d572cd1b06
This new features enabled to dedicate custom storage inline within operations.
This storage can be used as an alternative to attributes to store data that is
specific to an operation. Attribute can also be stored inside the properties
storage if desired, but any kind of data can be present as well. This offers
a way to store and mutate data without uniquing in the Context like Attribute.
See the OpPropertiesTest.cpp for an example where a struct with a
std::vector<> is attached to an operation and mutated in-place:
struct TestProperties {
int a = -1;
float b = -1.;
std::vector<int64_t> array = {-33};
};
More complex scheme (including reference-counting) are also possible.
The only constraint to enable storing a C++ object as "properties" on an
operation is to implement three functions:
- convert from the candidate object to an Attribute
- convert from the Attribute to the candidate object
- hash the object
Optional the parsing and printing can also be customized with 2 extra
functions.
A new options is introduced to ODS to allow dialects to specify:
let usePropertiesForAttributes = 1;
When set to true, the inherent attributes for all the ops in this dialect
will be using properties instead of being stored alongside discardable
attributes.
The TestDialect showcases this feature.
Another change is that we introduce new APIs on the Operation class
to access separately the inherent attributes from the discardable ones.
We envision deprecating and removing the `getAttr()`, `getAttrsDictionary()`,
and other similar method which don't make the distinction explicit, leading
to an entirely separate namespace for discardable attributes.
Differential Revision: https://reviews.llvm.org/D141742
6058 lines
239 KiB
C++
6058 lines
239 KiB
C++
//===- VectorOps.cpp - MLIR Vector Dialect Operations ---------------------===//
|
|
//
|
|
// 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 convenience types for working with super-vectorization
|
|
// operations, in particular super-vector loads and stores.
|
|
//
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
#include "mlir/Dialect/Vector/IR/VectorOps.h"
|
|
|
|
#include "mlir/Dialect/Arith/IR/Arith.h"
|
|
#include "mlir/Dialect/Arith/Utils/Utils.h"
|
|
#include "mlir/Dialect/MemRef/IR/MemRef.h"
|
|
#include "mlir/Dialect/Tensor/IR/Tensor.h"
|
|
#include "mlir/Dialect/Utils/IndexingUtils.h"
|
|
#include "mlir/Dialect/Utils/StructuredOpsUtils.h"
|
|
#include "mlir/IR/AffineExpr.h"
|
|
#include "mlir/IR/AffineMap.h"
|
|
#include "mlir/IR/Builders.h"
|
|
#include "mlir/IR/BuiltinAttributes.h"
|
|
#include "mlir/IR/BuiltinOps.h"
|
|
#include "mlir/IR/BuiltinTypes.h"
|
|
#include "mlir/IR/DialectImplementation.h"
|
|
#include "mlir/IR/IRMapping.h"
|
|
#include "mlir/IR/OpImplementation.h"
|
|
#include "mlir/IR/PatternMatch.h"
|
|
#include "mlir/IR/TypeUtilities.h"
|
|
#include "mlir/Support/LLVM.h"
|
|
#include "llvm/ADT/ArrayRef.h"
|
|
#include "llvm/ADT/STLExtras.h"
|
|
#include "llvm/ADT/SmallVector.h"
|
|
#include "llvm/ADT/StringSet.h"
|
|
#include "llvm/ADT/TypeSwitch.h"
|
|
#include "llvm/ADT/bit.h"
|
|
|
|
#include <cassert>
|
|
#include <cstdint>
|
|
#include <numeric>
|
|
|
|
#include "mlir/Dialect/Vector/IR/VectorOpsDialect.cpp.inc"
|
|
// Pull in all enum type and utility function definitions.
|
|
#include "mlir/Dialect/Vector/IR/VectorOpsEnums.cpp.inc"
|
|
|
|
using namespace mlir;
|
|
using namespace mlir::vector;
|
|
|
|
/// Helper enum to classify mask value.
|
|
enum class MaskFormat {
|
|
AllTrue = 0,
|
|
AllFalse = 1,
|
|
Unknown = 2,
|
|
};
|
|
|
|
/// Helper method to classify a mask value. Currently, the method
|
|
/// looks "under the hood" of a constant value with dense attributes
|
|
/// and a constant mask operation (since the client may be called at
|
|
/// various stages during progressive lowering).
|
|
static MaskFormat getMaskFormat(Value mask) {
|
|
if (auto c = mask.getDefiningOp<arith::ConstantOp>()) {
|
|
// Inspect constant dense values. We count up for bits that
|
|
// are set, count down for bits that are cleared, and bail
|
|
// when a mix is detected.
|
|
if (auto denseElts = c.getValue().dyn_cast<DenseIntElementsAttr>()) {
|
|
int64_t val = 0;
|
|
for (bool b : denseElts.getValues<bool>())
|
|
if (b && val >= 0)
|
|
val++;
|
|
else if (!b && val <= 0)
|
|
val--;
|
|
else
|
|
return MaskFormat::Unknown;
|
|
if (val > 0)
|
|
return MaskFormat::AllTrue;
|
|
if (val < 0)
|
|
return MaskFormat::AllFalse;
|
|
}
|
|
} else if (auto m = mask.getDefiningOp<ConstantMaskOp>()) {
|
|
// Inspect constant mask index. If the index exceeds the
|
|
// dimension size, all bits are set. If the index is zero
|
|
// or less, no bits are set.
|
|
ArrayAttr masks = m.getMaskDimSizes();
|
|
auto shape = m.getType().getShape();
|
|
bool allTrue = true;
|
|
bool allFalse = true;
|
|
for (auto [maskIdx, dimSize] : llvm::zip_equal(masks, shape)) {
|
|
int64_t i = maskIdx.cast<IntegerAttr>().getInt();
|
|
if (i < dimSize)
|
|
allTrue = false;
|
|
if (i > 0)
|
|
allFalse = false;
|
|
}
|
|
if (allTrue)
|
|
return MaskFormat::AllTrue;
|
|
if (allFalse)
|
|
return MaskFormat::AllFalse;
|
|
}
|
|
return MaskFormat::Unknown;
|
|
}
|
|
|
|
/// Default callback to build a region with a 'vector.yield' terminator with no
|
|
/// arguments.
|
|
void mlir::vector::buildTerminatedBody(OpBuilder &builder, Location loc) {
|
|
builder.create<vector::YieldOp>(loc);
|
|
}
|
|
|
|
// Helper for verifying combining kinds in contractions and reductions.
|
|
static bool isSupportedCombiningKind(CombiningKind combiningKind,
|
|
Type elementType) {
|
|
switch (combiningKind) {
|
|
case CombiningKind::ADD:
|
|
case CombiningKind::MUL:
|
|
return elementType.isIntOrIndexOrFloat();
|
|
case CombiningKind::MINUI:
|
|
case CombiningKind::MINSI:
|
|
case CombiningKind::MAXUI:
|
|
case CombiningKind::MAXSI:
|
|
case CombiningKind::AND:
|
|
case CombiningKind::OR:
|
|
case CombiningKind::XOR:
|
|
return elementType.isIntOrIndex();
|
|
case CombiningKind::MINF:
|
|
case CombiningKind::MAXF:
|
|
return elementType.isa<FloatType>();
|
|
}
|
|
return false;
|
|
}
|
|
|
|
/// Return true if the last dimension of the MemRefType has unit stride. Also
|
|
/// return true for memrefs with no strides.
|
|
bool mlir::vector::isLastMemrefDimUnitStride(MemRefType type) {
|
|
int64_t offset;
|
|
SmallVector<int64_t> strides;
|
|
auto successStrides = getStridesAndOffset(type, strides, offset);
|
|
return succeeded(successStrides) && (strides.empty() || strides.back() == 1);
|
|
}
|
|
|
|
AffineMap mlir::vector::getTransferMinorIdentityMap(ShapedType shapedType,
|
|
VectorType vectorType) {
|
|
int64_t elementVectorRank = 0;
|
|
VectorType elementVectorType =
|
|
shapedType.getElementType().dyn_cast<VectorType>();
|
|
if (elementVectorType)
|
|
elementVectorRank += elementVectorType.getRank();
|
|
// 0-d transfers are to/from tensor<t>/memref<t> and vector<1xt>.
|
|
// TODO: replace once we have 0-d vectors.
|
|
if (shapedType.getRank() == 0 &&
|
|
vectorType.getShape() == ArrayRef<int64_t>{1})
|
|
return AffineMap::get(
|
|
/*numDims=*/0, /*numSymbols=*/0,
|
|
getAffineConstantExpr(0, shapedType.getContext()));
|
|
return AffineMap::getMinorIdentityMap(
|
|
shapedType.getRank(), vectorType.getRank() - elementVectorRank,
|
|
shapedType.getContext());
|
|
}
|
|
|
|
bool mlir::vector::checkSameValueRAW(vector::TransferWriteOp defWrite,
|
|
vector::TransferReadOp read) {
|
|
return !defWrite.hasOutOfBoundsDim() && !defWrite.getMask() &&
|
|
!read.getMask() && defWrite.getIndices() == read.getIndices() &&
|
|
defWrite.getVectorType() == read.getVectorType() &&
|
|
defWrite.getPermutationMap() == read.getPermutationMap();
|
|
}
|
|
|
|
bool mlir::vector::checkSameValueWAW(vector::TransferWriteOp write,
|
|
vector::TransferWriteOp priorWrite) {
|
|
return priorWrite.getIndices() == write.getIndices() &&
|
|
priorWrite.getMask() == write.getMask() &&
|
|
priorWrite.getVectorType() == write.getVectorType() &&
|
|
priorWrite.getPermutationMap() == write.getPermutationMap();
|
|
}
|
|
|
|
bool mlir::vector::isDisjointTransferIndices(
|
|
VectorTransferOpInterface transferA, VectorTransferOpInterface transferB) {
|
|
// For simplicity only look at transfer of same type.
|
|
if (transferA.getVectorType() != transferB.getVectorType())
|
|
return false;
|
|
unsigned rankOffset = transferA.getLeadingShapedRank();
|
|
for (unsigned i = 0, e = transferA.indices().size(); i < e; i++) {
|
|
auto indexA = transferA.indices()[i].getDefiningOp<arith::ConstantOp>();
|
|
auto indexB = transferB.indices()[i].getDefiningOp<arith::ConstantOp>();
|
|
// If any of the indices are dynamic we cannot prove anything.
|
|
if (!indexA || !indexB)
|
|
continue;
|
|
|
|
if (i < rankOffset) {
|
|
// For leading dimensions, if we can prove that index are different we
|
|
// know we are accessing disjoint slices.
|
|
if (indexA.getValue().cast<IntegerAttr>().getInt() !=
|
|
indexB.getValue().cast<IntegerAttr>().getInt())
|
|
return true;
|
|
} else {
|
|
// For this dimension, we slice a part of the memref we need to make sure
|
|
// the intervals accessed don't overlap.
|
|
int64_t distance =
|
|
std::abs(indexA.getValue().cast<IntegerAttr>().getInt() -
|
|
indexB.getValue().cast<IntegerAttr>().getInt());
|
|
if (distance >= transferA.getVectorType().getDimSize(i - rankOffset))
|
|
return true;
|
|
}
|
|
}
|
|
return false;
|
|
}
|
|
|
|
bool mlir::vector::isDisjointTransferSet(VectorTransferOpInterface transferA,
|
|
VectorTransferOpInterface transferB) {
|
|
if (transferA.source() != transferB.source())
|
|
return false;
|
|
return isDisjointTransferIndices(transferA, transferB);
|
|
}
|
|
|
|
// Helper to iterate over n-D vector slice elements. Calculate the next
|
|
// `position` in the n-D vector of size `shape`, applying an offset `offsets`.
|
|
// Modifies the `position` in place. Returns a failure when `position` becomes
|
|
// the end position.
|
|
static LogicalResult incSlicePosition(MutableArrayRef<int64_t> position,
|
|
ArrayRef<int64_t> shape,
|
|
ArrayRef<int64_t> offsets) {
|
|
for (auto [posInDim, dimSize, offsetInDim] :
|
|
llvm::reverse(llvm::zip_equal(position, shape, offsets))) {
|
|
++posInDim;
|
|
if (posInDim < dimSize + offsetInDim)
|
|
return success();
|
|
|
|
// Carry the overflow to the next loop iteration.
|
|
posInDim = offsetInDim;
|
|
}
|
|
|
|
return failure();
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// CombiningKindAttr
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
namespace mlir {
|
|
namespace vector {
|
|
namespace detail {
|
|
struct BitmaskEnumStorage : public AttributeStorage {
|
|
using KeyTy = uint64_t;
|
|
|
|
BitmaskEnumStorage(KeyTy val) : value(val) {}
|
|
|
|
bool operator==(const KeyTy &key) const { return value == key; }
|
|
|
|
static BitmaskEnumStorage *construct(AttributeStorageAllocator &allocator,
|
|
const KeyTy &key) {
|
|
return new (allocator.allocate<BitmaskEnumStorage>())
|
|
BitmaskEnumStorage(key);
|
|
}
|
|
|
|
KeyTy value = 0;
|
|
};
|
|
} // namespace detail
|
|
} // namespace vector
|
|
} // namespace mlir
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// VectorDialect
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
void VectorDialect::initialize() {
|
|
addAttributes<
|
|
#define GET_ATTRDEF_LIST
|
|
#include "mlir/Dialect/Vector/IR/VectorOpsAttrDefs.cpp.inc"
|
|
>();
|
|
|
|
addOperations<
|
|
#define GET_OP_LIST
|
|
#include "mlir/Dialect/Vector/IR/VectorOps.cpp.inc"
|
|
>();
|
|
}
|
|
|
|
/// Materialize a single constant operation from a given attribute value with
|
|
/// the desired resultant type.
|
|
Operation *VectorDialect::materializeConstant(OpBuilder &builder,
|
|
Attribute value, Type type,
|
|
Location loc) {
|
|
return arith::ConstantOp::materialize(builder, value, type, loc);
|
|
}
|
|
|
|
IntegerType vector::getVectorSubscriptType(Builder &builder) {
|
|
return builder.getIntegerType(64);
|
|
}
|
|
|
|
ArrayAttr vector::getVectorSubscriptAttr(Builder &builder,
|
|
ArrayRef<int64_t> values) {
|
|
return builder.getI64ArrayAttr(values);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// MultiDimReductionOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
void vector::MultiDimReductionOp::build(OpBuilder &builder,
|
|
OperationState &result, Value source,
|
|
Value acc, ArrayRef<bool> reductionMask,
|
|
CombiningKind kind) {
|
|
SmallVector<int64_t> reductionDims;
|
|
for (const auto &en : llvm::enumerate(reductionMask))
|
|
if (en.value())
|
|
reductionDims.push_back(en.index());
|
|
build(builder, result, kind, source, acc,
|
|
builder.getI64ArrayAttr(reductionDims));
|
|
}
|
|
|
|
OpFoldResult MultiDimReductionOp::fold(FoldAdaptor adaptor) {
|
|
// Single parallel dim, this is a noop.
|
|
if (getSourceVectorType().getRank() == 1 && !isReducedDim(0))
|
|
return getSource();
|
|
return {};
|
|
}
|
|
|
|
std::optional<SmallVector<int64_t, 4>>
|
|
MultiDimReductionOp::getShapeForUnroll() {
|
|
return llvm::to_vector<4>(getSourceVectorType().getShape());
|
|
}
|
|
|
|
LogicalResult MultiDimReductionOp::verify() {
|
|
SmallVector<int64_t> targetShape;
|
|
Type inferredReturnType;
|
|
for (auto it : llvm::enumerate(getSourceVectorType().getShape()))
|
|
if (!llvm::any_of(getReductionDims().getValue(), [&](Attribute attr) {
|
|
return attr.cast<IntegerAttr>().getValue() == it.index();
|
|
}))
|
|
targetShape.push_back(it.value());
|
|
// TODO: update to also allow 0-d vectors when available.
|
|
if (targetShape.empty())
|
|
inferredReturnType = getSourceVectorType().getElementType();
|
|
else
|
|
inferredReturnType =
|
|
VectorType::get(targetShape, getSourceVectorType().getElementType());
|
|
if (getType() != inferredReturnType)
|
|
return emitOpError() << "destination type " << getType()
|
|
<< " is incompatible with source type "
|
|
<< getSourceVectorType();
|
|
|
|
return success();
|
|
}
|
|
|
|
/// Returns the mask type expected by this operation.
|
|
Type MultiDimReductionOp::getExpectedMaskType() {
|
|
auto vecType = getSourceVectorType();
|
|
return VectorType::get(vecType.getShape(),
|
|
IntegerType::get(vecType.getContext(), /*width=*/1));
|
|
}
|
|
|
|
namespace {
|
|
// Only unit dimensions that are being reduced are folded. If the dimension is
|
|
// unit, but not reduced, it is not folded, thereby keeping the output type the
|
|
// same. If not all dimensions which are reduced are of unit dimension, this
|
|
// transformation does nothing. This is just a generalization of
|
|
// ElideSingleElementReduction for ReduceOp.
|
|
struct ElideUnitDimsInMultiDimReduction
|
|
: public OpRewritePattern<MultiDimReductionOp> {
|
|
using OpRewritePattern::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(MultiDimReductionOp reductionOp,
|
|
PatternRewriter &rewriter) const override {
|
|
ArrayRef<int64_t> shape = reductionOp.getSourceVectorType().getShape();
|
|
for (const auto &dim : enumerate(shape)) {
|
|
if (reductionOp.isReducedDim(dim.index()) && dim.value() != 1)
|
|
return failure();
|
|
}
|
|
|
|
// Vector mask setup.
|
|
OpBuilder::InsertionGuard guard(rewriter);
|
|
Operation *rootOp;
|
|
Value mask;
|
|
if (reductionOp.isMasked()) {
|
|
rewriter.setInsertionPoint(reductionOp.getMaskingOp());
|
|
rootOp = reductionOp.getMaskingOp();
|
|
mask = reductionOp.getMaskingOp().getMask();
|
|
} else {
|
|
rootOp = reductionOp;
|
|
}
|
|
|
|
Location loc = reductionOp.getLoc();
|
|
Value acc = reductionOp.getAcc();
|
|
Value cast;
|
|
if (auto dstVecType = dyn_cast<VectorType>(reductionOp.getDestType())) {
|
|
if (mask) {
|
|
VectorType newMaskType =
|
|
VectorType::get(dstVecType.getShape(), rewriter.getI1Type());
|
|
mask = rewriter.create<vector::ShapeCastOp>(loc, newMaskType, mask);
|
|
}
|
|
cast = rewriter.create<vector::ShapeCastOp>(
|
|
loc, reductionOp.getDestType(), reductionOp.getSource());
|
|
} else {
|
|
// This means we are reducing all the dimensions, and all reduction
|
|
// dimensions are of size 1. So a simple extraction would do.
|
|
auto zeroAttr =
|
|
rewriter.getI64ArrayAttr(SmallVector<int64_t>(shape.size(), 0));
|
|
if (mask)
|
|
mask = rewriter.create<vector::ExtractOp>(loc, rewriter.getI1Type(),
|
|
mask, zeroAttr);
|
|
cast = rewriter.create<vector::ExtractOp>(
|
|
loc, reductionOp.getDestType(), reductionOp.getSource(), zeroAttr);
|
|
}
|
|
|
|
Value result = vector::makeArithReduction(
|
|
rewriter, loc, reductionOp.getKind(), acc, cast, mask);
|
|
rewriter.replaceOp(rootOp, result);
|
|
return success();
|
|
}
|
|
};
|
|
} // namespace
|
|
|
|
void MultiDimReductionOp::getCanonicalizationPatterns(
|
|
RewritePatternSet &results, MLIRContext *context) {
|
|
results.add<ElideUnitDimsInMultiDimReduction>(context);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// ReductionOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
void vector::ReductionOp::build(OpBuilder &builder, OperationState &result,
|
|
CombiningKind kind, Value vector) {
|
|
build(builder, result, kind, vector, /*acc=*/Value());
|
|
}
|
|
|
|
void vector::ReductionOp::build(OpBuilder &builder, OperationState &result,
|
|
CombiningKind kind, Value vector, Value acc) {
|
|
build(builder, result, vector.getType().cast<VectorType>().getElementType(),
|
|
kind, vector, acc);
|
|
}
|
|
|
|
LogicalResult ReductionOp::verify() {
|
|
// Verify for 0-D and 1-D vector.
|
|
int64_t rank = getSourceVectorType().getRank();
|
|
if (rank > 1)
|
|
return emitOpError("unsupported reduction rank: ") << rank;
|
|
|
|
// Verify supported reduction kind.
|
|
Type eltType = getDest().getType();
|
|
if (!isSupportedCombiningKind(getKind(), eltType))
|
|
return emitOpError("unsupported reduction type '")
|
|
<< eltType << "' for kind '" << stringifyCombiningKind(getKind())
|
|
<< "'";
|
|
|
|
return success();
|
|
}
|
|
|
|
ParseResult ReductionOp::parse(OpAsmParser &parser, OperationState &result) {
|
|
SmallVector<OpAsmParser::UnresolvedOperand, 2> operandsInfo;
|
|
Type redType;
|
|
Type resType;
|
|
CombiningKindAttr kindAttr;
|
|
if (parser.parseCustomAttributeWithFallback(kindAttr, Type{}, "kind",
|
|
result.attributes) ||
|
|
parser.parseComma() || parser.parseOperandList(operandsInfo) ||
|
|
parser.parseColonType(redType) ||
|
|
parser.parseKeywordType("into", resType) ||
|
|
(!operandsInfo.empty() &&
|
|
parser.resolveOperand(operandsInfo[0], redType, result.operands)) ||
|
|
(operandsInfo.size() > 1 &&
|
|
parser.resolveOperand(operandsInfo[1], resType, result.operands)) ||
|
|
parser.addTypeToList(resType, result.types))
|
|
return failure();
|
|
if (operandsInfo.empty() || operandsInfo.size() > 2)
|
|
return parser.emitError(parser.getNameLoc(),
|
|
"unsupported number of operands");
|
|
return success();
|
|
}
|
|
|
|
void ReductionOp::print(OpAsmPrinter &p) {
|
|
p << " ";
|
|
getKindAttr().print(p);
|
|
p << ", " << getVector();
|
|
if (getAcc())
|
|
p << ", " << getAcc();
|
|
p << " : " << getVector().getType() << " into " << getDest().getType();
|
|
}
|
|
|
|
// MaskableOpInterface methods.
|
|
|
|
/// Returns the mask type expected by this operation.
|
|
Type ReductionOp::getExpectedMaskType() {
|
|
auto vecType = getSourceVectorType();
|
|
return vecType.cloneWith(std::nullopt,
|
|
IntegerType::get(vecType.getContext(), /*width=*/1));
|
|
}
|
|
|
|
Value mlir::vector::getVectorReductionOp(arith::AtomicRMWKind op,
|
|
OpBuilder &builder, Location loc,
|
|
Value vector) {
|
|
switch (op) {
|
|
case arith::AtomicRMWKind::addf:
|
|
case arith::AtomicRMWKind::addi:
|
|
return builder.create<vector::ReductionOp>(vector.getLoc(),
|
|
CombiningKind::ADD, vector);
|
|
case arith::AtomicRMWKind::mulf:
|
|
case arith::AtomicRMWKind::muli:
|
|
return builder.create<vector::ReductionOp>(vector.getLoc(),
|
|
CombiningKind::MUL, vector);
|
|
case arith::AtomicRMWKind::minf:
|
|
return builder.create<vector::ReductionOp>(vector.getLoc(),
|
|
CombiningKind::MINF, vector);
|
|
case arith::AtomicRMWKind::mins:
|
|
return builder.create<vector::ReductionOp>(vector.getLoc(),
|
|
CombiningKind::MINSI, vector);
|
|
case arith::AtomicRMWKind::minu:
|
|
return builder.create<vector::ReductionOp>(vector.getLoc(),
|
|
CombiningKind::MINUI, vector);
|
|
case arith::AtomicRMWKind::maxf:
|
|
return builder.create<vector::ReductionOp>(vector.getLoc(),
|
|
CombiningKind::MAXF, vector);
|
|
case arith::AtomicRMWKind::maxs:
|
|
return builder.create<vector::ReductionOp>(vector.getLoc(),
|
|
CombiningKind::MAXSI, vector);
|
|
case arith::AtomicRMWKind::maxu:
|
|
return builder.create<vector::ReductionOp>(vector.getLoc(),
|
|
CombiningKind::MAXUI, vector);
|
|
case arith::AtomicRMWKind::andi:
|
|
return builder.create<vector::ReductionOp>(vector.getLoc(),
|
|
CombiningKind::AND, vector);
|
|
case arith::AtomicRMWKind::ori:
|
|
return builder.create<vector::ReductionOp>(vector.getLoc(),
|
|
CombiningKind::OR, vector);
|
|
// TODO: Add remaining reduction operations.
|
|
default:
|
|
(void)emitOptionalError(loc, "Reduction operation type not supported");
|
|
break;
|
|
}
|
|
return nullptr;
|
|
}
|
|
|
|
std::optional<SmallVector<int64_t, 4>> ReductionOp::getShapeForUnroll() {
|
|
return llvm::to_vector<4>(getSourceVectorType().getShape());
|
|
}
|
|
|
|
namespace {
|
|
struct ElideSingleElementReduction : public OpRewritePattern<ReductionOp> {
|
|
using OpRewritePattern::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(ReductionOp reductionOp,
|
|
PatternRewriter &rewriter) const override {
|
|
// Vector mask setup.
|
|
OpBuilder::InsertionGuard guard(rewriter);
|
|
auto maskableOp =
|
|
cast<vector::MaskableOpInterface>(reductionOp.getOperation());
|
|
Operation *rootOp;
|
|
Value mask;
|
|
if (maskableOp.isMasked()) {
|
|
rewriter.setInsertionPoint(maskableOp.getMaskingOp());
|
|
rootOp = maskableOp.getMaskingOp();
|
|
mask = maskableOp.getMaskingOp().getMask();
|
|
} else {
|
|
rootOp = reductionOp;
|
|
}
|
|
|
|
auto vectorType = reductionOp.getSourceVectorType();
|
|
if (vectorType.getRank() != 0 && vectorType.getDimSize(0) != 1)
|
|
return failure();
|
|
|
|
Location loc = reductionOp.getLoc();
|
|
Value result;
|
|
if (vectorType.getRank() == 0) {
|
|
if (mask)
|
|
mask = rewriter.create<ExtractElementOp>(loc, mask);
|
|
result = rewriter.create<ExtractElementOp>(loc, reductionOp.getVector());
|
|
} else {
|
|
if (mask) {
|
|
mask = rewriter.create<ExtractOp>(loc, rewriter.getI1Type(), mask,
|
|
rewriter.getI64ArrayAttr(0));
|
|
}
|
|
result = rewriter.create<ExtractOp>(loc, reductionOp.getType(),
|
|
reductionOp.getVector(),
|
|
rewriter.getI64ArrayAttr(0));
|
|
}
|
|
|
|
if (Value acc = reductionOp.getAcc())
|
|
result = vector::makeArithReduction(rewriter, loc, reductionOp.getKind(),
|
|
result, acc, mask);
|
|
|
|
rewriter.replaceOp(rootOp, result);
|
|
return success();
|
|
}
|
|
};
|
|
} // namespace
|
|
|
|
void ReductionOp::getCanonicalizationPatterns(RewritePatternSet &results,
|
|
MLIRContext *context) {
|
|
results.add<ElideSingleElementReduction>(context);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// ContractionOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
void vector::ContractionOp::build(OpBuilder &builder, OperationState &result,
|
|
Value lhs, Value rhs, Value acc,
|
|
ArrayRef<ArrayRef<AffineExpr>> indexingExprs,
|
|
ArrayRef<IteratorType> iteratorTypes) {
|
|
result.addOperands({lhs, rhs, acc});
|
|
result.addTypes(acc.getType());
|
|
result.addAttribute(getIndexingMapsAttrName(result.name),
|
|
builder.getAffineMapArrayAttr(
|
|
AffineMap::inferFromExprList(indexingExprs)));
|
|
result.addAttribute(
|
|
getIteratorTypesAttrName(result.name),
|
|
builder.getArrayAttr(llvm::to_vector(llvm::map_range(
|
|
iteratorTypes, [&](IteratorType t) -> mlir::Attribute {
|
|
return IteratorTypeAttr::get(builder.getContext(), t);
|
|
}))));
|
|
}
|
|
|
|
void vector::ContractionOp::build(OpBuilder &builder, OperationState &result,
|
|
Value lhs, Value rhs, Value acc,
|
|
ArrayAttr indexingMaps,
|
|
ArrayAttr iteratorTypes) {
|
|
build(builder, result, lhs, rhs, acc, indexingMaps, iteratorTypes,
|
|
ContractionOp::getDefaultKind());
|
|
}
|
|
|
|
void vector::ContractionOp::build(OpBuilder &builder, OperationState &result,
|
|
Value lhs, Value rhs, Value acc,
|
|
ArrayAttr indexingMaps,
|
|
ArrayAttr iteratorTypes, CombiningKind kind) {
|
|
result.addOperands({lhs, rhs, acc});
|
|
result.addTypes(acc.getType());
|
|
result.addAttribute(getIndexingMapsAttrName(result.name), indexingMaps);
|
|
result.addAttribute(getIteratorTypesAttrName(result.name), iteratorTypes);
|
|
result.addAttribute(getKindAttrName(result.name),
|
|
CombiningKindAttr::get(builder.getContext(), kind));
|
|
}
|
|
|
|
ParseResult ContractionOp::parse(OpAsmParser &parser, OperationState &result) {
|
|
OpAsmParser::UnresolvedOperand lhsInfo;
|
|
OpAsmParser::UnresolvedOperand rhsInfo;
|
|
OpAsmParser::UnresolvedOperand accInfo;
|
|
SmallVector<OpAsmParser::UnresolvedOperand, 2> masksInfo;
|
|
SmallVector<Type, 2> types;
|
|
Type resultType;
|
|
auto loc = parser.getCurrentLocation();
|
|
DictionaryAttr dictAttr;
|
|
// TODO: Unify linalg op attribute parsing.
|
|
if (parser.parseAttribute(dictAttr) || parser.parseOperand(lhsInfo) ||
|
|
parser.parseComma() || parser.parseOperand(rhsInfo) ||
|
|
parser.parseComma() || parser.parseOperand(accInfo) ||
|
|
parser.parseTrailingOperandList(masksInfo) ||
|
|
parser.parseOptionalAttrDict(result.attributes) ||
|
|
parser.parseColonTypeList(types) ||
|
|
parser.parseKeywordType("into", resultType) ||
|
|
parser.resolveOperand(lhsInfo, types[0], result.operands) ||
|
|
parser.resolveOperand(rhsInfo, types[1], result.operands) ||
|
|
parser.resolveOperand(accInfo, resultType, result.operands) ||
|
|
parser.addTypeToList(resultType, result.types))
|
|
return failure();
|
|
result.attributes.append(dictAttr.getValue().begin(),
|
|
dictAttr.getValue().end());
|
|
|
|
// Convert array of string into an array of IteratyType enums. This is needed,
|
|
// because tests still use the old format when 'iterator_types' attribute is
|
|
// represented as an array of strings.
|
|
// TODO: Remove this conversion once tests are fixed.
|
|
ArrayAttr iteratorTypes =
|
|
result.attributes.get(getIteratorTypesAttrName(result.name))
|
|
.cast<ArrayAttr>();
|
|
|
|
SmallVector<Attribute> iteratorTypeAttrs;
|
|
|
|
for (StringRef s : iteratorTypes.getAsValueRange<StringAttr>()) {
|
|
auto maybeIteratorType = symbolizeIteratorType(s);
|
|
if (!maybeIteratorType.has_value())
|
|
return parser.emitError(loc) << "unexpected iterator_type (" << s << ")";
|
|
|
|
iteratorTypeAttrs.push_back(
|
|
IteratorTypeAttr::get(parser.getContext(), maybeIteratorType.value()));
|
|
}
|
|
result.attributes.set(getIteratorTypesAttrName(result.name),
|
|
parser.getBuilder().getArrayAttr(iteratorTypeAttrs));
|
|
|
|
if (!result.attributes.get(getKindAttrName(result.name))) {
|
|
result.addAttribute(
|
|
getKindAttrName(result.name),
|
|
CombiningKindAttr::get(result.getContext(),
|
|
ContractionOp::getDefaultKind()));
|
|
}
|
|
if (masksInfo.empty())
|
|
return success();
|
|
if (masksInfo.size() != 2)
|
|
return parser.emitError(parser.getNameLoc(),
|
|
"expected zero or exactly 2 vector mask operands");
|
|
auto lhsType = types[0].cast<VectorType>();
|
|
auto rhsType = types[1].cast<VectorType>();
|
|
auto maskElementType = parser.getBuilder().getI1Type();
|
|
std::array<Type, 2> maskTypes = {
|
|
VectorType::Builder(lhsType).setElementType(maskElementType),
|
|
VectorType::Builder(rhsType).setElementType(maskElementType)};
|
|
if (parser.resolveOperands(masksInfo, maskTypes, loc, result.operands))
|
|
return failure();
|
|
return success();
|
|
}
|
|
|
|
void ContractionOp::print(OpAsmPrinter &p) {
|
|
// TODO: Unify printing code with linalg ops.
|
|
auto attrNames = getTraitAttrNames();
|
|
llvm::StringSet<> traitAttrsSet;
|
|
traitAttrsSet.insert(attrNames.begin(), attrNames.end());
|
|
SmallVector<NamedAttribute, 8> attrs;
|
|
for (auto attr : (*this)->getAttrs()) {
|
|
if (attr.getName() == getIteratorTypesAttrName()) {
|
|
auto iteratorTypes =
|
|
attr.getValue()
|
|
.cast<ArrayAttr>()
|
|
.getAsValueRange<IteratorTypeAttr, IteratorType>();
|
|
// Convert IteratorType enums into the string representation. This is
|
|
// needed, because tests still use the old format when 'iterator_types'
|
|
// attribute is represented as an array of strings.
|
|
// TODO: Remove this conversion once tests are fixed.
|
|
SmallVector<Attribute> iteratorTypeNames = llvm::to_vector(
|
|
llvm::map_range(iteratorTypes, [&](IteratorType t) -> Attribute {
|
|
return StringAttr::get(getContext(), stringifyIteratorType(t));
|
|
}));
|
|
|
|
attrs.emplace_back(getIteratorTypesAttrName(),
|
|
ArrayAttr::get(getContext(), iteratorTypeNames));
|
|
} else if (traitAttrsSet.count(attr.getName().strref()) > 0)
|
|
attrs.push_back(attr);
|
|
}
|
|
|
|
auto dictAttr = DictionaryAttr::get(getContext(), attrs);
|
|
p << " " << dictAttr << " " << getLhs() << ", ";
|
|
p << getRhs() << ", " << getAcc();
|
|
|
|
p.printOptionalAttrDict((*this)->getAttrs(), attrNames);
|
|
p << " : " << getLhs().getType() << ", " << getRhs().getType() << " into "
|
|
<< getResultType();
|
|
}
|
|
|
|
static bool verifyDimMap(VectorType lhsType, VectorType rhsType,
|
|
const std::vector<std::pair<int64_t, int64_t>> &map) {
|
|
for (auto &dimPair : map) {
|
|
if (dimPair.first < 0 || dimPair.first >= lhsType.getRank() ||
|
|
dimPair.second < 0 || dimPair.second >= rhsType.getRank() ||
|
|
lhsType.getDimSize(dimPair.first) != rhsType.getDimSize(dimPair.second))
|
|
return false;
|
|
}
|
|
return true;
|
|
}
|
|
|
|
static LogicalResult verifyOutputShape(
|
|
ContractionOp op, VectorType lhsType, VectorType rhsType, Type accType,
|
|
Type resType,
|
|
const std::vector<std::pair<int64_t, int64_t>> &contractingDimMap,
|
|
const std::vector<std::pair<int64_t, int64_t>> &batchDimMap) {
|
|
DenseSet<int64_t> lhsContractingDimSet;
|
|
DenseSet<int64_t> rhsContractingDimSet;
|
|
for (auto &dimPair : contractingDimMap) {
|
|
lhsContractingDimSet.insert(dimPair.first);
|
|
rhsContractingDimSet.insert(dimPair.second);
|
|
}
|
|
DenseSet<int64_t> rhsBatchDimSet;
|
|
for (auto &dimPair : batchDimMap)
|
|
rhsBatchDimSet.insert(dimPair.second);
|
|
|
|
// Add free and batch dimensions from 'lhsType' to 'expectedResultDims'.
