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[mlir] Flip more uses to prefixed accessor form (NFC).

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Try to keep the final flip small. Need to flip MemRef as there are many
templated cases with it and Tensor.
This commit is contained in:
Jacques Pienaar
2022-06-26 19:12:38 -07:00
parent ca05cc2064
commit 2d70eff802
7 changed files with 160 additions and 152 deletions
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22
mlir/include/mlir/Dialect/AMX/AMX.td
View File

@@ -97,7 +97,7 @@ def TileZeroOp : AMX_Op<"tile_zero", [NoSideEffect]> {
VectorOfRankAndType<[2], [F32, BF16, I32, I8]>:$res);
let extraClassDeclaration = [{
VectorType getVectorType() {
return res().getType().cast<VectorType>();
return getRes().getType().cast<VectorType>();
}
}];
let assemblyFormat = "attr-dict `:` type($res)";
@@ -128,10 +128,10 @@ def TileLoadOp : AMX_Op<"tile_load", [NoSideEffect]> {
VectorOfRankAndType<[2], [F32, BF16, I32, I8]>:$res);
let extraClassDeclaration = [{
MemRefType getMemRefType() {
return base().getType().cast<MemRefType>();
return getBase().getType().cast<MemRefType>();
}
VectorType getVectorType() {
return res().getType().cast<VectorType>();
return getRes().getType().cast<VectorType>();
}
}];
let assemblyFormat = "$base `[` $indices `]` attr-dict `:` "
@@ -158,10 +158,10 @@ def TileStoreOp : AMX_Op<"tile_store"> {
VectorOfRankAndType<[2], [F32, BF16, I32, I8]>:$val);
let extraClassDeclaration = [{
MemRefType getMemRefType() {
return base().getType().cast<MemRefType>();
return getBase().getType().cast<MemRefType>();
}
VectorType getVectorType() {
return val().getType().cast<VectorType>();
return getVal().getType().cast<VectorType>();
}
}];
let assemblyFormat = "$base `[` $indices `]` `,` $val attr-dict `:` "
@@ -194,13 +194,13 @@ def TileMulFOp : AMX_Op<"tile_mulf", [NoSideEffect, AllTypesMatch<["acc", "res"]
let results = (outs VectorOfRankAndType<[2], [F32, BF16]>:$res);
let extraClassDeclaration = [{
VectorType getLhsVectorType() {
return lhs().getType().cast<VectorType>();
return getLhs().getType().cast<VectorType>();
}
VectorType getRhsVectorType() {
return rhs().getType().cast<VectorType>();
return getRhs().getType().cast<VectorType>();
}
VectorType getVectorType() {
return res().getType().cast<VectorType>();
return getRes().getType().cast<VectorType>();
}
}];
let assemblyFormat = "$lhs `,` $rhs `,` $acc attr-dict `:` "
@@ -235,13 +235,13 @@ def TileMulIOp : AMX_Op<"tile_muli", [NoSideEffect, AllTypesMatch<["acc", "res"]
let results = (outs VectorOfRankAndType<[2], [I32, I8]>:$res);
let extraClassDeclaration = [{
VectorType getLhsVectorType() {
return lhs().getType().cast<VectorType>();
return getLhs().getType().cast<VectorType>();
}
VectorType getRhsVectorType() {
return rhs().getType().cast<VectorType>();
return getRhs().getType().cast<VectorType>();
}
VectorType getVectorType() {
return res().getType().cast<VectorType>();
return getRes().getType().cast<VectorType>();
}
}];
let assemblyFormat = "$lhs (`zext` $isZextLhs^)? `,` $rhs (`zext` $isZextRhs^)? `,` $acc attr-dict `:` "

18
mlir/include/mlir/Dialect/Tensor/IR/TensorOps.td
View File

@@ -27,7 +27,7 @@ class Tensor_OpWithOffsetSizesAndStrides<string mnemonic,
: Tensor_Op<mnemonic, traits> {
code extraBaseClassDeclaration = [{
/// Returns the dynamic sizes for this subview operation if specified.
::mlir::Operation::operand_range getDynamicSizes() { return sizes(); }
::mlir::Operation::operand_range getDynamicSizes() { return getSizes(); }
/// Return the list of Range (i.e. offset, size, stride). Each
/// Range entry contains either the dynamic value or a ConstantIndexOp
@@ -266,7 +266,7 @@ def Tensor_ExtractSliceOp : Tensor_OpWithOffsetSizesAndStrides<"extract_slice",
let extraClassDeclaration = extraBaseClassDeclaration # [{
/// Returns the type of the base tensor operand.
RankedTensorType getSourceType() {
return source().getType().cast<RankedTensorType>();
return getSource().getType().cast<RankedTensorType>();
}
/// The result of an extract_slice is always a tensor.
@@ -559,7 +559,7 @@ def Tensor_InsertSliceOp : Tensor_OpWithOffsetSizesAndStrides<"insert_slice", [
let extraClassDeclaration = extraBaseClassDeclaration # [{
/// Returns the type of the base tensor operand.
RankedTensorType getSourceType() {
return source().getType().cast<RankedTensorType>();
return getSource().getType().cast<RankedTensorType>();
}
/// The result of a insert_slice is always a tensor.
