1) Overloaded (instruction-based) method is a wrapper around the current (opcode-based) method.
2) This patch also changes a few callsites (VectorCombine.cpp,
SLPVectorizer.cpp, CodeGenPrepare.cpp) to call the overloaded method.
3) This is a split of D128302.
Differential Revision: https://reviews.llvm.org/D131114
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
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
Currently, for vectorised loops that use the get.active.lane.mask
intrinsic we only use the mask for predicated vector operations,
such as masked loads and stores, etc. The loop itself is still
controlled by comparing the canonical induction variable with the
trip count. However, for some targets this is inefficient when it's
cheap to use the mask itself to control the loop.
This patch adds support for using the active lane mask for control
flow by:
1. Generating the active lane mask for the next iteration of the
vector loop, rather than the current one. If there are still any
remaining iterations then at least the first bit of the mask will
be set.
2. Extract the first bit of this mask and use this bit for the
conditional branch.
I did this by creating a new VPActiveLaneMaskPHIRecipe that sets
up the initial PHI values in the vector loop pre-header. I've also
made use of the new BranchOnCond VPInstruction for the final
instruction in the loop region.
Differential Revision: https://reviews.llvm.org/D125301
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
This is required to query the legality more precisely in the LoopVectorizer.
This adds another TTI function named 'forceScalarizeMaskedGather/Scatter'
function to work around the hack introduced for MVE, where
isLegalMaskedGather/Scatter would return an answer by second-guessing
where the function was called from, based on the Type passed in (vector
vs scalar). The new interface makes this explicit. It is also used by
X86 to check for vector widths where gather/scatters aren't profitable
(or don't exist) for certain subtargets.
Differential Revision: https://reviews.llvm.org/D115329
Based off a discussion on D110100, we should be avoiding default CostKinds whenever possible.
This initial patch removes them from the 'inner' target implementation callbacks - these should only be used by the main TTI calls, so this should guarantee that we don't cause changes in CostKind by missing it in an inner call. This exposed a few missing arguments in getGEPCost and reduction cost calls that I've cleaned up.
Differential Revision: https://reviews.llvm.org/D110242
The class of instructions that write to narrow top/bottom lanes only
demand the even or odd elements of the input lanes. Which means that a
pair of VMOVNT; VMOVNB demands no lanes from the original input. This
teaches that to instcombine from the target hooks available through
ARMTTIImpl.
Differential Revision: https://reviews.llvm.org/D109325
I'm not sure this is the best way to approach this,
but the situation is rather not very detectable unless we explicitly call it out when refusing to advise to unroll.
Reviewed By: efriedma
Differential Revision: https://reviews.llvm.org/D107271
I have added a new FastMathFlags parameter to getArithmeticReductionCost
to indicate what type of reduction we are performing:
1. Tree-wise. This is the typical fast-math reduction that involves
continually splitting a vector up into halves and adding each
half together until we get a scalar result. This is the default
behaviour for integers, whereas for floating point we only do this
if reassociation is allowed.
2. Ordered. This now allows us to estimate the cost of performing
a strict vector reduction by treating it as a series of scalar
operations in lane order. This is the case when FP reassociation
is not permitted. For scalable vectors this is more difficult
because at compile time we do not know how many lanes there are,
and so we use the worst case maximum vscale value.
I have also fixed getTypeBasedIntrinsicInstrCost to pass in the
FastMathFlags, which meant fixing up some X86 tests where we always
assumed the vector.reduce.fadd/mul intrinsics were 'fast'.
New tests have been added here:
Analysis/CostModel/AArch64/reduce-fadd.ll
Analysis/CostModel/AArch64/sve-intrinsics.ll
Transforms/LoopVectorize/AArch64/strict-fadd-cost.ll
Transforms/LoopVectorize/AArch64/sve-strict-fadd-cost.ll
Differential Revision: https://reviews.llvm.org/D105432
This patch removes the IsPairwiseForm flag from the Reduction Cost TTI
hooks, along with some accompanying code for pattern matching reductions
from trees starting at extract elements. IsPairWise is now assumed to be
false, which was the predominant way that the value was used from both
the Loop and SLP vectorizers. Since the adjustments such as D93860, the
SLP vectorizer has not relied upon this distinction between paiwise and
non-pairwise reductions.
