| //===- ReshapeOpsUtils.cpp - Utilities used by structured ops -------------===// |
| // |
| // 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/Utils/ReshapeOpsUtils.h" |
| |
| #include "mlir/IR/AffineMap.h" |
| #include "mlir/IR/Builders.h" |
| |
| #include <numeric> |
| #include <optional> |
| |
| using namespace mlir; |
| |
| std::optional<SmallVector<ReassociationIndices>> |
| mlir::getReassociationIndicesForReshape(ShapedType sourceType, |
| ShapedType targetType) { |
| if (sourceType.getRank() > targetType.getRank()) |
| return getReassociationIndicesForCollapse(sourceType.getShape(), |
| targetType.getShape()); |
| if (sourceType.getRank() < targetType.getRank()) |
| return getReassociationIndicesForCollapse(targetType.getShape(), |
| sourceType.getShape()); |
| return std::nullopt; |
| } |
| |
| std::optional<SmallVector<ReassociationIndices>> |
| mlir::getReassociationIndicesForCollapse(ArrayRef<int64_t> sourceShape, |
| ArrayRef<int64_t> targetShape) { |
| if (sourceShape.size() <= targetShape.size()) |
| return std::nullopt; |
| unsigned sourceDim = 0; |
| SmallVector<ReassociationIndices> reassociationMap; |
| reassociationMap.reserve(targetShape.size()); |
| |
| ReassociationIndices currIndices; |
| int64_t prodOfCollapsedDims = 1; |
| while (sourceDim < sourceShape.size()) { |
| unsigned targetDim = reassociationMap.size(); |
| // If we have mapped all the target dimensions stop and handle the remaining |
| // tail of size-1 dimensions explicitly. |
| if (targetDim == targetShape.size()) |
| break; |
| |
| int64_t currTargetShape = targetShape[targetDim]; |
| while (sourceDim < (sourceShape.size() - 1) && |
| sourceShape[sourceDim] != ShapedType::kDynamic && |
| prodOfCollapsedDims * sourceShape[sourceDim] < currTargetShape) { |
| prodOfCollapsedDims *= sourceShape[sourceDim]; |
| currIndices.push_back(sourceDim++); |
| } |
| |
| // If the current expanded dimension is dynamic, then the collapsed |
| // dimensions should also be dynamic and product of all previous unprocessed |
| // dimensions of the expanded shape should be 1. |
| if (sourceShape[sourceDim] == ShapedType::kDynamic && |
| (currTargetShape != ShapedType::kDynamic || prodOfCollapsedDims != 1)) |
| return std::nullopt; |
| |
| // If the collapsed dim is dynamic, the current expanded dim should also |
| // be dynamic. |
| if (currTargetShape == ShapedType::kDynamic && |
| sourceShape[sourceDim] != ShapedType::kDynamic) |
| return std::nullopt; |
| |
| // For static shapes, if the product of dimensions of the expanded shape |
| // should match the collapsed dimension shape. |
| if (prodOfCollapsedDims * sourceShape[sourceDim] != currTargetShape) |
| return std::nullopt; |
| |
| currIndices.push_back(sourceDim++); |
| reassociationMap.emplace_back(ReassociationIndices{}); |
| std::swap(reassociationMap.back(), currIndices); |
| prodOfCollapsedDims = 1; |
| } |
| // All the dimensions in the target must have been processed. |
| if (reassociationMap.size() != targetShape.size()) |
| return std::nullopt; |
| // Process any remaining entries in the source shape. They all need to be |
| // 1 or dynamic. |
| for (; sourceDim < sourceShape.size(); sourceDim++) { |
| if (sourceShape[sourceDim] != ShapedType::kDynamic && |
| sourceShape[sourceDim] != 1) |
| return std::nullopt; |
| // The map is empty when the target type is a scalar. |
| if (!reassociationMap.empty()) |
| reassociationMap.back().push_back(sourceDim); |
| } |
| return reassociationMap; |
| } |
| |
| std::optional<SmallVector<ReassociationIndices>> |
| mlir::composeReassociationIndices( |
| ArrayRef<ReassociationIndices> producerReassociations, |
| ArrayRef<ReassociationIndices> consumerReassociations, |
| MLIRContext *context) { |
| SmallVector<ReassociationIndices> composedIndices; |
