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https://github.com/BerriAI/litellm.git
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fix(token_counter): retry a failed Hugging Face tokenizer download in the background
After the retry window, the count that finds the stored failure starts one background download and answers with tiktoken, so no live count waits on a retry. A failure from a download that started before the first failure no longer resets a running retry, and a failed retry never replaces a tokenizer another download loaded. The outcome store takes its clock and retry starter as dependencies, so the tests inject them instead of patching time.monotonic.
This commit is contained in:
mateo-berri
parent
d3234ca951
commit
a6260a9496
2 changed files with 282 additions and 200 deletions
122
litellm/utils.py
122
litellm/utils.py
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@ -30,7 +30,7 @@ import time
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import traceback
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from contextlib import nullcontext
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from dataclasses import dataclass, field, replace
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from functools import lru_cache, wraps
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from functools import lru_cache, partial, wraps
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from importlib import resources
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from inspect import iscoroutine
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from io import StringIO
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@ -2684,49 +2684,68 @@ _HuggingFaceTokenizerKey: TypeAlias = tuple[HuggingFaceTokenizerKind, Decision]
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_HuggingFaceTokenizerLoadOutcome: TypeAlias = _HuggingFaceTokenizerLoaded | _HuggingFaceTokenizerLoadFailure
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def _start_in_daemon_thread(work: Callable[[], None]) -> None:
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threading.Thread(target=work, name="litellm-huggingface-tokenizer-retry", daemon=True).start()
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@dataclass(slots=True)
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class _HuggingFaceTokenizerLoadOutcomes:
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clock: Callable[[], float] = time.monotonic
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start_retry: Callable[[Callable[[], None]], None] = _start_in_daemon_thread
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lock: threading.Lock = field(default_factory=threading.Lock)
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by_tokenizer: Mapping[_HuggingFaceTokenizerKey, _HuggingFaceTokenizerLoadOutcome] = field(
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default_factory=lambda: MappingProxyType({})
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)
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def claim(self, key: _HuggingFaceTokenizerKey) -> _HuggingFaceTokenizerLoadOutcome | None:
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def known(
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self, key: _HuggingFaceTokenizerKey, retry_when_due: Callable[[], None]
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) -> _HuggingFaceTokenizerLoadOutcome | None:
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with self.lock:
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known: Final = self.by_tokenizer.get(key)
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now: Final = time.monotonic()
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match known:
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case _HuggingFaceTokenizerLoadFailure(retry_at=retry_at, retrying=False) if now >= retry_at:
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self._remember(key, replace(known, retrying=True))
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return None
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case _:
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return known
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if (
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not isinstance(known, _HuggingFaceTokenizerLoadFailure)
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or known.retrying
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or self.clock() < known.retry_at
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):
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return known
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retrying: Final = replace(known, retrying=True)
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self._remember(key, retrying)
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self.start_retry(retry_when_due)
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return retrying
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def remember_loaded(self, key: _HuggingFaceTokenizerKey, tokenizer: HuggingFace) -> None:
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def remember_loaded(self, key: _HuggingFaceTokenizerKey, tokenizer: HuggingFace) -> _HuggingFaceTokenizerLoaded:
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loaded: Final = _HuggingFaceTokenizerLoaded(tokenizer)
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with self.lock:
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self._remember(key, _HuggingFaceTokenizerLoaded(tokenizer))
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self._remember(key, loaded)
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return loaded
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def remember_failure(self, key: _HuggingFaceTokenizerKey, reason: str) -> _HuggingFaceTokenizerLoadOutcome:
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failure: Final = _HuggingFaceTokenizerLoadFailure(
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reason=reason, retry_at=time.monotonic() + HUGGINGFACE_TOKENIZER_LOAD_RETRY_SECONDS
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)
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def remember_first_failure(self, key: _HuggingFaceTokenizerKey, reason: str) -> _HuggingFaceTokenizerLoadOutcome:
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with self.lock:
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known: Final = self.by_tokenizer.get(key)
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if known is not None:
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return known
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failure: Final = self._failure(reason)
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self._remember(key, failure)
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return failure