This also removes some code that was detecting reductions trees starting
from extract elements inside the costmodel. This case was
double-counting costs though, adding the individual costs on the
individual instruction _and_ the total cost of the reduction. Removing
it changes the costs in llvm/test/Analysis/CostModel/X86/reduction.ll to
not double count. The cost of reduction intrinsics is still tested
through the various tests in
llvm/test/Analysis/CostModel/X86/reduce-xyz.ll.
Differential Revision: https://reviews.llvm.org/D105484
This patch converts llvm.memcpy intrinsic into Tail Predicated
Hardware loops for a target that supports the Arm M-profile
Vector Extension (MVE).
From an implementation point of view, the patch
- adds an ARM specific SDAG Node (to which the llvm.memcpy intrinsic is lowered to, during first phase of ISel)
- adds a corresponding TableGen entry to generate a pseudo instruction, with a custom inserter,
on matching the above node.
- Adds a custom inserter function that expands the pseudo instruction into MIR suitable
to be (by later passes) into a WLSTP loop.
Reviewed By: dmgreen
Differential Revision: https://reviews.llvm.org/D99723
This patch converts llvm.memcpy intrinsic into Tail Predicated
Hardware loops for a target that supports the Arm M-profile
Vector Extension (MVE).
From an implementation point of view, the patch
- adds an ARM specific SDAG Node (to which the llvm.memcpy intrinsic is lowered to, during first phase of ISel)
- adds a corresponding TableGen entry to generate a pseudo instruction, with a custom inserter,
on matching the above node.
- Adds a custom inserter function that expands the pseudo instruction into MIR suitable
to be (by later passes) into a WLSTP loop.
Note: A cli option is used to control the conversion of memcpy to TP
loop and this option is currently disabled by default. It may be enabled
in the future after further downstream testing.
Reviewed By: dmgreen
Differential Revision: https://reviews.llvm.org/D99723
Added cost estimation for switch instruction, updated costs of branches, fixed
phi cost.
Had to increase `-amdgpu-unroll-threshold-if` default value since conditional
branch cost (size) was corrected to higher value.
Test renamed to "control-flow.ll".
Removed redundant code in `X86TTIImpl::getCFInstrCost()` and
`PPCTTIImpl::getCFInstrCost()`.
Reviewed By: rampitec
Differential Revision: https://reviews.llvm.org/D96805
This patch changes the interface to take a RegisterKind, to indicate
whether the register bitwidth of a scalar register, fixed-width vector
register, or scalable vector register must be returned.
Reviewed By: paulwalker-arm
Differential Revision: https://reviews.llvm.org/D98874
This uses the shuffle mask cost from D98206 to give a better cost of MVE
VREV instructions. This helps especially in VectorCombine where the cost
of shuffles is used to reorder bitcasts, which this helps keep the phase
ordering test for fp16 reductions producing optimal code. The isVREVMask
has been moved to a header file to allow it to be used across target
transform and isel lowering.
Differential Revision: https://reviews.llvm.org/D98210
This adds an Mask ArrayRef to getShuffleCost, so that if an exact mask
can be provided a more accurate cost can be provided by the backend.
For example VREV costs could be returned by the ARM backend. This should
be an NFC until then, laying the groundwork for that to be added.
Differential Revision: https://reviews.llvm.org/D98206
This patch provides two major changes:
1. Add getRelocationInfo to check if a constant will have static, dynamic, or
no relocations. (Also rename the original needsRelocation to needsDynamicRelocation.)
2. Only allow a constant with no relocations (static or dynamic) to be placed
in a mergeable section.
This will allow unused symbols that contain static relocations and happen to
fit in mergeable constant sections (.rodata.cstN) to instead be placed in
unique-named sections if -fdata-sections is used and subsequently garbage collected
by --gc-sections.
See https://lists.llvm.org/pipermail/llvm-dev/2021-February/148281.html.
Differential Revision: https://reviews.llvm.org/D95960
This refactors shouldFavorPostInc() and shouldFavorBackedgeIndex() into
getPreferredAddressingMode() so that we have one interface to steer LSR in
generating the preferred addressing mode.
