If a loop is in a function marked OptSize, Loop Access Analysis should refrain
from generating runtime checks for unit strides that will version the loop.
If a loop is in a function marked OptSize and its vectorization is enabled, it
should be vectorized w/o any versioning.
Fixes PR46228.
Differential Revision: https://reviews.llvm.org/D81345
Motivating examples are seen in the PhaseOrdering tests based on:
https://bugs.llvm.org/show_bug.cgi?id=43953#c2 - if we have
intrinsics there, some pass can fold them.
The intrinsics are still named "experimental" at this point, but
if there is no fallout from this patch, that will be a good
indicator that it is safe to finalize them.
Differential Revision: https://reviews.llvm.org/D80867
LV currently only supports power of 2 vectorization factors, which has
been made explicit with the assertion added in
840450549c.
However, if the widest type is not a power-of-2 the computed MaxVF won't
be a power-of-2 either. This patch updates computeFeasibleMaxVF to
ensure the returned value is a power-of-2 by rounding down to the
nearest power-of-2.
Fixes PR46139.
Reviewers: Ayal, gilr, rengolin
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D80870
Currently we unconditionally get the first lane of the condition
operand, even if we later use the full vector condition. This can result
in some unnecessary instructions being generated.
Suggested as follow-up in D80219.
This was reverted because of a miscompilation. At closer inspection, the
problem was actually visible in a changed llvm regression test too. This
one-line follow up fix/recommit will splat the IV, which is what we are trying
to avoid if unnecessary in general, if tail-folding is requested even if all
users are scalar instructions after vectorisation. Because with tail-folding,
the splat IV will be used by the predicate of the masked loads/stores
instructions. The previous version omitted this, which caused the
miscompilation. The original commit message was:
If tail-folding of the scalar remainder loop is applied, the primary induction
variable is splat to a vector and used by the masked load/store vector
instructions, thus the IV does not remain scalar. Because we now mark
that the IV does not remain scalar for these cases, we don't emit the vector IV
if it is not used. Thus, the vectoriser produces less dead code.
Thanks to Ayal Zaks for the direction how to fix this.
If tail-folding of the scalar remainder loop is applied, the primary induction
variable is splat to a vector and used by the masked load/store vector
instructions, thus the IV does not remain scalar. Because we now mark
that the IV does not remain scalar for these cases, we don't emit the vector IV
if it is not used. Thus, the vectoriser produces less dead code.
Thanks to Ayal Zaks for the direction how to fix this.
Differential Revision: https://reviews.llvm.org/D78911
The improvements to the x86 vector insert/extract element costs in D74976 resulted in the estimated costs for vector initialization and scalarization increasing higher than should be expected. This is particularly noticeable on pre-SSE4 targets where the available of legal INSERT_VECTOR_ELT ops is more limited.
This patch does 2 things:
1 - it implements X86TTIImpl::getScalarizationOverhead to more accurately represent the typical costs of a ISD::BUILD_VECTOR pattern.
2 - it adds a DemandedElts mask to getScalarizationOverhead to permit the SLP's BoUpSLP::getGatherCost to be rewritten to use it directly instead of accumulating raw vector insertion costs.
This fixes PR45418 where a v4i8 (zext'd to v4i32) was no longer vectorizing.
A future patch should extend X86TTIImpl::getScalarizationOverhead to tweak the EXTRACT_VECTOR_ELT scalarization costs as well.
Reviewed By: @craig.topper
Differential Revision: https://reviews.llvm.org/D78216
When folding tail, branch taken count is computed during initial VPlan execution
and recorded to be used by the compare computing the loop's mask. This recording
should directly set the State, instead of reusing Value2VPValue mapping which
serves original Values present prior to vectorization.
The branch taken count may be a constant Value, which may be used elsewhere in
the loop; trying to employ Value2VPValue for both leads to the issue reported in
https://reviews.llvm.org/D76992#inline-721028
Differential Revision: https://reviews.llvm.org/D78847
Introduce a new VPWidenCanonicalIVRecipe to generate a canonical vector
induction for use in fold-tail-with-masking, if a primary induction is absent.
The canonical scalar IV having start = 0 and step = VF*UF, created during code
-gen to control the vector loop, is widened into a canonical vector IV having
start = {<Part*VF, Part*VF+1, ..., Part*VF+VF-1> for 0 <= Part < UF} and
step = <VF*UF, VF*UF, ..., VF*UF>.
Differential Revision: https://reviews.llvm.org/D77635
Summary:
Add mapping from exp2 math functions
to corresponding SVML calls.
This is a follow up and extension for llvm diff
https://reviews.llvm.org/D19544
Test Plan:
- update test case and run ninja check.
