406 lines
15 KiB
Python
406 lines
15 KiB
Python
from __future__ import annotations
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import os
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import time
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import threading
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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
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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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class ModelHubClientPool:
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def __init__(self, clients: list[ModelHubClient], *, active_task_cap: int = 100) -> 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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self.active_task_cap = active_task_cap
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self._active_counts: list[int] = [0 for _ in clients]
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self._active_refresh_at: float = 0.0
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self._active_counts_ttl: float = 30.0
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self._state_lock = threading.Lock()
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# Per-cycle cache for search_by_model_id results (model_id -> merged verify result map)
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self._verify_cache: dict[str, dict[str, Any]] = {}
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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 _refresh_active_counts(self, *, force: bool = False) -> None:
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now = time.time()
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if (not force) and self._active_counts and (now - self._active_refresh_at) < self._active_counts_ttl:
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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 = {executor.submit(_to_indexed_result, index, client): index for index, client in enumerate(self.clients)}
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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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self._active_counts = [0 for _ in self.clients]
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for index, count in sorted(results, key=lambda item: item[0]):
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self._active_counts[index] = count
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# Use current time after refresh completes, not the stale 'now' from function start
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self._active_refresh_at = time.time()
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def active_task_counts(self) -> list[int]:
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with self._state_lock:
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now = time.time()
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if self._active_counts and (now - self._active_refresh_at) < self._active_counts_ttl:
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pass # use cached counts
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else:
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self._refresh_active_counts()
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return list(self._active_counts)
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def available_submit_slots(self) -> int:
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with self._state_lock:
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# Only refresh if cache is stale (respects TTL) or counts are empty
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now = time.time()
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if self._active_counts and (now - self._active_refresh_at) < self._active_counts_ttl:
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pass # use cached counts
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else:
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self._refresh_active_counts()
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return sum(max(0, self.active_task_cap - count) for count in self._active_counts)
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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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return dict(cached)
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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] = payload
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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 add_task(self, payload: dict[str, Any]) -> dict[str, Any]:
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with self._state_lock:
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self._refresh_active_counts(force=True)
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selected_index = None
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best_remaining = -1
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for index, active_count in enumerate(self._active_counts):
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remaining = self.active_task_cap - active_count
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if remaining > best_remaining:
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best_remaining = remaining
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selected_index = index
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if selected_index is None or best_remaining <= 0:
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raise ModelHubAPIError(
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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}）"
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)
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self._active_counts[selected_index] += 1
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selected_client = self.clients[selected_index]
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try:
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response = selected_client.add_task(payload)
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except Exception:
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with self._state_lock:
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self._active_counts[selected_index] -= 1
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raise
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return response
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def list_tasks_page(self, **kwargs): # noqa: ANN003, ANN001
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"""Single-page task listing via the reader client (no fanout)."""
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return self._reader.list_tasks_page(**kwargs)
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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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for client in self.clients:
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task_id = client.find_recent_task_id(model_id, gpu_type, submitted_after)
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if task_id is not None:
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return task_id
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return None
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