This ensures the right order in the sink-after map is maintained. If we
re-sink an instruction, it must be sunk after all earlier instructions
have been sunk.
Fixes https://github.com/llvm/llvm-project/issues/54223
Previous and OhterPrev may not be in the same block. Use DT::dominates
instead of local comesBefore. DT::dominates is already used earlier to
check the order of Previous and SinkCandidate.
Fixes https://github.com/llvm/llvm-project/issues/54195
This patch extends first-order recurrence handling to support cases
where we already sunk an instruction for a different recurrence, but
LastPrev comes before Previous.
To handle those cases correctly, we need to find the earliest entry for
the sink-after chain, because this is references the Previous from the
original recurrence. This is needed to ensure we use the correct
instruction as sink point.
Depends on D118558.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D118642
Extends getReductionOpChain to look through Phis which may be part of
the reduction chain. adjustRecipesForReductions will now also create a
CondOp for VPReductionRecipe if the block is predicated and not only if
foldTailByMasking is true.
Changes were required in tryToBlend to ensure that we don't attempt
to convert the reduction Phi into a select by returning a VPBlendRecipe.
The VPReductionRecipe will create a select between the Phi and the reduction.
Reviewed By: david-arm
Differential Revision: https://reviews.llvm.org/D117580
The code was relying upon the implicit conversion of TypeSize to
uint64_t and assuming the type in question was always fixed. However,
I discovered an issue when running the canon-freeze pass with some
IR loops that contains scalable vector types. I've changed the code
to bail out if the size is unknown at compile time, since we cannot
compute whether the step is a multiple of the type size or not.
I added a test here:
Transforms/CanonicalizeFreezeInLoops/phis.ll
Differential Revision: https://reviews.llvm.org/D118696
This is a bugfix in IVDescriptor.cpp.
The helper function `RecurrenceDescriptor::getExactFPMathInst()`
is supposed to return the 1st FP instruction that does not allow
reordering. However, when constructing the RecurrenceDescriptor,
we trace the use-def chain staring from a PHI node and for each
instruction in the use-def chain, its descriptor overrides the
previous one. Therefore in the final RecurrenceDescriptor we
constructed, we lose previous FP instructions that does not allow
reordering.
Reviewed By: kmclaughlin
Differential Revision: https://reviews.llvm.org/D118073
Instead use either Type::getPointerElementType() or
Type::getNonOpaquePointerElementType().
This is part of D117885, in preparation for deprecating the API.
For loops that contain in-loop reductions but no loads or stores, large
VFs are chosen because LoopVectorizationCostModel::getSmallestAndWidestTypes
has no element types to check through and so returns the default widths
(-1U for the smallest and 8 for the widest). This results in the widest
VF being chosen for the following example,
float s = 0;
for (int i = 0; i < N; ++i)
s += (float) i*i;
which, for more computationally intensive loops, leads to large loop
sizes when the operations end up being scalarized.
In this patch, for the case where ElementTypesInLoop is empty, the widest
type is determined by finding the smallest type used by recurrences in
the loop instead of falling back to a default value of 8 bits. This
results in the cost model choosing a more sensible VF for loops like
the one above.
Differential Revision: https://reviews.llvm.org/D113973
checkOrderedReductions looks for Phi nodes which can be classified as in-order,
meaning they can be vectorised without unsafe math. In order to vectorise the
reduction it should also be classified as in-loop by getReductionOpChain, which
checks that the reduction has two uses.
In this patch, a similar check is added to checkOrderedReductions so that we
now return false if there are more than two uses of the FAdd instruction.
This fixes PR52515.
Reviewed By: fhahn, david-arm
Differential Revision: https://reviews.llvm.org/D114002
At the moment, computeRecurrenceType does not include any sign bits in
the maximum bit width. If the value can be negative, this means the sign
bit will be missing and the sext won't properly extend the value.
If the value can be negative, increment the bitwidth by one to make sure
there is at least one sign bit in the result value.
