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jingyu-mlandClaude Opus 4.8 6b4ad85849 Qwen-Image diffusers PTQ: FP8 / NVFP4 / NVFP4-SVDQuant HF checkpoints (#1706) 
### What does this PR do?

Type of change: New feature

Adds **Qwen-Image** (`Qwen/Qwen-Image`, `QwenImageTransformer2DModel`)
to the diffusers quantization example and exports HuggingFace
checkpoints in three precisions — **FP8**, **NVFP4**, and **NVFP4 +
SVDQuant** — through the unified HF export.

- Registers `--model qwen-image` (lazy diffusers import; no
`trust_remote_code`).
- Transformer-block-range recipe: quantizes only the linears under
`transformer_blocks`, keeping the **first 2 / last 2** blocks (and
everything outside `transformer_blocks`) in original precision. Applied
**before** calibration so SVDQuant never mutates the excluded blocks.
Expressed with the top-level `enable` `QuantizerCfgEntry` field
(disable-all → re-enable `transformer_blocks` → disable first/last-N).
- SVDQuant export (AWQ-style): promotes quantizer-owned tensors to clean
module-level safetensors keys at export time —
`weight_quantizer.svdquant_lora_a/b → <module>.svdquant_lora_a/b` and
`input_quantizer._pre_quant_scale → <module>.pre_quant_scale` — with a
documented `NVFP4_SVD` `quantization_config` (`group_size`,
`has_zero_point: false`, `pre_quant_scale: true`, `lora_rank`). **Core
SVDQuant quantization code (`modelopt/torch/quantization`) is
unchanged.**
- Shared export-path change — **intentionally global** (applies to all
diffusers exports — SDXL / Flux / Wan, not just Qwen; the full export
suite was verified green on GB200): `hide_quantizers_from_state_dict`
now strips quantizer state from *all* modules (not just quant-linears)
so calibrated norm-layer input quantizers no longer leak
`input_quantizer._amax`. (An earlier `max_shard_size` workaround was
dropped after merging `main`: #1794 makes the ComfyUI layerwise-metadata
post-processing a no-op unless explicitly opted in, so a default sharded
export no longer hits the unsupported-sharded path.)

### Usage

```bash
python examples/diffusers/quantization/quantize.py \
    --model qwen-image --override-model-path <Qwen-Image> --model-dtype BFloat16 \
    --format fp4 --quant-algo svdquant --lowrank 32 \
    --calib-size 64 --n-steps 20 \
    --hf-ckpt-dir <out>
# FP8:   --format fp8 --quant-algo max
# NVFP4: --format fp4 --quant-algo max
```

### Testing

- Focused unit + example tests pass on GB200 (sm_100): block-range
recipe, `NVFP4_SVD` config schema, SVDQuant forward/fold (LoRA stays on
`weight_quantizer`), Qwen dummy-input / strict-QKV-fusion / promotion,
pipeline loading, and the diffusers HF-export test for Qwen FP8 / NVFP4
/ SVDQuant.
- Full `tests/examples/diffusers/test_export_diffusers_hf_ckpt.py` is
green (SDXL, Flux, Qwen, Wan2.2) — confirms the shared export changes do
not regress other models.
- End-to-end on the real `Qwen/Qwen-Image` (~20B): all three formats
export valid HF checkpoints — only `transformer_blocks` 2..57 quantized,
nothing outside, no quantizer/`_amax` leak, correct
`weight_scale`(`_2`)/`input_scale`, promoted SVDQuant keys
(rank-consistent shapes), and the expected `quantization_config`.

### Before your PR is "*Ready for review*"

- Is this change backward compatible?: ✅ <!-- live-model LoRA storage
unchanged; existing exports unaffected -->
- 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?: ❌ <!-- new-example feature; add a
CHANGELOG.rst entry if required -->
- Did you get Claude approval on this PR?: ❌ <!-- run /claude review -->

### Additional Information

All changes are confined to the diffusers example
(`examples/diffusers/quantization`) plus the shared export path
(`modelopt/torch/export`); the core quantization library is untouched.

