Vectorization.cpp File Reference

#include "mlir/Dialect/Affine/Utils.h"
#include "mlir/Analysis/SliceAnalysis.h"
#include "mlir/Dialect/Affine/IR/AffineOps.h"
#include "mlir/Dialect/Arith/IR/Arith.h"
#include "mlir/Dialect/Func/IR/FuncOps.h"
#include "mlir/Dialect/Linalg/IR/Linalg.h"
#include "mlir/Dialect/Linalg/Transforms/Transforms.h"
#include "mlir/Dialect/Linalg/Utils/Utils.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "mlir/Dialect/Utils/IndexingUtils.h"
#include "mlir/Dialect/Utils/StructuredOpsUtils.h"
#include "mlir/Dialect/Vector/IR/VectorOps.h"
#include "mlir/Dialect/Vector/Interfaces/MaskableOpInterface.h"
#include "mlir/Dialect/Vector/Utils/VectorUtils.h"
#include "mlir/IR/AffineExpr.h"
#include "mlir/IR/AffineMap.h"
#include "mlir/IR/Builders.h"
#include "mlir/IR/BuiltinTypeInterfaces.h"
#include "mlir/IR/BuiltinTypes.h"
#include "mlir/IR/OpDefinition.h"
#include "mlir/IR/PatternMatch.h"
#include "mlir/IR/Value.h"
#include "mlir/Support/LLVM.h"
#include "mlir/Transforms/RegionUtils.h"
#include "llvm/ADT/STLExtras.h"
#include "llvm/ADT/Sequence.h"
#include "llvm/ADT/SmallVector.h"
#include "llvm/ADT/TypeSwitch.h"
#include "llvm/Support/DebugLog.h"
#include "llvm/Support/InterleavedRange.h"
#include "llvm/Support/MathExtras.h"
#include "llvm/Support/raw_ostream.h"
#include <optional>

Go to the source code of this file.

Classes
struct	VectorizationState
	Contains the vectorization state and related methods used across the vectorization process of a given operation. More...
struct	VectorizationHookResult
	VectorizationHookResult contains the vectorized op returned from a CustomVectorizationHook. More...
struct	VectorizePadOpUserPattern< OpTy >
	Base pattern for rewriting tensor::PadOps whose result is consumed by a given operation type OpTy. More...
struct	PadOpVectorizationWithTransferReadPattern
	Rewrite use of tensor::PadOp result in TransferReadOp. More...
struct	PadOpVectorizationWithTransferWritePattern
	Rewrite use of tensor::PadOp result in TransferWriteOp. More...
struct	PadOpVectorizationWithInsertSlicePattern
	Rewrite use of tensor::PadOp result in InsertSliceOp. More...
struct	VectorizeConvolution

Macros
#define	DEBUG_TYPE   "linalg-vectorization"

Typedefs
using	CustomVectorizationPrecondition = std::function< LogicalResult(Operation *, bool)>
using	CustomVectorizationHook = std::function< VectorizationHookResult(Operation *, const IRMapping &)>

Enumerations
enum class	Conv1DOpOrder { W , Ncw , Nwc }
	Helper enum to represent conv1d input traversal order. More...
enum	VectorizationHookStatus { Failure = 0 , NoReplace , NewOp }
	Helper data structure to represent the result of vectorization for a single operation. More...
enum	VectorMemoryAccessKind { ScalarBroadcast , Contiguous , Gather }

