This allows assuming_all to be replaced when all inputs are known to be
statically passing witnesses.
Differential Revision: https://reviews.llvm.org/D80306
This will later be used during canonicalization and folding steps to replace
statically known passing constraints.
Differential Revision: https://reviews.llvm.org/D80307
The operation `num_elements` determines the number of elements for a given
shape.
That is the product of its dimensions.
Differential Revision: https://reviews.llvm.org/D80281
Add the two conversion operations `index_to_size` and `size_to_index` to the
shape dialect.
This facilitates the conversion of index types between the shape and the
standard dialect.
Differential Revision: https://reviews.llvm.org/D80280
Purely cosmetic change.
The operation implementations in `Shape.cpp` are now lexicographic order.
Differential Revision: https://reviews.llvm.org/D80277
Summary:
Index is the proper type for storing shapes when constant folding, so
this fixes the previous code (which was using i64).
Differential Revision: https://reviews.llvm.org/D80600
Take advantage of equality constrains to generate the type inference interface.
This is used for equality and trivially built types. The type inference method
is only generated when no type inference trait is specified already.
This reorders verification that changes some test error messages.
Differential Revision: https://reviews.llvm.org/D80484
Summary:
This op extracts an extent from a shape.
This also is the first op which constant folds to shape.const_size,
which revealed that shape.const_size needs a folder (ConstantLike ops
seem to always need folders for the constant folding infra to work).
Differential Revision: https://reviews.llvm.org/D80394
Summary:
Additionally, this adds traits and builder methods to AssumingYieldOp
and names the input witness to the AssumingOp.
Differential Revision: https://reviews.llvm.org/D80187
Summary:
This is a basic op needed for creating shapes from SSA values
representing the extents.
Differential Revision: https://reviews.llvm.org/D79833
This is a wrapper around vector of NamedAttributes that keeps track of whether sorted and does some minimal effort to remain sorted (doing more, e.g., appending attributes in sorted order, could be done in follow up). It contains whether sorted and if a DictionaryAttr is queried, it caches the returned DictionaryAttr along with whether sorted.
Change MutableDictionaryAttr to always return a non-null Attribute even when empty (reserve null cases for errors). To this end change the getter to take a context as input so that the empty DictionaryAttr could be queried. Also create one instance of the empty dictionary attribute that could be reused without needing to lock context etc.
Update infer type op interface to use DictionaryAttr and use NamedAttrList to avoid incurring multiple conversion costs.
Fix bug in sorting helper function.
Differential Revision: https://reviews.llvm.org/D79463
- Implement a first constant fold for shape.shape_of (more ops coming in subsequent patches)
- Implement the right builder interfaces for ShapeType and other types
- Splits shape.constant into shape.const_size and shape.const_shape which plays better with dyn_cast and building vs one polymorphic op.
Also, fix the RUN line in ops.mlir to properly verify round-tripping.
- add `to_extent_tensor`
- rename `create_shape` to `from_extent_tensor` for symmetry
- add `split_at` and `concat` ops for basic shape manipulations
This set of ops is inspired by the requirements of lowering a dynamic-shape-aware batch matmul op. For such an op, the "matrix" dimensions aren't subject to broadcasting but the others are, and so we need to slice, broadcast, and reconstruct the final output shape. Furthermore, the actual broadcasting op used downstream uses a tensor of extents as its preferred shape interface for the actual op that does the broadcasting.
However, this functionality is quite general. It's obvious that `to_extent_tensor` is needed long-term to support many common patterns that involve computations on shapes. We can evolve the shape manipulation ops introduced here. The specific choices made here took into consideration the potentially unranked nature of the !shape.shape type, which means that a simple listing of dimensions to extract isn't possible in general.
Differential Revision: https://reviews.llvm.org/D76817
Tres Popp