|
|
SmallVector<int64_t, 4> expectedResultDims;
|
|
for (int64_t i = 0, e = lhsType.getRank(); i < e; ++i) {
|
|
if (lhsContractingDimSet.count(i) > 0)
|
|
continue;
|
|
expectedResultDims.push_back(lhsType.getDimSize(i));
|
|
}
|
|
|
|
// Add free dimensions from 'rhsType' to 'expectedResultDims'.
|
|
for (int64_t i = 0, e = rhsType.getRank(); i < e; ++i) {
|
|
if (rhsContractingDimSet.count(i) > 0 || rhsBatchDimSet.count(i) > 0)
|
|
continue;
|
|
expectedResultDims.push_back(rhsType.getDimSize(i));
|
|
}
|
|
|
|
// Verify 'expectedResultDims'.
|
|
if (expectedResultDims.empty()) {
|
|
// No batch or free dimension implies a scalar result.
|
|
if (resType.isa<VectorType>() || accType.isa<VectorType>())
|
|
return op.emitOpError("invalid accumulator/result vector shape");
|
|
} else {
|
|
// At least one batch or free dimension implies a vector result.
|
|
auto resVectorType = resType.dyn_cast<VectorType>();
|
|
auto accVectorType = accType.dyn_cast<VectorType>();
|
|
if (!resVectorType || !accVectorType)
|
|
return op.emitOpError("invalid accumulator/result vector shape");
|
|
|
|
// Infer expected result vector type. Lhs + rhs map and lhs + rhs vector
|
|
// types fully define the result vector type. This assumes the affine maps
|
|
// are well-formed, which must have been verified already.
|
|
MLIRContext *ctx = op.getContext();
|
|
AffineMap lhsMap = op.getIndexingMapsArray()[0];
|
|
AffineMap rhsMap = op.getIndexingMapsArray()[1];
|
|
if (getUnusedDimsBitVector({lhsMap, rhsMap}).any())
|
|
return op.emitOpError(
|
|
"expected all dimensions to be either a LHS or a RHS dimension");
|
|
SmallVector<AffineExpr, 4> extents(lhsMap.getNumInputs());
|
|
for (auto pair :
|
|
{std::make_pair(lhsType, lhsMap), std::make_pair(rhsType, rhsMap)}) {
|
|
VectorType v = pair.first;
|
|
auto map = pair.second;
|
|
for (unsigned idx = 0, e = v.getRank(); idx < e; ++idx) {
|
|
unsigned pos = map.getDimPosition(idx);
|
|
if (!extents[pos])
|
|
extents[pos] = getAffineConstantExpr(v.getShape()[idx], ctx);
|
|
}
|
|
}
|
|
if (!llvm::all_of(extents, [](AffineExpr e) { return e; }))
|
|
return op.emitOpError("expected all dimensions to get an extent as "
|
|
"either a LHS or a RHS dimension");
|
|
|
|
AffineMap resMap = op.getIndexingMapsArray()[2];
|
|
auto extentsMap = AffineMap::get(/*dimCount=*/extents.size(),
|
|
/*symCount=*/0, extents, ctx);
|
|
// Compose the resMap with the extentsMap, which is a constant map.
|
|
AffineMap expectedMap = simplifyAffineMap(resMap.compose(extentsMap));
|
|
assert(llvm::all_of(
|
|
expectedMap.getResults(),
|
|
[](AffineExpr e) { return e.isa<AffineConstantExpr>(); }) &&
|
|
"expected constant extent along all dimensions.");
|
|
// Extract the expected shape and build the type.
|
|
auto expectedShape = llvm::to_vector<4>(
|
|
llvm::map_range(expectedMap.getResults(), [](AffineExpr e) {
|
|
return e.cast<AffineConstantExpr>().getValue();
|
|
}));
|
|
auto expected =
|
|
VectorType::get(expectedShape, resVectorType.getElementType());
|
|
if (resVectorType != expected || accVectorType != expected)
|
|
return op.emitOpError(
|
|
"invalid accumulator/result vector shape, expected: ")
|
|
<< expected;
|
|
}
|
|
return success();
|
|
}
|
|
|
|
LogicalResult ContractionOp::verify() {
|
|
VectorType lhsType = getLhsType();
|
|
VectorType rhsType = getRhsType();
|
|
Type accType = getAccType();
|
|
Type resType = getResultType();
|
|
|
|
if (lhsType.getElementType().isa<IntegerType>()) {
|
|
if (!lhsType.getElementType().isSignlessInteger())
|
|
return emitOpError("only supports signless integer types");
|
|
}
|
|
|
|
// Verify that an indexing map was specified for each vector operand.
|
|
if (getIndexingMapsArray().size() != 3)
|
|
return emitOpError("expected an indexing map for each vector operand");
|
|
|
|
// Verify that each index map has 'numIterators' inputs, no symbols, and
|
|
// that the number of map outputs equals the rank of its associated
|
|
// vector operand.
|
|
unsigned numIterators = getIteratorTypes().getValue().size();
|
|
for (const auto &it : llvm::enumerate(getIndexingMapsArray())) {
|
|
auto index = it.index();
|
|
auto map = it.value();
|
|
if (map.getNumSymbols() != 0)
|
|
return emitOpError("expected indexing map ")
|
|
<< index << " to have no symbols";
|
|
auto vectorType = getOperand(index).getType().dyn_cast<VectorType>();
|
|
unsigned rank = vectorType ? vectorType.getShape().size() : 0;
|
|
// Verify that the map has the right number of inputs, outputs, and indices.
|
|
// This also correctly accounts for (..) -> () for rank-0 results.
|
|
if (map.getNumDims() != numIterators)
|
|
return emitOpError("expected indexing map ")
|
|
<< index << " to have " << numIterators << " number of inputs";
|
|
if (map.getNumResults() != rank)
|
|
return emitOpError("expected indexing map ")
|
|
<< index << " to have " << rank << " number of outputs";
|
|
if (!map.isProjectedPermutation())
|
|
return emitOpError("expected indexing map ")
|
|
<< index << " to be a projected permutation of its inputs";
|
|
}
|
|
|
|
auto contractingDimMap = getContractingDimMap();
|
|
auto batchDimMap = getBatchDimMap();
|
|
|
|
// Verify at least one contracting dimension pair was specified.
|
|
if (contractingDimMap.empty())
|
|
return emitOpError("expected at least one contracting dimension pair");
|
|
|
|
// Verify contracting dimension map was properly constructed.
|
|
if (!verifyDimMap(lhsType, rhsType, contractingDimMap))
|
|
return emitOpError("invalid contracting dimension map");
|
|
|
|
// Verify batch dimension map was properly constructed.
|
|
if (!verifyDimMap(lhsType, rhsType, batchDimMap))
|
|
return emitOpError("invalid batch dimension map");
|
|
|
|
// Verify 'accType' and 'resType' shape.
|
|
if (failed(verifyOutputShape(*this, lhsType, rhsType, accType, resType,
|
|
contractingDimMap, batchDimMap)))
|
|
return failure();
|
|
|
|
// Verify supported combining kind.
|
|
auto vectorType = resType.dyn_cast<VectorType>();
|
|
auto elementType = vectorType ? vectorType.getElementType() : resType;
|
|
if (!isSupportedCombiningKind(getKind(), elementType))
|
|
return emitOpError("unsupported contraction type");
|
|
|
|
return success();
|
|
}
|
|
|
|
// MaskableOpInterface methods.
|
|
|
|
/// Returns the mask type expected by this operation. Mostly used for
|
|
/// verification purposes. It requires the operation to be vectorized."
|
|
Type ContractionOp::getExpectedMaskType() {
|
|
auto indexingMaps = this->getIndexingMapsArray();
|
|
AffineMap lhsIdxMap = indexingMaps[0];
|
|
AffineMap rhsIdxMap = indexingMaps[1];
|
|
VectorType lhsType = this->getLhsType();
|
|
VectorType rhsType = this->getRhsType();
|
|
|
|
unsigned numVecDims = lhsIdxMap.getNumDims();
|
|
SmallVector<int64_t> maskShape(numVecDims, ShapedType::kDynamic);
|
|
|
|
// Using the information in the indexing maps, extract the size of each
|
|
// dimension in the vector.contract operation from the two input operands.
|
|
for (auto [dimIdx, dimSize] : llvm::enumerate(lhsType.getShape()))
|
|
maskShape[lhsIdxMap.getDimPosition(dimIdx)] = dimSize;
|
|
for (auto [dimIdx, dimSize] : llvm::enumerate(rhsType.getShape()))
|
|
maskShape[rhsIdxMap.getDimPosition(dimIdx)] = dimSize;
|
|
|
|
assert(!ShapedType::isDynamicShape(maskShape) &&
|
|
"Mask shape couldn't be computed");
|
|
|
|
return VectorType::get(maskShape,
|
|
IntegerType::get(lhsType.getContext(), /*width=*/1));
|
|
}
|
|
|
|
SmallVector<StringRef> ContractionOp::getTraitAttrNames() {
|
|
return SmallVector<StringRef>{getIndexingMapsAttrName(),
|
|
getIteratorTypesAttrName(), getKindAttrName()};
|
|
}
|
|
|
|
static int64_t getResultIndex(AffineMap map, AffineExpr targetExpr) {
|
|
for (int64_t i = 0, e = map.getNumResults(); i < e; ++i)
|
|
if (targetExpr == map.getResult(i))
|
|
return i;
|
|
return -1;
|
|
}
|
|
|
|
static std::vector<std::pair<int64_t, int64_t>>
|
|
getDimMap(ArrayRef<AffineMap> indexingMaps, ArrayAttr iteratorTypes,
|
|
IteratorType targetIteratorType, MLIRContext *context) {
|
|
std::vector<std::pair<int64_t, int64_t>> dimMap;
|
|
for (const auto &it : llvm::enumerate(iteratorTypes)) {
|
|
auto iteratorType = it.value().cast<IteratorTypeAttr>().getValue();
|
|
if (iteratorType != targetIteratorType)
|
|
continue;
|
|
// Search lhs/rhs map results for 'targetExpr'.
|
|
auto targetExpr = getAffineDimExpr(it.index(), context);
|
|
int64_t lhsDim = getResultIndex(indexingMaps[0], targetExpr);
|
|
int64_t rhsDim = getResultIndex(indexingMaps[1], targetExpr);
|
|
if (lhsDim >= 0 && rhsDim >= 0)
|
|
dimMap.emplace_back(lhsDim, rhsDim);
|
|
}
|
|
return dimMap;
|
|
}
|
|
|
|
void ContractionOp::getIterationBounds(
|
|
SmallVectorImpl<int64_t> &iterationBounds) {
|
|
auto lhsShape = getLhsType().getShape();
|
|
auto resVectorType = getResultType().dyn_cast<VectorType>();
|
|
SmallVector<AffineMap, 4> indexingMaps(getIndexingMapsArray());
|
|
SmallVector<int64_t, 2> iterationShape;
|
|
for (const auto &it : llvm::enumerate(getIteratorTypes())) {
|
|
// Search lhs/rhs map results for 'targetExpr'.
|
|
auto targetExpr = getAffineDimExpr(it.index(), getContext());
|
|
auto iteratorType = it.value().cast<IteratorTypeAttr>().getValue();
|
|
if (iteratorType == IteratorType::reduction) {
|
|
// Get reduction dim size from lhs shape (same size in rhsShape).
|
|
int64_t lhsDimIndex = getResultIndex(indexingMaps[0], targetExpr);
|
|
assert(lhsDimIndex >= 0);
|
|
iterationBounds.push_back(lhsShape[lhsDimIndex]);
|
|
continue;
|
|
}
|
|
// Get parallel dimension size from result shape.
|
|
int64_t resDimIndex = getResultIndex(indexingMaps[2], targetExpr);
|
|
assert(resDimIndex >= 0);
|
|
assert(resVectorType != nullptr);
|
|
iterationBounds.push_back(resVectorType.getShape()[resDimIndex]);
|
|
}
|
|
}
|
|
|
|
void ContractionOp::getIterationIndexMap(
|
|
std::vector<DenseMap<int64_t, int64_t>> &iterationIndexMap) {
|
|
unsigned numMaps = getIndexingMapsArray().size();
|
|
iterationIndexMap.resize(numMaps);
|
|
for (const auto &it : llvm::enumerate(getIndexingMapsArray())) {
|
|
auto index = it.index();
|
|
auto map = it.value();
|
|
for (unsigned i = 0, e = map.getNumResults(); i < e; ++i) {
|
|
auto dim = map.getResult(i).cast<AffineDimExpr>();
|
|
iterationIndexMap[index][dim.getPosition()] = i;
|
|
}
|
|
}
|
|
}
|
|
|
|
std::vector<std::pair<int64_t, int64_t>> ContractionOp::getContractingDimMap() {
|
|
SmallVector<AffineMap, 4> indexingMaps(getIndexingMapsArray());
|
|
return getDimMap(indexingMaps, getIteratorTypes(), IteratorType::reduction,
|
|
getContext());
|
|
}
|
|
|
|
std::vector<std::pair<int64_t, int64_t>> ContractionOp::getBatchDimMap() {
|
|
SmallVector<AffineMap, 4> indexingMaps(getIndexingMapsArray());
|
|
return getDimMap(indexingMaps, getIteratorTypes(), IteratorType::parallel,
|
|
getContext());
|
|
}
|
|
|
|
std::optional<SmallVector<int64_t, 4>> ContractionOp::getShapeForUnroll() {
|
|
SmallVector<int64_t, 4> shape;
|
|
getIterationBounds(shape);
|
|
return shape;
|
|
}
|
|
|
|
/// Return a fused vector::ContractionOp which represents a patterns such as:
|
|
///
|
|
/// ```mlir
|
|
/// %c0 = vector.constant 0: ...
|
|
/// %c = vector.contract %a, %b, %c0: ...
|
|
/// %e = add %c, %d: ...
|
|
/// ```
|
|
///
|
|
/// by:
|
|
///
|
|
/// ```mlir
|
|
/// %e = vector.contract %a, %b, %d: ...
|
|
/// ```
|
|
///
|
|
/// Return null if the canonicalization does not apply.
|
|
// TODO: This should be a folding of Add into Contract in core but while they
|
|
// live in different dialects, it is not possible without unnatural
|
|
// dependencies.
|
|
template <typename AddOpType>
|
|
struct CanonicalizeContractAdd : public OpRewritePattern<AddOpType> {
|
|
using OpRewritePattern<AddOpType>::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(AddOpType addOp,
|
|
PatternRewriter &rewriter) const override {
|
|
auto canonicalize = [&](Value maybeContraction,
|
|
Value otherOperand) -> vector::ContractionOp {
|
|
vector::ContractionOp contractionOp =
|
|
dyn_cast_or_null<vector::ContractionOp>(
|
|
maybeContraction.getDefiningOp());
|
|
if (!contractionOp)
|
|
return vector::ContractionOp();
|
|
if (auto maybeZero = dyn_cast_or_null<arith::ConstantOp>(
|
|
contractionOp.getAcc().getDefiningOp())) {
|
|
if (maybeZero.getValue() ==
|
|
rewriter.getZeroAttr(contractionOp.getAcc().getType())) {
|
|
IRMapping bvm;
|
|
bvm.map(contractionOp.getAcc(), otherOperand);
|
|
auto newContraction =
|
|
cast<vector::ContractionOp>(rewriter.clone(*contractionOp, bvm));
|
|
rewriter.replaceOp(addOp, newContraction.getResult());
|
|
return newContraction;
|
|
}
|
|
}
|
|
return vector::ContractionOp();
|
|
};
|
|
|
|
Value a = addOp->getOperand(0), b = addOp->getOperand(1);
|
|
vector::ContractionOp contract = canonicalize(a, b);
|
|
contract = contract ? contract : canonicalize(b, a);
|
|
return contract ? success() : failure();
|
|
}
|
|
};
|
|
|
|
void ContractionOp::getCanonicalizationPatterns(RewritePatternSet &results,
|
|
MLIRContext *context) {
|
|
results.add<CanonicalizeContractAdd<arith::AddIOp>,
|
|
CanonicalizeContractAdd<arith::AddFOp>>(context);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// ExtractElementOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
void vector::ExtractElementOp::build(OpBuilder &builder, OperationState &result,
|
|
Value source) {
|
|
result.addOperands({source});
|
|
result.addTypes(source.getType().cast<VectorType>().getElementType());
|
|
}
|
|
|
|
LogicalResult vector::ExtractElementOp::verify() {
|
|
VectorType vectorType = getSourceVectorType();
|
|
if (vectorType.getRank() == 0) {
|
|
if (getPosition())
|
|
return emitOpError("expected position to be empty with 0-D vector");
|
|
return success();
|
|
}
|
|
if (vectorType.getRank() != 1)
|
|
return emitOpError("unexpected >1 vector rank");
|
|
if (!getPosition())
|
|
return emitOpError("expected position for 1-D vector");
|
|
return success();
|
|
}
|
|
|
|
OpFoldResult vector::ExtractElementOp::fold(FoldAdaptor adaptor) {
|
|
// Skip the 0-D vector here now.
|
|
if (!adaptor.getPosition())
|
|
return {};
|
|
|
|
Attribute src = adaptor.getVector();
|
|
Attribute pos = adaptor.getPosition();
|
|
|
|
// Fold extractelement (splat X) -> X.
|
|
if (auto splat = getVector().getDefiningOp<vector::SplatOp>())
|
|
return splat.getInput();
|
|
|
|
// Fold extractelement(broadcast(X)) -> X.
|
|
if (auto broadcast = getVector().getDefiningOp<vector::BroadcastOp>())
|
|
if (!broadcast.getSource().getType().isa<VectorType>())
|
|
return broadcast.getSource();
|
|
|
|
if (!pos || !src)
|
|
return {};
|
|
|
|
auto srcElements = src.cast<DenseElementsAttr>().getValues<Attribute>();
|
|
|
|
auto attr = pos.dyn_cast<IntegerAttr>();
|
|
uint64_t posIdx = attr.getInt();
|
|
|
|
return srcElements[posIdx];
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// ExtractOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
void vector::ExtractOp::build(OpBuilder &builder, OperationState &result,
|
|
Value source, ArrayRef<int64_t> position) {
|
|
build(builder, result, source, getVectorSubscriptAttr(builder, position));
|
|
}
|
|
|
|
// Convenience builder which assumes the values are constant indices.
|
|
void vector::ExtractOp::build(OpBuilder &builder, OperationState &result,
|
|
Value source, ValueRange position) {
|
|
SmallVector<int64_t, 4> positionConstants =
|
|
llvm::to_vector<4>(llvm::map_range(position, [](Value pos) {
|
|
return pos.getDefiningOp<arith::ConstantIndexOp>().value();
|
|
}));
|
|
build(builder, result, source, positionConstants);
|
|
}
|
|
|
|
LogicalResult
|
|
ExtractOp::inferReturnTypes(MLIRContext *, std::optional<Location>,
|
|
ValueRange operands, DictionaryAttr attributes,
|
|
OpaqueProperties properties, RegionRange,
|
|
SmallVectorImpl<Type> &inferredReturnTypes) {
|
|
ExtractOp::Adaptor op(operands, attributes);
|
|
auto vectorType = op.getVector().getType().cast<VectorType>();
|
|
if (static_cast<int64_t>(op.getPosition().size()) == vectorType.getRank()) {
|
|
inferredReturnTypes.push_back(vectorType.getElementType());
|
|
} else {
|
|
auto n =
|
|
std::min<size_t>(op.getPosition().size(), vectorType.getRank() - 1);
|
|
inferredReturnTypes.push_back(VectorType::get(
|
|
vectorType.getShape().drop_front(n), vectorType.getElementType()));
|
|
}
|
|
return success();
|
|
}
|
|
|
|
bool ExtractOp::isCompatibleReturnTypes(TypeRange l, TypeRange r) {
|
|
// Allow extracting 1-element vectors instead of scalars.
|
|
auto isCompatible = [](TypeRange l, TypeRange r) {
|
|
auto vectorType = l.front().dyn_cast<VectorType>();
|
|
return vectorType && vectorType.getShape().equals({1}) &&
|
|
vectorType.getElementType() == r.front();
|
|
};
|
|
if (l.size() == 1 && r.size() == 1 &&
|
|
(isCompatible(l, r) || isCompatible(r, l)))
|
|
return true;
|
|
return l == r;
|
|
}
|
|
|
|
LogicalResult vector::ExtractOp::verify() {
|
|
auto positionAttr = getPosition().getValue();
|
|
if (positionAttr.size() >
|
|
static_cast<unsigned>(getSourceVectorType().getRank()))
|
|
return emitOpError(
|
|
"expected position attribute of rank smaller than vector rank");
|
|
for (const auto &en : llvm::enumerate(positionAttr)) {
|
|
auto attr = en.value().dyn_cast<IntegerAttr>();
|
|
if (!attr || attr.getInt() < 0 ||
|
|
attr.getInt() >= getSourceVectorType().getDimSize(en.index()))
|
|
return emitOpError("expected position attribute #")
|
|
<< (en.index() + 1)
|
|
<< " to be a non-negative integer smaller than the corresponding "
|
|
"vector dimension";
|
|
}
|
|
return success();
|
|
}
|
|
|
|
template <typename IntType>
|
|
static SmallVector<IntType> extractVector(ArrayAttr arrayAttr) {
|
|
return llvm::to_vector<4>(llvm::map_range(
|
|
arrayAttr.getAsRange<IntegerAttr>(),
|
|
[](IntegerAttr attr) { return static_cast<IntType>(attr.getInt()); }));
|
|
}
|
|
|
|
/// Fold the result of chains of ExtractOp in place by simply concatenating the
|
|
/// positions.
|
|
static LogicalResult foldExtractOpFromExtractChain(ExtractOp extractOp) {
|
|
if (!extractOp.getVector().getDefiningOp<ExtractOp>())
|
|
return failure();
|
|
|
|
SmallVector<int64_t, 4> globalPosition;
|
|
ExtractOp currentOp = extractOp;
|
|
auto extrPos = extractVector<int64_t>(currentOp.getPosition());
|
|
globalPosition.append(extrPos.rbegin(), extrPos.rend());
|
|
while (ExtractOp nextOp = currentOp.getVector().getDefiningOp<ExtractOp>()) {
|
|
currentOp = nextOp;
|
|
auto extrPos = extractVector<int64_t>(currentOp.getPosition());
|
|
globalPosition.append(extrPos.rbegin(), extrPos.rend());
|
|
}
|
|
extractOp.setOperand(currentOp.getVector());
|
|
// OpBuilder is only used as a helper to build an I64ArrayAttr.
|
|
OpBuilder b(extractOp.getContext());
|
|
std::reverse(globalPosition.begin(), globalPosition.end());
|
|
extractOp->setAttr(ExtractOp::getPositionAttrStrName(),
|
|
b.getI64ArrayAttr(globalPosition));
|
|
return success();
|
|
}
|
|
|
|
namespace {
|
|
/// Fold an ExtractOp that is fed by a chain of InsertOps and TransposeOps.
|
|
/// Walk back a chain of InsertOp/TransposeOp until we hit a match.
|
|
/// Compose TransposeOp permutations as we walk back.
|
|
/// This helper class keeps an updated extraction position `extractPosition`
|
|
/// with extra trailing sentinels.
|
|
/// The sentinels encode the internal transposition status of the result vector.
|
|
/// As we iterate, extractPosition is permuted and updated.
|
|
class ExtractFromInsertTransposeChainState {
|
|
public:
|
|
ExtractFromInsertTransposeChainState(ExtractOp e);
|
|
|
|
/// Iterate over producing insert and transpose ops until we find a fold.
|
|
Value fold();
|
|
|
|
private:
|
|
/// Return true if the vector at position `a` is contained within the vector
|
|
/// at position `b`. Under insert/extract semantics, this is the same as `a`
|
|
/// is a prefix of `b`.
|
|
template <typename ContainerA, typename ContainerB>
|
|
bool isContainedWithin(const ContainerA &a, const ContainerB &b) {
|
|
return a.size() <= b.size() &&
|
|
std::equal(a.begin(), a.begin() + a.size(), b.begin());
|
|
}
|
|
|
|
/// Return true if the vector at position `a` intersects the vector at
|
|
/// position `b`. Under insert/extract semantics, this is the same as equality
|
|
/// of all entries of `a` that are >=0 with the corresponding entries of b.
|
|
/// Comparison is on the common prefix (i.e. zip).
|
|
template <typename ContainerA, typename ContainerB>
|
|
bool intersectsWhereNonNegative(const ContainerA &a, const ContainerB &b) {
|
|
for (auto [elemA, elemB] : llvm::zip(a, b)) {
|
|
if (elemA < 0 || elemB < 0)
|
|
continue;
|
|
if (elemA != elemB)
|
|
return false;
|
|
}
|
|
return true;
|
|
}
|
|
|
|
/// Folding is only possible in the absence of an internal permutation in the
|
|
/// result vector.
|
|
bool canFold() {
|
|
return (sentinels == ArrayRef(extractPosition).drop_front(extractedRank));
|
|
}
|
|
|
|
// Helper to get the next defining op of interest.
|
|
void updateStateForNextIteration(Value v) {
|
|
nextInsertOp = v.getDefiningOp<vector::InsertOp>();
|
|
nextTransposeOp = v.getDefiningOp<vector::TransposeOp>();
|
|
};
|
|
|
|
// Case 1. If we hit a transpose, just compose the map and iterate.
|
|
// Invariant: insert + transpose do not change rank, we can always compose.
|
|
LogicalResult handleTransposeOp();
|
|
|
|
// Case 2: the insert position matches extractPosition exactly, early return.
|
|
LogicalResult handleInsertOpWithMatchingPos(Value &res);
|
|
|
|
/// Case 3: if the insert position is a prefix of extractPosition, extract a
|
|
/// portion of the source of the insert.
|
|
/// Example:
|
|
/// ```
|
|
/// %ins = vector.insert %source, %vest[1]: vector<3x4> into vector<2x3x4x5>
|
|
/// // extractPosition == [1, 2, 3]
|
|
/// %ext = vector.extract %ins[1, 0]: vector<3x4x5>
|
|
/// // can fold to vector.extract %source[0, 3]
|
|
/// %ext = vector.extract %source[3]: vector<5x6>
|
|
/// ```
|
|
/// To traverse through %source, we need to set the leading dims to 0 and
|
|
/// drop the extra leading dims.
|
|
/// This method updates the internal state.
|
|
LogicalResult handleInsertOpWithPrefixPos(Value &res);
|
|
|
|
/// Try to fold in place to extract(source, extractPosition) and return the
|
|
/// folded result. Return null if folding is not possible (e.g. due to an
|
|
/// internal tranposition in the result).
|
|
Value tryToFoldExtractOpInPlace(Value source);
|
|
|
|
ExtractOp extractOp;
|
|
int64_t vectorRank;
|
|
int64_t extractedRank;
|
|
|
|
InsertOp nextInsertOp;
|
|
TransposeOp nextTransposeOp;
|
|
|
|
/// Sentinel values that encode the internal permutation status of the result.
|
|
/// They are set to (-1, ... , -k) at the beginning and appended to
|
|
/// `extractPosition`.
|
|
/// In the end, the tail of `extractPosition` must be exactly `sentinels` to
|
|
/// ensure that there is no internal transposition.
|
|
/// Internal transposition cannot be accounted for with a folding pattern.
|
|
// TODO: We could relax the internal transposition with an extra transposition
|
|
// operation in a future canonicalizer.
|
|
SmallVector<int64_t> sentinels;
|
|
SmallVector<int64_t> extractPosition;
|
|
};
|
|
} // namespace
|
|
|
|
ExtractFromInsertTransposeChainState::ExtractFromInsertTransposeChainState(
|
|
ExtractOp e)
|
|
: extractOp(e), vectorRank(extractOp.getSourceVectorType().getRank()),
|
|
extractedRank(extractOp.getPosition().size()) {
|
|
assert(vectorRank >= extractedRank && "extracted pos overflow");
|
|
sentinels.reserve(vectorRank - extractedRank);
|
|
for (int64_t i = 0, e = vectorRank - extractedRank; i < e; ++i)
|
|
sentinels.push_back(-(i + 1));
|
|
extractPosition = extractVector<int64_t>(extractOp.getPosition());
|
|
llvm::append_range(extractPosition, sentinels);
|
|
}
|
|
|
|
// Case 1. If we hit a transpose, just compose the map and iterate.
|
|
// Invariant: insert + transpose do not change rank, we can always compose.
|
|
LogicalResult ExtractFromInsertTransposeChainState::handleTransposeOp() {
|
|
if (!nextTransposeOp)
|
|
return failure();
|
|
auto permutation = extractVector<unsigned>(nextTransposeOp.getTransp());
|
|
AffineMap m = inversePermutation(
|
|
AffineMap::getPermutationMap(permutation, extractOp.getContext()));
|
|
extractPosition = applyPermutationMap(m, ArrayRef(extractPosition));
|
|
return success();
|
|
}
|
|
|
|
// Case 2: the insert position matches extractPosition exactly, early return.
|
|
LogicalResult
|
|
ExtractFromInsertTransposeChainState::handleInsertOpWithMatchingPos(
|
|
Value &res) {
|
|
auto insertedPos = extractVector<int64_t>(nextInsertOp.getPosition());
|
|
if (ArrayRef(insertedPos) !=
|
|
llvm::ArrayRef(extractPosition).take_front(extractedRank))
|
|
return failure();
|
|
// Case 2.a. early-exit fold.
|
|
res = nextInsertOp.getSource();
|
|
// Case 2.b. if internal transposition is present, canFold will be false.
|
|
return success(canFold());
|
|
}
|
|
|
|
/// Case 3: if inserted position is a prefix of extractPosition,
|
|
/// extract a portion of the source of the insertion.
|
|
/// This method updates the internal state.
|
|
LogicalResult
|
|
ExtractFromInsertTransposeChainState::handleInsertOpWithPrefixPos(Value &res) {
|
|
auto insertedPos = extractVector<int64_t>(nextInsertOp.getPosition());
|
|
if (!isContainedWithin(insertedPos, extractPosition))
|
|
return failure();
|
|
// Set leading dims to zero.
|
|
std::fill_n(extractPosition.begin(), insertedPos.size(), 0);
|
|
// Drop extra leading dims.
|
|
extractPosition.erase(extractPosition.begin(),
|
|
extractPosition.begin() + insertedPos.size());
|
|
extractedRank = extractPosition.size() - sentinels.size();
|
|
// Case 3.a. early-exit fold (break and delegate to post-while path).
|
|
res = nextInsertOp.getSource();
|
|
// Case 3.b. if internal transposition is present, canFold will be false.
|
|
return success();
|
|
}
|
|
|
|
/// Try to fold in place to extract(source, extractPosition) and return the
|
|
/// folded result. Return null if folding is not possible (e.g. due to an
|
|
/// internal tranposition in the result).
|
|
Value ExtractFromInsertTransposeChainState::tryToFoldExtractOpInPlace(
|
|
Value source) {
|
|
// If we can't fold (either internal transposition, or nothing to fold), bail.
|
|
bool nothingToFold = (source == extractOp.getVector());
|
|
if (nothingToFold || !canFold())
|
|
return Value();
|
|
// Otherwise, fold by updating the op inplace and return its result.
|
|
OpBuilder b(extractOp.getContext());
|
|
extractOp->setAttr(
|
|
extractOp.getPositionAttrName(),
|
|
b.getI64ArrayAttr(ArrayRef(extractPosition).take_front(extractedRank)));
|
|
extractOp.getVectorMutable().assign(source);
|
|
return extractOp.getResult();
|
|
}
|
|
|
|
/// Iterate over producing insert and transpose ops until we find a fold.
|
|
Value ExtractFromInsertTransposeChainState::fold() {
|
|
Value valueToExtractFrom = extractOp.getVector();
|
|
updateStateForNextIteration(valueToExtractFrom);
|
|
while (nextInsertOp || nextTransposeOp) {
|
|
// Case 1. If we hit a transpose, just compose the map and iterate.
|
|
// Invariant: insert + transpose do not change rank, we can always compose.
|
|
if (succeeded(handleTransposeOp())) {
|
|
valueToExtractFrom = nextTransposeOp.getVector();
|
|
updateStateForNextIteration(valueToExtractFrom);
|
|
continue;
|
|
}
|
|
|
|
Value result;
|
|
// Case 2: the position match exactly.
|
|
if (succeeded(handleInsertOpWithMatchingPos(result)))
|
|
return result;
|
|
|
|
// Case 3: if the inserted position is a prefix of extractPosition, we can
|
|
// just extract a portion of the source of the insert.
|
|
if (succeeded(handleInsertOpWithPrefixPos(result)))
|
|
return tryToFoldExtractOpInPlace(result);
|
|
|
|
// Case 4: extractPositionRef intersects insertedPosRef on non-sentinel
|
|
// values. This is a more difficult case and we bail.
|
|
auto insertedPos = extractVector<int64_t>(nextInsertOp.getPosition());
|
|
if (isContainedWithin(extractPosition, insertedPos) ||
|
|
intersectsWhereNonNegative(extractPosition, insertedPos))
|
|
return Value();
|
|
|
|
// Case 5: No intersection, we forward the extract to insertOp.dest().
|
|
valueToExtractFrom = nextInsertOp.getDest();
|
|
updateStateForNextIteration(valueToExtractFrom);
|
|
}
|
|
// If after all this we can fold, go for it.
|
|
return tryToFoldExtractOpInPlace(valueToExtractFrom);
|
|
}
|
|
|
|
/// Fold extractOp with scalar result coming from BroadcastOp or SplatOp.
|
|
static Value foldExtractFromBroadcast(ExtractOp extractOp) {
|
|
Operation *defOp = extractOp.getVector().getDefiningOp();
|
|
if (!defOp || !isa<vector::BroadcastOp, SplatOp>(defOp))
|
|
return Value();
|
|
Value source = defOp->getOperand(0);
|
|
if (extractOp.getType() == source.getType())
|
|
return source;
|
|
auto getRank = [](Type type) {
|
|
return type.isa<VectorType>() ? type.cast<VectorType>().getRank() : 0;
|
|
};
|
|
// If splat or broadcast from a scalar, just return the source scalar.
|
|
unsigned broadcastSrcRank = getRank(source.getType());
|
|
if (broadcastSrcRank == 0)
|
|
return source;
|
|
|
|
unsigned extractResultRank = getRank(extractOp.getType());
|
|
if (extractResultRank >= broadcastSrcRank)
|
|
return Value();
|
|
// Check that the dimension of the result haven't been broadcasted.
|
|
auto extractVecType = extractOp.getType().dyn_cast<VectorType>();
|
|
auto broadcastVecType = source.getType().dyn_cast<VectorType>();
|
|
if (extractVecType && broadcastVecType &&
|
|
extractVecType.getShape() !=
|
|
broadcastVecType.getShape().take_back(extractResultRank))
|
|
return Value();
|
|
|
|
auto broadcastOp = cast<vector::BroadcastOp>(defOp);
|
|
int64_t rankDiff = broadcastSrcRank - extractResultRank;
|
|
// Detect all the positions that come from "dim-1" broadcasting.
|
|
// These dimensions correspond to "dim-1" broadcasted dims; set the mathching
|
|
// extract position to `0` when extracting from the source operand.