Functions
static FailureOr< Operation * >	vectorizeConvolution (RewriterBase &rewriter, LinalgOp convOp, ArrayRef< int64_t > inputVecSizes={}, ArrayRef< bool > inputVecScalableFlags={}, bool flatten1DDepthwiseConv=false)
	Try to vectorize convOp as a convolution. More...
static LogicalResult	vectorizeAsInsertSliceOp (RewriterBase &rewriter, tensor::InsertSliceOp sliceOp, ArrayRef< int64_t > inputVectorSizes, SmallVectorImpl< Value > &newResults)
	Vectorize tensor::InsertSliceOp with: More...
static Value	getStaticPadVal (Operation *op)
	Returns the effective Pad value for the input op, provided it's a scalar. More...
template<typename OpType >
static OpType	getSingleOpOfType (Block &block)
	Return the unique instance of OpType in block if it is indeed unique. More...
static SmallVector< Value >	extractConvInputSlices (RewriterBase &rewriter, Location loc, Value input, int64_t nSize, int64_t wSize, int64_t cSize, int64_t kwSize, int strideW, int dilationW, int64_t wSizeStep, bool isSingleChanneled)
	Helper function to extract the input slices after filter is unrolled along kw. More...
static SmallVector< Value >	extractConvFilterSlices (RewriterBase &rewriter, Location loc, Value filter, int64_t kwSize)
	Helper function to extract the filter slices after filter is unrolled along kw. More...
static SmallVector< Value >	extractConvResultSlices (RewriterBase &rewriter, Location loc, Value res, int64_t nSize, int64_t wSize, int64_t fSize, int64_t wSizeStep, bool isSingleChanneled)
	Helper function to extract the result slices after filter is unrolled along kw. More...
static Value	insertConvResultSlices (RewriterBase &rewriter, Location loc, Value res, int64_t wSize, int64_t wSizeStep, SmallVectorImpl< Value > &resVals, bool isSingleChanneled)
	Helper function to insert the computed result slices. More...
static AffineMap	reindexIndexingMap (AffineMap map)
	Given an indexing map coming from a LinalgOp indexing, restricted to a projectedPermutation, compress the unused dimensions to serve as a permutation_map for a vector transfer operation. More...
static Operation *	matchLinalgReduction (OpOperand *outputOperand)
	Check whether outputOperand is a reduction with a single combiner operation. More...
static Value	broadcastIfNeeded (OpBuilder &b, Value value, Type dstType)
	Broadcast value to a vector of shape if possible. More...
static Operation *	buildMultiDimReduce (OpBuilder &b, Operation *reduceOp, Value valueToReduce, Value acc, ArrayRef< bool > dimsToMask)
	Create MultiDimReductionOp to compute the reduction for reductionOp. More...
static SmallVector< bool >	getDimsToReduce (LinalgOp linalgOp)
static bool	hasReductionIterator (LinalgOp &op)
	Check if op is a linalg.reduce or a linalg.generic that has at least one reduction iterator. More...
static Value	buildVectorWrite (RewriterBase &rewriter, Value value, OpOperand *outputOperand, VectorizationState &state)
	Build a vector.transfer_write of value into outputOperand at indices set to all 0; where outputOperand is an output operand of the LinalgOp currently being vectorized. More...
static VectorizationHookResult	vectorizeLinalgYield (RewriterBase &rewriter, Operation *op, const IRMapping &bvm, VectorizationState &state, LinalgOp linalgOp, SmallVectorImpl< Value > &newResults)
	Helper function to vectorize the terminator of a linalgOp. More...
static VectorizationHookResult	vectorizeLinalgIndex (RewriterBase &rewriter, VectorizationState &state, Operation *op, LinalgOp linalgOp)
	Helper function to vectorize the index operations of a linalgOp. More...
static LogicalResult	tensorExtractVectorizationPrecondition (Operation *op, bool vectorizeNDExtract)
	Helper function to check if the tensor.extract can be vectorized by the custom hook vectorizeTensorExtract. More...
static Value	calculateGatherOffset (RewriterBase &rewriter, VectorizationState &state, tensor::ExtractOp extractOp, const IRMapping &bvm)
	Calculates the offsets ($index_vec) for vector.gather operations generated from tensor.extract. More...
static uint64_t	getTrailingNonUnitLoopDimIdx (LinalgOp linalgOp)
	Find the index of the trailing non-unit dim in linalgOp. More...
static bool	isLoopInvariantIdx (LinalgOp &linalgOp, Value &val, VectorType resType)
	Checks whether val can be used for calculating a loop invariant index. More...
static bool	isContiguousLoadIdx (LinalgOp &linalgOp, Value &val, bool &foundIndexOp, VectorType resType)
	Check whether val could be used for calculating the trailing index for a contiguous load operation. More...
static VectorMemoryAccessKind	getTensorExtractMemoryAccessPattern (tensor::ExtractOp extractOp, LinalgOp &linalgOp, VectorType resType)
	Infer the memory access pattern for the input ExtractOp. More...
static VectorizationHookResult	vectorizeTensorExtract (RewriterBase &rewriter, VectorizationState &state, Operation *op, LinalgOp linalgOp, const IRMapping &bvm)
	Helper function to vectorize the tensor.extract operations. More...
static Operation *	reduceIfNeeded (OpBuilder &b, LinalgOp linalgOp, Operation *op, Value reduceValue, Value initialValue, const IRMapping &bvm)
	Emit reduction operations if the shapes of the value to reduce is different that the result shape. More...
static VectorizationHookResult	vectorizeOneOp (RewriterBase &rewriter, VectorizationState &state, LinalgOp linalgOp, Operation *op, const IRMapping &bvm, ArrayRef< CustomVectorizationHook > customVectorizationHooks)
	Generic vectorization for a single operation op, given already vectorized operands carried by bvm. More...
static LogicalResult	vectorizeAsLinalgGeneric (RewriterBase &rewriter, VectorizationState &state, LinalgOp linalgOp, SmallVectorImpl< Value > &newResults)
	Generic vectorization function that rewrites the body of a linalgOp into vector form. More...
static SmallVector< int64_t >	getTiledPackShape (linalg::PackOp packOp, ArrayRef< int64_t > destShape)
	Given a linalg::PackOp, return the dest shape before any packing permutations. More...
static bool	isMaskTriviallyFoldable (SmallVector< OpFoldResult > &maskSizes, SmallVector< Value > &writeIdxs, ArrayRef< int64_t > destShape, ArrayRef< int64_t > maskShape)
	Determines whether a mask for xfer_write is trivially "all true". More...
static Operation *	createWriteOrMaskedWrite (OpBuilder &builder, Location loc, Value vecToStore, Value dest, SmallVector< Value > writeIndices={}, bool useInBoundsInsteadOfMasking=false)
	Creates an optionally masked TransferWriteOp. More...
static LogicalResult	vectorizeAsTensorPackOp (RewriterBase &rewriter, linalg::PackOp packOp, ArrayRef< int64_t > inputVectorSizes, SmallVectorImpl< Value > &newResults)
	Vectorize linalg::PackOp with (1) static inner_tiles (2) constant padding value and (3) input vector sizes into: More...
static VectorType	getCollapsedVecType (VectorType type, ArrayRef< AffineMap > reassociation)
	Given the re-associations, "collapses" the input Vector type. More...
static LogicalResult	vectorizeAsTensorUnpackOp (RewriterBase &rewriter, linalg::UnPackOp unpackOp, ArrayRef< int64_t > inputVectorSizes, ArrayRef< bool > inputScalableVecDims, SmallVectorImpl< Value > &newResults)
	Vectorize linalg.unpack as: More...
static LogicalResult	vectorizeAsTensorPadOp (RewriterBase &rewriter, tensor::PadOp padOp, ArrayRef< int64_t > inputVectorSizes, SmallVectorImpl< Value > &newResults)
	Vectorize a padOp with (1) static result type, (2) constant padding value and (3) all-zero lowPad to transfer_write_in_bounds(transfer_read_masked(pad_source, pad_value)). More...
static LogicalResult	reductionPreconditions (LinalgOp op)
static LogicalResult	vectorizeDynamicConvOpPrecondition (linalg::LinalgOp conv, bool flatten1DDepthwiseConv)
static LogicalResult	vectorizeDynamicLinalgOpPrecondition (linalg::LinalgOp op, bool flatten1DDepthwiseConv)
static LogicalResult	vectorizeUnPackOpPrecondition (linalg::UnPackOp unpackOp, ArrayRef< int64_t > inputVectorSizes)
	This hook considers two cases: (1) If the input-vector-sizes are empty, then the vector sizes will be infered. More...
static LogicalResult	vectorizeInsertSliceOpPrecondition (tensor::InsertSliceOp sliceOp, ArrayRef< int64_t > inputVectorSizes)
static LogicalResult	vectorizeAsLinalgContraction (RewriterBase &rewriter, VectorizationState &state, LinalgOp linalgOp, SmallVectorImpl< Value > &newResults)
	Vectorize a named linalg contraction op into: vector::TransferReadOp - Reads vectors from the operands vector::ContractionOp - Performs contraction vector::TransferWriteOp - Write the result vector back to the destination The operands shapes are preserved and loaded directly into vectors. More...
static bool	isCastOfBlockArgument (Operation *op)
static std::optional< ConvOperationKind >	getConvOperationKind (Operation *reduceOp)
static bool	isSupportedPoolKind (vector::CombiningKind kind)
static LogicalResult	vectorizeConvOpPrecondition (linalg::LinalgOp convOp)
static LogicalResult	vectorizeLinalgOpPrecondition (LinalgOp linalgOp, ArrayRef< int64_t > inputVectorSizes, bool vectorizeNDExtract, bool flatten1DDepthwiseConv)
static LogicalResult	vectorizePackOpPrecondition (linalg::PackOp packOp, ArrayRef< int64_t > inputVectorSizes)
static LogicalResult	vectorizePadOpPrecondition (tensor::PadOp padOp, ArrayRef< int64_t > inputVectorSizes)
static LogicalResult	vectorizeScalableVectorPrecondition (Operation *op, ArrayRef< int64_t > inputVectorSizes, ArrayRef< bool > inputScalableVecDims)
	Preconditions for scalable vectors. More...
static void	convertAffineApply (RewriterBase &rewriter, LinalgOp linalgOp)
	Converts affine.apply Ops to arithmetic operations. More...
static bool	mayExistInterleavedUses (Operation *firstOp, Operation *secondOp, ValueRange values)
	Check whether there is any interleaved use of any values between firstOp and secondOp. More...
static memref::SubViewOp	getSubViewUseIfUnique (Value v)
	Return the unique subview use of v if it is indeed unique, null otherwise. More...
template<int N>
static void	bindShapeDims (ShapedType shapedType)
template<int N, typename IntTy , typename... IntTy2>
static void	bindShapeDims (ShapedType shapedType, IntTy &val, IntTy2 &...vals)
template<typename... IntTy>
static void	bindShapeDims (ShapedType shapedType, IntTy &...vals)
	Bind a pack of int& to the leading dimensions of shapedType.getShape(). More...

