maxjhandsome
/
llvm-project
forked from OSchip/llvm-project
1
0
Fork 0
Code Issues Pull Requests Packages Releases Wiki Activity

[Unroll] Implement a conservative and monotonically increasing cost tracking system during the full unroll heuristic analysis that avoids counting any instruction cost until that instruction becomes "live" through a side-effect or use outside the... 

Summary:
...loop after the last iteration.

This is really hard to do correctly. The core problem is that we need to
model liveness through the induction PHIs from iteration to iteration in
order to get the correct results, and we need to correctly de-duplicate
the common subgraphs of instructions feeding some subset of the
induction PHIs. All of this can be driven either from a side effect at
some iteration or from the loop values used after the loop finishes.

This patch implements this by storing the forward-propagating analysis
of each instruction in a cache to recall whether it was free and whether
it has become live and thus counted toward the total unroll cost. Then,
at each sink for a value in the loop, we recursively walk back through
every value that feeds the sink, including looping back through the
iterations as needed, until we have marked the entire input graph as
live. Because we cache this, we never visit instructions more than twice
-- once when we analyze them and put them into the cache, and once when
we count their cost towards the unrolled loop. Also, because the cache
is only two bits and because we are dealing with relatively small
iteration counts, we can store all of this very densely in memory to
avoid this from becoming an excessively slow analysis.

The code here is still pretty gross. I would appreciate suggestions
about better ways to factor or split this up, I've stared too long at
the algorithmic side to really have a good sense of what the design
should probably look at.

Also, it might seem like we should do all of this bottom-up, but I think
that is a red herring. Specifically, the simplification power is *much*
greater working top-down. We can forward propagate very effectively,
even across strange and interesting recurrances around the backedge.
Because we use data to propagate, this doesn't cause a state space
explosion. Doing this level of constant folding, etc, would be very
expensive to do bottom-up because it wouldn't be until the last moment
that you could collapse everything. The current solution is essentially
a top-down simplification with a bottom-up cost accounting which seems
to get the best of both worlds. It makes the simplification incremental
and powerful while leaving everything dead until we *know* it is needed.

Finally, a core property of this approach is its *monotonicity*. At all
times, the current UnrolledCost is a conservatively low estimate. This
ensures that we will never early-exit from the analysis due to exceeding
a threshold when if we had continued, the cost would have gone back
below the threshold. These kinds of bugs can cause incredibly hard to
track down random changes to behavior.

We could use a techinque similar (but much simpler) within the inliner
as well to avoid considering speculated code in the inline cost.

Reviewers: chandlerc

Subscribers: sanjoy, mzolotukhin, llvm-commits

Differential Revision: http://reviews.llvm.org/D11758

llvm-svn: 269388
This commit is contained in:
Michael Zolotukhin 2016-05-13 01:42:39 +00:00
parent a59a308e8d
commit b7b8052982
7 changed files with 236 additions and 18 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

1
llvm/include/llvm/Analysis/LoopUnrollAnalyzer.h
View File

@ -89,6 +89,7 @@ private:
bool visitLoad(LoadInst &I);
bool visitCastInst(CastInst &I);
bool visitCmpInst(CmpInst &I);
bool visitPHINode(PHINode &PN);
};
}
#endif

10
llvm/lib/Analysis/LoopUnrollAnalyzer.cpp
View File

@ -189,3 +189,13 @@ bool UnrolledInstAnalyzer::visitCmpInst(CmpInst &I) {
return Base::visitCmpInst(I);
}
bool UnrolledInstAnalyzer::visitPHINode(PHINode &PN) {
// Run base visitor first. This way we can gather some useful for later
// analysis information.
if (Base::visitPHINode(PN))
return true;
// The loop induction PHI nodes are definitionally free.
return PN.getParent() == L->getHeader();
}