|
|
llvm::SetVector<int64_t> broadcastedUnitDims =
|
|
broadcastOp.computeBroadcastedUnitDims();
|
|
auto extractPos = extractVector<int64_t>(extractOp.getPosition());
|
|
for (int64_t i = rankDiff, e = extractPos.size(); i < e; ++i)
|
|
if (broadcastedUnitDims.contains(i))
|
|
extractPos[i] = 0;
|
|
// `rankDiff` leading dimensions correspond to new broadcasted dims, drop the
|
|
// matching extract position when extracting from the source operand.
|
|
extractPos.erase(extractPos.begin(),
|
|
std::next(extractPos.begin(), extractPos.size() - rankDiff));
|
|
// OpBuilder is only used as a helper to build an I64ArrayAttr.
|
|
OpBuilder b(extractOp.getContext());
|
|
extractOp.setOperand(source);
|
|
extractOp->setAttr(ExtractOp::getPositionAttrStrName(),
|
|
b.getI64ArrayAttr(extractPos));
|
|
return extractOp.getResult();
|
|
}
|
|
|
|
// Fold extractOp with source coming from ShapeCast op.
|
|
static Value foldExtractFromShapeCast(ExtractOp extractOp) {
|
|
auto shapeCastOp = extractOp.getVector().getDefiningOp<vector::ShapeCastOp>();
|
|
if (!shapeCastOp)
|
|
return Value();
|
|
// Get the nth dimension size starting from lowest dimension.
|
|
auto getDimReverse = [](VectorType type, int64_t n) {
|
|
return type.getShape().take_back(n + 1).front();
|
|
};
|
|
int64_t destinationRank =
|
|
extractOp.getType().isa<VectorType>()
|
|
? extractOp.getType().cast<VectorType>().getRank()
|
|
: 0;
|
|
if (destinationRank > shapeCastOp.getSourceVectorType().getRank())
|
|
return Value();
|
|
if (destinationRank > 0) {
|
|
auto destinationType = extractOp.getResult().getType().cast<VectorType>();
|
|
for (int64_t i = 0; i < destinationRank; i++) {
|
|
// The lowest dimension of of the destination must match the lowest
|
|
// dimension of the shapecast op source.
|
|
// TODO: This case could be support in a canonicalization pattern.
|
|
if (getDimReverse(shapeCastOp.getSourceVectorType(), i) !=
|
|
getDimReverse(destinationType, i))
|
|
return Value();
|
|
}
|
|
}
|
|
// Extract the strides associated with the extract op vector source. Then use
|
|
// this to calculate a linearized position for the extract.
|
|
auto extractedPos = extractVector<int64_t>(extractOp.getPosition());
|
|
std::reverse(extractedPos.begin(), extractedPos.end());
|
|
SmallVector<int64_t, 4> strides;
|
|
int64_t stride = 1;
|
|
for (int64_t i = 0, e = extractedPos.size(); i < e; i++) {
|
|
strides.push_back(stride);
|
|
stride *=
|
|
getDimReverse(extractOp.getSourceVectorType(), i + destinationRank);
|
|
}
|
|
|
|
int64_t position = linearize(extractedPos, strides);
|
|
// Then extract the strides associated to the shapeCast op vector source and
|
|
// delinearize the position using those strides.
|
|
SmallVector<int64_t, 4> newStrides;
|
|
int64_t numDimension =
|
|
shapeCastOp.getSourceVectorType().getRank() - destinationRank;
|
|
stride = 1;
|
|
for (int64_t i = 0; i < numDimension; i++) {
|
|
newStrides.push_back(stride);
|
|
stride *=
|
|
getDimReverse(shapeCastOp.getSourceVectorType(), i + destinationRank);
|
|
}
|
|
std::reverse(newStrides.begin(), newStrides.end());
|
|
SmallVector<int64_t, 4> newPosition = delinearize(position, newStrides);
|
|
// OpBuilder is only used as a helper to build an I64ArrayAttr.
|
|
OpBuilder b(extractOp.getContext());
|
|
extractOp->setAttr(ExtractOp::getPositionAttrStrName(),
|
|
b.getI64ArrayAttr(newPosition));
|
|
extractOp.setOperand(shapeCastOp.getSource());
|
|
return extractOp.getResult();
|
|
}
|
|
|
|
/// Fold an ExtractOp from ExtractStridedSliceOp.
|
|
static Value foldExtractFromExtractStrided(ExtractOp extractOp) {
|
|
auto extractStridedSliceOp =
|
|
extractOp.getVector().getDefiningOp<vector::ExtractStridedSliceOp>();
|
|
if (!extractStridedSliceOp)
|
|
return Value();
|
|
// Return if 'extractStridedSliceOp' has non-unit strides.
|
|
if (extractStridedSliceOp.hasNonUnitStrides())
|
|
return Value();
|
|
|
|
// Trim offsets for dimensions fully extracted.
|
|
auto sliceOffsets =
|
|
extractVector<int64_t>(extractStridedSliceOp.getOffsets());
|
|
while (!sliceOffsets.empty()) {
|
|
size_t lastOffset = sliceOffsets.size() - 1;
|
|
if (sliceOffsets.back() != 0 ||
|
|
extractStridedSliceOp.getType().getDimSize(lastOffset) !=
|
|
extractStridedSliceOp.getSourceVectorType().getDimSize(lastOffset))
|
|
break;
|
|
sliceOffsets.pop_back();
|
|
}
|
|
unsigned destinationRank = 0;
|
|
if (auto vecType = extractOp.getType().dyn_cast<VectorType>())
|
|
destinationRank = vecType.getRank();
|
|
// The dimensions of the result need to be untouched by the
|
|
// extractStridedSlice op.
|
|
if (destinationRank > extractStridedSliceOp.getSourceVectorType().getRank() -
|
|
sliceOffsets.size())
|
|
return Value();
|
|
auto extractedPos = extractVector<int64_t>(extractOp.getPosition());
|
|
assert(extractedPos.size() >= sliceOffsets.size());
|
|
for (size_t i = 0, e = sliceOffsets.size(); i < e; i++)
|
|
extractedPos[i] = extractedPos[i] + sliceOffsets[i];
|
|
extractOp.getVectorMutable().assign(extractStridedSliceOp.getVector());
|
|
// OpBuilder is only used as a helper to build an I64ArrayAttr.
|
|
OpBuilder b(extractOp.getContext());
|
|
extractOp->setAttr(ExtractOp::getPositionAttrStrName(),
|
|
b.getI64ArrayAttr(extractedPos));
|
|
return extractOp.getResult();
|
|
}
|
|
|
|
/// Fold extract_op fed from a chain of insertStridedSlice ops.
|
|
static Value foldExtractStridedOpFromInsertChain(ExtractOp op) {
|
|
int64_t destinationRank = op.getType().isa<VectorType>()
|
|
? op.getType().cast<VectorType>().getRank()
|
|
: 0;
|
|
auto insertOp = op.getVector().getDefiningOp<InsertStridedSliceOp>();
|
|
while (insertOp) {
|
|
int64_t insertRankDiff = insertOp.getDestVectorType().getRank() -
|
|
insertOp.getSourceVectorType().getRank();
|
|
if (destinationRank > insertOp.getSourceVectorType().getRank())
|
|
return Value();
|
|
auto insertOffsets = extractVector<int64_t>(insertOp.getOffsets());
|
|
auto extractOffsets = extractVector<int64_t>(op.getPosition());
|
|
|
|
if (llvm::any_of(insertOp.getStrides(), [](Attribute attr) {
|
|
return attr.cast<IntegerAttr>().getInt() != 1;
|
|
}))
|
|
return Value();
|
|
bool disjoint = false;
|
|
SmallVector<int64_t, 4> offsetDiffs;
|
|
for (unsigned dim = 0, e = extractOffsets.size(); dim < e; ++dim) {
|
|
int64_t start = insertOffsets[dim];
|
|
int64_t size =
|
|
(dim < insertRankDiff)
|
|
? 1
|
|
: insertOp.getSourceVectorType().getDimSize(dim - insertRankDiff);
|
|
int64_t end = start + size;
|
|
int64_t offset = extractOffsets[dim];
|
|
// Check if the start of the extract offset is in the interval inserted.
|
|
if (start <= offset && offset < end) {
|
|
if (dim >= insertRankDiff)
|
|
offsetDiffs.push_back(offset - start);
|
|
continue;
|
|
}
|
|
disjoint = true;
|
|
break;
|
|
}
|
|
// The extract element chunk overlap with the vector inserted.
|
|
if (!disjoint) {
|
|
// If any of the inner dimensions are only partially inserted we have a
|
|
// partial overlap.
|
|
int64_t srcRankDiff =
|
|
insertOp.getSourceVectorType().getRank() - destinationRank;
|
|
for (int64_t i = 0; i < destinationRank; i++) {
|
|
if (insertOp.getSourceVectorType().getDimSize(i + srcRankDiff) !=
|
|
insertOp.getDestVectorType().getDimSize(i + srcRankDiff +
|
|
insertRankDiff))
|
|
return Value();
|
|
}
|
|
op.getVectorMutable().assign(insertOp.getSource());
|
|
// OpBuilder is only used as a helper to build an I64ArrayAttr.
|
|
OpBuilder b(op.getContext());
|
|
op->setAttr(ExtractOp::getPositionAttrStrName(),
|
|
b.getI64ArrayAttr(offsetDiffs));
|
|
return op.getResult();
|
|
}
|
|
// If the chunk extracted is disjoint from the chunk inserted, keep
|
|
// looking in the insert chain.
|
|
insertOp = insertOp.getDest().getDefiningOp<InsertStridedSliceOp>();
|
|
}
|
|
return Value();
|
|
}
|
|
|
|
OpFoldResult ExtractOp::fold(FoldAdaptor) {
|
|
if (getPosition().empty())
|
|
return getVector();
|
|
if (succeeded(foldExtractOpFromExtractChain(*this)))
|
|
return getResult();
|
|
if (auto res = ExtractFromInsertTransposeChainState(*this).fold())
|
|
return res;
|
|
if (auto res = foldExtractFromBroadcast(*this))
|
|
return res;
|
|
if (auto res = foldExtractFromShapeCast(*this))
|
|
return res;
|
|
if (auto val = foldExtractFromExtractStrided(*this))
|
|
return val;
|
|
if (auto val = foldExtractStridedOpFromInsertChain(*this))
|
|
return val;
|
|
return OpFoldResult();
|
|
}
|
|
|
|
namespace {
|
|
|
|
// Pattern to rewrite a ExtractOp(Broadcast) -> Broadcast.
|
|
class ExtractOpFromBroadcast final : public OpRewritePattern<ExtractOp> {
|
|
public:
|
|
using OpRewritePattern::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(ExtractOp extractOp,
|
|
PatternRewriter &rewriter) const override {
|
|
Operation *defOp = extractOp.getVector().getDefiningOp();
|
|
if (!defOp || !isa<vector::BroadcastOp, SplatOp>(defOp))
|
|
return failure();
|
|
|
|
Value source = defOp->getOperand(0);
|
|
if (extractOp.getType() == source.getType())
|
|
return failure();
|
|
auto getRank = [](Type type) {
|
|
return type.isa<VectorType>() ? type.cast<VectorType>().getRank() : 0;
|
|
};
|
|
unsigned broadcastSrcRank = getRank(source.getType());
|
|
unsigned extractResultRank = getRank(extractOp.getType());
|
|
// We only consider the case where the rank of the source is less than or
|
|
// equal to the rank of the extract dst. The other cases are handled in the
|
|
// folding patterns.
|
|
if (extractResultRank < broadcastSrcRank)
|
|
return failure();
|
|
|
|
// Special case if broadcast src is a 0D vector.
|
|
if (extractResultRank == 0) {
|
|
assert(broadcastSrcRank == 0 && source.getType().isa<VectorType>());
|
|
rewriter.replaceOpWithNewOp<vector::ExtractElementOp>(extractOp, source);
|
|
return success();
|
|
}
|
|
rewriter.replaceOpWithNewOp<vector::BroadcastOp>(
|
|
extractOp, extractOp.getType(), source);
|
|
return success();
|
|
}
|
|
};
|
|
|
|
// Pattern to rewrite a ExtractOp(splat ConstantOp) -> ConstantOp.
|
|
class ExtractOpSplatConstantFolder final : public OpRewritePattern<ExtractOp> {
|
|
public:
|
|
using OpRewritePattern::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(ExtractOp extractOp,
|
|
PatternRewriter &rewriter) const override {
|
|
// Return if 'ExtractOp' operand is not defined by a splat vector
|
|
// ConstantOp.
|
|
Value sourceVector = extractOp.getVector();
|
|
Attribute vectorCst;
|
|
if (!matchPattern(sourceVector, m_Constant(&vectorCst)))
|
|
return failure();
|
|
auto splat = vectorCst.dyn_cast<SplatElementsAttr>();
|
|
if (!splat)
|
|
return failure();
|
|
TypedAttr newAttr = splat.getSplatValue<TypedAttr>();
|
|
if (auto vecDstType = extractOp.getType().dyn_cast<VectorType>())
|
|
newAttr = DenseElementsAttr::get(vecDstType, newAttr);
|
|
rewriter.replaceOpWithNewOp<arith::ConstantOp>(extractOp, newAttr);
|
|
return success();
|
|
}
|
|
};
|
|
|
|
// Pattern to rewrite a ExtractOp(non-splat ConstantOp)[...] -> ConstantOp.
|
|
class ExtractOpNonSplatConstantFolder final
|
|
: public OpRewritePattern<ExtractOp> {
|
|
public:
|
|
using OpRewritePattern::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(ExtractOp extractOp,
|
|
PatternRewriter &rewriter) const override {
|
|
// Return if 'ExtractOp' operand is not defined by a compatible vector
|
|
// ConstantOp.
|
|
Value sourceVector = extractOp.getVector();
|
|
Attribute vectorCst;
|
|
if (!matchPattern(sourceVector, m_Constant(&vectorCst)))
|
|
return failure();
|
|
|
|
auto vecTy = sourceVector.getType().cast<VectorType>();
|
|
if (vecTy.isScalable())
|
|
return failure();
|
|
|
|
// The splat case is handled by `ExtractOpSplatConstantFolder`.
|
|
auto dense = vectorCst.dyn_cast<DenseElementsAttr>();
|
|
if (!dense || dense.isSplat())
|
|
return failure();
|
|
|
|
// Calculate the linearized position of the continuous chunk of elements to
|
|
// extract.
|
|
llvm::SmallVector<int64_t> completePositions(vecTy.getRank(), 0);
|
|
copy(getI64SubArray(extractOp.getPosition()), completePositions.begin());
|
|
int64_t elemBeginPosition =
|
|
linearize(completePositions, computeStrides(vecTy.getShape()));
|
|
auto denseValuesBegin = dense.value_begin<TypedAttr>() + elemBeginPosition;
|
|
|
|
TypedAttr newAttr;
|
|
if (auto resVecTy = extractOp.getType().dyn_cast<VectorType>()) {
|
|
SmallVector<Attribute> elementValues(
|
|
denseValuesBegin, denseValuesBegin + resVecTy.getNumElements());
|
|
newAttr = DenseElementsAttr::get(resVecTy, elementValues);
|
|
} else {
|
|
newAttr = *denseValuesBegin;
|
|
}
|
|
|
|
rewriter.replaceOpWithNewOp<arith::ConstantOp>(extractOp, newAttr);
|
|
return success();
|
|
}
|
|
};
|
|
|
|
} // namespace
|
|
|
|
void ExtractOp::getCanonicalizationPatterns(RewritePatternSet &results,
|
|
MLIRContext *context) {
|
|
results.add<ExtractOpSplatConstantFolder, ExtractOpNonSplatConstantFolder,
|
|
ExtractOpFromBroadcast>(context);
|
|
}
|
|
|
|
static void populateFromInt64AttrArray(ArrayAttr arrayAttr,
|
|
SmallVectorImpl<int64_t> &results) {
|
|
for (auto attr : arrayAttr)
|
|
results.push_back(attr.cast<IntegerAttr>().getInt());
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// FmaOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
std::optional<SmallVector<int64_t, 4>> FMAOp::getShapeForUnroll() {
|
|
return llvm::to_vector<4>(getVectorType().getShape());
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// BroadcastOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
/// Return the dimensions of the result vector that were formerly ones in the
|
|
/// source tensor and thus correspond to "dim-1" broadcasting.
|
|
static llvm::SetVector<int64_t>
|
|
computeBroadcastedUnitDims(ArrayRef<int64_t> srcShape,
|
|
ArrayRef<int64_t> dstShape) {
|
|
int64_t rankDiff = dstShape.size() - srcShape.size();
|
|
int64_t dstDim = rankDiff;
|
|
llvm::SetVector<int64_t> res;
|
|
for (auto [s1, s2] :
|
|
llvm::zip_equal(srcShape, dstShape.drop_front(rankDiff))) {
|
|
if (s1 != s2) {
|
|
assert(s1 == 1 && "expected dim-1 broadcasting");
|
|
res.insert(dstDim);
|
|
}
|
|
++dstDim;
|
|
}
|
|
return res;
|
|
}
|
|
|
|
llvm::SetVector<int64_t> BroadcastOp::computeBroadcastedUnitDims() {
|
|
// Scalar broadcast is without any unit dim broadcast.
|
|
auto srcVectorType = getSourceType().dyn_cast<VectorType>();
|
|
if (!srcVectorType)
|
|
return {};
|
|
return ::computeBroadcastedUnitDims(srcVectorType.getShape(),
|
|
getResultVectorType().getShape());
|
|
}
|
|
|
|
/// Broadcast `value` to a vector of `dstShape`, knowing that exactly the
|
|
/// `broadcastedDims` dimensions in the dstShape are broadcasted.
|
|
/// This requires (and asserts) that the broadcast is free of dim-1
|
|
/// broadcasting.
|
|
/// Since vector.broadcast only allows expanding leading dimensions, an extra
|
|
/// vector.transpose may be inserted to make the broadcast possible.
|
|
/// `value`, `dstShape` and `broadcastedDims` must be properly specified or
|
|
/// the helper will assert. This means:
|
|
/// 1. `dstShape` must not be empty.
|
|
/// 2. `broadcastedDims` must be confined to [0 .. rank(value.getVectorType)]
|
|
/// 2. `dstShape` trimmed of the dimensions specified in `broadcastedDims`
|
|
// must match the `value` shape.
|
|
Value BroadcastOp::createOrFoldBroadcastOp(
|
|
OpBuilder &b, Value value, ArrayRef<int64_t> dstShape,
|
|
const llvm::SetVector<int64_t> &broadcastedDims) {
|
|
assert(!dstShape.empty() && "unexpected empty dst shape");
|
|
|
|
// Well-formedness check.
|
|
SmallVector<int64_t> checkShape;
|
|
for (int i = 0, e = dstShape.size(); i < e; ++i) {
|
|
if (broadcastedDims.contains(i))
|
|
continue;
|
|
checkShape.push_back(dstShape[i]);
|
|
}
|
|
assert(broadcastedDims.size() == dstShape.size() - checkShape.size() &&
|
|
"ill-formed broadcastedDims contains values not confined to "
|
|
"destVectorShape");
|
|
|
|
Location loc = value.getLoc();
|
|
Type elementType = getElementTypeOrSelf(value.getType());
|
|
VectorType srcVectorType = value.getType().dyn_cast<VectorType>();
|
|
VectorType dstVectorType = VectorType::get(dstShape, elementType);
|
|
|
|
// Step 2. If scalar -> dstShape broadcast, just do it.
|
|
if (!srcVectorType) {
|
|
assert(checkShape.empty() &&
|
|
"ill-formed createOrFoldBroadcastOp arguments");
|
|
return b.createOrFold<vector::BroadcastOp>(loc, dstVectorType, value);
|
|
}
|
|
|
|
assert(srcVectorType.getShape().equals(checkShape) &&
|
|
"ill-formed createOrFoldBroadcastOp arguments");
|
|
|
|
// Step 3. Since vector.broadcast only allows creating leading dims,
|
|
// vector -> dstShape broadcast may require a transpose.
|
|
// Traverse the dims in order and construct:
|
|
// 1. The leading entries of the broadcastShape that is guaranteed to be
|
|
// achievable by a simple broadcast.
|
|
// 2. The induced permutation for the subsequent vector.transpose that will
|
|
// bring us from `broadcastShape` back to he desired `dstShape`.
|
|
// If the induced permutation is not the identity, create a vector.transpose.
|
|
SmallVector<int64_t> broadcastShape, permutation(dstShape.size(), -1);
|
|
broadcastShape.reserve(dstShape.size());
|
|
// Consider the example:
|
|
// srcShape = 2x4
|
|
// dstShape = 1x2x3x4x5
|
|
// broadcastedDims = [0, 2, 4]
|
|
//
|
|
// We want to build:
|
|
// broadcastShape = 1x3x5x2x4
|
|
// permutation = [0, 2, 4, 1, 3]
|
|
// ---V--- -----V-----
|
|
// leading broadcast part src shape part
|
|
//
|
|
// Note that the trailing dims of broadcastShape are exactly the srcShape
|
|
// by construction.
|
|
// nextSrcShapeDim is used to keep track of where in the permutation the
|
|
// "src shape part" occurs.
|
|
int64_t nextSrcShapeDim = broadcastedDims.size();
|
|
for (int64_t i = 0, e = dstShape.size(); i < e; ++i) {
|
|
if (broadcastedDims.contains(i)) {
|
|
// 3.a. For each dim in the dst shape, if it is a broadcasted dim,
|
|
// bring it to the head of the broadcastShape.
|
|
// It will need to be permuted back from `broadcastShape.size() - 1` into
|
|
// position `i`.
|
|
broadcastShape.push_back(dstShape[i]);
|
|
permutation[i] = broadcastShape.size() - 1;
|
|
} else {
|
|
// 3.b. Otherwise, the dim is not broadcasted, it comes from the src
|
|
// shape and needs to be permuted into position `i`.
|
|
// Don't touch `broadcastShape` here, the whole srcShape will be
|
|
// appended after.
|
|
permutation[i] = nextSrcShapeDim++;
|
|
}
|
|
}
|
|
// 3.c. Append the srcShape.
|
|
llvm::append_range(broadcastShape, srcVectorType.getShape());
|
|
|
|
// Ensure there are no dim-1 broadcasts.
|
|
assert(::computeBroadcastedUnitDims(srcVectorType.getShape(), broadcastShape)
|
|
.empty() &&
|
|
"unexpected dim-1 broadcast");
|
|
|
|
VectorType broadcastType = VectorType::get(broadcastShape, elementType);
|
|
assert(vector::isBroadcastableTo(value.getType(), broadcastType) ==
|
|
vector::BroadcastableToResult::Success &&
|
|
"must be broadcastable");
|
|
Value res = b.createOrFold<vector::BroadcastOp>(loc, broadcastType, value);
|
|
// Step 4. If we find any dimension that indeed needs to be permuted,
|
|
// immediately return a new vector.transpose.
|
|
for (int64_t i = 0, e = permutation.size(); i < e; ++i)
|
|
if (permutation[i] != i)
|
|
return b.createOrFold<vector::TransposeOp>(loc, res, permutation);
|
|
// Otherwise return res.
|
|
return res;
|
|
}
|
|
|
|
BroadcastableToResult
|
|
mlir::vector::isBroadcastableTo(Type srcType, VectorType dstVectorType,
|
|
std::pair<int, int> *mismatchingDims) {
|
|
// Broadcast scalar to vector of the same element type.
|
|
if (srcType.isIntOrIndexOrFloat() && dstVectorType &&
|
|
getElementTypeOrSelf(srcType) == getElementTypeOrSelf(dstVectorType))
|
|
return BroadcastableToResult::Success;
|
|
// From now on, only vectors broadcast.
|
|
VectorType srcVectorType = srcType.dyn_cast<VectorType>();
|
|
if (!srcVectorType)
|
|
return BroadcastableToResult::SourceTypeNotAVector;
|
|
|
|
int64_t srcRank = srcVectorType.getRank();
|
|
int64_t dstRank = dstVectorType.getRank();
|
|
if (srcRank > dstRank)
|
|
return BroadcastableToResult::SourceRankHigher;
|
|
// Source has an exact match or singleton value for all trailing dimensions
|
|
// (all leading dimensions are simply duplicated).
|
|
int64_t lead = dstRank - srcRank;
|
|
for (int64_t r = 0; r < srcRank; ++r) {
|
|
int64_t srcDim = srcVectorType.getDimSize(r);
|
|
int64_t dstDim = dstVectorType.getDimSize(lead + r);
|
|
if (srcDim != 1 && srcDim != dstDim) {
|
|
if (mismatchingDims) {
|
|
mismatchingDims->first = srcDim;
|
|
mismatchingDims->second = dstDim;
|
|
}
|
|
return BroadcastableToResult::DimensionMismatch;
|
|
}
|
|
}
|
|
|
|
return BroadcastableToResult::Success;
|
|
}
|
|
|
|
LogicalResult BroadcastOp::verify() {
|
|
std::pair<int, int> mismatchingDims;
|
|
BroadcastableToResult res = isBroadcastableTo(
|
|
getSourceType(), getResultVectorType(), &mismatchingDims);
|
|
if (res == BroadcastableToResult::Success)
|
|
return success();
|
|
if (res == BroadcastableToResult::SourceRankHigher)
|
|
return emitOpError("source rank higher than destination rank");
|
|
if (res == BroadcastableToResult::DimensionMismatch)
|
|
return emitOpError("dimension mismatch (")
|
|
<< mismatchingDims.first << " vs. " << mismatchingDims.second << ")";
|
|
if (res == BroadcastableToResult::SourceTypeNotAVector)
|
|
return emitOpError("source type is not a vector");
|
|
llvm_unreachable("unexpected vector.broadcast op error");
|
|
}
|
|
|
|
OpFoldResult BroadcastOp::fold(FoldAdaptor adaptor) {
|
|
if (getSourceType() == getResultVectorType())
|
|
return getSource();
|
|
if (!adaptor.getSource())
|
|
return {};
|
|
auto vectorType = getResultVectorType();
|
|
if (adaptor.getSource().isa<IntegerAttr, FloatAttr>())
|
|
return DenseElementsAttr::get(vectorType, adaptor.getSource());
|
|
if (auto attr = adaptor.getSource().dyn_cast<SplatElementsAttr>())
|
|
return DenseElementsAttr::get(vectorType, attr.getSplatValue<Attribute>());
|
|
return {};
|
|
}
|
|
|
|
namespace {
|
|
|
|
// Fold broadcast1(broadcast2(x)) into broadcast1(x).
|
|
struct BroadcastFolder : public OpRewritePattern<BroadcastOp> {
|
|
using OpRewritePattern::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(BroadcastOp broadcastOp,
|
|
PatternRewriter &rewriter) const override {
|
|
auto srcBroadcast = broadcastOp.getSource().getDefiningOp<BroadcastOp>();
|
|
if (!srcBroadcast)
|
|
return failure();
|
|
rewriter.replaceOpWithNewOp<BroadcastOp>(broadcastOp,
|
|
broadcastOp.getResultVectorType(),
|
|
srcBroadcast.getSource());
|
|
return success();
|
|
}
|
|
};
|
|
} // namespace
|
|
|
|
void BroadcastOp::getCanonicalizationPatterns(RewritePatternSet &results,
|
|
MLIRContext *context) {
|
|
// BroadcastToShapeCast is not a default canonicalization, it is opt-in by
|
|
// calling `populateCastAwayVectorLeadingOneDimPatterns`
|
|
results.add<BroadcastFolder>(context);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// ShuffleOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
void ShuffleOp::build(OpBuilder &builder, OperationState &result, Value v1,
|
|
Value v2, ArrayRef<int64_t> mask) {
|
|
build(builder, result, v1, v2, getVectorSubscriptAttr(builder, mask));
|
|
}
|
|
|
|
LogicalResult ShuffleOp::verify() {
|
|
VectorType resultType = getResultVectorType();
|
|
VectorType v1Type = getV1VectorType();
|
|
VectorType v2Type = getV2VectorType();
|
|
// Verify ranks.
|
|
int64_t resRank = resultType.getRank();
|
|
int64_t v1Rank = v1Type.getRank();
|
|
int64_t v2Rank = v2Type.getRank();
|
|
bool wellFormed0DCase = v1Rank == 0 && v2Rank == 0 && resRank == 1;
|
|
bool wellFormedNDCase = v1Rank == resRank && v2Rank == resRank;
|
|
if (!wellFormed0DCase && !wellFormedNDCase)
|
|
return emitOpError("rank mismatch");
|
|
|
|
// Verify all but leading dimension sizes.
|
|
for (int64_t r = 1; r < v1Rank; ++r) {
|
|
int64_t resDim = resultType.getDimSize(r);
|
|
int64_t v1Dim = v1Type.getDimSize(r);
|
|
int64_t v2Dim = v2Type.getDimSize(r);
|
|
if (resDim != v1Dim || v1Dim != v2Dim)
|
|
return emitOpError("dimension mismatch");
|
|
}
|
|
// Verify mask length.
|
|
auto maskAttr = getMask().getValue();
|
|
int64_t maskLength = maskAttr.size();
|
|
if (maskLength <= 0)
|
|
return emitOpError("invalid mask length");
|
|
if (maskLength != resultType.getDimSize(0))
|
|
return emitOpError("mask length mismatch");
|
|
// Verify all indices.
|
|
int64_t indexSize = (v1Type.getRank() == 0 ? 1 : v1Type.getDimSize(0)) +
|
|
(v2Type.getRank() == 0 ? 1 : v2Type.getDimSize(0));
|
|
for (const auto &en : llvm::enumerate(maskAttr)) {
|
|
auto attr = en.value().dyn_cast<IntegerAttr>();
|
|
if (!attr || attr.getInt() < 0 || attr.getInt() >= indexSize)
|
|
return emitOpError("mask index #") << (en.index() + 1) << " out of range";
|
|
}
|
|
return success();
|
|
}
|
|
|
|
LogicalResult
|
|
ShuffleOp::inferReturnTypes(MLIRContext *, std::optional<Location>,
|
|
ValueRange operands, DictionaryAttr attributes,
|
|
OpaqueProperties properties, RegionRange,
|
|
SmallVectorImpl<Type> &inferredReturnTypes) {
|
|
ShuffleOp::Adaptor op(operands, attributes);
|
|
auto v1Type = op.getV1().getType().cast<VectorType>();
|
|
auto v1Rank = v1Type.getRank();
|
|
// Construct resulting type: leading dimension matches mask
|
|
// length, all trailing dimensions match the operands.
|
|
SmallVector<int64_t, 4> shape;
|
|
shape.reserve(v1Rank);
|
|
shape.push_back(std::max<size_t>(1, op.getMask().size()));
|
|
// In the 0-D case there is no trailing shape to append.
|
|
if (v1Rank > 0)
|
|
llvm::append_range(shape, v1Type.getShape().drop_front());
|
|
inferredReturnTypes.push_back(
|
|
VectorType::get(shape, v1Type.getElementType()));
|
|
return success();
|
|
}
|
|
|
|
static bool isStepIndexArray(ArrayAttr idxArr, uint64_t begin, size_t width) {
|
|
uint64_t expected = begin;
|
|
return idxArr.size() == width &&
|
|
llvm::all_of(idxArr.getAsValueRange<IntegerAttr>(),
|
|
[&expected](auto attr) {
|
|
return attr.getZExtValue() == expected++;
|
|
});
|
|
}
|
|
|
|
OpFoldResult vector::ShuffleOp::fold(FoldAdaptor adaptor) {
|
|
VectorType v1Type = getV1VectorType();
|
|
// For consistency: 0-D shuffle return type is 1-D, this cannot be a folding
|
|
// but must be a canonicalization into a vector.broadcast.
|
|
if (v1Type.getRank() == 0)
|
|
return {};
|
|
|
|
// fold shuffle V1, V2, [0, 1, 2, 3] : <4xi32>, <2xi32> -> V1
|
|
if (!v1Type.isScalable() &&
|
|
isStepIndexArray(getMask(), 0, v1Type.getDimSize(0)))
|
|
return getV1();
|
|
// fold shuffle V1, V2, [4, 5] : <4xi32>, <2xi32> -> V2
|
|
if (!getV1VectorType().isScalable() && !getV2VectorType().isScalable() &&
|
|
isStepIndexArray(getMask(), getV1VectorType().getDimSize(0),
|
|
getV2VectorType().getDimSize(0)))
|
|
return getV2();
|
|
|
|
Attribute lhs = adaptor.getV1(), rhs = adaptor.getV2();
|
|
if (!lhs || !rhs)
|
|
return {};
|
|
|
|
auto lhsType = lhs.cast<DenseElementsAttr>().getType().cast<VectorType>();
|
|
// Only support 1-D for now to avoid complicated n-D DenseElementsAttr
|
|
// manipulation.
|
|
if (lhsType.getRank() != 1)
|
|
return {};
|
|
int64_t lhsSize = lhsType.getDimSize(0);
|
|
|
|
SmallVector<Attribute> results;
|
|
auto lhsElements = lhs.cast<DenseElementsAttr>().getValues<Attribute>();
|
|
auto rhsElements = rhs.cast<DenseElementsAttr>().getValues<Attribute>();
|
|
for (const auto &index : this->getMask().getAsValueRange<IntegerAttr>()) {
|
|
int64_t i = index.getZExtValue();
|
|
if (i >= lhsSize) {
|
|
results.push_back(rhsElements[i - lhsSize]);
|
|
} else {
|
|
results.push_back(lhsElements[i]);
|
|
}
|
|
}
|
|
|
|
return DenseElementsAttr::get(getResultVectorType(), results);
|
|
}
|
|
|
|
namespace {
|
|
|
|
// Pattern to rewrite a 0-D shuffle with [0] or [1] mask returning a 1-D vector
|
|
// to a broadcast.
|
|
struct Canonicalize0DShuffleOp : public OpRewritePattern<ShuffleOp> {
|
|
using OpRewritePattern::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(ShuffleOp shuffleOp,
|
|
PatternRewriter &rewriter) const override {
|
|
VectorType v1VectorType = shuffleOp.getV1VectorType();
|
|
ArrayAttr mask = shuffleOp.getMask();
|
|
if (v1VectorType.getRank() > 0)
|
|
return failure();
|
|
if (mask.size() != 1)
|
|
return failure();
|
|
Type resType = VectorType::Builder(v1VectorType).setShape({1});
|
|
if (mask[0].cast<IntegerAttr>().getInt() == 0)
|
|
rewriter.replaceOpWithNewOp<vector::BroadcastOp>(shuffleOp, resType,
|
|
shuffleOp.getV1());
|
|
else
|
|
rewriter.replaceOpWithNewOp<vector::BroadcastOp>(shuffleOp, resType,
|
|
shuffleOp.getV2());
|
|
return success();
|
|
}
|
|
};
|
|
|
|
/// Pattern to rewrite a ShuffleOp(SplatOp, SplatOp) to SplatOp.
|
|
class ShuffleSplat final : public OpRewritePattern<ShuffleOp> {
|
|
public:
|
|
using OpRewritePattern::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(ShuffleOp op,
|
|
PatternRewriter &rewriter) const override {
|
|
auto v1Splat = op.getV1().getDefiningOp<SplatOp>();
|
|
auto v2Splat = op.getV2().getDefiningOp<SplatOp>();
|
|
|
|
if (!v1Splat || !v2Splat)
|
|
return failure();
|
|
|
|
if (v1Splat.getInput() != v2Splat.getInput())
|
|
return failure();
|
|
|
|
rewriter.replaceOpWithNewOp<SplatOp>(op, op.getType(), v1Splat.getInput());
|
|
return success();
|
|
}
|
|
};
|
|
|
|
} // namespace
|
|
|
|
void ShuffleOp::getCanonicalizationPatterns(RewritePatternSet &results,
|
|
MLIRContext *context) {
|
|
results.add<ShuffleSplat, Canonicalize0DShuffleOp>(context);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// InsertElementOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
void InsertElementOp::build(OpBuilder &builder, OperationState &result,
|
|
Value source, Value dest) {
|
|
build(builder, result, source, dest, {});
|
|
}
|
|
|
|
LogicalResult InsertElementOp::verify() {
|
|
auto dstVectorType = getDestVectorType();
|
|
if (dstVectorType.getRank() == 0) {
|
|
if (getPosition())
|
|
return emitOpError("expected position to be empty with 0-D vector");
|
|
return success();
|
|
}
|
|
if (dstVectorType.getRank() != 1)
|
|
return emitOpError("unexpected >1 vector rank");
|
|
if (!getPosition())
|
|
return emitOpError("expected position for 1-D vector");
|
|
return success();
|
|
}
|
|
|
|
OpFoldResult vector::InsertElementOp::fold(FoldAdaptor adaptor) {
|
|
// Skip the 0-D vector here.