| // Make the producer the larger sized vector. If they are of same size, the |
| // resulting reshape is not a supported reshape op. |
| if (producerReassociations.size() == consumerReassociations.size()) |
| return std::nullopt; |
| if (producerReassociations.size() < consumerReassociations.size()) |
| std::swap(producerReassociations, consumerReassociations); |
| |
| // Handle the corner case of the result being a rank 0 shaped type. Return an |
| // empty reassociation. |
| if (consumerReassociations.empty()) |
| return composedIndices; |
| |
| size_t consumerDims = std::accumulate( |
| consumerReassociations.begin(), consumerReassociations.end(), 0, |
| [](size_t all, ReassociationIndicesRef indices) { |
| return all + indices.size(); |
| }); |
| if (producerReassociations.size() != consumerDims) |
| return std::nullopt; |
| |
| for (ReassociationIndicesRef consumerIndices : consumerReassociations) { |
| ReassociationIndices reassociations; |
| for (int64_t consumerIndex : consumerIndices) { |
| llvm::append_range(reassociations, producerReassociations[consumerIndex]); |
| } |
| composedIndices.push_back(std::move(reassociations)); |
| } |
| return composedIndices; |
| } |
| |
| SmallVector<SmallVector<AffineExpr, 2>, 2> |
| mlir::convertReassociationIndicesToExprs( |
| MLIRContext *context, ArrayRef<ReassociationIndices> reassociationIndices) { |
| SmallVector<SmallVector<AffineExpr, 2>, 2> reassociationMaps; |
| for (const auto &indices : reassociationIndices) { |
| SmallVector<AffineExpr, 2> reassociationMap; |
| reassociationMap.reserve(indices.size()); |
| for (int64_t index : indices) |
| reassociationMap.push_back(mlir::getAffineDimExpr(index, context)); |
| reassociationMaps.push_back(std::move(reassociationMap)); |
| } |
| return reassociationMaps; |
| } |
| |
| template <typename AffineExprTy> |
| unsigned getMaxPosOfType(ArrayRef<ReassociationExprs> exprArrays) { |
| unsigned pos = 0; |
| for (const auto &exprs : exprArrays) { |
| for (auto expr : exprs) { |
| expr.walk([&pos](AffineExpr e) { |
| if (auto d = dyn_cast<AffineExprTy>(e)) |
| pos = std::max(pos, d.getPosition()); |
| }); |
| } |
| } |
| return pos; |
| } |
| |
| ArrayAttr mlir::getReassociationIndicesAttribute( |
| Builder &b, ArrayRef<ReassociationIndices> reassociation) { |
| SmallVector<Attribute, 4> reassociationAttr = |
| llvm::to_vector<4>(llvm::map_range( |
| reassociation, [&](const ReassociationIndices &indices) -> Attribute { |
| return cast<Attribute>(b.getI64ArrayAttr(indices)); |
| })); |
| return b.getArrayAttr(reassociationAttr); |
| } |
| |
| SmallVector<ReassociationIndices, 2> mlir::convertReassociationMapsToIndices( |
| ArrayRef<ReassociationExprs> reassociationExprs) { |
| SmallVector<ReassociationIndices, 2> reassociationIndices; |
| for (const auto &exprs : reassociationExprs) { |
| ReassociationIndices indices; |
| indices.reserve(exprs.size()); |
| for (const auto &expr : exprs) |
| indices.push_back(cast<AffineDimExpr>(expr).getPosition()); |
| reassociationIndices.push_back(indices); |
| } |
| return reassociationIndices; |
| } |
| |
| SmallVector<AffineMap, 4> |
| mlir::getSymbolLessAffineMaps(ArrayRef<ReassociationExprs> reassociation) { |
| unsigned maxDim = getMaxPosOfType<AffineDimExpr>(reassociation); |
| assert(getMaxPosOfType<AffineSymbolExpr>(reassociation) == 0 && |
| "Expected symbol-less expressions"); |
| SmallVector<AffineMap, 4> maps; |
| maps.reserve(reassociation.size()); |
| for (const auto &exprs : reassociation) { |
| assert(!exprs.empty()); |
| maps.push_back(AffineMap::get(maxDim + 1, 0, exprs, exprs[0].getContext())); |
| } |
| return maps; |
| } |
| |
| bool mlir::isReassociationValid(ArrayRef<AffineMap> reassociation, |
| int *invalidIndex) { |
| if (reassociation.empty()) |
| return true; |
| unsigned nDims = reassociation[0].getNumDims(); |
