mirror of https://github.com/llvm/torch-mlir
f5dfa02523
This is our first op with error semantics, and stresses the system. There are a few design notes of special interest: - RefineTypes.cpp's note about shape inference in the presence of code that dynamically produces and error, and it is provable statically. - ATenToLinalg.cpp's notes about future automation of the ATen->linalg path. - The notes in Passes.td about using low-tech `std.assert` ops instead of `shape.assuming`. Note: Doesn't work on IREE yet due to the `std.assert` op (needs to be lowered to `vm.fail` on the IREE side). |
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| .. | ||
| __init__.py | ||
| cos_e2e.py | ||
| div_inplace_e2e.py | ||
| mm_e2e.py | ||
| mul_maximum_e2e.py | ||
| tanh_out_e2e.py | ||
| test_utils.py | ||
| torchscript_mm_e2e.py | ||
| torchscript_tanh_e2e.py | ||
| torchscript_tanh_e2e_iree.py | ||