### 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.
Javier De Jesus
f5e8a3acb3