Note that the increment is also needed *if* the value is *known* to be
negative, as a sign bit needs to be preserved for the sext to work.
Note that this at the moment prevents vectorization, because the
analysis computes i1 as type for the recurrence when looking through the
AND in lookThroughAnd.
Fixes PR51794, PR52485.
Reviewed By: spatel
Differential Revision: https://reviews.llvm.org/D113056
This patch adds further support for vectorisation of loops that involve
selecting an integer value based on a previous comparison. Consider the
following C++ loop:
int r = a;
for (int i = 0; i < n; i++) {
if (src[i] > 3) {
r = b;
}
src[i] += 2;
}
We should be able to vectorise this loop because all we are doing is
selecting between two states - 'a' and 'b' - both of which are loop
invariant. This just involves building a vector of values that contain
either 'a' or 'b', where the final reduced value will be 'b' if any lane
contains 'b'.
The IR generated by clang typically looks like this:
%phi = phi i32 [ %a, %entry ], [ %phi.update, %for.body ]
...
%pred = icmp ugt i32 %val, i32 3
%phi.update = select i1 %pred, i32 %b, i32 %phi
We already detect min/max patterns, which also involve a select + cmp.
However, with the min/max patterns we are selecting loaded values (and
hence loop variant) in the loop. In addition we only support certain
cmp predicates. This patch adds a new pattern matching function
(isSelectCmpPattern) and new RecurKind enums - SelectICmp & SelectFCmp.
We only support selecting values that are integer and loop invariant,
however we can support any kind of compare - integer or float.
Tests have been added here:
Transforms/LoopVectorize/AArch64/sve-select-cmp.ll
Transforms/LoopVectorize/select-cmp-predicated.ll
Transforms/LoopVectorize/select-cmp.ll
Differential Revision: https://reviews.llvm.org/D108136
This patch adds further support for vectorisation of loops that involve
selecting an integer value based on a previous comparison. Consider the
following C++ loop:
int r = a;
for (int i = 0; i < n; i++) {
if (src[i] > 3) {
r = b;
}
src[i] += 2;
}
We should be able to vectorise this loop because all we are doing is
selecting between two states - 'a' and 'b' - both of which are loop
invariant. This just involves building a vector of values that contain
either 'a' or 'b', where the final reduced value will be 'b' if any lane
contains 'b'.
The IR generated by clang typically looks like this:
%phi = phi i32 [ %a, %entry ], [ %phi.update, %for.body ]
...
%pred = icmp ugt i32 %val, i32 3
%phi.update = select i1 %pred, i32 %b, i32 %phi
We already detect min/max patterns, which also involve a select + cmp.
However, with the min/max patterns we are selecting loaded values (and
hence loop variant) in the loop. In addition we only support certain
cmp predicates. This patch adds a new pattern matching function
(isSelectCmpPattern) and new RecurKind enums - SelectICmp & SelectFCmp.
We only support selecting values that are integer and loop invariant,
however we can support any kind of compare - integer or float.
Tests have been added here:
Transforms/LoopVectorize/AArch64/sve-select-cmp.ll
Transforms/LoopVectorize/select-cmp-predicated.ll
Transforms/LoopVectorize/select-cmp.ll
Differential Revision: https://reviews.llvm.org/D108136
This extends the reduction logic in the vectorizer to handle intrinsic
versions of min and max, both the floating point variants already
created by instcombine under fastmath and the integer variants from
D98152.
As a bonus this allows us to match a chain of min or max operations into
a single reduction, similar to how add/mul/etc work.
Differential Revision: https://reviews.llvm.org/D109645
Store the used element type in the InductionDescriptor. For typed
pointers, it remains the pointer element type. For opaque pointers,
we always use an i8 element type, such that the step is a simple
offset.
A previous version of this patch instead tried to guess the element
type from an induction GEP, but this is not reliable, as the GEP
may be hidden (see @both in iv_outside_user.ll).
