tabbyAPI-ollama/common/utils.py
2025-04-21 01:46:18 +02:00

109 lines
2.8 KiB
Python

"""Common utility functions"""
import inspect
from types import NoneType
from typing import Dict, Optional, Type, Union, get_args, get_origin, TypeVar
T = TypeVar("T")
def unwrap(wrapped: Optional[T], default: T = None) -> T:
"""Unwrap function for Optionals."""
if wrapped is None:
return default
return wrapped
def coalesce(*args):
"""Coalesce function for multiple unwraps."""
return next((arg for arg in args if arg is not None), None)
def filter_none_values(collection: Union[dict, list]) -> Union[dict, list]:
"""Remove None values from a collection."""
if isinstance(collection, dict):
return {
k: filter_none_values(v) for k, v in collection.items() if v is not None
}
elif isinstance(collection, list):
return [filter_none_values(i) for i in collection if i is not None]
else:
return collection
def merge_dict(dict1: Dict, dict2: Dict) -> Dict:
"""Merge 2 dictionaries"""
for key, value in dict2.items():
if isinstance(value, dict) and key in dict1 and isinstance(dict1[key], dict):
merge_dict(dict1[key], value)
else:
dict1[key] = value
return dict1
def merge_dicts(*dicts: Dict) -> Dict:
"""Merge an arbitrary amount of dictionaries"""
result = {}
for dictionary in dicts:
result = merge_dict(result, dictionary)
return result
def flat_map(input_list):
"""Flattens a list of lists into a single list."""
return [item for sublist in input_list for item in sublist]
def is_list_type(type_hint) -> bool:
"""Checks if a type contains a list."""
if get_origin(type_hint) is list:
return True
# Recursively check for lists inside type arguments
type_args = get_args(type_hint)
if type_args:
return any(is_list_type(arg) for arg in type_args)
return False
def unwrap_optional_type(type_hint) -> Type:
"""
Unwrap Optional[type] annotations.
This is not the same as unwrap.
"""
if get_origin(type_hint) is Union:
args = get_args(type_hint)
if NoneType in args:
for arg in args:
if arg is not NoneType:
return arg
return type_hint
def calculate_rope_alpha(base_seq_len: int, target_seq_len: int):
"""
Converts a given max sequence length to a rope alpha value.
Args:
base_seq_len: The model's configured sequence length.
target_seq_len: The user-specified max sequence length.
"""
# Get the ratio of the model's max sequence length to the target
ratio = target_seq_len / base_seq_len
# Default to a 1 alpha if the sequence length is ever less
# than or equal to 1
if ratio <= 1.0:
alpha = 1
else:
alpha = -0.13436 + 0.80541 * ratio + 0.28833 * ratio**2
return alpha