Pass in the vector header instead of relying on ILV::LoopVectorBody.
This reduces the dependence on state from ILV. Where VPTransformState is
available, State.CFG.PrevBB can be used.
For the simple copy loop (see test case) vectorizer selects VF equal to 32 while the loop is known to have 17 iterations only. Such behavior makes no sense to me since such vector loop will never be executed. The only case we may want to select VF large than TC is masked vectoriztion. So I haven't touched that case.
Reviewed By: dmgreen
Differential Revision: https://reviews.llvm.org/D114528
Given a MLA reduction from two different types (say i8 and i16), we were
previously failing to find the reduction pattern, often making us chose
the lower vector factor. This improves that by using the largest of the
two extension types, allowing us to use the larger VF as the type of the
reduction.
As per https://godbolt.org/z/KP549EEYM the backend handles this
valiantly, leading to better performance.
Differential Revision: https://reviews.llvm.org/D115432
This patch simplifies handling of redundant induction casts, by
removing dead cast instructions after initial VPlan construction.
This has the following benefits:
1. fixes a crash
(see @test_optimized_cast_induction_feeding_first_order_recurrence)
2. Simplifies VPWidenIntOrFpInduction to a single-def recipes
3. Retires recordVectorLoopValueForInductionCast.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D115112
This patch uses a similar trick as in D113947 to only run the extra
passes after vectorization on functions where loops have been
vectorized.
The reason for running the 'extra vector passes' is
simplification/unswitching of the runtime checks created by LV, there
should be no need to run them if nothing got vectorized
To do that, a new dummy analysis ShouldRunExtraVectorPasses has been
added. If loops have been vectorized for a function, LV will cache the
analysis. At the moment it uses MadeCFGChanges as proxy for loop
vectorized, which isn't perfect (it could be too aggressive, e.g.
because no runtime checks have been added), but should be good enough
for now.
The extra passes are now managed by a new FunctionPassManager that
runs its passes only if ShouldRunExtraVectorPasses has been cached.
Without this patch, `-extra-vectorizer-passes` has the following
compile-time impact:
NewPM-O3: +4.86%
NewPM-ReleaseThinLTO: +3.56%
NewPM-ReleaseLTO-g: +7.17%
http://llvm-compile-time-tracker.com/compare.php?from=ead3979a92fc33add4710c4510d6906260dcb4ad&to=c292da649e2c6e88a31e702fdc474727d09c72bc&stat=instructions
With this patch, that gets reduced to
NewPM-O3: +1.43%
NewPM-ReleaseThinLTO: +1.00%
NewPM-ReleaseLTO-g: +1.58%
http://llvm-compile-time-tracker.com/compare.php?from=ead3979a92fc33add4710c4510d6906260dcb4ad&to=e67d86b57810011cf285eb9aa1944781be6096f0&stat=instructions
It is probably still too high to enable by default, but much better.
Reviewed By: aeubanks
Differential Revision: https://reviews.llvm.org/D115052
This allows easier access to the induction descriptor from VPlan,
without needing to go through Legal. VPReductionPHIRecipe already
contains a RecurrenceDescriptor in a similar fashion.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D115111
Both the entry and exit blocks of the top-region of a plan must be
VPBasicBlocks. They also must have no predecessors or successors
respectively.
This invariant was broken when splitting a block for sink-after. To fix
the issue, set the exit block of the region *after* sink-after is done.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D114586
ILV::scalarizeInstruction still uses the original IR operands to check
if an input value is uniform after vectorization.
There is no need to go back to the cost model to figure that out, as the
information is already explicit in the VPlan. Just check directly
whether the VPValue is defined outside the plan or is a uniform
VPReplicateRecipe.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D114253
If the condition of a select is a compare, pass its predicate to
TTI::getCmpSelInstrCost to get a more accurate cost value instead
of passing BAD_ICMP_PREDICATE.
I noticed that the commit message from D90070 had a comment about the
vectorized select predicate possibly being composed of other compares with
different predicate values, but I wasn't able to construct an example
where this was an actual issue. If this is an issue, I guess we could
add another check that the block isn't predicated for any reason.
Reviewed By: dmgreen, fhahn
Differential Revision: https://reviews.llvm.org/D114646
VPWidenIntOrFpInductionRecipes can either be constructed with a PHI and
an optional cast or a PHI and a trunc instruction. Reflect this in 2
separate constructors. This also simplifies a follow-up change.
The code in widenMemoryInstruction has already been transitioned
to only rely on information provided by VPWidenMemoryInstructionRecipe
directly.
