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Signed-off-by: lvliang-intel <liang1.lv@intel.com>
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Pull request overview
This PR stabilizes the Qwen3-Omni-MoE weight fidelity unit test by making model initialization deterministic, addressing intermittent CI failures attributed to non-deterministic random initialization.
Changes:
- Seed PyTorch RNG before constructing the tiny Qwen3-Omni-MoE model in the weight fidelity test.
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/azp run Unit-Test-CUDA-AutoRound |
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Azure Pipelines could not run because the pipeline triggers exclude this branch/path. |
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Description
https://dev.azure.com/lpot-inc/neural-compressor/_build/results?buildId=56973&view=logs&j=44c25250-aab3-5e31-d6d7-8ba2147b1266&t=262f41be-8379-5409-f492-e6c716395db9&s=883af604-c69a-512d-c028-4ffa383c1da9
Uninitialized memory producing Intermittent NaN Values, the test creates the model without a fixed random seed or explicit weight initialization.
Type of Change
Related Issues
Fixes or relates to #
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