Don't use RawReducer for activation shape collection in Fast Bias Correction #3642
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added 2 commits
August 28, 2025 16:37
daniil-lyakhov
approved these changes
Sep 2, 2025
Co-authored-by: Daniil Lyakhov <[email protected]>
Merged
3 tasks
andrey-churkin
approved these changes
Oct 10, 2025
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WC is passed: https://github.com/openvinotoolkit/nncf/actions/runs/18402892646 |
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Changes
As in the title.
Reason for changes
This PR reduces memory footprint when applying Fast Bias Correction algorithm: collecting raw activations is not required to obtain their shapes. Avoiding using raw reducers allows to save some memory otherwise allocated for the activations.
Example quantization run on vision encoder from
OpenGVLab/InternVL2-1Bwith 4 calibration data samples:Since there is no need to allocate so much memory, statistics collection time also improves.
Related tickets
172800
Tests
Existing tests cover the new changes.