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services/semif-serve is a FastAPI wrapper around SemIf's direct and shared torch scorers (SemIf-OpenJev @ 23cf1f39, MIT). Upstream ships only a batch CLI. The wrapper loads the pinned Qwen3.5-4B (851bf6e8, BF16) once from the offline HF cache and returns SemIf's result dicts unchanged, with an optional per-workload temperature-calibrated view. Contract: semif-serve.contract.md. Built with a short contract, TDD (39 tests, fake engine and fake torch, no GPU) and a heid bug-hunt panel (pending). On the card: - torch 2.10.0+cu128 with sm_120 kernels, which is SemIf's own stack; - a hard 12 GiB VRAM cap. Two defects surfaced only on the card, and each fix is covered by a test: - 0.1.1: an OOM raised as a chained exception kept the failed request's tensors alive (11.9 GiB after the 503). It is now raised unchained, after gc. - 0.1.2: a large request left 12.6 GB reserved on the shared card. After each call, reserved memory over the baseline + 512 MiB is now released. Acceptance against SemIf's committed torch predictions (authored144): - 142/144 same top choice; both misses are exact bf16 ties; - 144/144 identical prompt hashes; - deterministic A-vs-A; - negative control 14/144; - shared vs direct 72/72. 21 binary criteria over one state take 159 ms. The shared-mode capacity table under the cap is in stacks/semif/README.md. The Dockerfile installs dependencies from a manifest with the project version blanked, so a version bump reuses the ~4 GB torch layer. Verified: 41 s rebuild, dependency layer CACHED. DNS: semif.fv.internal. Token: vault semif/api-token.
104 lines
5.2 KiB
Markdown
104 lines
5.2 KiB
Markdown
# semif
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**SemIf option-logit decisions** on **fv-ml1 GPU 1**, the utility card beside
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`vllm-coder`, the erp/meromero seats and scriberr. Deployed 2026-09-27 at Prime's
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request. Hosting assessment: infra-hermes, 2026-09-25.
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SemIf ([TheoLeeCJ/SemIf-OpenJev](https://github.com/TheoLeeCJ/SemIf-OpenJev), MIT)
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asks a small model a typed question and reads the answer straight from the logits of
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the option letters, after **one forward pass with no decoding**. Upstream ships only a
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batch CLI, so `services/semif-serve/` wraps its two torch scorers in a small FastAPI
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service. The contract is `services/semif-serve/semif-serve.contract.md`.
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| **URL** | `http://10.251.50.54:8032` (`/health` is open; POSTs need `Authorization: Bearer $(secret get semif/api-token)`) |
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| **Model** | `Qwen/Qwen3.5-4B` @ `851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a`, BF16, from `/tank/aimodels/huggingface` (read-only, offline) |
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| **SemIf** | commit `23cf1f39fc9534fe81437200959b6dfc7106e45a`; torch `2.10.0+cu128`, transformers `5.17.0`, the same stack SemIf's committed predictions were made on |
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| **Image** | `semif-serve:<version>`, built on fv-ml1 from `services/semif-serve/` |
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| **State** | none. Weights re-download at the pinned revision. No backup needed beyond the host's `/opt/docker` restic. |
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## API
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```bash
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T=$(secret get semif/api-token)
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curl -s -H "Authorization: Bearer $T" http://10.251.50.54:8032/decide -d '{
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"id": "q1", "state": "Health checks passed in all three zones.",
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"question": "Did the deployment succeed?",
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"options": [{"id":"yes","description":"It succeeded."},{"id":"no","description":"It failed."}]}'
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```
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- `POST /decide`: one decision, returning SemIf's result dict unchanged (`option_ids`,
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`probabilities`, `option_logits`, `prompt_sha256`, `model`, …).
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- `POST /decide/shared`: `{state, decisions: [{id, question, options}]}`. It prefills
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the state once and scores every criterion in one batch. Use it when many questions
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share one long state.
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- `GET /health`: the pins, limits and calibrated workloads.
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## ⚠ Probabilities are uncalibrated
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SemIf labels its output `"conditional option score; uncalibrated as decision
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confidence"`, and means it: on WANLI the model is right ~64% of the time while
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reporting far higher confidence. **Before a caller thresholds on `probabilities`, it
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brings labelled rows (≥ ~150) for its workload.** We fit one temperature `T` with
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SemIf's `benchmarks/calibrate.py`, add `{"<workload>": T}` to
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`/opt/docker/conf/semif/calibration.json` (`stacks/semif/conf/`), and restart. The
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caller then passes `"workload": "<name>"` and gets a `calibrated` block beside the
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native scores. The argmax never changes.
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## VRAM: a hard cap, released after every burst
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`VRAM_CAP_GIB=12` becomes `torch.cuda.set_per_process_memory_fraction` before the
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weights load. At rest the process holds **~8.7 GB** (nvidia-smi); the weights are 7.84
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GiB. After any call that grows torch's reserved memory past the post-warm-up baseline
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+ 512 MiB, the engine calls `empty_cache()`, so a burst returns to the card and does
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not squeeze scriberr, which shares GPU 1. A request that would exceed the cap gets
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`503 out_of_memory`, memory returns to baseline, and the service stays up. Both
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behaviours were verified on the card (0.1.0 held 11.9 GiB after an OOM, and 12.6 GB
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after a large request; 0.1.2 returns to 7.85 GiB in both cases).
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**What fits under 12 GiB** (measured, `/decide/shared`, binary decisions):
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| state size (prefix tokens) | max decisions in one request |
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| ~140 | 52 |
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| ~520 | 43 |
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| ~1,960 | 19 |
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| ~3,900 | 13 |
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`/decide` fits at the full 4,096-token limit. Past the table you get a 503, so split
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the decisions across requests.
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## Acceptance (2026-09-27, v0.1.2)
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Raw: `services/semif-serve/acceptance/result-2026-09-27-v0.1.2.json`.
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| check | result |
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| parity with SemIf's committed torch predictions (authored144) | **142/144** same top choice; **144/144** identical prompt SHA-256; max prob gap 0.093 |
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| noise floor (same 144 twice) | 144/144, gap 0.0: deterministic |
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| negative control (option descriptions rotated) | 14/144: the check catches a wrong answer |
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| shared vs direct (36 shared states, 72 rows) | 72/72, max gap 0.045 |
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| 21 binary criteria over one state, from nh3-dev | shared **159 ms** (3 runs, 159–160) vs 21 sequential calls 981 ms |
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The two parity misses are **exact bf16 ties in our output** (top-2 margin 0.000),
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where upstream's prefix-cache path gave margins of 0.054 and 0.185; one of the two
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now matches the label. So the misses come from the numeric path, not the wrapper. The
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speed figure includes one network round trip (~27 ms). The burst release costs ~16 ms
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on it (0.1.1 measured 143 ms without the release).
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## Building
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```bash
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# from nh3-dev
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tar -C services/semif-serve -cf - --exclude=.venv --exclude=.pytest_cache --exclude=__pycache__ --exclude=acceptance . \
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| ssh infra-ops@10.251.50.54 'mkdir -p /opt/docker/src/semif-serve-X.Y.Z && tar -x -C /opt/docker/src/semif-serve-X.Y.Z'
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# on fv-ml1
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cd /opt/docker/src/semif-serve-X.Y.Z && docker build -t semif-serve:X.Y.Z .
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```
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To move SemIf forward: bump the commit in `services/semif-serve/pyproject.toml`
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(and `SEMIF_COMMIT` in `config.py`), run `uv lock`, rebuild, and **re-run the
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acceptance**. Upstream is research code that changes weekly, which is why it is
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pinned.
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