Macro Definition Documentation

DEBUG_TYPE

#define DEBUG_TYPE   "linalg-vectorization"

Definition at line 50 of file Vectorization.cpp.

Typedef Documentation

CustomVectorizationHook

using CustomVectorizationHook = std::function<VectorizationHookResult(Operation *, const IRMapping &)>

Definition at line 759 of file Vectorization.cpp.

CustomVectorizationPrecondition

using CustomVectorizationPrecondition = std::function<LogicalResult(Operation *, bool)>

Definition at line 753 of file Vectorization.cpp.

Enumeration Type Documentation

Conv1DOpOrder

enum Conv1DOpOrder

strong

Helper enum to represent conv1d input traversal order.

Enumerator
W
Ncw
Nwc

Definition at line 572 of file Vectorization.cpp.

VectorizationHookStatus

enum VectorizationHookStatus

Helper data structure to represent the result of vectorization for a single operation.

In certain specific cases, like terminators, we do not want to propagate.

Enumerator
Failure	Op failed to vectorize.
NoReplace	Op vectorized and custom function took care of replacement logic.
NewOp	Op vectorized into a new Op whose results will replace original Op's results.

Definition at line 581 of file Vectorization.cpp.

VectorMemoryAccessKind

enum VectorMemoryAccessKind

Enumerator
ScalarBroadcast
Contiguous
Gather

Definition at line 902 of file Vectorization.cpp.

Function Documentation

bindShapeDims() [1/3]

template<int N>

static void bindShapeDims ( ShapedType  shapedType )

static

Definition at line 3437 of file Vectorization.cpp.

Referenced by bindShapeDims().

bindShapeDims() [2/3]

template<typename... IntTy>

static void bindShapeDims ( ShapedType  shapedType, IntTy &...  vals  )

static

Bind a pack of int& to the leading dimensions of shapedType.getShape().

Definition at line 3447 of file Vectorization.cpp.

bindShapeDims() [3/3]

template<int N, typename IntTy , typename... IntTy2>

static void bindShapeDims ( ShapedType  shapedType, IntTy &  val, IntTy2 &...  vals  )

static

Definition at line 3440 of file Vectorization.cpp.

References bindShapeDims().

broadcastIfNeeded()

static Value broadcastIfNeeded ( OpBuilder &  b, Value  value, Type  dstType  )

static

Broadcast value to a vector of shape if possible.

Return value otherwise.

Definition at line 651 of file Vectorization.cpp.

References mlir::OpBuilder::createOrFold(), mlir::OpBuilder::getInsertionPoint(), mlir::Value::getType(), mlir::vector::isBroadcastableTo(), and mlir::vector::Success.

buildMultiDimReduce()

static Operation* buildMultiDimReduce ( OpBuilder &  b, Operation *  reduceOp, Value  valueToReduce, Value  acc, ArrayRef< bool >  dimsToMask  )

static

Create MultiDimReductionOp to compute the reduction for reductionOp.

This assumes that reductionOp has two operands and one of them is the reduction initial value.buildMultiDimReduce

Definition at line 668 of file Vectorization.cpp.

References mlir::linalg::getCombinerOpKind(), and mlir::Operation::getLoc().

Referenced by reduceIfNeeded().

buildVectorWrite()

static Value buildVectorWrite ( RewriterBase &  rewriter, Value  value, OpOperand *  outputOperand, VectorizationState &  state  )

static

Build a vector.transfer_write of value into outputOperand at indices set to all 0; where outputOperand is an output operand of the LinalgOp currently being vectorized.

If dest has null rank, build an memref.store. Return the produced value or null if no value is produced.

Definition at line 696 of file Vectorization.cpp.

calculateGatherOffset()

static Value calculateGatherOffset ( RewriterBase &  rewriter, VectorizationState &  state, tensor::ExtractOp  extractOp, const IRMapping &  bvm  )

static

Calculates the offsets ($index_vec) for vector.gather operations generated from tensor.extract.

The offset is calculated as follows (example using scalar values):

offset = extractOp.indices[0] for (i = 1; i < numIndices; i++) offset = extractOp.dimSize[i] * offset + extractOp.indices[i];

For tensor<45 x 80 x 15 x f32> and index [1, 2, 3], this leads to: offset = ( ( 1 ) * 80 + 2 ) * 15 + 3

Definition at line 871 of file Vectorization.cpp.

convertAffineApply()

static void convertAffineApply ( RewriterBase &  rewriter, LinalgOp  linalgOp  )

static

Converts affine.apply Ops to arithmetic operations.

Definition at line 2650 of file Vectorization.cpp.

References mlir::affine::expandAffineExpr(), mlir::RewriterBase::replaceOp(), and mlir::OpBuilder::setInsertionPoint().

createWriteOrMaskedWrite()

static Operation* createWriteOrMaskedWrite ( OpBuilder &  builder, Location  loc, Value  vecToStore, Value  dest, SmallVector< Value >  writeIndices = {}, bool  useInBoundsInsteadOfMasking = false  )

static

Creates an optionally masked TransferWriteOp.

