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Gwena Cunha
bdc04f174d
### What does this PR do? Type of change: Bug fix Regression bug introduced by the Autotune integration into ModelOpt ONNX quantization (https://github.com/NVIDIA/Model-Optimizer/pull/951), making ModelOpt dependent on TensorRT in all scenarios. This PR fixes this issue by requiring TensorRT only when `--autotune` is enabled. ### Usage ```python $ python -m modelopt.onnx.quantization --onnx_path=${MODEL_NAME}.onnx ``` ### Testing See bug 6056809. ### 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 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 <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **Bug Fixes** * Autotune dependency failures now surface as clearer runtime errors instead of only logging warnings. * **Chores** * Centralized autotune presets and numeric defaults into a shared configuration. * Core autotune components are conditionally exposed so initialization succeeds when optional acceleration libraries are absent. * Deferred autotune imports to runtime to improve failure handling. * **Tests** * Added a test ensuring the quantization CLI/parser initializes correctly without optional acceleration libraries. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: gcunhase <4861122+gcunhase@users.noreply.github.com>