1462 lines
63 KiB
C++
1462 lines
63 KiB
C++
//===- SparseTensorCodegen.cpp - Sparse tensor primitives conversion ------===//
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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//
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//===----------------------------------------------------------------------===//
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//
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// A pass that converts sparse tensor types and primitives to actual compiler
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// visible buffers and actual compiler IR that implements these primitives on
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// the selected sparse tensor storage schemes. This pass provides an alternative
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// to the SparseTensorConversion pass, eliminating the dependence on a runtime
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// support library, and providing much more opportunities for subsequent
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// compiler optimization of the generated code.
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//
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//===----------------------------------------------------------------------===//
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#include "CodegenUtils.h"
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#include "SparseTensorStorageLayout.h"
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#include "mlir/Dialect/Bufferization/IR/Bufferization.h"
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#include "mlir/Dialect/Func/IR/FuncOps.h"
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#include "mlir/Dialect/Linalg/Utils/Utils.h"
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#include "mlir/Dialect/MemRef/IR/MemRef.h"
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#include "mlir/Dialect/SparseTensor/IR/Enums.h"
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#include "mlir/Dialect/SparseTensor/IR/SparseTensor.h"
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#include "mlir/Dialect/SparseTensor/IR/SparseTensorType.h"
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#include "mlir/Dialect/SparseTensor/Transforms/Passes.h"
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#include "mlir/Dialect/Tensor/IR/Tensor.h"
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#include "mlir/Transforms/DialectConversion.h"
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#include "llvm/Support/FormatVariadic.h"
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#include <optional>
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using namespace mlir;
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using namespace mlir::sparse_tensor;
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namespace {
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using FuncGeneratorType =
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function_ref<void(OpBuilder &, ModuleOp, func::FuncOp, RankedTensorType)>;
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static constexpr const char kInsertFuncNamePrefix[] = "_insert_";
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//===----------------------------------------------------------------------===//
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// Helper methods.
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//===----------------------------------------------------------------------===//
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/// Flatten a list of operands that may contain sparse tensors.
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static void flattenOperands(ValueRange operands,
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SmallVectorImpl<Value> &flattened) {
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// In case of
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// sparse_tensor, c, sparse_tensor
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// ==>
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// memref ..., c, memref ...
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for (auto operand : operands) {
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if (getSparseTensorEncoding(operand.getType())) {
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auto tuple = getTuple(operand);
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// An unrealized_conversion_cast will be inserted by type converter to
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// inter-mix the gap between 1:N conversion between sparse tensors and
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// fields. In this case, take the operands in the cast and replace the
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// sparse tensor output with the flattened type array.
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flattened.append(tuple.getOperands().begin(), tuple.getOperands().end());
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} else {
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flattened.push_back(operand);
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}
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}
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}
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/// Generates a load with proper `index` typing.
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static Value genLoad(OpBuilder &builder, Location loc, Value mem, Value idx) {
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idx = genCast(builder, loc, idx, builder.getIndexType());
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return builder.create<memref::LoadOp>(loc, mem, idx);
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}
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/// Generates a store with proper `index` typing and proper value.
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static void genStore(OpBuilder &builder, Location loc, Value val, Value mem,
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Value idx) {
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idx = genCast(builder, loc, idx, builder.getIndexType());
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val = genCast(builder, loc, val,
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mem.getType().cast<ShapedType>().getElementType());
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builder.create<memref::StoreOp>(loc, val, mem, idx);
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}
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/// Creates a straightforward counting for-loop.
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static scf::ForOp createFor(OpBuilder &builder, Location loc, Value upper,
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MutableArrayRef<Value> fields,
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Value lower = Value()) {
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Type indexType = builder.getIndexType();
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if (!lower)
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lower = constantZero(builder, loc, indexType);
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Value one = constantOne(builder, loc, indexType);
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scf::ForOp forOp = builder.create<scf::ForOp>(loc, lower, upper, one, fields);
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for (unsigned i = 0, e = fields.size(); i < e; i++)
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fields[i] = forOp.getRegionIterArg(i);
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builder.setInsertionPointToStart(forOp.getBody());
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return forOp;
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}
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/// Gets the dimension size for the given sparse tensor at the given
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/// original dimension 'dim'.
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static Value sizeFromTensorAtDim(OpBuilder &builder, Location loc,
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SparseTensorDescriptor desc, Dimension dim) {
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const SparseTensorType stt(desc.getRankedTensorType());
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// Access into static dimension can query original type directly.
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// Note that this is typically already done by DimOp's folding.
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if (auto sz = stt.getStaticDimSize(dim))
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return constantIndex(builder, loc, *sz);
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// Any other query can consult the dimSizes array at field DimSizesIdx,
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// accounting for the reordering applied to the sparse storage.
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// FIXME: `toStoredDim` is deprecated.
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const Level lvl = toStoredDim(stt, dim);
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return desc.getLvlSize(builder, loc, lvl);
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}
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// Gets the dimension size at the given stored level 'lvl', either as a
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// constant for a static size, or otherwise dynamically through memSizes.
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static Value sizeFromTensorAtLvl(OpBuilder &builder, Location loc,
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SparseTensorDescriptor desc, Level lvl) {
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// FIXME: `toOrigDim` is deprecated.
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return sizeFromTensorAtDim(builder, loc, desc,
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toOrigDim(desc.getRankedTensorType(), lvl));
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}
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static void createPushback(OpBuilder &builder, Location loc,
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MutSparseTensorDescriptor desc,
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SparseTensorFieldKind kind, std::optional<Level> lvl,
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Value value, Value repeat = Value()) {
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Type etp = desc.getMemRefElementType(kind, lvl);
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Value field = desc.getMemRefField(kind, lvl);
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StorageSpecifierKind specFieldKind = toSpecifierKind(kind);
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auto pushBackOp = builder.create<PushBackOp>(
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loc, desc.getSpecifierField(builder, loc, specFieldKind, lvl), field,
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genCast(builder, loc, value, etp), repeat);
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desc.setMemRefField(kind, lvl, pushBackOp.getOutBuffer());
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desc.setSpecifierField(builder, loc, specFieldKind, lvl,
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pushBackOp.getNewSize());
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}
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/// Generates code that allocates a sparse storage scheme for given rank.
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static void allocSchemeForRank(OpBuilder &builder, Location loc,
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MutSparseTensorDescriptor desc, Level startLvl) {
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const SparseTensorType stt(desc.getRankedTensorType());
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Value linear = constantIndex(builder, loc, 1);
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const Level lvlRank = stt.getLvlRank();
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for (Level l = startLvl; l < lvlRank; l++) {
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const auto dlt = stt.getLvlType(l);
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if (isCompressedDLT(dlt)) {
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// Append linear x positions, initialized to zero. Since each compressed
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// dimension initially already has a single zero entry, this maintains
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// the desired "linear + 1" length property at all times.
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Value posZero = constantZero(builder, loc, stt.getPosType());
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createPushback(builder, loc, desc, SparseTensorFieldKind::PosMemRef, l,
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posZero, linear);
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return;
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}
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if (isSingletonDLT(dlt)) {
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return; // nothing to do
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}
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// Keep compounding the size, but nothing needs to be initialized
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// at this level. We will eventually reach a compressed level or
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// otherwise the values array for the from-here "all-dense" case.
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assert(isDenseDLT(dlt));
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Value size = sizeFromTensorAtLvl(builder, loc, desc, l);
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linear = builder.create<arith::MulIOp>(loc, linear, size);
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}
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// Reached values array so prepare for an insertion.
