This corresponds with the previous work to make shape.broadcast nary.
Additionally, simplify the ConvertShapeConstraints pass. It now doesn't
lower an implicit shape.is_broadcastable. This is still the same in
combination with shape-to-standard when the 2 passes are used in either
order.
Differential Revision: https://reviews.llvm.org/D96401
Enable querying shape function library ops from the module. Currently
supports singular or array of them (as long as array has all unique ops
in mappings). The preferred canonical form would have one library, but
given the invariant on the mapping, this can easily be achieved by a
simple merging pass.
Preferred the attribute approach vs naming convention as these could be
added in multiple different ways.
This op returns a boolean value indicating whether 2 ops are
broadcastable or not. This follows the same logic as the other ops with
broadcast in their names in the shape dialect.
Concretely, shape.is_broadcastable returning true implies that
shape.broadcast will not give an error, and shape.cstr_broadcastable
will not result in an assertion failure. Similarly, false implies an
error or assertion failure.
A "structural" type conversion is one where the underlying ops are
completely agnostic to the actual types involved and simply need to update
their types. An example of this is shape.assuming -- the shape.assuming op
and the corresponding shape.assuming_yield op need to update their types
accordingly to the TypeConverter, but otherwise don't care what type
conversions are happening.
Also, the previous conversion code would not correctly materialize
conversions for the shape.assuming_yield op. This should have caused a
verification failure, but shape.assuming's verifier wasn't calling
RegionBranchOpInterface::verifyTypes (which for reasons can't be called
automatically as part of the trait verification, and requires being
called manually). This patch also adds that verification.
Differential Revision: https://reviews.llvm.org/D89833
I realized when using this that one can't get very good error messages
without an additional message attribute.
Differential Revision: https://reviews.llvm.org/D87875
This op is a catch-all for creating witnesses from various random kinds
of constraints. In particular, I when dealing with extents directly,
which are of `index` type, one can directly use std ops for calculating
the predicates, and then use cstr_require for the final conversion to a
witness.
Differential Revision: https://reviews.llvm.org/D87871
This patch also fixes a minor issue that shape.rank should allow
returning !shape.size. The dialect doc has such an example for
shape.rank.
Differential Revision: https://reviews.llvm.org/D85556
This is an operation that can returns a new ValueShape with a different shape. Useful for composing shape function calls and reusing existing shape transfer functions.
Just adding the op in this change.
Differential Revision: https://reviews.llvm.org/D84217
In a context in which `shape.broadcast` is known not to produce an error value,
we want it to operate solely on extent tensors. The operation's behavior is
then undefined in the error case as the result type cannot hold this value.
Differential Revision: https://reviews.llvm.org/D84933
This adds conversions for const_size and to_extent_tensor. Also, cast-like operations are now folded away if the source and target types are the same.
Differential Revision: https://reviews.llvm.org/D84745
The current transformation to shape.reduce does not support tensor values.
This adds the required changes to make that work, including fixing the builder
for shape.reduce.
Differential Revision: https://reviews.llvm.org/D84744
Previous changes generalized some of the operands and results. Complete
a larger group of those to simplify progressive lowering. Also update
some of the declarative asm form due to generalization. Tried to keep it
mostly mechanical.
Based on https://reviews.llvm.org/D84439 but less restrictive, else we
don't allow shape_of to be able to produce a ranked output and doesn't
allow for iterative refinement here. We can consider making it more
restrictive later.
The operation `shape.shape_of` now returns an extent tensor `tensor<?xindex>` in
cases when no error are possible. All consuming operation will eventually accept
both, shapes and extent tensors.
Differential Revision: https://reviews.llvm.org/D84160
The operation `shape.const_shape` was used for constants of type shape only.
We can now also use it to create constant extent tensors.
Differential Revision: https://reviews.llvm.org/D84157
This folds shape.broadcast where at least one operand is a scalar to the
other operand.
Also add an assemblyFormat for shape.broadcast and shape.concat.
Differential Revision: https://reviews.llvm.org/D83854
Add `shape.shape_eq` operation to the shape dialect.
The operation allows to test shapes and extent tensors for equality.
Differential Revision: https://reviews.llvm.org/D82528
Summary:
Added canonicalization and folding was:
- Folding when either input is an attribute indicating a scalar input
which can always be broadcasted.
- Canonicalization where it can be determined that either input shape is
a scalar.
- Canonicalization where the partially specified input shapes can be
proven to be broadcastable always.
Differential Revision: https://reviews.llvm.org/D83194
Replace any `rank(shape_of(tensor))` that relies on a ranked tensor with the
corresponding constant `const_size`.
Differential Revision: https://reviews.llvm.org/D82077
