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llvm-project/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorRewriting.cpp
Aart Bik 22212ca745
[mlir][sparse] simplify some header code (#70989) 
This is a first revision in a small series of changes that removes
duplications between direct encoding methods and sparse tensor type
wrapper methods (in favor of the latter abstraction, since it provides
more safety). The goal is to simply end up with "just" SparseTensorType
2023-11-02 09:31:11 -07:00

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//===- SparseTensorRewriting.cpp - Sparse tensor rewriting rules ----------===//
//
// 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 rewriting rules that are specific to sparse tensors.
//
//===----------------------------------------------------------------------===//
#include "CodegenUtils.h"
#include "LoopEmitter.h"
#include "mlir/Dialect/Affine/IR/AffineOps.h"
#include "mlir/Dialect/Arith/IR/Arith.h"
#include "mlir/Dialect/Bufferization/IR/Bufferization.h"
#include "mlir/Dialect/Linalg/IR/Linalg.h"
#include "mlir/Dialect/Linalg/Utils/Utils.h"
#include "mlir/Dialect/MemRef/IR/MemRef.h"
#include "mlir/Dialect/SCF/IR/SCF.h"
#include "mlir/Dialect/SparseTensor/IR/SparseTensor.h"
#include "mlir/Dialect/SparseTensor/IR/SparseTensorType.h"
#include "mlir/Dialect/SparseTensor/Transforms/Passes.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "mlir/IR/AffineMap.h"
#include "mlir/IR/Matchers.h"
#include "mlir/Support/LLVM.h"
using namespace mlir;
using namespace mlir::bufferization;
using namespace mlir::linalg;
using namespace mlir::sparse_tensor;
//===---------------------------------------------------------------------===//
// Helper methods for the actual rewriting rules.
//===---------------------------------------------------------------------===//
// Helper method to match any typed zero.
static bool isZeroValue(Value val) {
return matchPattern(val, m_Zero()) || matchPattern(val, m_AnyZeroFloat());
}
// Helper to detect a sparse tensor type operand.
static bool isSparseTensor(Value v) {
auto enc = getSparseTensorEncoding(v.getType());
return enc && !llvm::all_of(enc.getLvlTypes(), [](auto dlt) {
return dlt == DimLevelType::Dense;
});
}
static bool isSparseTensor(OpOperand *op) { return isSparseTensor(op->get()); }
// Helper method to find zero/uninitialized tensor materialization.
static bool isMaterializing(OpOperand *op, bool isZero) {
Value val = op->get();
// Check allocation, with zero alloc when required.
if (auto alloc = val.getDefiningOp<AllocTensorOp>()) {
Value copy = alloc.getCopy();
if (isZero)
return copy && isZeroValue(copy);
return !copy;
}
// Check for empty tensor materialization.
if (auto empty = val.getDefiningOp<tensor::EmptyOp>())
return !isZero;
// Last resort for zero alloc: the whole value is zero.
return isZero && isZeroValue(val);
}
// Helper to detect sampling operation.
static bool isSampling(GenericOp op) {
auto yieldOp = cast<linalg::YieldOp>(op.getRegion().front().getTerminator());
if (auto *def = yieldOp.getOperand(0).getDefiningOp()) {
if (isa<arith::MulFOp>(def) || isa<arith::MulIOp>(def)) {
// Both scalar input arguments used exactly once.
Value s1 = op.getBlock()->getArgument(0);
Value s2 = op.getBlock()->getArgument(1);
return (def->getOperand(0) == s1 && def->getOperand(1) == s2) ||
(def->getOperand(1) == s1 && def->getOperand(0) == s2);
}
}
return false;
}
// Helper to detect chain of multiplications that do not involve x.
static bool isMulChain(Value val, Value x) {
if (auto arg = dyn_cast<BlockArgument>(val))
return arg != x;
if (auto *def = val.getDefiningOp()) {
if (isa<arith::MulFOp>(def) || isa<arith::MulIOp>(def))
return isMulChain(def->getOperand(0), x) &&
isMulChain(def->getOperand(1), x);
}
return false;
}
// Helper to detect x = x + <multiplications>.
static bool isSumOfMul(GenericOp op) {
auto yieldOp = cast<linalg::YieldOp>(op.getRegion().front().getTerminator());
if (auto *def = yieldOp.getOperand(0).getDefiningOp()) {
if (isa<arith::AddFOp>(def) || isa<arith::AddIOp>(def)) {
Value x = op.getBlock()->getArguments().back();
return (def->getOperand(0) == x && isMulChain(def->getOperand(1), x)) ||
(def->getOperand(1) == x && isMulChain(def->getOperand(0), x));
}
}
return false;
}
// Helper to detect direct yield of a zero value.
static bool isZeroYield(GenericOp op) {
auto yieldOp = cast<linalg::YieldOp>(op.getRegion().front().getTerminator());
if (auto arg = dyn_cast<BlockArgument>(yieldOp.getOperand(0))) {
if (arg.getOwner()->getParentOp() == op) {
return isZeroValue(op->getOperand(arg.getArgNumber()));
}
}
return isZeroValue(yieldOp.getOperand(0));
}
/// Populates given sizes array from type (for static sizes) and from
/// the tensor (for dynamic sizes).
static void sizesForTensor(OpBuilder &builder, SmallVectorImpl<Value> &sizes,
Location loc, ShapedType stp, Value tensor) {
for (const auto &d : enumerate(stp.getShape())) {
Value dim;
if (d.value() == ShapedType::kDynamic)
dim = builder.create<tensor::DimOp>(loc, tensor, d.index());
else
dim = constantIndex(builder, loc, d.value());
sizes.push_back(dim);
}
}
// TODO: The dim level property of the COO type relies on input tensors, the
// shape relies on the output tensor
static RankedTensorType getCOOType(const SparseTensorType &stt, bool ordered) {
return getCOOFromTypeWithOrdering(stt, stt.getDimToLvl(), ordered);
}
static RankedTensorType getBufferType(const SparseTensorType &stt,
bool needTmpCOO) {
return needTmpCOO ? getCOOType(stt, /*ordered=*/false)
: stt.getRankedTensorType();
}
/// Collects the dynamic dimension sizes for `tp` with the assumption that
/// `sizes` are the dimension sizes for the type. Stores the dynamic dimension
/// sizes to dynSizes.
static void getDynamicSizes(RankedTensorType tp, ValueRange sizes,
SmallVectorImpl<Value> &dynSizes) {
for (const auto &d : enumerate(tp.getShape())) {
if (d.value() == ShapedType::kDynamic)
dynSizes.push_back(sizes[d.index()]);
}
}
static LogicalResult genForeachOnSparseConstant(ForeachOp op,
RewriterBase &rewriter,
SparseElementsAttr attr) {
auto loc = op.getLoc();
SmallVector<Value> reduc = op.getInitArgs();
// Foreach on constant.
