---
title: semif-serve
kind: module-contract
status: draft
owner: infra-ops
created: 2026-09-27
depends_on:
  - SemIf-OpenJev (MIT) at commit 23cf1f39fc9534fe81437200959b6dfc7106e45a, package semif_phase1
  - Qwen/Qwen3.5-4B at revision 851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a, BF16
---

# semif-serve: an HTTP wrapper around SemIf's direct and shared scorers

## Purpose

SemIf decides by reading the logits of the option letters after one forward pass.
It ships as a batch CLI only. semif-serve loads the model **once** and exposes the
two torch scorers over HTTP, so fleet callers can ask typed questions without
decoding. It adds nothing to the scoring itself: every score it returns is what
`semif_phase1.direct.score` or `semif_phase1.shared.score_shared` returned,
unchanged, and it adds an optional calibrated view next to it.

Operator decisions (Prime, 2026-09-27): runs on fv-ml1 GPU 1 under a hard VRAM
cap; built by infra-ops with a light process (this contract → TDD → heid bug
hunt); no consumer is named yet.

## Endpoints

Every POST takes and returns JSON. Every endpoint except `GET /health` requires
`Authorization: Bearer <token>`.

| method | path | body | success |
|---|---|---|---|
| GET | `/health` | — | 200 `{status: "ok", semif_commit, model, vram_cap_gib, max_tokens, max_decisions, workloads}` |
| POST | `/decide` | one SemIf row `{id, state, question, options[2..16]}` + optional `workload` | 200 the `direct.score` dict + optional `calibrated` |
| POST | `/decide/shared` | `{state, decisions: [{id, question, options}], workload?}` | 200 `{results: [...], timing: {...}}` from `score_shared` |

`options` items are `{id, description}`, as in SemIf. `state` is a nonempty
string, object or array. For `/decide/shared`, each decision becomes a SemIf row
by adding the shared `state`.

**Calibration.** `workload` is optional. When it names an entry in the
calibration table, each result gains `calibrated: {workload, temperature,
probabilities}` with `softmax(option_logits / T)`. The argmax never changes. The
native `probabilities` and `probability_status` stay untouched. An unknown
`workload` is a 422. With no `workload`, no `calibrated` key appears.

**Order averaging (0.1.3, Prime 2026-09-27).** A decision (the `/decide` body, or an
entry in `decisions`) may set `orderings`, whose default is `"none"`:

- `"rotations"`: the n cyclic shifts of the caller's option list, starting with
  the caller's order, so every option sits in every position exactly once.
- `"all"`: every permutation (n!), the caller's order first. It is allowed only
  when n ≤ 4, else 422.

Every ordering of every decision in the request becomes its own SemIf row
(`id` = `"<id>#o<k>"`, same state and question, reordered options). **All rows go
to the engine in ONE `shared` call**, which includes `/decide`. The result for an
averaged decision is:

```
{id, option_ids (caller's order),
 combined: {method, orderings: n, probabilities, top, agreement, spread: {option_id: [min, max]}},
 orderings: [the SemIf result for each ordering, unchanged]}
```

- `probabilities`: per ordering, log-softmax of `option_logits`; averaged per
  option id; renormalised; reported in the caller's order.
- `agreement`: the fraction of orderings whose top option equals `combined.top`.
- `spread`: each option's min and max native probability across orderings.

Every expanded row counts toward `SEMIF_MAX_DECISIONS`. `workload` together with
`orderings` is a 422, because a temperature is fitted per method and none is
fitted on combined scores yet. Decisions without `orderings` keep the exact
pre-0.1.3 result shape. Averaging cancels any additive position bias exactly.
Measured by the 2026-09-27 spike: 3 rotations take SemIf's labelled sets from
78.6% to 87.7% accuracy, and unanimous agreement is 94.4% accurate.

## Invariants

- **INV-1 pass-through.** `option_ids`, `probabilities`, `option_logits`,
  `prompt_sha256`, `prompt_version`, `model` and `probability_status` are exactly
  what SemIf returned. The wrapper never rewrites a score.
- **INV-2 one model, one inference at a time.** The model is loaded at startup,
  and a process-wide lock serialises every scorer call. The app runs as one worker.
  Scorer calls run off the event loop, so `/health` answers while one is in progress.
- **INV-3 fail-closed startup.** Startup refuses to serve unless the model sits
  on a CUDA device, torch's arch list includes the card's `sm_XY`, and one
  warm-up decision scores. `device=cpu` is allowed only when set explicitly.
- **INV-4 VRAM cap.** When `SEMIF_VRAM_CAP_GIB` is set, the process is capped at
  that share of the card (`torch.cuda.set_per_process_memory_fraction`) before
  the model loads. An out-of-memory error during a request is a 503
  `out_of_memory`, followed by `torch.cuda.empty_cache()`. The process stays up.
  **After every scorer call**, when the reserved memory exceeds the post-warm-up
  baseline by more than 512 MiB, the engine calls `empty_cache()`. A burst must not
  keep the card's shared headroom: on 2026-09-27 a 64-decision request left the
  process holding 12.6 GB, leaving scriberr 3.5 GB.
  **Any other scorer failure except `ValueError`** also releases before it is
  reported: it is logged with its traceback, then re-raised unchained as
  `ScoringFailed` after `gc.collect()` + `empty_cache()` (bug hunt C5). A
  `RuntimeError` whose message says "out of memory" (cuBLAS/cuDNN allocation
  failures) counts as an OOM → 503 (S9). An OOM with an empty message is reported
  as "CUDA out of memory" rather than crashing the handler (C4).
- **INV-5 no network at runtime.** Weights come from the mounted HF cache at the
  pinned revision. The entry point sets `HF_HUB_OFFLINE=1` itself before torch or
  transformers load, so this holds outside the image too (S10).
- **INV-6 constant-time auth.** Token comparison uses `hmac.compare_digest`. The
  token is ≥ 32 characters of visible ASCII (33–126). Startup refuses anything
  else, because a CR, LF or NUL in the token can never arrive in a header (S2).