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def remember_failed_retry(self, key: _HuggingFaceTokenizerKey, reason: str) -> _HuggingFaceTokenizerLoadOutcome:
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with self.lock:
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known: Final = self.by_tokenizer.get(key)
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if isinstance(known, _HuggingFaceTokenizerLoaded):
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return known
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failure: Final = self._failure(reason)
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self._remember(key, failure)
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return failure
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def end_retry(self, key: _HuggingFaceTokenizerKey) -> None:
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with self.lock:
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known: Final = self.by_tokenizer.get(key)
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if isinstance(known, _HuggingFaceTokenizerLoadFailure) and known.retrying:
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self._remember(key, replace(known, retrying=False))
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return failure
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def flush_cache(self) -> None:
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with self.lock:
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self.by_tokenizer = MappingProxyType({})
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def _failure(self, reason: str) -> _HuggingFaceTokenizerLoadFailure:
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return _HuggingFaceTokenizerLoadFailure(
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reason=reason, retry_at=self.clock() + HUGGINGFACE_TOKENIZER_LOAD_RETRY_SECONDS
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)
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def _remember(self, key: _HuggingFaceTokenizerKey, outcome: _HuggingFaceTokenizerLoadOutcome) -> None:
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self.by_tokenizer = MappingProxyType({**self.by_tokenizer, key: outcome})
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@ -2743,24 +2762,25 @@ def _warn_huggingface_tokenizer_fallback(model: str, reason: str) -> None:
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)
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def _return_huggingface_tokenizer(model: str) -> SelectTokenizerResponse | _HuggingFaceTokenizerLoadFailure | None:
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def _return_huggingface_tokenizer(
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model: str, outcomes: _HuggingFaceTokenizerLoadOutcomes = _HUGGINGFACE_TOKENIZER_LOAD_OUTCOMES
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) -> SelectTokenizerResponse | _HuggingFaceTokenizerLoadFailure | None:
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kind: Final = huggingface_tokenizer_kind(model)
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if kind is None or (kind not in _BUNDLED_TOKENIZER_LOAD_LOCKS and is_offline_mode()):
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return None
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backend: Final = _huggingface_tokenizer_backend()
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key: Final = (kind, _huggingface_tokenizer_backend())
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with _BUNDLED_TOKENIZER_LOAD_LOCKS.get(kind, nullcontext()):
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known: Final = _HUGGINGFACE_TOKENIZER_LOAD_OUTCOMES.claim((kind, backend))
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if isinstance(known, _HuggingFaceTokenizerLoadFailure):
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return known
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tokenizer: Final = (
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known.tokenizer
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if isinstance(known, _HuggingFaceTokenizerLoaded)
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else _load_huggingface_tokenizer_remembering_outcome(kind, backend)
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known: Final = outcomes.known(key, partial(_retry_huggingface_tokenizer_load, model, key, outcomes))
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outcome: Final = (
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known
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if known is not None
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else _load_huggingface_tokenizer_remembering_outcome(model, key, outcomes, outcomes.remember_first_failure)
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)
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if isinstance(tokenizer, _HuggingFaceTokenizerLoadFailure):
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_warn_huggingface_tokenizer_fallback(model, tokenizer.reason)
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return tokenizer
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return {"type": "huggingface_tokenizer", "tokenizer": tokenizer}
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match outcome:
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case _HuggingFaceTokenizerLoaded(tokenizer=tokenizer):
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return {"type": "huggingface_tokenizer", "tokenizer": tokenizer}
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case _HuggingFaceTokenizerLoadFailure():
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return outcome
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def _load_huggingface_tokenizer(kind: HuggingFaceTokenizerKind) -> HuggingFace:
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@ -2784,9 +2804,12 @@ def _load_huggingface_tokenizer(kind: HuggingFaceTokenizerKind) -> HuggingFace:
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def _load_huggingface_tokenizer_remembering_outcome(
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kind: HuggingFaceTokenizerKind, backend: Decision
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) -> HuggingFace | _HuggingFaceTokenizerLoadFailure:
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key: Final = (kind, backend)
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model: str,
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key: _HuggingFaceTokenizerKey,
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outcomes: _HuggingFaceTokenizerLoadOutcomes,
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remember_failure: Callable[[_HuggingFaceTokenizerKey, str], _HuggingFaceTokenizerLoadOutcome],
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) -> _HuggingFaceTokenizerLoadOutcome:
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kind, _ = key
