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llvm-project/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorCodegen.cpp
Aliia Khasanova 399638f98c Merge kDynamicSize and kDynamicSentinel into one constant. 
resolve conflicts

Differential Revision: https://reviews.llvm.org/D138282
2022-11-21 13:01:26 +00:00

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//===- SparseTensorCodegen.cpp - Sparse tensor primitives conversion ------===//
//
// 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
//
//===----------------------------------------------------------------------===//
//
// A pass that converts sparse tensor types and primitives to actual compiler
// visible buffers and actual compiler IR that implements these primitives on
// the selected sparse tensor storage schemes. This pass provides an alternative
// to the SparseTensorConversion pass, eliminating the dependence on a runtime
// support library, and providing much more opportunities for subsequent
// compiler optimization of the generated code.
//
//===----------------------------------------------------------------------===//
#include "CodegenUtils.h"
#include "mlir/Dialect/Bufferization/IR/Bufferization.h"
#include "mlir/Dialect/Func/IR/FuncOps.h"
#include "mlir/Dialect/Linalg/Utils/Utils.h"
#include "mlir/Dialect/MemRef/IR/MemRef.h"
#include "mlir/Dialect/SparseTensor/IR/SparseTensor.h"
#include "mlir/Dialect/SparseTensor/Transforms/Passes.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "mlir/Transforms/DialectConversion.h"
using namespace mlir;
using namespace mlir::sparse_tensor;
namespace {
static constexpr uint64_t dimSizesIdx = 0;
static constexpr uint64_t memSizesIdx = 1;
static constexpr uint64_t fieldsIdx = 2;
//===----------------------------------------------------------------------===//
// Helper methods.
//===----------------------------------------------------------------------===//
/// Returns the "tuple" value of the adapted tensor.
static UnrealizedConversionCastOp getTuple(Value tensor) {
return llvm::cast<UnrealizedConversionCastOp>(tensor.getDefiningOp());
}
/// Packs the given values as a "tuple" value.
static Value genTuple(OpBuilder &builder, Location loc, Type tp,
ValueRange values) {
return builder.create<UnrealizedConversionCastOp>(loc, TypeRange(tp), values)
.getResult(0);
}
/// Flatten a list of operands that may contain sparse tensors.
static void flattenOperands(ValueRange operands,
SmallVectorImpl<Value> &flattened) {
// In case of
// sparse_tensor, c, sparse_tensor
// ==>
// memref ..., c, memref ...
for (auto operand : operands) {
if (getSparseTensorEncoding(operand.getType())) {
auto tuple = getTuple(operand);
// An unrealized_conversion_cast will be inserted by type converter to
// inter-mix the gap between 1:N conversion between sparse tensors and
// fields. In this case, take the operands in the cast and replace the
// sparse tensor output with the flattened type array.
flattened.append(tuple.getOperands().begin(), tuple.getOperands().end());
} else {
flattened.push_back(operand);
}
}
}
/// Adds index conversions where needed.
static Value toType(OpBuilder &builder, Location loc, Value value, Type tp) {
if (value.getType() != tp)
return builder.create<arith::IndexCastOp>(loc, tp, value);
return value;
}
/// Generates a load with proper index typing.
static Value genLoad(OpBuilder &builder, Location loc, Value mem, Value idx) {
idx = toType(builder, loc, idx, builder.getIndexType());
return builder.create<memref::LoadOp>(loc, mem, idx);
}
/// Generates a store with proper index typing and (for indices) proper value.
static void genStore(OpBuilder &builder, Location loc, Value val, Value mem,
Value idx) {
idx = toType(builder, loc, idx, builder.getIndexType());
val = toType(builder, loc, val,
mem.getType().cast<ShapedType>().getElementType());
builder.create<memref::StoreOp>(loc, val, mem, idx);
}
/// Creates a straightforward counting for-loop.
static scf::ForOp createFor(OpBuilder &builder, Location loc, Value upper,
SmallVectorImpl<Value> &fields,
Value lower = Value()) {
Type indexType = builder.getIndexType();
if (!lower)
lower = constantZero(builder, loc, indexType);
Value one = constantOne(builder, loc, indexType);
scf::ForOp forOp = builder.create<scf::ForOp>(loc, lower, upper, one, fields);
for (unsigned i = 0, e = fields.size(); i < e; i++)
fields[i] = forOp.getRegionIterArg(i);
builder.setInsertionPointToStart(forOp.getBody());
return forOp;
}
/// Gets the dimension size for the given sparse tensor at the given
/// original dimension 'dim'. Returns None if no sparse encoding is
/// attached to the given tensor type.
static Optional<Value> sizeFromTensorAtDim(OpBuilder &builder, Location loc,
RankedTensorType tensorTp,
Value adaptedValue, unsigned dim) {
auto enc = getSparseTensorEncoding(tensorTp);
if (!enc)
return llvm::None;
// Access into static dimension can query original type directly.
// Note that this is typically already done by DimOp's folding.
auto shape = tensorTp.getShape();
if (!ShapedType::isDynamic(shape[dim]))
return constantIndex(builder, loc, shape[dim]);
// Any other query can consult the dimSizes array at field DimSizesIdx,
// accounting for the reordering applied to the sparse storage.
auto tuple = getTuple(adaptedValue);
Value idx = constantIndex(builder, loc, toStoredDim(tensorTp, dim));
return builder
.create<memref::LoadOp>(loc, tuple.getInputs()[dimSizesIdx], idx)
.getResult();
}
// Gets the dimension size at the given stored dimension 'd', either as a
// constant for a static size, or otherwise dynamically through memSizes.
