File size: 9,819 Bytes
81c1867 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 | from __future__ import annotations
import argparse
import json
import os
import subprocess
import sys
import time
from dataclasses import dataclass, field
from datetime import datetime
from pathlib import Path
from typing import Any
ROOT = Path(__file__).resolve().parents[1]
LOG_FILE = ROOT / "logs" / "token_holdem.jsonl"
@dataclass
class ModelEvidence:
model_id: str
loads: bool = False
generates: bool = False
json_valid: bool = False
legal_action: bool = False
action_applied: bool = False
full_hand: bool = False
arena_verified: bool = False
fallback_used: bool = False
failures: list[str] = field(default_factory=list)
latencies: list[float] = field(default_factory=list)
def _run(command: list[str], *, enabled: bool = True) -> None:
if not enabled:
return
print(f"$ {' '.join(command)}", flush=True)
subprocess.run(command, cwd=ROOT, check=True)
def _log_offset() -> int:
if not LOG_FILE.exists():
return 0
return LOG_FILE.stat().st_size
def _read_new_logs(offset: int) -> list[dict[str, Any]]:
if not LOG_FILE.exists():
return []
rows: list[dict[str, Any]] = []
with LOG_FILE.open("r", encoding="utf-8") as handle:
handle.seek(offset)
for line in handle:
line = line.strip()
if not line:
continue
try:
rows.append(json.loads(line))
except json.JSONDecodeError:
continue
return rows
def _run_direct_roster_decisions() -> None:
from token_holdem.agents import ROSTER
from token_holdem.model_runtime import ModalRuntime
runtime = ModalRuntime()
legal = {
"actions": ["fold", "call", "raise", "all_in"],
"to_call": 20,
"raise_presets": {"min": 40, "half_pot": 80, "pot": 140, "all_in": 1000},
}
for idx, profile in enumerate(ROSTER, start=1):
state = {
"hand_no": idx,
"street": "preflop",
"hole_cards": ["As", "Kd"],
"community_cards": [],
"stack": 1000,
"pot": 30,
"legal": legal,
"history": ["small blind posts 10", "big blind posts 20"],
"recent_chats": [],
"seed": 9100 + idx,
"session_id": "release-direct",
"hand_id": f"release-direct-h{idx:03d}",
"orbit_id": "release-direct-o01",
}
started = time.perf_counter()
result = runtime.decide(profile, state)
elapsed = time.perf_counter() - started
print(
json.dumps(
{
"stage": "direct_decision",
"model": profile.name,
"model_id": profile.model_id,
"source": result.source,
"status": result.status,
"decision": result.decision,
"elapsed_seconds": round(elapsed, 3),
},
default=str,
),
flush=True,
)
def _run_arena(hands: int, seed: int) -> None:
from app import run_arena
for _ in run_arena(seed, hands):
pass
def _parse_evidence(rows: list[dict[str, Any]]) -> dict[str, ModelEvidence]:
from token_holdem.agents import ROSTER
from token_holdem.model_runtime import SUPPORTED_TRANSFORMERS_MODELS
evidence = {
profile.name: ModelEvidence(SUPPORTED_TRANSFORMERS_MODELS.get(profile.name, profile.model_id))
for profile in ROSTER
}
pending: dict[tuple[str, str, str, str], list[datetime]] = {}
completed_hands = {row.get("hand_id") for row in rows if row.get("message") == "hand_completed"}
for row in rows:
player = row.get("player")
if player not in evidence:
continue
item = evidence[player]
message = row.get("message")
key = (row.get("session_id", ""), row.get("hand_id", ""), row.get("orbit_id", ""), player)
if message == "model_runtime_modal_call_started":
item.loads = True
try:
pending.setdefault(key, []).append(datetime.strptime(row["time"], "%Y-%m-%dT%H:%M:%S%z"))
except (KeyError, ValueError):
pass
elif message == "model_runtime_modal_success":
item.loads = True
item.generates = True
item.json_valid = True
item.legal_action = row.get("action") is not None
if row.get("hand_id") in completed_hands:
item.full_hand = True
raw_text = str(row.get("raw_text", ""))
if "used persona fallback" in raw_text:
item.fallback_used = True
if "repair=" in raw_text:
item.failures.append("repair prompt used")
starts = pending.get(key) or []
if starts:
try:
ended = datetime.strptime(row["time"], "%Y-%m-%dT%H:%M:%S%z")
item.latencies.append((ended - starts.pop(0)).total_seconds())
except (KeyError, ValueError):
