Add a verifier checking that if a transform operation consumes a handle
(which is associated with a payload operation being erased or
recreated), it also indicates modification of the payload IR. This
hasn't been consistent in the past because of the "no-aliasing"
assumption where we couldn't have had more than one handle to an
operation, requiring some handle-manipulation operations, such as
`transform.merge_handles` to consume their operands. That assumption has
been liften and it is no longer necessary for these operations to
consume handles and thus make the life harder for the clients.
Additionally, remove TransformEffects.td that uses the ODS mechanism for
indicating side effects that works only for operands and results. It
was being used incorrectly to also indicate effects on the payload IR,
not assocaited with any IR value, and lacked the consume/produce
semantics available via helpers in C++.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D142361
Adapt the implementation of TransformEachOpTrait to the existence of
parameter values recently introduced into the transform dialect. In
particular, allow `applyToOne` hooks to return a list containing a mix
of `Operation *` that will be associated with handles and `Attribute`
that will be associated with parameter values by the trait
implementation of the transform interface's `apply` method.
Disentangle the "transposition" of the list of per-payload op partial
results to decrease its overall complexity and detemplatize the code
that doesn't really need templates. This removes the poorly documented
special handling for single-result ops with TransformEachOpTrait that
could have assigned null pointer values to handles.
Reviewed By: springerm
Differential Revision: https://reviews.llvm.org/D140979
Introduce a new transform operation to replace `tensor.empty` with
`alloc_tensor` operations. The operation is a pass-through if the target
operation is already a `alloc_tensor`; otherwise, it expects a
`tensor.empty` as a target. Currently, it does not return any results.
The operation is expected to run before `one_shot_bufferize` as
`one_shot_bufferize` rejects `tensor.empty`.
Reviewed By: springerm
Differential Revision: https://reviews.llvm.org/D140026
Expose `function-boundary-type-conversion` in `OneShotBufferizeOp`. To
reuse options between passes and transform operations, create a
`BufferizationEnums.td`.
Reviewed By: springerm
Differential Revision: https://reviews.llvm.org/D137833
In the Transform dialect extensions, provide the separate mechanism to
declare dependent dialects (the dialects the transform IR depends on)
and the generated dialects (the dialects the payload IR may be
transformed into). This allows the Transform dialect clients that are
only constructing the transform IR to avoid loading the dialects
relevant for the payload IR along with the Transform dialect itself,
thus decreasing the build/link time.
Reviewed By: springerm
Differential Revision: https://reviews.llvm.org/D130289
Introduce a transform dialect op that allows one to attempt different
transformation sequences on the same piece of payload IR until one of them
succeeds. This op fundamentally expands the scope of possibilities in the
transform dialect that, until now, could only propagate transformation failure,
at least using in-tree operations. This requires a more detailed specification
of the execution model for the transform dialect that now indicates how failure
is handled and propagated.
Transformations described by transform operations now have tri-state results,
with some errors being fundamentally irrecoverable (e.g., generating malformed
IR) and some others being recoverable by containing ops. Existing transform ops
directly implementing the `apply` interface method are updated to produce this
directly. Transform ops with the `TransformEachTransformOpTrait` are currently
considered to produce only irrecoverable failures and will be updated
separately.
Reviewed By: springerm
Differential Revision: https://reviews.llvm.org/D127724
This commit allows for One-Shot Bufferize to be used through the transform dialect. No op handle is currently returned for the bufferized IR.
Differential Revision: https://reviews.llvm.org/D125098