191
llvm/lib/Transforms/Scalar/LoopUnrollPass.cpp
View File

@ -184,6 +184,40 @@ static TargetTransformInfo::UnrollingPreferences gatherUnrollingPreferences(
return UP;
}
namespace {
/// A struct to densely store the state of an instruction after unrolling at
/// each iteration.
///
/// This is designed to work like a tuple of <Instruction *, int> for the
/// purposes of hashing and lookup, but to be able to associate two boolean
/// states with each key.
struct UnrolledInstState {
Instruction *I;
int Iteration : 30;
unsigned IsFree : 1;
unsigned IsCounted : 1;
};
/// Hashing and equality testing for a set of the instruction states.
struct UnrolledInstStateKeyInfo {
typedef DenseMapInfo<Instruction *> PtrInfo;
typedef DenseMapInfo<std::pair<Instruction *, int>> PairInfo;
static inline UnrolledInstState getEmptyKey() {
return {PtrInfo::getEmptyKey(), 0, 0, 0};
}
static inline UnrolledInstState getTombstoneKey() {
return {PtrInfo::getTombstoneKey(), 0, 0, 0};
}
static inline unsigned getHashValue(const UnrolledInstState &S) {
return PairInfo::getHashValue({S.I, S.Iteration});
}
static inline bool isEqual(const UnrolledInstState &LHS,
const UnrolledInstState &RHS) {
return PairInfo::isEqual({LHS.I, LHS.Iteration}, {RHS.I, RHS.Iteration});
}
};
}
namespace {
struct EstimatedUnrollCost {
/// \brief The estimated cost after unrolling.
@ -218,18 +252,25 @@ analyzeLoopUnrollCost(const Loop *L, unsigned TripCount, DominatorTree &DT,
assert(UnrollMaxIterationsCountToAnalyze < (INT_MAX / 2) &&
"The unroll iterations max is too large!");
// Only analyze inner loops. We can't properly estimate cost of nested loops
// and we won't visit inner loops again anyway.
if (!L->empty())
return None;
// Don't simulate loops with a big or unknown tripcount
if (!UnrollMaxIterationsCountToAnalyze || !TripCount ||
TripCount > UnrollMaxIterationsCountToAnalyze)
return None;
SmallSetVector<BasicBlock *, 16> BBWorklist;
SmallSetVector<std::pair<BasicBlock *, BasicBlock *>, 4> ExitWorklist;
DenseMap<Value *, Constant *> SimplifiedValues;
SmallVector<std::pair<Value *, Constant *>, 4> SimplifiedInputValues;
// The estimated cost of the unrolled form of the loop. We try to estimate
// this by simplifying as much as we can while computing the estimate.
int UnrolledCost = 0;
// We also track the estimated dynamic (that is, actually executed) cost in
// the rolled form. This helps identify cases when the savings from unrolling
// aren't just exposing dead control flows, but actual reduced dynamic
@ -237,6 +278,97 @@ analyzeLoopUnrollCost(const Loop *L, unsigned TripCount, DominatorTree &DT,
// unrolling.
int RolledDynamicCost = 0;
// We track the simplification of each instruction in each iteration. We use
// this to recursively merge costs into the unrolled cost on-demand so that
// we don't count the cost of any dead code. This is essentially a map from
// <instruction, int> to <bool, bool>, but stored as a densely packed struct.
