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//===- Transforms/Utils/SampleProfileInference.h ----------*- C++ -*-===//
//
// 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
//
//===----------------------------------------------------------------------===//
//
/// \file
/// This file provides the interface for the profile inference algorithm, profi.
//
//===----------------------------------------------------------------------===//
#ifndef LLVM_TRANSFORMS_UTILS_SAMPLEPROFILEINFERENCE_H
#define LLVM_TRANSFORMS_UTILS_SAMPLEPROFILEINFERENCE_H
#include "llvm/ADT/DenseMap.h"
#include "llvm/ADT/DepthFirstIterator.h"
#include "llvm/ADT/SmallVector.h"
namespace llvm {
struct FlowJump;
/// A wrapper of a binary basic block.
struct FlowBlock {
uint64_t Index;
uint64_t Weight{0};
bool HasUnknownWeight{true};
bool IsUnlikely{false};
uint64_t Flow{0};
std::vector<FlowJump *> SuccJumps;
std::vector<FlowJump *> PredJumps;
/// Check if it is the entry block in the function.
bool isEntry() const { return PredJumps.empty(); }
/// Check if it is an exit block in the function.
bool isExit() const { return SuccJumps.empty(); }
};
/// A wrapper of a jump between two basic blocks.
struct FlowJump {
uint64_t Source;
uint64_t Target;
uint64_t Weight{0};
bool HasUnknownWeight{true};
bool IsUnlikely{false};
uint64_t Flow{0};
};
/// A wrapper of binary function with basic blocks and jumps.
struct FlowFunction {
/// Basic blocks in the function.
std::vector<FlowBlock> Blocks;
/// Jumps between the basic blocks.
std::vector<FlowJump> Jumps;
/// The index of the entry block.
uint64_t Entry{0};
};
/// Various thresholds and options controlling the behavior of the profile
/// inference algorithm. Default values are tuned for several large-scale
/// applications, and can be modified via corresponding command-line flags.
struct ProfiParams {
/// Evenly distribute flow when there are multiple equally likely options.
bool EvenFlowDistribution{false};
/// Evenly re-distribute flow among unknown subgraphs.
bool RebalanceUnknown{false};
/// Join isolated components having positive flow.
bool JoinIslands{false};
/// The cost of increasing a block's count by one.
unsigned CostBlockInc{0};
/// The cost of decreasing a block's count by one.
unsigned CostBlockDec{0};
/// The cost of increasing a count of zero-weight block by one.
unsigned CostBlockZeroInc{0};
/// The cost of increasing the entry block's count by one.
unsigned CostBlockEntryInc{0};
/// The cost of decreasing the entry block's count by one.
unsigned CostBlockEntryDec{0};
/// The cost of increasing an unknown block's count by one.
unsigned CostBlockUnknownInc{0};
/// The cost of increasing a jump's count by one.
unsigned CostJumpInc{0};
/// The cost of increasing a fall-through jump's count by one.
unsigned CostJumpFTInc{0};
/// The cost of decreasing a jump's count by one.
unsigned CostJumpDec{0};
/// The cost of decreasing a fall-through jump's count by one.
unsigned CostJumpFTDec{0};
/// The cost of increasing an unknown jump's count by one.
unsigned CostJumpUnknownInc{0};
/// The cost of increasing an unknown fall-through jump's count by one.
unsigned CostJumpUnknownFTInc{0};
/// The cost of taking an unlikely block/jump.
const int64_t CostUnlikely = ((int64_t)1) << 30;
};
void applyFlowInference(const ProfiParams &Params, FlowFunction &Func);
void applyFlowInference(FlowFunction &Func);
/// Sample profile inference pass.
template <typename FT> class SampleProfileInference {
public:
using NodeRef = typename GraphTraits<FT *>::NodeRef;
using BasicBlockT = std::remove_pointer_t<NodeRef>;
using FunctionT = FT;
using Edge = std::pair<const BasicBlockT *, const BasicBlockT *>;
using BlockWeightMap = DenseMap<const BasicBlockT *, uint64_t>;
using EdgeWeightMap = DenseMap<Edge, uint64_t>;
using BlockEdgeMap =
DenseMap<const BasicBlockT *, SmallVector<const BasicBlockT *, 8>>;
SampleProfileInference(FunctionT &F, BlockEdgeMap &Successors,
BlockWeightMap &SampleBlockWeights)
: F(F), Successors(Successors), SampleBlockWeights(SampleBlockWeights) {}
/// Apply the profile inference algorithm for a given function
void apply(BlockWeightMap &BlockWeights, EdgeWeightMap &EdgeWeights);
private:
/// Initialize flow function blocks, jumps and misc metadata.
FlowFunction
createFlowFunction(const std::vector<const BasicBlockT *> &BasicBlocks,
DenseMap<const BasicBlockT *, uint64_t> &BlockIndex);
/// Try to infer branch probabilities mimicking implementation of
/// BranchProbabilityInfo. Unlikely taken branches are marked so that the
/// inference algorithm can avoid sending flow along corresponding edges.
void findUnlikelyJumps(const std::vector<const BasicBlockT *> &BasicBlocks,
BlockEdgeMap &Successors, FlowFunction &Func);
/// Determine whether the block is an exit in the CFG.
bool isExit(const BasicBlockT *BB);
/// Function.
const FunctionT &F;
/// Successors for each basic block in the CFG.
BlockEdgeMap &Successors;
/// Map basic blocks to their sampled weights.
