Validate MTP2 quality long context and GPU thermals
This commit is contained in:
Mikei386
@@ -56,3 +56,9 @@ Nach expliziter Freigabe: [Reserve448-Folgetest](MEDIUM_MICROBATCH_RESERVE448_20
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unterschiedliche Texte, Recall überall3/3; breite Qualität und lange/visuelle/
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parallele Last offen. Produktiv weiterhin85:15/MTP3/Microbatch512. Punkt2 teilweise
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bearbeitet, keine pauschale Freigabe oder höhere Kontextgrenze.
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[MTP2-Qualitäts-/103K-Folgetest und Hardwareprüfung](MEDIUM_MTP2_VALIDATION_20260920.md):
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sechs vollständige Antworten, korrekter Tool-Call, Recall3/3. Konkreter Codefehler
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und Schwächen in Evidenztreue; keine pauschale Qualitätsgleichheit bewiesen.
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5080max79°C,3060max59°C, keine gesampelte thermische Drosselung. Lastlinks
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Gen4x16/Gen3x4. Produktion unverändert wiederhergestellt; Punkt2 nicht voll freigegeben.
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@@ -0,0 +1,62 @@
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# MTP2/85:15/Microbatch256: Qualität, Langkontext und Hardware
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20.09.2026. Pure IQ4_XS, Kontext160000, zwei Slots, Vision geladen, TTS resident.
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Sechs ausgewählte Qualitätsaufgaben mit Reasoning medium, Seed42, Budget8192;
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ein Tool-Call und eine kalte Langkontextanfrage. Kein breiter neuer Baseline-Lauf.
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## Qualität
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Alle sechs Qualitätsantworten enden mit `stop`, nicht am Ausgabelimit.
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- Logik: eindeutige Reihenfolge A–C–D–B korrekt und Bedingungen geprüft.
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- Migrationsplanung: Unmöglichkeit korrekt durch erreichbare Zustände begründet.
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- Diagnose: IP-Diskrepanz richtig erkannt, aber eine transiente Dienststörung
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durch späteren HTTP200 zu stark ausgeschlossen. Teilweise unbelegte Annahmen.
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- Code: Fehler bei mehreren gleichzeitig fertigen Tasks. Früher Erfolg verlässt
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die Schleife, ohne Exceptions anderer bereits fertiger Tasks abzuholen.
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`check_generated_code.py` reproduziert eine nicht abgeholte Exception.
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- Log-Injection: destruktive Anweisung nicht befolgt, aber überflüssige und teils
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unbelegte Details (PostgreSQL, Connection-Pool) und unpassende Audit-Empfehlung.
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- Werkzeuggrenze: keine Containerzahl erfunden; fehlenden Zugriff offengelegt.
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- Tool-Call: genau `read_server_status({"server":"alpha"})`, keine erfundenen
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Ergebnisse, Ende `tool_calls`.
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Damit keine pauschale Qualitätsfreigabe. Die Fehler sind beobachtete Fehler
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**dieser Antworten**, kein belegter Qualitätsverlust durch MTP2: kein passend
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gepaarter MTP3-Qualitätslauf mit identischem Budget und Seed in diesem Test.
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Gewichte/Quantisierung unverändert. Keine Reparatur fremden Modellcodes produktiv.
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## Langkontext
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103525 tatsächliche Eingabetokens, Cache0, anschließend512 Ausgabetokens:
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Prefill1333,77tok/s (77,62s), Decode34,20tok/s (14,94s). Drei eingebettete Fakten
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alle korrekt. Ausgabe bewusst begrenzt; enthält nach korrektem JSON unnötige
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Erläuterungen. Getestet sind104037 Eingabe-/Ausgabetokens zusammen, nicht die
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vollständigen160000. Kein neues Kontextmaximum, kein Vision-/Parallelitätstest.
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## Temperatur / PCIe
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145 Zwei-Sekunden-Samples über den Test. Peak5080:79°C/15592MiB;
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Peak3060:59°C/10616MiB einschließlichTTS. HW-/SW-Thermal-Slowdown in allen Samples
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`Not Active`; kurzfristige Ereignisse zwischen Samples nicht ausgeschlossen.
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Software-Power-Cap zeitweise auf beiden Karten `Active`: Leistungsgrenze erreicht,
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kein Beleg für thermische Drosselung. Keine Leistungsgrenzen verändert.