Differential Revision: https://reviews.llvm.org/D96600
The vector reduction intrinsics started life as experimental ops, so backend support
was lacking. As part of promoting them to 1st-class intrinsics, however, codegen
support was added/improved:
D58015
D90247
So I think it is safe to now remove this complication from IR.
Note that we still have an IR-level codegen expansion pass for these as discussed
in D95690. Removing that is another step in simplifying the logic. Also note that
x86 was already unconditionally forming reductions in IR, so there should be no
difference for x86.
I spot checked a couple of the tests here by running them through opt+llc and did
not see any asm diffs.
If we do find functional differences for other targets, it should be possible
to (at least temporarily) restore the shuffle IR with the ExpandReductions IR
pass.
Differential Revision: https://reviews.llvm.org/D96552
This adds cost modelling for the inloop vectorization added in
745bf6cf44. Up until now they have been modelled as the original
underlying instruction, usually an add. This happens to works OK for MVE
with instructions that are reducing into the same type as they are
working on. But MVE's instructions can perform the equivalent of an
extended MLA as a single instruction:
%sa = sext <16 x i8> A to <16 x i32>
%sb = sext <16 x i8> B to <16 x i32>
%m = mul <16 x i32> %sa, %sb
%r = vecreduce.add(%m)
->
R = VMLADAV A, B
There are other instructions for performing add reductions of
v4i32/v8i16/v16i8 into i32 (VADDV), for doing the same with v4i32->i64
(VADDLV) and for performing a v4i32/v8i16 MLA into an i64 (VMLALDAV).
The i64 are particularly interesting as there are no native i64 add/mul
instructions, leading to the i64 add and mul naturally getting very
high costs.
Also worth mentioning, under NEON there is the concept of a sdot/udot
instruction which performs a partial reduction from a v16i8 to a v4i32.
They extend and mul/sum the first four elements from the inputs into the
first element of the output, repeating for each of the four output
lanes. They could possibly be represented in the same way as above in
llvm, so long as a vecreduce.add could perform a partial reduction. The
vectorizer would then produce a combination of in and outer loop
reductions to efficiently use the sdot and udot instructions. Although
this patch does not do that yet, it does suggest that separating the
input reduction type from the produced result type is a useful concept
to model. It also shows that a MLA reduction as a single instruction is
fairly common.
This patch attempt to improve the costmodelling of in-loop reductions
by:
- Adding some pattern matching in the loop vectorizer cost model to
match extended reduction patterns that are optionally extended and/or
MLA patterns. This marks the cost of the reduction instruction correctly
and the sext/zext/mul leading up to it as free, which is otherwise
difficult to tell and may get a very high cost. (In the long run this
can hopefully be replaced by vplan producing a single node and costing
it correctly, but that is not yet something that vplan can do).
- getExtendedAddReductionCost is added to query the cost of these
extended reduction patterns.
- Expanded the ARM costs to account for these expanded sizes, which is a
fairly simple change in itself.
- Some minor alterations to allow inloop reduction larger than the highest
vector width and i64 MVE reductions.
- An extra InLoopReductionImmediateChains map was added to the vectorizer
for it to efficiently detect which instructions are reductions in the
cost model.
- The tests have some updates to show what I believe is optimal
vectorization and where we are now.
Put together this can greatly improve performance for reduction loop
under MVE.
Differential Revision: https://reviews.llvm.org/D93476
This adds some basic MVE masked load/store costs, notably changing the
cost of legal loads/stores to the MVECostFactor and the cost of
scalarized instructions to 8*NumElts.
Differential Revision: https://reviews.llvm.org/D86538
Hook up legalizations for VECREDUCE_SEQ_FMUL. This is following up on the VECREDUCE_SEQ_FADD work from D90247.
Differential Revision: https://reviews.llvm.org/D90644
If an instruction will be lowered to a call there is no advantage of
using a low overhead loop as the LR register will need to be spilled and
reloaded around the call, and the low overhead will end up being
reverted. This teaches our hardware loop lowering that these memory
intrinsics will be calls under certain situations.
Differential Revision: https://reviews.llvm.org/D90439
Mingming Liu