Unfortunately sinking recipes for first-order recurrences relies on
the original position of recipes. So if a recipes needs to be sunk after
an optimized induction, it needs to stay in the original position, until
sinking is done. This is causing PR52460.
To fix the crash, keep the recipes in the original position until
sink-after is done.
Post-commit follow-up to c45045bfd0 to address PR52460.
This reverts commit 7cd273c339.
Several patches with tests fixes have been applied:
0cada82f0a "[Test] Remove incorrect test in GVN"
97cb13615d "[Test] Separate IndVars test into AArch64 and X86 parts"
985cc490f1 "[Test] Remove separated test in IndVars",
and test failures caused by 5ec2386 should be resolved now.
When creating a splat of 0 for scalable vectors we tend to create them
with using a combination of shufflevector and insertelement, i.e.
shufflevector (<vscale x 4 x i32> insertelement (<vscale x 4 x i32> poison, i32 0, i32 0),
<vscale x 4 x i32> poison, <vscale x 4 x i32> zeroinitializer)
However, for the case of a zero splat we can actually just replace the
above with zeroinitializer instead. This makes the IR a lot simpler and
easier to read. I have changed ConstantFoldShuffleVectorInstruction to
use zeroinitializer when creating a splat of integer 0 or FP +0.0 values.
Differential Revision: https://reviews.llvm.org/D113394
Changes VPReplicateRecipe to extract the last lane from an unconditional,
uniform store instruction. collectLoopUniforms will also add stores to
the list of uniform instructions where Legal->isUniformMemOp is true.
setCostBasedWideningDecision now sets the widening decision for
all uniform memory ops to Scalarize, where previously GatherScatter
may have been chosen for scalable stores.
This fixes an assert ("Cannot yet scalarize uniform stores") in
setCostBasedWideningDecision when we have a loop containing a
uniform i1 store and a scalable VF, which we cannot create a scatter for.
Reviewed By: sdesmalen, david-arm, fhahn
Differential Revision: https://reviews.llvm.org/D112725
This reapplies patch db289340c8.
The test failures on build with expensive checks caused by the patch happened due
to the fact that we sorted loop Phis in replaceCongruentIVs using llvm::sort,
which shuffles the given container if the expensive checks are enabled,
so equivalent Phis in the sorted vector had different mutual order from run
to run. replaceCongruentIVs tries to replace narrow Phis with truncations
of wide ones. In some test cases there were several Phis with the same
width, so if their order differs from run to run, the narrow Phis would
be replaced with a different Phi, depending on the shuffling result.
The patch ae14fae0ff fixed this issue by
replacing llvm::sort with llvm::stable_sort.
All phi-like recipes should be at the beginning of a VPBasicBlock with
no other recipes in between. Ensure that the recurrence-splicing recipe
is not added between phi-like recipes, but after them.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D111301
When targeting a specific CPU with scalable vectorization, the knowledge
of that particular CPU's vscale value can be used to tune the cost-model
and make the cost per lane less pessimistic.
If the target implements 'TTI.getVScaleForTuning()', the cost-per-lane
is calculated as:
Cost / (VScaleForTuning * VF.KnownMinLanes)
Otherwise, it assumes a value of 1 meaning that the behavior
is unchanged and calculated as:
Cost / VF.KnownMinLanes
Reviewed By: kmclaughlin, david-arm
Differential Revision: https://reviews.llvm.org/D113209
In IndVarSimplify after simplifying and extending loop IVs we call 'replaceCongruentIVs'.
This function optionally takes a TTI argument to be able to replace narrow IVs uses
with truncates of the widest one.
For some reason the TTI wasn't passed to the function, so it couldn't perform such
transform.
This patch fixes it.
Reviewed By: mkazantsev
Differential Revision: https://reviews.llvm.org/D113024
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've added a test for a loop containing a conditional uniform load for
a target that supports masked loads. The test just ensures that we
correctly use gather instructions and have the correct mask.
Differential Revision: https://reviews.llvm.org/D112619
This patch updates VPReductionRecipe::execute so that the fast-math
flags associated with the underlying instruction of the VPRecipe are
propagated through to the reductions which are created.
Differential Revision: https://reviews.llvm.org/D112548
We never expect the runtime VF to be negative so we should use
the uitofp instruction instead of sitofp.
Differential revision: https://reviews.llvm.org/D112610
This patch updates recipe creation to ensure all
VPWidenIntOrFpInductionRecipes are in the header block. At the moment,
new induction recipes can be created in different blocks when trying to
optimize casts and induction variables.
Having all induction recipes in the header makes it easier to
analyze/transform them in VPlan.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D111300
Upon further investigation and discussion,
this is actually the opposite direction from what we should be taking,
and this direction wouldn't solve the motivational problem anyway.
Additionally, some more (polly) tests have escaped being updated.
So, let's just take a step back here.
This reverts commit f3190dedee.
This reverts commit 749581d21f.
This reverts commit f3df87d57e.
This reverts commit ab1dbcecd6.
There's precedent for that in `CreateOr()`/`CreateAnd()`.
The motivation here is to avoid bloating the run-time check's IR
in `SCEVExpander::generateOverflowCheck()`.
Refs. https://reviews.llvm.org/D109368#3089809
It's a no-op, no overflow happens ever: https://alive2.llvm.org/ce/z/Zw89rZ
While generally i don't like such hacks,
we have a very good reason to do this: here we are expanding
a run-time correctness check for the vectorization,
and said `umul_with_overflow` will not be optimized out
before we query the cost of the checks we've generated.
Which means, the cost of run-time checks would be artificially inflated,
and after https://reviews.llvm.org/D109368 that will affect
the minimal trip count for which these checks are even evaluated.
And if they aren't even evaluated, then the vectorized code
certainly won't be run.
We could consider doing this in IRBuilder, but then we'd need to
also teach `CreateExtractValue()` to look into chain of `insertvalue`'s,
and i'm not sure there's precedent for that.
Refs. https://reviews.llvm.org/D109368#3089809
While we could emit such a tautological `select`,
it will stick around until the next instsimplify invocation,
which may happen after we count the cost of this redundant `select`.
Which is precisely what happens with loop vectorization legality checks,
and that artificially increases the cost of said checks,
which is bad.
There is prior art for this in `IRBuilderBase::CreateAnd()`/`IRBuilderBase::CreateOr()`.
Refs. https://reviews.llvm.org/D109368#3089809
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
The math here is:
Cost of 1 load = cost of n loads / n
Cost of live loads = num live loads * Cost of 1 load
Cost of live loads = num live loads * (cost of n loads / n)
Cost of live loads = cost of n loads * (num live loads / n)
But, all the variables here are integers,
and integer division rounds down,
but this calculation clearly expects float semantics.
Instead multiply upfront, and then perform round-up-division.
Reviewed By: RKSimon
Differential Revision: https://reviews.llvm.org/D112302
This patch introduces a new function:
AArch64Subtarget::getVScaleForTuning
that returns a value for vscale that can be used for tuning the cost
model when using scalable vectors. The VScaleForTuning option in
AArch64Subtarget is initialised according to the following rules:
1. If the user has specified the CPU to tune for we use that, else
2. If the target CPU was specified we use that, else
3. The tuning is set to "generic".
For CPUs of type "generic" I have assumed that vscale=2.
New tests added here:
Analysis/CostModel/AArch64/sve-gather.ll
Analysis/CostModel/AArch64/sve-scatter.ll
Transforms/LoopVectorize/AArch64/sve-strict-fadd-cost.ll
Differential Revision: https://reviews.llvm.org/D110259
Right now when we see -O# we add the corresponding 'default<O#>' into
the list of passes to run when translating legacy -pass-name. This has
the side effect of not using the default AA pipeline.
Instead, treat -O# as -passes='default<O#>', but don't allow any other
-passes or -pass-name. I think we can keep `opt -O#` as shorthand for
`opt -passes='default<O#>` but disallow anything more than just -O#.
Tests need to be updated to not use `opt -O# -pass-name`.
Reviewed By: asbirlea
Differential Revision: https://reviews.llvm.org/D112036
This simplifies the return value of addRuntimeCheck from a pair of
instructions to a single `Value *`.
The existing users of addRuntimeChecks were ignoring the first element
of the pair, hence there is not reason to track FirstInst and return
it.
Additionally all users of addRuntimeChecks use the second returned
`Instruction *` just as `Value *`, so there is no need to return an
`Instruction *`. Therefore there is no need to create a redundant
dummy `and X, true` instruction any longer.
Effectively this change should not impact the generated code because the
redundant AND will be folded by later optimizations. But it is easy to
avoid creating it in the first place and it allows more accurately
estimating the cost of the runtime checks.
These cases use the same codegen as AVX2 (pshuflw/pshufd) for the sub-128bit vector deinterleaving, and unpcklqdq for v2i64.
It's going to take a while to add full interleaved cost coverage, but since these are the same for SSE2 -> AVX2 it should be an easy win.
Fixes PR47437
Differential Revision: https://reviews.llvm.org/D111938
And another attempt to start untangling this ball of threads around gather.
There's `TTI::prefersVectorizedAddressing()`hoop, which confusingly defaults to `true`,
which tells LV to try to vectorize the addresses that lead to loads,
but X86 generally can not deal with vectors of addresses,
the only instructions that support that are GATHER/SCATTER,
but even those aren't available until AVX2, and aren't really usable until AVX512.
