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[OpenVINO backend] suggesting disable reusing compiled model until the bug get fixed #21659
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Summary of Changes
Hello @Mohamed-Ashraf273, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
This change addresses a critical bug in the OpenVINO backend that affects the reliability of model predictions when compiled models are reused. By temporarily disabling the caching of these models, the PR ensures consistent and correct inference results, prioritizing accuracy over potential performance gains from caching until the underlying issue is resolved.
Highlights
- Disabling OpenVINO Compiled Model Reuse: This pull request temporarily disables the caching mechanism for OpenVINO compiled models. This is a workaround for a known bug (openvinotoolkit/openvino#32045) where reusing a compiled model can lead to inconsistent predictions for the same input, particularly observed with the Gemma model.
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Code Review
This pull request disables the caching of compiled OpenVINO models as a temporary workaround for a bug causing inconsistent predictions. The change is straightforward and effectively prevents the bug by forcing re-compilation. I've suggested a minor improvement to the implementation to use a boolean flag instead of commenting out code, which should improve readability and make it easier to re-enable the cache in the future.
Codecov Report❌ Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## master #21659 +/- ##
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- Coverage 82.55% 82.54% -0.01%
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Files 571 571
Lines 57626 57629 +3
Branches 9001 9002 +1
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Hits 47572 47572
- Misses 7759 7761 +2
- Partials 2295 2296 +1
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Please not merge for while we take a look on OpenVINO side
@rkazants
@fchollet
Based on issue: openvinotoolkit/openvino#32045
Discovered from PR: keras-team/keras-hub#2389
I suggest disabling reusing compiled models until the bug gets fixed.
Note: the bug is not related to the PR: keras-team/keras-hub#2389 itself, it is related to getting the predictions with the same input for the same compiled model being different (for now, this issue has been found only on the Gemma model).