llvm-project/mlir/lib/IR/PatternMatch.cpp

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//===- PatternMatch.cpp - Base classes for pattern match ------------------===//
//
// 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
//
//===----------------------------------------------------------------------===//
#include "mlir/IR/PatternMatch.h"
#include "mlir/IR/IRMapping.h"
using namespace mlir;
//===----------------------------------------------------------------------===//
// PatternBenefit
//===----------------------------------------------------------------------===//
PatternBenefit::PatternBenefit(unsigned benefit) : representation(benefit) {
assert(representation == benefit && benefit != ImpossibleToMatchSentinel &&
"This pattern match benefit is too large to represent");
}
unsigned short PatternBenefit::getBenefit() const {
assert(!isImpossibleToMatch() && "Pattern doesn't match");
return representation;
}
//===----------------------------------------------------------------------===//
// Pattern
//===----------------------------------------------------------------------===//
//===----------------------------------------------------------------------===//
// OperationName Root Constructors
Pattern::Pattern(StringRef rootName, PatternBenefit benefit,
MLIRContext *context, ArrayRef<StringRef> generatedNames)
: Pattern(OperationName(rootName, context).getAsOpaquePointer(),
RootKind::OperationName, generatedNames, benefit, context) {}
//===----------------------------------------------------------------------===//
// MatchAnyOpTypeTag Root Constructors
Pattern::Pattern(MatchAnyOpTypeTag tag, PatternBenefit benefit,
MLIRContext *context, ArrayRef<StringRef> generatedNames)
: Pattern(nullptr, RootKind::Any, generatedNames, benefit, context) {}
//===----------------------------------------------------------------------===//
// MatchInterfaceOpTypeTag Root Constructors
Pattern::Pattern(MatchInterfaceOpTypeTag tag, TypeID interfaceID,
PatternBenefit benefit, MLIRContext *context,
ArrayRef<StringRef> generatedNames)
: Pattern(interfaceID.getAsOpaquePointer(), RootKind::InterfaceID,
generatedNames, benefit, context) {}
//===----------------------------------------------------------------------===//
// MatchTraitOpTypeTag Root Constructors
Pattern::Pattern(MatchTraitOpTypeTag tag, TypeID traitID,
PatternBenefit benefit, MLIRContext *context,
ArrayRef<StringRef> generatedNames)
: Pattern(traitID.getAsOpaquePointer(), RootKind::TraitID, generatedNames,
benefit, context) {}
//===----------------------------------------------------------------------===//
// General Constructors
Pattern::Pattern(const void *rootValue, RootKind rootKind,
ArrayRef<StringRef> generatedNames, PatternBenefit benefit,
MLIRContext *context)
: rootValue(rootValue), rootKind(rootKind), benefit(benefit),
contextAndHasBoundedRecursion(context, false) {
if (generatedNames.empty())
return;
generatedOps.reserve(generatedNames.size());
std::transform(generatedNames.begin(), generatedNames.end(),
std::back_inserter(generatedOps), [context](StringRef name) {
return OperationName(name, context);
});
}
//===----------------------------------------------------------------------===//
// RewritePattern
//===----------------------------------------------------------------------===//
void RewritePattern::rewrite(Operation *op, PatternRewriter &rewriter) const {
llvm_unreachable("need to implement either matchAndRewrite or one of the "
"rewrite functions!");
}
LogicalResult RewritePattern::match(Operation *op) const {
llvm_unreachable("need to implement either match or matchAndRewrite!");
}
/// Out-of-line vtable anchor.
