This stops Negator from transforming:
`C1 - shl X, C2 --> mul X, (1<<C2) + C1`
...in the general case. There does not seem to be any analysis
benefit to using mul in IR, and there's definitely downside in
codegen (particularly when the multiply has to be expanded).
If `C1` is 0, then there's a stronger argument that the single
mul is a better canonicalization than negate-of-shl, but we may
want to remove that too.
This was noted as a potential conflict for D133667.
Differential Revision: https://reviews.llvm.org/D134310
Epilogue vectorization uses isScalarAfterVectorization to check if
widened versions for inductions need to be generated and bails out in
those cases.
At the moment, there are scenarios where isScalarAfterVectorization
returns true but VPWidenPointerInduction::onlyScalarsGenerated would
return false, causing widening.
This can lead to widened phis with incorrect start values being created
in the epilogue vector body.
This patch addresses the issue by storing the cost-model decision in
VPWidenPointerInductionRecipe and restoring the behavior before 151c144.
This effectively reverts 151c144, but the long-term fix is to properly
support widened inductions during epilogue vectorization
Fixes#57712.
This is the first patch in a series intended for removing flag
-enable-new-pm=0 from lit tests. This is part of a bigger
effort of completely removing legacy code related to legacy
pass manager in favor of currently default new pass manager.
In this patch flag has been removed only from tests where no significant
change has been required because checks has been duplicated for
both PMs.
Reviewed By: fhahn
Differential Revision: https://reviews.llvm.org/D134150
This was originally part of D133788. There are no visible
regressions. All of the diffs show a large unsigned constant
becoming a small negative constant. This should be better
for analysis (and slightly less compile-time) and codegen.
Vector shift by const uniform is the cheapest shift instruction we have, non-const uniform have a marginally higher cost - some targets 'splat' the amount internally to use the shift-per-element instruction, others see a higher cost for the explicit zeroing of the upper bits for the (64-bit) shift amount.
This was achieved with an updated version of the 'cost-tables vs llvm-mca' script D103695 (I'll update the patch soon for reference)
This patch adds cost model for vector insert/extract element instructions. In RVV, we could use vector scalar move instruction to insert or extract the first element, and use vslide to move it. But for mask vector or i64 vector in i32 target, we need special instructions to make it.
Reviewed By: reames
Differential Revision: https://reviews.llvm.org/D133007
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
Users of LCSSA may not expect non-phi uses when checking the uses
outside a loop, which may cause crashes. This is due to the fact that we
do not update uses in unreachable blocks.
To ensure all reachable uses outside the loop are phis, update uses in
unreachable blocks to use poison in dead code.
Fixes#57508.
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
It looks like the vector loops in the modified test cases
unintentionally never get executed. Update the exit condition to ensure
it does to avoid them getting optimized away in upcoming changes.
This change enables the use of RISCV's variable length vector registers for fixed length vectors in the IR, and implicitly enables various IR transforms which generate fixed length vectors if legal (e.g. LoopVectorize). Specifically, this enables fixed length vectors which are known to be inbounds of the underlying variable hardware size.
For context, remember that the +V extension provides a minimum VLEN of 128. The embedded variants provide lower minimums. The analogy here is essentially vectorizing for SSE on a machine which may or may not include AVX2/AVX512. We won't get full utilization by default, but we will get some benefit. And of course, with an explicit mcpu we can vectorize to the exact target hardware.
The LV impact is mostly related to vectorizer robustness. In cases we haven't yet fully implemented scalable vectorization support, we can fall back to fixed length vectorization.
SLP has been disabled for now, even when fixed vectors are enabled. See a310637 and associated review. There are a few addiitional code quality issues which need worked through before turning SLP on would be reasonable.
Differential Revision: https://reviews.llvm.org/D131508
The simpler diff-checks require pointers with add-recs from the same
innermost loop, but this property wasn't check completely. Add the
missing check to ensure both addrecs are in the innermost loop.
Fixes#57315.
Add a variation of @nested_loop_outer_iv_addrec_invariant_in_inner with
the dependence sink and source swapped to extend test coverage.
