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//===- OneShotAnalysis.cpp - One-Shot (Single Pass) Analysis --------------===//
//
// 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
//
//===----------------------------------------------------------------------===//
//
// One-Shot Analysis analyzes function bodies. By default, function boundaries
// (FuncOp bbArgs, CallOps, ReturnOps) are treated as "unknown" ops.
// OneShotModuleBufferization.cpp is an extension of One-Shot Analysis for
// simple call graphs without loops.
//
// One-Shot Bufferize consists of three phases.
//
// 1. Analyze ops to decide which OpOperands can bufferize inplace, i.e.,
// without inserting buffer copies. The analysis queries op bufferization
// semantics via `BufferizableOpInterface`.
// 2. Insert copies for OpOperands that were decided to bufferize out-of-place
// in tensor land during `TensorCopyInsertion`.
// 3. Bufferize ops by calling `BufferizableOpInterface::bufferize`.
//
// This file contains only the analysis. For convenience, this file also
// contains a helper function `runOneShotBufferize` that analyzes an op (and its
// nested ops) and then bufferizes it.
//
// Inplace bufferization decisions are passed from the analysis to the
// `TensorCopyInsertion` phase via `AnalysisState`. They can be printed for
// debugging purposes with `testAnalysisOnly`.
//
// Ops that do not implement `BufferizableOpInterface` can be analyzed but are
// treated conservatively. E.g., the analysis has to assume that their tensor
// OpOperands bufferize to memory writes. While such ops can be analyzed, they
// are not bufferized and remain in the IR. to_tensor and to_memref ops are
// inserted at the bufferization boundary.
//
// This analysis caters to high-performance codegen where buffer reuse is deemed
// critical: the analysis should fail if the bufferized form of the function
// needs to return a buffer, unless `allowReturnAllocs` is enabled.
#include "mlir/Dialect/Bufferization/Transforms/OneShotAnalysis.h"
#include <random>
#include <optional>
#include "mlir/Dialect/Bufferization/IR/BufferizableOpInterface.h"
#include "mlir/Dialect/Bufferization/IR/Bufferization.h"
#include "mlir/Dialect/Bufferization/Transforms/Bufferize.h"
#include "mlir/Dialect/Bufferization/Transforms/Transforms.h"
#include "mlir/Dialect/Func/IR/FuncOps.h"
#include "mlir/Dialect/MemRef/IR/MemRef.h"
#include "mlir/IR/AsmState.h"
#include "mlir/IR/Dominance.h"
#include "mlir/IR/Operation.h"
#include "mlir/IR/TypeUtilities.h"
#include "mlir/Interfaces/ControlFlowInterfaces.h"
#include "llvm/ADT/DenseSet.h"
#include "llvm/ADT/SetVector.h"
MLIR_DEFINE_EXPLICIT_TYPE_ID(mlir::bufferization::OneShotAnalysisState)
// Run mlir-opt with `-debug-only="one-shot-analysis"` for detailed debug
// output.
#define DEBUG_TYPE "one-shot-analysis"
using namespace mlir;
using namespace mlir::bufferization;
static bool isaTensor(Type t) { return t.isa<TensorType>(); }
//===----------------------------------------------------------------------===//
// Bufferization-specific attribute manipulation.
// These are for testing and debugging only. Bufferization information is stored
// in BufferizationAliasInfo. When run with `testAnalysisOnly`, the IR is
// annotated with the results of the analysis, so that they can be checked in
// tests.
//===----------------------------------------------------------------------===//
/// Attribute marker to specify op operands that bufferize in-place.
constexpr StringLiteral kInPlaceOperandsAttrName = "__inplace_operands_attr__";
/// Mark whether OpOperand will be bufferized inplace.
static void setInPlaceOpOperand(OpOperand &opOperand, bool inPlace) {
Operation *op = opOperand.getOwner();
SmallVector<StringRef> inPlaceVector;
if (auto attr = op->getAttr(kInPlaceOperandsAttrName)) {
inPlaceVector = SmallVector<StringRef>(llvm::to_vector<4>(
attr.cast<ArrayAttr>().getAsValueRange<StringAttr>()));
} else {
inPlaceVector = SmallVector<StringRef>(op->getNumOperands(), "none");
for (OpOperand &opOperand : op->getOpOperands())
if (opOperand.get().getType().isa<TensorType>())
inPlaceVector[opOperand.getOperandNumber()] = "false";
}
inPlaceVector[opOperand.getOperandNumber()] = inPlace ? "true" : "false";
op->setAttr(kInPlaceOperandsAttrName,
OpBuilder(op).getStrArrayAttr(inPlaceVector));
}
//===----------------------------------------------------------------------===//
// BufferizationAliasInfo
//===----------------------------------------------------------------------===//
BufferizationAliasInfo::BufferizationAliasInfo(Operation *rootOp) {
rootOp->walk([&](Operation *op) {
for (Value v : op->getResults())
if (v.getType().isa<TensorType>())
createAliasInfoEntry(v);
for (Region &r : op->getRegions())
for (Block &b : r.getBlocks())
for (auto bbArg : b.getArguments())
if (bbArg.getType().isa<TensorType>())
createAliasInfoEntry(bbArg);
});
}
/// Add a new entry for `v` in the `aliasInfo` and `equivalentInfo`. In the
/// beginning the alias and equivalence sets only contain `v` itself.
void BufferizationAliasInfo::createAliasInfoEntry(Value v) {
aliasInfo.insert(v);
equivalentInfo.insert(v);
}
/// Insert an info entry for `newValue` and merge its alias set with that of
/// `alias`.
void BufferizationAliasInfo::insertNewBufferAlias(Value newValue, Value alias) {
createAliasInfoEntry(newValue);
aliasInfo.unionSets(newValue, alias);
}
/// Insert an info entry for `newValue` and merge its alias set with that of
/// `alias`. Additionally, merge their equivalence classes.