void RewritePattern::anchor() {}
[mlir][PDL] Add support for PDL bytecode and expose PDL support to OwningRewritePatternList PDL patterns are now supported via a new `PDLPatternModule` class. This class contains a ModuleOp with the pdl::PatternOp operations representing the patterns, as well as a collection of registered C++ functions for native constraints/creations/rewrites/etc. that may be invoked via the pdl patterns. Instances of this class are added to an OwningRewritePatternList in the same fashion as C++ RewritePatterns, i.e. via the `insert` method. The PDL bytecode is an in-memory representation of the PDL interpreter dialect that can be efficiently interpreted/executed. The representation of the bytecode boils down to a code array(for opcodes/memory locations/etc) and a memory buffer(for storing attributes/operations/values/any other data necessary). The bytecode operations are effectively a 1-1 mapping to the PDLInterp dialect operations, with a few exceptions in cases where the in-memory representation of the bytecode can be more efficient than the MLIR representation. For example, a generic `AreEqual` bytecode op can be used to represent AreEqualOp, CheckAttributeOp, and CheckTypeOp. The execution of the bytecode is split into two phases: matching and rewriting. When matching, all of the matched patterns are collected to avoid the overhead of re-running parts of the matcher. These matched patterns are then considered alongside the native C++ patterns, which rewrite immediately in-place via `RewritePattern::matchAndRewrite`, for the given root operation. When a PDL pattern is matched and has the highest benefit, it is passed back to the bytecode to execute its rewriter. Differential Revision: https://reviews.llvm.org/D89107
2020-12-01 14:30:18 -08:00
//===----------------------------------------------------------------------===//
// PDLValue
//===----------------------------------------------------------------------===//
void PDLValue::print(raw_ostream &os) const {
if (!value) {
os << "<NULL-PDLValue>";
[mlir][PDL] Add support for PDL bytecode and expose PDL support to OwningRewritePatternList PDL patterns are now supported via a new `PDLPatternModule` class. This class contains a ModuleOp with the pdl::PatternOp operations representing the patterns, as well as a collection of registered C++ functions for native constraints/creations/rewrites/etc. that may be invoked via the pdl patterns. Instances of this class are added to an OwningRewritePatternList in the same fashion as C++ RewritePatterns, i.e. via the `insert` method. The PDL bytecode is an in-memory representation of the PDL interpreter dialect that can be efficiently interpreted/executed. The representation of the bytecode boils down to a code array(for opcodes/memory locations/etc) and a memory buffer(for storing attributes/operations/values/any other data necessary). The bytecode operations are effectively a 1-1 mapping to the PDLInterp dialect operations, with a few exceptions in cases where the in-memory representation of the bytecode can be more efficient than the MLIR representation. For example, a generic `AreEqual` bytecode op can be used to represent AreEqualOp, CheckAttributeOp, and CheckTypeOp. The execution of the bytecode is split into two phases: matching and rewriting. When matching, all of the matched patterns are collected to avoid the overhead of re-running parts of the matcher. These matched patterns are then considered alongside the native C++ patterns, which rewrite immediately in-place via `RewritePattern::matchAndRewrite`, for the given root operation. When a PDL pattern is matched and has the highest benefit, it is passed back to the bytecode to execute its rewriter. Differential Revision: https://reviews.llvm.org/D89107
2020-12-01 14:30:18 -08:00
return;
}
switch (kind) {
case Kind::Attribute:
os << cast<Attribute>();
break;
case Kind::Operation:
os << *cast<Operation *>();
break;
case Kind::Type:
os << cast<Type>();
break;
case Kind::TypeRange:
llvm::interleaveComma(cast<TypeRange>(), os);
break;
case Kind::Value:
os << cast<Value>();
break;
case Kind::ValueRange:
llvm::interleaveComma(cast<ValueRange>(), os);
break;
[mlir][PDL] Add support for PDL bytecode and expose PDL support to OwningRewritePatternList PDL patterns are now supported via a new `PDLPatternModule` class. This class contains a ModuleOp with the pdl::PatternOp operations representing the patterns, as well as a collection of registered C++ functions for native constraints/creations/rewrites/etc. that may be invoked via the pdl patterns. Instances of this class are added to an OwningRewritePatternList in the same fashion as C++ RewritePatterns, i.e. via the `insert` method. The PDL bytecode is an in-memory representation of the PDL interpreter dialect that can be efficiently interpreted/executed. The representation of the bytecode boils down to a code array(for opcodes/memory locations/etc) and a memory buffer(for storing attributes/operations/values/any other data necessary). The bytecode operations are effectively a 1-1 mapping to the PDLInterp dialect operations, with a few exceptions in cases where the in-memory representation of the bytecode can be more efficient than the MLIR representation. For example, a generic `AreEqual` bytecode op can be used to represent AreEqualOp, CheckAttributeOp, and CheckTypeOp. The execution of the bytecode is split into two phases: matching and rewriting. When matching, all of the matched patterns are collected to avoid the overhead of re-running parts of the matcher. These matched patterns are then considered alongside the native C++ patterns, which rewrite immediately in-place via `RewritePattern::matchAndRewrite`, for the given root operation. When a PDL pattern is matched and has the highest benefit, it is passed back to the bytecode to execute its rewriter. Differential Revision: https://reviews.llvm.org/D89107