### Follow-up (next step): fused-QKV SVDQuant for sglang / Nunchaku

This export keeps attention `q/k/v` (and `add_q/k/v_proj`) as
**separate** projections — the diffusers-native layout. That matches
sglang's bf16 / FP8 / plain-NVFP4 paths (which also keep QKV separate)
and ModelOpt/TRT-LLM consumers, so those load 1:1.

sglang's **NVFP4-SVDQuant (Nunchaku)** path, however, builds a **fused**
`to_qkv` with a *single* fused rank-r LoRA in Nunchaku-native format
(`proj_down`/`proj_up`, `smooth_factor`, `wscales`/`wtscale`). Our
per-projection tensors (`svdquant_lora_a/b` + `pre_quant_scale`; three
independent rank-r decompositions) are not directly loadable there — and
cannot be fused at load time, because the fp16 weight residual needed to
derive a single fused rank-r is not preserved after export.

**Planned next step:** an opt-in fused-QKV SVDQuant export mode that
fuses q/k/v **before** SVDQuant calibration (yielding one rank-r over
the fused weight) and emits a Nunchaku-compatible layout, enabling
lower-latency fused-QKV inference in sglang. Tracked as a separate
follow-up.


<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit

* **New Features**
* Added Qwen-Image (`QWEN_IMAGE`) model quantization and Diffusers
export support
  * Added NVFP4_SVD (SVDQuant) export configuration support
* Added transformer block-range quantization recipes (exclude first/last
blocks)
* **Bug Fixes**
  * Improved missing-pipeline error messaging for Qwen-Image
* Prevented quantizer-related tensor/buffer leakage by promoting and
cleaning quantizer outputs during export
* **Tests**
  * Added Qwen-Image HF checkpoint export tests and offline fixtures
  * Added unit coverage for SVDQuant promotion/clean state-dict keys
<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Signed-off-by: Jingyu Xin <jingyux@nvidia.com>
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
 2026-07-07 17:21:31 -07:00
jingyu-ml 26ae8da517 [2/3] Implicit Gemm NVFP4 (#1227) 
### What does this PR do?

Type of change: new feature <!-- Use one of the following: Bug fix, new
feature, new example, new tests, documentation. -->

- Add Conv3D implicit GEMM kernel with BF16 WMMA tensor cores and fused
NVFP4 activation quantization for video diffusion VAE layers
- Integrate into _QuantConv3d via QuantModuleRegistry — automatically
dispatched when NVFP4 quantization is applied to nn.Conv3d
- Move kernel from `experimental/conv/ to modelopt/torch/kernels/conv/`;
move tests to `tests/gpu/torch/quantization/kernels/`

### Testing
<!-- Mention how have you tested your change if applicable. -->

- Added test cases to measure the difference between cuDNN and our CUDA
implicit GEMM kernel
- Added an NVFP4 fake quantization test using CUDA code

### Before your PR is "*Ready for review*"

Make sure you read and follow [Contributor
guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md)
and your commits are signed (`git commit -s -S`).

Make sure you read and follow the [Security Best
Practices](https://github.com/NVIDIA/Model-Optimizer/blob/main/SECURITY.md#security-coding-practices-for-contributors)
(e.g. avoiding hardcoded `trust_remote_code=True`, `torch.load(...,
weights_only=False)`, `pickle`, etc.).

