElementType() != resultType.getElementType())
    return op.emitOpError("element types of source and destination tensor "
                          "types should be the same");

  int64_t shapeSize =
      op.shape().getType().cast<RankedTensorType>().getDimSize(0);
  auto resultRankedType = resultType.dyn_cast<RankedTensorType>();
  auto operandRankedType = operandType.dyn_cast<RankedTensorType>();

  if (resultRankedType) {
    if (operandRankedType && resultRankedType.hasStaticShape() &&
        operandRankedType.hasStaticShape()) {
      if (GetNumElements(operandRankedType) != GetNumElements(resultRankedType))
        return op.emitOpError("source and destination tensor should have the "
                              "same number of elements");
    }
    if (shapeSize == TensorType::kDynamicSize)
      return op.emitOpError("cannot use shape operand with dynamic length to "
                            "reshape to statically-ranked tensor type");
    if (shapeSize != resultRankedType.getRank())
      return op.emitOpError(
          "length of shape operand differs from the result's tensor rank");
  }
  return success();
}

//===----------------------------------------------------------------------===//
// ExtractSliceOp
//===----------------------------------------------------------------------===//

/// An extract_slice op result type can be fully inferred from the source type
/// and the static representation of offsets, sizes and strides. Special
/// sentinels encode the dynamic case.
Type ExtractSliceOp::inferResultType(RankedTensorType sourceRankedTensorType,
                                     ArrayRef<int64_t> leadingStaticOffsets,
                                     ArrayRef<int64_t> leadingStaticSizes,
                                     ArrayRef<int64_t> leadingStaticStrides) {
  // An extract_slice op may specify only a leading subset of offset/sizes/
  // strides in which case we complete with offset=0, sizes from memref type and
  // strides=1.
  unsigned rank = sourceRankedTensorType.getRank();
  assert(leadingStaticSizes.size() <= rank &&
         "unexpected leadingStaticSizes overflow");
  auto staticSizes = llvm::to_vector<4>(leadingStaticSizes);
  unsigned numTrailingSizes = rank - staticSizes.size();
  llvm::append_range(staticSizes, sourceRankedTensorType.getShape().take_back(
                                      numTrailingSizes));
  return RankedTensorType::get(staticSizes,
                               sourceRankedTensorType.getElementType());
}

Type ExtractSliceOp::inferResultType(
    RankedTensorType sourceRankedTensorType,
    ArrayRef<OpFoldResult> leadingStaticOffsets,
    ArrayRef<OpFoldResult> leadingStaticSizes,
    ArrayRef<OpFoldResult> leadingStaticStrides) {
  SmallVector<int64_t> staticOffsets, staticSizes, staticStrides;
  SmallVector<Value> dynamicOffsets, dynamicSizes, dynamicStrides;
  dispatchIndexOpFoldResults(leadingStaticOffsets, dynamicOffsets,
                             staticOffsets, ShapedType::kDynamicStrideOrOffset);
  dispatchIndexOpFoldResults(leadingStaticSizes, dynamicSizes, staticSizes,
                             ShapedType::kDynamicSize);
  dispatchIndexOpFoldResults(leadingStaticStrides, dynamicStrides,
                             staticStrides, ShapedType::kDynamicStrideOrOffset);
  return ExtractSliceOp::inferResultType(sourceRankedTensorType, staticOffsets,
                                         staticSizes, staticStrides);
}

/// An extract_slice op result type can be fully inferred from the source type
/// and the static representation of offsets, sizes and strides. Special
/// sentinels encode the dynamic case.
Type ExtractSliceOp::inferRankReducedResultType(
    unsigned resultRank, RankedTensorType sourceRankedTensorType,
    ArrayRef<int64_t> leadingStaticOffsets,
    ArrayRef<int64_t> leadingStaticSizes,
    ArrayRef<int64_t> leadingStaticStrides) {
  auto inferredType =
      inferResultType(sourceRankedTensorType, leadingStaticOffsets,
                      leadingStaticSizes, leadingStaticStrides)
          .cast<RankedTensorType>();
  int rankDiff = inferredType.getRank() - resultRank;
  if (rankDiff > 0) {
    auto shape = inferredType.getShape();
    llvm::SmallDenseSet<unsigned> dimsToProject;
    mlir::getPositionsOfShapeOne(rankDiff, shape, dimsToProject);
    SmallVector<int64_t> projectedShape;
    for (unsigned pos = 0, e = shape.size(); pos < e; ++pos)
      if (!dimsToProject.contains(pos))
        projectedShape.push_back(shape[pos]);
    inferredType =
        RankedTensorType::get(projectedShape, inferredType.getElementType());
  }
  return inferredType;
}