|
|
if (!adaptor.getPosition())
|
|
return {};
|
|
|
|
Attribute src = adaptor.getSource();
|
|
Attribute dst = adaptor.getDest();
|
|
Attribute pos = adaptor.getPosition();
|
|
if (!src || !dst || !pos)
|
|
return {};
|
|
|
|
auto dstElements = dst.cast<DenseElementsAttr>().getValues<Attribute>();
|
|
|
|
SmallVector<Attribute> results(dstElements);
|
|
|
|
auto attr = pos.dyn_cast<IntegerAttr>();
|
|
uint64_t posIdx = attr.getInt();
|
|
|
|
results[posIdx] = src;
|
|
|
|
return DenseElementsAttr::get(getDestVectorType(), results);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// InsertOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
void InsertOp::build(OpBuilder &builder, OperationState &result, Value source,
|
|
Value dest, ArrayRef<int64_t> position) {
|
|
result.addOperands({source, dest});
|
|
auto positionAttr = getVectorSubscriptAttr(builder, position);
|
|
result.addTypes(dest.getType());
|
|
result.addAttribute(getPositionAttrStrName(), positionAttr);
|
|
}
|
|
|
|
// Convenience builder which assumes the values are constant indices.
|
|
void InsertOp::build(OpBuilder &builder, OperationState &result, Value source,
|
|
Value dest, ValueRange position) {
|
|
SmallVector<int64_t, 4> positionConstants =
|
|
llvm::to_vector<4>(llvm::map_range(position, [](Value pos) {
|
|
return pos.getDefiningOp<arith::ConstantIndexOp>().value();
|
|
}));
|
|
build(builder, result, source, dest, positionConstants);
|
|
}
|
|
|
|
LogicalResult InsertOp::verify() {
|
|
auto positionAttr = getPosition().getValue();
|
|
auto destVectorType = getDestVectorType();
|
|
if (positionAttr.size() > static_cast<unsigned>(destVectorType.getRank()))
|
|
return emitOpError(
|
|
"expected position attribute of rank smaller than dest vector rank");
|
|
auto srcVectorType = getSourceType().dyn_cast<VectorType>();
|
|
if (srcVectorType &&
|
|
(static_cast<unsigned>(srcVectorType.getRank()) + positionAttr.size() !=
|
|
static_cast<unsigned>(destVectorType.getRank())))
|
|
return emitOpError("expected position attribute rank + source rank to "
|
|
"match dest vector rank");
|
|
if (!srcVectorType &&
|
|
(positionAttr.size() != static_cast<unsigned>(destVectorType.getRank())))
|
|
return emitOpError(
|
|
"expected position attribute rank to match the dest vector rank");
|
|
for (const auto &en : llvm::enumerate(positionAttr)) {
|
|
auto attr = en.value().dyn_cast<IntegerAttr>();
|
|
if (!attr || attr.getInt() < 0 ||
|
|
attr.getInt() >= destVectorType.getDimSize(en.index()))
|
|
return emitOpError("expected position attribute #")
|
|
<< (en.index() + 1)
|
|
<< " to be a non-negative integer smaller than the corresponding "
|
|
"dest vector dimension";
|
|
}
|
|
return success();
|
|
}
|
|
|
|
namespace {
|
|
|
|
// If insertOp is only inserting unit dimensions it can be transformed to a
|
|
// broadcast.
|
|
class InsertToBroadcast final : public OpRewritePattern<InsertOp> {
|
|
public:
|
|
using OpRewritePattern::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(InsertOp insertOp,
|
|
PatternRewriter &rewriter) const override {
|
|
auto srcVecType = insertOp.getSourceType().dyn_cast<VectorType>();
|
|
if (!srcVecType || insertOp.getDestVectorType().getNumElements() !=
|
|
srcVecType.getNumElements())
|
|
return failure();
|
|
rewriter.replaceOpWithNewOp<BroadcastOp>(
|
|
insertOp, insertOp.getDestVectorType(), insertOp.getSource());
|
|
return success();
|
|
}
|
|
};
|
|
|
|
/// Pattern to rewrite a InsertOp(SplatOp, SplatOp) to SplatOp.
|
|
class InsertSplatToSplat final : public OpRewritePattern<InsertOp> {
|
|
public:
|
|
using OpRewritePattern::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(InsertOp op,
|
|
PatternRewriter &rewriter) const override {
|
|
auto srcSplat = op.getSource().getDefiningOp<SplatOp>();
|
|
auto dstSplat = op.getDest().getDefiningOp<SplatOp>();
|
|
|
|
if (!srcSplat || !dstSplat)
|
|
return failure();
|
|
|
|
if (srcSplat.getInput() != dstSplat.getInput())
|
|
return failure();
|
|
|
|
rewriter.replaceOpWithNewOp<SplatOp>(op, op.getType(), srcSplat.getInput());
|
|
return success();
|
|
}
|
|
};
|
|
|
|
// Pattern to rewrite a InsertOp(ConstantOp into ConstantOp) -> ConstantOp.
|
|
class InsertOpConstantFolder final : public OpRewritePattern<InsertOp> {
|
|
public:
|
|
using OpRewritePattern::OpRewritePattern;
|
|
|
|
// Do not create constants with more than `vectorSizeFoldThreashold` elements,
|
|
// unless the source vector constant has a single use.
|
|
static constexpr int64_t vectorSizeFoldThreshold = 256;
|
|
|
|
LogicalResult matchAndRewrite(InsertOp op,
|
|
PatternRewriter &rewriter) const override {
|
|
// Return if 'InsertOp' operand is not defined by a compatible vector
|
|
// ConstantOp.
|
|
TypedValue<VectorType> destVector = op.getDest();
|
|
Attribute vectorDestCst;
|
|
if (!matchPattern(destVector, m_Constant(&vectorDestCst)))
|
|
return failure();
|
|
|
|
VectorType destTy = destVector.getType();
|
|
if (destTy.isScalable())
|
|
return failure();
|
|
|
|
// Make sure we do not create too many large constants.
|
|
if (destTy.getNumElements() > vectorSizeFoldThreshold &&
|
|
!destVector.hasOneUse())
|
|
return failure();
|
|
|
|
auto denseDest = vectorDestCst.cast<DenseElementsAttr>();
|
|
|
|
Value sourceValue = op.getSource();
|
|
Attribute sourceCst;
|
|
if (!matchPattern(sourceValue, m_Constant(&sourceCst)))
|
|
return failure();
|
|
|
|
// Calculate the linearized position of the continuous chunk of elements to
|
|
// insert.
|
|
llvm::SmallVector<int64_t> completePositions(destTy.getRank(), 0);
|
|
copy(getI64SubArray(op.getPosition()), completePositions.begin());
|
|
int64_t insertBeginPosition =
|
|
linearize(completePositions, computeStrides(destTy.getShape()));
|
|
|
|
SmallVector<Attribute> insertedValues;
|
|
if (auto denseSource = sourceCst.dyn_cast<DenseElementsAttr>())
|
|
llvm::append_range(insertedValues, denseSource.getValues<Attribute>());
|
|
else
|
|
insertedValues.push_back(sourceCst);
|
|
|
|
auto allValues = llvm::to_vector(denseDest.getValues<Attribute>());
|
|
copy(insertedValues, allValues.begin() + insertBeginPosition);
|
|
auto newAttr = DenseElementsAttr::get(destTy, allValues);
|
|
|
|
rewriter.replaceOpWithNewOp<arith::ConstantOp>(op, newAttr);
|
|
return success();
|
|
}
|
|
};
|
|
|
|
} // namespace
|
|
|
|
void InsertOp::getCanonicalizationPatterns(RewritePatternSet &results,
|
|
MLIRContext *context) {
|
|
results.add<InsertToBroadcast, BroadcastFolder, InsertSplatToSplat,
|
|
InsertOpConstantFolder>(context);
|
|
}
|
|
|
|
// Eliminates insert operations that produce values identical to their source
|
|
// value. This happens when the source and destination vectors have identical
|
|
// sizes.
|
|
OpFoldResult vector::InsertOp::fold(FoldAdaptor adaptor) {
|
|
if (getPosition().empty())
|
|
return getSource();
|
|
return {};
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// InsertStridedSliceOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
void InsertStridedSliceOp::build(OpBuilder &builder, OperationState &result,
|
|
Value source, Value dest,
|
|
ArrayRef<int64_t> offsets,
|
|
ArrayRef<int64_t> strides) {
|
|
result.addOperands({source, dest});
|
|
auto offsetsAttr = getVectorSubscriptAttr(builder, offsets);
|
|
auto stridesAttr = getVectorSubscriptAttr(builder, strides);
|
|
result.addTypes(dest.getType());
|
|
result.addAttribute(getOffsetsAttrStrName(), offsetsAttr);
|
|
result.addAttribute(getStridesAttrStrName(), stridesAttr);
|
|
}
|
|
|
|
// TODO: Should be moved to Tablegen ConfinedAttr attributes.
|
|
template <typename OpType>
|
|
static LogicalResult isIntegerArrayAttrSmallerThanShape(OpType op,
|
|
ArrayAttr arrayAttr,
|
|
ArrayRef<int64_t> shape,
|
|
StringRef attrName) {
|
|
if (arrayAttr.size() > shape.size())
|
|
return op.emitOpError("expected ")
|
|
<< attrName << " attribute of rank smaller than vector rank";
|
|
return success();
|
|
}
|
|
|
|
// Returns true if all integers in `arrayAttr` are in the half-open [min, max}
|
|
// interval. If `halfOpen` is true then the admissible interval is [min, max).
|
|
// Otherwise, the admissible interval is [min, max].
|
|
template <typename OpType>
|
|
static LogicalResult
|
|
isIntegerArrayAttrConfinedToRange(OpType op, ArrayAttr arrayAttr, int64_t min,
|
|
int64_t max, StringRef attrName,
|
|
bool halfOpen = true) {
|
|
for (auto attr : arrayAttr) {
|
|
auto val = attr.cast<IntegerAttr>().getInt();
|
|
auto upper = max;
|
|
if (!halfOpen)
|
|
upper += 1;
|
|
if (val < min || val >= upper)
|
|
return op.emitOpError("expected ") << attrName << " to be confined to ["
|
|
<< min << ", " << upper << ")";
|
|
}
|
|
return success();
|
|
}
|
|
|
|
// Returns true if all integers in `arrayAttr` are in the half-open [min, max}
|
|
// interval. If `halfOpen` is true then the admissible interval is [min, max).
|
|
// Otherwise, the admissible interval is [min, max].
|
|
template <typename OpType>
|
|
static LogicalResult
|
|
isIntegerArrayAttrConfinedToShape(OpType op, ArrayAttr arrayAttr,
|
|
ArrayRef<int64_t> shape, StringRef attrName,
|
|
bool halfOpen = true, int64_t min = 0) {
|
|
for (auto [index, attrDimPair] :
|
|
llvm::enumerate(llvm::zip_first(arrayAttr, shape))) {
|
|
int64_t val =
|
|
std::get<0>(attrDimPair).template cast<IntegerAttr>().getInt();
|
|
int64_t max = std::get<1>(attrDimPair);
|
|
if (!halfOpen)
|
|
max += 1;
|
|
if (val < min || val >= max)
|
|
return op.emitOpError("expected ")
|
|
<< attrName << " dimension " << index << " to be confined to ["
|
|
<< min << ", " << max << ")";
|
|
}
|
|
return success();
|
|
}
|
|
|
|
// Returns true if all integers in `arrayAttr` are in the interval [min, max}.
|
|
// interval. If `halfOpen` is true then the admissible interval is [min, max).
|
|
// Otherwise, the admissible interval is [min, max].
|
|
template <typename OpType>
|
|
static LogicalResult isSumOfIntegerArrayAttrConfinedToShape(
|
|
OpType op, ArrayAttr arrayAttr1, ArrayAttr arrayAttr2,
|
|
ArrayRef<int64_t> shape, StringRef attrName1, StringRef attrName2,
|
|
bool halfOpen = true, int64_t min = 1) {
|
|
assert(arrayAttr1.size() <= shape.size());
|
|
assert(arrayAttr2.size() <= shape.size());
|
|
for (auto [index, it] :
|
|
llvm::enumerate(llvm::zip(arrayAttr1, arrayAttr2, shape))) {
|
|
auto val1 = std::get<0>(it).template cast<IntegerAttr>().getInt();
|
|
auto val2 = std::get<1>(it).template cast<IntegerAttr>().getInt();
|
|
int64_t max = std::get<2>(it);
|
|
if (!halfOpen)
|
|
max += 1;
|
|
if (val1 + val2 < 0 || val1 + val2 >= max)
|
|
return op.emitOpError("expected sum(")
|
|
<< attrName1 << ", " << attrName2 << ") dimension " << index
|
|
<< " to be confined to [" << min << ", " << max << ")";
|
|
}
|
|
return success();
|
|
}
|
|
|
|
static ArrayAttr makeI64ArrayAttr(ArrayRef<int64_t> values,
|
|
MLIRContext *context) {
|
|
auto attrs = llvm::map_range(values, [context](int64_t v) -> Attribute {
|
|
return IntegerAttr::get(IntegerType::get(context, 64), APInt(64, v));
|
|
});
|
|
return ArrayAttr::get(context, llvm::to_vector<8>(attrs));
|
|
}
|
|
|
|
LogicalResult InsertStridedSliceOp::verify() {
|
|
auto sourceVectorType = getSourceVectorType();
|
|
auto destVectorType = getDestVectorType();
|
|
auto offsets = getOffsetsAttr();
|
|
auto strides = getStridesAttr();
|
|
if (offsets.size() != static_cast<unsigned>(destVectorType.getRank()))
|
|
return emitOpError(
|
|
"expected offsets of same size as destination vector rank");
|
|
if (strides.size() != static_cast<unsigned>(sourceVectorType.getRank()))
|
|
return emitOpError("expected strides of same size as source vector rank");
|
|
if (sourceVectorType.getRank() > destVectorType.getRank())
|
|
return emitOpError(
|
|
"expected source rank to be smaller than destination rank");
|
|
|
|
auto sourceShape = sourceVectorType.getShape();
|
|
auto destShape = destVectorType.getShape();
|
|
SmallVector<int64_t, 4> sourceShapeAsDestShape(
|
|
destShape.size() - sourceShape.size(), 0);
|
|
sourceShapeAsDestShape.append(sourceShape.begin(), sourceShape.end());
|
|
auto offName = InsertStridedSliceOp::getOffsetsAttrName();
|
|
auto stridesName = InsertStridedSliceOp::getStridesAttrName();
|
|
if (failed(isIntegerArrayAttrConfinedToShape(*this, offsets, destShape,
|
|
offName)) ||
|
|
failed(isIntegerArrayAttrConfinedToRange(*this, strides, 1, 1,
|
|
stridesName,
|
|
/*halfOpen=*/false)) ||
|
|
failed(isSumOfIntegerArrayAttrConfinedToShape(
|
|
*this, offsets,
|
|
makeI64ArrayAttr(sourceShapeAsDestShape, getContext()), destShape,
|
|
offName, "source vector shape",
|
|
/*halfOpen=*/false, /*min=*/1)))
|
|
return failure();
|
|
|
|
return success();
|
|
}
|
|
|
|
namespace {
|
|
/// Pattern to rewrite an InsertStridedSliceOp(SplatOp(X):src_type,
|
|
/// SplatOp(X):dst_type) to SplatOp(X):dst_type.
|
|
class FoldInsertStridedSliceSplat final
|
|
: public OpRewritePattern<InsertStridedSliceOp> {
|
|
public:
|
|
using OpRewritePattern::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(InsertStridedSliceOp insertStridedSliceOp,
|
|
PatternRewriter &rewriter) const override {
|
|
auto srcSplatOp =
|
|
insertStridedSliceOp.getSource().getDefiningOp<vector::SplatOp>();
|
|
auto destSplatOp =
|
|
insertStridedSliceOp.getDest().getDefiningOp<vector::SplatOp>();
|
|
|
|
if (!srcSplatOp || !destSplatOp)
|
|
return failure();
|
|
|
|
if (srcSplatOp.getInput() != destSplatOp.getInput())
|
|
return failure();
|
|
|
|
rewriter.replaceOp(insertStridedSliceOp, insertStridedSliceOp.getDest());
|
|
return success();
|
|
}
|
|
};
|
|
|
|
/// Pattern to rewrite an InsertStridedSliceOp(ExtractStridedSliceOp(dst), dst)
|
|
/// to dst.
|
|
class FoldInsertStridedSliceOfExtract final
|
|
: public OpRewritePattern<InsertStridedSliceOp> {
|
|
public:
|
|
using OpRewritePattern::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(InsertStridedSliceOp insertStridedSliceOp,
|
|
PatternRewriter &rewriter) const override {
|
|
auto extractStridedSliceOp =
|
|
insertStridedSliceOp.getSource()
|
|
.getDefiningOp<vector::ExtractStridedSliceOp>();
|
|
|
|
if (!extractStridedSliceOp)
|
|
return failure();
|
|
|
|
if (extractStridedSliceOp.getOperand() != insertStridedSliceOp.getDest())
|
|
return failure();
|
|
|
|
// Check if have the same strides and offsets.
|
|
if (extractStridedSliceOp.getStrides() !=
|
|
insertStridedSliceOp.getStrides() ||
|
|
extractStridedSliceOp.getOffsets() != insertStridedSliceOp.getOffsets())
|
|
return failure();
|
|
|
|
rewriter.replaceOp(insertStridedSliceOp, insertStridedSliceOp.getDest());
|
|
return success();
|
|
}
|
|
};
|
|
|
|
// Pattern to rewrite an InsertStridedSliceOp(ConstantOp into ConstantOp) ->
|
|
// ConstantOp.
|
|
class InsertStridedSliceConstantFolder final
|
|
: public OpRewritePattern<InsertStridedSliceOp> {
|
|
public:
|
|
using OpRewritePattern::OpRewritePattern;
|
|
|
|
// Do not create constants with more than `vectorSizeFoldThreashold` elements,
|
|
// unless the source vector constant has a single use.
|
|
static constexpr int64_t vectorSizeFoldThreshold = 256;
|
|
|
|
LogicalResult matchAndRewrite(InsertStridedSliceOp op,
|
|
PatternRewriter &rewriter) const override {
|
|
// Return if 'InsertOp' operand is not defined by a compatible vector
|
|
// ConstantOp.
|
|
TypedValue<VectorType> destVector = op.getDest();
|
|
Attribute vectorDestCst;
|
|
if (!matchPattern(destVector, m_Constant(&vectorDestCst)))
|
|
return failure();
|
|
|
|
VectorType destTy = destVector.getType();
|
|
if (destTy.isScalable())
|
|
return failure();
|
|
|
|
// Make sure we do not create too many large constants.
|
|
if (destTy.getNumElements() > vectorSizeFoldThreshold &&
|
|
!destVector.hasOneUse())
|
|
return failure();
|
|
|
|
auto denseDest = vectorDestCst.cast<DenseElementsAttr>();
|
|
|
|
TypedValue<VectorType> sourceValue = op.getSource();
|
|
Attribute sourceCst;
|
|
if (!matchPattern(sourceValue, m_Constant(&sourceCst)))
|
|
return failure();
|
|
|
|
// TODO: Handle non-unit strides when they become available.
|
|
if (op.hasNonUnitStrides())
|
|
return failure();
|
|
|
|
VectorType sliceVecTy = sourceValue.getType();
|
|
ArrayRef<int64_t> sliceShape = sliceVecTy.getShape();
|
|
int64_t rankDifference = destTy.getRank() - sliceVecTy.getRank();
|
|
SmallVector<int64_t, 4> offsets = getI64SubArray(op.getOffsets());
|
|
SmallVector<int64_t, 4> destStrides = computeStrides(destTy.getShape());
|
|
|
|
// Calcualte the destination element indices by enumerating all slice
|
|
// positions within the destination and linearizing them. The enumeration
|
|
// order is lexicographic which yields a sequence of monotonically
|
|
// increasing linearized position indices.
|
|
// Because the destination may have higher dimensionality then the slice,
|
|
// we keep track of two overlapping sets of positions and offsets.
|
|
auto denseSlice = sourceCst.cast<DenseElementsAttr>();
|
|
auto sliceValuesIt = denseSlice.value_begin<Attribute>();
|
|
auto newValues = llvm::to_vector(denseDest.getValues<Attribute>());
|
|
SmallVector<int64_t> currDestPosition(offsets.begin(), offsets.end());
|
|
MutableArrayRef<int64_t> currSlicePosition(
|
|
currDestPosition.begin() + rankDifference, currDestPosition.end());
|
|
ArrayRef<int64_t> sliceOffsets(offsets.begin() + rankDifference,
|
|
offsets.end());
|
|
do {
|
|
int64_t linearizedPosition = linearize(currDestPosition, destStrides);
|
|
assert(linearizedPosition < destTy.getNumElements() && "Invalid index");
|
|
assert(sliceValuesIt != denseSlice.value_end<Attribute>() &&
|
|
"Invalid slice element");
|
|
newValues[linearizedPosition] = *sliceValuesIt;
|
|
++sliceValuesIt;
|
|
} while (succeeded(
|
|
incSlicePosition(currSlicePosition, sliceShape, sliceOffsets)));
|
|
|
|
auto newAttr = DenseElementsAttr::get(destTy, newValues);
|
|
rewriter.replaceOpWithNewOp<arith::ConstantOp>(op, newAttr);
|
|
return success();
|
|
}
|
|
};
|
|
|
|
} // namespace
|
|
|
|
void vector::InsertStridedSliceOp::getCanonicalizationPatterns(
|
|
RewritePatternSet &results, MLIRContext *context) {
|
|
results.add<FoldInsertStridedSliceSplat, FoldInsertStridedSliceOfExtract,
|
|
InsertStridedSliceConstantFolder>(context);
|
|
}
|
|
|
|
OpFoldResult InsertStridedSliceOp::fold(FoldAdaptor adaptor) {
|
|
if (getSourceVectorType() == getDestVectorType())
|
|
return getSource();
|
|
return {};
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// OuterProductOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
/// Build an op without mask, use the type of `acc` as the return type.
|
|
void OuterProductOp::build(OpBuilder &builder, OperationState &result,
|
|
Value lhs, Value rhs, Value acc) {
|
|
result.addOperands({lhs, rhs, acc});
|
|
result.addTypes(acc.getType());
|
|
}
|
|
|
|
void OuterProductOp::print(OpAsmPrinter &p) {
|
|
p << " " << getLhs() << ", " << getRhs();
|
|
if (!getAcc().empty()) {
|
|
p << ", " << getAcc();
|
|
p.printOptionalAttrDict((*this)->getAttrs());
|
|
}
|
|
p << " : " << getLhs().getType() << ", " << getRhs().getType();
|
|
}
|
|
|
|
ParseResult OuterProductOp::parse(OpAsmParser &parser, OperationState &result) {
|
|
SmallVector<OpAsmParser::UnresolvedOperand, 3> operandsInfo;
|
|
Type tLHS, tRHS;
|
|
if (parser.parseOperandList(operandsInfo) ||
|
|
parser.parseOptionalAttrDict(result.attributes) ||
|
|
parser.parseColonType(tLHS) || parser.parseComma() ||
|
|
parser.parseType(tRHS))
|
|
return failure();
|
|
if (operandsInfo.size() < 2)
|
|
return parser.emitError(parser.getNameLoc(),
|
|
"expected at least 2 operands");
|
|
VectorType vLHS = tLHS.dyn_cast<VectorType>();
|
|
VectorType vRHS = tRHS.dyn_cast<VectorType>();
|
|
if (!vLHS)
|
|
return parser.emitError(parser.getNameLoc(),
|
|
"expected vector type for operand #1");
|
|
|
|
unsigned numScalableDims = vLHS.getNumScalableDims();
|
|
VectorType resType;
|
|
if (vRHS) {
|
|
numScalableDims += vRHS.getNumScalableDims();
|
|
resType = VectorType::get({vLHS.getDimSize(0), vRHS.getDimSize(0)},
|
|
vLHS.getElementType(), numScalableDims);
|
|
} else {
|
|
// Scalar RHS operand
|
|
resType = VectorType::get({vLHS.getDimSize(0)}, vLHS.getElementType(),
|
|
numScalableDims);
|
|
}
|
|
|
|
if (!result.attributes.get(OuterProductOp::getKindAttrStrName())) {
|
|
result.attributes.append(
|
|
OuterProductOp::getKindAttrStrName(),
|
|
CombiningKindAttr::get(result.getContext(),
|
|
OuterProductOp::getDefaultKind()));
|
|
}
|
|
|
|
return failure(
|
|
parser.resolveOperand(operandsInfo[0], tLHS, result.operands) ||
|
|
parser.resolveOperand(operandsInfo[1], tRHS, result.operands) ||
|
|
(operandsInfo.size() > 2 &&
|
|
parser.resolveOperand(operandsInfo[2], resType, result.operands)) ||
|
|
parser.addTypeToList(resType, result.types));
|
|
}
|
|
|
|
LogicalResult OuterProductOp::verify() {
|
|
Type tRHS = getOperandTypeRHS();
|
|
VectorType vLHS = getOperandVectorTypeLHS(),
|
|
vRHS = tRHS.dyn_cast<VectorType>(),
|
|
vACC = getOperandVectorTypeACC(), vRES = getResultVectorType();
|
|
|
|
if (vLHS.getRank() != 1)
|
|
return emitOpError("expected 1-d vector for operand #1");
|
|
|
|
if (vRHS) {
|
|
// Proper OUTER operation.
|
|
if (vRHS.getRank() != 1)
|
|
return emitOpError("expected 1-d vector for operand #2");
|
|
if (vRES.getRank() != 2)
|
|
return emitOpError("expected 2-d vector result");
|
|
if (vLHS.getDimSize(0) != vRES.getDimSize(0))
|
|
return emitOpError("expected #1 operand dim to match result dim #1");
|
|
if (vRHS.getDimSize(0) != vRES.getDimSize(1))
|
|
return emitOpError("expected #2 operand dim to match result dim #2");
|
|
if (vRHS.isScalable() != vLHS.isScalable())
|
|
return emitOpError("expected either all or none of vector operands #1 "
|
|
"and #2 to be scalable");
|
|
} else {
|
|
// An AXPY operation.
|
|
if (vRES.getRank() != 1)
|
|
return emitOpError("expected 1-d vector result");
|
|
if (vLHS.getDimSize(0) != vRES.getDimSize(0))
|
|
return emitOpError("expected #1 operand dim to match result dim #1");
|
|
}
|
|
|
|
if (vACC && vACC != vRES)
|
|
return emitOpError("expected operand #3 of same type as result type");
|
|
|
|
// Verify supported combining kind.
|
|
if (!isSupportedCombiningKind(getKind(), vRES.getElementType()))
|
|
return emitOpError("unsupported outerproduct type");
|
|
|
|
return success();
|
|
}
|
|
|
|
// MaskableOpInterface methods.
|
|
|
|
/// Returns the mask type expected by this operation. Mostly used for
|
|
/// verification purposes. It requires the operation to be vectorized."
|
|
Type OuterProductOp::getExpectedMaskType() {
|
|
auto vecType = this->getResultVectorType();
|
|
return VectorType::get(vecType.getShape(),
|
|
IntegerType::get(vecType.getContext(), /*width=*/1));
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// ReshapeOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
LogicalResult ReshapeOp::verify() {
|
|
// Verify that rank(numInputs/outputs) + numFixedVec dim matches vec rank.
|
|
auto inputVectorType = getInputVectorType();
|
|
auto outputVectorType = getOutputVectorType();
|
|
int64_t inputShapeRank = getNumInputShapeSizes();
|
|
int64_t outputShapeRank = getNumOutputShapeSizes();
|
|
SmallVector<int64_t, 4> fixedVectorSizes;
|
|
getFixedVectorSizes(fixedVectorSizes);
|
|
int64_t numFixedVectorSizes = fixedVectorSizes.size();
|
|
|
|
if (inputVectorType.getRank() != inputShapeRank + numFixedVectorSizes)
|
|
return emitError("invalid input shape for vector type ") << inputVectorType;
|
|
|
|
if (outputVectorType.getRank() != outputShapeRank + numFixedVectorSizes)
|
|
return emitError("invalid output shape for vector type ")
|
|
<< outputVectorType;
|
|
|
|
// Verify that the 'fixedVectorSizes' match an input/output vector shape
|
|
// suffix.
|
|
unsigned inputVectorRank = inputVectorType.getRank();
|
|
for (unsigned i = 0; i < numFixedVectorSizes; ++i) {
|
|
unsigned index = inputVectorRank - numFixedVectorSizes - i;
|
|
if (fixedVectorSizes[i] != inputVectorType.getShape()[index])
|
|
return emitError("fixed vector size must match input vector for dim ")
|
|
<< i;
|
|
}
|
|
|
|
unsigned outputVectorRank = outputVectorType.getRank();
|
|
for (unsigned i = 0; i < numFixedVectorSizes; ++i) {
|
|
unsigned index = outputVectorRank - numFixedVectorSizes - i;
|
|
if (fixedVectorSizes[i] != outputVectorType.getShape()[index])
|
|
return emitError("fixed vector size must match output vector for dim ")
|
|
<< i;
|
|
}
|
|
|
|
// If all shape operands are produced by constant ops, verify that product
|
|
// of dimensions for input/output shape match.
|
|
auto isDefByConstant = [](Value operand) {
|
|
return isa_and_nonnull<arith::ConstantIndexOp>(operand.getDefiningOp());
|
|
};
|
|
if (llvm::all_of(getInputShape(), isDefByConstant) &&
|
|
llvm::all_of(getOutputShape(), isDefByConstant)) {
|
|
int64_t numInputElements = 1;
|
|
for (auto operand : getInputShape())
|
|
numInputElements *=
|
|
cast<arith::ConstantIndexOp>(operand.getDefiningOp()).value();
|
|
int64_t numOutputElements = 1;
|
|
for (auto operand : getOutputShape())
|
|
numOutputElements *=
|
|
cast<arith::ConstantIndexOp>(operand.getDefiningOp()).value();
|
|
if (numInputElements != numOutputElements)
|
|
return emitError("product of input and output shape sizes must match");
|
|
}
|
|
return success();
|
|
}
|
|
|
|
void ReshapeOp::getFixedVectorSizes(SmallVectorImpl<int64_t> &results) {
|
|
populateFromInt64AttrArray(getFixedVectorSizes(), results);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// ExtractStridedSliceOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
// Inference works as follows:
|
|
// 1. Add 'sizes' from prefix of dims in 'offsets'.
|
|
// 2. Add sizes from 'vectorType' for remaining dims.
|
|
static Type inferStridedSliceOpResultType(VectorType vectorType,
|
|
ArrayAttr offsets, ArrayAttr sizes,
|
|
ArrayAttr strides) {
|
|
assert(offsets.size() == sizes.size() && offsets.size() == strides.size());
|
|
SmallVector<int64_t, 4> shape;
|
|
shape.reserve(vectorType.getRank());
|
|
unsigned idx = 0;
|
|
for (unsigned e = offsets.size(); idx < e; ++idx)
|
|
shape.push_back(sizes[idx].cast<IntegerAttr>().getInt());
|
|
for (unsigned e = vectorType.getShape().size(); idx < e; ++idx)
|
|
shape.push_back(vectorType.getShape()[idx]);
|
|
|
|
return VectorType::get(shape, vectorType.getElementType());
|
|
}
|
|
|
|
void ExtractStridedSliceOp::build(OpBuilder &builder, OperationState &result,
|
|
Value source, ArrayRef<int64_t> offsets,
|
|
ArrayRef<int64_t> sizes,
|
|
ArrayRef<int64_t> strides) {
|
|
result.addOperands(source);
|
|
auto offsetsAttr = getVectorSubscriptAttr(builder, offsets);
|
|
auto sizesAttr = getVectorSubscriptAttr(builder, sizes);
|
|
auto stridesAttr = getVectorSubscriptAttr(builder, strides);
|
|
result.addTypes(
|
|
inferStridedSliceOpResultType(source.getType().cast<VectorType>(),
|
|
offsetsAttr, sizesAttr, stridesAttr));
|
|
result.addAttribute(getOffsetsAttrStrName(), offsetsAttr);
|
|
result.addAttribute(getSizesAttrStrName(), sizesAttr);
|
|
result.addAttribute(getStridesAttrStrName(), stridesAttr);
|
|
}
|
|
|
|
LogicalResult ExtractStridedSliceOp::verify() {
|
|
auto type = getSourceVectorType();
|
|
auto offsets = getOffsetsAttr();
|
|
auto sizes = getSizesAttr();
|
|
auto strides = getStridesAttr();
|
|
if (offsets.size() != sizes.size() || offsets.size() != strides.size())
|
|
return emitOpError(
|
|
"expected offsets, sizes and strides attributes of same size");
|
|
|
|
auto shape = type.getShape();
|
|
auto offName = getOffsetsAttrName();
|
|
auto sizesName = getSizesAttrName();
|
|
auto stridesName = getStridesAttrName();
|
|
if (failed(
|
|
isIntegerArrayAttrSmallerThanShape(*this, offsets, shape, offName)) ||
|
|
failed(
|
|
isIntegerArrayAttrSmallerThanShape(*this, sizes, shape, sizesName)) ||
|
|
failed(isIntegerArrayAttrSmallerThanShape(*this, strides, shape,
|
|
stridesName)) ||
|
|
failed(
|
|
isIntegerArrayAttrConfinedToShape(*this, offsets, shape, offName)) ||
|
|
failed(isIntegerArrayAttrConfinedToShape(*this, sizes, shape, sizesName,
|
|
/*halfOpen=*/false,
|
|
/*min=*/1)) ||
|
|
failed(isIntegerArrayAttrConfinedToRange(*this, strides, 1, 1,
|
|
stridesName,
|
|
/*halfOpen=*/false)) ||
|
|
failed(isSumOfIntegerArrayAttrConfinedToShape(*this, offsets, sizes,
|
|
shape, offName, sizesName,
|
|
/*halfOpen=*/false)))
|
|
return failure();
|
|
|
|
auto resultType = inferStridedSliceOpResultType(getSourceVectorType(),
|
|
offsets, sizes, strides);
|
|
if (getResult().getType() != resultType)
|
|
return emitOpError("expected result type to be ") << resultType;
|
|
|
|
return success();
|
|
}
|
|
|
|
// When the source of ExtractStrided comes from a chain of InsertStrided ops try
|
|
// to use the source of the InsertStrided ops if we can detect that the
|
|
// extracted vector is a subset of one of the vector inserted.
|
|
static LogicalResult
|
|
foldExtractStridedOpFromInsertChain(ExtractStridedSliceOp op) {
|
|
// Helper to extract integer out of ArrayAttr.
|
|
auto getElement = [](ArrayAttr array, int idx) {
|
|
return array[idx].cast<IntegerAttr>().getInt();
|
|
};
|
|
ArrayAttr extractOffsets = op.getOffsets();
|
|
ArrayAttr extractStrides = op.getStrides();
|
|
ArrayAttr extractSizes = op.getSizes();
|
|
auto insertOp = op.getVector().getDefiningOp<InsertStridedSliceOp>();
|
|
while (insertOp) {
|
|
if (op.getSourceVectorType().getRank() !=
|
|
insertOp.getSourceVectorType().getRank())
|
|
return failure();
|
|
ArrayAttr insertOffsets = insertOp.getOffsets();
|
|
ArrayAttr insertStrides = insertOp.getStrides();
|
|
// If the rank of extract is greater than the rank of insert, we are likely
|
|
// extracting a partial chunk of the vector inserted.
