Depends On D115008
This change opens the way for D115012, and removes some corner cases in `CodegenUtils.cpp`. The `SparseTensorAttrDefs.td` already specifies that we allow `0` bitwidth for the two overhead types and that it is interpreted to mean the architecture's native width.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D115010
data point using the 3-dim tensor nell-2.tns
MLIR:
READ FILE INTO COO: 24424.369294 ms ---> improves to ----> 9638.501044 ms
SORT COO BEFORE PACK: 762.834831 ms
PACK COO TO TENSOR: 1243.376245 ms
TACO:
b file read: 13270.9 ms
b pack: 7137.74 ms
b size: (12092 x 9184 x 28818), 925300328 bytes
https://github.com/llvm/llvm-project/issues/52679
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D115696
Add convertFromMLIRSparseTensor to the supporting C shared library to convert
SparseTensorStorage to COO-flavor format.
Add Python routine sparse_tensor_to_coo_tensor to convert sparse tensor storage
pointer to numpy values for COO-flavor format tensor.
Add a Python test for sparse tensor output.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D115557
This revision implements sparse outputs (from scratch) in all cases where
the loops can be reordered with all but one parallel loops outer. If the
inner parallel loop appears inside one or more reductions loops, then an
access pattern expansion is required (aka. workspaces in TACO speak).
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D115091
First version was vectors only. With some clever "path" insertion,
we now support any d-dimensional tensor. Up next: reductions too
Reviewed By: bixia, wrengr
Differential Revision: https://reviews.llvm.org/D114024
This revision contains all "sparsification" ops and rewriting necessary to support sparse output tensors when the kernel has no reduction (viz. insertions occur in lexicographic order and are "injective"). This will be later generalized to allow reductions too. Also, this first revision only supports sparse 1-d tensors (viz. vectors) as output in the runtime support library. This will be generalized to n-d tensors shortly. But this way, the revision is kept to a manageable size.
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D113705