foreachInSparseConstant(
rewriter, loc, attr, op.getOrder().value_or(AffineMap()),
[&reduc, &rewriter, op](ArrayRef<Value> cvs, Value v) mutable {
SmallVector<Value> args;
args.append(cvs.begin(), cvs.end());
args.push_back(v);
args.append(reduc);
// Clones the foreach op to get a copy of the loop body.
auto cloned = cast<ForeachOp>(rewriter.clone(*op.getOperation()));
assert(args.size() == cloned.getBody()->getNumArguments());
Operation *yield = cloned.getBody()->getTerminator();
rewriter.inlineBlockBefore(cloned.getBody(), op, args);
// clean up
rewriter.eraseOp(cloned);
reduc = yield->getOperands();
rewriter.eraseOp(yield);
});
rewriter.replaceOp(op, reduc);
return success();
}
/// Populates the given sizes array for concatenation from types (for static
/// sizes) and from the source tensors (for dynamic sizes).
static void concatSizesFromInputs(OpBuilder &builder,
SmallVectorImpl<Value> &sizes, Location loc,
ShapedType dstTp, ValueRange srcs,
unsigned dim) {
auto dstShape = dstTp.getShape();
sizesFromSrc(builder, sizes, loc, srcs[0]);
// Sum up on the `dim` if the dimension is dynamic.
if (dstShape[dim] != ShapedType::kDynamic) {
// Faithfully take the static size.
sizes[dim] = constantIndex(builder, loc, dstShape[dim]);
} else {
// Else, compute the shape dynamically.
for (const auto &src : srcs.drop_front()) {
Value srcSz = linalg::createOrFoldDimOp(builder, loc, src, dim);
// Sum up all the sizes.
sizes[dim] = builder.create<arith::AddIOp>(loc, sizes[dim], srcSz);
}
}
}
//===---------------------------------------------------------------------===//
// The actual sparse tensor rewriting rules.
//===---------------------------------------------------------------------===//
namespace {
/// Rewriting rule that converts direct yield of zero with initial allocation.
struct FoldInvariantYield : public OpRewritePattern<GenericOp> {
public:
using OpRewritePattern<GenericOp>::OpRewritePattern;
LogicalResult matchAndRewrite(GenericOp op,
PatternRewriter &rewriter) const override {
if (!op.hasTensorSemantics() || op.getNumResults() != 1 ||
!isMaterializing(op.getDpsInitOperand(0), /*isZero=*/false) ||
!isZeroYield(op) || !op.getDpsInitOperand(0)->get().hasOneUse())
return failure();
auto outputType = getRankedTensorType(op.getResult(0));
// Yielding zero on newly materialized sparse tensor can be
// optimized directly (regardless of dynamic or static size).
if (getSparseTensorEncoding(outputType)) {
rewriter.replaceOp(op, op.getDpsInitOperand(0)->get());
return success();
}
// Use static zero value directly instead of materialization.
if (!outputType.hasStaticShape())
return failure();
Operation *def = op.getDpsInitOperand(0)->get().getDefiningOp();
rewriter.replaceOp(op, constantZero(rewriter, op.getLoc(), outputType));
rewriter.eraseOp(def);
return success();
}
};
/// Rewriting rule that converts two kernels:
///
/// T(i,j) = SUM(k, A(i,j,k) * B(i,j,k) * ... )
/// X(i,j) = S(i,j) * T(i,j)
///
/// into a single kernel, using distributive law:
///
/// X(i,j) = SUM(k, S(i,j) * A(i,j,k) * B(i,j,k) * ... )
///
/// This kind of fusion (merging two ops into one but using arithmetic
/// equalities that may not hold for floating-point computations) would
/// be undesirable in the dense case, since we distribute the multiplication
/// into the reduction loop. However, for sparse sampling tensor S, such
/// a fusion may actually reduce the asymptotic complexity of the kernel,
/// since intermediate results may be nullified.
struct FuseSparseMultiplyOverAdd : public OpRewritePattern<GenericOp> {
public:
using OpRewritePattern<GenericOp>::OpRewritePattern;
LogicalResult matchAndRewrite(GenericOp op,
PatternRewriter &rewriter) const override {
// Check consumer.
if (!op.hasTensorSemantics() || op.getNumDpsInputs() != 2 ||
op.getNumResults() != 1 ||
op.getNumParallelLoops() != op.getNumLoops() ||
!op.getMatchingIndexingMap(op.getDpsInitOperand(0)).isIdentity() ||
!op.getMatchingIndexingMap(op.getDpsInputOperand(0)).isIdentity() ||
!op.getMatchingIndexingMap(op.getDpsInputOperand(1)).isIdentity())
return failure();
// Find consuming OP2(sparse, other) or OP2(other, sparse). The other
// operand can be sparse or dense, since the point of this rewriting rule
// is detecting a situation in which *more* sparsity is introduced into
// a computation, be it already sparse or still dense.
unsigned other = 0;
if (isSparseTensor(op.getDpsInputOperand(0)))
other = 1;
else if (!isSparseTensor(op.getDpsInputOperand(1)))
return failure();
// Check producer.
auto prod = dyn_cast_or_null<GenericOp>(
op.getDpsInputOperand(other)->get().getDefiningOp());
if (!prod || !prod.hasTensorSemantics() || prod.getNumResults() != 1 ||
!prod.getResult(0).hasOneUse())
return failure();
// Sampling consumer and sum of multiplication chain producer.
if (!isMaterializing(op.getDpsInitOperand(0), /*isZero=*/false) ||
!isMaterializing(prod.getDpsInitOperand(0), /*isZero=*/true) ||
!isSampling(op) || !isSumOfMul(prod))
return failure();
// Modify operand structure of producer and consumer.
Location loc = prod.getLoc();
SmallVector<Value> inputOps = prod.getInputs();
SmallVector<Value> outputOps = op.getOutputs();
SmallVector<AffineMap> fusedIndexMaps = prod.getIndexingMapsArray();
inputOps.push_back(op.getDpsInputOperand(1 - other)->get());
fusedIndexMaps.push_back(fusedIndexMaps.back()); // mimic other
// Fuse producer and consumer into a new generic op.
auto fusedOp = rewriter.create<GenericOp>(
loc, op.getResult(0).getType(), inputOps, outputOps,
rewriter.getAffineMapArrayAttr(fusedIndexMaps), prod.getIteratorTypes(),
/*doc=*/nullptr, /*library_call=*/nullptr);
Block &prodBlock = prod.getRegion().front();
Block &consBlock = op.getRegion().front();
IRMapping mapper;
Block *fusedBlock = new Block();
fusedOp.getRegion().push_back(fusedBlock);
unsigned num = prodBlock.getNumArguments();
for (unsigned i = 0; i < num - 1; i++)
addArg(mapper, fusedBlock, prodBlock.getArgument(i));
addArg(mapper, fusedBlock, consBlock.getArgument(1 - other));
addArg(mapper, fusedBlock, prodBlock.getArgument(num - 1));
// Clone bodies of the producer and consumer in new evaluation order.
auto *acc = prodBlock.getTerminator()->getOperand(0).getDefiningOp();
auto *sampler = consBlock.getTerminator()->getOperand(0).getDefiningOp();
rewriter.setInsertionPointToStart(fusedBlock);
Value last;
for (auto &op : prodBlock.without_terminator())
if (&op != acc) {
last = op.getResult(0);
rewriter.clone(op, mapper);
}
mapper.map(consBlock.getArgument(other), fusedBlock->back().getResult(0));
mapper.map(last, rewriter.clone(*sampler, mapper)->getResult(0));
last = rewriter.clone(*acc, mapper)->getResult(0);
rewriter.create<linalg::YieldOp>(loc, last);
// Force initial value on merged allocation for dense outputs.
