When the SME attributes tell that a function is or may be executed in Streaming
SVE mode, we currently need to be conservative and disable _any_ vectorization
(fixed or scalable) because the code-generator does not yet support generating
streaming-compatible code.
Scalable auto-vec will be gradually enabled in the future when we have
confidence that the loop-vectorizer won't use any SVE or NEON instructions
that are illegal in Streaming SVE mode.
Reviewed By: paulwalker-arm
Differential Revision: https://reviews.llvm.org/D135950
Currently, AArch64 doesn't support vectorization for non temporal loads because `isLegalNTLoad` is not implemented for the target.
This patch applies similar functionality as `D73158` but for non temporal loads
Reviewed By: fhahn
Differential Revision: https://reviews.llvm.org/D131964
Follow up to D133580; adjust the cost model to prefer uniform store lowering for scalable stores which are unpredicated.
The impact here isn't in the uniform store lowering quality itself. InstCombine happily converts the scatter form into the single store form. The main impact is in letting the rest of the cost model make choices based on the knowledge that the vector will be scalarized on use.
Differential Revision: https://reviews.llvm.org/D134460
The dependent code has been changed quite a lot since 151c144 which
b73d2c8 effectively reverts. Now we run into a case where lowering
didn't expect/support the behavior pre 151c144 any longer.
Update the code dealing with scalable pointer inductions to also check
for uniformity in combination with isScalarAfterVectorization. This
should ensure scalable pointer inductions are handled properly during
epilogue vectorization.
Fixes#57912.
This extends the previously added uniform store case to handle stores of loop varying values to a loop invariant address. Note that the placement of this code only allows unpredicated stores; this is important for correctness. (That is "IsPredicated" is always false at this point in the function.)
This patch does not include scalable types. The diff felt "large enough" as it were; I'll handle that in a separate patch. (It requires some changes to cost modeling.)
Differential Revision: https://reviews.llvm.org/D133580
This stops Negator from transforming:
`C1 - shl X, C2 --> mul X, (1<<C2) + C1`
...in the general case. There does not seem to be any analysis
benefit to using mul in IR, and there's definitely downside in
codegen (particularly when the multiply has to be expanded).
If `C1` is 0, then there's a stronger argument that the single
mul is a better canonicalization than negate-of-shl, but we may
want to remove that too.
This was noted as a potential conflict for D133667.
Differential Revision: https://reviews.llvm.org/D134310
Epilogue vectorization uses isScalarAfterVectorization to check if
widened versions for inductions need to be generated and bails out in
those cases.
At the moment, there are scenarios where isScalarAfterVectorization
returns true but VPWidenPointerInduction::onlyScalarsGenerated would
return false, causing widening.
This can lead to widened phis with incorrect start values being created
in the epilogue vector body.
This patch addresses the issue by storing the cost-model decision in
VPWidenPointerInductionRecipe and restoring the behavior before 151c144.
This effectively reverts 151c144, but the long-term fix is to properly
support widened inductions during epilogue vectorization
Fixes#57712.
This was originally part of D133788. There are no visible
regressions. All of the diffs show a large unsigned constant
becoming a small negative constant. This should be better
for analysis (and slightly less compile-time) and codegen.
This patch moves the cost-based decision whether to use an intrinsic or
library call to the point where the recipe is created. This untangles
code-gen from the cost model and also avoids doing some extra work as
the information is already computed at construction.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D132585
It looks like the vector loops in the modified test cases
unintentionally never get executed. Update the exit condition to ensure
it does to avoid them getting optimized away in upcoming changes.
This patch adds support for vectorizing conditionally executed div/rem operations via a variant of widening. The existing support for predicated divrem in the vectorizer requires scalarization which we can't do for scalable vectors.
The basic idea is that we can always divide (take remainder) by 1 without executing UB. As such, we can use the active lane mask to conditional select either the actual divisor for active lanes, or a constant one for inactive lanes. We already account for the cost of the active lane mask, so the only additional cost is a splat of one and the vector select. This is one of several possible approaches to this problem; see the review thread for discussion on some of the others. This one was chosen mostly because it was straight forward, and none of the others seemed oviously better.
I enabled the new code only for scalable vectors. We could also legally enable it for fixed vectors as well, but I haven't thought through the cost tradeoffs between widening and scalarization enough to know if that's profitable. This will be explored in future patches.
Differential Revision: https://reviews.llvm.org/D130164
Whilst writing a patch to add extra tail-folding RUN lines to
existing tests I noticed a few areas where they can be
cleaned up a little:
1. scalable-reductions.ll: fmin_fast does not mark fcmp as fast.
2. sve-inductions-unusual-types.ll: remove direct references to
SSA variable names.
3. sve-strict-fadd-cost.ll: don't force vector width so we see
costs for different VFs in one go. This will be important for
the follow-on patch.
4. sve-vector-reverse.ll,vector-reverse-mask4.ll: add noalias
keyword to simplify IR.
