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llvm-project/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorRewriting.cpp
wren romano 46a384dfbe [mlir][sparse] Preliminary code changes for ExprId, LatPointId, LatSetId newtypes 
This commit contains several code changes which are ultimately required for converting the varions `Merger` identifiers from typedefs to newtypes.  The actual implementation of the newtypes themselves has been split off into separate commits, in hopes of simplifying the review process.

Depends On D146561

Reviewed By: aartbik

Differential Revision: https://reviews.llvm.org/D146684
2023-03-29 18:01:56 -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/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(OpOperand *op) {
auto enc = getSparseTensorEncoding(op->get().getType());
return enc &&
llvm::is_contained(enc.getDimLevelType(), DimLevelType::Compressed);
}
// Helper method to find zero/uninitialized allocation.
static bool isAlloc(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;
}
// 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 = val.dyn_cast<BlockArgument>())
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 = yieldOp.getOperand(0).dyn_cast<BlockArgument>()) {
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
getUnorderedCOOFromTypeWithOrdering(RankedTensorType src, AffineMap ordering) {
return getCOOFromTypeWithOrdering(src, ordering, false);
}
static RankedTensorType getUnorderedCOOFromType(RankedTensorType src) {
return getCOOFromType(src, false);
}
/// 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,
const SmallVectorImpl<Value> &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(
loc, rewriter, 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 ||
!isAlloc(op.getDpsInitOperand(0), /*isZero=*/false) ||
!isZeroYield(op) || !op.getDpsInitOperand(0)->get().hasOneUse())
return failure();
auto outputType = getRankedTensorType(op.getResult(0));
// Yielding zero on newly allocated (all-zero) sparse tensors can be
// optimized out directly (regardless of dynamic or static size).
if (getSparseTensorEncoding(outputType)) {
rewriter.replaceOp(op, op.getDpsInitOperand(0)->get());
return success();
}
// Incorporate zero value into allocation copy.
if (!outputType.hasStaticShape())
return failure();
Value zero = constantZero(rewriter, op.getLoc(), op.getResult(0).getType());
AllocTensorOp a =
op.getDpsInitOperand(0)->get().getDefiningOp<AllocTensorOp>();
rewriter.updateRootInPlace(a, [&]() { a.getCopyMutable().assign(zero); });
rewriter.replaceOp(op, op.getDpsInitOperand(0)->get());
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 (!isAlloc(op.getDpsInitOperand(0), /*isZero=*/false) ||
!isAlloc(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.
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()));
}
};
/// 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();
auto srcTp = getRankedTensorType(srcTensor);
auto dstTp = getRankedTensorType(op.getResult());
SparseTensorEncodingAttr encSrc = getSparseTensorEncoding(srcTp);
SparseTensorEncodingAttr encDst = getSparseTensorEncoding(dstTp);
if (!encDst || !encSrc) {
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.hasStaticShape()) {
for (auto d : dstTp.getShape())
dstSizes.push_back(constantIndex(rewriter, loc, d));
} else {
ArrayRef<int64_t> dstShape = dstTp.getShape();
genReshapeDstShape(loc, rewriter, dstSizes, srcSizes, dstShape,
op.getReassociationIndices());
for (auto [idx, shape] : llvm::enumerate(dstShape)) {
if (shape == ShapedType::kDynamic)
dstDynSizes.push_back(dstSizes[idx]);
}
}
// Implement the sparse2sparse reshape as follows:
// %tmp = bufferization.alloc_tensor : unordered COO
// foreach srcCoords %srcTensor
// insert reshapeCvs(srcCoords), %tmp
// %t = sparse_tensor.cast %tmp
Value nnz = rewriter.create<NumberOfEntriesOp>(loc, srcTensor);
RankedTensorType cooTp = getUnorderedCOOFromType(dstTp);
Value cooBuffer =
rewriter
.create<AllocTensorOp>(loc, cooTp, dstDynSizes, Value(),
/*sizeHint=*/nnz, Attribute())
.getResult();
ForeachOp foreachOp = rewriter.create<ForeachOp>(
loc, srcTensor, cooBuffer,
[&](OpBuilder &builder, Location loc, ValueRange srcLcvs, Value v,
ValueRange reduc) {
const Dimension dimRank = srcTp.getRank();
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);
});
auto t = rewriter.create<LoadOp>(loc, foreachOp.getResult(0), true);
auto converted = rewriter.create<ConvertOp>(loc, dstTp, t).getResult();
rewriter.create<DeallocTensorOp>(loc, t);
rewriter.replaceOp(op, converted);
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();
}
};
struct ConcatenateRewriter : public OpRewritePattern<ConcatenateOp> {
using OpRewritePattern::OpRewritePattern;
LogicalResult matchAndRewrite(ConcatenateOp op,
PatternRewriter &rewriter) const override {
const Location loc = op.getLoc();
const auto dstTp = getSparseTensorType(op);
const Dimension dimRank = dstTp.getDimRank();
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
// %t = convert_to_dest_tensor(%tmp)
//
// NOTE: this cannot be `const` because it will be changed when
// `needTmpCOO`, but that's buried in the conditional below and
// thus not easily extracted.
