Use VPExpandSCEVRecipe to expand the step of pointer inductions. This
cleanup addresses a corresponding FIXME.
It should be NFC, as steps for pointer induction must be constants,
which makes expansion trivial.
This extends the safe-divisor widening scheme recently added for scalable vectors to handle fixed vectors as well.
Differential Revision: https://reviews.llvm.org/D132591
VPReplicateRecipe::isUniform actually means uniform-per-parts, hence a
scalar instruction is generated per-part.
This is a potential alternative D132892. For now the current patch only
catches cases where the address is trivially invariant (defined outside
VPlan), while D132892 catches any address that is considered invariant
by SCEV AFAICT.
It should be possible to hoist fully invariant recipes feeding loads out
of the vector loop region as well, but in practice LICM should do that
already.
This version of the patch artificially limits this to loads to make it
easier to compare, but this restriction should be easily liftable.
Reviewed By: reames
Differential Revision: https://reviews.llvm.org/D133019
This patch moves the cost-based decision whether to use an intrinsic or
library call to the point where the recipe is created. This untangles
code-gen from the cost model and also avoids doing some extra work as
the information is already computed at construction.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D132585
I keep finding myself needing to rule this out as a possible source of scalarization, so add debug output like we have for other instructions we decide to scalarize.
I'd extracted isUniform, and Florian moved isUniformAfterVectorization out of VPlan at basically the same time. Let's go ahead and merge them.
For the VPTransformState::get path, a VPValue without a def (which corresponds to an external IR value outside of VPLan) is explicitly handled above the uniform check. On the scalarizeInstruction path, I'm less sure why the change isn't visible, but test cases which would seem likely to hit it were already being handled as uniform through some other mechanism. It would be correct to consider values defined outside of vplan uniform here.
When rebasing the review which became f79214d1, I forgot to adjust for the changed semantics introduced by 531dd3634. Functionally, this had no impact, but semantically it resulted in an incorrect result for isPredicatedInst. I noticed this while doing a follow up change.
This patch adds support for vectorizing conditionally executed div/rem operations via a variant of widening. The existing support for predicated divrem in the vectorizer requires scalarization which we can't do for scalable vectors.
The basic idea is that we can always divide (take remainder) by 1 without executing UB. As such, we can use the active lane mask to conditional select either the actual divisor for active lanes, or a constant one for inactive lanes. We already account for the cost of the active lane mask, so the only additional cost is a splat of one and the vector select. This is one of several possible approaches to this problem; see the review thread for discussion on some of the others. This one was chosen mostly because it was straight forward, and none of the others seemed oviously better.
I enabled the new code only for scalable vectors. We could also legally enable it for fixed vectors as well, but I haven't thought through the cost tradeoffs between widening and scalarization enough to know if that's profitable. This will be explored in future patches.
Differential Revision: https://reviews.llvm.org/D130164
The existing cost model for fixed-order recurrences models the phi as an
extract shuffle of a v1 vector. The shuffle produced should be a splice,
as they take two vectors inputs are extracting from a subset of the
lanes. On certain architectures the existing cost model can drastically
under-estimate the correct cost for the shuffle, so this changes it to a
SK_Splice and passes a correct Mask through to the getShuffleCost call.
I believe this might be the first use of a SK_Splice shuffle cost model
outside of scalable vectors, and some targets may require additions to
the cost-model to correctly account for them. In tree targets appear to
all have been updated where needed.
Differential Revision: https://reviews.llvm.org/D132308
This removes the last use of OperandValueKind from the client side API, and (once this is fully plumbed through TTI implementation) allow use of the same properties in store costing as arithmetic costing.
OperandValueKind and OperandValueProperties both provide facts about the operands of an instruction for purposes of cost modeling. We've discussed merging them several times; before I plumb through more flags, let's go ahead and do so.
This change only adds the client side interface for getArithmeticInstrCost and makes a couple of minor changes in client code to prove that it works. Target TTI implementations still use the split flags. I'm deliberately splitting what could be one big change into a series of smaller ones so that I can lean on the compiler to catch errors along the way.
