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"))
Attention in one batch (reference implementation) → tokens. The reference against which this family is checked.
The most straightforward way of writing it: actually build the (T, T) score matrix and then apply softmax. attention_tiled / attention_linear / attention_grouped show their correctness by agreeing with this.
Args:
query: (T, d) float.
key: (S, d) float.
value: (S, dv) float.
mask: None / "causal" / "window" / a (T, S) bool array.
window: window width when mask="window".
scale: score multiplier. None means 1/√d.
Returns:
tokens (T, dv) float: the sequence mixed by attention.
• 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.
• poc_attention_identities — py -3.11 examples/poc_attention_identities.py
tokens as input)rms_norm · rope_rotate · attention_scores · attention_apply · attention_tiled · attention_linear · attention_grouped · kv_cache_decode
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).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.