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//===- 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;
}
/// The permutation can be obtained from two permutations:
/// a) Compute the permutation vector to move the last `numPackedDims` into
/// the `innerPosDims` of a shape of rank `rank`.
/// b) Compute the permutation vector to move outer dims if the
/// `outerPerm` parameter is not empty.
/// Apply (b) permutation on (a) permutation to get the final permutation.
static SmallVector<int64_t>
computePackUnPackPerm(int64_t rank, ArrayRef<int64_t> &innerDimsPos,
ArrayRef<int64_t> &outerPerm,
PackingMetadata &packingMetadata) {
int64_t numPackedDims = innerDimsPos.size();
auto lastDims =
llvm::to_vector(llvm::seq<int64_t>(rank - numPackedDims, rank));
packingMetadata = computePackingMetadata(rank, innerDimsPos);
SmallVector<int64_t> innerPositionsPerm =
computePermutationVector(rank, lastDims, packingMetadata.insertPositions);
SmallVector<int64_t> outerPos = packingMetadata.outerPositions;
if (!outerPerm.empty())
applyPermutationToVector(outerPos, outerPerm);
SmallVector<int64_t> outerPositionPerm =
computePermutationVector(rank, packingMetadata.outerPositions, outerPos);
SmallVector<int64_t> packInverseDestPermutation = innerPositionsPerm;
applyPermutationToVector(packInverseDestPermutation, outerPositionPerm);
return packInverseDestPermutation;
}
/// Shell function to compute the Destination Permutation of PackOp
/// This function uses the helper function `computePackUnPackPerm` to get
/// the permutation vector. Only major difference between UnPack and Pack is
/// that packOp uses destination rank whereas unpack Uses source rank.
SmallVector<int64_t> mlir::tensor::getPackInverseDestPerm(PackOp packOp) {
PackingMetadata pMetadata;
int64_t packedRank = packOp.getDestType().getRank();
ArrayRef<int64_t> innerDimPos = packOp.getInnerDimsPos();
ArrayRef<int64_t> outerPerm = packOp.getOuterDimsPerm();
SmallVector<int64_t> packInvDestPerm =
computePackUnPackPerm(packedRank, innerDimPos, outerPerm, pMetadata);
return packInvDestPerm;
}
/// Shell function to compute the Source Permutation of unPackOp.
/// This function, like the getPackInverseDestPerm uses the helper function
/// computePackUnPackPerm` to get the permutation vector.
/// Only major difference between UnPack and Pack is that packOp uses
/// destination rank whereas unpack Uses source rank.
SmallVector<int64_t> mlir::tensor::getUnPackInverseSrcPerm(UnPackOp unpackOp) {
PackingMetadata metadata;
return mlir::tensor::getUnPackInverseSrcPerm(unpackOp, metadata);
}
/// Shell function to compute the Source rank permutation for unpackOp
/// Unpack requires some packing metadata data information, so created
/// another function where this value is passed by reference.
SmallVector<int64_t>
mlir::tensor::getUnPackInverseSrcPerm(UnPackOp unpackOp,
PackingMetadata &metadata) {
int64_t unpackRank = unpackOp.getSourceType().getRank();
ArrayRef<int64_t> innerDimPos = unpackOp.getInnerDimsPos();
ArrayRef<int64_t> outerPerm = unpackOp.getOuterDimsPerm();
SmallVector<int64_t> unpackInvSrcPerm =
computePackUnPackPerm(unpackRank, innerDimPos, outerPerm, metadata);
return unpackInvSrcPerm;
}
bool mlir::tensor::isCastLikeInsertSliceOp(InsertSliceOp op) {
llvm::SmallBitVector droppedDims = op.getDroppedDims();
int64_t srcDim = 0;
RankedTensorType resultType = op.getDestType();
// Source dims and destination dims (apart from dropped dims) must have the
// same size.
for (int64_t resultDim = 0; resultDim < resultType.getRank(); ++resultDim) {
if (droppedDims.test(resultDim)) {
// InsertSlice may expand unit dimensions that result from inserting a
// size-1 slice into a non-size-1 result dimension.
if (resultType.getDimSize(resultDim) != 1)
return false;
continue;
}
FailureOr<bool> equalDimSize = ValueBoundsConstraintSet::areEqual(
{op.getSource(), srcDim}, {op.getResult(), 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(), dim}, {op.getResult(), resultDim});
if (failed(equalDimSize) || !*equalDimSize)
return false;
++resultDim;
}
return true;
}
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