Value sizeAtStoredDim(OpBuilder &builder, Location loc, RankedTensorType rtp,
SmallVectorImpl<Value> &fields, unsigned d) {
unsigned dim = toOrigDim(rtp, d);
auto shape = rtp.getShape();
if (!ShapedType::isDynamic(shape[dim]))
return constantIndex(builder, loc, shape[dim]);
return genLoad(builder, loc, fields[dimSizesIdx],
constantIndex(builder, loc, d));
}
/// Translates field index to memSizes index.
static unsigned getMemSizesIndex(unsigned field) {
assert(fieldsIdx <= field);
return field - fieldsIdx;
}
/// Creates a pushback op for given field and updates the fields array
/// accordingly. This operation also updates the memSizes contents.
static void createPushback(OpBuilder &builder, Location loc,
SmallVectorImpl<Value> &fields, unsigned field,
Value value, Value repeat = Value()) {
assert(fieldsIdx <= field && field < fields.size());
Type etp = fields[field].getType().cast<ShapedType>().getElementType();
fields[field] = builder.create<PushBackOp>(
loc, fields[field].getType(), fields[memSizesIdx], fields[field],
toType(builder, loc, value, etp), APInt(64, getMemSizesIndex(field)),
repeat);
}
/// Returns field index of sparse tensor type for pointers/indices, when set.
static unsigned getFieldIndex(Type type, unsigned ptrDim, unsigned idxDim) {
assert(getSparseTensorEncoding(type));
RankedTensorType rType = type.cast<RankedTensorType>();
unsigned field = fieldsIdx; // start past header
for (unsigned r = 0, rank = rType.getShape().size(); r < rank; r++) {
if (isCompressedDim(rType, r)) {
if (r == ptrDim)
return field;
field++;
if (r == idxDim)
return field;
field++;
} else if (isSingletonDim(rType, r)) {
if (r == idxDim)
return field;
field++;
} else {
assert(isDenseDim(rType, r)); // no fields
}
}
assert(ptrDim == -1u && idxDim == -1u);
return field + 1; // return values field index
}
/// Maps a sparse tensor type to the appropriate compounded buffers.
static Optional<LogicalResult>
convertSparseTensorType(Type type, SmallVectorImpl<Type> &fields) {
auto enc = getSparseTensorEncoding(type);
if (!enc)
return llvm::None;
// Construct the basic types.
auto *context = type.getContext();
unsigned idxWidth = enc.getIndexBitWidth();
unsigned ptrWidth = enc.getPointerBitWidth();
RankedTensorType rType = type.cast<RankedTensorType>();
Type indexType = IndexType::get(context);
Type idxType = idxWidth ? IntegerType::get(context, idxWidth) : indexType;
Type ptrType = ptrWidth ? IntegerType::get(context, ptrWidth) : indexType;
Type eltType = rType.getElementType();
//
// Sparse tensor storage scheme for rank-dimensional tensor is organized
// as a single compound type with the following fields. Note that every
// memref with ? size actually behaves as a "vector", i.e. the stored
// size is the capacity and the used size resides in the memSizes array.
//
// struct {
// memref<rank x index> dimSizes ; size in each dimension
// memref<n x index> memSizes ; sizes of ptrs/inds/values
// ; per-dimension d:
// ; if dense:
// <nothing>
// ; if compresed:
// memref<? x ptr> pointers-d ; pointers for sparse dim d
// memref<? x idx> indices-d ; indices for sparse dim d
// ; if singleton:
// memref<? x idx> indices-d ; indices for singleton dim d
// memref<? x eltType> values ; values
// };
//
unsigned rank = rType.getShape().size();
unsigned lastField = getFieldIndex(type, -1u, -1u);
// The dimSizes array and memSizes array.
fields.push_back(MemRefType::get({rank}, indexType));
fields.push_back(MemRefType::get({getMemSizesIndex(lastField)}, indexType));
// Per-dimension storage.
for (unsigned r = 0; r < rank; r++) {
// Dimension level types apply in order to the reordered dimension.
// As a result, the compound type can be constructed directly in the given
// order. Clients of this type know what field is what from the sparse
// tensor type.
if (isCompressedDim(rType, r)) {
fields.push_back(MemRefType::get({ShapedType::kDynamic}, ptrType));
fields.push_back(MemRefType::get({ShapedType::kDynamic}, idxType));
} else if (isSingletonDim(rType, r)) {
fields.push_back(MemRefType::get({ShapedType::kDynamic}, idxType));
} else {
assert(isDenseDim(rType, r)); // no fields
}
}
// The values array.
fields.push_back(MemRefType::get({ShapedType::kDynamic}, eltType));
assert(fields.size() == lastField);
return success();
}
/// Generates code that allocates a sparse storage scheme for given rank.
static void allocSchemeForRank(OpBuilder &builder, Location loc,
RankedTensorType rtp,
SmallVectorImpl<Value> &fields, unsigned field,
unsigned r0) {
unsigned rank = rtp.getShape().size();
Value linear = constantIndex(builder, loc, 1);
for (unsigned r = r0; r < rank; r++) {
if (isCompressedDim(rtp, r)) {
// Append linear x pointers, initialized to zero. Since each compressed
// dimension initially already has a single zero entry, this maintains
// the desired "linear + 1" length property at all times.
