a4d4592864
Scoring progress: docker-exec benches (humaneval etc.) score for minutes with zero feedback -- the bar sat at 'generating 100%' and looked hung. evaluate() now takes on_scored(i, n) (atomic counter, fires from worker threads), run_eval passes it through, and the CLI shows 'scoring 42/164' on the bar + milestone log lines every 10% (also fixes the phase match: 'scoring' never matched the capitalized 'Scoring predictions...' status message, so the bar never even switched its label). PooledAdapter: - one release per acquire: the exception path released True (inner finally) AND False (except handler), double-decrementing _inflight (over-admission) and applying the x0.7 backoff twice - AdaptiveGate: rebuild the Condition + probe task when the event loop changes -- pools are cached across benchmarks and the CLI runs asyncio.run() per bench/repeat; a loop-bound Condition from a closed loop raises 'bound to a different event loop' under contention Co-Authored-By: Claude <noreply@anthropic.com>
278 lines
11 KiB
Python
278 lines
11 KiB
Python
"""Multi-endpoint load balancing: one logical model, N local ports.
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from evalharness.model.pool import PooledAdapter
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from evalharness.model.adapter import resolve_adapter
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base = resolve_adapter('openai/http://127.0.0.1:8123/v1?Qwen3-8B')
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pool = PooledAdapter([resolve_adapter(f'openai/http://127.0.0.1:{p}/v1?Qwen3-8B')
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for p in range(8123, 8131)])
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out = await pool.generate(...) # round-robin over instances
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Traffic management: round-robin keeps per-endpoint traffic even; backends
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that fail repeatedly enter a cool-down window and are skipped until it
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expires, so one sick endpoint cannot absorb its share of the load.
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"""
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import asyncio
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import contextlib
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import itertools
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import time
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from typing import Dict, List, Optional
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from ..data.sample import ChatMessage
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from .adapter import ModelAdapter
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from .output import ModelOutput, Usage
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class PooledAdapter(ModelAdapter):
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"""Round-robin over N equivalent backend instances with health cooling."""
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name = 'pool'
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COOLDOWN_S = 60.0 # a backend that failed EVERY attempt rests this long
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COOLDOWN_AFTER = 2 # consecutive full-pass failures before cooling
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def __init__(self, adapters: List[ModelAdapter]):
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if not adapters:
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raise ValueError('PooledAdapter needs at least one backend')
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super().__init__(model=adapters[0].model, api_base=adapters[0].api_base)
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self.adapters = adapters
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self._cycle = itertools.cycle(range(len(adapters)))
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self.usage = Usage()
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# request outcome counters (success rate accounting)
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self.stats = {'requests': 0, 'ok': 0, 'failed': 0, 'retried': 0}
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# per-backend health: consecutive_failures, cooling_until, per-endpoint counts
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self._health = [{ 'fails': 0, 'until': 0.0, 'ok': 0, 'req': 0}
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for _ in adapters]
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# adaptive per-endpoint concurrency gates (AIMD over /metrics signals)
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self._gates = [AdaptiveGate(a) for a in adapters]
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def _next(self) -> ModelAdapter:
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"""Round-robin, skipping endpoints inside their cool-down window."""
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n = len(self.adapters)
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now = time.time()
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for _ in range(n):
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i = next(self._cycle)
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h = self._health[i]
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if h['until'] <= now or all(x['until'] <= now for x in self._health):
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self._health[i]['req'] += 1
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return self.adapters[i]
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# everything cooling: take the next anyway (better to try than stall)
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i = next(self._cycle)
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self._health[i]['req'] += 1
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return self.adapters[i]
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def _mark(self, adapter: ModelAdapter, ok: bool) -> None:
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try:
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i = self.adapters.index(adapter)
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except ValueError:
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return
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h = self._health[i]
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if ok:
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h['fails'] = 0
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h['until'] = 0.0
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h['ok'] += 1
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else:
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h['fails'] += 1
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if h['fails'] >= self.COOLDOWN_AFTER:
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h['until'] = time.time() + self.COOLDOWN_S
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h['fails'] = 0
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def request_stats(self) -> Dict[str, float]:
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"""Success-rate + per-endpoint traffic view (load-balance audit)."""
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n = self.stats['requests']
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out = {
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'requests': n,
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'success_rate': self.stats['ok'] / n if n else 0.0,
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'retry_rate': self.stats['retried'] / n if n else 0.0,
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'failure_rate': self.stats['failed'] / n if n else 0.0,
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}
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for i, (a, h) in enumerate(zip(self.adapters, self._health)):
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tag = a.api_base.rsplit('//', 1)[-1].replace('/', '_')
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out[f'ep{i}_{tag}_reqs'] = h['req']
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out[f'ep{i}_{tag}_ok'] = h['ok']
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if i < len(self._gates):
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for k, v in self._gates[i].report().items():
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out[f'ep{i}_{tag}_{k}'] = v
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return out
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async def generate(self, messages: List[ChatMessage],
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tools: Optional[list] = None, **kw) -> ModelOutput:
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last_exc = None
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self.stats['requests'] += 1
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for _ in range(len(self.adapters)): # try each instance once
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adapter = self._next()
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try:
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i = self.adapters.index(adapter)
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await self._gates[i].acquire()
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ok = False
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try:
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out = await adapter.generate(messages, tools=tools, **kw)
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ok = True
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finally:
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# ONE release per acquire: the old code released True in
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# this finally AND False in the except handler, double-
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# decrementing _inflight (gate over-admits) and applying
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# the x0.7 backoff twice per failure
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self._gates[i].release(ok)
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self.usage = self.usage + out.usage
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self.stats['ok'] += 1
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self._mark(adapter, True)
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if out.usage.retries:
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self.stats['retried'] += 1
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return out
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except Exception as e: # dead/overloaded instance -> next
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last_exc = e
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self._mark(adapter, False)
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# 4xx (e.g. 400 overloaded) still worth trying ANOTHER instance:
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# one backend's state can differ from the rest
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continue
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self.stats['failed'] += 1
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raise last_exc
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async def close(self) -> None:
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for a in self.adapters:
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await a.close()
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for g in self._gates:
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g.stop()
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class AdaptiveGate:
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"""Per-endpoint adaptive concurrency limiter (AIMD + server signals).