4. sve-widen-gep.ll,sve-widen-phi.ll: regenerate using script.
These changes will make the subsequent patch adding RUN lines much
easier to review!
Differential Revision: https://reviews.llvm.org/D132219
After D121595 was commited, I noticed regressions assosicated with small trip
count numbersvectorisation by tail folding with scalable vectors. As a solution
for those issues I propose to introduce the minimal trip count threshold value.
Differential Revision: https://reviews.llvm.org/D130755
If we have interleave groups in the loop we want to vectorise then
we should fall back on normal vectorisation with a scalar epilogue. In
such cases when tail-folding is enabled we'll almost certainly go on to
create vplans with very high costs for all vector VFs and fall back on
VF=1 anyway. This is likely to be worse than if we'd just used an
unpredicated vector loop in the first place.
Once the vectoriser has proper support for analysing all the costs
for each combination of VF and vectorisation style, then we should
be able to remove this.
Added an extra test here:
Transforms/LoopVectorize/AArch64/sve-tail-folding-option.ll
Differential Revision: https://reviews.llvm.org/D128342
This patch adds the AArch64 hook for preferPredicateOverEpilogue,
which currently returns true if SVE is enabled and one of the
following conditions (non-exhaustive) is met:
1. The "sve-tail-folding" option is set to "all", or
2. The "sve-tail-folding" option is set to "all+noreductions"
and the loop does not contain reductions,
3. The "sve-tail-folding" option is set to "all+norecurrences"
and the loop has no first-order recurrences.
Currently the default option is "disabled", but this will be
changed in a later patch.
I've added new tests to show the options behave as expected here:
Transforms/LoopVectorize/AArch64/sve-tail-folding-option.ll
Differential Revision: https://reviews.llvm.org/D129560
This patch is in preparation for enabling vectorisation with tail-folding
by default for SVE targets. Once we do that many existing tests will
break that depend upon having normal unpredicated vector loops. For
all such tests I have added the flag:
-prefer-predicate-over-epilogue=scalar-epilogue
Differential Revision: https://reviews.llvm.org/D129137
By default if SVE is enabled we want the select instruction used for
reductions to be inside the loop, rather than outside. This makes it
possible for the backend to fold the select into the operation to
produce a single predicated add, fadd, etc.
Differential Revision: https://reviews.llvm.org/D129763
In sve-tail-folding-reductions.ll I've also added an extra RUN line
to test normal reductions, i.e. not in-loop. This patch is a pre-commit
in preparation for a follow-on patch that changes how reduction selects
are generated in the vector loop.
Differential Revision: https://reviews.llvm.org/D129761
I've simplified all of the SVE vectoriser tail-folding tests to
only care about testing the flag:
-prefer-predicate-over-epiloge=predicate-else-scalar-epilogue
In practice we always want to fall back on unpredicated vector
loops if tail-folding is not possible.
Differential Revision: https://reviews.llvm.org/D129843
At the moment, the cost of runtime checks for scalable vectors is
overestimated due to creating separate vscale * VF expressions for each
check. Instead re-use the first expression.
For scalable vectors, it is not sufficient to only check
MinProfitableTripCount if it is >= VF.getKnownMinValue() * UF, because
this property may not holder for larger values of vscale. In those
cases, compute umax(VF * UF, MinProfTC) instead.
This should fix
https://lab.llvm.org/buildbot/#/builders/197/builds/2262
The test shows a case where the minimum trip count check incorrectly
only checks the minimum profitable trip count computed due to runtime
checks. This is incorrect for scalable VFs, because the VF * UF may
exceed the minimum profitable trip count for vscale > 1.
This is the likely reason for
https://lab.llvm.org/buildbot/#/builders/197/builds/2262 failing.
When vectorising ordered reductions we call a function
LoopVectorizationPlanner::adjustRecipesForReductions to replace the
existing VPWidenRecipe for the fadd instruction with a new
VPReductionRecipe. We attempt to insert the new recipe in the same
place, but this is wrong because createBlockInMask may have
generated new recipes that VPReductionRecipe now depends upon. I
have changed the insertion code to append the recipe to the
VPBasicBlock instead.
Added a new RUN with tail-folding enabled to the existing test:
Transforms/LoopVectorize/AArch64/scalable-strict-fadd.ll
Differential Revision: https://reviews.llvm.org/D129550
When calculating the cost of Instruction::Br in getInstructionCost
we query PredicatedBBsAfterVectorization to see if there is a
scalar predicated block. However, this meant that the decisions
being made for a given fixed-width VF were affecting the cost for a
scalable VF. As a result we were returning InstructionCost::Invalid
pointlessly for a scalable VF that should have a low cost. I
encountered this for some loops when enabling tail-folding for
scalable VFs.
Test added here:
Transforms/LoopVectorize/AArch64/sve-tail-folding-cost.ll
Differential Revision: https://reviews.llvm.org/D128272
Currently, for vectorised loops that use the get.active.lane.mask
intrinsic we only use the mask for predicated vector operations,
such as masked loads and stores, etc. The loop itself is still
controlled by comparing the canonical induction variable with the
trip count. However, for some targets this is inefficient when it's
cheap to use the mask itself to control the loop.