unsigned ptrWidth = getSparseTensorEncoding(rtp).getPointerBitWidth();
Type indexType = builder.getIndexType();
Type ptrType = ptrWidth ? builder.getIntegerType(ptrWidth) : indexType;
Value ptrZero = constantZero(builder, loc, ptrType);
createPushback(builder, loc, fields, field, ptrZero, linear);
return;
}
if (isSingletonDim(rtp, r)) {
return; // nothing to do
} // Keep compounding the size, but nothing needs to be initialized
// at this level. We will eventually reach a compressed level or
// otherwise the values array for the from-here "all-dense" case.
assert(isDenseDim(rtp, r));
Value size = sizeAtStoredDim(builder, loc, rtp, fields, r);
linear = builder.create<arith::MulIOp>(loc, linear, size);
}
// Reached values array so prepare for an insertion.
Value valZero = constantZero(builder, loc, rtp.getElementType());
createPushback(builder, loc, fields, field, valZero, linear);
assert(fields.size() == ++field);
}
/// Creates allocation operation.
static Value createAllocation(OpBuilder &builder, Location loc, Type type,
Value sz, bool enableInit) {
auto memType = MemRefType::get({ShapedType::kDynamic}, type);
Value buffer = builder.create<memref::AllocOp>(loc, memType, sz);
if (enableInit) {
Value fillValue =
builder.create<arith::ConstantOp>(loc, type, builder.getZeroAttr(type));
builder.create<linalg::FillOp>(loc, fillValue, buffer);
}
return buffer;
}
/// Creates allocation for each field in sparse tensor type. Note that
/// for all dynamic memrefs, the memory size is really the capacity of
/// the "vector", while the actual size resides in the sizes array.
///
/// TODO: for efficiency, we will need heuristis to make educated guesses
/// on the required capacities (see heuristic variable).
///
static void createAllocFields(OpBuilder &builder, Location loc, Type type,
ValueRange dynSizes, bool enableInit,
SmallVectorImpl<Value> &fields) {
auto enc = getSparseTensorEncoding(type);
assert(enc);
// Construct the basic types.
unsigned idxWidth = enc.getIndexBitWidth();
unsigned ptrWidth = enc.getPointerBitWidth();
RankedTensorType rtp = type.cast<RankedTensorType>();
Type indexType = builder.getIndexType();
Type idxType = idxWidth ? builder.getIntegerType(idxWidth) : indexType;
Type ptrType = ptrWidth ? builder.getIntegerType(ptrWidth) : indexType;
Type eltType = rtp.getElementType();
auto shape = rtp.getShape();
unsigned rank = shape.size();
Value heuristic = constantIndex(builder, loc, 16);
// Build original sizes.
SmallVector<Value> sizes;
for (unsigned r = 0, o = 0; r < rank; r++) {
if (ShapedType::isDynamic(shape[r]))
sizes.push_back(dynSizes[o++]);
else
sizes.push_back(constantIndex(builder, loc, shape[r]));
}
// The dimSizes array and memSizes array.
unsigned lastField = getFieldIndex(type, -1u, -1u);
Value dimSizes =
builder.create<memref::AllocOp>(loc, MemRefType::get({rank}, indexType));
Value memSizes = builder.create<memref::AllocOp>(
loc, MemRefType::get({getMemSizesIndex(lastField)}, indexType));
fields.push_back(dimSizes);
fields.push_back(memSizes);
// Per-dimension storage.
for (unsigned r = 0; r < rank; r++) {
if (isCompressedDim(rtp, r)) {
fields.push_back(
createAllocation(builder, loc, ptrType, heuristic, enableInit));
fields.push_back(
createAllocation(builder, loc, idxType, heuristic, enableInit));
} else if (isSingletonDim(rtp, r)) {
fields.push_back(
createAllocation(builder, loc, idxType, heuristic, enableInit));
} else {
assert(isDenseDim(rtp, r)); // no fields
}
}
// The values array.
fields.push_back(
createAllocation(builder, loc, eltType, heuristic, enableInit));
assert(fields.size() == lastField);
// Initialize the storage scheme to an empty tensor. Initialized memSizes
// to all zeros, sets the dimSizes to known values and gives all pointer
// fields an initial zero entry, so that it is easier to maintain the
// "linear + 1" length property.