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Value valZero = constantZero(builder, loc, stt.getElementType());
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createPushback(builder, loc, desc, SparseTensorFieldKind::ValMemRef,
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std::nullopt, valZero, linear);
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}
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/// Creates allocation operation.
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static Value createAllocation(OpBuilder &builder, Location loc,
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MemRefType memRefType, Value sz,
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bool enableInit) {
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Value buffer = builder.create<memref::AllocOp>(loc, memRefType, sz);
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Type elemType = memRefType.getElementType();
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if (enableInit) {
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Value fillValue = constantZero(builder, loc, elemType);
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builder.create<linalg::FillOp>(loc, fillValue, buffer);
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}
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return buffer;
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}
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/// Creates allocation for each field in sparse tensor type. Note that
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/// for all dynamic memrefs, the memory size is really the capacity of
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/// the "vector", while the actual size resides in the sizes array.
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///
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/// TODO: for efficiency, we will need heuristics to make educated guesses
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/// on the required capacities (see heuristic variable).
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///
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static void createAllocFields(OpBuilder &builder, Location loc,
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SparseTensorType stt, ValueRange dynSizes,
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bool enableInit, SmallVectorImpl<Value> &fields,
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Value sizeHint) {
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// Build original sizes.
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assert((dynSizes.size() == static_cast<size_t>(stt.getNumDynamicDims())) &&
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"Got wrong number of dynamic sizes");
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const Dimension dimRank = stt.getDimRank();
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SmallVector<Value> dimSizes;
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dimSizes.reserve(dimRank);
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unsigned i = 0; // cumulative index into `dynSizes`.
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for (const DynSize sh : stt.getDimShape())
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dimSizes.push_back(ShapedType::isDynamic(sh)
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? dynSizes[i++]
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: constantIndex(builder, loc, sh));
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// Set up some heuristic sizes. We try to set the initial
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// size based on available information. Otherwise we just
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// initialize a few elements to start the reallocation chain.
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// TODO: refine this
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Value posHeuristic, crdHeuristic, valHeuristic;
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if (stt.isAllDense()) {
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valHeuristic = dimSizes[0];
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for (const Value sz : ArrayRef<Value>{dimSizes}.drop_front())
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valHeuristic = builder.create<arith::MulIOp>(loc, valHeuristic, sz);
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} else if (sizeHint) {
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if (getCOOStart(stt.getEncoding()) == 0) {
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posHeuristic = constantIndex(builder, loc, 2);
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crdHeuristic = builder.create<arith::MulIOp>(
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loc, constantIndex(builder, loc, dimRank), sizeHint); // AOS
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} else if (dimRank == 2 && stt.isDenseLvl(0) && stt.isCompressedLvl(1)) {
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posHeuristic = builder.create<arith::AddIOp>(
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loc, sizeHint, constantIndex(builder, loc, 1));
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crdHeuristic = sizeHint;
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} else {
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posHeuristic = crdHeuristic = constantIndex(builder, loc, 16);
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}
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valHeuristic = sizeHint;
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} else {
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posHeuristic = crdHeuristic = valHeuristic =
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constantIndex(builder, loc, 16);
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}
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foreachFieldAndTypeInSparseTensor(
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stt,
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[&builder, &fields, stt, loc, posHeuristic, crdHeuristic, valHeuristic,
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enableInit](Type fType, FieldIndex fIdx, SparseTensorFieldKind fKind,
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Level /*lvl*/, DimLevelType /*dlt*/) -> bool {
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assert(fields.size() == fIdx);
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Value field;
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switch (fKind) {
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case SparseTensorFieldKind::StorageSpec:
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field = SparseTensorSpecifier::getInitValue(builder, loc, stt);
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break;
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case SparseTensorFieldKind::PosMemRef:
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case SparseTensorFieldKind::CrdMemRef:
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case SparseTensorFieldKind::ValMemRef:
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field = createAllocation(
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builder, loc, fType.cast<MemRefType>(),
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(fKind == SparseTensorFieldKind::PosMemRef) ? posHeuristic
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: (fKind == SparseTensorFieldKind::CrdMemRef) ? crdHeuristic
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: valHeuristic,
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enableInit);
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break;
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}
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assert(field);
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fields.push_back(field);
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// Returns true to continue the iteration.
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return true;
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});
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MutSparseTensorDescriptor desc(stt, fields);
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// Initialize the storage scheme to an empty tensor. Initialized memSizes
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// to all zeros, sets the dimSizes to known values and gives all position
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// fields an initial zero entry, so that it is easier to maintain the
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// "linear + 1" length property.
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Value posZero = constantZero(builder, loc, stt.getPosType());
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for (Level lvlRank = stt.getLvlRank(), l = 0; l < lvlRank; l++) {
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// Fills dim sizes array.
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// FIXME: `toOrigDim` is deprecated.
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desc.setLvlSize(builder, loc, l, dimSizes[toOrigDim(stt, l)]);
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// Pushes a leading zero to positions memref.
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if (stt.isCompressedLvl(l))
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createPushback(builder, loc, desc, SparseTensorFieldKind::PosMemRef, l,
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posZero);
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}
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allocSchemeForRank(builder, loc, desc, /*rank=*/0);
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}
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/// Helper method that generates block specific to compressed case:
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///
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/// // given: parentPos = posCursor[lvl-1]
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/// pstart = desc.positions[lvl][parentPos]
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/// pstop = desc.positions[lvl][parentPos+1]
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/// plast = pstop - 1
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/// msz = desc.coordinates[lvl].size()
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/// if (pstart < pstop) {
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/// isPresent = (desc.coordinates[lvl][plast] == lvlCoords[lvl])
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/// } else { // first insertion
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/// isPresent = false
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/// desc.positions[lvl][parentPos] = msz
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/// }
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/// if (isPresent) { // coordinate is already present
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/// pnext = plast
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/// } else {
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/// desc.coordinates[lvl].push_back(lvlCoords[lvl])
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/// desc.positions[lvl][parentPos+1] = msz+1
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/// pnext = msz
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/// <prepare level lvl+1>
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/// }
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/// posCursor[lvl] = pnext
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static Value genCompressed(OpBuilder &builder, Location loc,
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MutSparseTensorDescriptor desc, ValueRange lvlCoords,
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Value /*unused*/, Value parentPos, Level lvl) {
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const SparseTensorType stt(desc.getRankedTensorType());
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const Level lvlRank = stt.getLvlRank();
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assert(lvl < lvlRank && "Level is out of bounds");
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assert(lvlCoords.size() == static_cast<size_t>(lvlRank) &&
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"Level-rank mismatch");
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SmallVector<Type> types;
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Type indexType = builder.getIndexType();
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Type boolType = builder.getIntegerType(1);
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unsigned crdFidx;
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unsigned crdStride;
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std::tie(crdFidx, crdStride) = desc.getCrdMemRefIndexAndStride(lvl);
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const Value one = constantIndex(builder, loc, 1);
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const Value pp1 = builder.create<arith::AddIOp>(loc, parentPos, one);
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const Value positionsAtLvl = desc.getPosMemRef(lvl);
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const Value pstart = genLoad(builder, loc, positionsAtLvl, parentPos);
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const Value pstop = genLoad(builder, loc, positionsAtLvl, pp1);
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const Value crdMsz = desc.getCrdMemSize(builder, loc, lvl);
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const Value crdStrideC =
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crdStride > 1 ? constantIndex(builder, loc, crdStride) : Value();
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const Value msz =
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crdStrideC ? builder.create<arith::DivUIOp>(loc, crdMsz, crdStrideC)
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: crdMsz;
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const Value plast = builder.create<arith::SubIOp>(
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loc, genCast(builder, loc, pstop, indexType), one);
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// Conditional expression.
