99 lines
3.7 KiB
C++
99 lines
3.7 KiB
C++
//===- ElementwiseToLinalg.cpp - conversion of elementwise to linalg ------===//
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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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#include "mlir/Dialect/Linalg/Passes.h"
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#include "PassDetail.h"
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#include "mlir/Dialect/Linalg/IR/LinalgOps.h"
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#include "mlir/Dialect/StandardOps/IR/Ops.h"
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#include "mlir/Transforms/DialectConversion.h"
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using namespace mlir;
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static bool isElementwiseMappableOpOnRankedTensors(Operation *op) {
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if (!op->hasTrait<OpTrait::ElementwiseMappable>())
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return false;
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// TODO: The conversion pattern can be made to work for `any_of` here, but
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// it's more complex as it requires tracking which operands are scalars.
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return llvm::all_of(op->getOperandTypes(),
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[](Type type) { return type.isa<RankedTensorType>(); });
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}
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namespace {
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struct ConvertAnyElementwiseMappableOpOnRankedTensors : public RewritePattern {
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ConvertAnyElementwiseMappableOpOnRankedTensors()
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: RewritePattern(/*benefit=*/1, MatchAnyOpTypeTag()) {}
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LogicalResult matchAndRewrite(Operation *op,
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PatternRewriter &rewriter) const final {
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if (!isElementwiseMappableOpOnRankedTensors(op))
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return rewriter.notifyMatchFailure(
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op, "requires elementwise op on ranked tensors");
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auto rank = op->getResult(0).getType().cast<RankedTensorType>().getRank();
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SmallVector<AffineMap, 3> indexingMaps(
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op->getNumResults() + op->getNumOperands(),
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rewriter.getMultiDimIdentityMap(rank));
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SmallVector<StringRef, 6> iteratorTypes(rank,
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getParallelIteratorTypeName());
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rewriter.replaceOpWithNewOp<linalg::GenericOp>(
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op, /*resultTensorTypes=*/op->getResultTypes(),
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/*inputs=*/op->getOperands(),
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/*outputBuffers=*/ValueRange(),
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/*initTensors=*/ValueRange(),
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/*indexingMaps=*/indexingMaps,
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/*iteratorTypes=*/iteratorTypes,
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/*bodyBuilder=*/
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[&](OpBuilder &builder, Location loc, ValueRange regionArgs) {
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OperationState state(loc, op->getName());
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state.addAttributes(op->getAttrs());
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state.addOperands(regionArgs);
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auto resultTypes = llvm::to_vector<6>(
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llvm::map_range(op->getResultTypes(), [](Type type) {
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return type.cast<TensorType>().getElementType();
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}));
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state.addTypes(resultTypes);
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auto *scalarOp = builder.createOperation(state);
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builder.create<linalg::YieldOp>(loc, scalarOp->getResults());
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});
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return success();
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}
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};
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} // namespace
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void mlir::populateElementwiseToLinalgConversionPatterns(
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OwningRewritePatternList &patterns, MLIRContext *) {
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patterns.insert<ConvertAnyElementwiseMappableOpOnRankedTensors>();
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}
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namespace {
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class ConvertElementwiseToLinalgPass
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: public ConvertElementwiseToLinalgBase<ConvertElementwiseToLinalgPass> {
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void runOnFunction() final {
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auto func = getOperation();
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auto *context = &getContext();
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ConversionTarget target(*context);
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OwningRewritePatternList patterns;
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populateElementwiseToLinalgConversionPatterns(patterns, context);
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target.markUnknownOpDynamicallyLegal([](Operation *op) {
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return !isElementwiseMappableOpOnRankedTensors(op);
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});
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if (failed(applyPartialConversion(func, target, std::move(patterns))))
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signalPassFailure();
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}
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};
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} // namespace
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std::unique_ptr<OperationPass<FuncOp>>
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mlir::createConvertElementwiseToLinalgPass() {
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return std::make_unique<ConvertElementwiseToLinalgPass>();
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}
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