Differential Revision: https://reviews.llvm.org/D104795
If a reduction Phi has a single user which `AND`s the Phi with a type mask,
`lookThroughAnd` will return the user of the Phi and the narrower type represented
by the mask. Currently this is only used for arithmetic reductions, whereas loops
containing logical reductions will create a reduction intrinsic using the widened
type, for example:
for.body:
%phi = phi i32 [ %and, %for.body ], [ 255, %entry ]
%mask = and i32 %phi, 255
%gep = getelementptr inbounds i8, i8* %ptr, i32 %iv
%load = load i8, i8* %gep
%ext = zext i8 %load to i32
%and = and i32 %mask, %ext
...
^ this will generate an and reduction intrinsic such as the following:
call i32 @llvm.vector.reduce.and.v8i32(<8 x i32>...)
The same example for an add instruction would create an intrinsic of type i8:
call i8 @llvm.vector.reduce.add.v8i8(<8 x i8>...)
This patch changes AddReductionVar to call lookThroughAnd for other integer
reductions, allowing loops similar to the example above with reductions such
as and, or & xor to vectorize.
Reviewed By: david-arm, dmgreen
Differential Revision: https://reviews.llvm.org/D105632
The Exit instruction passed in for checking if it's an ordered reduction need not be
an FPAdd operation. We need to bail out at that point instead of
assuming it is an FPAdd (and hence has two operands). See added testcase.
It crashes without the patch because the Exit instruction is a phi with
exactly one operand.
This latent bug was exposed by 95346ba which added support for
multi-exit loops for vectorization.
Reviewed-By: kmclaughlin
Differential Revision: https://reviews.llvm.org/D106843
If a reduction Phi has a single user which `AND`s the Phi with a type mask,
`lookThroughAnd` will return the user of the Phi and the narrower type represented
by the mask. Currently this is only used for arithmetic reductions, whereas loops
containing logical reductions will create a reduction intrinsic using the widened
type, for example:
for.body:
%phi = phi i32 [ %and, %for.body ], [ 255, %entry ]
%mask = and i32 %phi, 255
%gep = getelementptr inbounds i8, i8* %ptr, i32 %iv
%load = load i8, i8* %gep
%ext = zext i8 %load to i32
%and = and i32 %mask, %ext
...
^ this will generate an and reduction intrinsic such as the following:
call i32 @llvm.vector.reduce.and.v8i32(<8 x i32>...)
The same example for an add instruction would create an intrinsic of type i8:
call i8 @llvm.vector.reduce.add.v8i8(<8 x i8>...)
This patch changes AddReductionVar to call lookThroughAnd for other integer
reductions, allowing loops similar to the example above with reductions such
as and, or & xor to vectorize.
Reviewed By: david-arm, dmgreen
Differential Revision: https://reviews.llvm.org/D105632
Update isFirstOrderRecurrence to explore all uses of a recurrence phi
and check if we can sink them. If there are multiple users to sink, they
are all mapped to the previous instruction.
Fixes PR44286 (and another PR or two).
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D84951
Such attributes can either be unset, or set to "true" or "false" (as string).
throughout the codebase, this led to inelegant checks ranging from
if (Fn->getFnAttribute("no-jump-tables").getValueAsString() == "true")
to
if (Fn->hasAttribute("no-jump-tables") && Fn->getFnAttribute("no-jump-tables").getValueAsString() == "true")
Introduce a getValueAsBool that normalize the check, with the following
behavior:
no attributes or attribute set to "false" => return false
attribute set to "true" => return true
Differential Revision: https://reviews.llvm.org/D99299
Previously we could only vectorize FP reductions if fast math was enabled, as this allows us to
reorder FP operations. However, it may still be beneficial to vectorize the loop by moving
the reduction inside the vectorized loop and making sure that the scalar reduction value
be an input to the horizontal reduction, e.g:
%phi = phi float [ 0.0, %entry ], [ %reduction, %vector_body ]
%load = load <8 x float>
%reduction = call float @llvm.vector.reduce.fadd.v8f32(float %phi, <8 x float> %load)
This patch adds a new flag (IsOrdered) to RecurrenceDescriptor and makes use of the changes added
by D75069 as much as possible, which already teaches the vectorizer about in-loop reductions.