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try:
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tokenizer: Final = _load_huggingface_tokenizer(kind)
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except Exception as error:
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@ -2794,13 +2817,22 @@ def _load_huggingface_tokenizer_remembering_outcome(
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if isinstance(error, (ForkedAfterNativeRuntimeStarted, ProcessReservedForForking)):
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raise
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outcome: Final = _HUGGINGFACE_TOKENIZER_LOAD_OUTCOMES.remember_failure(key, str(error))
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return outcome.tokenizer if isinstance(outcome, _HuggingFaceTokenizerLoaded) else outcome
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else:
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_HUGGINGFACE_TOKENIZER_LOAD_OUTCOMES.remember_loaded(key, tokenizer)
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return tokenizer
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finally:
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_HUGGINGFACE_TOKENIZER_LOAD_OUTCOMES.end_retry(key)
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outcome: Final = remember_failure(key, str(error))
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if isinstance(outcome, _HuggingFaceTokenizerLoadFailure):
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_warn_huggingface_tokenizer_fallback(model, outcome.reason)
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return outcome
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return outcomes.remember_loaded(key, tokenizer)
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def _retry_huggingface_tokenizer_load(
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model: str, key: _HuggingFaceTokenizerKey, outcomes: _HuggingFaceTokenizerLoadOutcomes
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) -> None:
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from litellm.rust_bridge.fork_guard import ForkedAfterNativeRuntimeStarted, ProcessReservedForForking
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try:
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_load_huggingface_tokenizer_remembering_outcome(model, key, outcomes, outcomes.remember_failed_retry)
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except (ForkedAfterNativeRuntimeStarted, ProcessReservedForForking) as error:
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outcomes.remember_failed_retry(key, str(error))
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def encode(model="", text="", custom_tokenizer: dict | None = None):
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@ -978,6 +978,9 @@ import unittest
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from litellm.utils import (
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_HUGGINGFACE_TOKENIZER_LOAD_OUTCOMES,
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_HuggingFaceTokenizerLoadFailure,
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_HuggingFaceTokenizerLoadOutcomes,
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_return_huggingface_tokenizer,
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_select_tokenizer_helper,
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claude_json_str,
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encoding,
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@ -1085,56 +1088,19 @@ class TestTokenizerSelection(unittest.TestCase):
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):
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_HUGGINGFACE_TOKENIZER_LOAD_OUTCOMES.flush_cache()
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try:
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with patch("litellm.utils.time.monotonic", return_value=0.0):
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mock_from_pretrained.side_effect = RuntimeError("HF unavailable")
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model: Final = "databricks/databricks-meta-llama-3-3-70b-instruct"
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text: Final = "Count the tokens for this message."
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expected: Final = len(encoding.encode(text, disallowed_special=()))
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first_count: Final = litellm.token_counter(model=model, text=text)
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first_attempts: Final = mock_from_pretrained.call_count
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second_count: Final = litellm.token_counter(model=model, text=text)
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assert first_attempts > 0
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assert mock_from_pretrained.call_count == first_attempts
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assert first_count == expected
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assert second_count == expected
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finally:
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_HUGGINGFACE_TOKENIZER_LOAD_OUTCOMES.flush_cache()
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@patch("litellm.utils.time.monotonic", return_value=299.0)
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@patch("litellm.utils.tokenizer_dispatch.from_pretrained")
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def test_failed_huggingface_tokenizer_download_is_retried_after_retry_window(
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self, mock_from_pretrained: MagicMock, mock_monotonic: MagicMock
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):
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_HUGGINGFACE_TOKENIZER_LOAD_OUTCOMES.flush_cache()
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try:
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def from_pretrained_side_effect(_: str) -> object:
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if mock_from_pretrained.call_count == 1:
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mock_monotonic.return_value = 322.0
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raise RuntimeError("HF unavailable")
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return sentinel.tokenizer
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mock_from_pretrained.side_effect = from_pretrained_side_effect
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mock_from_pretrained.side_effect = RuntimeError("HF unavailable")
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model: Final = "databricks/databricks-meta-llama-3-3-70b-instruct"
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text: Final = "Count the tokens for this message."