- run tests locally
Reviewers: wenlei, hoyFB, mmasten, mzolotukhin, spatel
Reviewed By: spatel
Subscribers: llvm-commits
Tags: #llvm
Differential Revision: https://reviews.llvm.org/D77114
This tries to improve the accuracy of extract/insert element costs by accounting for subvector extraction/insertion for >128-bit vectors and the shuffling of elements to/from the 0'th index.
It also adds INSERTPS for f32 types and PINSR/PEXTR costs for integer types (at the moment we assume the same cost as MOVD/MOVQ - which isn't always true).
Differential Revision: https://reviews.llvm.org/D74976
Summary:
Previosly we simply always said that `SCEVMinMaxExpr` is too costly to expand.
But this isn't really true, it expands into just a comparison+swap pair.
And again much like with add/mul, there will be one less such pair
than the number of operands. And we need to count the cost of operands themselves.
This does change a number of testcases, and as far as i can tell,
all of these changes are improvements, in the sense that
we fixed up more latches to do the [in]equality comparison.
This concludes cost-modelling changes, no other SCEV expressions exist as of now.
This is a part of addressing [[ https://bugs.llvm.org/show_bug.cgi?id=44668 | PR44668 ]].
Reviewers: reames, mkazantsev, wmi, sanjoy
Reviewed By: mkazantsev
Subscribers: hiraditya, javed.absar, llvm-commits
Tags: #llvm
Differential Revision: https://reviews.llvm.org/D73744
Summary:
Currently, `SCEVExpander::isHighCostExpansionHelper()` has the following logic:
```
if (auto *UDivExpr = dyn_cast<SCEVUDivExpr>(S)) {
// If the divisor is a power of two and the SCEV type fits in a native
// integer (and the LHS not expensive), consider the division cheap
// irrespective of whether it occurs in the user code since it can be
// lowered into a right shift.
if (auto *SC = dyn_cast<SCEVConstant>(UDivExpr->getRHS()))
if (SC->getAPInt().isPowerOf2()) {
if (isHighCostExpansionHelper(UDivExpr->getLHS(), L, At,
BudgetRemaining, TTI, Processed))
return true;
const DataLayout &DL =
L->getHeader()->getParent()->getParent()->getDataLayout();
unsigned Width = cast<IntegerType>(UDivExpr->getType())->getBitWidth();
return DL.isIllegalInteger(Width);
}
```
Since this test does not have a datalayout specified,
`SCEVExpander::isHighCostExpansionHelper()` says that
`[[TMP2:%.*]] = lshr exact i64 [[TMP1]], 5` is high-cost, and didn't perform it.
But future patches will change that logic to solely rely on cost-model,
without any such datalayout checks, so i think it is best to show
that that change is ephemeral, and can already happen without costmodel changes.
Reviewers: reames, fhahn, sanjoy, craig.topper, RKSimon
Reviewed By: RKSimon
Subscribers: javed.absar, llvm-commits
Tags: #llvm
Differential Revision: https://reviews.llvm.org/D73717
This patch adds initial support for a DemandedElts mask to the internal computeKnownBits/ComputeNumSignBits methods, matching the SelectionDAG and GlobalISel equivalents.
So far only a couple of instructions have been setup to handle the DemandedElts, the remainder still using the existing 'all elements' default. The plan is to extend support as we have test coverage.
Differential Revision: https://reviews.llvm.org/D73435
Don't overwrite existing target-cpu attributes.
I've often found the replacement behavior annoying, and this is
inconsistent with how the fast math command line flags interact with
the function attributes.
Does not yet change target-features, since I think that should behave
as a concatenation.
A sequence of additions or multiplications that is known not to wrap, may wrap
if it's order is changed (i.e., reassociated). Therefore when vectorizing
integer sum or product reductions, their no-wrap flags need to be removed.
Fixes PR43828
Patch by Denis Antrushin
Differential Revision: https://reviews.llvm.org/D69563
Fix PR40816: avoid considering scalar-with-predication instructions as also
uniform-after-vectorization.
Instructions identified as "scalar with predication" will be "vectorized" using
a replicating region. If such instructions are also optimized as "uniform after
vectorization", namely when only the first of VF lanes is used, such a
replicating region becomes erroneous - only the first instance of the region can
and should be formed. Fix such cases by not considering such instructions as
"uniform after vectorization".
Differential Revision: https://reviews.llvm.org/D70298
I'm not sure what the effect of this change will be on all of the affected
tests or a larger benchmark, but it fixes the horizontal add/sub problems
noted here:
https://reviews.llvm.org/D59710?vs=227972&id=228095&whitespace=ignore-most#toc
The costs are based on reciprocal throughput numbers in Agner's tables for
PEXTR*; these appear to be very slow ops on Silvermont.
