This patch introduces a new ComputeReductionResult opcode to compute the
final reduction result in the middle block. The code from fixReduction
has been moved to ComputeReductionResult, after some earlier cleanup
changes to model parts of fixReduction explicitly elsewhere as needed.
The recipe may be broken down further in the future.
Note that the phi nodes to merge the reduction result from the trip
count check and the middle block, to be used as resume value for the
scalar remainder loop are also generated based on
ComputeReductionResult.
Once we have a VPValue for the reduction result, this can also be
modeled explicitly and moved out of the recipe.
Reductions with intermediate stores currently need to be fixed in order
of their intermediate stores. Instead of doing this at fixup time after
code has been generated, sort the reductions in adjustRecipesForReductions.
This makes the order explicit in VPlan and will enable removing
fixReductions with modeling computing the final reduction result in
VPlan, followed by also modeling the intermediate stores explicitly.
This patch updates the mask creation code to always create compares of
the form (ICMP_ULE, wide canonical IV, backedge-taken-count) up front
when tail folding and introduce active-lane-mask as later
transformation.
This effectively makes (ICMP_ULE, wide canonical IV, backedge-taken-count)
the canonical form for tail-folding early on. Introducing more specific
active-lane-mask recipes is treated as a VPlan-to-VPlan optimization.
This has the advantage of keeping the logic (and complexity) of
introducing active-lane-mask recipes in a single place, instead of
spreading the logic out across multiple functions. It also simplifies
initial VPlan construction and enables treating introducing EVL as
similar optimization.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D158779
Extend VPRecipeWithIRFlags to also manage predicates for compares. This
allows removing the custom ICmpULE opcode from VPInstruction which was a
workaround for missing proper predicate handling.
This simplifies the code a bit while also allowing compares with any
predicates. It also fixes a case where the compare predixcate wasn't
printed properly for VPReplicateRecipes.
Discussed/split off from D150398.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D158992
Replace ConditionalAssume set by treating conditional assumes like other
predicated instructions (i.e. create a VPReplicateRecipe with a mask)
and later remove any assume recipes with masks during VPlan cleanup.
This reduces coupling of VPlan construction and Legal by removing a
shared set between the 2 and results in a cleaner code structure
overall.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D157034
If a candidate VF for epilogue vectorization is greater than the number of
remaining iterations, the epilogue loop would be dead. Skip such factors.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D154264
If a candidate VF for epilogue vectorization is less than the number of
remaining iterations, the epilogue loop would be dead. Skip such factors.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D154264
Split off from D143938. This moves the planning logic to select the
vectorization factor to LoopVectorizationPlanner as a step towards only
computing costs for individual VFs in LoopVectorizationCostModel and do
planning in LVP.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D150197
When generating code for the epilogue vector loop, we need to re-use the
expansion results for induction steps generated for the main vector
loop, as the pre-header of the epilogue vector loop may not dominate the
vector preheader of the epilogue.
This fixes a reported crash. Note that this is a workaround which should
be removed soon once induction resume value creation is handled in VPlan
directly.
Update skeleton creation logic to use SCEV expansion results from
expanding the pre-header. This avoids another set of SCEV expansions
that may happen after the CFG has been modified.
Fixes#58811.
Depends on D147964.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D147965
The function checks if there's a plan with the specified VF. Update the
comment to match the implementation.
Pointed out as independent improvement in D143938.
Update the planning code constructing VPlan to allow building VPlans to
fail. This allows us to gradually shift some legality checks to VPlan
construction. The first candidate is checking if all users of
first-order recurrence phis can be sunk past the recipe computing the
previous value.
The new functionality will be used by D142886 which is approved and will
be landed shortly.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D142885
This patch updates LV to sink recipes directly using the VPlan use
chains. The initial patch only moves sinking to be purely VPlan-based.
Follow-up patches will move legality checks to VPlan as well.
At the moment, there's a single test failure remaining.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D142589
This patch is a simple piece of refactoring that now permits users
to create VPInstructions and specify the name of the value being
generated. This is useful for creating more readable/meaningful
names in IR.
Differential Revision: https://reviews.llvm.org/D128982
Now that removeDeadRecipes can remove most dead recipes across a whole
VPlan, there is no need to first collect some dead instructions.
Instead removeDeadRecipes can simply clean them up.
Depends D127580.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D128408
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
At the moment, the same VPlan can be used code generation of both the
main vector and epilogue vector loop. This can lead to wrong results, if
the plan is optimized based on the VF of the main vector loop and then
re-used for the epilogue loop.
One example where this is problematic is if the scalar loops need to
execute at least one iteration, e.g. due to interleave groups.
To prevent mis-compiles in the short-term, disable optimizing exit
conditions for VPlans when using epilogue vectorization. The proper fix
is to avoid re-using the same plan for both loops, which will require
support for cloning plans first.
Fixes#56319.
This patch updates LV to generate runtime after the VF & IC are selected. It
allows deciding whether to vectorize with runtime checks or not based on
their cost compared to the vector loop.
It also updates VectorizationFactor to include the scalar cost.
Reviewed By: lebedev.ri, dmgreen
Differential Revision: https://reviews.llvm.org/D75981
The runtime check threshold should also restrict interleave count.
Otherwise, too many runtime checks will be generated for some cases.