// TODO: deal with non alloc tensor here one day
if (!getSparseTensorEncoding(op.getResult(0).getType())) {
Value init = prod.getDpsInitOperand(0)
->get()
.getDefiningOp<AllocTensorOp>()
.getCopy();
AllocTensorOp a =
op.getDpsInitOperand(0)->get().getDefiningOp<AllocTensorOp>();
rewriter.updateRootInPlace(a, [&]() { a.getCopyMutable().assign(init); });
}
// Replace consumer with fused operation. Old producer
// and consumer ops will be removed by DCE.
rewriter.replaceOp(op, fusedOp->getResults());
return success();
}
private:
// Helper to add argument and record the mapping.
static void addArg(IRMapping &mapper, Block *b, BlockArgument a) {
mapper.map(a, b->addArgument(a.getType(), a.getLoc()));
}
};
// Fuse a tensor cast into producing operation. Note that a tensor.cast
// should really not be used to convert between sparse encodings. Since
// the pattern currently appears as a result of some prior rewriting
// we make an attempt to repair very obvious cases.
// TODO: audit the pure tensor dialect rewriting rules
struct FuseTensorCast : public OpRewritePattern<tensor::CastOp> {
public:
using OpRewritePattern<tensor::CastOp>::OpRewritePattern;
LogicalResult matchAndRewrite(tensor::CastOp op,
PatternRewriter &rewriter) const override {
Type srcType = op.getSource().getType();
Type dstType = op.getDest().getType();
// A nop cast simply folds away.
if (srcType == dstType) {
rewriter.replaceOp(op, op->getResults());
return success();
}
// See if a sparsity changing cast can be fused into producer.
if (tensor::isSameTypeWithoutEncoding(srcType, dstType)) {
if (Operation *def = op.getSource().getDefiningOp()) {
if (def->hasOneUse() && isa<tensor::ExtractSliceOp>(def)) {
rewriter.updateRootInPlace(def, [&]() {
def->getResult(0).setType(op->getResultTypes()[0]);
});
rewriter.replaceOp(op, def->getResult(0));
return success();
}
}
}
// Repair tensor casts with at least one sparse operand into the
// the properly supported sparse_tensor.convert.
if (getSparseTensorEncoding(srcType) || getSparseTensorEncoding(dstType)) {
rewriter.replaceOpWithNewOp<ConvertOp>(op, dstType, op.getSource());
return success();
}
// Fail otherwise.
return failure();
}
};
/// Rewrites a sequence of operations for sparse tensor selections in to
/// semi-ring operations such that they can be compiled correctly by the sparse
/// compiler. E.g., transforming the following sequence
///
/// %sel = arith.select %cond, %sp1, %sp2
///
/// to
///
/// %sel = binary %sp1, %sp2:
/// both (%l, %r) {yield select %cond, %l, %r}
/// left (%l) {yield select %cond, %l, 0}
/// right (%r) {yield select %cond, 0, %r}
///
/// TODO: We require that the tensor used for extracting conditions to be dense
/// to sparsify the code. To support a sparse condition tensor, we need a
/// tri-nary operation.
struct GenSemiRingSelect : public OpRewritePattern<GenericOp> {
public:
using OpRewritePattern<GenericOp>::OpRewritePattern;
LogicalResult matchAndRewrite(GenericOp op,
PatternRewriter &rewriter) const override {
// Rejects non sparse kernels.
if (!op.hasTensorSemantics() || !hasAnySparseOperand(op))
return failure();
Location loc = op.getLoc();
SmallVector<std::pair<Operation *, sparse_tensor::BinaryOp>> semiRings;
for (Operation &inst : *op.getBody()) {
// Matches pattern.
auto matched = isRewritablePattern(op, &inst);
if (!matched.has_value())
continue;
rewriter.setInsertionPoint(&inst);
auto [c, t, f] = matched.value();
assert(t.getType() == f.getType());
auto selTp = t.getType();
auto c0 = constantZero(rewriter, loc, selTp);
auto binOp = rewriter.create<sparse_tensor::BinaryOp>(loc, selTp, t, f);
// Initializes all the blocks.
rewriter.createBlock(&binOp.getOverlapRegion(), {}, {selTp, selTp},
{t.getLoc(), f.getLoc()});
rewriter.createBlock(&binOp.getRightRegion(), {}, selTp, f.getLoc());
rewriter.createBlock(&binOp.getLeftRegion(), {}, selTp, t.getLoc());
for (auto *r : binOp.getRegions()) {
Block *b = &r->front();
rewriter.setInsertionPointToStart(b);
IRMapping irMap;
// Clones the cmp operations into the region to make the binary op
// admissible.
Value newC = c;
if (auto *def = c.getDefiningOp())
newC = rewriter.clone(*def, irMap)->getResult(0);
irMap.map(c, newC);
if (r == &binOp.getLeftRegion()) {
irMap.map(t, b->getArgument(0));
irMap.map(f, c0);
} else if (r == &binOp.getRightRegion()) {
irMap.map(t, c0);
irMap.map(f, b->getArgument(0));
} else {
irMap.map(t, b->getArgument(0));
irMap.map(f, b->getArgument(1));
}
auto y = rewriter.clone(inst, irMap)->getResult(0);
rewriter.create<sparse_tensor::YieldOp>(loc, y);
}
// We successfully rewrited a operation. We can not do replacement here
// becuase it invalidate the iterator for the current loop to traverse
// the instructions.
semiRings.emplace_back(&inst, binOp);
}
// Finalizes the replacement.
for (auto [sel, semi] : semiRings)
rewriter.replaceOp(sel, semi->getResults());
return success(!semiRings.empty());
}
private:
static std::optional<std::tuple<Value, BlockArgument, BlockArgument>>
isRewritablePattern(GenericOp op, Operation *v) {
auto sel = dyn_cast<arith::SelectOp>(v);
if (!sel)
return std::nullopt;
auto tVal = sel.getTrueValue().dyn_cast<BlockArgument>();
auto fVal = sel.getFalseValue().dyn_cast<BlockArgument>();
// TODO: For simplicity, we only handle cases where both true/false value
// are directly loaded the input tensor. We can probably admit more cases
// in theory.
