Those helpers model properties of a user and they should also be
available to non-recipe users. This will be used in D123537 for a new
exit value user.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D124936
This patch adds initial support for a pointer diff based runtime check
scheme for vectorization. This scheme requires fewer computations and
checks than the existing full overlap checking, if it is applicable.
The main idea is to only check if source and sink of a dependency are
far enough apart so the accesses won't overlap in the vector loop. To do
so, it is sufficient to compute the difference and compare it to the
`VF * UF * AccessSize`. It is sufficient to check
`(Sink - Src) <u VF * UF * AccessSize` to rule out a backwards
dependence in the vector loop with the given VF and UF. If Src >=u Sink,
there is not dependence preventing vectorization, hence the overflow
should not matter and using the ULT should be sufficient.
Note that the initial version is restricted in multiple ways:
1. Pointers must only either be read or written, by a single
instruction (this allows re-constructing source/sink for
dependences with the available information)
2. Source and sink pointers must be add-recs, with matching steps
3. The step must be a constant.
3. abs(step) == AccessSize.
Most of those restrictions can be relaxed in the future.
See https://github.com/llvm/llvm-project/issues/53590.
Reviewed By: dmgreen
Differential Revision: https://reviews.llvm.org/D119078
When the loop vectoriser encounters a known low trip count it tries
to create a single predicated loop in order to get the benefit of
vectorisation and eliminate the scalar tail. However, until now the
vectoriser prevented the use of scalable vectors in this case due
to concerns in the past about stability. I believe that tail-folded
loops using scalable vectors are now sufficiently well tested that
we can enable this. For the same reason I've also enabled it when
optimising for code size too.
Tests added here:
Transforms/LoopVectorize/AArch64/sve-low-trip-count.ll
Transforms/LoopVectorize/AArch64/sve-tail-folding-optsize.ll
Transforms/LoopVectorize/RISCV/low-trip-count.ll
Differential Revision: https://reviews.llvm.org/D121595
Under some circumstances, SCEVExpander will insert new instructions when
expanding a predicate, but the final result of the expansion can be a
false constant.
In those cases, the expanded instructions may later be used by other
expansions, e.g. the trip count. This may trigger an assertion during
SCEVExpander cleanup. To avoid this, always mark the result as used.
Fixes#55100.
If alternate node has only 2 instructions and the tree is already big
enough, better to skip the vectorization of such nodes, they are not
very profitable (the resulting code cotains 3 instructions instead of
original 2 scalars). SLP can try to vectorize the buildvector sequence
in the next attempt, if it is profitable.
Metric: SLP.NumVectorInstructions
Program SLP.NumVectorInstructions
results results0 diff
test-suite :: MultiSource/Benchmarks/DOE-ProxyApps-C/miniAMR/miniAMR.test 72.00 73.00 1.4%
test-suite :: MultiSource/Benchmarks/Prolangs-C/TimberWolfMC/timberwolfmc.test 1186.00 1198.00 1.0%
test-suite :: MultiSource/Benchmarks/DOE-ProxyApps-C++/miniFE/miniFE.test 241.00 242.00 0.4%
test-suite :: MultiSource/Applications/JM/lencod/lencod.test 2131.00 2139.00 0.4%
test-suite :: External/SPEC/CINT2017rate/523.xalancbmk_r/523.xalancbmk_r.test 6377.00 6384.00 0.1%
test-suite :: External/SPEC/CINT2017speed/623.xalancbmk_s/623.xalancbmk_s.test 6377.00 6384.00 0.1%
test-suite :: External/SPEC/CFP2017rate/510.parest_r/510.parest_r.test 12650.00 12658.00 0.1%
test-suite :: External/SPEC/CFP2017rate/526.blender_r/526.blender_r.test 26169.00 26147.00 -0.1%
test-suite :: MultiSource/Benchmarks/Trimaran/enc-3des/enc-3des.test 99.00 86.00 -13.1%
Gains:
526.blender_r - more vectorized trees.
enc-3des - same.
