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319 lines
15 KiB
Python
319 lines
15 KiB
Python
"""有界、可取消的模型子流程，以 CPU 作为默认设备。"""
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from __future__ import annotations
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import asyncio
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import json
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import os
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import time
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import hashlib
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from collections import OrderedDict
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from contextlib import closing
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from contextvars import ContextVar
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from functools import wraps
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from pathlib import Path
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from typing import Literal
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from pydantic import BaseModel, Field
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from app.config import BACKEND_DIR
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from app.database.db import connect
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from app.errors import ApiError
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from app.local_models.catalog import CATALOG
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from app.local_models.manager import model_path, read_state
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from app.providers.base import ProviderError
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class RuntimeConfig(BaseModel):
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device: Literal["cpu", "cuda"] = "cpu"
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cpu_threads: int = Field(default=2, ge=1, le=32)
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memory_limit_mb: int = Field(default=8192, ge=1024, le=131072)
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gpu_memory_limit_mb: int = Field(default=4096, ge=512, le=65536)
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timeout_seconds: int = Field(default=1800, ge=30, le=14400)
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embedding_model: Literal["bekko", "granite"] = "bekko"
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version: int = Field(default=1, ge=1)
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runtime_context = ContextVar("runtime_config", default=None)
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runtime_progress = ContextVar("runtime_progress", default=None)
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embedding_priority = ContextVar("embedding_priority", default=0)
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def background_embeddings(operation):
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@wraps(operation)
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async def wrapped(*args, **kwargs):
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token = embedding_priority.set(20)
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try:
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return await operation(*args, **kwargs)
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finally:
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embedding_priority.reset(token)
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return wrapped
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def configuration():
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if runtime_context.get() is not None:
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return runtime_context.get()
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with closing(connect()) as conn:
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conn.execute("CREATE TABLE IF NOT EXISTS local_runtime_config (id INTEGER PRIMARY KEY CHECK(id=1), config_json TEXT NOT NULL)")
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row = conn.execute("SELECT config_json FROM local_runtime_config WHERE id=1").fetchone()
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return RuntimeConfig.model_validate_json(row[0]) if row else RuntimeConfig()
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def configure(request):
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from app.database.db import transaction
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configuration()
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with closing(connect()) as conn, transaction(conn):
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row = conn.execute("SELECT config_json FROM local_runtime_config WHERE id=1").fetchone()
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previous = RuntimeConfig.model_validate_json(row[0]) if row else RuntimeConfig()
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if request.version != previous.version:
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raise ApiError(409, "VERSION_CONFLICT", "Local runtime settings changed; reload first.")
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request = request.model_copy(update={"version": request.version + 1})
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conn.execute("INSERT OR REPLACE INTO local_runtime_config VALUES (1,?)", (request.model_dump_json(),))
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return request
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def interpreter(config=None):
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from app.local_models import components
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requested_device = (config or configuration()).device
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if not os.getenv("APP_MODEL_PYTHON") and requested_device == "cuda" and components.ready():
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return components.ROOT / "Scripts/python.exe"
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return Path(os.getenv("APP_MODEL_PYTHON", str(BACKEND_DIR / ".venv-models" / ("Scripts/python.exe" if os.name == "nt" else "bin/python"))))
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class Runtime:
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def __init__(self):
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self.active = {}
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self.active_files = {}
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self.waiters = []
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self.counter = 0
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self.diagnostics = []
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def in_use(self, key):
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return key in self.active.values()
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def media_in_use(self, path):
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target = str(Path(path).resolve())
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return any(target in paths for paths in self.active_files.values())
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async def infer(self, key, operation, payload, *, priority=10):
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from app.services import model_diagnostics
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config = configuration().model_copy(deep=True)
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self.counter += 1
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ticket = (priority, self.counter)
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self.waiters.append(ticket)
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queued_at = time.monotonic()
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reason = None
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from app.services.usage_service import usage_context
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from uuid import uuid4
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context = dict(usage_context.get() or {})
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context.setdefault("request_id", uuid4().hex)
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usage_token = usage_context.set(context)
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try:
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while self.active or ticket != min(self.waiters):
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await asyncio.sleep(0.05)
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self.waiters.remove(ticket)
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self.active[ticket] = key
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self.active_files[ticket] = {str(Path(payload[name]).resolve()) for name in ("source", "reference") if payload.get(name)}
