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#include "Utils/CodegenUtils.h"
#include "Utils/SparseTensorIterator.h"
#include "mlir/Dialect/SCF/IR/SCF.h"
#include "mlir/Dialect/SparseTensor/IR/SparseTensor.h"
#include "mlir/Dialect/SparseTensor/Transforms/Passes.h"
#include "mlir/Transforms/OneToNTypeConversion.h"
using namespace mlir;
using namespace mlir::sparse_tensor;
void convertLevelType(SparseTensorEncodingAttr enc, Level lvl,
SmallVectorImpl<Type> &fields) {
// Position and coordinate buffer in the sparse structure.
if (enc.getLvlType(lvl).isWithPosLT())
fields.push_back(enc.getPosMemRefType());
if (enc.getLvlType(lvl).isWithCrdLT())
fields.push_back(enc.getCrdMemRefType());
// One index for shape bound (result from lvlOp).
fields.push_back(IndexType::get(enc.getContext()));
}
static std::optional<LogicalResult>
convertIterSpaceType(IterSpaceType itSp, SmallVectorImpl<Type> &fields) {
auto idxTp = IndexType::get(itSp.getContext());
for (Level l = itSp.getLoLvl(); l < itSp.getHiLvl(); l++)
convertLevelType(itSp.getEncoding(), l, fields);
// Two indices for lower and upper bound (we only need one pair for the last
// iteration space).
fields.append({idxTp, idxTp});
return success();
}
static std::optional<LogicalResult>
convertIteratorType(IteratorType itTp, SmallVectorImpl<Type> &fields) {
// The actually Iterator Values (that are updated every iteration).
auto idxTp = IndexType::get(itTp.getContext());
// TODO: handle batch dimension.
assert(itTp.getEncoding().getBatchLvlRank() == 0);
if (!itTp.isUnique()) {
// Segment high for non-unique iterator.
fields.push_back(idxTp);
}
fields.push_back(idxTp);
return success();
}
namespace {
/// Sparse codegen rule for number of entries operator.
class ExtractIterSpaceConverter
: public OneToNOpConversionPattern<ExtractIterSpaceOp> {
public:
using OneToNOpConversionPattern::OneToNOpConversionPattern;
LogicalResult
matchAndRewrite(ExtractIterSpaceOp op, OpAdaptor adaptor,
OneToNPatternRewriter &rewriter) const override {
Location loc = op.getLoc();
const OneToNTypeMapping &resultMapping = adaptor.getResultMapping();
// Construct the iteration space.
SparseIterationSpace space(loc, rewriter, op.getTensor(), 0,
op.getLvlRange(), adaptor.getParentIter());
SmallVector<Value> result = space.toValues();
rewriter.replaceOp(op, result, resultMapping);
return success();
}
};
class SparseIterateOpConverter : public OneToNOpConversionPattern<IterateOp> {
public:
using OneToNOpConversionPattern::OneToNOpConversionPattern;
LogicalResult
matchAndRewrite(IterateOp op, OpAdaptor adaptor,
OneToNPatternRewriter &rewriter) const override {
if (!op.getCrdUsedLvls().empty())
return rewriter.notifyMatchFailure(
op, "non-empty coordinates list not implemented.");
Location loc = op.getLoc();
auto iterSpace = SparseIterationSpace::fromValues(
op.getIterSpace().getType(), adaptor.getIterSpace(), 0);
std::unique_ptr<SparseIterator> it =
iterSpace.extractIterator(rewriter, loc);
if (it->iteratableByFor()) {
auto [lo, hi] = it->genForCond(rewriter, loc);
Value step = constantIndex(rewriter, loc, 1);
SmallVector<Value> ivs;
for (ValueRange inits : adaptor.getInitArgs())
llvm::append_range(ivs, inits);
scf::ForOp forOp = rewriter.create<scf::ForOp>(loc, lo, hi, step, ivs);
Block *loopBody = op.getBody();
OneToNTypeMapping bodyTypeMapping(loopBody->getArgumentTypes());
if (failed(typeConverter->convertSignatureArgs(
loopBody->getArgumentTypes(), bodyTypeMapping)))
return failure();
rewriter.applySignatureConversion(loopBody, bodyTypeMapping);
rewriter.eraseBlock(forOp.getBody());
Region &dstRegion = forOp.getRegion();
rewriter.inlineRegionBefore(op.getRegion(), dstRegion, dstRegion.end());
auto yieldOp =
llvm::cast<sparse_tensor::YieldOp>(forOp.getBody()->getTerminator());
rewriter.setInsertionPointToEnd(forOp.getBody());
// replace sparse_tensor.yield with scf.yield.
rewriter.create<scf::YieldOp>(loc, yieldOp.getResults());
rewriter.eraseOp(yieldOp);
const OneToNTypeMapping &resultMapping = adaptor.getResultMapping();
rewriter.replaceOp(op, forOp.getResults(), resultMapping);
} else {
SmallVector<Value> ivs;
llvm::append_range(ivs, it->getCursor());
for (ValueRange inits : adaptor.getInitArgs())
llvm::append_range(ivs, inits);
assert(llvm::all_of(ivs, [](Value v) { return v != nullptr; }));
TypeRange types = ValueRange(ivs).getTypes();
auto whileOp = rewriter.create<scf::WhileOp>(loc, types, ivs);
SmallVector<Location> l(types.size(), op.getIterator().getLoc());
// Generates loop conditions.
Block *before = rewriter.createBlock(&whileOp.getBefore(), {}, types, l);
rewriter.setInsertionPointToStart(before);
ValueRange bArgs = before->getArguments();
auto [whileCond, remArgs] = it->genWhileCond(rewriter, loc, bArgs);
assert(remArgs.size() == adaptor.getInitArgs().size());
rewriter.create<scf::ConditionOp>(loc, whileCond, before->getArguments());
// Generates loop body.
Block *loopBody = op.getBody();
OneToNTypeMapping bodyTypeMapping(loopBody->getArgumentTypes());
if (failed(typeConverter->convertSignatureArgs(
loopBody->getArgumentTypes(), bodyTypeMapping)))
return failure();
rewriter.applySignatureConversion(loopBody, bodyTypeMapping);
Region &dstRegion = whileOp.getAfter();
// TODO: handle uses of coordinate!
rewriter.inlineRegionBefore(op.getRegion(), dstRegion, dstRegion.end());
ValueRange aArgs = whileOp.getAfterArguments();
auto yieldOp = llvm::cast<sparse_tensor::YieldOp>(
whileOp.getAfterBody()->getTerminator());
rewriter.setInsertionPointToEnd(whileOp.getAfterBody());
aArgs = it->linkNewScope(aArgs);
ValueRange nx = it->forward(rewriter, loc);
SmallVector<Value> yields;
llvm::append_range(yields, nx);
llvm::append_range(yields, yieldOp.getResults());
// replace sparse_tensor.yield with scf.yield.
rewriter.eraseOp(yieldOp);
rewriter.create<scf::YieldOp>(loc, yields);
const OneToNTypeMapping &resultMapping = adaptor.getResultMapping();
rewriter.replaceOp(
op, whileOp.getResults().drop_front(it->getCursor().size()),
resultMapping);
}
return success();
}
};
} // namespace
mlir::SparseIterationTypeConverter::SparseIterationTypeConverter() {
addConversion([](Type type) { return type; });
addConversion(convertIteratorType);
addConversion(convertIterSpaceType);
addSourceMaterialization([](OpBuilder &builder, IterSpaceType spTp,
ValueRange inputs,
Location loc) -> std::optional<Value> {
return builder
.create<UnrealizedConversionCastOp>(loc, TypeRange(spTp), inputs)
.getResult(0);
});
}
void mlir::populateLowerSparseIterationToSCFPatterns(
TypeConverter &converter, RewritePatternSet &patterns) {
IterateOp::getCanonicalizationPatterns(patterns, patterns.getContext());
patterns.add<ExtractIterSpaceConverter, SparseIterateOpConverter>(
converter, patterns.getContext());
}
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