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//===- Generalization.cpp - linalg named ops to generic ops --------------===//
//
// 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
//
//===----------------------------------------------------------------------===//
//
// This file implements the Linalg generalization pass. It converts named
// Linalg ops to linalg.generic ops.
//
//===----------------------------------------------------------------------===//
#include "mlir/Dialect/Linalg/Passes.h"
#include "mlir/Dialect/Func/IR/FuncOps.h"
#include "mlir/Dialect/Linalg/IR/Linalg.h"
#include "mlir/Dialect/Linalg/Transforms/Transforms.h"
#include "mlir/IR/AffineMap.h"
#include "mlir/IR/Attributes.h"
#include "mlir/IR/Builders.h"
#include "mlir/IR/ImplicitLocOpBuilder.h"
#include "mlir/IR/PatternMatch.h"
#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
#include "llvm/ADT/SmallVector.h"
#include "llvm/Support/Debug.h"
namespace mlir {
#define GEN_PASS_DEF_LINALGGENERALIZATION
#include "mlir/Dialect/Linalg/Passes.h.inc"
} // namespace mlir
#define DEBUG_TYPE "linalg-generalization"
using namespace mlir;
using namespace mlir::linalg;
static LogicalResult generalizeNamedOpPrecondition(LinalgOp linalgOp) {
// Bailout if `linalgOp` is already a generic or a linalg.map. We cannot
// trivially generalize a `linalg.map`, as it does not use the output as
// region arguments in the block.
if (isa<GenericOp>(linalgOp) || isa<MapOp>(linalgOp))
return failure();
// Check if the operation has exactly one region.
if (linalgOp->getNumRegions() != 1) {
assert(linalgOp->getNumRegions() == 0 && "op with multiple regions");
// TOD: Otherwise it needs to be built explicitly from the region builder.
return failure();
}
return success();
}
FailureOr<GenericOp> mlir::linalg::generalizeNamedOp(RewriterBase &rewriter,
LinalgOp linalgOp) {
if (failed(generalizeNamedOpPrecondition(linalgOp)))
return rewriter.notifyMatchFailure(linalgOp, "preconditions not met");
SmallVector<Value> inputs = linalgOp.getDpsInputOperands();
SmallVector<Value> outputs = linalgOp.getDpsInitOperands();
SmallVector<AffineMap> indexingMaps = linalgOp.getIndexingMapsArray();
SmallVector<utils::IteratorType> iterators = linalgOp.getIteratorTypesArray();
SmallVector<Type> resultTypes = linalgOp.hasTensorSemantics()
? TypeRange(ValueRange(outputs))
: TypeRange{};
// All named ops have a region attached that can be inlined.
assert(linalgOp->getNumRegions() == 1 &&
"expect named op to have one region attached");
GenericOp genericOp = rewriter.create<GenericOp>(
linalgOp.getLoc(), resultTypes, inputs, outputs, indexingMaps, iterators);
rewriter.inlineRegionBefore(linalgOp->getRegion(0), genericOp.getRegion(),
genericOp.getRegion().begin());
rewriter.replaceOp(linalgOp, genericOp->getResults());
return genericOp;
}
namespace {
struct LinalgGeneralizationPass
: public impl::LinalgGeneralizationBase<LinalgGeneralizationPass> {
void runOnOperation() override;
};
} // namespace
void LinalgGeneralizationPass::runOnOperation() {
RewritePatternSet patterns(&getContext());
populateLinalgNamedOpsGeneralizationPatterns(patterns);
(void)applyPatternsAndFoldGreedily(getOperation(), std::move(patterns));
}
void mlir::linalg::populateLinalgNamedOpsGeneralizationPatterns(
RewritePatternSet &patterns) {
patterns.add<LinalgGeneralizationPattern>(patterns.getContext());
}
std::unique_ptr<Pass> mlir::createLinalgGeneralizationPass() {
return std::make_unique<LinalgGeneralizationPass>();
}
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