Generates the following operation: res = vector.transfer_write vecToStore into dest

If shape(vecToStore) != shape(dest), masking is used to ensure correctness:

mask = vector.create_mask(destShape) : vecToStoreShape res = vector.mask mask { vector.transfer_write vecToStore into dest }

The mask shape is identical to vecToStore (with the element type == i1), and the mask values are based on the shape of the dest tensor.

If useInBoundsInsteadOfMasking is set to true, the in_bounds attribute is used instead of masking:

write = vector.transfer_write vecToStore into dest in_bounds_flags = (...) res = vector.transfer_write input into dest {in_bounds = in_bounds_flags}

Finally, writeIndices specifies the offsets to use. If empty, all indices are set to 0.

Definition at line 1663 of file Vectorization.cpp.

Referenced by vectorizeAsInsertSliceOp(), vectorizeAsTensorPackOp(), vectorizeAsTensorPadOp(), and vectorizeAsTensorUnpackOp().

extractConvFilterSlices()

static SmallVector<Value> extractConvFilterSlices ( RewriterBase &  rewriter, Location  loc, Value  filter, int64_t  kwSize  )

static

Helper function to extract the filter slices after filter is unrolled along kw.

Definition at line 145 of file Vectorization.cpp.

extractConvInputSlices()

static SmallVector<Value> extractConvInputSlices ( RewriterBase &  rewriter, Location  loc, Value  input, int64_t  nSize, int64_t  wSize, int64_t  cSize, int64_t  kwSize, int  strideW, int  dilationW, int64_t  wSizeStep, bool  isSingleChanneled  )

static

Helper function to extract the input slices after filter is unrolled along kw.

Definition at line 109 of file Vectorization.cpp.

extractConvResultSlices()

static SmallVector<Value> extractConvResultSlices ( RewriterBase &  rewriter, Location  loc, Value  res, int64_t  nSize, int64_t  wSize, int64_t  fSize, int64_t  wSizeStep, bool  isSingleChanneled  )

static

Helper function to extract the result slices after filter is unrolled along kw.

Definition at line 161 of file Vectorization.cpp.

getCollapsedVecType()

static VectorType getCollapsedVecType ( VectorType  type, ArrayRef< AffineMap >  reassociation  )

static

Given the re-associations, "collapses" the input Vector type.

This is similar to CollapseShapeOp::inferCollapsedType with two notable differences:

• We can safely assume that there are no dynamic sizes.
• Scalable flags are updated alongside regular dims.

When collapsing scalable flags, conservatively avoids cases with two scalable dims. We could re-visit this in the future.

EXAMPLE: type = vector<4x16x[8]x16xf32> reassociation = [(d0, d1, d2, d3) -> (d0, d1), (d0, d1, d2, d3) -> (d2, d3)] Result: vector<64x[128]xf32>

Definition at line 1851 of file Vectorization.cpp.

References mlir::get(), and mlir::isReassociationValid().

Referenced by vectorizeAsTensorUnpackOp().

getConvOperationKind()

static std::optional<ConvOperationKind> getConvOperationKind ( Operation *  reduceOp )

static

Definition at line 2250 of file Vectorization.cpp.

References mlir::Operation::getOperands(), mlir::Operation::getResultTypes(), isCastOfBlockArgument(), and mlir::Operation::operand_end().

Referenced by vectorizeConvOpPrecondition().

getDimsToReduce()

static SmallVector<bool> getDimsToReduce ( LinalgOp  linalgOp )

static

Definition at line 677 of file Vectorization.cpp.

References mlir::linalg::isReductionIterator().

Referenced by reduceIfNeeded().

getSingleOpOfType()

template<typename OpType >

static OpType getSingleOpOfType ( Block &  block )

static

Return the unique instance of OpType in block if it is indeed unique.

Return null if none or more than 1 instances exist.

Definition at line 93 of file Vectorization.cpp.

References mlir::WalkResult::advance(), mlir::WalkResult::interrupt(), and mlir::Block::walk().

getStaticPadVal()

static Value getStaticPadVal ( Operation *  op )

static

Returns the effective Pad value for the input op, provided it's a scalar.

Many Ops exhibit pad-like behaviour, but this isn't always explicit. If this Op performs padding, retrieve the padding value provided that it's a scalar and static/fixed for all the padded values. Returns an empty value otherwise.

Many Ops exhibit pad-like behaviour, but this isn't always explicit. If this Op performs padding, retrieve the padding value provided that it's a scalar and static/fixed for all the padded values. Returns an empty value otherwise.

TODO: This is used twice (when checking vectorization pre-conditions and when vectorizing). Cache results instead of re-running.

Definition at line 3045 of file Vectorization.cpp.

Referenced by vectorizeAsInsertSliceOp(), and vectorizeInsertSliceOpPrecondition().

getSubViewUseIfUnique()

static memref::SubViewOp getSubViewUseIfUnique ( Value  v )

static

Return the unique subview use of v if it is indeed unique, null otherwise.

Definition at line 3285 of file Vectorization.cpp.

References mlir::Value::getUses().

Referenced by mlir::linalg::LinalgCopyVTRForwardingPattern::matchAndRewrite(), and mlir::linalg::LinalgCopyVTWForwardingPattern::matchAndRewrite().

getTensorExtractMemoryAccessPattern()

static VectorMemoryAccessKind getTensorExtractMemoryAccessPattern ( tensor::ExtractOp  extractOp, LinalgOp &  linalgOp, VectorType  resType  )

static

Infer the memory access pattern for the input ExtractOp.

Based on the ExtratOp result shape and the access indices, decides whether this Op corresponds to a contiguous load (including a broadcast of a scalar) or a gather load. When analysing the ExtractOp indices (to identify contiguous laods), this method looks for "loop" invariant indices (e.g. block arguments) and indices that change linearly (e.g. via linalg.index Op).

Note that it is always safe to use gather load operations for contiguous loads (albeit slow), but not vice-versa. When in doubt, bail out and assume that extractOp is a gather load.

Definition at line 1051 of file Vectorization.cpp.

References Contiguous, mlir::detail::enumerate(), Gather, isContiguousLoadIdx(), isLoopInvariantIdx(), and ScalarBroadcast.

getTiledPackShape()

static SmallVector<int64_t> getTiledPackShape ( linalg::PackOp  packOp, ArrayRef< int64_t >  destShape  )

static

Given a linalg::PackOp, return the dest shape before any packing permutations.

