[2/N] Added KDLoss based AutoQuantize

[realAsma]

What does this PR do?

Type of change: ? New Feature

Overview:

This PR extends AutoQuantize with KL Divergence Loss-based sensitivity measurement as an alternative to the existing gradient-based approach. KD Loss uses a binary searcher similar to the binary searcher in FastNAS.

AutoQuantize gradient is faster than KL Divergence based AutoQuantize. However KL Divergence does not need the model implementation to support gradient backward. In addition, AutoQuantize collected KL Divergence is useful for sensitivity analysis of the model. KL Divergence is a more direct measure of sensitivity than gradient scores.

Usage

see tests/unit/torch/quantization/test_autoquant.py

Testing

Testes with unit tests.

Result for Qwen3 8B

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