void BufferizationAliasInfo::insertNewBufferEquivalence(Value newValue,
Value alias) {
insertNewBufferAlias(newValue, alias);
equivalentInfo.unionSets(newValue, alias);
}
/// Return `true` if a value was marked as in-place bufferized.
bool BufferizationAliasInfo::isInPlace(OpOperand &operand) const {
return inplaceBufferized.contains(&operand);
}
/// Set the inPlace bufferization spec to true.
void BufferizationAliasInfo::bufferizeInPlace(OpOperand &operand,
AnalysisState &state) {
if (inplaceBufferized.contains(&operand))
return;
markInPlace(operand);
for (OpResult result : state.getAliasingOpResult(operand))
aliasInfo.unionSets(result, operand.get());
++statNumTensorInPlace;
}
/// Set the inPlace bufferization spec to false.
void BufferizationAliasInfo::bufferizeOutOfPlace(OpOperand &operand) {
assert(!inplaceBufferized.contains(&operand) &&
"OpOperand was already decided to bufferize inplace");
++statNumTensorOutOfPlace;
}
/// Apply `fun` to all the members of the equivalence class of `v`.
void BufferizationAliasInfo::applyOnEquivalenceClass(
Value v, function_ref<void(Value)> fun) const {
auto leaderIt = equivalentInfo.findLeader(v);
for (auto mit = leaderIt, meit = equivalentInfo.member_end(); mit != meit;
++mit) {
fun(*mit);
}
}
/// Apply `fun` to all aliases of `v`.
void BufferizationAliasInfo::applyOnAliases(
Value v, function_ref<void(Value)> fun) const {
auto leaderIt = aliasInfo.findLeader(v);
for (auto mit = leaderIt, meit = aliasInfo.member_end(); mit != meit; ++mit) {
fun(*mit);
}
}
BufferizationAliasInfo::EquivalenceClassRangeType
BufferizationAliasInfo::getAliases(Value v) const {
DenseSet<Value> res;
auto it = aliasInfo.findValue(aliasInfo.getLeaderValue(v));
for (auto mit = aliasInfo.member_begin(it), meit = aliasInfo.member_end();
mit != meit; ++mit) {
res.insert(static_cast<Value>(*mit));
}
return BufferizationAliasInfo::EquivalenceClassRangeType(
aliasInfo.member_begin(it), aliasInfo.member_end());
}
//===----------------------------------------------------------------------===//
// OneShotAnalysisState
//===----------------------------------------------------------------------===//
OneShotAnalysisState::OneShotAnalysisState(
Operation *op, const OneShotBufferizationOptions &options)
: AnalysisState(options, TypeID::get<OneShotAnalysisState>()),
aliasInfo(op) {
// Set up alias sets for OpResults that must bufferize in-place. This should
// be done before making any other bufferization decisions.
op->walk([&](BufferizableOpInterface bufferizableOp) {
if (!options.isOpAllowed(bufferizableOp))
return WalkResult::skip();
for (OpOperand &opOperand : bufferizableOp->getOpOperands())
if (opOperand.get().getType().isa<TensorType>())
if (bufferizableOp.mustBufferizeInPlace(opOperand, *this))
aliasInfo.bufferizeInPlace(opOperand, *this);
return WalkResult::advance();
});
}
bool OneShotAnalysisState::isInPlace(OpOperand &opOperand) const {
return aliasInfo.isInPlace(opOperand);
}
bool OneShotAnalysisState::areEquivalentBufferizedValues(Value v1,
Value v2) const {
return aliasInfo.areEquivalentBufferizedValues(v1, v2);
}
bool OneShotAnalysisState::areAliasingBufferizedValues(Value v1,
Value v2) const {
return aliasInfo.areAliasingBufferizedValues(v1, v2);
}
// Gather yielded tensors in `yieldedTensors` by querying all aliases. This is
// to ensure that such information is available during bufferization time.
// Alias information can no longer be queried through BufferizationAliasInfo
// once we have started modifying the IR.
void OneShotAnalysisState::gatherYieldedTensors(Operation *op) {
op->walk([&](Operation *returnOp) {
if (!isRegionReturnLike(returnOp) || !getOptions().isOpAllowed(returnOp))
return WalkResult::advance();
for (OpOperand &returnValOperand : returnOp->getOpOperands()) {
Value returnVal = returnValOperand.get();
// Skip non-tensor values.
if (!returnVal.getType().isa<TensorType>())
continue;
// Add all aliases of the returned value. But only the ones that are in
// the same block.
aliasInfo.applyOnAliases(returnVal, [&](Value v) {
if (auto bbArg = v.dyn_cast<BlockArgument>()) {
if (bbArg.getOwner()->getParentOp() == returnOp->getParentOp())
yieldedTensors.insert(bbArg);
return;
}
Operation *definingOp = v.getDefiningOp();
if (definingOp->getParentOp() == returnOp->getParentOp())
yieldedTensors.insert(v);
});
}
return WalkResult::advance();
});
}
void OneShotAnalysisState::gatherUndefinedTensorUses(Operation *op) {
op->walk([&](Operation *op) {
// Skip unknown ops.
auto bufferizableOp = getOptions().dynCastBufferizableOp(op);
if (!bufferizableOp)
return WalkResult::skip();
// Check all tensor OpResults.
for (OpResult opResult : op->getOpResults()) {
if (!opResult.getType().isa<TensorType>())
continue;
// If there is no preceding memory write, the tensor contents are
// undefined.
// Note: If `findLastPrecedingWrite` reaches the end of the reverse SSA
// use-def chain, it returns that value, regardless of whether it is a
// memory write or not.