Definition at line 1535 of file Vectorization.cpp.

References mlir::applyPermutation(), and mlir::linalg::getPackInverseDestPerm().

Referenced by vectorizeAsTensorPackOp().

getTrailingNonUnitLoopDimIdx()

static uint64_t getTrailingNonUnitLoopDimIdx ( LinalgOp  linalgOp )

static

Find the index of the trailing non-unit dim in linalgOp.

This hook is used when checking whether tensor.extract Op (within a linalg.generic Op) represents a contiguous load operation.

Note that when calling this hook, it is assumed that the output vector is effectively 1D. Other cases (i.e. reading n-D vectors) should've been labelled as a gather load before entering this method.

Following on from the above, it is assumed that:

• for statically shaped loops, when no masks are used, only one dim is != 1 (that's what the shape of the output vector is based on).
• for dynamically shaped loops, there might be more non-unit dims as the output vector type is user-specified.

TODO: Statically shaped loops + vector masking

Definition at line 919 of file Vectorization.cpp.

Referenced by isContiguousLoadIdx().

hasReductionIterator()

static bool hasReductionIterator ( LinalgOp &  op )

static

Check if op is a linalg.reduce or a linalg.generic that has at least one reduction iterator.

Definition at line 684 of file Vectorization.cpp.

References mlir::linalg::isReductionIterator().

Referenced by vectorizeDynamicLinalgOpPrecondition(), and vectorizeScalableVectorPrecondition().

insertConvResultSlices()

static Value insertConvResultSlices ( RewriterBase &  rewriter, Location  loc, Value  res, int64_t  wSize, int64_t  wSizeStep, SmallVectorImpl< Value > &  resVals, bool  isSingleChanneled  )

static

Helper function to insert the computed result slices.

Definition at line 189 of file Vectorization.cpp.

isCastOfBlockArgument()

static bool isCastOfBlockArgument ( Operation *  op )

static

Definition at line 2234 of file Vectorization.cpp.

References mlir::Operation::getNumOperands(), and mlir::Operation::getOperand().

Referenced by getConvOperationKind().

isContiguousLoadIdx()

static bool isContiguousLoadIdx ( LinalgOp &  linalgOp, Value &  val, bool &  foundIndexOp, VectorType  resType  )

static

Check whether val could be used for calculating the trailing index for a contiguous load operation.

There are currently 3 types of values that are allowed here:

1. loop-invariant values,
2. values that increment by 1 with every loop iteration,
3. results of basic arithmetic operations (linear and continuous) involving 1., 2. and 3. This method returns True if indeed only such values are used in calculating val.

Additionally, the trailing index for a contiguous load operation should increment by 1 with every loop iteration, i.e. be based on:

• linalg.index <dim> , where <dim> is the trailing non-unit dim of the iteration space (this way, linalg.index <dim> increments by 1 with every loop iteration). foundIndexOp is updated to true when such Op is found.

Definition at line 996 of file Vectorization.cpp.

References mlir::Value::getDefiningOp(), and getTrailingNonUnitLoopDimIdx().

Referenced by getTensorExtractMemoryAccessPattern().

isLoopInvariantIdx()

static bool isLoopInvariantIdx ( LinalgOp &  linalgOp, Value &  val, VectorType  resType  )

static

Checks whether val can be used for calculating a loop invariant index.

Definition at line 937 of file Vectorization.cpp.

References mlir::Value::getDefiningOp().

Referenced by getTensorExtractMemoryAccessPattern().

isMaskTriviallyFoldable()

static bool isMaskTriviallyFoldable ( SmallVector< OpFoldResult > &  maskSizes, SmallVector< Value > &  writeIdxs, ArrayRef< int64_t >  destShape, ArrayRef< int64_t >  maskShape  )

static

Determines whether a mask for xfer_write is trivially "all true".

Given all the inputs required to generate a mask (mask sizes and shapes), and an xfer_write operation (write indices and the destination tensor shape), determines whether the corresponding mask would be trivially foldable (i.e., trivially "all true").

Use this method to avoid generating spurious masks and relaying on vectorization post-processing to remove them.

Pre-conditions for a mask to be trivially foldable:

• All involved shapes (mask + destination tensor) are static.
• All write indices are constant.
• All mask sizes are constant (including arith.constant).

If the pre-conditions are met, the method checks for each destination dimension d: (1) destDimSize[rankDiff + d] <= maskShape[d] (2) destDimSize[rankDiff + d] <= writeIndex[d] + maskSize[d]

rankDiff = rank(dest) - rank(mask).

This method takes a conservative view: it may return false even if the mask is technically foldable.

EXAMPLE 1 (trivially foldable, all shapes match, mask sizes match the shape of the dest tensor): c0 = arith.constant 0 : index mask = vector.create_mask 5, 1 vector.mask mask { vector.transfer_write vecToStore_1, dest{[c0, c0] {in_bounds = [true, true]} : vector<5x1xi32>, tensor<5x1xi32> }

EXAMPLE 2 (not trivially foldable - vector shape exceeds the tensor shape, mask is required to avoid out-of-bounds write): c0 = arith.constant 0 : index mask = vector.create_mask 5, 1 vector.mask mask { vector.transfer_write vecToStore_2, dest[c0, c0] {in_bounds = [true, true]} : vector<8x1xi32>, tensor<5x1xi32> }

TODO: Re-use in createReadOrMaskedRead

Definition at line 1586 of file Vectorization.cpp.

References clamp(), mlir::detail::enumerate(), mlir::getConstantIntValue(), mlir::m_ConstantInt(), and mlir::matchPattern().

isSupportedPoolKind()

static bool isSupportedPoolKind ( vector::CombiningKind  kind )

static

Definition at line 2292 of file Vectorization.cpp.

References kind, and MINUI.

Referenced by vectorizeConvOpPrecondition().

matchLinalgReduction()

static Operation* matchLinalgReduction ( OpOperand *  outputOperand )

static

Check whether outputOperand is a reduction with a single combiner operation.

Return the combiner operation of the reduction. Return nullptr otherwise. Multiple reduction operations would impose an ordering between reduction dimensions and is currently unsupported in Linalg. This limitation is motivated by the fact that e.g. min(max(X)) != max(min(X))

Definition at line 635 of file Vectorization.cpp.

References mlir::OpOperand::getOperandNumber(), mlir::detail::IROperandBase::getOwner(), and mlir::matchReduction().

