556 lines
22 KiB
Python
556 lines
22 KiB
Python
from __future__ import annotations
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import hashlib
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import os
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import threading
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import time
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from datetime import datetime, timedelta
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from typing import Any
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from common import format_modelhub_datetime, parse_datetime, runtime_instance_id
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from defaults import EMBEDDED_MODELHUB_XC_TOKEN
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from http_json import HttpJsonError, JsonHttpClient
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class ModelHubAPIError(RuntimeError):
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def __init__(self, message: str, *, code: int | None = None, payload: Any = None) -> None:
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super().__init__(message)
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self.code = code
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self.payload = payload
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class ModelHubClient:
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def __init__(
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self,
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*,
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token: str | None = None,
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base_url: str = "https://modelhub.org.cn",
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timeout: int = 30,
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retries: int = 2,
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http_client: JsonHttpClient | None = None,
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) -> None:
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self.token = token or os.getenv("MODELHUB_XC_TOKEN") or os.getenv("XC_TOKEN") or EMBEDDED_MODELHUB_XC_TOKEN
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if not self.token and http_client is None:
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raise ValueError("ModelHub token is required. Set MODELHUB_XC_TOKEN or XC_TOKEN.")
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default_headers = {"Xc-Token": self.token} if self.token else {}
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self.http_client = http_client or JsonHttpClient(
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base_url=base_url,
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default_headers=default_headers,
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timeout=timeout,
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retries=retries,
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)
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def search_by_model_id(self, model_id: str) -> dict[str, Any]:
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return self._request("GET", "/api/computility/models/search-by-model-id", query={"modelId": model_id})
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def is_model_processed_for_gpu(self, model_id: str, target_gpu: str) -> bool:
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gpu_result = self.get_verify_result_map(model_id).get(target_gpu)
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if gpu_result is None:
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return False
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return "result" in gpu_result or "records" in gpu_result
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def get_verify_result_map(self, model_id: str) -> dict[str, Any]:
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payload = self.search_by_model_id(model_id)
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return ((payload.get("data") or {}).get("verifyResult") or {})
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def processed_gpus_for_model(self, model_id: str) -> set[str]:
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return set(self.get_verify_result_map(model_id).keys())
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def list_tasks_page(
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self,
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*,
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current: int = 1,
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page_size: int = 50,
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only_mine: bool = True,
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begin_time: datetime | None = None,
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end_time: datetime | None = None,
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gpu_type: str | None = None,
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model_id: str | None = None,
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) -> dict[str, Any]:
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return self._request(
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"GET",
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"/api/adapt/task/page",
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query={
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"current": current,
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"pageSize": page_size,
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"onlyMine": str(only_mine).lower(),
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"beginTime": format_modelhub_datetime(begin_time) if begin_time else None,
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"endTime": format_modelhub_datetime(end_time) if end_time else None,
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"gpuType": gpu_type,
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"modelId": model_id,
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},
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)
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def list_tasks(
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self,
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*,
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page_size: int = 50,
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only_mine: bool = True,
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begin_time: datetime | None = None,
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end_time: datetime | None = None,
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gpu_type: str | None = None,
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model_id: str | None = None,
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) -> list[dict[str, Any]]:
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current = 1
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records: list[dict[str, Any]] = []
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while True:
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page = self.list_tasks_page(
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current=current,
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page_size=page_size,
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only_mine=only_mine,
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begin_time=begin_time,
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end_time=end_time,
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gpu_type=gpu_type,
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model_id=model_id,
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)
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page_data = page.get("data") or {}
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page_records = page_data.get("records") or []
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records.extend(page_records)
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pages = int(page_data.get("pages") or 0)
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if pages <= current or not page_records:
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break
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current += 1
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return records
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def add_task(self, payload: dict[str, Any]) -> dict[str, Any]:
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return self._request("POST", "/api/adapt/task/add", data=payload)
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def find_recent_task_id(self, model_id: str, gpu_type: str, submitted_after: datetime) -> str | None:
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recent_tasks = self.list_tasks(
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page_size=20,
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only_mine=True,
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begin_time=submitted_after - timedelta(hours=1),
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end_time=submitted_after + timedelta(hours=6),
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gpu_type=gpu_type,
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model_id=model_id,
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)
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if not recent_tasks:
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return None
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recent_tasks.sort(key=lambda task: parse_datetime(task.get("updateTime")) or submitted_after, reverse=True)
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return str(recent_tasks[0].get("taskId")) if recent_tasks[0].get("taskId") is not None else None
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def count_active_tasks(
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self,
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*,
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page_size: int = 100,
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only_mine: bool = True,
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begin_time: datetime | None = None,
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end_time: datetime | None = None,
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max_count: int | None = None,
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) -> int:
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"""Count active tasks with early pagination termination.