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queue_seconds = time.monotonic() - queued_at
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# 用 CPU 进程替换失败的 CUDA 进程时，继续占用原有资源配额。
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for device in (["cuda", "cpu"] if config.device == "cuda" else ["cpu"]):
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started = time.monotonic()
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diagnostics = dict(model=CATALOG[key].repository, revision=CATALOG[key].revision,
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operation=operation, source="local", requested_device=config.device,
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attempted_device=device, queue_seconds=queue_seconds, fallback_reason=reason, request_id=context["request_id"])
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try:
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result = await self._execute(key, operation, payload, config.model_copy(update={"device": device}), diagnostics)
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diagnostics.update(result.get("diagnostics", {}))
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diagnostics.update(requested_device=config.device, status="completed")
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if reason:
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diagnostics["fallback_reason"] = reason
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return result["result"]
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except asyncio.CancelledError:
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diagnostics.update(status="cancelled", error_code="LOCAL_MODEL_CANCELLED")
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raise
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except ProviderError as exc:
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diagnostics.update(status="failed", error_code=exc.code)
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if device == "cuda" and exc.code in {"LOCAL_CUDA_INIT_FAILED", "LOCAL_CUDA_OOM"}:
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reason = exc.code
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callback = runtime_progress.get()
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if callback:
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callback({"reset": True, "progress": 0})
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continue
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raise
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except Exception:
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diagnostics.update(status="failed", error_code="LOCAL_MODEL_INVALID_RESPONSE")
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raise ProviderError("LOCAL_MODEL_INVALID_RESPONSE", "本地模型返回无效数据。") from None
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finally:
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diagnostics["requested_device"] = config.device
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diagnostics["elapsed_seconds"] = time.monotonic() - started
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self.diagnostics.append(model_diagnostics.record(**diagnostics))
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self.diagnostics = self.diagnostics[-100:]
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except asyncio.CancelledError:
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if ticket not in self.active:
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model_diagnostics.record(model=CATALOG[key].repository, operation=operation,
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source="local", status="cancelled", error_code="LOCAL_QUEUE_CANCELLED",
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requested_device=config.device, queue_seconds=time.monotonic() - queued_at)
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raise
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finally:
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if ticket in self.waiters:
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self.waiters.remove(ticket)
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self.active.pop(ticket, None)
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self.active_files.pop(ticket, None)
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usage_context.reset(usage_token)
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async def _execute(self, key, operation, payload, config, diagnostics):
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if read_state(key)["status"] != "installed":
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raise ProviderError("LOCAL_MODEL_NOT_INSTALLED", "请先下载本地模型。")
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executable = interpreter(config)
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if not executable.is_file():
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raise ProviderError("LOCAL_RUNTIME_NOT_INSTALLED", "请先安装本地模型运行环境。")
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from app.services.usage_service import UsageAttempt
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attempt = UsageAttempt("local-models", CATALOG[key].repository, "local", operation, source="local")
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diagnostics.update(attempt_id=attempt.attempt_id, request_id=attempt.request_id)
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process = None
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try:
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env = {**os.environ, "HF_HUB_OFFLINE": "1", "TRANSFORMERS_OFFLINE": "1",
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"HF_HUB_DISABLE_TELEMETRY": "1", "OMP_NUM_THREADS": str(config.cpu_threads),
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"PYTHONIOENCODING": "utf-8"}
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args = (str(executable), str(Path(__file__).with_name("worker.py")))
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options = {"env": env, "limit": 16 * 1024 * 1024,
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**({"creationflags": 0x08000000} if os.name == "nt" else {})}
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try:
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process = await asyncio.create_subprocess_exec(*args,
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stdin=asyncio.subprocess.PIPE, stdout=asyncio.subprocess.PIPE,
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stderr=asyncio.subprocess.DEVNULL, **options)
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except NotImplementedError:
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from app.local_models.process import ThreadedProcess
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process = ThreadedProcess(args, **options)
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request = {"key": key, "operation": operation, "model_path": str(model_path(key).resolve()),
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"config": config.model_dump(), "payload": payload}
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async def receive():
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process.stdin.write(json.dumps(request).encode())
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await process.stdin.drain()
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process.stdin.close()
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final = None
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vectors = []
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while line := await process.stdout.readline():
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message = json.loads(line)
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if "embedding_chunk" in message:
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chunk = message['embedding_chunk']
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if (operation != 'embedding' or not isinstance(chunk, list)
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or message.get('embedding_offset') != len(vectors)
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or len(vectors) + len(chunk) > len(payload.get('texts', []))):
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raise ProviderError('LOCAL_MODEL_INVALID_RESPONSE', '本地向量传输顺序或数量无效。')
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vectors.extend(chunk)
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elif "progress" in message:
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callback = runtime_progress.get()
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if callback:
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callback(message)
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else:
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final = message
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await process.wait()
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if isinstance(final, dict) and 'embedding_count' in final:
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if (final['embedding_count'] != len(vectors)
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or len(vectors) != len(payload.get('texts', []))):
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raise ProviderError('LOCAL_MODEL_INVALID_RESPONSE', '本地向量传输不完整。')
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final['result'] = vectors
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elif vectors:
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raise ProviderError('LOCAL_MODEL_INVALID_RESPONSE', '本地向量传输缺少结束标记。')
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return final
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try:
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result = await asyncio.wait_for(receive(), config.timeout_seconds)
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except TimeoutError as exc:
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raise ProviderError("LOCAL_MODEL_TIMEOUT", "本地模型处理超时。") from exc
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if process.returncode != 0:
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raise ProviderError("LOCAL_MODEL_PROCESS_FAILED", "本地模型进程退出，请检查依赖与资源预算。")
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if not isinstance(result, dict):
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raise ProviderError("LOCAL_MODEL_INVALID_RESPONSE", "本地模型进程未返回有效结果。")
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diagnostics.update(result.get("diagnostics", {}))
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if "error_code" in result:
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raise ProviderError(result["error_code"], result.get("message", "本地推理失败。"))
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attempt.observe(result)
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attempt.completed = True
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return result
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finally:
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if process is not None and process.returncode is None:
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process.kill()
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await process.wait()
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if process is not None and hasattr(process, "close"):
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await process.close()
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attempt.persist()
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runtime = Runtime()
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# 对确定性的单文本本地向量做有界内存复用。键包含模型目录、不可变版本和冻结运行配置；
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# 远程 API 响应以及模型不可用时的回退结果都不进入缓存。
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_embedding_cache = OrderedDict()
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_EMBEDDING_CACHE_TTL = 600
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class LocalEmbedding:
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dim = 384
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def __init__(self, config=None):
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self._config = config
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def snapshot(self):
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return LocalEmbedding((self._config or configuration()).model_copy(deep=True))
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@property
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def model_id(self):
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spec = CATALOG[(self._config or configuration()).embedding_model]
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return f"{spec.repository}@{spec.revision}"
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@property
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def version(self):
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return CATALOG[(self._config or configuration()).embedding_model].revision
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@property
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def available(self):
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return read_state(configuration().embedding_model)["status"] == "installed" and interpreter().is_file()
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async def embed_documents(self, texts):
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config = (self._config or configuration()).model_copy(deep=True)
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from app.retrieval.provenance import record_embedding
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cache_key = None
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if len(texts) == 1 and read_state(config.embedding_model)['status'] == 'installed' and interpreter(config).is_file():
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cache_key = (str(model_path(config.embedding_model).resolve()), config.model_dump_json(),
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hashlib.sha256(texts[0].encode()).hexdigest())
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cached = _embedding_cache.get(cache_key)
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if cached and time.monotonic() - cached[0] < _EMBEDDING_CACHE_TTL:
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_embedding_cache.move_to_end(cache_key)
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record_embedding(query_embedding_cache='hit')
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return [list(cached[1])]
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record_embedding(query_embedding_cache='miss')
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token = runtime_context.set(config)
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try:
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vectors = await runtime.infer(config.embedding_model, "embedding", {"texts": texts}, priority=embedding_priority.get())
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if cache_key and len(vectors) == 1:
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_embedding_cache[cache_key] = (time.monotonic(), tuple(vectors[0]))
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while len(_embedding_cache) > 128: _embedding_cache.popitem(last=False)
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return vectors
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finally:
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runtime_context.reset(token)
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async def embed_query(self, query):
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return (await self.embed_documents([query]))[0]
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class LocalSpeech:
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@property
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def available(self):
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return self.available_for("transcription")
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def available_for(self, capability):
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key = "qwen3-asr" if capability == "transcription" else "eres2netv2"
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return read_state(key)["status"] == "installed" and interpreter().is_file()
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async def transcribe(self, source, language):
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from app.providers.routing import RoutedTranscript
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from app.contracts import TranscriptSegment
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result = await runtime.infer("qwen3-asr", "transcription", {"source": str(source.resolve()), "language": language})
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return RoutedTranscript(text=result["text"], source="local",
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segments=[TranscriptSegment(**s) for s in result["segments"]], warnings=result.get("warnings", []))
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async def match(self, source, reference):
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result = await runtime.infer("eres2netv2", "speaker_matching",
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{"source": str(source.resolve()), "reference": str(reference.resolve())}, priority=0)
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return result["score"]
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