auto encDst = dstTp.getEncoding();
Value dst; // Destination tensor for inserting source tensor values.
bool needTmpCOO = true;
const bool allDense = dstTp.hasEncoding() && dstTp.isAllDense();
Value annotatedDenseDst;
if (dstTp.hasEncoding()) {
bool allOrdered = false;
// When concatenating on dimension 0, and all inputs are sorted and have
// an identity dimOrdering, the concatenate will generate coords in
// lexOrder thus no need for the tmp COO buffer.
// TODO: When conDim != 0, as long as conDim is the first dimension
// in all input/output buffers, and all input/output buffers have the same
// dimOrdering, the tmp COO buffer is still unnecessary (e.g, concatenate
// CSC matrices along column).
if (!allDense && conDim == 0 && dstTp.isIdentity()) {
for (auto i : op.getInputs()) {
const auto stt = getSparseTensorType(i);
allOrdered = stt.isAllOrdered() && stt.isIdentity();
if (!allOrdered)
break;
}
}
needTmpCOO = !allDense && !allOrdered;
const RankedTensorType tp = needTmpCOO ? getUnorderedCOOFromType(dstTp)
: dstTp.getRankedTensorType();
encDst = needTmpCOO ? getSparseTensorEncoding(tp) : encDst;
SmallVector<Value> dynSizes;
getDynamicSizes(dstTp, sizes, dynSizes);
dst = rewriter.create<AllocTensorOp>(loc, tp, dynSizes).getResult();
if (allDense) {
// Create a view of the values buffer to match the unannotated dense
// tensor.
Value valuesBuffer = genToValues(rewriter, loc, dst);
Value dimCoords =
genAlloca(rewriter, loc, dimRank, rewriter.getIndexType(),
/*staticShape=*/true);
annotatedDenseDst = dst;
dst = reshapeValuesToLevels(rewriter, loc, encDst, sizes, valuesBuffer,
dimCoords);
}
} else {
// TODO: Dense buffers should be allocated/deallocated via the callback
// in BufferizationOptions.
dst = allocDenseTensor(rewriter, loc, dstTp, sizes);
}
Value offset = constantIndex(rewriter, loc, 0);
SmallVector<Value> initArgs;
if (encDst && !allDense)
initArgs.push_back(dst);
ForeachOp foreachOp;
for (Value input : op.getInputs()) {
// Build a for op for each input tensor to append new values into the
// output tensor.
foreachOp = rewriter.create<ForeachOp>(
loc, input, initArgs,
[&](OpBuilder &builder, Location loc, ValueRange dcvs, Value v,
ValueRange reduc) {
SmallVector<Value> dstLcvs(dstTp.getLvlRank());
for (Dimension d = 0; d < dimRank; d++) {
Value crd = dcvs[d];
if (d == conDim)
// Transform coordinates for the concatenating dim.