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Value lt = builder.create<arith::CmpIOp>(loc, arith::CmpIPredicate::ult,
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pstart, pstop);
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types.push_back(boolType);
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scf::IfOp ifOp1 = builder.create<scf::IfOp>(loc, types, lt, /*else*/ true);
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types.pop_back();
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builder.setInsertionPointToStart(&ifOp1.getThenRegion().front());
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Value crd =
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genLoad(builder, loc, desc.getMemRefField(crdFidx),
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crdStrideC ? builder.create<arith::MulIOp>(loc, plast, crdStrideC)
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: plast);
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Value eq = builder.create<arith::CmpIOp>(
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loc, arith::CmpIPredicate::eq, genCast(builder, loc, crd, indexType),
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lvlCoords[lvl]);
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builder.create<scf::YieldOp>(loc, eq);
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builder.setInsertionPointToStart(&ifOp1.getElseRegion().front());
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if (lvl > 0)
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genStore(builder, loc, msz, positionsAtLvl, parentPos);
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builder.create<scf::YieldOp>(loc, constantI1(builder, loc, false));
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builder.setInsertionPointAfter(ifOp1);
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// If present construct. Note that for a non-unique dimension level, we
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// simply set the condition to false and rely on CSE/DCE to clean up the IR.
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//
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// TODO: generate less temporary IR?
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//
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for (unsigned i = 0, e = desc.getNumFields(); i < e; i++)
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types.push_back(desc.getField(i).getType());
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types.push_back(indexType);
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const Value p = stt.isUniqueLvl(lvl) ? ifOp1.getResult(0)
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: constantI1(builder, loc, false);
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scf::IfOp ifOp2 = builder.create<scf::IfOp>(loc, types, p, /*else*/ true);
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// If present (fields unaffected, update pnext to plast).
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builder.setInsertionPointToStart(&ifOp2.getThenRegion().front());
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// FIXME: This does not looks like a clean way, but probably the most
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// efficient way.
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desc.getFields().push_back(plast);
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builder.create<scf::YieldOp>(loc, desc.getFields());
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desc.getFields().pop_back();
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// If !present (changes fields, update pnext).
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builder.setInsertionPointToStart(&ifOp2.getElseRegion().front());
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Value mszp1 = builder.create<arith::AddIOp>(loc, msz, one);
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genStore(builder, loc, mszp1, positionsAtLvl, pp1);
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createPushback(builder, loc, desc, SparseTensorFieldKind::CrdMemRef, lvl,
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lvlCoords[lvl]);
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// Prepare the next level "as needed".
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if ((lvl + 1) < lvlRank)
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allocSchemeForRank(builder, loc, desc, lvl + 1);
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desc.getFields().push_back(msz);
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builder.create<scf::YieldOp>(loc, desc.getFields());
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desc.getFields().pop_back();
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// Update fields and return next pos.
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builder.setInsertionPointAfter(ifOp2);
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unsigned o = 0;
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for (unsigned i = 0, e = desc.getNumFields(); i < e; i++)
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desc.setField(i, ifOp2.getResult(o++));
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return ifOp2.getResult(o);
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}
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/// Generates code along an insertion path without the need for a "cursor".
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/// This current insertion strategy comes at the expense of some testing
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/// overhead for each insertion. The strategy will be optimized later for
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/// common insertion patterns. The current insertion strategy also assumes
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/// insertions occur in "a reasonable order" that enables building the
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/// storage scheme in an appending/inserting kind of fashion (i.e. no
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/// in-between insertions that need data movement). The implementation
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/// relies on CSE/DCE to clean up all bookkeeping that is not needed.
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///
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/// TODO: better unord/not-unique; also generalize, optimize, specialize!
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///
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static void genInsertBody(OpBuilder &builder, ModuleOp module,
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func::FuncOp func, RankedTensorType rtp) {
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const OpBuilder::InsertionGuard insertionGuard(builder);
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Block *const entryBlock = func.addEntryBlock();
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builder.setInsertionPointToStart(entryBlock);
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const ValueRange args = entryBlock->getArguments();
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const Location loc = func.getLoc();
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const SparseTensorType stt(rtp);
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const Level lvlRank = stt.getLvlRank();
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// Extract fields and coordinates from args.
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SmallVector<Value> fields = llvm::to_vector(args.drop_back(lvlRank + 1));
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MutSparseTensorDescriptor desc(rtp, fields);
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const SmallVector<Value> coordinates =
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llvm::to_vector(args.take_back(lvlRank + 1).drop_back());
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Value value = args.back();
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Value parentPos = constantZero(builder, loc, builder.getIndexType());
|
|
// Generate code for every level.
|
|
for (Level l = 0; l < lvlRank; l++) {
|
|
const auto dlt = stt.getLvlType(l);
|
|
if (isCompressedDLT(dlt)) {
|
|
// Create:
|
|
// if (!present) {
|
|
// coordinates[l].push_back(coords[l])
|
|
// <update positions and prepare level l + 1>
|
|
// }
|
|
// positions[l] = coordinates.size() - 1
|
|
// <insert @ positions[l] at next level l + 1>
|
|
parentPos =
|
|
genCompressed(builder, loc, desc, coordinates, value, parentPos, l);
|
|
} else if (isSingletonDLT(dlt)) {
|
|
// Create:
|
|
// coordinates[l].push_back(coords[l])
|
|
// positions[l] = positions[l-1]
|
|
// <insert @ positions[l] at next level l + 1>
|
|
createPushback(builder, loc, desc, SparseTensorFieldKind::CrdMemRef, l,
|
|
coordinates[l]);
|
|
} else {
|
|
assert(isDenseDLT(dlt));
|
|
// Construct the new position as:
|
|
// positions[l] = size * positions[l-1] + coords[l]
|
|
// <insert @ positions[l] at next level l + 1>
|
|
Value size = sizeFromTensorAtLvl(builder, loc, desc, l);
|
|
Value mult = builder.create<arith::MulIOp>(loc, size, parentPos);
|
|
parentPos = builder.create<arith::AddIOp>(loc, mult, coordinates[l]);
|
|
}
|
|
}
|
|
// Reached the actual value append/insert.
|
|
if (!stt.isDenseLvl(lvlRank - 1))
|
|
createPushback(builder, loc, desc, SparseTensorFieldKind::ValMemRef,
|
|
std::nullopt, value);
|
|
else
|
|
genStore(builder, loc, value, desc.getValMemRef(), parentPos);
|
|
builder.create<func::ReturnOp>(loc, fields);
|
|
}
|
|
|
|
/// Generates a call to a function to perform an insertion operation. If the
|
|
/// function doesn't exist yet, call `createFunc` to generate the function.
|
|
static void genInsertionCallHelper(OpBuilder &builder,
|
|
MutSparseTensorDescriptor desc,
|
|
SmallVectorImpl<Value> &lcvs, Value value,
|
|
func::FuncOp insertPoint,
|
|
StringRef namePrefix,
|
|
FuncGeneratorType createFunc) {
|
|
// The mangled name of the function has this format:
|
|
// <namePrefix>_<DLT>_<shape>_<ordering>_<eltType>_<crdWidth>_<posWidth>
|
|
const SparseTensorType stt(desc.getRankedTensorType());
|
|
SmallString<32> nameBuffer;
|
|
llvm::raw_svector_ostream nameOstream(nameBuffer);
|
|
nameOstream << namePrefix;
|
|
const Level lvlRank = stt.getLvlRank();
|
|
assert(lcvs.size() == static_cast<size_t>(lvlRank));
|
|
for (Level l = 0; l < lvlRank; l++)
|
|
nameOstream << toMLIRString(stt.getLvlType(l)) << "_";
|
|
// Static dim sizes are used in the generated code while dynamic sizes are
|
|
// loaded from the dimSizes buffer. This is the reason for adding the shape
|
|
// to the function name.