builder.create<linalg::FillOp>(
loc, ValueRange{constantZero(builder, loc, indexType)},
ValueRange{memSizes}); // zero memSizes
Value ptrZero = constantZero(builder, loc, ptrType);
for (unsigned r = 0, field = fieldsIdx; r < rank; r++) {
unsigned ro = toOrigDim(rtp, r);
genStore(builder, loc, sizes[ro], dimSizes, constantIndex(builder, loc, r));
if (isCompressedDim(rtp, r)) {
createPushback(builder, loc, fields, field, ptrZero);
field += 2;
} else if (isSingletonDim(rtp, r)) {
field += 1;
}
}
allocSchemeForRank(builder, loc, rtp, fields, fieldsIdx, /*rank=*/0);
}
/// Helper method that generates block specific to compressed case:
///
/// plo = pointers[d][pos[d-1]]
/// phi = pointers[d][pos[d-1]+1]
/// msz = indices[d].size()
/// if (plo < phi) {
/// present = indices[d][phi-1] == i[d]
/// } else { // first insertion
/// present = false
/// pointers[d][pos[d-1]] = msz
/// }
/// if (present) { // index already present
/// next = phi-1
/// } else {
/// indices[d].push_back(i[d])
/// pointers[d][pos[d-1]+1] = msz+1
/// next = msz
/// <prepare dimension d + 1>
/// }
/// pos[d] = next
static Value genCompressed(OpBuilder &builder, Location loc,
RankedTensorType rtp, SmallVectorImpl<Value> &fields,
SmallVectorImpl<Value> &indices, Value value,
Value pos, unsigned field, unsigned d) {
unsigned rank = rtp.getShape().size();
SmallVector<Type> types;
Type indexType = builder.getIndexType();
Type boolType = builder.getIntegerType(1);
Value one = constantIndex(builder, loc, 1);
Value pp1 = builder.create<arith::AddIOp>(loc, pos, one);
Value plo = genLoad(builder, loc, fields[field], pos);
Value phi = genLoad(builder, loc, fields[field], pp1);
Value psz = constantIndex(builder, loc, getMemSizesIndex(field + 1));
Value msz = genLoad(builder, loc, fields[memSizesIdx], psz);
Value phim1 = builder.create<arith::SubIOp>(
loc, toType(builder, loc, phi, indexType), one);
// Conditional expression.
Value lt =
builder.create<arith::CmpIOp>(loc, arith::CmpIPredicate::ult, plo, phi);
types.push_back(boolType);
scf::IfOp ifOp1 = builder.create<scf::IfOp>(loc, types, lt, /*else*/ true);
types.pop_back();
builder.setInsertionPointToStart(&ifOp1.getThenRegion().front());
Value crd = genLoad(builder, loc, fields[field + 1], phim1);
Value eq = builder.create<arith::CmpIOp>(loc, arith::CmpIPredicate::eq,
toType(builder, loc, crd, indexType),
indices[d]);
builder.create<scf::YieldOp>(loc, eq);
builder.setInsertionPointToStart(&ifOp1.getElseRegion().front());
if (d > 0)
genStore(builder, loc, msz, fields[field], pos);
builder.create<scf::YieldOp>(loc, constantI1(builder, loc, false));
builder.setInsertionPointAfter(ifOp1);
Value p = ifOp1.getResult(0);
// If present construct. Note that for a non-unique dimension level, we simply
// set the condition to false and rely on CSE/DCE to clean up the IR.
//
// TODO: generate less temporary IR?
//
for (unsigned i = 0, e = fields.size(); i < e; i++)
types.push_back(fields[i].getType());
types.push_back(indexType);
if (!isUniqueDim(rtp, d))
p = constantI1(builder, loc, false);
scf::IfOp ifOp2 = builder.create<scf::IfOp>(loc, types, p, /*else*/ true);
// If present (fields unaffected, update next to phim1).
builder.setInsertionPointToStart(&ifOp2.getThenRegion().front());
fields.push_back(phim1);
builder.create<scf::YieldOp>(loc, fields);
fields.pop_back();
// If !present (changes fields, update next).
builder.setInsertionPointToStart(&ifOp2.getElseRegion().front());
Value mszp1 = builder.create<arith::AddIOp>(loc, msz, one);
genStore(builder, loc, mszp1, fields[field], pp1);
createPushback(builder, loc, fields, field + 1, indices[d]);
// Prepare the next dimension "as needed".
if ((d + 1) < rank)
allocSchemeForRank(builder, loc, rtp, fields, field + 2, d + 1);
fields.push_back(msz);
builder.create<scf::YieldOp>(loc, fields);
fields.pop_back();
// Update fields and return next pos.
builder.setInsertionPointAfter(ifOp2);
unsigned o = 0;
for (unsigned i = 0, e = fields.size(); i < e; i++)
fields[i] = ifOp2.getResult(o++);
return ifOp2.getResult(o);
}
/// Generates code along an insertion path without the need for a "cursor".
/// This current insertion strategy comes at the expense of some testing
/// overhead for each insertion. The strategy will be optimized later for
/// common insertion patterns. The current insertion strategy also assumes
/// insertions occur in "a reasonable order" that enables building the
/// storage scheme in an appending/inserting kind of fashion (i.e. no
/// in-between insertions that need data movement). The implementation
/// relies on CSE/DCE to clean up all bookkeeping that is not needed.
///
/// TODO: better unord/not-unique; also generalize, optimize, specialize!