Type ExtractSliceOp::inferRankReducedResultType(
    unsigned resultRank, RankedTensorType sourceRankedTensorType,
    ArrayRef<OpFoldResult> leadingStaticOffsets,
    ArrayRef<OpFoldResult> leadingStaticSizes,
    ArrayRef<OpFoldResult> leadingStaticStrides) {
  SmallVector<int64_t> staticOffsets, staticSizes, staticStrides;
  SmallVector<Value> dynamicOffsets, dynamicSizes, dynamicStrides;
  dispatchIndexOpFoldResults(leadingStaticOffsets, dynamicOffsets,
                             staticOffsets, ShapedType::kDynamicStrideOrOffset);
  dispatchIndexOpFoldResults(leadingStaticSizes, dynamicSizes, staticSizes,
                             ShapedType::kDynamicSize);
  dispatchIndexOpFoldResults(leadingStaticStrides, dynamicStrides,
                             staticStrides, ShapedType::kDynamicStrideOrOffset);
  return ExtractSliceOp::inferRankReducedResultType(
      resultRank, sourceRankedTensorType, staticOffsets, staticSizes,
      staticStrides);
}

/// Build an ExtractSliceOp with mixed static and dynamic entries and custom
/// result type. If the type passed is nullptr, it is inferred.
void ExtractSliceOp::build(OpBuilder &b, OperationState &result,
                           RankedTensorType resultType, Value source,
                           ArrayRef<OpFoldResult> offsets,
                           ArrayRef<OpFoldResult> sizes,
                           ArrayRef<OpFoldResult> strides,
                           ArrayRef<NamedAttribute> attrs) {
  SmallVector<int64_t> staticOffsets, staticSizes, staticStrides;
  SmallVector<Value> dynamicOffsets, dynamicSizes, dynamicStrides;
  dispatchIndexOpFoldResults(offsets, dynamicOffsets, staticOffsets,
                             ShapedType::kDynamicStrideOrOffset);
  dispatchIndexOpFoldResults(sizes, dynamicSizes, staticSizes,
                             ShapedType::kDynamicSize);
  dispatchIndexOpFoldResults(strides, dynamicStrides, staticStrides,
                             ShapedType::kDynamicStrideOrOffset);
  auto sourceRankedTensorType = source.getType().cast<RankedTensorType>();
  // Structuring implementation this way avoids duplication between builders.
  if (!resultType) {
    resultType =
        ExtractSliceOp::inferResultType(sourceRankedTensorType, staticOffsets,
                                        staticSizes, staticStrides)
            .cast<RankedTensorType>();
  }
  build(b, result, resultType, source, dynamicOffsets, dynamicSizes,
        dynamicStrides, b.getI64ArrayAttr(staticOffsets),
        b.getI64ArrayAttr(staticSizes), b.getI64ArrayAttr(staticStrides));
  result.addAttributes(attrs);
}

/// Build an ExtractSliceOp with mixed static and dynamic entries and inferred
/// result type.
void ExtractSliceOp::build(OpBuilder &b, OperationState &result, Value source,
                           ArrayRef<OpFoldResult> offsets,
                           ArrayRef<OpFoldResult> sizes,
                           ArrayRef<OpFoldResult> strides,
                           ArrayRef<NamedAttribute> attrs) {
  build(b, result, RankedTensorType(), source, offsets, sizes, strides, attrs);
}