Referenced by reductionPreconditions(), and vectorizeConvOpPrecondition().

mayExistInterleavedUses()

static bool mayExistInterleavedUses ( Operation *  firstOp, Operation *  secondOp, ValueRange  values  )

static

Check whether there is any interleaved use of any values between firstOp and secondOp.

Conservatively return true if any op or value is in a different block.

Definition at line 3258 of file Vectorization.cpp.

References mlir::Operation::getBlock(), and mlir::Operation::isBeforeInBlock().

Referenced by mlir::linalg::LinalgCopyVTRForwardingPattern::matchAndRewrite(), and mlir::linalg::LinalgCopyVTWForwardingPattern::matchAndRewrite().

reduceIfNeeded()

static Operation* reduceIfNeeded ( OpBuilder &  b, LinalgOp  linalgOp, Operation *  op, Value  reduceValue, Value  initialValue, const IRMapping &  bvm  )

static

Emit reduction operations if the shapes of the value to reduce is different that the result shape.

Definition at line 1267 of file Vectorization.cpp.

References buildMultiDimReduce(), getDimsToReduce(), mlir::Value::getType(), and mlir::IRMapping::lookup().

reductionPreconditions()

static LogicalResult reductionPreconditions ( LinalgOp  op )

static

Definition at line 2013 of file Vectorization.cpp.

References mlir::linalg::getCombinerOpKind(), mlir::AffineMap::isPermutation(), mlir::linalg::isReductionIterator(), and matchLinalgReduction().

Referenced by vectorizeDynamicLinalgOpPrecondition(), and vectorizeLinalgOpPrecondition().

reindexIndexingMap()

static AffineMap reindexIndexingMap ( AffineMap  map )

static

Given an indexing map coming from a LinalgOp indexing, restricted to a projectedPermutation, compress the unused dimensions to serve as a permutation_map for a vector transfer operation.

For example, given a linalg op such as:

%0 = linalg.generic {
     indexing_maps = affine_map<(d0, d1, d2, d3, d4) -> (d4, d0, d2)>,
     indexing_maps = affine_map<(d0, d1, d2, d3, d4) -> (d1, d3)>
   }
  ins(%0 : tensor<2x3x4xf32>)
 outs(%1 : tensor<5x6xf32>)

the iteration domain size of the linalg op is 3x5x4x6x2. The first affine map is reindexed to affine_map<(d0, d1, d2) -> (d2, d0, d1)>, the second affine map is reindexed to affine_map<(d0, d1) -> (d0, d1)>.

Definition at line 561 of file Vectorization.cpp.

References mlir::compressUnusedDims(), and mlir::AffineMap::isProjectedPermutation().

tensorExtractVectorizationPrecondition()

static LogicalResult tensorExtractVectorizationPrecondition ( Operation *  op, bool  vectorizeNDExtract  )

static

Helper function to check if the tensor.extract can be vectorized by the custom hook vectorizeTensorExtract.

Definition at line 838 of file Vectorization.cpp.

Referenced by vectorizeLinalgOpPrecondition().

vectorizeAsInsertSliceOp()

static LogicalResult vectorizeAsInsertSliceOp ( RewriterBase &  rewriter, tensor::InsertSliceOp  sliceOp, ArrayRef< int64_t >  inputVectorSizes, SmallVectorImpl< Value > &  newResults  )

static

Vectorize tensor::InsertSliceOp with:

• vector::TransferReadOp + vector::TransferWriteOp The vector sizes are either:
• user-provided in inputVectorSizes, or
• inferred from the static dims in the input and output tensors. Bails out if:
• vector sizes are not user-provided, and
• at least one dim is dynamic (in both the input and output tensors).

Before: !t_in_type = tensor<1x2x3xf32> !t_out_type = tensor<9x8x7x1x2x3xf32> !v_type = vector<1x2x3xf32> inserted_slice = tensor.insert_slice src into dest ... : !t_in_type into !t_out_type After: read = vector.transfer_read src[...], pad ... : !t_in_type, !v_type write = vector.transfer_write read, dest ... : !v_type, !t_out_type

Definition at line 3089 of file Vectorization.cpp.

References mlir::arith::ConstantIndexOp::create(), mlir::vector::createReadOrMaskedRead(), createWriteOrMaskedWrite(), mlir::get(), mlir::Operation::getResult(), getStaticPadVal(), mlir::getValueOrCreateConstantIndexOp(), mlir::Builder::getZeroAttr(), and mlir::OpBuilder::setInsertionPoint().

vectorizeAsLinalgContraction()

static LogicalResult vectorizeAsLinalgContraction ( RewriterBase &  rewriter, VectorizationState &  state, LinalgOp  linalgOp, SmallVectorImpl< Value > &  newResults  )

static

Vectorize a named linalg contraction op into: vector::TransferReadOp - Reads vectors from the operands vector::ContractionOp - Performs contraction vector::TransferWriteOp - Write the result vector back to the destination The operands shapes are preserved and loaded directly into vectors.

Any further permutations or numerical casting remain within contraction op.

Definition at line 2152 of file Vectorization.cpp.

vectorizeAsLinalgGeneric()

static LogicalResult vectorizeAsLinalgGeneric ( RewriterBase &  rewriter, VectorizationState &  state, LinalgOp  linalgOp, SmallVectorImpl< Value > &  newResults  )

static

Generic vectorization function that rewrites the body of a linalgOp into vector form.

Generic vectorization proceeds as follows:

1. Verify the linalgOp has one non-empty region.
2. Values defined above the region are mapped to themselves and will be broadcasted on a per-need basis by their consumers.
3. Each region argument is vectorized into a vector.transfer_read (or 0-d load). TODO: Reuse opportunities for RAR dependencies. 4a. Register CustomVectorizationHook for YieldOp to capture the results. 4rewriter. Register CustomVectorizationHook for IndexOp to access the iteration indices.
4. Iteratively call vectorizeOneOp on the region operations.

When broadcastToMaximalCommonShape is set to true, eager broadcasting is performed to the maximal common vector size implied by the linalgOp iteration space. This eager broadcasting is introduced in the permutation_map of the vector.transfer_read operations. The eager broadcasting makes it trivial to determine where broadcast, transposes and reductions should occur, without any bookkeeping. The tradeoff is that, in the absence of good canonicalizations, the amount of work increases. This is not deemed a problem as we expect canonicalizations and foldings to aggressively clean up the useless work.