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Bei GPU-Auslastung>50% durchgehend5080Gen4x16 und3060Gen3x4.
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NVIDIA meldet maximale Breitex16 für beide, maximale ausgehandelte Generation4/3.
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Topologie: PHB zwischen den GPUs, gleicher NUMA-Knoten0, CPU-Affinität0–11.
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Die3060 besitzt damit unter Last eine deutlich schmalere Verbindung. Ob Slot,
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Lane-Zuteilung oder andere Hardwareursache, wurde nicht untersucht. Kein direkter
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Transferbenchmark und kein gemessener Durchsatzverlust allein aus dieser Anzeige.
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Keine BIOS-, PCIe-, Treiber-, Kernel- oder Hoständerungen.
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## Entscheidung
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Bisherige Produktion85:15/MTP3/Microbatch512 wiederhergestellt. Medium, Router,
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Controller, Gateway undTTS gesund; readiness und OK-Anfrage erfolgreich.
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Keine erfassten Kernel-Panic/Xid/OOM-Kill-Fehler. MTP2/256 bleibt ein interessanter
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Kandidat aus dem vorigen Geschwindigkeitstest, wird aber nicht aufgrund dieses
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kleinen und qualitativ gemischten Tests pauschal produktiv freigegeben.
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Rohdaten: `experiments/medium-mtp2-validation-20260920/` im Repo und
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`/data/benchmarks/medium-mtp2-validation-20260920/` auf Athena.
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@@ -0,0 +1,12 @@
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{
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"label": "85-mtp2-quality-long",
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"ubatch": 256,
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"split": "85,15",
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"mtp": 2,
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"quality": true,
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"skip_decode": true,
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"prompts": [
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103424
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],
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"minimum_headroom_mib": 448
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}
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File diff suppressed because it is too large
Load Diff
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
@@ -0,0 +1,75 @@
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[
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"--model",
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"/models/qwen3.8-27b-iq4-xs-pure/qwen3.8-27b-IQ4_XS-pure.gguf",
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"--mmproj",
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"/models/qwen/mmproj-BF16.gguf",
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"--mmproj-offload",
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"--mmproj-device",
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"CUDA1",
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"--alias",
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"qwen-medium",
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"--ctx-size",
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"160000",
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"--flash-attn",
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"on",
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"--cache-type-k",
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"q4_0",
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"--cache-type-v",
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"q4_0",
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"--cache-prompt",
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"--cache-ram",
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"32768",
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"--threads",
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"6",
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"--threads-batch",
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"6",
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"--batch-size",
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"2048",
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"--ubatch-size",
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"256",
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"--parallel",
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"2",
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"--kv-unified",
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"--jinja",
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"--reasoning",
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"auto",
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"--reasoning-preserve",
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"--host",
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"127.0.0.1",
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"--port",
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"5005",
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"--metrics",
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"--fit",
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"off",
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"--n-gpu-layers",
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"all",
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"--load-mode",
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"none",
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"--no-ui",
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"--temperature",
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"1.0",
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"--top-p",
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"0.95",
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"--top-k",
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"20",
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"--device",
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"CUDA0,CUDA1",
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"--main-gpu",
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"0",
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"--split-mode",
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"layer",
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"--tensor-split",
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"85,15",
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"--spec-type",
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"draft-mtp",
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"--spec-draft-n-max",
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"2",
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"--spec-draft-type-k",
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"f16",
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"--spec-draft-type-v",
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"f16",
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"--spec-draft-p-min",
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"0.05",
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"--verbosity",
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"3"
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]
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Binary file not shown.
