The "Dim" prefix is a legacy left-over that no longer makes sense, since
we have a very strict "Dimension" vs. "Level" definition for sparse
tensor types and their storage.
The "dimension" before "level" does not really make sense Note that
renaming the actual type DimLevelType to LevelType is still TBD, since
this is an externally visible change (e.g. visible to Python API).
Changes:
1. For both dimToLvl and lvlToDim, always returns the actual map instead
of AffineMap() for identity map.
2. Updated custom builder for encoding to have default values.
3. Non-inferable lvlToDim will still return AffineMap() during
inference, so it will be caught by verifier.
Updates:
1. Verification of block sparsity.
2. Verification of singleton level type can only follow compressed or
loose_compressed levels. And all level types after singleton should be
singleton.
3. Added getBlockSize function.
4. Added an invalid encoding test for an incorrect lvlToDim map that
user provides.
Currently, dimlvlmap with identity affine map will be treated as empty
affine map. But the new syntax would treat it as an actual identity
affine map such as {d0} -> {d0}. This mismatch could raise an error when
we are comparing sparse encodings.
These methods are needed for use with `Diagnostic::operator<<` etc.
The definitions follow the pattern of `Diagnostic::str` by simply wrapping the underlying `print(raw_ostream)` method. Although there is some overhead for constructing the `std::string`, this seems like the overall most-efficient option: since this overhead only occurs on the error path (under the current intended usage). An alternative approach would be to have one method construct a `Twine` directly, and then have the print method pass the twine to the stream; however, that would mean introducing the overhead of twine construction on the common/happy path of simply printing things out.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D157643
These new methods help clean up some code for doing LvlExpr-analysis during DimExpr-inference.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D157647
This commit makes `DimLvlMap::print` consistent with `DimLvlMapParser` with respect to both the forward-decls per se, as well as the all-or-none constraint on LvlVar-bindings in LvlSpecs.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D156768
Previously, the commented out code in the `DimLvlMap` ctor would result in `VarSet::add` raising an OOB error; which should be impossible because the ctor asserted `DimLvlMap::isWF` which ensures that all variables occuring in the map are within bounds for the ranks.
The root cause of that bug was the `VarSet` ctor using `SmallBitVector::reserve` which does not actually change the size of the bitvectors (hence the subsequent OOB). This is corrected by using any of `SmallBitVector::resize`, the move-ctor, or the copy-ctor. Since the default-initialized bitvectors being modified/overwritten have size zero, there shouldn't be any significant performance difference between these three implementations.
Reviewed By: Peiming
Differential Revision: https://reviews.llvm.org/D155999
(1) uses the previously introduce API to reuse AffineExpr parser without codedup
(2) solves the look-ahead problem when parsing level spec
Reviewed By: Peiming
Differential Revision: https://reviews.llvm.org/D154254
We are in the progress of migrating to a much improved surface syntax for the Sparse Tensor Encoding Attribute (STEA).
You can see a preview of this in the StableHLO RFC at
https://github.com/openxla/stablehlo/blob/main/rfcs/20230210-sparsity.md
//**This design is courtesy Wren Romano.**//
This initial revision
(1) Introduces the first version of a new parser written by Wren Romano
(2) Introduces a simple "migration plan" using NEW_SYNTAX on the STEA, which will allow us to test the new parser with new examples, as well as migrate existing examples over without the need to rewrite them all
This first "drop" merely provides the entry points to parse the new syntax. The parser is still under active development. For example, we need to address the "lookahead" issue when parsing the lvl spec (viz. do we see l0 = d0 or a direct d0). Another larger task is to actually implement "affine" parsing (since the MLIR affine parser is not accessible in other parts of the tree).
EXAMPLE:
Currently, CSR looks like
#CSR = #sparse_tensor.encoding<{
lvlTypes = ["dense","compressed"],
dimToLvl = affine_map<(i,j) -> (i,j)>
}>
but you can "force" the new parser with
#CSR = #sparse_tensor.encoding<{
NEW_SYNTAX =
(d0, d1) -> (l0 = d0 : dense, l1 = d1 : compressed)
}>
Reviewed By: Peiming
Differential Revision: https://reviews.llvm.org/D153997