Moving the code directly to VPWidenMemoryInstructionRecipe::execute
completes the transition for the recipe.
It provides the following advantages:
1. Less indirection, easier to see what's going on.
2. Removes accesses to fields of ILV.
2) in particular ensures that no dependencies on
fields in ILV for vector code generation are re-introduced.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D114324
The code in widenSelectInstruction has already been transitioned
to only rely on information provided by VPWidenSelectRecipe directly.
Moving the code directly to VPWidenSelectRecipe::execute completes
the transition for the recipe.
It provides the following advantages:
1. Less indirection, easier to see what's going on.
2. Removes accesses to fields of ILV.
2) in particular ensures that no dependencies on
fields in ILV for vector code generation are re-introduced.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D114323
We ask `TTI.getAddressComputationCost()` about the cost of computing vector address,
and then multiply it by the vector width. This doesn't make any sense,
it implies that we'd do a vector GEP and then scalarize the vector of pointers,
but there is no such thing in the vectorized IR, we perform scalar GEP's.
This is *especially* bad on X86, and was effectively prohibiting any scalarized
vectorization of gathers/scatters, because `X86TTIImpl::getAddressComputationCost()`
says that cost of vector address computation is `10` as compared to `1` for scalar.
The computed costs are similar to the ones with D111222+D111220,
but we end up without masked memory intrinsics that we'd then have to
expand later on, without much luck. (D111363)
Differential Revision: https://reviews.llvm.org/D111460
The code in widenInstruction has already been transitioned to
only rely on information provided by VPWidenRecipe directly.
Moving the code directly to VPWidenRecipe::execute completes
the transition for the recipe.
It provides the following advantages:
1. Less indirection, easier to see what's going on.
2. Removes accesses to fields of ILV.
2) in particular ensures that no dependencies on
fields in ILV for vector code generation are re-introduced.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D114322
The code in widenGEP has already been transitioned to only rely on
information provided by VPWidenGEPRecipe directly.
Moving the code directly to VPWidenGEPRecipe::execute completes
the transition for the recipe.
It provides the following advantages:
1. Less indirection, easier to see what's going on.
2. Removes accesses to fields of ILV.
2) in particular ensures that no dependencies on
fields in ILV for GEP code generation are re-introduced.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D114321
collectLoopScalars should only add non-uniform nodes to the list if they
are used by a load/store instruction that is marked as CM_Scalarize.
Before this patch, the LV incorrectly marked pointer induction variables
as 'scalar' when they required to be widened by something else,
such as a compare instruction, and weren't used by a node marked as
'CM_Scalarize'. This case is covered by sve-widen-phi.ll.
This change also allows removing some code where the LV tried to
widen the PHI nodes with a stepvector, even though it was marked as
'scalarAfterVectorization'. Now that this code is more careful about
marking instructions that need widening as 'scalar', this code has
become redundant.
Differential Revision: https://reviews.llvm.org/D114373
In VPRecipeBuilder::handleReplication if we believe the instruction
is predicated we then proceed to create new VP region blocks even
when the load is uniform and only predicated due to tail-folding.
I have updated isPredicatedInst to avoid treating a uniform load as
predicated when tail-folding, which means we can do a single scalar
load and a vector splat of the value.
Tests added here:
Transforms/LoopVectorize/AArch64/tail-fold-uniform-memops.ll
Differential Revision: https://reviews.llvm.org/D112552
This patch updates the cost model for ordered reductions so that a call
to the llvm.fmuladd intrinsic is modelled as a normal fmul instruction
plus the cost of an ordered fadd reduction.
Differential Revision: https://reviews.llvm.org/D111630
In-loop vector reductions which use the llvm.fmuladd intrinsic involve
the creation of two recipes; a VPReductionRecipe for the fadd and a
VPInstruction for the fmul. If the call to llvm.fmuladd has fast-math flags
these should be propagated through to the fmul instruction, so an
interface setFastMathFlags has been added to the VPInstruction class to
enable this.
Differential Revision: https://reviews.llvm.org/D113125
This patch fixes PR52111. The problem is that LV propagates poison-generating flags (`nuw`/`nsw`, `exact`
and `inbounds`) in instructions that contribute to the address computation of widen loads/stores that are
guarded by a condition. It may happen that when the code is vectorized and the control flow within the loop
is linearized, these flags may lead to generating a poison value that is effectively used as the base address
of the widen load/store. The fix drops all the integer poison-generating flags from instructions that
contribute to the address computation of a widen load/store whose original instruction was in a basic block
that needed predication and is not predicated after vectorization.