This is a small step towards the larger motivation discussed in PR43605:
https://bugs.llvm.org/show_bug.cgi?id=43605
Also, it seems likely that insert/extract is the source of perf regressions on
other CPUs (up to 30%) that were cited as part of the reason to revert D59710,
so maybe we'll extend the table-based approach to other subtargets.
Differential Revision: https://reviews.llvm.org/D70607
Summary:
Getelementptr has vector type if any of its operands are vectors
(the scalar operands being implicitly broadcast to all vector elements).
Extractelement applied to a vector getelementptr can be folded by
applying the extractelement in turn to all of the vector operands.
Subscribers: hiraditya, llvm-commits
Tags: #llvm
Differential Revision: https://reviews.llvm.org/D69379
Currently we may do iterleaving by more than estimated trip count
coming from the profile or computed maximum trip count. The solution is to
use "best known" trip count instead of exact one in interleaving analysis.
Patch by Evgeniy Brevnov.
Differential Revision: https://reviews.llvm.org/D67948
In loop-vectorize, interleave count and vector factor depend on target register number. Currently, it does not
estimate different register pressure for different register class separately(especially for scalar type,
float type should not be on the same position with int type), so it's not accurate. Specifically,
it causes too many times interleaving/unrolling, result in too many register spills in loop body and hurting performance.
So we need classify the register classes in IR level, and importantly these are abstract register classes,
and are not the target register class of backend provided in td file. It's used to establish the mapping between
the types of IR values and the number of simultaneous live ranges to which we'd like to limit for some set of those types.
For example, POWER target, register num is special when VSX is enabled. When VSX is enabled, the number of int scalar register is 32(GPR),
float is 64(VSR), but for int and float vector register both are 64(VSR). So there should be 2 kinds of register class when vsx is enabled,
and 3 kinds of register class when VSX is NOT enabled.
It runs on POWER target, it makes big(+~30%) performance improvement in one specific bmk(503.bwaves_r) of spec2017 and no other obvious degressions.
Differential revision: https://reviews.llvm.org/D67148
llvm-svn: 374634
Also Revert "[LoopVectorize] Fix non-debug builds after rL374017"
This reverts commit 9f41deccc0.
This reverts commit 18b6fe07bc.
The patch is breaking PowerPC internal build, checked with author, reverting
on behalf of him for now due to timezone.
llvm-svn: 374091
In loop-vectorize, interleave count and vector factor depend on target register number. Currently, it does not
estimate different register pressure for different register class separately(especially for scalar type,
float type should not be on the same position with int type), so it's not accurate. Specifically,
it causes too many times interleaving/unrolling, result in too many register spills in loop body and hurting performance.
So we need classify the register classes in IR level, and importantly these are abstract register classes,
and are not the target register class of backend provided in td file. It's used to establish the mapping between
the types of IR values and the number of simultaneous live ranges to which we'd like to limit for some set of those types.
For example, POWER target, register num is special when VSX is enabled. When VSX is enabled, the number of int scalar register is 32(GPR),
float is 64(VSR), but for int and float vector register both are 64(VSR). So there should be 2 kinds of register class when vsx is enabled,
and 3 kinds of register class when VSX is NOT enabled.
It runs on POWER target, it makes big(+~30%) performance improvement in one specific bmk(503.bwaves_r) of spec2017 and no other obvious degressions.
Differential revision: https://reviews.llvm.org/D67148
llvm-svn: 374017
Implement a TODO from rL371452, and handle loop invariant addresses in predicated blocks. If we can prove that the load is safe to speculate into the header, then we can avoid using a masked.load in favour of a normal load.
This is mostly about vectorization robustness. In the common case, it's generally expected that LICM/LoadStorePromotion would have eliminated such loads entirely.
Differential Revision: https://reviews.llvm.org/D67372
llvm-svn: 371745
If we're vectorizing a load in a predicated block, check to see if the load can be speculated rather than predicated. This allows us to generate a normal vector load instead of a masked.load.
To do so, we must prove that all bytes accessed on any iteration of the original loop are dereferenceable, and that all loads (across all iterations) are properly aligned. This is equivelent to proving that hoisting the load into the loop header in the original scalar loop is safe.
Note: There are a couple of code motion todos in the code. My intention is to wait about a day - to be sure this sticks - and then perform the NFC motion without furthe review.
Differential Revision: https://reviews.llvm.org/D66688
llvm-svn: 371452
Ayal Zaks