### What does this PR do?
Type of change: Bug fix
During max calibration, `enable_stats_collection()` calls
`disable_quant()`, which sets `_if_quant=False`. The fused causal P-QDQ
attention paths bypass `TensorQuantizer.forward()` and previously
selected the Triton/Kitchen path from the configured enabled state
alone, so P quant-dequant could still execute while quantization was
inactive.
This change:
- enters the Triton P-QDQ path only when `p_bmm_quantizer._if_quant` is
true;
- bypasses all fused P-QDQ paths when the P quantizer is disabled or
quantization is inactive;
- adds focused parameterized regression tests covering Triton and
Kitchen dispatch across enabled, `disable()`, and `disable_quant()`
states.
### Usage
N/A. This restores the existing `disable_quant()` contract and does not
introduce a new API.
### Testing
- `pytest
tests/unit/torch/quantization/plugins/test_attention_quant.py`: 14
passed
- Targeted pre-commit checks passed
- Kitchen coverage verifies the full `{disable(), disable_quant()} ×
{lazy, already initialized}` dispatch matrix remains bypassed while
inactive and resumes after re-enabling
- Reproduced with the exact same ModelOpt 0.47.0rc1 wheel on both sides
on B300: [module regression build
#121](http://dlswqa-nas.nvidia.com:18880/view/yiguo/job/modelopt-quant-module/121/)
- Controlled B300 isolation passed when only the P-BMM quantizers were
hard-disabled during calibration, and also passed when the existing
single Triton attention configuration was forced
- Fixed-code B300 validation passed in three independent runs: [Jenkins
build
123](http://dlswqa-nas.nvidia.com:18880/job/modelopt-quant-module/123/),
[Jenkins build
124](http://dlswqa-nas.nvidia.com:18880/job/modelopt-quant-module/124/),
and [Jenkins build
125](http://dlswqa-nas.nvidia.com:18880/job/modelopt-quant-module/125/).
Jenkins build 123 compared all 178 module outputs byte-identically and
found 0/377 `amax` and 0/377 `scale` changes.
The confirmed impact is incorrect calibration behavior plus unstable
quantizer state and module outputs; downstream benchmark accuracy impact
has not been established.
### Before your PR is "*Ready for review*"
- Is this change backward compatible?: ✅
- If you copied code from any other sources or added a new PIP
dependency, did you follow guidance in `CONTRIBUTING.md`: N/A
- Did you write any new necessary tests?: ✅
- Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?:
N/A
- Did you get Claude approval on this PR?: N/A
### Additional Information
The failure was isolated to fused P-QDQ runtime-state dispatch.
Quantizer topology and configuration were identical in the failing
same-wheel comparison.
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
- **Bug Fixes**
- Corrected attention dispatch so disabled or inactive quantization uses
the original attention implementation.
- Preserved optimized quantized attention when quantization is enabled.
- Ensured attention masks remain unchanged when using the original
attention implementation.
- Improved fallback behavior for fused attention paths, including
correct initialization and reuse when quantization is re-enabled.
- **Tests**
- Added coverage for enabled and disabled quantization states,
attention-mask handling, fallback selection, and result consistency.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
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Signed-off-by: yingguo-trt <244492186+yingguo-trt@users.noreply.github.com>
