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# 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.
"""Utils for tokenization."""
import warnings
__all__ = ["hf_tokenizer", "hf_processor"]
def set_pad_token_id(tokenizer):
"""Set pad_token_id to eos_token_id if it is None.
Args:
tokenizer (transformers.PreTrainedTokenizer): The tokenizer to be set.
"""
if tokenizer.pad_token_id is None:
tokenizer.pad_token_id = tokenizer.eos_token_id
warnings.warn(f"tokenizer.pad_token_id is None. Now set to {tokenizer.eos_token_id}", stacklevel=1)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
warnings.warn(f"tokenizer.pad_token is None. Now set to {tokenizer.eos_token}", stacklevel=1)
def hf_tokenizer(name_or_path, correct_pad_token=True, correct_gemma2=True, **kwargs):
"""Create a huggingface pretrained tokenizer which correctness handles eos and pad tokens.
Args:
name (str): The name of the tokenizer.
correct_pad_token (bool): Whether to correct the pad token id.
correct_gemma2 (bool): Whether to correct the gemma2 tokenizer.
Returns:
transformers.PreTrainedTokenizer: The pretrained tokenizer.
"""
from transformers import AutoTokenizer
if correct_gemma2 and isinstance(name_or_path, str) and "gemma-2-2b-it" in name_or_path:
# the EOS token in gemma2 is ambiguious, which may worsen RL performance.
# https://huggingface.co/google/gemma-2-2b-it/commit/17a01657f5c87135bcdd0ec7abb4b2dece04408a
warnings.warn("Found gemma-2-2b-it tokenizer. Set eos_token and eos_token_id to <end_of_turn> and 107.", stacklevel=1)
kwargs["eos_token"] = "<end_of_turn>"
kwargs["eos_token_id"] = 107
tokenizer = AutoTokenizer.from_pretrained(name_or_path, **kwargs)
if correct_pad_token:
set_pad_token_id(tokenizer)
return tokenizer
def hf_processor(name_or_path, **kwargs):
"""Create a huggingface processor to process multimodal data.
Args:
name_or_path (str): The name of the processor.
Returns:
transformers.ProcessorMixin: The pretrained processor.
"""
from transformers import AutoProcessor
try:
processor = AutoProcessor.from_pretrained(name_or_path, **kwargs)
except Exception:
processor = None
# Avoid load tokenizer, see:
# https://github.com/huggingface/transformers/blob/v4.49.0/src/transformers/models/auto/processing_auto.py#L344
if processor is not None and "Processor" not in processor.__class__.__name__:
processor = None
return processor
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