///
static void genInsert(OpBuilder &builder, Location loc, RankedTensorType rtp,
SmallVectorImpl<Value> &fields,
SmallVectorImpl<Value> &indices, Value value) {
unsigned rank = rtp.getShape().size();
assert(rank == indices.size());
unsigned field = fieldsIdx; // start past header
Value pos = constantZero(builder, loc, builder.getIndexType());
// Generate code for every dimension.
for (unsigned d = 0; d < rank; d++) {
if (isCompressedDim(rtp, d)) {
// Create:
// if (!present) {
// indices[d].push_back(i[d])
// <update pointers and prepare dimension d + 1>
// }
// pos[d] = indices.size() - 1
// <insert @ pos[d] at next dimension d + 1>
pos = genCompressed(builder, loc, rtp, fields, indices, value, pos, field,
d);
field += 2;
} else if (isSingletonDim(rtp, d)) {
// Create:
// indices[d].push_back(i[d])
// pos[d] = pos[d-1]
// <insert @ pos[d] at next dimension d + 1>
createPushback(builder, loc, fields, field, indices[d]);
field += 1;
} else {
assert(isDenseDim(rtp, d));
// Construct the new position as:
// pos[d] = size * pos[d-1] + i[d]
// <insert @ pos[d] at next dimension d + 1>
Value size = sizeAtStoredDim(builder, loc, rtp, fields, d);
Value mult = builder.create<arith::MulIOp>(loc, size, pos);
pos = builder.create<arith::AddIOp>(loc, mult, indices[d]);
}
}
// Reached the actual value append/insert.
if (!isDenseDim(rtp, rank - 1))
createPushback(builder, loc, fields, field++, value);
else
genStore(builder, loc, value, fields[field++], pos);
assert(fields.size() == field);
}
/// Generations insertion finalization code.
static void genEndInsert(OpBuilder &builder, Location loc, RankedTensorType rtp,
SmallVectorImpl<Value> &fields) {
unsigned rank = rtp.getShape().size();
unsigned field = fieldsIdx; // start past header
for (unsigned d = 0; d < rank; d++) {
if (isCompressedDim(rtp, d)) {
// Compressed dimensions need a pointer cleanup for all entries
// that were not visited during the insertion pass.
//
// TODO: avoid cleanup and keep compressed scheme consistent at all times?
//
if (d > 0) {
unsigned ptrWidth = getSparseTensorEncoding(rtp).getPointerBitWidth();
Type indexType = builder.getIndexType();
Type ptrType = ptrWidth ? builder.getIntegerType(ptrWidth) : indexType;
Value mz = constantIndex(builder, loc, getMemSizesIndex(field));
Value hi = genLoad(builder, loc, fields[memSizesIdx], mz);
Value zero = constantIndex(builder, loc, 0);
Value one = constantIndex(builder, loc, 1);
// Vector of only one, but needed by createFor's prototype.
SmallVector<Value, 1> inits{genLoad(builder, loc, fields[field], zero)};
scf::ForOp loop = createFor(builder, loc, hi, inits, one);
Value i = loop.getInductionVar();
Value oldv = loop.getRegionIterArg(0);
Value newv = genLoad(builder, loc, fields[field], i);
Value ptrZero = constantZero(builder, loc, ptrType);
Value cond = builder.create<arith::CmpIOp>(
loc, arith::CmpIPredicate::eq, newv, ptrZero);
scf::IfOp ifOp = builder.create<scf::IfOp>(loc, TypeRange(ptrType),
cond, /*else*/ true);
builder.setInsertionPointToStart(&ifOp.getThenRegion().front());
genStore(builder, loc, oldv, fields[field], i);
builder.create<scf::YieldOp>(loc, oldv);
builder.setInsertionPointToStart(&ifOp.getElseRegion().front());
builder.create<scf::YieldOp>(loc, newv);
builder.setInsertionPointAfter(ifOp);
builder.create<scf::YieldOp>(loc, ifOp.getResult(0));
builder.setInsertionPointAfter(loop);
}
field += 2;
} else if (isSingletonDim(rtp, d)) {
field++;
} else {
assert(isDenseDim(rtp, d));
}
}
assert(fields.size() == ++field);
}
//===----------------------------------------------------------------------===//
// Codegen rules.
//===----------------------------------------------------------------------===//
/// Sparse tensor storage conversion rule for returns.
class SparseReturnConverter : public OpConversionPattern<func::ReturnOp> {
public:
using OpConversionPattern::OpConversionPattern;
LogicalResult
matchAndRewrite(func::ReturnOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
SmallVector<Value> flattened;
flattenOperands(adaptor.getOperands(), flattened);
// Create a return with the flattened value extracted from sparse tensors.
rewriter.replaceOpWithNewOp<func::ReturnOp>(op, flattened);
return success();
}
};
/// Sparse tensor storage conversion rule for calls.
class SparseCallConverter : public OpConversionPattern<func::CallOp> {
public:
// The default CallOp converter can not handle 1:N type conversion.
using OpConversionPattern::OpConversionPattern;
LogicalResult
matchAndRewrite(func::CallOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
Location loc = op.getLoc();
// In case of:
// sparse_tensor, f, sparse_tensor = call @foo(...)
// ==>
// memref..., f, memref = call @foo(...) replace with
// cast(memref...)->sparse_tensor, f, cast(memref...)->sparse_tensor
SmallVector<Type> finalRetTy;
if (failed(typeConverter->convertTypes(op.getResultTypes(), finalRetTy)))
return failure();
// (1) Genereates new call with flattened return value.
SmallVector<Value> flattened;
flattenOperands(adaptor.getOperands(), flattened);
auto newCall = rewriter.create<func::CallOp>(loc, op.getCallee(),
finalRetTy, flattened);
// (2) Create cast operation for sparse tensor returns.
SmallVector<Value> castedRet;
// Tracks the offset of current return value (of the orignal call)
// relative to the new call (after sparse tensor flattening);
unsigned retOffset = 0;
// Temporal buffer to hold the flattened list of type for
// a sparse tensor.
