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[Refactor] litellm/init.py: lazy load default encoding from client decorator (#18059)

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Alexsander Hamir 2025-12-16 07:45:24 -08:00 • committed by GitHub
parent f8168f5063
commit 8f976df651
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2 changed files with 34 additions and 8 deletions
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23
litellm/_lazy_imports.py
View file

@ -1,10 +1,31 @@
from typing import Any, cast
from typing import Any, Optional, cast
import sys
def _get_litellm_globals() -> dict:
"""Helper to get the globals dictionary of the litellm module."""
return sys.modules["litellm"].__dict__
# Lazy loader for default encoding to avoid importing tiktoken at module import time
_default_encoding: Optional[Any] = None
def _get_default_encoding() -> Any:
"""
Lazily load and cache the default OpenAI encoding.
This avoids importing `litellm.litellm_core_utils.default_encoding` (and thus tiktoken)
at `litellm` import time. The encoding is cached after the first import.
This is used internally by utils.py functions that need the encoding but shouldn't
trigger its import during module load.
"""
global _default_encoding
if _default_encoding is None:
from litellm.litellm_core_utils.default_encoding import encoding
_default_encoding = encoding
return _default_encoding
# Cost calculator names that support lazy loading via _lazy_import_cost_calculator
COST_CALCULATOR_NAMES = (
"completion_cost",

19
litellm/utils.py
View file

@ -1,3 +1,6 @@
# from __future__ import annotations must be the first non-comment statement
from __future__ import annotations
# +-----------------------------------------------+
# | |
# | Give Feedback / Get Help |
@ -96,11 +99,11 @@ from litellm.litellm_core_utils.core_helpers import (
process_response_headers,
)
from litellm.litellm_core_utils.credential_accessor import CredentialAccessor
from litellm.litellm_core_utils.default_encoding import encoding
from litellm.litellm_core_utils.dot_notation_indexing import (
delete_nested_value,
is_nested_path,
)
from litellm._lazy_imports import _get_default_encoding
from litellm.litellm_core_utils.exception_mapping_utils import (
_get_response_headers,
exception_type,
@ -260,12 +263,16 @@ from litellm.llms.base_llm.base_utils import (
BaseLLMModelInfo,
type_to_response_format_param,
)
if TYPE_CHECKING:
# Heavy types that are only needed for type checking; avoid importing
# their modules at runtime during `litellm` import.
from litellm.llms.base_llm.files.transformation import BaseFilesConfig
from litellm.llms.base_llm.batches.transformation import BaseBatchesConfig
from litellm.llms.base_llm.chat.transformation import BaseConfig
from litellm.llms.base_llm.completion.transformation import BaseTextCompletionConfig
from litellm.llms.base_llm.containers.transformation import BaseContainerConfig
from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig
from litellm.llms.base_llm.files.transformation import BaseFilesConfig
from litellm.llms.base_llm.image_edit.transformation import BaseImageEditConfig
from litellm.llms.base_llm.image_generation.transformation import (
BaseImageGenerationConfig,
@ -293,6 +300,7 @@ from .caching.caching import (
RedisSemanticCache,
S3Cache,
)
from .exceptions import (
APIConnectionError,
APIError,
@ -1752,7 +1760,7 @@ def _select_tokenizer_helper(model: str) -> SelectTokenizerResponse:
def _return_openai_tokenizer(model: str) -> SelectTokenizerResponse:
return {"type": "openai_tokenizer", "tokenizer": encoding}
return {"type": "openai_tokenizer", "tokenizer": _get_default_encoding()}
def _return_huggingface_tokenizer(model: str) -> Optional[SelectTokenizerResponse]:
@ -5842,7 +5850,7 @@ def prompt_token_calculator(model, messages):
anthropic_obj = Anthropic()
num_tokens = anthropic_obj.count_tokens(text) # type: ignore
else:
num_tokens = len(encoding.encode(text))
num_tokens = len(_get_default_encoding().encode(text))
return num_tokens
@ -8187,9 +8195,6 @@ def extract_duration_from_srt_or_vtt(srt_or_vtt_content: str) -> Optional[float]
return max(durations) if durations else None
import httpx
def _add_path_to_api_base(api_base: str, ending_path: str) -> str:
"""
Adds an ending path to an API base URL while preventing duplicate path segments.