When we vectorize a scalar constant, the vector constant is inserted before its
first user if the scalar constant is defined outside the loops to be vectorized.
It is possible that the vector constant does not dominate all its users. To fix
the problem, we find the innermost vectorized loop that encloses that first user
and insert the vector constant at the top of the loop body.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D106609
This patch adds support for vectorizing loops with 'iter_args'
implementing known reductions along the vector dimension. Comparing to
the non-vector-dimension case, two additional things are done during
vectorization of such loops:
- The resulting vector returned from the loop is reduced to a scalar
using `vector.reduce`.
- In some cases a mask is applied to the vector yielded at the end of
the loop to prevent garbage values from being written to the
accumulator.
Vectorization of reduction loops is disabled by default. To enable it, a
map from loops to array of reduction descriptors should be explicitly passed to
`vectorizeAffineLoops`, or `vectorize-reductions=true` should be passed
to the SuperVectorize pass.
Current limitations:
- Loops with a non-unit step size are not supported.
- n-D vectorization with n > 1 is not supported.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D100694
Introduce a basic support for parallelizing affine loops with reductions
expressed using iteration arguments. Affine parallelism detector now has a flag
to assume such reductions are parallel. The transformation handles a subset of
parallel reductions that are can be expressed using affine.parallel:
integer/float addition and multiplication. This requires to detect the
reduction operation since affine.parallel only supports a fixed set of
reduction operators.
Reviewed By: chelini, kumasento, bondhugula
Differential Revision: https://reviews.llvm.org/D101171
This patch adds support for vectorizing loops with 'iter_args' when those loops
are not a vector dimension. This allows vectorizing outer loops with an inner
'iter_args' loop (e.g., reductions). Vectorizing scenarios where 'iter_args'
loops are vector dimensions would require more work (e.g., analysis,
generating horizontal reduction, etc.) not included in this patch.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D97892
This patch replaces the root-terminal vectorization approach implemented in the
Affine vectorizer with a topological order approach that vectorizes all the
operations within the target loop nest. These are the most important changes
introduced by the new algorithm:
* Removed tracking of root and terminal ops. Existing vectorization
functionality is preserved and extended so that loop nests without
root-terminal chains can be vectorized.
* Vectorizing a loop nest now only requires a single topological traversal.
* A new vector loop nest is incrementally built along the vectorization
process. The original scalar loop is kept intact. No cloning guard is needed
to recover the scalar loop if vectorization fails. This approach also
simplifies the challenging task of replacing a loop operation amid the
vectorization process without invalidating the analysis information that
depends on the original loop.
* Vectorization of specific operations has been implemented as independent,
preparing them to be moved to a potential vectorization interface.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D97442
This patch fixes a heap-use-after-free introduced by the recent changes
in the vectorizer: https://reviews.llvm.org/rG95db7b4aeaad590f37720898e339a6d54313422f
The problem is due to the way candidate loops are visited. All candidate loops
are pattern-matched beforehand using the 'NestedMatch' utility. These matches may
intersect with each other so it may happen that we try to vectorize a loop that
was previously vectorized. The new vectorization algorithm replaces the original
loops that are vectorized with new loops and, therefore, any reference to the
original loops in the pre-computed matches becomes invalid.
This patch fixes the problem by classifying the candidate matches into buckets
before vectorization. Each bucket contains all the matches that intersect. The
vectorizer uses these buckets to make sure that we only vectorize *one* match from
each bucket, at most.
Differential Revision: https://reviews.llvm.org/D98382
This patch adds support for vectorizing loops with 'iter_args' when those loops
are not a vector dimension. This allows vectorizing outer loops with an inner
'iter_args' loop (e.g., reductions). Vectorizing scenarios where 'iter_args'
loops are vector dimensions would require more work (e.g., analysis,
generating horizontal reduction, etc.) not included in this patch.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D97892
This patch replaces the root-terminal vectorization approach implemented in the
Affine vectorizer with a topological order approach that vectorizes all the
operations within the target loop nest. These are the most important changes
introduced by the new algorithm:
* Removed tracking of root and terminal ops. Existing vectorization
functionality is preserved and extended so that loop nests without
root-terminal chains can be vectorized.
* Vectorizing a loop nest now only requires a single topological traversal.
* A new vector loop nest is incrementally built along the vectorization
process. The original scalar loop is kept intact. No cloning guard is needed
to recover the scalar loop if vectorization fails. This approach also
simplifies the challenging task of replacing a loop operation amid the
vectorization process without invalidating the analysis information that
depends on the original loop.
