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
This reverts the revert 02c5ba8679
Fix:
Pass was registered as DUMMY_FUNCTION_PASS causing the newpm-pass
functions to be doubly defined. Triggered in -DLLVM_ENABLE_MODULE=1
builds.
Original commit:
This patch implements expansion of llvm.vp.* intrinsics
(https://llvm.org/docs/LangRef.html#vector-predication-intrinsics).
VP expansion is required for targets that do not implement VP code
generation. Since expansion is controllable with TTI, targets can switch
on the VP intrinsics they do support in their backend offering a smooth
transition strategy for VP code generation (VE, RISC-V V, ARM SVE,
AVX512, ..).
Reviewed By: rogfer01
Differential Revision: https://reviews.llvm.org/D78203
This patch implements expansion of llvm.vp.* intrinsics
(https://llvm.org/docs/LangRef.html#vector-predication-intrinsics).
VP expansion is required for targets that do not implement VP code
generation. Since expansion is controllable with TTI, targets can switch
on the VP intrinsics they do support in their backend offering a smooth
transition strategy for VP code generation (VE, RISC-V V, ARM SVE,
AVX512, ..).
Reviewed By: rogfer01
Differential Revision: https://reviews.llvm.org/D78203
This patch adds the missing implementation for
TargetTransformInfo::hasActiveVectorLength() without which using
hasActiveVectorLength() causes linker error.
Patch by Vineet Kumar!
Differential Revision: https://reviews.llvm.org/D100941
Alexey Bataev