Frank
7c77326c33
The 2026-10-08 rename moved the harness model ids and the homepage cards; the served aliases on the wire stayed frank-primary/frank-large/ frank-secondary. This closes the gap: the alias each endpoint announces is now the family name, while the services, units, scripts, env files and runbooks keep the old names (container identity stays stable). - scripts/frank-secondary.env ALIAS, scripts/frank-large.env HALOGEN_MODEL_ID, scripts/frank-primary.env FRANK_PRIMARY_SERVED_NAME (+ the two serve-script defaults/gate messages) - seams re-pinned: frank_secondary/frank_large/frank_primary unit bats - fleet harnesses retargeted: bench-probe, deep-probe, calib-push, fleet-probes-swift15, autoresearch(+measure), bench-threads-batch-http, bench-decode - shared/homepage/docs.html: api-id annotations and the "switchover will rename the ids" footnote removed — the page's names are now the ids; opencode/Pi/curl examples send frank-zealot - dankclaw/hermes/config.yaml.example: frank-large -> frank-dragoon and the live 262144 context (Hermes routes by served id; see Hermes.md) Consumers with live state re-keyed out-of-band in the same window: OpenWebUI (model row, access_grant, openai.api_configs model_ids) and the Hermes config.yaml.
111 lines
3.8 KiB
Python
Executable File
111 lines
3.8 KiB
Python
Executable File
#!/usr/bin/env python3
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"""calib-push.py — drive a calibration corpus through a radiance scratch
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server's prefill so its tap writes layer Hessians (docs/MOE-W4.md).
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Runs ON DANKSERVER next to the scratch server (the one autoresearch- /
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serve-style scratch container booted with RADIANCE_CALIB_DIR set).
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Reads the corpus JSONL produced by make-calib-corpus.py
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({"prompt": "..."} a line), POSTs each prompt to /v1/completions with
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max_tokens=1 (prefill-only cost, one token out), at N-way concurrency
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matching the scratch server's --max-num-seqs budget.
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Corpus rounds repeat until the engine prints the tap's completion line
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("calibration wrote layer Hessians") in `podman logs <container>` — that
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is the ground truth that RADIANCE_CALIB_TOKENS was reached. After that
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line, further prompts are pure waste, so the driver exits.
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Mechanics notes:
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- requests carry no sampling params that matter (max_tokens=1, greedy).
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- a per-request timeout of 30 min: at ~1k tok/s prefill a 12k-token
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prompt is a few seconds; the timeout only catches a wedged engine.
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- failures print and continue: the tap pools over tokens, a few lost
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prompts do not bias the Hessians (the corpus is drawn round-robin over
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source classes, so loss is class-neutral as long as failures are rare;
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a systematic 4xx is loud in the log and must be fixed, not ignored).
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Usage: calib-push.py [--port 7770] [--parallel 64] [--container arbench]
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[--corpus /home/dank/corpora/out/calib.jsonl] [--max-rounds 4]
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"""
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import argparse
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import concurrent.futures
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import json
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import subprocess
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import sys
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import time
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import urllib.request
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DONE_MARK = "calibration wrote"
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def tap_done(container):
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"""True if the engine logged the tap's completion line."""
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try:
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out = subprocess.run(
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["podman", "logs", "--tail", "50", container],
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capture_output=True, text=True, timeout=30,
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)
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except Exception:
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return False
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return DONE_MARK in (out.stdout + out.stderr)
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def push_one(url, prompt):
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body = json.dumps({
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"model": "frank-archon",
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"prompt": prompt,
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"max_tokens": 1,
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"temperature": 0,
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"stream": False,
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}).encode()
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req = urllib.request.Request(
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url, data=body, headers={"Content-Type": "application/json"})
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try:
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with urllib.request.urlopen(req, timeout=1800) as r:
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r.read()
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return True
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except Exception as e:
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print(f"FAIL {str(e)[:120]}", flush=True)
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return False
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument("--port", type=int, default=7770)
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ap.add_argument("--parallel", type=int, default=64)
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ap.add_argument("--container", default="arbench")
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ap.add_argument("--corpus", default="/home/dank/corpora/out/calib.jsonl")
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ap.add_argument("--max-rounds", type=int, default=4)
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a = ap.parse_args()
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url = f"http://127.0.0.1:{a.port}/v1/completions"
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prompts = []
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with open(a.corpus) as f:
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for line in f:
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line = line.strip()
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if line:
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prompts.append(json.loads(line)["prompt"])
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print(f"corpus: {len(prompts)} prompts", flush=True)
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for rnd in range(1, a.max_rounds + 1):
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if tap_done(a.container):
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break
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t0 = time.time()
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ok = 0
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with concurrent.futures.ThreadPoolExecutor(a.parallel) as ex:
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for good in ex.map(lambda p: push_one(url, p), prompts):
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ok += 1 if good else 0
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dt = time.time() - t0
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print(f"round {rnd}: {ok}/{len(prompts)} ok in {dt:.0f}s "
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f"({sum(len(p) for p in prompts)/4/dt:.0f} chars/s est)",
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flush=True)
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if tap_done(a.container):
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break
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else:
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print("max rounds exhausted, tap not done", flush=True)
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sys.exit(1)
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print("TAP_DONE: calibration wrote layer Hessians", flush=True)
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if __name__ == "__main__":
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main()
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