/// Build an ExtractSliceOp with dynamic entries and custom result type. If the
/// type passed is nullptr, it is inferred.
void ExtractSliceOp::build(OpBuilder &b, OperationState &result,
                           RankedTensorType resultType, Value source,
                           ValueRange offsets, ValueRange sizes,
                           ValueRange strides, ArrayRef<NamedAttribute> attrs) {
  SmallVector<OpFoldResult> offsetValues = llvm::to_vector<4>(
      llvm::map_range(offsets, [](Value v) -> OpFoldResult { return v; }));
  SmallVector<OpFoldResult> sizeValues = llvm::to_vector<4>(
      llvm::map_range(sizes, [](Value v) -> OpFoldResult { return v; }));
  SmallVector<OpFoldResult> strideValues = llvm::to_vector<4>(
      llvm::map_range(strides, [](Value v) -> OpFoldResult { return v; }));
  build(b, result, resultType, source, offsetValues, sizeValues, strideValues);
}

/// Build an ExtractSliceOp with dynamic entries and inferred result type.
void ExtractSliceOp::build(OpBuilder &b, OperationState &result, Value source,
                           ValueRange offsets, ValueRange sizes,
                           ValueRange strides, ArrayRef<NamedAttribute> attrs) {
  build(b, result, RankedTensorType(), source, offsets, sizes, strides, attrs);
}

enum SliceVerificationResult {
  Success,
  RankTooLarge,
  SizeMismatch,
  ElemTypeMismatch,
};

/// Checks if `original` Type type can be rank reduced to `reduced` type.
/// This function is slight variant of `is subsequence` algorithm where
/// not matching dimension must be 1.
static SliceVerificationResult
isRankReducedType(Type originalType, Type candidateReducedType,
                  std::string *errMsg = nullptr) {
  if (originalType == candidateReducedType)
    return SliceVerificationResult::Success;
  if (!originalType.isa<RankedTensorType>())
    return SliceVerificationResult::Success;
  if (originalType.isa<RankedTensorType>() &&
      !candidateReducedType.isa<RankedTensorType>())
    return SliceVerificationResult::Success;

  ShapedType originalShapedType = originalType.cast<ShapedType>();
  ShapedType candidateReducedShapedType =
      candidateReducedType.cast<ShapedType>();

  // Rank and size logic is valid for all ShapedTypes.
  ArrayRef<int64_t> originalShape = originalShapedType.getShape();
  ArrayRef<int64_t> candidateReducedShape =
      candidateReducedShapedType.getShape();
  unsigned originalRank = originalShape.size(),
           candidateReducedRank = candidateReducedShape.size();
  if (candidateReducedRank > originalRank)
    return SliceVerificationResult::RankTooLarge;

  auto optionalUnusedDimsMask =
      computeRankReductionMask(originalShape, candidateReducedShape);

  // Sizes cannot be matched in case empty vector is returned.
  if (!optionalUnusedDimsMask.hasValue())
    return SliceVerificationResult::SizeMismatch;

  if (originalShapedType.getElementType() !=
      candidateReducedShapedType.getElementType())
    return SliceVerificationResult::ElemTypeMismatch;

  // We are done for the tensor case.
  if (originalType.isa<RankedTensorType>())
    return SliceVerificationResult::Success;

  return SliceVerificationResult::Success;
}

template <typename OpTy>
static LogicalResult produceSliceErrorMsg(SliceVerificationResult result,
                                          OpTy op, Type expectedType,
                                          StringRef errMsg = "") {
  auto memrefType = expectedType.cast<ShapedType>();
  switch (result) {
  case SliceVerificationResult::Success:
    return success();
  case SliceVerificationResult::RankTooLarge:
    return op.emitError("expected result rank to be smaller or equal to ")
           << "the source rank. " << errMsg;
  case SliceVerificationResult::SizeMismatch:
    return op.emitError("expected result type to be ")
           << expectedType
           << " or a rank-reduced version. (mismatch of result sizes) "
           << errMsg;
  case SliceVerificationResult::ElemTypeMismatch:
    return op.emitError("expected result element type to be ")
           << memrefType.getElementType() << errMsg;
  }
  llvm_unreachable("unexpected extract_slice op verification result");
}

/// Verifier for ExtractSliceOp.
static LogicalResult verify(ExtractSliceOp op) {
  // Verify result type against inferred type.
  auto expectedType = ExtractSliceOp::inferResultType(
      op.getSourceType(), extractFromI64ArrayAttr(op.static_offsets()),
      extractFromI64ArrayAttr(op.static_sizes()),
      extractFromI64ArrayAttr(op.static_strides()));
  auto result = isRankReducedType(expectedType, op.getType());
  return produceSliceErrorMsg(result, op, expectedType);
}