BlockWeightMap &SampleBlockWeights;
};
template <typename BT>
void SampleProfileInference<BT>::apply(BlockWeightMap &BlockWeights,
EdgeWeightMap &EdgeWeights) {
// Find all forwards reachable blocks which the inference algorithm will be
// applied on.
df_iterator_default_set<const BasicBlockT *> Reachable;
for (auto *BB : depth_first_ext(&F, Reachable))
(void)BB /* Mark all reachable blocks */;
// Find all backwards reachable blocks which the inference algorithm will be
// applied on.
df_iterator_default_set<const BasicBlockT *> InverseReachable;
for (const auto &BB : F) {
// An exit block is a block without any successors.
if (isExit(&BB)) {
for (auto *RBB : inverse_depth_first_ext(&BB, InverseReachable))
(void)RBB;
}
}
// Keep a stable order for reachable blocks
DenseMap<const BasicBlockT *, uint64_t> BlockIndex;
std::vector<const BasicBlockT *> BasicBlocks;
BlockIndex.reserve(Reachable.size());
BasicBlocks.reserve(Reachable.size());
for (const auto &BB : F) {
if (Reachable.count(&BB) && InverseReachable.count(&BB)) {
BlockIndex[&BB] = BasicBlocks.size();
BasicBlocks.push_back(&BB);
}
}
BlockWeights.clear();
EdgeWeights.clear();
bool HasSamples = false;
for (const auto *BB : BasicBlocks) {
auto It = SampleBlockWeights.find(BB);
if (It != SampleBlockWeights.end() && It->second > 0) {
HasSamples = true;
BlockWeights[BB] = It->second;
}
}
// Quit early for functions with a single block or ones w/o samples
if (BasicBlocks.size() <= 1 || !HasSamples) {
return;
}
// Create necessary objects
FlowFunction Func = createFlowFunction(BasicBlocks, BlockIndex);
// Create and apply the inference network model.
applyFlowInference(Func);
// Extract the resulting weights from the control flow
// All weights are increased by one to avoid propagation errors introduced by
// zero weights.
for (const auto *BB : BasicBlocks) {
BlockWeights[BB] = Func.Blocks[BlockIndex[BB]].Flow;
}
for (auto &Jump : Func.Jumps) {
Edge E = std::make_pair(BasicBlocks[Jump.Source], BasicBlocks[Jump.Target]);
EdgeWeights[E] = Jump.Flow;
}
#ifndef NDEBUG
// Unreachable blocks and edges should not have a weight.
for (auto &I : BlockWeights) {
assert(Reachable.contains(I.first));
assert(InverseReachable.contains(I.first));
}
for (auto &I : EdgeWeights) {
assert(Reachable.contains(I.first.first) &&
Reachable.contains(I.first.second));
assert(InverseReachable.contains(I.first.first) &&
InverseReachable.contains(I.first.second));
}
#endif
}
template <typename BT>
FlowFunction SampleProfileInference<BT>::createFlowFunction(
const std::vector<const BasicBlockT *> &BasicBlocks,
DenseMap<const BasicBlockT *, uint64_t> &BlockIndex) {
FlowFunction Func;
Func.Blocks.reserve(BasicBlocks.size());
// Create FlowBlocks
for (const auto *BB : BasicBlocks) {
FlowBlock Block;
if (SampleBlockWeights.contains(BB)) {
Block.HasUnknownWeight = false;
Block.Weight = SampleBlockWeights[BB];
} else {
Block.HasUnknownWeight = true;
Block.Weight = 0;
}
Block.Index = Func.Blocks.size();
Func.Blocks.push_back(Block);
}
// Create FlowEdges
for (const auto *BB : BasicBlocks) {
for (auto *Succ : Successors[BB]) {
if (!BlockIndex.count(Succ))
continue;
FlowJump Jump;
Jump.Source = BlockIndex[BB];
Jump.Target = BlockIndex[Succ];
Func.Jumps.push_back(Jump);
}
}
for (auto &Jump : Func.Jumps) {
uint64_t Src = Jump.Source;
uint64_t Dst = Jump.Target;
Func.Blocks[Src].SuccJumps.push_back(&Jump);
Func.Blocks[Dst].PredJumps.push_back(&Jump);
}
// Try to infer probabilities of jumps based on the content of basic block
findUnlikelyJumps(BasicBlocks, Successors, Func);
// Find the entry block
for (size_t I = 0; I < Func.Blocks.size(); I++) {
if (Func.Blocks[I].isEntry()) {
Func.Entry = I;
break;
}
}
assert(Func.Entry == 0 && "incorrect index of the entry block");
// Pre-process data: make sure the entry weight is at least 1
auto &EntryBlock = Func.Blocks[Func.Entry];
if (EntryBlock.Weight == 0 && !EntryBlock.HasUnknownWeight) {
EntryBlock.Weight = 1;
EntryBlock.HasUnknownWeight = false;
}
return Func;
}
template <typename BT>
inline void SampleProfileInference<BT>::findUnlikelyJumps(
const std::vector<const BasicBlockT *> &BasicBlocks,
BlockEdgeMap &Successors, FlowFunction &Func) {}
template <typename BT>
inline bool SampleProfileInference<BT>::isExit(const BasicBlockT *BB) {
return BB->succ_empty();
}
} // end namespace llvm
#endif // LLVM_TRANSFORMS_UTILS_SAMPLEPROFILEINFERENCE_H
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