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
This patch introduces a new VPWidenMemoryRecipe base class and distinct
sub-classes to model loads and stores.
This is a first step in an effort to simplify and modularize code
generation for widened loads and stores and enable adding further more
specialized memory recipes.
PR: https://github.com/llvm/llvm-project/pull/87411
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
Add a new PtrAdd opcode to VPInstruction that corresponds to
IRBuilder::CreatePtrAdd, which creates a GEP with source element type
i8.
This is then used to model scalarizing VPWidenPointerInductionRecipe by
introducing scalar-steps to model the index increment followed by a
PtrAdd.
Note that PtrAdd needs to be able to generate code for only the first
lane or for all lanes. This may warrant introducing a separate recipe
for scalarizing that can be created without relying on the underlying
IR.
Depends on https://github.com/llvm/llvm-project/pull/80271
PR: https://github.com/llvm/llvm-project/pull/83068
A VPlan contains multiple live-ins without underlying IR, like VFxUF or
VectorTripCount. Trying to infer the scalar type of those causes a crash
at the moment.
Update VPTypeAnalysis to take a VPlan in its constructor and assign
types to those live-ins up front. All those live-ins share the type of
the canonical IV.
PR: https://github.com/llvm/llvm-project/pull/80723
Add a new recipe to model scalar cast instructions, without relying on
an underlying instruction.
This allows creating scalar casts, without relying on an underlying
instruction (like the current VPReplicateRecipe). The new recipe is
used to explicitly model both truncating the induction step and the
VPDerivedIVRecipe, thus simplifying both the recipe and code
needed to introduce it.
Truncating VPWidenIntOrFpInductionRecipes should also be modeled using
the new recipe, as follow-up.
PR: https://github.com/llvm/llvm-project/pull/78113
This patch adds initial type inferrence for VPValues. It infers the
scalar type of a VPValue, by bottom-up traversing through defining
recipes until root nodes with known types are reached (e.g. live-ins or
load recipes). The types are then propagated top down through
operations.
This is intended as building block for a VPlan-based cost model, which
will need access to type information for VPValues/recipes.
Initial testing is done by asserting the inferred type matches the type
of the result value generated for a widen and replicate recipes.
