### What does this PR do?
- Add experimental support for transformers >=5.0 and remove deprecated
usages:
https://github.com/huggingface/transformers/blob/main/MIGRATION_GUIDE_V5.md
- ⚠️ For accelerate examples that used `--warmup-ratio: float`
(deprecated in 5.x), we now change it to `--warmup-steps: float | int`
which works as ratio if float but only for 5.x. For 4.x, it will error
out if float and prompt user to change back to `--warmup-ratio` or pass
an int absolute step count.
- ⚠️ Unified Hugging Face checkpoint export for quantized checkpoints
may not work for some models with transformers>=5.0 yet as it requires a
lot of fixes (e.g. change in how MoE experts are organized)
- ~Add Workaround for TRT-LLM's import of deprecated transformers
functions so trt-llm based gpu unit tests work fine. Still deployment
for models needs proper fixes directly in TRT-LLM hence llm/vlm ptq
example tests still run with transformers 4.57~
- Everything except PTQ and Export (mainly MoE) should work fine with
transformers>=5.0
- Bump min torch to 2.8 and enable 2.11 cicd testing
- NOTE: Upcoming Nemo:26.04 container comes with transformers 5.3
### Testing
<!-- Mention how have you tested your change if applicable. -->
- [x] CI/CD tests passing
- [x] Manually tested unit tests, gpu tests with transformers 4.56 and
5.4
- [x] Manually tested example tests (except trt-llm container tests)
with transformers 4.56 and 5.4
- [x] 2-gpu nightly CICD tests manually triggered and passing: [gpu
tests](https://github.com/NVIDIA/Model-Optimizer/actions/runs/23867257540),
[example
tests](https://github.com/NVIDIA/Model-Optimizer/actions/runs/23867260643)
### 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`, using
`torch.load(..., weights_only=True)`, avoiding `pickle`, etc.).
- Is this change backward compatible?: ✅ <!--- If ❌, explain why. -->
- If you copied code from any other source, did you follow IP policy in
[CONTRIBUTING.md](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md#-copying-code-from-other-sources)?:
N/A <!--- 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. -->
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **New Features**
* Make remote-code usage opt-in via a configurable --trust_remote_code
flag across examples and tools.
* **Bug Fixes**
* Improve checkpoint/resume detection and related training guidance to
avoid erroneous errors.
* **Refactor**
* Consolidate dtype/config naming, switch warmup settings from ratio →
steps, and unify tokenizer invocation patterns.
* **Documentation**
* Simplify changelog title and add misc notes for release 0.44.
* **Chores**
* Remove scheduled PR-branch cleanup workflow and relax/remove several
transformers version pins.
* **Tests**
* Adjust test gates, skips, and structures to align with updated deps
and behaviors.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
### What does this PR do?
- Add secure checkpoint loading support using
`torch.serialization.add_safe_globals([cls])`. This also removes 1
existing pickle usage.
- Remove hard-coded `trust_remote_code=True`
- Replaces https://github.com/NVIDIA/Model-Optimizer/pull/1056 by
@RinZ27
### Testing
<!-- Mention how have you tested your change if applicable. -->
CICD tests ran
### 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
NVBug: 5999336
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **New Features**
* Added safe checkpoint save/load helpers and a --trust_remote_code CLI
flag in examples to control remote-code loading.
* **Bug Fixes**
* Checkpoint loading now defaults to safer, weights-only semantics to
reduce arbitrary-code exposure.
* **Documentation**
* CHANGELOG updated with security guidance and opt-in procedure for
unsafe checkpoint loading.
* **Tests**
* New unit tests validating the safe-load behavior.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: RinZ27 <222222878+RinZ27@users.noreply.github.com>
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
Co-authored-by: RinZ27 <222222878+RinZ27@users.noreply.github.com>
## What does this PR do?
**Type of change:** ? <!-- Use one of the following: Bug fix, new
feature, new example, new tests, documentation. -->
new feature
**Overview:** ?
- This PR adds the sparse attention calibration algorithm
- Chunked prefill to support long ctx_len
- Separated calibration for prefill and decode
## Usage
<!-- You can potentially add a usage example below. -->
```python
import modelopt.torch.sparsity.attention_sparsity as mtsa
# Apply sparse attention with calibration
model = mtsa.sparsify(model, config=SKIP_SOFTMAX_CALIB)
# Print summary - now shows actual thresholds
mtsa.print_sparse_attention_summary(model)
# Output:
# Method: flash_skip_softmax, Threshold: Dynamic (λ=437.395926)
# Or llm_eval integration
# HuggingFace sparse attention example
python examples/llm_sparsity/attention_sparsity/hf_sa.py \
--pyt_ckpt_path Qwen/Qwen3-4B \
--sparse_attn skip_softmax_calib
```
# The calibration method
## Calibration Algorithm
- Implemented the Inverse Power model: scale_factor = k / (1 -
sparsity)^p
- Fit model parameters (k, p) per phase using scipy.optimize.curve_fit
- At inference: threshold = k / (1 - target_sparsity)^p / seqlen
## Why Choosing the Inverse Power model?
The inverse power model better fits the relationship between sparsity
ratio and threshold_scale_factor.
<img width="2388" height="1082" alt="sparsity_model_analysis"
src="https://github.com/user-attachments/assets/4dfb45d4-8c16-4f15-a878-c8e08a9b6128"
/>
## Runtime Flexibility
- Target sparsity can be changed at inference time without recalibration
- Users can adjust module._sparse_method_instance.target_sparse_ratio
dynamically
- Threshold automatically adapts to sequence length
## Testing
<!-- Mention how have you tested your change if applicable. -->
The calibration results for `Qwen/Qwen3-30B-A3B-Thinking-2507` are shown
below and are mostly consistent with the ground-truth numbers collected
from the kernel side.
```
Prefill Calibration Results:
Model: scale_factor = k / (1 - sparsity)^p
Fitted k: 1003.3990
Fitted p: 1.2589
R-squared: 0.827549
Scale factors for different target sparsities:
Target Scale Factor
---------- ---------------
50% 2401.35
70% 4568.26
80% 7610.98
90% 18214.70
95% 43591.65
```
## 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/TensorRT-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/TensorRT-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. -->
---------
Signed-off-by: Kai Xu <kaix@nvidia.com>
Keval Morabia