252 Commits (41aa562fb48aa11f1d30badf278cc9fb00ff80bd)

Author	SHA1	Message	Date
Sean Silva	1b769f7841	Extend GlobalizeObjectGraph to handle torch.prim.GetAttr returning NnModuleType ... This happens in practice. With this, we can globalize slots for the non-trivial classifier layer obtained from https://github.com/NVIDIA/NeMo/blob/main/tutorials/nlp/Joint_Intent_and_Slot_Classification.ipynb This also adds support for tuple return types, which were needed by that model.	2021-02-19 10:23:25 -08:00
Sean Silva	158c5c484d	Implement GlobalizeObjectGraph transformation. ... This required restructuring of how we model TorchScript on import. The main difference is that now we split out a `torch.class_type` that holds methods and declarations of the types of each slot. This is more consistent with TorchScript (our previous representation was "denormalized"). Recommended reading order: 1. check out the description of `torch.class_type` in `TorchOps.td` and look at `test/Dialect/Torch/ops.mlir` and `frontends/pytorch/test/module_import/` to familiarize with the new representation. - Just look at the new IR. The diff between the old names and new names is confusing. 2. check out `test/Dialect/Torch/globalize-object-graph*.mlir` and read along with the pass description in `include/npcomp/Dialect/Torch/Transforms/Passes.td` 3. Read the code in `GlobalizeObjectGraph.cpp` and miscellaneous changes in `ivalue_importer.cpp`, `TorchOps.cpp`, etc.	2021-02-18 18:18:47 -08:00