Reviewed By: fhahn, dmgreen
Differential Revision: https://reviews.llvm.org/D122126
At the moment in LoopVectorizationCostModel::selectEpilogueVectorizationFactor
we bail out if the main vector loop uses a scalable VF. This patch adds
support for generating epilogue vector loops using a fixed-width VF when the
main vector loop uses a scalable VF.
I've changed LoopVectorizationCostModel::selectEpilogueVectorizationFactor
so that we convert the scalable VF into a fixed-width VF and do profitability
checks on that instead. In addition, since the scalable and fixed-width VFs
live in different VPlans that means I had to change the calls to
LVP.hasPlanWithVFs so that we only pass in the fixed-width VF.
New tests added here:
Transforms/LoopVectorize/AArch64/sve-epilog-vect.ll
Differential Revision: https://reviews.llvm.org/D109432
I have removed LoopVectorizationPlanner::setBestPlan, since this
function is quite aggressive because it deletes all other plans
except the one containing the <VF,UF> pair required. The code is
currently written to assume that all <VF,UF> pairs will live in the
same vplan. This is overly restrictive, since scalable VFs live in
different plans to fixed-width VFS. When we add support for
vectorising epilogue loops when the main loop uses scalable vectors
then we will the vplan for the main loop will be different to the
epilogue.
Instead I have added a new function called
LoopVectorizationPlanner::getBestPlanFor
that returns the best vplan for the <VF,UF> pair requested and leaves
all the vplans untouched. We then pass this best vplan to
LoopVectorizationPlanner::executePlan
which now takes an additional VPlanPtr argument.
Differential revision: https://reviews.llvm.org/D111125
After refactoring the phi recipes, we can now iterate over all header
phis in a VPlan to detect reductions when it comes to fixing them up
when tail folding.
This reduces the coupling with the cost model & legal by using the
information directly available in VPlan. It also removes a call to
getOrAddVPValue, which references the original IR value which may
become outdated after VPlan transformations.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D100102
Currently we will allow loops with a fixed width VF of 1 to vectorize
if the -enable-strict-reductions flag is set. However, the loop vectorizer
will not use ordered reductions if `VF.isScalar()` and the resulting
vectorized loop will be out of order.
This patch removes `VF.isVector()` when checking if ordered reductions
should be used. Also, instead of converting the FAdds to reductions if the
VF = 1, operands of the FAdds are changed such that the order is preserved.
Reviewed By: david-arm
Differential Revision: https://reviews.llvm.org/D104533
Update isFirstOrderRecurrence to explore all uses of a recurrence phi
and check if we can sink them. If there are multiple users to sink, they
are all mapped to the previous instruction.
Fixes PR44286 (and another PR or two).
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D84951
This patch introduces a new class, MaxVFCandidates, that holds the
maximum vectorization factors that have been computed for both scalable
and fixed-width vectors.
This patch is intended to be NFC for fixed-width vectors, although
considering a scalable max VF (which is disabled by default) pessimises
tail-loop elimination, since it can no longer determine if any chosen VF
(less than fixed/scalable MaxVFs) is guaranteed to handle all vector
iterations if the trip-count is known. This issue will be addressed in
a future patch.
Reviewed By: fhahn, david-arm
Differential Revision: https://reviews.llvm.org/D98721
Rather than maintaining two separate values, a `float` for the per-lane
cost and a Width for the VF, maintain a single VectorizationFactor which
comprises the two and also removes the need for converting an integer value
to float.
This simplifies the query when asking if one VF is more profitable than
another when we want to extend this for scalable vectors (which may
require additional options to determine if e.g. a scalable VF of the
some cost, is more profitable than a fixed VF of the same cost).
The patch isn't entirely NFC because it also fixes an issue in
selectEpilogueVectorizationFactor, where the cost passed to ProfitableVFs
no longer truncates the floating-point cost from `float` to `unsigned` to
then perform the calculation on the truncated cost. It now does
a cost comparison with the correct precision.
Reviewed By: dmgreen
Differential Revision: https://reviews.llvm.org/D100121
Re-apply 25fbe803d4db, with a small update to emit the right remark
class.
Original message:
[LV] Move runtime pointer size check to LVP::plan().
This removes the need for the remaining doesNotMeet check and instead
directly checks if there are too many runtime checks for vectorization
in the planner.
A subsequent patch will adjust the logic used to decide whether to
vectorize with runtime to consider their cost more accurately.
Reviewed By: lebedev.ri
This removes the need for the remaining doesNotMeet check and instead
directly checks if there are too many runtime checks for vectorization
in the planner.
A subsequent patch will adjust the logic used to decide whether to
vectorize with runtime to consider their cost more accurately.
Reviewed By: lebedev.ri
Differential Revision: https://reviews.llvm.org/D98634
I foresee two uses for this:
1) It's easier to use those in debugger.
2) Once we start implementing more VPlan-to-VPlan transformations (especially
inner loop massaging stuff), using the vectorized LLVM IR as CHECK targets in
LIT test would become too obscure. I can imagine that we'd want to CHECK
against VPlan dumps after multiple transformations instead. That would be
easier with plain text dumps than with DOT format.
Reviewed By: fhahn
Differential Revision: https://reviews.llvm.org/D96628