| unsigned nextExpectedDim = 0; |
| for (const auto &it : llvm::enumerate(reassociation)) { |
| auto m = it.value(); |
| if (m.getNumDims() != nDims || m.getNumSymbols() != 0) { |
| if (invalidIndex) |
| *invalidIndex = it.index(); |
| return false; |
| } |
| for (auto e : m.getResults()) { |
| auto d = dyn_cast<AffineDimExpr>(e); |
| if (!d || d.getPosition() != nextExpectedDim++) { |
| if (invalidIndex) |
| *invalidIndex = it.index(); |
| return false; |
| } |
| } |
| } |
| if (nextExpectedDim != nDims) { |
| if (invalidIndex) |
| *invalidIndex = reassociation.size() - 1; |
| return false; |
| } |
| return true; |
| } |
| |
| LogicalResult mlir::reshapeLikeShapesAreCompatible( |
| function_ref<LogicalResult(const Twine &)> emitError, |
| ArrayRef<int64_t> collapsedShape, ArrayRef<int64_t> expandedShape, |
| ArrayRef<ReassociationIndices> reassociationMaps, bool isExpandingReshape) { |
| unsigned expandedDimStart = 0; |
| for (const auto &map : llvm::enumerate(reassociationMaps)) { |
| bool foundDynamicShape = false; |
| int64_t linearizedStaticShape = 1; |
| |
| for (const auto &dim : llvm::enumerate( |
| expandedShape.slice(expandedDimStart, map.value().size()))) { |
| if (ShapedType::isDynamic(dim.value())) |
| foundDynamicShape = true; |
| else |
| linearizedStaticShape *= dim.value(); |
| } |
| if (foundDynamicShape) { |
| if (!ShapedType::isDynamic(collapsedShape[map.index()])) { |
| return emitError( |
| "expected dimension " + Twine(map.index()) + |
| " of collapsed type to be dynamic since one or more of the " |
| "corresponding dimensions in the expanded type is dynamic"); |
| } |
| } else { |
| if (collapsedShape[map.index()] != linearizedStaticShape) { |
| return emitError("expected dimension " + Twine(map.index()) + |
| " of collapsed type to be static value of " + |
| Twine(linearizedStaticShape)); |
| } |
| } |
| expandedDimStart += map.value().size(); |
| } |
| return success(); |
| } |
| |
| bool mlir::hasNonIdentityLayout(Type type) { |
| if (auto memrefType = dyn_cast<MemRefType>(type)) |
| return !memrefType.getLayout().isIdentity(); |
| return false; |
| } |
| |
| llvm::SmallBitVector |
| mlir::getSlicedDimensions(ArrayRef<OpFoldResult> sliceInputShape, |
| ArrayRef<Range> sliceParams) { |
| assert(sliceParams.size() == sliceInputShape.size() && |
| "only supports non rank-reducing case"); |
| llvm::SmallBitVector mask(sliceInputShape.size()); |
| unsigned idx = 0; |
| for (const auto &[offset, size, stride] : sliceParams) { |
| std::optional<int64_t> offsetConst = getConstantIntValue(offset); |
| std::optional<int64_t> strideConst = getConstantIntValue(stride); |
| mask[idx] = !isEqualConstantIntOrValue(size, sliceInputShape[idx]) || |
| (!strideConst || *strideConst != 1) || |
| (!offsetConst || *offsetConst != 0); |
| idx++; |
| } |
| return mask; |
| } |
| |
| llvm::SmallBitVector mlir::getLinearizedDimensions( |
| ArrayRef<ReassociationIndices> reassociationIndices) { |
| llvm::SmallBitVector result(reassociationIndices.size()); |
| for (const auto &it : llvm::enumerate(reassociationIndices)) |
| result[it.index()] = it.value().size() > 1; |
| return result; |
| } |
| |
| SmallVector<Range> SliceFromCollapseHelper::getExtractSliceParams( |
| MLIRContext *ctx, ArrayRef<ValueRange> multiIndices) { |
| unsigned loopIdx = 0; |
| auto oneAttr = IntegerAttr::get(IndexType::get(ctx), 1); |
| auto zeroAttr = IntegerAttr::get(IndexType::get(ctx), 0); |
| SmallVector<Range> offsetsSizesAndStrides; |
| offsetsSizesAndStrides.reserve(collapseShapeInputShape.size()); |
| for (const auto &it : llvm::enumerate(reassociationIndices)) { |
| // Case 1: Linearized dimensions that have also been sliced. These |
| // are size of 1 because we are iterating over these dimensions. The |
| // offsets are exactly the de-linearized multi-indices. |
