| """Completion checks and bounded cleanup around the unmodified Prime launcher.""" |
| import asyncio |
| import contextlib |
| import os |
| from pathlib import Path |
| import signal |
|
|
|
|
| async def stop_launcher(process, grace=30): |
| import psutil |
| from prime_rl.utils.process import cleanup_process |
|
|
| children = [] |
| if process.returncode is None: |
| with contextlib.suppress(psutil.NoSuchProcess): |
| children = psutil.Process(process.pid).children(recursive=True) |
| cleanup_process(process.pid, signal.SIGTERM) |
| |
| with contextlib.suppress(ProcessLookupError): |
| os.killpg(process.pid, signal.SIGTERM) |
| try: |
| await asyncio.wait_for(process.wait(), grace) |
| except asyncio.TimeoutError: |
| with contextlib.suppress(ProcessLookupError): |
| os.killpg(process.pid, signal.SIGKILL) |
| with contextlib.suppress(ProcessLookupError): |
| process.kill() |
| await asyncio.wait_for(process.wait(), 5) |
| _, alive = await asyncio.to_thread(psutil.wait_procs, children, timeout=grace) |
| for child in alive: |
| with contextlib.suppress(psutil.NoSuchProcess): |
| child.kill() |
| |
| with contextlib.suppress(ProcessLookupError): |
| os.killpg(process.pid, signal.SIGKILL) |
| _, alive = await asyncio.to_thread(psutil.wait_procs, alive, timeout=5) |
| survivors = [p.pid for p in alive if p.is_running() and p.status() != psutil.STATUS_ZOMBIE] |
| if survivors: |
| raise RuntimeError(f"Launcher descendants still running: {survivors}") |
|
|
|
|
| def verify_checkpoint(step_dir, expected_step, orchestrator_expected_step=None): |
| """Read native DCP metadata, check shard extents and load saved progress.""" |
| import torch |
| import torch.distributed.checkpoint as dcp |
|
|
| step_dir = Path(step_dir) |
| trainer = step_dir / "trainer" |
| if not (trainer / ".metadata").is_file(): |
| raise RuntimeError(f"Incomplete native DCP checkpoint: {trainer / '.metadata'} missing") |
| metadata = dcp.FileSystemReader(trainer).read_metadata() |
| keys = metadata.state_dict_metadata |
| for prefix in ("app.model.", "app.optimizers.", "app.scheduler.", "app.progress."): |
| if not any(key.startswith(prefix) for key in keys): |
| raise RuntimeError(f"Native checkpoint has no {prefix} state") |
| if not metadata.storage_data: |
| raise RuntimeError("Native checkpoint contains no storage records") |
| shards = set() |
| for item in metadata.storage_data.values(): |
| shard = trainer / item.relative_path |
| if not shard.is_file() or shard.stat().st_size < item.offset + item.length: |
| raise RuntimeError(f"Missing or truncated native checkpoint shard: {shard}") |
| shards.add(shard.name) |
| state = {"app": {"progress": {"step": 0, "total_tokens": 0, "total_samples": 0}}} |
| dcp.load(state, checkpoint_id=trainer) |
| if state["app"]["progress"]["step"] != expected_step: |
| raise RuntimeError(f"Trainer checkpoint progress disagrees with step {expected_step}") |
| saved = torch.load(step_dir / "orchestrator" / "progress.pt", weights_only=False, map_location="cpu") |
| |
| |
| if orchestrator_expected_step is None: |
| orchestrator_expected_step = expected_step |
| if saved["progress"].step != orchestrator_expected_step: |
| raise RuntimeError(f"Orchestrator progress {saved['progress'].step} != expected {orchestrator_expected_step}") |
| if "train_source" not in saved: |
| raise RuntimeError("Orchestrator checkpoint has no train_source state") |
| return {"step": expected_step, "metadata_readable": True, "shards": sorted(shards), |
| "progress_loaded": True, "orchestrator_progress_step": saved["progress"].step, |
| "full_resume_tested": False} |
|
|
|
|
| if __name__ == "__main__": |
| import json |
| import sys |
| print(json.dumps(verify_checkpoint(sys.argv[1], int(sys.argv[2]), int(sys.argv[3]) if len(sys.argv)>3 else None))) |
|
|