2020-12-01 14:30:18 -08:00
}
}
void PDLValue::print(raw_ostream &os, Kind kind) {
switch (kind) {
case Kind::Attribute:
os << "Attribute";
break;
case Kind::Operation:
os << "Operation";
break;
case Kind::Type:
os << "Type";
break;
case Kind::TypeRange:
os << "TypeRange";
break;
case Kind::Value:
os << "Value";
break;
case Kind::ValueRange:
os << "ValueRange";
break;
}
}
[mlir][PDL] Add support for PDL bytecode and expose PDL support to OwningRewritePatternList PDL patterns are now supported via a new `PDLPatternModule` class. This class contains a ModuleOp with the pdl::PatternOp operations representing the patterns, as well as a collection of registered C++ functions for native constraints/creations/rewrites/etc. that may be invoked via the pdl patterns. Instances of this class are added to an OwningRewritePatternList in the same fashion as C++ RewritePatterns, i.e. via the `insert` method. The PDL bytecode is an in-memory representation of the PDL interpreter dialect that can be efficiently interpreted/executed. The representation of the bytecode boils down to a code array(for opcodes/memory locations/etc) and a memory buffer(for storing attributes/operations/values/any other data necessary). The bytecode operations are effectively a 1-1 mapping to the PDLInterp dialect operations, with a few exceptions in cases where the in-memory representation of the bytecode can be more efficient than the MLIR representation. For example, a generic `AreEqual` bytecode op can be used to represent AreEqualOp, CheckAttributeOp, and CheckTypeOp. The execution of the bytecode is split into two phases: matching and rewriting. When matching, all of the matched patterns are collected to avoid the overhead of re-running parts of the matcher. These matched patterns are then considered alongside the native C++ patterns, which rewrite immediately in-place via `RewritePattern::matchAndRewrite`, for the given root operation. When a PDL pattern is matched and has the highest benefit, it is passed back to the bytecode to execute its rewriter. Differential Revision: https://reviews.llvm.org/D89107
2020-12-01 14:30:18 -08:00
//===----------------------------------------------------------------------===//
// PDLPatternModule
//===----------------------------------------------------------------------===//
void PDLPatternModule::mergeIn(PDLPatternModule &&other) {
// Ignore the other module if it has no patterns.
if (!other.pdlModule)
return;
// Steal the functions and config of the other module.
for (auto &it : other.constraintFunctions)
registerConstraintFunction(it.first(), std::move(it.second));
for (auto &it : other.rewriteFunctions)
registerRewriteFunction(it.first(), std::move(it.second));
for (auto &it : other.configs)
configs.emplace_back(std::move(it));
for (auto &it : other.configMap)
configMap.insert(it);
[mlir][PDL] Add support for PDL bytecode and expose PDL support to OwningRewritePatternList PDL patterns are now supported via a new `PDLPatternModule` class. This class contains a ModuleOp with the pdl::PatternOp operations representing the patterns, as well as a collection of registered C++ functions for native constraints/creations/rewrites/etc. that may be invoked via the pdl patterns. Instances of this class are added to an OwningRewritePatternList in the same fashion as C++ RewritePatterns, i.e. via the `insert` method. The PDL bytecode is an in-memory representation of the PDL interpreter dialect that can be efficiently interpreted/executed. The representation of the bytecode boils down to a code array(for opcodes/memory locations/etc) and a memory buffer(for storing attributes/operations/values/any other data necessary). The bytecode operations are effectively a 1-1 mapping to the PDLInterp dialect operations, with a few exceptions in cases where the in-memory representation of the bytecode can be more efficient than the MLIR representation. For example, a generic `AreEqual` bytecode op can be used to represent AreEqualOp, CheckAttributeOp, and CheckTypeOp. The execution of the bytecode is split into two phases: matching and rewriting. When matching, all of the matched patterns are collected to avoid the overhead of re-running parts of the matcher. These matched patterns are then considered alongside the native C++ patterns, which rewrite immediately in-place via `RewritePattern::matchAndRewrite`, for the given root operation. When a PDL pattern is matched and has the highest benefit, it is passed back to the bytecode to execute its rewriter. Differential Revision: https://reviews.llvm.org/D89107
2020-12-01 14:30:18 -08:00
// Steal the other state if we have no patterns.