crd = builder.create<arith::AddIOp>(loc, crd, offset);
// FIXME: `toStoredDim` is deprecated
dstLcvs[toStoredDim(encDst, d)] = crd;
}
if (encDst && !allDense) {
Value cond = genIsNonzero(rewriter, loc, v);
scf::IfOp ifOp = builder.create<scf::IfOp>(
loc, TypeRange(reduc.front().getType()), cond, /*else*/ true);
builder.setInsertionPointToStart(&ifOp.getThenRegion().front());
Value t =
builder.create<InsertOp>(loc, v, reduc.front(), dstLcvs);
rewriter.create<scf::YieldOp>(loc, t);
rewriter.setInsertionPointToStart(&ifOp.getElseRegion().front());
rewriter.create<scf::YieldOp>(loc, reduc.front());
rewriter.setInsertionPointAfter(ifOp);
rewriter.create<sparse_tensor::YieldOp>(loc, ifOp.getResult(0));
} else {
builder.create<memref::StoreOp>(loc, v, dst, dstLcvs);
builder.create<sparse_tensor::YieldOp>(loc);
}
});
// Accumulates the offset. Note that only static-shaped inputs are allowed
// by concatenate op verifier, which saves us from computing the offset
// dynamically.
const auto sh = getSparseTensorType(input).getStaticDimSize(conDim);
assert(sh.has_value());
offset = rewriter.create<arith::AddIOp>(
loc, offset, constantIndex(rewriter, loc, *sh));
if (encDst && !allDense) {
dst = foreachOp.getResult(0);
initArgs[0] = dst;
}
}
// Temp variable to avoid needing to call `getRankedTensorType`
// in the three use-sites below.
const RankedTensorType dstRTT = dstTp;
if (!encDst) {
rewriter.replaceOpWithNewOp<bufferization::ToTensorOp>(op, dstRTT, dst);
} else if (allDense) {
rewriter.replaceOp(
op, rewriter.create<ConvertOp>(loc, dstRTT, annotatedDenseDst)
.getResult());
} else {
dst = rewriter.create<LoadOp>(loc, dst, true);
if (needTmpCOO) {
Value tmpCoo = dst;
dst = rewriter.create<ConvertOp>(loc, dstRTT, tmpCoo).getResult();
rewriter.create<DeallocTensorOp>(loc, tmpCoo);
}
rewriter.replaceOp(op, dst);
}
return success();
}
};
/// Sparse rewriting rule for the convert operator.
struct ConvertRewriter : public OpRewritePattern<ConvertOp> {
using OpRewritePattern::OpRewritePattern;
LogicalResult matchAndRewrite(ConvertOp op,
PatternRewriter &rewriter) const override {
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();
}
// TODO: Add a cast before generating InsertOp.
assert(op.getSource().getType().getElementType() ==
op.getDest().getType().getElementType());
if (encSrc && encDst)
return sparse2SparseRewrite(op, rewriter);
if (encSrc && !encDst)
return sparse2DenseRewrite(op, rewriter);
if (!encSrc && encDst)
return dense2SparseRewrite(op, rewriter);
// Dense-to-dense convert is a nop and handled by canonicalization.
return failure();
}
private:
// Handles sparse constant to sparse tensor or dense tensor to sparse tensor
// conversion as follows:
// t = new sparse COO tensor
// fill t using src
// dst = convert t
//
// To fill the COO tensor from a dense tensor:
// for i1 in dim1
// ..
// for ik in dimk
// val = a[i1,..,ik]
// if val != 0
// t->add(val, [i1,..,ik], [p1,..,pk])
//
// To fill the COO tensor from a sparse constant in COO format:
// for i in range(NNZ)
// val = values[i]
// [i1,..,ik] = coordinates[i]
// t->add(val, [i1,..,ik], [p1,..,pk])
LogicalResult dense2SparseRewrite(ConvertOp op,
PatternRewriter &rewriter) const {
Location loc = op.getLoc();
Value src = op.getSource();
const auto dstTp = getSparseTensorType(op);
SmallVector<Value> sizes;
sizesFromSrc(rewriter, sizes, loc, src);
SmallVector<Value> dynSizes;
getDynamicSizes(dstTp, sizes, dynSizes);
bool fromSparseConst = false;
if (auto constOp = op.getSource().getDefiningOp<arith::ConstantOp>()) {
if (constOp.getValue().dyn_cast<SparseElementsAttr>()) {
fromSparseConst = true;
}
}
const auto encDst = dstTp.getEncoding();
// We don't need a temporary COO tensor if the destination has an identity
// ordering. Otherwise, we use the destination ordering for the temporary
// COO tensor.