SmallVector<Type> sparseFlat;
for (auto ret : op.getResults()) {
assert(retOffset < newCall.getNumResults());
auto retType = ret.getType();
if (failed(typeConverter->convertType(retType, sparseFlat)))
// This should never happen.
llvm_unreachable("Failed to convert type in sparse tensor codegen");
// Converted types can not be empty when the type conversion succeed.
assert(!sparseFlat.empty());
if (sparseFlat.size() > 1) {
auto flatSize = sparseFlat.size();
ValueRange fields(iterator_range<ResultRange::iterator>(
newCall.result_begin() + retOffset,
newCall.result_begin() + retOffset + flatSize));
castedRet.push_back(genTuple(rewriter, loc, retType, fields));
retOffset += flatSize;
} else {
// If this is an 1:1 conversion, no need for casting.
castedRet.push_back(newCall.getResult(retOffset));
retOffset++;
}
sparseFlat.clear();
}
assert(castedRet.size() == op.getNumResults());
rewriter.replaceOp(op, castedRet);
return success();
}
};
/// Sparse codegen rule for dimension accesses.
class SparseDimOpConverter : public OpConversionPattern<tensor::DimOp> {
public:
using OpConversionPattern::OpConversionPattern;
LogicalResult
matchAndRewrite(tensor::DimOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
Optional<int64_t> index = op.getConstantIndex();
if (!index)
return failure();
auto sz =
sizeFromTensorAtDim(rewriter, op.getLoc(),
op.getSource().getType().cast<RankedTensorType>(),
adaptor.getSource(), *index);
if (!sz)
return failure();
rewriter.replaceOp(op, *sz);
return success();
}
};
/// Sparse codegen rule for trivial tensor casts.
class SparseCastConverter : public OpConversionPattern<tensor::CastOp> {
public:
using OpConversionPattern::OpConversionPattern;
LogicalResult
matchAndRewrite(tensor::CastOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
// Only rewrite identically annotated source/dest.
auto encDst = getSparseTensorEncoding(op.getType());
auto encSrc = getSparseTensorEncoding(op.getSource().getType());
if (!encDst || encDst != encSrc)
return failure();
rewriter.replaceOp(op, adaptor.getOperands());
return success();
}
};
/// Sparse codgen rule for the alloc operator.
class SparseTensorAllocConverter
: public OpConversionPattern<bufferization::AllocTensorOp> {
public:
using OpConversionPattern::OpConversionPattern;
SparseTensorAllocConverter(TypeConverter &typeConverter, MLIRContext *context,
bool enableInit)
: OpConversionPattern(typeConverter, context),
enableBufferInitialization(enableInit) {}
LogicalResult
matchAndRewrite(bufferization::AllocTensorOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
RankedTensorType resType = op.getType();
auto enc = getSparseTensorEncoding(resType);
if (!enc)
return failure();
if (op.getCopy())
return rewriter.notifyMatchFailure(op, "tensor copy not implemented");
// Construct allocation for each field.
Location loc = op.getLoc();
SmallVector<Value> fields;
createAllocFields(rewriter, loc, resType, adaptor.getOperands(),
enableBufferInitialization, fields);
// Replace operation with resulting memrefs.
rewriter.replaceOp(op, genTuple(rewriter, loc, resType, fields));
return success();
}
private:
bool enableBufferInitialization;
};
/// Sparse codegen rule for the dealloc operator.
class SparseTensorDeallocConverter
: public OpConversionPattern<bufferization::DeallocTensorOp> {
public:
using OpConversionPattern::OpConversionPattern;
LogicalResult
matchAndRewrite(bufferization::DeallocTensorOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
auto enc = getSparseTensorEncoding(op.getTensor().getType());
if (!enc)
return failure();
// Replace the sparse tensor deallocation with field deallocations.
Location loc = op.getLoc();
auto tuple = getTuple(adaptor.getTensor());
for (auto input : tuple.getInputs())
// Deallocate every buffer used to store the sparse tensor handler.
rewriter.create<memref::DeallocOp>(loc, input);
rewriter.eraseOp(op);
return success();
}
};
/// Sparse codegen rule for tensor rematerialization.
class SparseTensorLoadConverter : public OpConversionPattern<LoadOp> {
public:
using OpConversionPattern::OpConversionPattern;
LogicalResult
matchAndRewrite(LoadOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
RankedTensorType srcType =
op.getTensor().getType().cast<RankedTensorType>();
auto tuple = getTuple(adaptor.getTensor());
// Prepare fields.
SmallVector<Value> fields(tuple.getInputs());
// Generate optional insertion finalization code.
if (op.getHasInserts())
genEndInsert(rewriter, op.getLoc(), srcType, fields);
// Replace operation with resulting memrefs.
rewriter.replaceOp(op, genTuple(rewriter, op.getLoc(), srcType, fields));
return success();
}
};
/// Sparse codegen rule for the expand op.
class SparseExpandConverter : public OpConversionPattern<ExpandOp> {
public:
using OpConversionPattern::OpConversionPattern;
LogicalResult
matchAndRewrite(ExpandOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
Location loc = op->getLoc();
RankedTensorType srcType =
op.getTensor().getType().cast<RankedTensorType>();
Type eltType = srcType.getElementType();
Type boolType = rewriter.getIntegerType(1);
Type idxType = rewriter.getIndexType();
// All initialization should be done on entry of the loop nest.
rewriter.setInsertionPointAfter(op.getTensor().getDefiningOp());
// Determine the size for access expansion (always the innermost stored
// dimension size, translated back to original dimension). Note that we
// recursively rewrite the new DimOp on the **original** tensor.
unsigned innerDim = toOrigDim(srcType, srcType.getRank() - 1);
auto sz = sizeFromTensorAtDim(rewriter, loc, srcType, adaptor.getTensor(),
innerDim);
assert(sz); // This for sure is a sparse tensor
// Generate a memref for `sz` elements of type `t`.
auto genAlloc = [&](Type t) {
auto memTp = MemRefType::get({ShapedType::kDynamic}, t);
return rewriter.create<memref::AllocOp>(loc, memTp, ValueRange{*sz});
};
// Allocate temporary buffers for values/filled-switch and added.
// We do not use stack buffers for this, since the expanded size may
// be rather large (as it envelops a single expanded dense dimension).
Value values = genAlloc(eltType);
Value filled = genAlloc(boolType);
Value added = genAlloc(idxType);
Value zero = constantZero(rewriter, loc, idxType);
// Reset the values/filled-switch to all-zero/false. Note that this
// introduces an O(N) operation into the computation, but this reset
// operation is amortized over the innermost loops for the access
// pattern expansion. As noted in the operation doc, we would like
// to amortize this setup cost even between kernels.
rewriter.create<linalg::FillOp>(
loc, ValueRange{constantZero(rewriter, loc, eltType)},
ValueRange{values});
rewriter.create<linalg::FillOp>(
loc, ValueRange{constantZero(rewriter, loc, boolType)},
ValueRange{filled});
// Replace expansion op with these buffers and initial index.
assert(op.getNumResults() == 4);
rewriter.replaceOp(op, {values, filled, added, zero});
return success();
}
};
/// Sparse codegen rule for the compress operator.
class SparseCompressConverter : public OpConversionPattern<CompressOp> {
public:
using OpConversionPattern::OpConversionPattern;
LogicalResult
matchAndRewrite(CompressOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
Location loc = op->getLoc();
RankedTensorType dstType =
op.getTensor().getType().cast<RankedTensorType>();
Type eltType = dstType.getElementType();
auto tuple = getTuple(adaptor.getTensor());
Value values = adaptor.getValues();
Value filled = adaptor.getFilled();
Value added = adaptor.getAdded();
Value count = adaptor.getCount();
// Prepare fields and indices.
SmallVector<Value> fields(tuple.getInputs());
SmallVector<Value> indices(adaptor.getIndices());
// If the innermost dimension is ordered, we need to sort the indices
// in the "added" array prior to applying the compression.
unsigned rank = dstType.getShape().size();
if (isOrderedDim(dstType, rank - 1))
rewriter.create<SortOp>(loc, count, ValueRange{added}, ValueRange{});
// While performing the insertions, we also need to reset the elements
// of the values/filled-switch by only iterating over the set elements,
// to ensure that the runtime complexity remains proportional to the
// sparsity of the expanded access pattern.
//
// Generate
// out_memrefs = for (i = 0; i < count; i++)(in_memrefs) {
// index = added[i];
// value = values[index];
// insert({prev_indices, index}, value);
// new_memrefs = insert(in_memrefs, {prev_indices, index}, value);
// values[index] = 0;
// filled[index] = false;
// yield new_memrefs
// }
scf::ForOp loop = createFor(rewriter, loc, count, fields);
Value i = loop.getInductionVar();
Value index = genLoad(rewriter, loc, added, i);
Value value = genLoad(rewriter, loc, values, index);
indices.push_back(index);
// TODO: faster for subsequent insertions?
genInsert(rewriter, loc, dstType, fields, indices, value);
genStore(rewriter, loc, constantZero(rewriter, loc, eltType), values,
index);
genStore(rewriter, loc, constantI1(rewriter, loc, false), filled, index);
rewriter.create<scf::YieldOp>(loc, fields);
rewriter.setInsertionPointAfter(loop);
Value result = genTuple(rewriter, loc, dstType, loop->getResults());
// Deallocate the buffers on exit of the full loop nest.
Operation *parent = getTop(op);
rewriter.setInsertionPointAfter(parent);
rewriter.create<memref::DeallocOp>(loc, values);
rewriter.create<memref::DeallocOp>(loc, filled);
rewriter.create<memref::DeallocOp>(loc, added);
// Replace operation with resulting memrefs.
rewriter.replaceOp(op, result);
return success();
}
};
/// Sparse codegen rule for the insert operator.
class SparseInsertConverter : public OpConversionPattern<InsertOp> {
public:
using OpConversionPattern::OpConversionPattern;
LogicalResult
matchAndRewrite(InsertOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
RankedTensorType dstType =
op.getTensor().getType().cast<RankedTensorType>();
auto tuple = getTuple(adaptor.getTensor());
// Prepare fields and indices.
SmallVector<Value> fields(tuple.getInputs());
SmallVector<Value> indices(adaptor.getIndices());
// Generate insertion.