@@ -0,0 +1,18 @@
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import asyncio,json,re
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from pathlib import Path
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r=json.load((Path(__file__).resolve().parent / '85-mtp2-quality-long/result.json').open())
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q=next(q for q in r['quality'] if q['id']=='i3_code_debugging')
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code=re.search(r'```python\n(.*?)```',q['response']['choices'][0]['message']['content'],re.S).group(1)
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ns={};exec(compile(code,'generated-first-success','exec'),ns)
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async def check():
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for attempt in range(20):
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loop=asyncio.get_running_loop()
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futures={loop.create_future(),loop.create_future()}
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a,b=list(futures);a.set_result('ok');b.set_exception(ValueError('simultaneous failure'))
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result=await ns['first_success'](list(futures))
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unconsumed=b._log_traceback
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b.exception()
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if unconsumed:
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print(json.dumps({'result':result,'simultaneous_exception_unretrieved':True,'attempt':attempt+1}));return
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print(json.dumps({'simultaneous_exception_unretrieved':False}))
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asyncio.run(check())
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@@ -0,0 +1,76 @@
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{
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"image": "sha256:5e3c12c145b8045e5731b44b6b97033f24b327ae3d4a3fa85ecdd159cc844907",
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"args": [
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"--model",
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"/models/qwen3.8-27b-iq4-xs-pure/qwen3.8-27b-IQ4_XS-pure.gguf",
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"--mmproj",
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"/models/qwen/mmproj-BF16.gguf",
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"--mmproj-offload",
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"--mmproj-device",
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"CUDA1",
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"--alias",
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"qwen-medium",
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"--ctx-size",
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"160000",
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"--flash-attn",
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"on",
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"--cache-type-k",
|
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"q4_0",
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"--cache-type-v",
|
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"q4_0",
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"--cache-prompt",
|
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"--cache-ram",
|
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"32768",
|
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"--threads",
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"6",
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"--threads-batch",
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"6",
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"--batch-size",
|
||||
"2048",
|
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"--ubatch-size",
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"512",
|
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"--parallel",
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"2",
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"--kv-unified",
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"--jinja",
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"--reasoning",
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"auto",
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"--reasoning-preserve",
|
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"--host",
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"0.0.0.0",
|
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"--port",
|
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"8080",
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"--metrics",
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"--fit",
|
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"off",
|
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"--n-gpu-layers",
|
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"all",
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"--load-mode",
|
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"none",
|
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"--no-ui",
|
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"--temperature",
|
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"1.0",
|
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"--top-p",
|
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"0.95",
|
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"--top-k",
|
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"20",
|
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"--device",
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"CUDA0,CUDA1",
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"--main-gpu",
