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https://github.com/NVIDIA/Model-Optimizer.git
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Ajinkya RasaneandCodex
5b1f7e86cc
### What does this PR do?
Type of change: documentation
Align the ONNX PTQ README, guide, and executable example with the
implemented contracts:
- use the canonical `--calibration_data_path` CLI option;
- load `.npy` calibration data before passing it to the Python API;
- document the supported Autotune modes and calibration methods;
- correct the minimum opsets to INT8 19, FP8 19, and INT4 21; and
- describe the no-data fallback as random calibration inputs.
This also removes an inaccurate source comment without changing runtime
behavior.
### Usage
```bash
python -m modelopt.onnx.quantization \
--onnx_path=model.onnx \
--quantize_mode=int8 \
--calibration_data_path=calib.npy \
--output_path=model.quant.onnx
```
### Testing
- `pre-commit run --files docs/source/guides/_onnx_quantization.rst
examples/onnx_ptq/README.md modelopt/onnx/quantization/quantize.py
tests/examples/test_onnx_ptq.sh`
- `bash -n tests/examples/test_onnx_ptq.sh`
- `CUDA_VISIBLE_DEVICES="" python -m pytest -o addopts="" -p
no:cacheprovider --confcutdir=tests/unit/onnx/quantization -q
tests/unit/onnx/quantization/test_autotune_quantization_integration.py`
(4 passed)
- `nox -s docs` (passed; Sphinx built 881 HTML files)
- Focused before/after contract probe covering the documented CLI
option, API data type, Autotune modes and methods, opset minimums, and
random-input wording
### 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 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?: N/A
- 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
Tracking: [6701308]
> 🤖 _Generated by Codex (AI agent)._
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
- **Documentation**
- Clarified that random calibration inputs are used when no calibration
dataset is provided.
- Updated ONNX post-training quantization examples with minimum opset
requirements and the `calibration_data_path` argument.
- Clarified Autotune support for FP8 and INT8 calibration methods using
`max` or `entropy`.
- **Tests**
- Updated quantization command examples to use the current calibration
data path option.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
Signed-off-by: ajrasane <131806219+ajrasane@users.noreply.github.com>
Co-authored-by: Codex <codex@openai.com>