@@ -685,7 +685,7 @@ class Tensor_ReassociativeReshapeOp<string mnemonic, list<Trait> traits = []> :
SmallVector<ReassociationExprs, 4> getReassociationExprs();
SmallVector<ReassociationIndices, 4> getReassociationIndices() {
SmallVector<ReassociationIndices, 4> reassociationIndices;
for (auto attr : reassociation())
for (auto attr : getReassociation())
reassociationIndices.push_back(llvm::to_vector<2>(
llvm::map_range(attr.cast<ArrayAttr>(), [&](Attribute indexAttr) {
return indexAttr.cast<IntegerAttr>().getInt();
@@ -693,10 +693,10 @@ class Tensor_ReassociativeReshapeOp<string mnemonic, list<Trait> traits = []> :
return reassociationIndices;
};
RankedTensorType getSrcType() {
return src().getType().cast<RankedTensorType>();
return getSrc().getType().cast<RankedTensorType>();
}
RankedTensorType getResultType() {
return result().getType().cast<RankedTensorType>();
return getResult().getType().cast<RankedTensorType>();
}
}];
@@ -930,7 +930,7 @@ def Tensor_PadOp : Tensor_Op<"pad", [AttrSizedOperandSegments, NoSideEffect,
}
RankedTensorType getSourceType() {
return source().getType().cast<RankedTensorType>();
return getSource().getType().cast<RankedTensorType>();
}
RankedTensorType getResultType() {
return getResult().getType().cast<RankedTensorType>();
@@ -965,10 +965,10 @@ def Tensor_PadOp : Tensor_Op<"pad", [AttrSizedOperandSegments, NoSideEffect,
return res;
}
SmallVector<OpFoldResult> getMixedLowPad() {
return getMixedPadImpl(static_low(), low());
return getMixedPadImpl(getStaticLow(), getLow());
}
SmallVector<OpFoldResult> getMixedHighPad() {
return getMixedPadImpl(static_high(), high());
return getMixedPadImpl(getStaticHigh(), getHigh());
}
// Return true if low padding is guaranteed to be 0.
bool hasZeroLowPad() {

29
mlir/lib/Conversion/AffineToStandard/AffineToStandard.cpp
View File

@@ -100,7 +100,7 @@ public:
LogicalResult matchAndRewrite(AffineMinOp op,
PatternRewriter &rewriter) const override {
Value reduced =
lowerAffineMapMin(rewriter, op.getLoc(), op.map(), op.operands());
lowerAffineMapMin(rewriter, op.getLoc(), op.getMap(), op.operands());
if (!reduced)
return failure();
@@ -116,7 +116,7 @@ public:
LogicalResult matchAndRewrite(AffineMaxOp op,
PatternRewriter &rewriter) const override {
Value reduced =
lowerAffineMapMax(rewriter, op.getLoc(), op.map(), op.operands());
lowerAffineMapMax(rewriter, op.getLoc(), op.getMap(), op.operands());
if (!reduced)
return failure();
@@ -156,7 +156,7 @@ public:
auto scfForOp = rewriter.create<scf::ForOp>(loc, lowerBound, upperBound,
step, op.getIterOperands());
rewriter.eraseBlock(scfForOp.getBody());
rewriter.inlineRegionBefore(op.region(), scfForOp.getRegion(),
rewriter.inlineRegionBefore(op.getRegion(), scfForOp.getRegion(),
scfForOp.getRegion().end());
rewriter.replaceOp(op, scfForOp.getResults());
return success();
@@ -193,20 +193,20 @@ public:
return rewriter.notifyMatchFailure(op, "couldn't convert upper bounds");
upperBoundTuple.push_back(upper);
}
steps.reserve(op.steps().size());
for (int64_t step : op.steps())
steps.reserve(op.getSteps().size());
for (int64_t step : op.getSteps())
steps.push_back(rewriter.create<arith::ConstantIndexOp>(loc, step));
// Get the terminator op.
Operation *affineParOpTerminator = op.getBody()->getTerminator();
scf::ParallelOp parOp;
if (op.results().empty()) {
if (op.getResults().empty()) {
// Case with no reduction operations/return values.
parOp = rewriter.create<scf::ParallelOp>(loc, lowerBoundTuple,
upperBoundTuple, steps,
/*bodyBuilderFn=*/nullptr);
rewriter.eraseBlock(parOp.getBody());
rewriter.inlineRegionBefore(op.region(), parOp.getRegion(),
rewriter.inlineRegionBefore(op.getRegion(), parOp.getRegion(),
parOp.getRegion().end());
rewriter.replaceOp(op, parOp.getResults());
return success();
@@ -214,7 +214,7 @@ public:
// Case with affine.parallel with reduction operations/return values.
// scf.parallel handles the reduction operation differently unlike
// affine.parallel.
ArrayRef<Attribute> reductions = op.reductions().getValue();
ArrayRef<Attribute> reductions = op.getReductions().getValue();
for (auto pair : llvm::zip(reductions, op.getResultTypes())) {
// For each of the reduction operations get the identity values for
// initialization of the result values.
@@ -234,7 +234,7 @@ public:
// Copy the body of the affine.parallel op.
rewriter.eraseBlock(parOp.getBody());
rewriter.inlineRegionBefore(op.region(), parOp.getRegion(),
rewriter.inlineRegionBefore(op.getRegion(), parOp.getRegion(),
parOp.getRegion().end());
assert(reductions.size() == affineParOpTerminator->getNumOperands() &&
"Unequal number of reductions and operands.");
@@ -299,13 +299,14 @@ public:
: rewriter.create<arith::ConstantIntOp>(loc, /*value=*/1,
/*width=*/1);
bool hasElseRegion = !op.elseRegion().empty();
bool hasElseRegion = !op.getElseRegion().empty();
auto ifOp = rewriter.create<scf::IfOp>(loc, op.getResultTypes(), cond,
hasElseRegion);
rewriter.inlineRegionBefore(op.thenRegion(), &ifOp.getThenRegion().back());
rewriter.inlineRegionBefore(op.getThenRegion(),
&ifOp.getThenRegion().back());
rewriter.eraseBlock(&ifOp.getThenRegion().back());
if (hasElseRegion) {
rewriter.inlineRegionBefore(op.elseRegion(),
rewriter.inlineRegionBefore(op.getElseRegion(),
&ifOp.getElseRegion().back());
rewriter.eraseBlock(&ifOp.getElseRegion().back());
}
@@ -375,8 +376,8 @@ public:
// Build memref.prefetch memref[expandedMap.results].
