Even though tensor.cast is not part of the sparse tensor dialect,
it may be used to cast static dimension sizes to dynamic dimension
sizes for sparse tensors without changing the actual sparse tensor
itself. Those cases should be lowered properly when replacing sparse
tensor types with their opaque pointers. Likewise, no op sparse
conversions are handled by this revision in a similar manner.
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D112173
Next step towards supporting sparse tensors outputs.
Also some minor refactoring of enum constants as well
as replacing tensor arguments with proper buffer arguments
(latter is required for more general sizes arguments for
the sparse_tensor.init operation, as well as more general
spares_tensor.convert operations later)
Reviewed By: wrengr
Differential Revision: https://reviews.llvm.org/D111771
Precursor: https://reviews.llvm.org/D110200
Removed redundant ops from the standard dialect that were moved to the
`arith` or `math` dialects.
Renamed all instances of operations in the codebase and in tests.
Reviewed By: rriddle, jpienaar
Differential Revision: https://reviews.llvm.org/D110797
The sparse constant provides a constant tensor in coordinate format. We first split the sparse constant into a constant tensor for indices and a constant tensor for values. We then generate a loop to fill a sparse tensor in coordinate format using the tensors for the indices and the values. Finally, we convert the sparse tensor in coordinate format to the destination sparse tensor format.
Add tests.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D110373
When generating code to add an element to SparseTensorCOO (e.g., when doing dense=>sparse conversion), we used to check for nonzero values on the runtime side, whereas now we generate MLIR code to do that check.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D110121
Rationale:
Passing in a pointer to the memref data in order to implement the
dense to sparse conversion was a bit too low-level. This revision
improves upon that approach with a cleaner solution of generating
a loop nest in MLIR code itself that prepares the COO object before
passing it to our "swiss army knife" setup. This is much more
intuitive *and* now also allows for dynamic shapes.
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D108491
Implements lowering dense to sparse conversion, for static tensor types only.
First step towards general sparse_tensor.convert support.
Reviewed By: ThomasRaoux
Differential Revision: https://reviews.llvm.org/D107681
Controlled by a compiler option, if 32-bit indices can be handled
with zero/sign-extention alike (viz. no worries on non-negative
indices), scatter/gather operations can use the more efficient
32-bit SIMD version.
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D103632
The experimental flag for "inplace" bufferization in the sparse
compiler can be replaced with the new inplace attribute. This gives
a uniform way of expressing the more efficient way of bufferization.
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D102538
A very elaborate, but also very fun revision because all
puzzle pieces are finally "falling in place".
1. replaces lingalg annotations + flags with proper sparse tensor types
2. add rigorous verification on sparse tensor type and sparse primitives
3. removes glue and clutter on opaque pointers in favor of sparse tensor types
4. migrates all tests to use sparse tensor types
NOTE: next CL will remove *all* obsoleted sparse code in Linalg
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D102095
This revision migrates more code from Linalg into the new permanent home of
SparseTensor. It replaces the test passes with proper compiler passes.
NOTE: the actual removal of the last glue and clutter in Linalg will follow
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D101811
wren romano