SetVector<Value> lastWrites = findLastPrecedingWrite(opResult);
bool isUndefined = llvm::none_of(lastWrites, [&](Value lastWrite) {
if (auto bufferizableOp = getOptions().dynCastBufferizableOp(lastWrite))
return bufferizableOp.isMemoryWrite(lastWrite.cast<OpResult>(),
*this);
return true;
});
if (isUndefined)
for (OpOperand &use : opResult.getUses())
undefinedTensorUses.insert(&use);
}
return WalkResult::advance();
});
}
bool OneShotAnalysisState::hasUndefinedContents(OpOperand *opOperand) const {
return undefinedTensorUses.contains(opOperand);
}
bool OneShotAnalysisState::isTensorYielded(Value tensor) const {
return yieldedTensors.contains(tensor);
}
bool OneShotAnalysisState::isValueWritten(Value value) const {
bool isWritten = false;
aliasInfo.applyOnAliases(value, [&](Value val) {
for (OpOperand &use : val.getUses())
if (isInPlace(use) && bufferizesToMemoryWrite(use))
isWritten = true;
});
return isWritten;
}
bool OneShotAnalysisState::isWritable(Value value) const {
// TODO: Out-of-place bufferized value could be considered writable.
if (auto bufferizableOp = getOptions().dynCastBufferizableOp(value))
return bufferizableOp.isWritable(value, *this);
// Query BufferizableOpInterface to see if the BlockArgument is writable.
if (auto bbArg = value.dyn_cast<BlockArgument>())
if (auto bufferizableOp =
getOptions().dynCastBufferizableOp(bbArg.getOwner()->getParentOp()))
return bufferizableOp.isWritable(bbArg, *this);
// Not a bufferizable op: The conservative answer is "not writable".
return false;
}
OneShotAnalysisState::Extension::~Extension() = default;
//===----------------------------------------------------------------------===//
// Bufferization-specific alias analysis.
//===----------------------------------------------------------------------===//
/// Return true if opOperand has been decided to bufferize in-place.
static bool isInplaceMemoryWrite(OpOperand &opOperand,
const BufferizationAliasInfo &aliasInfo,
const AnalysisState &state) {
// OpOperands that do not bufferize to a memory write do not write in-place.
if (!state.bufferizesToMemoryWrite(opOperand))
return false;
// Check current bufferization decisions.
return aliasInfo.isInPlace(opOperand);
}
/// Return true if `a` happens before `b`, i.e., `a` or one of its ancestors
/// properly dominates `b` and `b` is not inside `a`.
static bool happensBefore(Operation *a, Operation *b,
const DominanceInfo &domInfo) {
do {
// TODO: Instead of isProperAncestor + properlyDominates, we should use
// properlyDominatesImpl(a, b, /*enclosingOpOk=*/false)
if (a->isProperAncestor(b))
return false;
if (domInfo.properlyDominates(a, b))
return true;
} while ((a = a->getParentOp()));
return false;
}
/// Return `true` if the given tensor value is a memory write. Most values are
/// tensor writes, but ops that define a tensor SSA value without specifying its
/// contents (e.g., alloc_tensor) are not.
static bool isMemoryWrite(Value value, const AnalysisState &state) {
auto opResult = value.dyn_cast<OpResult>();
if (!opResult)
return true;
auto bufferizableOp = state.getOptions().dynCastBufferizableOp(value);
if (!bufferizableOp)
return true;
return bufferizableOp.isMemoryWrite(opResult, state);
}
/// Return `true` if op dominance can be used to rule out read-after-write
/// conflicts wrt. the given reads and writes.
///
/// Op dominance can often be used to rule out potential conflicts such as
/// "read" happens before "write". E.g., the following IR is not a RaW conflict
/// because the the read happens *before* the write.
///
/// %0 = ... : tensor<?xf32>
/// "reading_op"(%0) : tensor<?xf32>
/// %1 = "writing_op"(%0) : tensor<?xf32> -> tensor<?xf32>
///
/// This is no longer true inside loops (or repetitive regions). In such cases,
/// there may not be a meaningful `happensBefore` relationship because ops
/// could be executed multiple times. E.g.:
///
/// %0 = ... : tensor<?xf32>
/// scf.for ... {
/// "reading_op"(%0) : tensor<?xf32>
/// %1 = "writing_op"(%0) : tensor<?xf32> -> tensor<?xf32>
/// ...
/// }
///
/// In the above example, reading_op happens before writing_op according to
/// op dominance. However, both ops may happen multiple times; in
/// particular, the second execution of reading_op happens after the first
/// execution of writing_op. This is problematic because the tensor %0 they
/// operate on (i.e., the "definition") is defined outside of the loop.
///
/// Counter example:
///
/// scf.for ... {
/// %0 = ... : tensor<?xf32>
/// "reading_op"(%0) : tensor<?xf32>
/// %1 = "writing_op"(%0) : tensor<?xf32> -> tensor<?xf32>
/// ...
/// }
///
/// In this example, the definition %0 is in the same repetitive region as
/// "writing_op", so op dominance can be used to compute the `happensBefore`
/// relationship.
///
/// Whether op dominance can be used or not is decided as follows: Find the
/// closest enclosing repetitive region of all buffer writes wrt. the given
/// tensor reads and writes. (The given sets of reads and writes contain the
/// entire alias set.) In case of a read, we look at the op that defines the
/// read value. In case of a write, we look at the op that is writing. If all of
/// those ops are in the same closest enclosing repetitive region (nullptr in
/// case of "no repetitive region" found at all), then op dominance can be used.
/// Otherwise, it cannot be used.
///
/// Example: The common enclosing repetitive region is the scf.for loop.
/// Op dominance can be used.
/// scf.for ... {
/// %0 = tensor.generate
/// "read"(%0)
/// }
///
/// Example: The common enclosing repetitive region is nullptr: There is no
/// repetitive region around the tensor.generate. Op dominance can be
/// used.
/// %0 = tensor.generate
/// scf.for ... { "read"(%0) }
///
/// Example: The common enclosing repetitive regions of tensor.generate and
/// "write" differ. Op dominance cannot be used.
/// %0 = tensor.generate
/// scf.for ... {
/// "read"(%0)
/// "write"(%0)
/// }
///
/// Example: The common enclosing repetitive regions of tensor.generate and
/// "write" differ, but there is no read of %0, so op dominance can be
/// used.