rewriter.replaceOpWithNewOp<memref::PrefetchOp>(
op, op.memref(), *resultOperands, op.isWrite(), op.localityHint(),
op.isDataCache());
op, op.getMemref(), *resultOperands, op.getIsWrite(),
op.getLocalityHint(), op.getIsDataCache());
return success();
}
};

2
mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
View File

@@ -61,7 +61,7 @@ static mlir::Value applyPad(Location loc, Value input, ArrayRef<int64_t> pad,
return tensor::createPadScalarOp(RankedTensorType::get(paddedShape, inputETy),
input, padValue, lowIndices, highIndices,
/*nofold=*/false, loc, rewriter)
.result();
.getResult();
}
static mlir::Value reifyConstantDim(Attribute attr,

7
mlir/lib/Dialect/SCF/IR/SCF.cpp
View File

@@ -889,12 +889,12 @@ struct ForOpTensorCastFolder : public OpRewritePattern<ForOp> {
// Must be a tensor.cast op pair with matching types.
if (outgoingCastOp.getResult().getType() !=
incomingCast.source().getType())
incomingCast.getSource().getType())
continue;
// Create a new ForOp with that iter operand replaced.
auto newForOp = replaceTensorCastForOpIterArg(rewriter, iterOpOperand,
incomingCast.source());
incomingCast.getSource());
// Insert outgoing cast and use it to replace the corresponding result.
rewriter.setInsertionPointAfter(newForOp);
@@ -902,7 +902,8 @@ struct ForOpTensorCastFolder : public OpRewritePattern<ForOp> {
unsigned returnIdx =
iterOpOperand.getOperandNumber() - op.getNumControlOperands();
replacements[returnIdx] = rewriter.create<tensor::CastOp>(
op.getLoc(), incomingCast.dest().getType(), replacements[returnIdx]);
op.getLoc(), incomingCast.getDest().getType(),
replacements[returnIdx]);
rewriter.replaceOp(op, replacements);
return success();
}

232
mlir/lib/Dialect/Tensor/IR/TensorOps.cpp
View File

@@ -97,7 +97,7 @@ bool mlir::tensor::canFoldIntoConsumerOp(CastOp castOp) {
// Can fold if the source of cast has at least as much static information as
// its results.
return preservesStaticInformation(castOp.getType(),
castOp.source().getType());
castOp.getSource().getType());
}
/// Determines whether the tensor::CastOp casts to a more static version of the
@@ -123,7 +123,7 @@ bool mlir::tensor::canFoldIntoConsumerOp(CastOp castOp) {
bool mlir::tensor::canFoldIntoProducerOp(CastOp castOp) {
if (!castOp)
return false;
return preservesStaticInformation(castOp.source().getType(),
return preservesStaticInformation(castOp.getSource().getType(),
castOp.getType());
}
@@ -250,13 +250,15 @@ struct TensorCastExtractSlice : public OpRewritePattern<CastOp> {
tensorCast.getOperand().getDefiningOp<ExtractSliceOp>();
if (!extractOperand || !canFoldIntoProducerOp(tensorCast) ||
tensorCast.getType().getShape() ==
tensorCast.source().getType().cast<RankedTensorType>().getShape())
tensorCast.getType().getShape() == tensorCast.getSource()
.getType()
.cast<RankedTensorType>()
.getShape())
return failure();
SmallVector<OpFoldResult, 4> sizes = extractOperand.getMixedSizes();
auto dimMask = computeRankReductionMask(
extractFromI64ArrayAttr(extractOperand.static_sizes()),
extractFromI64ArrayAttr(extractOperand.getStaticSizes()),
extractOperand.getType().getShape());
size_t dimIndex = 0;
for (size_t i = 0, e = sizes.size(); i < e; i++) {
@@ -270,7 +272,7 @@ struct TensorCastExtractSlice : public OpRewritePattern<CastOp> {
rewriter.replaceOpWithNewOp<ExtractSliceOp>(
tensorCast, tensorCast.getType().cast<RankedTensorType>(),
extractOperand.source(), extractOperand.getMixedOffsets(), sizes,
extractOperand.getSource(), extractOperand.getMixedOffsets(), sizes,
extractOperand.getMixedStrides());
return success();
}
@@ -295,7 +297,7 @@ void DimOp::build(OpBuilder &builder, OperationState &result, Value source,
}
Optional<int64_t> DimOp::getConstantIndex() {
if (auto constantOp = index().getDefiningOp<arith::ConstantOp>())
if (auto constantOp = getIndex().getDefiningOp<arith::ConstantOp>())
return constantOp.getValue().cast<IntegerAttr>().getInt();
return {};
}
@@ -307,7 +309,7 @@ LogicalResult DimOp::verify() {
return success();
// Check that constant index is not knowingly out of range.
auto type = source().getType();
auto type = getSource().getType();
if (auto tensorType = type.dyn_cast<RankedTensorType>()) {
if (*index >= tensorType.getRank())
return emitOpError("index is out of range");
@@ -326,7 +328,7 @@ OpFoldResult DimOp::fold(ArrayRef<Attribute> operands) {
return {};
// Folding for unranked types (UnrankedTensorType) is not supported.
auto tensorType = source().getType().dyn_cast<RankedTensorType>();
auto tensorType = getSource().getType().dyn_cast<RankedTensorType>();
if (!tensorType)
return {};
@@ -336,7 +338,7 @@ OpFoldResult DimOp::fold(ArrayRef<Attribute> operands) {
return builder.getIndexAttr(tensorType.getShape()[index.getInt()]);
}
Operation *definingOp = source().getDefiningOp();
Operation *definingOp = getSource().getDefiningOp();
// Fold dim to the operand of tensor.generate.
if (auto fromElements = dyn_cast_or_null<tensor::GenerateOp>(definingOp)) {
@@ -347,7 +349,7 @@ OpFoldResult DimOp::fold(ArrayRef<Attribute> operands) {
assert(ShapedType::isDynamic(resultType.getShape()[index.getInt()]));
// Find the operand of the fromElements that corresponds to this index.
