* llama: create new fused op for QSA scoring
* enroll glm5.3-next too
* tests : initialize the remaining tensors in test_kpool_mask
The harness sentinels were left uninitialized, so leftover memory could read as NaN and fail the NaN check.
* ggml: generalize the k-pool mask op into ggml_top_k_mask
The k-pool mask op only covered the pooled QSA selection of qwen4exp
and glm5-next, while the DSA models and the DeepSeek V4 CSA build the
same top-k mask by hand with fill, set_rows and add.
ggml_top_k_mask(mask, top_k, score, blocks, extra) keeps the mask
cells of the live picks and the extra cells, and sets the rest to
-inf. Every row tensor is 4D in the layout of top_k, and the mask is
broadcast to those rows, so a selection can also be made per head.
score, blocks and extra are optional: without score every pick is
live, without blocks a pick is a cell, and extra holds the cells a
row keeps without a pick.
llm_graph_context::build_fused_top_k_mask views the row tensors in
the layout of kq_mask and registers the fused node. The k-pool
models, the DSA attention of build_attn and the DeepSeek V4 CSA mask
use it when the probe enables the op, and keep their current path on
backends without it.
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Co-authored-by: Pascal <admin@serveurperso.com>