if (!pdlModule) {
pdlModule = std::move(other.pdlModule);
return;
}
// Merge the pattern operations from the other module into this one.
Block *block = pdlModule->getBody();
block->getOperations().splice(block->end(),
other.pdlModule->getBody()->getOperations());
}
void PDLPatternModule::attachConfigToPatterns(ModuleOp module,
PDLPatternConfigSet &configSet) {
// Attach the configuration to the symbols within the module. We only add
// to symbols to avoid hardcoding any specific operation names here (given
// that we don't depend on any PDL dialect). We can't use
// cast<SymbolOpInterface> here because patterns may be optional symbols.
module->walk([&](Operation *op) {
if (op->hasTrait<SymbolOpInterface::Trait>())
configMap[op] = &configSet;
});
}
[mlir][PDL] Add support for PDL bytecode and expose PDL support to OwningRewritePatternList PDL patterns are now supported via a new `PDLPatternModule` class. This class contains a ModuleOp with the pdl::PatternOp operations representing the patterns, as well as a collection of registered C++ functions for native constraints/creations/rewrites/etc. that may be invoked via the pdl patterns. Instances of this class are added to an OwningRewritePatternList in the same fashion as C++ RewritePatterns, i.e. via the `insert` method. The PDL bytecode is an in-memory representation of the PDL interpreter dialect that can be efficiently interpreted/executed. The representation of the bytecode boils down to a code array(for opcodes/memory locations/etc) and a memory buffer(for storing attributes/operations/values/any other data necessary). The bytecode operations are effectively a 1-1 mapping to the PDLInterp dialect operations, with a few exceptions in cases where the in-memory representation of the bytecode can be more efficient than the MLIR representation. For example, a generic `AreEqual` bytecode op can be used to represent AreEqualOp, CheckAttributeOp, and CheckTypeOp. The execution of the bytecode is split into two phases: matching and rewriting. When matching, all of the matched patterns are collected to avoid the overhead of re-running parts of the matcher. These matched patterns are then considered alongside the native C++ patterns, which rewrite immediately in-place via `RewritePattern::matchAndRewrite`, for the given root operation. When a PDL pattern is matched and has the highest benefit, it is passed back to the bytecode to execute its rewriter. Differential Revision: https://reviews.llvm.org/D89107
2020-12-01 14:30:18 -08:00
//===----------------------------------------------------------------------===//
// Function Registry
void PDLPatternModule::registerConstraintFunction(
StringRef name, PDLConstraintFunction constraintFn) {
// TODO: Is it possible to diagnose when `name` is already registered to
// a function that is not equivalent to `constraintFn`?
// Allow existing mappings in the case multiple patterns depend on the same
// constraint.