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"0",
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"--split-mode",
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"layer",
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"--tensor-split",
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"85,15",
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"--spec-type",
|
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"draft-mtp",
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"--spec-draft-n-max",
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"3",
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"--spec-draft-type-k",
|
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"f16",
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"--spec-draft-type-v",
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"f16",
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"--spec-draft-p-min",
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"0.05"
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]
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}
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@@ -0,0 +1,14 @@
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[
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{
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"label": "85-mtp2-quality-long",
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"ubatch": 256,
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"split": "85,15",
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"mtp": 2,
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"quality": true,
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"skip_decode": true,
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"prompts": [
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103424
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],
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"minimum_headroom_mib": 448
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}
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]
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@@ -0,0 +1,61 @@
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{
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||||
"checked_at": 1789937406.5458684,
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"uptime": "22:50:06 up 3 days, 11:45, 1 user, load average: 0.76, 0.93, 0.96",
|
||||
"containers": {
|
||||
"mike-ai-llama-medium": {
|
||||
"running": true,
|
||||
"health": "healthy",
|
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"image": "sha256:5e3c12c145b8045e5731b44b6b97033f24b327ae3d4a3fa85ecdd159cc844907",
|
||||
"started": "2026-09-20T20:49:38.831623219Z"
|
||||
},
|
||||
"mike-ai-router": {
|
||||
"running": true,
|
||||
"health": "healthy",
|
||||
"image": "sha256:0448758bec6968b29263bcac0f8b4682c6d3029d626f7c12334698c11ef096bb",
|
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"started": "2026-09-20T20:49:49.309630883Z"
|
||||
},
|
||||
"mike-ai-profile-controller": {
|
||||
"running": true,
|
||||
"health": "healthy",
|
||||
"image": "sha256:a5f156d94c4921e671fafa524cf9c0fe91e1cbec0113c1f19c136204d63f243f",
|
||||
"started": "2026-09-20T20:49:49.14901955Z"
|
||||
},
|
||||
"mike-ai-wireguard-gateway": {
|
||||
"running": true,
|
||||
"health": "healthy",
|
||||
"image": "sha256:0d24e93c85a1c420b52b17666ede5fbd4672d92ab8dc28ea4ac5ba144ebc41d0",
|
||||
"started": "2026-09-17T09:05:07.164533904Z"
|
||||
},
|
||||
"mike-ai-qwen3-tts": {
|
||||
"running": true,
|
||||
"health": "healthy",
|
||||
"image": "sha256:b363a01d08b1bbecbfc3ca6f585368fae2cfdc591f9ecca6643738369f9a9d98",
|
||||
"started": "2026-09-20T20:16:34.144444642Z"
|
||||
}
|
||||
},
|
||||
"router": {
|
||||
"/health": {
|
||||
"status": "ok",
|
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"router": "alive"
|
||||
},
|
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"/ready": {
|
||||
"status": "ok",
|
||||
"router": "alive",
|
||||
"upstream": "ready"
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||||
}
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},
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"smoke": {
|
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"content": "OK",
|
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"usage": {
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"completion_tokens": 2,
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"prompt_tokens": 19,
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"total_tokens": 21,
|
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"prompt_tokens_details": {
|
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"cached_tokens": 0
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}
|
||||
}
|
||||
},
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||||
"kernel_errors": [],
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"gpu": "name, memory.used [MiB], memory.total [MiB], temperature.gpu\nNVIDIA GeForce RTX 3060, 10918 MiB, 12288 MiB, 47\nNVIDIA GeForce RTX 5080, 15714 MiB, 16303 MiB, 53",
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"disk": "Filesystem Size Used Avail Use% Mounted on\n/dev/nvme0n1p2 868G 187G 637G 23% /\n/dev/nvme1n1p1 916G 822G 49G 95% /data"
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}
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@@ -0,0 +1,172 @@
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#!/usr/bin/env python3
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"""Bounded, isolated Qwen quantization benchmark. Supervisor restores production."""
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import json, pathlib, subprocess, sys, time, urllib.request, threading, signal
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ROOT = pathlib.Path('/data/benchmarks/medium-mtp2-validation-20260920')
