createInductionResumeValues only uses its loop argument only to get the
pre-header, but the pre-header is already known (we created/cached it
earlier). Remove the unneeded loop argument.
completeLoopSkeleton only uses its loop argument only to get the
pre-header, but the pre-header is already known (we created/cached it
earlier). Remove the unneeded loop argument.
Update functions that previously took a loop pointer but only to get the
pre-header. Instead, pass the block directly. This removes the
requirement for the loop object to be created up-front.
This uses the existing VPlan helpers to check whether there are scalar
uses of a phi recipe. It remove one of the few remaining dependencies on
the cost model from VPlan code generation.
Depends on D121612.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D121613
This patch ensures scalars (except for uniforms) are no
longer collected (prior to LVP planning phase) for
scalable vectorization.
This is to avoid the chances of generating scalarized
instructions later (during LVP execute phase) as they
are not supported for scalable vectorization.
Relevant test has also been added.
Differential Revision: https://reviews.llvm.org/D121452
Update places still referencing LoopVectorBody to use the vector loop to
get the vector loop header. This is needed to move vector loop
code-generation to VPlan completely, which in turn is needed to model
pre-header & exit blocks in VPlan as well.
The insertion point for the builder used during VPlan code generation is
set during code generation. Setting the insert point here is dead code
and can be removed.
This patch is a follow-up to D115953. It updates optimizeInductions
to also introduce new VPScalarIVStepsRecipes if an IV has both vector
and scalar uses.
It updates all uses that only need scalar values to use the newly
created recipe for the scalar steps.
This completes untangling of VPWidenIntOrFpInductionRecipe
code-generation. Now the recipe *only* creates the widened vector
values, as it says on the tin.
The code to genereate IR has been moved directly to
VPWidenIntOrFpInductionRecipe::execute.
Note that the recipe has been updated to hold a reference to
ScalarEvolution, which is needed to expand the step, until we can place
the corresponding SCEV expansion in the pre-header.
Depends on D120827.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D120828
The analysis passes output function name encapsulated in `'` braces,
but LV uses `"`. Harmonizing this may help in creating an update script
for the LV costmodel test checks.
Reviewed By: fhahn
Differential Revision: https://reviews.llvm.org/D121105
Instead of relying on underlying instructions, this patch updates
VPScalarIVStepsRecipe to only store the required type information.
This removes access to unrelated information, as well as avoiding issues
with the same underlying instruction being shared by multiple recipes.
This change should only change the debug output and not cause any
codegen changes, hence NFCI.
This reverts the revert commit ff93260bf6bddfbad1fa65c4d5184988885b900f.
The underlying issue causing the PPC bot failures has been fixed in
cbaac1473403 and a corresponding test case has been added in
ad2cad1c521c.
Original message:
This patch adds a new VPScalarIVStepsRecipe to handle building scalar
steps.
In the first patch, it only handles the case where there is no vector
induction variable needed.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D115953
This patch adds a new VPScalarIVStepsRecipe to handle building scalar
steps.
In the first patch, it only handles the case where there is no vector
induction variable needed.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D115953
This patch adds a new transform to remove dead recipes. For now, it only
removes dead recipes in the header, to keep the number tests that require
updating manageable. Future patches will extend this to remove dead
recipes across the whole plan.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D118051
Extends getReductionOpChain to look through Phis which may be part of
the reduction chain. adjustRecipesForReductions will now also create a
CondOp for VPReductionRecipe if the block is predicated and not only if
foldTailByMasking is true.
Changes were required in tryToBlend to ensure that we don't attempt
to convert the reduction Phi into a select by returning a VPBlendRecipe.
The VPReductionRecipe will create a select between the Phi and the reduction.
Reviewed By: david-arm
Differential Revision: https://reviews.llvm.org/D117580
Note that this doesn't actually cause the top level predicate to become a non-union just yet.
The * above comes from a case in the LoopVectorizer where a predicate which is later proven no longer blocks vectorization due to a change from checking if predicates exists to whether the predicate is possibly false.
This reverts commit 77a0da926c9ea86afa9baf28158d79c7678fc6b9 as we've
received multiple reports of this significantly impacting performance,
in ways that don't seem to just be target specific cost models going
wrong. I would offer some reproducers, but the test changes here seem to
be full of them!
Reverting for now and hopefully we can remove the "hack" more carefully
as we go.