if (!tVal || !fVal)
return std::nullopt;
// Helper lambda to determine whether the value is loaded from a dense input
// or is a loop invariant.
auto isValFromDenseInputOrInvariant = [&op](Value v) -> bool {
if (auto bArg = v.dyn_cast<BlockArgument>();
bArg && !isSparseTensor(op.getDpsInputOperand(bArg.getArgNumber())))
return true;
// If the value is defined outside the loop, it is a loop invariant.
return v.getDefiningOp() && v.getDefiningOp()->getBlock() != op.getBody();
};
// If the condition value is load directly from a dense tensor or
// loop-invariants, we can sparsify the kernel.
auto cond = sel.getCondition();
if (isValFromDenseInputOrInvariant(cond))
return std::make_tuple(cond, tVal, fVal);
Value cmpL, cmpR;
if (matchPattern(cond, m_Op<arith::CmpIOp>(matchers::m_Any(&cmpL),
matchers::m_Any(&cmpR))) ||
matchPattern(cond, m_Op<arith::CmpFOp>(matchers::m_Any(&cmpL),
matchers::m_Any(&cmpR)))) {
// TODO: we can do it recursively to check whether all the leaf values are
// loaded from dense tensors or are loop invariants.
if (isValFromDenseInputOrInvariant(cmpL) ||
isValFromDenseInputOrInvariant(cmpR))
return std::make_tuple(cond, tVal, fVal);
}
return std::nullopt;
};
};
/// Rewrites a sparse reduction that would not sparsify directly since
/// doing so would only iterate over the stored elements, ignoring the
/// implicit zeros, into a semi-ring. Applies to all prod/and/min/max
/// (note that reductions like add/sub/or/xor can directly be sparsified
/// since the implicit zeros do not contribute to the final result).
/// Note that prod/and are still included since, even though they often
/// are nullified in sparse data, they may still occur for special
/// situations in which e.g. some rows in a sparse matrix are fully
/// dense. For min/max, including the implicit zeros is a much more
/// common situation.
///
/// TODO: this essentially "densifies" the operation; we want to implement
/// this much more efficiently by performing the reduction over the
/// stored values, and feed in the zero once if there were *any*
/// implicit zeros as well; but for now, at least we provide
/// the functionality
///
struct GenSemiRingReduction : public OpRewritePattern<GenericOp> {
public:
using OpRewritePattern<GenericOp>::OpRewritePattern;
LogicalResult matchAndRewrite(GenericOp op,
PatternRewriter &rewriter) const override {
// Reject non-reductions.
if (!op.hasTensorSemantics() || op.getNumDpsInputs() != 1 ||
op.getNumReductionLoops() == 0 || op.getNumResults() != 1)
return failure();
auto inp = op.getDpsInputOperand(0);
auto init = op.getDpsInitOperand(0);
if (!isSparseTensor(inp))
return failure();
// Look for direct x = x OP y for semi-ring ready reductions.
auto red = cast<linalg::YieldOp>(op.getRegion().front().getTerminator())
.getOperand(0)
.getDefiningOp();
if (!isa<arith::AndIOp, arith::MulIOp, arith::MulFOp, arith::MinimumFOp,
arith::MinSIOp, arith::MinUIOp, arith::MaximumFOp, arith::MaxSIOp,
arith::MaxUIOp>(red))
return failure();
Value s0 = op.getBlock()->getArgument(0);
Value s1 = op.getBlock()->getArgument(1);
if ((red->getOperand(0) != s0 || red->getOperand(1) != s1) &&
(red->getOperand(0) != s1 || red->getOperand(1) != s0))
return failure();
// Identity.
Location loc = op.getLoc();
Value identity =
rewriter.create<tensor::ExtractOp>(loc, init->get(), ValueRange());
// Unary {
// present -> value
// absent -> zero.
// }
Type rtp = s0.getType();
rewriter.setInsertionPointToStart(&op.getRegion().front());
auto semiring = rewriter.create<sparse_tensor::UnaryOp>(loc, rtp, s0);
Block *present =
rewriter.createBlock(&semiring.getPresentRegion(), {}, rtp, loc);
rewriter.setInsertionPointToStart(&semiring.getPresentRegion().front());
rewriter.create<sparse_tensor::YieldOp>(loc, present->getArgument(0));
rewriter.createBlock(&semiring.getAbsentRegion(), {}, {}, {});
rewriter.setInsertionPointToStart(&semiring.getAbsentRegion().front());
auto zero =
rewriter.create<arith::ConstantOp>(loc, rewriter.getZeroAttr(rtp));
rewriter.create<sparse_tensor::YieldOp>(loc, zero);
rewriter.setInsertionPointAfter(semiring);
// CustomReduce {
// x = x REDUC y, identity
// }
auto custom = rewriter.create<sparse_tensor::ReduceOp>(
loc, rtp, semiring.getResult(), s1, identity);
Block *region =
rewriter.createBlock(&custom.getRegion(), {}, {rtp, rtp}, {loc, loc});
rewriter.setInsertionPointToStart(&custom.getRegion().front());
IRMapping irMap;
irMap.map(red->getOperand(0), region->getArgument(0));
irMap.map(red->getOperand(1), region->getArgument(1));
auto cloned = rewriter.clone(*red, irMap);
rewriter.create<sparse_tensor::YieldOp>(loc, cloned->getResult(0));
rewriter.setInsertionPointAfter(custom);
rewriter.replaceOp(red, custom.getResult());
return success();
}
};
/// Sparse rewriting rule for sparse-to-sparse reshape operator.
struct TensorReshapeRewriter : public OpRewritePattern<tensor::ReshapeOp> {
public:
using OpRewritePattern<tensor::ReshapeOp>::OpRewritePattern;
LogicalResult matchAndRewrite(tensor::ReshapeOp op,
PatternRewriter &rewriter) const override {
Location loc = op.getLoc();
Value srcTensor = op.getSource();
const auto srcTp = getSparseTensorType(srcTensor);
const auto dstTp = getSparseTensorType(op.getResult());
if (!srcTp.hasEncoding() || !dstTp.hasEncoding() ||
!dstTp.hasStaticDimShape())
return failure();
SmallVector<Value> srcSizes;
sizesForTensor(rewriter, srcSizes, loc, srcTp, srcTensor);
SmallVector<Value> dstSizes;
for (Dimension d : dstTp.getDimShape())
dstSizes.push_back(constantIndex(rewriter, loc, d));
Value nnz = rewriter.create<NumberOfEntriesOp>(loc, srcTensor);
// Only need an unordered COO buffer if input and output are not sorted
// in the same way.
Type bufferTp = getBufferType(
dstTp.withoutDimToLvl(),
!srcTp.isAllOrdered() || !srcTp.isIdentity() || !dstTp.isIdentity());
SmallVector<Value> dynSizes;
Value buffer = rewriter
.create<AllocTensorOp>(loc, bufferTp, dynSizes, Value(),
nnz, Attribute())
.getResult();
// Convert src coordinates to dst coordinates by first collapsing it to 1D
// and then expand it to the match the rank of the destination tensor.
