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)
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.
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.
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
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
Generally, the cost of a memory op will scale with the number of vector registers accessed. Machines might exist which have a narrow memory access than vector register width, but machines with a wider memory access width than vector register width seem unlikely.
I noticed this because we were preferring wide loads + deinterleaves on examples where the cost of a short gather (actually a strided load) would be better. Touching 8 vector registers instead of doing a 4 element gather is not a good tradeoff.
Differential Revision: https://reviews.llvm.org/D147470
Previously, while calculating register usage due to invariants, it was assumed that invariant would always be part of widening
instructions. This resulted in calculating vector register types for vectors which cant be legalized(check the newly added test for more details).
An invariant might not always need a vector register. For e.g., invariant might just be used for iteration check.
This patch checks if the invariant is part of any widening instruction and considers register usage accordingly. Fixes issue 60493
Differential Revision: https://reviews.llvm.org/D143422
Previously, while calculating register usage due to invariants, it was assumed that invariant would always be part of widening
instructions. This resulted in calculating vector register types for vectors which cant be legalized(check the newly added test for more details).
An invariant might not always need a vector register. For e.g., invariant might just be used for iteration check.
This patch checks if the invariant is part of any widening instruction and considers register usage accordingly. Fixes issue 60493
Differential Revision: https://reviews.llvm.org/D143422
It's less clear with scalable vectors than fixed length vectors that
interleaving exposes more ILP, as scalable vectors can be thought of a
sort of hardware form of interleaving, especially with larger LMULs.
This also addresses the unexpected additional unrolling that occurs when
using larger LMULs in the loop vectorizer.
Reviewed By: craig.topper
Differential Revision: https://reviews.llvm.org/D144485
This reuses the routine implemented in 0e6f0b7 to implement several existing TODOs. Many of the operations scale linearly with LMUL; this change represents that in the cost model.
Differential Revision: https://reviews.llvm.org/D139039
This reverts commit bf15f1e489aa2f1ac13268c9081a992a8963eb5b.
The updated version fixes a crash by checking the induction kind instead
of the opcode; for integer inductions, the step is always added, but the
opcode might not be set.
This patch implements getArithmeticInstrCost for RISCV, supports cost
model for integer and float vector arithmetic instructions.
Differential Revision: https://reviews.llvm.org/D133552 (Original patch by jacquesguan. Subset by me with todos added.)
This patch splits off the logic to transform the canonical IV to a
a value for an induction with a different start and step. This
transformation only needs to be done once (independent of VF/UF) and
enables sinking of VPScalarIVStepsRecipe as follow-up.
Reviewed By: Ayal
Differential Revision: https://reviews.llvm.org/D133758
```
void vector_reverse_i64(int *A, int *B, int n) {
#pragma clang loop vectorize_width(4, scalable)
for (int i = n-1; i >= 0; i--)
A[i] = B[i] + 1;
}
```
When option: scalable-vectorization is on (or set #pragma clang loop vectorize_width(elements, scalable)), Reverse Iterators can't loop vectorization as <vscale x elements x elementType>
Reviewed By: craig.topper
Differential Revision: https://reviews.llvm.org/D125866