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expected: Final = len(encoding.encode(text, disallowed_special=()))
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first_result: Final = _select_tokenizer_helper(model)
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assert first_result["type"] == "openai_tokenizer"
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mock_monotonic.return_value = 323.0
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second_result: Final = _select_tokenizer_helper(model)
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assert second_result["type"] == "openai_tokenizer", "tokenizer was retried at t=323"
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assert mock_from_pretrained.call_count == 1
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first_count: Final = litellm.token_counter(model=model, text=text)
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first_attempts: Final = mock_from_pretrained.call_count
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second_count: Final = litellm.token_counter(model=model, text=text)
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mock_monotonic.return_value = 322.0 + HUGGINGFACE_TOKENIZER_LOAD_RETRY_SECONDS - 1
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third_result: Final = _select_tokenizer_helper(model)
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assert third_result["type"] == "openai_tokenizer"
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assert mock_from_pretrained.call_count == 1
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mock_monotonic.return_value = 322.0 + HUGGINGFACE_TOKENIZER_LOAD_RETRY_SECONDS + 1
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retried_result: Final = _select_tokenizer_helper(model)
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assert retried_result["type"] == "huggingface_tokenizer"
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assert retried_result["tokenizer"] is sentinel.tokenizer
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assert mock_from_pretrained.call_count == 2
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assert first_attempts > 0
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assert mock_from_pretrained.call_count == first_attempts
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assert first_count == expected
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assert second_count == expected
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finally:
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_HUGGINGFACE_TOKENIZER_LOAD_OUTCOMES.flush_cache()
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@ -1298,128 +1264,212 @@ def test_loaded_tokenizer_serves_later_counts_without_downloading_it_again():
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_HUGGINGFACE_TOKENIZER_LOAD_OUTCOMES.flush_cache()
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def test_counts_during_a_retry_after_the_window_use_tiktoken_instead_of_downloading_again():
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_HUGGINGFACE_TOKENIZER_LOAD_OUTCOMES.flush_cache()
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def _run_now(retry: Callable[[], None]) -> None:
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retry()
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def _start_retry_resolving(ended: Future[None]) -> Callable[[Callable[[], None]], None]:
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def start(retry: Callable[[], None]) -> None:
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def run_then_resolve() -> None:
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retry()
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ended.set_result(None)
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threading.Thread(target=run_then_resolve, daemon=True).start()
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return start
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def _eventually(probe: Callable[[], bool]) -> bool:
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deadline: Final = time.monotonic() + 5
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while time.monotonic() < deadline:
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if probe():
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return True
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time.sleep(0.01)
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return False
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def test_failed_download_is_retried_once_per_window_until_it_loads():