constraintFunctions.try_emplace(name, std::move(constraintFn));
[mlir][PDL] Add support for PDL bytecode and expose PDL support to OwningRewritePatternList PDL patterns are now supported via a new `PDLPatternModule` class. This class contains a ModuleOp with the pdl::PatternOp operations representing the patterns, as well as a collection of registered C++ functions for native constraints/creations/rewrites/etc. that may be invoked via the pdl patterns. Instances of this class are added to an OwningRewritePatternList in the same fashion as C++ RewritePatterns, i.e. via the `insert` method. The PDL bytecode is an in-memory representation of the PDL interpreter dialect that can be efficiently interpreted/executed. The representation of the bytecode boils down to a code array(for opcodes/memory locations/etc) and a memory buffer(for storing attributes/operations/values/any other data necessary). The bytecode operations are effectively a 1-1 mapping to the PDLInterp dialect operations, with a few exceptions in cases where the in-memory representation of the bytecode can be more efficient than the MLIR representation. For example, a generic `AreEqual` bytecode op can be used to represent AreEqualOp, CheckAttributeOp, and CheckTypeOp. The execution of the bytecode is split into two phases: matching and rewriting. When matching, all of the matched patterns are collected to avoid the overhead of re-running parts of the matcher. These matched patterns are then considered alongside the native C++ patterns, which rewrite immediately in-place via `RewritePattern::matchAndRewrite`, for the given root operation. When a PDL pattern is matched and has the highest benefit, it is passed back to the bytecode to execute its rewriter. Differential Revision: https://reviews.llvm.org/D89107
2020-12-01 14:30:18 -08:00
}
[mlir][PDL] Add support for PDL bytecode and expose PDL support to OwningRewritePatternList PDL patterns are now supported via a new `PDLPatternModule` class. This class contains a ModuleOp with the pdl::PatternOp operations representing the patterns, as well as a collection of registered C++ functions for native constraints/creations/rewrites/etc. that may be invoked via the pdl patterns. Instances of this class are added to an OwningRewritePatternList in the same fashion as C++ RewritePatterns, i.e. via the `insert` method. The PDL bytecode is an in-memory representation of the PDL interpreter dialect that can be efficiently interpreted/executed. The representation of the bytecode boils down to a code array(for opcodes/memory locations/etc) and a memory buffer(for storing attributes/operations/values/any other data necessary). The bytecode operations are effectively a 1-1 mapping to the PDLInterp dialect operations, with a few exceptions in cases where the in-memory representation of the bytecode can be more efficient than the MLIR representation. For example, a generic `AreEqual` bytecode op can be used to represent AreEqualOp, CheckAttributeOp, and CheckTypeOp. The execution of the bytecode is split into two phases: matching and rewriting. When matching, all of the matched patterns are collected to avoid the overhead of re-running parts of the matcher. These matched patterns are then considered alongside the native C++ patterns, which rewrite immediately in-place via `RewritePattern::matchAndRewrite`, for the given root operation. When a PDL pattern is matched and has the highest benefit, it is passed back to the bytecode to execute its rewriter. Differential Revision: https://reviews.llvm.org/D89107
2020-12-01 14:30:18 -08:00
void PDLPatternModule::registerRewriteFunction(StringRef name,
PDLRewriteFunction rewriteFn) {
// TODO: Is it possible to diagnose when `name` is already registered to
// a function that is not equivalent to `rewriteFn`?
// Allow existing mappings in the case multiple patterns depend on the same
// rewrite.
rewriteFunctions.try_emplace(name, std::move(rewriteFn));
[mlir][PDL] Add support for PDL bytecode and expose PDL support to OwningRewritePatternList PDL patterns are now supported via a new `PDLPatternModule` class. This class contains a ModuleOp with the pdl::PatternOp operations representing the patterns, as well as a collection of registered C++ functions for native constraints/creations/rewrites/etc. that may be invoked via the pdl patterns. Instances of this class are added to an OwningRewritePatternList in the same fashion as C++ RewritePatterns, i.e. via the `insert` method. The PDL bytecode is an in-memory representation of the PDL interpreter dialect that can be efficiently interpreted/executed. The representation of the bytecode boils down to a code array(for opcodes/memory locations/etc) and a memory buffer(for storing attributes/operations/values/any other data necessary). The bytecode operations are effectively a 1-1 mapping to the PDLInterp dialect operations, with a few exceptions in cases where the in-memory representation of the bytecode can be more efficient than the MLIR representation. For example, a generic `AreEqual` bytecode op can be used to represent AreEqualOp, CheckAttributeOp, and CheckTypeOp. The execution of the bytecode is split into two phases: matching and rewriting. When matching, all of the matched patterns are collected to avoid the overhead of re-running parts of the matcher. These matched patterns are then considered alongside the native C++ patterns, which rewrite immediately in-place via `RewritePattern::matchAndRewrite`, for the given root operation. When a PDL pattern is matched and has the highest benefit, it is passed back to the bytecode to execute its rewriter. Differential Revision: https://reviews.llvm.org/D89107
2020-12-01 14:30:18 -08:00
}
//===----------------------------------------------------------------------===//
// RewriterBase
//===----------------------------------------------------------------------===//
bool RewriterBase::Listener::classof(const OpBuilder::Listener *base) {
return base->getKind() == OpBuilder::ListenerBase::Kind::RewriterBaseListener;
}
RewriterBase::~RewriterBase() {
// Out of line to provide a vtable anchor for the class.