Value value = adaptor.getValue();
genInsert(rewriter, op->getLoc(), dstType, fields, indices, value);
// Replace operation with resulting memrefs.
rewriter.replaceOp(op, genTuple(rewriter, op.getLoc(), dstType, fields));
return success();
}
};
/// Base class for getter-like operations, e.g., to_indices, to_pointers.
template <typename SourceOp, typename Base>
class SparseGetterOpConverter : public OpConversionPattern<SourceOp> {
public:
using OpAdaptor = typename SourceOp::Adaptor;
using OpConversionPattern<SourceOp>::OpConversionPattern;
LogicalResult
matchAndRewrite(SourceOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
// Replace the requested pointer access with corresponding field.
// The cast_op is inserted by type converter to intermix 1:N type
// conversion.
auto tuple = getTuple(adaptor.getTensor());
unsigned idx = Base::getIndexForOp(tuple, op);
auto fields = tuple.getInputs();
assert(idx < fields.size());
rewriter.replaceOp(op, fields[idx]);
return success();
}
};
/// Sparse codegen rule for pointer accesses.
class SparseToPointersConverter
: public SparseGetterOpConverter<ToPointersOp, SparseToPointersConverter> {
public:
using SparseGetterOpConverter::SparseGetterOpConverter;
// Callback for SparseGetterOpConverter.
static unsigned getIndexForOp(UnrealizedConversionCastOp /*tuple*/,
ToPointersOp op) {
uint64_t dim = op.getDimension().getZExtValue();
return getFieldIndex(op.getTensor().getType(), /*ptrDim=*/dim, -1u);
}
};
/// Sparse codegen rule for index accesses.
class SparseToIndicesConverter
: public SparseGetterOpConverter<ToIndicesOp, SparseToIndicesConverter> {
public:
using SparseGetterOpConverter::SparseGetterOpConverter;
// Callback for SparseGetterOpConverter.
static unsigned getIndexForOp(UnrealizedConversionCastOp /*tuple*/,
ToIndicesOp op) {
uint64_t dim = op.getDimension().getZExtValue();
return getFieldIndex(op.getTensor().getType(), -1u, /*idxDim=*/dim);
}
};
/// Sparse codegen rule for value accesses.
class SparseToValuesConverter
: public SparseGetterOpConverter<ToValuesOp, SparseToValuesConverter> {
public:
using SparseGetterOpConverter::SparseGetterOpConverter;
// Callback for SparseGetterOpConverter.
static unsigned getIndexForOp(UnrealizedConversionCastOp tuple,
ToValuesOp /*op*/) {
// The last field holds the value buffer.
return tuple.getInputs().size() - 1;
}
};
/// Sparse codegen rule for the convert operator.
class SparseConvertConverter : public OpConversionPattern<ConvertOp> {
public:
using OpConversionPattern::OpConversionPattern;
LogicalResult
matchAndRewrite(ConvertOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
SparseTensorEncodingAttr encDst = getSparseTensorEncoding(op.getType());
SparseTensorEncodingAttr encSrc =
getSparseTensorEncoding(op.getSource().getType());
if (encDst != encSrc) {
// This should be handled by rewriting before codegen.
return failure();
}
rewriter.replaceOp(op, adaptor.getSource());
return success();
}
};
/// Sparse codegen rule for number of entries operator.
class SparseNumberOfEntriesConverter
: public OpConversionPattern<NumberOfEntriesOp> {
public:
using OpConversionPattern::OpConversionPattern;
LogicalResult
matchAndRewrite(NumberOfEntriesOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
// Query memSizes for the actually stored values size.
auto tuple = getTuple(adaptor.getTensor());
auto fields = tuple.getInputs();
unsigned lastField = fields.size() - 1;
Value field =
constantIndex(rewriter, op.getLoc(), getMemSizesIndex(lastField));
rewriter.replaceOpWithNewOp<memref::LoadOp>(op, fields[memSizesIdx], field);
return success();
}
};
} // namespace
//===----------------------------------------------------------------------===//
// Sparse tensor type conversion into an actual buffer.
//===----------------------------------------------------------------------===//
mlir::SparseTensorTypeToBufferConverter::SparseTensorTypeToBufferConverter() {
addConversion([](Type type) { return type; });
addConversion(convertSparseTensorType);
// Required by scf.for 1:N type conversion.
addSourceMaterialization([](OpBuilder &builder, RankedTensorType tp,
ValueRange inputs,
Location loc) -> Optional<Value> {
if (!getSparseTensorEncoding(tp))
// Not a sparse tensor.
return llvm::None;
// Sparse compiler knows how to cancel out these casts.
return genTuple(builder, loc, tp, inputs);
});
}
//===----------------------------------------------------------------------===//
// Public method for populating conversion rules.
//===----------------------------------------------------------------------===//
/// Populates the given patterns list with conversion rules required for
/// the sparsification of linear algebra operations.
void mlir::populateSparseTensorCodegenPatterns(
TypeConverter &typeConverter, RewritePatternSet &patterns,
bool enableBufferInitialization) {
patterns.add<SparseReturnConverter, SparseCallConverter, SparseDimOpConverter,
SparseCastConverter, SparseTensorDeallocConverter,
SparseTensorLoadConverter, SparseExpandConverter,
SparseCompressConverter, SparseInsertConverter,
SparseToPointersConverter, SparseToIndicesConverter,
SparseToValuesConverter, SparseConvertConverter,
SparseNumberOfEntriesConverter>(typeConverter,
patterns.getContext());
patterns.add<SparseTensorAllocConverter>(typeConverter, patterns.getContext(),
enableBufferInitialization);
}
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