/// %0 = tensor.generate
/// scf.for ... {
/// "write"(%0)
/// }
///
/// Note: iter_args of loops are not aliases of their respective block
/// arguments, so op domanice can be used when analyzing ops that operate
/// on them.
bool canUseOpDominance(const DenseSet<OpOperand *> &usesRead,
const DenseSet<OpOperand *> &usesWrite,
const AnalysisState &state) {
const BufferizationOptions &options = state.getOptions();
std::optional<Region *> commonEnclosingRegion;
// In case of a write, take the region in which the write takes place.
for (OpOperand *uWrite : usesWrite) {
Region *r = getEnclosingRepetitiveRegion(uWrite->getOwner(), options);
if (!commonEnclosingRegion.has_value()) {
commonEnclosingRegion = r;
continue;
}
if (*commonEnclosingRegion != r)
return false;
}
// In case of a read, take the region which the read value is defined.
for (OpOperand *uRead : usesRead) {
// Optimization: Skip reads of values that have no defined contents.
if (!isMemoryWrite(uRead->get(), state))
continue;
Region *r = getEnclosingRepetitiveRegion(uRead->get(), options);
if (!commonEnclosingRegion.has_value()) {
commonEnclosingRegion = r;
continue;
}
if (*commonEnclosingRegion != r)
return false;
}
return commonEnclosingRegion.has_value();
}
/// Annotate IR with details about the detected RaW conflict.
static void annotateConflict(OpOperand *uRead, OpOperand *uConflictingWrite,
Value lastWrite) {
static uint64_t counter = 0;
Operation *readingOp = uRead->getOwner();
Operation *conflictingWritingOp = uConflictingWrite->getOwner();
OpBuilder b(conflictingWritingOp->getContext());
std::string id = "C_" + std::to_string(counter++);
std::string conflictingWriteAttr =
id +
"[CONFL-WRITE: " + std::to_string(uConflictingWrite->getOperandNumber()) +
"]";
conflictingWritingOp->setAttr(conflictingWriteAttr, b.getUnitAttr());
std::string readAttr =
id + "[READ: " + std::to_string(uRead->getOperandNumber()) + "]";
readingOp->setAttr(readAttr, b.getUnitAttr());
if (auto opResult = lastWrite.dyn_cast<OpResult>()) {
std::string lastWriteAttr = id + "[LAST-WRITE: result " +
std::to_string(opResult.getResultNumber()) +
"]";
opResult.getDefiningOp()->setAttr(lastWriteAttr, b.getUnitAttr());
} else {
auto bbArg = lastWrite.cast<BlockArgument>();
std::string lastWriteAttr =
id + "[LAST-WRITE: bbArg " + std::to_string(bbArg.getArgNumber()) + "]";
bbArg.getOwner()->getParentOp()->setAttr(lastWriteAttr, b.getUnitAttr());
}
}
/// Given sets of uses and writes, return true if there is a RaW conflict under
/// the assumption that all given reads/writes alias the same buffer and that
/// all given writes bufferize inplace.
///
/// A conflict is: According to SSA use-def chains, a read R is supposed to read
/// the result of a write W1. But because of bufferization decisions, R actually
/// reads another write W2.
static bool hasReadAfterWriteInterference(
const DenseSet<OpOperand *> &usesRead,
const DenseSet<OpOperand *> &usesWrite, const DominanceInfo &domInfo,
AnalysisState &state, const BufferizationAliasInfo &aliasInfo) {
const BufferizationOptions &options = state.getOptions();
// Check if op dominance can be used to rule out read-after-write conflicts.
bool useDominance = canUseOpDominance(usesRead, usesWrite, state);
LLVM_DEBUG(llvm::dbgs() << "\n- useDominance = " << useDominance << "\n");
for (OpOperand *uRead : usesRead) {
Operation *readingOp = uRead->getOwner();
// Find most recent writes of uRead by following the SSA use-def chain.
// E.g.:
//
// %0 = "writing_op"(%t) : tensor<?x32> -> tensor<?xf32>
// %1 = "aliasing_op"(%0) : tensor<?x32> -> tensor<?xf32>
// %2 = "reading_op"(%1) : : tensor<?x32> -> not_a_tensor_type
//
// In the above example, if uRead is the OpOperand of reading_op, lastWrite
// is %0. Note that operations that create an alias but do not write (such
// as ExtractSliceOp) are skipped.
SetVector<Value> lastWrites = state.findLastPrecedingWrite(uRead->get());
// Look for conflicting memory writes. Potential conflicts are writes to an
// alias that have been decided to bufferize inplace.
for (OpOperand *uConflictingWrite : usesWrite) {
LLVM_DEBUG(llvm::dbgs() << "\n- check conflict:\n");
LLVM_DEBUG(llvm::dbgs()
<< " uRead = operand " << uRead->getOperandNumber() << " of "
<< *uRead->getOwner() << "\n");
LLVM_DEBUG(llvm::dbgs() << " unConflictingWrite = operand "
<< uConflictingWrite->getOperandNumber() << " of "
<< *uConflictingWrite->getOwner() << "\n");
// Throughout this loop, check for multiple requirements that have to be
// met for uConflictingWrite to be an actual conflict.
Operation *conflictingWritingOp = uConflictingWrite->getOwner();
// Inside of repetitive regions, ops may be executed multiple times and op
// dominance cannot be used to rule out conflicts.
if (useDominance) {
// No conflict if the readingOp dominates conflictingWritingOp, i.e.,
// the write is not visible when reading.
//
// Note: If ops are executed multiple times (e.g., because they are
// inside a loop), there may be no meaningful `happensBefore`
// relationship.
if (happensBefore(readingOp, conflictingWritingOp, domInfo)) {
LLVM_DEBUG(llvm::dbgs()
<< " no conflict: read happens before write\n");
continue;
}
// No conflict if the reading use equals the use of the conflicting
// write. A use cannot conflict with itself.
//
// Note: Just being the same op is not enough. It has to be the same
// use.
