Corback
328 lines
13 KiB
Python
328 lines
13 KiB
Python
"""
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LLM Crash Test — Fallout: Venice of Wasteland
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Test de performance des deux LLM (MJ 14b + PNJ 7b) en conditions réelles.
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Modes:
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--mode simultane : les deux LLM tournent en parallèle (threads)
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--mode decale : push progressif PNJ → MJ (pipeline producteur/consommateur)
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--mode solo-mj : seulement le 14b
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--mode solo-pnj : seulement le 7b
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Usage:
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python llm_crash_test.py --mode simultane --rounds 5
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python llm_crash_test.py --mode decale --rounds 10 --pnj-count 3
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"""
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import os, sys, time, json, argparse, threading, queue
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import urllib.request
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from datetime import datetime
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OLLAMA_URL = os.getenv("OLLAMA_URL", "http://localhost:11434")
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MODEL_MJ = os.getenv("MODEL_MJ", "qwen2.5:14b")
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MODEL_PNJ = os.getenv("MODEL_PNJ", "qwen2.5:7b")
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# ---------------------------------------------------------------------------
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# Prompts de test réalistes (contexte Fallout Venice)
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# ---------------------------------------------------------------------------
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PNJ_PROMPTS = [
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"Tu es Remy Tureaud, marchand créole de Pearl River. Réponds en 2 phrases à : 'T'as des piles ?'",
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"Tu es Sœur Eulalie, soigneuse de L'Union. Réponds en 2 phrases à : 'J'ai une blessure par balle.'",
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"Tu es Jacques-Henri, garde de la Régie. Réponds en 2 phrases à : 'Laisse-moi passer le checkpoint.'",
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"Tu es Mama Voodoo, goule du Grand Krewe. Réponds en 2 phrases à : 'C'est quoi ce tatouage ?'",
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"Tu es un Écumeur anonyme. Réponds en 2 phrases à : 'On veut juste passer.'",
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]
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MJ_PROMPTS = [
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"Résume en 3 phrases la situation politique entre L'Union et la CdA à New Orleans post-apo.",
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"Décris en 3 phrases une rencontre de nuit dans les bayous de Pearl River.",
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"En 3 phrases, quelles sont les conséquences d'un blocus sur la route de Baton Rouge ?",
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"Décris en 3 phrases l'ambiance d'un marché noir dans le Vieux Carré de NOLA.",
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"En 3 phrases, comment réagit le Grand Krewe si des étrangers fouillent leurs ruines ?",
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]
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# ---------------------------------------------------------------------------
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# LLM call
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# ---------------------------------------------------------------------------
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def call_llm(model: str, prompt: str, timeout: int = 60) -> dict:
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"""Appel Ollama, retourne {tokens, elapsed, error, text}."""
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t0 = time.time()
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body = json.dumps({
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"model": model,
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"prompt": prompt,
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"stream": False,
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"options": {"num_predict": 150, "temperature": 0.7},
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}).encode()
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req = urllib.request.Request(
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f"{OLLAMA_URL}/api/generate", data=body, method="POST",
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headers={"Content-Type": "application/json"}
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)
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try:
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with urllib.request.urlopen(req, timeout=timeout) as r:
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data = json.loads(r.read())
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elapsed = time.time() - t0
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tokens = data.get("eval_count", 0)
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return {
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"model": model,
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"tokens": tokens,
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"elapsed": round(elapsed, 2),
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"tok_s": round(tokens / elapsed, 1) if elapsed > 0 else 0,
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"error": None,
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"text": data.get("response", "")[:120],
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}
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except Exception as e:
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return {
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"model": model,
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"tokens": 0,
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"elapsed": round(time.time() - t0, 2),
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"tok_s": 0,
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"error": str(e)[:80],
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"text": "",
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}
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# ---------------------------------------------------------------------------
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# Modes
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# ---------------------------------------------------------------------------
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def run_solo(model: str, prompts: list[str], rounds: int) -> list[dict]:
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results = []
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for i in range(rounds):
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prompt = prompts[i % len(prompts)]
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print(f" [{model}] Round {i+1}/{rounds}...", end="", flush=True)
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r = call_llm(model, prompt)
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print(f" {r['tok_s']} tok/s | {r['elapsed']}s | {'ERR: '+r['error'] if r['error'] else 'OK'}")
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results.append(r)
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return results
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def run_simultane(rounds: int, pnj_count: int = 2) -> dict:
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"""Les deux LLM tournent en parallèle via threads."""