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Unlike list_tasks() which fetches all pages, this method stops
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fetching pages as soon as max_count active tasks are found.
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"""
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max_count_int: int | None = max_count
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if max_count_int is not None:
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max_count_int = max(0, int(max_count_int))
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if max_count_int == 0:
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return 0
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active_count = 0
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current = 1
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while True:
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page = self.list_tasks_page(
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current=current,
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page_size=page_size,
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only_mine=only_mine,
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begin_time=begin_time,
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end_time=end_time,
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)
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page_data = page.get("data") or {}
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page_records = page_data.get("records") or []
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for task in page_records:
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if is_active_task(task):
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active_count += 1
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if max_count_int is not None and active_count >= max_count_int:
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return active_count
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pages = int(page_data.get("pages") or 0)
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if pages <= current or not page_records:
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break
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current += 1
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return active_count
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def _request(
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self,
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method: str,
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path: str,
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*,
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query: dict[str, Any] | None = None,
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data: dict[str, Any] | None = None,
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) -> dict[str, Any]:
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try:
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payload = self.http_client.request_json(method, path, query=query, data=data)
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except HttpJsonError as exc:
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if isinstance(exc.payload, dict) and "message" in exc.payload:
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raise ModelHubAPIError(exc.payload["message"], payload=exc.payload, code=exc.status_code) from exc
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raise ModelHubAPIError(str(exc), payload=exc.payload, code=exc.status_code) from exc
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if not isinstance(payload, dict):
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raise ModelHubAPIError("Unexpected API response shape", payload=payload)
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code = payload.get("code")
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if code != 0:
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raise ModelHubAPIError(payload.get("message") or "ModelHub API request failed", code=code, payload=payload)
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return payload
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ACTIVE_TASK_STATUSES = {
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"waiting",
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"running",
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"processing",
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"queued",
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"pending",
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"validating",
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"submitting",
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"initializing",
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}
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TERMINAL_TASK_STATUSES = {
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"success",
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"failed",
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"error",
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"cancelled",
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"canceled",
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"rejected",
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"timeout",
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"completed",
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"complete",
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"done",
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}
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def is_active_task(task: dict[str, Any]) -> bool:
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status = str(task.get("status") or "").strip().lower()
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if not status:
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return False
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if status in ACTIVE_TASK_STATUSES:
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return True
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if status in TERMINAL_TASK_STATUSES:
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return False
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return True
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CAPACITY_ERROR_MARKERS = (
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"达到上限",
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"达上限",
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"任务数量已达",
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"队列已满",
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"queue is full",
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"queue full",
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"capacity",
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"too many active",
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"active task limit",
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)
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def is_capacity_error(error: ModelHubAPIError) -> bool:
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if error.code in {409, 429}:
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return True
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message = str(error).strip().lower()
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if isinstance(error.payload, dict):
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message = f"{message} {error.payload.get('message') or ''}".lower()
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return any(marker in message for marker in CAPACITY_ERROR_MARKERS)
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class ModelHubClientPool:
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def __init__(
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self,
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clients: list[ModelHubClient],
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*,
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active_task_cap: int | None = None,
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active_counts_ttl: float | None = None,
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reservation_ttl: float | None = None,
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instance_id: str | None = None,
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) -> None:
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if not clients:
