litellm/tests/benchmarks/conftest.py
devin-ai-integration[bot] 0abd9267c1
feat(tokenizer): preserve Python defaults with opt-in Rust dispatch (#42174)
* ci: benchmark and gate an installed release wheel

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

* ci: simplify installed-wheel benchmark check

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

* feat(rust): add native tokenizer codec

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

* refactor(tokenizer): route Python tokenization through the Rust extension

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

* style(lint): format tokenizer call

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

* fix(packaging): restore runtime dependencies and native images

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

* fix(tokenizer): preserve Python SDK behavior with Rust tokenizers

* fix(tokenizer): restore compatibility paths

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

* refactor(tokenizer): count custom tokenizers directly

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

* fix(tokenizer): preserve caller-supplied Python tokenizer counts

* fix(tokenizer): reuse packaged vocabularies in the native wheel

* refactor(rust_bridge): route token counting through the catalog as RUST_OPT_IN

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

* fix(spend_tracking): compare tokenizer groups by value

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

* chore(deps): re-resolve filelock under the <4.0 pin

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

* fix(llms): align transformation override signatures with base configs

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

* build(rust): use fat LTO to keep the native wheel under the 35 MB limit

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

* feat(tokenizer): preserve Python defaults with opt-in Rust dispatch

* test(proxy): tolerate missing litellm.utils.Tokenizer when patching it

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

* test(proxy): patch the tokenizer dispatch function instead of the removed alias

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

* feat(tokenizer): give the Rust wrappers the tiktoken and tokenizers surface

Callers of litellm.encoding and litellm.create_tokenizer must see the same
read-only API whichever backend the catalog selects.

- OpenAIEncoding mirrors tiktoken.Encoding: n_vocab, max_token_value,
  token_byte_values, encode_single_token, encode_with_unstable,
  encode_to_numpy, decode_with_offsets, is_special_token, repr; the Rust
  tiktoken crate keeps a Vocabulary beside each CoreBPE and reports the
  requested encoding name (gpt2 stays gpt2).
- HuggingFaceTokenizer mirrors the read-only tokenizers.Tokenizer surface
  (token_to_id, id_to_token, get_vocab, get_vocab_size,
  get_added_tokens_decoder, num_special_tokens_to_add, padding, truncation,
  encode_special_tokens, from_buffer); HuggingFaceEncoding gains the
  char/word/token lookups, pad, truncate, set_sequence_id and merge.
  Mutators stay on the Python tokenizer.
- from_json/from_pretrained claim the fork gate only when the huggingface
  feature is compiled in; the surrogate fallback matches on the Codec.
- Tokenizer caching is keyed on the same catalog Context the dispatch runs
  on; rust_tokenizer reads the encoding name without loading an encoding;
  LITELLM_RUST parsing is cached.
- Drop the unused tiktoken_encoding_for_model export and Error::Download.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

* fix(tokenizer): close the exhaustive matches with assert_never

CodeQL reads a `match` over a Literal with no default arm as an implicit
`None` return. `assert_never` makes the exhaustiveness explicit for both the
HuggingFace tokenizer loader and the Rust token-counter factory.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

* feat(tokenizer): derive the fast counter from the shared tokenizer

The count-only counter (`fast` feature) and the codec each parsed the same
artifact: TokenCounter took the Anthropic JSON and the tiktoken rank files
from Python while Tokenizer loaded them again. One parse now serves both.

- FastTokenizer builds from a model another loader holds: `from_shared`
  takes the Arc<tokenizers::Tokenizer> the HF codec keeps, and
  `from_*_pairs` take the ranks the tiktoken vocabulary already parsed.
- `FastCounter::fast_counter` in the core crate derives it from either codec;
  encodings the fast scanner does not reproduce are refused.
- Native `Tokenizer.count(text, fast=False)` opts into that counter, built
  once per tokenizer on first use; `TokenCounter.from_tokenizer(tokenizer,
  fast=False)` replaces the JSON and rank-file constructors.
- The Python route counts over the native tokenizers the codec path shares
  (`native_encoding`, `native_anthropic`) and no longer reads rank files;
  the packaged Anthropic tokenizer has one loader, `tokenizer_dispatch.anthropic`.
- Public wrappers gain `count(text, fast=False)`.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

---------

Co-authored-by: Yujong Lee <yujong@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: Claude Fable 5.1 <noreply@anthropic.com>
2026-09-22 04:41:11 +00:00

54 lines
1.8 KiB
Python

"""Shared setup keeping CodSpeed measurements hermetic.
CodSpeed's callgrind instrumentation counts instructions from every thread while
a measurement window is open, and valgrind serializes all threads onto one
virtual CPU. Work deferred to litellm's shared logging executor would therefore
be attributed to whichever benchmark the valgrind scheduler resumes it under,
flipping results between runs. Running the executor inline keeps each
benchmark's cost self-contained and deterministic.
"""
import os
import sys
from collections.abc import Callable, Iterator
from concurrent.futures import Future
from pathlib import Path
from typing import ParamSpec, TypeVar
import pytest
from litellm.litellm_core_utils.thread_pool_executor import executor
P = ParamSpec("P")
R = TypeVar("R")
def pytest_configure(config: pytest.Config) -> None:
if os.environ.get("LITELLM_REQUIRE_INSTALLED_WHEEL") != "1":
return
import litellm
import litellm.rust_bridge._native as native
prefix = Path(sys.prefix).resolve()
for name, module_file in (("litellm", litellm.__file__), ("litellm.rust_bridge._native", native.__file__)):
path = Path(module_file).resolve()
if not path.is_relative_to(prefix):
raise pytest.UsageError(f"{name} resolved outside the benchmark environment: {path}")
print(f"{name}: {path}") # noqa: T201 # provenance evidence must be visible in CI logs
def _submit_inline(fn: Callable[P, R], /, *args: P.args, **kwargs: P.kwargs) -> Future[R]:
future: Future[R] = Future()
try:
future.set_result(fn(*args, **kwargs))
except BaseException as exc:
future.set_exception(exc)
return future
@pytest.fixture(autouse=True, scope="session")
def inline_logging_executor() -> Iterator[None]:
executor.submit = _submit_inline
yield
del executor.submit