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1faccd4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 | # Copyright 2024 Bytedance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Compatibility utilities for different versions of transformers library.
"""
import importlib.metadata
from functools import lru_cache
from typing import Optional
from packaging import version
# Handle version compatibility for flash_attn_supports_top_left_mask
# This function was added in newer versions of transformers
try:
from transformers.modeling_flash_attention_utils import flash_attn_supports_top_left_mask
except ImportError:
# For older versions of transformers that don't have this function
# Default to False as a safe fallback for older versions
def flash_attn_supports_top_left_mask():
"""Fallback implementation for older transformers versions.
Returns False to disable features that require this function.
"""
return False
@lru_cache
def is_transformers_version_in_range(min_version: Optional[str] = None, max_version: Optional[str] = None) -> bool:
try:
# Get the installed version of the transformers library
transformers_version_str = importlib.metadata.version("transformers")
except importlib.metadata.PackageNotFoundError as e:
raise ModuleNotFoundError("The `transformers` package is not installed.") from e
transformers_version = version.parse(transformers_version_str)
lower_bound_check = True
if min_version is not None:
lower_bound_check = version.parse(min_version) <= transformers_version
upper_bound_check = True
if max_version is not None:
upper_bound_check = transformers_version <= version.parse(max_version)
return lower_bound_check and upper_bound_check
@lru_cache
def get_auto_model_for_vision2seq():
"""Return the available VL auto model class across transformers versions."""
try:
# Prefer the newer class when available. In transformers 4.x this class has
# a broader mapping than AutoModelForVision2Seq, and AutoModelForVision2Seq
# is deprecated for removal in v5.
from transformers import AutoModelForImageTextToText
except ImportError:
from transformers import AutoModelForVision2Seq
return AutoModelForVision2Seq
return AutoModelForImageTextToText
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