pass
elif message == "model_runtime_modal_failed":
item.failures.append(str(row.get("error", "Modal failure"))[:240])
elif message == "ai_decision":
if row.get("source") == "modal_model":
item.arena_verified = row.get("session_id") not in {"release-direct", "test-session"}
elif message == "action_applied":
item.action_applied = True
if row.get("hand_id") in completed_hands:
item.full_hand = True
elif message == "ai_decision_blocked":
item.failures.append(str(row.get("error", "decision blocked"))[:240])
elif message in {"model_runtime_partial_fallback", "model_runtime_deterministic_dev"}:
item.fallback_used = True
return evidence
def _write_report(evidence: dict[str, ModelEvidence], rows: list[dict[str, Any]], path: Path) -> None:
completed = [row for row in rows if row.get("message") == "hand_completed"]
payload = {
"generated_at": datetime.now().isoformat(),
"completed_hands": len(completed),
"models": {
name: {
"model_id": item.model_id,
"loads": item.loads,
"generates": item.generates,
"json_valid": item.json_valid,
"legal_action": item.legal_action,
"action_applied": item.action_applied,
"full_hand": item.full_hand,
"arena_verified": item.arena_verified,
"fallback_used": item.fallback_used,
"latency_avg_seconds": round(sum(item.latencies) / len(item.latencies), 3) if item.latencies else None,
"latency_max_seconds": max(item.latencies) if item.latencies else None,
"failures": item.failures,
}
for name, item in evidence.items()
},
}
path.parent.mkdir(parents=True, exist_ok=True)
path.write_text(json.dumps(payload, indent=2, ensure_ascii=True), encoding="utf-8")
print(json.dumps(payload, indent=2, ensure_ascii=True), flush=True)
def _assert_release_ready(evidence: dict[str, ModelEvidence], rows: list[dict[str, Any]]) -> None:
failures: list[str] = []
if not any(row.get("message") == "hand_completed" for row in rows):
failures.append("No AI Arena hand completed.")
for name, item in evidence.items():
for field_name in ("loads", "generates", "json_valid", "legal_action", "action_applied", "full_hand", "arena_verified"):
if not getattr(item, field_name):
failures.append(f"{name}: missing {field_name}")
if item.fallback_used:
failures.append(f"{name}: fallback used")
if item.failures:
failures.append(f"{name}: failures: {'; '.join(item.failures)}")
if failures:
raise SystemExit("Release validation failed:\n" + "\n".join(f"- {failure}" for failure in failures))
def main() -> None:
parser = argparse.ArgumentParser(description="Deploy and validate Token Hold'em Modal release readiness.")
parser.add_argument("--deploy", action="store_true", help="Run modal deploy before validation.")
parser.add_argument("--setup-cache", action="store_true", help="Pre-download enabled model snapshots.")
parser.add_argument("--warmup", action="store_true", help="Warm all enabled Modal model workers.")
parser.add_argument("--skip-direct", action="store_true", help="Skip direct per-model Modal decisions.")
parser.add_argument("--skip-arena", action="store_true", help="Skip AI Arena validation.")
parser.add_argument("--arena-hands", type=int, default=6)
parser.add_argument("--seed", type=int, default=20260615)
parser.add_argument("--report", type=Path, default=Path("logs/release_modal_validation.json"))
args = parser.parse_args()
os.environ["USE_MODAL_INFERENCE"] = "true"
_run(["uv", "run", "modal", "deploy", "modal_inference.py"], enabled=args.deploy)
_run(["uv", "run", "modal", "run", "modal_inference.py::setup_cache"], enabled=args.setup_cache)
_run(["uv", "run", "modal", "run", "modal_inference.py::warmup_demo"], enabled=args.warmup)
offset = _log_offset()
if not args.skip_direct:
_run_direct_roster_decisions()
if not args.skip_arena:
_run_arena(args.arena_hands, args.seed)
rows = _read_new_logs(offset)
evidence = _parse_evidence(rows)
report_path = args.report if args.report.is_absolute() else ROOT / args.report
_write_report(evidence, rows, report_path)
_assert_release_ready(evidence, rows)
if __name__ == "__main__":
sys.path.insert(0, str(ROOT))
main()
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