- Is this change backward compatible?: ✅ <!--- If ❌, explain why. -->
- If you copied code from any other sources or added a new PIP
dependency, did you follow guidance in `CONTRIBUTING.md`: ✅ <!---
Mandatory -->
- Did you write any new necessary tests?: ✅ <!--- Mandatory for new
features or examples. -->
- Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?:
✅ <!--- Only for new features, API changes, critical bug fixes or
backward incompatible changes. -->

### Additional Information
<!-- E.g. related issue. -->


<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit

* **New Features**
* Per-backbone quantization/export in a single run with per-backbone
checkpoints and backbone-aware quant filters
* Configurable NVFP4 block-size via CLI/config; improved NVFP4 Conv3D
inference path and Wan 2.2 quantization support
* **Bug Fixes**
* Video-model calibration now respects extra params and forces video
decoding during calibration
* **Documentation**
* Added comprehensive Conv3D implicit‑GEMM kernel documentation; removed
experimental Conv3D prototype docs/benchmark
* **Tests**
* New Wan 2.2 quantization/export tests and expanded Conv3D/FP4 kernel
test coverage
<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Signed-off-by: Jingyu Xin <jingyux@nvidia.com>
 2026-04-19 12:20:14 +05:30
jingyu-ml 1070d895dc Flux2-Dev Quantization (#947) 
## What does this PR do?

**Type of change:** new example <!-- Use one of the following: Bug fix,
new feature, new example, new tests, documentation. -->

**Overview:** 

- Register Flux2Attention and Flux2ParallelSelfAttention in the
quantization plugin so bmm quantizers are patched (enables
--quantize-mha).
- Add Flux2-specific dummy input generation for HF checkpoint export.
- Guard check_conv_and_mha with hasattr for bmm quantizer attributes 

## Usage
<!-- You can potentially add a usage example below. -->

```bash
python quantize.py \
    --model flux2-dev \
    --model-dtype BFloat16 \
    --format fp4 --batch-size 2 --calib-size 1 \
    --n-steps 20 --quantized-torch-ckpt-save-path ./flux2-dev-fp4.pt --collect-method default \
    --hf-ckpt-dir ./flux2-dev-fp4
```

## Testing
<!-- Mention how have you tested your change if applicable. -->

## Before your PR is "*Ready for review*"
<!-- If you haven't finished some of the above items you can still open
`Draft` PR. -->

- **Make sure you read and follow [Contributor
guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md)**
and your commits are signed.
- **Is this change backward compatible?**: Yes<!--- If No, explain why.
-->
- **Did you write any new necessary tests?**: No
- **Did you add or update any necessary documentation?**: No
- **Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?**:
No <!--- Only for new features, API changes, critical bug fixes or bw
breaking changes. -->

## Additional Information
<!-- E.g. related issue. -->


<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit

* **New Features**
* Added Flux2-dev model support with Flux2-compatible dummy input
generation and default inference params (768×1024, guidance scale 4.0).

* **Refactor**
* Made attention quantization disabling more robust by iterating
available quantizers before disabling.

* **Infrastructure**
* Flux2 attention components are now optional and registered only when
present to avoid import issues.

* **Tests**
* Added Flux2 test helpers and coverage validating Flux2 dummy input
shapes.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Signed-off-by: Jingyu Xin <jingyux@nvidia.com>
 2026-03-14 11:42:02 -05:00
jingyu-ml 812e8c60a2 Minor update on the LTX2 NVFP4 recipe (#1010) 
### What does this PR do?

Type of change: minor code change <!-- Use one of the following: Bug
fix, new feature, new example, new tests, documentation. -->

1. Update the default calibration dataset for LTX_VIDEO_DEV and LTX2
from Gustavosta/Stable-Diffusion-Prompts to nkp37/OpenVid-1M, which
provides video-specific captions better suited for video model
calibration.
2. update the default recipe for ltx2: first 3 and last 3 layers stays
at higher precision.

### Usage

### Testing
<!-- Mention how have you tested your change if applicable. -->

### Before your PR is "*Ready for review*"

Make sure you read and follow [Contributor
guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md)
and your commits are signed (`git commit -s -S`).

Make sure you read and follow the [Security Best
Practices](https://github.com/NVIDIA/Model-Optimizer/blob/main/SECURITY.md#security-coding-practices-for-contributors)
(e.g. avoiding hardcoded `trust_remote_code=True`, `torch.load(...,
weights_only=False)`, `pickle`, etc.).