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raise ValueError("At least one ModelHub client is required")
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self.clients = clients
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configured_cap = active_task_cap if active_task_cap is not None else os.getenv("MODELHUB_AGENT_ACTIVE_TASK_CAP", "100")
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self.active_task_cap = max(1, int(configured_cap))
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configured_ttl = active_counts_ttl if active_counts_ttl is not None else os.getenv("MODELHUB_AGENT_ACTIVE_COUNTS_TTL_SECONDS", "15")
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configured_reservation_ttl = (
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reservation_ttl
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if reservation_ttl is not None
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else os.getenv("MODELHUB_AGENT_RESERVATION_TTL_SECONDS", "120")
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)
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self._active_counts_ttl = max(1.0, float(configured_ttl))
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self._reservation_ttl = max(self._active_counts_ttl * 2, float(configured_reservation_ttl))
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self._remote_counts: list[int] = [0 for _ in clients]
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self._active_refresh_at: float = 0.0
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self._counts_initialized = False
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self._state_lock = threading.Lock()
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self._refresh_lock = threading.Lock()
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self._reservations: list[dict[int, dict[str, Any]]] = [{} for _ in clients]
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self._reservation_sequence = 0
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identity = instance_id or runtime_instance_id()
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identity_hash = hashlib.sha256(identity.encode("utf-8")).hexdigest()
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self._selection_cursor = int(identity_hash[:12], 16) % len(clients)
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self._verify_cache: dict[str, tuple[float, dict[str, Any]]] = {}
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self._verify_cache_ttl = max(1.0, float(os.getenv("MODELHUB_AGENT_VERIFY_CACHE_TTL_SECONDS", "30")))
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# Single reader client to avoid fanout on read operations
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self._reader = clients[0]
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def _safe_count_active_tasks(self, client: ModelHubClient) -> int:
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try:
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return client.count_active_tasks(max_count=self.active_task_cap, page_size=200)
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except Exception:
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return self.active_task_cap
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def _safe_search_by_model_id(self, client: ModelHubClient, model_id: str) -> dict[str, Any]:
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try:
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payload = client.search_by_model_id(model_id)
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return payload if isinstance(payload, dict) else {}
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except Exception:
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return {}
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def _safe_list_tasks(self, client: ModelHubClient, kwargs: dict[str, Any]) -> list[dict[str, Any]]:
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try:
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return client.list_tasks(**kwargs)
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except Exception:
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return []
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def _counts_are_fresh_locked(self, now: float) -> bool:
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return self._counts_initialized and (now - self._active_refresh_at) < self._active_counts_ttl
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def _refresh_active_counts(self, *, force: bool = False) -> None:
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now = time.monotonic()
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with self._state_lock:
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if not force and self._counts_are_fresh_locked(now):
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return
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# Do not hold the scheduler lock during remote I/O. One refresher is
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# enough; all submitting threads can continue using their reservations.
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with self._refresh_lock:
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now = time.monotonic()
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with self._state_lock:
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if not force and self._counts_are_fresh_locked(now):
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return
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def _to_indexed_result(index: int, client: ModelHubClient) -> tuple[int, int]:
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return index, self._safe_count_active_tasks(client)
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results: list[tuple[int, int]] = []
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with ThreadPoolExecutor(max_workers=min(len(self.clients), 12)) as executor:
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futures = {
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executor.submit(_to_indexed_result, index, client): index
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for index, client in enumerate(self.clients)
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}
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for future in as_completed(futures):
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index = futures[future]
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try:
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results.append(future.result())
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except Exception:
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results.append((index, self.active_task_cap))
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refreshed_at = time.monotonic()
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with self._state_lock:
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for index, count in results:
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old_remote_count = self._remote_counts[index]
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new_remote_count = min(self.active_task_cap, max(0, int(count)))
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acknowledged = max(0, new_remote_count - old_remote_count)
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completed_reservations = sorted(
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(
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(reservation_id, reservation)
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for reservation_id, reservation in self._reservations[index].items()
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if not reservation["inflight"]
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),
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key=lambda item: item[1]["updated_at"],
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)
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for reservation_id, _reservation in completed_reservations[:acknowledged]:
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self._reservations[index].pop(reservation_id, None)
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# A remote count can stay flat when one old task finishes as
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# one new task appears. Expiry prevents that net-zero update
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# from reserving a slot forever.