// TODO: enhance foreachOp to take ordering to remove the need of a
// temporary COO tensor here.
const RankedTensorType bufferTp = dstTp.isIdentity() || fromSparseConst
? dstTp.getRankedTensorType()
: getUnorderedCOOFromTypeWithOrdering(
dstTp, dstTp.getDimToLvlMap());
// Only imposes foreach order on dense constant (which will be statically
// sorted by the sparse compiler), otherwise the rotated loop sequence
// results to bad cache locality.
AffineMapAttr foreachOrder = nullptr;
if (encDst.getDimOrdering() && fromSparseConst)
foreachOrder = AffineMapAttr::get(encDst.getDimOrdering());
auto buffer =
rewriter.create<AllocTensorOp>(loc, bufferTp, dynSizes).getResult();
auto foreachOp = rewriter.create<ForeachOp>(
loc, src, buffer, foreachOrder,
[&](OpBuilder &builder, Location loc, ValueRange dcvs, Value v,
ValueRange reduc) {
Value input = reduc.front();
const Dimension dimRank = dstTp.getDimRank();
const Level lvlRank = dstTp.getLvlRank();
SmallVector<Value> lcvs(lvlRank);
for (Dimension d = 0; d < dimRank; d++)
// FIXME: `toStoredDim` is deprecated
lcvs[toStoredDim(encDst, d)] = dcvs[d];
if (fromSparseConst) {
input = builder.create<InsertOp>(loc, v, input, lcvs);
} else {
Value cond = genIsNonzero(builder, loc, v);
auto ifOp = builder.create<scf::IfOp>(
loc, TypeRange(input.getType()), cond, /*else*/ true);
builder.setInsertionPointToStart(&ifOp.getThenRegion().front());
Value insert = builder.create<InsertOp>(loc, v, input, lcvs);
builder.create<scf::YieldOp>(loc, insert);
builder.setInsertionPointToStart(&ifOp.getElseRegion().front());
builder.create<scf::YieldOp>(loc, input);
builder.setInsertionPointAfter(ifOp);
input = ifOp.getResult(0);
}
builder.create<sparse_tensor::YieldOp>(loc, input);
});
rewriter.setInsertionPointAfter(op);
src = rewriter.create<LoadOp>(loc, foreachOp.getResult(0), true);
if (bufferTp != dstTp) {
rewriter.replaceOpWithNewOp<ConvertOp>(op, dstTp.getRankedTensorType(),
src);
rewriter.create<DeallocTensorOp>(loc, src);
} else {
rewriter.replaceOp(op, src);
}
return success();
}
// Handles sparse tensor to dense tensor conversion as follows:
// dst = new dense tensor;
// foreach elemment in src
// dst[element.coords] = element.value
LogicalResult sparse2DenseRewrite(ConvertOp op,
PatternRewriter &rewriter) const {
Location loc = op->getLoc();
RankedTensorType dstTp = getRankedTensorType(op);
Value src = op.getSource();
RankedTensorType srcTp = getRankedTensorType(src);
SmallVector<Value> sizes;
sizesForTensor(rewriter, sizes, loc, srcTp, src);
Value dst = allocDenseTensor(rewriter, loc, dstTp, sizes);
Block *insertionBlock = rewriter.getInsertionBlock();
bool noEscape = bufferization::allocationDoesNotEscape(op->getOpResult(0));
rewriter.create<ForeachOp>(loc, src, std::nullopt,
[&](OpBuilder &builder, Location loc,
ValueRange args, Value v, ValueRange reduc) {
builder.create<memref::StoreOp>(loc, v, dst,
args);
builder.create<sparse_tensor::YieldOp>(loc);
});
rewriter.replaceOpWithNewOp<bufferization::ToTensorOp>(op, dstTp, dst);
// Deallocate the buffer.
if (noEscape) {
rewriter.setInsertionPoint(insertionBlock->getTerminator());
deallocDenseTensor(rewriter, loc, dst);
}
return success();
}
// Handles sparse tensor to sparse tensor conversion as follows:
// if src is not COO
// construct a COO to represent the src
// sort the src COO
// foreach elemment in the sorted src COO
// insert element to dst
LogicalResult sparse2SparseRewrite(ConvertOp op,
PatternRewriter &rewriter) const {
const Location loc = op->getLoc();
// These two variables cannot be `const` because they're conditionally
// changed below. Ideally we'd use `SparseTensorType` for `srcRTT`;
// however that class's copy-ctor is implicitly deleted.