auto dynExtents = fromElements.dynamicExtents().begin();
auto dynExtents = fromElements.getDynamicExtents().begin();
for (auto dim : resultType.getShape().take_front(index.getInt()))
if (ShapedType::isDynamic(dim))
dynExtents++;
@@ -381,11 +383,11 @@ struct DimOfCastOp : public OpRewritePattern<DimOp> {
LogicalResult matchAndRewrite(DimOp dimOp,
PatternRewriter &rewriter) const override {
auto castOp = dimOp.source().getDefiningOp<CastOp>();
auto castOp = dimOp.getSource().getDefiningOp<CastOp>();
if (!castOp)
return failure();
Value newSource = castOp.getOperand();
rewriter.replaceOpWithNewOp<DimOp>(dimOp, newSource, dimOp.index());
rewriter.replaceOpWithNewOp<DimOp>(dimOp, newSource, dimOp.getIndex());
return success();
}
};
@@ -402,8 +404,8 @@ void DimOp::getCanonicalizationPatterns(RewritePatternSet &results,
LogicalResult ExtractOp::verify() {
// Verify the # indices match if we have a ranked type.
if (auto tensorType = tensor().getType().dyn_cast<RankedTensorType>())
if (tensorType.getRank() != static_cast<int64_t>(indices().size()))
if (auto tensorType = getTensor().getType().dyn_cast<RankedTensorType>())
if (tensorType.getRank() != static_cast<int64_t>(getIndices().size()))
return emitOpError("incorrect number of indices for extract_element");
return success();
@@ -425,7 +427,7 @@ OpFoldResult ExtractOp::fold(ArrayRef<Attribute> operands) {
}
// Fold extract(from_elements(...)).
if (auto fromElementsOp = tensor().getDefiningOp<FromElementsOp>()) {
if (auto fromElementsOp = getTensor().getDefiningOp<FromElementsOp>()) {
auto tensorType = fromElementsOp.getType().cast<RankedTensorType>();
auto rank = tensorType.getRank();
assert(static_cast<int64_t>(indices.size()) == tensorType.getRank() &&
@@ -439,10 +441,10 @@ OpFoldResult ExtractOp::fold(ArrayRef<Attribute> operands) {
}
// Prevent out of bounds accesses. This can happen in invalid code that will
// never execute.
if (static_cast<int>(fromElementsOp.elements().size()) <= flatIndex ||
if (static_cast<int>(fromElementsOp.getElements().size()) <= flatIndex ||
flatIndex < 0)
return {};
return fromElementsOp.elements()[flatIndex];
return fromElementsOp.getElements()[flatIndex];
}
// If this is an elements attribute, query the value at the given indices.
@@ -503,14 +505,14 @@ struct ExtractElementFromIndexCast
LogicalResult matchAndRewrite(tensor::ExtractOp extract,
PatternRewriter &rewriter) const final {
Location loc = extract.getLoc();
auto indexCast = extract.tensor().getDefiningOp<arith::IndexCastOp>();
auto indexCast = extract.getTensor().getDefiningOp<arith::IndexCastOp>();
if (!indexCast)
return failure();
Type elementTy = getElementTypeOrSelf(indexCast.getIn());
auto newExtract = rewriter.create<tensor::ExtractOp>(
loc, elementTy, indexCast.getIn(), extract.indices());
loc, elementTy, indexCast.getIn(), extract.getIndices());
rewriter.replaceOpWithNewOp<arith::IndexCastOp>(extract, extract.getType(),
newExtract);
@@ -532,8 +534,8 @@ void FromElementsOp::getCanonicalizationPatterns(RewritePatternSet &results,
LogicalResult InsertOp::verify() {
// Verify the # indices match if we have a ranked type.
if (auto destType = dest().getType().dyn_cast<RankedTensorType>())
if (destType.getRank() != static_cast<int64_t>(indices().size()))
if (auto destType = getDest().getType().dyn_cast<RankedTensorType>())
if (destType.getRank() != static_cast<int64_t>(getIndices().size()))
return emitOpError("incorrect number of indices");
return success();
}
@@ -581,16 +583,16 @@ LogicalResult GenerateOp::verify() {
LogicalResult GenerateOp::verifyRegions() {
RankedTensorType resultTy = getType().cast<RankedTensorType>();
// Ensure that region arguments span the index space.
if (!llvm::all_of(body().getArgumentTypes(),
if (!llvm::all_of(getBody().getArgumentTypes(),
[](Type ty) { return ty.isIndex(); }))
return emitError("all body arguments must be index");
if (body().getNumArguments() != resultTy.getRank())
if (getBody().getNumArguments() != resultTy.getRank())
return emitError("must have one body argument per input dimension");
// Ensure that the region yields an element of the right type.
