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[https://nvbugspro.nvidia.com/bug/6778095] Fix fused P-QDQ to respect disabled quantization during calibration - #2434
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cjluo-nv merged 3 commits intoSep 22, 2026
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ChangesAttention quantization fallback
Priority: ➖ Normal Estimated code review effort: 2 (Simple) | ~10 minutes Change: Bug fix Merge Risk: ⚪ Minimal · up to The change respects disabled P quantization by using the original attention path, with regression coverage for enabled and disabled dispatch states. It is ready to merge. 🚥 Pre-merge checks | ✅ 5 | ❌ 1❌ Failed checks (1 warning)
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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.pytests/unit/torch/quantization/plugins/test_attention_quant.py
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## main #2434 +/- ##
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Nudge: the runtime-state guard looks correct, but Kitchen bypass coverage and fixed-commit GPU validation remain incomplete.
Needs action:
- Extend
test_attention_quant.pyto coverdisable()anddisable_quant()with Kitchen available, including an already-initialized Kitchen callable; assert neither initialization nor execution occurs while disabled and dispatch resumes after re-enabling. - Run the pending fixed-commit GPU calibration regression and record the result in the PR body.
No action needed:
- The new FP8 dispatch test covers active/inactive quantization and preserves the positional attention mask. Existing tests are unchanged.
sychen52
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Good cache. Thanks! |
Signed-off-by: yingguo-trt <244492186+yingguo-trt@users.noreply.github.com>
Signed-off-by: yingguo-trt <244492186+yingguo-trt@users.noreply.github.com>
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@cjluo-nv Addressed both follow-ups in
Could an authorized vetter please run |
Signed-off-by: yingguo-trt <244492186+yingguo-trt@users.noreply.github.com>
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Approve: the prior correctness and validation concerns are resolved, with focused regression coverage and three reported fixed-code B300 passes.
No action needed:
- ✔️ Resolved since the last review: positional-mask duplicate binding, Kitchen bypass/re-enable coverage across all four runtime-state cases, and fixed-code GPU validation.
- Existing tests are unchanged; the added tests reuse the shared attention fixture.
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/ok to test 264a571 |
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… disabled quantization during calibration (#2434) ### 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 --> --------- Signed-off-by: yingguo-trt <244492186+yingguo-trt@users.noreply.github.com> Signed-off-by: Chad Voegele <cvoegele@nvidia.com>
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… disabled quantization during calibration (#2434) ### 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 --> --------- Signed-off-by: yingguo-trt <244492186+yingguo-trt@users.noreply.github.com> Signed-off-by: Chad Voegele <cvoegele@nvidia.com>
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#2438 #2402 #2434 (#2504) ## Cherry-picked PRs - #2202 - #2317 - #2314 - #2403 - #2203 - #2413 - #2436 - #2451 - #2336 - #2438 - #2402 - #2434 <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **New Features** * Added Step-3.7 PTQ recipes for routed experts and MLPs using NVFP4, with FP8 KV-cache support. * Added SDXL FP4 quantization with FP8 convolution support. * Expanded FP4 ONNX export guidance to include Flux and SDXL. * Added BF16 compatibility for FP8 ONNX export. * **Bug Fixes** * Improved ONNX handling for large models, external data, metadata, opsets, and output types. * Improved attention quantization and invalid dynamic-scale handling. * Quantization now reports unmatched weight configurations, with an override for valid pipeline-parallel workflows. * **Documentation** * Updated calibration, ONNX opset, CLI, Step-3.7, and TensorRT-LLM deployment guidance. * Updated deployment guidance to use unified Hugging Face exports with the TensorRT-LLM PyTorch backend. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Zhiyu Cheng <zhiyuc@nvidia.com> Signed-off-by: Chad Voegele <cvoegele@nvidia.com> Signed-off-by: ajrasane <131806219+ajrasane@users.noreply.github.com> Signed-off-by: Noey Yang <174223378+noeyy-mino@users.noreply.github.com> Signed-off-by: Ajinkya Rasane <ajinkyaashwin@gmail.com> Signed-off-by: Yue <yueshen@nvidia.com> Signed-off-by: yingguo-trt <244492186+yingguo-trt@users.noreply.github.com> Co-authored-by: Zhiyu <zhiyuc@nvidia.com> Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com> Co-authored-by: Ajinkya Rasane <131806219+ajrasane@users.noreply.github.com> Co-authored-by: Codex <noreply@openai.com> Co-authored-by: Codex <codex@openai.com> Co-authored-by: noeyy-mino <174223378+noeyy-mino@users.noreply.github.com> Co-authored-by: Ajinkya Rasane <ajinkyaashwin@gmail.com> Co-authored-by: yueshen2016 <39203804+yueshen2016@users.noreply.github.com> Co-authored-by: yingguo-trt <244492186+yingguo-trt@users.noreply.github.com>
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What does this PR do?
Type of change: Bug fix
During max calibration,
enable_stats_collection()callsdisable_quant(), which sets_if_quant=False. The fused causal P-QDQ attention paths bypassTensorQuantizer.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:
p_bmm_quantizer._if_quantis true;disable(), anddisable_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{disable(), disable_quant()} × {lazy, already initialized}dispatch matrix remains bypassed while inactive and resumes after re-enablingamaxand 0/377scalechanges.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"
CONTRIBUTING.md: N/AAdditional 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
Tests