DenseSet<UnrolledInstState, UnrolledInstStateKeyInfo> InstCostMap;
// A small worklist used to accumulate cost of instructions from each
// observable and reached root in the loop.
SmallVector<Instruction *, 16> CostWorklist;
// PHI-used worklist used between iterations while accumulating cost.
SmallVector<Instruction *, 4> PHIUsedList;
// Helper function to accumulate cost for instructions in the loop.
auto AddCostRecursively = [&](Instruction &RootI, int Iteration) {
assert(Iteration >= 0 && "Cannot have a negative iteration!");
assert(CostWorklist.empty() && "Must start with an empty cost list");
assert(PHIUsedList.empty() && "Must start with an empty phi used list");
CostWorklist.push_back(&RootI);
for (;; --Iteration) {
do {
Instruction *I = CostWorklist.pop_back_val();
// InstCostMap only uses I and Iteration as a key, the other two values
// don't matter here.
auto CostIter = InstCostMap.find({I, Iteration, 0, 0});
if (CostIter == InstCostMap.end())
// If an input to a PHI node comes from a dead path through the loop
// we may have no cost data for it here. What that actually means is
// that it is free.
continue;
auto &Cost = *CostIter;
if (Cost.IsCounted)
// Already counted this instruction.
continue;
// Mark that we are counting the cost of this instruction now.
Cost.IsCounted = true;
// If this is a PHI node in the loop header, just add it to the PHI set.
if (auto *PhiI = dyn_cast<PHINode>(I))
if (PhiI->getParent() == L->getHeader()) {
assert(Cost.IsFree && "Loop PHIs shouldn't be evaluated as they "
"inherently simplify during unrolling.");
if (Iteration == 0)
continue;
// Push the incoming value from the backedge into the PHI used list
// if it is an in-loop instruction. We'll use this to populate the
// cost worklist for the next iteration (as we count backwards).
if (auto *OpI = dyn_cast<Instruction>(
PhiI->getIncomingValueForBlock(L->getLoopLatch())))
if (L->contains(OpI))
PHIUsedList.push_back(OpI);
continue;
}
// First accumulate the cost of this instruction.
if (!Cost.IsFree) {
UnrolledCost += TTI.getUserCost(I);
DEBUG(dbgs() << "Adding cost of instruction (iteration " << Iteration
<< "): ");
DEBUG(I->dump());
}
// We must count the cost of every operand which is not free,
// recursively. If we reach a loop PHI node, simply add it to the set
// to be considered on the next iteration (backwards!).
for (Value *Op : I->operands()) {
// Check whether this operand is free due to being a constant or
// outside the loop.
auto *OpI = dyn_cast<Instruction>(Op);
if (!OpI || !L->contains(OpI))
continue;
// Otherwise accumulate its cost.
CostWorklist.push_back(OpI);
}
} while (!CostWorklist.empty());
if (PHIUsedList.empty())
// We've exhausted the search.
break;
assert(Iteration > 0 &&
"Cannot track PHI-used values past the first iteration!");
CostWorklist.append(PHIUsedList.begin(), PHIUsedList.end());
PHIUsedList.clear();
}
};
// Ensure that we don't violate the loop structure invariants relied on by
// this analysis.
