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//===- RankReductionPatterns.cpp - Patterns related to rank reductions ----===//
//
// 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/Tensor/IR/Tensor.h"
#include "mlir/Dialect/Tensor/Transforms/Transforms.h"
#include "mlir/IR/PatternMatch.h"
#include "llvm/Support/Debug.h"
using namespace mlir;
using namespace mlir::tensor;
namespace {
/// Fold expand_shape(extract_slice) ops that cancel itself out.
struct FoldExpandOfRankReducingExtract
: public OpRewritePattern<ExpandShapeOp> {
using OpRewritePattern<ExpandShapeOp>::OpRewritePattern;
LogicalResult matchAndRewrite(ExpandShapeOp expandShapeOp,
PatternRewriter &rewriter) const override {
RankedTensorType resultType = expandShapeOp.getResultType();
auto extractSliceOp =
expandShapeOp.getSrc().getDefiningOp<ExtractSliceOp>();
if (!extractSliceOp)
return failure();
RankedTensorType srcType = extractSliceOp.getSourceType();
// Only cases where the ExpandShapeOp can be folded away entirely are
// supported. Moreover, only simple cases where the resulting ExtractSliceOp
// has no rank-reduction anymore are supported at the moment.
RankedTensorType nonReducingExtractType = ExtractSliceOp::inferResultType(
srcType, extractSliceOp.getStaticOffsets(),
extractSliceOp.getStaticSizes(), extractSliceOp.getStaticStrides());
if (nonReducingExtractType != resultType)
return failure();
SmallVector<OpFoldResult> mixedOffsets = extractSliceOp.getMixedOffsets();
SmallVector<OpFoldResult> mixedSizes = extractSliceOp.getMixedSizes();
SmallVector<OpFoldResult> mixedStrides = extractSliceOp.getMixedStrides();
rewriter.replaceOpWithNewOp<tensor::ExtractSliceOp>(
expandShapeOp, extractSliceOp.getSource(), mixedOffsets, mixedSizes,
mixedStrides);
return success();
}
};
/// Fold insert_slice(collapse_shape) ops that cancel itself out.
template <typename OpTy>
struct FoldInsertOfRankReducingInsert : public OpRewritePattern<OpTy> {
using OpRewritePattern<OpTy>::OpRewritePattern;
LogicalResult matchAndRewrite(OpTy insertSliceOp,
PatternRewriter &rewriter) const override {
auto collapseShapeOp =
insertSliceOp.getSource().template getDefiningOp<CollapseShapeOp>();
if (!collapseShapeOp)
return failure();
RankedTensorType srcType = collapseShapeOp.getSrcType();
// Only cases where the CollapseShapeOp can be folded away entirely are
// supported. Moreover, only simple cases where the resulting InsertSliceOp
// has no rank-reduction anymore are supported at the moment.
RankedTensorType nonReducingInsertType =
RankedTensorType::get(insertSliceOp.getStaticSizes(),
insertSliceOp.getDestType().getElementType());
if (nonReducingInsertType != srcType)
return failure();
SmallVector<OpFoldResult> mixedOffsets = insertSliceOp.getMixedOffsets();
SmallVector<OpFoldResult> mixedSizes = insertSliceOp.getMixedSizes();
SmallVector<OpFoldResult> mixedStrides = insertSliceOp.getMixedStrides();
rewriter.replaceOpWithNewOp<OpTy>(insertSliceOp, collapseShapeOp.getSrc(),
insertSliceOp.getDest(), mixedOffsets,
mixedSizes, mixedStrides);
return success();
}
};
} // namespace
void mlir::tensor::populateReassociativeReshapeFoldingPatterns(
RewritePatternSet &patterns) {
patterns.add<FoldExpandOfRankReducingExtract,
FoldInsertOfRankReducingInsert<tensor::InsertSliceOp>,
FoldInsertOfRankReducingInsert<tensor::ParallelInsertSliceOp>>(
patterns.getContext());
}
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