For now in-order reduction support is off by default and controlled with the `-enable-strict-reductions` flag.
Reviewed By: david-arm
Differential Revision: https://reviews.llvm.org/D98435
Changes getRecurrenceIdentity to always return a neutral value of -0.0 for FAdd.
Reviewed By: dmgreen, spatel
Differential Revision: https://reviews.llvm.org/D98963
We are tracking an FP instruction that does *not* have FMF (reassoc)
properties, so calling that "Unsafe" seems opposite of the common
reading.
I also removed one getter method by rolling the null check into
the access. Further simplification seems possible.
The motivation is to clean up the interactions between FMF and
function-level attributes in these classes and their callers.
This is a mess, but this is hopefully no-functional-change.
The 'Prev' descriptor is only used for min/max recurrences
or when starting a match from a phi, so it should not be a
factor when propagating FMF for fmul/fadd.
The API is confusing (and should be reduced in subsequent steps)
because the "UnsafeAlgebraInst" appears to actually be a placeholder
for a recurrence that does NOT have FMF, but we still want to
treat it as reassociative.
This is a fix for https://llvm.org/PR49215 either before/after
we make a verifier enhancement for vector reductions with D96904.
I'm not sure what the current thinking is for pointer math/logic
in IR. We allow icmp on pointer values. Therefore, we match min/max
patterns, so without this patch, the vectorizer could form a vector
reduction from that sequence.
But the LangRef definitions for min/max and vector reduction
intrinsics do not allow pointer types:
https://llvm.org/docs/LangRef.html#llvm-smax-intrinsichttps://llvm.org/docs/LangRef.html#llvm-vector-reduce-umax-intrinsic
So we would crash/assert at some point - either in IR verification,
in the cost model, or in codegen. If we do want to allow this kind
of transform, we will need to update the LangRef and all of those
parts of the compiler.
Differential Revision: https://reviews.llvm.org/D97047
Currently, setting the `no-nans-fp-math` attribute to true will allow
loops with fmin/fmax to vectorize, though we should be requiring that
`no-signed-zeros-fp-math` is also set.
This patch adds the check for no-signed-zeros at the function level and includes
tests to make sure we don't vectorize functions with only one of the attributes
associated.
Reviewed By: spatel
Differential Revision: https://reviews.llvm.org/D96604
This is another step (see D95452) towards correcting fast-math-flags
bugs in vector reductions.
There are multiple bugs visible in the test diffs, and this is still
not working as it should. We still use function attributes (rather
than FMF) to drive part of the logic, but we are not checking for
the correct FP function attributes.
Note that FMF may not be propagated optimally on selects (example
in https://llvm.org/PR35607 ). That's why I'm proposing to union the
FMF of a fcmp+select pair and avoid regressions on existing vectorizer
tests.
Differential Revision: https://reviews.llvm.org/D95690
While here, rename the inaccurate getRecurrenceBinOp()
because that was also used to get CmpInst opcodes.
The recurrence/reduction kind should always refer to the
expected opcode for a reduction. SLP appears to be the
only direct caller of createSimpleTargetReduction(), and
that calling code ideally should not be carrying around
both an opcode and a reduction kind.
This should allow us to generalize reduction matching to
use intrinsics instead of only binops.
This is almost all mechanical search-and-replace and
no-functional-change-intended (NFC). Having a single
enum makes it easier to match/reason about the
reduction cases.
The goal is to remove `Opcode` from reduction matching
code in the vectorizers because that makes it harder to
adapt the code to handle intrinsics.
The code in RecurrenceDescriptor::AddReductionVar() is
the only place that required closer inspection. It uses
a RecurrenceDescriptor and a second InstDesc to sometimes
overwrite part of the struct. It seem like we should be
able to simplify that logic, but it's not clear exactly
which cmp+sel patterns that we are trying to handle/avoid.