|
|
if (extractOffsets.size() > insertOffsets.size())
|
|
return failure();
|
|
bool patialoverlap = false;
|
|
bool disjoint = false;
|
|
SmallVector<int64_t, 4> offsetDiffs;
|
|
for (unsigned dim = 0, e = extractOffsets.size(); dim < e; ++dim) {
|
|
if (getElement(extractStrides, dim) != getElement(insertStrides, dim))
|
|
return failure();
|
|
int64_t start = getElement(insertOffsets, dim);
|
|
int64_t end = start + insertOp.getSourceVectorType().getDimSize(dim);
|
|
int64_t offset = getElement(extractOffsets, dim);
|
|
int64_t size = getElement(extractSizes, dim);
|
|
// Check if the start of the extract offset is in the interval inserted.
|
|
if (start <= offset && offset < end) {
|
|
// If the extract interval overlaps but is not fully included we may
|
|
// have a partial overlap that will prevent any folding.
|
|
if (offset + size > end)
|
|
patialoverlap = true;
|
|
offsetDiffs.push_back(offset - start);
|
|
continue;
|
|
}
|
|
disjoint = true;
|
|
break;
|
|
}
|
|
// The extract element chunk is a subset of the insert element.
|
|
if (!disjoint && !patialoverlap) {
|
|
op.setOperand(insertOp.getSource());
|
|
// OpBuilder is only used as a helper to build an I64ArrayAttr.
|
|
OpBuilder b(op.getContext());
|
|
op->setAttr(ExtractStridedSliceOp::getOffsetsAttrStrName(),
|
|
b.getI64ArrayAttr(offsetDiffs));
|
|
return success();
|
|
}
|
|
// If the chunk extracted is disjoint from the chunk inserted, keep looking
|
|
// in the insert chain.
|
|
if (disjoint)
|
|
insertOp = insertOp.getDest().getDefiningOp<InsertStridedSliceOp>();
|
|
else {
|
|
// The extracted vector partially overlap the inserted vector, we cannot
|
|
// fold.
|
|
return failure();
|
|
}
|
|
}
|
|
return failure();
|
|
}
|
|
|
|
OpFoldResult ExtractStridedSliceOp::fold(FoldAdaptor adaptor) {
|
|
if (getSourceVectorType() == getResult().getType())
|
|
return getVector();
|
|
if (succeeded(foldExtractStridedOpFromInsertChain(*this)))
|
|
return getResult();
|
|
return {};
|
|
}
|
|
|
|
void ExtractStridedSliceOp::getOffsets(SmallVectorImpl<int64_t> &results) {
|
|
populateFromInt64AttrArray(getOffsets(), results);
|
|
}
|
|
|
|
namespace {
|
|
|
|
// Pattern to rewrite an ExtractStridedSliceOp(ConstantMaskOp) to
|
|
// ConstantMaskOp.
|
|
class StridedSliceConstantMaskFolder final
|
|
: public OpRewritePattern<ExtractStridedSliceOp> {
|
|
public:
|
|
using OpRewritePattern::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(ExtractStridedSliceOp extractStridedSliceOp,
|
|
PatternRewriter &rewriter) const override {
|
|
// Return if 'extractStridedSliceOp' operand is not defined by a
|
|
// ConstantMaskOp.
|
|
auto *defOp = extractStridedSliceOp.getVector().getDefiningOp();
|
|
auto constantMaskOp = dyn_cast_or_null<ConstantMaskOp>(defOp);
|
|
if (!constantMaskOp)
|
|
return failure();
|
|
// Return if 'extractStridedSliceOp' has non-unit strides.
|
|
if (extractStridedSliceOp.hasNonUnitStrides())
|
|
return failure();
|
|
// Gather constant mask dimension sizes.
|
|
SmallVector<int64_t, 4> maskDimSizes;
|
|
populateFromInt64AttrArray(constantMaskOp.getMaskDimSizes(), maskDimSizes);
|
|
// Gather strided slice offsets and sizes.
|
|
SmallVector<int64_t, 4> sliceOffsets;
|
|
populateFromInt64AttrArray(extractStridedSliceOp.getOffsets(),
|
|
sliceOffsets);
|
|
SmallVector<int64_t, 4> sliceSizes;
|
|
populateFromInt64AttrArray(extractStridedSliceOp.getSizes(), sliceSizes);
|
|
|
|
// Compute slice of vector mask region.
|
|
SmallVector<int64_t, 4> sliceMaskDimSizes;
|
|
sliceMaskDimSizes.reserve(maskDimSizes.size());
|
|
for (auto [maskDimSize, sliceOffset, sliceSize] :
|
|
llvm::zip(maskDimSizes, sliceOffsets, sliceSizes)) {
|
|
int64_t sliceMaskDimSize = std::max(
|
|
static_cast<int64_t>(0),
|
|
std::min(sliceOffset + sliceSize, maskDimSize) - sliceOffset);
|
|
sliceMaskDimSizes.push_back(sliceMaskDimSize);
|
|
}
|
|
// Add unchanged dimensions.
|
|
if (sliceMaskDimSizes.size() < maskDimSizes.size())
|
|
for (size_t i = sliceMaskDimSizes.size(); i < maskDimSizes.size(); ++i)
|
|
sliceMaskDimSizes.push_back(maskDimSizes[i]);
|
|
// If any of 'sliceMaskDimSizes' are zero, then set all to zero (masked
|
|
// region is a conjunction of mask dim intervals).
|
|
if (llvm::is_contained(sliceMaskDimSizes, 0))
|
|
sliceMaskDimSizes.assign(maskDimSizes.size(), 0);
|
|
|
|
// Replace 'extractStridedSliceOp' with ConstantMaskOp with sliced mask
|
|
// region.
|
|
rewriter.replaceOpWithNewOp<ConstantMaskOp>(
|
|
extractStridedSliceOp, extractStridedSliceOp.getResult().getType(),
|
|
vector::getVectorSubscriptAttr(rewriter, sliceMaskDimSizes));
|
|
return success();
|
|
}
|
|
};
|
|
|
|
// Pattern to rewrite a ExtractStridedSliceOp(splat ConstantOp) -> ConstantOp.
|
|
class StridedSliceSplatConstantFolder final
|
|
: public OpRewritePattern<ExtractStridedSliceOp> {
|
|
public:
|
|
using OpRewritePattern::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(ExtractStridedSliceOp extractStridedSliceOp,
|
|
PatternRewriter &rewriter) const override {
|
|
// Return if 'ExtractStridedSliceOp' operand is not defined by a splat
|
|
// ConstantOp.
|
|
Value sourceVector = extractStridedSliceOp.getVector();
|
|
Attribute vectorCst;
|
|
if (!matchPattern(sourceVector, m_Constant(&vectorCst)))
|
|
return failure();
|
|
|
|
auto splat = vectorCst.dyn_cast<SplatElementsAttr>();
|
|
if (!splat)
|
|
return failure();
|
|
|
|
auto newAttr = SplatElementsAttr::get(extractStridedSliceOp.getType(),
|
|
splat.getSplatValue<Attribute>());
|
|
rewriter.replaceOpWithNewOp<arith::ConstantOp>(extractStridedSliceOp,
|
|
newAttr);
|
|
return success();
|
|
}
|
|
};
|
|
|
|
// Pattern to rewrite a ExtractStridedSliceOp(non-splat ConstantOp) ->
|
|
// ConstantOp.
|
|
class StridedSliceNonSplatConstantFolder final
|
|
: public OpRewritePattern<ExtractStridedSliceOp> {
|
|
public:
|
|
using OpRewritePattern::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(ExtractStridedSliceOp extractStridedSliceOp,
|
|
PatternRewriter &rewriter) const override {
|
|
// Return if 'ExtractStridedSliceOp' operand is not defined by a non-splat
|
|
// ConstantOp.
|
|
Value sourceVector = extractStridedSliceOp.getVector();
|
|
Attribute vectorCst;
|
|
if (!matchPattern(sourceVector, m_Constant(&vectorCst)))
|
|
return failure();
|
|
|
|
// The splat case is handled by `StridedSliceSplatConstantFolder`.
|
|
auto dense = vectorCst.dyn_cast<DenseElementsAttr>();
|
|
if (!dense || dense.isSplat())
|
|
return failure();
|
|
|
|
// TODO: Handle non-unit strides when they become available.
|
|
if (extractStridedSliceOp.hasNonUnitStrides())
|
|
return failure();
|
|
|
|
auto sourceVecTy = sourceVector.getType().cast<VectorType>();
|
|
ArrayRef<int64_t> sourceShape = sourceVecTy.getShape();
|
|
SmallVector<int64_t, 4> sourceStrides = computeStrides(sourceShape);
|
|
|
|
VectorType sliceVecTy = extractStridedSliceOp.getType();
|
|
ArrayRef<int64_t> sliceShape = sliceVecTy.getShape();
|
|
int64_t sliceRank = sliceVecTy.getRank();
|
|
|
|
// Expand offsets and sizes to match the vector rank.
|
|
SmallVector<int64_t, 4> offsets(sliceRank, 0);
|
|
copy(getI64SubArray(extractStridedSliceOp.getOffsets()), offsets.begin());
|
|
|
|
SmallVector<int64_t, 4> sizes(sourceShape.begin(), sourceShape.end());
|
|
copy(getI64SubArray(extractStridedSliceOp.getSizes()), sizes.begin());
|
|
|
|
// Calculate the slice elements by enumerating all slice positions and
|
|
// linearizing them. The enumeration order is lexicographic which yields a
|
|
// sequence of monotonically increasing linearized position indices.
|
|
auto denseValuesBegin = dense.value_begin<Attribute>();
|
|
SmallVector<Attribute> sliceValues;
|
|
sliceValues.reserve(sliceVecTy.getNumElements());
|
|
SmallVector<int64_t> currSlicePosition(offsets.begin(), offsets.end());
|
|
do {
|
|
int64_t linearizedPosition = linearize(currSlicePosition, sourceStrides);
|
|
assert(linearizedPosition < sourceVecTy.getNumElements() &&
|
|
"Invalid index");
|
|
sliceValues.push_back(*(denseValuesBegin + linearizedPosition));
|
|
} while (
|
|
succeeded(incSlicePosition(currSlicePosition, sliceShape, offsets)));
|
|
|
|
assert(static_cast<int64_t>(sliceValues.size()) ==
|
|
sliceVecTy.getNumElements() &&
|
|
"Invalid number of slice elements");
|
|
auto newAttr = DenseElementsAttr::get(sliceVecTy, sliceValues);
|
|
rewriter.replaceOpWithNewOp<arith::ConstantOp>(extractStridedSliceOp,
|
|
newAttr);
|
|
return success();
|
|
}
|
|
};
|
|
|
|
// Pattern to rewrite an ExtractStridedSliceOp(BroadcastOp) to
|
|
// BroadcastOp(ExtractStrideSliceOp).
|
|
class StridedSliceBroadcast final
|
|
: public OpRewritePattern<ExtractStridedSliceOp> {
|
|
public:
|
|
using OpRewritePattern::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(ExtractStridedSliceOp op,
|
|
PatternRewriter &rewriter) const override {
|
|
auto broadcast = op.getVector().getDefiningOp<BroadcastOp>();
|
|
if (!broadcast)
|
|
return failure();
|
|
auto srcVecType = broadcast.getSource().getType().dyn_cast<VectorType>();
|
|
unsigned srcRank = srcVecType ? srcVecType.getRank() : 0;
|
|
auto dstVecType = op.getType().cast<VectorType>();
|
|
unsigned dstRank = dstVecType.getRank();
|
|
unsigned rankDiff = dstRank - srcRank;
|
|
// Check if the most inner dimensions of the source of the broadcast are the
|
|
// same as the destination of the extract. If this is the case we can just
|
|
// use a broadcast as the original dimensions are untouched.
|
|
bool lowerDimMatch = true;
|
|
for (unsigned i = 0; i < srcRank; i++) {
|
|
if (srcVecType.getDimSize(i) != dstVecType.getDimSize(i + rankDiff)) {
|
|
lowerDimMatch = false;
|
|
break;
|
|
}
|
|
}
|
|
Value source = broadcast.getSource();
|
|
// If the inner dimensions don't match, it means we need to extract from the
|
|
// source of the orignal broadcast and then broadcast the extracted value.
|
|
// We also need to handle degenerated cases where the source is effectively
|
|
// just a single scalar.
|
|
bool isScalarSrc = (srcRank == 0 || srcVecType.getNumElements() == 1);
|
|
if (!lowerDimMatch && !isScalarSrc) {
|
|
source = rewriter.create<ExtractStridedSliceOp>(
|
|
op->getLoc(), source,
|
|
getI64SubArray(op.getOffsets(), /* dropFront=*/rankDiff),
|
|
getI64SubArray(op.getSizes(), /* dropFront=*/rankDiff),
|
|
getI64SubArray(op.getStrides(), /* dropFront=*/rankDiff));
|
|
}
|
|
rewriter.replaceOpWithNewOp<BroadcastOp>(op, op.getType(), source);
|
|
return success();
|
|
}
|
|
};
|
|
|
|
/// Pattern to rewrite an ExtractStridedSliceOp(SplatOp) to SplatOp.
|
|
class StridedSliceSplat final : public OpRewritePattern<ExtractStridedSliceOp> {
|
|
public:
|
|
using OpRewritePattern::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(ExtractStridedSliceOp op,
|
|
PatternRewriter &rewriter) const override {
|
|
auto splat = op.getVector().getDefiningOp<SplatOp>();
|
|
if (!splat)
|
|
return failure();
|
|
rewriter.replaceOpWithNewOp<SplatOp>(op, op.getType(), splat.getInput());
|
|
return success();
|
|
}
|
|
};
|
|
|
|
} // namespace
|
|
|
|
void ExtractStridedSliceOp::getCanonicalizationPatterns(
|
|
RewritePatternSet &results, MLIRContext *context) {
|
|
// Pattern to rewrite a ExtractStridedSliceOp(ConstantMaskOp) ->
|
|
// ConstantMaskOp and ExtractStridedSliceOp(ConstantOp) -> ConstantOp.
|
|
results.add<StridedSliceConstantMaskFolder, StridedSliceSplatConstantFolder,
|
|
StridedSliceNonSplatConstantFolder, StridedSliceBroadcast,
|
|
StridedSliceSplat>(context);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// TransferReadOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
/// 1. Builder that sets padding to zero and an empty mask (variant with attrs).
|
|
void TransferReadOp::build(OpBuilder &builder, OperationState &result,
|
|
VectorType vectorType, Value source,
|
|
ValueRange indices, AffineMapAttr permutationMapAttr,
|
|
/*optional*/ ArrayAttr inBoundsAttr) {
|
|
Type elemType = source.getType().cast<ShapedType>().getElementType();
|
|
Value padding = builder.create<arith::ConstantOp>(
|
|
result.location, elemType, builder.getZeroAttr(elemType));
|
|
build(builder, result, vectorType, source, indices, permutationMapAttr,
|
|
padding, /*mask=*/Value(), inBoundsAttr);
|
|
}
|
|
|
|
/// 2. Builder that sets padding to zero an empty mask (variant without attrs).
|
|
void TransferReadOp::build(OpBuilder &builder, OperationState &result,
|
|
VectorType vectorType, Value source,
|
|
ValueRange indices, AffineMap permutationMap,
|
|
std::optional<ArrayRef<bool>> inBounds) {
|
|
auto permutationMapAttr = AffineMapAttr::get(permutationMap);
|
|
auto inBoundsAttr = (inBounds && !inBounds.value().empty())
|
|
? builder.getBoolArrayAttr(inBounds.value())
|
|
: ArrayAttr();
|
|
build(builder, result, vectorType, source, indices, permutationMapAttr,
|
|
inBoundsAttr);
|
|
}
|
|
|
|
/// 3. Builder that sets permutation map to 'getMinorIdentityMap'.
|
|
void TransferReadOp::build(OpBuilder &builder, OperationState &result,
|
|
VectorType vectorType, Value source,
|
|
ValueRange indices, Value padding,
|
|
std::optional<ArrayRef<bool>> inBounds) {
|
|
AffineMap permutationMap = getTransferMinorIdentityMap(
|
|
source.getType().cast<ShapedType>(), vectorType);
|
|
auto permutationMapAttr = AffineMapAttr::get(permutationMap);
|
|
auto inBoundsAttr = (inBounds && !inBounds.value().empty())
|
|
? builder.getBoolArrayAttr(inBounds.value())
|
|
: ArrayAttr();
|
|
build(builder, result, vectorType, source, indices, permutationMapAttr,
|
|
padding,
|
|
/*mask=*/Value(), inBoundsAttr);
|
|
}
|
|
|
|
/// 4. Builder that sets padding to zero and permutation map to
|
|
/// 'getMinorIdentityMap'.
|
|
void TransferReadOp::build(OpBuilder &builder, OperationState &result,
|
|
VectorType vectorType, Value source,
|
|
ValueRange indices,
|
|
std::optional<ArrayRef<bool>> inBounds) {
|
|
Type elemType = source.getType().cast<ShapedType>().getElementType();
|
|
Value padding = builder.create<arith::ConstantOp>(
|
|
result.location, elemType, builder.getZeroAttr(elemType));
|
|
build(builder, result, vectorType, source, indices, padding, inBounds);
|
|
}
|
|
|
|
template <typename EmitFun>
|
|
static LogicalResult verifyPermutationMap(AffineMap permutationMap,
|
|
EmitFun emitOpError) {
|
|
SmallVector<bool, 8> seen(permutationMap.getNumInputs(), false);
|
|
for (auto expr : permutationMap.getResults()) {
|
|
auto dim = expr.dyn_cast<AffineDimExpr>();
|
|
auto zero = expr.dyn_cast<AffineConstantExpr>();
|
|
if (zero) {
|
|
if (zero.getValue() != 0) {
|
|
return emitOpError(
|
|
"requires a projected permutation_map (at most one dim or the zero "
|
|
"constant can appear in each result)");
|
|
}
|
|
continue;
|
|
}
|
|
if (!dim) {
|
|
return emitOpError("requires a projected permutation_map (at most one "
|
|
"dim or the zero constant can appear in each result)");
|
|
}
|
|
if (seen[dim.getPosition()]) {
|
|
return emitOpError(
|
|
"requires a permutation_map that is a permutation (found one dim "
|
|
"used more than once)");
|
|
}
|
|
seen[dim.getPosition()] = true;
|
|
}
|
|
return success();
|
|
}
|
|
|
|
static LogicalResult
|
|
verifyTransferOp(VectorTransferOpInterface op, ShapedType shapedType,
|
|
VectorType vectorType, VectorType maskType,
|
|
VectorType inferredMaskType, AffineMap permutationMap,
|
|
ArrayAttr inBounds) {
|
|
if (op->hasAttr("masked")) {
|
|
return op->emitOpError("masked attribute has been removed. "
|
|
"Use in_bounds instead.");
|
|
}
|
|
|
|
if (!shapedType.isa<MemRefType, RankedTensorType>())
|
|
return op->emitOpError(
|
|
"requires source to be a memref or ranked tensor type");
|
|
|
|
auto elementType = shapedType.getElementType();
|
|
DataLayout dataLayout = DataLayout::closest(op);
|
|
if (auto vectorElementType = elementType.dyn_cast<VectorType>()) {
|
|
// Memref or tensor has vector element type.
|
|
unsigned sourceVecSize =
|
|
dataLayout.getTypeSizeInBits(vectorElementType.getElementType()) *
|
|
vectorElementType.getShape().back();
|
|
unsigned resultVecSize =
|
|
dataLayout.getTypeSizeInBits(vectorType.getElementType()) *
|
|
vectorType.getShape().back();
|
|
if (resultVecSize % sourceVecSize != 0)
|
|
return op->emitOpError(
|
|
"requires the bitwidth of the minor 1-D vector to be an integral "
|
|
"multiple of the bitwidth of the minor 1-D vector of the source");
|
|
|
|
unsigned sourceVecEltRank = vectorElementType.getRank();
|
|
unsigned resultVecRank = vectorType.getRank();
|
|
if (sourceVecEltRank > resultVecRank)
|
|
return op->emitOpError(
|
|
"requires source vector element and vector result ranks to match.");
|
|
unsigned rankOffset = resultVecRank - sourceVecEltRank;
|
|
// Check that permutation map results match 'rankOffset' of vector type.
|
|
if (permutationMap.getNumResults() != rankOffset)
|
|
return op->emitOpError("requires a permutation_map with result dims of "
|
|
"the same rank as the vector type");
|
|
|
|
if (maskType)
|
|
return op->emitOpError("does not support masks with vector element type");
|
|
} else {
|
|
// Memref or tensor has scalar element type.
|
|
unsigned minorSize =
|
|
vectorType.getRank() == 0 ? 1 : vectorType.getShape().back();
|
|
unsigned resultVecSize =
|
|
dataLayout.getTypeSizeInBits(vectorType.getElementType()) * minorSize;
|
|
if (resultVecSize % dataLayout.getTypeSizeInBits(elementType) != 0)
|
|
return op->emitOpError(
|
|
"requires the bitwidth of the minor 1-D vector to be an integral "
|
|
"multiple of the bitwidth of the source element type");
|
|
|
|
// Check that permutation map results match rank of vector type.
|
|
if (permutationMap.getNumResults() != vectorType.getRank())
|
|
return op->emitOpError("requires a permutation_map with result dims of "
|
|
"the same rank as the vector type");
|
|
}
|
|
|
|
if (permutationMap.getNumSymbols() != 0)
|
|
return op->emitOpError("requires permutation_map without symbols");
|
|
|
|
if (permutationMap.getNumInputs() != shapedType.getRank())
|
|
return op->emitOpError("requires a permutation_map with input dims of the "
|
|
"same rank as the source type");
|
|
|
|
if (maskType && maskType != inferredMaskType)
|
|
return op->emitOpError("inferred mask type (")
|
|
<< inferredMaskType << ") and mask operand type (" << maskType
|
|
<< ") don't match";
|
|
|
|
if (inBounds) {
|
|
if (permutationMap.getNumResults() != static_cast<int64_t>(inBounds.size()))
|
|
return op->emitOpError("expects the optional in_bounds attr of same rank "
|
|
"as permutation_map results: ")
|
|
<< AffineMapAttr::get(permutationMap)
|
|
<< " vs inBounds of size: " << inBounds.size();
|
|
for (unsigned int i = 0; i < permutationMap.getNumResults(); ++i)
|
|
if (permutationMap.getResult(i).isa<AffineConstantExpr>() &&
|
|
!inBounds.getValue()[i].cast<BoolAttr>().getValue())
|
|
return op->emitOpError("requires broadcast dimensions to be in-bounds");
|
|
}
|
|
|
|
return success();
|
|
}
|
|
|
|
static void printTransferAttrs(OpAsmPrinter &p, VectorTransferOpInterface op) {
|
|
SmallVector<StringRef, 3> elidedAttrs;
|
|
elidedAttrs.push_back(TransferReadOp::getOperandSegmentSizeAttr());
|
|
if (op.permutation_map().isMinorIdentity())
|
|
elidedAttrs.push_back(op.getPermutationMapAttrStrName());
|
|
bool elideInBounds = true;
|
|
if (auto inBounds = op.in_bounds()) {
|
|
for (auto attr : *inBounds) {
|
|
if (attr.template cast<BoolAttr>().getValue()) {
|
|
elideInBounds = false;
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
if (elideInBounds)
|
|
elidedAttrs.push_back(op.getInBoundsAttrStrName());
|
|
p.printOptionalAttrDict(op->getAttrs(), elidedAttrs);
|
|
}
|
|
|
|
void TransferReadOp::print(OpAsmPrinter &p) {
|
|
p << " " << getSource() << "[" << getIndices() << "], " << getPadding();
|
|
if (getMask())
|
|
p << ", " << getMask();
|
|
printTransferAttrs(p, *this);
|
|
p << " : " << getShapedType() << ", " << getVectorType();
|
|
}
|
|
|
|
/// Infers the mask type for a transfer op given its vector type and
|
|
/// permutation map. The mask in a transfer op operation applies to the
|
|
/// tensor/buffer part of it and its type should match the vector shape
|
|
/// *before* any permutation or broadcasting.
|
|
static VectorType inferTransferOpMaskType(VectorType vecType,
|
|
AffineMap permMap) {
|
|
auto i1Type = IntegerType::get(permMap.getContext(), 1);
|
|
AffineMap invPermMap = inversePermutation(compressUnusedDims(permMap));
|
|
assert(invPermMap && "Inversed permutation map couldn't be computed");
|
|
SmallVector<int64_t, 8> maskShape = invPermMap.compose(vecType.getShape());
|
|
return VectorType::get(maskShape, i1Type);
|
|
}
|
|
|
|
ParseResult TransferReadOp::parse(OpAsmParser &parser, OperationState &result) {
|
|
auto &builder = parser.getBuilder();
|
|
SMLoc typesLoc;
|
|
OpAsmParser::UnresolvedOperand sourceInfo;
|
|
SmallVector<OpAsmParser::UnresolvedOperand, 8> indexInfo;
|
|
OpAsmParser::UnresolvedOperand paddingInfo;
|
|
SmallVector<Type, 2> types;
|
|
OpAsmParser::UnresolvedOperand maskInfo;
|
|
// Parsing with support for paddingValue.
|
|
if (parser.parseOperand(sourceInfo) ||
|
|
parser.parseOperandList(indexInfo, OpAsmParser::Delimiter::Square) ||
|
|
parser.parseComma() || parser.parseOperand(paddingInfo))
|
|
return failure();
|
|
ParseResult hasMask = parser.parseOptionalComma();
|
|
if (hasMask.succeeded()) {
|
|
if (parser.parseOperand(maskInfo))
|
|
return failure();
|
|
}
|
|
if (parser.parseOptionalAttrDict(result.attributes) ||
|
|
parser.getCurrentLocation(&typesLoc) || parser.parseColonTypeList(types))
|
|
return failure();
|
|
if (types.size() != 2)
|
|
return parser.emitError(typesLoc, "requires two types");
|
|
auto indexType = builder.getIndexType();
|
|
auto shapedType = types[0].dyn_cast<ShapedType>();
|
|
if (!shapedType || !shapedType.isa<MemRefType, RankedTensorType>())
|
|
return parser.emitError(typesLoc, "requires memref or ranked tensor type");
|
|
VectorType vectorType = types[1].dyn_cast<VectorType>();
|
|
if (!vectorType)
|
|
return parser.emitError(typesLoc, "requires vector type");
|
|
auto permMapAttrName = TransferReadOp::getPermutationMapAttrStrName();
|
|
Attribute permMapAttr = result.attributes.get(permMapAttrName);
|
|
AffineMap permMap;
|
|
if (!permMapAttr) {
|
|
permMap = getTransferMinorIdentityMap(shapedType, vectorType);
|
|
result.attributes.set(permMapAttrName, AffineMapAttr::get(permMap));
|
|
} else {
|
|
permMap = permMapAttr.cast<AffineMapAttr>().getValue();
|
|
}
|
|
if (parser.resolveOperand(sourceInfo, shapedType, result.operands) ||
|
|
parser.resolveOperands(indexInfo, indexType, result.operands) ||
|
|
parser.resolveOperand(paddingInfo, shapedType.getElementType(),
|
|
result.operands))
|
|
return failure();
|
|
if (hasMask.succeeded()) {
|
|
if (shapedType.getElementType().dyn_cast<VectorType>())
|
|
return parser.emitError(
|
|
maskInfo.location, "does not support masks with vector element type");
|
|
// Instead of adding the mask type as an op type, compute it based on the
|
|
// vector type and the permutation map (to keep the type signature small).
|
|
auto maskType = inferTransferOpMaskType(vectorType, permMap);
|
|
if (parser.resolveOperand(maskInfo, maskType, result.operands))
|
|
return failure();
|
|
}
|
|
result.addAttribute(TransferReadOp::getOperandSegmentSizeAttr(),
|
|
builder.getDenseI32ArrayAttr(
|
|
{1, static_cast<int32_t>(indexInfo.size()), 1,
|
|
static_cast<int32_t>(hasMask.succeeded())}));
|
|
return parser.addTypeToList(vectorType, result.types);
|
|
}
|
|
|
|
LogicalResult TransferReadOp::verify() {
|
|
// Consistency of elemental types in source and vector.
|
|
ShapedType shapedType = getShapedType();
|
|
VectorType vectorType = getVectorType();
|
|
VectorType maskType = getMaskType();
|
|
auto paddingType = getPadding().getType();
|
|
auto permutationMap = getPermutationMap();
|
|
VectorType inferredMaskType =
|
|
maskType ? inferTransferOpMaskType(vectorType, permutationMap)
|
|
: VectorType();
|
|
auto sourceElementType = shapedType.getElementType();
|
|
|
|
if (static_cast<int64_t>(getIndices().size()) != shapedType.getRank())
|
|
return emitOpError("requires ") << shapedType.getRank() << " indices";
|
|
|
|
if (failed(verifyTransferOp(cast<VectorTransferOpInterface>(getOperation()),
|
|
shapedType, vectorType, maskType,
|
|
inferredMaskType, permutationMap,
|
|
getInBounds() ? *getInBounds() : ArrayAttr())))
|
|
return failure();
|
|
|
|
if (auto sourceVectorElementType = sourceElementType.dyn_cast<VectorType>()) {
|
|
// Source has vector element type.
|
|
// Check that 'sourceVectorElementType' and 'paddingType' types match.
|
|
if (sourceVectorElementType != paddingType)
|
|
return emitOpError(
|
|
"requires source element type and padding type to match.");
|
|
|
|
} else {
|
|
// Check that 'paddingType' is valid to store in a vector type.
|
|
if (!VectorType::isValidElementType(paddingType))
|
|
return emitOpError("requires valid padding vector elemental type");
|
|
|
|
// Check that padding type and vector element types match.
|
|
if (paddingType != sourceElementType)
|
|
return emitOpError(
|
|
"requires formal padding and source of the same elemental type");
|
|
}
|
|
|
|
return verifyPermutationMap(permutationMap,
|
|
[&](Twine t) { return emitOpError(t); });
|
|
}
|
|
|
|
// MaskableOpInterface methods.
|
|
|
|
/// Returns the mask type expected by this operation. Mostly used for
|
|
/// verification purposes. It requires the operation to be vectorized."
|
|
Type TransferReadOp::getExpectedMaskType() {
|
|
return inferTransferOpMaskType(getVectorType(), getPermutationMap());
|
|
}
|
|
|
|
template <typename TransferOp>
|
|
static bool isInBounds(TransferOp op, int64_t resultIdx, int64_t indicesIdx) {
|
|
// TODO: support more aggressive createOrFold on:
|
|
// `op.indices()[indicesIdx] + vectorType < dim(op.source(), indicesIdx)`
|
|
if (op.getShapedType().isDynamicDim(indicesIdx))
|
|
return false;
|
|
Value index = op.getIndices()[indicesIdx];
|
|
auto cstOp = index.getDefiningOp<arith::ConstantIndexOp>();
|
|
if (!cstOp)
|
|
return false;
|
|
|
|
int64_t sourceSize = op.getShapedType().getDimSize(indicesIdx);
|
|
int64_t vectorSize = op.getVectorType().getDimSize(resultIdx);
|
|
|
|
return cstOp.value() + vectorSize <= sourceSize;
|
|
}
|
|
|
|
template <typename TransferOp>
|
|
static LogicalResult foldTransferInBoundsAttribute(TransferOp op) {
|
|
// TODO: support 0-d corner case.
|
|
// TODO: Be less conservative.
|
|
if (op.getTransferRank() == 0)
|
|
return failure();
|
|
AffineMap permutationMap = op.getPermutationMap();
|
|
bool changed = false;
|
|
SmallVector<bool, 4> newInBounds;
|
|
newInBounds.reserve(op.getTransferRank());
|
|
for (unsigned i = 0; i < op.getTransferRank(); ++i) {
|
|
// Already marked as in-bounds, nothing to see here.
|
|
if (op.isDimInBounds(i)) {
|
|
newInBounds.push_back(true);
|
|
continue;
|
|
}
|
|
// Currently out-of-bounds, check whether we can statically determine it is
|
|
// inBounds.
|
|
auto dimExpr = permutationMap.getResult(i).dyn_cast<AffineDimExpr>();
|
|
assert(dimExpr && "Broadcast dims must be in-bounds");
|
|
auto inBounds =
|
|
isInBounds(op, /*resultIdx=*/i, /*indicesIdx=*/dimExpr.getPosition());
|
|
newInBounds.push_back(inBounds);
|
|
// We commit the pattern if it is "more inbounds".
|
|
changed |= inBounds;
|
|
}
|
|
if (!changed)
|
|
return failure();
|
|
// OpBuilder is only used as a helper to build an I64ArrayAttr.
|
|
OpBuilder b(op.getContext());
|
|
op->setAttr(TransferOp::getInBoundsAttrStrName(),
|
|
b.getBoolArrayAttr(newInBounds));
|
|
return success();
|
|
}
|
|
|
|
/// ```
|
|
/// %w0 = vector.transfer_write %v0, %arg0[%c1, %c0] {in_bounds = [true, true]}
|
|
/// : vector<1x4xf32>, tensor<4x4xf32>
|
|
/// %0 = vector.transfer_read %w0[%c1, %c0], %cf0 {in_bounds = [true, true]}
|
|
/// : tensor<4x4xf32>, vector<1x4xf32>
|
|
/// ```
|
|
/// -> Folds into
|
|
/// ```
|
|
/// %v0
|
|
/// ```
|
|
static Value foldRAW(TransferReadOp readOp) {
|
|
if (!readOp.getShapedType().isa<RankedTensorType>())
|
|
return {};
|
|
auto defWrite = readOp.getSource().getDefiningOp<vector::TransferWriteOp>();
|
|
while (defWrite) {
|
|
if (checkSameValueRAW(defWrite, readOp))
|
|
return defWrite.getVector();
|
|
if (!isDisjointTransferIndices(
|
|
cast<VectorTransferOpInterface>(defWrite.getOperation()),
|
|
cast<VectorTransferOpInterface>(readOp.getOperation())))
|
|
break;
|
|
defWrite = defWrite.getSource().getDefiningOp<vector::TransferWriteOp>();
|
|
}
|
|
return {};
|
|
}
|
|
|
|
OpFoldResult TransferReadOp::fold(FoldAdaptor) {
|
|
if (Value vec = foldRAW(*this))
|
|
return vec;
|
|
/// transfer_read(memrefcast) -> transfer_read
|
|
if (succeeded(foldTransferInBoundsAttribute(*this)))
|
|
return getResult();
|
|
if (succeeded(memref::foldMemRefCast(*this)))
|
|
return getResult();
|
|
if (succeeded(tensor::foldTensorCast(*this)))
|
|
return getResult();
|
|
return OpFoldResult();
|
|
}
|
|
|
|
std::optional<SmallVector<int64_t, 4>> TransferReadOp::getShapeForUnroll() {
|
|
return llvm::to_vector<4>(getVectorType().getShape());
|
|
}
|
|
|
|
void TransferReadOp::getEffects(
|
|
SmallVectorImpl<SideEffects::EffectInstance<MemoryEffects::Effect>>
|
|
&effects) {
|
|
if (getShapedType().isa<MemRefType>())
|
|
effects.emplace_back(MemoryEffects::Read::get(), getSource(),
|
|
SideEffects::DefaultResource::get());
|
|
}
|
|
|
|
/// Returns true if all rank reduced in the given `extractOp` happen in leading
|
|
/// dimensions earlier than last `trailingRank` dimensions.
|
|
static bool areAllRankReducedLeadingDim(tensor::ExtractSliceOp extractOp,
|
|
unsigned trailingRank) {
|
|
// If no ranks are reduced at all, it's a degenerated case; always true.