* Vectorization of specific operations has been implemented as independent,
preparing them to be moved to a potential vectorization interface.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D97442
This makes ignoring a result explicit by the user, and helps to prevent accidental errors with dropped results. Marking LogicalResult as no discard was always the intention from the beginning, but got lost along the way.
Differential Revision: https://reviews.llvm.org/D95841
Given that OpState already implicit converts to Operator*, this seems reasonable.
The alternative would be to add more functions to OpState which forward to Operation.
Reviewed By: rriddle, ftynse
Differential Revision: https://reviews.llvm.org/D92266
Adding missing code that should have been part of "D85869: Utility to
vectorize loop nest using strategy."
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D88346
This patch adds a utility based on SuperVectorizer to vectorize an
affine loop nest using a given vectorization strategy. This strategy allows
targeting specific loops for vectorization instead of relying of the
SuperVectorizer analysis to choose the right loops to vectorize.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D85869
Rename 'setInsertionPointAfter(Value)' API to avoid ambiguity with
'setInsertionPointAfter(Operation *)' for SingleResult operations which
implicitly convert to Value (see D86756).
Differential Revision: https://reviews.llvm.org/D87155
This patch adds basic support for vectorization of uniform values to SuperVectorizer.
For now, only invariant values to the target vector loops are considered uniform. This
enables the vectorization of loops that use function arguments and external definitions
to the vector loops. We could extend uniform support in the future if we implement some
kind of divergence analysis algorithm.
Reviewed By: nicolasvasilache, aartbik
Differential Revision: https://reviews.llvm.org/D86756
This patch refactors a small part of the Super Vectorizer code to
a utility so that it can be used independently from the pass. This
aligns vectorization with other utilities that we already have for loop
transformations, such as fusion, interchange, tiling, etc.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D84289
Summary:
Vector transfer ops semantic is extended to allow specifying a per-dimension `masked`
attribute. When the attribute is false on a particular dimension, lowering to LLVM emits
unmasked load and store operations.
Differential Revision: https://reviews.llvm.org/D80098
Summary:
This revision makes the use of vector transfer operatons more idiomatic by
allowing to omit and inferring the permutation_map.
Differential Revision: https://reviews.llvm.org/D80092
Summary:
This makes a common pattern of
`dyn_cast_or_null<OpTy>(v.getDefiningOp())` more concise.
Differential Revision: https://reviews.llvm.org/D79681
This revision refactors the structure of the operand storage such that there is no additional memory cost for resizable operand lists until it is required. This is done by using two different internal representations for the operand storage:
* One using trailing operands
* One using a dynamically allocated std::vector<OpOperand>
This allows for removing the resizable operand list bit, and will free up APIs from needing to workaround non-resizable operand lists.
Differential Revision: https://reviews.llvm.org/D78875
Summary: Functional.h contains many different methods that have a direct, and more efficient, equivalent in LLVM. This revision replaces all usages with the LLVM equivalent, and removes the header. This is part of larger cleanup, pr45513, merging MLIR support facilities into LLVM.
Differential Revision: https://reviews.llvm.org/D78053
Summary: Pass options are a better choice for various reasons and avoid the need for static constructors.
Differential Revision: https://reviews.llvm.org/D77707
Summary:
This is much cleaner, and fits the same structure as many other tablegen backends. This was not done originally as the CRTP in the pass classes made it overly verbose/complex.
Differential Revision: https://reviews.llvm.org/D77367
This revision removes all of the CRTP from the pass hierarchy in preparation for using the tablegen backend instead. This creates a much cleaner interface in the C++ code, and naturally fits with the rest of the infrastructure. A new utility class, PassWrapper, is added to replicate the existing behavior for passes not suitable for using the tablegen backend.
Differential Revision: https://reviews.llvm.org/D77350
This revision adds support for generating utilities for passes such as options/statistics/etc. that can be inferred from the tablegen definition. This removes additional boilerplate from the pass, and also makes it easier to remove the reliance on the pass registry to provide certain things(e.g. the pass argument).
Differential Revision: https://reviews.llvm.org/D76659
This generates a Passes.td for all of the dialects that have transformation passes. This removes the need for global registration for all of the dialect passes.
Differential Revision: https://reviews.llvm.org/D76657
Move some of the affine transforms and their test cases to their
respective dialect directory. This patch does not complete the move, but
takes care of a good part.
Renames: prefix 'affine' to affine loop tiling cl options,
vectorize -> super-vectorize
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Differential Revision: https://reviews.llvm.org/D76565
Diego Caballero