attention_softmax — LLMCORE `attend` op

• Data kinds: tokens × tokens × tokens → tokens

• Call: import fullseye as fs; fs.ledger.attention_softmax(query, key, value, mask=None, window=None, scale=None) -> 'np.ndarray' (to call the implementation directly, import llmcore; llmcore.attention_softmax(query, key, value, mask=None, window=None, scale=None) -> 'np.ndarray'; from the registry, opsllmcore.get("attention_softmax"))

Usage

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

一括の注意(参照実装)→ tokens。この族の答え合わせの基準。

(T, T) のスコア行列を実際に作ってから softmax する、いちばん素直な書き方。

`attention_tiled / attention_linear / attention_grouped` はこれと

一致することで正しさを示す。

Args:

query: (T, d) float。

key: (S, d) float。

value: (S, dv) float。

mask: None / `"causal" / "window"` / (T, S) の bool 配列。

window: `mask="window"` のときの窓幅。

scale: スコアの倍率。None なら 1/√d。

Returns:

tokens (T, dv) float: 注意で混ぜた列。

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_apply · attention_tiled · attention_linear · attention_grouped · kv_cache_decode

Same category (`attend`)

attention_tiled · attention_linear · attention_grouped

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

attention_softmax — LLMCORE attend op