Update VPBlendRecipe::execute to support generating code for first-lane
only. This fixes a crash in the newly added test
@test_not_first_lane_only_wide_compare_incoming_order_swapped.
The support for interleaved accesses for scalable vector with a factor
of 2 is enabled in vectorizer. Therefore, the patch removed the
restriction for scalable vector with a factor of 2.
This reverts the revert commit c6e01627acf859.
This patch includes a fix for any-of reductions and epilogue
vectorization. Extra test coverage for the issue that caused the revert
has been added in bce3bfced5fe0b019 and an assertion has been added in
c7209cbb8be7a3c65813.
--------------------------------
Original commit message:
Update AnyOf reduction code generation to only keep track of the AnyOf
property in a boolean vector in the loop, only selecting either the new
or start value in the middle block.
The patch incorporates feedback from https://reviews.llvm.org/D153697.
This fixes the #62565, as now there aren't multiple uses of the
start/new values.
Fixes https://github.com/llvm/llvm-project/issues/62565
PR: https://github.com/llvm/llvm-project/pull/78304
Support for predicated vector reverse intrinsic was added some time ago.
Adds support for predicated reversed loads/stores in the loop
vectorizer.
Reviewers: fhahn
Reviewed By: fhahn
Pull Request: https://github.com/llvm/llvm-project/pull/88025
This patch is moving out following intrinsics:
* vector.interleave2/deinterleave2
* vector.reverse
* vector.splice
from the experimental namespace.
All these intrinsics exist in LLVM for more than a year now, and are
widely used, so should not be considered as experimental.
Introduce new subclasses of VPWidenMemoryRecipe for VP
(vector-predicated) loads and stores to address multiple TODOs from
https://github.com/llvm/llvm-project/pull/76172
Note that the introduction of the new recipes also improves code-gen for
VP gather/scatters by removing the redundant header mask. With the new
approach, it is not sufficient to look at users of the widened canonical
IV to find all uses of the header mask.
In some cases, a widened IV is used instead of separately widening the
canonical IV. To handle that, first collect all VPValues representing header
masks (by looking at users of both the canonical IV and widened inductions
that are canonical) and then checking all users (recursively) of those header
masks.
Depends on https://github.com/llvm/llvm-project/pull/87411.
PR: https://github.com/llvm/llvm-project/pull/87816
VPBlendRecipe does not use the first mask operand. Removing it allows
VPlan-based DCE to remove unused mask computations.
This also fixes#87410, where unused Not VPInstructions are considered
having only their first lane demanded, but some of their operands
providing a vector value due to other users.
Fixes https://github.com/llvm/llvm-project/issues/87410
PR: https://github.com/llvm/llvm-project/pull/87770
This reverts the revert commit 589c7abb03448.
This patch includes a fix for any-of reductions and epilogue
vectorization. Extra test coverage for the issue that caused the revert
has been added in 399ff08e29d.
--------------------------------
Original commit message:
Update AnyOf reduction code generation to only keep track of the AnyOf
property in a boolean vector in the loop, only selecting either the new
or start value in the middle block.
The patch incorporates feedback from https://reviews.llvm.org/D153697.
This fixes the #62565, as now there aren't multiple uses of the
start/new values.
Fixes https://github.com/llvm/llvm-project/issues/62565
PR: https://github.com/llvm/llvm-project/pull/78304
This patch introduces generating VP intrinsics in the Loop Vectorizer.
Currently the Loop Vectorizer supports vector predication in a very
limited capacity via tail-folding and masked load/store/gather/scatter
intrinsics. However, this does not let architectures with active vector
length predication support take advantage of their capabilities.
Architectures with general masked predication support also can only take
advantage of predication on memory operations. By having a way for the
Loop Vectorizer to generate Vector Predication intrinsics, which (will)
provide a target-independent way to model predicated vector
instructions. These architectures can make better use of their
predication capabilities.
Our first approach (implemented in this patch) builds on top of the
existing tail-folding mechanism in the LV (just adds a new tail-folding
mode using EVL), but instead of generating masked intrinsics for memory
operations it generates VP intrinsics for loads/stores instructions. The
patch adds a new VPlanTransforms to replace the wide header predicate
compare with EVL and updates codegen for load/stores to use VP
store/load with EVL.
