# Autoresearch Worklog: latency-p95-chat-completions

## Session Summary
- Objective: reduce end-to-end p95 latency for local `POST /v1/chat/completions`
- Primary metric: `p95_ms` (lower is better)
- Secondary metrics: `p50_ms`, `mean_ms`, `completion_tps_mean`, `success_count`
- Benchmark harness: `./autoresearch.sh`

## Key Insights
- The first stable end-to-end benchmark is around 0.94 s p95 for a short non-streaming coding request on the local 4B GGUF model.
- The benchmark harness must use the canonical model id derived from the GGUF filename; the current server build does not actually honor the intended `local-model` alias path yet.
- Prompt prefill is only about 25 ms for this workload, so almost all latency sits in token generation rather than request parsing or prompt construction.
- A micro-optimization in per-token detokenization made both latency and throughput worse, which suggests the dominant cost is deeper in the generation/runtime path rather than heap allocation in Rust glue code.
- Shrinking the `Turbo4B` context and batch sizes also hurt throughput, so the current 4B profile appears closer to the local optimum for this hardware/workload than expected.
- Disabling internal llama.cpp perf counters produced the first real p95 improvement over baseline, which suggests a small but measurable amount of hot-path CPU overhead was coming from instrumentation rather than model math.
- Better stop behavior improved average and median latency, but it still failed to beat baseline p95, so the worst outliers are not explained solely by turn-boundary overruns.
- Switching to a Qwen-style chat template produced a severe p95 tail regression while median latency stayed near baseline, which strongly suggests worse stop behavior or occasional runaway generations rather than uniformly slower decoding.

## Next Ideas
- Measure baseline first, then inspect the hottest latency contributors in prompt preparation, request handling, and session creation.
- Investigate why `ModelCatalog` rejects `local-model` despite intending to expose aliases.

### Run 1: baseline - p95_ms=941.252 (KEEP)
- Timestamp: 2026-03-26 17:32
- What changed: Established the autoresearch harness, built the release server, started it against the local Qwen GGUF, and measured a fixed non-streaming coding request workload.
- Result: p95_ms=941.252, p50_ms=908.457, mean_ms=917.123, completion_tps_mean=52.427, success_count=7.
- Insight: The baseline is stable enough to compare future server/runtime changes, and the benchmark immediately exposed a compatibility bug in alias resolution that should be kept separate from true latency experiments.
- Next: fix low-risk benchmark-adjacent correctness issues only if they distort measurement; otherwise inspect hot latency contributors in the request/session path.

### Run 2: stack buffer detokenization - p95_ms=1032.315 (DISCARD)
- Timestamp: 2026-03-26 17:38
- What changed: Replaced the per-token heap allocation in `decode_token_piece_with()` with a 256-byte stack buffer and heap fallback in `crates/inference-core/src/session.rs`.
- Result: p95_ms=1032.315, p50_ms=1020.402, mean_ms=1011.466, completion_tps_mean=47.671, success_count=7.
- Insight: This path is not the practical bottleneck for end-to-end latency here; generation got slower overall despite the reduced allocation churn.
- Next: discard this branch state and focus on runtime/profile-level experiments that can improve generation throughput directly.

### Run 3: slim turbo4b runtime profile - p95_ms=1004.435 (DISCARD)
- Timestamp: 2026-03-26 17:40
- What changed: Reduced `Turbo4B` defaults in `crates/inference-core/src/profile.rs` from `n_ctx=4096`, `n_batch=512`, `n_ubatch=128` to `n_ctx=2048`, `n_batch=256`, `n_ubatch=64`.
- Result: p95_ms=1004.435, p50_ms=998.919, mean_ms=990.771, completion_tps_mean=48.528, success_count=7.
- Insight: The leaner profile still reduced effective generation speed on this RTX 3070 + Q6_K workload, so the current batch/context sizing is likely not the source of the baseline latency.
- Next: revert to the best-known commit and target generation semantics or model-facing prompt/template strategy rather than detokenization or profile shrinkage.

### Run 6: disable llama perf counters in hot path - p95_ms=925.587 (KEEP)
- Timestamp: 2026-03-26 17:57
- What changed: Set `llama_context_params.no_perf = true` in `crates/inference-core/src/engine.rs` and `sampler_chain_default_params.no_perf = true` in `crates/inference-core/src/session.rs`.
- Result: p95_ms=925.587, p50_ms=893.913, mean_ms=899.895, completion_tps_mean=53.618, success_count=7.
- Insight: Disabling internal perf bookkeeping shaved about 1.7% off baseline p95 with slightly better overall throughput, making this the first experiment worth keeping.
- Next: continue from this commit and look for adjacent hot-path flags or runtime settings that reduce generation overhead without changing model behavior.

### Run 5: default turn-boundary stops - p95_ms=965.663 (DISCARD)
- Timestamp: 2026-03-26 17:54
- What changed: Added rolling multi-token stop detection in `crates/inference-core/src/session.rs` and injected default turn-boundary stop sequences in `crates/inference-server/src/compat.rs` when the request did not specify `stop`.
- Result: p95_ms=965.663, p50_ms=877.030, mean_ms=897.251, completion_tps_mean=53.686, success_count=7.
- Insight: This reduced median and mean latency and slightly improved throughput, but the p95 still stayed above baseline, so tail latency likely comes from a smaller number of deeper generation stalls rather than just missed stop boundaries.
- Next: inspect per-sample latency spread and target the remaining long-tail outliers directly, likely in warmup/first-request behavior or occasional decode stalls.

### Run 4: qwen-style chat template - p95_ms=3027.145 (DISCARD)
- Timestamp: 2026-03-26 17:50
- What changed: Replaced the plain `System/User/Assistant:` prompt format in `crates/inference-server/src/compat.rs` with a Qwen-style `<|im_start|>...<|im_end|>` chat template.
- Result: p95_ms=3027.145, p50_ms=974.937, mean_ms=1467.026, completion_tps_mean=41.360, success_count=7.
- Insight: The template did not make typical requests much faster and introduced severe tail latency, likely because some generations stopped much later or followed a worse decoding path for this server/model combination.
- Next: revert this template change and focus on experiments that directly bound generation length or improve stop behavior without overhauling the prompt format.

## Segment 1: Dynamic Chat Templates
- New objective: add dynamic prompt templates by model family with built-in coverage for Qwen, Gemma, Llama, Kimi, and a generic fallback.
- Benchmark driver: `./autoresearch.sh` now runs ignored template-specific tests and reports `template_score`, `passed_cases`, `failed_cases`, and `total_cases`.
- Keep/discard rule for this segment is test-driven: higher `template_score` wins, while the regular non-ignored test suite must remain green.

### Run 7: baseline - template_score=20.0 (KEEP)
- Timestamp: 2026-03-26 18:14
- What changed: Re-initialized autoresearch to a new segment focused on dynamic chat templates, added a dedicated ignored test suite for Qwen, Gemma, Llama, Kimi, and generic fallback behavior, and switched the benchmark harness to score that suite.
- Result: template_score=20.0, passed_cases=1, failed_cases=4, total_cases=5.
- Insight: Only the generic fallback works today; all named family-template expectations fail because prompt rendering is still generic and model-agnostic.
- Next: add a minimal model-family detector and template registry in `crates/inference-server/src/compat.rs`, then route `prepare_prompt()` through it.