### What does this PR do?
Type of change: Bug fix
Remote autotuning is supported in TensorRT from version 10.15, but fails
with Autotune as it's checking for 10.16+. This PR fixes that check and
updates documentation accordingly.
### Usage
```python
# Add a code snippet demonstrating how to use this
```
### Testing
See bug 6034518.
### 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 <!---
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)?:
✅ <!--- 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
* **Documentation**
* Added a Remote Autotuning guide for TensorRT 10.15+ with CLI examples;
updated examples to require `--safe --skipInference`.
* **Updates**
* Lowered TensorRT minimum requirement for remote autotuning from 10.16
to 10.15.
* Clarified CLI help text for trtexec/autotune arguments.
* **Bug Fixes**
* trtexec-based autotuning now verifies the trtexec executable version
when checking compatibility.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: gcunhase <4861122+gcunhase@users.noreply.github.com>
Signed-off-by: dmoodie <dmoodie@nvidia.com>
Co-authored-by: dmoodie <dmoodie@nvidia.com>
### What does this PR do?
Added an extensive guide for the ModelOpt recipe system: recipe
structure, YAML schema (quantization-focused), built-in discovery and
path resolution, floating-point shorthand conversion, example usage
(Python/CLI), authoring guidance, repository layout, and future
directions.
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **Documentation**
* Added comprehensive guide for ModelOpt recipes, introducing
declarative YAML-based optimization specifications. Documents recipe
structure, configuration loading, path resolution, and the three-layer
system architecture. Includes built-in recipe discovery conventions and
detailed instructions for authoring custom recipes with practical
examples.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
### 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?
#### Summary
Redesigns the `quant_cfg` configuration format in ModelOpt's PyTorch
quantization stack, replacing the previous dict-based format with an
**ordered list of typed `QuantizerCfgEntry` dicts**.
##### Motivation
The old `quant_cfg` dict had several pain points:
- **Ambiguous precedence**: no explicit way to reason about which entry
wins when multiple keys match a quantizer
- **Mixed key namespaces**: wildcard paths and PyTorch class names lived
in the same dict level, requiring ad-hoc dispatch
- **Magic `"default"` key**: an implicit, undocumented catch-all that
was easy to misuse
- **Poor composability**: merging two configs required dict updates that
silently discarded keys
- **No YAML round-trip fidelity**: the nested structure couldn't be
expressed cleanly in YAML
##### New format
`quant_cfg` is now an ordered list of `QuantizerCfgEntry` TypedDicts.
Each entry has:
- `quantizer_name` *(required)*: `fnmatch` wildcard matched against
quantizer module names
- `cfg` *(optional)*: dict (or list of dicts) of
`QuantizerAttributeConfig` fields
- `enable` *(optional)*: toggles quantizer on/off independently of `cfg`
- `parent_class` *(optional)*: restricts match to quantizers whose
parent module is of the given PyTorch class (e.g. `"nn.BatchNorm2d"`)
Entries are applied in list order; later entries override earlier ones.
The canonical pattern is deny-all first (`_base_disable_all`), then
selectively re-enable and configure, then apply standard exclusions
(`_default_disabled_quantizer_cfg`).
##### Changes
**Core library (`modelopt/torch/quantization/`)**
- **`config.py`**:
- Added `QuantizerCfgEntry` TypedDict (line 163) and
`find_quant_cfg_entry_by_path()` helper for exact-match lookup of
entries by path.
- Added `normalize_quant_cfg_list()` (line 1539) that converts legacy
formats (flat dict, single-key dicts, `nn.*`-scoped dicts, `"default"`
key) to canonical `QuantizerCfgEntry` lists. After normalization every
entry is guaranteed to have explicit `quantizer_name`, `enable`, and
`cfg` keys.
- Converted `_default_disabled_quantizer_cfg` and
`_mamba_moe_disabled_quantizer_cfg` from dicts to lists of
`QuantizerCfgEntry`.
- Added `_base_disable_all` (line 205): canonical deny-all entry
(`[{"quantizer_name": "*", "enable": False}]`).
