This clarifies that these methods only work in append mode, not for general insertions. This is a prospective change towards https://github.com/llvm/llvm-project/issues/51652 which also performs random-access insertions, so we want to avoid confusion.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D120929
Previously, convertToMLIRSparseTensor assumes identity storage ordering and all
compressed dimensions. This change extends the function with two parameters for
users to specify the storage ordering and the sparsity of each dimension.
Modify PyTACO to reflect this change.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D120643
These routines will need to be specialized a lot more based on value types,
index types, pointer types, and permutation/dimension ordering. This is a
careful first step, providing some functionality needed in PyTACO bridge.
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D120154
While testing LLVM 14.0.0 rc1 on Solaris, I ran into a compile failure:
from /var/llvm/llvm-14.0.0-rc1/rc1/llvm-project/mlir/lib/ExecutionEngine/SparseTensorUtils.cpp:22:
/usr/include/sys/types.h:103:16: error: conflicting declaration ‘typedef short int index_t’
103 | typedef short index_t;
| ^~~~~~~
In file included from
/var/llvm/llvm-14.0.0-rc1/rc1/llvm-project/mlir/lib/ExecutionEngine/SparseTensorUtils.cpp:17:
/var/llvm/llvm-14.0.0-rc1/rc1/llvm-project/mlir/include/mlir/ExecutionEngine/SparseTensorUtils.h:26:7:
note: previous declaration as ‘using index_t = uint64_t’
26 | using index_t = uint64_t;
| ^~~~~~~
The same issue had already occured in the past and fixed in D72619
<https://reviews.llvm.org/D72619>. More detailed explanation can also be
found there.
Tested on `amd64-pc-solaris2.11` and `sparcv9-solaris2.11`.
Differential Revision: https://reviews.llvm.org/D119323
Rationale:
Although file I/O is a bit alien to MLIR itself, we provide two convenient ways
for sparse tensor I/O. The input part was already there (behind the swiss army
knife sparse_tensor.new). Now we have a sparse_tensor.out to write out data. As
before, the ops are kept vague and may change in the future. For now this
allows us to compare TACO vs MLIR very easily.
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D117850
This guarantees the preconditions of fromCOO; whereas prior to this, one could call the constructor directly with an unsorted tensor, which would cause fromCOO to misbehave.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D117167
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
