torch-mlir/frontends/pytorch/python
Sean Silva 179105ca3e Add basic MLP's to the e2e curriculum.
These tests pass on the reference backend.

- Add aten.linear op + shape xfer function + ATen->Linalg lowering.
 - Note: this needs to be more automated, and needs to cover more cases.
 - Current not implemented caveats:
  - size-1 broadcasting for bias vector (either static-size-1 or ? case)
  - higher-rank aten.linear ops (not produced by torch.nn.Linear though)
  - type promotion (still don't even know the exact rules here)
- Add folder for torch.derefine op. Now the inliner can clean it up as
  it inlines. (call boundaries are a main place we need to insert
  torch.derefine) This is brittle -- the other important case is control
  flow which will need to be handled via an extension to
  RefineTypes.cpp (as will more robust call handling). River has an
  in-flight patch to update it to the new dataflow framework so I didn't
  want to do anything intrusive here.
    - Also adjust torch.derefine syntax to use the keyword `to` instead of
      `->`, as most type-only, cast-like ops do.
2021-04-27 12:18:54 -07:00
..
torch_mlir Miscellaneous changes while trying to work on ResNet18 2021-04-27 11:51:11 -07:00
torch_mlir_utils/codegen Add basic MLP's to the e2e curriculum. 2021-04-27 12:18:54 -07:00
CMakeLists.txt Create public API for torch_mlir python code. 2020-10-13 16:36:49 -07:00