- Converted all ~30 built-in config constants (`INT8_DEFAULT_CFG`,
`FP8_DEFAULT_CFG`, `NVFP4_DEFAULT_CFG`, etc.) to list format using
`*_base_disable_all` and `*_default_disabled_quantizer_cfg` unpacking.
- KV-cache configs (`FP8_KV_CFG`, `NVFP4_KV_CFG`, etc.) are now minimal
lists designed to be concatenated with a primary config — they
intentionally omit `_base_disable_all` and `"algorithm"`.
- Added two `QuantizeConfig` Pydantic field validators: a
`mode="before"` validator that calls `normalize_quant_cfg_list()`, and a
`mode="after"` validator that validates `cfg` dicts against
`QuantizerAttributeConfig`.
- Updated `need_calibration()` to iterate the normalized list instead of
the old dict.
- Changed `QuantizeQuantCfgType` alias from `dict[str | Callable, ...]`
to `list[QuantizerCfgEntry]`.
- **`conversion.py`**:
- Rewrote `set_quantizer_by_cfg()` (line 217) to iterate the list
directly. Each entry's `parent_class` is resolved via
`QuantModuleRegistry[parent_class_name]` (the existing `_DMRegistryCls`
registry).
- Added `set_quantizer_attributes_full()` (line 314): full replacement
of quantizer attributes from a `QuantizerAttributeConfig`. Unspecified
fields revert to defaults, enforcing entry atomicity. Can also upgrade
`TensorQuantizer` → `SequentialQuantizer` or downgrade the reverse.
- Added `set_quantizer_attributes_partial()` (line 384): merges a
partial `dict` of attributes into existing quantizer state. Does NOT
change quantizer structure. Used for enable-only entries.
- Added `set_quantizer_by_cfg_context()` context manager (line 447) that
temporarily applies a `quant_cfg` list and restores original quantizer
state on exit.
- Deprecated `set_quantizer_attribute()` (line 525) with a
`DeprecationWarning` pointing to the new functions.
- **`tensor_quantizer.py`**:
- `TensorQuantizer.set_from_attribute_config()`: narrowed type hint from
`dict` to `dict[str, Any]`.
- Added `_axis_setter` and `_block_sizes_setter` custom setters so that
`axis` and `block_sizes` changes properly propagate to the calibrator
and maintain mutual exclusivity.
- `SequentialQuantizer.set_from_attribute_config()`: narrowed signature
to `list[QuantizerAttributeConfig] | list[dict[str, Any]]` (removed the
old union with single values).
- **`algorithms.py`**:
- Updated `_match_quantizer_cfg()` to iterate the list and return
`(matched_cfg, matched_enable)` tuple with last-match-wins.
- Updated `_cfg_to_dict()`, `estimate_quant_compression()`, and
`QuantRecipe` to work with the list-based format.
- Updated `get_auto_quantize_config()` to emit list-format `quant_cfg`.
- **`model_quant.py`**: `disable_quantizer()` / `enable_quantizer()` now
call `set_quantizer_attributes_partial()` directly instead of the
deprecated `set_quantizer_attribute()`. Updated docstrings and code
examples to show the list format.
- **`utils/core_utils.py`**: `disable_lora_quantizers_in_config()` and
`update_quant_cfg_with_kv_cache_quant()` updated to append
`QuantizerCfgEntry` dicts to the list.
- **Other**: minor updates to `backends/fp8_per_tensor_gemm.py`,
`backends/nvfp4_gemm.py`, `compress.py`, `model_calib.py`,
`export/unified_export_hf.py`, and
`sparsity/attention_sparsity/conversion.py` to use the list format.
- **`onnx/llm_export_utils/quantization_utils.py`**: Updated
quantization config construction to use list format.