|
|
for (const auto sh : stt.getDimShape())
|
|
nameOstream << sh << "_";
|
|
// Permutation information is also used in generating insertion.
|
|
if (!stt.isIdentity())
|
|
nameOstream << stt.getDimToLvlMap() << "_";
|
|
nameOstream << stt.getElementType() << "_";
|
|
nameOstream << stt.getCrdWidth() << "_" << stt.getPosWidth();
|
|
|
|
// Look up the function.
|
|
ModuleOp module = insertPoint->getParentOfType<ModuleOp>();
|
|
MLIRContext *context = module.getContext();
|
|
auto result = SymbolRefAttr::get(context, nameOstream.str());
|
|
auto func = module.lookupSymbol<func::FuncOp>(result.getAttr());
|
|
|
|
// Construct operands: fields, coords, and value.
|
|
SmallVector<Value> operands = llvm::to_vector(desc.getFields());
|
|
operands.append(lcvs);
|
|
operands.push_back(value);
|
|
Location loc = insertPoint.getLoc();
|
|
|
|
if (!func) {
|
|
// Create the function.
|
|
OpBuilder::InsertionGuard insertionGuard(builder);
|
|
builder.setInsertionPoint(insertPoint);
|
|
|
|
func = builder.create<func::FuncOp>(
|
|
loc, nameOstream.str(),
|
|
FunctionType::get(context, ValueRange(operands).getTypes(),
|
|
ValueRange(desc.getFields()).getTypes()));
|
|
func.setPrivate();
|
|
createFunc(builder, module, func, stt);
|
|
}
|
|
|
|
// Generate a call to perform the insertion and update `fields` with values
|
|
// returned from the call.
|
|
func::CallOp call = builder.create<func::CallOp>(loc, func, operands);
|
|
for (size_t i = 0, e = desc.getNumFields(); i < e; i++) {
|
|
desc.getFields()[i] = call.getResult(i);
|
|
}
|
|
}
|
|
|
|
/// Generations insertion finalization code.
|
|
static void genEndInsert(OpBuilder &builder, Location loc,
|
|
SparseTensorDescriptor desc) {
|
|
const SparseTensorType stt(desc.getRankedTensorType());
|
|
const Level lvlRank = stt.getLvlRank();
|
|
for (Level l = 0; l < lvlRank; l++) {
|
|
const auto dlt = stt.getLvlType(l);
|
|
if (isCompressedDLT(dlt)) {
|
|
// Compressed dimensions need a position cleanup for all entries
|
|
// that were not visited during the insertion pass.
|
|
//
|
|
// TODO: avoid cleanup and keep compressed scheme consistent at all
|
|
// times?
|
|
//
|
|
if (l > 0) {
|
|
Type posType = stt.getPosType();
|
|
Value posMemRef = desc.getPosMemRef(l);
|
|
Value hi = desc.getPosMemSize(builder, loc, l);
|
|
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, posMemRef, zero)};
|
|
scf::ForOp loop = createFor(builder, loc, hi, inits, one);
|
|
Value i = loop.getInductionVar();
|
|
Value oldv = loop.getRegionIterArg(0);
|
|
Value newv = genLoad(builder, loc, posMemRef, i);
|
|
Value posZero = constantZero(builder, loc, posType);
|
|
Value cond = builder.create<arith::CmpIOp>(
|
|
loc, arith::CmpIPredicate::eq, newv, posZero);
|
|
scf::IfOp ifOp = builder.create<scf::IfOp>(loc, TypeRange(posType),
|
|
cond, /*else*/ true);
|
|
builder.setInsertionPointToStart(&ifOp.getThenRegion().front());
|
|
genStore(builder, loc, oldv, posMemRef, 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);
|
|
}
|
|
} else {
|
|
assert(isDenseDLT(dlt) || isSingletonDLT(dlt));
|
|
}
|
|
}
|
|
}
|
|
|
|
/// Returns a memref that fits the requested length (reallocates if requested
|
|
/// length is larger, or creates a subview if it is smaller).
|
|
static Value reallocOrSubView(OpBuilder &builder, Location loc, int64_t len,
|
|
Value buffer) {
|
|
MemRefType memTp = getMemRefType(buffer);
|
|
auto retTp = MemRefType::get(ArrayRef{len}, memTp.getElementType());
|
|
|
|
Value targetLen = constantIndex(builder, loc, len);
|
|
Value bufferLen = linalg::createOrFoldDimOp(builder, loc, buffer, 0);
|
|
// Reallocates if target length is greater than the actual buffer len.
|
|
Value reallocP = builder.create<arith::CmpIOp>(loc, arith::CmpIPredicate::ugt,
|
|
targetLen, bufferLen);
|
|
scf::IfOp ifOp = builder.create<scf::IfOp>(loc, retTp, reallocP, true);
|
|
// If targetLen > bufferLen, reallocate to get enough sparse to return.
|
|
builder.setInsertionPointToStart(&ifOp.getThenRegion().front());
|
|
Value reallocBuf = builder.create<memref::ReallocOp>(loc, retTp, buffer);
|
|
builder.create<scf::YieldOp>(loc, reallocBuf);
|
|
// Else, return a subview to fit the size.
|
|
builder.setInsertionPointToStart(&ifOp.getElseRegion().front());
|
|
Value subViewBuf = builder.create<memref::SubViewOp>(
|
|
loc, retTp, buffer, /*offset=*/ArrayRef<int64_t>{0},
|
|
/*size=*/ArrayRef<int64_t>{len},
|
|
/*stride=*/ArrayRef<int64_t>{1});
|
|
builder.create<scf::YieldOp>(loc, subViewBuf);
|
|
// Resets insertion point.
|
|
builder.setInsertionPointAfter(ifOp);
|
|
return ifOp.getResult(0);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// 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 {
|
|
std::optional<int64_t> dim = op.getConstantIndex();
|
|
if (!dim || !getSparseTensorEncoding(adaptor.getSource().getType()))
|
|
return failure();
|
|
|
|
auto desc = getDescriptorFromTensorTuple(adaptor.getSource());
|
|
auto sz = sizeFromTensorAtDim(rewriter, op.getLoc(), desc, *dim);
|
|
|
|
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 {
|
|
const auto resType = getSparseTensorType(op);
|
|
if (!resType.hasEncoding())
|
|
return failure();
|
|
if (op.getCopy())
|
|
return rewriter.notifyMatchFailure(op, "tensor copy not implemented");
|
|
|
|
// Construct allocation for each field.
|
|
const Location loc = op.getLoc();
|
|
const Value sizeHint = op.getSizeHint();
|
|
const ValueRange dynSizes = adaptor.getDynamicSizes();
|
|
const size_t found = dynSizes.size();
|
|
const int64_t expected = resType.getNumDynamicDims();
|
|
if (found != static_cast<size_t>(expected))
|
|
return rewriter.notifyMatchFailure(
|
|
op, llvm::formatv(
|
|
"Got wrong number of dynamic sizes: Found={0}, Expected={1}",
|
|
found, expected));
|
|
SmallVector<Value> fields;
|
|
createAllocFields(rewriter, loc, resType, dynSizes,
|
|
enableBufferInitialization, fields, sizeHint);
|
|
// 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 desc = getDescriptorFromTensorTuple(adaptor.getTensor());
|
|
for (auto input : desc.getMemRefFields())
|
|
// 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 {
|
|
// Prepare descriptor.