auto yieldOp = cast<YieldOp>(body().getBlocks().front().getTerminator());
auto yieldOp = cast<YieldOp>(getBody().getBlocks().front().getTerminator());
if (yieldOp.value().getType() != resultTy.getElementType())
if (yieldOp.getValue().getType() != resultTy.getElementType())
return emitOpError(
"body must be terminated with a `yield` operation of the tensor "
"element type");
@@ -634,7 +636,7 @@ struct StaticTensorGenerate : public OpRewritePattern<GenerateOp> {
SmallVector<Value, 4> newOperands;
SmallVector<int64_t, 4> newShape;
auto operandsIt = tensorFromElements.dynamicExtents().begin();
auto operandsIt = tensorFromElements.getDynamicExtents().begin();
for (int64_t dim : resultType.getShape()) {
if (!ShapedType::isDynamic(dim)) {
@@ -651,15 +653,15 @@ struct StaticTensorGenerate : public OpRewritePattern<GenerateOp> {
operandsIt++;
}
if (newOperands.size() == tensorFromElements.dynamicExtents().size())
if (newOperands.size() == tensorFromElements.getDynamicExtents().size())
return failure();
auto loc = tensorFromElements.getLoc();
auto newOp = rewriter.create<GenerateOp>(
loc, RankedTensorType::get(newShape, resultType.getElementType()),
newOperands);
rewriter.inlineRegionBefore(tensorFromElements.body(), newOp.body(),
newOp.body().begin());
rewriter.inlineRegionBefore(tensorFromElements.getBody(), newOp.getBody(),
newOp.getBody().begin());
rewriter.replaceOpWithNewOp<tensor::CastOp>(tensorFromElements, resultType,
newOp);
return success();
@@ -682,19 +684,19 @@ struct ExtractFromTensorGenerate : public OpRewritePattern<tensor::ExtractOp> {
LogicalResult matchAndRewrite(tensor::ExtractOp extract,
PatternRewriter &rewriter) const final {
auto tensorFromElements = extract.tensor().getDefiningOp<GenerateOp>();
auto tensorFromElements = extract.getTensor().getDefiningOp<GenerateOp>();
if (!tensorFromElements || !wouldOpBeTriviallyDead(tensorFromElements))
return failure();
BlockAndValueMapping mapping;
Block *body = &tensorFromElements.getBody().front();
mapping.map(body->getArguments(), extract.indices());
mapping.map(body->getArguments(), extract.getIndices());
for (auto &op : body->without_terminator())
rewriter.clone(op, mapping);
auto yield = cast<YieldOp>(body->getTerminator());
rewriter.replaceOp(extract, mapping.lookupOrDefault(yield.value()));
rewriter.replaceOp(extract, mapping.lookupOrDefault(yield.getValue()));
return success();
}
};
@@ -712,12 +714,12 @@ struct ExtractFromTensorCast : public OpRewritePattern<tensor::ExtractOp> {
LogicalResult matchAndRewrite(tensor::ExtractOp extract,
PatternRewriter &rewriter) const final {
auto tensorCast = extract.tensor().getDefiningOp<tensor::CastOp>();
auto tensorCast = extract.getTensor().getDefiningOp<tensor::CastOp>();
if (!tensorCast)
return failure();
rewriter.replaceOpWithNewOp<tensor::ExtractOp>(extract, tensorCast.source(),
extract.indices());
rewriter.replaceOpWithNewOp<tensor::ExtractOp>(
extract, tensorCast.getSource(), extract.getIndices());
return success();
}
};
@@ -756,14 +758,15 @@ static int64_t getNumElements(ShapedType type) {
}
LogicalResult ReshapeOp::verify() {
TensorType operandType = source().getType().cast<TensorType>();
TensorType resultType = result().getType().cast<TensorType>();
TensorType operandType = getSource().getType().cast<TensorType>();
TensorType resultType = getResult().getType().cast<TensorType>();
if (operandType.getElementType() != resultType.getElementType())
return emitOpError("element types of source and destination tensor "
"types should be the same");
int64_t shapeSize = shape().getType().cast<RankedTensorType>().getDimSize(0);
int64_t shapeSize =
getShape().getType().cast<RankedTensorType>().getDimSize(0);
auto resultRankedType = resultType.dyn_cast<RankedTensorType>();
auto operandRankedType = operandType.dyn_cast<RankedTensorType>();
@@ -891,7 +894,7 @@ struct FoldReshapeWithConstant : OpRewritePattern<TensorReshapeOp> {
LogicalResult matchAndRewrite(TensorReshapeOp reshapeOp,
PatternRewriter &rewriter) const override {
DenseElementsAttr attr;
if (!matchPattern(reshapeOp.src(), m_Constant(&attr)))
if (!matchPattern(reshapeOp.getSrc(), m_Constant(&attr)))
return failure();
if (!attr || !attr.isSplat())
return failure();
@@ -910,7 +913,7 @@ struct FoldReshapeWithFromElements : OpRewritePattern<TensorReshapeOp> {
LogicalResult matchAndRewrite(TensorReshapeOp reshapeOp,
PatternRewriter &rewriter) const override {
auto fromElements =
reshapeOp.src().template getDefiningOp<FromElementsOp>();
reshapeOp.getSrc().template getDefiningOp<FromElementsOp>();
if (!fromElements)
return failure();
@@ -920,7 +923,7 @@ struct FoldReshapeWithFromElements : OpRewritePattern<TensorReshapeOp> {
return failure();
rewriter.replaceOpWithNewOp<FromElementsOp>(reshapeOp, reshapeOp.getType(),
fromElements.elements());
fromElements.getElements());
return success();
}
};
@@ -1208,7 +1211,7 @@ public:
}))
return failure();
auto castOp = sliceOp.source().getDefiningOp<tensor::CastOp>();
auto castOp = sliceOp.getSource().getDefiningOp<tensor::CastOp>();
if (!castOp)
return failure();
@@ -1221,9 +1224,9 @@ public:
sliceOp.getMixedOffsets(), sliceOp.getMixedSizes(),
sliceOp.getMixedStrides());
Value newSlice = rewriter.create<ExtractSliceOp>(
sliceOp.getLoc(), resultType, castOp.source(), sliceOp.offsets(),
sliceOp.sizes(), sliceOp.strides(), sliceOp.static_offsets(),
sliceOp.static_sizes(), sliceOp.static_strides());
sliceOp.getLoc(), resultType, castOp.getSource(), sliceOp.getOffsets(),
sliceOp.getSizes(), sliceOp.getStrides(), sliceOp.getStaticOffsets(),
sliceOp.getStaticSizes(), sliceOp.getStaticStrides());
rewriter.replaceOpWithNewOp<tensor::CastOp>(sliceOp, sliceOp.getType(),
newSlice);
return success();
@@ -1277,7 +1280,7 @@ public:
LogicalResult matchAndRewrite(ExtractSliceOp op,
PatternRewriter &rewriter) const override {
DenseElementsAttr attr;
if (!matchPattern(op.source(), m_Constant(&attr)))
if (!matchPattern(op.getSource(), m_Constant(&attr)))
return failure();
// A constant splat is handled by fold().