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model: Final = "databricks/databricks-meta-llama-3-3-70b-instruct"
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retry_started: Final = threading.Event()
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other_counts_finished: Final = threading.Event()
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clock: Final = MagicMock(return_value=299.0)
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outcomes: Final = _HuggingFaceTokenizerLoadOutcomes(clock=clock, start_retry=_run_now)
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def download(_: str) -> object:
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if threading.current_thread().name.startswith("retry"):
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retry_started.set()
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other_counts_finished.wait(timeout=5)
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return sentinel.tokenizer
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raise RuntimeError("HF unavailable")
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if from_pretrained.call_count == 1:
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clock.return_value = 322.0
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raise RuntimeError("HF unavailable")
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if from_pretrained.call_count == 2:
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raise RuntimeError("HF rate limited")
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return sentinel.tokenizer
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try:
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with (
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patch("litellm.utils.tokenizer_dispatch.from_pretrained", side_effect=download) as from_pretrained,
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patch("litellm.utils.time.monotonic", return_value=0.0) as clock,
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):
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first_type: Final = _select_tokenizer_helper(model)["type"]
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clock.return_value = HUGGINGFACE_TOKENIZER_LOAD_RETRY_SECONDS + 1.0
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with ThreadPoolExecutor(max_workers=1, thread_name_prefix="retry") as pool:
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retry: Final = pool.submit(_select_tokenizer_helper, model)
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assert retry_started.wait(timeout=5)
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types_during_retry: Final = tuple(_select_tokenizer_helper(model)["type"] for _ in range(3))
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other_counts_finished.set()
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retried: Final = retry.result(timeout=5)
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downloads: Final = from_pretrained.call_count
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assert first_type == "openai_tokenizer"
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assert types_during_retry == ("openai_tokenizer",) * 3
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assert downloads == 2
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assert retried["tokenizer"] is sentinel.tokenizer
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finally:
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_HUGGINGFACE_TOKENIZER_LOAD_OUTCOMES.flush_cache()
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with patch("litellm.utils.tokenizer_dispatch.from_pretrained", side_effect=download) as from_pretrained:
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first: Final = _return_huggingface_tokenizer(model, outcomes)
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clock.return_value = 322.0 + HUGGINGFACE_TOKENIZER_LOAD_RETRY_SECONDS - 1
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inside_the_window: Final = _return_huggingface_tokenizer(model, outcomes)
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downloads_inside_the_window: Final = from_pretrained.call_count
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clock.return_value = 322.0 + HUGGINGFACE_TOKENIZER_LOAD_RETRY_SECONDS
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claimant: Final = _return_huggingface_tokenizer(model, outcomes)
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inside_the_next_window: Final = _return_huggingface_tokenizer(model, outcomes)