|
|
auto desc = getDescriptorFromTensorTuple(adaptor.getTensor());
|
|
// Generate optional insertion finalization code.
|
|
if (op.getHasInserts())
|
|
genEndInsert(rewriter, op.getLoc(), desc);
|
|
// Replace operation with resulting memrefs.
|
|
rewriter.replaceOp(op, genTuple(rewriter, op.getLoc(), desc));
|
|
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 {
|
|
if (!getSparseTensorEncoding(op.getTensor().getType()))
|
|
return failure();
|
|
Location loc = op->getLoc();
|
|
auto desc = getDescriptorFromTensorTuple(adaptor.getTensor());
|
|
const auto srcType = getSparseTensorType(op.getTensor());
|
|
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.
|
|
// FIXME: `toOrigDim` is deprecated.
|
|
const Dimension innerDim = toOrigDim(srcType, srcType.getLvlRank() - 1);
|
|
const auto sz = sizeFromTensorAtDim(rewriter, loc, desc, innerDim);
|
|
// Generate a memref for `sz` elements of type `t`.
|
|
const auto genAlloc = [&](Type t) {
|
|
const 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 coordinate.
|
|
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();
|
|
SmallVector<Value> fields;
|
|
auto desc = getMutDescriptorFromTensorTuple(adaptor.getTensor(), fields);
|
|
Value values = adaptor.getValues();
|
|
Value filled = adaptor.getFilled();
|
|
Value added = adaptor.getAdded();
|
|
Value count = adaptor.getCount();
|
|
const SparseTensorType dstType(desc.getRankedTensorType());
|
|
Type eltType = dstType.getElementType();
|
|
// Prepare level-coords.
|
|
SmallVector<Value> lcvs(adaptor.getLvlCoords());
|
|
// If the innermost level is ordered, we need to sort the coordinates
|
|
// in the "added" array prior to applying the compression.
|
|
if (dstType.isOrderedLvl(dstType.getLvlRank() - 1))
|
|
rewriter.create<SortOp>(loc, count, ValueRange{added}, ValueRange{},
|
|
SparseTensorSortKind::HybridQuickSort);
|
|
// 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) {
|
|
// crd = added[i];
|
|
// value = values[crd];
|
|
// insert({lvlCoords, crd}, value);
|
|
// new_memrefs = insert(in_memrefs, {lvlCoords, crd}, value);
|
|
// values[crd] = 0;
|
|
// filled[crd] = false;
|
|
// yield new_memrefs
|
|
// }
|
|
scf::ForOp loop = createFor(rewriter, loc, count, desc.getFields());
|
|
Value i = loop.getInductionVar();
|
|
Value crd = genLoad(rewriter, loc, added, i);
|
|
Value value = genLoad(rewriter, loc, values, crd);
|
|
lcvs.push_back(crd);
|
|
// TODO: faster for subsequent insertions?
|
|
auto insertPoint = op->template getParentOfType<func::FuncOp>();
|
|
genInsertionCallHelper(rewriter, desc, lcvs, value, insertPoint,
|
|
kInsertFuncNamePrefix, genInsertBody);
|
|
genStore(rewriter, loc, constantZero(rewriter, loc, eltType), values, crd);
|
|
genStore(rewriter, loc, constantI1(rewriter, loc, false), filled, crd);
|
|
rewriter.create<scf::YieldOp>(loc, desc.getFields());
|
|
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 {
|
|
SmallVector<Value> fields;
|
|
auto desc = getMutDescriptorFromTensorTuple(adaptor.getTensor(), fields);
|
|
SmallVector<Value> lcvs(adaptor.getLvlCoords());
|
|
// Generate insertion.
|
|
Value value = adaptor.getValue();
|
|
auto insertPoint = op->template getParentOfType<func::FuncOp>();
|
|
genInsertionCallHelper(rewriter, desc, lcvs, value, insertPoint,
|
|
kInsertFuncNamePrefix, genInsertBody);
|
|
|
|
// Replace operation with resulting memrefs.
|
|
rewriter.replaceOp(op, genTuple(rewriter, op.getLoc(), desc));
|
|
return success();
|
|
}
|
|
};
|
|
|
|
/// Sparse codegen rule for position accesses.
|
|
class SparseToPositionsConverter : public OpConversionPattern<ToPositionsOp> {
|
|
public:
|
|
using OpAdaptor = typename ToPositionsOp::Adaptor;
|
|
using OpConversionPattern<ToPositionsOp>::OpConversionPattern;
|
|
LogicalResult
|
|
matchAndRewrite(ToPositionsOp op, OpAdaptor adaptor,
|
|
ConversionPatternRewriter &rewriter) const override {
|
|
// Replace the requested position access with corresponding field.
|
|
// The cast_op is inserted by type converter to intermix 1:N type
|
|
// conversion.
|
|
auto desc = getDescriptorFromTensorTuple(adaptor.getTensor());
|
|
rewriter.replaceOp(op, desc.getPosMemRef(op.getLevel()));
|
|
return success();
|
|
}
|
|
};
|
|
|
|
/// Sparse codegen rule for accessing the coordinates arrays.
|
|
class SparseToCoordinatesConverter
|
|
: public OpConversionPattern<ToCoordinatesOp> {
|
|
public:
|
|
using OpAdaptor = typename ToCoordinatesOp::Adaptor;
|
|
using OpConversionPattern<ToCoordinatesOp>::OpConversionPattern;
|
|
LogicalResult
|
|
matchAndRewrite(ToCoordinatesOp op, OpAdaptor adaptor,
|
|
ConversionPatternRewriter &rewriter) const override {
|
|
// Replace the requested coordinates access with corresponding field.
|
|
// The cast_op is inserted by type converter to intermix 1:N type
|
|
// conversion.
|
|
Location loc = op.getLoc();
|
|
auto desc = getDescriptorFromTensorTuple(adaptor.getTensor());
|
|
Value field = desc.getCrdMemRefOrView(rewriter, loc, op.getLevel());
|
|
|
|
// Insert a cast to bridge the actual type to the user expected type. If the
|
|
// actual type and the user expected type aren't compatible, the compiler or
|
|
// the runtime will issue an error.
|
|
Type resType = op.getResult().getType();
|
|
if (resType != field.getType())
|
|
field = rewriter.create<memref::CastOp>(loc, resType, field);
|
|
rewriter.replaceOp(op, field);
|
|
|
|
return success();
|
|
}
|
|
};
|
|
|
|
/// Sparse codegen rule for accessing the linear coordinates buffer.
|
|
class SparseToCoordinatesBufferConverter
|
|
: public OpConversionPattern<ToCoordinatesBufferOp> {
|
|
public:
|
|
using OpAdaptor = typename ToCoordinatesBufferOp::Adaptor;
|
|
using OpConversionPattern<ToCoordinatesBufferOp>::OpConversionPattern;
|
|
LogicalResult
|
|
matchAndRewrite(ToCoordinatesBufferOp op, OpAdaptor adaptor,
|
|
ConversionPatternRewriter &rewriter) const override {
|
|
// Replace the requested coordinates access with corresponding field.
|
|
// The cast_op is inserted by type converter to intermix 1:N type
|
|
// conversion.
|
|
auto desc = getDescriptorFromTensorTuple(adaptor.getTensor());
|
|
rewriter.replaceOp(op, desc.getAOSMemRef());
|
|
|
|
return success();
|
|
}
|
|
};
|
|
|
|
/// Sparse codegen rule for value accesses.