Others:
510.parest_r - no changes.
miniFE - same
623.xalancbmk_s - some (non-profitable) parts of the trees are not
vectorized.
523.xalancbmk_r - same
lencod - same
timberwolfmc - same
miniAMR - same
Differential Revision: https://reviews.llvm.org/D125571
If the insert indes was used already or is not constant, we should stop
looking for unique buildvector sequence, it mustbe splitted to
2 different buildvectors.
In InnerLoopVectorizer::getOrCreateVectorTripCount there is an
assert that the known minimum value for the VF is a power of 2
when tail-folding is enabled. However, for scalable vectors the
value of vscale may not be a power of 2, which means we have
to worry about the possibility of overflow. I have solved this
problem by adding preheader checks that prevent us from entering
the vector body if the canonical IV would overflow, i.e.
if ((IntMax - TripCount) < (VF * UF)) ... skip vector loop ...
Differential Revision: https://reviews.llvm.org/D125235
We commonly want to create either an inbounds or non-inbounds GEP
based on a boolean value, e.g. when preserving inbounds from
existing GEPs. Directly accept such a boolean in the API, rather
than requiring a ternary between CreateGEP and CreateInBoundsGEP.
This change is not entirely NFC, because we now preserve an
inbounds flag in a constant expression edge-case in InstCombine.
Further improvement of the cost model for the scalars used in
buildvectors sequences. The main functionality is outlined into
a separate function.
The cost is calculated in the following way:
1. If the Base vector is not undef vector, resizing the very first mask to
have common VF and perform action for 2 input vectors (including non-undef
Base). Other shuffle masks are combined with the resulting after the 1 stage and processed as a shuffle of 2 elements.
2. If the Base is undef vector and have only 1 shuffle mask, perform the
action only for 1 vector with the given mask, if it is not the identity
mask.
3. If > 2 masks are used, perform serie of shuffle actions for 2 vectors,
combing the masks properly between the steps.
The original implementation misses the very first analysis for the Base
vector, so the cost might too optimistic in some cases. But it improves
the cost for the insertelements which are part of the current SLP graph.
Part of D107966.
Differential Revision: https://reviews.llvm.org/D115750
With opaque pointers, both the stored value and the address can be the
same. Only consider the recipe using the first lane only *if* the
address is not stored.
Fixes#55375.
The current reordering scheme only checks the ordering of in-tree operands.
There are some cases, however, where we need to adjust the ordering based on
the ordering of a future SLP-tree who's instructions are not part of the
current tree, but are external users.
This patch is a simple implementation of this. We keep track of scalar stores
that are users of TreeEntries and if they look profitable to vectorize, then
we keep track of their ordering. During the reordering step we take this new
index order into account. This can remove some shuffles in cases like in the
lit test.
Differential Revision: https://reviews.llvm.org/D125111
If the same scalar is inserted several times into the same buildvector,
the mask index can be used already. In this case need to check, that
this scalar is already part of the vectorized buildvector.
This reverts commit 8b48223447311af8b3022697dd58858e1ce6975f.
This triggers an assertion on a test case mentioned in D123720.
Revert while I investigate.
We can try to vectorize number of stores less than MinVecRegSize
/ scalar_value_size, if it is allowed by target. Gives an extra
opportunity for the vectorization.
Fixes PR54985.
Differential Revision: https://reviews.llvm.org/D124284
Need to use actual index instead of the tree entry position, since the
insert index may be different than 0. It mean, that we vectorized part
of the buildvector starting from not initial insertelement instruction
beause of some reason.
Given a commutative reduction leading from a shuffle, the order of the
lanes on the shuffle are not important for the result. This means we can
reorder the shuffle to something simpler, which we try shuffling the
first vector lanes first. This was D123494.