Also simplifies the test by removing an unneeded reduction.
When we have a dependency with a dependence distance which can only be hit on an iteration beyond the actual trip count of the loop, we can ignore that dependency when analyzing said loop. We already had this code, but had restricted it solely to unknown dependence distances. This change applies it to all dependence distances.
Without this code, we relied on the vectorizer reducing VF such that our infeasible dependence was respected. This usually worked out to about the same result, but not always. For fixed length vectorization, this could mean a smaller VF than optimal being chosen or additional runtime checks. For scalable vectorization - where the bounds on access implied by VF are broader - we could often not find a feasible VF at all.
Differential Revision: https://reviews.llvm.org/D131924
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
Don't demand low order bits from the LHS of an Add if:
- they are not demanded in the result, and
- they are known to be zero in the RHS, so they can't possibly
overflow and affect higher bit positions
This is intended to avoid a regression from a future patch to change
the order of canonicalization of ADD and AND.
Differential Revision: https://reviews.llvm.org/D130075
(X op Y) op Z --> (Y op Z) op X
This isn't a complete solution (see TODO tests for possible refinements),
but it shows some nice wins and doesn't seem to cause any harm. I think
the most potential danger is from conflicting with other folds and causing
an infinite loop - that's the reason for avoiding patterns with constant
operands.
Alternatively, we could try this in the reassociate pass, but we would not
immediately see all of the logic folds that instcombine provides. I also
looked at improving ValueTracking's isImpliedCondition() (and we should
still add some enhancements there), but that would not work in general for
bitwise logic reduction.
The tests that reduce completely to 0/-1 are motivated by issue #56653.
Differential Revision: https://reviews.llvm.org/D131356
Whilst writing a patch to add extra tail-folding RUN lines to
existing tests I noticed a few areas where they can be
cleaned up a little:
1. scalable-reductions.ll: fmin_fast does not mark fcmp as fast.
2. sve-inductions-unusual-types.ll: remove direct references to
SSA variable names.
3. sve-strict-fadd-cost.ll: don't force vector width so we see
costs for different VFs in one go. This will be important for
the follow-on patch.
4. sve-vector-reverse.ll,vector-reverse-mask4.ll: add noalias
keyword to simplify IR.
4. sve-widen-gep.ll,sve-widen-phi.ll: regenerate using script.
These changes will make the subsequent patch adding RUN lines much
easier to review!
Differential Revision: https://reviews.llvm.org/D132219
On known hardware, reductions, gather, and scatter operations have execution latencies which correlated with the vector length (VL) of the operation. Most other operations (e.g. simply arithmetic) don't correlated in this way, and instead essentially fixed cost as VL varies.
When I'd implemented initial scalable cost model support for reductions, gather, and scatter operations, I had used an upper bound on the statically unknown VL. The argument at the time was that this prevented falsely low costs, and biased the vectorizer away from generating bad (on some hardware) code. Unfortunately, practical experience shows we were a bit too effective at that goal, and the high costs defacto prevents vectorization using these constructs at all.
This patch reverses course, and ties the returned cost not to the maximum possible VL, but the VL which would correspond to VScaleForTuning. This parameter is the same one the vectorizer uses when normalizing loop costs, so the term effectively cancels out. The result is that the vectorizer now sees these constructs as comparable in cost to their fixed length variants.
This does introduce the possibility of the cost for these operations being a significant under estimate on platforms where actual VLEN is far from that implied by VScaleForTuning. On such platforms, we might make poor heuristic choices. Probably not in LV itself (due to the cancellation mentioned above), but possibly during e.g. lowering. I'm not currently aware of any concrete examples of this, but this patch does open a concern which did not previously exist.
Previously, we had the problem of overestimating costs causing the same problem on machines much closer to default values for vscale for tuning. With this patch, we still have that problem potentially if vscale for tuning is set high (manually), and then the code is run on a narrow VLEN machine.
Differential Revision: https://reviews.llvm.org/D131519
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
