/*===- TableGen'erated file -------------------------------------*- C++ -*-===*\ |* *| |* Interface Definitions *| |* *| |* Automatically generated file, do not edit! *| |* *| \*===----------------------------------------------------------------------===*/ /// Method to decompose the operation into simpler operations. /// /// On success, this method returns one `Value` per result in the /// original operation. /// The order of the returned values must match the order of the /// original values. /// In other words, the returned vector can be used directly with /// `RewriterBase::replaceOp(this, returnedValues)`. FailureOr<SmallVector<Value>> mlir::linalg::AggregatedOpInterface::decomposeOperation(OpBuilder & b) { … } /// Returns the left-hand side operand. Value mlir::linalg::ContractionOpInterface::lhs() { … } /// Returns the right-hand side operand. Value mlir::linalg::ContractionOpInterface::rhs() { … } /// Returns whether the given op has indexing maps that correspond to a /// row-major matmul operation. bool mlir::linalg::ContractionOpInterface::isRowMajorMatmul() { … } /// Returns whether the given op has indexing maps that correspond to a /// column-major matmul operation. bool mlir::linalg::ContractionOpInterface::isColumnMajorMatmul() { … } /// Returns whether the given op has indexing maps that correspond to a /// row-major batch matmul operation. bool mlir::linalg::ContractionOpInterface::isRowMajorBatchMatmul() { … } /// Returns whether the given op has indexing maps that correspond to a /// vector-matrix multiplication. bool mlir::linalg::ContractionOpInterface::isVecmat() { … } /// Returns whether the given op has indexing maps that correspond to a /// batched vector-matrix multiplication. bool mlir::linalg::ContractionOpInterface::isBatchVecmat() { … } /// Returns whether the given op has indexing maps that correspond to a /// matrix-vector multiplication. bool mlir::linalg::ContractionOpInterface::isMatvec() { … } /// Returns whether the given op has indexing maps that correspond to a /// batched matrix-vector multiplication. bool mlir::linalg::ContractionOpInterface::isBatchMatvec() { … } /// Return the image operand. Value mlir::linalg::ConvolutionOpInterface::image() { … } /// Return the filter operand. Value mlir::linalg::ConvolutionOpInterface::filter() { … } /// Return the fill value. Value mlir::linalg::FillOpInterface::value() { … } /// Return the output operand. Value mlir::linalg::FillOpInterface::output() { … } /// Return the result. Value mlir::linalg::FillOpInterface::result() { … } /// Return the number of parallel loops. unsigned mlir::linalg::LinalgOp::getNumParallelLoops() { … } /// Return true if all loops are parallel. bool mlir::linalg::LinalgOp::isAllParallelLoops() { … } /// Return the dims that are parallel loops. void mlir::linalg::LinalgOp::getParallelDims(SmallVectorImpl<unsigned> & res) { … } /// Return the number of reduction loops. unsigned mlir::linalg::LinalgOp::getNumReductionLoops() { … } /// Return the dims that are reduction loops. void mlir::linalg::LinalgOp::getReductionDims(SmallVectorImpl<unsigned> & res) { … } /// Return the total number of loops within the current operation. unsigned mlir::linalg::LinalgOp::getNumLoops() { … } /// Returns true if the current operation has only one loop and it's a /// reduction loop. bool mlir::linalg::LinalgOp::hasSingleReductionLoop() { … } /// Return true if the payload uses the value loaded from `opOperand`. This /// is useful to avoid loading from "write-only" memory that may be /// uninitialized, as well as properly cloning "read-write" operands. bool mlir::linalg::LinalgOp::payloadUsesValueFromOperand(OpOperand * opOperand) { … } /// Returns true only if linalgOp takes one input and produces one result. bool mlir::linalg::LinalgOp::isSingleInputOutput() { … } /// Return true if `opOperand` is an init tensor. This is true when it is /// an output tensor operand whose value is used in the payload region. bool mlir::linalg::LinalgOp::isInitTensor(OpOperand * opOperand) { … } /// Return the `opOperand` rank or zero for scalars or vectors not wrapped within a tensor or a memref. int64_t mlir::linalg::LinalgOp::getRank(OpOperand* opOperand) { … } /// Return the input block arguments of the region. Block::BlockArgListType mlir::linalg::LinalgOp::getRegionInputArgs() { … } /// Return the output block arguments of the region. Block::BlockArgListType mlir::linalg::LinalgOp::getRegionOutputArgs() { … } /// Return the `opOperand` shape or an empty vector for scalars or vectors /// not wrapped within a tensor or a memref. ArrayRef<int64_t> mlir::linalg::LinalgOp::getShape(OpOperand* opOperand) { … } /// Return the block argument for an `opOperand`. BlockArgument mlir::linalg::LinalgOp::getMatchingBlockArgument(OpOperand * opOperand) { … } /// Return the operand for a `blockArgument`. OpOperand *mlir::linalg::LinalgOp::getMatchingOpOperand(BlockArgument blockArgument) { … } /// Return the input or output indexing map for `opOperand`. AffineMap mlir::linalg::LinalgOp::getMatchingIndexingMap(OpOperand* opOperand) { … } /// Return the indexing map for a `result`. AffineMap mlir::linalg::LinalgOp::getIndexingMapMatchingResult(OpResult result) { … } /// Return the value yielded by the region corresponding to an output /// `opOperand`. OpOperand *mlir::linalg::LinalgOp::getMatchingYieldValue(OpOperand* opOperand) { … } /// Return the single block constituting the body of the operation by /// calling the getBody method on the concrete operation. Block*mlir::linalg::LinalgOp::getBlock() { … } /// Return iterator types in the current operation. /// /// Default implementation