The MLIR classes Type/Attribute/Operation/Op/Value support cast/dyn_cast/isa/dyn_cast_or_null functionality through llvm's doCast functionality in addition to defining methods with the same name. This change begins the migration of uses of the method to the corresponding function call as has been decided as more consistent. Note that there still exist classes that only define methods directly, such as AffineExpr, and this does not include work currently to support a functional cast/isa call. Caveats include: - This clang-tidy script probably has more problems. - This only touches C++ code, so nothing that is being generated. Context: - https://mlir.llvm.org/deprecation/ at "Use the free function variants for dyn_cast/cast/isa/…" - Original discussion at https://discourse.llvm.org/t/preferred-casting-style-going-forward/68443 Implementation: This first patch was created with the following steps. The intention is to only do automated changes at first, so I waste less time if it's reverted, and so the first mass change is more clear as an example to other teams that will need to follow similar steps. Steps are described per line, as comments are removed by git: 0. Retrieve the change from the following to build clang-tidy with an additional check: https://github.com/llvm/llvm-project/compare/main...tpopp:llvm-project:tidy-cast-check 1. Build clang-tidy 2. Run clang-tidy over your entire codebase while disabling all checks and enabling the one relevant one. Run on all header files also. 3. Delete .inc files that were also modified, so the next build rebuilds them to a pure state. 4. Some changes have been deleted for the following reasons: - Some files had a variable also named cast - Some files had not included a header file that defines the cast functions - Some files are definitions of the classes that have the casting methods, so the code still refers to the method instead of the function without adding a prefix or removing the method declaration at the same time. ``` ninja -C $BUILD_DIR clang-tidy run-clang-tidy -clang-tidy-binary=$BUILD_DIR/bin/clang-tidy -checks='-*,misc-cast-functions'\ -header-filter=mlir/ mlir/* -fix rm -rf $BUILD_DIR/tools/mlir/**/*.inc git restore mlir/lib/IR mlir/lib/Dialect/DLTI/DLTI.cpp\ mlir/lib/Dialect/Complex/IR/ComplexDialect.cpp\ mlir/lib/**/IR/\ mlir/lib/Dialect/SparseTensor/Transforms/SparseVectorization.cpp\ mlir/lib/Dialect/Vector/Transforms/LowerVectorMultiReduction.cpp\ mlir/test/lib/Dialect/Test/TestTypes.cpp\ mlir/test/lib/Dialect/Transform/TestTransformDialectExtension.cpp\ mlir/test/lib/Dialect/Test/TestAttributes.cpp\ mlir/unittests/TableGen/EnumsGenTest.cpp\ mlir/test/python/lib/PythonTestCAPI.cpp\ mlir/include/mlir/IR/ ``` Differential Revision: https://reviews.llvm.org/D150123
427 lines
17 KiB
C++
427 lines
17 KiB
C++
//===- EraseUnusedOperandsAndResults.cpp ----------------------------------===//
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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/Transforms/Transforms.h"
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#include "mlir/Dialect/Linalg/IR/Linalg.h"
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using namespace mlir;
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using namespace mlir::linalg;
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/// Return `true` if the `result` of an operation `genericOp` is dead.
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static bool isResultValueDead(linalg::GenericOp genericOp, OpResult result) {
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if (!result.use_empty())
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return false;
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// If out operand not used in payload, we can drop it.
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OpOperand *outputOpOperand =
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genericOp.getDpsInitOperand(result.getResultNumber());
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if (!genericOp.payloadUsesValueFromOperand(outputOpOperand))
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return true;
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// The out operand that is part of a payload can be dropped if
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// these conditions are met:
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// - Result from out operand is dead.
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// - User of arg is yield.
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// - outArg data is not being used by other outArgs.
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// Check block arg and cycle from out operand has a single use.
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BlockArgument outputArg =
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genericOp.getRegionOutputArgs()[result.getResultNumber()];
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if (!outputArg.hasOneUse())
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return false;
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Operation *argUserOp = *outputArg.user_begin();
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// Check argUser has no other use.
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if (!argUserOp->use_empty())
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return false;
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// Check that argUser is a yield.