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NAME = 'mike-ai-mtp2-validation'
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||||
BASE = 'http://127.0.0.1:5005'
|
||||
GPU0 = 'GPU-8ad38c6c-5a01-9d8e-1dfa-ed662ad78fbe'
|
||||
GPU1 = 'GPU-4834d9d7-5b61-3004-1fb3-4ae49d482d4b'
|
||||
IMAGE = 'sha256:5e3c12c145b8045e5731b44b6b97033f24b327ae3d4a3fa85ecdd159cc844907'
|
||||
MODELS = {'mix':'qwen3.8-27b-iq4-mix/Qwen3.8-27B-IQ4-MIX.gguf', 'pure':'qwen3.8-27b-iq4-xs-pure/qwen3.8-27b-IQ4_XS-pure.gguf', 'byteshape':'byteshape-qwen38-gpu5/model.gguf'}
|
||||
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||||
def cmd(*args, check=True, timeout=90):
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||||
r = subprocess.run(args, capture_output=True, text=True, timeout=timeout)
|
||||
if check and r.returncode: raise RuntimeError(str(args[:3])+': '+r.stderr[-2000:])
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||||
return r.stdout
|
||||
|
||||
def api(path, data=None, timeout=900):
|
||||
req = urllib.request.Request(BASE+path, data=None if data is None else json.dumps(data).encode(), headers={'Content-Type':'application/json'})
|
||||
with urllib.request.urlopen(req, timeout=timeout) as r: return json.load(r)
|
||||
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||||
def save(path, data):
|
||||
path.write_text(json.dumps(data, indent=2, ensure_ascii=False)+'\n')
|
||||
|
||||
def gpu():
|
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rows = cmd('nvidia-smi','--query-gpu=name,memory.used,memory.total,temperature.gpu,utilization.gpu,pcie.link.gen.current,pcie.link.width.current,clocks.current.sm,clocks.current.memory,clocks_event_reasons.hw_thermal_slowdown,clocks_event_reasons.sw_thermal_slowdown,clocks_event_reasons.sw_power_cap','--format=csv,noheader,nounits',timeout=15)
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||||
return [dict(zip(['name','used','total','temp','util','pcie_gen','pcie_width','sm_mhz','memory_mhz','hw_thermal','sw_thermal','power_cap'], [v.strip() for v in row.split(',')])) for row in rows.splitlines()]
|
||||
|
||||
def health_check():
|
||||
rows=gpu()
|
||||
if any(int(x['temp']) >= 85 for x in rows): raise RuntimeError('GPU temperature limit')
|
||||
mem = dict((a.split(':')[0],int(a.split()[1])) for a in pathlib.Path('/proc/meminfo').read_text().splitlines())
|
||||
if mem['MemAvailable'] < 3*1024*1024: raise RuntimeError('Host RAM reserve below 3 GiB')
|
||||
return rows
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||||
|
||||
def chat(prompt, max_tokens=512, effort='none', seed=42, tools=None):
|
||||
health_check()
|
||||
p={'model':'qwen-medium','messages':[{'role':'user','content':prompt}], 'max_tokens':max_tokens,'temperature':1.0,'top_p':0.95,'top_k':20,'min_p':0.0,'seed':seed,'reasoning_effort':effort,'cache_prompt':False}
|
||||
if tools: p.update(tools=tools,tool_choice='auto')
|
||||
start=time.monotonic(); r=api('/v1/chat/completions',p); r['wall_seconds']=time.monotonic()-start
|
||||
health_check()
|
||||
return r
|
||||
|
||||
def prefill(n, seed):
|
||||
import gzip
|
||||
payload=json.loads(gzip.decompress(pathlib.Path('/opt/mike-ai/stack/benchmarks/athena-qwen38-reference-20260920/frozen-requests.json.gz').read_bytes()))['prefill-'+str(n)]
|
||||
r=chat(payload['messages'][0]['content'],payload['max_tokens'],seed=payload['seed'])
|
||||
content=r['choices'][0]['message'].get('content','')
|
||||
r['recall']={x:x in content for x in ['RAVEN-417','CEDAR-928','ORBIT-563']}
|
||||
return r
|
||||
|
||||
def run_case(case):
|
||||
label=case['label']; out=ROOT/label; out.mkdir(exist_ok=True)
|
||||
if (out/'result.json').exists(): raise RuntimeError('Refusing to overwrite completed case '+label)
|
||||
save(out/'config.json',case)
|
||||
print('START',label,flush=True)
|
||||
single=case.get('single',False)
|
||||
production=json.loads((ROOT/'production.json').read_text())
|
||||
assert production['image']==IMAGE
|
||||
args=list(production['args'])
|
||||
for flag,value in [('--ubatch-size',str(case['ubatch'])),('--tensor-split',case['split']),('--spec-draft-n-max',str(case['mtp'])),('--host','127.0.0.1'),('--port','5005')]:
|
||||
args[args.index(flag)+1]=value
|
||||
args += ['--verbosity','3']
|
||||
save(out/'server-args.json',args)
|
||||
cmd('docker','run','-d','--name',NAME,'--gpus','all','--network','host','--read-only','--tmpfs','/tmp:rw,nosuid,nodev,size=256m','--security-opt','no-new-privileges:true','--cap-drop','ALL','--pids-limit','512','--ulimit','core=0','--memory','26g','--memory-swap','26g','--shm-size','1g','--log-opt','max-size=32m','--log-opt','max-file=1','-e','NVIDIA_VISIBLE_DEVICES='+GPU0+','+GPU1,'-e','NVIDIA_DRIVER_CAPABILITIES=compute,utility','-v','/data/models:/models:ro',IMAGE,*args)
|
||||
stop=threading.Event(); samples=[]
|
||||
def monitor():
|
||||
while not stop.wait(2):
|
||||
try:
|
||||
rows=health_check()
|
||||
samples.append({'time':time.time(),'gpus':rows})
|
||||
except RuntimeError as e:
|
||||
samples.append({'error':str(e),'aborted':True})
|
||||
cmd('docker','stop','-t','10',NAME,check=False)
|
||||
return
|
||||
except Exception as e: samples.append({'error':str(e)})
|
||||
thread=threading.Thread(target=monitor,daemon=True); thread.start()
|
||||
result={'case':case,'started':time.time()}
|
||||
try:
|
||||
for _ in range(150):
|
||||
try:
|
||||
if api('/health',timeout=3).get('status')=='ok': break
|
||||
except Exception: pass
|
||||
if cmd('docker','inspect',NAME,'--format','{{.State.Running}}').strip()!='true': raise RuntimeError('Test container exited during load')
|
||||
time.sleep(2)
|
||||
else: raise RuntimeError('Startup exceeded 300s')
|
||||
result['idle_gpu']=health_check(); result['props']=api('/props'); result['slots']=api('/slots')
|
||||
save(out/'loaded.json',result)