The new shuffle may not be profitable though, and if it is not we can
try the folding of select shuffles from D123911. This, with some
adjustment as the output lane ordering is now unimportant, can allow the
final shuffle to simplify given the inputs to the patterns from D123911.
Where as each transformation on their own are not profitable, the
combination is.
We can only support a single shuffle when called from reductions, but we
are able to sort the ReconstructMask, potentially allowing it to
simplify to an identity or concat mask.
Differential Revision: https://reviews.llvm.org/D125086
Given a load without a better order, this patch partially sorts the
elements to form clusters of adjacent elements in memory. These clusters
can potentially be loaded in fewer loads, meaning less overall shuffling
(for example loading v4i8 clusters of a v16i8 as a single f32 loads, as
opposed to multiple independent bytes loads and inserts).
Differential Revision: https://reviews.llvm.org/D122145
This patch adds a combine to attempt to reduce the costs of certain
select-shuffle patterns. The form of code it attempts to detect is:
%x = shuffle ...
%y = shuffle ...
%a = binop %x, %y
%b = binop %x, %y
shuffle %a, %b, selectmask
A classic select-mask will pick items from each lane of a or b. These
do not always have a great lowering on many architectures. This patch
attempts to pack a and b into the lower elements, creating a differently
ordered shuffle for reconstructing the orignal which may be better than
the select mask. This can be better for performance, especially if less
elements of a and b need to be computed and the input shuffles are
cheaper.
Because select-masks are just one form of shuffle, we generalize to any
mask. So long as the backend has decent costmodel for the shuffles, this
can generally improve things when they come up. For more basic cost
models the folds do not appear to be profitable, not getting past the
cost checks.
Differential Revision: https://reviews.llvm.org/D123911
Further improvement of the cost model for the scalars used in
buildvectors sequences. The main functionality is outlined into
a separate function.
The cost is calculated in the following way:
1. If the Base vector is not undef vector, resizing the very first mask to
have common VF and perform action for 2 input vectors (including non-undef
Base). Other shuffle masks are combined with the resulting after the 1 stage and processed as a shuffle of 2 elements.
2. If the Base is undef vector and have only 1 shuffle mask, perform the
action only for 1 vector with the given mask, if it is not the identity
mask.
3. If > 2 masks are used, perform serie of shuffle actions for 2 vectors,
combing the masks properly between the steps.
The original implementation misses the very first analysis for the Base
vector, so the cost might too optimistic in some cases. But it improves
the cost for the insertelements which are part of the current SLP graph.
Part of D107966.
Differential Revision: https://reviews.llvm.org/D115750
This reverts commit f4e1eaa3755a13f85696be3b74b387122b74a558.
The patch was originally reverted because it uncovered an issue that has
now been fixed in 0ef8ca6d88aa7e4abc.
Running iwyu-diff on LLVM codebase since fa5a4e1b95c8f37796 detected a few
regressions, fixing them.
Differential Revision: https://reviews.llvm.org/D124847
Adds ability to vectorize loops containing a store to a loop-invariant
address as part of a reduction that isn't converted to SSA form due to
lack of aliasing info. Runtime checks are generated to ensure the store
does not alias any other accesses in the loop.
Ordered fadd reductions are not yet supported.
Differential Revision: https://reviews.llvm.org/D110235
This adds fptosi_sat and fptoui_sat to the list of trivially
vectorizable functions, mainly so that the loop vectorizer can vectorize
the instruction. Marking them as trivially vectorizable also allows them
to be SLP vectorized, and Scalarized.
The signature of a fptosi_sat requires two type overrides
(@llvm.fptosi.sat.v2i32.v2f32), unlike other intrinsics that often only
take a single. This patch alters hasVectorInstrinsicOverloadedScalarOpd
to isVectorIntrinsicWithOverloadTypeAtArg, so that it can mark the first
operand of the intrinsic as a overloaded (but not scalar) operand.