/// Infer the canonical type of the result of an extract_slice op. Returns a
/// type with rank `resultRank` that is either the rank of the rank-reduced
/// type, or the non-rank-reduced type.
static RankedTensorType
getCanonicalSliceResultType(unsigned resultRank, RankedTensorType sourceType,
                            ArrayRef<OpFoldResult> mixedOffsets,
                            ArrayRef<OpFoldResult> mixedSizes,
                            ArrayRef<OpFoldResult> mixedStrides) {
  auto resultType =
      ExtractSliceOp::inferRankReducedResultType(
          resultRank, sourceType, mixedOffsets, mixedSizes, mixedStrides)
          .cast<RankedTensorType>();
  if (resultType.getRank() != resultRank) {
    resultType = ExtractSliceOp::inferResultType(sourceType, mixedOffsets,
                                                 mixedSizes, mixedStrides)
                     .cast<RankedTensorType>();
  }
  return resultType;
}

namespace {
/// Pattern to rewrite an extract_slice op with tensor::Cast arguments.
/// This essentially pushes memref_cast past its consuming slice when
/// `canFoldIntoConsumerOp` is true.
///
/// Example:
/// ```
///   %0 = tensor.cast %V : tensor<16x16xf32> to tensor<?x?xf32>
///   %1 = tensor.extract_slice %0[0, 0][3, 4][1, 1] : tensor<?x?xf32> to
///   tensor<3x4xf32>
/// ```
/// is rewritten into:
/// ```
///   %0 = tensor.extract_slice %V[0, 0][3, 4][1, 1] : tensor<16x16xf32> to
///   tensor<3x4xf32> %1 = tensor.cast %0: tensor<3x4xf32> to tensor<3x4xf32>
/// ```
class ExtractSliceOpCastFolder final : public OpRewritePattern<ExtractSliceOp> {
public:
  using OpRewritePattern<ExtractSliceOp>::OpRewritePattern;

  LogicalResult matchAndRewrite(ExtractSliceOp sliceOp,
                                PatternRewriter &rewriter) const override {
    // Any constant operand, just return to let SubViewOpConstantFolder kick in.
    if (llvm::any_of(sliceOp.getOperands(), [](Value operand) {
          return matchPattern(operand, matchConstantIndex());
        }))
      return failure();

    auto castOp = sliceOp.source().getDefiningOp<tensor::CastOp>();
    if (!castOp)
      return failure();

    if (!canFoldIntoConsumerOp(castOp))
      return failure();

    /// Deduce the type of the result to use for the canonicalized operation.
    RankedTensorType resultType = getCanonicalSliceResultType(
        sliceOp.getType().getRank(), sliceOp.getSourceType(),
        sliceOp.getMixedOffsets(), sliceOp.getMixedSizes(),
        sliceOp.getMixedStrides());
    Value newSlice = rewriter.create<ExtractSliceOp>(
        sliceOp.getLoc(), resultType, castOp.source(), sliceOp.offsets(),
        sliceOp.sizes(), sliceOp.strides(), sliceOp.static_offsets(),
        sliceOp.static_sizes(), sliceOp.static_strides());
    rewriter.replaceOpWithNewOp<tensor::CastOp>(sliceOp, sliceOp.getType(),
                                                newSlice);
    return success();
  }
};
} // namespace

/// Return the canonical type of the result of an extract_slice op.
struct SliceReturnTypeCanonicalizer {
  RankedTensorType operator()(ExtractSliceOp op,
                              ArrayRef<OpFoldResult> mixedOffsets,
                              ArrayRef<OpFoldResult> mixedSizes,
                              ArrayRef<OpFoldResult> mixedStrides) {
    return getCanonicalSliceResultType(op.getType().getRank(),
                                       op.getSourceType(), mixedOffsets,
                                       mixedSizes, mixedStrides);
  }
};