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print(f"\n[SIMULTANE] {MODEL_MJ} + {MODEL_PNJ} x{pnj_count} PNJ — {rounds} rounds\n")
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all_results = {"mj": [], "pnj": []}
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lock = threading.Lock()
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def mj_worker(round_idx: int):
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prompt = MJ_PROMPTS[round_idx % len(MJ_PROMPTS)]
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r = call_llm(MODEL_MJ, prompt)
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with lock:
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all_results["mj"].append(r)
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status = f"ERR: {r['error']}" if r['error'] else f"{r['tok_s']} tok/s"
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print(f" [MJ 14b] R{round_idx+1} → {status} ({r['elapsed']}s)")
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def pnj_worker(round_idx: int, pnj_idx: int):
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prompt = PNJ_PROMPTS[(round_idx * pnj_count + pnj_idx) % len(PNJ_PROMPTS)]
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r = call_llm(MODEL_PNJ, prompt)
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with lock:
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all_results["pnj"].append(r)
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status = f"ERR: {r['error']}" if r['error'] else f"{r['tok_s']} tok/s"
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print(f" [PNJ 7b] R{round_idx+1} PNJ{pnj_idx+1} → {status} ({r['elapsed']}s)")
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t_start = time.time()
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for i in range(rounds):
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threads = []
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t = threading.Thread(target=mj_worker, args=(i,))
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threads.append(t)
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for j in range(pnj_count):
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t = threading.Thread(target=pnj_worker, args=(i, j))
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threads.append(t)
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for t in threads:
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t.start()
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for t in threads:
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t.join()
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print()
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total = time.time() - t_start
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return {**all_results, "total_elapsed": round(total, 1)}
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def run_decale(rounds: int, pnj_count: int = 3) -> dict:
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"""Pipeline producteur/consommateur : les PNJ accumulent des infos → poussé vers le MJ."""
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print(f"\n[DÉCALÉ] {pnj_count} PNJ 7b → buffer → MJ 14b — {rounds} rounds\n")
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pnj_queue: queue.Queue = queue.Queue()
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all_results = {"mj": [], "pnj": [], "latency_pnj_to_mj": []}
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stop_flag = threading.Event()
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def pnj_producer():
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idx = 0
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while not stop_flag.is_set():
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prompt = PNJ_PROMPTS[idx % len(PNJ_PROMPTS)]
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r = call_llm(MODEL_PNJ, prompt)
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r["produced_at"] = time.time()
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pnj_queue.put(r)
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with threading.Lock():
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status = f"ERR: {r['error']}" if r['error'] else f"{r['tok_s']} tok/s"
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print(f" [PNJ 7b ] prod #{idx+1} → {status} ({r['elapsed']}s) | queue={pnj_queue.qsize()}")
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idx += 1
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def mj_consumer():
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consumed = 0
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while consumed < rounds:
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# Attendre qu'il y ait au moins 1 item dans la queue (ou timeout)
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try:
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pnj_result = pnj_queue.get(timeout=90)
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except queue.Empty:
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print(" [MJ 14b] Timeout attente PNJ")
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break
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latency = time.time() - pnj_result["produced_at"]
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# Construire le prompt MJ à partir du résultat PNJ
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pnj_text = pnj_result.get("text", "(vide)")
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mj_prompt = (
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f"Un PNJ vient de parler : \"{pnj_text}\"\n"
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f"En tant que MJ, en 2 phrases, quelle est la conséquence narrative ?"
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)
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r = call_llm(MODEL_MJ, mj_prompt)
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r["latency_from_pnj"] = round(latency, 2)
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all_results["mj"].append(r)
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all_results["latency_pnj_to_mj"].append(round(latency + r["elapsed"], 2))
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status = f"ERR: {r['error']}" if r['error'] else f"{r['tok_s']} tok/s"
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print(f" [MJ 14b ] cons #{consumed+1} → {status} ({r['elapsed']}s) | pipeline={round(latency+r['elapsed'],1)}s")
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consumed += 1
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pnj_queue.task_done()
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print()
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# Lancer N threads PNJ + 1 thread MJ
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pnj_threads = [threading.Thread(target=pnj_producer, daemon=True) for _ in range(pnj_count)]
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mj_thread = threading.Thread(target=mj_consumer)
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t_start = time.time()
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for t in pnj_threads:
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t.start()
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mj_thread.start()
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mj_thread.join()
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stop_flag.set()
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# Récupérer tous les résultats PNJ dans la queue restante
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while not pnj_queue.empty():
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all_results["pnj"].append(pnj_queue.get())
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total = time.time() - t_start
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return {**all_results, "total_elapsed": round(total, 1)}
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# ---------------------------------------------------------------------------
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# Rapport
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# ---------------------------------------------------------------------------
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def print_report(results: dict, mode: str):
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def stats(lst: list[dict]) -> dict:
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if not lst:
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return {}
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ok = [r for r in lst if not r.get("error")]
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err = len(lst) - len(ok)
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if not ok:
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return {"calls": len(lst), "errors": err}
