616 lines
24 KiB
Python
616 lines
24 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 datetime import datetime, timedelta
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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 utc_now, 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 _load_model_last_modified(
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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, datetime], dict[str, str]]:
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values: dict[str, datetime] = {}
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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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with ThreadPoolExecutor(max_workers=workers) as executor:
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futures = {
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executor.submit(discovery.get_model_last_modified, model_id): model_id
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for model_id in sorted(model_ids)
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}
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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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value = future.result()
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if value is None:
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errors[model_id] = "model_last_modified_unknown"
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else:
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values[model_id] = value
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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] age_scan={completed}/{len(model_ids)} "
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f"complete={len(values)} unknown={len(errors)}"
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)
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return values, 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 find_old_overflow_tasks(
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tasks: list[OwnedTask],
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*,
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model_last_modified: dict[str, datetime],
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queue_threshold: int | dict[int, int] = 80,
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recent_model_days: int = 7,
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reference_time: datetime | None = None,
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incomplete_accounts: set[int] | None = None,
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) -> tuple[list[dict[str, Any]], dict[str, int]]:
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recent_days = max(1, int(recent_model_days))
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cutoff = (reference_time or utc_now()) - timedelta(days=recent_days)
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incomplete_accounts = incomplete_accounts or set()
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grouped: dict[int, list[OwnedTask]] = {}
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for task in tasks:
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grouped.setdefault(task.account_index, []).append(task)
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decisions: list[dict[str, Any]] = []
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skipped = {
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"accountsWithIncompleteListing": len(incomplete_accounts),
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"withinFirstQueuePositions": 0,
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"recentOverflowTasks": 0,
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"modelAgeUnknown": 0,
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"accountThresholdUnknown": 0,
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}
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for account_index, account_tasks in sorted(grouped.items()):
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if account_index in incomplete_accounts:
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continue
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if isinstance(queue_threshold, dict):
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configured_threshold = queue_threshold.get(account_index)
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if configured_threshold is None:
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skipped["accountThresholdUnknown"] += 1
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continue
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else:
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configured_threshold = queue_threshold
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threshold = max(0, int(configured_threshold))
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ordered = sorted(account_tasks, key=lambda item: item.task_id)
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skipped["withinFirstQueuePositions"] += min(threshold, len(ordered))
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for position, task in enumerate(ordered, start=1):
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if position <= threshold:
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continue
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last_modified = model_last_modified.get(task.model_id)
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if last_modified is None:
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skipped["modelAgeUnknown"] += 1
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continue
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if last_modified >= cutoff:
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skipped["recentOverflowTasks"] += 1
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continue
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decisions.append(
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{
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"accountIndex": 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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"queuePosition": position,
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"queueThreshold": threshold,
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"modelLastModified": last_modified.isoformat(),
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"recentCutoff": cutoff.isoformat(),
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"reason": "old_model_beyond_account_queue_threshold",
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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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age_reserved_slots: int | None = None,
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reference_time: datetime | None = None,
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log: Callable[[str], None] = print,
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) -> dict[str, Any]:
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"""Stop deterministic OOM tasks and old tasks beyond each account's protected prefix."""
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clients = list(modelhub.clients)
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reference_time = reference_time or utc_now()
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configured_reserved_slots = (
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age_reserved_slots
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if age_reserved_slots is not None
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else getattr(modelhub, "recent_model_reserve_slots", 10)
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)
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reserved_slots = max(0, int(configured_reserved_slots or 0))
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recent_model_days = max(1, int(getattr(modelhub, "recent_model_days", 7) or 7))
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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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observed_active_counts: list[int | None] = [0 for _ in clients]
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for task in tasks:
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current_count = observed_active_counts[task.account_index]
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if current_count is not None:
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observed_active_counts[task.account_index] = current_count + 1
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for account_index in listing_errors:
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observed_active_counts[account_index] = None
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if hasattr(modelhub, "observe_capacity_lower_bounds"):
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account_caps = list(modelhub.observe_capacity_lower_bounds(observed_active_counts))
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elif hasattr(modelhub, "account_capacity_limits"):
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account_caps = list(modelhub.account_capacity_limits())
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else:
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default_cap = max(1, int(getattr(modelhub, "active_task_cap", 100) or 100))
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account_caps = [default_cap for _ in clients]
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queue_thresholds = {
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index: max(0, int(account_caps[index]) - reserved_slots)
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for index in range(min(len(clients), len(account_caps)))
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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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oom_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(oom_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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oom_task_keys = {
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(int(decision["accountIndex"]) - 1, int(decision["taskId"]))
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for decision in oom_decisions
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}
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# OOM tasks are stopped first. Rank the age-policy queue as it will look
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# after those certain failures are gone, so an old task moving into the
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# protected first N positions is not over-cancelled.
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age_rank_tasks = [
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task for task in tasks if (task.account_index, task.task_id) not in oom_task_keys
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]
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overflow_model_ids = {
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task.model_id
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for account_index in range(len(clients))
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if account_index not in listing_errors
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for task in sorted(
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(item for item in age_rank_tasks if item.account_index == account_index),
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key=lambda item: item.task_id,
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)[queue_thresholds.get(account_index, len(age_rank_tasks)):]
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}
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model_last_modified, age_errors = _load_model_last_modified(
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overflow_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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old_overflow_decisions, age_skipped = find_old_overflow_tasks(
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age_rank_tasks,
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model_last_modified=model_last_modified,
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queue_threshold=queue_thresholds,
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recent_model_days=recent_model_days,
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reference_time=reference_time,
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incomplete_accounts=set(listing_errors),
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)
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log(
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f"[queue-cleanup] old_overflow={len(old_overflow_decisions)} "
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f"thresholds={','.join(str(queue_thresholds[index]) for index in sorted(queue_thresholds))} "
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f"reserve_recent_slots={reserved_slots} recent_days={recent_model_days} "
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f"recent_overflow={age_skipped['recentOverflowTasks']} "
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f"age_unknown={age_skipped['modelAgeUnknown']}"
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)
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decisions_by_key: dict[tuple[int, int], dict[str, Any]] = {}
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for decision in oom_decisions:
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enriched = dict(decision)
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enriched["cleanupReasons"] = [decision["reason"]]
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decisions_by_key[(int(decision["accountIndex"]), int(decision["taskId"]))] = enriched
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for decision in old_overflow_decisions:
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key = (int(decision["accountIndex"]), int(decision["taskId"]))
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existing = decisions_by_key.get(key)
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if existing is None:
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enriched = dict(decision)
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enriched["cleanupReasons"] = [decision["reason"]]
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decisions_by_key[key] = enriched
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continue
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existing["cleanupReasons"].append(decision["reason"])
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for field in ("queuePosition", "queueThreshold", "modelLastModified", "recentCutoff"):
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existing[field] = decision[field]
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decisions = sorted(
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decisions_by_key.values(),
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key=lambda item: (int(item["accountIndex"]), int(item["taskId"])),
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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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policy_no_longer_applies: 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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active_positions_by_account: dict[int, dict[int, 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 in range(len(clients)):
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planned_oom_ids = {
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int(decision["taskId"])
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for decision in oom_decisions
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if int(decision["accountIndex"]) - 1 == account_index
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}
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ordered_ids = sorted(
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task.task_id
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for task in refreshed_tasks
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if task.account_index == account_index and task.task_id not in planned_oom_ids
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)
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active_positions_by_account[account_index] = {
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task_id: position for position, task_id in enumerate(ordered_ids, start=1)
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}
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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,
|
|
}
|
|
)
|
|
|
|
by_account: dict[int, list[dict[str, Any]]] = {}
|
|
for decision in decisions:
|
|
account_index = int(decision["accountIndex"]) - 1
|
|
if account_index in refresh_errors:
|
|
continue
|
|
if int(decision["taskId"]) not in active_ids_by_account.get(account_index, set()):
|
|
disappeared.append(decision)
|
|
continue
|
|
cleanup_reasons = set(decision.get("cleanupReasons") or [decision.get("reason")])
|
|
age_only = cleanup_reasons == {"old_model_beyond_account_queue_threshold"}
|
|
current_position = active_positions_by_account.get(account_index, {}).get(int(decision["taskId"]))
|
|
account_queue_threshold = queue_thresholds.get(account_index)
|
|
if age_only and (
|
|
current_position is None
|
|
or account_queue_threshold is None
|
|
or current_position <= account_queue_threshold
|
|
):
|
|
policy_no_longer_applies.append(
|
|
{**decision, "recheckedQueuePosition": current_position}
|
|
)
|
|
continue
|
|
if current_position is not None:
|
|
decision["recheckedQueuePosition"] = current_position
|
|
by_account.setdefault(account_index, []).append(decision)
|
|
|
|
batch_size = max(1, min(100, int(stop_batch_size)))
|
|
stop_failed = False
|
|
for account_index in sorted(by_account):
|
|
if stop_failed:
|
|
break
|
|
decisions_by_id = {int(item["taskId"]): item for item in by_account[account_index]}
|
|
phase_ids = [
|
|
sorted(
|
|
task_id
|
|
for task_id, decision in decisions_by_id.items()
|
|
if ("certain_oom_repository_size_exceeds_gpu_capacity" in decision["cleanupReasons"])
|
|
== is_oom_phase
|
|
)
|
|
for is_oom_phase in (True, False)
|
|
]
|
|
for task_ids in phase_ids:
|
|
for batch in _chunks(task_ids, batch_size):
|
|
try:
|
|
clients[account_index].stop_tasks(batch)
|
|
except Exception as exc:
|
|
stop_errors.append(
|
|
{
|
|
"accountIndex": account_index + 1,
|
|
"taskIds": batch,
|
|
"error": f"{type(exc).__name__}: {exc}",
|
|
}
|
|
)
|
|
stop_failed = True
|
|
break
|
|
cancelled.extend(decisions_by_id[task_id] for task_id in batch)
|
|
log(
|
|
f"[queue-cleanup] account={account_index + 1:02d} "
|
|
f"cancelled_batch={len(batch)} cancelled_total={len(cancelled)}"
|
|
)
|
|
if stop_failed:
|
|
break
|
|
|
|
if cancelled and hasattr(modelhub, "refresh_active_counts"):
|
|
modelhub.refresh_active_counts()
|
|
|
|
return {
|
|
"dryRun": bool(dry_run),
|
|
"accounts": len(clients),
|
|
"activeScanned": len(tasks),
|
|
"uniqueModels": len(model_ids),
|
|
"repositorySizesComplete": len(repository_sizes),
|
|
"repositorySizeErrors": size_errors,
|
|
"modelAgeMetadataComplete": len(model_last_modified),
|
|
"modelAgeErrors": age_errors,
|
|
"listingErrors": {str(index + 1): values for index, values in listing_errors.items()},
|
|
"certainOomCount": len(oom_decisions),
|
|
"certainOomTasks": oom_decisions,
|
|
"oldOverflowCount": len(old_overflow_decisions),
|
|
"oldOverflowTasks": old_overflow_decisions,
|
|
"oldModelQueueThresholds": [
|
|
queue_thresholds.get(index) for index in range(len(clients))
|
|
],
|
|
"accountCapacityLimits": [
|
|
account_caps[index] if index < len(account_caps) else None
|
|
for index in range(len(clients))
|
|
],
|
|
"recentModelReserveSlots": reserved_slots,
|
|
"recentModelDays": recent_model_days,
|
|
"agePolicySkipped": age_skipped,
|
|
"cleanupCandidateCount": len(decisions),
|
|
"skipped": skipped,
|
|
"cancelledCount": len(cancelled),
|
|
"cancelledTasks": cancelled,
|
|
"noLongerActiveCount": len(disappeared),
|
|
"policyNoLongerAppliesCount": len(policy_no_longer_applies),
|
|
"policyNoLongerAppliesTasks": policy_no_longer_applies,
|
|
"stopErrors": stop_errors,
|
|
}
|
|
|
|
|
|
def build_parser() -> argparse.ArgumentParser:
|
|
parser = argparse.ArgumentParser(description="Safely stop deterministic OOM and over-threshold old ModelHub tasks.")
|
|
parser.add_argument("--execute", action="store_true", help="Actually terminate selected tasks; otherwise only print a preview")
|
|
parser.add_argument("--read-concurrency", type=int, default=6)
|
|
parser.add_argument("--stop-batch-size", type=int, default=DEFAULT_STOP_BATCH_SIZE)
|
|
parser.add_argument("--key-path", default=str(DEFAULT_KEY_PATH))
|
|
parser.add_argument("--modelhub-base-url", default="https://modelhub.org.cn")
|
|
parser.add_argument("--hf-base-url", default="https://modelscope.cn")
|
|
parser.add_argument("--modelhub-token", default=None)
|
|
parser.add_argument("--hf-token", default=None)
|
|
parser.add_argument("--modelscope-token", default=None)
|
|
parser.add_argument("--report-path", default=".modelhub_state/queue_cleanup_latest.json")
|
|
return parser
|
|
|
|
|
|
def main(argv: list[str] | None = None) -> int:
|
|
args = build_parser().parse_args(argv)
|
|
ensure_tokens(args)
|
|
tokens = list(getattr(args, "modelhub_tokens", None) or [args.modelhub_token])
|
|
clients = [ModelHubClient(token=token, base_url=args.modelhub_base_url) for token in tokens]
|
|
pool = ModelHubClientPool(clients, capacity_state_path=None)
|
|
discovery = HuggingFaceDiscovery(base_url=args.hf_base_url)
|
|
summary = cleanup_certain_oom_tasks(
|
|
pool,
|
|
discovery,
|
|
dry_run=not args.execute,
|
|
read_concurrency=args.read_concurrency,
|
|
stop_batch_size=args.stop_batch_size,
|
|
)
|
|
report_path = Path(args.report_path)
|
|
write_json(report_path, summary)
|
|
print(
|
|
f"[queue-cleanup] finished certain_oom={summary['certainOomCount']} "
|
|
f"old_overflow={summary['oldOverflowCount']} "
|
|
f"cancelled={summary['cancelledCount']} stop_errors={len(summary['stopErrors'])} "
|
|
f"report={report_path}",
|
|
flush=True,
|
|
)
|
|
return 1 if summary["stopErrors"] else 0
|
|
|
|
|
|
if __name__ == "__main__":
|
|
raise SystemExit(main())
|