## Limits and errors

- `SEMIF_MAX_TOKENS` (default 4096) is passed to the scorers. A longer prompt is a
  422, never truncated (SemIf raises).
- `SEMIF_MAX_DECISIONS` (default 64) caps `decisions` per shared request. It must
  hold 1..max entries, else 422.
- Request body ≤ `SEMIF_MAX_BODY_BYTES` (default 1 MiB), else 413. The limit is
  checked **before** each chunk is kept, so no more than the limit is ever held
  (C2). A declared `Content-Length` is trusted only if it is ASCII digits (S3).
- **Admission:** at most `SEMIF_MAX_QUEUE` (default 32) POSTs may be in progress,
  counting both queued and scoring. The next one is refused with 429 `busy`
  before its body is read (C6).

| status | code | when |
|---|---|---|
| 401 | `unauthorized` | missing or wrong bearer |
| 413 | `request_too_large` | body over the limit |
| 422 | `invalid_request` | bad JSON shape, a SemIf `ValueError` (validation, token limit, tokenisation), unknown workload, too many decisions |
| 429 | `busy` | `SEMIF_MAX_QUEUE` requests already in progress |
| 503 | `out_of_memory` | CUDA OOM during scoring |
| 500 | `scoring_failed` | any other scorer exception, **or a failure while building the response** from a scorer result (calibration, averaging): always the envelope, never a bare 500 (C3) |

The error body is `{error: {code, message}}`.

## Configuration (env)

`SEMIF_API_TOKEN` (required), `SEMIF_MODEL` (default `Qwen/Qwen3.5-4B`),
`SEMIF_REVISION` (default the pinned SHA), `SEMIF_DEVICE` (default `cuda`),
`SEMIF_VRAM_CAP_GIB`, `SEMIF_MAX_TOKENS`, `SEMIF_MAX_DECISIONS`,
`SEMIF_MAX_BODY_BYTES`, `SEMIF_MAX_QUEUE`, `SEMIF_CALIBRATION` (path to a JSON
`{workload: T}`).

Startup validates every value and refuses a bad one with a `ValueError` naming
the variable (C1, S1, S8):
- the VRAM cap, when set, is finite and > 0 (`0` used to mean uncapped);
- every limit is an integer ≥ 1;
- each T is a finite number, not a bool, in [0.05, 20] (a tiny T overflowed to
  NaN, and the response then failed to render);
- the calibration file must exist and parse.

## Tests (TDD, fake scorer: no torch, no model)

auth required on POSTs and not on /health; a short token is refused at startup;
`/decide` passes the row through and returns the scorer dict unchanged;
`/decide/shared` builds rows with the shared state and returns results + timing;
calibration adds `calibrated` and keeps the argmax; an unknown workload → 422; a
scorer `ValueError` → 422; the engine's `OutOfMemory` → 503; the torch engine,
against a fake torch, turns `torch.cuda.OutOfMemoryError` into an **unchained**
`OutOfMemory` and calls `empty_cache()` only after the failed call's tensors are freed
(found on the card: a chained exception kept 11.9 GiB allocated after the 503); after
a call, reserved memory over baseline + 512 MiB is released and at or under it is left
alone; any
other exception → 500; malformed
JSON or a wrong body shape → 422; too many decisions → 422; an oversized body → 413; requests
are serialised (two concurrent calls never overlap inside the scorer); `/health`
answers while a scorer call is blocked. **Averaging:** `rotations` sends n rows
in one shared call, each option once per position, with ids `<id>#o<k>`, and
cancels a position bias exactly; `all` sends n! rows and is 422 above 4 options;
agreement and spread are computed from the orderings; a mixed shared request
(averaged + plain) is one engine call, with results in request order and plain
results unchanged; expanded rows count toward the cap; `workload` + `orderings`
→ 422.

## Acceptance (on fv-ml1, real model; not unit tests)

1. **Parity:** our `/decide` over SemIf's `authored144` against their committed
   torch predictions (top choice and max probability gap).
2. **Noise floor:** the same run twice (A-vs-A).
3. **Negative control:** shuffled option descriptions must break agreement.
4. **Shared vs direct:** the same rows agree within the A-vs-A floor.
5. **Speed:** 21 binary criteria over one state, N ≥ 3, p50 + spread.
6. **VRAM:** the peak at a 4096-token input sets `SEMIF_VRAM_CAP_GIB`.