@@ -1285,8 +1288,8 @@ public:
return failure();
// Dynamic result shape is not supported.
auto sourceType = op.source().getType().cast<ShapedType>();
auto resultType = op.result().getType().cast<ShapedType>();
auto sourceType = op.getSource().getType().cast<ShapedType>();
auto resultType = op.getResult().getType().cast<ShapedType>();
if (!sourceType.hasStaticShape() || !resultType.hasStaticShape())
return failure();
@@ -1299,13 +1302,13 @@ public:
return failure();
// Check if there are any dynamic parts, which are not supported.
auto offsets = extractFromI64ArrayAttr(op.static_offsets());
auto offsets = extractFromI64ArrayAttr(op.getStaticOffsets());
if (llvm::is_contained(offsets, ShapedType::kDynamicStrideOrOffset))
return failure();
auto sizes = extractFromI64ArrayAttr(op.static_sizes());
auto sizes = extractFromI64ArrayAttr(op.getStaticSizes());
if (llvm::is_contained(sizes, ShapedType::kDynamicSize))
return failure();
auto strides = extractFromI64ArrayAttr(op.static_strides());
auto strides = extractFromI64ArrayAttr(op.getStaticStrides());
if (llvm::is_contained(strides, ShapedType::kDynamicStrideOrOffset))
return failure();
@@ -1414,25 +1417,25 @@ foldIdentityOffsetSizeAndStrideOpInterface(OffsetSizeAndStrideOpInterface op,
// TODO: This only checks the immediate producer; extend to go up the
// insert/extract chain if the slices are disjoint.
static Value foldExtractAfterInsertSlice(ExtractSliceOp extractOp) {
auto insertOp = extractOp.source().getDefiningOp<InsertSliceOp>();
auto insertOp = extractOp.getSource().getDefiningOp<InsertSliceOp>();
auto isSame = [](OpFoldResult a, OpFoldResult b) { return a == b; };
if (insertOp && insertOp.source().getType() == extractOp.getType() &&
if (insertOp && insertOp.getSource().getType() == extractOp.getType() &&
insertOp.isSameAs(extractOp, isSame))
return insertOp.source();
return insertOp.getSource();
return {};
}
OpFoldResult ExtractSliceOp::fold(ArrayRef<Attribute> operands) {
if (auto splat = operands[0].dyn_cast_or_null<SplatElementsAttr>()) {
auto resultType = result().getType().cast<ShapedType>();
auto resultType = getResult().getType().cast<ShapedType>();
if (resultType.hasStaticShape())
return splat.resizeSplat(resultType);
}
if (getSourceType() == getType() &&
succeeded(foldIdentityOffsetSizeAndStrideOpInterface(*this, getType())))
return this->source();
return this->getSource();
if (Value slice = foldExtractAfterInsertSlice(*this))
return slice;
@@ -1518,8 +1521,8 @@ verifyInsertSliceOp(ShapedType srcType, ShapedType dstType,
LogicalResult InsertSliceOp::verify() {
ShapedType expectedType;
auto result =
verifyInsertSliceOp(getSourceType(), getType(), static_offsets(),
static_sizes(), static_strides(), &expectedType);
verifyInsertSliceOp(getSourceType(), getType(), getStaticOffsets(),
getStaticSizes(), getStaticStrides(), &expectedType);
return produceSliceErrorMsg(result, *this, expectedType);
}
@@ -1539,15 +1542,15 @@ LogicalResult InsertSliceOp::verify() {
/// %1 = tensor.insert_slice %slice1 into %input[0, 0] [64, 64] [1, 1]
/// ```
static LogicalResult foldInsertAfterInsertSlice(InsertSliceOp insertOp) {
auto prevInsertOp = insertOp.dest().getDefiningOp<InsertSliceOp>();
auto prevInsertOp = insertOp.getDest().getDefiningOp<InsertSliceOp>();
auto isSame = [](OpFoldResult a, OpFoldResult b) { return a == b; };
if (!prevInsertOp ||
prevInsertOp.source().getType() != insertOp.source().getType() ||
prevInsertOp.getSource().getType() != insertOp.getSource().getType() ||
!prevInsertOp.isSameAs(insertOp, isSame))
return failure();
insertOp.destMutable().assign(prevInsertOp.dest());
insertOp.getDestMutable().assign(prevInsertOp.getDest());
return success();
}
@@ -1555,7 +1558,7 @@ OpFoldResult InsertSliceOp::fold(ArrayRef<Attribute>) {
if (getSourceType().hasStaticShape() && getType().hasStaticShape() &&
getSourceType() == getType() &&
succeeded(foldIdentityOffsetSizeAndStrideOpInterface(*this, getType())))
return this->source();
return this->getSource();
if (succeeded(foldInsertAfterInsertSlice(*this)))
return getResult();
return OpFoldResult();
@@ -1566,7 +1569,7 @@ LogicalResult InsertSliceOp::reifyResultShapes(
reifiedReturnShapes.resize(1, SmallVector<Value>(getType().getRank()));
for (auto dim : llvm::seq<int64_t>(0, getType().getRank())) {
reifiedReturnShapes[0][dim] =
builder.createOrFold<tensor::DimOp>(getLoc(), dest(), dim);
builder.createOrFold<tensor::DimOp>(getLoc(), getDest(), dim);
}
return success();
}
@@ -1600,13 +1603,13 @@ public:
auto sourceType = ExtractSliceOp::inferRankReducedResultType(
insertSliceOp.getSourceType().getRank(), insertSliceOp.getType(),
mixedOffsets, mixedSizes, mixedStrides);
Value toInsert = insertSliceOp.source();
Value toInsert = insertSliceOp.getSource();
if (sourceType != insertSliceOp.getSourceType())
toInsert = rewriter.create<tensor::CastOp>(insertSliceOp.getLoc(),
sourceType, toInsert);
