UPSTREAM PR #14891: imatrix: calculate activation-based statistics for new format (GGUF) imatrices

[DajanaV]

Mirrored from ggml-org/llama.cpp#14891

Following up from #9400 and #12718, I've started tinkering with activation-based statistics, in addition to what's currently available via --show-statistics.

At the moment, I'm exploring three options going from from easy to implement and OK approximation, to some assembly required but fairly accurate:

1. L2 norm of activation difference: where larger values would suggest the tensor has significantly transformed the input with respect to the previous layer.
2. KL Divergence reduction using a pre-computed logit file: using a similar approach as described by nostalgebraist in logit lens, and based on a pre-computed logit file (e.g. from a previous llama-perplexity --save-all-logits run)
3. Given that llama-imatrix already generates the actual logits to compute PPL, use Thông T. Nguyễn's logit prism approach to calculate the exact contribution of each layer to the final logit scores

Sharing with the readers, and in particular @compilade and @jukofyork, in case anyone's willing to double check assumptions and/or suggest alternative approaches I haven't considered.