Other important part of this approach is how the Explicit Vector Length
is computed. (VP intrinsics define this vector length parameter as
Explicit Vector Length (EVL)). We use an experimental intrinsic
`get_vector_length`, that can be lowered to architecture specific
instruction(s) to compute EVL.
Also, added a new recipe to emit instructions for computing EVL. Using
VPlan in this way will eventually help build and compare VPlans
corresponding to different strategies and alternatives.
Differential Revision: https://reviews.llvm.org/D99750
Some optimizations are apply after UF and VF have been chosen. This
patch adds an extra print of the final VPlan just before
codegen/execution.
In the future, there will be additional transforms that are applied
later (interleaving for example).
PR: https://github.com/llvm/llvm-project/pull/82269
Spent a bunch of time tracing down an odd issue "in SCEV" which turned out
to be the fact that SCEV doesn't have access to TTI. As a result, the only
way for it to get range facts on vscales (to avoid collapsing ranges of
element counts and type sizes to trivial ranges on multiplies) is to look
at the vscale_range attribute. Since vscale_range is set by clang by
default, manually setting it in the tests shouldn't interfere with the
test intent.
At the moment, block and edge masks are created on demand, which means
that they are inserted at the point where they are demanded and then
cached. It is possible that the mask for a block is looked up later at a
point that's not dominated by the point where the mask has been
inserted.
To avoid this, create masks up front on entry to the corresponding basic
block and leave it to VPlan simplification to remove unneeded masks.
Note that we need to create masks for all blocks, if any of the blocks
in the loop needs predication, as computing the mask of a block depends
on the masks of its predecessor.
Needed for #76090.
https://github.com/llvm/llvm-project/pull/76635
Move vector pointer generation to a separate VPVectorPointerRecipe.
This untangles address computation from the memory recipes future
and is also needed to enable explicit unrolling in VPlan.
https://github.com/llvm/llvm-project/pull/72164
This patch starts initial modeling of VF * UF in VPlan.
Initially, introduce a dedicated VFxUF VPValue, which is then
populated during VPlan::prepareToExecute. Initially, the VF * UF
applies only to the main vector loop region. Once we extend the
scope of VPlan in the future, we may want to associate different VFxUFs
with different vector loop regions (e.g. the epilogue vector loop)
This allows explicitly parameterizing recipes that rely on the
VF * UF, like the canonical induction increment. At the moment, this
mainly helps to avoid generating some duplicated calls to vscale with
scalable vectors. It should also allow using EVL as induction increments
explicitly in D99750. Referring to VF * UF is also needed in other
places that we plan to migrate to VPlan, like the minimum trip count
check during skeleton creation.
The first version creates the value for VF * UF directly in
prepareToExecute to limit the scope of the patch. A follow-on patch will
model VF * UF computation explicitly in VPlan using recipes.
Moved from Phabricator (https://reviews.llvm.org/D157322)
There are many tests that specify a target triple/CPU flags but no
DataLayout which can lead to IR being generated that has unusual
behaviour. This commit attempts to use the default DataLayout based
on the relevant flags if there is no explicit override on the command
line or in the IR file.
One thing that is not currently possible to differentiate from a missing
datalayout `target datalayout = ""` in the IR file since the current
APIs don't allow detecting this case. If it is considered useful to
support this case (instead of passing "-data-layout=" on the command
line), I can change IR parsers to track whether they have seen such a
directive and change the callback type.
Differential Revision: https://reviews.llvm.org/D141060
Need to add NumSrcElts param to is..Mask functions in
ShuffleVectorInstruction class for better mask analysis. Mask.size() not
always matches the sizes of the permuted vector(s). Allows to better
estimate the cost in SLP and fix uses of the functions in other cases.
Differential Revision: https://reviews.llvm.org/D158449
Since the getMaximisedVFForTarget function is called twice, once for fixed-width and once for scalable, it adds no value to always return a fixed-width VF. Instead, when we are tail-folding, we can use either fixed-width or scalable vectors.
Need to add NumSrcElts param to is..Mask functions in
ShuffleVectorInstruction class for better mask analysis. Mask.size() not
always matches the sizes of the permuted vector(s). Allows to better
estimate the cost in SLP and fix uses of the functions in other cases.
