Rewrite load-store-vectorizer.

The motivation for this change is a workload generated by the XLA compiler
targeting nvidia GPUs.

This kernel has a few hundred i8 loads and stores. Merging is criti

Rewrite load-store-vectorizer.

The motivation for this change is a workload generated by the XLA compiler
targeting nvidia GPUs.

This kernel has a few hundred i8 loads and stores. Merging is critical for
performance.

The current LSV doesn't merge these well because it only considers instructions
within a block of 64 loads+stores. This limit is necessary to contain the
O(n^2) behavior of the pass. I'm hesitant to increase the limit, because this
pass is already one of the slowest parts of compiling an XLA program.

So we rewrite basically the whole thing to use a new algorithm. Before, we
compared every load/store to every other to see if they're consecutive. The
insight (from tra@) is that this is redundant. If we know the offset from PtrA
to PtrB, then we don't need to compare PtrC to both of them in order to tell
whether C may be adjacent to A or B.

So that's what we do. When scanning a basic block, we maintain a list of
chains, where we know the offset from every element in the chain to the first
element in the chain. Each instruction gets compared only to the leaders of
all the chains.

In the worst case, this is still O(n^2), because all chains might be of length
1. To prevent compile time blowup, we only consider the 64 most recently used
chains. Thus we do no more comparisons than before, but we have the potential
to make much longer chains.

This rewrite affects many tests. The changes to tests fall into two
categories.

1. The old code had what appears to be a bug when deciding whether a misaligned
vectorized load is fast. Suppose TTI reports that load <i32 x 4> align 4
has relative speed 1, and suppose that load i32 align 4 has relative speed
32.

The intent of the code seems to be that we prefer the scalar load, because
it's faster. But the old code would choose the vectorized load.
accessIsMisaligned would set RelativeSpeed to 0 for the scalar load (and not
even call into TTI to get the relative speed), because the scalar load is
aligned.

After this patch, we will prefer the scalar load if it's faster.

2. This patch changes the logic for how we vectorize. Usually this results in
vectorizing more.

Explanation of changes to tests:

- AMDGPU/adjust-alloca-alignment.ll: #1
- AMDGPU/flat_atomic.ll: #2, we vectorize more.
- AMDGPU/int_sideeffect.ll: #2, there are two possible locations for the call to @foo, and the pass is brittle to this. Before, we'd vectorize in case 1 and not case 2. Now we vectorize in case 2 and not case 1. So we just move the call.
- AMDGPU/adjust-alloca-alignment.ll: #2, we vectorize more
- AMDGPU/insertion-point.ll: #2 we vectorize more
- AMDGPU/merge-stores-private.ll: #1 (undoes changes from git rev 86f9117d476, which appear to have hit the bug from #1)
- AMDGPU/multiple_tails.ll: #1
- AMDGPU/vect-ptr-ptr-size-mismatch.ll: Fix alignment (I think related to #1 above).
- AMDGPU CodeGen: I have difficulty commenting on these changes, but many of them look like #2, we vectorize more.
- NVPTX/4x2xhalf.ll: Fix alignment (I think related to #1 above).
- NVPTX/vectorize_i8.ll: We don't generate <3 x i8> vectors on NVPTX because they're not legal (and eventually get split)
- X86/correct-order.ll: #2, we vectorize more, probably because of changes to the chain-splitting logic.
- X86/subchain-interleaved.ll: #2, we vectorize more
- X86/vector-scalar.ll: #2, we can now vectorize scalar float + <1 x float>
- X86/vectorize-i8-nested-add-inseltpoison.ll: Deleted the nuw test because it was nonsensical. It was doing `add nuw %v0, -1`, but this is equivalent to `add nuw %v0, 0xffff'ffff`, which is equivalent to asserting that %v0 == 0.
- X86/vectorize-i8-nested-add.ll: Same as nested-add-inseltpoison.ll

Differential Revision: https://reviews.llvm.org/D149893

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[LoadStoreVectorizer] Convert some tests to opaque pointers (NFC)

[NFC] Port all LoadStoreVectorizer tests to `-passes=` syntax

LoadStoreVectorizer: support different operand orders in the add sequence match

First we refactor the code which does no wrapping add sequences
match: we need to allow different operand orders for
t

LoadStoreVectorizer: support different operand orders in the add sequence match

First we refactor the code which does no wrapping add sequences
match: we need to allow different operand orders for
the key add instructions involved in the match.

Then we use the refactored code trying 4 variants of matching operands.

Originally the code relied on the fact that the matching operands
of the two last add instructions of memory index calculations
had the same LHS argument. But which operand is the same
in the two instructions is actually not essential, so now we allow
that to be any of LHS or RHS of each of the two instructions.
This increases the chances of vectorization to happen.

Reviewed By: volkan

Differential Revision: https://reviews.llvm.org/D103912

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Change the context instruction for computeKnownBits in LoadStoreVectorizer pass

This change enables cases for which the index value for the first
load/store instruction in a pair could be a function

Change the context instruction for computeKnownBits in LoadStoreVectorizer pass

This change enables cases for which the index value for the first
load/store instruction in a pair could be a function argument. This
allows using llvm.assume to provide known bits information in such
cases.

Patch by Viacheslav Nikolaev. Thanks!

Differential Revision: https://reviews.llvm.org/D101680

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Add support for llvm.assume intrinsic to the LoadStoreVectorizer pass

Patch by Viacheslav Nikolaev. Thanks!

LoadStoreVectorizer: Match nested adds to prove vectorization is safe

If both OpA and OpB is an add with NSW/NUW and with the same LHS operand,
we can guarantee that the transformation is safe if we

LoadStoreVectorizer: Match nested adds to prove vectorization is safe

If both OpA and OpB is an add with NSW/NUW and with the same LHS operand,
we can guarantee that the transformation is safe if we can prove that OpA
won't overflow when IdxDiff added to the RHS of OpA.

Review: https://reviews.llvm.org/D79817

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