This is a first revision in a small series of changes that removes
duplications between direct encoding methods and sparse tensor type
wrapper methods (in favor of the latter abstraction, since it provides
more safety). The goal is to simply end up with "just" SparseTensorType
Rather than extending sparsifier codegen with higher order
non-permutations, we follow the path of rewriting linalg generic ops
into higher order operations. That way, code generation will simply work
out of the box. This is a very first proof-of-concept rewriting of that
idea.
This commit changes the SparseTensor LLVM dialect lowering from using
`llvm.ptr<i8>` to `llvm.ptr`. This change ensures that the lowering now
properly relies on opaque pointers, instead of working with already type
erased i8 pointers.
This test used to be here, but somehow got lost while linalg rewrote
their interfaces. It is essential to test this on entry of
sparsification, however, since all subsequent analysis simply assumes
tensor types.
Fixes:
https://github.com/llvm/llvm-project/issues/64325
Making the materialize-from-reader method part of the Swiss army knife
suite again removes a lot of redundant boiler plate code and unifies the
parameter setup into a single centralized utility. Furthermore, we now
have minimized the number of entry points into the library that need a
non-permutation map setup, simplifying what comes next
`StorageSpecifierToLLVMPass` does not have to be part of the
bufferization mini pipeline. It can run after the bufferization
pipeline. This is desirable because it keeps the bufferization pipeline
smaller.
Also fix incorrect bufferization API usage: `bufferizeOp` instead of
`bufferizeModuleOp` was used, even though function boundaries were
bufferized.
This completely centralizes all set up related to dim2lvl and lvl2dim
for the runtime library (and even parts of direct IR codegen) into one
place! And all comptatible with the MapRef data structure that should be
used in all remaining clients of dim2lvl and lvl2dim.
NOTE: the convert_x2y.mlir tests were becoming too overloaded
so I decided to bring them back to the basics; if e.g.
more coverage of the foreach is required, they should
go into isolated smalle tests
This revision introduces a MapRef, which will support a future
generalization beyond permutations (e.g. block sparsity). This revision
also unifies the conversion/codegen paths for the sparse_tensor.new
operation from file (eg. the readers). Note that more unification is
planned as well as general affine dim2lvl and lvl2dim (all marked with
TODOs).