**YAML recipes (`modelopt_recipes/`)**
- Converted all 5 general PTQ recipes to the new list format:
- `general/ptq/fp8_default-fp8_kv.yml`
- `general/ptq/nvfp4_default-fp8_kv.yml`
- `general/ptq/nvfp4_experts_only-fp8_kv.yml`
- `general/ptq/nvfp4_mlp_only-fp8_kv.yml`
- `general/ptq/nvfp4_omlp_only-fp8_kv.yml`
- Converted model-specific recipe:
`models/Step3.5-Flash/nvfp4-mlp-only.yaml`
**Documentation (`docs/`)**
- New guide: `docs/source/guides/_quant_cfg.rst` — comprehensive
reference covering entry format, ordering semantics, entry atomicity,
`enable` vs `cfg` independence, `parent_class` filtering, and common
patterns (deny-all-then-enable, customizing a built-in config, building
from scratch).
- Updated `_pytorch_quantization.rst` code examples to show the list
format with `copy.deepcopy` and `.append()`.
- Added `_quant_cfg.rst` to the quantization guide table of contents.
**Examples**
- Updated all quantization examples to use the list format:
`deepseek/ptq.py`, `diffusers/quantization/config.py`,
`llm_ptq/hf_ptq.py`, `llm_qat/main.py`, `vllm_serve/vllm_ptq_utils.py`,
`llm_autodeploy/run_auto_quantize.py`, `llm_eval/quantization_utils.py`,
`llm_ptq/example_utils.py`,
`windows/torch_onnx/diffusers/qad_example/sample_example_qad_diffusers.py`,
and 2 notebooks.
**Tests**
- New test file:
`tests/unit/torch/quantization/test_config_validation.py` — unit tests
for `need_calibration()`, `normalize_quant_cfg_list()` (new format,
legacy format conversions, error cases),
`find_quant_cfg_entry_by_path()`, `_match_quantizer_cfg()`, and
`QuantizeConfig` Pydantic validators.
- Extended `tests/unit/torch/quantization/test_quantize_cpu.py` with
tests for `set_quantizer_attributes_full()` (atomicity, parent_class
filtering, SequentialQuantizer creation), list ordering, enable-only
entry behavior, and end-to-end legacy dict format.
- Updated 20+ existing test files across `tests/unit/`, `tests/gpu/`,
`tests/gpu_megatron/`, and `tests/_test_utils/` to use the list format.
##### Backward compatibility
`normalize_quant_cfg_list()` is called automatically by the
`QuantizeConfig` Pydantic `mode="before"` validator, so existing code
passing the old dict-based format (flat dict like `{"*weight_quantizer":
{"num_bits": 8}}`, single-key dict lists, or `nn.*`-scoped dicts with
`parent_class` semantics) continues to work without modification. The
legacy `"default"` key is converted to `quantizer_name: "*"`.
`set_quantizer_attribute()` is preserved as a deprecated wrapper around
`set_quantizer_attributes_partial()`.
#### Test coverage
- **Unit tests**: new `test_config_validation.py` with tests for
normalization, validation, path lookup, and cfg matching. Extended
`test_quantize_cpu.py` with tests for full/partial attribute setting,
ordering, atomicity, and legacy backward compatibility.
- **System testing**:
```
python examples/llm_ptq/hf_ptq.py \
--model Qwen/Qwen3-8B \
--recipe general/ptq/fp8_default-fp8_kv \
--export_path=build/fp8_default-fp8_kv42 \
--calib_size=16 \
--batch_size=0 \
--trust_remote_code \
--export_fmt=hf
```
### Additional Information
<!-- E.g. related issue. -->
---------
Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
### What does this PR do?
Type of change: Minor documentation update
- Update documentation (olive installation instructions) to mention
install commands for CUDA EP packages for ORT / ORT-genai.
### Testing
- Locally checked the readme, doc.
### 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
* **Documentation**
* Updated Windows Olive installation guidance to recommend CUDA-based
ONNX Runtime packages instead of DirectML and added a link to ONNX
Runtime’s Execution-Provider docs for alternative EPs and requirements.
