torch-mlir

Commit Graph

Author	SHA1	Message	Date
Sean Silva	c424c24ed8	Bump llvm-project to c68d2895a1f4019b387c69d1e5eec31b0eb5e7b0 - dialect registration - StringAttr::get: order of context arg - math dialect - LogicalResult nodiscard - error message for invalid broadcast	2021-02-22 12:23:24 -08:00
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

Author

SHA1

Message

Date

Sean Silva

c424c24ed8

Bump llvm-project to c68d2895a1f4019b387c69d1e5eec31b0eb5e7b0

- dialect registration
- StringAttr::get: order of context arg
- math dialect
- LogicalResult nodiscard
- error message for invalid broadcast

2021-02-22 12:23:24 -08:00

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

3 Commits (c424c24ed8330483ebe91fe7f1001e57243e3561)