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
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
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
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
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
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
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
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
The expansion for these was updated in https://reviews.llvm.org/D47927 but the cost model was not adjusted.
I believe the cost model was also incorrect for the old expansion.
The expansion prior to D47927 used 3 icmps using LHS, RHS, and Result
to calculate theirs signs. Then 2 icmps to compare the signs. Followed
by an And. The previous cost model was using 3 icmps and 2 selects.
Digging back through git blame, those 2 selects in the cost model used to
be 2 icmps, but were changed in https://reviews.llvm.org/D90681
Differential Revision: https://reviews.llvm.org/D110739
In ValueTracking.cpp we use a function called
computeKnownBitsFromOperator to determine the known bits of a value.
For the vscale intrinsic if the function contains the vscale_range
attribute we can use the maximum and minimum values of vscale to
determine some known zero and one bits. This should help to improve
code quality by allowing certain optimisations to take place.
Tests added here:
Transforms/InstCombine/icmp-vscale.ll
Differential Revision: https://reviews.llvm.org/D109883
For SVE, when scalarising the PHI instruction the whole vector part is
generated as opposed to creating instructions for each lane for fixed-
width vectors. However, in some cases the lane values may be needed
later (e.g for a load instruction) so we still need to calculate
these values to avoid extractelement being called on the vector part.
Differential Revision: https://reviews.llvm.org/D109445
Reverted (manually due to merge conflicts) while regressions reported on PR51540 are investigated
As noticed on D106352, after we've folded "(select C, (gep Ptr, Idx), Ptr) -> (gep Ptr, (select C, Idx, 0))" if the inner Ptr was also a (now one use) gep we could then merge the geps, using the sum of the indices instead.
I've limited this to basic 2-op geps - a more general case further down InstCombinerImpl.visitGetElementPtrInst doesn't have the one-use limitation but only creates the add if it can be created via SimplifyAddInst.
https://alive2.llvm.org/ce/z/f8pLfD (Thanks Roman!)
Differential Revision: https://reviews.llvm.org/D106450
Adjusting the reduction recipes still relies on references to the
original IR, which can become outdated by the first-order recurrence
handling. Until reduction recipe construction does not require IR
references, move it before first-order recurrence handling, to prevent a
crash as exposed by D106653.
This reverts commit f4122398e7 to
investigate a crash exposed by it.
The patch breaks building the code below with `clang -O2 --target=aarch64-linux`
int a;
double b, c;
void d() {
for (; a; a++) {
b += c;
c = a;
}
}
I have added a new TTI interface called enableOrderedReductions() that
controls whether or not ordered reductions should be enabled for a
given target. By default this returns false, whereas for AArch64 it
returns true and we rely upon the cost model to make sensible
vectorisation choices. It is still possible to override the new TTI
interface by setting the command line flag:
-force-ordered-reductions=true|false
I have added a new RUN line to show that we use ordered reductions by
default for SVE and Neon:
Transforms/LoopVectorize/AArch64/strict-fadd.ll
Transforms/LoopVectorize/AArch64/scalable-strict-fadd.ll
Differential Revision: https://reviews.llvm.org/D106653
For tight loops like this:
float r = 0;
for (int i = 0; i < n; i++) {
r += a[i];
}
it's better not to vectorise at -O3 using fixed-width ordered reductions
on AArch64 targets. Although the resulting number of instructions in the
generated code ends up being comparable to not vectorising at all, there
may be additional costs on some CPUs, for example perhaps the scheduling
is worse. It makes sense to deter vectorisation in tight loops.
Differential Revision: https://reviews.llvm.org/D108292
Removed AArch64 usage of the getMaxVScale interface, replacing it with
the vscale_range(min, max) IR Attribute.
Reviewed By: paulwalker-arm
Differential Revision: https://reviews.llvm.org/D106277
Previously we emitted a "does not support scalable vectors"
remark for all targets whenever vectorisation is attempted. This
pollutes the output for architectures that don't support scalable
vectors and is likely confusing to the user.
Instead this patch introduces a debug message that reports when
scalable vectorisation is allowed by the target and only issues
the previous remark when scalable vectorisation is specifically
requested, for example:
#pragma clang loop vectorize_width(2, scalable)
Differential Revision: https://reviews.llvm.org/D108028
I have added RUN lines to both:
Transforms/LoopVectorize/AArch64/strict-fadd.ll
Transforms/LoopVectorize/AArch64/scalable-strict-fadd.ll
to show the default behaviour is to not vectorise when the following
flag is unset:
-force-ordered-reductions
This patch updates ConstantVector::getSplat to use poison instead
of undef when using insertelement/shufflevector to splat.
This follows on from D93793.
Differential Revision: https://reviews.llvm.org/D107751
Since all operands to ExtractValue must be loop-invariant when we deem
the loop vectorizable, we can consider ExtractValue to be uniform.
Reviewed By: david-arm
Differential Revision: https://reviews.llvm.org/D107286
This change wasn't strictly necessary for D106164 and could be removed.
This patch addresses the post-commit comments from @fhahn on D106164, and
also changes sve-widen-gep.ll to use the same IR test as shown in
pointer-induction.ll.
Reviewed By: fhahn
Differential Revision: https://reviews.llvm.org/D106878
I'm renaming the flag because a future patch will add a new
enableOrderedReductions() TTI interface and so the meaning of this
flag will change to be one of forcing the target to enable/disable
them. Also, since other places in LoopVectorize.cpp use the word
'Ordered' instead of 'strict' I changed the flag to match.
Differential Revision: https://reviews.llvm.org/D107264
It was writing to the source directory (which may not be writeable),
rather than using %t.
