365 lines
14 KiB
Python
365 lines
14 KiB
Python
from __future__ import annotations
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import argparse
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from dataclasses import dataclass
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from pathlib import Path
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from typing import Any, Callable, Iterable
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from candidate_preflight import CandidatePreflightAdvisor, MODEL_LOAD_OVERHEAD
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from common import write_json
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from hf_discovery import HuggingFaceDiscovery, inspect_repo_tree
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from modelhub_client import ModelHubClient, ModelHubClientPool
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from runner_common import DEFAULT_KEY_PATH, ensure_tokens
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ACTIVE_FILTER_STATUSES = ("waiting", "running")
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DEFAULT_STOP_BATCH_SIZE = 50
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@dataclass(frozen=True)
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class OwnedTask:
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account_index: int
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task_id: int
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model_id: str
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gpu_type: str
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status: str
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def _normalize_status(value: Any) -> str:
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return str(value or "").strip().lower()
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def _parse_owned_task(account_index: int, record: dict[str, Any]) -> OwnedTask | None:
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task_id = record.get("taskId")
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model_id = str(record.get("modelId") or "").strip()
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gpu_type = str(record.get("gpuType") or "").strip()
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status = _normalize_status(record.get("status"))
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if not model_id or not gpu_type or status not in ACTIVE_FILTER_STATUSES:
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return None
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try:
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numeric_task_id = int(str(task_id).strip())
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except (TypeError, ValueError):
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return None
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if numeric_task_id <= 0:
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return None
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return OwnedTask(
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account_index=account_index,
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task_id=numeric_task_id,
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model_id=model_id,
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gpu_type=gpu_type,
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status=status,
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)
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def _fetch_status_tasks(
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client: ModelHubClient,
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*,
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account_index: int,
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status: str,
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page_size: int = 100,
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) -> list[OwnedTask]:
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current = 1
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results: list[OwnedTask] = []
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while True:
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payload = client.list_tasks_page(
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current=current,
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page_size=page_size,
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only_mine=True,
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status=status,
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)
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page = payload.get("data") or {}
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records = page.get("records") or []
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if not isinstance(records, list):
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raise ValueError("ModelHub task page records are invalid")
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for record in records:
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if not isinstance(record, dict):
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continue
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task = _parse_owned_task(account_index, record)
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# The platform has silently ignored unknown status filters before.
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# Accept only records whose returned state is explicitly active.
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if task is not None:
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results.append(task)
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pages = int(page.get("pages") or 0)
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if not records or current >= pages:
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break
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current += 1
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return results
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def collect_active_tasks(
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clients: list[ModelHubClient],
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*,
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read_concurrency: int = 6,
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statuses: Iterable[str] = ACTIVE_FILTER_STATUSES,
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) -> tuple[list[OwnedTask], dict[int, list[str]]]:
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"""Fetch active tasks from every account without sharing account-scoped reads."""
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requested_statuses = tuple(dict.fromkeys(_normalize_status(status) for status in statuses))
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tasks: list[OwnedTask] = []
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errors: dict[int, list[str]] = {}
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jobs = [(index, client, status) for index, client in enumerate(clients) for status in requested_statuses]
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workers = min(max(1, int(read_concurrency)), max(1, len(jobs)))
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with ThreadPoolExecutor(max_workers=workers) as executor:
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futures = {
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executor.submit(
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_fetch_status_tasks,
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client,
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account_index=index,
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status=status,
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): (index, status)
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for index, client, status in jobs
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}
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for future in as_completed(futures):
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index, status = futures[future]
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try:
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tasks.extend(future.result())
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except Exception as exc:
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errors.setdefault(index, []).append(f"{status}: {type(exc).__name__}: {exc}")
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deduped: dict[tuple[int, int], OwnedTask] = {}
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for task in tasks:
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deduped[(task.account_index, task.task_id)] = task
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return sorted(deduped.values(), key=lambda item: (item.account_index, item.task_id)), errors
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def _load_repository_sizes(
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model_ids: set[str],
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*,
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discovery: HuggingFaceDiscovery,
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read_concurrency: int,
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log: Callable[[str], None],
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) -> tuple[dict[str, int], dict[str, str]]:
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sizes: dict[str, int] = {}
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errors: dict[str, str] = {}
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completed = 0
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workers = min(max(1, int(read_concurrency)), max(1, len(model_ids)))
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def inspect(model_id: str) -> tuple[str, int | None]:
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tree = discovery.list_repo_tree(model_id)
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return model_id, inspect_repo_tree(model_id, tree).repository_size_bytes
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with ThreadPoolExecutor(max_workers=workers) as executor:
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futures = {executor.submit(inspect, model_id): model_id for model_id in sorted(model_ids)}
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for future in as_completed(futures):
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model_id = futures[future]
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try:
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returned_model_id, size = future.result()
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if size is None:
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errors[returned_model_id] = "recursive_repository_size_incomplete"
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else:
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sizes[returned_model_id] = size
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except Exception as exc:
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errors[model_id] = f"{type(exc).__name__}: {exc}"
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completed += 1
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if completed == len(model_ids) or completed % 50 == 0:
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log(
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f"[queue-cleanup] size_scan={completed}/{len(model_ids)} "
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f"complete={len(sizes)} unknown={len(errors)}"
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)
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return sizes, errors
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def find_certain_oom_tasks(
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tasks: list[OwnedTask],
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*,
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repository_sizes: dict[str, int],
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gpu_memory_gib: dict[str, float] | None = None,
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) -> tuple[list[dict[str, Any]], dict[str, int]]:
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capacities = dict(CandidatePreflightAdvisor(gpu_memory_gib=gpu_memory_gib).gpu_memory_gib)
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decisions: list[dict[str, Any]] = []
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skipped = {
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"repositorySizeUnknown": 0,
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"gpuCapacityUnknown": 0,
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"fitsKnownCapacity": 0,
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}
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for task in tasks:
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size_bytes = repository_sizes.get(task.model_id)
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if size_bytes is None:
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skipped["repositorySizeUnknown"] += 1
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continue
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capacity_gib = capacities.get(task.gpu_type)
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if capacity_gib is None:
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skipped["gpuCapacityUnknown"] += 1
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continue
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repository_gib = size_bytes / (1024**3)
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required_gib = repository_gib * MODEL_LOAD_OVERHEAD
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if required_gib <= capacity_gib:
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skipped["fitsKnownCapacity"] += 1
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continue
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decisions.append(
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{
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"accountIndex": task.account_index + 1,
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"taskId": task.task_id,
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"modelId": task.model_id,
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"gpuType": task.gpu_type,
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"status": task.status,
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"repositorySizeGiB": round(repository_gib, 3),
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"requiredGiB": round(required_gib, 3),
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"gpuCapacityGiB": float(capacity_gib),
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"reason": "certain_oom_repository_size_exceeds_gpu_capacity",
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}
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)
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return decisions, skipped
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def _chunks(values: list[int], size: int) -> Iterable[list[int]]:
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for offset in range(0, len(values), size):
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yield values[offset : offset + size]
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def cleanup_certain_oom_tasks(
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modelhub: ModelHubClientPool,
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discovery: HuggingFaceDiscovery,
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*,
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dry_run: bool = False,
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read_concurrency: int = 6,
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stop_batch_size: int = DEFAULT_STOP_BATCH_SIZE,
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gpu_memory_gib: dict[str, float] | None = None,
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log: Callable[[str], None] = print,
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) -> dict[str, Any]:
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"""Stop only active tasks that cannot fit the selected GPU by known capacity."""
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clients = list(modelhub.clients)
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tasks, listing_errors = collect_active_tasks(clients, read_concurrency=read_concurrency)
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log(
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f"[queue-cleanup] active_scanned={len(tasks)} accounts={len(clients)} "
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f"listing_errors={sum(len(items) for items in listing_errors.values())}"
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)
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model_ids = {task.model_id for task in tasks}
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repository_sizes, size_errors = _load_repository_sizes(
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model_ids,
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discovery=discovery,
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read_concurrency=read_concurrency,
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log=log,
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)
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decisions, skipped = find_certain_oom_tasks(
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tasks,
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repository_sizes=repository_sizes,
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gpu_memory_gib=gpu_memory_gib,
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)
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log(
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f"[queue-cleanup] certain_oom={len(decisions)} "
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f"fits={skipped['fitsKnownCapacity']} size_unknown={skipped['repositorySizeUnknown']} "
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f"gpu_unknown={skipped['gpuCapacityUnknown']} dry_run={str(bool(dry_run)).lower()}"
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)
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cancelled: list[dict[str, Any]] = []
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disappeared: list[dict[str, Any]] = []
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stop_errors: list[dict[str, Any]] = []
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if decisions and not dry_run:
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# Re-read each account immediately before mutation. If any active-state
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# query fails for that account, fail closed and do not terminate its tasks.