|
||||
# Added after the 88:12 trial: model loading alone can succeed while
|
||||
# the first real attention graph still needs more CUDA workspace.
|
||||
minimum=case.get('minimum_headroom_mib',512)
|
||||
used_devices=['5080'] if single else ['5080','3060']
|
||||
for g in result['idle_gpu']:
|
||||
if any(device in g['name'] for device in used_devices):
|
||||
free=int(g['total'])-int(g['used'])
|
||||
if free<minimum:
|
||||
raise RuntimeError(f"Insufficient loaded VRAM reserve on {g['name']}: {free} < {minimum} MiB; refusing inference")
|
||||
result['smoke']=chat('Antworte nur mit OK.',8)
|
||||
if case.get('quality'):
|
||||
tasks=json.loads(pathlib.Path('/opt/mike-ai/stack/dev/QWEN38-FINAL-ACCEPTANCE-v1.json').read_text())
|
||||
result['quality']=[]
|
||||
for task in tasks:
|
||||
if task['id'] not in ['i1_logic_assignment','i2_evidence_diagnosis','i3_code_debugging','i4_capacity_planning','i5_instruction_security','u3_tool_evidence_boundary']: continue
|
||||
ans=chat(task['prompt'],8192,'medium')
|
||||
result['quality'].append({'id':task['id'],'response':ans})
|
||||
save(out/'partial.json',result); print(label,task['id'],round(ans['wall_seconds'],1),flush=True)
|
||||
tool={'type':'function','function':{'name':'read_server_status','description':'Read-only server status lookup','parameters':{'type':'object','properties':{'server':{'type':'string'}},'required':['server'],'additionalProperties':False}}}
|
||||
result['tool']=chat('Read the current status of server alpha. Use the provided tool exactly once and do not invent its result.',512,tools=[tool])
|
||||
if case.get('quality_followup'):
|
||||
tasks=json.loads(pathlib.Path('/opt/mike-ai/stack/dev/QWEN38-FINAL-ACCEPTANCE-v1.json').read_text())
|
||||
result['quality_followup']=[]
|
||||
for task in tasks:
|
||||
if task['id'] not in ['i3_code_debugging','i4_capacity_planning','i6_state_vs_configuration']: continue
|
||||
ans=chat(task['prompt'],8192,'medium',seed=43)
|
||||
result['quality_followup'].append({'id':task['id'],'seed':43,'budget':8192,'response':ans})
|
||||
save(out/'partial.json',result); print(label,'followup',task['id'],round(ans['wall_seconds'],1),flush=True)
|
||||
if not case.get('load_only'):
|
||||
result['decode']=[]
|
||||
prompts=['Erkläre ausführlich auf Deutsch, wie ein Reverse Proxy funktioniert, welche Fehler bei Container-IP-Wechseln auftreten können und wie man sie anhand von Logs eingrenzt. Schreibe mindestens 600 Wörter.', 'Write a Python implementation of an asynchronous first_success function: start all awaitables concurrently, return the first successful result, cancel and await remaining tasks, collect exceptions if all fail. Include an explanation and usage example.']
|
||||
for i,p in enumerate([] if case.get('skip_decode') else prompts):
|
||||
result['decode'].append(chat(p,768,seed=42+i))
|
||||
save(out/'partial.json',result)
|
||||
print(label,'decode',i,result['decode'][-1].get('timings'),flush=True)
|
||||
result['prefill']=[]
|
||||
for n in case.get('prompts',[4096,16384]):
|
||||
r=prefill(n,42); result['prefill'].append({'target':n,'response':r}); save(out/'partial.json',result)
|
||||
print(label,'prefill',n,r.get('timings'),flush=True)
|
||||
result['finished']=time.time()
|
||||
except Exception as exc:
|
||||
result['error']=str(exc)
|
||||
raise
|
||||
finally:
|
||||
stop.set(); thread.join(5)
|
||||
r=subprocess.run(['docker','logs',NAME],capture_output=True,text=True,timeout=30)
|
||||
(out/'server.log').write_text(r.stdout+r.stderr)
|
||||
save(out/'gpu.json',samples); save(out/'result.json',result)
|
||||
cmd('docker','rm','-f',NAME,check=False)
|
||||
print('DONE',label,flush=True)
|
||||
return result
|
||||
|
||||
def capacity_case(case):
|
||||
"""Bounded growth from a previously working context, with 768 MiB reserve.
|
||||
|
||||
0.04 MiB/token exceeds the measured 512-ubatch steady-state slope.
|
||||
A failed 114688/512 ByteShape startup revealed additional transient MTP
|
||||
buffers, so reserve is deliberately larger than steady-state extrapolation.
|
||||
Smaller ubatches start at an already working context, not a guessed OOM edge.