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downloads_inside_the_next_window: Final = from_pretrained.call_count
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clock.return_value = 322.0 + 2 * HUGGINGFACE_TOKENIZER_LOAD_RETRY_SECONDS
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_return_huggingface_tokenizer(model, outcomes)
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after_the_second_retry: Final = _return_huggingface_tokenizer(model, outcomes)
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assert isinstance(first, _HuggingFaceTokenizerLoadFailure)
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assert isinstance(inside_the_window, _HuggingFaceTokenizerLoadFailure)
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assert downloads_inside_the_window == 1
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assert isinstance(claimant, _HuggingFaceTokenizerLoadFailure)
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assert isinstance(inside_the_next_window, _HuggingFaceTokenizerLoadFailure)
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assert downloads_inside_the_next_window == 2
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assert from_pretrained.call_count == 3
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assert after_the_second_retry == {"type": "huggingface_tokenizer", "tokenizer": sentinel.tokenizer}
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def test_retry_slower_than_the_window_stays_the_only_download_until_it_ends():
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_HUGGINGFACE_TOKENIZER_LOAD_OUTCOMES.flush_cache()
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def test_retry_after_the_window_runs_in_the_background_while_every_count_answers_at_once():
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model: Final = "databricks/databricks-meta-llama-3-3-70b-instruct"
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clock: Final = MagicMock(return_value=0.0)
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outcomes: Final = _HuggingFaceTokenizerLoadOutcomes(clock=clock)
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retry_started: Final = threading.Event()
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other_counts_finished: Final = threading.Event()
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def download(_: str) -> object:
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if threading.current_thread().name.startswith("retry"):
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retry_started.set()
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other_counts_finished.wait(timeout=5)
|
||||
return sentinel.tokenizer
|
||||
raise RuntimeError("HF unavailable")
|
||||
|
||||
try:
|
||||
with (
|
||||
patch("litellm.utils.tokenizer_dispatch.from_pretrained", side_effect=download) as from_pretrained,
|
||||
patch("litellm.utils.time.monotonic", return_value=0.0) as clock,
|
||||
):
|
||||
_select_tokenizer_helper(model)
|
||||
clock.return_value = HUGGINGFACE_TOKENIZER_LOAD_RETRY_SECONDS + 1.0
|
||||
with ThreadPoolExecutor(max_workers=1, thread_name_prefix="retry") as pool:
|
||||
retry: Final = pool.submit(_select_tokenizer_helper, model)
|
||||
assert retry_started.wait(timeout=5)
|
||||
clock.return_value = 5 * HUGGINGFACE_TOKENIZER_LOAD_RETRY_SECONDS
|
||||
types_while_retry_runs: Final = tuple(_select_tokenizer_helper(model)["type"] for _ in range(3))
|
||||
downloads_while_retry_runs: Final = from_pretrained.call_count
|
||||
other_counts_finished.set()
|
||||
retried: Final = retry.result(timeout=5)
|
||||
assert types_while_retry_runs == ("openai_tokenizer",) * 3
|
||||
assert downloads_while_retry_runs == 2
|
||||
assert retried["tokenizer"] is sentinel.tokenizer
|
||||
finally:
|
||||
_HUGGINGFACE_TOKENIZER_LOAD_OUTCOMES.flush_cache()
|
||||
|
||||
|
||||
def test_fork_guard_error_during_a_retry_leaves_the_next_count_free_to_retry():
|
||||
from litellm.rust_bridge.fork_guard import ForkedAfterNativeRuntimeStarted
|
||||
|
||||
_HUGGINGFACE_TOKENIZER_LOAD_OUTCOMES.flush_cache()
|
||||
model: Final = "databricks/databricks-meta-llama-3-3-70b-instruct"
|
||||
try:
|
||||
with (
|
||||
patch(
|
||||
"litellm.utils.tokenizer_dispatch.from_pretrained", side_effect=RuntimeError("HF unavailable")
|
||||
) as from_pretrained,
|
||||
patch("litellm.utils.time.monotonic", return_value=0.0) as clock,
|
||||
):
|
||||
_select_tokenizer_helper(model)
|
||||
clock.return_value = HUGGINGFACE_TOKENIZER_LOAD_RETRY_SECONDS + 1.0
|
||||
