Project: release an incremental service-event decoder

Freeze a session grammar

Represent service events with an explicit start token, end token and padding token, plus domain events such as opened, assigned, repaired and closed. Split by service session and, where appropriate, customer or asset; do not let a session prefix appear in training while its suffix becomes a held-out target. Construct shifted targets so each position predicts the next event and ignore padding in loss. The target-shift lesson gives the training audit. Keep tokenizer and event-ID mapping versioned.

Establish full-prefix behavior

Train a modest causal decoder or start with an existing one and save logits for fixed held-out prefixes. Verify that appending a future event cannot change earlier-position logits. Include a one-event prefix, a padded mixed-length batch and a prefix exactly at the context boundary. A high teacher-forced next-token score is insufficient if deployment decoding has a different mask or position convention. The attention leakage project gives the causal test pattern.

Prove cache parity step by step

Prefill the same prefix and compare each next-event logit vector from the full-prefix path with the cached incremental path across several append steps. Keep dropout disabled, masks aligned and the same precision. Deliberately alter one prefix or model revision and verify its cache is rejected. If beam search is enabled, reorder every cache layer when parent beams change. The code below supplies a small pure-Python release checker over measured maximum logit gaps and request identities; the model-specific cache implementation must produce those measurements. The cache lesson explains the memory trade.

Exercise stop and invalid-order cases

For a fixed held-out prefix set, compare greedy, sampled and beam policies under the same maximum new-event count. Verify that end stops output, padding never appears as an emitted event, and unfinished sequences are tagged as capped. Reject impossible orderings such as closed followed by assigned unless the domain explicitly allows reopening. Measure invalid-event rate and sequence duplication, not only token accuracy. The policy lesson defines the controls.

Publish a bounded serving artifact

Package model revision, tokenizer, event-order rules, cache strategy, stopping policy, maximum prefix and output lengths, and the exact parity tolerance. Measure first-event and p95 whole-request latency, peak cache memory and invalid-sequence rate at intended concurrency. A parity failure blocks cache acceleration; a policy failure blocks the new decoding setting even if model logits are correct. Keep a full-prefix or previous-version fallback until fixed-batch replay and monitored rollout pass.

Implementation

from dataclasses import dataclass

@dataclass(frozen=True)
class DecodeAudit:
    matching_prefix: bool
    maximum_logit_gap: float
    emitted_padding: int
    invalid_event_orders: int
    capped_sequences: int

def release_failures(result: DecodeAudit, allowed_gap: float,
                     allowed_capped: int) -> list[str]:
    failures = []
    if not result.matching_prefix or result.maximum_logit_gap > allowed_gap:
        failures.append("cache parity")
    if result.emitted_padding:
        failures.append("padding emission")
    if result.invalid_event_orders:
        failures.append("event grammar")
    if result.capped_sequences > allowed_capped:
        failures.append("end-token coverage")
    return failures

heldout_audit = DecodeAudit(True, 0.00003, 0, 1, 2)
assert release_failures(heldout_audit, 0.0001, 3) == ["event grammar"]

Performance and operating cost

Full-prefix parity evaluation repeats a growing prefix and can be costly across many held-out sessions, but it is a one-time release check rather than a serving path. Cache memory grows with retained context, layers and concurrent requests; beam width can multiply it. The pure-Python gate is O(1) after metrics are collected. Runtime benchmarks must include tokenizer work, prefill, each decode step and session-specific masks. An illustrative passing tolerance does not replace a device-specific numerical study.

Common Mistakes

Do not cache one request prefix and reuse it for a different service session.
Do not mark capped sequences complete when no end token was emitted.
Do not release a fast decoder with an invalid event-order rate hidden inside average token accuracy.