- Is this change backward compatible?: ✅ / ❌ / N/A <!--- If ❌, explain
why. -->
- If you copied code from any other sources or added a new PIP
dependency, did you follow guidance in `CONTRIBUTING.md`: ✅ / ❌ / N/A
<!--- Mandatory -->
- Did you write any new necessary tests?: ✅ / ❌ / N/A <!--- Mandatory
for new features or examples. -->
- Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?:
✅ / ❌ / N/A <!--- Only for new features, API changes, critical bug fixes
or backward incompatible changes. -->

### Additional Information
<!-- E.g. related issue. -->


<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->

## Summary by CodeRabbit

* **Chores**
  * Updated default dataset configuration for LTX-Video and LTX2 models.
* Refined model filtering pattern for LTX-Video to support additional
model components.

<!-- end of auto-generated comment: release notes by coderabbit.ai -->

Signed-off-by: Jingyu Xin <jingyux@nvidia.com>
 2026-03-13 22:42:48 -05:00
jingyu-ml d78797b466 Update the LTX2 API calls during the calibration (#926) 
## What does this PR do?

**Type of change:** Bug fix <!-- Use one of the following: Bug fix, new
feature, new example, new tests, documentation. -->

**Overview:** 

Update LTX-2 integration to match latest upstream API

1. The LTX-2 codebase removed/replaced several APIs. This MR updates all
affected files:
2. Replace cfg_guidance_scale with MultiModalGuiderParams: The pipeline
__call__ no longer accepts a single cfg_guidance_scale float. It now
requires two MultiModalGuiderParams objects (video_guider_params and
audio_guider_params) that control CFG, STG, rescale, cross-modality
guidance, and skip-step settings. Updated in ltx-2.py, ltx-2-fp8.py,
ltx-2-onestage.py, calibration.py, and models_utils.py.
3. Replace fp8transformer with QuantizationPolicy: The
TI2VidTwoStagesPipeline constructor no longer accepts the fp8transformer
boolean flag. FP8 quantization is now configured via
quantization=QuantizationPolicy.fp8_cast(). Updated in ltx-2-fp8.py and
pipeline_manager.py (with backwards-compatible support for the old
--extra-param fp8transformer=true CLI flag).
4. Remove DEFAULT_CFG_GUIDANCE_SCALE constant: Replaced by
DEFAULT_VIDEO_GUIDER_PARAMS and DEFAULT_AUDIO_GUIDER_PARAMS in all
import sites.

## Usage
<!-- You can potentially add a usage example below. -->

```bash
python quantize.py --model ltx-2 --format fp4 --batch-size 1 --calib-size 1 --n-steps 40 --extra-param checkpoint_path=./ltx-2-19b-dev-fp8.safetensors --extra-param distilled_lora_path=./ltx-2-19b-distilled-lora-384.safetensors --extra-param spatial_upsampler_path=./ltx-2-spatial-upscaler-x2-1.0.safetensors --extra-param gemma_root=./gemma-3-12b-it-qat-q4_0-unquantized --extra-param fp8transformer=true --hf-ckpt-dir ./ltx2-nvfp4
```

## Testing
<!-- Mention how have you tested your change if applicable. -->

## Before your PR is "*Ready for review*"
<!-- If you haven't finished some of the above items you can still open
`Draft` PR. -->

- **Make sure you read and follow [Contributor
guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md)**
and your commits are signed.
- **Is this change backward compatible?**: Yes/No <!--- If No, explain
why. -->
- **Did you write any new necessary tests?**: Yes/No
- **Did you add or update any necessary documentation?**: Yes/No
- **Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?**:
Yes/No <!--- Only for new features, API changes, critical bug fixes or
bw breaking changes. -->

## Additional Information
<!-- E.g. related issue. -->


<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->

## Summary by CodeRabbit

## Release Notes

* **Updates**
  * Default resolution for LTX2 models adjusted to 768x1280
* Guidance parameter configuration updated for video and audio pipelines
  * FP8 quantization parameter handling refined

<!-- end of auto-generated comment: release notes by coderabbit.ai -->

Signed-off-by: Jingyu Xin <jingyux@nvidia.com>
 2026-02-24 14:23:28 -08:00
jingyu-ml 2e43c80609 [2/4] Diffusion Quantized ckpt export (#810) 
## What does this PR do?