Definition at line 1418 of file Vectorization.cpp.

vectorizeAsTensorPackOp()

static LogicalResult vectorizeAsTensorPackOp ( RewriterBase &  rewriter, linalg::PackOp  packOp, ArrayRef< int64_t >  inputVectorSizes, SmallVectorImpl< Value > &  newResults  )

static

Vectorize linalg::PackOp with (1) static inner_tiles (2) constant padding value and (3) input vector sizes into:

masked_transfer_read->shape_cast->transpose->transfer_write_in_bounds

As in the following example: pack = tensor.pack src inner_dims_pos = [2, 1] inner_tiles = [16, 2] into dst : tensor<32x8x16xf32> -> tensor<32x4x1x16x2xf32>

This pack would be vectorized to:

load = vector.mask mask { vector.transfer_read arg0[c0, c0, c0], cst {in_bounds = [true, true, true]} : tensor<32x7x16xf32>, vector<32x8x16xf32> } : vector<32x8x16xi1> -> vector<32x8x16xf32> shape_cast = vector.shape_cast load : vector<32x8x16xf32> to vector<32x4x2x1x16xf32> transpose = vector.transpose shape_cast, [0, 1, 3, 4, 2] : vector<32x4x2x1x16xf32> to vector<32x4x1x16x2xf32> write = vector.transfer_write transpose, empty[c0_0, c0_0, c0_0, c0_0, c0_0] {in_bounds = [true, true, true, true, true]} : vector<32x4x1x16x2xf32>, tensor<32x4x1x16x2xf32>

If the (3) input vector sizes are not provided, the vector sizes are determined by the result tensor shape and the in_bounds attribute is used instead of masking to mark out-of-bounds accesses.

NOTE: The input vector sizes specify the dimensions corresponding to the outer dimensions of the output tensor. The remaining dimensions are computed based on, e.g., the static inner tiles. Supporting dynamic inner tiles will require the user to specify the missing vector sizes. This is left as a TODO.

Definition at line 1765 of file Vectorization.cpp.

References mlir::applyPermutationToVector(), mlir::vector::createReadOrMaskedRead(), createWriteOrMaskedWrite(), mlir::detail::enumerate(), mlir::get(), mlir::linalg::getPackInverseDestPerm(), mlir::Operation::getResult(), getTiledPackShape(), mlir::Builder::getZeroAttr(), innerDimsPos, innerTiles, mlir::invertPermutationVector(), outerDimsPerm, and mlir::OpBuilder::setInsertionPoint().

vectorizeAsTensorPadOp()

static LogicalResult vectorizeAsTensorPadOp ( RewriterBase &  rewriter, tensor::PadOp  padOp, ArrayRef< int64_t >  inputVectorSizes, SmallVectorImpl< Value > &  newResults  )

static

Vectorize a padOp with (1) static result type, (2) constant padding value and (3) all-zero lowPad to transfer_write_in_bounds(transfer_read_masked(pad_source, pad_value)).

Definition at line 1983 of file Vectorization.cpp.

References mlir::vector::createReadOrMaskedRead(), createWriteOrMaskedWrite(), mlir::Operation::getResult(), and mlir::OpBuilder::setInsertionPoint().

vectorizeAsTensorUnpackOp()

static LogicalResult vectorizeAsTensorUnpackOp ( RewriterBase &  rewriter, linalg::UnPackOp  unpackOp, ArrayRef< int64_t >  inputVectorSizes, ArrayRef< bool >  inputScalableVecDims, SmallVectorImpl< Value > &  newResults  )

static

Vectorize linalg.unpack as:

• xfer_read -> vector.transpose -> vector.shape_cast -> xfer_write

The input-vector-sizes specify the read vector sizes (i.e. the vector sizes for the xfer_read operation). This is sufficient to infer the other vector sizes required here.

If the vector sizes are not provided:

• the vector sizes are determined from the input tensor static shape.
• the inBounds attribute is used instead of masking.

EXAMPLE (no vector sizes):

%unpack = linalg.unpack  %src
 inner_dims_pos = [0, 1]
 inner_tiles = [8, 8]
 into %dest : tensor<1x1x8x8xf32> -> tensor<8x8xf32>

is vectorized as:

%read = vector.transfer_read %src
  : tensor<1x1x8x8xf32>, vector<1x1x8x8xf32>
%tr = vector.transpose %read, [0, 2, 1, 3]
  : vector<1x1x8x8xf32> to vector<1x8x1x8xf32>
%sc = vector.shape_cast %tr
  : vector<1x8x1x8xf32> to vector<8x8xf32>
%vector = vector.transfer_write %sc into %dest
  : vector<8x8xf32>, tensor<8x8xf32>

Definition at line 1912 of file Vectorization.cpp.

References mlir::convertReassociationIndicesToExprs(), mlir::vector::createReadOrMaskedRead(), createWriteOrMaskedWrite(), getCollapsedVecType(), mlir::Builder::getContext(), mlir::Operation::getResult(), mlir::getSymbolLessAffineMaps(), mlir::linalg::getUnPackInverseSrcPerm(), mlir::Builder::getZeroAttr(), and mlir::OpBuilder::setInsertionPoint().

vectorizeConvolution()

static FailureOr< Operation * > vectorizeConvolution ( RewriterBase &  rewriter, LinalgOp  convOp, ArrayRef< int64_t >  inputVecSizes = {}, ArrayRef< bool >  inputVecScalableFlags = {}, bool  flatten1DDepthwiseConv = false  )

static

Try to vectorize convOp as a convolution.

Helper function to vectorize a LinalgOp with convolution semantics.

Definition at line 4208 of file Vectorization.cpp.

Referenced by VectorizeConvolution::matchAndRewrite().

vectorizeConvOpPrecondition()

static LogicalResult vectorizeConvOpPrecondition ( linalg::LinalgOp  convOp )

static

Definition at line 2309 of file Vectorization.cpp.

References mlir::linalg::getCombinerOpKind(), getConvOperationKind(), isSupportedPoolKind(), and matchLinalgReduction().

Referenced by vectorizeLinalgOpPrecondition().

vectorizeDynamicConvOpPrecondition()

static LogicalResult vectorizeDynamicConvOpPrecondition ( linalg::LinalgOp  conv, bool  flatten1DDepthwiseConv  )

static

Definition at line 2033 of file Vectorization.cpp.

References mlir::Value::getType().

Referenced by vectorizeDynamicLinalgOpPrecondition().

vectorizeDynamicLinalgOpPrecondition()

static LogicalResult vectorizeDynamicLinalgOpPrecondition ( linalg::LinalgOp  op, bool  flatten1DDepthwiseConv  )

static

Definition at line 2061 of file Vectorization.cpp.

