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
enabled
The C++20 Coroutines couldn't be compiled to WebAssembly due to an
optimization named symmetric transfer requires the support for musttail
calls but WebAssembly doesn't support it yet.
This patch tries to fix the problem by adding a supportsTailCalls
method to TargetTransformImpl to skip the symmetric transfer when
tail-call feature is not supported.
Reviewed By: tlively
Differential Revision: https://reviews.llvm.org/D128794
During the reordering transformation we should try to avoid reordering bundles
like fadd,fsub because this may block them being matched into a single vector
instruction in x86.
We do this by checking if a TreeEntry is such a pattern and adding it to the
list of TreeEntries with orders that need to be considered.
Differential Revision: https://reviews.llvm.org/D125712
In some passes we need a valid number of cache line size to do analysis or
transformation, e.g., loop cache analysis and loop date prefetch. However,
for some backend targets, `TTIImpl->getCacheLineSize()` is not implemented
and hence 'TTI.getCacheLineSize()' would just return 0 which eventually might
produce invalid result.
In this patch we add a user-specified opt/llc option for cache line size.
If the option is specified by users we use the value supplied, otherwise we
fall-back to the default value obtained from `TTIImpl->->getCacheLineSize()`.
The powerpc target already has such an option, this patch generalizes
this option to TargetTransformInfo.cpp.
Reviewed By: bmahjour, #loopoptwg
Differential Revision: https://reviews.llvm.org/D127342
Previously, `getRegUsageForType` was implemented using
`getTypeLegalizationCost`. `getRegUsageForType` is used by the loop
vectorizer to estimate the register pressure caused by using a vector
type. However, `getTypeLegalizationCost` currently only appears to
understand splitting and not scalarization, so significantly
underestimates the register requirements.
Instead, use `getNumRegisters`, which understands when scalarization
can occur (via computeRegisterProperties).
This was discovered while investigating D118979 (Set maximum VF with
shouldMaximizeVectorBandwidth), where under fixed-length 512-bit SVE the
loop vectorizer previously ends up costing an v128i1 as 2 v64i*
registers where it actually occupies 128 i32 registers.
I'm sending this patch early for comment, I'm still doing some sanity checking
with LNT. I note that getRegisterClassForType appears to return VectorRC even
though the type in question (large vNi1 types) end up occupying scalar
registers. That might be worth fixing too.
Differential Revision: https://reviews.llvm.org/D125918
We can try to vectorize number of stores less than MinVecRegSize
/ scalar_value_size, if it is allowed by target. Gives an extra
opportunity for the vectorization.
Fixes PR54985.
Differential Revision: https://reviews.llvm.org/D124284
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
Currently, the utility supports lowering of non atomic memory transfer routines only. This patch adds support for atomic version of memcopy. This may be useful for targets not supporting atomic memcopy.
Reviewed By: arsenm
Differential Revision: https://reviews.llvm.org/D118443
Splat loads are inexpensive in X86. For a 2-lane vector we need just one
instruction: `movddup (%reg), xmm0`. Using the standard Splat score leads
to worse code. This patch adds a new score dedicated for splat loads.
Please note that a splat is usually three IR instructions:
- It is usually a load and 2 inserts:
%ld = load double, double* %gep
%ins1 = insertelement <2 x double> poison, double %ld, i32 0
%ins2 = insertelement <2 x double> %ins1, double %ld, i32 1
- But it can also be a load, an insert and a shuffle:
%ld = load double, double* %gep
%ins = insertelement <2 x double> poison, double %ld, i32 0
%shf = shufflevector <2 x double> %ins, <2 x double> poison, <2 x i32> zeroinitializer
Because of this some of the lit tests contain more IR instructions.
Differential Revision: https://reviews.llvm.org/D121354
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
The areFunctionArgsABICompatible() hook currently accepts a list of
pointer arguments, though what we're actually interested in is the
ABI compatibility after these pointer arguments have been converted
into value arguments.
This means that a) the current API is incompatible with opaque
pointers (because it requires inspection of pointee types) and
b) it can only be used in the specific context of ArgPromotion.
I would like to reuse the API when inspecting calls during inlining.
This patch converts it into an areTypesABICompatible() hook, which
accepts a list of types. This makes the method more generally usable,
and compatible with opaque pointers from an API perspective (the
actual usage in ArgPromotion/Attributor is still incompatible,
I'll follow up on that in separate patches).
Differential Revision: https://reviews.llvm.org/D116031
The availability of SVE should be sufficient to enable scalable
auto-vectorization.
This patch adds a new TTI interface to query the target what style of
vectorization it wants when scalable vectors are available. For other
targets than AArch64, this currently defaults to 'FixedWidthOnly'.