**Type of change:** New feature <!-- Use one of the following: Bug fix,
new feature, new example, new tests, documentation. -->

**Overview:** 

This MR adds HuggingFace checkpoint export support for LTX‑2 by treating
TI2VidTwoStagesPipeline as a diffusion-like pipeline, exporting only the
stage‑1 transformer (with QKV-fusion-enabled dummy inputs) and falling
back to writing model.safetensors when save_pretrained isn’t available.
It also preserves the original forward in DynamicModule patching
(_forward_pre_dm) so downstream callers can still invoke the pre-patched
forward implementation.

**Changes**

1. Added the calibration & quantization support of the LTX2, even with
FP8 precision.
2. Preserve original forward before `DynamicModule` patching: when
patching forward, we now stash the pre-patched implementation in
`self._forward_pre_dm` (once) so downstream code can still call the
original forward, then re-bind forward to the class implementation. This
is needed for the LTX2 FP8 calibration.
3. Added LTX‑2 HF export path: `export_hf_checkpoint()` now also treats
ltx_pipelines.ti2vid_two_stages.TI2VidTwoStagesPipeline as a
“diffusion-like” object and routes it through
_export_diffusers_checkpoint() (import guarded; no hard dependency).
4. Generalized component discovery: introduced
get_diffusion_components() (aliasing the old get_diffusers_components)
to support non-diffusers pipelines; for LTX‑2 it returns only
stage_1_transformer.
5. Enabled QKV fusion for LTX‑2 backbone: added a model-aware dummy
forward generator (generate_diffusion_dummy_forward_fn) that builds
minimal LTX Modality inputs (including correct timesteps broadcasting)
so shared-input hooks can run and fuse QKV when applicable.
6. Export fallback for non-save_pretrained modules: when a component
lacks save_pretrained (LTX‑2 transformer), export now writes
model.safetensors + minimal config.json instead of pytorch_model.bin.

Plans

- [x] [1/4] Add the basic functionalities to support limited image
models with NVFP4 + FP8, with some refactoring on the previous LLM code
and the diffusers example. PIC: @jingyu-ml
- [x] [2/4] Add support to more video gen models. PIC: @jingyu-ml 
- [ ] [3/4] Add test cases, refactor on the doc, and all related README.
PIC: @jingyu-ml
- [ ] [4/4] Add the final support to ComfyUI. PIC @jingyu-ml 

## Usage
<!-- You can potentially add a usage example below. -->
```bash
python quantize.py --model ltx-2 --format fp4 --batch-size 64 --calib-size 1 --n-steps 40 --extra-param checkpoint_path=/home/scratch.omniml_data_2/jingyux/models/LTX-2/ltx-2-19b-dev-fp8.safetensors --extra-param distilled_lora_path=/home/scratch.omniml_data_2/jingyux/models/LTX-2/ltx-2-19b-distilled-lora-384.safetensors --extra-param spatial_upsampler_path=/home/scratch.omniml_data_2/jingyux/models/LTX-2/ltx-2-spatial-upscaler-x2-1.0.safetensors --extra-param gemma_root=/home/scratch.omniml_data_2/jingyux/models/LTX-2/gemma-3-12b-it-qat-q4_0-unquantized --extra-param fp8transformer=true --hf-ckpt-dir ./ltx2-nvfp4
```