* Simplified Windows examples by removing explicit package install
commands and pointing users to the consolidated Olive setup
instructions.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: vipandya <vipandya@nvidia.com>
### What does this PR do?
- Remove `examples/nemo_run` and other deprecated Nemo 2.0 references
- Add Megatron-Bridge example links where missing
<!-- Details about the change. -->
### 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?: ✅ <!--- 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)?:
✅ <!--- 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
* **Deprecations**
* Removed deprecated NeMo 2.0 support and related example flows and
utilities.
* **Documentation**
* Updated docs and examples to emphasize Megatron-Bridge / Megatron-LM
and refreshed technique/deployment guidance and links.
* **New Features**
* Added CLI options for additional parallelism (context/expert
tensor/expert model) in Megatron-Bridge distillation.
* **Chores**
* Removed legacy CI configs and refreshed container image tags across
examples and docs.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
Fixes https://github.com/NVIDIA/Model-Optimizer/issues/217
## Summary
- Bump `requires-python` from `>=3.10,<3.13` to `>=3.10,<3.14` to
formally include Python 3.13
- Add explicit Python 3.10–3.13 PyPI classifiers for better
discoverability
- Add `py313` to tox CPU unit test and partial-install environment
matrices
- Add Python 3.10–3.13 to the `multi-py` CI matrix in `unit_tests.yml`
## Background
Python 3.13 was previously excluded by the `<3.13` upper bound. Testing
in a related repo with `--ignore-requires-python` confirmed that the
library installs and runs correctly under Python 3.13. This PR lifts the
restriction and wires up CI to verify it going forward.
## Test plan
- [ ] CI `multi-py` job passes on `py313-torch210-tf_latest-unit`
- [ ] `tox -e py313-torch210-tf_latest-unit` passes locally (requires
Python 3.13 installed)
- [ ] `tox -e py313-partial-unit-torch` passes locally
- [ ] No regressions on existing Python 3.10/3.11/3.12 matrix jobs
🤖 Generated with [Claude Code](https://claude.com/claude-code)
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **Chores**
* Extended Python support: minimum remains 3.10; added official support
up through 3.13 (upper bound advanced accordingly).
* **Tests**
* CI and test matrix expanded to include experimental Python 3.13
coverage.
* **Documentation**
* Installation docs and changelog updated to reflect Python 3.13
support.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Ivan Basov <ibasov@nvidia.com>
Signed-off-by: Ivan Basov <5455484+ivanbasov@users.noreply.github.com>
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Co-authored-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
### What does this PR do?
**Type of change**: documentation
**Overview**: This PR updates the documentation and does some folder
re-structuring and file re-naming related to
https://github.com/NVIDIA/Model-Optimizer/pull/951.
### Usage
Documentation
### Testing
Documentation
### 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`: ✅
- 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)?:
✅ (renamed `AutoQDQ` to `Autotune`)
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **Documentation**
* Renamed AutoQDQ to Autotune across guides and changelog.
* Updated Autotune guide descriptions and wording.
* Added a new section on optimizing Q/DQ node placement with Autotune,
including CLI usage and API links (appears twice in one README).
* Applied minor grammar and capitalization corrections.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: gcunhase <4861122+gcunhase@users.noreply.github.com>
## What does this PR do?
This PR upload user guide of Automated QDQ placement tool. This tool
automatically search QDQ insertion points with better performance.
**Overview:** ?
## Usage
<!-- You can potentially add a usage example below. -->
```python
# Add a code snippet demonstrating how to use this
```
## 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
* **Documentation**
* Added comprehensive guide for Automated Q/DQ Placement Optimization
workflow, including quick start instructions, advanced usage patterns,
configuration options, best practices, and troubleshooting.
* **New Features**
* Exposed public API for CLI parser programmatic access.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Will Guo <willg@nvidia.com>
Signed-off-by: Gwena Cunha <4861122+gcunhase@users.noreply.github.com>
Co-authored-by: Gwena Cunha <4861122+gcunhase@users.noreply.github.com>
Co-authored-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
Since we no longer have any compiled packages, all releases are for all
platforms so we dont have separate windows releases hence merging
changelog as well.