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refreshed_tasks, refresh_errors = collect_active_tasks(clients, read_concurrency=read_concurrency)
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active_ids_by_account: dict[int, set[int]] = {}
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for task in refreshed_tasks:
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active_ids_by_account.setdefault(task.account_index, set()).add(task.task_id)
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for account_index, details in sorted(refresh_errors.items()):
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stop_errors.append(
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{
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"accountIndex": account_index + 1,
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"error": "active_task_recheck_failed",
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"details": details,
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}
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)
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by_account: dict[int, list[dict[str, Any]]] = {}
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for decision in decisions:
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account_index = int(decision["accountIndex"]) - 1
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if account_index in refresh_errors:
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continue
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if int(decision["taskId"]) not in active_ids_by_account.get(account_index, set()):
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disappeared.append(decision)
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continue
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by_account.setdefault(account_index, []).append(decision)
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batch_size = max(1, min(100, int(stop_batch_size)))
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stop_failed = False
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for account_index in sorted(by_account):
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if stop_failed:
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break
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decisions_by_id = {int(item["taskId"]): item for item in by_account[account_index]}
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task_ids = sorted(decisions_by_id)
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for batch in _chunks(task_ids, batch_size):
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try:
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clients[account_index].stop_tasks(batch)
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except Exception as exc:
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stop_errors.append(
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{
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"accountIndex": account_index + 1,
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"taskIds": batch,
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"error": f"{type(exc).__name__}: {exc}",
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}
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)
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stop_failed = True
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break
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cancelled.extend(decisions_by_id[task_id] for task_id in batch)
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log(
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f"[queue-cleanup] account={account_index + 1:02d} "
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f"cancelled_batch={len(batch)} cancelled_total={len(cancelled)}"
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)
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if cancelled and hasattr(modelhub, "refresh_active_counts"):
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modelhub.refresh_active_counts()
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return {
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"dryRun": bool(dry_run),
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"accounts": len(clients),
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"activeScanned": len(tasks),
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"uniqueModels": len(model_ids),
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"repositorySizesComplete": len(repository_sizes),
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"repositorySizeErrors": size_errors,
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"listingErrors": {str(index + 1): values for index, values in listing_errors.items()},
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"certainOomCount": len(decisions),
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"certainOomTasks": decisions,
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"skipped": skipped,
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"cancelledCount": len(cancelled),
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"cancelledTasks": cancelled,
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"noLongerActiveCount": len(disappeared),
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"stopErrors": stop_errors,
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}
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def build_parser() -> argparse.ArgumentParser:
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parser = argparse.ArgumentParser(description="Safely stop active ModelHub tasks that are certain to run out of GPU memory.")
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parser.add_argument("--execute", action="store_true", help="Actually terminate selected tasks; otherwise only print a preview")
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parser.add_argument("--read-concurrency", type=int, default=6)
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parser.add_argument("--stop-batch-size", type=int, default=DEFAULT_STOP_BATCH_SIZE)
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parser.add_argument("--key-path", default=str(DEFAULT_KEY_PATH))
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parser.add_argument("--modelhub-base-url", default="https://modelhub.org.cn")
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parser.add_argument("--hf-base-url", default="https://modelscope.cn")
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parser.add_argument("--modelhub-token", default=None)
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parser.add_argument("--hf-token", default=None)
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parser.add_argument("--modelscope-token", default=None)
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parser.add_argument("--report-path", default=".modelhub_state/queue_cleanup_latest.json")
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return parser
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def main(argv: list[str] | None = None) -> int:
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args = build_parser().parse_args(argv)
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ensure_tokens(args)
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tokens = list(getattr(args, "modelhub_tokens", None) or [args.modelhub_token])
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clients = [ModelHubClient(token=token, base_url=args.modelhub_base_url) for token in tokens]
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pool = ModelHubClientPool(clients, capacity_state_path=None)
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discovery = HuggingFaceDiscovery(base_url=args.hf_base_url)
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summary = cleanup_certain_oom_tasks(
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pool,
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discovery,
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dry_run=not args.execute,
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read_concurrency=args.read_concurrency,
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stop_batch_size=args.stop_batch_size,
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)
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report_path = Path(args.report_path)
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write_json(report_path, summary)
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print(
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f"[queue-cleanup] finished certain_oom={summary['certainOomCount']} "
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f"cancelled={summary['cancelledCount']} stop_errors={len(summary['stopErrors'])} "
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f"report={report_path}",
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flush=True,
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)
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return 1 if summary["stopErrors"] else 0
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if __name__ == "__main__":
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raise SystemExit(main())
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