283 lines
11 KiB
Python
283 lines
11 KiB
Python
from typing import List, Optional, Tuple, Union
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from transformers import (AutoTokenizer, PreTrainedTokenizer,
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PreTrainedTokenizerFast)
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from vllm.logger import init_logger
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from vllm.lora.request import LoRARequest
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from vllm.transformers_utils.tokenizers import *
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from vllm.utils import make_async
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logger = init_logger(__name__)
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def get_cached_tokenizer(
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tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast]
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) -> Union[PreTrainedTokenizer, PreTrainedTokenizerFast]:
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"""Get tokenizer with cached properties.
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This will patch the tokenizer object in place.
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By default, transformers will recompute multiple tokenizer properties
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each time they are called, leading to a significant slowdown. This
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function caches these properties for faster access."""
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tokenizer_all_special_ids = set(tokenizer.all_special_ids)
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tokenizer_all_special_tokens_extended = (
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tokenizer.all_special_tokens_extended)
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tokenizer_all_special_tokens = set(tokenizer.all_special_tokens)
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tokenizer_len = len(tokenizer)
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class CachedTokenizer(tokenizer.__class__):
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@property
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def all_special_ids(self):
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return tokenizer_all_special_ids
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@property
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def all_special_tokens(self):
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return tokenizer_all_special_tokens
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@property
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def all_special_tokens_extended(self):
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return tokenizer_all_special_tokens_extended
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def __len__(self):
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return tokenizer_len
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CachedTokenizer.__name__ = f"Cached{tokenizer.__class__.__name__}"
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tokenizer.__class__ = CachedTokenizer
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return tokenizer
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def get_tokenizer(
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tokenizer_name: str,
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*args,
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tokenizer_mode: str = "auto",
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trust_remote_code: bool = False,
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tokenizer_revision: Optional[str] = None,
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**kwargs,
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) -> Union[PreTrainedTokenizer, PreTrainedTokenizerFast]:
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"""Gets a tokenizer for the given model name via Huggingface."""
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if tokenizer_mode == "slow":
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if kwargs.get("use_fast", False):
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raise ValueError(
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"Cannot use the fast tokenizer in slow tokenizer mode.")
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kwargs["use_fast"] = False
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try:
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tokenizer = AutoTokenizer.from_pretrained(
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tokenizer_name,
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*args,
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trust_remote_code=trust_remote_code,
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tokenizer_revision=tokenizer_revision,
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**kwargs)
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except ValueError as e:
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# If the error pertains to the tokenizer class not existing or not
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# currently being imported, suggest using the --trust-remote-code flag.
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if (not trust_remote_code and
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("does not exist or is not currently imported." in str(e)
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or "requires you to execute the tokenizer file" in str(e))):
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err_msg = (
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"Failed to load the tokenizer. If the tokenizer is a custom "
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"tokenizer not yet available in the HuggingFace transformers "
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"library, consider setting `trust_remote_code=True` in LLM "
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"or using the `--trust-remote-code` flag in the CLI.")
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raise RuntimeError(err_msg) from e
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else:
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raise e
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except AttributeError as e:
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if "BaichuanTokenizer" in str(e):
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# This is for the error "'BaichuanTokenizer' object has no
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# attribute 'sp_model'".
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tokenizer = BaichuanTokenizer.from_pretrained(
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tokenizer_name,
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*args,
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trust_remote_code=trust_remote_code,
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tokenizer_revision=tokenizer_revision,
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**kwargs)
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else:
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raise e
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if not isinstance(tokenizer, PreTrainedTokenizerFast):
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logger.warning(
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"Using a slow tokenizer. This might cause a significant "
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"slowdown. Consider using a fast tokenizer instead.")
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return get_cached_tokenizer(tokenizer)
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def get_lora_tokenizer(lora_request: LoRARequest, *args,
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**kwargs) -> Optional[PreTrainedTokenizer]:
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if lora_request is None:
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return None
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try:
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tokenizer = get_tokenizer(lora_request.lora_local_path, *args,
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**kwargs)
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except OSError as e:
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# No tokenizer was found in the LoRA folder,
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# use base model tokenizer
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logger.warning(
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f"No tokenizer found in {lora_request.lora_local_path}, "
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"using base model tokenizer instead. "
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f"(Exception: {str(e)})")
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tokenizer = None
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return tokenizer
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get_lora_tokenizer_async = make_async(get_lora_tokenizer)
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def _convert_tokens_to_string_with_added_encoders(
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tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast],
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output_tokens: List[str],
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skip_special_tokens: bool,
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spaces_between_special_tokens: bool,
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) -> str:
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# Adapted from
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# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/tokenization_utils.py#L921
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# NOTE(woosuk): The following code is slow because it runs a for loop over
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# the output_tokens. In Python, running a for loop over a list can be slow
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# even when the loop body is very simple.