Move out the induction step creation from emitTransformedIndex to the
callers. In some places (e.g. widenIntOrFpInduction) the step is already
created. Passing the step in ensures the steps are kept in sync.
This makes the function independent of shared state in ILV (ensures no
new dependencies on things like the cost model are introduced) and allows
for use directly in recipe's ::execute functions.
The vectorizer will choose at times to "vectorize" loops with a scalar
factor (VF=1) with interleaving (IC > 1). This can occasionally produce
better code than the unroller (notable for reductions where it can
produce independent reduction chains that are combined after the loop).
At times this is not very beneficial though, for example when runtime
checks are needed or when the scalar code requires predication.
This addresses the second point, preventing the vectorizer from
interleaving when the scalar loop will require predication. This
prevents it from making a bit of a mess, that is worse than the original
and better left for the unroller to unroll if beneficial. It helps
reverse some of the regressions from D118090.
Differential Revision: https://reviews.llvm.org/D118566
D43208 extracted `useEmulatedMaskMemRefHack()` from legality into cost model.
What it essentially does is prevents scalarized vectorization of masked memory operations:
```
// TODO: Cost model for emulated masked load/store is completely
// broken. This hack guides the cost model to use an artificially
// high enough value to practically disable vectorization with such
// operations, except where previously deployed legality hack allowed
// using very low cost values. This is to avoid regressions coming simply
// from moving "masked load/store" check from legality to cost model.
// Masked Load/Gather emulation was previously never allowed.
// Limited number of Masked Store/Scatter emulation was allowed.
```
While i don't really understand about what specifically `is completely broken`
was talking about, i believe that at least on X86 with AVX2-or-later,
this is no longer true. (or at least, i would like to know what is still broken).
So i would like to follow suit after D111460, and like wise disable that hack for AVX2+.
But since this was added for X86 specifically, let's just instead completely remove this hack.
Reviewed By: RKSimon
Differential Revision: https://reviews.llvm.org/D114779
In scalarizeInstruction(), isUniformAfterVectorization is used to detect
cases where it is sufficient to always access the first lane. This
should map directly checking whether the operand is a uniform replicate
recipe.
Differential Revision: https://reviews.llvm.org/D116654
When the main loop is e.g. VF=vscale x 1 and the epilogue VF cannot
be any smaller, the vectorizer should try to estimate how many lanes are
executed at runtime and allow a suitable fixed-width VF to be chosen. It
can use VScaleForTuning to figure out what a suitable fixed-width VF could
be. For the case where the main loop VF is VF=vscale x 1, and VScaleForTuning=8,
it could still choose an epilogue VF upto VF=4.
This was a bit tricky to test, so this patch also introduces a wrapper
function to get 'VScaleForTuning' by also considering vscale_range.
If min and max are equal, then that will be the vscale we compile for.
It makes little sense to tune for a different width if the code
will not be portable for other widths.
Reviewed By: david-arm
Differential Revision: https://reviews.llvm.org/D118709
For some reason we limited the epilogue VF to be fixed-width, but there
is not necessarily a reason for doing so. If the main VF=vscale x 16, the
epilogue VF could be either fixed-width, or a scalable VF upto vscale x 8.
Reviewed By: david-arm
Differential Revision: https://reviews.llvm.org/D118688
This removes another instance of recipe execution still relying on
the cost model.
Depends on D116554.
Reviewed By: david-arm
Differential Revision: https://reviews.llvm.org/D116656
This removes the remaining dependence on LoopVectorizationCostModel from
buildScalarSteps and is required so it can be moved out of ILV.
It also improves allows us to remove a few unneeded instructions.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D116554
This patch tries to use an existing VPWidenCanonicalIVRecipe
instead of creating another step-vector for canonical
induction recipes in widenIntOrFpInduction.
This has the following benefits:
1. First step to avoid setting both vector and scalar values for the
same induction def.
2. Reducing complexity of widenIntOrFpInduction through making things
more explicit in VPlan
3. Only need to splat the vector IV for block in masks.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D116123
This explicitly records whether a scalar IV is needed in the
VPWidenIntOrFpInductionRecipe, to remove a dependence on the cost-model
during its ::execute.
