This implements 2 different vectorisation fallback strategies if tail-folding
fails: 1) don't vectorise at all, or 2) vectorise using a scalar epilogue. This
can be controlled with option -prefer-predicate-over-epilogue, that has been
changed to take a numeric value corresponding to the tail-folding preference
and preferred fallback.
Patch by: Pierre van Houtryve, Sjoerd Meijer.
Differential Revision: https://reviews.llvm.org/D79783
This adapts LV to the new semantics of get.active.lane.mask as discussed in
D86147, which means that the LV now emits intrinsic get.active.lane.mask with
the loop tripcount instead of the backedge-taken count as its second argument.
The motivation for this is described in D86147.
Differential Revision: https://reviews.llvm.org/D86304
This refactors option -disable-mve-tail-predication to take different arguments
so that we have 1 option to control tail-predication rather than several
different ones.
This is also a prep step for D82953, in which we want to reject reductions
unless that is requested with this option.
Differential Revision: https://reviews.llvm.org/D83133
This emits new IR intrinsic @llvm.get.active.mask for tail-folded vectorised
loops if the intrinsic is supported by the backend, which is checked by
querying TargetTransform hook emitGetActiveLaneMask.
This intrinsic creates a mask representing active and inactive vector lanes,
which is used by the masked load/store instructions that are created for
tail-folded loops. The semantics of @llvm.get.active.mask are described here in
LangRef:
https://llvm.org/docs/LangRef.html#llvm-get-active-lane-mask-intrinsics
This intrinsic is also used to provide a hint to the backend. That is, the
second argument of the intrinsic represents the back-edge taken count of the
loop. For MVE, for example, we use that to set up tail-predication, which is a
new form of predication in MVE for vector loops that implicitely predicates the
last vector loop iteration by implicitely setting active/inactive lanes, i.e.
the tail loop is predicated. In order to set up a tail-predicated vector loop,
we need to know the number of data elements processed by the vector loop, which
corresponds the the tripcount of the scalar loop, which we can now reconstruct
using @llvm.get.active.mask.
Differential Revision: https://reviews.llvm.org/D79100
D77635 added support to recognise primary induction variables for counting-down
loops. This allows us to fold the scalar tail loop into the main vector body,
which we need for MVE tail-predication. This adds some ARM tail-folding test
cases that we want to support.
This test was extracted from D76838, which implemented a different approach to
reverse and thus find a primary induction variable.
This addresses a vectorisation regression for tail-folded loops that are
counting down, e.g. loops as simple as this:
void foo(char *A, char *B, char *C, uint32_t N) {
while (N > 0) {
*C++ = *A++ + *B++;
N--;
}
}
These are loops that can be vectorised, but when tail-folding is requested, it
can't find a primary induction variable which we do need for predicating the
loop. As a result, the loop isn't vectorised at all, which it is able to do
when tail-folding is not attempted. So, this adds a check for the primary
induction variable where we decide how to lower the scalar epilogue. I.e., when
there isn't a primary induction variable, a scalar epilogue loop is allowed
(i.e. don't request tail-folding) so that vectorisation could still be
triggered.
Having this check for the primary induction variable make sense anyway, and in
addition, in a follow-up of this I will look into discovering earlier the
primary induction variable for counting down loops, so that this can also be
tail-folded.
Differential revision: https://reviews.llvm.org/D72324
Sjoerd Meijer