}
/// This method replaces the uses of the results of `op` with the values in
/// `newValues` when the provided `functor` returns true for a specific use.
/// The number of values in `newValues` is required to match the number of
/// results of `op`.
void RewriterBase::replaceOpWithIf(
Operation *op, ValueRange newValues, bool *allUsesReplaced,
llvm::unique_function<bool(OpOperand &) const> functor) {
assert(op->getNumResults() == newValues.size() &&
"incorrect number of values to replace operation");
// Notify the listener that we're about to replace this op.
if (auto *rewriteListener = dyn_cast_if_present<Listener>(listener))
rewriteListener->notifyOperationReplaced(op, newValues);
// Replace each use of the results when the functor is true.
bool replacedAllUses = true;
for (auto it : llvm::zip(op->getResults(), newValues)) {
replaceUsesWithIf(std::get<0>(it), std::get<1>(it), functor);
replacedAllUses &= std::get<0>(it).use_empty();
}
if (allUsesReplaced)
*allUsesReplaced = replacedAllUses;
}
/// This method replaces the uses of the results of `op` with the values in
/// `newValues` when a use is nested within the given `block`. The number of
/// values in `newValues` is required to match the number of results of `op`.
/// If all uses of this operation are replaced, the operation is erased.
void RewriterBase::replaceOpWithinBlock(Operation *op, ValueRange newValues,
Block *block, bool *allUsesReplaced) {
replaceOpWithIf(op, newValues, allUsesReplaced, [block](OpOperand &use) {
return block->getParentOp()->isProperAncestor(use.getOwner());
});
}
/// This method replaces the results of the operation with the specified list of
/// values. The number of provided values must match the number of results of
/// the operation. The replaced op is erased.
void RewriterBase::replaceOp(Operation *op, ValueRange newValues) {
assert(op->getNumResults() == newValues.size() &&
"incorrect # of replacement values");
// Notify the listener that we're about to replace this op.
if (auto *rewriteListener = dyn_cast_if_present<Listener>(listener))
rewriteListener->notifyOperationReplaced(op, newValues);
// Replace results one-by-one. Also notifies the listener of modifications.
for (auto it : llvm::zip(op->getResults(), newValues))
replaceAllUsesWith(std::get<0>(it), std::get<1>(it));
// Erase the op.
eraseOp(op);
}
/// This method replaces the results of the operation with the specified new op
/// (replacement). The number of results of the two operations must match. The
/// replaced op is erased.
void RewriterBase::replaceOp(Operation *op, Operation *newOp) {
assert(op && newOp && "expected non-null op");
assert(op->getNumResults() == newOp->getNumResults() &&
"ops have different number of results");
// Notify the listener that we're about to replace this op.
if (auto *rewriteListener = dyn_cast_if_present<Listener>(listener))
rewriteListener->notifyOperationReplaced(op, newOp);
// Replace results one-by-one. Also notifies the listener of modifications.
for (auto it : llvm::zip(op->getResults(), newOp->getResults()))
replaceAllUsesWith(std::get<0>(it), std::get<1>(it));
// Erase the old op.
eraseOp(op);
}
/// This method erases an operation that is known to have no uses. The uses of
/// the given operation *must* be known to be dead.
