diff options
| author | Aart Bik <ajcbik@google.com> | 2020-11-17 12:13:18 -0800 |
|---|---|---|
| committer | Aart Bik <ajcbik@google.com> | 2020-11-17 13:10:42 -0800 |
| commit | eced4a8e6fe3041b699bd22b5b89bea47c84c51a (patch) | |
| tree | d61cff7d3f89c9052e3b9a664c30cbc7a1c44531 /llvm/lib/DebugInfo/PDB/Native/NativeSession.cpp | |
| parent | 792f8e1114afa7f40227eee2820c6e425ee07c3b (diff) | |
| download | llvm-eced4a8e6fe3041b699bd22b5b89bea47c84c51a.zip llvm-eced4a8e6fe3041b699bd22b5b89bea47c84c51a.tar.gz llvm-eced4a8e6fe3041b699bd22b5b89bea47c84c51a.tar.bz2 | |
[mlir] [sparse] start of sparse tensor compiler support
As discussed in https://llvm.discourse.group/t/mlir-support-for-sparse-tensors/2020
this CL is the start of sparse tensor compiler support in MLIR. Starting with a
"dense" kernel expressed in the Linalg dialect together with per-dimension
sparsity annotations on the tensors, the compiler automatically lowers the
kernel to sparse code using the methods described in Fredrik Kjolstad's thesis.
Many details are still TBD. For example, the sparse "bufferization" is purely
done locally since we don't have a global solution for propagating sparsity
yet. Furthermore, code to input and output the sparse tensors is missing.
Nevertheless, with some hand modifications, the generated MLIR can be
easily converted into runnable code already.
Reviewed By: nicolasvasilache, ftynse
Differential Revision: https://reviews.llvm.org/D90994
Diffstat (limited to 'llvm/lib/DebugInfo/PDB/Native/NativeSession.cpp')
0 files changed, 0 insertions, 0 deletions