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for reservation_id, reservation in list(self._reservations[index].items()):
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if reservation["inflight"]:
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continue
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if refreshed_at - reservation["updated_at"] >= self._reservation_ttl:
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self._reservations[index].pop(reservation_id, None)
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self._remote_counts[index] = new_remote_count
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self._counts_initialized = True
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self._active_refresh_at = refreshed_at
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def _effective_count_locked(self, index: int) -> int:
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return self._remote_counts[index] + len(self._reservations[index])
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def _counts_snapshot_locked(self) -> list[int]:
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return [min(self.active_task_cap, self._effective_count_locked(index)) for index in range(len(self.clients))]
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def active_task_counts(self) -> list[int]:
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self._refresh_active_counts()
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with self._state_lock:
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return self._counts_snapshot_locked()
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def available_submit_slots(self) -> int:
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self._refresh_active_counts()
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with self._state_lock:
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return sum(
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max(0, self.active_task_cap - self._effective_count_locked(index))
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for index in range(len(self.clients))
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)
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def list_tasks(self, **kwargs): # noqa: ANN003, ANN001
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# Default: use only the reader client to avoid fanout amplification.
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# The fanout_all parameter allows explicit cross-account merging when needed.
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fanout_all = kwargs.pop("_fanout_all", False)
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if not fanout_all:
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return self._safe_list_tasks(self._reader, kwargs)
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merged: list[dict[str, Any]] = []
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seen: set[str] = set()
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with ThreadPoolExecutor(max_workers=min(len(self.clients), 12)) as executor:
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futures = {executor.submit(self._safe_list_tasks, client, kwargs): index for index, client in enumerate(self.clients)}
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for future in as_completed(futures):
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try:
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tasks = future.result()
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except Exception:
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tasks = []
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for task in tasks:
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task_id = str(task.get("taskId")) if task.get("taskId") is not None else None
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if task_id and task_id in seen:
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continue
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if task_id:
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seen.add(task_id)
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merged.append(task)
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return merged
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def _read_from_cache(self, model_id: str) -> dict[str, Any] | None:
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cached = self._verify_cache.get(model_id)
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if cached is None:
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return None
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cached_at, payload = cached
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if time.monotonic() - cached_at >= self._verify_cache_ttl:
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self._verify_cache.pop(model_id, None)
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return None
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return dict(payload)
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def _write_to_cache(self, model_id: str, payload: dict[str, Any]) -> None:
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self._verify_cache[model_id] = (time.monotonic(), payload)
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def begin_cycle(self) -> None:
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"""Drop model verification cache entries from the previous scan cycle."""
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with self._state_lock:
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self._verify_cache.clear()
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def search_by_model_id(self, model_id: str) -> dict[str, Any]:
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# Check cache first (per-cycle cache to avoid repeated API calls for the same model)
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with self._state_lock:
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cached = self._read_from_cache(model_id)
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if cached is not None:
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return cached
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# Only query ONE client (the reader) instead of fanning out to all clients.
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# verifyResult is model-specific platform data, not account-specific.