from_pretrained.side_effect = ForkedAfterNativeRuntimeStarted("native runtime started before fork")
|
||||
with pytest.raises(ForkedAfterNativeRuntimeStarted):
|
||||
_select_tokenizer_helper(model)
|
||||
from_pretrained.side_effect = None
|
||||
from_pretrained.return_value = sentinel.tokenizer
|
||||
retried: Final = _select_tokenizer_helper(model)
|
||||
assert retried["tokenizer"] is sentinel.tokenizer
|
||||
finally:
|
||||
_HUGGINGFACE_TOKENIZER_LOAD_OUTCOMES.flush_cache()
|
||||
|
||||
|
||||
def test_counts_after_an_idle_stretch_of_several_windows_still_share_one_retry_download():
|
||||
_HUGGINGFACE_TOKENIZER_LOAD_OUTCOMES.flush_cache()
|
||||
model: Final = "databricks/databricks-meta-llama-3-3-70b-instruct"
|
||||
count_during_retry: Final = Future[SelectTokenizerResponse]()
|
||||
retry_released: Final = threading.Event()
|
||||
|
||||
def download(_: str) -> object:
|
||||
if from_pretrained.call_count == 1:
|
||||
raise RuntimeError("HF unavailable")
|
||||
if from_pretrained.call_count == 2:
|
||||
count_during_retry.set_result(_select_tokenizer_helper(model))
|
||||
retry_started.set()
|
||||
retry_released.wait(timeout=5)
|
||||
return sentinel.tokenizer
|
||||
|
||||
try:
|
||||
with (
|
||||
patch("litellm.utils.tokenizer_dispatch.from_pretrained", side_effect=download) as from_pretrained,
|
||||
patch("litellm.utils.time.monotonic", return_value=0.0) as clock,
|
||||
):
|
||||
first_type: Final = _select_tokenizer_helper(model)["type"]
|
||||
clock.return_value = 2 * HUGGINGFACE_TOKENIZER_LOAD_RETRY_SECONDS + 1.0
|
||||
retried: Final = _select_tokenizer_helper(model)
|
||||
downloads: Final = from_pretrained.call_count
|
||||
assert first_type == "openai_tokenizer"
|
||||
assert count_during_retry.result(timeout=0)["type"] == "openai_tokenizer"
|
||||
assert downloads == 2
|
||||
assert retried["tokenizer"] is sentinel.tokenizer
|
||||
finally:
|
||||
_HUGGINGFACE_TOKENIZER_LOAD_OUTCOMES.flush_cache()
|
||||
with patch("litellm.utils.tokenizer_dispatch.from_pretrained", side_effect=download) as from_pretrained:
|
||||
_return_huggingface_tokenizer(model, outcomes)
|
||||
clock.return_value = 3 * HUGGINGFACE_TOKENIZER_LOAD_RETRY_SECONDS
|
||||
counts_while_retry_runs: Final = tuple(_return_huggingface_tokenizer(model, outcomes) for _ in range(4))
|
||||
assert retry_started.wait(timeout=5)
|
||||
clock.return_value = 9 * HUGGINGFACE_TOKENIZER_LOAD_RETRY_SECONDS
|
||||
later_counts_while_retry_runs: Final = tuple(_return_huggingface_tokenizer(model, outcomes) for _ in range(2))
|
||||
downloads_while_retry_runs: Final = from_pretrained.call_count
|
||||
retry_released.set()
|
||||
retried: Final = _eventually(
|
||||
lambda: _return_huggingface_tokenizer(model, outcomes)
|
||||
== {"type": "huggingface_tokenizer", "tokenizer": sentinel.tokenizer}
|
||||
)
|
||||
assert all(
|
||||
isinstance(count, _HuggingFaceTokenizerLoadFailure)
|
||||
for count in counts_while_retry_runs + later_counts_while_retry_runs
|
||||
)
|
||||
assert downloads_while_retry_runs == 2
|
||||
assert retried
|
||||
assert from_pretrained.call_count == 2
|
||||
|
||||
|
||||
def test_download_from_before_the_first_failure_ending_during_a_retry_leaves_it_the_only_retry():
|
||||
model: Final = "databricks/databricks-meta-llama-3-3-70b-instruct"
|
||||
clock: Final = MagicMock(return_value=0.0)
|
||||
outcomes: Final = _HuggingFaceTokenizerLoadOutcomes(clock=clock)
|
||||
early_download_started: Final = threading.Event()
|
||||
early_download_released: Final = threading.Event()
|
||||
retry_started: Final = threading.Event()
|
||||
retry_released: Final = threading.Event()
|
||||
|
||||
def download(_: str) -> object:
|
||||
if from_pretrained.call_count == 1:
|
||||
early_download_started.set()
|
||||
early_download_released.wait(timeout=5)
|
||||
raise RuntimeError("HF unavailable")
|
||||
if from_pretrained.call_count == 2:
|
||||
raise RuntimeError("HF unavailable")
|
||||
retry_started.set()
|
||||
retry_released.wait(timeout=5)
|
||||
return sentinel.tokenizer
|
||||
|
||||
with (
|
||||
patch("litellm.utils.tokenizer_dispatch.from_pretrained", side_effect=download) as from_pretrained,
|
||||
ThreadPoolExecutor(max_workers=1) as early_pool,
|
||||
):
|
||||
early_count: Final = early_pool.submit(_return_huggingface_tokenizer, model, outcomes)
|
||||
assert early_download_started.wait(timeout=5)
|
||||
_return_huggingface_tokenizer(model, outcomes)
|
||||
clock.return_value = HUGGINGFACE_TOKENIZER_LOAD_RETRY_SECONDS + 1.0
|
||||
_return_huggingface_tokenizer(model, outcomes)
|
||||
assert retry_started.wait(timeout=5)
|
||||
