attention_grouped — LLMCORE attend op

• Data kinds: tokens × tokens × tokens → tokens

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

Usage

Attention that groups heads (GQA) → tokens. Exactly matches MHA when n_kv_heads == n_heads, and MQA when == 1.

Ainslie et al. 2023. The query is split into n_heads heads, while key/value hold only n_kv_heads heads and are reused. Both extremes fall exactly onto the 2 existing schemes (MHA / MQA) (0.0e+00 for both), so the claim "it takes the middle ground" can be cross-checked at the ends.

Args:

query: (T, n_heads·dh) float. Laid out per head with width dh.

key: (S, n_kv_heads·dh) float.

value: (S, n_kv_heads·dh) float.

n_heads: number of query heads. Must be a multiple of n_kv_heads.

n_kv_heads: number of key/value heads. 1 gives MQA, n_heads gives MHA.

mask: None / "causal" / "window" / (T, S) bool array.

window: window width when mask="window".

Returns:

tokens (T, n_heads·dh) float: the per-head outputs concatenated side by side.

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_softmax · attention_tiled · attention_linear · kv_cache_decode

Same category (attend)

attention_softmax · attention_tiled · attention_linear

*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.