|
|
class SparseToValuesConverter : public OpConversionPattern<ToValuesOp> {
|
|
public:
|
|
using OpAdaptor = typename ToValuesOp::Adaptor;
|
|
using OpConversionPattern<ToValuesOp>::OpConversionPattern;
|
|
LogicalResult
|
|
matchAndRewrite(ToValuesOp op, OpAdaptor adaptor,
|
|
ConversionPatternRewriter &rewriter) const override {
|
|
// Replace the requested values access with corresponding field.
|
|
// The cast_op is inserted by type converter to intermix 1:N type
|
|
// conversion.
|
|
auto desc = getDescriptorFromTensorTuple(adaptor.getTensor());
|
|
rewriter.replaceOp(op, desc.getValMemRef());
|
|
return success();
|
|
}
|
|
};
|
|
|
|
/// 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());
|
|
// Different encoding (except for different bitwidth) should be handled by
|
|
// rewriting.
|
|
if (encDst.withoutBitWidths() != encSrc.withoutBitWidths()) {
|
|
return failure();
|
|
}
|
|
|
|
Type retElemTp = op.getResult().getType().getElementType();
|
|
Type srcElemTp = op.getSource().getType().getElementType();
|
|
// Fold the trivial cases.
|
|
if (retElemTp == srcElemTp && encDst == encSrc) {
|
|
rewriter.replaceOp(op, adaptor.getSource());
|
|
return success();
|
|
}
|
|
//
|
|
// Do element-wise type conversion without using InsertOp.
|
|
//
|
|
// for each memref in srcTensor:
|
|
// dst = memref.alloc
|
|
// if srcMemRefType != dstMemRefType:
|
|
// for every dst[i] = cast(src[i])
|
|
// else:
|
|
// dst = memref.copy(src)
|
|
Location loc = op.getLoc();
|
|
auto srcDesc = getDescriptorFromTensorTuple(adaptor.getSource());
|
|
SmallVector<Value> fields;
|
|
foreachFieldAndTypeInSparseTensor(
|
|
SparseTensorType(op.getResult().getType().cast<RankedTensorType>()),
|
|
[&rewriter, &fields, srcDesc,
|
|
loc](Type fTp, FieldIndex fIdx, SparseTensorFieldKind fKind, Level lvl,
|
|
DimLevelType /*dlt*/) -> bool {
|
|
// Simply reuses the storage specifier as it is an SSA value.
|
|
if (fKind == SparseTensorFieldKind::StorageSpec) {
|
|
fields.push_back(srcDesc.getSpecifier());
|
|
} else {
|
|
// Allocates new memrefs
|
|
Value srcMem = srcDesc.getMemRefField(fIdx);
|
|
// TODO: We can instead use the actual memSize in specifier, that
|
|
// would require a subViewOp to avoid overflow when copying
|
|
// values.
|
|
Value sz = linalg::createOrFoldDimOp(rewriter, loc, srcMem, 0);
|
|
auto dstMem = rewriter.create<memref::AllocOp>(
|
|
loc, fTp.cast<MemRefType>(), sz);
|
|
if (fTp != srcMem.getType()) {
|
|
// Converts elements type.
|
|
scf::buildLoopNest(
|
|
rewriter, loc, constantIndex(rewriter, loc, 0), sz,
|
|
constantIndex(rewriter, loc, 1),
|
|
[srcMem, &dstMem](OpBuilder &builder, Location loc,
|
|
ValueRange ivs) {
|
|
Value v = builder.create<memref::LoadOp>(loc, srcMem, ivs);
|
|
Value casted = genCast(builder, loc, v,
|
|
dstMem.getType().getElementType());
|
|
builder.create<memref::StoreOp>(loc, casted, dstMem, ivs);
|
|
});
|
|
} else {
|
|
// TODO: We can even reuse the same memref for the new tensor,
|
|
// but that requires a `ref-counting` based memory management
|
|
// for shared memrefs between multiple sparse tensors.
|
|
rewriter.create<memref::CopyOp>(loc, srcMem, dstMem);
|
|
}
|
|
fields.push_back(dstMem);
|
|
}
|
|
return true;
|
|
});
|
|
|
|
rewriter.replaceOp(
|
|
op, genTuple(rewriter, loc, op.getResult().getType(), fields));
|
|
return success();
|
|
}
|
|
};
|
|
|
|
class SparseExtractSliceCoverter
|
|
: public OpConversionPattern<tensor::ExtractSliceOp> {
|
|
public:
|
|
using OpConversionPattern::OpConversionPattern;
|
|
LogicalResult
|
|
matchAndRewrite(tensor::ExtractSliceOp op, OpAdaptor adaptor,
|
|
ConversionPatternRewriter &rewriter) const override {
|
|
auto srcEnc = getSparseTensorEncoding(op.getSourceType());
|
|
auto dstEnc = getSparseTensorEncoding(op.getResult().getType());
|
|
if (!srcEnc && !dstEnc)
|
|
return failure();
|
|
|
|
// TODO: We should check these in ExtractSliceOp::verify.
|
|
assert(srcEnc && dstEnc && dstEnc.isSlice());
|
|
assert(srcEnc.getDimLevelType() == dstEnc.getDimLevelType());
|
|
assert(srcEnc.getDimOrdering() == dstEnc.getDimOrdering());
|
|
assert(srcEnc.getHigherOrdering() == dstEnc.getHigherOrdering());
|
|
assert(srcEnc.getPosWidth() == dstEnc.getPosWidth());
|
|
assert(srcEnc.getCrdWidth() == dstEnc.getCrdWidth());
|
|
|
|
// TODO: support dynamic slices.
|
|
for (int i = 0, e = op.getSourceType().getRank(); i < e; i++) {
|
|
assert(op.getStaticStrides()[i] == dstEnc.getStaticDimSliceStride(i));
|
|
assert(op.getStaticOffsets()[i] == dstEnc.getStaticDimSliceOffset(i));
|
|
assert(op.getStaticSizes()[i] == dstEnc.getStaticDimSliceSize(i));
|
|
}
|
|
|
|
// TODO: create a new specifer for slices (need to encode slice metadata).
|
|
// It does not matter now because only constant offset/stride are allowed.
|
|
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.
|
|
rewriter.replaceOp(
|
|
op, genValMemSize(rewriter, op.getLoc(), adaptor.getTensor()));
|
|
return success();
|
|
}
|
|
};
|
|
|
|
struct SparsePackOpConverter : public OpConversionPattern<PackOp> {
|
|
using OpConversionPattern::OpConversionPattern;
|
|
LogicalResult
|
|
matchAndRewrite(PackOp op, OpAdaptor adaptor,
|
|
ConversionPatternRewriter &rewriter) const override {
|
|
|
|
const auto rtp = getRankedTensorType(op.getResult());
|
|
assert(isUniqueCOOType(rtp));
|
|
|
|
SmallVector<Value> fields;
|
|
Location loc = op.getLoc();
|
|
|
|
foreachFieldAndTypeInSparseTensor(
|
|
rtp,
|
|
[&rewriter, &fields, &op, rtp,
|
|
loc](Type fType, FieldIndex fIdx, SparseTensorFieldKind fKind,
|
|
Level /*lvl*/, DimLevelType /*dlt*/) -> bool {
|
|
assert(fields.size() == fIdx);
|
|
auto enc = getSparseTensorEncoding(rtp);
|
|
Value field;
|
|
switch (fKind) {
|
|
case SparseTensorFieldKind::StorageSpec:
|
|
field = SparseTensorSpecifier::getInitValue(rewriter, loc, rtp);
|
|
break;
|
|
case SparseTensorFieldKind::PosMemRef: {
|
|
// TACO-style COO starts with a PosBuffer
|
|
// By creating a constant value for it, we avoid the complexity of
|
|
// memory management.
