decode op• Data kinds: tokens × tokens × tokens → tokens
• Call: import fullseye as fs; fs.ledger.kv_cache_decode(query_step, key_cache, value_cache, scale=None) -> 'np.ndarray' (to call the implementation directly, import llmcore; llmcore.kv_cache_decode(query_step, key_cache, value_cache, scale=None) -> 'np.ndarray'; from the registry, opsllmcore.get("kv_cache_decode"))
Inference that advances by only 1 token (KV Cache) → tokens (1, dv).
Under a causal mask, rewriting the future does not move the earlier outputs by even 1 bit (measured 0.0e+00), so you can keep the finished key/value and compute just 1 row — advancing sequentially 1 row at a time gives the same result as passing the whole sequence at once (relative difference 1.6e-16). The cost for 1 step is O(T d), equal to the batch O(T² d) divided over T steps.
Args:
query_step: (1, d) float. The 1 token about to be generated.
key_cache: (T, d) float. The keys seen so far (including itself).
value_cache: (T, dv) float. Values of the same length.
scale: score multiplier. None means 1/√d.
Returns:
tokens (1, dv) float: matches the last row of the batch pass.
• 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_softmax · attention_tiled · attention_linear · attention_grouped
decode)—
*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.