Fixes: a5dd6c6cf9 ("[LoopVectorize] Don't interleave scalar ordered reductions for inner loops")
Consider the following loop:
void foo(float *dst, float *src, int N) {
for (int i = 0; i < N; i++) {
dst[i] = 0.0;
for (int j = 0; j < N; j++) {
dst[i] += src[(i * N) + j];
}
}
}
When we are not building with -Ofast we may attempt to vectorise the
inner loop using ordered reductions instead. In addition we also try
to select an appropriate interleave count for the inner loop. However,
when choosing a VF=1 the inner loop will be scalar and there is existing
code in selectInterleaveCount that limits the interleave count to 2
for reductions due to concerns about increasing the critical path.
For ordered reductions this problem is even worse due to the additional
data dependency, and so I've added code to simply disable interleaving
for scalar ordered reductions for now.
Test added here:
Transforms/LoopVectorize/AArch64/strict-fadd-vf1.ll
Differential Revision: https://reviews.llvm.org/D106646
The loop vectorizer may decide to use tail folding when the trip-count
is low. When that happens, scalable VFs are no longer a candidate,
since tail folding/predication is not yet supported for scalable vectors.
This can be re-enabled in a future patch.
Reviewed By: kmclaughlin
Differential Revision: https://reviews.llvm.org/D106657
Invalid costs can be used to avoid vectorization with a given VF, which is
used for scalable vectors to avoid things that the code-generator cannot
handle. If we override the cost using the -force-target-instruction-cost
option of the LV, we would override this mechanism, rendering the flag useless.
This change ensures the cost is only overriden when the original cost that
was calculated is valid. That allows the flag to be used in combination
with the -scalable-vectorization option.
Reviewed By: david-arm
Differential Revision: https://reviews.llvm.org/D106677
This change moves most of `sve-inductions.ll` to non-AArch64 specific
LV tests using the `-target-supports-scalable-vectors` flag, because they're
not explicitly AArch64-specific. One test builds on AArch64-specific
knowledge regarding masked loads/stores, and remains in sve-inductions.ll.
Scalarization for scalable vectors is not (yet) supported, so the
LV discards a VF when scalarization is chosen as the widening
decision. It should therefore not assert that the VF is not scalable
when it computes the decision to scalarize.
The code can get here when both the interleave-cost, gather/scatter cost
and scalarization-cost are all illegal. This may e.g. happen for SVE
when the VF=1, to avoid generating `<vscale x 1 x eltty>` types that
the code-generator cannot yet handle.
Reviewed By: david-arm
Differential Revision: https://reviews.llvm.org/D106656
This fixes an issue that was found in D105199, where a GEP instruction
is used both as the address of a store, as well as the value of a store.
For the former, the value is scalar after vectorization, but the latter
(as value) requires widening.
Other code in that function seems to prevent similar cases from happening,
but it seems this case was missed.
Reviewed By: david-arm
Differential Revision: https://reviews.llvm.org/D106164
I have added a new FastMathFlags parameter to getArithmeticReductionCost
to indicate what type of reduction we are performing:
1. Tree-wise. This is the typical fast-math reduction that involves
continually splitting a vector up into halves and adding each
half together until we get a scalar result. This is the default
behaviour for integers, whereas for floating point we only do this
if reassociation is allowed.
2. Ordered. This now allows us to estimate the cost of performing
a strict vector reduction by treating it as a series of scalar
operations in lane order. This is the case when FP reassociation
is not permitted. For scalable vectors this is more difficult
because at compile time we do not know how many lanes there are,
and so we use the worst case maximum vscale value.
I have also fixed getTypeBasedIntrinsicInstrCost to pass in the
FastMathFlags, which meant fixing up some X86 tests where we always
assumed the vector.reduce.fadd/mul intrinsics were 'fast'.
New tests have been added here:
Analysis/CostModel/AArch64/reduce-fadd.ll
Analysis/CostModel/AArch64/sve-intrinsics.ll
Transforms/LoopVectorize/AArch64/strict-fadd-cost.ll
Transforms/LoopVectorize/AArch64/sve-strict-fadd-cost.ll
Differential Revision: https://reviews.llvm.org/D105432
This patch avoids computing discounts for predicated instructions when the
VF is scalable.
There is no support for vectorization of loops with division because the
vectorizer cannot guarantee that zero divisions will not happen.
This loop now does not use VF scalable
```
for (long long i = 0; i < n; i++)
if (cond[i])
a[i] /= b[i];
```
Differential Revision: https://reviews.llvm.org/D101916
As noticed on D106352, after we've folded "(select C, (gep Ptr, Idx), Ptr) -> (gep Ptr, (select C, Idx, 0))" if the inner Ptr was also a (now one use) gep we could then merge the geps, using the sum of the indices instead.
I've limited this to basic 2-op geps - a more general case further down InstCombinerImpl.visitGetElementPtrInst doesn't have the one-use limitation but only creates the add if it can be created via SimplifyAddInst.
https://alive2.llvm.org/ce/z/f8pLfD (Thanks Roman!)
Differential Revision: https://reviews.llvm.org/D106450
This patch returns an Invalid cost from getInstructionCost() for alloca
instructions if the VF is scalable, as otherwise loops which contain
these instructions will crash when attempting to scalarize the alloca.
Reviewed By: sdesmalen
Differential Revision: https://reviews.llvm.org/D105824
The original patch was:
https://reviews.llvm.org/D105806
There were some issues with undeterministic behaviour of the sorting
function, which led to scalable-call.ll passing and/or failing. This
patch fixes the issue by numbering all instructions in the array first,
and using that number as the order, which should provide a consistent
ordering.
This reverts commit a607f64118.
Kerry McLaughlin