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//===- Canonicalizer.cpp - Canonicalize MLIR operations -------------------===//
//
// Copyright 2019 The MLIR Authors.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
// =============================================================================
//
// This transformation pass converts operations into their canonical forms by
// folding constants, applying operation identity transformations etc.
//
//===----------------------------------------------------------------------===//
#include "mlir/IR/MLIRContext.h"
#include "mlir/IR/PatternMatch.h"
#include "mlir/Pass/Pass.h"
#include "mlir/Transforms/Passes.h"
using namespace mlir;
namespace {
/// Canonicalize operations in nested regions.
struct Canonicalizer : public OperationPass<Canonicalizer> {
void runOnOperation() override {
OwningRewritePatternList patterns;
// TODO: Instead of adding all known patterns from the whole system lazily
// add and cache the canonicalization patterns for ops we see in practice
// when building the worklist. For now, we just grab everything.
auto *context = &getContext();
for (auto *op : context->getRegisteredOperations())
op->getCanonicalizationPatterns(patterns, context);
Operation *op = getOperation();
applyPatternsGreedily(op->getRegions(), patterns);
}
};
} // end anonymous namespace
/// Create a Canonicalizer pass.
std::unique_ptr<Pass> mlir::createCanonicalizerPass() {
return std::make_unique<Canonicalizer>();
}
static PassRegistration<Canonicalizer> pass("canonicalize",
"Canonicalize operations");
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