References hasReductionIterator(), mlir::linalg::isElementwise(), reductionPreconditions(), and vectorizeDynamicConvOpPrecondition().

Referenced by vectorizeLinalgOpPrecondition().

vectorizeInsertSliceOpPrecondition()

static LogicalResult vectorizeInsertSliceOpPrecondition ( tensor::InsertSliceOp  sliceOp, ArrayRef< int64_t >  inputVectorSizes  )

static

Definition at line 2113 of file Vectorization.cpp.

References getStaticPadVal().

Referenced by mlir::linalg::vectorizeOpPrecondition().

vectorizeLinalgIndex()

static VectorizationHookResult vectorizeLinalgIndex ( RewriterBase &  rewriter, VectorizationState &  state, Operation *  op, LinalgOp  linalgOp  )

static

Helper function to vectorize the index operations of a linalgOp.

Return VectorizationHookStatus::NewOp to signal the vectorization algorithm that it should map the produced operations. This function is meant to be used as a CustomVectorizationHook.

Definition at line 794 of file Vectorization.cpp.

vectorizeLinalgOpPrecondition()

static LogicalResult vectorizeLinalgOpPrecondition ( LinalgOp  linalgOp, ArrayRef< int64_t >  inputVectorSizes, bool  vectorizeNDExtract, bool  flatten1DDepthwiseConv  )

static

Definition at line 2354 of file Vectorization.cpp.

References mlir::linalg::allIndexingsAreProjectedPermutation(), mlir::remark::failed(), mlir::Region::front(), mlir::Operation::getRegion(), mlir::linalg::isElementwise(), mlir::vector::isValidMaskedInputVector(), reductionPreconditions(), tensorExtractVectorizationPrecondition(), vectorizeConvOpPrecondition(), and vectorizeDynamicLinalgOpPrecondition().

Referenced by mlir::linalg::vectorizeOpPrecondition().

vectorizeLinalgYield()

static VectorizationHookResult vectorizeLinalgYield ( RewriterBase &  rewriter, Operation *  op, const IRMapping &  bvm, VectorizationState &  state, LinalgOp  linalgOp, SmallVectorImpl< Value > &  newResults  )

static

Helper function to vectorize the terminator of a linalgOp.

New result vector values are appended to newResults. Return VectorizationHookStatus::NoReplace to signal the vectorization algorithm that it should not try to map produced operations and instead return the results using the newResults vector making them available to the vectorization algorithm for RAUW. This function is meant to be used as a CustomVectorizationHook.

Definition at line 770 of file Vectorization.cpp.

vectorizeOneOp()

static VectorizationHookResult vectorizeOneOp ( RewriterBase &  rewriter, VectorizationState &  state, LinalgOp  linalgOp, Operation *  op, const IRMapping &  bvm, ArrayRef< CustomVectorizationHook >  customVectorizationHooks  )

static

Generic vectorization for a single operation op, given already vectorized operands carried by bvm.

Vectorization occurs as follows:

1. Try to apply any of the customVectorizationHooks and return its result on success.
2. Clone any constant in the current scope without vectorization: each consumer of the constant will later determine the shape to which the constant needs to be broadcast to.
3. Fail on any remaining non ElementwiseMappable op. It is the purpose of the customVectorizationHooks to cover such cases.
4. Clone op in vector form to a vector of shape prescribed by the first operand of maximal rank. Other operands have smaller rank and are broadcast accordingly. It is assumed this broadcast is always legal, otherwise, it means one of the customVectorizationHooks is incorrect.

This function assumes all operands of op have been vectorized and are in the bvm mapping. As a consequence, this function is meant to be called on a topologically-sorted list of ops. This function does not update bvm but returns a VectorizationHookStatus that instructs the caller what bvm update needs to occur.

Definition at line 1303 of file Vectorization.cpp.

vectorizePackOpPrecondition()

static LogicalResult vectorizePackOpPrecondition ( linalg::PackOp  packOp, ArrayRef< int64_t >  inputVectorSizes  )

static

Definition at line 2423 of file Vectorization.cpp.

References mlir::remark::failed(), mlir::vector::isValidMaskedInputVector(), mlir::m_Constant(), and mlir::matchPattern().

Referenced by mlir::linalg::vectorizeOpPrecondition().

vectorizePadOpPrecondition()

static LogicalResult vectorizePadOpPrecondition ( tensor::PadOp  padOp, ArrayRef< int64_t >  inputVectorSizes  )

static

Definition at line 2457 of file Vectorization.cpp.

References mlir::detail::enumerate(), mlir::remark::failed(), and mlir::vector::isValidMaskedInputVector().

Referenced by mlir::linalg::vectorizeOpPrecondition().

vectorizeScalableVectorPrecondition()

static LogicalResult vectorizeScalableVectorPrecondition ( Operation *  op, ArrayRef< int64_t >  inputVectorSizes, ArrayRef< bool >  inputScalableVecDims  )

static

Preconditions for scalable vectors.

For Ops implementing the LinalgOp interface, this is quite restrictive - it models the fact that in practice we would only make selected dimensions scalable. For other Ops (e.g. linalg.unpack), this will succeed unconditionally - we are yet to identify meaningful conditions.

Definition at line 2502 of file Vectorization.cpp.

References hasReductionIterator(), and mlir::linalg::isElementwise().

Referenced by mlir::linalg::vectorizeOpPrecondition().

vectorizeTensorExtract()

static VectorizationHookResult vectorizeTensorExtract ( RewriterBase &  rewriter, VectorizationState &  state, Operation *  op, LinalgOp  linalgOp, const IRMapping &  bvm  )

static

Helper function to vectorize the tensor.extract operations.

Returns VectorizationHookStatus::NewOp to signal the vectorization algorithm that it should map the produced operations. This function is meant to be used as a CustomVectorizationHook.

Definition at line 1132 of file Vectorization.cpp.

vectorizeUnPackOpPrecondition()

static LogicalResult vectorizeUnPackOpPrecondition ( linalg::UnPackOp  unpackOp, ArrayRef< int64_t >  inputVectorSizes  )

static

This hook considers two cases: (1) If the input-vector-sizes are empty, then the vector sizes will be infered.

This is only possible when all shapes are static. (2) If the input-vector-sizes are non-empty (i.e. user provided), then carry out basic sanity-checking.

Definition at line 2086 of file Vectorization.cpp.

References mlir::remark::failed(), and mlir::vector::isValidMaskedInputVector().

Referenced by mlir::linalg::vectorizeOpPrecondition().