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[mlir][sparse] cleanup small vector constant hints

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Following advise from

https://llvm.org/docs/ProgrammersManual.html#llvm-adt-smallvector-h

This revision removes the size hints from SmallVector (unless we are
certain of the resulting number of elements). Also, this replaces
SmallVector references with SmallVectorImpl references.

Reviewed By: Peiming

Differential Revision: https://reviews.llvm.org/D138063
This commit is contained in:
Aart Bik
2022-11-15 14:15:35 -08:00
parent dbd31935ed
commit 0e1708ff64
10 changed files with 110 additions and 112 deletions
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28
mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorCodegen.cpp
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@@ -324,7 +324,7 @@ static void createAllocFields(OpBuilder &builder, Location loc, Type type,
unsigned rank = shape.size();
Value heuristic = constantIndex(builder, loc, 16);
// Build original sizes.
SmallVector<Value, 8> sizes;
SmallVector<Value> sizes;
for (unsigned r = 0, o = 0; r < rank; r++) {
if (ShapedType::isDynamic(shape[r]))
sizes.push_back(dynSizes[o++]);
@@ -403,7 +403,7 @@ static Value genCompressed(OpBuilder &builder, Location loc,
SmallVectorImpl<Value> &indices, Value value,
Value pos, unsigned field, unsigned d) {
unsigned rank = rtp.getShape().size();
SmallVector<Type, 4> types;
SmallVector<Type> types;
Type indexType = builder.getIndexType();
Type boolType = builder.getIntegerType(1);
Value one = constantIndex(builder, loc, 1);
@@ -543,7 +543,7 @@ static void genEndInsert(OpBuilder &builder, Location loc, RankedTensorType rtp,
Value hi = genLoad(builder, loc, fields[memSizesIdx], mz);
Value zero = constantIndex(builder, loc, 0);
Value one = constantIndex(builder, loc, 1);
SmallVector<Value, 1> inits;
SmallVector<Value, 1> inits; // only one
inits.push_back(genLoad(builder, loc, fields[field], zero));
scf::ForOp loop = createFor(builder, loc, hi, inits, one);
Value i = loop.getInductionVar();
@@ -584,7 +584,7 @@ public:
LogicalResult
matchAndRewrite(func::ReturnOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
SmallVector<Value, 8> flattened;
SmallVector<Value> flattened;
flattenOperands(adaptor.getOperands(), flattened);
// Create a return with the flattened value extracted from sparse tensors.
rewriter.replaceOpWithNewOp<func::ReturnOp>(op, flattened);
@@ -606,23 +606,23 @@ public:
// ==>
// memref..., f, memref = call @foo(...) replace with
// cast(memref...)->sparse_tensor, f, cast(memref...)->sparse_tensor
SmallVector<Type, 8> finalRetTy;
SmallVector<Type> finalRetTy;
if (failed(typeConverter->convertTypes(op.getResultTypes(), finalRetTy)))
return failure();
// (1) Genereates new call with flattened return value.
SmallVector<Value, 8> flattened;
SmallVector<Value> flattened;
flattenOperands(adaptor.getOperands(), flattened);
auto newCall = rewriter.create<func::CallOp>(loc, op.getCallee(),
finalRetTy, flattened);
// (2) Create cast operation for sparse tensor returns.
SmallVector<Value, 4> castedRet;
SmallVector<Value> castedRet;
// Tracks the offset of current return value (of the orignal call)
// relative to the new call (after sparse tensor flattening);
unsigned retOffset = 0;
// Temporal buffer to hold the flattened list of type for
// a sparse tensor.
SmallVector<Type, 8> sparseFlat;
SmallVector<Type> sparseFlat;
for (auto ret : op.getResults()) {
assert(retOffset < newCall.getNumResults());
auto retType = ret.getType();
@@ -713,7 +713,7 @@ public:
// Construct allocation for each field.
Location loc = op.getLoc();
SmallVector<Value, 8> fields;
SmallVector<Value> fields;
createAllocFields(rewriter, loc, resType, adaptor.getOperands(),
enableBufferInitialization, fields);
// Replace operation with resulting memrefs.
@@ -760,7 +760,7 @@ public:
op.getTensor().getType().cast<RankedTensorType>();
auto tuple = getTuple(adaptor.getTensor());
// Prepare fields.
SmallVector<Value, 8> fields(tuple.getInputs());
SmallVector<Value> fields(tuple.getInputs());
// Generate optional insertion finalization code.
if (op.getHasInserts())
genEndInsert(rewriter, op.getLoc(), srcType, fields);
@@ -839,8 +839,8 @@ public:
Value added = adaptor.getAdded();
Value count = adaptor.getCount();
// Prepare fields and indices.
SmallVector<Value, 8> fields(tuple.getInputs());
SmallVector<Value, 8> indices(adaptor.getIndices());
SmallVector<Value> fields(tuple.getInputs());
SmallVector<Value> indices(adaptor.getIndices());
// If the innermost dimension is ordered, we need to sort the indices
// in the "added" array prior to applying the compression.
unsigned rank = dstType.getShape().size();
@@ -897,8 +897,8 @@ public:
op.getTensor().getType().cast<RankedTensorType>();
auto tuple = getTuple(adaptor.getTensor());
// Prepare fields and indices.
SmallVector<Value, 8> fields(tuple.getInputs());
SmallVector<Value, 8> indices(adaptor.getIndices());
SmallVector<Value> fields(tuple.getInputs());
SmallVector<Value> indices(adaptor.getIndices());
// Generate insertion.
Value value = adaptor.getValue();
genInsert(rewriter, op->getLoc(), dstType, fields, indices, value);