[https://nvbugspro.nvidia.com/bug/6778095] Fix fused P-QDQ to respect disabled quantization during calibration

[yingguo-trt]

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 a focused regression test covering causal attention with disable_quant().

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: 9 passed
• Targeted pre-commit checks passed
• Reproduced with the exact same ModelOpt 0.47.0rc1 wheel on both sides on B300: module regression build #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

The fixed-commit GPU end-to-end validation is still pending. 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?: 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.

Summary by CodeRabbit

• Bug Fixes

  • Corrected attention behavior so disabled or non-quantizing configurations use the original attention implementation instead of the quantized path.
  • Preserved optimized quantized attention when quantization is enabled.
  • Preserved fallback support for fused attention where applicable.
  • Ensured attention masks remain unchanged when using the original attention implementation.

• Tests

  • Added coverage for enabled and disabled quantization states, attention-mask handling, fallback selection, and result consistency.

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• tests/unit/torch/quantization/plugins/test_attention_quant.py

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📝 Walkthrough

Walkthrough

_quantized_attention now uses Triton P-QDQ attention only when P quantization is enabled. Otherwise, it removes a duplicate attention_mask and calls the original attention implementation. Parameterized tests verify both dispatch paths.

Changes

Attention quantization fallback

Layer / File(s)	Summary
Guard fused attention and verify fallback modelopt/torch/quantization/plugins/huggingface.py, tests/unit/torch/quantization/plugins/test_attention_quant.py	_quantized_attention checks p_bmm_quantizer._if_quant before using Triton P-QDQ attention. Tests cover enabled and disabled states, mask forwarding, result preservation, and exclusion of Kitchen initialization.

Priority: ⬇️ Low

Estimated code review effort: 2 (Simple) | ~10 minutes

Change: Bug fix

Merge Risk: ⚪ Minimal · up to 0f816

The disabled-quantization fallback preserves the tested positional attention-mask behavior and no merge-blocking issue remains.

🚥 Pre-merge checks | ✅ 5 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 40.00% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 5 functions across 2 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (5 passed)

Check name	Status	Explanation
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Security Anti-Patterns	✅ Passed	PASS. The pull request changes only modelopt/torch/quantization/plugins/huggingface.py and a test file. The added modelopt lines only gate fused attention dispatch and call the original attention im…
Description Check	✅ Passed	Check skipped - CodeRabbit’s high-level summary is enabled.
Title check	✅ Passed	The title clearly identifies the main change: fused P-QDQ now respects disabled quantization during calibration.

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👉 Steps to fix this

Actionable comments posted: 1

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Inline comments:
In `@modelopt/torch/quantization/plugins/huggingface.py`:
- Around line 257-258: Update the causal fallback around
original_attention_interface so a positional attention_mask is not forwarded
alongside the normalized kwargs["attention_mask"] when _if_quant is false;
preserve the mask exactly once while retaining other positional arguments. Add
regression coverage for a positional mask with a callback that also declares an
explicit attention_mask parameter.

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• modelopt/torch/quantization/plugins/huggingface.py
• tests/unit/torch/quantization/plugins/test_attention_quant.py

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[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 71.41%. Comparing base (3c87751) to head (0f816a2).

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #2434      +/-   ##
==========================================
- Coverage   71.42%   71.41%   -0.01%     
==========================================
  Files         590      590              
  Lines       64698    64703       +5     
==========================================
  Hits        46209    46209              
- Misses      18489    18494       +5     

Flag	Coverage Δ
unit	57.81% <100.00%> (+<0.01%)	⬆️

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