early_download_released.set()
|
||||
early: Final = early_count.result(timeout=5)
|
||||
clock.return_value = 5 * HUGGINGFACE_TOKENIZER_LOAD_RETRY_SECONDS
|
||||
counts_while_retry_runs: Final = tuple(_return_huggingface_tokenizer(model, outcomes) for _ in range(3))
|
||||
downloads_while_retry_runs: Final = from_pretrained.call_count
|
||||
retry_released.set()
|
||||
retried: Final = _eventually(
|
||||
lambda: _return_huggingface_tokenizer(model, outcomes)
|
||||
== {"type": "huggingface_tokenizer", "tokenizer": sentinel.tokenizer}
|
||||
)
|
||||
assert isinstance(early, _HuggingFaceTokenizerLoadFailure)
|
||||
assert all(isinstance(count, _HuggingFaceTokenizerLoadFailure) for count in counts_while_retry_runs)
|
||||
assert downloads_while_retry_runs == 3
|
||||
assert retried
|
||||
|
||||
|
||||
def test_retry_failing_after_an_earlier_download_loaded_the_tokenizer_keeps_it():
|
||||
model: Final = "databricks/databricks-meta-llama-3-3-70b-instruct"
|
||||
clock: Final = MagicMock(return_value=0.0)
|
||||
retry_ended: Final = Future[None]()
|
||||
outcomes: Final = _HuggingFaceTokenizerLoadOutcomes(clock=clock, start_retry=_start_retry_resolving(retry_ended))
|
||||
early_download_started: Final = threading.Event()
|
||||
early_download_released: Final = threading.Event()
|
||||
retry_started: Final = threading.Event()
|
||||
retry_released: Final = threading.Event()
|
||||
|
||||
def download(_: str) -> object:
|
||||
if from_pretrained.call_count == 1:
|
||||
early_download_started.set()
|
||||
early_download_released.wait(timeout=5)
|
||||
return sentinel.tokenizer
|
||||
if from_pretrained.call_count == 2:
|
||||
raise RuntimeError("HF unavailable")
|
||||
retry_started.set()
|
||||
retry_released.wait(timeout=5)
|
||||
raise RuntimeError("HF rate limited")
|
||||
|
||||
with (
|
||||
patch("litellm.utils.tokenizer_dispatch.from_pretrained", side_effect=download) as from_pretrained,
|
||||
ThreadPoolExecutor(max_workers=1) as early_pool,
|
||||
):
|
||||
early_count: Final = early_pool.submit(_return_huggingface_tokenizer, model, outcomes)
|
||||
assert early_download_started.wait(timeout=5)
|
||||
_return_huggingface_tokenizer(model, outcomes)
|
||||
clock.return_value = HUGGINGFACE_TOKENIZER_LOAD_RETRY_SECONDS
|
||||
_return_huggingface_tokenizer(model, outcomes)
|
||||
assert retry_started.wait(timeout=5)
|
||||
early_download_released.set()
|
||||
early: Final = early_count.result(timeout=5)
|
||||
retry_released.set()
|
||||
retry_ended.result(timeout=5)
|
||||
later_counts: Final = tuple(_return_huggingface_tokenizer(model, outcomes) for _ in range(3))
|
||||
loaded: Final = {"type": "huggingface_tokenizer", "tokenizer": sentinel.tokenizer}
|
||||
assert early == loaded
|
||||
assert from_pretrained.call_count == 3
|
||||
assert later_counts == (loaded,) * 3
|
||||
|
||||
|
||||
def test_fork_guard_error_in_a_retry_waits_out_the_next_window_like_any_failed_retry():
|
||||
from litellm.rust_bridge.fork_guard import ForkedAfterNativeRuntimeStarted
|
||||
|
||||
model: Final = "databricks/databricks-meta-llama-3-3-70b-instruct"
|
||||
clock: Final = MagicMock(return_value=0.0)
|
||||
outcomes: Final = _HuggingFaceTokenizerLoadOutcomes(clock=clock, start_retry=_run_now)
|
||||
with patch(
|
||||
"litellm.utils.tokenizer_dispatch.from_pretrained", side_effect=RuntimeError("HF unavailable")
|
||||
) as from_pretrained:
|
||||
_return_huggingface_tokenizer(model, outcomes)
|
||||
clock.return_value = HUGGINGFACE_TOKENIZER_LOAD_RETRY_SECONDS
|
||||
from_pretrained.side_effect = ForkedAfterNativeRuntimeStarted("native runtime started before fork")
|
||||
_return_huggingface_tokenizer(model, outcomes)
|
||||
from_pretrained.side_effect = None
|
||||
from_pretrained.return_value = sentinel.tokenizer
|
||||
inside_the_next_window: Final = tuple(_return_huggingface_tokenizer(model, outcomes) for _ in range(3))
|
||||
downloads_inside_the_next_window: Final = from_pretrained.call_count
|
||||
clock.return_value = 2 * HUGGINGFACE_TOKENIZER_LOAD_RETRY_SECONDS
|
||||
_return_huggingface_tokenizer(model, outcomes)
|
||||
after_the_next_window: Final = _return_huggingface_tokenizer(model, outcomes)
|
||||
assert all(isinstance(count, _HuggingFaceTokenizerLoadFailure) for count in inside_the_next_window)
|
||||
assert downloads_inside_the_next_window == 2
|
||||
assert after_the_next_window == {"type": "huggingface_tokenizer", "tokenizer": sentinel.tokenizer}
|
||||
|
||||
|
||||
def test_fork_guard_error_while_loading_a_tokenizer_is_raised_and_not_remembered():
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue