Spaces:
Sleeping
Sleeping
File size: 4,711 Bytes
9731ebe | 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 | from __future__ import annotations
import json
import os
import sys
from datetime import datetime
from pathlib import Path
from typing import Any
sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
from app.env import NervousSystemEnv
from app.models import SREAction
RESULTS_DIR = "results"
PATCH_FILES = [
"model/transformer.py",
"model/attention.py",
"model/feedforward.py",
"model/embedding.py",
]
def snapshot(
trace: list[dict[str, Any]],
label: str,
action: dict[str, Any] | None,
result: Any,
env: NervousSystemEnv,
) -> None:
obs = env.get_state()
grade = None
if label == "final_grade":
grade_result = env.grade("cascade")
grade = {
"score": grade_result.score,
"passed": grade_result.passed,
"breakdown": grade_result.breakdown,
"explanation": grade_result.explanation,
}
trace.append(
{
"label": label,
"step": obs.step_count,
"action": action,
"reward": (
result.reward.model_dump() if result is not None and hasattr(result, "reward") else None
),
"job_status": obs.training.job_status,
"throughput": obs.training.throughput_tokens_per_sec,
"stale_telemetry": obs.stale_telemetry,
"log_retention_steps": obs.log_retention_steps,
"visible_logs": obs.visible_logs,
"grade": grade,
}
)
def step(env: NervousSystemEnv, trace: list[dict[str, Any]], label: str, action: dict[str, Any]) -> None:
result = env.step(SREAction(**action))
snapshot(trace, label, action, result, env)
def main() -> None:
os.makedirs(RESULTS_DIR, exist_ok=True)
env = NervousSystemEnv(seed=42)
obs = env.reset(task_id="cascade", seed=42)
trace: list[dict[str, Any]] = []
snapshot(trace, "reset", None, None, env)
failing_rank = next(node.node_id for node in obs.nodes if node.health_status == "failed")
step(
env,
trace,
"phase1_diagnose_oom",
{"action_type": "inspect_flight_recorder", "parameters": {"rank_id": failing_rank}},
)
for index in range(2, 13):
step(env, trace, f"monitor_stale_surface_{index}", {"action_type": "noop", "parameters": {}})
step(
env,
trace,
"refresh_diagnostics_after_stale_warning",
{"action_type": "query_nccl_logs", "parameters": {"time_window": 8}},
)
for index in range(14, 22):
step(env, trace, f"wait_for_phase2_{index}", {"action_type": "noop", "parameters": {}})
step(
env,
trace,
"phase2_rack_local_topology_fix",
{"action_type": "topo_reorder", "parameters": {"affinity": "rack"}},
)
for index in range(23, 51):
action = (
{"action_type": "query_nccl_logs", "parameters": {"time_window": 5}}
if index in {30, 40, 50}
else {"action_type": "noop", "parameters": {}}
)
step(env, trace, f"monitor_recovery_and_delayed_desync_{index}", action)
step(
env,
trace,
"phase3_investigate_desync",
{"action_type": "query_nccl_logs", "parameters": {"time_window": 10}},
)
selected_file = PATCH_FILES[0]
for file_name in PATCH_FILES:
action = {
"action_type": "patch_divergent_code",
"parameters": {"file": file_name, "fix_type": "identify_file"},
}
result = env.step(SREAction(**action))
snapshot(trace, f"identify_candidate_{file_name}", action, result, env)
if "stage 1" in result.reward.info.lower():
selected_file = file_name
break
step(
env,
trace,
"propose_patch_diff",
{
"action_type": "patch_divergent_code",
"parameters": {"file": selected_file, "fix_type": "propose_diff"},
},
)
step(
env,
trace,
"apply_synchronize_conditional_patch",
{
"action_type": "patch_divergent_code",
"parameters": {"file": selected_file, "fix_type": "synchronize_conditional"},
},
)
snapshot(trace, "final_grade", None, None, env)
result = {
"timestamp": datetime.now().isoformat(),
"task_id": "cascade",
"seed": 42,
"trace_length": len(trace),
"trace": trace,
}
path = os.path.join(RESULTS_DIR, "cascade_long_horizon_trace.json")
with open(path, "w", encoding="utf-8") as file:
json.dump(result, file, indent=2)
print(f"Saved {path} with {len(trace)} trace events")
if __name__ == "__main__":
main()
|