Defaults to TCK_RecipThroughput - as most explicit calls were assuming TCK_RecipThroughput (vectorizers) or was just doing a before-vs-after comparison (vectorcombiner). Calls via getInstructionCost were just dropping the CostKind, so again there should be no change at this time (as getShuffleCost and its expansions don't use CostKind yet) - but it will make it easier for us to better account for size/latency shuffle costs in inline/unroll passes in the future.
Differential Revision: https://reviews.llvm.org/D132287
If the incoming previous value of a fixed-order recurrence is a phi in
the header, go through incoming values from the latch until we find a
non-phi value. Use this as the new Previous, all uses in the header
will be dominated by the original phi, but need to be moved after
the non-phi previous value.
At the moment, fixed-order recurrences are modeled as a chain of
first-order recurrences.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D119661
This change reorganizes the code and comments to make the expected semantics of these routines more clear. However, this is *not* an NFC change. The functional change is having isScalarWithPredication return false if the instruction does not need predicated. Specifically, for the case of a uniform memory operation we were previously considering it *not* to be a predicated instruction, but *were* considering it to be scalable with predication.
As can be seen with the test changes, this causes uniform memory ops which should have been lowered as uniform-per-parts values to instead be lowering via naive scalarization or if scalarization is infeasible (i.e. scalable vectors) aborted entirely. I also don't trust the code to bail out correctly 100% of the time, so it's possible we had a crash or miscompile from trying to scalarize something which isn't scalaralizable. I haven't found a concrete example here, but I am suspicious.
Differential Revision: https://reviews.llvm.org/D131093
After D121595 was commited, I noticed regressions assosicated with small trip
count numbersvectorisation by tail folding with scalable vectors. As a solution
for those issues I propose to introduce the minimal trip count threshold value.
Differential Revision: https://reviews.llvm.org/D130755
This is mostly a stylistic change to make the uniform memop widening cost
code fit more naturally with the sourounding code. Its not strictly
speaking NFC as I added in the store with invariant value case, and we
could in theory have a target where a gather/scatter is cheaper than a
single load/store... but it's probably NFC in practice. Note that the
scatter/gather result can still be overriden later if the result is
uniform-by-parts.
This extends the handling of uniform memory operations to handle the case where a store is storing a loop invariant value. Unlike the general case of a store to an invariant address where we must use the last active lane, in this case we can use any lane since all lanes must produce the same result.
For context, the basic structure of the existing code and how the change fits in:
* First, we select a widening strategy. (The result is irrelevant for this patch.)
* Then we determine if a computation is uniform within all lanes of VF. (Note this is the uniform-per-part definition, not LAI's uniform across all unrolled iterations definition.)
* If it is, we overrule the widening strategy, and unconditionally scalarize.
* VPReplicationRecipe - which is what actually does the scalarization - knows how to handle unform-per-part values including for scalable vectors. However, we do need to know that the expression is safe to execute without predication - e.g. the uniform mem op was unconditional in the original loop. (This part was split off and already landed.)
An obvious question is why not simply implement the generic case? The answer is that I'm going to, but doing so without a canonicalization towards uniform causes regressions due to bad interaction with scalarization/uniformity of values feeding the uniform mem-op. This patch is needed to avoid those regressions.
Differential Revision: https://reviews.llvm.org/D130364
If we have interleave groups in the loop we want to vectorise then
we should fall back on normal vectorisation with a scalar epilogue. In
such cases when tail-folding is enabled we'll almost certainly go on to
create vplans with very high costs for all vector VFs and fall back on
VF=1 anyway. This is likely to be worse than if we'd just used an
unpredicated vector loop in the first place.
Once the vectoriser has proper support for analysing all the costs
for each combination of VF and vectorisation style, then we should
be able to remove this.