/// A canonicalizer wrapper to replace ExtractSliceOps.
struct SliceCanonicalizer {
  void operator()(PatternRewriter &rewriter, ExtractSliceOp op,
                  ExtractSliceOp newOp) {
    Value replacement = newOp.getResult();
    if (replacement.getType() != op.getType())
      replacement = rewriter.create<tensor::CastOp>(op.getLoc(), op.getType(),
                                                    replacement);
    rewriter.replaceOp(op, replacement);
  }
};

void ExtractSliceOp::getCanonicalizationPatterns(RewritePatternSet &results,
                                                 MLIRContext *context) {
  results.add<
      OpWithOffsetSizesAndStridesConstantArgumentFolder<
          ExtractSliceOp, SliceReturnTypeCanonicalizer, SliceCanonicalizer>,
      ExtractSliceOpCastFolder>(context);
}

//
static LogicalResult
foldIdentityOffsetSizeAndStrideOpInterface(OffsetSizeAndStrideOpInterface op,
                                           ShapedType shapedType) {
  OpBuilder b(op.getContext());
  for (OpFoldResult ofr : op.getMixedOffsets())
    if (getConstantIntValue(ofr) != static_cast<int64_t>(0))
      return failure();
  // Rank-reducing noops only need to inspect the leading dimensions: llvm::zip
  // is appropriate.
  auto shape = shapedType.getShape();
  for (auto it : llvm::zip(op.getMixedSizes(), shape))
    if (getConstantIntValue(std::get<0>(it)) != std::get<1>(it))
      return failure();
  for (OpFoldResult ofr : op.getMixedStrides())
    if (getConstantIntValue(ofr) != static_cast<int64_t>(1))
      return failure();
  return success();
}

OpFoldResult ExtractSliceOp::fold(ArrayRef<Attribute>) {
  if (getSourceType() == getType() &&
      succeeded(foldIdentityOffsetSizeAndStrideOpInterface(*this, getType())))
    return this->source();
  return OpFoldResult();
}

//===----------------------------------------------------------------------===//
// InsertSliceOp
//===----------------------------------------------------------------------===//

// Build a InsertSliceOp with mixed static and dynamic entries.
void InsertSliceOp::build(OpBuilder &b, OperationState &result, Value source,
                          Value dest, ArrayRef<OpFoldResult> offsets,
                          ArrayRef<OpFoldResult> sizes,
                          ArrayRef<OpFoldResult> strides,
                          ArrayRef<NamedAttribute> attrs) {
  SmallVector<int64_t> staticOffsets, staticSizes, staticStrides;
  SmallVector<Value> dynamicOffsets, dynamicSizes, dynamicStrides;
  dispatchIndexOpFoldResults(offsets, dynamicOffsets, staticOffsets,
                             ShapedType::kDynamicStrideOrOffset);
  dispatchIndexOpFoldResults(sizes, dynamicSizes, staticSizes,
                             ShapedType::kDynamicSize);
  dispatchIndexOpFoldResults(strides, dynamicStrides, staticStrides,
                             ShapedType::kDynamicStrideOrOffset);
  build(b, result, dest.getType(), source, dest, dynamicOffsets, dynamicSizes,
        dynamicStrides, b.getI64ArrayAttr(staticOffsets),
        b.getI64ArrayAttr(staticSizes), b.getI64ArrayAttr(staticStrides));
  result.addAttributes(attrs);
}

// Build a InsertSliceOp with dynamic entries.
void InsertSliceOp::build(OpBuilder &b, OperationState &result, Value source,
                          Value dest, ValueRange offsets, ValueRange sizes,
                          ValueRange strides, ArrayRef<NamedAttribute> attrs) {
  SmallVector<OpFoldResult> offsetValues = llvm::to_vector<4>(
      llvm::map_range(offsets, [](Value v) -> OpFoldResult { return v; }));
  SmallVector<OpFoldResult> sizeValues = llvm::to_vector<4>(
      llvm::map_range(sizes, [](Value v) -> OpFoldResult { return v; }));
  SmallVector<OpFoldResult> strideValues = llvm::to_vector<4>(
      llvm::map_range(strides, [](Value v) -> OpFoldResult { return v; }));
  build(b, result, source, dest, offsetValues, sizeValues, strideValues);
}