Differential Revision: https://reviews.llvm.org/D158449
Need to add NumSrcElts param to is..Mask functions in
ShuffleVectorInstruction class for better mask analysis. Mask.size() not
always matches the sizes of the permuted vector(s). Allows to better
estimate the cost in SLP and fix uses of the functions in other cases.
Differential Revision: https://reviews.llvm.org/D158449
Need to add NumSrcElts param to is..Mask functions in
ShuffleVectorInstruction class for better mask analysis. Mask.size() not
always matches the sizes of the permuted vector(s). Allows to better
estimate the cost in SLP and fix uses of the functions in other cases.
Differential Revision: https://reviews.llvm.org/D158449
Need to add NumSrcElts param to is..Mask functions in
ShuffleVectorInstruction class for better mask analysis. Mask.size() not
always matches the sizes of the permuted vector(s). Allows to better
estimate the cost in SLP and fix uses of the functions in other cases.
Differential Revision: https://reviews.llvm.org/D158449
This patch implements getCFInstrCost TTI hook that mostly affects
LoopVectorizer decisions. It sets zero cost for PHI nodes and zero
throughput cost for branches (assuming that branches are likely to
be predicted). The implementation is similar to X86/AArch64/PowerPC
targets and reduces loop cost by excluding induction PHIs/loop latch
branches, which in turn leads to selecting smaller vectorization
factor.
Add first VPlan-based recipe simplification to fold (MUL A, 1) -> A.
Among other things, this enables additional simplifications after
applying versioned strides, as follow up to D147783.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D159200
Split off from D150398 to avoid builder-related diff changes there.
Using IRBuilder to create ICmps simplifies the result if both operands
are constants.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D158332
Model wrap flags directly using VPRecipeWithIRFlags and clean up the
duplicated *NUW opcodes.
D157144 will build on this and also model FMFs for VPInstruction.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D157194
Use the printOperands for printing VPInstruction's operands to be more
in line with other recipes and ensure consistent printing after D15719.
Also removes some stray spaces in print output.
This reverts commit 245ec675a4e41f7ec24dfc998720bffdc46a6c53.
Recommits eea9258648ce with a fix to only erase the instruction from the
first part if it is defined outside the loop. This fixes a
use-after-free error reported.
This reverts commit eea9258648ce73507f6f85c395de978af659d498.
That commit triggered crashes in the following testcase:
$ cat reduced.c
typedef struct {
int a[8]
} b;
typedef struct {
b *c;
short d
} e;
void f() {
int g;
char *h;
e *i = f;
short j = i->d;
int a = i->c->a[0];
for (;;)
for (; g < a; g++) {
*h = j * i->d >> 8;
h++;
}
}
$ clang -target aarch64-linux-gnu -w -c -O2 reduced.c
vrgather.vv across multiple vector registers (i.e. LMUL > 1) requires all to all data movement. This includes two conceptual sets of changes:
For permutes, we were modeling these as being linear in LMUL.
For reverse, we were modeling them as being fixed cost in LMUL.
Both were wrong, and have been adjusted to O(LMUL^2). Noticed via code inspection while looking at something else.
Its worth asking whether we should be lowering reverse to something other than a vrgather at high LMULs. That shuffle is quite expensive. (Future work)
Differential Revision: https://reviews.llvm.org/D152019
After constructing the initial VPlan, replace VPValues for versioned
strides with their constant counterparts.
Differential Revision: https://reviews.llvm.org/D147783
Now that IR flags are modeled as part of VPRecipeWithIRFlags, include
the flags when printing recipes.
Depends on D150027.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D150029
This is a follow-up to b71edfaa4ec3c998aadb35255ce2f60bba2940b0
since I forgot the lit.local.cfg files in that one.
Reformatting is done with `black`.
If you end up having problems merging this commit because you
have made changes to a python file, the best way to handle that
is to run git checkout --ours <yourfile> and then reformat it
with black.
If you run into any problems, post to discourse about it and
we will try to help.
RFC Thread below:
https://discourse.llvm.org/t/rfc-document-and-standardize-python-code-style
Reviewed By: barannikov88, kwk
Differential Revision: https://reviews.llvm.org/D150762
