In DeepSeek models there are two additional tensors, *attn_k_b.weight and *attn_v_b.weight required for MLA. When MLA is enabled, these will get used for attention computation. When standard attention is used, then the *attn_kv_b.weight tensors are used instead. Hence, when one has used standard attention to compute the imatrix, there will be no data for *attn_k_b.weight and *attn_v_b.weight; if one uses MLA, then there will be no data for *attn_kv_b.weight. As the *attn_v_b.weight tensors are simply the lower half of *attn_kv_b.weight (i.e., the second half of rows), they "see" the exact same activations as the *attn_kv_b.weight tensors. This PR takes advantage of this and enables the usage of *attn_kv_b.weight imatrix data for *attn_v_b.weight and vice versa.

The situation with *attn_k_b.weight is more tricky and will require a much bigger change to be fixed. *attn_k_b.weight is the transposed upper half of *attn_kv_b.weight. The *attn_kv_b.weight tensors have a shape of 512 x 4096, so the upper half is 512 x 2048. At run time it multiplies activations X to produce a 2048 x n_token tensor, which is then viewed as 128 x n_token x 16 for further processing by the 16 attention heads. On the other hand, *attn_k_b.weight is stored as 128 x 8192 and is then viewed as 128 x 512 x 16 for multiplication with the query Q, so the imatrix data collection functions sees a matrix with just 128 columns, so quite useless to actually guide the quantization process. To make this actually useful, a modification in the imatrix tool is required to collect data for 128 x 16 columns, along with a modification in the quantization function to make use of imatrix data with 128 x 16 columns. This is left for a future PR, so for now there will be no imatrix data for *attn_k_b.weight even if the imatrix was computed with MLA enabled.