attention_apply — LLMCORE `score` op

• Data kinds: attnmap × tokens → tokens

• Call: import fullseye as fs; fs.ledger.attention_apply(weights, value) -> 'np.ndarray' (to call the implementation directly, import llmcore; llmcore.attention_apply(weights, value) -> 'np.ndarray'; from the registry, opsllmcore.get("attention_apply"))

Usage

> This operator's description has not been translated yet. The original text follows as it is.

注意の重みで値を混ぜる → tokens。行和 1 なので出力は入力の凸結合。

Args:

weights: (T, S) float。行和が 1 であること(`attention_weights` の出力)。

value: (S, dv) float。混ぜられる側。

Returns:

tokens (T, dv) float: 各行が value の行の凸結合。値域は value を出ない

(同時に幅は縮む —— 平均は対比を潰す)。

Detailed usage guide

• llmcore family guide

References (sample data, literature)

• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).

• Operator provenance and references — the sources of the research/methods this op family came from.

• The canonical algorithm (author, year) and its uses are named in the family usage guide above.

Runnable examples (verified samples that actually call this op)

• poc_attention_identities — py -3.11 examples/poc_attention_identities.py

Ops the type connects to (they accept `tokens` as input)

rms_norm · rope_rotate · attention_scores · attention_softmax · attention_tiled · attention_linear · attention_grouped · kv_cache_decode

Same category (`score`)

attention_scores · attention_weights

*Provenance: llmcore.py — LLMCORE operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

attention_apply — LLMCORE score op