This might also make it easier to adapt if we want
to match min/max intrinsics rather than cmp+sel idioms.
The 'const' part is to potentially avoid confusion
in calling code. There's some surprising and possibly
wrong behavior related to matching min/max reductions
differently than other reductions.
The last use of the function was removed on Sep 18, 2016 in commit
5f8cc0c346.
The function was later moved to llvm/lib/Analysis/IVDescriptors.cpp on
Sep 12, 2018 in commit 7e98d69847.
1. Removed #include "...AliasAnalysis.h" in other headers and modules.
2. Cleaned up includes in AliasAnalysis.h.
Reviewed By: RKSimon
Differential Revision: https://reviews.llvm.org/D92489
This expands upon the inloop reductions added in e9761688e41cb9e976,
allowing them to be inserted into tail folded loops. Reductions are
generates with the form:
x = select(mask, vecop, zero)
v = vecreduce.add(x)
c = add chain, v
Where zero here is chosen as the identity value for add reductions. The
backend is then expected to fold the select and the vecreduce into a
single predicated instruction.
Most of the code is fairly straight forward, except for the creation of
blockmasks which need to ensure they are created in dominance order. The
order they are added is altered to be after any phis, keeping the
requirements for the underlying IR.
Differential Revision: https://reviews.llvm.org/D84451
Arm MVE has multiple instructions such as VMLAVA.s8, which (in this
case) can take two 128bit vectors, sign extend the inputs to i32,
multiplying them together and sum the result into a 32bit general
purpose register. So taking 16 i8's as inputs, they can multiply and
accumulate the result into a single i32 without any rounding/truncating
along the way. There are also reduction instructions for plain integer
add and min/max, and operations that sum into a pair of 32bit registers
together treated as a 64bit integer (even though MVE does not have a
plain 64bit addition instruction). So giving the vectorizer the ability
to use these instructions both enables us to vectorize at higher
bitwidths, and to vectorize things we previously could not.
In order to do that we need a way to represent that the reduction
operation, specified with a llvm.experimental.vector.reduce when
vectorizing for Arm, occurs inside the loop not after it like most
reductions. This patch attempts to do that, teaching the vectorizer
about in-loop reductions. It does this through a vplan recipe
representing the reductions that the original chain of reduction
operations is replaced by. Cost modelling is currently just done through
a prefersInloopReduction TTI hook (which follows in a later patch).
Differential Revision: https://reviews.llvm.org/D75069
This reverts commit e9761688e4. It breaks the build:
```
~/src/llvm-project/llvm/lib/Analysis/IVDescriptors.cpp:868:10: error: no viable conversion from returned value of type 'SmallVector<[...], 8>' to function return type 'SmallVector<[...], 4>'
return ReductionOperations;
```
Arm MVE has multiple instructions such as VMLAVA.s8, which (in this
case) can take two 128bit vectors, sign extend the inputs to i32,
multiplying them together and sum the result into a 32bit general
purpose register. So taking 16 i8's as inputs, they can multiply and
accumulate the result into a single i32 without any rounding/truncating
along the way. There are also reduction instructions for plain integer
add and min/max, and operations that sum into a pair of 32bit registers
together treated as a 64bit integer (even though MVE does not have a
plain 64bit addition instruction). So giving the vectorizer the ability
to use these instructions both enables us to vectorize at higher
bitwidths, and to vectorize things we previously could not.
In order to do that we need a way to represent that the reduction
operation, specified with a llvm.experimental.vector.reduce when
vectorizing for Arm, occurs inside the loop not after it like most
reductions. This patch attempts to do that, teaching the vectorizer
about in-loop reductions. It does this through a vplan recipe
representing the reductions that the original chain of reduction
operations is replaced by. Cost modelling is currently just done through
a prefersInloopReduction TTI hook (which follows in a later patch).
Differential Revision: https://reviews.llvm.org/D75069
Forward declare DemandedBits in IVDescriptors, and move include
into the cpp file. Also drop the include from LoopUtils, which
does not need it at all.
Florian Hahn