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.
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
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 c515b2f39e, 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.
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
Before this patch `Args` was used to pass a broadcat's arguments by SLP.
This patch changes this. `Args` is now used for passing the operands of
the shuffle.
Differential Revision: https://reviews.llvm.org/D124202
tryToVectorize() method implements one of searching paths for vectorizable tree roots in SLP vectorizer,
specifically for binary and comparison operations. Order of making probes for various scalar pairs
was defined by its implementation: the instruction operands, then climb over one operand if
the instruction is its sole user and then perform same actions for another operand if previous
attempts failed. Problem with this approach is that among these options we can have more than a
single vectorizable tree candidate and it is not necessarily the one that encountered first.
Trying to build vectorizable tree for each possible combination for just evaluation is expensive.
But we already have lookahead heuristics mechanism which we use for finding best pick among
operands of commutative instructions. It calculates cumulative score for candidates in two
consecutive lanes. This patch introduces use of the heuristics for choosing the best pair among
several combinations. We only try one that looks as most promising for vectorization.
Additional benefit is that we reduce total number of vectorization trees built for probes
because we skip those looking non-profitable early.
Reviewed By: Alexey Bataev (ABataev), Vasileios Porpodas (vporpo)
Differential Revision: https://reviews.llvm.org/D124309
Minor refactoring to reduce size of functional change D124309:
look-ahead scoring routines pulled out of VLOperands and formed
new LookAheadHeuristics helper class.
Reviewed By: Alexey Bataev (ABataev), Vasileios Porpodas (vporpo)
Differential Revision: https://reviews.llvm.org/D124313
This patch extends the scope of VPlan to also include the exit (aka
middle) block.
For now, the exit block remains empty, but handling of exit values will
subsequently be moved to VPlan, by adding recipes to model exit values
in the exit block.
As a first step, this will allow fixing #51366.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D123457
This patch moves SCEV expansion of steps used by
VPWidenIntOrFpInductionRecipes to the pre-header using
VPExpandSCEVRecipe. This ensures that those steps are expanded while the
CFG is in a valid state. Previously, SCEV expansion may happen during
vector body code-generation, during which the CFG may be invalid,
causing issues with SCEV expansion.
Depends on D122095.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D122096
Vasileios Porpodas