Fix oob dereference (#29012)
This pull request strengthens shape inference validation for several
custom BERT-related ONNX operators by adding explicit rank checks for
input tensors. These changes ensure that input tensors meet minimum rank
requirements, improving error messaging and preventing incorrect shape
propagation.
**Enhanced shape validation for custom ONNX operators:**
*RelativePositionBias and GatedRelativePositionBias:*
- Added checks to ensure `bias_table` (for `RelativePositionBias`) and
`token_offset` (for `GatedRelativePositionBias`) inputs have rank ≥ 2,
with clear error messages if not.
[[1]](diffhunk://#diff-8bf31275168b1e4a2aecd6760acf0ef92347134b003dfdc687c5d3cec4a178ecR1959-R1965)
[[2]](diffhunk://#diff-8bf31275168b1e4a2aecd6760acf0ef92347134b003dfdc687c5d3cec4a178ecR2228-R2230)
*CausalConvWithState:*
- Added checks to ensure both `input` and `weight` tensors have rank ≥
2, failing shape inference with descriptive errors if violated.
*LinearAttention:*
- Added checks to ensure `query` and `value` tensors have rank ≥ 3 for
both output and state shape inference, with early returns or errors if
requirements are not met.
[[1]](diffhunk://#diff-8bf31275168b1e4a2aecd6760acf0ef92347134b003dfdc687c5d3cec4a178ecR2460-R2465)
[[2]](diffhunk://#diff-8bf31275168b1e4a2aecd6760acf0ef92347134b003dfdc687c5d3cec4a178ecR2483-R2486)
*SkipLayerNormalization:*
- Added a check to ensure the `input` tensor has rank ≥ 1, improving
error reporting for invalid input shapes.