assumes that the operation has an attribute /// `iterator_types`, but it's not always the case. Sometimes iterator types /// can be infered from other parameters and in such cases default /// getIteratorTypesArray should be overriden. SmallVector<utils::IteratorType> mlir::linalg::LinalgOp::getIteratorTypesArray() { … } /// Return true if the indexing map is depending on the current op instance. /// This means that the indexing map is dynamically synthesized by using the /// op instance's concrete attributes, instead of being static for all /// instances of the same op kind. bool mlir::linalg::LinalgOp::hasDynamicIndexingMaps() { … } /// Verify all attributes used by indexing maps are valid. LogicalResult mlir::linalg::LinalgOp::verifyIndexingMapRequiredAttributes() { … } /// Return the indexing maps attribute within the current operation. ArrayAttr mlir::linalg::LinalgOp::getIndexingMaps() { … } /// Return the indexing maps within the current operation. SmallVector<AffineMap> mlir::linalg::LinalgOp::getIndexingMapsArray() { … } /// Return true if any of the operands has a dynamic shape. bool mlir::linalg::LinalgOp::hasDynamicShape() { … } /// Return the name registered for this op when lowering to an external /// library call. std::string mlir::linalg::LinalgOp::getLibraryCallName() { … } /// Return whether the op accesses the iteration indices. bool mlir::linalg::LinalgOp::hasIndexSemantics() { … } /// Return op operands that have a corresponding argument in the basic block. /// By default, the block should have an argument for each operand, but there /// are expection. For example, in `map` output operand isn't used in /// the block. ::llvm::SmallVector<OpOperand *> mlir::linalg::LinalgOp::getOpOperandsMatchingBBargs() { … } /// Given a dimension of the iteration space of a Linalg operation, finds an /// operand in the operation that is defined on such dimension. Returns /// whether such operand was found or not. If found, also returns the /// operand value and the dimension position within the operand. LogicalResult mlir::linalg::LinalgOp::mapIterationSpaceDimToOperandDim(unsigned dimPos, ::mlir::Value & operand, unsigned & operandDimPos) { … } /// Given a dimension of the iteration space of a Linalg operation, finds /// all the operands in the operation that are defined on such dimension. /// Returns all the operand values found and their dimension positions in /// `operandDimPairs`. void mlir::linalg::LinalgOp::mapIterationSpaceDimToAllOperandDims(unsigned dimPos, mlir::SmallVectorImpl<std::pair<Value, unsigned>>& operandDimPairs) { … } /// Hook to provide a custom AffineMap used to compute all the operand /// subshapes given loop bounds. This is used to answer the question: "given /// an iteration space over the codomain, what are the subshapes of the /// operands involved in the computation". /// The default behavior is to just concatenate all the indexing maps. /// A custom AffineMap allows providing a map that can be used to /// compute subshapes even in cases where the concatenation of indexing maps /// (i.e. the data traversal order) is not a simple permutation of the loop /// traversal order. It is then possible to define ops with skewed data /// traversal order for which we can still easily compute hyperrectangular /// loop bounds and subviews. AffineMap mlir::linalg::LinalgOp::getLoopsToShapesMap() { … } /// Hook to provide a custom AffineMap used to construct the /// hyperrectangular loop iteration space given all the operand subshapes. /// This is used to answer the question: /// "Given a list of operand ranges, what is the subportion of the iteration /// space involved in the computation". /// This is the inverse problem of `getLoopsToShapesMap`. /// Return the empty AffineMap when such an AffineMap cannot be constructed. /// The default behavior is based on a very simple inference procedure that /// only works with permutation affine maps. /// A more advanced Tensor-Comprehension like inference is possible but has /// proven to be ambiguous in unfavorable case. /// A safer and more robust alternative is to allow each op to define /// its own AffineMap. AffineMap mlir::linalg::LinalgOp::getShapesToLoopsMap() { … } /// Checks if the given operands can be dropped, and the remaining /// operands can still compute the bounds of the op. bool mlir::linalg::LinalgOp::canOpOperandsBeDropped(ArrayRef<OpOperand *> droppedOperands) { … } /// Like `getShape`, but only returns statically-known information, without /// generating any new IR. For each shape dimension, returns >=0 if that /// dimension is statically known, or ShapedType::kDynamic otherwise. SmallVector<int64_t> mlir::linalg::LinalgOp::getStaticShape() { … } /// Returns the statically-known loop ranges. Composes /// `getShapesToLoopsMap()` with the result of `getStaticShape`. /// Returns ShapedType::kDynamic for non-statically-known loop ranges. /// This is expected to be called by a valid Linalg op SmallVector<int64_t, 4> mlir::linalg::LinalgOp::getStaticLoopRanges() { … } /// Returns the region builder for constructing the body for linalg.generic. /// Returns a null function if this named op does not define a region /// builder. std::function<void(ImplicitLocOpBuilder &, Block &, ArrayRef<NamedAttribute>)> mlir::linalg::LinalgOp::getRegionBuilder() { … } /// Return true if all the indexing maps are projected permutations. /// Otherwise return false. bool mlir::linalg::LinalgOp::hasOnlyProjectedPermutations() { … } /// Return start and end indices of the init operands range. ::mlir::MutableOperandRange mlir::linalg::LinalgOp::getDpsInitsMutable() { … }
Codebase Browser
llvm