bolt/deps/llvm-18.1.8/mlir/lib/Dialect/Tensor/Utils/Utils.cpp

//===- Utils.cpp - Utilities to support the Tensor dialect ----------------===//
//
// 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
//
//===----------------------------------------------------------------------===//
//
// This file implements utilities for the Tensor dialect.
//
//===----------------------------------------------------------------------===//

#include "mlir/Dialect/Tensor/Utils/Utils.h"

#include "mlir/Dialect/Affine/IR/AffineOps.h"
#include "mlir/Dialect/Arith/IR/Arith.h"
#include "mlir/Dialect/Arith/Utils/Utils.h"
#include "mlir/Dialect/Utils/IndexingUtils.h"
#include "mlir/Interfaces/ValueBoundsOpInterface.h"

using namespace mlir;
using namespace mlir::tensor;

PadOp mlir::tensor::createPadHighOp(RankedTensorType type, Value source,
                                    Value pad, bool nofold, Location loc,
                                    OpBuilder &b) {
  SmallVector<OpFoldResult> low(type.getRank(), b.getIndexAttr(0));
  SmallVector<OpFoldResult> high(type.getRank(), b.getIndexAttr(0));
  for (const auto &en : enumerate(type.getShape())) {
    // Pad only the static dimensions of the result tensor type.
    if (ShapedType::isDynamic(en.value()))
      continue;
    // Compute the padding width.
    AffineExpr d0;
    bindDims(b.getContext(), d0);
    OpFoldResult sz = tensor::getMixedSize(b, loc, source, en.index());
    high[en.index()] =
        affine::makeComposedFoldedAffineApply(b, loc, en.value() - d0, {sz});
  }
  return b.create<PadOp>(loc, type, source, low, high, pad, nofold);
}

SmallVector<Value> mlir::tensor::createDynamicDimValues(OpBuilder &b,
                                                        Location loc,
                                                        Value rankedTensor) {
  auto tensorTy = cast<RankedTensorType>(rankedTensor.getType());
  SmallVector<Value> dynamicDims;
  for (const auto &en : llvm::enumerate(tensorTy.getShape())) {
    if (en.value() == ShapedType::kDynamic)
      dynamicDims.push_back(
          b.create<tensor::DimOp>(loc, rankedTensor, en.index()));
  }
  return dynamicDims;
}

FailureOr<RankedTensorType>
mlir::tensor::computeTransposedType(RankedTensorType rankedTensorType,
                                    ArrayRef<int64_t> transposeVector) {
  if (transposeVector.empty())
    return rankedTensorType;

  if (!isPermutationVector(transposeVector) ||
      transposeVector.size() != static_cast<size_t>(rankedTensorType.getRank()))
    return failure();

  SmallVector<int64_t> transposedShape(rankedTensorType.getShape().begin(),
                                       rankedTensorType.getShape().end());
  applyPermutationToVector(transposedShape, transposeVector);

  using RTTBuilder = RankedTensorType::Builder;
  RankedTensorType transposedTensorType =
      RTTBuilder(rankedTensorType).setShape(transposedShape);
  return transposedTensorType;
}

bool mlir::tensor::isCastLikeInsertSliceOp(InsertSliceOp op) {
  llvm::SmallBitVector droppedDims = op.getDroppedDims();
  int64_t srcDim = 0;
  // Source dims and destination dims (apart from dropped dims) must have the
  // same size.
  for (int64_t resultDim = 0; resultDim < op.getDestType().getRank();
       ++resultDim) {
    if (droppedDims.test(resultDim)) {
      continue;
    }
    FailureOr<bool> equalDimSize = ValueBoundsConstraintSet::areEqual(
        op.getSource(), op.getResult(), srcDim, resultDim);
    if (failed(equalDimSize) || !*equalDimSize)
      return false;
    ++srcDim;
  }

  return true;
}

bool mlir::tensor::isCastLikeExtractSliceOp(ExtractSliceOp op) {
  llvm::SmallBitVector droppedDims = op.getDroppedDims();
  int64_t resultDim = 0;
  // Source dims and result dims (apart from dropped dims) must have the same
  // size.
  RankedTensorType sourceType = op.getSourceType();
  for (int64_t dim = 0, e = sourceType.getRank(); dim < e; ++dim) {
    if (droppedDims.test(dim)) {
      // ExtractSlice may drop unit dimensions that result from taking a size-1
      // slice from a non-size-1 source dimension.
      if (sourceType.getDimSize(dim) != 1)
        return false;
      continue;
    }
    FailureOr<bool> equalDimSize = ValueBoundsConstraintSet::areEqual(
        op.getSource(), op.getResult(), dim, resultDim);
    if (failed(equalDimSize) || !*equalDimSize)
      return false;
    ++resultDim;
  }

  return true;
}