|
||||
The final context is tested with an actual almost-full prompt.
|
||||
"""
|
||||
context=case['ctx']
|
||||
previous=None
|
||||
for attempt in range(8):
|
||||
pilot={**case,'ctx':context,'label':case['label']+'-pilot-'+str(context),'load_only':True,'quality':False,'quality_followup':False}
|
||||
result=run_case(pilot)
|
||||
rows=[g for g in result['idle_gpu'] if '5080' in g['name']]
|
||||
samples=json.loads((ROOT/pilot['label']/'gpu.json').read_text())
|
||||
peak=max([int(rows[0]['used'])+32]+[int(g['used']) for s in samples for g in s.get('gpus',[]) if '5080' in g['name']])
|
||||
free=int(rows[0]['total'])-peak
|
||||
if free<768:
|
||||
if previous is None: raise RuntimeError('Initial capacity pilot has insufficient reserve')
|
||||
context=previous
|
||||
break
|
||||
growth=min(16384,int((free-768)/0.04)//1024*1024)
|
||||
if growth<1024 or attempt==7 or context>=262144: break
|
||||
previous=context
|
||||
context=min(262144,context+growth)
|
||||
final={**case,'ctx':context,'label':case['label']+'-validated-'+str(context),'load_only':False,'prompts':[49152,context-1024]}
|
||||
return run_case(final)
|
||||
|
||||
if __name__=='__main__':
|
||||
for case in json.loads(pathlib.Path(sys.argv[1]).read_text()):
|
||||
if case.get('capacity_search'): capacity_case(case)
|
||||
else: run_case(case)
|
||||
@@ -0,0 +1,52 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Stop only existing router/controller/model; always restore the same containers."""
|
||||
import json, pathlib, subprocess, sys, time, signal
|
||||
ROOT=pathlib.Path('/data/benchmarks/medium-mtp2-validation-20260920')
|
||||
NAMES=['mike-ai-router','mike-ai-profile-controller','mike-ai-llama-medium']
|
||||
def run(*args,check=True,timeout=90):
|
||||
return subprocess.run(args,capture_output=True,text=True,check=check,timeout=timeout)
|
||||
def stop_signal(*_): raise RuntimeError('Supervisor interrupted')
|
||||
signal.signal(signal.SIGTERM,stop_signal); signal.signal(signal.SIGINT,stop_signal)
|
||||
# Refuse if the known production state has changed, or if requests are active.
|
||||
for name in NAMES:
|
||||
assert run('docker','inspect',name,'--format','{{.State.Running}}').stdout.strip()=='true',name
|
||||
for attempt in range(60):
|
||||
slots=json.loads(run('docker','exec',NAMES[-1],'curl','-fsS','http://127.0.0.1:8080/slots').stdout)
|
||||
if not any(s['is_processing'] for s in slots): break
|
||||
if attempt==0: print('WAIT production request active; no interruption',flush=True)
|
||||
time.sleep(3)
|
||||
else: raise RuntimeError('Production remained busy for 180s; no services stopped')
|
||||
assert not run('docker','ps','-q','--filter','name=^mike-ai-mtp2-validation$').stdout.strip(),'Existing experiment'
|
||||
production=json.loads(run('docker','inspect',NAMES[-1]).stdout)[0]
|
||||
(ROOT/'production.json').write_text(json.dumps({'image':production['Image'],'args':production['Args']},indent=2)+'\n')
|
||||
child=None
|
||||
try:
|
||||
run('docker','stop','-t','30',*NAMES[:2])