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Goal: keep the backend SATURATED (high XPU util / throughput) without
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pushing it over the cliff (500s / child crashes). Signals:
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- server /metrics: num_queue_reqs > 0 means WE are pushing too hard
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for the current mix; idle (no queue, low running) means room to grow
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- request failures: multiplicative decrease (survive first)
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Control law (classic AIMD):
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+1 concurrency per probe interval when the endpoint looks underfed
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-1 when the server reports a queue (gentle)
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x0.7 on any failed request (fast backoff), floor at LO
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Purely additive to PooledAdapter: one gate per backend, no caller change.
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"""
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LO = 2 # never go below: progress beats perfection
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HI = 96 # sane ceiling for one endpoint
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PROBE_S = 5.0 # metrics probe interval
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def __init__(self, adapter: ModelAdapter):
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self.adapter = adapter
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self.limit = 8.0 # float for smooth x0.7; compare with int()
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self._inflight = 0
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self._cond: Optional[asyncio.Condition] = None
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self._task: Optional[asyncio.Task] = None
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self._stopped = False
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self._loop = None # loop the cond/probe-task are bound to
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self.stats = {'probe': 0, 'ramp': 0, 'hold_queue': 0, 'backoff_fail': 0,
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'backoff_queue': 0}
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# ---- gate semantics ----
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async def acquire(self) -> None:
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loop = asyncio.get_running_loop()
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if self._cond is None or self._loop is not loop or self._stopped:
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# lazy init OR LOOP CHANGE: pools are cached across benchmarks,
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# and the CLI runs asyncio.run() per bench (per repeat!) -- a new
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# run means a new event loop while this gate survives. A Condition
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# is loop-bound: reusing the old one raises "bound to a different
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# event loop" under contention, and the old probe task died with
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# the closed loop. Rebuild both; inflight resets to 0 (nothing is
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# in flight on a fresh loop by construction).
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self._loop = loop
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self._cond = asyncio.Condition()
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self._inflight = 0
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self._stopped = False
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if self._task is not None:
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self._task.cancel() # dead task from the closed loop; no-op
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self._task = loop.create_task(self._probe_loop())
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while self._inflight >= max(1, int(self.limit)):
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await self._cond.acquire()
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try:
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await self._cond.wait()
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finally:
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self._cond.release()
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self._inflight += 1
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def release(self, ok: bool) -> None:
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self._inflight = max(0, self._inflight - 1)
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if not ok: # multiplicative decrease -- survival first
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before = self.limit
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self.limit = max(self.LO, self.limit * 0.7)
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if before != self.limit:
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self.stats['backoff_fail'] += 1
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self._wake()
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def _wake(self) -> None:
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if self._cond is not None:
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# fire-and-forget notify (loop may not be ours -- best effort)
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try:
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fut = asyncio.ensure_future(self._notify_all())
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fut.add_done_callback(lambda f: None)
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except RuntimeError:
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pass
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async def _notify_all(self) -> None:
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async with self._cond:
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self._cond.notify_all()
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# ---- server-signal probe ----
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async def _probe_once(self) -> None:
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import urllib.request
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url = f'{self.adapter.api_base.rstrip("/")}/metrics'
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try:
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with urllib.request.urlopen(url, timeout=4) as resp:
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text = resp.read().decode('utf-8', 'ignore')
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except Exception:
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return # no metrics (or busy): hold current limit
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running = queue = None
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for line in text.splitlines():
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if line.startswith('sglang:num_running_reqs'):
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running = float(line.rsplit(' ', 1)[-1])
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elif line.startswith('sglang:num_queue_reqs'):
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queue = float(line.rsplit(' ', 1)[-1])
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self.stats['probe'] += 1
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if queue is None and running is None:
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return
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if queue is not None and queue >= 2:
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# server is queuing OUR excess: gentle additive decrease
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self.limit = max(self.LO, self.limit - 1)
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self.stats['backoff_queue'] += 1
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elif (queue or 0) == 0 and (running is None or running < max(2, int(self.limit))):
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# underfed: no queue and running below our own cap -> ramp up
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self.limit = min(self.HI, self.limit + 1)
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self.stats['ramp'] += 1
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else:
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self.stats['hold_queue'] += 1
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self._wake()
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async def _probe_loop(self) -> None:
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import contextlib
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while not self._stopped:
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with contextlib.suppress(Exception):
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await self._probe_once()
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await asyncio.sleep(self.PROBE_S)
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def stop(self) -> None:
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self._stopped = True
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if self._task is not None:
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self._task.cancel()
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def report(self) -> Dict[str, float]:
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return {'limit': max(1, int(self.limit)), 'inflight': self._inflight,
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**{f'gate_{k}': v for k, v in self.stats.items()}}
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def pooled(specs: List[str], api_key: str = '') -> PooledAdapter:
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"""['openai/http://127.0.0.1:8123/v1?M', ...] -> PooledAdapter.
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api_key: explicit key applied to EVERY member (two-key setups should
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build two pools, or use env resolution per host)."""
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from .adapter import resolve_adapter
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members = [resolve_adapter(s) for s in specs]
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if api_key:
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for m in members:
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m.api_key = api_key
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return PooledAdapter(members)
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