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auto yieldOp = dyn_cast<linalg::YieldOp>(argUserOp);
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if (!yieldOp)
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return false;
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// Check outArg data is not being used by other outArgs.
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if (yieldOp.getOperand(result.getResultNumber()) != outputArg)
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return false;
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return true;
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}
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namespace {
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struct DeduplicateAndRemoveDeadOperandsAndResults
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: public OpRewritePattern<GenericOp> {
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DeduplicateAndRemoveDeadOperandsAndResults(MLIRContext *ctx,
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bool removeOutputs)
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: OpRewritePattern<GenericOp>(ctx), removeOutputs(removeOutputs) {}
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LogicalResult matchAndRewrite(GenericOp genericOp,
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PatternRewriter &rewriter) const override {
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// Create a map from argument position in the original op to the argument
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// position in the new op. If the argument is dropped it wont have an entry.
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SmallVector<OpOperand *> droppedOpOperands;
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// Information needed to build the new op.
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SmallVector<Value> newInputOperands, newOutputOperands;
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SmallVector<AffineMap> newIndexingMaps;
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// Gather information about duplicate input operands.
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llvm::SmallDenseMap<unsigned, unsigned> origInsToNewInsPos =
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deduplicateInputOperands(genericOp, droppedOpOperands, newInputOperands,
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newIndexingMaps);
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// Gather information about the dropped outputs.
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llvm::SmallDenseMap<unsigned, unsigned> origOutsToNewOutsPos =
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deduplicateOutputOperands(genericOp, droppedOpOperands,
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newOutputOperands, newIndexingMaps);
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// Check if there is any change to operands.
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if (newInputOperands.size() + newOutputOperands.size() ==
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genericOp->getNumOperands())
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return failure();
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// Create the new op with the body being empty.
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Location loc = genericOp.getLoc();
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SmallVector<Type> newResultTypes;
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for (Value v : newOutputOperands)
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if (isa<TensorType>(v.getType()))
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newResultTypes.push_back(v.getType());
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auto newOp = rewriter.create<GenericOp>(
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loc, newResultTypes, newInputOperands, newOutputOperands,
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rewriter.getAffineMapArrayAttr(newIndexingMaps),
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genericOp.getIteratorTypes(), genericOp.getDocAttr(),
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genericOp.getLibraryCallAttr(),
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[](OpBuilder & /*builder*/, Location /*loc*/, ValueRange /*args*/) {
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return;
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});
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// Copy over unknown attributes. They might be load bearing for some flow.
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ArrayRef<StringRef> odsAttrs = genericOp.getAttributeNames();
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for (NamedAttribute kv : genericOp->getAttrs())
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if (!llvm::is_contained(odsAttrs, kv.getName().getValue()))
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newOp->setAttr(kv.getName(), kv.getValue());
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// Fix up the payload of the canonicalized operation.
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populateOpPayload(genericOp, newOp, origInsToNewInsPos,
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origOutsToNewOutsPos, rewriter);
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// Replace all live uses of the op.
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SmallVector<Value> replacementsVals(genericOp->getNumResults(), nullptr);
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for (const auto &result : llvm::enumerate(genericOp.getResults())) {
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auto it = origOutsToNewOutsPos.find(result.index());
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if (it == origOutsToNewOutsPos.end())
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continue;
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replacementsVals[result.index()] = newOp.getResult(it->second);
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}
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rewriter.replaceOp(genericOp, replacementsVals);
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return success();
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}
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private:
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/// If unset, outputs are not modified by this pattern.
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bool removeOutputs;
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// Deduplicate input operands, and return the
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// - Mapping from operand position in the original op, to operand position in
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// the canonicalized op.
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// - The preserved input operands list (by reference).
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llvm::SmallDenseMap<unsigned, unsigned>
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deduplicateInputOperands(GenericOp genericOp,
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SmallVector<OpOperand *> &droppedOpOperands,
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SmallVector<Value> &newInputOperands,
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SmallVector<AffineMap> &newIndexingMaps) const {
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llvm::SmallDenseMap<unsigned, unsigned> origToNewPos;
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llvm::SmallDenseMap<std::pair<Value, AffineMap>, unsigned> dedupedInputs;
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for (const auto &en : llvm::enumerate(genericOp.getDpsInputOperands())) {
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OpOperand *inputOpOperand = en.value();
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// Check if operand is dead and if dropping the indexing map makes the
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// loops to shape computation invalid.