OpFoldResult InsertSliceOp::fold(ArrayRef<Attribute>) {
  if (getSourceType().hasStaticShape() && getType().hasStaticShape() &&
      getSourceType() == getType() &&
      succeeded(foldIdentityOffsetSizeAndStrideOpInterface(*this, getType())))
    return this->source();
  return OpFoldResult();
}

namespace {
/// Pattern to rewrite a insert_slice op with constant arguments.
class InsertSliceOpConstantArgumentFolder final
    : public OpRewritePattern<InsertSliceOp> {
public:
  using OpRewritePattern<InsertSliceOp>::OpRewritePattern;

  LogicalResult matchAndRewrite(InsertSliceOp insertSliceOp,
                                PatternRewriter &rewriter) const override {
    // No constant operand, just return.
    if (llvm::none_of(insertSliceOp.getOperands(), [](Value operand) {
          return matchPattern(operand, matchConstantIndex());
        }))
      return failure();

    // At least one of offsets/sizes/strides is a new constant.
    // Form the new list of operands and constant attributes from the
    // existing.
    SmallVector<OpFoldResult> mixedOffsets(insertSliceOp.getMixedOffsets());
    SmallVector<OpFoldResult> mixedSizes(insertSliceOp.getMixedSizes());
    SmallVector<OpFoldResult> mixedStrides(insertSliceOp.getMixedStrides());
    canonicalizeSubViewPart(mixedOffsets, ShapedType::isDynamicStrideOrOffset);
    canonicalizeSubViewPart(mixedSizes, ShapedType::isDynamic);
    canonicalizeSubViewPart(mixedStrides, ShapedType::isDynamicStrideOrOffset);

    // Create the new op in canonical form.
    rewriter.replaceOpWithNewOp<InsertSliceOp>(
        insertSliceOp, insertSliceOp.source(), insertSliceOp.dest(),
        mixedOffsets, mixedSizes, mixedStrides);
    return success();
  }
};

/// Fold tensor_casts with insert_slice operations.
struct InsertSliceOpCastFolder final : public OpRewritePattern<InsertSliceOp> {
  using OpRewritePattern<InsertSliceOp>::OpRewritePattern;

  LogicalResult matchAndRewrite(InsertSliceOp insertSliceOp,
                                PatternRewriter &rewriter) const override {
    if (llvm::any_of(insertSliceOp.getOperands(), [](Value operand) {
          return matchPattern(operand, matchConstantIndex());
        }))
      return failure();

    auto getSourceOfCastOp = [](Value v) -> Optional<Value> {
      auto castOp = v.getDefiningOp<tensor::CastOp>();
      if (!castOp || !canFoldIntoConsumerOp(castOp))
        return llvm::None;
      return castOp.source();
    };
    Optional<Value> sourceCastSource =
        getSourceOfCastOp(insertSliceOp.source());
    Optional<Value> destCastSource = getSourceOfCastOp(insertSliceOp.dest());
    if (!sourceCastSource && !destCastSource)
      return failure();

    Value replacement = rewriter.create<InsertSliceOp>(
        insertSliceOp.getLoc(),
        (sourceCastSource ? *sourceCastSource : insertSliceOp.source()),
        (destCastSource ? *destCastSource : insertSliceOp.dest()),
        insertSliceOp.getMixedOffsets(), insertSliceOp.getMixedSizes(),
        insertSliceOp.getMixedStrides());

    if (replacement.getType() != insertSliceOp.getType()) {
      replacement = rewriter.create<tensor::CastOp>(
          insertSliceOp.getLoc(), insertSliceOp.getType(), replacement);
    }
    rewriter.replaceOp(insertSliceOp, replacement);
    return success();
  }
};
} // namespace

void InsertSliceOp::getCanonicalizationPatterns(RewritePatternSet &results,
                                                MLIRContext *context) {
  results.add<InsertSliceOpConstantArgumentFolder, InsertSliceOpCastFolder>(
      context);
}

//===----------------------------------------------------------------------===//
// TableGen'd op method definitions
//===----------------------------------------------------------------------===//

#define GET_OP_CLASSES
#include "mlir/Dialect/Tensor/IR/TensorOps.cpp.inc"