Differential Revision: https://reviews.llvm.org/D124358
Currently SLP vectorizer walks through the instructions and selects
3 main classes of values: 1) reduction operations - instructions with same
reduction opcode (add, mul, min/max, etc.), which build the reduction,
2) reduced values - instructions with the same opcodes, but different
from the reduction opcode, 3) extra arguments - all other values,
instructions from the different basic block rather than the root node,
instructions with to many/less uses.
This scheme is not very efficient. It excludes some instructions and all
non-instruction values from the reductions (constants, proficient
gathers), to many possibly reduced values are marked as extra arguments.
Patch improves this process by introducing a bit extended analysis
stage. During this stage, we still try to select 3 classes of the
values: 1) reduction operations - same as before, 2) possibly reduced
values - all instructions from the current block/non-instructions, which
may build a vectorization tree, 3) extra arguments - instructions from
the different basic blocks. Additionally, an extra sorting of the
possibly reduced values occurs to build the scalar sequences which
highly likely will bed vectorized, e.g. loads are grouped by the
distance between them, constants are grouped together, cmp instructions
are sorted by their compare types and predicates, extractelement
instructions are sorted by the vector operand, etc. Also, these groups
are reordered by their length so the longest group is the first in the
list of the possibly reduced values.
The vectorization process tries to emit the reductions for all these
groups. These reductions, remaining non-vectorized possible reduced
values and extra arguments are then combined into the final expression
just like it was before.
Differential Revision: https://reviews.llvm.org/D114171
'Widen' recipe are only used when actual vector values are generated.
Fix tryToWidenCall to do not create VPWidenCallRecipes for scalar vector
factors.
This was exposed by D123720, because the widened recipes are considered
vector users.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D124718
Similar to c515b2f39e77, If there are no loops in the function as seen
through LI, we should avoid computing the remaining expensive analyses
(such as SCEV, BPI). Reordered the analyses requests and early return
if there are no loops.
The logic of avoiding expensive analyses is applied to LoopVectorizer,
LoopLoadElimination and LoopUnrollPass, i.e. all function passes which operate
on loops.
This is an NFC with compile time improvement.
Differential Revision: https://reviews.llvm.org/D124529
The name CountRoundDown is potentially misleading, as the number of
iterations can be rounded up when folding the tail.
Reviewed By: fhahn
Differential Revision: https://reviews.llvm.org/D119681
I think this sort comparator was overly complex, and the windows
expensive check bot agreed, failing as it was not giving a strict weak
ordering. Change it to use the comparison of the mask values as unsigned
integers. This should sort the undef elements to the end whilst keeping
X<Y otherwise.
This reverts commit 43842b887e0a7b918bb2d6c9f672025b2c621f8a while I
investigate a buildbot failure.
It also reverts the follow-up commit
2883de05145fc5b4afb99b91f69ebb835af36af5.
Given a shuffle feeding a commutative reduction, the lane ordering of
the shuffle will not alter the result. This is also true if there are a
number of operations between the reduction and the shuffle, providing
they only operate lane-wise. This patch searches for cases like that in
Vector Combine, allowing us to check the cost of the shuffle vs an
in-order identity shuffle and replace the order if possible. This only
handles a single shuffle at the moment to keep things simple, and is
able to ignore splats that produce results where every result is the
same.
This is a more powerful version of a combine that already happens in
instrcombine, capable of optimizing more cases by looking through more
instructions and being able to cost the shuffle.
Differential Revision: https://reviews.llvm.org/D123494
Introduced masks where they are not added and improved target dependent
cost models to avoid returning of the incorrect cost results after
adding masks.
Differential Revision: https://reviews.llvm.org/D100486
Remove one of the last remaining uses of ::needsVectorIV, preparing for
its removal. Now that usesScalars is available and based on the
information explicit in VPlan, there is no need to use the pre-computed
needsVectorIV.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D123720
Introduced masks where they are not added and improved target dependent
cost models to avoid returning of the incorrect cost results after
adding masks.
Differential Revision: https://reviews.llvm.org/D100486
