Text lineage: openai/gpt-oss-120b

benchmaxxing/gateway.py

@@ -488,6 +488,17 @@ class LocalOpenAICompatibleBackend(OpenAIBackend):
     API; this backend reuses the ``openai`` client (and ``OpenAIBackend.complete``) against a
     custom ``base_url``. Local servers usually ignore the key, so a placeholder ``api_key`` is
     sent by default. A ``client`` can be injected for offline tests.
+
+    ``max_retries`` defaults to 0 on purpose. The SDK retries internally by default, so leaving it
+    at the default puts a hidden retry loop underneath every caller's own retry wrapper: one logical
+    call can become many unpaced HTTP requests, which defeats rate pacing and spends a rate bucket
+    the caller thinks it is metering. Retries belong to the caller (``gateway.RetryBackend`` and
+    ``experiments/_lane.paced_complete``), not here.
+
+    ``timeout`` defaults to 60 s, which suits a hosted endpoint that answers a burst by holding the
+    socket open rather than refusing: failing fast there turns a stall into a retryable error. A
+    locally served model is the opposite case, where a long completion past 60 s is legitimate, so
+    raise it per call site rather than editing this default.
     """
 
     def __init__(
@@ -497,6 +508,8 @@ def __init__(
         api_key: str = "not-needed",
         client: object | None = None,
         default_decoding: dict | None = None,
+        timeout: float = 60.0,
+        max_retries: int = 0,
     ):
         self.model = model
         self.base_url = base_url
@@ -513,4 +526,5 @@ def __init__(
                 "installed. Install the models extra: pip install 'benchmaxxing[models]' "
                 "(or: pip install openai)."
             ) from exc
-        self._client = OpenAI(base_url=base_url, api_key=api_key)
+        self._client = OpenAI(base_url=base_url, api_key=api_key,
+                              timeout=timeout, max_retries=max_retries)

experiments/_lane.py

@@ -0,0 +1,328 @@
+"""Shared model dispatch for the text lanes.
+
+Every text runner used to hardcode ``MODEL = "gemini-2.5-flash-lite"`` and build
+``GeminiBackend`` directly, so a contributor assigned a second-vendor model had nothing to run.
+This module is the one place that maps a model id to its key, its backend and its output cap, so a
+runner only has to take ``--model`` and pass it through.
+
+The cache key is ``sha256(model \\x00 prompt)``, unchanged from the per-runner caches it replaces, so
+every committed Gemini cache still replays byte for byte with no new API calls.
+"""
+from __future__ import annotations
+
+import hashlib
+import json
+import os
+import re
+import threading
+import time
+from pathlib import Path
+
+from benchmaxxing import gateway
+from benchmaxxing.extract import declared_mcq_choice
+
+DEFAULT_MODEL = "gemini-2.5-flash-lite"
+NIM_BASE_URL = "https://integrate.api.nvidia.com/v1"
+DEEPSEEK_BASE_URL = "https://api.deepseek.com"
+# An open-weights model served on the machine that runs the experiment has no vendor endpoint, no
+# key and no request ceiling. BENCHMAXXING_LOCAL_BASE_URL names that server, and setting it is
+# enough to point the OpenAI-compatible backend at it, skip the key lookup and switch pacing off.
+# Gemini and DeepSeek ids keep their vendor routing whatever it is set to, so one variable cannot
+# silently redirect a committed comparator arm to a different model behind the same id.
+LOCAL_BASE_URL = os.environ.get("BENCHMAXXING_LOCAL_BASE_URL", "").strip()
+# Reasoning models need headroom. A cap that lands mid-reasoning returns the truncated chain of
+# thought in `content`, which the legacy parsers would then score as if it were an answer. Whatever
+# a cap still truncates is recorded as undeclared by `declared()` and excluded rather than scored.
+MAX_TOKENS = 8192
+# The NVIDIA endpoint allows about 40 requests per minute and penalises concurrent bursts, which
+# #416 measured and documented when it introduced this vendor; it returns HTTP 429 with no
+# Retry-After header, so the retry wrapper burns its five attempts against a closed door. Pace the
+# run instead of racing it: NIM_RPM is that documented ceiling, and BENCHMAXXING_MIN_CALL_INTERVAL
+# overrides the interval directly when a vendor needs something different. Pacing is measured
+# across threads, so it holds whatever max_workers a runner uses, and it is off for Gemini, which
+# has no such restriction.