It will also be used in D116123 to determine if a vector phi will be
generated.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D118167
isCandidateForEpilogueVectorization will currently return false for loops
which contain reductions. This patch removes this restriction and makes
the following changes to support epilogue vectorisation with reductions:
- `fixReduction`: If fixReduction is being called during vectorisation of the
epilogue, the phi node it creates will need to additionally carry incoming
values from the middle block of the main loop.
- `createEpilogueVectorizedLoopSkeleton`: The incoming values of the phi
created by fixReduction are updated after the vec.epilog.iter.check block
is added. The phi is also moved to the preheader of the epilogue.
- `processLoop`: The start value of any VPReductionPHIRecipes are updated before
vectorising the epilogue loop. The getResumeInstr function added to the ILV
will return the resume instruction associated with the recurrence descriptor.
Reviewed By: sdesmalen
Differential Revision: https://reviews.llvm.org/D116928
This patch updates createBlockInMask to always generate
VPWidenCanonicalIVRecipe and adds a transform to optimize it away later,
if it is not needed.
This is a step towards breaking up VPWidenIntOrFpInductionRecipe and
explicitly distinguishing between vector phis and scalarizing.
Split off from D116123.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D117140
This patch adds VPWidenIntOrFpInductionRecipe::isCanonical to check if
an induction recipe is canonical. The code is also updated to use it
instead of isCanonicalID.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D117551
After d4a8fc3a87a1 LV stopped adding metadata to disable runtime
unrolling to the vectorized epilogue loop. This was missed because
278aa65cc495 removed the relevant test coverage.
This patch fixes that by adding the relevant metadata after
vector loop generation.
This reverts the revert commit 073c27b5e5851f13d99d383e047309299b68827d.
A reduced test case has been added in 5e4966cbae7ba5 and the code has
been updated to handle the case where getInductionOpcode returns
BinaryOpsEnd. In this case, the original code was always using
Instruction::Add. Do the same in the patch.
Note this commit may slightly change the value naming, because it now
also assigns the 'induction' name in the floating point case.
Causes a crash with the following (creduce'd) test-case:
clang -O3 '--target=aarch64-grtev4-linux-gnu' -xc - -c -o /dev/null <<EOF
int *e;
int f;
int g() {
int h;
int *j = 0;
while (&f - j > 0) {
int k;
k = j;
if (e == j && *e)
k = 5;
h = k;
j++;
}
return h;
}
EOF
This reverts commit 7ce48be0fd83fb4fe3d0104f324bbbcfcc82983c.
This patch adds a new BranchOnCount VPInstruction opcode with 2
operands. It first compares its 2 operands (increment of canonical
induction and vector trip count), followed by a branch to either the
exit block or back to the vector header.
It must be the last recipe in the exit block of the topmost vector loop
region.
This extracts parts from D113224 and was discussed in D113223.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D116479
This is required to query the legality more precisely in the LoopVectorizer.
This adds another TTI function named 'forceScalarizeMaskedGather/Scatter'
function to work around the hack introduced for MVE, where
isLegalMaskedGather/Scatter would return an answer by second-guessing
where the function was called from, based on the Type passed in (vector
vs scalar). The new interface makes this explicit. It is also used by
X86 to check for vector widths where gather/scatters aren't profitable
(or don't exist) for certain subtargets.
Differential Revision: https://reviews.llvm.org/D115329
This patch fixes up an issue with InnerLoopVectorizer::getOrCreateVectorTripCount
whereby we weren't correctly generating the runtime trip count
for scalable vectors when tail-folding.
It also removes some asserts in the tail-folding path for cases when
the VF is not scalable.
In this patch I have only permitted tail-folding to be enabled
explicitly for scalable vectors when the user has specified one
of the following flags:
-prefer-predicate-over-epilogue=predicate-dont-vectorize
-prefer-predicate-over-epilogue=predicate-else-scalar-epilogue
For now it's best not to enable tail-folding with scalable vectors for
low trip counts or when optimising for code size, since there has been
no analysis on whether this is worth it.
Various tests have been added here:
Transforms/LoopVectorize/AArch64/sve-tail-folding.ll
Transforms/LoopVectorize/AArch64/sve-tail-folding-forced.ll
The tests cannot be target independent because they require masked
load/store support, i.e. TTI.isLegalMaskedLoad and TTI.isLegalMaskedStore
need to return true.
Differential Revision: https://reviews.llvm.org/D113003