// Note: If the op is executed multiple times (e.g., because it is
// inside a loop), it may be conflicting with itself.
if (uConflictingWrite == uRead) {
LLVM_DEBUG(llvm::dbgs()
<< " no conflict: read and write are same use\n");
continue;
}
// Ops are not conflicting if they are in mutually exclusive regions.
//
// Note: If ops are executed multiple times (e.g., because they are
// inside a loop), mutually exclusive regions may be executed
// multiple times.
if (insideMutuallyExclusiveRegions(readingOp, conflictingWritingOp)) {
LLVM_DEBUG(llvm::dbgs() << " no conflict: read and write are in "
"mutually exclusive regions\n");
continue;
}
}
// No conflict if the op interface says so.
if (auto bufferizableOp = options.dynCastBufferizableOp(readingOp)) {
if (bufferizableOp.isNotConflicting(uRead, uConflictingWrite, state)) {
LLVM_DEBUG(llvm::dbgs()
<< " no conflict: op interace of reading op says 'no'\n");
continue;
}
}
if (conflictingWritingOp != readingOp) {
if (auto bufferizableOp =
options.dynCastBufferizableOp(conflictingWritingOp)) {
if (bufferizableOp.isNotConflicting(uRead, uConflictingWrite,
state)) {
LLVM_DEBUG(
llvm::dbgs()
<< " no conflict: op interace of writing op says 'no'\n");
continue;
}
}
}
// Check all possible last writes.
for (Value lastWrite : lastWrites) {
LLVM_DEBUG(llvm::dbgs() << " * lastWrite = " << lastWrite << "\n");
// No conflict if the conflicting write happens before the last
// write.
if (Operation *writingOp = lastWrite.getDefiningOp()) {
if (happensBefore(conflictingWritingOp, writingOp, domInfo)) {
// conflictingWritingOp happens before writingOp. No conflict.
LLVM_DEBUG(llvm::dbgs()
<< " no conflict: write happens before last write\n");
continue;
}
// No conflict if conflictingWritingOp is contained in writingOp.
if (writingOp->isProperAncestor(conflictingWritingOp)) {
LLVM_DEBUG(
llvm::dbgs()
<< " no conflict: write is contained in last write\n");
continue;
}
} else {
auto bbArg = lastWrite.cast<BlockArgument>();
Block *block = bbArg.getOwner();
if (!block->findAncestorOpInBlock(*conflictingWritingOp)) {
LLVM_DEBUG(llvm::dbgs() << " no conflict: last write is bbArg "
"and write happens outside of block\n");
// conflictingWritingOp happens outside of the block. No
// conflict.
continue;
}
}
// No conflict if the conflicting write and the last write are the same
// use.
SmallVector<OpResult> aliasingOpResult =
state.getAliasingOpResult(*uConflictingWrite);
if (aliasingOpResult.size() == 1 && aliasingOpResult[0] == lastWrite) {
LLVM_DEBUG(llvm::dbgs()
<< " no conflict: last write and write are same\n");
continue;
}
// All requirements are met. Conflict found!
if (options.printConflicts)
annotateConflict(uRead, uConflictingWrite, lastWrite);
LLVM_DEBUG(llvm::dbgs() << " => RaW CONFLICT FOUND\n");
return true;
}
}
}
return false;
}
// Helper function to iterate on aliases of `root` and capture the writes.
static void getAliasingInplaceWrites(DenseSet<OpOperand *> &res, Value root,
const BufferizationAliasInfo &aliasInfo,
const AnalysisState &state) {
aliasInfo.applyOnAliases(root, [&](Value alias) {
for (auto &use : alias.getUses())
// Inplace write to a value that aliases root.
if (isInplaceMemoryWrite(use, aliasInfo, state))
res.insert(&use);
});
}
// Helper function to iterate on aliases of `root` and capture the reads.
static void getAliasingReads(DenseSet<OpOperand *> &res, Value root,
const BufferizationAliasInfo &aliasInfo,
const AnalysisState &state) {
aliasInfo.applyOnAliases(root, [&](Value alias) {
for (auto &use : alias.getUses()) {
// Read of a value that aliases root.
if (state.bufferizesToMemoryRead(use)) {
res.insert(&use);
continue;
}
// Read of a dependent value in the SSA use-def chain. E.g.:
//
// %0 = ...
// %1 = tensor.extract_slice %0 {not_analyzed_yet}
// "read"(%1)
//
// In the above example, getAliasingReads(%0) includes the first OpOperand
// of the tensor.extract_slice op. The extract_slice itself does not read
// but its aliasing result is eventually fed into an op that does.
//
// Note: This is considered a "read" only if the use does not bufferize to
// a memory write. (We already ruled out memory reads. In case of a memory
// write, the buffer would be entirely overwritten; in the above example
// there would then be no flow of data from the extract_slice operand to
// its result's uses.)
if (!state.bufferizesToMemoryWrite(use)) {
SmallVector<OpResult> opResults = state.getAliasingOpResult(use);
if (llvm::any_of(opResults,
[&](OpResult r) { return state.isValueRead(r); }))
res.insert(&use);
}
}
});
}
/// Return true if bufferizing `operand` inplace would create a conflict. A read
/// R and a write W of the same alias set is a conflict if inplace bufferization
/// of W changes the value read by R to a value different from the one that
/// would be expected by tracing back R's origin through SSA use-def chains.
/// A conflict can only be introduced by a new alias and/or an inplace
/// bufferization decision.
///
/// Example:
/// %0 = tensor.extract_slice %t[...][...][1, 1] {inplace?}
/// %1 = vector.transfer_write %v1, %t {inplace} : vector<5xf32>, tensor<?xf32>
/// %e = tensor.extract_slice %1
/// %2 = vector.transfer_write %v2, %0 {inplace} : vector<6xf32>, tensor<?xf32>
/// %3 = vector.transfer_read %e, %cst : tensor<?xf32>, vector<7xf32>
///
/// In the above example, the two TransferWriteOps have already been decided to
/// bufferize inplace. Bufferizing the ExtractSliceOp inplace would create a
/// conflict because:
/// * According to SSA use-def chains, we expect to read the result of %1.
