45ccff175b
This can be a pre-processing for bufferization and allows for more efficient lowerings without an alloc. Differential Revision: https://reviews.llvm.org/D142205
79 lines
3.1 KiB
C++
79 lines
3.1 KiB
C++
//===- ConvertToDestinationStyle.cpp - Convert non-DPS to DPS ops ---------===//
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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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// This file contains patterns to convert non-DPS ops to DPS ops. New
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// tensor.empty ops are inserted as a destination. Such tensor.empty can be
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// eliminated with "empty tensor elimination", allowing them to bufferize
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// without an allocation (assuming there are no further conflicts).
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//
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//===----------------------------------------------------------------------===//
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//
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#include "mlir/Dialect/Arith/IR/Arith.h"
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#include "mlir/Dialect/Linalg/IR/Linalg.h"
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#include "mlir/Dialect/Linalg/Transforms/Transforms.h"
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#include "mlir/Dialect/Tensor/IR/Tensor.h"
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#include "mlir/IR/PatternMatch.h"
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#include "llvm/Support/Debug.h"
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using namespace mlir;
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using namespace mlir::tensor;
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namespace {
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/// Lower tensor.generate to linalg.generic.
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struct GenerateOpConverter : public OpRewritePattern<GenerateOp> {
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using OpRewritePattern<GenerateOp>::OpRewritePattern;
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LogicalResult matchAndRewrite(GenerateOp generateOp,
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PatternRewriter &rewriter) const override {
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// Only ops with exactly one block are supported.
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if (!generateOp.getBody().hasOneBlock())
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return failure();
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Location loc = generateOp.getLoc();
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RankedTensorType tensorType = generateOp.getType().cast<RankedTensorType>();
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// Create tensor.empty.
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auto emptyOp = rewriter.create<EmptyOp>(loc, tensorType,
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generateOp.getDynamicExtents());
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// Create linalg.generic.
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SmallVector<utils::IteratorType> iteratorTypes(
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tensorType.getRank(), utils::IteratorType::parallel);
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SmallVector<AffineMap> indexingMaps(
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1, rewriter.getMultiDimIdentityMap(tensorType.getRank()));
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auto genericOp = rewriter.create<linalg::GenericOp>(
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loc, tensorType, /*inputs=*/ValueRange(),
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/*outputs=*/ValueRange{emptyOp.getResult()}, /*indexingMaps=*/
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indexingMaps, iteratorTypes);
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Block *body = rewriter.createBlock(&genericOp->getRegion(0), {},
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tensorType.getElementType(), loc);
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rewriter.setInsertionPointToStart(body);
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SmallVector<Value> bbArgReplacements;
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for (int64_t i = 0; i < tensorType.getRank(); ++i)
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bbArgReplacements.push_back(rewriter.create<linalg::IndexOp>(loc, i));
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rewriter.mergeBlocks(&generateOp.getBody().front(), body,
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bbArgReplacements);
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// Update terminator.
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auto yieldOp = cast<tensor::YieldOp>(body->getTerminator());
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rewriter.replaceOpWithNewOp<linalg::YieldOp>(yieldOp, yieldOp.getValue());
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// Replace tensor.generate.
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rewriter.replaceOp(generateOp, genericOp->getResult(0));
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return success();
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}
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};
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} // namespace
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void linalg::populateConvertToDestinationStylePatterns(
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RewritePatternSet &patterns) {
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patterns.insert<GenerateOpConverter>(patterns.getContext());
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}
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