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tok_s = [r["tok_s"] for r in ok]
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elaps = [r["elapsed"] for r in ok]
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return {
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"calls": len(lst),
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"errors": err,
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"avg_tok_s": round(sum(tok_s) / len(tok_s), 1),
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"min_tok_s": min(tok_s),
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"max_tok_s": max(tok_s),
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"avg_elapsed": round(sum(elaps) / len(elaps), 1),
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"total_tokens": sum(r["tokens"] for r in ok),
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}
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sep = "=" * 60
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print(f"\n{sep}")
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print(f" RAPPORT — MODE {mode.upper()}")
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print(f" {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
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print(sep)
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if "mj" in results and results["mj"]:
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s = stats(results["mj"])
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print(f"\n MJ 14b ({MODEL_MJ})")
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for k, v in s.items():
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print(f" {k:20s} : {v}")
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if "pnj" in results and results["pnj"]:
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s = stats(results["pnj"])
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print(f"\n PNJ 7b ({MODEL_PNJ})")
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for k, v in s.items():
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print(f" {k:20s} : {v}")
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if "latency_pnj_to_mj" in results and results["latency_pnj_to_mj"]:
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lat = results["latency_pnj_to_mj"]
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print(f"\n Pipeline PNJ→MJ (latence totale)")
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print(f" avg_pipeline_s : {round(sum(lat)/len(lat), 1)}")
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print(f" min_pipeline_s : {min(lat)}")
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print(f" max_pipeline_s : {max(lat)}")
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print(f"\n Durée totale : {results.get('total_elapsed', '?')}s")
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# Score de viabilité
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mj_ok = not any(r.get("error") for r in results.get("mj", []))
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pnj_ok = not any(r.get("error") for r in results.get("pnj", []))
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mj_speed = stats(results.get("mj", [])).get("avg_tok_s", 0)
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pnj_speed = stats(results.get("pnj", [])).get("avg_tok_s", 0)
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print(f"\n DIAGNOSTIC")
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print(f" MJ stable : {'✓' if mj_ok else '✗ ERREURS'}")
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print(f" PNJ stable : {'✓' if pnj_ok else '✗ ERREURS'}")
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if mj_speed:
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viable = "✓ VIABLE" if mj_speed > 8 else "⚠ LENT (< 8 tok/s)" if mj_speed > 3 else "✗ TROP LENT"
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print(f" MJ débit : {mj_speed} tok/s → {viable}")
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if pnj_speed:
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viable = "✓ VIABLE" if pnj_speed > 15 else "⚠ LENT" if pnj_speed > 5 else "✗ TROP LENT"
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print(f" PNJ débit : {pnj_speed} tok/s → {viable}")
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print(sep)
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# Export JSON
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RESULTS_PATH = os.getenv("CRASH_RESULTS_PATH", "/home/ubuntu/fallout-venice/src/config/crash_results.json")
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report_path = RESULTS_PATH
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try:
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with open(report_path, "w") as f:
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json.dump({"mode": mode, "results": results,
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"stats": {"mj": stats(results.get("mj",[])),
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"pnj": stats(results.get("pnj",[]))}}, f, indent=2)
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print(f"\n Rapport JSON : {report_path}")
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except Exception:
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pass
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# ---------------------------------------------------------------------------
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# CLI
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# ---------------------------------------------------------------------------
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def main():
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parser = argparse.ArgumentParser(description="LLM Crash Test — Fallout Venice")
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parser.add_argument("--mode", choices=["simultane","decale","solo-mj","solo-pnj"],
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default="simultane")
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parser.add_argument("--rounds", type=int, default=5, help="Nombre de rounds MJ")
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parser.add_argument("--pnj-count", type=int, default=2, help="PNJ parallèles (modes simultane/decale)")
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args = parser.parse_args()
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print(f"=== LLM CRASH TEST — {args.mode.upper()} ===")
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print(f"Ollama : {OLLAMA_URL}")
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print(f"MJ : {MODEL_MJ}")
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print(f"PNJ : {MODEL_PNJ}")
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print(f"Rounds : {args.rounds} | PNJ parallèles : {args.pnj_count}")
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# Vérifier que Ollama répond
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try:
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with urllib.request.urlopen(f"{OLLAMA_URL}/api/tags", timeout=5) as r:
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models = [m["name"] for m in json.loads(r.read()).get("models", [])]
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print(f"Modèles disponibles : {', '.join(models[:5])}")
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except Exception as e:
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print(f"[ERR] Ollama inaccessible : {e}")
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sys.exit(1)
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t_global = time.time()
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if args.mode == "simultane":
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results = run_simultane(args.rounds, args.pnj_count)
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elif args.mode == "decale":
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results = run_decale(args.rounds, args.pnj_count)
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elif args.mode == "solo-mj":
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r = run_solo(MODEL_MJ, MJ_PROMPTS, args.rounds)
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results = {"mj": r, "total_elapsed": round(time.time() - t_global, 1)}
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elif args.mode == "solo-pnj":
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r = run_solo(MODEL_PNJ, PNJ_PROMPTS, args.rounds)
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results = {"pnj": r, "total_elapsed": round(time.time() - t_global, 1)}
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print_report(results, args.mode)
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if __name__ == "__main__":
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main()
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