|
|
const auto posTp = enc.getPosType();
|
|
auto tensorType = RankedTensorType::get({2}, posTp);
|
|
auto memrefType = MemRefType::get(tensorType.getShape(),
|
|
tensorType.getElementType());
|
|
auto cstPtr = rewriter.create<arith::ConstantOp>(
|
|
loc, tensorType,
|
|
DenseElementsAttr::get(
|
|
tensorType,
|
|
ArrayRef<Attribute>{
|
|
IntegerAttr::get(posTp, 0),
|
|
IntegerAttr::get(
|
|
posTp, op.getValues().getType().getShape()[0])}));
|
|
field = rewriter.create<bufferization::ToMemrefOp>(loc, memrefType,
|
|
cstPtr);
|
|
break;
|
|
}
|
|
case SparseTensorFieldKind::CrdMemRef: {
|
|
auto tensorType = op.getCoordinates().getType();
|
|
auto memrefType = MemRefType::get(tensorType.getShape(),
|
|
tensorType.getElementType());
|
|
auto crdMemRef = rewriter.create<bufferization::ToMemrefOp>(
|
|
op->getLoc(), memrefType, op.getCoordinates());
|
|
ReassociationIndices reassociation;
|
|
for (int i = 0, e = tensorType.getRank(); i < e; i++)
|
|
reassociation.push_back(i);
|
|
|
|
// Flattened the indices buffer to rank 1.
|
|
field = rewriter.create<memref::CollapseShapeOp>(
|
|
loc, crdMemRef, ArrayRef<ReassociationIndices>(reassociation));
|
|
break;
|
|
}
|
|
case SparseTensorFieldKind::ValMemRef: {
|
|
auto tensorType = op.getValues().getType();
|
|
auto memrefType = MemRefType::get(tensorType.getShape(),
|
|
tensorType.getElementType());
|
|
field = rewriter.create<bufferization::ToMemrefOp>(
|
|
op->getLoc(), memrefType, op.getValues());
|
|
break;
|
|
}
|
|
}
|
|
|
|
assert(field);
|
|
if (fType != field.getType())
|
|
field = rewriter.create<memref::CastOp>(loc, fType, field);
|
|
fields.push_back(field);
|
|
// Returns true to continue the iteration.
|
|
return true;
|
|
});
|
|
|
|
MutSparseTensorDescriptor desc(rtp, fields);
|
|
auto noe = linalg::createOrFoldDimOp(rewriter, loc, op.getValues(), 0);
|
|
// FIXME: should use `SparseTensorType::getLvlRank` in lieu of
|
|
// `RankedTensorType::getRank`, because the latter introduces dim/lvl
|
|
// ambiguity.
|
|
for (Level lvl = 0, lvlRank = rtp.getRank(); lvl < lvlRank; lvl++) {
|
|
const auto sh = rtp.getShape()[lvl];
|
|
assert(!ShapedType::isDynamic(sh));
|
|
desc.setLvlSize(rewriter, loc, lvl, constantIndex(rewriter, loc, sh));
|
|
if (lvl == 0)
|
|
desc.setPosMemSize(rewriter, loc, lvl, constantIndex(rewriter, loc, 2));
|
|
|
|
desc.setCrdMemSize(rewriter, loc, lvl, noe);
|
|
}
|
|
desc.setValMemSize(rewriter, loc, noe);
|
|
|
|
rewriter.replaceOp(op, genTuple(rewriter, loc, desc));
|
|
return success();
|
|
}
|
|
};
|
|
|
|
struct SparseUnpackOpConverter : public OpConversionPattern<UnpackOp> {
|
|
using OpConversionPattern::OpConversionPattern;
|
|
LogicalResult
|
|
matchAndRewrite(UnpackOp op, OpAdaptor adaptor,
|
|
ConversionPatternRewriter &rewriter) const override {
|
|
auto desc = getDescriptorFromTensorTuple(adaptor.getTensor());
|
|
Location loc = op.getLoc();
|
|
const auto srcTp = getSparseTensorType(op.getTensor());
|
|
const Level lvlRank = srcTp.getLvlRank();
|
|
|
|
assert(isUniqueCOOType(srcTp) && desc.getFields().size() == 4);
|
|
|
|
Value flatBuf = lvlRank == 1 ? desc.getCrdMemRefOrView(rewriter, loc, 0)
|
|
: desc.getAOSMemRef();
|
|
Value valuesBuf = desc.getValMemRef();
|
|
|
|
// If frontend requests a static buffer, we reallocate the
|
|
// values/coordinates to ensure that we meet their need.
|
|
const auto valuesTp = getRankedTensorType(op.getValues());
|
|
if (valuesTp.hasStaticShape()) {
|
|
valuesBuf =
|
|
reallocOrSubView(rewriter, loc, valuesTp.getShape()[0], valuesBuf);
|
|
}
|
|
|
|
const auto coordinatesTp = getRankedTensorType(op.getCoordinates());
|
|
if (coordinatesTp.hasStaticShape()) {
|
|
auto len = coordinatesTp.getShape()[0] * coordinatesTp.getShape()[1];
|
|
flatBuf = reallocOrSubView(rewriter, loc, len, flatBuf);
|
|
}
|
|
|
|
Value coordinatesBuf = rewriter.create<memref::ExpandShapeOp>(
|
|
loc,
|
|
MemRefType::get(coordinatesTp.getShape(),
|
|
coordinatesTp.getElementType()),
|
|
flatBuf, ArrayRef{ReassociationIndices{0, 1}});
|
|
|
|
// Converts MemRefs back to Tensors.
|
|
Value values = rewriter.create<bufferization::ToTensorOp>(loc, valuesBuf);
|
|
Value coordinates =
|
|
rewriter.create<bufferization::ToTensorOp>(loc, coordinatesBuf);
|
|
Value nse = genCast(rewriter, loc, desc.getValMemSize(rewriter, loc),
|
|
op.getNse().getType());
|
|
|
|
rewriter.replaceOp(op, {values, coordinates, nse});
|
|
return success();
|
|
}
|
|
};
|
|
|
|
struct SparseNewOpConverter : public OpConversionPattern<NewOp> {
|
|
using OpConversionPattern::OpConversionPattern;
|
|
LogicalResult
|
|
matchAndRewrite(NewOp op, OpAdaptor adaptor,
|
|
ConversionPatternRewriter &rewriter) const override {
|
|
Location loc = op.getLoc();
|
|
const auto dstTp = getSparseTensorType(op.getResult());
|
|
// Creating COO with NewOp is handled by direct IR codegen. All other cases
|
|
// are handled by rewriting.
|
|
if (!dstTp.hasEncoding() || getCOOStart(dstTp.getEncoding()) != 0)
|
|
return failure();
|
|
|
|
// Implement the NewOp(filename) as follows:
|
|
// %reader = @getSparseTensorReader(%filename)
|
|
// %nse = @getSparseTensorNSE(%reader)
|
|
// %coo = bufferization.alloc_tensor an ordered COO with
|
|
// dst dim ordering, size_hint = %nse
|
|
// %coordinates = sparse_tensor.coordinates_buffer(%coo)
|
|
// %values = sparse_tensor.values(%coo)
|
|
// %isSorted = @sparseTensorReaderReadToBuffers(%coordinates, %values)
|
|
// if (! %isSorted) sparse_tensor.sort_coo(%nse, %coordinates, %values)
|
|
// update storage specifier
|
|
// @delSparseTensorReader(%reader)
|
|
|
|
// Create a sparse tensor reader.
