mirror of https://github.com/llvm/torch-mlir
a710237437
* [custom op] Generalize shape library logic to work with dtypes This commit generalizes the shape library logic, so that dtype rules for ops can also be expressed using the same mechanism. In other words, each op can now have a shape function and a dtype function specified in Python that is imported during lowering to calculate the shapes and dtypes throught a program. For more information about how to specify a dtype function, see the updated `docs/adding_a_shape_and_dtype_function.md`. For those not familiar with how the shape library works, the file `docs/calculations_lib.md` provides an overview. |
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| images | ||
| abstract_interp_lib.md | ||
| adding_abstract_interpretation_functions.md | ||
| adding_an_e2e_test.md | ||
| architecture.md | ||
| code_owners.md | ||
| development.md | ||
| long_term_roadmap.md | ||
| ltc_backend.md | ||
| ltc_examples.md | ||