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if (!genericOp.payloadUsesValueFromOperand(inputOpOperand)) {
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// Add the current operands to the list of potentially droppable
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// operands. If it cannot be dropped, this needs to be popped back.
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droppedOpOperands.push_back(inputOpOperand);
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if (genericOp.canOpOperandsBeDropped(droppedOpOperands))
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continue;
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droppedOpOperands.pop_back();
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}
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// Check if this operand is a duplicate.
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AffineMap indexingMap = genericOp.getMatchingIndexingMap(inputOpOperand);
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auto it = dedupedInputs.find(
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std::make_pair(inputOpOperand->get(), indexingMap));
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if (it != dedupedInputs.end()) {
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origToNewPos[en.index()] = it->second;
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droppedOpOperands.push_back(inputOpOperand);
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continue;
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}
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// This is a preserved argument.
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origToNewPos[en.index()] = newInputOperands.size();
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dedupedInputs[{inputOpOperand->get(), indexingMap}] =
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newInputOperands.size();
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newInputOperands.push_back(inputOpOperand->get());
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newIndexingMaps.push_back(indexingMap);
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}
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return origToNewPos;
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}
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// Deduplicate output operands, and return the
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// - Mapping from operand position in the original op, to operand position in
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// the canonicalized op.
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// - The preserved output operands list (by reference).
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llvm::SmallDenseMap<unsigned, unsigned>
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deduplicateOutputOperands(GenericOp genericOp,
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SmallVector<OpOperand *> &droppedOpOperands,
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SmallVector<Value> &newOutputOperands,
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SmallVector<AffineMap> &newIndexingMaps) const {
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llvm::SmallDenseMap<unsigned, unsigned> origToNewPos;
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llvm::SmallDenseMap<std::tuple<Value, AffineMap, Value>, unsigned>
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dedupedOutpts;
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// If the op doesn't have tensor semantics or outputs should not be removed,
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// keep all the outputs as preserved.
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if (!genericOp.hasTensorSemantics() || !removeOutputs) {
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for (const auto &en : llvm::enumerate(genericOp.getDpsInitOperands())) {
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origToNewPos[en.index()] = newOutputOperands.size();
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newOutputOperands.push_back(en.value()->get());
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newIndexingMaps.push_back(genericOp.getMatchingIndexingMap(en.value()));
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}
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return origToNewPos;
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}
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// Output argument can be dropped if the result has
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// - no users, and
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// - it is not used in the payload, and
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// - the corresponding indexing maps are not needed for loop bound
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// computation.
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auto yieldOp = cast<YieldOp>(genericOp.getBody()->getTerminator());
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for (const auto &outputOpOperand :
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llvm::enumerate(genericOp.getDpsInitOperands())) {
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OpResult result = genericOp.getTiedOpResult(outputOpOperand.value());
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AffineMap indexingMap =
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genericOp.getMatchingIndexingMap(outputOpOperand.value());
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auto key = std::make_tuple(outputOpOperand.value()->get(), indexingMap,
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yieldOp->getOperand(outputOpOperand.index()));
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if (isResultValueDead(genericOp, result)) {
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// Check if the opoperand can be dropped without affecting loop
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// bound computation. Add the operand to the list of dropped op
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// operand for checking. If it cannot be dropped, need to pop the
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// value back.
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droppedOpOperands.push_back(outputOpOperand.value());
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if (genericOp.canOpOperandsBeDropped(droppedOpOperands)) {
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continue;
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}
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droppedOpOperands.pop_back();
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}
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if (!genericOp.payloadUsesValueFromOperand(outputOpOperand.value())) {
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// The out operand can also be dropped if it is computed redundantly
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// by another result, the conditions for that are
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// - The same operand is used as the out operand
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// - The same indexing map is used
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// - The same yield value is used.