void RewriterBase::eraseOp(Operation *op) {
assert(op->use_empty() && "expected 'op' to have no uses");
if (auto *rewriteListener = dyn_cast_if_present<Listener>(listener))
rewriteListener->notifyOperationRemoved(op);
op->erase();
}
void RewriterBase::eraseBlock(Block *block) {
for (auto &op : llvm::make_early_inc_range(llvm::reverse(*block))) {
assert(op.use_empty() && "expected 'op' to have no uses");
eraseOp(&op);
}
block->erase();
}
void RewriterBase::finalizeRootUpdate(Operation *op) {
// Notify the listener that the operation was modified.
if (auto *rewriteListener = dyn_cast_if_present<Listener>(listener))
rewriteListener->notifyOperationModified(op);
}
/// Find uses of `from` and replace them with `to` if the `functor` returns
/// true. It also marks every modified uses and notifies the rewriter that an
/// in-place operation modification is about to happen.
void RewriterBase::replaceUsesWithIf(Value from, Value to,
function_ref<bool(OpOperand &)> functor) {
for (OpOperand &operand : llvm::make_early_inc_range(from.getUses())) {
if (functor(operand))
updateRootInPlace(operand.getOwner(), [&]() { operand.set(to); });
}
}
void RewriterBase::inlineBlockBefore(Block *source, Block *dest,
Block::iterator before,
ValueRange argValues) {
assert(argValues.size() == source->getNumArguments() &&
"incorrect # of argument replacement values");
// The source block will be deleted, so it should not have any users (i.e.,
// there should be no predecessors).
assert(source->hasNoPredecessors() &&
"expected 'source' to have no predecessors");
if (dest->end() != before) {
// The source block will be inserted in the middle of the dest block, so
// the source block should have no successors. Otherwise, the remainder of
// the dest block would be unreachable.
assert(source->hasNoSuccessors() &&
"expected 'source' to have no successors");
} else {
// The source block will be inserted at the end of the dest block, so the
// dest block should have no successors. Otherwise, the inserted operations
// will be unreachable.
assert(dest->hasNoSuccessors() && "expected 'dest' to have no successors");
}
// Replace all of the successor arguments with the provided values.
for (auto it : llvm::zip(source->getArguments(), argValues))
replaceAllUsesWith(std::get<0>(it), std::get<1>(it));
// Move operations from the source block to the dest block and erase the
// source block.
dest->getOperations().splice(before, source->getOperations());
source->erase();
}
void RewriterBase::inlineBlockBefore(Block *source, Operation *op,
ValueRange argValues) {
inlineBlockBefore(source, op->getBlock(), op->getIterator(), argValues);
}
void RewriterBase::mergeBlocks(Block *source, Block *dest,
ValueRange argValues) {
inlineBlockBefore(source, dest, dest->end(), argValues);
}
/// Split the operations starting at "before" (inclusive) out of the given
/// block into a new block, and return it.
Block *RewriterBase::splitBlock(Block *block, Block::iterator before) {
return block->splitBlock(before);
}
/// Move the blocks that belong to "region" before the given position in
/// another region. The two regions must be different. The caller is in
/// charge to update create the operation transferring the control flow to the
/// region and pass it the correct block arguments.
void RewriterBase::inlineRegionBefore(Region &region, Region &parent,
Region::iterator before) {
parent.getBlocks().splice(before, region.getBlocks());
}
void RewriterBase::inlineRegionBefore(Region &region, Block *before) {
inlineRegionBefore(region, *before->getParent(), before->getIterator());
}
/// Clone the blocks that belong to "region" before the given position in
/// another region "parent". The two regions must be different. The caller is
/// responsible for creating or updating the operation transferring flow of
/// control to the region and passing it the correct block arguments.
void RewriterBase::cloneRegionBefore(Region &region, Region &parent,
Region::iterator before,
IRMapping &mapping) {
region.cloneInto(&parent, before, mapping);
}
void RewriterBase::cloneRegionBefore(Region &region, Region &parent,
Region::iterator before) {
IRMapping mapping;
cloneRegionBefore(region, parent, before, mapping);
}
void RewriterBase::cloneRegionBefore(Region &region, Block *before) {
cloneRegionBefore(region, *before->getParent(), before->getIterator());
}