rewriter.replaceOpWithNewOp<InsertSliceOp>(
insertSliceOp, toInsert, insertSliceOp.dest(), mixedOffsets, mixedSizes,
mixedStrides);
insertSliceOp, toInsert, insertSliceOp.getDest(), mixedOffsets,
mixedSizes, mixedStrides);
return success();
}
};
@@ -1643,22 +1646,23 @@ struct InsertSliceOpCastFolder final : public OpRewritePattern<InsertSliceOp> {
auto castOp = v.getDefiningOp<tensor::CastOp>();
if (!castOp || !canFoldIntoConsumerOp(castOp))
return llvm::None;
return castOp.source();
return castOp.getSource();
};
Optional<Value> sourceCastSource =
getSourceOfCastOp(insertSliceOp.source());
Optional<Value> destCastSource = getSourceOfCastOp(insertSliceOp.dest());
getSourceOfCastOp(insertSliceOp.getSource());
Optional<Value> destCastSource = getSourceOfCastOp(insertSliceOp.getDest());
if (!sourceCastSource && !destCastSource)
return failure();
auto src = (sourceCastSource ? *sourceCastSource : insertSliceOp.source());
auto dst = (destCastSource ? *destCastSource : insertSliceOp.dest());
auto src =
(sourceCastSource ? *sourceCastSource : insertSliceOp.getSource());
auto dst = (destCastSource ? *destCastSource : insertSliceOp.getDest());
auto srcType = src.getType().cast<ShapedType>();
auto dstType = dst.getType().cast<ShapedType>();
if (verifyInsertSliceOp(srcType, dstType, insertSliceOp.static_offsets(),
insertSliceOp.static_sizes(),
insertSliceOp.static_strides()) !=
if (verifyInsertSliceOp(srcType, dstType, insertSliceOp.getStaticOffsets(),
insertSliceOp.getStaticSizes(),
insertSliceOp.getStaticStrides()) !=
SliceVerificationResult::Success)
return failure();
@@ -1724,9 +1728,9 @@ struct InsertSliceOpSourceCastInserter final
// 3) Cast-compatible with srcType.
// Insert the cast.
Value cast = rewriter.create<tensor::CastOp>(
insertSliceOp.getLoc(), newSrcType, insertSliceOp.source());
insertSliceOp.getLoc(), newSrcType, insertSliceOp.getSource());
rewriter.replaceOpWithNewOp<InsertSliceOp>(
insertSliceOp, cast, insertSliceOp.dest(),
insertSliceOp, cast, insertSliceOp.getDest(),
insertSliceOp.getMixedOffsets(), insertSliceOp.getMixedSizes(),
insertSliceOp.getMixedStrides());
return success();
@@ -1781,11 +1785,11 @@ ParseResult parseInferType(OpAsmParser &parser,
}
LogicalResult PadOp::verify() {
auto sourceType = source().getType().cast<RankedTensorType>();
auto resultType = result().getType().cast<RankedTensorType>();
auto expectedType =
PadOp::inferResultType(sourceType, extractFromI64ArrayAttr(static_low()),
extractFromI64ArrayAttr(static_high()));
auto sourceType = getSource().getType().cast<RankedTensorType>();
auto resultType = getResult().getType().cast<RankedTensorType>();
auto expectedType = PadOp::inferResultType(
sourceType, extractFromI64ArrayAttr(getStaticLow()),
extractFromI64ArrayAttr(getStaticHigh()));
for (int i = 0, e = sourceType.getRank(); i < e; ++i) {
if (resultType.getDimSize(i) == expectedType.getDimSize(i))
continue;
@@ -1801,7 +1805,7 @@ LogicalResult PadOp::verify() {
LogicalResult PadOp::verifyRegions() {
auto &region = getRegion();
unsigned rank = result().getType().cast<RankedTensorType>().getRank();
unsigned rank = getResult().getType().cast<RankedTensorType>().getRank();
Block &block = region.front();
if (block.getNumArguments() != rank)
return emitError("expected the block to have ") << rank << " arguments";
@@ -1815,7 +1819,7 @@ LogicalResult PadOp::verifyRegions() {
// Ensure that the region yields an element of the right type.
auto yieldOp = llvm::cast<YieldOp>(block.getTerminator());
if (yieldOp.value().getType() !=
if (yieldOp.getValue().getType() !=
getType().cast<ShapedType>().getElementType())
return emitOpError("expected yield type to match shape element type");
@@ -1919,10 +1923,11 @@ struct FoldStaticZeroPadding : public OpRewritePattern<PadOp> {
PatternRewriter &rewriter) const override {
if (!padTensorOp.hasZeroLowPad() || !padTensorOp.hasZeroHighPad())
return failure();
if (padTensorOp.nofold())
if (padTensorOp.getNofold())
return failure();
rewriter.replaceOpWithNewOp<tensor::CastOp>(
padTensorOp, padTensorOp.result().getType(), padTensorOp.source());
padTensorOp, padTensorOp.getResult().getType(),
padTensorOp.getSource());
return success();
}
};
@@ -1933,25 +1938,26 @@ struct FoldSourceTensorCast : public OpRewritePattern<PadOp> {
LogicalResult matchAndRewrite(PadOp padTensorOp,
PatternRewriter &rewriter) const override {
auto castOp = padTensorOp.source().getDefiningOp<tensor::CastOp>();
auto castOp = padTensorOp.getSource().getDefiningOp<tensor::CastOp>();
if (!tensor::canFoldIntoConsumerOp(castOp))
return failure();
auto newResultType = PadOp::inferResultType(
castOp.source().getType().cast<RankedTensorType>(),
extractFromI64ArrayAttr(padTensorOp.static_low()),
extractFromI64ArrayAttr(padTensorOp.static_high()),
castOp.getSource().getType().cast<RankedTensorType>(),
extractFromI64ArrayAttr(padTensorOp.getStaticLow()),
extractFromI64ArrayAttr(padTensorOp.getStaticHigh()),