| if (slicedDimensions[it.index()] && linearizedDimensions[it.index()]) { |
| llvm::append_range( |
| offsetsSizesAndStrides, |
| llvm::map_range(multiIndices[loopIdx++], [&](Value v) -> Range { |
| return Range{getAsOpFoldResult(v), oneAttr, oneAttr}; |
| })); |
| continue; |
| } |
| |
| // Case 2: One or possibly multiple combined input dimensions, but we |
| // have proven that these are not sliced. In this case we just take |
| // the full extent of each dimension in the reassociation list. |
| if (linearizedDimensions[it.index()]) { |
| llvm::append_range( |
| offsetsSizesAndStrides, |
| llvm::map_range(it.value(), [&](int64_t idx) -> Range { |
| return {zeroAttr, collapseShapeInputShape[idx], oneAttr}; |
| })); |
| continue; |
| } |
| |
| // Case 3: A single index, but it may be sliced. |
| offsetsSizesAndStrides.push_back(sliceParams[it.index()]); |
| } |
| return offsetsSizesAndStrides; |
| } |
| |
| SmallVector<Range> |
| SliceFromCollapseHelper::getInsertSliceParams(MLIRContext *ctx, |
| ValueRange tileIndices) { |
| auto one = IntegerAttr::get(IndexType::get(ctx), 1); |
| auto zero = IntegerAttr::get(IndexType::get(ctx), 0); |
| SmallVector<Range> insertParams; |
| insertParams.reserve(linearizedDimensions.size()); |
| unsigned loopIdx = 0; |
| for (unsigned i = 0; i < linearizedDimensions.size(); i++) { |
| if (linearizedDimensions[i] && slicedDimensions[i]) { |
| insertParams.push_back(Range{tileIndices[loopIdx++], one, one}); |
| continue; |
| } |
| insertParams.push_back(Range{zero, sliceParams[i].size, one}); |
| } |
| return insertParams; |
| } |
| |
| /// Returns the index of the only non-unit dimension among `indices` of `shape`, |
| /// if such a dimension exists and `indices` has more than one element. |
| /// Otherwise, return std::nullopt. |
| static std::optional<int64_t> getUniqueNonUnitDim(ArrayRef<int64_t> indices, |
| ArrayRef<int64_t> shape) { |
| // Return false if more than one of the dimensions in this group are not 1. |
| std::optional<int64_t> dimIndex; |
| if (indices.size() < 2) |
| return std::nullopt; |
| for (int64_t idx : indices) { |
| if (shape[idx] != 1) { |
| if (dimIndex != std::nullopt) |
| return std::nullopt; |
| dimIndex = idx; |
| } |
| } |
| return dimIndex; |
| } |
| |
| // For each segment in the reassociation indices, check whether we can |
| // simplify that segment with a rank-reducing extract slice. We can do this if |
| // all but (exactly) one of the corresponding source dims is 1. |
| static SmallVector<std::optional<int64_t>> getCollapseShapeTrivialSegments( |
| RankedTensorType sourceType, |
| ArrayRef<ReassociationIndices> reassociationIndices) { |
| SmallVector<std::optional<int64_t>> trivialSegments; |
| for (const auto &indices : reassociationIndices) |
| trivialSegments.push_back( |
| getUniqueNonUnitDim(indices, sourceType.getShape())); |
| return trivialSegments; |
| } |
| |
| /// Returns true if any of the segments of the reassociation indices for a |
| /// collapsing reshape can be simplified using a rank-reducing slice. |
| static FailureOr<SmallVector<std::optional<int64_t>>> |
| canCollapseShapeBeSimplifiedByRankReducingSlice( |
| RankedTensorType sourceType, |
| ArrayRef<ReassociationIndices> reassociationIndices) { |
| SmallVector<std::optional<int64_t>> trivialSegments = |
| getCollapseShapeTrivialSegments(sourceType, reassociationIndices); |
| if (!llvm::any_of(trivialSegments, [](const std::optional<int64_t> &idx) { |
| return idx.has_value(); |
| })) |
| return failure(); |
| return trivialSegments; |
| } |
| |
| FailureOr<CollapseShapeRankReducingSliceSimplificationInfo> |
| mlir::getSimplifyCollapseShapeWithRankReducingSliceInfo( |
| RankedTensorType sourceType, |
| ArrayRef<ReassociationIndices> reassociationIndices) { |
