clang-p2996/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/Tensor/IR/Tensor.h"
#include "mlir/IR/Builders.h"
#include "mlir/IR/PatternMatch.h"
#include "mlir/IR/TypeUtilities.h"
#include "llvm/ADT/STLExtras.h"

using namespace mlir;
using namespace mlir::tensor;

//===----------------------------------------------------------------------===//
// 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 `subtensor` 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;
}

bool CastOp::areCastCompatible(Type a, Type b) {
  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));
}

OpFoldResult CastOp::fold(ArrayRef<Attribute> operands) {
  return impl::foldCastOp(*this);
}

/// 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(OwningRewritePatternList &results,
                                         MLIRContext *context) {
  results.insert<ChainedTensorCast>(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 {};
}

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

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