//===- 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 { 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 mlir::createCanonicalizerPass() { return std::make_unique(); } static PassRegistration pass("canonicalize", "Canonicalize operations");