Introduce transform ops for "for" loops, in particular for peeling, software
pipelining and unrolling, along with a couple of "IR navigation" ops. These ops
are intended to be generalized to different kinds of loops when possible and
therefore use the "loop" prefix. They currently live in the SCF dialect as
there is no clear place to put transform ops that may span across several
dialects, this decision is postponed until the ops actually need to handle
non-SCF loops.
Additionally refactor some common utilities for transform ops into trait or
interface methods, and change the loop pipelining to be a returning pattern.
Reviewed By: springerm
Differential Revision: https://reviews.llvm.org/D127300
Vectorization is a key transformation to achieve high performance on most
architectures. In the transform dialect, vectorization is implemented as a
parameterizable transform op. It currently applies to a scope of payload IR
delimited by some isolated-from-above op, mainly because several enabling
transformations (such as affine simplification) are needed to perform
vectorization and these transformation would apply to ops other than the "main"
computational payload op. A separate "navigation" transform op that obtains the
isolated-from-above ancestor of an op is introduced in the core transform
dialect. Even though it is currently only useful for vectorization,
isolated-from-above ops are a common anchor for transformations (usually
implemented as passes) that is likely to be reused in the future.
Depends On D126374
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D126542
The Transform dialect uses the side effect modeling mechanism to record the
effects of the transform ops on the mapping between Transform IR values and
Payload IR ops. Introduce a checker pass that warns if a Transform IR value is
used after it has been freed (consumed). This pass is mostly intended as a
debugging aid in addition to the verification/assertion mechanisms in the
transform interpreter. It reports all potential use-after-free situations.
The implementation makes a series of simplifying assumptions to be simple and
conservative. A more advanced implementation would rely on the data flow-like
analysis associated with a side-effect resource rather than a value, which is
currently not supported by the analysis infrastructure.
Reviewed By: springerm
Differential Revision: https://reviews.llvm.org/D126381
Currently, the sequence of Transform dialect operations only supports a single
use of each operand (verified by the `transform.sequence` operation). This was
originally motivated by the need to guard against accessing a payload IR
operation associated with a transform IR value after this operation has likely
been rewritten by a transformation. However, not all Transform dialect
operations rewrite payload IR, in particular the "navigation" operation such as
`transform.pdl_match` do not.
Introduce memory effects to the Transform dialect operations to describe their
effect on the payload IR and the mapping between payload IR opreations and
transform IR values. Use these effects to replace the single-use rule, allowing
repeated reads and disallowing use-after-free, where operations with the "free"
effect are considered to "consume" the transform IR value and rewrite the
corresponding payload IR operations). As an additional improvement, this
enables code motion transformation on the transform IR itself.
Reviewed By: Mogball
Differential Revision: https://reviews.llvm.org/D124181
This introduces a pair of ops to the Transform dialect that connect it to PDL
patterns. Transform dialect relies on PDL for matching the Payload IR ops that
are about to be transformed. For this purpose, it provides a container op for
patterns, a "pdl_match" op and transform interface implementations that call
into the pattern matching infrastructure.
To enable the caching of compiled patterns, this also provides the extension
mechanism for TransformState. Extensions allow one to store additional
information in the TransformState and thus communicate it between different
Transform dialect operations when they are applied. They can be added and
removed when applying transform ops. An extension containing a symbol table in
which the pattern names are resolved and a pattern compilation cache is
introduced as the first client.
Depends On D123664
Reviewed By: Mogball
Differential Revision: https://reviews.llvm.org/D124007
Sequence is an important transform combination primitive that just indicates
transform ops being applied in a row. The simplest version requires fails
immediately if any transformation in the sequence fails. Introducing this
operation allows one to start placing transform IR within other IR.
Depends On D123135
Reviewed By: Mogball, rriddle
Differential Revision: https://reviews.llvm.org/D123664
