[Quantization] Refactor initialize for activation shape inference

[kylesayrs]

Purpose

• Refactor initialize to support passing the shapes of activations
  • While the true shapes of activations are determined at inference/calibration time, the last dimensions can be known from the weight
  • This information is necessary to support group/head quantization, since these strategies need to know the shape of the activation at init time

Changes

• Use the module weight to infer activation and output quantization shapes
• Rename _initialize_scale_zero_point to initialize_qparams
• Introduce strategy_cdiv to share logic related to validating divisibility

[kylesayrs]

Drafting for now before release

[kylesayrs]

I have no clue with tests/test_utils/test_helpers.py is reported to fail, nor why the CI seems to stall. All other tests pass.

[kylesayrs]

https://github.com/neuralmagic/llm-compressor-testing/actions/runs/18167585866 (passes with known/unrelated failures)

[brian-dellabetta]

one nit, otherwise LGTM

[shanjiaz]

Thanks for adding the division helper.