javierdejesusda opened a new pull request, #19645:
URL: https://github.com/apache/tvm/pull/19645
### Motivation
`torch.logical_not` accepts an input tensor of any dtype (treating any
nonzero
element as `True`) and always returns a `bool` tensor.
The PyTorch frontend previously lowered it with
`self._unary_op(relax.op.logical_not)`.
`relax.op.logical_not` is a unary arithmetic op that passes its input dtype
through,
so a non-bool input (for example `float32`) produced a `float32` result
instead of
the `bool` result PyTorch returns. This is a dtype mismatch against the
reference
PyTorch semantics for both the FX and ExportedProgram frontends.
### Changes
- Add a shared `_logical_not` converter in `BaseFXGraphImporter` that casts
non-bool
inputs to `bool` before applying `relax.op.logical_not`. Bool inputs are
passed
through unchanged (no redundant cast).
- Point the `logical_not` (FX) and `logical_not.default` (ExportedProgram)
registrations at the new converter.
- Update the FX test and add a standalone ExportedProgram `test_logical_not`
to assert
the corrected IR (`astype` to bool, then `logical_not`, 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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