noeyy-mino a1bcda4727 Fix protobuf size-check failures in ONNX deployment (#2403) ...

### What does this PR do?

Type of change:  Bug fix：6701737

The ONNX deployment path assumed that ModelProto.ByteSize() would always
return a valid size. With newer protobuf versions, querying the size of
a model exceeding the protobuf serialization limit can itself raise
EncodeError: Failed to serialize proto.

Replaced both direct size checks with the existing
is_model_too_large_for_protobuf() helper. This helper handles size-query
failures conservatively and checks the protobuf size limit:

Shape inference now selects the external-data/file-based path when
ByteSize() fails or the model is too large.
Metadata creation uses the same safe check instead of raising another
serialization error.
The unused TWO_GB constant was also removed.

### Usage

```
python examples/diffusers/quantization/diffusion_trt.py --model flux-dev --benchmark --skip-image
```

### Testing
the above test case pass on B100

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- 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)?:
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### Additional Information
N/A


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

## Summary by CodeRabbit

* **Bug Fixes**
* Improved ONNX model size detection during shape inference and export
processing.
* Ensured large models consistently use the appropriate external-data
handling path.

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

Signed-off-by: Noey Yang <174223378+noeyy-mino@users.noreply.github.com>

2026-09-11 10:46:10 -04:00

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torch

Fix protobuf size-check failures in ONNX deployment (#2403)

2026-09-11 10:46:10 -04:00

__init__.py

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