assert(L->isLoopSimplifyForm() && "Must put loop into normal form first.");
@ -291,22 +423,32 @@ analyzeLoopUnrollCost(const Loop *L, unsigned TripCount, DominatorTree &DT,
// it. We don't change the actual IR, just count optimization
// opportunities.
for (Instruction &I : *BB) {
int InstCost = TTI.getUserCost(&I);
// Track this instruction's expected baseline cost when executing the
// rolled loop form.
RolledDynamicCost += TTI.getUserCost(&I);
// Visit the instruction to analyze its loop cost after unrolling,
// and if the visitor returns false, include this instruction in the
// unrolled cost.
if (!Analyzer.visit(I))
UnrolledCost += InstCost;
else {
DEBUG(dbgs() << " " << I
<< " would be simplified if loop is unrolled.\n");
(void)0;
}
// and if the visitor returns true, mark the instruction as free after
// unrolling and continue.
bool IsFree = Analyzer.visit(I);
bool Inserted = InstCostMap.insert({&I, (int)Iteration, IsFree,
/*IsCounted*/ false})
.second;
(void)Inserted;
assert(Inserted && "Cannot have a state for an unvisited instruction!");
// Also track this instructions expected cost when executing the rolled
// loop form.
RolledDynamicCost += InstCost;
if (IsFree)
continue;
// If the instruction might have a side-effect recursively account for
// the cost of it and all the instructions leading up to it.
if (I.mayHaveSideEffects())
AddCostRecursively(I, Iteration);
// Can't properly model a cost of a call.
// FIXME: With a proper cost model we should be able to do it.
if(isa<CallInst>(&I))
return None;
// If unrolled body turns out to be too big, bail out.
if (UnrolledCost > MaxUnrolledLoopSize) {
@ -335,6 +477,8 @@ analyzeLoopUnrollCost(const Loop *L, unsigned TripCount, DominatorTree &DT,
cast<ConstantInt>(SimpleCond)->isZero() ? 1 : 0);
if (L->contains(Succ))
BBWorklist.insert(Succ);
else
ExitWorklist.insert({BB, Succ});
continue;
}
}
@ -350,6 +494,8 @@ analyzeLoopUnrollCost(const Loop *L, unsigned TripCount, DominatorTree &DT,
.getCaseSuccessor();
if (L->contains(Succ))
BBWorklist.insert(Succ);
else
ExitWorklist.insert({BB, Succ});
continue;
}
}
@ -358,6 +504,8 @@ analyzeLoopUnrollCost(const Loop *L, unsigned TripCount, DominatorTree &DT,
for (BasicBlock *Succ : successors(BB))
if (L->contains(Succ))
BBWorklist.insert(Succ);
else
ExitWorklist.insert({BB, Succ});
}
// If we found no optimization opportunities on the first iteration, we
@ -368,6 +516,23 @@ analyzeLoopUnrollCost(const Loop *L, unsigned TripCount, DominatorTree &DT,
return None;
}
}
while (!ExitWorklist.empty()) {
BasicBlock *ExitingBB, *ExitBB;
std::tie(ExitingBB, ExitBB) = ExitWorklist.pop_back_val();
for (Instruction &I : *ExitBB) {
auto *PN = dyn_cast<PHINode>(&I);
if (!PN)
break;
Value *Op = PN->getIncomingValueForBlock(ExitingBB);
if (auto *OpI = dyn_cast<Instruction>(Op))
if (L->contains(OpI))
AddCostRecursively(*OpI, TripCount - 1);
}
}
DEBUG(dbgs() << "Analysis finished:\n"
<< "UnrolledCost: " << UnrolledCost << ", "
<< "RolledDynamicCost: " << RolledDynamicCost << "\n");