This patch adds support for using the active lane mask for control
flow by:
1. Generating the active lane mask for the next iteration of the
vector loop, rather than the current one. If there are still any
remaining iterations then at least the first bit of the mask will
be set.
2. Extract the first bit of this mask and use this bit for the
conditional branch.
I did this by creating a new VPActiveLaneMaskPHIRecipe that sets
up the initial PHI values in the vector loop pre-header. I've also
made use of the new BranchOnCond VPInstruction for the final
instruction in the loop region.
Differential Revision: https://reviews.llvm.org/D125301
This patch replaces the tight hard cut-off for the number of runtime
checks with a more accurate cost-driven approach.
The new approach allows vectorization with a larger number of runtime
checks in general, but only executes the vector loop (and runtime checks) if
considered profitable at runtime. Profitable here means that the cost-model
indicates that the runtime check cost + vector loop cost < scalar loop cost.
To do that, LV computes the minimum trip count for which runtime check cost
+ vector-loop-cost < scalar loop cost.
Note that there is still a hard cut-off to avoid excessive compile-time/code-size
increases, but it is much larger than the original limit.
The performance impact on standard test-suites like SPEC2006/SPEC2006/MultiSource
is mostly neutral, but the new approach can give substantial gains in cases where
we failed to vectorize before due to the over-aggressive cut-offs.
On AArch64 with -O3, I didn't observe any regressions outside the noise level (<0.4%)
and there are the following execution time improvements. Both `IRSmk` and `srad` are relatively short running, but the changes are far above the noise level for them on my benchmark system.
```
CFP2006/447.dealII/447.dealII -1.9%
CINT2017rate/525.x264_r/525.x264_r -2.2%
ASC_Sequoia/IRSmk/IRSmk -9.2%
Rodinia/srad/srad -36.1%
```
`size` regressions on AArch64 with -O3 are
```
MultiSource/Applications/hbd/hbd 90256.00 106768.00 18.3%
MultiSourc...ks/ASCI_Purple/SMG2000/smg2000 240676.00 257268.00 6.9%
MultiSourc...enchmarks/mafft/pairlocalalign 472603.00 489131.00 3.5%
External/S...2017rate/525.x264_r/525.x264_r 613831.00 630343.00 2.7%
External/S...NT2006/464.h264ref/464.h264ref 818920.00 835448.00 2.0%
External/S...te/538.imagick_r/538.imagick_r 1994730.00 2027754.00 1.7%
MultiSourc...nchmarks/tramp3d-v4/tramp3d-v4 1236471.00 1253015.00 1.3%
MultiSource/Applications/oggenc/oggenc 2108147.00 2124675.00 0.8%
External/S.../CFP2006/447.dealII/447.dealII 4742999.00 4759559.00 0.3%
External/S...rate/510.parest_r/510.parest_r 14206377.00 14239433.00 0.2%
```
Reviewed By: lebedev.ri, ebrevnov, dmgreen
Differential Revision: https://reviews.llvm.org/D109368
TTI::prefersVectorizedAddressing() try to vectorize the addresses that lead to loads.
For aarch64, only gather/scatter (supported by SVE) can deal with vectors of addresses.
This patch specializes the hook for AArch64, to return true only when we enable SVE.
Reviewed By: dmgreen
Differential Revision: https://reviews.llvm.org/D124612
Try to simplify BranchOnCount to `BranchOnCond true` if TC <= UF * VF.
This is an alternative to D121899 which simplifies the VPlan directly
instead of doing so late in code-gen.
The potential benefit of doing this in VPlan is that this may help
cost-modeling in the future. The reason this is done in prepareToExecute
at the moment is that a single plan may be used for multiple VFs/UFs.
There are further simplifications that can be applied as follow ups:
1. Replace inductions with constants
2. Replace vector region with regular block.
Fixes#55354.
Depends on D126679.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D126680
Now that SimpleLoopUnswitch and other transforms no longer introduce
branch on poison, enable the -branch-on-poison-as-ub option by
default. The practical impact of this is mostly better flag
preservation in SCEV, and some freeze instructions no longer being
necessary.
Differential Revision: https://reviews.llvm.org/D125299
When compiling the attached new test in scalable-reductions-tf.ll we
were hitting this assertion in fixReduction:
Assertion `isa<PHINode>(U) && "Reduction exit must feed Phi's or select"
The loop contains a reduction and an intermediate store of the reduction
value. When vectorising with tail-folding the contains of 'U' in the
assertion above happened to be a scatter_store. It turns out that we
were still creating a widen recipe for the invariant store, despite
knowing that we can actually sink it. The simplest fix is to change
buildVPlanWithVPRecipes so that we look for invariant stores before
attempting to widen it.
Differential Revision: https://reviews.llvm.org/D126295
Sander de Smalen