+NIM_RPM = 40
+_NIM_INTERVAL = 60.0 / NIM_RPM * 1.05  # a 5% margin, since the window is not published exactly
+# 40 RPM is the documented ceiling, but a free-tier key sustains far less: the bucket is small and
+# refills slowly, so a long run settles nearer 3 calls a minute. Measured on this account, one call
+# every 20s completes 9 attempts in 10, and a single call succeeds again after 60s of idle.
+NIM_SUSTAINED_INTERVAL = 20.0
+# A 429 outlives RetryBackend's five quick attempts, which is what killed whole arms mid-run: the
+# backoff schedule expires while the bucket is still empty. Wait for a refill instead of failing.
+RATE_LIMIT_SLEEP = 90.0
+RATE_LIMIT_TRIES = 12
+TRANSIENT_SLEEP = 15  # a dropped connection needs a pause, not the full rate-limit cooldown
+MIN_CALL_INTERVAL = float(os.environ.get("BENCHMAXXING_MIN_CALL_INTERVAL", "0") or 0)
+
+
+def is_local(model: str) -> bool:
+    """True when this model is served locally rather than by a vendor endpoint."""
+    m = model.lower()
+    return bool(LOCAL_BASE_URL) and "gemini" not in m and "deepseek" not in m
+
+
+def interval_for(model: str) -> float:
+    """Seconds to leave between outgoing calls for a model's endpoint."""
+    if MIN_CALL_INTERVAL > 0:
+        return MIN_CALL_INTERVAL
+    if is_local(model):
+        return 0.0
+    if "gemini" in model.lower():
+        return 0.0
+    return NIM_SUSTAINED_INTERVAL
+
+
+def _is_rate_limited(exc: Exception) -> bool:
+    """True for a 429 from any vendor, without importing the vendor SDKs."""
+    if type(exc).__name__ in ("RateLimitError", "ResourceExhausted"):
+        return True
+    if getattr(exc, "status_code", None) == 429 or getattr(exc, "code", None) == 429:
+        return True
+    return "429" in str(exc) or "too many requests" in str(exc).lower()
+
+
+def _is_transient(exc: Exception) -> bool:
+    """True for a dropped or timed-out connection, which is worth retrying like a 429.
+
+    A second-vendor endpoint under load holds the socket open and then drops it rather than
+    answering, so a long arm sees ``APIConnectionError`` or ``ReadTimeout`` even when paced well
+    inside the rate limit. Without this the retry wrapper gives up and the whole arm dies, losing
+    the run but not the calls already cached; observed on three of thirteen ablation arms.
+    """
+    name = type(exc).__name__.lower()
+    if "timeout" in name or "connect" in name:
+        return True
+    # The NVIDIA endpoint under load also answers 503 "Service temporarily overloaded", 502/504, and an
+    # intermittent 404 for a model that /v1/models still lists and that answers 200 a minute later
+    # (observed 7 Sept 2026 on nemotron-3-super, 466 calls into an arm). Retrying is bounded by
+    # RATE_LIMIT_TRIES, so a model that has genuinely been withdrawn still fails, just not on the first 404.
+    text = str(exc).lower()
+    return (
+        "internalserver" in name or "serviceunavailable" in name or "notfound" in name
+        or any(code in text for code in ("error code: 502", "error code: 503", "error code: 504", "error code: 404"))
+        or "temporarily overloaded" in text
+    )
+
+
+_lock = threading.Lock()
+_pace_lock = threading.Lock()
+_last_call = [0.0]
+
+
+def _pace(model: str):
+    """Block until this model's minimum interval has passed since the previous outgoing call."""
+    gap = interval_for(model)
+    if gap <= 0:
+        return
+    with _pace_lock:
+        wait = gap - (time.monotonic() - _last_call[0])
+        if wait > 0:
+            time.sleep(wait)
+        _last_call[0] = time.monotonic()
+
+
+def is_gemini(model: str) -> bool:
+    """The one lineage whose key, backend and pacing differ from every second-vendor model."""
+    return "gemini" in model.lower()
+
+
+GEMINI_IDS = ("gemini-2.5-flash", "gemini-2.5-flash-lite", "gemini-2.5-pro")
+
+
+def rebind_models(namespace: dict, model: str) -> int:
+    """Rebind every Gemini id in a runner's module constants to `model`, in place.
+
+    The Gemini-only runners name their seats with module constants such as HOLDOUT, MODELS, TIERS
+    or MEMBERS, as strings, lists of strings, lists of (name, id) pairs or dicts of ids. When a
+    second model is requested, every one of those seats becomes that model, so a committee runner
+    compares the requested model's committee against Gemini's rather than mixing lineages. Returns
+    the number of ids rebound; zero means the runner had nothing to rebind, which is a bug.
+    """
+    def swap(v):
+        if isinstance(v, str):
+            return (model, 1) if v in GEMINI_IDS else (v, 0)
+        if isinstance(v, tuple):
+            items = [swap(x) for x in v]
+            return tuple(x for x, _ in items), sum(n for _, n in items)
+        if isinstance(v, list):
+            items = [swap(x) for x in v]
+            out = [x for x, _ in items]
+            if all(isinstance(x, str) for x in out):
+                # A list of tiers collapses to one entry per distinct model, so a runner that loops
+                # over tiers does not run the same model twice.
+                out = list(dict.fromkeys(out))
+            return out, sum(n for _, n in items)
+        if isinstance(v, dict):
+            items = {k: swap(x) for k, x in v.items()}
+            return {k: x for k, (x, _) in items.items()}, sum(n for _, n in items.values())
+        return v, 0
+
+    total = 0
+    for name, value in list(namespace.items()):
+        if name.isupper() and not name.startswith("_") and isinstance(value, (str, list, tuple, dict)):
+            new, n = swap(value)
+            if n:
+                namespace[name] = new
+                total += n
+    return total
+
+
+def key_name(model: str) -> str:
+    """Name the environment variable a model's key comes from."""
+    m = model.lower()
+    if "gemini" in m:
+        return "GEMINI_API_KEY"
+    if "deepseek" in m:
+        return "DEEPSEEK_API_KEY"
+    return "NVIDIA_API_KEY"
+
+
+def key_for(model: str):
+    """Resolve the API key strictly from the model id, as the imaging lane does."""
+    if is_local(model):
+        # A cache miss on a local endpoint must not exit for a key that no server checks.
+        return "not-needed"
+    m = model.lower()
+    if "gemini" in m:
+        return os.environ.get("GEMINI_API_KEY") or os.environ.get("GOOGLE_API_KEY")
+    if "deepseek" in m:
+        return os.environ.get("DEEPSEEK_API_KEY")
+    return os.environ.get("NVIDIA_API_KEY")
+
+
+def backend_for(model: str, key, client=None):
+    """Gemini through the Google SDK, everything else through the OpenAI-compatible path.
+
+    ``client`` is the gateway's own injection hook, so dispatch is testable without constructing
+    an SDK client.
+    """
+    if "gemini" in model.lower():
+        return gateway.GeminiBackend(model=model, api_key=key)
+    if is_local(model):
+        base_url = LOCAL_BASE_URL
+    elif "deepseek" in model.lower():
+        base_url = DEEPSEEK_BASE_URL
+    else:
+        base_url = NIM_BASE_URL
+    # A locally served model has no rate limit but can legitimately take minutes on a long
+    # completion, so it gets a generous timeout; a hosted endpoint keeps the 60 s fast-fail, where a
+    # stall is the failure mode worth converting into a retryable error.
+    return gateway.LocalOpenAICompatibleBackend(
+        model=model, base_url=base_url, api_key=key, client=client,
+        default_decoding={"max_tokens": MAX_TOKENS},
+        timeout=600.0 if is_local(model) else 60.0,
+    )
+
+
+def letters(n: int) -> list[str]:
+    return [chr(65 + i) for i in range(n)]
+
+
+_TERMINAL_LETTER = re.compile(r"^\s*\**\(?([A-E])\)?\**[.:]?\s*$")
+
+
+def declared(text: str, options) -> str | None:
+    """The option letter the model actually committed to, or None if it committed to nothing.
+
+    Prefers the shared declaration detector added in #418, and falls back to a bare option letter
+    on the final non-empty line, which is the form the text prompts ask for. A completion that ends
+    mid-reasoning, or in prose that merely mentions options, is undeclared and must not be scored:
+    the legacy parser will still find *some* letter in it.