// Implemented as follows:
// foreach srcCoords %srcTensor
// collapsedCoords = reshapeCvs(srcCoords, [1, ..., srcRank])
// expandedCoords = reshapeCvs(collapsedCoords, [1, ..., dstRank])
// insert expandedCoords, %buffer
//
// followed by an optional
// %t = sparse_tensor.cast %tmp
// depending on whether the input/output are sorted in the same way.
const auto encSrc = srcTp.getEncoding();
ForeachOp foreachOp = rewriter.create<ForeachOp>(
loc, srcTensor, buffer,
[&](OpBuilder &builder, Location loc, ValueRange srcLcvs, Value v,
ValueRange reduc) {
const Dimension srcRank = srcTp.getDimRank();
SmallVector<Value> srcDcvs;
srcDcvs.reserve(srcRank);
for (Dimension d = 0; d < srcRank; d++) {
// FIXME: `toStoredDim` is deprecated
Level lvl = toStoredDim(encSrc, d);
srcDcvs.push_back(srcLcvs[lvl]);
}
Value collapseSize = constantIndex(builder, loc, 1);
for (Dimension d = 0; d < srcRank; d++)
collapseSize =
builder.create<arith::MulIOp>(loc, collapseSize, srcSizes[d]);
SmallVector<Value, 1> collapsedSizes = {collapseSize};
ReassociationIndices collapseIdx;
for (Dimension i = 0; i < srcRank; i++)
collapseIdx.push_back(i);
SmallVector<ReassociationIndices, 1> collapseReass = {collapseIdx};
SmallVector<Value, 1> collapsedDcvs;
reshapeCvs(builder, loc, collapseReass, srcSizes, srcDcvs,
collapsedSizes, collapsedDcvs);
ReassociationIndices expandIdx;
for (Dimension i = 0; i < dstTp.getDimRank(); i++)
expandIdx.push_back(i);
SmallVector<ReassociationIndices, 1> expandReass = {expandIdx};
SmallVector<Value> dstDcvs;
reshapeCvs(builder, loc, expandReass, collapsedSizes, collapsedDcvs,
dstSizes, dstDcvs);
auto t = builder.create<InsertOp>(loc, v, reduc.front(), dstDcvs);
builder.create<sparse_tensor::YieldOp>(loc, t);
});
Value t = rewriter.create<LoadOp>(loc, foreachOp.getResult(0), true);
if (bufferTp != dstTp) {
auto dstRTT = dstTp.getRankedTensorType();
Value converted = rewriter.create<ConvertOp>(loc, dstRTT, t).getResult();
rewriter.create<DeallocTensorOp>(loc, t);
t = converted;
}
rewriter.replaceOp(op, t);
return success();
}
};
/// Sparse rewriting rule for sparse-to-sparse reshape operator.
template <typename ReshapeOp>
struct Sparse2SparseReshapeRewriter : public OpRewritePattern<ReshapeOp> {
public:
using OpRewritePattern<ReshapeOp>::OpRewritePattern;
LogicalResult matchAndRewrite(ReshapeOp op,
PatternRewriter &rewriter) const override {
Location loc = op.getLoc();
Value srcTensor = op.getSrc();
const auto srcTp = getSparseTensorType(srcTensor);
const auto dstTp = getSparseTensorType(op.getResult());
if (!srcTp.hasEncoding() || !dstTp.hasEncoding())
return failure();
// Generate code to represent the static dimension constants or compute
// the dynamic dimension values.
SmallVector<Value> srcSizes;
sizesForTensor(rewriter, srcSizes, loc, srcTp, srcTensor);
SmallVector<Value> dstSizes;
SmallVector<Value> dstDynSizes;
if (dstTp.hasStaticDimShape()) {
for (Dimension d : dstTp.getDimShape())
dstSizes.push_back(constantIndex(rewriter, loc, d));
} else {
ArrayRef<Size> dstShape = dstTp.getDimShape();
genReshapeDstShape(rewriter, loc, dstSizes, srcSizes, dstShape,
op.getReassociationIndices());
for (auto [idx, shape] : llvm::enumerate(dstShape)) {
if (shape == ShapedType::kDynamic)
dstDynSizes.push_back(dstSizes[idx]);
}
}
Value nnz = rewriter.create<NumberOfEntriesOp>(loc, srcTensor);
// Only need a unordered COO buffer if input and output are not sorted
// in the same way.
Type bufferTp = getBufferType(
dstTp.withoutDimToLvl(),
!srcTp.isAllOrdered() || !srcTp.isIdentity() || !dstTp.isIdentity());
Value buffer =
rewriter
.create<AllocTensorOp>(loc, bufferTp, dstDynSizes, Value(),
/*sizeHint=*/nnz, Attribute())
.getResult();
// Implement the sparse2sparse reshape as follows:
// foreach srcCoords %srcTensor
// insert reshapeCvs(srcCoords), %buffer
//
// followed by an optional
// %t = sparse_tensor.cast %tmp
// depending on whether the input/output are sorted in the same way.
const auto encSrc = srcTp.getEncoding();
ForeachOp foreachOp = rewriter.create<ForeachOp>(
loc, srcTensor, buffer,
[&](OpBuilder &builder, Location loc, ValueRange srcLcvs, Value v,
ValueRange reduc) {
const Dimension dimRank = srcTp.getDimRank();
SmallVector<Value> srcDcvs;
srcDcvs.reserve(dimRank);
for (Dimension d = 0; d < dimRank; d++) {
// FIXME: `toStoredDim` is deprecated
Level lvl = toStoredDim(encSrc, d);
srcDcvs.push_back(srcLcvs[lvl]);
}
SmallVector<Value> dstDcvs;
reshapeCvs(builder, loc, op.getReassociationIndices(), srcSizes,
srcDcvs, dstSizes, dstDcvs);
auto t = builder.create<InsertOp>(loc, v, reduc.front(), dstDcvs);
builder.create<sparse_tensor::YieldOp>(loc, t);
});
Value t = rewriter.create<LoadOp>(loc, foreachOp.getResult(0), true);
if (bufferTp != dstTp) {
auto dstRTT = dstTp.getRankedTensorType();
Value converted = rewriter.create<ConvertOp>(loc, dstRTT, t).getResult();
rewriter.create<DeallocTensorOp>(loc, t);
t = converted;
}
rewriter.replaceOp(op, t);
return success();
}
};
/// Sparse rewriting rule for sparse-to-dense and dense-to-sparse reshape
/// operator.
template <typename ReshapeOp>
struct ReshapeRewriter : public OpRewritePattern<ReshapeOp> {
public:
using OpRewritePattern<ReshapeOp>::OpRewritePattern;
LogicalResult matchAndRewrite(ReshapeOp op,
PatternRewriter &rewriter) const override {
Location loc = op->getLoc();
auto encDst = getSparseTensorEncoding(op.getResult().getType());
auto encSrc = getSparseTensorEncoding(op.getSrc().getType());
// Since a pure dense expansion is very cheap (change of view), for
// a sparse2dense or dense2sparse, we can simply unfuse a sparse
// conversion from the reshape operation itself.
// All other cases are handled elsewhere.
if (encDst && encSrc) {
return failure();
}
if (encSrc) {
auto rtp = getRankedTensorType(op.getSrc());
auto denseTp =
RankedTensorType::get(rtp.getShape(), rtp.getElementType());
auto convert = rewriter.create<ConvertOp>(loc, denseTp, op.getSrc());
rewriter.updateRootInPlace(op, [&]() { op->setOperand(0, convert); });
return success();
}
if (encDst) {
auto rtp = getRankedTensorType(op.getResult());
auto denseTp =
RankedTensorType::get(rtp.getShape(), rtp.getElementType());
auto reshape = rewriter.create<ReshapeOp>(loc, denseTp, op.getSrc(),
op.getReassociation());
Value convert = rewriter.create<ConvertOp>(loc, rtp, reshape);
rewriter.replaceOp(op, convert);
return success();
}
return failure();
}
};
// A trivial wrapper to help generate different operations for dense/sparse
// tensors.
struct TensorLike {
TensorLike(OpBuilder &builder, Location loc, RankedTensorType rtt,
ValueRange sizes) {
SmallVector<Value> dynSzs;
getDynamicSizes(rtt, sizes, dynSzs);
val = builder.create<AllocTensorOp>(loc, rtt, dynSzs);
if (!isSparse()) {
Value c0 = constantZero(builder, loc, rtt.getElementType());
val = builder.create<linalg::FillOp>(loc, c0, val).getResult(0);
}
}
void insert(OpBuilder &builder, Location loc, Value v, ValueRange crds) {
val = builder.create<tensor::InsertOp>(loc, v, val, crds);
}
Value finalize(OpBuilder &builder, Location loc, RankedTensorType rtp) const {
if (isSparse())
return builder.create<LoadOp>(loc, val, true);
return val;
}
bool isSparse() const {
return getSparseTensorEncoding(val.getType()) != nullptr;
}
Value val;
};
struct SparseTensorDimOpRewriter : public OpRewritePattern<tensor::DimOp> {
using OpRewritePattern::OpRewritePattern;
LogicalResult matchAndRewrite(tensor::DimOp op,
PatternRewriter &rewriter) const override {
std::optional<int64_t> dim = op.getConstantIndex();
auto stt = getSparseTensorType(op.getSource());
if (!dim || !stt.hasEncoding())
return failure();
if (stt.isPermutation()) {
rewriter.replaceOpWithNewOp<LvlOp>(op, op.getSource(),
toStoredDim(stt, *dim));
return success();
}
// Non-permutation dim2lvl/lvl2dim maps.
// Compute as follows:
// affine.apply #map (l0 - 1, l1 - 1, ...) + 1
// Note that it is not the most efficient way (but a more general one) for
// the lvl to dim translation, e.g., for BSR, the dimension size for can be
// computed simply by lvl_size * block_size.
Location loc = op.getLoc();
SmallVector<Value> maxLvlCrds;
for (Level l = 0; l < stt.getLvlRank(); l++) {
Value lvlSz = rewriter.create<LvlOp>(loc, op.getSource(), l);
Value maxLvlCrd = rewriter.create<arith::SubIOp>(
loc, lvlSz, constantOne(rewriter, loc, rewriter.getIndexType()));
maxLvlCrds.push_back(maxLvlCrd);
}
AffineExpr lvl2DimExp = stt.getLvlToDim().getResult(*dim);
Value maxDimCrd = rewriter.create<affine::AffineApplyOp>(
op.getLoc(), AffineMap::get(stt.getLvlRank(), 0, lvl2DimExp),
maxLvlCrds);
Value dimSz = rewriter.create<arith::AddIOp>(
loc, maxDimCrd, constantOne(rewriter, loc, rewriter.getIndexType()));
rewriter.replaceOp(op, dimSz);
return success();
}
};
struct ConcatenateRewriter : public OpRewritePattern<ConcatenateOp> {
using OpRewritePattern::OpRewritePattern;
LogicalResult matchAndRewrite(ConcatenateOp op,
PatternRewriter &rewriter) const override {
if (op.needsExtraSort())
op.emitError("ConcatenateOp not staged");
const Location loc = op.getLoc();
const auto dstTp = getSparseTensorType(op);
const Dimension conDim = op.getDimension();
SmallVector<Value> sizes;
concatSizesFromInputs(rewriter, sizes, loc, dstTp, op.getInputs(), conDim);
// %t = concatenate %s1, %s2, %s3 {dim = 1}
// ==>
// if (isSparseDst)
// if (allDense)
// %tmp = bufferization.alloc_tensor dstTp
// else
// %tmp = bufferization.alloc_tensor : unordered COO
// else
// %tmp = memref.alloc : dense tensor
// foreach in %s1 : insert d0, d1, %tmp
// foreach in %s2 : insert d0, d1 + size(s1), %tmp
// foreach in %s3 : insert d0, d1 + size(s1) + size(s2), %tmp
TensorLike dstBuf(rewriter, loc, dstTp.getRankedTensorType(), sizes);
Value offset = constantIndex(rewriter, loc, 0);
Value iterArg = dstBuf.val;
ForeachOp foreachOp;
for (Value input : op.getInputs()) {
// Builds a for op for each input tensor to append new values into the
// output tensor.
foreachOp = rewriter.create<ForeachOp>(
loc, input, iterArg,
[&](OpBuilder &builder, Location loc, ValueRange dcvs, Value v,
ValueRange reduc) {
SmallVector<Value> offDimCrd(dcvs);
offDimCrd[conDim] =
builder.create<arith::AddIOp>(loc, offDimCrd[conDim], offset);
// Enters foreach, updates the SSA chain.
dstBuf.val = reduc.front();
if (!dstTp.isAllDense()) {
Value cond = genIsNonzero(builder, loc, v);
auto ifOp = builder.create<scf::IfOp>(loc, reduc.getTypes(), cond,
/*else*/ true);
builder.setInsertionPointToStart(&ifOp.getElseRegion().front());
builder.create<scf::YieldOp>(loc, dstBuf.val);
builder.setInsertionPointToStart(&ifOp.getThenRegion().front());
dstBuf.insert(builder, loc, v, offDimCrd);
builder.create<scf::YieldOp>(loc, dstBuf.val);
// Exits the ifOp, update the sparse tensor SSA value.
builder.setInsertionPointAfter(ifOp);
dstBuf.val = ifOp.getResult(0);
} else {
dstBuf.insert(builder, loc, v, offDimCrd);
}
builder.create<sparse_tensor::YieldOp>(loc, dstBuf.val);
});
// Accumulates the offset. Note that only static-shaped inputs are allowed
// by concatenate op verifier, which saves us from computing the offset
// dynamically.
const Size sz = getSparseTensorType(input).getDynamicDimSize(conDim);
assert(!ShapedType::isDynamic(sz));
offset = rewriter.create<arith::AddIOp>(loc, offset,
constantIndex(rewriter, loc, sz));
iterArg = foreachOp.getResult(0);
dstBuf.val = iterArg;
}
dstBuf.val = iterArg;
Value ret = dstBuf.finalize(rewriter, loc, dstTp.getRankedTensorType());
rewriter.replaceOp(op, ret);
return success();
}
};
struct DirectConvertRewriter : public OpRewritePattern<ConvertOp> {
using OpRewritePattern::OpRewritePattern;
LogicalResult matchAndRewrite(ConvertOp op,
PatternRewriter &rewriter) const override {
if (op.needsExtraSort())
return op.emitError("ConvertOp not staged.");
// TODO: Maybe we want a different operation for this too.
auto encDst = getSparseTensorEncoding(op.getType());
auto encSrc = getSparseTensorEncoding(op.getSource().getType());
if (encDst && encSrc && !encSrc.isSlice() &&
encSrc.withoutBitWidths() == encDst.withoutBitWidths()) {
// Trivial tensor conversion and simple element type conversion is handled
// in codegen.
return failure();
}
Location loc = op.getLoc();
Value src = op.getSource();
SparseTensorType srcStt = getSparseTensorType(op.getSource());
SparseTensorType dstStt = getSparseTensorType(op.getDest());
bool fromSparseConst = false;
if (auto constOp = op.getSource().getDefiningOp<arith::ConstantOp>())
if (dyn_cast<SparseElementsAttr>(constOp.getValue()))
fromSparseConst = true;
const AffineMapAttr foreachOrder =
(!dstStt.isIdentity() && fromSparseConst)
? AffineMapAttr::get(dstStt.getExpandedDimToLvl())
: nullptr;
bool skipZeroCheck = srcStt.hasEncoding() || fromSparseConst;
SmallVector<Value> sizes;
sizesFromSrc(rewriter, sizes, loc, src);
ValueRange vs;
TensorLike dstBuf(rewriter, loc, dstStt.getRankedTensorType(), sizes);
auto foreachOp = rewriter.create<ForeachOp>(
loc, src, dstBuf.val, foreachOrder,
[&](OpBuilder &builder, Location loc, ValueRange dcvs, Value v,
ValueRange reduc) {
// Enters the loop, update the SSA value for insertion chain.
dstBuf.val = reduc.front();
if (!skipZeroCheck) {
Value cond = genIsNonzero(builder, loc, v);
auto ifOp = builder.create<scf::IfOp>(loc, reduc.getTypes(), cond,
/*else*/ true);
builder.setInsertionPointToStart(&ifOp.getElseRegion().front());
builder.create<scf::YieldOp>(loc, dstBuf.val);
builder.setInsertionPointToStart(&ifOp.getThenRegion().front());
dstBuf.insert(builder, loc, v, dcvs);
builder.create<scf::YieldOp>(loc, dstBuf.val);
// Exits the ifOp, update the sparse tensor SSA value.
builder.setInsertionPointAfter(ifOp);
dstBuf.val = ifOp.getResult(0);
} else {
dstBuf.insert(builder, loc, v, dcvs);
}
builder.create<sparse_tensor::YieldOp>(loc, dstBuf.val);
});
rewriter.setInsertionPointAfter(foreachOp);
// Exits the for loop, links the SSA chain.
dstBuf.val = foreachOp.getResult(0);
Value ret = dstBuf.finalize(rewriter, loc, dstStt.getRankedTensorType());
rewriter.replaceOp(op, ret);
return success();
}
};
struct CrdTranslateRewriter : public OpRewritePattern<CrdTranslateOp> {
using OpRewritePattern::OpRewritePattern;
LogicalResult matchAndRewrite(CrdTranslateOp op,
PatternRewriter &rewriter) const override {
AffineMap map = op.getDirection() == CrdTransDirectionKind::dim2lvl
? op.getEncoder().getDimToLvl()
: op.getEncoder().getLvlToDim();
SmallVector<Value> outCrds;
for (AffineExpr result : map.getResults()) {
// TODO: we should probably expand the affine map to IR using our own
// rules, since affine.apply assume signed value, while the cooridinates
// we provided must always be signless.
Value trans = rewriter.create<affine::AffineApplyOp>(
op.getLoc(), AffineMap::get(map.getNumDims(), 0, result),
op.getInCrds());
outCrds.push_back(trans);
}
rewriter.replaceOp(op, outCrds);
return success();
}
};
/// Sparse rewriting rule for the foreach operator.
struct ForeachRewriter : public OpRewritePattern<ForeachOp> {
public:
using OpRewritePattern::OpRewritePattern;
LogicalResult matchAndRewrite(ForeachOp op,
PatternRewriter &rewriter) const override {
auto loc = op.getLoc();
Value input = op.getTensor();
SmallVector<Value> reduc = op.getInitArgs();
const auto stt = getSparseTensorType(input);
const Level lvlRank = stt.getLvlRank();
// Special-case: for each over a sparse constant uses its own rewriting
// rule.
if (auto constOp = input.getDefiningOp<arith::ConstantOp>()) {
if (auto attr = dyn_cast<SparseElementsAttr>(constOp.getValue())) {
return genForeachOnSparseConstant(op, rewriter, attr);
}
}
// Otherwise, use loop emitter to generate loops.
const auto enc = stt.getEncoding();
// 1. Generates loop for the sparse input.
LoopEmitter loopEmitter(
ValueRange{input},
StringAttr::get(getContext(), ForeachOp::getOperationName()));
loopEmitter.initializeLoopEmit(rewriter, loc);
for (Level l = 0; l < lvlRank; l++) {
// TODO: provide utility function for loop sequences that only contains
// one for loop?
const SmallVector<TensorLevel, 1> tidLvls{
loopEmitter.makeTensorLevel(0, l)};
loopEmitter.enterNewLoopSeq(rewriter, loc, tidLvls);
// Note that reduc will be taken care of by loop emitter and get updated
// in place.
loopEmitter.enterCoIterationOverTensorsAtLvls(rewriter, loc, tidLvls,
reduc);
}
SmallVector<Value> lcvs = loopEmitter.getLoopIVs();
if (op.getOrder()) {
// FIXME: There is some dim/lvl confusion here since `dimRank != lvlRank`
const Dimension dimRank = stt.getDimRank();
SmallVector<Value> dcvs = lcvs; // keep a copy
for (Dimension d = 0; d < dimRank; d++) {
auto l = op.getOrder()->getDimPosition(d);
lcvs[l] = dcvs[d];
}
}
Value vals = loopEmitter.getValBuffer()[0];
Value pos = loopEmitter.getPosits()[0].back();
// Loads the value from sparse tensor using position-index;
// loads the value from dense tensor using coords.
Value val = enc ? rewriter.create<memref::LoadOp>(loc, vals, pos)
: rewriter.create<memref::LoadOp>(loc, vals, lcvs);
// 2. Inline the block in the foreach operator.
Block *srcBlock = op.getBody();
// Remap coordinates.
SmallVector<Value> args =
enc.translateCrds(rewriter, loc, lcvs, CrdTransDirectionKind::lvl2dim);
// Remap value.
args.push_back(val);
// Remap reduction variables.
args.append(reduc);
// Remove sparse_tensor.yield.
SmallVector<Value> reducValue = srcBlock->getTerminator()->getOperands();
rewriter.eraseOp(srcBlock->getTerminator());
// Inline body.
if (!reducValue.empty()) {
rewriter.mergeBlocks(srcBlock, rewriter.getBlock(), args);
} else {
// This is annoying, since scf.for inserts a implicit yield op when
// there is no reduction variable upon creation, in this case we need to
// merge the block *before* the yield op.
rewriter.inlineBlockBefore(srcBlock, &*rewriter.getInsertionPoint(),
args);
}
for (Level l = 0; l < lvlRank; l++) {
// Link the reduction chain. Note that loop emitter update the reducValue
// in place.
loopEmitter.exitCurrentLoop(rewriter, loc, reducValue);
loopEmitter.exitCurrentLoopSeq(rewriter, loc);
}
// Replace the foreach operator with the value returned by the outtermost
// for loop.
rewriter.replaceOp(op, reducValue);
return success();
}
};
/// Sparse rewriting rule for the new operator.
struct NewRewriter : public OpRewritePattern<NewOp> {
using OpRewritePattern::OpRewritePattern;
LogicalResult matchAndRewrite(NewOp op,
PatternRewriter &rewriter) const override {
Location loc = op.getLoc();
const auto dstTp = getSparseTensorType(op.getResult());
const auto encDst = dstTp.getEncoding();
if (!dstTp.hasEncoding() || getCOOStart(encDst) == 0)
return failure();
// Implement the NewOp as follows:
// %orderedCoo = sparse_tensor.new %filename
// %t = sparse_tensor.convert %orderedCoo
RankedTensorType cooTp = getCOOType(dstTp, /*ordered=*/true);
Value cooTensor = rewriter.create<NewOp>(loc, cooTp, op.getSource());
Value convert = rewriter.replaceOpWithNewOp<ConvertOp>(
op, dstTp.getRankedTensorType(), cooTensor);
// Release the ordered COO tensor.
rewriter.setInsertionPointAfterValue(convert);
rewriter.create<DeallocTensorOp>(loc, cooTensor);
return success();
}
};
struct OutRewriter : public OpRewritePattern<OutOp> {
using OpRewritePattern::OpRewritePattern;
LogicalResult matchAndRewrite(OutOp op,
PatternRewriter &rewriter) const override {
Location loc = op.getLoc();
// Calculate NNZ.
Value src = op.getTensor();
Value nnz = rewriter.create<NumberOfEntriesOp>(loc, src);
// Allocate a temporary buffer for storing dimension-sizes/coordinates.
const auto srcTp = getSparseTensorType(src);
const Dimension dimRank = srcTp.getDimRank();
Type indexTp = rewriter.getIndexType();
Value dimSizes = genAlloca(rewriter, loc, dimRank, indexTp);
// Generate code to calculate dimension size values and store the values to
// the buffer.
SmallVector<Value> dims;
sizesForTensor(rewriter, dims, loc, srcTp, src);
for (Dimension d = 0; d < dimRank; d++) {
rewriter.create<memref::StoreOp>(loc, dims[d], dimSizes,
constantIndex(rewriter, loc, d));
}
// Create a sparse tensor writer and output meta data.
Type opaqueTp = getOpaquePointerType(rewriter);
Value writer =
createFuncCall(rewriter, loc, "createSparseTensorWriter", {opaqueTp},
{op.getDest()}, EmitCInterface::Off)
.getResult(0);
Value rankValue = constantIndex(rewriter, loc, dimRank);
createFuncCall(rewriter, loc, "outSparseTensorWriterMetaData", {},
{writer, rankValue, nnz, dimSizes}, EmitCInterface::On);
Value dimCoords = dimSizes; // Reuse the dimSizes buffer for dimCoords.
Type eltTp = srcTp.getElementType();
SmallString<29> outNextFuncName{"outSparseTensorWriterNext",
primaryTypeFunctionSuffix(eltTp)};
Value value = genAllocaScalar(rewriter, loc, eltTp);
ModuleOp module = op->getParentOfType<ModuleOp>();
// For each element in the source tensor, output the element.
rewriter.create<ForeachOp>(
loc, src, std::nullopt,
[&](OpBuilder &builder, Location loc, ValueRange dcvs, Value v,
ValueRange reduc) {
for (Dimension d = 0; d < dimRank; d++) {
rewriter.create<memref::StoreOp>(loc, dcvs[d], dimCoords,
constantIndex(builder, loc, d));
}
rewriter.create<memref::StoreOp>(loc, v, value);
SmallVector<Value> operands{writer, rankValue, dimCoords, value};
FlatSymbolRefAttr fn = getFunc(module, outNextFuncName, {}, operands,
EmitCInterface::On);
builder.create<func::CallOp>(loc, TypeRange(), fn, operands);
builder.create<sparse_tensor::YieldOp>(loc);
});
// Release the writer.
createFuncCall(rewriter, loc, "delSparseTensorWriter", {}, {writer},
EmitCInterface::Off);
rewriter.eraseOp(op);
return success();
}
};
} // namespace
//===---------------------------------------------------------------------===//
// Methods that add patterns described in this file to a pattern list.
//===---------------------------------------------------------------------===//
void mlir::populatePreSparsificationRewriting(RewritePatternSet &patterns) {
patterns.add<FoldInvariantYield, FuseSparseMultiplyOverAdd, FuseTensorCast,
GenSemiRingReduction, GenSemiRingSelect>(patterns.getContext());
}
void mlir::populateLowerSparseOpsToForeachPatterns(RewritePatternSet &patterns,
bool enableRT,
bool enableConvert) {
patterns.add<ConcatenateRewriter, ReshapeRewriter<tensor::ExpandShapeOp>,
ReshapeRewriter<tensor::CollapseShapeOp>,
Sparse2SparseReshapeRewriter<tensor::ExpandShapeOp>,
Sparse2SparseReshapeRewriter<tensor::CollapseShapeOp>,
SparseTensorDimOpRewriter, TensorReshapeRewriter, OutRewriter>(
patterns.getContext());
if (enableConvert)
patterns.add<DirectConvertRewriter>(patterns.getContext());
if (!enableRT)
patterns.add<NewRewriter>(patterns.getContext());
}
void mlir::populateLowerForeachToSCFPatterns(RewritePatternSet &patterns) {
// Run CrdTranslateRewriter later in the pipeline so that operation can be
// folded before lowering to affine.apply
patterns.add<CrdTranslateRewriter, ForeachRewriter>(patterns.getContext());
}
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