/// * However, adding an alias {%0, %t} would mean that the second
/// TransferWriteOp overwrites the first one. Therefore, the TransferReadOp
/// would no longer be reading the result of %1.
///
/// If `checkConsistencyOnly` is true, this function checks if there is a
/// read-after-write conflict without bufferizing `operand` inplace. This would
/// indicate a problem with the current inplace bufferization decisions.
///
/// Note: If `checkConsistencyOnly`, this function may be called with a null
/// OpResult. In that case, only the consistency of bufferization decisions
/// involving aliases of the given OpOperand are checked.
static bool wouldCreateReadAfterWriteInterference(
OpOperand &operand, const DominanceInfo &domInfo, AnalysisState &state,
const BufferizationAliasInfo &aliasInfo,
bool checkConsistencyOnly = false) {
// Collect reads and writes of all aliases of OpOperand and OpResult.
DenseSet<OpOperand *> usesRead, usesWrite;
getAliasingReads(usesRead, operand.get(), aliasInfo, state);
getAliasingInplaceWrites(usesWrite, operand.get(), aliasInfo, state);
for (OpResult result : state.getAliasingOpResult(operand)) {
getAliasingReads(usesRead, result, aliasInfo, state);
getAliasingInplaceWrites(usesWrite, result, aliasInfo, state);
}
if (!checkConsistencyOnly && state.bufferizesToMemoryWrite(operand))
usesWrite.insert(&operand);
return hasReadAfterWriteInterference(usesRead, usesWrite, domInfo, state,
aliasInfo);
}
/// Annotate IR with details about the detected non-writability conflict.
static void annotateNonWritableTensor(Value value) {
static int64_t counter = 0;
OpBuilder b(value.getContext());
std::string id = "W_" + std::to_string(counter++);
if (auto opResult = value.dyn_cast<OpResult>()) {
std::string attr = id + "[NOT-WRITABLE: result " +
std::to_string(opResult.getResultNumber()) + "]";
opResult.getDefiningOp()->setAttr(attr, b.getUnitAttr());
} else {
auto bbArg = value.cast<BlockArgument>();
std::string attr = id + "[NOT-WRITABLE: bbArg " +
std::to_string(bbArg.getArgNumber()) + "]";
bbArg.getOwner()->getParentOp()->setAttr(attr, b.getUnitAttr());
}
}
/// Check the reverse SSA use-def chain (following aliasing OpOperands) for
/// non-writable tensor values. Stop searching when an out-of-place bufferized
/// OpOperand was found (or when the OpOperand was not bufferized yet).
/// `currentOpOperand` is assumed to be in-place, even if that decision was not
/// materialized in `aliasInfo` yet.
static bool
hasPrecedingAliasingNonWritableTensor(Value value, OpOperand *currentOpOperand,
const BufferizationAliasInfo &aliasInfo,
const OneShotAnalysisState &state) {
SmallVector<Value> worklist;
worklist.push_back(value);
while (!worklist.empty()) {
Value nextVal = worklist.pop_back_val();
if (!state.isWritable(nextVal)) {
if (state.getOptions().printConflicts)
annotateNonWritableTensor(nextVal);
return true;
}
// If `nextVal` is not a BlockArgument: End of use-def chain reached.
auto opResult = nextVal.dyn_cast<OpResult>();
if (!opResult)
continue;
// Follow reverse SSA use-def chain.
SmallVector<OpOperand *> aliasingOpOperands =
state.getAliasingOpOperand(opResult);
for (OpOperand *opOperand : aliasingOpOperands)
if (aliasInfo.isInPlace(*opOperand) || currentOpOperand == opOperand)
worklist.push_back(opOperand->get());
}
return false;
}
/// Return true if bufferizing `operand` inplace would create a write to a
/// non-writable buffer.
static bool wouldCreateWriteToNonWritableBuffer(
OpOperand &operand, const BufferizationAliasInfo &aliasInfo,
OneShotAnalysisState &state, bool checkConsistencyOnly = false) {
// Collect writes of all aliases of OpOperand and OpResult.
DenseSet<OpOperand *> usesWrite;
getAliasingInplaceWrites(usesWrite, operand.get(), aliasInfo, state);
for (OpResult result : state.getAliasingOpResult(operand)) {
getAliasingInplaceWrites(usesWrite, result, aliasInfo, state);
}
if (!checkConsistencyOnly && state.bufferizesToMemoryWrite(operand))
usesWrite.insert(&operand);
// Assuming that `operand` bufferizes in-place: For each write (to each
// alias), check if there is a non-writable tensor in the reverse SSA use-def
// chain.
for (OpOperand *uWrite : usesWrite) {
if (hasPrecedingAliasingNonWritableTensor(uWrite->get(), &operand,
aliasInfo, state)) {
LLVM_DEBUG(llvm::dbgs() << "=> NOT WRITABLE\n");
return true;
}
}
return false;
}
//===----------------------------------------------------------------------===//
// Bufferization analyses.
//===----------------------------------------------------------------------===//
/// Determine if `operand` can be bufferized in-place.
static LogicalResult bufferizableInPlaceAnalysisImpl(
OpOperand &operand, BufferizationAliasInfo &aliasInfo,
OneShotAnalysisState &state, const DominanceInfo &domInfo) {
LLVM_DEBUG(
llvm::dbgs() << "//===-------------------------------------------===//\n"
<< "Analyzing operand #" << operand.getOperandNumber()
<< " of " << *operand.getOwner() << "\n");
bool foundInterference =
wouldCreateWriteToNonWritableBuffer(operand, aliasInfo, state) ||
wouldCreateReadAfterWriteInterference(operand, domInfo, state, aliasInfo);
if (foundInterference)
aliasInfo.bufferizeOutOfPlace(operand);
else
aliasInfo.bufferizeInPlace(operand, state);
LLVM_DEBUG(llvm::dbgs()
<< "//===-------------------------------------------===//\n");
return success();
}
/// Analyze the `ops` to determine which OpOperands are inplaceable. Walk ops in
/// reverse and bufferize ops greedily. This is a good starter heuristic.