padTensorOp.getResultType().getShape());
if (newResultType == padTensorOp.getResultType()) {
rewriter.updateRootInPlace(padTensorOp, [&]() {
padTensorOp.sourceMutable().assign(castOp.source());
padTensorOp.getSourceMutable().assign(castOp.getSource());
});
} else {
auto newOp = rewriter.create<PadOp>(
padTensorOp->getLoc(), newResultType, padTensorOp.source(),
padTensorOp.low(), padTensorOp.high(), padTensorOp.static_low(),
padTensorOp.static_high(), padTensorOp.nofold());
padTensorOp->getLoc(), newResultType, padTensorOp.getSource(),
padTensorOp.getLow(), padTensorOp.getHigh(),
padTensorOp.getStaticLow(), padTensorOp.getStaticHigh(),
padTensorOp.getNofold());
BlockAndValueMapping mapper;
padTensorOp.getRegion().cloneInto(&newOp.getRegion(), mapper);
@@ -1969,25 +1975,25 @@ struct FoldTargetTensorCast : public OpRewritePattern<PadOp> {
LogicalResult matchAndRewrite(PadOp padTensorOp,
PatternRewriter &rewriter) const override {
if (!padTensorOp.result().hasOneUse())
if (!padTensorOp.getResult().hasOneUse())
return failure();
auto tensorCastOp =
dyn_cast<tensor::CastOp>(*padTensorOp->getUsers().begin());
if (!tensorCastOp)
return failure();
if (!tensor::preservesStaticInformation(padTensorOp.result().getType(),
tensorCastOp.dest().getType()))
if (!tensor::preservesStaticInformation(padTensorOp.getResult().getType(),
tensorCastOp.getDest().getType()))
return failure();
auto replacementOp = rewriter.create<PadOp>(
padTensorOp.getLoc(), tensorCastOp.dest().getType(),
padTensorOp.source(), padTensorOp.low(), padTensorOp.high(),
padTensorOp.static_low(), padTensorOp.static_high(),
padTensorOp.nofold());
replacementOp.region().takeBody(padTensorOp.region());
padTensorOp.getLoc(), tensorCastOp.getDest().getType(),
padTensorOp.getSource(), padTensorOp.getLow(), padTensorOp.getHigh(),
padTensorOp.getStaticLow(), padTensorOp.getStaticHigh(),
padTensorOp.getNofold());
replacementOp.getRegion().takeBody(padTensorOp.getRegion());
rewriter.replaceOp(padTensorOp, replacementOp.result());
rewriter.replaceOp(tensorCastOp, replacementOp.result());
rewriter.replaceOp(padTensorOp, replacementOp.getResult());
rewriter.replaceOp(tensorCastOp, replacementOp.getResult());
return success();
}
};
@@ -2031,13 +2037,13 @@ struct FoldOrthogonalPaddings : public OpRewritePattern<PadOp> {
LogicalResult matchAndRewrite(PadOp padOp,
PatternRewriter &rewriter) const override {
auto innerSliceOp = padOp.source().getDefiningOp<ExtractSliceOp>();
auto innerSliceOp = padOp.getSource().getDefiningOp<ExtractSliceOp>();
if (!innerSliceOp)
return failure();
auto outerPadOp = innerSliceOp.source().getDefiningOp<PadOp>();
if (!outerPadOp || outerPadOp.nofold())
auto outerPadOp = innerSliceOp.getSource().getDefiningOp<PadOp>();
if (!outerPadOp || outerPadOp.getNofold())
return failure();
auto outerSliceOp = outerPadOp.source().getDefiningOp<ExtractSliceOp>();
auto outerSliceOp = outerPadOp.getSource().getDefiningOp<ExtractSliceOp>();
if (!outerSliceOp)
return failure();
@@ -2136,11 +2142,11 @@ struct FoldOrthogonalPaddings : public OpRewritePattern<PadOp> {
// Create a new tensor::ExtractSliceOp, tensor::PadOp pair that performs the
// two paddings in one step.
auto newSliceOp = rewriter.create<ExtractSliceOp>(
padOp.getLoc(), outerSliceOp.source(), newOffsets, newSizes,
padOp.getLoc(), outerSliceOp.getSource(), newOffsets, newSizes,
innerSliceOp.getMixedStrides());
auto newPadOp = rewriter.create<PadOp>(
padOp.getLoc(), padOp.getResultType(), newSliceOp.getResult(),
padOp.getMixedLowPad(), newHighPad, padOp.nofold());
padOp.getMixedLowPad(), newHighPad, padOp.getNofold());
rewriter.inlineRegionBefore(padOp.getRegion(), newPadOp.getRegion(),
newPadOp.getRegion().begin());
rewriter.replaceOp(padOp, newPadOp.getResult());
@@ -2169,7 +2175,7 @@ Value PadOp::getConstantPaddingValue() {
auto yieldOp = dyn_cast<YieldOp>(getRegion().front().getTerminator());
if (!yieldOp)
return {};
Value padValue = yieldOp.value();
Value padValue = yieldOp.getValue();
// Check if yield value is a constant.
if (matchPattern(padValue, m_Constant()))
return padValue;
@@ -2182,8 +2188,8 @@ Value PadOp::getConstantPaddingValue() {
OpFoldResult PadOp::fold(ArrayRef<Attribute>) {
if (getResultType().hasStaticShape() && getResultType() == getSourceType() &&
!nofold())
return source();
!getNofold())
return getSource();
return {};
}

2
mlir/lib/Dialect/Tensor/Transforms/SplitPadding.cpp
View File

@@ -75,7 +75,7 @@ struct SplitPadding final : public OpRewritePattern<tensor::PadOp> {
// Build the scf.if op itself. For the "then" branch, we can elide the
// padding. For the "else" branch, we retain the clone op.
auto thenBuilder = [&padOp](OpBuilder &builder, Location loc) {
builder.create<scf::YieldOp>(loc, padOp.source());
builder.create<scf::YieldOp>(loc, padOp.getSource());
};
auto elseBuilder = [&padOp](OpBuilder &builder, Location loc) {
Operation *newOp = builder.clone(*padOp);