Reviewed By: fhahn, spatel, nlopes
Differential Revision: https://reviews.llvm.org/D111846
A first step towards modeling preheader and exit blocks in VPlan as well.
Keeping the vector loop in a region allows for changing the VF as we
traverse region boundaries.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D113182
When asking how many parts are required for a scalable vector type
there are occasions when it cannot be computed. For example, <vscale x 1 x i3>
is one such vector for AArch64+SVE because at the moment no matter how we
promote the i3 type we never end up with a legal vector. This means
that getTypeConversion returns TypeScalarizeScalableVector as the
LegalizeKind, and then getTypeLegalizationCost returns an invalid cost.
This then causes BasicTTImpl::getNumberOfParts to dereference an invalid
cost, which triggers an assert. This patch changes getNumberOfParts to
return 0 for such cases, since the definition of getNumberOfParts in
TargetTransformInfo.h states that we can use a return value of 0 to represent
an unknown answer.
Currently, LoopVectorize.cpp is the only place where we need to check for
0 as a return value, because all other instances will not currently
ask for the number of parts for <vscale x 1 x iX> types.
In addition, I have changed the target-independent interface for
getNumberOfParts to return 1 and assume there is a single register
that can fit the type. The loop vectoriser has lots of tests that are
target-independent and they relied upon the 0 value to mean the
answer is known and that we are not scalarising the vector.
I have added tests here that show we correctly return an invalid cost
for VF=vscale x 1 when the loop contains unusual types such as i7:
Transforms/LoopVectorize/AArch64/sve-inductions-unusual-types.ll
Differential Revision: https://reviews.llvm.org/D113772
The interface is a convenience function to ask if a block requires
predication when widening, but it's important that there are two
separate concepts to consider:
(A) The block was predicated in the original loop.
(B) The block was unpredicated in the original loop, but requires
predication because of tail folding.
In the case of (B) we know that at least one lane of the vector will
be executed, which means we can implementing a load from a uniform address
with a scalar load + splat (D112552). In the case of predication because
of (A), we cannot do this, because the scalar load itself requires
predication.
The name 'blockNeedsPredication' does not make the distinction between
(A) and (B), hence the reason to rename it.
Reviewed By: david-arm
Differential Revision: https://reviews.llvm.org/D113392
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 c45045bfd04af9 to address PR52460.
This reverts commit 0d748b4d32cbddf58a1ff83f3ff178ec1ad49edc.
This is causing some failures when building Spec2017 with scalable
vectors. Reverting to investigate.
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 patch adds a function to verify general properties of VPlans. The
first check makes sure that all phi-like recipes are at the beginning of
a block, with no other recipes in between.
Note that this currently may not hold for VPBlendRecipes at the moment,
as other recipes may be inserted before the VPBlendRecipe during mask
creation.
Note that this patch depends on D111300 and D111301, which fix code that
breaks the checked invariant.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D111302
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
The common use case for calling createStepForVF is currently something
like:
Value *Step = createStepForVF(Builder, ConstantInt::get(Ty, UF), VF);
and it makes more sense to reduce overall lines of code and change the
function to let it create the constant instead. With my patch this
becomes:
Value *Step = createStepForVF(Builder, Ty, VF, UF);
and the ConstantInt is created instead createStepForVF. A side-effect of
this is that the code in createStepForVF is also becomes simpler.
As part of this patch I've also replaced some calls to getRuntimeVF
with calls to createStepForVF, i.e.
getRuntimeVF(Builder, Count->getType(), VFactor * UFactor) ->
createStepForVF(Builder, Count->getType(), VFactor, UFactor)
because this feels semantically better.
Differential Revision: https://reviews.llvm.org/D113122
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
The public API for this functionality is forgetValue(). There was
only one call from LoopVectorize, which was directly next to a
forgetValue() call and as such redundant.
The recipe produces exactly one VPValue and can inherit directly from
it. This is in line with other recipes and avoids having to use
getVPSingleValue.
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
This patch changes the definition of getStepVector from:
Value *getStepVector(Value *Val, int StartIdx, Value *Step, ...
to
Value *getStepVector(Value *Val, Value *StartIdx, Value *Step, ...
because:
1. it seems inconsistent to pass some values as Value* and some as
integer, and
2. future work will require the StartIdx to be an expression made up
of runtime calculations of the VF.
In widenIntOrFpInduction I've changed the code to pass in the
value returned from getRuntimeVF, but the presence of the assert:
assert(!VF.isScalable() && "scalable vectors not yet supported.");
means that currently this code path is only exercised for fixed-width
VFs and so the patch is still NFC.
Differential revision: https://reviews.llvm.org/D111882