## Testing
<!-- Mention how have you tested your change if applicable. -->

## Before your PR is "*Ready for review*"
<!-- If you haven't finished some of the above items you can still open
`Draft` PR. -->

- **Make sure you read and follow [Contributor
guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md)**
and your commits are signed.
- **Is this change backward compatible?**: Yes <!--- If No, explain why.
-->
- **Did you write any new necessary tests?**:No
- **Did you add or update any necessary documentation?**:No
- **Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?**:No
<!--- Only for new features, API changes, critical bug fixes or bw
breaking changes. -->

## Additional Information
<!-- E.g. related issue. -->

<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->

## Summary by CodeRabbit

## Release Notes

* **New Features**
* Added LTX-2 video model support with complete quantization and export
pipeline integration
* Introduced `--extra-param` CLI option for flexible model configuration
and parameter passing
* Enhanced export capabilities with broader diffusion model
compatibility

* **Chores**
* Changed default model data type from Half to BFloat16 for improved
numerical stability

<sub>✏️ Tip: You can customize this high-level summary in your review
settings.</sub>

<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Signed-off-by: Jingyu Xin <jingyux@nvidia.com>
 2026-02-04 10:41:33 +00:00
jingyu-ml 668b8a19e8 [1/3] Diffusion ckpt export for NVFP4 & FP8 (#781) 
## What does this PR do?

**Type of change:** New feature <!-- Use one of the following: Bug fix,
new feature, new example, new tests, documentation. -->

**Overview:** 

This PR adds support for exporting quantized diffusers models (DiT,
Flux, SD3, UNet, etc.) to HuggingFace checkpoint format, enabling
deployment to inference frameworks like SGLang, vLLM, and TensorRT-LLM.

**Changes**

New file: `diffusers_utils.py`
- Dummy input generation for various diffusion models
- Pipeline component extraction helpers
- QKV projection detection and grouping
- `hide_quantizers_from_state_dict()` context manager for clean saves

Refactored: `unified_export_hf.py`
- New `_fuse_qkv_linears_diffusion()` for QKV amax fusion
- `_export_diffusers_checkpoint()` to export full pipelines (models +
tokenizers + schedulers etc.)

Plans

- [x] [1/3] Add the basic functionalities to support limited image
models with NVFP4 + FP8, with some refactoring on the previous LLM code
and the diffusers example. PIC: @jingyu-ml
- [ ] [2/3] Add support to more video gen modelsPIC: @jingyu-ml 
- [ ] [3/3] Add test cases, refactor on the doc, and all related README.
PIC: @jingyu-ml

## Usage
<!-- You can potentially add a usage example below. -->
```
mtq.quantize(pipe, quant_config, forward_call)
export_hf_checkpoint(pipe, export_dir=hf_ckpt_dir)
```

## Testing
<!-- Mention how have you tested your change if applicable. -->

## Before your PR is "*Ready for review*"
<!-- If you haven't finished some of the above items you can still open
`Draft` PR. -->

- **Make sure you read and follow [Contributor
guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md)**
and your commits are signed.
- **Is this change backward compatible?**: Yes <!--- If No, explain why.
-->
- **Did you write any new necessary tests?**:No
- **Did you add or update any necessary documentation?**:No
- **Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?**:No
<!--- Only for new features, API changes, critical bug fixes or bw
breaking changes. -->

## Additional Information
<!-- E.g. related issue. -->


<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit

## New Features
* Added HuggingFace checkpoint export support for quantized diffusion
models with configurable output directory
* Introduced new `--hf-ckpt-dir` CLI argument for specifying checkpoint
export destination
* Extended export functionality to support selective component exports
from diffusion pipelines
* Enhanced quantized model export with improved component handling and
multi-stage checkpoint generation

<sub>✏️ Tip: You can customize this high-level summary in your review
settings.</sub>
<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Signed-off-by: Jingyu Xin <jingyux@nvidia.com>
 2026-01-21 23:11:09 +00:00