|
|
if (extractOp.getSourceType().getRank() == extractOp.getType().getRank())
|
|
return true;
|
|
|
|
RankedTensorType inferredType = extractOp.inferResultType(
|
|
extractOp.getSourceType(), extractOp.getMixedOffsets(),
|
|
extractOp.getMixedSizes(), extractOp.getMixedStrides());
|
|
return extractOp.getType().getShape().take_back(trailingRank) ==
|
|
inferredType.getShape().take_back(trailingRank);
|
|
}
|
|
|
|
namespace {
|
|
/// Fold transfer_reads of a tensor.extract_slice op. E.g.:
|
|
///
|
|
/// ```
|
|
/// %0 = tensor.extract_slice %t[%a, %b] [%c, %d] [1, 1]
|
|
/// : tensor<?x?xf32> to tensor<?x?xf32>
|
|
/// %1 = vector.transfer_read %0[%e, %f], %cst {in_bounds = [true, true]}
|
|
/// : tensor<?x?xf32>, vector<4x5xf32>
|
|
/// ```
|
|
/// is rewritten to:
|
|
/// ```
|
|
/// %p0 = arith.addi %a, %e : index
|
|
/// %p1 = arith.addi %b, %f : index
|
|
/// %1 = vector.transfer_read %t[%p0, %p1], %cst {in_bounds = [true, true]}
|
|
/// : tensor<?x?xf32>, vector<4x5xf32>
|
|
/// ```
|
|
// TODO: this is brittle and should be deprecated in favor of a more general
|
|
// pattern that applies on-demand.
|
|
struct FoldExtractSliceIntoTransferRead
|
|
: public OpRewritePattern<TransferReadOp> {
|
|
public:
|
|
using OpRewritePattern::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(TransferReadOp xferOp,
|
|
PatternRewriter &rewriter) const override {
|
|
// TODO: support 0-d corner case.
|
|
if (xferOp.getTransferRank() == 0)
|
|
return failure();
|
|
if (xferOp.hasOutOfBoundsDim())
|
|
return failure();
|
|
if (!xferOp.getPermutationMap().isMinorIdentity())
|
|
return failure();
|
|
if (xferOp.getMask())
|
|
return failure();
|
|
auto extractOp = xferOp.getSource().getDefiningOp<tensor::ExtractSliceOp>();
|
|
if (!extractOp)
|
|
return failure();
|
|
if (!extractOp.hasUnitStride())
|
|
return failure();
|
|
|
|
// Bail on illegal rank-reduction: we need to check that the rank-reduced
|
|
// dims are exactly the leading dims. I.e. the following is illegal:
|
|
// ```
|
|
// %0 = tensor.extract_slice %t[0,0,0][2,1,4][1,1,1] :
|
|
// tensor<2x1x4xf32> to tensor<2x4xf32>
|
|
// %1 = vector.transfer_read %0[0,0], %cst :
|
|
// tensor<2x4xf32>, vector<2x4xf32>
|
|
// ```
|
|
//
|
|
// Cannot fold into:
|
|
// ```
|
|
// %0 = vector.transfer_read %t[0,0,0], %cst :
|
|
// tensor<2x1x4xf32>, vector<2x4xf32>
|
|
// ```
|
|
// For this, check the trailing `vectorRank` dims of the extract_slice
|
|
// result tensor match the trailing dims of the inferred result tensor.
|
|
if (!areAllRankReducedLeadingDim(extractOp, extractOp.getType().getRank()))
|
|
return failure();
|
|
|
|
int64_t rankReduced =
|
|
extractOp.getSourceType().getRank() - extractOp.getType().getRank();
|
|
|
|
SmallVector<Value> newIndices;
|
|
// In case this is a rank-reducing ExtractSliceOp, copy rank-reduced
|
|
// indices first.
|
|
for (int64_t i = 0; i < rankReduced; ++i) {
|
|
OpFoldResult offset = extractOp.getMixedOffsets()[i];
|
|
newIndices.push_back(getValueOrCreateConstantIndexOp(
|
|
rewriter, extractOp.getLoc(), offset));
|
|
}
|
|
for (const auto &it : llvm::enumerate(xferOp.getIndices())) {
|
|
OpFoldResult offset =
|
|
extractOp.getMixedOffsets()[it.index() + rankReduced];
|
|
newIndices.push_back(rewriter.create<arith::AddIOp>(
|
|
xferOp->getLoc(), it.value(),
|
|
getValueOrCreateConstantIndexOp(rewriter, extractOp.getLoc(),
|
|
offset)));
|
|
}
|
|
SmallVector<bool> inBounds(xferOp.getTransferRank(), true);
|
|
rewriter.replaceOpWithNewOp<TransferReadOp>(
|
|
xferOp, xferOp.getVectorType(), extractOp.getSource(), newIndices,
|
|
xferOp.getPadding(), ArrayRef<bool>{inBounds});
|
|
|
|
return success();
|
|
}
|
|
};
|
|
|
|
/// Store to load forwarding for transfer operations with permuation maps.
|
|
/// Even if the permutation maps are different we can still propagate the store
|
|
/// into the load if the size of the dimensions read and written match. Then we
|
|
/// can replace the transfer_read + transfer_write by vector.broadcast and
|
|
/// vector.transpose.
|
|
/// Example:
|
|
/// ```
|
|
/// %w0 = vector.transfer_write %v0, %arg0[%c0, %c0, %c0]
|
|
/// {in_bounds = [true, true],
|
|
/// permutation_map = affine_map<(d0, d1, d2) -> (d2, d1)>} :
|
|
/// vector<4x1xf32>, tensor<4x4x4xf32>
|
|
/// %r = vector.transfer_read %w0[%c0, %c0, %c0], %cf0
|
|
/// {in_bounds = [true, true, true, true],
|
|
/// permutation_map = affine_map<(d0, d1, d2) -> (d1, 0, d2, 0)>} :
|
|
/// tensor<4x4x4xf32>, vector<1x100x4x5xf32>
|
|
/// ```
|
|
/// To:
|
|
/// ```
|
|
/// %0 = vector.broadcast %arg1 : vector<4x1xf32> to vector<100x5x4x1xf32>
|
|
/// %r = vector.transpose %0, [3, 0, 2, 1] :
|
|
/// vector<100x5x4x1xf32> to vector<1x100x4x5xf32>
|
|
/// ```
|
|
struct TransferReadAfterWriteToBroadcast
|
|
: public OpRewritePattern<TransferReadOp> {
|
|
using OpRewritePattern::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(TransferReadOp readOp,
|
|
PatternRewriter &rewriter) const override {
|
|
if (readOp.hasOutOfBoundsDim() ||
|
|
!readOp.getShapedType().isa<RankedTensorType>())
|
|
return failure();
|
|
auto defWrite = readOp.getSource().getDefiningOp<vector::TransferWriteOp>();
|
|
if (!defWrite)
|
|
return failure();
|
|
|
|
SmallVector<int64_t> readDims = readOp.getTransferChunkAccessed();
|
|
Value vec;
|
|
if (readOp.getIndices() == defWrite.getIndices() &&
|
|
readOp.getMask() == defWrite.getMask()) {
|
|
SmallVector<int64_t> writeDims = defWrite.getTransferChunkAccessed();
|
|
// TODO: If the writeDim is a superset of the read dims we could do an
|
|
// extract_strided_slice.
|
|
if (writeDims == readDims)
|
|
vec = defWrite.getVector();
|
|
}
|
|
// TODO: loop through the chain of transfer_write if we can prove that they
|
|
// don't overlap with the transfer_read. This requires improving
|
|
// `isDisjointTransferIndices` helper.
|
|
if (!vec)
|
|
return failure();
|
|
SmallVector<unsigned> permutation;
|
|
AffineMap readMap = compressUnusedDims(readOp.getPermutationMap());
|
|
AffineMap writeMap = compressUnusedDims(defWrite.getPermutationMap());
|
|
AffineMap map = readMap.compose(writeMap);
|
|
if (map.getNumResults() == 0)
|
|
return failure();
|
|
// Calculate the permuation to apply to go from the vector stored to the
|
|
// vector read.
|
|
if (!map.isPermutationOfMinorIdentityWithBroadcasting(permutation))
|
|
return failure();
|
|
|
|
Location loc = readOp.getLoc();
|
|
// Calculate the broadcast shape by applying the reverse permuation to the
|
|
// final shape we want.
|
|
ArrayRef<int64_t> destShape = readOp.getVectorType().getShape();
|
|
SmallVector<int64_t> broadcastShape(destShape.size());
|
|
for (const auto &pos : llvm::enumerate(permutation))
|
|
broadcastShape[pos.value()] = destShape[pos.index()];
|
|
VectorType broadcastedType = VectorType::get(
|
|
broadcastShape, defWrite.getVectorType().getElementType());
|
|
vec = rewriter.create<vector::BroadcastOp>(loc, broadcastedType, vec);
|
|
SmallVector<int64_t> transposePerm(permutation.begin(), permutation.end());
|
|
rewriter.replaceOpWithNewOp<vector::TransposeOp>(readOp, vec,
|
|
transposePerm);
|
|
return success();
|
|
}
|
|
};
|
|
} // namespace
|
|
|
|
void TransferReadOp::getCanonicalizationPatterns(RewritePatternSet &results,
|
|
MLIRContext *context) {
|
|
// clang-format off
|
|
results.add <
|
|
// TODO: this is brittle and should be deprecated in favor of a
|
|
// more general pattern that applies on-demand.
|
|
FoldExtractSliceIntoTransferRead,
|
|
TransferReadAfterWriteToBroadcast>(context);
|
|
// clang-format on
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// TransferWriteOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
/// 1. Builder with type inference.
|
|
void TransferWriteOp::build(OpBuilder &builder, OperationState &result,
|
|
Value vector, Value dest, ValueRange indices,
|
|
AffineMapAttr permutationMapAttr,
|
|
/*optional*/ Value mask,
|
|
/*optional*/ ArrayAttr inBoundsAttr) {
|
|
Type resultType = dest.getType().dyn_cast<RankedTensorType>();
|
|
build(builder, result, resultType, vector, dest, indices, permutationMapAttr,
|
|
mask, inBoundsAttr);
|
|
}
|
|
|
|
/// 2. Builder with type inference that sets an empty mask (variant with attrs).
|
|
void TransferWriteOp::build(OpBuilder &builder, OperationState &result,
|
|
Value vector, Value dest, ValueRange indices,
|
|
AffineMapAttr permutationMapAttr,
|
|
/*optional*/ ArrayAttr inBoundsAttr) {
|
|
build(builder, result, vector, dest, indices, permutationMapAttr,
|
|
/*mask=*/Value(), inBoundsAttr);
|
|
}
|
|
|
|
/// 3. Builder with type inference that sets an empty mask (variant without
|
|
/// attrs)
|
|
void TransferWriteOp::build(OpBuilder &builder, OperationState &result,
|
|
Value vector, Value dest, ValueRange indices,
|
|
AffineMap permutationMap,
|
|
std::optional<ArrayRef<bool>> inBounds) {
|
|
auto permutationMapAttr = AffineMapAttr::get(permutationMap);
|
|
auto inBoundsAttr = (inBounds && !inBounds.value().empty())
|
|
? builder.getBoolArrayAttr(inBounds.value())
|
|
: ArrayAttr();
|
|
build(builder, result, vector, dest, indices, permutationMapAttr,
|
|
/*mask=*/Value(), inBoundsAttr);
|
|
}
|
|
|
|
/// 4. Builder with type inference that sets an empty mask and sets permutation
|
|
/// map to 'getMinorIdentityMap'.
|
|
void TransferWriteOp::build(OpBuilder &builder, OperationState &result,
|
|
Value vector, Value dest, ValueRange indices,
|
|
std::optional<ArrayRef<bool>> inBounds) {
|
|
auto vectorType = vector.getType().cast<VectorType>();
|
|
AffineMap permutationMap = getTransferMinorIdentityMap(
|
|
dest.getType().cast<ShapedType>(), vectorType);
|
|
build(builder, result, vector, dest, indices, permutationMap, inBounds);
|
|
}
|
|
|
|
ParseResult TransferWriteOp::parse(OpAsmParser &parser,
|
|
OperationState &result) {
|
|
auto &builder = parser.getBuilder();
|
|
SMLoc typesLoc;
|
|
OpAsmParser::UnresolvedOperand vectorInfo, sourceInfo;
|
|
SmallVector<OpAsmParser::UnresolvedOperand, 8> indexInfo;
|
|
SmallVector<Type, 2> types;
|
|
OpAsmParser::UnresolvedOperand maskInfo;
|
|
if (parser.parseOperand(vectorInfo) || parser.parseComma() ||
|
|
parser.parseOperand(sourceInfo) ||
|
|
parser.parseOperandList(indexInfo, OpAsmParser::Delimiter::Square))
|
|
return failure();
|
|
ParseResult hasMask = parser.parseOptionalComma();
|
|
if (hasMask.succeeded() && parser.parseOperand(maskInfo))
|
|
return failure();
|
|
if (parser.parseOptionalAttrDict(result.attributes) ||
|
|
parser.getCurrentLocation(&typesLoc) || parser.parseColonTypeList(types))
|
|
return failure();
|
|
if (types.size() != 2)
|
|
return parser.emitError(typesLoc, "requires two types");
|
|
auto indexType = builder.getIndexType();
|
|
VectorType vectorType = types[0].dyn_cast<VectorType>();
|
|
if (!vectorType)
|
|
return parser.emitError(typesLoc, "requires vector type");
|
|
ShapedType shapedType = types[1].dyn_cast<ShapedType>();
|
|
if (!shapedType || !shapedType.isa<MemRefType, RankedTensorType>())
|
|
return parser.emitError(typesLoc, "requires memref or ranked tensor type");
|
|
auto permMapAttrName = TransferWriteOp::getPermutationMapAttrStrName();
|
|
auto permMapAttr = result.attributes.get(permMapAttrName);
|
|
AffineMap permMap;
|
|
if (!permMapAttr) {
|
|
permMap = getTransferMinorIdentityMap(shapedType, vectorType);
|
|
result.attributes.set(permMapAttrName, AffineMapAttr::get(permMap));
|
|
} else {
|
|
permMap = permMapAttr.cast<AffineMapAttr>().getValue();
|
|
}
|
|
if (parser.resolveOperand(vectorInfo, vectorType, result.operands) ||
|
|
parser.resolveOperand(sourceInfo, shapedType, result.operands) ||
|
|
parser.resolveOperands(indexInfo, indexType, result.operands))
|
|
return failure();
|
|
if (hasMask.succeeded()) {
|
|
if (shapedType.getElementType().dyn_cast<VectorType>())
|
|
return parser.emitError(
|
|
maskInfo.location, "does not support masks with vector element type");
|
|
auto maskType = inferTransferOpMaskType(vectorType, permMap);
|
|
if (parser.resolveOperand(maskInfo, maskType, result.operands))
|
|
return failure();
|
|
}
|
|
result.addAttribute(TransferWriteOp::getOperandSegmentSizeAttr(),
|
|
builder.getDenseI32ArrayAttr(
|
|
{1, 1, static_cast<int32_t>(indexInfo.size()),
|
|
static_cast<int32_t>(hasMask.succeeded())}));
|
|
return failure(shapedType.isa<RankedTensorType>() &&
|
|
parser.addTypeToList(shapedType, result.types));
|
|
}
|
|
|
|
void TransferWriteOp::print(OpAsmPrinter &p) {
|
|
p << " " << getVector() << ", " << getSource() << "[" << getIndices() << "]";
|
|
if (getMask())
|
|
p << ", " << getMask();
|
|
printTransferAttrs(p, *this);
|
|
p << " : " << getVectorType() << ", " << getShapedType();
|
|
}
|
|
|
|
LogicalResult TransferWriteOp::verify() {
|
|
// Consistency of elemental types in shape and vector.
|
|
ShapedType shapedType = getShapedType();
|
|
VectorType vectorType = getVectorType();
|
|
VectorType maskType = getMaskType();
|
|
auto permutationMap = getPermutationMap();
|
|
VectorType inferredMaskType =
|
|
maskType ? inferTransferOpMaskType(vectorType, permutationMap)
|
|
: VectorType();
|
|
|
|
if (llvm::size(getIndices()) != shapedType.getRank())
|
|
return emitOpError("requires ") << shapedType.getRank() << " indices";
|
|
|
|
// We do not allow broadcast dimensions on TransferWriteOps for the moment,
|
|
// as the semantics is unclear. This can be revisited later if necessary.
|
|
if (hasBroadcastDim())
|
|
return emitOpError("should not have broadcast dimensions");
|
|
|
|
if (failed(verifyTransferOp(cast<VectorTransferOpInterface>(getOperation()),
|
|
shapedType, vectorType, maskType,
|
|
inferredMaskType, permutationMap,
|
|
getInBounds() ? *getInBounds() : ArrayAttr())))
|
|
return failure();
|
|
|
|
return verifyPermutationMap(permutationMap,
|
|
[&](Twine t) { return emitOpError(t); });
|
|
}
|
|
|
|
// MaskableOpInterface methods.
|
|
|
|
/// Returns the mask type expected by this operation. Mostly used for
|
|
/// verification purposes.
|
|
Type TransferWriteOp::getExpectedMaskType() {
|
|
return inferTransferOpMaskType(getVectorType(), getPermutationMap());
|
|
}
|
|
|
|
/// Fold:
|
|
/// ```
|
|
/// %t1 = ...
|
|
/// %v = vector.transfer_read %t0[%c0...], {in_bounds = [true...]} :
|
|
/// tensor<static_sizesxf32>, vector<static_sizesxf32>
|
|
/// %t2 = vector.transfer_write %v, %t1[%c0...] {in_bounds = [true...]} :
|
|
/// vector<static_sizesxf32>, tensor<static_sizesxf32>
|
|
/// ```
|
|
///
|
|
/// into:
|
|
///
|
|
/// ```
|
|
/// %t0
|
|
/// ```
|
|
///
|
|
/// The producer of t1 may or may not be DCE'd depending on whether it is a
|
|
/// block argument or has side effects.
|
|
static LogicalResult foldReadInitWrite(TransferWriteOp write,
|
|
ArrayRef<Attribute>,
|
|
SmallVectorImpl<OpFoldResult> &results) {
|
|
// TODO: support 0-d corner case.
|
|
if (write.getTransferRank() == 0)
|
|
return failure();
|
|
auto rankedTensorType =
|
|
write.getSource().getType().dyn_cast<RankedTensorType>();
|
|
// If not operating on tensors, bail.
|
|
if (!rankedTensorType)
|
|
return failure();
|
|
// If no read, bail.
|
|
auto read = write.getVector().getDefiningOp<vector::TransferReadOp>();
|
|
if (!read)
|
|
return failure();
|
|
// TODO: support 0-d corner case.
|
|
if (read.getTransferRank() == 0)
|
|
return failure();
|
|
// For now, only accept minor identity. Future: composition is minor identity.
|
|
if (!read.getPermutationMap().isMinorIdentity() ||
|
|
!write.getPermutationMap().isMinorIdentity())
|
|
return failure();
|
|
// Bail on mismatching ranks.
|
|
if (read.getTransferRank() != write.getTransferRank())
|
|
return failure();
|
|
// Bail on potential out-of-bounds accesses.
|
|
if (read.hasOutOfBoundsDim() || write.hasOutOfBoundsDim())
|
|
return failure();
|
|
// Tensor types must be the same.
|
|
if (read.getSource().getType() != rankedTensorType)
|
|
return failure();
|
|
// Vector types must be the same.
|
|
if (read.getVectorType() != write.getVectorType())
|
|
return failure();
|
|
// Vector and Tensor shapes must match.
|
|
if (read.getVectorType().getShape() != rankedTensorType.getShape())
|
|
return failure();
|
|
// If any index is nonzero.
|
|
auto isNotConstantZero = [](Value v) {
|
|
auto cstOp = v.getDefiningOp<arith::ConstantIndexOp>();
|
|
return !cstOp || cstOp.value() != 0;
|
|
};
|
|
if (llvm::any_of(read.getIndices(), isNotConstantZero) ||
|
|
llvm::any_of(write.getIndices(), isNotConstantZero))
|
|
return failure();
|
|
// Success.
|
|
results.push_back(read.getSource());
|
|
return success();
|
|
}
|
|
|
|
static bool checkSameValueWAR(vector::TransferReadOp read,
|
|
vector::TransferWriteOp write) {
|
|
return read.getSource() == write.getSource() &&
|
|
read.getIndices() == write.getIndices() &&
|
|
read.getPermutationMap() == write.getPermutationMap() &&
|
|
read.getVectorType() == write.getVectorType() && !read.getMask() &&
|
|
!write.getMask();
|
|
}
|
|
/// Fold transfer_write write after read:
|
|
/// ```
|
|
/// %t0 = ...
|
|
/// %v = vector.transfer_read %t0[%c0...] :
|
|
/// tensor<static_sizesxf32>, vector<static_sizesxf32>
|
|
/// %t1 = vector.transfer_write %v, %t0[%c0...] :
|
|
/// vector<static_sizesxf32>, tensor<static_sizesxf32>
|
|
/// ```
|
|
///
|
|
/// into:
|
|
///
|
|
/// ```
|
|
/// %t0
|
|
/// ```
|
|
static LogicalResult foldWAR(TransferWriteOp write,
|
|
SmallVectorImpl<OpFoldResult> &results) {
|
|
if (!write.getSource().getType().isa<RankedTensorType>())
|
|
return failure();
|
|
auto read = write.getVector().getDefiningOp<vector::TransferReadOp>();
|
|
if (!read)
|
|
return failure();
|
|
|
|
if (!checkSameValueWAR(read, write))
|
|
return failure();
|
|
results.push_back(read.getSource());
|
|
return success();
|
|
}
|
|
|
|
LogicalResult TransferWriteOp::fold(FoldAdaptor adaptor,
|
|
SmallVectorImpl<OpFoldResult> &results) {
|
|
if (succeeded(foldReadInitWrite(*this, adaptor.getOperands(), results)))
|
|
return success();
|
|
if (succeeded(foldWAR(*this, results)))
|
|
return success();
|
|
if (succeeded(foldTransferInBoundsAttribute(*this)))
|
|
return success();
|
|
return memref::foldMemRefCast(*this);
|
|
}
|
|
|
|
std::optional<SmallVector<int64_t, 4>> TransferWriteOp::getShapeForUnroll() {
|
|
return llvm::to_vector<4>(getVectorType().getShape());
|
|
}
|
|
|
|
void TransferWriteOp::getEffects(
|
|
SmallVectorImpl<SideEffects::EffectInstance<MemoryEffects::Effect>>
|
|
&effects) {
|
|
if (getShapedType().isa<MemRefType>())
|
|
effects.emplace_back(MemoryEffects::Write::get(), getSource(),
|
|
SideEffects::DefaultResource::get());
|
|
}
|
|
|
|
namespace {
|
|
/// Remove dead transfer write from the SSA chain so that it an be eliminated by
|
|
/// DCE
|
|
/// ```
|
|
/// %w0 = vector.transfer_write %v0, %arg0[%c1, %c0] {in_bounds = [true, true]}
|
|
/// : vector<1x4xf32>, tensor<4x4xf32>
|
|
/// %w1 = vector.transfer_write %v0, %w0[%c2, %c0] {in_bounds = [true, true]}
|
|
/// : vector<1x4xf32>, tensor<4x4xf32>
|
|
/// %w2 = vector.transfer_write %v1, %w1[%c1, %c0] {in_bounds = [true, true]}
|
|
/// : vector<1x4xf32>, tensor<4x4xf32>
|
|
/// ```
|
|
///
|
|
/// into:
|
|
///
|
|
/// ```
|
|
/// %w0 = vector.transfer_write %v0, %arg0[%c1, %c0] {in_bounds = [true, true]}
|
|
/// : vector<1x4xf32>, tensor<4x4xf32>
|
|
/// %w1 = vector.transfer_write %v0, %arg0[%c2, %c0] {in_bounds = [true, true]}
|
|
/// : vector<1x4xf32>, tensor<4x4xf32>
|
|
/// %w2 = vector.transfer_write %v1, %w1[%c1, %c0] {in_bounds = [true, true]}
|
|
/// : vector<1x4xf32>, tensor<4x4xf32>
|
|
/// ```
|
|
///
|
|
/// `%w0 = vector.transfer_write` op will be removed by DCE if it doesn't have
|
|
/// any other uses.
|
|
class FoldWaw final : public OpRewritePattern<TransferWriteOp> {
|
|
public:
|
|
using OpRewritePattern::OpRewritePattern;
|
|
LogicalResult matchAndRewrite(TransferWriteOp writeOp,
|
|
PatternRewriter &rewriter) const override {
|
|
if (!writeOp.getShapedType().isa<RankedTensorType>())
|
|
return failure();
|
|
vector::TransferWriteOp writeToModify = writeOp;
|
|
|
|
auto defWrite =
|
|
writeOp.getSource().getDefiningOp<vector::TransferWriteOp>();
|
|
while (defWrite) {
|
|
if (checkSameValueWAW(writeOp, defWrite)) {
|
|
rewriter.updateRootInPlace(writeToModify, [&]() {
|
|
writeToModify.getSourceMutable().assign(defWrite.getSource());
|
|
});
|
|
return success();
|
|
}
|
|
if (!isDisjointTransferIndices(
|
|
cast<VectorTransferOpInterface>(defWrite.getOperation()),
|
|
cast<VectorTransferOpInterface>(writeOp.getOperation())))
|
|
break;
|
|
// If the previous write op doesn't have any other use we an safely look
|
|
// at the previous store to see if it can be removed.
|
|
if (!defWrite->hasOneUse())
|
|
break;
|
|
writeToModify = defWrite;
|
|
defWrite = defWrite.getSource().getDefiningOp<vector::TransferWriteOp>();
|
|
}
|
|
return failure();
|
|
}
|
|
};
|
|
|
|
/// Fold tensor.insert_slice into vector.transfer_write if the transfer_write
|
|
/// could directly write to the insert_slice's destination. E.g.:
|
|
///
|
|
/// ```
|
|
/// %0 = vector.transfer_write %v, %t1[%c0, %c0] {in_bounds = [true, true]}
|
|
/// : vector<4x5xf32>, tensor<4x5xf32>
|
|
/// %1 = tensor.insert_slice %0 into %t2[%a, %b] [4, 5] [1, 1]
|
|
/// : tensor<4x5xf32> into tensor<?x?xf32>
|
|
/// ```
|
|
/// is rewritten to:
|
|
/// ```
|
|
/// %1 = vector.transfer_write %v, %t2[%a, %b] {in_bounds = [true, true]}
|
|
/// : vector<4x5xf32>, tensor<?x?xf32>
|
|
/// ```
|
|
// TODO: this is brittle and should be deprecated in favor of a more general
|
|
// pattern that applies on-demand.
|
|
struct FoldInsertSliceIntoTransferWrite
|
|
: public OpRewritePattern<tensor::InsertSliceOp> {
|
|
public:
|
|
using OpRewritePattern::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(tensor::InsertSliceOp insertOp,
|
|
PatternRewriter &rewriter) const override {
|
|
if (!insertOp.hasUnitStride())
|
|
return failure();
|
|
|
|
auto xferOp = insertOp.getSource().getDefiningOp<TransferWriteOp>();
|
|
if (!xferOp)
|
|
return failure();
|
|
// TODO: support 0-d corner case.
|
|
if (xferOp.getTransferRank() == 0)
|
|
return failure();
|
|
|
|
if (xferOp.hasOutOfBoundsDim())
|
|
return failure();
|
|
if (xferOp.getVectorType().getRank() != xferOp.getShapedType().getRank())
|
|
return failure();
|
|
if (xferOp.getMask())
|
|
return failure();
|
|
// Fold only if the TransferWriteOp completely overwrites the `source` with
|
|
// a vector. I.e., the result of the TransferWriteOp is a new tensor whose
|
|
// content is the data of the vector.
|
|
if (!llvm::equal(xferOp.getVectorType().getShape(),
|
|
xferOp.getShapedType().getShape()))
|
|
return failure();
|
|
if (!xferOp.getPermutationMap().isIdentity())
|
|
return failure();
|
|
|
|
// Bail on illegal rank-reduction: we need to check that the rank-reduced
|
|
// dims are exactly the leading dims. I.e. the following is illegal:
|
|
// ```
|
|
// %0 = vector.transfer_write %v, %t[0,0], %cst :
|
|
// vector<2x4xf32>, tensor<2x4xf32>
|
|
// %1 = tensor.insert_slice %0 into %tt[0,0,0][2,1,4][1,1,1] :
|
|
// tensor<2x4xf32> into tensor<2x1x4xf32>
|
|
// ```
|
|
//
|
|
// Cannot fold into:
|
|
// ```
|
|
// %0 = vector.transfer_write %v, %t[0,0,0], %cst :
|
|
// vector<2x4xf32>, tensor<2x1x4xf32>
|
|
// ```
|
|
// For this, check the trailing `vectorRank` dims of the insert_slice result
|
|
// tensor match the trailing dims of the inferred result tensor.
|
|
int64_t rankReduced =
|
|
insertOp.getType().getRank() - insertOp.getSourceType().getRank();
|
|
int64_t vectorRank = xferOp.getVectorType().getRank();
|
|
RankedTensorType inferredSourceTensorType =
|
|
tensor::ExtractSliceOp::inferResultType(
|
|
insertOp.getType(), insertOp.getMixedOffsets(),
|
|
insertOp.getMixedSizes(), insertOp.getMixedStrides());
|
|
auto actualSourceTensorShape = insertOp.getSourceType().getShape();
|
|
if (rankReduced > 0 &&
|
|
actualSourceTensorShape.take_back(vectorRank) !=
|
|
inferredSourceTensorType.getShape().take_back(vectorRank))
|
|
return failure();
|
|
|
|
SmallVector<Value> indices = getValueOrCreateConstantIndexOp(
|
|
rewriter, insertOp.getLoc(), insertOp.getMixedOffsets());
|
|
SmallVector<bool> inBounds(xferOp.getTransferRank(), true);
|
|
rewriter.replaceOpWithNewOp<TransferWriteOp>(insertOp, xferOp.getVector(),
|
|
insertOp.getDest(), indices,
|
|
ArrayRef<bool>{inBounds});
|
|
return success();
|
|
}
|
|
};
|
|
|
|
/// Rewrite tensor::ExtractSliceOp(vector::TransferWriteOp) to
|
|
/// vector::TransferWriteOp(tensor::ExtractSliceOp) if the full slice is
|
|
/// overwritten and inserted into another tensor. After this rewrite, the
|
|
/// operations bufferize in-place since all of them work on the same slice.
|
|
///
|
|
/// For example:
|
|
/// ```mlir
|
|
/// %0 = vector.transfer_write %vec, %init_tensor[%c0, %c0]
|
|
/// : vector<8x16xf32>, tensor<8x16xf32>
|
|
/// %1 = tensor.extract_slice %0[0, 0] [%sz0, %sz1] [1, 1]
|
|
/// : tensor<8x16xf32> to tensor<?x?xf32>
|
|
/// %r = tensor.insert_slice %1 into %iter_arg[%iv0, %iv1] [%sz0, %sz1] [1, 1]
|
|
/// : tensor<?x?xf32> into tensor<27x37xf32>
|
|
/// ```
|
|
/// folds to
|
|
/// ```mlir
|
|
/// %0 = tensor.extract_slice %iter_arg[%iv0, %iv1] [%sz0, %sz1] [1, 1]
|
|
/// : tensor<27x37xf32> to tensor<?x?xf32>
|
|
/// %1 = vector.transfer_write %vec, %0[%c0, %c0]
|
|
/// : vector<8x16xf32>, tensor<?x?xf32>
|
|
/// %r = tensor.insert_slice %1 into %iter_arg[%iv0, %iv1] [%sz0, %sz1] [1, 1]
|
|
/// : tensor<?x?xf32> into tensor<27x37xf32>
|
|
/// ```
|
|
struct SwapExtractSliceOfTransferWrite
|
|
: public OpRewritePattern<tensor::InsertSliceOp> {
|
|
public:
|
|
using OpRewritePattern::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(tensor::InsertSliceOp insertOp,
|
|
PatternRewriter &rewriter) const override {
|
|
if (!insertOp.hasUnitStride())
|
|
return failure();
|
|
auto extractOp =
|
|
insertOp.getSource().getDefiningOp<tensor::ExtractSliceOp>();
|
|
if (!extractOp || !extractOp.hasUnitStride() || !extractOp->hasOneUse())
|
|
return failure();
|
|
auto transferOp = extractOp.getSource().getDefiningOp<TransferWriteOp>();
|
|
if (!transferOp || !transferOp->hasOneUse())
|
|
return failure();
|
|
|
|
// Fail if vector::TransferWriteOp or tensor::ExtractSliceOp is
|
|
// rank-reducing.
|
|
if (insertOp.getSourceType().getRank() != transferOp.getTransferRank()) {
|
|
return rewriter.notifyMatchFailure(insertOp,
|
|
"use-def chain is rank-reducing");
|
|
}
|
|
|
|
// Fail if tensor::ExtractSliceOp has non-zero offset.
|
|
if (!extractOp.hasZeroOffset()) {
|
|
return rewriter.notifyMatchFailure(insertOp,
|
|
"ExtractSliceOp has non-zero offset");
|
|
}
|
|
|
|
// Fail if tensor::TransferWriteOp has non-zero offset.
|
|
if (!llvm::all_of(transferOp.getIndices(), [](Value value) {
|
|
return getConstantIntValue(value) == static_cast<int64_t>(0);
|
|
})) {
|
|
return rewriter.notifyMatchFailure(insertOp,
|
|
"TranferWriteOp has non-zero offset");
|
|
}
|
|
|
|
// Fail if tensor::ExtractSliceOp and tensor::InsertSliceOp sizes differ.
|
|
if (insertOp.getMixedSizes().size() != extractOp.getMixedSizes().size()) {
|
|
return rewriter.notifyMatchFailure(
|
|
insertOp, "InsertSliceOp and ExtractSliceOp ranks differ");
|
|
}
|
|
|
|
for (auto [insertSize, extractSize] :
|
|
llvm::zip_equal(insertOp.getMixedSizes(), extractOp.getMixedSizes())) {
|
|
if (!isEqualConstantIntOrValue(insertSize, extractSize)) {
|
|
return rewriter.notifyMatchFailure(
|
|
insertOp, "InsertSliceOp and ExtractSliceOp sizes differ");
|
|
}
|
|
}
|
|
|
|
// Fail if the vector::TransferWriteOp may not overwrite the full tensor.
|
|
assert(transferOp.getVectorType().hasStaticShape() &&
|
|
"expected vector to have a static shape");
|
|
ArrayRef<int64_t> vectorShape = transferOp.getVectorType().getShape();
|
|
SmallVector<int64_t> resultShape = applyPermutationMap(
|
|
transferOp.getPermutationMap(), transferOp.getShapedType().getShape());
|
|
if (transferOp.getMask() || !vectorShape.equals(resultShape)) {
|
|
return rewriter.notifyMatchFailure(
|
|
insertOp, "TransferWriteOp may not write the full tensor.");
|
|
}
|
|
|
|
// Swap the tensor::ExtractSliceOp in front of the vector::TransferWriteOp.