Going forward, windows can test on latest linux version and if fixes
needed, they can go in next usual monthly linux release
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **Documentation**
* Reorganized changelog structure to consolidate Windows and Linux
release information in a unified view.
* Expanded Windows Support documentation across recent release versions.
* **Chores**
* Updated Windows example release badge to dynamically reflect the
latest PyPI release version.
<!-- 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 Security related coding practices in `SECURITY.md` and merge with
`2_security.rst`
- Update `CONTRIBUTING.md` for instructions to follow if copying code
from other repositories
- Update PR template
- Cleanup dependency files
- New API `mto.load_modelopt_state` doing the insecure `torch.load(f,
weights_only=False)` instead of doing it separately everywhere. This
also allows us to later improve the input validation for
`modelopt_state_path` or use safer alternatives to `torch.load`
### Testing
<!-- Mention how have you tested your change if applicable. -->
N/A
### 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. hardcoded `trust_remote_code=True`, `torch.load(...,
weights_only=True)`, `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)?:
✅ <!--- Mandatory -->
- Did you write any new necessary tests?: NA <!--- Mandatory for new
features or examples. -->
- Did you add or update any necessary documentation and update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?:
NA <!--- 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
* **Documentation**
* Expanded and reorganized security guidance and contributor procedures;
updated PR template and several READMEs with clearer security,
submission, and installation instructions
* Replaced an older security document with an enhanced, centralized
security guidance
* **Chores**
* Adjusted example dependency lists and optional extras (adds, removals,
and version constraints)
* Enabled automated incremental reviews, added pre-merge security
checks, and introduced a knowledge-base of coding/security guidelines
<!-- 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?
**Type of change:** Bug fix
**Overview:** Upgrade ORT to 1.24.x to fix various bugs (5615343,
5597780, 5371126).
TODO: Verify no regressions by bumping ORT once @ajrasane is back
## Usage
See each bug.
## Testing
See each bug.
## 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)?**:
Yes <!--- Only for new features, API changes, critical bug fixes or bw
breaking changes. -->
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
## Bug Fixes
* Upgraded ONNX Runtime to version 1.24.2, addressing multiple reported
issues
* Updated system requirements and installation documentation to reflect
the new ONNX Runtime version
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: gcunhase <4861122+gcunhase@users.noreply.github.com>
Signed-off-by: Gwena Cunha <4861122+gcunhase@users.noreply.github.com>
Co-authored-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
## What does this PR do?
- Upgrade CICD test containers to latest
- Enable torch 2.10 testing in CICD
## Testing
<!-- Mention how have you tested your change if applicable. -->
CI/CD in this PR should pass
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **New Features**
* Added support for mixed-precision gradient handling with FSDP2.
* **Documentation**
* Updated Linux installation guide with CUDA 13.x support and cupy
dependency guidance.
* **Chores**
* Updated CI/CD workflows and test infrastructure to support PyTorch
2.10 and CUDA 13.
* Updated container image versions and test environment configurations.
* Updated TensorRT-LLM version requirements.
<!-- 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?
**Type of change:** documentation <!-- Use one of the following: Bug
fix, new feature, new example, new tests, documentation. -->
**Overview:**
1. Added multi‑backbone support for quantization: --backbone now accepts
space- or comma-separated lists and resolves to a list of backbone
modules.
2. Introduced PipelineManager.iter_backbones() to iterate named backbone
modules and updated get_backbone() to return a single module or a
ModuleList for multi‑backbone.
3. Updated ExportManager to save/restore per‑backbone checkpoints when a
directory is provided, with {backbone_name}.pt files, and to create
target directories when missing.
4. Simplified save_checkpoint() calls to rely on the registered
pipeline_manager by default.