///
/// Even if an op does not read or write, it may still create an alias when
/// bufferized in-place. An example of such ops is tensor.extract_slice.
///
/// Rationale for bufferizing `%1 = tensor.extract_slice %0[...]` inplace:
///
/// When bufferized out of place, an ExtractSliceOp lowers to alloc + copy. This
/// cannot change the flow of information for either the source or the
/// result buffers.
///
/// When bufferized inplace, an ExtractSliceOp does not by itself create any
/// read or write from memory. Instead, it has the effect of merging the alias
/// sets of the source and the result buffers.
///
/// An analysis is required to ensure inplace bufferization would not result in
/// RaW dependence violations.
static LogicalResult inPlaceAnalysis(SmallVector<Operation *> &ops,
BufferizationAliasInfo &aliasInfo,
OneShotAnalysisState &state,
const DominanceInfo &domInfo,
unsigned analysisFuzzerSeed = 0) {
if (analysisFuzzerSeed) {
// This is a fuzzer. For testing purposes only. Randomize the order in which
// operations are analyzed. The bufferization quality is likely worse, but
// we want to make sure that no assertions are triggered anywhere.
std::mt19937 g(analysisFuzzerSeed);
llvm::shuffle(ops.begin(), ops.end(), g);
}
// Analyze a single op.
auto analyzeOp = [&](Operation *op) {
for (OpOperand &opOperand : op->getOpOperands())
if (opOperand.get().getType().isa<TensorType>())
if (auto bufferizableOp = state.getOptions().dynCastBufferizableOp(op))
if (failed(bufferizableInPlaceAnalysisImpl(opOperand, aliasInfo,
state, domInfo)))
return failure();
return success();
};
OneShotBufferizationOptions::AnalysisHeuristic heuristic =
state.getOptions().analysisHeuristic;
if (heuristic == OneShotBufferizationOptions::AnalysisHeuristic::BottomUp) {
// Default: Walk ops in reverse for better interference analysis.
for (Operation *op : reverse(ops))
if (failed(analyzeOp(op)))
return failure();
} else if (heuristic ==
OneShotBufferizationOptions::AnalysisHeuristic::TopDown) {
for (Operation *op : ops)
if (failed(analyzeOp(op)))
return failure();
} else {
llvm_unreachable("unsupported heuristic");
}
return success();
}
/// Return true if the given op has a tensor result or a tensor operand.
static bool hasTensorSemantics(Operation *op) {
bool hasTensorResult = any_of(op->getResultTypes(), isaTensor);
bool hasTensorOperand = any_of(op->getOperandTypes(), isaTensor);
return hasTensorResult || hasTensorOperand;
}
/// Analyze all ops that are contained in `op`.
static LogicalResult inPlaceAnalysis(Operation *op,
BufferizationAliasInfo &aliasInfo,
OneShotAnalysisState &state,
const DominanceInfo &domInfo,
unsigned analysisFuzzerSeed = 0) {
// Collect ops so we can build our own reverse traversal.
SmallVector<Operation *> ops;
op->walk([&](Operation *op) {
// No tensors => no buffers.
if (!hasTensorSemantics(op))
return;
ops.push_back(op);
});
return inPlaceAnalysis(ops, aliasInfo, state, domInfo, analysisFuzzerSeed);
}
/// Analyze equivalence of tied OpResult/OpOperand pairs of the given ops.
static void equivalenceAnalysis(SmallVector<Operation *> &ops,
BufferizationAliasInfo &aliasInfo,
AnalysisState &state) {
for (Operation *op : ops)
if (auto bufferizableOp = state.getOptions().dynCastBufferizableOp(op))
for (OpResult opResult : op->getOpResults())
if (opResult.getType().isa<TensorType>())
for (OpOperand *opOperand :
bufferizableOp.getAliasingOpOperand(opResult, state))
if (state.isInPlace(*opOperand))
if (bufferizableOp.bufferRelation(opResult, state) ==
BufferRelation::Equivalent)
aliasInfo.unionEquivalenceClasses(opResult, opOperand->get());
}
/// Analyze equivalence of tied OpResult/OpOperand pairs of all ops contained
/// in `op`.
static void equivalenceAnalysis(Operation *op,
BufferizationAliasInfo &aliasInfo,
AnalysisState &state) {
// Traverse ops in PostOrder: Nested ops first, then enclosing ops.
SmallVector<Operation *> ops;
op->walk<WalkOrder::PostOrder>([&](Operation *op) {
// No tensors => no buffers.
if (none_of(op->getResultTypes(), isaTensor))
return;
ops.push_back(op);
});
equivalenceAnalysis(ops, aliasInfo, state);
}
/// Assert that the current bufferization decisions are consistent.
static LogicalResult
checkAliasInfoConsistency(Operation *op, const DominanceInfo &domInfo,
AnalysisState &state,
const BufferizationAliasInfo &aliasInfo) {
const BufferizationOptions &options = state.getOptions();
WalkResult walkResult = op->walk([&](BufferizableOpInterface op) {
// Skip ops that are not in the filter.
if (!options.isOpAllowed(op.getOperation()))
return WalkResult::advance();
// Input IR may not contain any ToMemrefOps. These are not supported because
// the analysis cannot follow the data flow through memrefs.
if (isa<ToMemrefOp>(op.getOperation())) {
op->emitError("to_memref ops not supported during One-Shot Analysis");
return WalkResult::interrupt();
}
for (OpOperand &opOperand : op->getOpOperands()) {
if (opOperand.get().getType().isa<TensorType>()) {
if (wouldCreateReadAfterWriteInterference(
opOperand, domInfo, state, aliasInfo,
/*checkConsistencyOnly=*/true)) {
// This error can happen if certain "mustBufferizeInPlace" interface
// methods are implemented incorrectly, such that the IR already has
// a RaW conflict before making any bufferization decisions.