|
|
SmallVector<int64_t> newResultShape = applyPermutationMap(
|
|
transferOp.getPermutationMap(), insertOp.getSourceType().getShape());
|
|
SmallVector<bool> newInBounds;
|
|
for (const auto &en : enumerate(newResultShape))
|
|
newInBounds.push_back(en.value() == vectorShape[en.index()]);
|
|
auto newExtractOp = rewriter.create<tensor::ExtractSliceOp>(
|
|
extractOp.getLoc(), insertOp.getSourceType(), insertOp.getDest(),
|
|
insertOp.getMixedOffsets(), insertOp.getMixedSizes(),
|
|
insertOp.getMixedStrides());
|
|
auto newTransferWriteOp = rewriter.create<TransferWriteOp>(
|
|
transferOp.getLoc(), transferOp.getVector(), newExtractOp.getResult(),
|
|
transferOp.getIndices(), transferOp.getPermutationMapAttr(),
|
|
rewriter.getBoolArrayAttr(newInBounds));
|
|
rewriter.updateRootInPlace(insertOp, [&]() {
|
|
insertOp.getSourceMutable().assign(newTransferWriteOp.getResult());
|
|
});
|
|
return success();
|
|
}
|
|
};
|
|
|
|
} // namespace
|
|
|
|
void TransferWriteOp::getCanonicalizationPatterns(RewritePatternSet &results,
|
|
MLIRContext *context) {
|
|
// clang-format off
|
|
results.add<FoldWaw,
|
|
// TODO: this is brittle and should be deprecated in favor of a
|
|
// more general pattern that applies on-demand.
|
|
FoldInsertSliceIntoTransferWrite,
|
|
SwapExtractSliceOfTransferWrite>(context);
|
|
// clang-format on
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// LoadOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
static LogicalResult verifyLoadStoreMemRefLayout(Operation *op,
|
|
MemRefType memRefTy) {
|
|
if (!isLastMemrefDimUnitStride(memRefTy))
|
|
return op->emitOpError("most minor memref dim must have unit stride");
|
|
return success();
|
|
}
|
|
|
|
LogicalResult vector::LoadOp::verify() {
|
|
VectorType resVecTy = getVectorType();
|
|
MemRefType memRefTy = getMemRefType();
|
|
|
|
if (failed(verifyLoadStoreMemRefLayout(*this, memRefTy)))
|
|
return failure();
|
|
|
|
// Checks for vector memrefs.
|
|
Type memElemTy = memRefTy.getElementType();
|
|
if (auto memVecTy = memElemTy.dyn_cast<VectorType>()) {
|
|
if (memVecTy != resVecTy)
|
|
return emitOpError("base memref and result vector types should match");
|
|
memElemTy = memVecTy.getElementType();
|
|
}
|
|
|
|
if (resVecTy.getElementType() != memElemTy)
|
|
return emitOpError("base and result element types should match");
|
|
if (llvm::size(getIndices()) != memRefTy.getRank())
|
|
return emitOpError("requires ") << memRefTy.getRank() << " indices";
|
|
return success();
|
|
}
|
|
|
|
OpFoldResult LoadOp::fold(FoldAdaptor) {
|
|
if (succeeded(memref::foldMemRefCast(*this)))
|
|
return getResult();
|
|
return OpFoldResult();
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// StoreOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
LogicalResult vector::StoreOp::verify() {
|
|
VectorType valueVecTy = getVectorType();
|
|
MemRefType memRefTy = getMemRefType();
|
|
|
|
if (failed(verifyLoadStoreMemRefLayout(*this, memRefTy)))
|
|
return failure();
|
|
|
|
// Checks for vector memrefs.
|
|
Type memElemTy = memRefTy.getElementType();
|
|
if (auto memVecTy = memElemTy.dyn_cast<VectorType>()) {
|
|
if (memVecTy != valueVecTy)
|
|
return emitOpError(
|
|
"base memref and valueToStore vector types should match");
|
|
memElemTy = memVecTy.getElementType();
|
|
}
|
|
|
|
if (valueVecTy.getElementType() != memElemTy)
|
|
return emitOpError("base and valueToStore element type should match");
|
|
if (llvm::size(getIndices()) != memRefTy.getRank())
|
|
return emitOpError("requires ") << memRefTy.getRank() << " indices";
|
|
return success();
|
|
}
|
|
|
|
LogicalResult StoreOp::fold(FoldAdaptor adaptor,
|
|
SmallVectorImpl<OpFoldResult> &results) {
|
|
return memref::foldMemRefCast(*this);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// MaskedLoadOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
LogicalResult MaskedLoadOp::verify() {
|
|
VectorType maskVType = getMaskVectorType();
|
|
VectorType passVType = getPassThruVectorType();
|
|
VectorType resVType = getVectorType();
|
|
MemRefType memType = getMemRefType();
|
|
|
|
if (resVType.getElementType() != memType.getElementType())
|
|
return emitOpError("base and result element type should match");
|
|
if (llvm::size(getIndices()) != memType.getRank())
|
|
return emitOpError("requires ") << memType.getRank() << " indices";
|
|
if (resVType.getDimSize(0) != maskVType.getDimSize(0))
|
|
return emitOpError("expected result dim to match mask dim");
|
|
if (resVType != passVType)
|
|
return emitOpError("expected pass_thru of same type as result type");
|
|
return success();
|
|
}
|
|
|
|
namespace {
|
|
class MaskedLoadFolder final : public OpRewritePattern<MaskedLoadOp> {
|
|
public:
|
|
using OpRewritePattern::OpRewritePattern;
|
|
LogicalResult matchAndRewrite(MaskedLoadOp load,
|
|
PatternRewriter &rewriter) const override {
|
|
switch (getMaskFormat(load.getMask())) {
|
|
case MaskFormat::AllTrue:
|
|
rewriter.replaceOpWithNewOp<vector::LoadOp>(
|
|
load, load.getType(), load.getBase(), load.getIndices());
|
|
return success();
|
|
case MaskFormat::AllFalse:
|
|
rewriter.replaceOp(load, load.getPassThru());
|
|
return success();
|
|
case MaskFormat::Unknown:
|
|
return failure();
|
|
}
|
|
llvm_unreachable("Unexpected 1DMaskFormat on MaskedLoad");
|
|
}
|
|
};
|
|
} // namespace
|
|
|
|
void MaskedLoadOp::getCanonicalizationPatterns(RewritePatternSet &results,
|
|
MLIRContext *context) {
|
|
results.add<MaskedLoadFolder>(context);
|
|
}
|
|
|
|
OpFoldResult MaskedLoadOp::fold(FoldAdaptor) {
|
|
if (succeeded(memref::foldMemRefCast(*this)))
|
|
return getResult();
|
|
return OpFoldResult();
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// MaskedStoreOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
LogicalResult MaskedStoreOp::verify() {
|
|
VectorType maskVType = getMaskVectorType();
|
|
VectorType valueVType = getVectorType();
|
|
MemRefType memType = getMemRefType();
|
|
|
|
if (valueVType.getElementType() != memType.getElementType())
|
|
return emitOpError("base and valueToStore element type should match");
|
|
if (llvm::size(getIndices()) != memType.getRank())
|
|
return emitOpError("requires ") << memType.getRank() << " indices";
|
|
if (valueVType.getDimSize(0) != maskVType.getDimSize(0))
|
|
return emitOpError("expected valueToStore dim to match mask dim");
|
|
return success();
|
|
}
|
|
|
|
namespace {
|
|
class MaskedStoreFolder final : public OpRewritePattern<MaskedStoreOp> {
|
|
public:
|
|
using OpRewritePattern::OpRewritePattern;
|
|
LogicalResult matchAndRewrite(MaskedStoreOp store,
|
|
PatternRewriter &rewriter) const override {
|
|
switch (getMaskFormat(store.getMask())) {
|
|
case MaskFormat::AllTrue:
|
|
rewriter.replaceOpWithNewOp<vector::StoreOp>(
|
|
store, store.getValueToStore(), store.getBase(), store.getIndices());
|
|
return success();
|
|
case MaskFormat::AllFalse:
|
|
rewriter.eraseOp(store);
|
|
return success();
|
|
case MaskFormat::Unknown:
|
|
return failure();
|
|
}
|
|
llvm_unreachable("Unexpected 1DMaskFormat on MaskedStore");
|
|
}
|
|
};
|
|
} // namespace
|
|
|
|
void MaskedStoreOp::getCanonicalizationPatterns(RewritePatternSet &results,
|
|
MLIRContext *context) {
|
|
results.add<MaskedStoreFolder>(context);
|
|
}
|
|
|
|
LogicalResult MaskedStoreOp::fold(FoldAdaptor adaptor,
|
|
SmallVectorImpl<OpFoldResult> &results) {
|
|
return memref::foldMemRefCast(*this);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// GatherOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
LogicalResult GatherOp::verify() {
|
|
VectorType indVType = getIndexVectorType();
|
|
VectorType maskVType = getMaskVectorType();
|
|
VectorType resVType = getVectorType();
|
|
ShapedType baseType = getBaseType();
|
|
|
|
if (!baseType.isa<MemRefType, RankedTensorType>())
|
|
return emitOpError("requires base to be a memref or ranked tensor type");
|
|
|
|
if (resVType.getElementType() != baseType.getElementType())
|
|
return emitOpError("base and result element type should match");
|
|
if (llvm::size(getIndices()) != baseType.getRank())
|
|
return emitOpError("requires ") << baseType.getRank() << " indices";
|
|
if (resVType.getShape() != indVType.getShape())
|
|
return emitOpError("expected result dim to match indices dim");
|
|
if (resVType.getShape() != maskVType.getShape())
|
|
return emitOpError("expected result dim to match mask dim");
|
|
if (resVType != getPassThruVectorType())
|
|
return emitOpError("expected pass_thru of same type as result type");
|
|
return success();
|
|
}
|
|
|
|
// MaskableOpInterface methods.
|
|
|
|
/// Returns the mask type expected by this operation. Mostly used for
|
|
/// verification purposes. It requires the operation to be vectorized."
|
|
Type GatherOp::getExpectedMaskType() {
|
|
auto vecType = this->getIndexVectorType();
|
|
return VectorType::get(vecType.getShape(),
|
|
IntegerType::get(vecType.getContext(), /*width=*/1));
|
|
}
|
|
|
|
std::optional<SmallVector<int64_t, 4>> GatherOp::getShapeForUnroll() {
|
|
return llvm::to_vector<4>(getVectorType().getShape());
|
|
}
|
|
|
|
namespace {
|
|
class GatherFolder final : public OpRewritePattern<GatherOp> {
|
|
public:
|
|
using OpRewritePattern::OpRewritePattern;
|
|
LogicalResult matchAndRewrite(GatherOp gather,
|
|
PatternRewriter &rewriter) const override {
|
|
switch (getMaskFormat(gather.getMask())) {
|
|
case MaskFormat::AllTrue:
|
|
return failure(); // no unmasked equivalent
|
|
case MaskFormat::AllFalse:
|
|
rewriter.replaceOp(gather, gather.getPassThru());
|
|
return success();
|
|
case MaskFormat::Unknown:
|
|
return failure();
|
|
}
|
|
llvm_unreachable("Unexpected 1DMaskFormat on GatherFolder");
|
|
}
|
|
};
|
|
} // namespace
|
|
|
|
void GatherOp::getCanonicalizationPatterns(RewritePatternSet &results,
|
|
MLIRContext *context) {
|
|
results.add<GatherFolder>(context);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// ScatterOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
LogicalResult ScatterOp::verify() {
|
|
VectorType indVType = getIndexVectorType();
|
|
VectorType maskVType = getMaskVectorType();
|
|
VectorType valueVType = getVectorType();
|
|
MemRefType memType = getMemRefType();
|
|
|
|
if (valueVType.getElementType() != memType.getElementType())
|
|
return emitOpError("base and valueToStore element type should match");
|
|
if (llvm::size(getIndices()) != memType.getRank())
|
|
return emitOpError("requires ") << memType.getRank() << " indices";
|
|
if (valueVType.getDimSize(0) != indVType.getDimSize(0))
|
|
return emitOpError("expected valueToStore dim to match indices dim");
|
|
if (valueVType.getDimSize(0) != maskVType.getDimSize(0))
|
|
return emitOpError("expected valueToStore dim to match mask dim");
|
|
return success();
|
|
}
|
|
|
|
namespace {
|
|
class ScatterFolder final : public OpRewritePattern<ScatterOp> {
|
|
public:
|
|
using OpRewritePattern::OpRewritePattern;
|
|
LogicalResult matchAndRewrite(ScatterOp scatter,
|
|
PatternRewriter &rewriter) const override {
|
|
switch (getMaskFormat(scatter.getMask())) {
|
|
case MaskFormat::AllTrue:
|
|
return failure(); // no unmasked equivalent
|
|
case MaskFormat::AllFalse:
|
|
rewriter.eraseOp(scatter);
|
|
return success();
|
|
case MaskFormat::Unknown:
|
|
return failure();
|
|
}
|
|
llvm_unreachable("Unexpected 1DMaskFormat on ScatterFolder");
|
|
}
|
|
};
|
|
} // namespace
|
|
|
|
void ScatterOp::getCanonicalizationPatterns(RewritePatternSet &results,
|
|
MLIRContext *context) {
|
|
results.add<ScatterFolder>(context);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// ExpandLoadOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
LogicalResult ExpandLoadOp::verify() {
|
|
VectorType maskVType = getMaskVectorType();
|
|
VectorType passVType = getPassThruVectorType();
|
|
VectorType resVType = getVectorType();
|
|
MemRefType memType = getMemRefType();
|
|
|
|
if (resVType.getElementType() != memType.getElementType())
|
|
return emitOpError("base and result element type should match");
|
|
if (llvm::size(getIndices()) != memType.getRank())
|
|
return emitOpError("requires ") << memType.getRank() << " indices";
|
|
if (resVType.getDimSize(0) != maskVType.getDimSize(0))
|
|
return emitOpError("expected result dim to match mask dim");
|
|
if (resVType != passVType)
|
|
return emitOpError("expected pass_thru of same type as result type");
|
|
return success();
|
|
}
|
|
|
|
namespace {
|
|
class ExpandLoadFolder final : public OpRewritePattern<ExpandLoadOp> {
|
|
public:
|
|
using OpRewritePattern::OpRewritePattern;
|
|
LogicalResult matchAndRewrite(ExpandLoadOp expand,
|
|
PatternRewriter &rewriter) const override {
|
|
switch (getMaskFormat(expand.getMask())) {
|
|
case MaskFormat::AllTrue:
|
|
rewriter.replaceOpWithNewOp<vector::LoadOp>(
|
|
expand, expand.getType(), expand.getBase(), expand.getIndices());
|
|
return success();
|
|
case MaskFormat::AllFalse:
|
|
rewriter.replaceOp(expand, expand.getPassThru());
|
|
return success();
|
|
case MaskFormat::Unknown:
|
|
return failure();
|
|
}
|
|
llvm_unreachable("Unexpected 1DMaskFormat on ExpandLoadFolder");
|
|
}
|
|
};
|
|
} // namespace
|
|
|
|
void ExpandLoadOp::getCanonicalizationPatterns(RewritePatternSet &results,
|
|
MLIRContext *context) {
|
|
results.add<ExpandLoadFolder>(context);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// CompressStoreOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
LogicalResult CompressStoreOp::verify() {
|
|
VectorType maskVType = getMaskVectorType();
|
|
VectorType valueVType = getVectorType();
|
|
MemRefType memType = getMemRefType();
|
|
|
|
if (valueVType.getElementType() != memType.getElementType())
|
|
return emitOpError("base and valueToStore element type should match");
|
|
if (llvm::size(getIndices()) != memType.getRank())
|
|
return emitOpError("requires ") << memType.getRank() << " indices";
|
|
if (valueVType.getDimSize(0) != maskVType.getDimSize(0))
|
|
return emitOpError("expected valueToStore dim to match mask dim");
|
|
return success();
|
|
}
|
|
|
|
namespace {
|
|
class CompressStoreFolder final : public OpRewritePattern<CompressStoreOp> {
|
|
public:
|
|
using OpRewritePattern::OpRewritePattern;
|
|
LogicalResult matchAndRewrite(CompressStoreOp compress,
|
|
PatternRewriter &rewriter) const override {
|
|
switch (getMaskFormat(compress.getMask())) {
|
|
case MaskFormat::AllTrue:
|
|
rewriter.replaceOpWithNewOp<vector::StoreOp>(
|
|
compress, compress.getValueToStore(), compress.getBase(),
|
|
compress.getIndices());
|
|
return success();
|
|
case MaskFormat::AllFalse:
|
|
rewriter.eraseOp(compress);
|
|
return success();
|
|
case MaskFormat::Unknown:
|
|
return failure();
|
|
}
|
|
llvm_unreachable("Unexpected 1DMaskFormat on CompressStoreFolder");
|
|
}
|
|
};
|
|
} // namespace
|
|
|
|
void CompressStoreOp::getCanonicalizationPatterns(RewritePatternSet &results,
|
|
MLIRContext *context) {
|
|
results.add<CompressStoreFolder>(context);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// ShapeCastOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
/// Returns true if each element of 'a' is equal to the product of a contiguous
|
|
/// sequence of the elements of 'b'. Returns false otherwise.
|
|
static bool isValidShapeCast(ArrayRef<int64_t> a, ArrayRef<int64_t> b) {
|
|
unsigned rankA = a.size();
|
|
unsigned rankB = b.size();
|
|
assert(rankA < rankB);
|
|
|
|
unsigned i = 0;
|
|
unsigned j = 0;
|
|
while (i < rankA && j < rankB) {
|
|
int64_t dimA = a[i];
|
|
int64_t dimB = 1;
|
|
while (dimB < dimA && j < rankB)
|
|
dimB *= b[j++];
|
|
if (dimA != dimB)
|
|
break;
|
|
++i;
|
|
|
|
// Handle the case when trailing dimensions are of size 1.
|
|
// Include them into the contiguous sequence.
|
|
auto isOne = [](int64_t v) { return v == 1; };
|
|
if (i < rankA && llvm::all_of(a.slice(i), isOne))
|
|
i = rankA;
|
|
if (j < rankB && llvm::all_of(b.slice(j), isOne))
|
|
j = rankB;
|
|
}
|
|
|
|
return i == rankA && j == rankB;
|
|
}
|
|
|
|
static LogicalResult verifyVectorShapeCast(Operation *op,
|
|
VectorType sourceVectorType,
|
|
VectorType resultVectorType) {
|
|
// Check that element type is the same.
|
|
if (sourceVectorType.getElementType() != resultVectorType.getElementType())
|
|
return op->emitOpError("source/result vectors must have same element type");
|
|
auto sourceShape = sourceVectorType.getShape();
|
|
auto resultShape = resultVectorType.getShape();
|
|
|
|
// Check that product of source dim sizes matches product of result dim sizes.
|
|
int64_t sourceDimProduct = std::accumulate(
|
|
sourceShape.begin(), sourceShape.end(), 1LL, std::multiplies<int64_t>{});
|
|
int64_t resultDimProduct = std::accumulate(
|
|
resultShape.begin(), resultShape.end(), 1LL, std::multiplies<int64_t>{});
|
|
if (sourceDimProduct != resultDimProduct)
|
|
return op->emitOpError("source/result number of elements must match");
|
|
|
|
// Check that expanding/contracting rank cases.
|
|
unsigned sourceRank = sourceVectorType.getRank();
|
|
unsigned resultRank = resultVectorType.getRank();
|
|
if (sourceRank < resultRank) {
|
|
if (!isValidShapeCast(sourceShape, resultShape))
|
|
return op->emitOpError("invalid shape cast");
|
|
} else if (sourceRank > resultRank) {
|
|
if (!isValidShapeCast(resultShape, sourceShape))
|
|
return op->emitOpError("invalid shape cast");
|
|
}
|
|
return success();
|
|
}
|
|
|
|
LogicalResult ShapeCastOp::verify() {
|
|
auto sourceVectorType = getSource().getType().dyn_cast_or_null<VectorType>();
|
|
auto resultVectorType = getResult().getType().dyn_cast_or_null<VectorType>();
|
|
|
|
// Check if source/result are of vector type.
|
|
if (sourceVectorType && resultVectorType)
|
|
return verifyVectorShapeCast(*this, sourceVectorType, resultVectorType);
|
|
|
|
return success();
|
|
}
|
|
|
|
OpFoldResult ShapeCastOp::fold(FoldAdaptor adaptor) {
|
|
// No-op shape cast.
|
|
if (getSource().getType() == getResult().getType())
|
|
return getSource();
|
|
|
|
// Canceling shape casts.
|
|
if (auto otherOp = getSource().getDefiningOp<ShapeCastOp>()) {
|
|
if (getResult().getType() == otherOp.getSource().getType())
|
|
return otherOp.getSource();
|
|
|
|
// Only allows valid transitive folding.
|
|
VectorType srcType = otherOp.getSource().getType().cast<VectorType>();
|
|
VectorType resultType = getResult().getType().cast<VectorType>();
|
|
if (srcType.getRank() < resultType.getRank()) {
|
|
if (!isValidShapeCast(srcType.getShape(), resultType.getShape()))
|
|
return {};
|
|
} else if (srcType.getRank() > resultType.getRank()) {
|
|
if (!isValidShapeCast(resultType.getShape(), srcType.getShape()))
|
|
return {};
|
|
} else {
|
|
return {};
|
|
}
|
|
|
|
setOperand(otherOp.getSource());
|
|
return getResult();
|
|
}
|
|
|
|
// Cancelling broadcast and shape cast ops.
|
|
if (auto bcastOp = getSource().getDefiningOp<BroadcastOp>()) {
|
|
if (bcastOp.getSourceType() == getType())
|
|
return bcastOp.getSource();
|
|
}
|
|
|
|
return {};
|
|
}
|
|
|
|
namespace {
|
|
// Pattern to rewrite a ShapeCast(splat ConstantOp) -> ConstantOp.
|
|
class ShapeCastConstantFolder final : public OpRewritePattern<ShapeCastOp> {
|
|
public:
|
|
using OpRewritePattern::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(ShapeCastOp shapeCastOp,
|
|
PatternRewriter &rewriter) const override {
|
|
auto constantOp =
|
|
shapeCastOp.getSource().getDefiningOp<arith::ConstantOp>();
|
|
if (!constantOp)
|
|
return failure();
|
|
// Only handle splat for now.
|
|
auto dense = constantOp.getValue().dyn_cast<SplatElementsAttr>();
|
|
if (!dense)
|
|
return failure();
|
|
auto newAttr =
|
|
DenseElementsAttr::get(shapeCastOp.getType().cast<VectorType>(),
|
|
dense.getSplatValue<Attribute>());
|
|
rewriter.replaceOpWithNewOp<arith::ConstantOp>(shapeCastOp, newAttr);
|
|
return success();
|
|
}
|
|
};
|
|
|
|
/// Pattern to rewrite a ShapeCast(Broadcast) -> Broadcast.
|
|
/// This only applies when the shape of the broadcast source is a suffix of the
|
|
/// shape of the result (i.e. when broadcast without reshape is expressive
|
|
/// enough to capture the result in a single op).
|
|
class ShapeCastBroadcastFolder final : public OpRewritePattern<ShapeCastOp> {
|
|
public:
|
|
using OpRewritePattern::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(ShapeCastOp shapeCastOp,
|
|
PatternRewriter &rewriter) const override {
|
|
auto broadcastOp =
|
|
shapeCastOp.getSource().getDefiningOp<vector::BroadcastOp>();
|
|
if (!broadcastOp)
|
|
return failure();
|
|
|
|
auto broadcastSourceVectorType =
|
|
broadcastOp.getSourceType().dyn_cast<VectorType>();
|
|
auto broadcastSourceShape = broadcastSourceVectorType
|
|
? broadcastSourceVectorType.getShape()
|
|
: ArrayRef<int64_t>{};
|
|
auto shapeCastTargetShape = shapeCastOp.getResultVectorType().getShape();
|
|
|
|
// Bail if `broadcastSourceShape` is not a suffix of the result.
|
|
bool isSuffix = (broadcastSourceShape == shapeCastTargetShape.take_back(
|
|
broadcastSourceShape.size()));
|
|
if (!isSuffix)
|
|
return failure();
|
|
|
|
rewriter.replaceOpWithNewOp<vector::BroadcastOp>(
|
|
shapeCastOp, shapeCastOp.getResultVectorType(),
|
|
broadcastOp.getSource());
|
|
return success();
|
|
}
|
|
};
|
|
|
|
} // namespace
|
|
|
|
void ShapeCastOp::getCanonicalizationPatterns(RewritePatternSet &results,
|
|
MLIRContext *context) {
|
|
results.add<ShapeCastConstantFolder, ShapeCastBroadcastFolder>(context);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// VectorBitCastOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
LogicalResult BitCastOp::verify() {
|
|
auto sourceVectorType = getSourceVectorType();
|
|
auto resultVectorType = getResultVectorType();
|
|
|
|
for (int64_t i = 0, e = sourceVectorType.getRank() - 1; i < e; i++) {
|
|
if (sourceVectorType.getDimSize(i) != resultVectorType.getDimSize(i))
|
|
return emitOpError("dimension size mismatch at: ") << i;
|
|
}
|
|
|
|
DataLayout dataLayout = DataLayout::closest(*this);
|
|
auto sourceElementBits =
|
|
dataLayout.getTypeSizeInBits(sourceVectorType.getElementType());
|
|
auto resultElementBits =
|
|
dataLayout.getTypeSizeInBits(resultVectorType.getElementType());
|
|
|
|
if (sourceVectorType.getRank() == 0) {
|
|
if (sourceElementBits != resultElementBits)
|
|
return emitOpError("source/result bitwidth of the 0-D vector element "
|
|
"types must be equal");
|
|
} else if (sourceElementBits * sourceVectorType.getShape().back() !=
|
|
resultElementBits * resultVectorType.getShape().back()) {
|
|
return emitOpError(
|
|
"source/result bitwidth of the minor 1-D vectors must be equal");
|
|
}
|
|
|
|
return success();
|
|
}
|
|
|
|
OpFoldResult BitCastOp::fold(FoldAdaptor adaptor) {
|
|
// Nop cast.
|
|
if (getSource().getType() == getResult().getType())
|
|
return getSource();
|
|
|
|
// Canceling bitcasts.
|
|
if (auto otherOp = getSource().getDefiningOp<BitCastOp>()) {
|
|
if (getResult().getType() == otherOp.getSource().getType())
|
|
return otherOp.getSource();
|
|
|
|
setOperand(otherOp.getSource());
|
|
return getResult();
|
|
}
|
|
|
|
Attribute sourceConstant = adaptor.getSource();
|
|
if (!sourceConstant)
|
|
return {};
|
|
|
|
Type srcElemType = getSourceVectorType().getElementType();
|
|
Type dstElemType = getResultVectorType().getElementType();
|
|
|
|
if (auto floatPack = sourceConstant.dyn_cast<DenseFPElementsAttr>()) {
|
|
if (floatPack.isSplat()) {
|
|
auto splat = floatPack.getSplatValue<FloatAttr>();
|
|
|
|
// Casting fp16 into fp32.
|
|
if (srcElemType.isF16() && dstElemType.isF32()) {
|
|
uint32_t bits = static_cast<uint32_t>(
|
|
splat.getValue().bitcastToAPInt().getZExtValue());
|
|
// Duplicate the 16-bit pattern.
|
|
bits = (bits << 16) | (bits & 0xffff);
|
|
APInt intBits(32, bits);
|
|
APFloat floatBits(llvm::APFloat::IEEEsingle(), intBits);
|
|
return DenseElementsAttr::get(getResultVectorType(), floatBits);
|
|
}
|
|
}
|
|
}
|
|
|
|
if (auto intPack = sourceConstant.dyn_cast<DenseIntElementsAttr>()) {
|
|
if (intPack.isSplat()) {
|
|
auto splat = intPack.getSplatValue<IntegerAttr>();
|
|
|
|
if (dstElemType.isa<IntegerType>()) {
|
|
uint64_t srcBitWidth = srcElemType.getIntOrFloatBitWidth();
|
|
uint64_t dstBitWidth = dstElemType.getIntOrFloatBitWidth();
|
|
|
|
// Casting to a larger integer bit width.
|
|
if (dstBitWidth > srcBitWidth && dstBitWidth % srcBitWidth == 0) {
|
|
APInt intBits = splat.getValue().zext(dstBitWidth);
|
|
|
|
// Duplicate the lower width element.
|
|
for (uint64_t i = 0; i < dstBitWidth / srcBitWidth - 1; i++)
|
|
intBits = (intBits << srcBitWidth) | intBits;
|
|
return DenseElementsAttr::get(getResultVectorType(), intBits);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
return {};
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// TypeCastOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
static SmallVector<int64_t, 8> extractShape(MemRefType memRefType) {
|
|
auto vectorType = memRefType.getElementType().dyn_cast<VectorType>();
|
|
SmallVector<int64_t, 8> res(memRefType.getShape().begin(),
|
|
memRefType.getShape().end());
|
|
if (vectorType)
|
|
res.append(vectorType.getShape().begin(), vectorType.getShape().end());
|
|
return res;
|
|
}
|
|
|
|
/// Build the canonical memRefType with a single vector.
|
|
/// E.g. memref<4 x 5 x vector<6 x f32>> -> memref<vector<4 x 5 x 6 x f32>>.
|
|
void TypeCastOp::build(OpBuilder &builder, OperationState &result,
|
|
Value source) {
|
|
result.addOperands(source);
|
|
MemRefType memRefType = source.getType().cast<MemRefType>();
|
|
VectorType vectorType =
|
|
VectorType::get(extractShape(memRefType),
|
|
getElementTypeOrSelf(getElementTypeOrSelf(memRefType)));
|
|
result.addTypes(MemRefType::get({}, vectorType, MemRefLayoutAttrInterface(),
|
|
memRefType.getMemorySpace()));
|
|
}
|
|
|
|
LogicalResult TypeCastOp::verify() {
|
|
MemRefType canonicalType = canonicalizeStridedLayout(getMemRefType());
|
|
if (!canonicalType.getLayout().isIdentity())
|
|
return emitOpError("expects operand to be a memref with identity layout");
|
|
if (!getResultMemRefType().getLayout().isIdentity())
|
|
return emitOpError("expects result to be a memref with identity layout");
|
|
if (getResultMemRefType().getMemorySpace() !=
|
|
getMemRefType().getMemorySpace())
|
|
return emitOpError("expects result in same memory space");
|
|
|
|
auto sourceType = getMemRefType();
|
|
auto resultType = getResultMemRefType();
|
|
if (getElementTypeOrSelf(getElementTypeOrSelf(sourceType)) !=
|
|
getElementTypeOrSelf(getElementTypeOrSelf(resultType)))
|
|
return emitOpError(
|
|
"expects result and operand with same underlying scalar type: ")
|
|
<< resultType;
|
|
if (extractShape(sourceType) != extractShape(resultType))
|
|
return emitOpError(
|
|
"expects concatenated result and operand shapes to be equal: ")
|
|
<< resultType;
|
|
return success();
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// TransposeOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
void vector::TransposeOp::build(OpBuilder &builder, OperationState &result,
|
|
Value vector, ArrayRef<int64_t> transp) {
|
|
VectorType vt = vector.getType().cast<VectorType>();
|
|
SmallVector<int64_t, 4> transposedShape(vt.getRank());
|
|
for (unsigned i = 0; i < transp.size(); ++i)
|
|
transposedShape[i] = vt.getShape()[transp[i]];
|
|
|
|
result.addOperands(vector);
|
|
result.addTypes(VectorType::get(transposedShape, vt.getElementType()));
|
|
result.addAttribute(getTranspAttrStrName(), builder.getI64ArrayAttr(transp));
|
|
}
|
|
|
|
OpFoldResult vector::TransposeOp::fold(FoldAdaptor adaptor) {
|
|
// Eliminate splat constant transpose ops.
|
|
if (auto attr = adaptor.getVector().dyn_cast_or_null<DenseElementsAttr>())
|
|
if (attr.isSplat())
|
|
return attr.reshape(getResultVectorType());
|
|
|
|
// Eliminate identity transpose ops. This happens when the dimensions of the
|
|
// input vector remain in their original order after the transpose operation.
|
|
SmallVector<int64_t, 4> transp;
|
|
getTransp(transp);
|
|
|
|
// Check if the permutation of the dimensions contains sequential values:
|
|
// {0, 1, 2, ...}.
|
|
for (int64_t i = 0, e = transp.size(); i < e; i++) {
|
|
if (transp[i] != i)
|
|
return {};
|
|
}
|
|
|
|
return getVector();
|
|
}
|
|
|
|
LogicalResult vector::TransposeOp::verify() {
|
|
VectorType vectorType = getSourceVectorType();
|
|
VectorType resultType = getResultVectorType();
|
|
int64_t rank = resultType.getRank();
|
|
if (vectorType.getRank() != rank)
|
|
return emitOpError("vector result rank mismatch: ") << rank;
|
|
// Verify transposition array.
|
|
auto transpAttr = getTransp().getValue();
|
|
int64_t size = transpAttr.size();
|
|
if (rank != size)
|
|
return emitOpError("transposition length mismatch: ") << size;
|
|
SmallVector<bool, 8> seen(rank, false);
|
|
for (const auto &ta : llvm::enumerate(transpAttr)) {
|
|
int64_t i = ta.value().cast<IntegerAttr>().getInt();
|
|
if (i < 0 || i >= rank)
|
|
return emitOpError("transposition index out of range: ") << i;
|
|
if (seen[i])
|
|
return emitOpError("duplicate position index: ") << i;
|
|
seen[i] = true;
|
|
if (resultType.getDimSize(ta.index()) != vectorType.getDimSize(i))
|
|
return emitOpError("dimension size mismatch at: ") << i;
|
|
}
|
|
return success();
|
|
}
|
|
|
|
std::optional<SmallVector<int64_t, 4>> TransposeOp::getShapeForUnroll() {
|
|
return llvm::to_vector<4>(getResultVectorType().getShape());
|
|
}
|
|
|
|
namespace {
|
|
|
|
// Rewrites two back-to-back TransposeOp operations into a single TransposeOp.
|
|
class TransposeFolder final : public OpRewritePattern<vector::TransposeOp> {
|
|
public:
|
|
using OpRewritePattern::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(vector::TransposeOp transposeOp,
|
|
PatternRewriter &rewriter) const override {
|
|
// Wrapper around vector::TransposeOp::getTransp() for cleaner code.
|
|
auto getPermutation = [](vector::TransposeOp transpose) {
|
|
SmallVector<int64_t, 4> permutation;
|
|
transpose.getTransp(permutation);
|
|
return permutation;
|
|
};
|
|
|
|
// Composes two permutations: result[i] = permutation1[permutation2[i]].
|
|
auto composePermutations = [](ArrayRef<int64_t> permutation1,
|
|
ArrayRef<int64_t> permutation2) {
|
|
SmallVector<int64_t, 4> result;
|
|
for (auto index : permutation2)
|
|
result.push_back(permutation1[index]);
|
|
return result;
|
|
};
|
|
|
|
// Return if the input of 'transposeOp' is not defined by another transpose.
|
|
vector::TransposeOp parentTransposeOp =
|
|
transposeOp.getVector().getDefiningOp<vector::TransposeOp>();
|
|
if (!parentTransposeOp)
|
|
return failure();
|
|
|
|
SmallVector<int64_t, 4> permutation = composePermutations(
|
|
getPermutation(parentTransposeOp), getPermutation(transposeOp));
|
|
// Replace 'transposeOp' with a new transpose operation.
|
|
rewriter.replaceOpWithNewOp<vector::TransposeOp>(
|
|
transposeOp, transposeOp.getResult().getType(),
|
|
parentTransposeOp.getVector(),
|
|
vector::getVectorSubscriptAttr(rewriter, permutation));
|
|
return success();
|
|
}
|
|
};
|
|
|
|
// Folds transpose(broadcast(<scalar>)) into brodcast(<scalar>).
|
|
struct FoldTransposedScalarBroadcast final
|
|
: public OpRewritePattern<vector::TransposeOp> {
|
|
using OpRewritePattern::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(vector::TransposeOp transposeOp,
|
|
PatternRewriter &rewriter) const override {
|
|
auto bcastOp = transposeOp.getVector().getDefiningOp<vector::BroadcastOp>();
|
|
if (!bcastOp)
|
|
return failure();
|
|
|
|
auto srcVectorType = bcastOp.getSourceType().dyn_cast<VectorType>();
|
|
if (!srcVectorType || srcVectorType.getNumElements() == 1) {
|
|
rewriter.replaceOpWithNewOp<vector::BroadcastOp>(
|
|
transposeOp, transposeOp.getResultVectorType(), bcastOp.getSource());
|
|
return success();
|
|
}
|
|
|
|
return failure();
|
|
}
|
|
};
|
|
|
|
// Folds transpose(splat x : src_type) : res_type into splat x : res_type.
