mirror of https://github.com/llvm/torch-mlir
be8375d350
This PR introduces a sparse_jit wrapper that can run simple models with sparse tensor inputs end-to-end. The implementation shows all required components on modifying sparse tensor types with a 1:N relation on the call sites. Two tests shows that the JIT runs end-to-end while computing the correct results. More details to follow (generalizing to COO and different ranks, as well as support for *output* sparse tensors), but the general concepts are all here now. **_Update: Thanks to Rob, bump to proper LLVM/MLIR hash is done!_** _**NOTE that all parameter passing changes are nicely done "downstream" in MLIR, so very little changes are required in torch-mlir code proper**_ --------- Co-authored-by: Franz Haniel <77495327+frafranz@users.noreply.github.com> Co-authored-by: Franz Haniel <franz.haniel@amd.com> |
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| .. | ||
| e2e_testing | ||
| examples | ||
| python | ||
| test | ||
| tools | ||
| CMakeLists.txt | ||