mirror of https://github.com/llvm/torch-mlir
6dddb4d4fe
1. Added a simplified version of torch.aten.batch_norm which only handles inference and assumes the weight, bias, running_mean, running_var are not None. 2. Removed the primitive types check in verifyLinalgCompatibleTypes check since now we have proper type converter to handle torch types conversion. The checks for RankedTensorType is kept because the type converter doesn't guarantee the converted builtin tensor type is ranked. A separate verification pass to verify the invariant expected by later passes will need to be added before those can be removed as well. |
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| pytorch | ||
| README.md | ||
| __init__.py | ||
README.md
NPComp - Frontends
NPComp maintains in-tree frontends for various popular numeric-python based frameworks. In general these are:
- Considered optional components
- Target dialects maintained at the top-level of the project
- Maintained in isolation so as to facilitate moving them out to dedicated projects at an appropriate point of the lifecycle (i.e. if NPComp is successful as a general purpose target for such frameworks, then it may make sense to contribute/build each frontend to their respective up-stream project).
Frontends try to stylistically fit into the outer project except for when it is more clear/advantageous to align them with the conventions of the source project. This is approached on a case by case basis as needed. Deviations should be documented in a local style guide for the frontend.