|
|
class FoldTransposeSplat final : public OpRewritePattern<TransposeOp> {
|
|
public:
|
|
using OpRewritePattern::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(TransposeOp transposeOp,
|
|
PatternRewriter &rewriter) const override {
|
|
auto splatOp = transposeOp.getVector().getDefiningOp<vector::SplatOp>();
|
|
if (!splatOp)
|
|
return failure();
|
|
|
|
rewriter.replaceOpWithNewOp<vector::SplatOp>(
|
|
transposeOp, transposeOp.getResultVectorType(), splatOp.getInput());
|
|
return success();
|
|
}
|
|
};
|
|
|
|
/// Folds transpose(create_mask) into a new transposed create_mask.
|
|
class FoldTransposeCreateMask final : public OpRewritePattern<TransposeOp> {
|
|
public:
|
|
using OpRewritePattern::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(TransposeOp transpOp,
|
|
PatternRewriter &rewriter) const override {
|
|
Value transposeSrc = transpOp.getVector();
|
|
auto createMaskOp = transposeSrc.getDefiningOp<vector::CreateMaskOp>();
|
|
auto constantMaskOp = transposeSrc.getDefiningOp<vector::ConstantMaskOp>();
|
|
if (!createMaskOp && !constantMaskOp)
|
|
return failure();
|
|
|
|
// Get the transpose permutation and apply it to the vector.create_mask or
|
|
// vector.constant_mask operands.
|
|
SmallVector<int64_t> permutation;
|
|
transpOp.getTransp(permutation);
|
|
|
|
if (createMaskOp) {
|
|
auto maskOperands = createMaskOp.getOperands();
|
|
SmallVector<Value> newOperands(maskOperands.begin(), maskOperands.end());
|
|
applyPermutationToVector(newOperands, permutation);
|
|
|
|
rewriter.replaceOpWithNewOp<vector::CreateMaskOp>(
|
|
transpOp, transpOp.getResultVectorType(), newOperands);
|
|
return success();
|
|
}
|
|
|
|
// ConstantMaskOp case.
|
|
auto maskDimSizes = constantMaskOp.getMaskDimSizes();
|
|
SmallVector<Attribute> newMaskDimSizes(maskDimSizes.getValue());
|
|
applyPermutationToVector(newMaskDimSizes, permutation);
|
|
|
|
rewriter.replaceOpWithNewOp<vector::ConstantMaskOp>(
|
|
transpOp, transpOp.getResultVectorType(),
|
|
ArrayAttr::get(transpOp.getContext(), newMaskDimSizes));
|
|
return success();
|
|
}
|
|
};
|
|
|
|
} // namespace
|
|
|
|
void vector::TransposeOp::getCanonicalizationPatterns(
|
|
RewritePatternSet &results, MLIRContext *context) {
|
|
results.add<FoldTransposeCreateMask, FoldTransposedScalarBroadcast,
|
|
TransposeFolder, FoldTransposeSplat>(context);
|
|
}
|
|
|
|
void vector::TransposeOp::getTransp(SmallVectorImpl<int64_t> &results) {
|
|
populateFromInt64AttrArray(getTransp(), results);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// ConstantMaskOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
LogicalResult ConstantMaskOp::verify() {
|
|
auto resultType = getResult().getType().cast<VectorType>();
|
|
// Check the corner case of 0-D vectors first.
|
|
if (resultType.getRank() == 0) {
|
|
if (getMaskDimSizes().size() != 1)
|
|
return emitError("array attr must have length 1 for 0-D vectors");
|
|
auto dim = getMaskDimSizes()[0].cast<IntegerAttr>().getInt();
|
|
if (dim != 0 && dim != 1)
|
|
return emitError("mask dim size must be either 0 or 1 for 0-D vectors");
|
|
return success();
|
|
}
|
|
|
|
// Verify that array attr size matches the rank of the vector result.
|
|
if (static_cast<int64_t>(getMaskDimSizes().size()) != resultType.getRank())
|
|
return emitOpError(
|
|
"must specify array attr of size equal vector result rank");
|
|
// Verify that each array attr element is in bounds of corresponding vector
|
|
// result dimension size.
|
|
auto resultShape = resultType.getShape();
|
|
SmallVector<int64_t, 4> maskDimSizes;
|
|
for (const auto &it : llvm::enumerate(getMaskDimSizes())) {
|
|
int64_t attrValue = it.value().cast<IntegerAttr>().getInt();
|
|
if (attrValue < 0 || attrValue > resultShape[it.index()])
|
|
return emitOpError(
|
|
"array attr of size out of bounds of vector result dimension size");
|
|
maskDimSizes.push_back(attrValue);
|
|
}
|
|
// Verify that if one mask dim size is zero, they all should be zero (because
|
|
// the mask region is a conjunction of each mask dimension interval).
|
|
bool anyZeros = llvm::is_contained(maskDimSizes, 0);
|
|
bool allZeros = llvm::all_of(maskDimSizes, [](int64_t s) { return s == 0; });
|
|
if (anyZeros && !allZeros)
|
|
return emitOpError("expected all mask dim sizes to be zeros, "
|
|
"as a result of conjunction with zero mask dim");
|
|
// Verify that if the mask type is scalable, dimensions should be zero because
|
|
// constant scalable masks can only be defined for the "none set" or "all set"
|
|
// cases, and there is no VLA way to define an "all set" case for
|
|
// `vector.constant_mask`. In the future, a convention could be established
|
|
// to decide if a specific dimension value could be considered as "all set".
|
|
if (resultType.isScalable() &&
|
|
getMaskDimSizes()[0].cast<IntegerAttr>().getInt() != 0)
|
|
return emitOpError("expected mask dim sizes for scalable masks to be 0");
|
|
return success();
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// CreateMaskOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
void CreateMaskOp::build(OpBuilder &builder, OperationState &result,
|
|
VectorType type,
|
|
ArrayRef<OpFoldResult> mixedOperands) {
|
|
SmallVector<Value> operands =
|
|
getValueOrCreateConstantIndexOp(builder, result.location, mixedOperands);
|
|
build(builder, result, type, operands);
|
|
}
|
|
|
|
LogicalResult CreateMaskOp::verify() {
|
|
auto vectorType = getResult().getType().cast<VectorType>();
|
|
// Verify that an operand was specified for each result vector each dimension.
|
|
if (vectorType.getRank() == 0) {
|
|
if (getNumOperands() != 1)
|
|
return emitOpError(
|
|
"must specify exactly one operand for 0-D create_mask");
|
|
} else if (getNumOperands() !=
|
|
getResult().getType().cast<VectorType>().getRank()) {
|
|
return emitOpError(
|
|
"must specify an operand for each result vector dimension");
|
|
}
|
|
return success();
|
|
}
|
|
|
|
namespace {
|
|
|
|
// Pattern to rewrite a CreateMaskOp with a ConstantMaskOp.
|
|
class CreateMaskFolder final : public OpRewritePattern<CreateMaskOp> {
|
|
public:
|
|
using OpRewritePattern::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(CreateMaskOp createMaskOp,
|
|
PatternRewriter &rewriter) const override {
|
|
// Return if any of 'createMaskOp' operands are not defined by a constant.
|
|
auto isNotDefByConstant = [](Value operand) {
|
|
return !isa_and_nonnull<arith::ConstantIndexOp>(operand.getDefiningOp());
|
|
};
|
|
if (llvm::any_of(createMaskOp.getOperands(), isNotDefByConstant))
|
|
return failure();
|
|
|
|
// CreateMaskOp for scalable vectors can be folded only if all dimensions
|
|
// are negative or zero.
|
|
if (auto vType = createMaskOp.getType().dyn_cast<VectorType>()) {
|
|
if (vType.isScalable())
|
|
for (auto opDim : createMaskOp.getOperands()) {
|
|
APInt intVal;
|
|
if (matchPattern(opDim, m_ConstantInt(&intVal)) &&
|
|
intVal.isStrictlyPositive())
|
|
return failure();
|
|
}
|
|
}
|
|
|
|
// Gather constant mask dimension sizes.
|
|
SmallVector<int64_t, 4> maskDimSizes;
|
|
maskDimSizes.reserve(createMaskOp->getNumOperands());
|
|
for (auto [operand, maxDimSize] : llvm::zip_equal(
|
|
createMaskOp.getOperands(), createMaskOp.getType().getShape())) {
|
|
Operation *defOp = operand.getDefiningOp();
|
|
int64_t dimSize = cast<arith::ConstantIndexOp>(defOp).value();
|
|
dimSize = std::min(dimSize, maxDimSize);
|
|
// If one of dim sizes is zero, set all dims to zero.
|
|
if (dimSize <= 0) {
|
|
maskDimSizes.assign(createMaskOp.getType().getRank(), 0);
|
|
break;
|
|
}
|
|
maskDimSizes.push_back(dimSize);
|
|
}
|
|
// Replace 'createMaskOp' with ConstantMaskOp.
|
|
rewriter.replaceOpWithNewOp<ConstantMaskOp>(
|
|
createMaskOp, createMaskOp.getResult().getType(),
|
|
vector::getVectorSubscriptAttr(rewriter, maskDimSizes));
|
|
return success();
|
|
}
|
|
};
|
|
|
|
} // namespace
|
|
|
|
void CreateMaskOp::getCanonicalizationPatterns(RewritePatternSet &results,
|
|
MLIRContext *context) {
|
|
results.add<CreateMaskFolder>(context);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// MaskOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
void MaskOp::build(
|
|
OpBuilder &builder, OperationState &result, Value mask,
|
|
Operation *maskableOp,
|
|
function_ref<void(OpBuilder &, Operation *)> maskRegionBuilder) {
|
|
assert(maskRegionBuilder &&
|
|
"builder callback for 'maskRegion' must be present");
|
|
|
|
result.addOperands(mask);
|
|
OpBuilder::InsertionGuard guard(builder);
|
|
Region *maskRegion = result.addRegion();
|
|
builder.createBlock(maskRegion);
|
|
maskRegionBuilder(builder, maskableOp);
|
|
}
|
|
|
|
void MaskOp::build(
|
|
OpBuilder &builder, OperationState &result, TypeRange resultTypes,
|
|
Value mask, Operation *maskableOp,
|
|
function_ref<void(OpBuilder &, Operation *)> maskRegionBuilder) {
|
|
build(builder, result, resultTypes, mask, /*passthru=*/Value(), maskableOp,
|
|
maskRegionBuilder);
|
|
}
|
|
|
|
void MaskOp::build(
|
|
OpBuilder &builder, OperationState &result, TypeRange resultTypes,
|
|
Value mask, Value passthru, Operation *maskableOp,
|
|
function_ref<void(OpBuilder &, Operation *)> maskRegionBuilder) {
|
|
build(builder, result, mask, maskableOp, maskRegionBuilder);
|
|
if (passthru)
|
|
result.addOperands(passthru);
|
|
result.addTypes(resultTypes);
|
|
}
|
|
|
|
ParseResult MaskOp::parse(OpAsmParser &parser, OperationState &result) {
|
|
// Create the op region.
|
|
result.regions.reserve(1);
|
|
Region &maskRegion = *result.addRegion();
|
|
|
|
auto &builder = parser.getBuilder();
|
|
|
|
// Parse all the operands.
|
|
OpAsmParser::UnresolvedOperand mask;
|
|
if (parser.parseOperand(mask))
|
|
return failure();
|
|
|
|
// Optional passthru operand.
|
|
OpAsmParser::UnresolvedOperand passthru;
|
|
ParseResult parsePassthru = parser.parseOptionalComma();
|
|
if (parsePassthru.succeeded() && parser.parseOperand(passthru))
|
|
return failure();
|
|
|
|
// Parse op region.
|
|
if (parser.parseRegion(maskRegion, /*arguments=*/{}, /*argTypes=*/{}))
|
|
return failure();
|
|
|
|
MaskOp::ensureTerminator(maskRegion, builder, result.location);
|
|
|
|
// Parse the optional attribute list.
|
|
if (parser.parseOptionalAttrDict(result.attributes))
|
|
return failure();
|
|
|
|
// Parse all the types.
|
|
Type maskType;
|
|
if (parser.parseColonType(maskType))
|
|
return failure();
|
|
|
|
SmallVector<Type> resultTypes;
|
|
if (parser.parseOptionalArrowTypeList(resultTypes))
|
|
return failure();
|
|
result.types.append(resultTypes);
|
|
|
|
// Resolve operands.
|
|
if (parser.resolveOperand(mask, maskType, result.operands))
|
|
return failure();
|
|
|
|
if (parsePassthru.succeeded())
|
|
if (parser.resolveOperand(passthru, resultTypes[0], result.operands))
|
|
return failure();
|
|
|
|
return success();
|
|
}
|
|
|
|
void mlir::vector::MaskOp::print(OpAsmPrinter &p) {
|
|
p << " " << getMask();
|
|
if (getPassthru())
|
|
p << ", " << getPassthru();
|
|
|
|
// Print single masked operation and skip terminator.
|
|
p << " { ";
|
|
Block *singleBlock = &getMaskRegion().getBlocks().front();
|
|
if (singleBlock && !singleBlock->getOperations().empty())
|
|
p.printCustomOrGenericOp(&singleBlock->front());
|
|
p << " }";
|
|
|
|
p.printOptionalAttrDict(getOperation()->getAttrs());
|
|
|
|
p << " : " << getMask().getType();
|
|
if (getNumResults() > 0)
|
|
p << " -> " << getResultTypes();
|
|
}
|
|
|
|
void MaskOp::ensureTerminator(Region ®ion, Builder &builder, Location loc) {
|
|
OpTrait::SingleBlockImplicitTerminator<vector::YieldOp>::Impl<
|
|
MaskOp>::ensureTerminator(region, builder, loc);
|
|
// Keep the default yield terminator if the number of masked operations is not
|
|
// the expected. This case will trigger a verification failure.
|
|
Block &block = region.front();
|
|
if (block.getOperations().size() != 2)
|
|
return;
|
|
|
|
// Replace default yield terminator with a new one that returns the results
|
|
// from the masked operation.
|
|
OpBuilder opBuilder(builder.getContext());
|
|
Operation *maskedOp = &block.front();
|
|
Operation *oldYieldOp = &block.back();
|
|
assert(isa<vector::YieldOp>(oldYieldOp) && "Expected vector::YieldOp");
|
|
|
|
// Empty vector.mask op.
|
|
if (maskedOp == oldYieldOp)
|
|
return;
|
|
|
|
opBuilder.setInsertionPoint(oldYieldOp);
|
|
opBuilder.create<vector::YieldOp>(loc, maskedOp->getResults());
|
|
oldYieldOp->dropAllReferences();
|
|
oldYieldOp->erase();
|
|
}
|
|
|
|
LogicalResult MaskOp::verify() {
|
|
// Structural checks.
|
|
Block &block = getMaskRegion().getBlocks().front();
|
|
if (block.getOperations().empty())
|
|
return emitOpError("expects a terminator within the mask region");
|
|
if (block.getOperations().size() > 2)
|
|
return emitOpError("expects only one operation to mask");
|
|
|
|
// Terminator checks.
|
|
auto terminator = dyn_cast<vector::YieldOp>(block.back());
|
|
if (!terminator)
|
|
return emitOpError("expects a terminator within the mask region");
|
|
|
|
if (terminator->getNumOperands() != getNumResults())
|
|
return emitOpError(
|
|
"expects number of results to match mask region yielded values");
|
|
|
|
auto maskableOp = dyn_cast<MaskableOpInterface>(block.front());
|
|
// Empty vector.mask. Nothing else to check.
|
|
if (!maskableOp)
|
|
return success();
|
|
|
|
// Result checks.
|
|
if (maskableOp->getNumResults() != getNumResults())
|
|
return emitOpError("expects number of results to match maskable operation "
|
|
"number of results");
|
|
|
|
if (!llvm::equal(maskableOp->getResultTypes(), getResultTypes()))
|
|
return emitOpError(
|
|
"expects result type to match maskable operation result type");
|
|
|
|
if (llvm::count_if(maskableOp->getResultTypes(),
|
|
[](Type t) { return t.isa<VectorType>(); }) > 1)
|
|
return emitOpError("multiple vector results not supported");
|
|
|
|
// Mask checks.
|
|
Type expectedMaskType = maskableOp.getExpectedMaskType();
|
|
if (getMask().getType() != expectedMaskType)
|
|
return emitOpError("expects a ")
|
|
<< expectedMaskType << " mask for the maskable operation";
|
|
|
|
// Passthru checks.
|
|
Value passthru = getPassthru();
|
|
if (passthru) {
|
|
if (!maskableOp.supportsPassthru())
|
|
return emitOpError(
|
|
"doesn't expect a passthru argument for this maskable operation");
|
|
|
|
if (maskableOp->getNumResults() != 1)
|
|
return emitOpError("expects result when passthru argument is provided");
|
|
|
|
if (passthru.getType() != maskableOp->getResultTypes()[0])
|
|
return emitOpError("expects passthru type to match result type");
|
|
}
|
|
|
|
return success();
|
|
}
|
|
|
|
// Elides empty vector.mask operations with or without return values. Propagates
|
|
// the yielded values by the vector.yield terminator, if any, or erases the op,
|
|
// otherwise.
|
|
class ElideEmptyMaskOp : public OpRewritePattern<MaskOp> {
|
|
using OpRewritePattern::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(MaskOp maskOp,
|
|
PatternRewriter &rewriter) const override {
|
|
auto maskingOp = cast<MaskingOpInterface>(maskOp.getOperation());
|
|
if (maskingOp.getMaskableOp())
|
|
return failure();
|
|
|
|
Block *block = maskOp.getMaskBlock();
|
|
if (block->getOperations().size() > 1)
|
|
return failure();
|
|
|
|
auto terminator = cast<vector::YieldOp>(block->front());
|
|
if (terminator.getNumOperands() == 0)
|
|
rewriter.eraseOp(maskOp);
|
|
else
|
|
rewriter.replaceOp(maskOp, terminator.getOperands());
|
|
|
|
return success();
|
|
}
|
|
};
|
|
|
|
void MaskOp::getCanonicalizationPatterns(RewritePatternSet &results,
|
|
MLIRContext *context) {
|
|
results.add<ElideEmptyMaskOp>(context);
|
|
}
|
|
|
|
// MaskingOpInterface definitions.
|
|
|
|
/// Returns the operation masked by this 'vector.mask'.
|
|
Operation *MaskOp::getMaskableOp() {
|
|
Block *block = getMaskBlock();
|
|
if (block->getOperations().size() < 2)
|
|
return nullptr;
|
|
|
|
return &block->front();
|
|
}
|
|
|
|
/// Returns true if 'vector.mask' has a passthru value.
|
|
bool MaskOp::hasPassthru() { return getPassthru() != Value(); }
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// ScanOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
LogicalResult ScanOp::verify() {
|
|
VectorType srcType = getSourceType();
|
|
VectorType initialType = getInitialValueType();
|
|
// Check reduction dimension < rank.
|
|
int64_t srcRank = srcType.getRank();
|
|
int64_t reductionDim = getReductionDim();
|
|
if (reductionDim >= srcRank)
|
|
return emitOpError("reduction dimension ")
|
|
<< reductionDim << " has to be less than " << srcRank;
|
|
|
|
// Check that rank(initial_value) = rank(src) - 1.
|
|
int64_t initialValueRank = initialType.getRank();
|
|
if (initialValueRank != srcRank - 1)
|
|
return emitOpError("initial value rank ")
|
|
<< initialValueRank << " has to be equal to " << srcRank - 1;
|
|
|
|
// Check shapes of initial value and src.
|
|
ArrayRef<int64_t> srcShape = srcType.getShape();
|
|
ArrayRef<int64_t> initialValueShapes = initialType.getShape();
|
|
SmallVector<int64_t> expectedShape;
|
|
for (int i = 0; i < srcRank; i++) {
|
|
if (i != reductionDim)
|
|
expectedShape.push_back(srcShape[i]);
|
|
}
|
|
if (!llvm::equal(initialValueShapes, expectedShape)) {
|
|
return emitOpError("incompatible input/initial value shapes");
|
|
}
|
|
|
|
// Verify supported reduction kind.
|
|
Type eltType = getDestType().getElementType();
|
|
if (!isSupportedCombiningKind(getKind(), eltType))
|
|
return emitOpError("unsupported reduction type ")
|
|
<< eltType << " for kind '" << stringifyCombiningKind(getKind())
|
|
<< "'";
|
|
|
|
return success();
|
|
}
|
|
|
|
void mlir::vector::populateVectorToVectorCanonicalizationPatterns(
|
|
RewritePatternSet &patterns, PatternBenefit benefit) {
|
|
patterns
|
|
.add<CreateMaskFolder, MaskedLoadFolder, MaskedStoreFolder, GatherFolder,
|
|
ScatterFolder, ExpandLoadFolder, CompressStoreFolder,
|
|
StridedSliceConstantMaskFolder, TransposeFolder>(
|
|
patterns.getContext(), benefit);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// SplatOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
OpFoldResult SplatOp::fold(FoldAdaptor adaptor) {
|
|
auto constOperand = adaptor.getInput();
|
|
if (!constOperand.isa_and_nonnull<IntegerAttr, FloatAttr>())
|
|
return {};
|
|
|
|
// SplatElementsAttr::get treats single value for second arg as being a splat.
|
|
return SplatElementsAttr::get(getType(), {constOperand});
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// WarpExecuteOnLane0Op
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
void WarpExecuteOnLane0Op::print(OpAsmPrinter &p) {
|
|
p << "(" << getLaneid() << ")";
|
|
|
|
SmallVector<StringRef> coreAttr = {getWarpSizeAttrName()};
|
|
auto warpSizeAttr = getOperation()->getAttr(getWarpSizeAttrName());
|
|
p << "[" << warpSizeAttr.cast<IntegerAttr>().getInt() << "]";
|
|
|
|
if (!getArgs().empty())
|
|
p << " args(" << getArgs() << " : " << getArgs().getTypes() << ")";
|
|
if (!getResults().empty())
|
|
p << " -> (" << getResults().getTypes() << ')';
|
|
p << " ";
|
|
p.printRegion(getRegion(),
|
|
/*printEntryBlockArgs=*/true,
|
|
/*printBlockTerminators=*/!getResults().empty());
|
|
p.printOptionalAttrDict(getOperation()->getAttrs(), coreAttr);
|
|
}
|
|
|
|
ParseResult WarpExecuteOnLane0Op::parse(OpAsmParser &parser,
|
|
OperationState &result) {
|
|
// Create the region.
|
|
result.regions.reserve(1);
|
|
Region *warpRegion = result.addRegion();
|
|
|
|
auto &builder = parser.getBuilder();
|
|
OpAsmParser::UnresolvedOperand laneId;
|
|
|
|
// Parse predicate operand.
|
|
if (parser.parseLParen() ||
|
|
parser.parseOperand(laneId, /*allowResultNumber=*/false) ||
|
|
parser.parseRParen())
|
|
return failure();
|
|
|
|
int64_t warpSize;
|
|
if (parser.parseLSquare() || parser.parseInteger(warpSize) ||
|
|
parser.parseRSquare())
|
|
return failure();
|
|
result.addAttribute(getWarpSizeAttrName(OperationName(getOperationName(),
|
|
builder.getContext())),
|
|
builder.getI64IntegerAttr(warpSize));
|
|
|
|
if (parser.resolveOperand(laneId, builder.getIndexType(), result.operands))
|
|
return failure();
|
|
|
|
llvm::SMLoc inputsOperandsLoc;
|
|
SmallVector<OpAsmParser::UnresolvedOperand> inputsOperands;
|
|
SmallVector<Type> inputTypes;
|
|
if (succeeded(parser.parseOptionalKeyword("args"))) {
|
|
if (parser.parseLParen())
|
|
return failure();
|
|
|
|
inputsOperandsLoc = parser.getCurrentLocation();
|
|
if (parser.parseOperandList(inputsOperands) ||
|
|
parser.parseColonTypeList(inputTypes) || parser.parseRParen())
|
|
return failure();
|
|
}
|
|
if (parser.resolveOperands(inputsOperands, inputTypes, inputsOperandsLoc,
|
|
result.operands))
|
|
return failure();
|
|
|
|
// Parse optional results type list.
|
|
if (parser.parseOptionalArrowTypeList(result.types))
|
|
return failure();
|
|
// Parse the region.
|
|
if (parser.parseRegion(*warpRegion, /*arguments=*/{},
|
|
/*argTypes=*/{}))
|
|
return failure();
|
|
WarpExecuteOnLane0Op::ensureTerminator(*warpRegion, builder, result.location);
|
|
|
|
// Parse the optional attribute list.
|
|
if (parser.parseOptionalAttrDict(result.attributes))
|
|
return failure();
|
|
return success();
|
|
}
|
|
|
|
void WarpExecuteOnLane0Op::getSuccessorRegions(
|
|
std::optional<unsigned> index, ArrayRef<Attribute> operands,
|
|
SmallVectorImpl<RegionSuccessor> ®ions) {
|
|
if (index) {
|
|
regions.push_back(RegionSuccessor(getResults()));
|
|
return;
|
|
}
|
|
|
|
// The warp region is always executed
|
|
regions.push_back(RegionSuccessor(&getWarpRegion()));
|
|
}
|
|
|
|
void WarpExecuteOnLane0Op::build(OpBuilder &builder, OperationState &result,
|
|
TypeRange resultTypes, Value laneId,
|
|
int64_t warpSize) {
|
|
build(builder, result, resultTypes, laneId, warpSize,
|
|
/*operands=*/std::nullopt, /*argTypes=*/std::nullopt);
|
|
}
|
|
|
|
void WarpExecuteOnLane0Op::build(OpBuilder &builder, OperationState &result,
|
|
TypeRange resultTypes, Value laneId,
|
|
int64_t warpSize, ValueRange args,
|
|
TypeRange blockArgTypes) {
|
|
result.addOperands(laneId);
|
|
result.addAttribute(getAttributeNames()[0],
|
|
builder.getI64IntegerAttr(warpSize));
|
|
result.addTypes(resultTypes);
|
|
result.addOperands(args);
|
|
assert(args.size() == blockArgTypes.size());
|
|
OpBuilder::InsertionGuard guard(builder);
|
|
Region *warpRegion = result.addRegion();
|
|
Block *block = builder.createBlock(warpRegion);
|
|
for (auto [type, arg] : llvm::zip_equal(blockArgTypes, args))
|
|
block->addArgument(type, arg.getLoc());
|
|
}
|
|
|
|
/// Helper check if the distributed vector type is consistent with the expanded
|
|
/// type and distributed size.
|
|
static LogicalResult verifyDistributedType(Type expanded, Type distributed,
|
|
int64_t warpSize, Operation *op) {
|
|
// If the types matches there is no distribution.
|
|
if (expanded == distributed)
|
|
return success();
|
|
auto expandedVecType = expanded.dyn_cast<VectorType>();
|
|
auto distributedVecType = distributed.dyn_cast<VectorType>();
|
|
if (!expandedVecType || !distributedVecType)
|
|
return op->emitOpError("expected vector type for distributed operands.");
|
|
if (expandedVecType.getRank() != distributedVecType.getRank() ||
|
|
expandedVecType.getElementType() != distributedVecType.getElementType())
|
|
return op->emitOpError(
|
|
"expected distributed vectors to have same rank and element type.");
|
|
bool foundDistributedDim = false;
|
|
for (int64_t i = 0, e = expandedVecType.getRank(); i < e; i++) {
|
|
if (expandedVecType.getDimSize(i) == distributedVecType.getDimSize(i))
|
|
continue;
|
|
if (expandedVecType.getDimSize(i) ==
|
|
distributedVecType.getDimSize(i) * warpSize) {
|
|
if (foundDistributedDim)
|
|
return op->emitOpError()
|
|
<< "expected only one dimension to be distributed from "
|
|
<< expandedVecType << " to " << distributedVecType;
|
|
foundDistributedDim = true;
|
|
continue;
|
|
}
|
|
return op->emitOpError() << "incompatible distribution dimensions from "
|
|
<< expandedVecType << " to " << distributedVecType;
|
|
}
|
|
return success();
|
|
}
|
|
|
|
LogicalResult WarpExecuteOnLane0Op::verify() {
|
|
if (getArgs().size() != getWarpRegion().getNumArguments())
|
|
return emitOpError(
|
|
"expected same number op arguments and block arguments.");
|
|
auto yield =
|
|
cast<YieldOp>(getWarpRegion().getBlocks().begin()->getTerminator());
|
|
if (yield.getNumOperands() != getNumResults())
|
|
return emitOpError(
|
|
"expected same number of yield operands and return values.");
|
|
int64_t warpSize = getWarpSize();
|
|
for (auto [regionArg, arg] :
|
|
llvm::zip_equal(getWarpRegion().getArguments(), getArgs())) {
|
|
if (failed(verifyDistributedType(regionArg.getType(), arg.getType(),
|
|
warpSize, getOperation())))
|
|
return failure();
|
|
}
|
|
for (auto [yieldOperand, result] :
|
|
llvm::zip_equal(yield.getOperands(), getResults())) {
|
|
if (failed(verifyDistributedType(yieldOperand.getType(), result.getType(),
|
|
warpSize, getOperation())))
|
|
return failure();
|
|
}
|
|
return success();
|
|
}
|
|
|
|
bool WarpExecuteOnLane0Op::areTypesCompatible(Type lhs, Type rhs) {
|
|
return succeeded(
|
|
verifyDistributedType(lhs, rhs, getWarpSize(), getOperation()));
|
|
}
|
|
|
|
Value mlir::vector::makeArithReduction(OpBuilder &b, Location loc,
|
|
CombiningKind kind, Value v1, Value acc,
|
|
Value mask) {
|
|
Type t1 = getElementTypeOrSelf(v1.getType());
|
|
Type tAcc = getElementTypeOrSelf(acc.getType());
|
|
Value result;
|
|
|
|
switch (kind) {
|
|
case CombiningKind::ADD:
|
|
if (t1.isIntOrIndex() && tAcc.isIntOrIndex())
|
|
result = b.createOrFold<arith::AddIOp>(loc, v1, acc);
|
|
else if (t1.isa<FloatType>() && tAcc.isa<FloatType>())
|
|
result = b.createOrFold<arith::AddFOp>(loc, v1, acc);
|
|
else
|
|
llvm_unreachable("invalid value types for ADD reduction");
|
|
break;
|
|
case CombiningKind::AND:
|
|
assert(t1.isIntOrIndex() && tAcc.isIntOrIndex() && "expected int values");
|
|
result = b.createOrFold<arith::AndIOp>(loc, v1, acc);
|
|
break;
|
|
case CombiningKind::MAXF:
|
|
assert(t1.isa<FloatType>() && tAcc.isa<FloatType>() &&
|
|
"expected float values");
|
|
result = b.createOrFold<arith::MaxFOp>(loc, v1, acc);
|
|
break;
|
|
case CombiningKind::MINF:
|
|
assert(t1.isa<FloatType>() && tAcc.isa<FloatType>() &&
|
|
"expected float values");
|
|
result = b.createOrFold<arith::MinFOp>(loc, v1, acc);
|
|
break;
|
|
case CombiningKind::MAXSI:
|
|
assert(t1.isIntOrIndex() && tAcc.isIntOrIndex() && "expected int values");
|
|
result = b.createOrFold<arith::MaxSIOp>(loc, v1, acc);
|
|
break;
|
|
case CombiningKind::MINSI:
|
|
assert(t1.isIntOrIndex() && tAcc.isIntOrIndex() && "expected int values");
|
|
result = b.createOrFold<arith::MinSIOp>(loc, v1, acc);
|
|
break;
|
|
case CombiningKind::MAXUI:
|
|
assert(t1.isIntOrIndex() && tAcc.isIntOrIndex() && "expected int values");
|
|
result = b.createOrFold<arith::MaxUIOp>(loc, v1, acc);
|
|
break;
|
|
case CombiningKind::MINUI:
|
|
assert(t1.isIntOrIndex() && tAcc.isIntOrIndex() && "expected int values");
|
|
result = b.createOrFold<arith::MinUIOp>(loc, v1, acc);
|
|
break;
|
|
case CombiningKind::MUL:
|
|
if (t1.isIntOrIndex() && tAcc.isIntOrIndex())
|
|
result = b.createOrFold<arith::MulIOp>(loc, v1, acc);
|
|
else if (t1.isa<FloatType>() && tAcc.isa<FloatType>())
|
|
result = b.createOrFold<arith::MulFOp>(loc, v1, acc);
|
|
else
|
|
llvm_unreachable("invalid value types for MUL reduction");
|
|
break;
|
|
case CombiningKind::OR:
|
|
assert(t1.isIntOrIndex() && tAcc.isIntOrIndex() && "expected int values");
|
|
result = b.createOrFold<arith::OrIOp>(loc, v1, acc);
|
|
break;
|
|
case CombiningKind::XOR:
|
|
assert(t1.isIntOrIndex() && tAcc.isIntOrIndex() && "expected int values");
|
|
result = b.createOrFold<arith::XOrIOp>(loc, v1, acc);
|
|
break;
|
|
};
|
|
|
|
assert(result && "unknown CombiningKind");
|
|
return selectPassthru(b, mask, result, acc);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// Vector Masking Utilities
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
/// Create the vector.yield-ended region of a vector.mask op with `maskableOp`
|
|
/// as masked operation.
|
|
void mlir::vector::createMaskOpRegion(OpBuilder &builder,
|
|
Operation *maskableOp) {
|
|
assert(maskableOp->getBlock() && "MaskableOp must be inserted into a block");
|
|
Block *insBlock = builder.getInsertionBlock();
|
|
// Create a block and move the op to that block.
|
|
insBlock->getOperations().splice(
|
|
insBlock->begin(), maskableOp->getBlock()->getOperations(), maskableOp);
|
|
builder.create<YieldOp>(maskableOp->getLoc(), maskableOp->getResults());
|
|
}
|
|
|
|
/// Creates a vector.mask operation around a maskable operation. Returns the
|
|
/// vector.mask operation if the mask provided is valid. Otherwise, returns
|
|
/// the maskable operation itself.
|
|
Operation *mlir::vector::maskOperation(OpBuilder &builder,
|
|
Operation *maskableOp, Value mask,
|
|
Value passthru) {
|
|
if (!mask)
|
|
return maskableOp;
|
|
if (passthru)
|
|
return builder.create<MaskOp>(maskableOp->getLoc(),
|
|
maskableOp->getResultTypes(), mask, passthru,
|
|
maskableOp, createMaskOpRegion);
|
|
return builder.create<MaskOp>(maskableOp->getLoc(),
|
|
maskableOp->getResultTypes(), mask, maskableOp,
|
|
createMaskOpRegion);
|
|
}
|
|
|
|
/// Creates a vector select operation that picks values from `newValue` or
|
|
/// `passthru` for each result vector lane based on `mask`. This utility is used
|
|
/// to propagate the pass-thru value of vector.mask or for cases where only the
|
|
/// pass-thru value propagation is needed. VP intrinsics do not support
|
|
/// pass-thru values and every mask-out lane is set to poison. LLVM backends are
|
|
/// usually able to match op + select patterns and fold them into a native
|
|
/// target instructions.
|
|
Value mlir::vector::selectPassthru(OpBuilder &builder, Value mask,
|
|
Value newValue, Value passthru) {
|
|
if (!mask)
|
|
return newValue;
|
|
|
|
return builder.create<arith::SelectOp>(newValue.getLoc(), newValue.getType(),
|
|
mask, newValue, passthru);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// TableGen'd op method definitions
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
#define GET_ATTRDEF_CLASSES
|
|
#include "mlir/Dialect/Vector/IR/VectorOpsAttrDefs.cpp.inc"
|
|
|
|
#define GET_OP_CLASSES
|
|
#include "mlir/Dialect/Vector/IR/VectorOps.cpp.inc"
|