|
|
const Value fileName = op.getSource();
|
|
const Type opaqueTp = getOpaquePointerType(rewriter);
|
|
// FIXME: use `createCheckedSparseTensorReader` instead, because
|
|
// `createSparseTensorReader` is unsafe.
|
|
Value reader = createFuncCall(rewriter, loc, "createSparseTensorReader",
|
|
{opaqueTp}, {fileName}, EmitCInterface::Off)
|
|
.getResult(0);
|
|
|
|
const Type indexTp = rewriter.getIndexType();
|
|
const Dimension dimRank = dstTp.getDimRank();
|
|
const Level lvlRank = dstTp.getLvlRank();
|
|
|
|
// If the result tensor has dynamic dimensions, get the dynamic sizes from
|
|
// the sparse tensor reader.
|
|
SmallVector<Value> dynSizes;
|
|
if (dstTp.hasDynamicDimShape()) {
|
|
// FIXME: call `getSparseTensorReaderDimSizes` instead, because
|
|
// `copySparseTensorReaderDimSizes` copies the memref over,
|
|
// instead of just accessing the reader's memory directly.
|
|
Value dimSizes = genAlloca(rewriter, loc, dimRank, indexTp);
|
|
createFuncCall(rewriter, loc, "copySparseTensorReaderDimSizes", {},
|
|
{reader, dimSizes}, EmitCInterface::On)
|
|
.getResult(0);
|
|
for (const auto &d : llvm::enumerate(dstTp.getDimShape()))
|
|
if (ShapedType::isDynamic(d.value()))
|
|
dynSizes.push_back(rewriter.create<memref::LoadOp>(
|
|
loc, dimSizes, constantIndex(rewriter, loc, d.index())));
|
|
}
|
|
|
|
Value nse = createFuncCall(rewriter, loc, "getSparseTensorReaderNSE",
|
|
{indexTp}, {reader}, EmitCInterface::Off)
|
|
.getResult(0);
|
|
// Construct allocation for each field.
|
|
SmallVector<Value> fields;
|
|
createAllocFields(rewriter, loc, dstTp, dynSizes, /*enableInit=*/false,
|
|
fields, nse);
|
|
MutSparseTensorDescriptor desc(dstTp, fields);
|
|
|
|
// Construct the `dim2lvl` buffer for handing off to the runtime library.
|
|
// FIXME: This code is (mostly) copied from the SparseTensorConversion.cpp
|
|
// handling of `NewOp`, and only handles permutations. Fixing this
|
|
// requires waiting for wrengr to finish redoing the CL that handles
|
|
// all dim<->lvl stuff more robustly.
|
|
SmallVector<Value> dim2lvlValues(dimRank);
|
|
if (!dstTp.isIdentity()) {
|
|
const auto dimOrder = dstTp.getDimToLvlMap();
|
|
assert(dimOrder.isPermutation() && "Got non-permutation");
|
|
for (Level l = 0; l < lvlRank; l++) {
|
|
const Dimension d = dimOrder.getDimPosition(l);
|
|
dim2lvlValues[d] = constantIndex(rewriter, loc, l);
|
|
}
|
|
} else {
|
|
// The `SparseTensorType` ctor already ensures `dimRank == lvlRank`
|
|
// when `isIdentity`; so no need to re-assert it here.
|
|
for (Dimension d = 0; d < dimRank; d++)
|
|
dim2lvlValues[d] = constantIndex(rewriter, loc, d);
|
|
}
|
|
Value dim2lvl = allocaBuffer(rewriter, loc, dim2lvlValues);
|
|
|
|
// Read the COO tensor data.
|
|
Value xs = desc.getAOSMemRef();
|
|
Value ys = desc.getValMemRef();
|
|
|
|
const Type boolTp = rewriter.getIntegerType(1);
|
|
const Type elemTp = dstTp.getElementType();
|
|
const Type crdTp = dstTp.getCrdType();
|
|
// FIXME: This function name is weird; should rename to
|
|
// "sparseTensorReaderReadToBuffers".
|
|
SmallString<32> readToBuffersFuncName{"getSparseTensorReaderRead",
|
|
overheadTypeFunctionSuffix(crdTp),
|
|
primaryTypeFunctionSuffix(elemTp)};
|
|
Value isSorted =
|
|
createFuncCall(rewriter, loc, readToBuffersFuncName, {boolTp},
|
|
{reader, dim2lvl, xs, ys}, EmitCInterface::On)
|
|
.getResult(0);
|
|
|
|
// If the destination tensor is a sorted COO, we need to sort the COO tensor
|
|
// data if the input elements aren't sorted yet.
|
|
if (dstTp.isOrderedLvl(lvlRank - 1)) {
|
|
Value kFalse = constantI1(rewriter, loc, false);
|
|
Value notSorted = rewriter.create<arith::CmpIOp>(
|
|
loc, arith::CmpIPredicate::eq, isSorted, kFalse);
|
|
scf::IfOp ifOp =
|
|
rewriter.create<scf::IfOp>(loc, notSorted, /*else*/ false);
|
|
rewriter.setInsertionPointToStart(&ifOp.getThenRegion().front());
|
|
rewriter.create<SortCooOp>(
|
|
loc, nse, xs, ValueRange{ys}, rewriter.getIndexAttr(lvlRank),
|
|
rewriter.getIndexAttr(0), SparseTensorSortKind::HybridQuickSort);
|
|
rewriter.setInsertionPointAfter(ifOp);
|
|
}
|
|
|
|
// Set PosMemRef0[1] = nse.
|
|
const Value c1 = constantIndex(rewriter, loc, 1);
|
|
const Value posMemref0 = desc.getPosMemRef(0);
|
|
const Type posTp = dstTp.getPosType();
|
|
const Value posNse = genCast(rewriter, loc, nse, posTp);
|
|
rewriter.create<memref::StoreOp>(loc, posNse, posMemref0, c1);
|
|
|
|
// Update storage specifier.
|
|
Value coordinatesSize = rewriter.create<arith::MulIOp>(
|
|
loc, nse, constantIndex(rewriter, loc, lvlRank));
|
|
desc.setSpecifierField(rewriter, loc, StorageSpecifierKind::CrdMemSize, 0,
|
|
coordinatesSize);
|
|
desc.setSpecifierField(rewriter, loc, StorageSpecifierKind::ValMemSize,
|
|
std::nullopt, nse);
|
|
|
|
// Release the sparse tensor reader.
|
|
createFuncCall(rewriter, loc, "delSparseTensorReader", {}, {reader},
|
|
EmitCInterface::Off);
|
|
|
|
// Replace operation with resulting memrefs.
|
|
rewriter.replaceOp(op, genTuple(rewriter, loc, dstTp, fields));
|
|
return success();
|
|
}
|
|
};
|
|
|
|
} // namespace
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// 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<SparsePackOpConverter, SparseUnpackOpConverter,
|
|
SparseReturnConverter, SparseCallConverter, SparseDimOpConverter,
|
|
SparseCastConverter, SparseTensorDeallocConverter,
|
|
SparseExtractSliceCoverter, SparseTensorLoadConverter,
|
|
SparseExpandConverter, SparseCompressConverter,
|
|
SparseInsertConverter, SparseToPositionsConverter,
|
|
SparseToCoordinatesConverter, SparseToCoordinatesBufferConverter,
|
|
SparseToValuesConverter, SparseConvertConverter,
|
|
SparseNewOpConverter, SparseNumberOfEntriesConverter>(
|
|
typeConverter, patterns.getContext());
|
|
patterns.add<SparseTensorAllocConverter>(typeConverter, patterns.getContext(),
|
|
enableBufferInitialization);
|
|
}
|