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@anzr299 anzr299 commented Aug 25, 2025

Changes

Re-worked the apply method in WC algorithm to use an extra method to return weights compression params such that the apply method is more concise and only contains algorithm, quantization logic etc.

Reason for changes

This is done so that OpenVINO quantizer can obtain the final collection of weights compression parameters for all the nodes so that are to be compressed.

@anzr299 anzr299 marked this pull request as ready for review August 27, 2025 09:37
@anzr299 anzr299 requested a review from a team as a code owner August 27, 2025 09:37
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@daniil-lyakhov daniil-lyakhov left a comment

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Aamir, I think it would be beneficial to split this function on two: get_weight_compression_parameters and get_weight_compression_statistics as it seem like the statistic collection does not relate to the collection of weight parameters in any way and could be split without any effort. This way it would be clear: statistic points and datasets have nothing to do with the choice of the WC configuration.

Other thing - what a processed parameters means? What is an unprocessed parameter than?

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anzr299 commented Aug 27, 2025

Aamir, I think it would be beneficial to split this function on two: get_weight_compression_parameters and get_weight_compression_statistics as it seem like the statistic collection does not relate to the collection of weight parameters in any way and could be split without any effort. This way it would be clear: statistic points and datasets have nothing to do with the choice of the WC configuration.

Other thing - what a processed parameters means? What is an unprocessed parameter than?

Sure, that sounds good.
as for "processed" parameters, what I mean is that originally all the WC parameters are set to backup precision and in the later steps of this function, the parameters are changed as per user defined WC configurations like all_layers, backup_mode and so on.

@AlexanderDokuchaev AlexanderDokuchaev merged commit 9ed7c8b into openvinotoolkit:develop Sep 10, 2025
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3 participants