Value src = op.getSource();
auto srcRTT = getRankedTensorType(src);
const auto dstTp = getSparseTensorType(op);
const auto encDst = dstTp.getEncoding();
const Level dstLvlRank = dstTp.getLvlRank();
const Dimension dimRank = dstTp.getDimRank();
// This assertion should be guaranteed by validity of the op,
// but just for paranoia's sake.
assert(static_cast<Dimension>(srcRTT.getRank()) == dimRank);
SmallVector<Value> srcSizes;
sizesForTensor(rewriter, srcSizes, loc, srcRTT, src);
Value tmpCoo = Value();
Value nnz = rewriter.create<NumberOfEntriesOp>(loc, src);
// We need a tmp COO buffer if and only if
// 1. the src tensor is not a COO and
// 2. the src tensor is not ordered in the same way as the target
// tensor (e.g., src tensor is not ordered or src tensor haves a different
// dimOrdering).
if (const SparseTensorType srcTp(srcRTT);
!(srcTp.isAllOrdered() && srcTp.hasSameDimToLvlMap(dstTp))) {
// Construct a COO tensor from the src tensor.
// TODO: there may be cases for which more efficiently without
// going through an intermediate COO, such as cases that only change
// the overhead types.
SmallVector<Value> dynSrcSizes;
getDynamicSizes(srcRTT, srcSizes, dynSrcSizes);
srcRTT =
getUnorderedCOOFromTypeWithOrdering(srcRTT, dstTp.getDimToLvlMap());
// Ensure that mutating `srcRTT` didn't invalidate `dimRank`.
assert(static_cast<Dimension>(srcRTT.getRank()) == dimRank);
tmpCoo = rewriter
.create<AllocTensorOp>(loc, srcRTT, dynSrcSizes, Value(),
/*sizeHint=*/nnz, Attribute())
.getResult();
auto foreachOp = rewriter.create<ForeachOp>(
loc, src, tmpCoo,
[&](OpBuilder &builder, Location loc, ValueRange dcvs, Value v,
ValueRange reduc) {
SmallVector<Value> dstLcvs(dstLvlRank);
for (Dimension d = 0; d < dimRank; d++) {
// FIXME: `toStoredDim` is deprecated
Level l = toStoredDim(encDst, d);
dstLcvs[l] = dcvs[d];
}
auto t = builder.create<InsertOp>(loc, v, reduc.front(), dstLcvs);
builder.create<sparse_tensor::YieldOp>(loc, t);
});
src = rewriter.create<LoadOp>(loc, foreachOp.getResult(0), true);
}
// Now that the conditional is done, we can use `SparseTensorType`.
const SparseTensorType srcTp(srcRTT);
// Only need to sort if the srcTp is not already sorted (we faithfully take
// the guarantee from the sparse tensor encoding).
if (!srcTp.isAllOrdered()) {
// Retrieve the values-array.
Value y = genToValues(rewriter, loc, src);
const auto encSrc = srcTp.getEncoding();
// Sort the COO tensor so that its elements are ordered via increasing
// coordinates for the storage ordering of the dst tensor. Use SortCoo
// if the COO tensor has the same ordering as the dst tensor.
if (dimRank > 1 && srcTp.hasSameDimToLvlMap(dstTp)) {
MemRefType coordsTp =
get1DMemRefType(encSrc.getCrdType(), /*withLayout=*/false);
Value xs = rewriter.create<ToCoordinatesBufferOp>(loc, coordsTp, src);
rewriter.create<SortCooOp>(
loc, nnz, xs, ValueRange{y}, rewriter.getIndexAttr(dimRank),
rewriter.getIndexAttr(0), SparseTensorSortKind::HybridQuickSort);
} else {
// Gather the coordinates-arrays in the dst tensor storage order.
SmallVector<Value> xs(dstLvlRank);
const Level srcLvlRank = srcTp.getLvlRank();
for (Level srcLvl = 0; srcLvl < srcLvlRank; srcLvl++) {
// FIXME: `toOrigDim` is deprecated
Dimension dim = toOrigDim(encSrc, srcLvl);
// FIXME: `toStoredDim` is deprecated
Level dstLvl = toStoredDim(encDst, dim);
xs[dstLvl] =
genToCoordinates(rewriter, loc, src, srcLvl, /*cooStart=*/0);
}
rewriter.create<SortOp>(loc, nnz, xs, ValueRange{y},
SparseTensorSortKind::HybridQuickSort);
}
}
// For each element in the COO tensor, insert the element to the dst tensor.