2
llvm/test/Transforms/LoopUnroll/full-unroll-heuristics-2.ll
View File

@ -1,4 +1,4 @@
; RUN: opt < %s -S -loop-unroll -unroll-max-iteration-count-to-analyze=1000 -unroll-threshold=10 -unroll-percent-dynamic-cost-saved-threshold=50 -unroll-dynamic-cost-savings-discount=90 | FileCheck %s
; RUN: opt < %s -S -loop-unroll -unroll-max-iteration-count-to-analyze=1000 -unroll-threshold=10 -unroll-percent-dynamic-cost-saved-threshold=70 -unroll-dynamic-cost-savings-discount=90 | FileCheck %s
target datalayout = "e-m:o-i64:64-f80:128-n8:16:32:64-S128"
@unknown_global = internal unnamed_addr global [9 x i32] [i32 0, i32 -1, i32 0, i32 -1, i32 5, i32 -1, i32 0, i32 -1, i32 0], align 16

38
llvm/test/Transforms/LoopUnroll/full-unroll-heuristics-dce.ll Normal file
View File

@ -0,0 +1,38 @@
; RUN: opt < %s -S -loop-unroll -unroll-max-iteration-count-to-analyze=100 -unroll-dynamic-cost-savings-discount=1000 -unroll-threshold=10 -unroll-percent-dynamic-cost-saved-threshold=60 | FileCheck %s
target datalayout = "e-m:o-i64:64-f80:128-n8:16:32:64-S128"
@known_constant = internal unnamed_addr constant [10 x i32] [i32 0, i32 0, i32 0, i32 0, i32 1, i32 0, i32 0, i32 0, i32 0, i32 0], align 16
; If a load becomes a constant after loop unrolling, we sometimes can simplify
; CFG. This test verifies that we handle such cases.
; After one operand in an instruction is constant-folded and the
; instruction is simplified, the other operand might become dead.
; In this test we have::
; for i in 1..10:
; r += A[i] * B[i]
; A[i] is 0 almost at every iteration, so there is no need in loading B[i] at
; all.
; CHECK-LABEL: @unroll_dce
; CHECK-NOT: br i1 %exitcond, label %for.end, label %for.body
define i32 @unroll_dce(i32* noalias nocapture readonly %b) {
entry:
br label %for.body
for.body: ; preds = %for.body, %entry
%iv.0 = phi i64 [ 0, %entry ], [ %iv.1, %for.body ]
%r.0 = phi i32 [ 0, %entry ], [ %r.1, %for.body ]
%arrayidx1 = getelementptr inbounds [10 x i32], [10 x i32]* @known_constant, i64 0, i64 %iv.0
%x1 = load i32, i32* %arrayidx1, align 4
%arrayidx2 = getelementptr inbounds i32, i32* %b, i64 %iv.0
%x2 = load i32, i32* %arrayidx2, align 4
%mul = mul i32 %x1, %x2
%r.1 = add i32 %mul, %r.0
%iv.1 = add nuw nsw i64 %iv.0, 1
%exitcond = icmp eq i64 %iv.1, 10
br i1 %exitcond, label %for.end, label %for.body
for.end: ; preds = %for.body
ret i32 %r.1
}

2
llvm/test/Transforms/LoopUnroll/full-unroll-heuristics-geps.ll
View File

@ -1,4 +1,4 @@
; RUN: opt < %s -S -loop-unroll -unroll-max-iteration-count-to-analyze=100 -unroll-dynamic-cost-savings-discount=1000 -unroll-threshold=10 -unroll-percent-dynamic-cost-saved-threshold=40 | FileCheck %s
; RUN: opt < %s -S -loop-unroll -unroll-max-iteration-count-to-analyze=100 -unroll-dynamic-cost-savings-discount=1000 -unroll-threshold=10 -unroll-percent-dynamic-cost-saved-threshold=60 | FileCheck %s
target datalayout = "e-m:o-i64:64-f80:128-n8:16:32:64-S128"
; When examining gep-instructions we shouldn't consider them simplified if the

10
llvm/unittests/Analysis/UnrollAnalyzer.cpp
View File

@ -134,6 +134,7 @@ TEST(UnrollAnalyzerTest, OuterLoopSimplification) {
" br label %outer.loop\n"
"outer.loop:\n"
" %iv.outer = phi i64 [ 0, %entry ], [ %iv.outer.next, %outer.loop.latch ]\n"
" %iv.outer.next = add nuw nsw i64 %iv.outer, 1\n"
" br label %inner.loop\n"
"inner.loop:\n"
" %iv.inner = phi i64 [ 0, %outer.loop ], [ %iv.inner.next, %inner.loop ]\n"
@ -141,7 +142,6 @@ TEST(UnrollAnalyzerTest, OuterLoopSimplification) {
" %exitcond.inner = icmp eq i64 %iv.inner.next, 1000\n"
" br i1 %exitcond.inner, label %outer.loop.latch, label %inner.loop\n"
"outer.loop.latch:\n"
" %iv.outer.next = add nuw nsw i64 %iv.outer, 1\n"
" %exitcond.outer = icmp eq i64 %iv.outer.next, 40\n"
" br i1 %exitcond.outer, label %exit, label %outer.loop\n"
"exit:\n"
@ -163,11 +163,15 @@ TEST(UnrollAnalyzerTest, OuterLoopSimplification) {
BasicBlock *InnerBody = &*FI++;
BasicBlock::iterator BBI = Header->begin();
Instruction *Y1 = &*BBI++;
BBI++;
Instruction *Y1 = &*BBI;
BBI = InnerBody->begin();
Instruction *Y2 = &*BBI++;
BBI++;
Instruction *Y2 = &*BBI;
// Check that we can simplify IV of the outer loop, but can't simplify the IV
// of the inner loop if we only know the iteration number of the outer loop.
//
// Y1 is %iv.outer.next, Y2 is %iv.inner.next
auto I1 = SimplifiedValuesVector[0].find(Y1);
EXPECT_TRUE(I1 != SimplifiedValuesVector[0].end());
auto I2 = SimplifiedValuesVector[0].find(Y2);