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auto it = dedupedOutpts.find(key);
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if (it != dedupedOutpts.end()) {
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origToNewPos[outputOpOperand.index()] = it->second;
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droppedOpOperands.push_back(outputOpOperand.value());
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continue;
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}
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}
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origToNewPos[outputOpOperand.index()] = newOutputOperands.size();
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dedupedOutpts[key] = newOutputOperands.size();
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newOutputOperands.push_back(outputOpOperand.value()->get());
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newIndexingMaps.push_back(
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genericOp.getMatchingIndexingMap(outputOpOperand.value()));
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}
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return origToNewPos;
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}
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// Populate the body of the canonicalized operation.
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void populateOpPayload(
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GenericOp genericOp, GenericOp newOp,
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const llvm::SmallDenseMap<unsigned, unsigned> &origInsToNewInsPos,
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const llvm::SmallDenseMap<unsigned, unsigned> &origOutsToNewOutsPos,
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PatternRewriter &rewriter) const {
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// Merge the body of the original op with the new op.
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Block *newOpBlock = &newOp.getRegion().front();
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assert(newOpBlock->empty() && "expected new op to have an empty payload");
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Block *origOpBlock = &genericOp.getRegion().front();
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SmallVector<Value> replacements(origOpBlock->getNumArguments(), nullptr);
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// Replace all arguments in the original op, with arguments from the
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// canonicalized op.
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auto updateReplacements =
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[&](OpOperandVector &origOperands, OpOperandVector &newOperands,
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const llvm::SmallDenseMap<unsigned, unsigned> &map) {
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for (const auto &origOperand : llvm::enumerate(origOperands)) {
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auto it = map.find(origOperand.index());
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if (it == map.end())
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continue;
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OpOperand *newOperand = newOperands[it->second];
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replacements[origOperand.value()->getOperandNumber()] =
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newOpBlock->getArgument(newOperand->getOperandNumber());
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}
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};
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OpOperandVector origInputOperands = genericOp.getDpsInputOperands();
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OpOperandVector newInputOperands = newOp.getDpsInputOperands();
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updateReplacements(origInputOperands, newInputOperands, origInsToNewInsPos);
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OpOperandVector origOutputOperands = genericOp.getDpsInitOperands();
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OpOperandVector newOutputOperands = newOp.getDpsInitOperands();
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updateReplacements(origOutputOperands, newOutputOperands,
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origOutsToNewOutsPos);
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// Drop the unused yield args.
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if (newOp.getNumDpsInits() != genericOp.getNumDpsInits()) {
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OpBuilder::InsertionGuard g(rewriter);
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YieldOp origYieldOp = cast<YieldOp>(origOpBlock->getTerminator());
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rewriter.setInsertionPoint(origYieldOp);
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SmallVector<Value> newYieldVals(newOp.getNumDpsInits(), nullptr);
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for (const auto &yieldOpOperands :
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llvm::enumerate(origYieldOp.getValues())) {
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auto it = origOutsToNewOutsPos.find(yieldOpOperands.index());
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if (it == origOutsToNewOutsPos.end())
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continue;
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newYieldVals[it->second] = yieldOpOperands.value();
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}
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rewriter.replaceOpWithNewOp<YieldOp>(origYieldOp, newYieldVals);
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}
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rewriter.mergeBlocks(origOpBlock, newOpBlock, replacements);
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}
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};
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/// Remove unused cycles.
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/// We can remove unused cycle within a payload of generic region
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/// if these conditions are met:
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/// - Result from out operand is dead.
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/// - Block arg from out operand has a single use in the %cycle
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/// instruction.
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/// - Cycle has a single use and it is in yield.
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struct RemoveUnusedCycleInGenericOp : public OpRewritePattern<GenericOp> {
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using OpRewritePattern<GenericOp>::OpRewritePattern;
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LogicalResult matchAndRewrite(GenericOp genericOp,
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PatternRewriter &rewriter) const override {
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// If the op doesnt have tensor semantics, preserve the outputs as is.
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if (!genericOp.hasTensorSemantics())
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return failure();
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bool hasRemovedCycles = false;
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// Iterate over output operands and remove any unused cycles.