**Usage: **
```bash
python quantize.py --model wan2.2-t2v-14b --format fp4 --batch-size 1 --calib-size 32 \
--n-steps 30 --backbone transformer transformer_2 --model-dtype BFloat16 \
--quantized-torch-ckpt-save-path ./wan22_mo_ckpts \
--hf-ckpt-dir ./wan2.2-t2v-14b
```
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
- [x] [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
## 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?**: No <!--- If No, explain why.
-->
- **Did you write any new necessary tests?**:No
- **Did you add or update any necessary documentation?**: Yes
- **Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?**:
Yes <!--- 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**
* Unified Hugging Face export support for diffusers pipelines and
components
* LTX-2 and Wan2.2 (T2V) support in diffusers quantization workflow
* Comprehensive ONNX export and TensorRT engine build documentation for
diffusion models
* **Documentation**
* Updated to clarify support for both transformers and diffusers models
in unified export API
* Expanded diffusers examples with LoRA fusion guidance and additional
model options (Flux, SD3, SDXL variants)
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Jingyu Xin <jingyux@nvidia.com>
## What does this PR do?
Documentation
**Overview:**
- Update support matrix, changelog, deployment page, example readmes as
per recent feature and model support on Windows side.
## Testing
- No testing, its just documentation change
## 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
* **New Features**
* Added ONNX Mixed Precision Weight-only quantization (INT4/INT8)
support.
* Introduced diffusion-model quantization on Windows.
* Added new accuracy benchmarks (Perplexity and KL-Divergence).
* Expanded deployment with multiple ONNX Runtime Execution Providers
(CUDA, DirectML, TensorRT-RTX).
* **Bug Fixes**
* Fixed ONNX 1.19 compatibility issue with CuPy during INT4 AWQ
quantization.
* **Documentation**
* Updated installation guides with system requirements and multiple
backend options.
* Reorganized deployment documentation with comprehensive execution
provider guidance.
* Expanded example workflows with improved setup instructions and
support matrices.
<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: vipandya <vipandya@nvidia.com>
Add security considerations to docs suggested by Nvidia Security team
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* Added comprehensive security considerations documentation for ModelOpt
covering multiple risk areas including untrusted input handling,
deserialization safety, input validation, resource exhaustion
prevention, data protection for transit and storage, logging and
observability practices, supply chain security, and structured
mitigation approaches with practical implementation examples.
<!-- 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?
**Type of change:** New feature
**Overview:**
AutoCast runs full type inference to get the new types after adding
casts. ONNX doesn't have a separate function for type inference, and it
is done as part of shape inference. Shape inference is a much more
complex task than type inference, especially when dynamic shapes are
involved. We're seeing some shape inference related bugs in AutoCast.
Typically we can WAR, but it's cumbersome. A local implementation might
allow users to WAR shape inference related issues. This is opt-in and
marked as experimental.
## Usage
python -m modelopt.onnx.autocast --onnx_path /path/to/input.onnx
[options] --use_standalone_type_inference
## Testing
Added use_standalone_type_inference=True to all existing
PrecisionConverter tests.
## 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
- **Did you write any new necessary tests?**: Yes
- **Did you add or update any necessary documentation?**: Yes
- **Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?**:
Yes <!--- Only for new features, API changes, critical bug fixes or bw
breaking changes. -->
## Additional Information
A more permanent fix would be to decouple type and shape inference in
ONNX, we should invest in that when we have the resources - see
https://github.com/onnx/onnx/issues/7100
. This is a quick fix, which is also why it is opt-in and not the
default mode.
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **New Features**
* Added `--use_standalone_type_inference` flag to ONNX AutoCast,
enabling type-only inference as an alternative to standard shape
inference. Useful as a workaround when shape inference fails or to
reduce computational overhead.
* **Documentation**
* Added "Type Inference Control" section with usage examples and caveats
for the new standalone type inference option.
* **Tests**
* Extended test coverage to validate both standard and standalone type
inference paths.
<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: Gal Hubara Agam <96368689+galagam@users.noreply.github.com>
- [x] Address feedback on Threat and Vuln Analysis (TAVA) doc by ProdSec
team
- [x] Add note on safe usage of pickle deserialization of
modelopt-generated state files
**TODO: [Separate PR]** Replace pickle usage in
`modelopt/torch/opt/plugins/megatron.py` - Needs fix on
TransformerEngine first as we copy from
https://github.com/NVIDIA/TransformerEngine/blob/3ff0b8d4/transformer_engine/pytorch/module/base.py#L863
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **New Features**
* Added `--trust_calibration_data` CLI flag for secure ONNX quantization
with pickle data files.