This specializes the hook for X86, to return true only if we have AVX512 or AVX2 w/ fast gather.
Reviewed By: RKSimon
Differential Revision: https://reviews.llvm.org/D111546
While i've modelled most of the relevant tuples for AVX2,
that only covered fully-interleaved groups.
By definition, interleaving load of stride N means:
load N*VF elements, and shuffle them into N VF-sized vectors,
with 0'th vector containing elements `[0, VF)*stride + 0`,
and 1'th vector containing elements `[0, VF)*stride + 1`.
Example: https://godbolt.org/z/df561Me5E (i64 stride 4 vf 2 => cost 6)
Now, not fully interleaved load, is when not all of these vectors is demanded.
So at worst, we could just pretend that everything is demanded,
and discard the non-demanded vectors. What this means is that the cost
for not-fully-interleaved group should be not greater than the cost
for the same fully-interleaved group, but perhaps somewhat less.
Examples:
https://godbolt.org/z/a78dK5Geq (i64 stride 4 (indices 012u) vf 2 => cost 4)
https://godbolt.org/z/G91ceo8dM (i64 stride 4 (indices 01uu) vf 2 => cost 2)
https://godbolt.org/z/5joYob9rx (i64 stride 4 (indices 0uuu) vf 2 => cost 1)
As we have established over the course of last ~70 patches, (wow)
`BaseT::getInterleavedMemoryOpCos()` is absolutely bogus,
it is usually almost an order of magnitude overestimation,
so i would claim that we should at least use the hardcoded costs
of fully interleaved load groups.
We could go further and adjust them e.g. by the number of demanded indices,
but then i'm somewhat fearful of underestimating the cost.
Reviewed By: RKSimon
Differential Revision: https://reviews.llvm.org/D111174
`X86TTIImpl::getGSScalarCost()` has (at least) two issues:
* it naively computes the cost of sequence of `insertelement`/`extractelement`.
If we are operating not on the XMM (but YMM/ZMM),
this widely overestimates the cost of subvector insertions/extractions.
* Gather/scatter takes a vector of pointers, and scalarization results in us performing
scalar memory operation for each of these pointers, but we never account for the cost
of extracting these pointers out of the vector of pointers.
Reviewed By: RKSimon
Differential Revision: https://reviews.llvm.org/D111222
This patch fixes another crash revealed by PR51614:
when *deciding* to vectorize with masked interleave groups, check if the access
is reverse (which is currently not supported).
Differential Revision: https://reviews.llvm.org/D108900
collectLoopScalars collects pointer induction updates in ScalarPtrs, assuming
that the instruction will be scalar after vectorization. This may crash later
in VPReplicateRecipe::execute() if there there is another user of the instruction
other than the Phi node which needs to be widened.
This changes collectLoopScalars so that if there are any other users of
Update other than a Phi node, it is not added to ScalarPtrs.
Reviewed By: david-arm, fhahn
Differential Revision: https://reviews.llvm.org/D111294
At the moment, a VPValue is created for the backedge-taken count, which
is used by some recipes. To make it easier to identify the operands of
recipes using the backedge-taken count, print it at the beginning of the
VPlan if it is used.
Reviewed By: a.elovikov
Differential Revision: https://reviews.llvm.org/D111298
This patch adds further support for vectorisation of loops that involve
selecting an integer value based on a previous comparison. Consider the
following C++ loop:
int r = a;
for (int i = 0; i < n; i++) {
if (src[i] > 3) {
r = b;
}
src[i] += 2;
}
We should be able to vectorise this loop because all we are doing is
selecting between two states - 'a' and 'b' - both of which are loop
invariant. This just involves building a vector of values that contain
either 'a' or 'b', where the final reduced value will be 'b' if any lane
contains 'b'.
The IR generated by clang typically looks like this:
%phi = phi i32 [ %a, %entry ], [ %phi.update, %for.body ]
...
%pred = icmp ugt i32 %val, i32 3
%phi.update = select i1 %pred, i32 %b, i32 %phi
We already detect min/max patterns, which also involve a select + cmp.
However, with the min/max patterns we are selecting loaded values (and
hence loop variant) in the loop. In addition we only support certain
cmp predicates. This patch adds a new pattern matching function
(isSelectCmpPattern) and new RecurKind enums - SelectICmp & SelectFCmp.
We only support selecting values that are integer and loop invariant,
however we can support any kind of compare - integer or float.
Tests have been added here:
Transforms/LoopVectorize/AArch64/sve-select-cmp.ll
Transforms/LoopVectorize/select-cmp-predicated.ll
Transforms/LoopVectorize/select-cmp.ll
Differential Revision: https://reviews.llvm.org/D108136
Florian Hahn