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for (const auto &outputOpOperand :
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llvm::enumerate(genericOp.getDpsInitOperands())) {
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// Check that result from out operand is dead.
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Value result = genericOp.getResult(outputOpOperand.index());
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if (!result.use_empty())
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continue;
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// Check that outputArg has one use in cycle.
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BlockArgument outputArg =
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genericOp.getRegionOutputArgs()[outputOpOperand.index()];
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if (!outputArg.hasOneUse())
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continue;
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// Check cycle has at most one use.
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Operation *cycleOp = *outputArg.user_begin();
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if (!cycleOp->hasOneUse())
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continue;
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// Check that the cycleUser is a yield.
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Operation *cycleUserOp = *cycleOp->user_begin();
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if (!isa<linalg::YieldOp>(cycleUserOp))
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continue;
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// Check that argIndex matches yieldIndex, else data is being used.
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if (cycleUserOp->getOperand(outputOpOperand.index()) !=
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cycleOp->getResult(0))
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continue;
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// Directly replace the cycle with the blockArg such that
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// Deduplicate pattern can eliminate it along with unused yield.
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rewriter.replaceOp(cycleOp, outputArg);
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rewriter.updateRootInPlace(genericOp, [] {});
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hasRemovedCycles = true;
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}
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if (hasRemovedCycles) {
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return success();
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}
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return failure();
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}
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};
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/// Fold uses of duplicate inputs in the body of a linalg.generic. E.g.:
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/// ```
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/// linalg.generic ins(%a, %b, %a, %b) outs(%a)
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/// ^bb0(%in0, %in1, %in2, %in3, %out1)
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/// ```
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/// Assuming that all %a and %b have the same index map:
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/// * All uses of %in0 and %in2 are replaced with %out1
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/// * All uses of %in1 are replaced with %in3
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/// This pattern can enable additional canonicalizations: In the above example,
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/// %in0, %in1 and %in3 have no uses anymore and their corresponding operands
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/// can be folded away. This pattern does not modify uses of output block args.
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struct FoldDuplicateInputBbArgs : public OpRewritePattern<GenericOp> {
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using OpRewritePattern<GenericOp>::OpRewritePattern;
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LogicalResult matchAndRewrite(GenericOp genericOp,
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PatternRewriter &rewriter) const override {
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// Find replacement bbArgs for all input bbArg.
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DenseMap<int, int> replacements;
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for (int i = 0; i < genericOp.getNumDpsInputs(); ++i) {
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// Skip bbArgs that have no uses.
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if (genericOp.getBody()->getArgument(i).getUses().empty())
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continue;
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// Find replacement bbArg. This can be an input or an output bbArg.
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for (int j = genericOp->getNumOperands() - 1; j > i; --j) {
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if (genericOp->getOperand(i) == genericOp->getOperand(j) &&
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genericOp.getIndexingMapsArray()[i] ==
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genericOp.getIndexingMapsArray()[j]) {
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replacements[i] = j;
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break;
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}
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}
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}
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// Stop here if no replacements were found.
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if (replacements.empty())
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return failure();
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// Rewrite the op.
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rewriter.updateRootInPlace(genericOp, [&]() {
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for (auto [before, after] : replacements) {
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BlockArgument bbArg = genericOp.getBody()->getArgument(before);
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BlockArgument replacement = genericOp.getBody()->getArgument(after);
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rewriter.replaceAllUsesWith(bbArg, replacement);
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}
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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::linalg::populateEraseUnusedOperandsAndResultsPatterns(
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RewritePatternSet &patterns) {
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patterns.insert<DeduplicateAndRemoveDeadOperandsAndResults>(
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patterns.getContext(), /*removeOutputs=*/true);
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patterns.insert<RemoveUnusedCycleInGenericOp>(patterns.getContext());
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}
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|
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void mlir::linalg::populateEraseUnnecessaryInputsPatterns(
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RewritePatternSet &patterns) {
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|
patterns.insert<DeduplicateAndRemoveDeadOperandsAndResults>(
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|
patterns.getContext(), /*removeOutputs=*/false);
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|
patterns.insert<FoldDuplicateInputBbArgs>(patterns.getContext());
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|
}
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