* **Improvements**
* Enhanced security validation for generated quantization code.
* Simplified data loading by removing pickle-based caching—data is now
always loaded fresh.
* Added security guidance throughout model state loading operations.
* **Documentation**
* Updated guides with security best practices for model state handling.
<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: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
## What does this PR do?
- Deprecate `num_query_groups` parameter pruning support in
`mcore_minitron` mode. This parameter pruning was not part of the paper
but added hacky support in the past which makes refactoring current
implementation a bit challenging hence removed its support.
- Besides, `num_query_groups` should be multiple of TP so its less
likely to be pruned.
- Users can still prune it if they want to by using ModelOpt 0.40.0 or
earlier
- Going forward, `num_query_groups` if passed in pruning `export_config`
should be same as original else raises an Exception.
- As a follow-up PR, I will de-couple DynamicModule with forward hooks /
hparam importance logic so its easy for users to change to different
scoring logic
## Testing
<!-- Mention how have you tested your change if applicable. -->
- [x] CI/CD Tests pass
- [x] Compare MMLU for pruning Qwen3-8B (nmm-sanbox) with previous and
current implementation
## 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?**: No. See reasoning above <!---
If No, explain why. -->
- **Did you write any new necessary tests?**: No
- **Did you add or update any necessary documentation?**: Yes
- **Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?**:
Yes <!--- Only for new features, API changes, critical bug fixes or bw
breaking changes. -->
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
## Release Notes
* **Deprecations**
* num_query_groups parameter in Minitron pruning is deprecated. Use
ModelOpt 0.40.0 or earlier if pruning with this parameter is needed.
* **Documentation**
* Updated NAS search parameters documentation
* Refined pruning scope documentation for Transformer, MoE, and Mamba
models
* Updated example configurations and support matrix reflecting current
supported pruning parameters
<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: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
## What does this PR do?
**Type of change:**
New Feature
**Overview:**
- Enable ONNX export for auto quantized models
- Update documentation and changelog
## Usage
<!-- You can potentially add a usage example below. -->
```
python torch_quant_to_onnx.py --quantize_mode=auto \
--onnx_save_path=./vit_base_patch16_224.nvfp4_fp8.onnx \
--calibration_data_size 64 \
--auto_quantization_formats NVFP4_AWQ_LITE_CFG FP8_DEFAULT_CFG \
--batch_size 128
```
## Testing
<!-- Mention how have you tested your change if applicable. -->
```
python evaluate.py --onnx_path=vit_base_patch16_224.nvfp4_fp8.onnx \
--model_name=vit_base_patch16_224 \
--results_path=./results.txt \
--batch_size 128
```
Accuracy results
```
The top1 accuracy of the model is 84.15%
The top5 accuracy of the model is 97.396%
```
Reference accuracy for fp16
```
The top1 accuracy of the model is 85.102%
The top5 accuracy of the model is 97.526%
```
## 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
- **Did you write any new necessary tests?**: No
- **Did you add or update any necessary documentation?**: Yes
- **Did you update
[Changelog](https://github.com/NVIDIA/TensorRT-Model-Optimizer/blob/main/CHANGELOG.rst)?**:
Yes <!--- Only for new features, API changes, critical bug fixes or bw
breaking changes. -->
---------
Signed-off-by: ajrasane <131806219+ajrasane@users.noreply.github.com>
## What does this PR do?
**Type of change:** Bug fix
**Overview:** Autocast crashes if the input batch size in the ONNX model
is different to the calibration data input batch size. For example:
calibration data has shape `[10, 6, 3, 480, 800]` and ONNX model has
shape `[1, 6, 3, 480, 800]`. The quantization workflow interprets this
as 10 calibration samples, so ideally, Autocast would also interpret
them similarly. This PR just allows Autocast to exit gracefully with a
custom message.
