javierdejesusda opened a new pull request, #19679:
URL: https://github.com/apache/tvm/pull/19679
### Motivation
`torch.logical_and` accepts input tensors of any dtype (treating any nonzero
element as `True`) and always returns a `bool` tensor.
The PyTorch frontend did not produce that `bool` result. The ExportedProgram
frontend lowered `logical_and.default` with
`self._binary_op(relax.op.logical_and, operator.and_)`, which kept the
operand
dtype and emitted `relax.op.logical_and` on non-bool inputs (for example
`float32`). `relax.op.logical_and` requires boolean inputs and otherwise
fails
`LegalizeOps` in the TOPI `logical_and`. The FX frontend did not register
`logical_and` at all, so the op was unsupported there.
### Changes
- Add a shared `_logical_and` converter in `BaseFXGraphImporter` that casts
non-bool operands to `bool` before applying `relax.op.logical_and`. Bool
operands are passed through unchanged (no redundant cast).
- Point the `logical_and.default` (ExportedProgram) registration at the new
converter, and add a `logical_and` (FX) registration that was previously
missing, matching the existing `logical_not` converter.
- Add a standalone `test_logical_and` to both the FX and ExportedProgram test
suites asserting the corrected IR (`astype` to bool on each operand, then
`logical_and`, 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.
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: [email protected]
For queries about this service, please contact Infrastructure at:
[email protected]
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