Differential Revision: https://reviews.llvm.org/D115651
Added TTI queries for the cost of a VP Memory operation, and added Opcode,
DataType and Alignment to the hasActiveVectorLength() interface.
Reviewed By: Roland Froese
Differential Revision: https://reviews.llvm.org/D109416
It is trivial to produce DemandedSrcElts given DemandedReplicatedElts,
so don't pass the former. Also, it isn't really useful so far
to have the overload taking the Mask, so just inline it.
- CUDA cannot associate memory space with pointer types. Even though Clang could add extra attributes to specify the address space explicitly on a pointer type, it breaks the portability between Clang and NVCC.
- This change proposes to assume the address space from a pointer from the assumption built upon target-specific address space predicates, such as `__isGlobal` from CUDA. E.g.,
```
foo(float *p) {
__builtin_assume(__isGlobal(p));
// From there, we could assume p is a global pointer instead of a
// generic one.
}
```
This makes the code portable without introducing the implementation-specific features.
Note that NVCC starts to support __builtin_assume from version 11.
Reviewed By: arsenm
Differential Revision: https://reviews.llvm.org/D112041
When targeting a specific CPU with scalable vectorization, the knowledge
of that particular CPU's vscale value can be used to tune the cost-model
and make the cost per lane less pessimistic.
If the target implements 'TTI.getVScaleForTuning()', the cost-per-lane
is calculated as:
Cost / (VScaleForTuning * VF.KnownMinLanes)
Otherwise, it assumes a value of 1 meaning that the behavior
is unchanged and calculated as:
Cost / VF.KnownMinLanes
Reviewed By: kmclaughlin, david-arm
Differential Revision: https://reviews.llvm.org/D113209
This finally creates proper test coverage for replication shuffles,
that are used by LV for conditional loads, and will allow to add
proper costmodel at least for AVX512.
Reviewed By: RKSimon
Differential Revision: https://reviews.llvm.org/D113324
Hiding it in `getInterleavedMemoryOpCost()` is problematic for a number of reasons,
including testability and reuse, let's do better.
In a followup `getUserCost()` will be taught to use to to estimate the mask costs,
which will allow for better cost model tests for it.
Reviewed By: RKSimon
Differential Revision: https://reviews.llvm.org/D113313
This reapplies commit 7dbba3376f, or, put
differently, this reverts commit d9a8d20827.
The test now requires the amdgpu and nvptx backend explicitly as it
won't work without properly.
Not all address spaces support initializers for globals and we can
therefore not set them without checking if they are allowed. This
patch adds a hook into TTI to check if an AS allows non-undef
initializers. We disable it for all but address space 0 by default,
NVPTX and AMDGPU targets allow all but address space 3.
Reviewed By: tra
Differential Revision: https://reviews.llvm.org/D109337
This reverts commit f4122398e7 to
investigate a crash exposed by it.
The patch breaks building the code below with `clang -O2 --target=aarch64-linux`
int a;
double b, c;
void d() {
for (; a; a++) {
b += c;
c = a;
}
}
I have added a new TTI interface called enableOrderedReductions() that
controls whether or not ordered reductions should be enabled for a
given target. By default this returns false, whereas for AArch64 it
returns true and we rely upon the cost model to make sensible
vectorisation choices. It is still possible to override the new TTI
interface by setting the command line flag:
-force-ordered-reductions=true|false
I have added a new RUN line to show that we use ordered reductions by
default for SVE and Neon:
Transforms/LoopVectorize/AArch64/strict-fadd.ll
Transforms/LoopVectorize/AArch64/scalable-strict-fadd.ll
Differential Revision: https://reviews.llvm.org/D106653
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 adds a TTI function, isElementTypeLegalForScalableVector, to query
whether it is possible to vectorize a given element type. This is called by
isLegalToVectorizeInstTypesForScalable to reject scalable vectorization if
any of the instruction types in the loop are unsupported, e.g:
int foo(__int128_t* ptr, int N)
#pragma clang loop vectorize_width(4, scalable)
for (int i=0; i<N; ++i)
ptr[i] = ptr[i] + 42;
This example currently crashes if we attempt to vectorize since i128 is not a
supported type for scalable vectorization.
Reviewed By: sdesmalen, david-arm
Differential Revision: https://reviews.llvm.org/D102253
We were passing the RecurrenceDescriptor by value to most of the reduction analysis methods, despite it being rather bulky with TrackingVH members (that can be costly to copy). In all these cases we're only using the RecurrenceDescriptor for rather basic purposes (access to types/kinds etc.).
Differential Revision: https://reviews.llvm.org/D104029
David Sherwood