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sub_texts = []
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current_sub_text = []
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all_special_tokens = set(tokenizer.all_special_tokens)
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for token in output_tokens:
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if skip_special_tokens and token in all_special_tokens:
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continue
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if token in tokenizer.get_added_vocab():
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if current_sub_text:
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sub_text = tokenizer.convert_tokens_to_string(current_sub_text)
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sub_texts.append(sub_text)
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current_sub_text = []
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sub_texts.append(token)
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else:
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current_sub_text.append(token)
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if current_sub_text:
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sub_text = tokenizer.convert_tokens_to_string(current_sub_text)
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sub_texts.append(sub_text)
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if spaces_between_special_tokens:
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return " ".join(sub_texts)
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else:
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return "".join(sub_texts)
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# 5 is an arbitrary value that should work for all
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# tokenizers (bigger = more conservative).
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INITIAL_INCREMENTAL_DETOKENIZATION_OFFSET = 5
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def convert_prompt_ids_to_tokens(
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tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast],
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prompt_ids: List[int],
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skip_special_tokens: bool = False,
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) -> Tuple[List[str], int, int]:
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"""Converts the prompt ids to tokens and returns the tokens and offsets
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for incremental detokenization.
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Note that not all tokens are converted to strings. Only the tokens that
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are necessary for incremental detokenization are converted to strings.
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"""
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# Offset a little more in case we have special tokens.
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prefix_offset = max(
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len(prompt_ids) - INITIAL_INCREMENTAL_DETOKENIZATION_OFFSET - 2, 0)
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# We do not need to convert the whole prompt to tokens.
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new_tokens = tokenizer.convert_ids_to_tokens(
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prompt_ids[prefix_offset:], skip_special_tokens=skip_special_tokens)
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prefix_offset = max(
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len(new_tokens) - INITIAL_INCREMENTAL_DETOKENIZATION_OFFSET, 0)
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read_offset = len(new_tokens)
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return new_tokens, prefix_offset, read_offset
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# Based on
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# https://github.com/huggingface/text-generation-inference/blob/v0.9.4/server/text_generation_server/models/model.py#L62C9-L62C15
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# under Apache 2.0 license
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def detokenize_incrementally(
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tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast],
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all_input_ids: List[int],
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prev_tokens: Optional[List[str]],
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prefix_offset: int,
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read_offset: int,
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skip_special_tokens: bool = False,
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spaces_between_special_tokens: bool = True,
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) -> Tuple[List[str], str, int, int]:
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"""Detokenizes the input ids incrementally and returns the new tokens
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and the new text.
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If `prev_tokens` is None, this function will convert the input ids to
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tokens and return the tokens and the new text. Otherwise, it will return the
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new tokens and the new text.
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This function will also return the new prefix offset and the new read
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offset to be used in the next iteration.
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The offsets are necessary to defeat cleanup algorithms in the decode which
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decide to add a space or not depending on the surrounding ids.
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Args:
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tokenizer: The tokenizer to use.
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all_input_ids: The input ids. The last id is the new token id.
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prev_tokens: The previous tokens. If None, this function will convert
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the input ids to tokens and return the tokens and the new text.
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prefix_offset: The prefix offset.
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read_offset: The read offset.
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skip_special_tokens: Whether to skip special tokens.
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spaces_between_special_tokens: Whether to add spaces between special
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tokens.
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"""
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new_token_id = all_input_ids[-1]
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# This is the first iteration for this sequence
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is_first_iter = prev_tokens is None
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if is_first_iter:
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(prev_tokens, prefix_offset,
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read_offset) = convert_prompt_ids_to_tokens(
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tokenizer,
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all_input_ids[:-1],
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skip_special_tokens=skip_special_tokens)
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# If the new token id is out of bounds, return an empty string.
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if new_token_id >= len(tokenizer):
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new_tokens = [""]
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else:
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# Put new_token_id in a list so skip_special_tokens is respected
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new_tokens = tokenizer.convert_ids_to_tokens(
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[new_token_id], skip_special_tokens=skip_special_tokens)
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output_tokens = prev_tokens + new_tokens
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# If this is the first iteration, return all tokens.
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if is_first_iter:
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new_tokens = output_tokens
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# The prefix text is necessary only to defeat cleanup algorithms in
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# the decode which decide to add a space or not depending on the
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# surrounding ids.
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if tokenizer.is_fast or not tokenizer.get_added_vocab():
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prefix_text = tokenizer.convert_tokens_to_string(
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output_tokens[prefix_offset:read_offset])
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new_text = tokenizer.convert_tokens_to_string(
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output_tokens[prefix_offset:])
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else:
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prefix_text = _convert_tokens_to_string_with_added_encoders(
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tokenizer,
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output_tokens[prefix_offset:read_offset],
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skip_special_tokens=skip_special_tokens,
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spaces_between_special_tokens=spaces_between_special_tokens,
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)
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new_text = _convert_tokens_to_string_with_added_encoders(
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tokenizer,
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output_tokens[prefix_offset:],
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skip_special_tokens=skip_special_tokens,
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spaces_between_special_tokens=spaces_between_special_tokens,
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)
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if len(new_text) > len(prefix_text) and not new_text.endswith("<EFBFBD>"):
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# utf-8 char at the end means it's a potential unfinished byte sequence
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# from byte fallback tokenization.
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# If it's in the middle, it's probably a real invalid id generated
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# by the model
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new_text = new_text[len(prefix_text):]
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return new_tokens, new_text, read_offset, len(output_tokens)
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else:
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return new_tokens, "", prefix_offset, read_offset
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