op->emitError("input IR has RaW conflict");
return WalkResult::interrupt();
}
}
}
return WalkResult::advance();
});
return success(!walkResult.wasInterrupted());
}
/// Annotate the IR with the result of the analysis. For testing/debugging only.
static void
annotateOpsWithBufferizationMarkers(Operation *op,
const BufferizationAliasInfo &aliasInfo,
const BufferizationOptions &options) {
// Add __inplace_operands_attr__.
op->walk([&](BufferizableOpInterface bufferizableOp) {
if (options.isOpAllowed(bufferizableOp.getOperation()))
for (OpOperand &opOperand : bufferizableOp->getOpOperands())
if (opOperand.get().getType().isa<TensorType>())
setInPlaceOpOperand(opOperand, aliasInfo.isInPlace(opOperand));
});
}
/// Assert that every allocation can be deallocated in the same block. I.e.,
/// every value that is returned or yielded from a block is:
/// * guaranteed to be aliasing a bbArg of that block or a parent block, or
/// * guaranteed to be aliasing an OpResult of a op in a parent block.
///
/// In that case, buffer deallocation is simple: Every allocated buffer can be
/// deallocated in the same block. Otherwise, the buffer deallocation pass must
/// be run.
///
/// Note: The current implementation checks for equivalent values instead of
/// aliasing values, which is stricter than needed. We can currently not check
/// for aliasing values because the analysis is a maybe-alias analysis and we
/// need a must-alias analysis here.
///
/// Example:
/// ```
/// %0 = "some_op" : tensor<?xf32>
/// %1 = scf.if %c -> (tensor<?xf32>) {
/// scf.yield %0 : tensor<?xf32>
/// } else {
/// %t = linalg.alloc_tensor : tensor<?xf32>
/// scf.yield %t : tensor<?xf32>
/// }
/// ```
///
/// In the above example, the second scf.yield op is problematic because the
/// yielded value %t is defined in the same block as the scf.yield op and
/// and bufferizes to a new allocation.
// TODO: Remove buffer deallocation from One-Shot Bufferize and fix the buffer
// deallocation pass.
static LogicalResult assertNoAllocsReturned(Operation *op,
const BufferizationOptions &options,
BufferizationAliasInfo &aliasInfo) {
LogicalResult status = success();
DominanceInfo domInfo(op);
op->walk([&](Operation *returnOp) {
if (!isRegionReturnLike(returnOp) || !options.isOpAllowed(returnOp))
return WalkResult::advance();
for (OpOperand &returnValOperand : returnOp->getOpOperands()) {
Value returnVal = returnValOperand.get();
// Skip non-tensor values.
if (!returnVal.getType().isa<TensorType>())
continue;
bool foundEquivValue = false;
aliasInfo.applyOnEquivalenceClass(returnVal, [&](Value equivVal) {
if (auto bbArg = equivVal.dyn_cast<BlockArgument>()) {
Operation *definingOp = bbArg.getOwner()->getParentOp();
if (definingOp->isProperAncestor(returnOp))
foundEquivValue = true;
return;
}
Operation *definingOp = equivVal.getDefiningOp();
if (definingOp->getBlock()->findAncestorOpInBlock(
*returnOp->getParentOp()))
// Skip ops that happen after `returnOp` and parent ops.
if (happensBefore(definingOp, returnOp, domInfo))
foundEquivValue = true;
});
// Note: Returning/yielding buffer allocations is allowed only if
// `allowReturnAllocs` is set.
if (!foundEquivValue)
status = returnOp->emitError()
<< "operand #" << returnValOperand.getOperandNumber()
<< " may return/yield a new buffer allocation";
}
return WalkResult::advance();
});
return status;
}
LogicalResult bufferization::analyzeOp(Operation *op,
OneShotAnalysisState &state,
BufferizationStatistics *statistics) {
DominanceInfo domInfo(op);
BufferizationAliasInfo &aliasInfo = state.getAliasInfo();
const OneShotBufferizationOptions &options = state.getOptions();
if (failed(checkAliasInfoConsistency(op, domInfo, state, aliasInfo)))
return failure();
// If the analysis fails, just return.
if (failed(inPlaceAnalysis(op, aliasInfo, state, domInfo,
options.analysisFuzzerSeed)))
return failure();
if (statistics) {
statistics->numTensorInPlace = aliasInfo.getStatNumTensorInPlace();
statistics->numTensorOutOfPlace = aliasInfo.getStatNumTensorOutOfPlace();
}
equivalenceAnalysis(op, aliasInfo, state);
bool failedAnalysis = false;
if (!options.allowReturnAllocs)
failedAnalysis |= failed(assertNoAllocsReturned(op, options, aliasInfo));
// Gather some extra analysis data.
state.gatherYieldedTensors(op);
state.gatherUndefinedTensorUses(op);
// Analysis verification: After setting up alias/equivalence sets, each op
// can check for expected invariants/limitations and fail the analysis if
// necessary.
op->walk([&](Operation *op) {
if (BufferizableOpInterface bufferizableOp =
options.dynCastBufferizableOp(op))
failedAnalysis |= failed(bufferizableOp.verifyAnalysis(state));
});
// Annotate operations if we only want to report the analysis.
if (options.testAnalysisOnly)
annotateOpsWithBufferizationMarkers(op, aliasInfo, options);
return success(!failedAnalysis);
}
LogicalResult
bufferization::runOneShotBufferize(Operation *op,
const OneShotBufferizationOptions &options,
BufferizationStatistics *statistics) {
assert(!(options.copyBeforeWrite && options.testAnalysisOnly) &&
"invalid combination of bufferization flags");
if (!options.copyBeforeWrite) {
// If a buffer is copied before every write, no analysis is needed.
if (failed(insertTensorCopies(op, options, statistics)))
return failure();
}
if (options.testAnalysisOnly)
return success();
return bufferizeOp(op, options, /*copyBeforeWrite=*/options.copyBeforeWrite,
/*opFilter=*/nullptr, statistics);
}
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