## Usage
```python
$ python -m modelopt.onnx.autocast --onnx_path=$MODEL_NAME.onnx --calibration_data=calib_data_10.npz
```
## Testing
See bug 5676209.
## 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
- **Did you write any new necessary tests?**: No
- **Did you add or update any necessary documentation?**: Yes
- **Did you update
[Changelog](https://github.com/NVIDIA/TensorRT-Model-Optimizer/blob/main/CHANGELOG.rst)?**:
No
## Additional Information
Original error:
```sh
polygraphy.exception.exception.PolygraphyException: Input tensor: image | Received incompatible shape: (10, 6, 3, 480, 800).
Note: Expected a shape compatible with: BoundedShape([1, 6, 3, 480, 800], min=None, max=None)
```
Autocast error:
```sh
ValueError: Input shape from 'image' does not match provided input shape: [1, 6, 3, 480, 800] vs [10, 6, 3, 480, 800]. Please make sure that your calibration data matches the ONNX input shapes.
```
---------
Signed-off-by: gcunhase <4861122+gcunhase@users.noreply.github.com>
- [x] Product Rename: TensorRT Model Optimizer to Model Optimizer
(OMNIML-3033)
- [x] Mention in Latest News section with date on the date of merging
this PR (12/08)
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
## What does this PR do?
Bump TRT-LLM docker from 1.1.0rc2.post2 (CUDA 12) to 1.2.0rc4 (CUDA 13)
## Testing
<!-- Mention how have you tested your change if applicable. -->
- [ ] 2-gpu tests for all examples: ?
## 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 <!--- If No, explain why.
-->
- **Did you write any new necessary tests?**: No
- **Did you add or update any necessary documentation?**: Yes
- **Did you update
[Changelog](https://github.com/NVIDIA/TensorRT-Model-Optimizer/blob/main/CHANGELOG.rst)?**:
Yes <!--- Only for new features, API changes, critical bug fixes or bw
breaking changes. -->
---------
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
## What does this PR do?
**Type of change:** New feature <!-- Use one of the following: Bug fix,
new feature, new example, new tests, documentation. -->
- Support pruning `num_moe_experts`, `moe_ffn_hidden_size`, and
`moe_shared_expert_intermediate_size` in `mcore_minitron` pruning
## 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/TensorRT-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?**: Yes
- **Did you add or update any necessary documentation?**: Yes
- **Did you update
[Changelog](https://github.com/NVIDIA/TensorRT-Model-Optimizer/blob/main/CHANGELOG.rst)?**:
Yes <!--- Only for new features, API changes, critical bug fixes or bw
breaking changes. -->
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
## Release Notes
* **New Features**
* Added Mixture of Experts (MoE) pruning support with new configurable
dimensions for expert count and intermediate sizes
* Extended NAS architecture search capabilities to include MoE model
parameters
* **Documentation**
* Updated support matrix and pruning documentation for MoE-compatible
models
* Clarified available pruning dimensions and parameters for MoE
architectures
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
Co-authored-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
## What does this PR do?
new feature
**Overview:**
AutoCast: Allow configuring target opset from CLI and main API.
## Usage
<!-- You can potentially add a usage example below. -->
python -m modelopt.onnx.autocast --onnx_path model.onnx --opset 22
```python
# Add a code snippet demonstrating how to use this
```
## Testing
<!-- Mention how have you tested your change if applicable. -->
pytest tests/unit/onnx/autocast/test_autocast.py -k test_opset
## 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
- **Did you write any new necessary tests?**: Yes
- **Did you add or update any necessary documentation?**: Yes
- **Did you update
[Changelog](https://github.com/NVIDIA/TensorRT-Model-Optimizer/blob/main/CHANGELOG.rst)?**:
No - minor API addition <!--- Only for new features, API changes,
critical bug fixes or bw breaking changes. -->
## Additional Information
<!-- E.g. related issue. -->
Signed-off-by: Gal Hubara Agam <96368689+galagam@users.noreply.github.com>