SmallVector<Value> dynDstSizes;
getDynamicSizes(dstTp, srcSizes, dynDstSizes);
Value dst = rewriter
.create<AllocTensorOp>(loc, dstTp.getRankedTensorType(),
dynDstSizes, Value(),
/*sizeHint=*/nnz, Attribute())
.getResult();
SmallVector<Value> dstLcvs(dstLvlRank);
auto foreachOp = rewriter.create<ForeachOp>(
loc, src, dst,
[&](OpBuilder &builder, Location loc, ValueRange dcvs, Value v,
ValueRange reduc) {
for (Dimension d = 0; d < dimRank; d++) {
// FIXME: `toStoredDim` is deprecated
Level l = toStoredDim(encDst, d);
dstLcvs[l] = dcvs[d];
}
auto t = builder.create<InsertOp>(loc, v, reduc.front(), dstLcvs);
builder.create<sparse_tensor::YieldOp>(loc, t);
});
// Release the temporary COO if it is created. Note that tmpCoo is
// invalidated due to foreach and updated to src.
if (tmpCoo)
rewriter.create<DeallocTensorOp>(loc, src);
// Directly replace op with dst results in bufferization error message
// "sparse tensor allocation should not escape function".
// As such, we insert a trivial tensor convert which will be removed by
// codegen.
rewriter.setInsertionPointAfter(op);
auto t = rewriter.create<LoadOp>(loc, foreachOp.getResult(0), true);
rewriter.replaceOpWithNewOp<ConvertOp>(op, dstTp.getRankedTensorType(), t);
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 Dimension dimRank = stt.getDimRank();
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 = constOp.getValue().dyn_cast<SparseElementsAttr>()) {
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?
// FIXME(wrengr): what is this "ld" supposed to be really?
const Level ld = op.getOrder() ? op.getOrder()->getDimPosition(l) : l;
const SmallVector<TensorId, 1> tids{0};
loopEmitter.enterNewLoopSeq(rewriter, loc, tids, ld);
// Note that reduc will be taken care of by loop emitter and get updated
// in place.
loopEmitter.enterLoopOverTensorAtLvl(rewriter, loc, tids, l, reduc);
}
SmallVector<Value> lcvs;
lcvs.reserve(lvlRank);
loopEmitter.getLoopIVs(lcvs);
if (op.getOrder()) {
// FIXME: There is some dim/lvl confusion here since `dimRank != lvlRank`
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;
for (Dimension d = 0; d < dimRank; d++) {
// FIXME: `toStoredDim` is deprecated
Value dimCrd = lcvs[toStoredDim(enc, d)];
args.push_back(dimCrd);
}
// 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 (Dimension d = 0; d < dimRank; d++) {
// Link the reduction chain. Note that loop emitter update the reducValue
// in place.
loopEmitter.exitCurrentLoop(rewriter, loc, reducValue);
loopEmitter.exitCurrentLoopSeq();
}
// 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 =
getCOOFromTypeWithOrdering(dstTp, encDst.getDimOrdering(), 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>(
patterns.getContext());
}
void mlir::populatePostSparsificationRewriting(RewritePatternSet &patterns,
bool enableRT,
bool enableForeach,
bool enableConvert) {
patterns.add<ReshapeRewriter<tensor::ExpandShapeOp>,
ReshapeRewriter<tensor::CollapseShapeOp>>(patterns.getContext());
if (enableForeach)
patterns.add<ForeachRewriter>(patterns.getContext());
// TODO: If RT not enabled, rewrite concatenate ops, etc here.
if (!enableRT) {
patterns.add<ConcatenateRewriter, NewRewriter, OutRewriter,
Sparse2SparseReshapeRewriter<tensor::ExpandShapeOp>,
Sparse2SparseReshapeRewriter<tensor::CollapseShapeOp>>(
patterns.getContext());
if (enableConvert)
patterns.add<ConvertRewriter>(patterns.getContext());
}
}
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