mirror of https://github.com/llvm/torch-mlir
b4c7ae1e0c
* A bit gross because I took the chance to upgrade all of the backend bits to the new MLIR Python bindings and we still co-mingle the old and new for now. * Since the Python created PassManagers are configured for explicit nesting, I had to upgrade some of the pass pipelines to be explicit. * The demo in mul_maximum_e2e.py now compiles, runs through PyTorch and through the JIT, prints and asserts the same results. * I am not claiming that this is the prettiest API in this patch: consider that this is just directly using low-level APIs and there should be an intervening high level API. |
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| .. | ||
| Backend | ||
| Conversion | ||
| Dialect | ||
| Python | ||
| RefBackend | ||
| Typing | ||
| CMakeLists.txt | ||
| InitAll.h | ||