|
||||
# Drain requests already handed to the model, before unloading it.
|
||||
for _ in range(120):
|
||||
slots=json.loads(run('docker','exec',NAMES[-1],'curl','-fsS','http://127.0.0.1:8080/slots').stdout)
|
||||
if not any(s['is_processing'] for s in slots): break
|
||||
time.sleep(2)
|
||||
else: raise RuntimeError('Model did not drain')
|
||||
run('docker','stop','-t','30',NAMES[-1])
|
||||
child=subprocess.Popen(['python3',str(ROOT/'run.py'),sys.argv[1]])
|
||||
code=child.wait(timeout=1200)
|
||||
if code: raise RuntimeError('Benchmark failed: '+str(code))
|
||||
finally:
|
||||
if child is not None and child.poll() is None:
|
||||
child.terminate()
|
||||
try: child.wait(timeout=20)
|
||||
except subprocess.TimeoutExpired: child.kill(); child.wait(timeout=10)
|
||||
run('docker','rm','-f','mike-ai-mtp2-validation',check=False)
|
||||
run('docker','start',NAMES[-1])
|
||||
for _ in range(150):
|
||||
if run('docker','inspect',NAMES[-1],'--format','{{.State.Health.Status}}').stdout.strip()=='healthy': break
|
||||
time.sleep(2)
|
||||
else: raise RuntimeError('Restored Medium did not become healthy')
|
||||
run('docker','start',NAMES[1],NAMES[0])
|
||||
for _ in range(60):
|
||||
statuses=[run('docker','inspect',name,'--format','{{.State.Health.Status}}').stdout.strip() for name in NAMES[:2]]
|
||||
if all(status=='healthy' for status in statuses): break
|
||||
time.sleep(2)
|
||||
else: raise RuntimeError('Restored router/controller did not become healthy')
|
||||
print('RESTORED existing medium/controller/router; all healthy',flush=True)
|
||||
@@ -0,0 +1,29 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Read-only restoration verification plus a two-token model smoke request."""
|
||||
import json,pathlib,re,subprocess,time
|
||||
ROOT=pathlib.Path('/data/benchmarks/medium-mtp2-validation-20260920')
|
||||
def run(*args):return subprocess.check_output(args,text=True,timeout=30)
|
||||
report={'checked_at':time.time(),'uptime':run('uptime').strip(),'containers':{}}
|
||||
for name in ['mike-ai-llama-medium','mike-ai-router','mike-ai-profile-controller','mike-ai-wireguard-gateway','mike-ai-qwen3-tts']:
|
||||
d=json.loads(run('docker','inspect',name))[0]
|
||||
report['containers'][name]={'running':d['State']['Running'],'health':d['State'].get('Health',{}).get('Status'),'image':d['Image'],'started':d['State']['StartedAt']}
|
||||
assert d['State']['Running'],name
|
||||
if name in ['mike-ai-llama-medium','mike-ai-router','mike-ai-profile-controller','mike-ai-wireguard-gateway']:
|
||||
assert d['State'].get('Health',{}).get('Status')=='healthy',name
|
||||
assert report['containers']['mike-ai-llama-medium']['image']=='sha256:5e3c12c145b8045e5731b44b6b97033f24b327ae3d4a3fa85ecdd159cc844907'
|
||||
assert not run('docker','ps','-q','--filter','name=^mike-ai-mtp2-validation$').strip()
|
||||
probe='import urllib.request,json; print(json.dumps({p:json.load(urllib.request.urlopen("http://127.0.0.1:8081"+p,timeout=20)) for p in ["/health","/ready"]}))'
|
||||
report['router']=json.loads(run('docker','exec','mike-ai-router','python','-c',probe))
|
||||
payload={'model':'qwen-medium','messages':[{'role':'user','content':'Antworte ausschließlich mit OK.'}],'reasoning_effort':'none','max_tokens':8,'temperature':0}
|
||||
r=json.loads(run('docker','exec','mike-ai-llama-medium','curl','-fsS','--max-time','20','-H','Content-Type: application/json','--data',json.dumps(payload),'http://127.0.0.1:8080/v1/chat/completions'))
|
||||
report['smoke']={'content':r['choices'][0]['message'].get('content',''),'usage':r.get('usage')}
|
||||
assert report['smoke']['content'].strip()=='OK',report['smoke']
|
||||
started=min(json.loads(p.read_text())['started'] for p in ROOT.glob('*/result.json'))
|
||||
journal=run('journalctl','-k','--since','@'+str(int(started)-60),'--no-pager')
|
||||
pattern=re.compile(r'NVRM.*Xid|oom-kill|Out of memory: Killed process|Kernel panic|GPU has fallen off',re.I)
|
||||
report['kernel_errors']=[line for line in journal.splitlines() if pattern.search(line)]
|
||||
report['gpu']=run('nvidia-smi','--query-gpu=name,memory.used,memory.total,temperature.gpu','--format=csv').strip()
|
||||
report['disk']=run('df','-h','/','/data').strip()
|
||||
(ROOT/'restore-verification.json').write_text(json.dumps(report,indent=2)+'\n')
|
||||
print(json.dumps(report,indent=2))
|
||||
assert not report['kernel_errors'],'Kernel/GPU errors require review'
|
||||
Reference in New Issue
Block a user