+    """
+    if not text:
+        return None
+    options = list(options)
+    letter_of = letters(len(options))
+    # declared_mcq_choice returns the option TEXT, so map it back to its letter.
+    choice, ok = declared_mcq_choice(text, options)
+    if ok and choice in options:
+        return letter_of[options.index(choice)]
+    valid = set(letter_of)
+    lines = [ln for ln in text.strip().splitlines() if ln.strip()]
+    if lines:
+        m = _TERMINAL_LETTER.match(lines[-1])
+        if m and m.group(1) in valid:
+            return m.group(1)
+    return None
+
+
+def add_model_arg(ap, default: str = DEFAULT_MODEL):
+    ap.add_argument("--model", default=default,
+                    help="Model id. Gemini ids go through the Google SDK; anything else through "
+                         "the OpenAI-compatible endpoint (NVIDIA NIM by default).")
+
+
+def scoped(model: str, out: str, default_cache: str, cache: str | None = None):
+    """Model-scoped output directory and cache path.
+
+    The default model keeps the committed paths untouched so its results and cache stay exactly
+    where the paper's numbers were computed; every other model gets its own subdirectory and its
+    own cache file, which also keeps a thirteen-way fan-out off one shared, conflict-prone file.
+    """
+    slug = model.replace("/", "_")
+    out_dir = Path(out) if model == DEFAULT_MODEL else Path(out) / slug
+    if cache:
+        cache_path = Path(cache)
+    elif model == DEFAULT_MODEL:
+        cache_path = Path(default_cache)
+    else:
+        p = Path(default_cache)
+        cache_path = p.with_name(f"{slug}_{p.name}")
+    out_dir.mkdir(parents=True, exist_ok=True)
+    return out_dir, cache_path
+
+
+def paced_complete(model: str, key, prompt: str, decoding=None, client=None):
+    """One completion, paced to the model's rate and retried through a 429 or a transient fault.
+
+    This is the single call every runner cache goes through. The inner ``RetryBackend`` covers the
+    quick retries; this loop covers the slow ones: an empty rate bucket (wait RATE_LIMIT_SLEEP) or a
+    dropped connection, 5xx or intermittent 404 (wait TRANSIENT_SLEEP). Anything else, and the last
+    attempt of anything, is re-raised so a real fault still fails the run.
+    """
+    backend = gateway.RetryBackend(backend_for(model, key, client=client), tries=5, backoff=3.0)
+    for attempt in range(RATE_LIMIT_TRIES):
+        _pace(model)
+        try:
+            return backend.complete(prompt, decoding=decoding or {"temperature": 0})
+        except Exception as exc:  # noqa: BLE001  (re-raised below unless it is a 429 or transient)
+            root = exc
+            while root.__cause__ is not None:
+                root = root.__cause__
+            limited = _is_rate_limited(root)
+            if attempt == RATE_LIMIT_TRIES - 1 or not (limited or _is_transient(root)):
+                raise
+            time.sleep(RATE_LIMIT_SLEEP if limited else TRANSIENT_SLEEP)
+    return None
+
+
+class Cache:
+    """Prompt cache keyed on (model, prompt); a fully cached run needs no API key."""
+
+    def __init__(self, path, key, model):
+        self.path, self.key, self.model, self.store, self.calls = Path(path), key, model, {}, 0
+        if self.path.exists():
+            for line in self.path.read_text().splitlines():
+                if line.strip():
+                    r = json.loads(line)
+                    self.store[r["k"]] = r["resp"]
+
+    def complete(self, prompt, model=None):
+        model = model or self.model
+        k = hashlib.sha256(f"{model}\x00{prompt}".encode()).hexdigest()
+        with _lock:
+            if k in self.store:
+                return self.store[k]
+        if not self.key:
+            raise SystemExit(f"Cache miss and no {key_name(model)} set for {model} "
+                             "(a fully cached run needs no key).")
+        resp = paced_complete(model, self.key, prompt, decoding={"temperature": 0})
+        if resp is None:
+            raise SystemExit(f"{model} returned an empty completion (content=None). Reasoning-only "
+                             "models are not usable here: the parsers read `content`.")
+        with _lock:
+            self.store[k] = resp
+            self.calls += 1
+            with open(self.path, "a") as f:
+                f.write(json.dumps({"k": k, "model": model, "resp": resp}) + "\n")
+        return resp