| FailureOr<SmallVector<std::optional<int64_t>>> trivialSegments = |
| canCollapseShapeBeSimplifiedByRankReducingSlice(sourceType, |
| reassociationIndices); |
| if (failed(trivialSegments)) |
| return failure(); |
| |
| // Create the expected result shape of the rank-reducing slice. |
| SmallVector<int64_t> sliceShape; |
| for (const auto &[nonUnitDim, indices] : |
| llvm::zip(*trivialSegments, reassociationIndices)) { |
| if (nonUnitDim) { |
| sliceShape.push_back(sourceType.getDimSize(*nonUnitDim)); |
| continue; |
| } |
| llvm::append_range(sliceShape, llvm::map_range(indices, [&](int64_t idx) { |
| return sourceType.getDimSize(idx); |
| })); |
| } |
| auto sliceType = |
| RankedTensorType::get(sliceShape, sourceType.getElementType()); |
| |
| // If the rank-reducing slice simplified every segment, then we are done. |
| if (sliceShape.size() == reassociationIndices.size()) |
| return CollapseShapeRankReducingSliceSimplificationInfo{sliceType, |
| std::nullopt}; |
| |
| // Otherwise, we need to create a new collapse_shape op for the segments that |
| // weren't covered by the slice. By design, the new reassociation indices has |
| // the same number of groups as the old reassociation indices. |
| SmallVector<ReassociationIndices> newReassociationIndices; |
| SmallVector<int64_t, 2> reassociation; |
| int64_t groupIdx = 0; |
| for (int64_t dimIdx = 0; dimIdx < sliceType.getRank(); dimIdx++) { |
| reassociation.push_back(dimIdx); |
| if ((*trivialSegments)[groupIdx] || |
| reassociation.size() == reassociationIndices[groupIdx].size()) { |
| newReassociationIndices.push_back(reassociation); |
| reassociation.clear(); |
| groupIdx++; |
| } |
| } |
| |
| return CollapseShapeRankReducingSliceSimplificationInfo{ |
| sliceType, newReassociationIndices}; |
| } |
| |
| PackingMetadata mlir::computePackingMetadata(int64_t packedRank, |
| ArrayRef<int64_t> innerDimPos) { |
| PackingMetadata res; |
| res.insertPositions.reserve(innerDimPos.size()); |
| // The pack insert position is the position + the number of previously |
| // inserted positions + offset. |
| // The offset controls whether the packing dimension is the first or last. |
| // |
| // Example |
| // ======= |
| // Consider packing from a hypothetical ABCD layout to ABCDba whose |
| // pack.inner_dims is [1, 0]. The first step consists in undoing the |
| // permutation and producing AaBbCD. This is achieved purely by computing the |
| // insert positions of `b` and `a` into `ABCD`, starting from [1, 0]. One |
| // possibility, is to produce insert positions [2, 0], this would result in an |
| // aAbBCD layout (i.e. offset 0). The other possibility, is to produce insert |
| // positions [3, 1], this would result in an AaBbCD layout (i.e. offset 1). |
| // The latter is what we expect from packing. |
| int64_t offset = 1; |
| for (int64_t pos : innerDimPos) { |
| int64_t numInsertedBefore = llvm::count_if( |
| innerDimPos, [&pos](int64_t pos2) { return pos > pos2; }); |
| res.insertPositions.push_back(pos + numInsertedBefore + offset); |
| } |
| |
| DenseSet<int64_t> posSet(res.insertPositions.begin(), |
| res.insertPositions.end()); |
| res.reassociations.reserve(packedRank); |
| for (int64_t i = 1; i <= packedRank; ++i) { |
| res.outerPositions.push_back(i - 1); |
| if (!posSet.contains(i)) { |
| res.reassociations.push_back(ReassociationIndices{i - 1}); |
| continue; |
| } |
| res.reassociations.push_back(ReassociationIndices{i - 1, i}); |
| ++i; |
| } |
| return res; |
| } |
| |
| OpFoldResult mlir::reshapeConstantSource(DenseElementsAttr source, |
| TensorType result, |
| std::optional<Attribute> cst) { |
| if (source && source.isSplat() && result.hasStaticShape() && |
| (!cst.has_value() || source.getSplatValue<Attribute>() == cst.value())) |
| return source.resizeSplat(result); |
| |
| return {}; |
| } |
