llvm-project/mlir/lib/Dialect/SparseTensor/Transforms/SparseReinterpretMap.cpp

83 lines
3.3 KiB
C++

//===- SparseReinterpretMap.cpp - reinterpret sparse tensor maps ----------===//
//
// 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
//
//===----------------------------------------------------------------------===//
#include "mlir/Dialect/Affine/IR/AffineOps.h"
#include "mlir/Dialect/SparseTensor/IR/SparseTensor.h"
#include "mlir/Dialect/SparseTensor/IR/SparseTensorType.h"
#include "mlir/Dialect/SparseTensor/Transforms/Passes.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "mlir/IR/AffineMap.h"
using namespace mlir;
using namespace mlir::sparse_tensor;
namespace {
// TODO:
// (1) insert the zero-cost sparse_tensor.reinterpret_map ops
// (2) rewrite linalg.generic ops traits on level crds
// (3) compute topsort, and resolve cyles with sparse_tensor.convert ops
//===----------------------------------------------------------------------===//
// Reiterpret Map Rewriters for operations other than linalg.generics
//===----------------------------------------------------------------------===//
struct CrdTranslateRewriter : public OpRewritePattern<CrdTranslateOp> {
using OpRewritePattern::OpRewritePattern;
LogicalResult matchAndRewrite(CrdTranslateOp op,
PatternRewriter &rewriter) const override {
AffineMap map = op.getDirection() == CrdTransDirectionKind::dim2lvl
? op.getEncoder().getDimToLvl()
: op.getEncoder().getLvlToDim();
SmallVector<Value> outCrds;
for (AffineExpr result : map.getResults()) {
// TODO: we should probably expand the affine map to IR using our own
// rules, since affine.apply assume signed value, while the cooridinates
// we provided must always be signless.
Value trans = rewriter.create<affine::AffineApplyOp>(
op.getLoc(), AffineMap::get(map.getNumDims(), 0, result),
op.getInCrds());
outCrds.push_back(trans);
}
rewriter.replaceOp(op, outCrds);
return success();
}
};
struct TensorInsertRewriter : public OpRewritePattern<tensor::InsertOp> {
using OpRewritePattern::OpRewritePattern;
LogicalResult matchAndRewrite(tensor::InsertOp op,
PatternRewriter &rewriter) const override {
if (!op.getResult().getType().getEncoding())
return failure();
Location loc = op.getLoc();
auto stt = getSparseTensorType(op.getResult());
ValueRange lvlCrd = stt.translateCrds(rewriter, loc, op.getIndices(),
CrdTransDirectionKind::dim2lvl);
Value t = rewriter.create<ReinterpretMapOp>(
loc, stt.getEncoding().withoutDimToLvl(), op.getDest());
t = rewriter.create<sparse_tensor::InsertOp>(loc, op.getScalar(), t,
lvlCrd);
rewriter.replaceOpWithNewOp<ReinterpretMapOp>(op, op.getType(), t);
return success();
}
};
} // namespace
void mlir::populateSparseReinterpretMap(RewritePatternSet &patterns,
ReinterpretMapScope scope) {
if (scope == ReinterpretMapScope::kAll ||
scope == ReinterpretMapScope::kExceptGeneric) {
patterns.add<CrdTranslateRewriter, TensorInsertRewriter>(
patterns.getContext());
}
}