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response = self._safe_search_by_model_id(self._reader, model_id)
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if not isinstance(response, dict):
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response = {"code": 0, "data": {"verifyResult": {}}}
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with self._state_lock:
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self._write_to_cache(model_id, response)
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return response
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def get_verify_result_map(self, model_id: str) -> dict[str, Any]:
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payload = self.search_by_model_id(model_id)
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return ((payload.get("data") or {}).get("verifyResult") or {})
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def processed_gpus_for_model(self, model_id: str) -> set[str]:
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return set(self.get_verify_result_map(model_id).keys())
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def _reserve_account(self, excluded: set[int]) -> tuple[int, int] | None:
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with self._state_lock:
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remaining_by_index = {
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index: self.active_task_cap - self._effective_count_locked(index)
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for index in range(len(self.clients))
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if index not in excluded
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}
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usable = {index: remaining for index, remaining in remaining_by_index.items() if remaining > 0}
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if not usable:
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return None
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best_remaining = max(usable.values())
|
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tied = [index for index, remaining in usable.items() if remaining == best_remaining]
|
||
selected_index = min(
|
||
tied,
|
||
key=lambda index: (index - self._selection_cursor) % len(self.clients),
|
||
)
|
||
self._selection_cursor = (selected_index + 1) % len(self.clients)
|
||
self._reservation_sequence += 1
|
||
reservation_id = self._reservation_sequence
|
||
self._reservations[selected_index][reservation_id] = {
|
||
"inflight": True,
|
||
"updated_at": time.monotonic(),
|
||
}
|
||
return selected_index, reservation_id
|
||
|
||
def _finish_reservation(self, index: int, reservation_id: int, *, succeeded: bool) -> None:
|
||
with self._state_lock:
|
||
reservation = self._reservations[index].get(reservation_id)
|
||
if reservation is None:
|
||
return
|
||
if not succeeded:
|
||
self._reservations[index].pop(reservation_id, None)
|
||
return
|
||
reservation["inflight"] = False
|
||
reservation["updated_at"] = time.monotonic()
|
||
|
||
def _mark_account_saturated(self, index: int) -> None:
|
||
with self._state_lock:
|
||
self._remote_counts[index] = self.active_task_cap
|
||
self._counts_initialized = True
|
||
self._active_refresh_at = time.monotonic()
|
||
|
||
def add_task(self, payload: dict[str, Any]) -> dict[str, Any]:
|
||
self._refresh_active_counts()
|
||
attempted_accounts: set[int] = set()
|
||
forced_refresh_done = False
|
||
last_capacity_error: ModelHubAPIError | None = None
|
||
|
||
while True:
|
||
reservation = self._reserve_account(attempted_accounts)
|
||
if reservation is None:
|
||
if not forced_refresh_done:
|
||
self._refresh_active_counts(force=True)
|
||
forced_refresh_done = True
|
||
continue
|
||
if last_capacity_error is not None:
|
||
raise last_capacity_error
|
||
raise ModelHubAPIError(
|
||
f"当前等待中或运行中的异步模型验证任务数量已达上限（{self.active_task_cap}）"
|
||
)
|
||
|
||
selected_index, reservation_id = reservation
|
||
selected_client = self.clients[selected_index]
|
||
try:
|
||
response = selected_client.add_task(payload)
|
||
except ModelHubAPIError as exc:
|
||
self._finish_reservation(selected_index, reservation_id, succeeded=False)
|
||
if not is_capacity_error(exc):
|
||
raise
|
||
# Another process may have filled this account after our count
|
||
# refresh. Mark it full locally and immediately try another one.
|
||
self._mark_account_saturated(selected_index)
|
||
attempted_accounts.add(selected_index)
|
||
last_capacity_error = exc
|
||
continue
|
||
except Exception:
|
||
self._finish_reservation(selected_index, reservation_id, succeeded=False)
|
||
raise
|
||
|
||
self._finish_reservation(selected_index, reservation_id, succeeded=True)
|
||
return response
|
||
|
||
def list_tasks_page(self, **kwargs): # noqa: ANN003, ANN001
|
||
"""Single-page task listing via the reader client (no fanout)."""
|
||
return self._reader.list_tasks_page(**kwargs)
|
||
|
||
def find_recent_task_id(self, model_id: str, gpu_type: str, submitted_after: datetime) -> str | None:
|
||
for client in self.clients:
|
||
task_id = client.find_recent_task_id(model_id, gpu_type, submitted_after)
|
||
if task_id is not None:
|
||
return task_id
|
||
return None
|