javierdejesusda opened a new pull request, #19756:
URL: https://github.com/apache/tvm/pull/19756
### Motivation
`torch.logical_or` and `torch.logical_xor` accept input tensors of any dtype
(treating any nonzero element as `True`) and always return a `bool` tensor.
Neither op was handled by the PyTorch frontend. The ExportedProgram frontend
did
not register `logical_or.default` / `logical_xor.default`, and the FX
frontend
did not register `logical_or` / `logical_xor`, so importing a model that uses
either op failed early with `Unsupported function types`.
This follows up on #19679 (`logical_and`) and addresses the explicit question
raised in #19743: whether `logical_or` and `logical_xor` need the same
handling.
### Changes
- Add shared `_logical_or` and `_logical_xor` converters in
`BaseFXGraphImporter`
that cast non-bool operands to `bool` before applying
`relax.op.logical_or` /
`relax.op.logical_xor`. Bool operands are passed through unchanged (no
redundant cast).
- Register `logical_or.default` / `logical_xor.default` (ExportedProgram) and
`logical_or` / `logical_xor` (FX), matching the existing `logical_and`
converter.
- Add standalone `test_logical_or` and `test_logical_xor` to both the FX and
ExportedProgram test suites, asserting the corrected IR (`astype` to bool
on
each operand, then the logical op, producing a `bool` output).
### Notes
The cast to `bool` lowers to an elementwise nonzero test, so it matches
PyTorch's "nonzero is True" semantics for float, integer, and NaN inputs.
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