Added an extra test here:
Transforms/LoopVectorize/AArch64/sve-tail-folding-option.ll
Differential Revision: https://reviews.llvm.org/D128342
Now the API getExtendedAddReductionCost is used to determine the cost of extended Add reduction with optional Mul. For Arm, it could cover the cases. But for other target, for example: RISCV, they support other kinds of extended recution, such as FAdd.
This patch does the following changes:
1, Split getExtendedAddReductionCost into 2 new API: getExtendedReductionCost which handles the extended reduction with addtional input of Opcode; getMulAccReductionCost which handle the MLA cases the getExtendedAddReductionCost.
2, Refactor getReductionPatternCost, add some contraint condition to make sure the getMulAccReductionCost should only handle the reuction of Add + Mul.
Differential Revision: https://reviews.llvm.org/D130868
We already had the reasoning about uniform mem op loads; if the address is accessed at least once, we know the instruction doesn't need predicated to ensure fault safety. For stores, we do need to ensure that the values visible in memory are the same with and without predication. The easiest sub-case to check for is that all the values being stored are the same. Since we know that at least one lane is active, this tells us that the value must be visible.
Warning on confusing terminology: "uniform" vs "uniform mem op" mean two different things here, and this patch is specific to the later. It would *not* be legal to make this same change for merely "uniform" operations.
Differential Revision: https://reviews.llvm.org/D130637
Reorganize the code to make it clear what is and isn't handle, and why.
Restructure bailout to remove (false and confusing) dependence on
CM_Scalarize; just return invalid cost and propagate, that's what it
is for.
This code confuses LV's "Uniform" and LVL/LAI's "Uniform". Despite the
common name, these are different.
* LVs notion means that only the first lane *of each unrolled part* is
required. That is, lanes within a single unroll factor are considered
uniform. This allows e.g. widenable memory ops to be considered
uses of uniform computations.
* LVL and LAI's notion refers to all lanes across all unrollings.
IsUniformMem is in turn defined in terms of LAI's notion. Thus a
UniformMemOpmeans is a memory operation with a loop invariant address.
This means the same address is accessed in every iteration.
The tweaked piece of code was trying to match a uniform mem op (i.e.
fully loop invariant address), but instead checked for LV's notion of
uniformity. In theory, this meant with UF > 1, we could speculate
a load which wasn't safe to execute.
This ends up being mostly silent in current code as it is nearly
impossible to create the case where this difference is visible. The
closest I've come in the test case from 54cb87, but even then, the
incorrect result is only visible in the vplan debug output; before this
change we sink the unsafely speculated load back into the user's predicate
blocks before emitting IR. Both before and after IR are correct so the
differences aren't "interesting".
The other test changes are uninteresting. They're cases where LV's uniform
analysis is slightly weaker than SCEV isLoopInvariant.
This patch adds the AArch64 hook for preferPredicateOverEpilogue,
which currently returns true if SVE is enabled and one of the
following conditions (non-exhaustive) is met:
1. The "sve-tail-folding" option is set to "all", or
2. The "sve-tail-folding" option is set to "all+noreductions"
and the loop does not contain reductions,
3. The "sve-tail-folding" option is set to "all+norecurrences"
and the loop has no first-order recurrences.
Currently the default option is "disabled", but this will be
changed in a later patch.
I've added new tests to show the options behave as expected here:
Transforms/LoopVectorize/AArch64/sve-tail-folding-option.ll
Differential Revision: https://reviews.llvm.org/D129560
An srem or sdiv has two cases which can cause undefined behavior, not just one. The existing code did not account for this, and as a result, we miscompiled when we encountered e.g. a srem i64 %v, -1 in a conditional block.
Instead of hand rolling the logic, just use the utility function which exists exactly for this purpose.
Differential Revision: https://reviews.llvm.org/D130106
At the moment, the VPPRedInstPHIRecipe is not used in subsequent uses of
the predicate recipe. This incorrectly models the def-use chains, as all
later uses should use the phi recipe. Fix that by delaying recording of
the recipe.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D129436
