mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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331 lines
11 KiB
Python
331 lines
11 KiB
Python
# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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from abc import ABC, abstractmethod
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from paddlenlp.transformers import Llama3Tokenizer, LlamaTokenizer
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from paddlenlp.utils.llm_utils import get_eos_token_id
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from server.engine.config import Config
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from server.utils import data_processor_logger
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class BaseDataProcessor(ABC):
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"""base class for data processor"""
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def __init__(self):
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"""
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Returns:
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None
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"""
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self.tokenizer = self._load_tokenizer()
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self.tokenizer.bos_token_id = self.tokenizer._convert_token_to_id(self.tokenizer.bos_token)
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self.tokenizer.cls_token_id = self.tokenizer._convert_token_to_id(self.tokenizer.cls_token)
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self.tokenizer.sep_token_id = self.tokenizer._convert_token_to_id(self.tokenizer.sep_token)
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self.tokenizer.eos_token_id = self.tokenizer._convert_token_to_id(self.tokenizer.eos_token)
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self.tokenizer.mask_token_id = self.tokenizer._convert_token_to_id(self.tokenizer.mask_token)
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data_processor_logger.info((f"tokenizer infomation: bos_token is {self.tokenizer.bos_token}, {self.tokenizer.bos_token_id}, ",
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f"cls_token is {self.tokenizer.cls_token}, {self.tokenizer.cls_token_id}, "
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f"sep_token is {self.tokenizer.sep_token}, {self.tokenizer.sep_token_id}, "
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f"eos_token is {self.tokenizer.eos_token}, {self.tokenizer.eos_token_id}, "
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f"mask_token is {self.tokenizer.mask_token}, {self.tokenizer.mask_token_id}"))
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@abstractmethod
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def process_request(self, request, **kwargs):
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"""
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Preprocess the request
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Args:
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request (Dict): may contain text and messages fields
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**kwargs: others
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Returns:
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bool: Whether preprocessing is successful
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str: error message
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"""
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raise NotImplementedError
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@abstractmethod
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def process_response(self, response_dict):
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"""
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Preprocess the response
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Args:
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response_dict (Dict): response for engine, contain ids fields
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Returns:
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Dict: response contain text fields
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"""
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raise NotImplementedError
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def text2ids(self, text):
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"""
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text to token ids
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Args:
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text (str): text
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Returns:
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List[int]: token ids list
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"""
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raise NotImplementedError
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def messages2ids(self, messages):
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"""
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Convert multi-turn messages into ID sequences.
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Args:
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messages (List[List[Dict[str, Any]]]): multi-turn messages.
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Returns:
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List[int]: ID sequences
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"""
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raise NotImplementedError
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def ids2tokens(self, token_ids, task_id=None):
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"""
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token ids to strings
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Args:
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token_ids (List[int]): token ids
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task_id (str): task id
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Returns:
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List[str]: strings
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"""
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raise NotImplementedError
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@abstractmethod
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def _load_tokenizer(self):
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"""
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load tokenizer
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Returns:
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tokenizer (AutoTokenizer)
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"""
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raise NotImplementedError
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class DataProcessor(BaseDataProcessor):
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def __init__(self):
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self.config = Config()
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max_length = self.config.get_model_config().get('max_length', 1024)
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self.src_length = max_length - self.config.seq_len_limit
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self.decode_status = dict()
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self.tokenizer = self._load_tokenizer()
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data_processor_logger.info(f"tokenizer infomation: bos_token is {self.tokenizer.bos_token}, {self.tokenizer.bos_token_id}, "+
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f"eos_token is {self.tokenizer.eos_token}, {self.tokenizer.eos_token_id}, ")
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def process_request(self, request, max_seq_len=None):
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"""
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Preprocess the request
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Args:
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request (Dict): may contain text and messages fields
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Returns:
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bool: Whether preprocessing is successful
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str: error message
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"""
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if "eos_token_ids" not in request or request["eos_token_ids"] == [None]:
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request["eos_token_ids"] = []
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request["eos_token_ids"].extend(get_eos_token_id(self.tokenizer, self.config.generation_config))
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if "input_ids" in request:
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input_ids = request["input_ids"]
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else:
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input_ids = self.text2ids(request['text'])
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if max_seq_len is not None and len(input_ids) > max_seq_len:
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input_ids = input_ids[:max_seq_len-1]
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request["input_ids"] = input_ids
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data_processor_logger.info(f"processed request: {request}")
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return request
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def process_response(self, response_dict, **kwargs):
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"""
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Preprocess the response
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Args:
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response_dict (Dict): response for engine, contain ids fields
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Returns:
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Dict: response contain text fields
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"""
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is_end = response_dict.get("is_end", 0)
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req_id = response_dict.get("req_id")
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if "choices" in response_dict:
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for i in range(len(response_dict["choices"])):
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response_dict["token"] = self.ids2tokens(response_dict["choices"][i]["token_ids"], req_id)
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return response_dict
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token_ids = response_dict.get("token_ids", [])
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response_dict["token"] = self.ids2tokens(token_ids, response_dict["req_id"])
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response_dict["usage"] = {"completion_tokens" : response_dict["send_idx"] + 1}
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if is_end:
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response_dict["tokens_all"] = self.clear_request_status(req_id)
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return response_dict
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def text2ids(self, text):
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"""
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text to token ids
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Args:
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text (str): text
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Returns:
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List[int]: token ids list
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"""
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if self.config.use_hf_tokenizer:
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tokens = self.tokenizer(
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text,
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return_tensors="np",
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padding=True,
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truncation=True,
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)
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else:
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if self.tokenizer.chat_template is not None:
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text = [text] if isinstance(text, str) else text
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text = [self.tokenizer.apply_chat_template(sentence, tokenize=False) for sentence in text]
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tokens = self.tokenizer(
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text,
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return_tensors="np",
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padding=True,
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truncation=True,
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max_length=self.src_length,
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add_special_tokens=self.tokenizer.chat_template is None,
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)
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return tokens["input_ids"][0]
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def messages2ids(self, messages):
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"""
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Convert multi-turn messages into ID sequences.
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Args:
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messages (List[List[Dict[str, Any]]]): multi-turn messages.
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Returns:
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List[int]: ID sequences
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"""
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return
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def ids2tokens(self, token_id, task_id):
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"""
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token ids to strings
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Args:
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token_ids (List[int]): token ids
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task_id (str): task id
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Returns:
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List[str]: strings
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"""
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if self.config.use_hf_tokenizer:
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if task_id not in self.decode_status:
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# history token ids & history token strings & befer decode str
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self.decode_status[task_id] = [[], [], ""]
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previous_token_ids = self.decode_status[task_id][0]
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decode_str = self.tokenizer.batch_decode([previous_token_ids + token_id],
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skip_special_tokens=True,
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clean_up_tokenization_spaces=False)
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if isinstance(decode_str, list) and len(decode_str):
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new_str = decode_str[0].replace(self.decode_status[task_id][2], "", 1)
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self.decode_status[task_id][1].append(new_str)
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self.decode_status[task_id][2] = decode_str[0]
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else:
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new_str = ""
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self.decode_status[task_id][0] += token_id
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return new_str
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else:
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if task_id not in self.decode_status:
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# prefix offset & read offset & history token ids & history token strings
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self.decode_status[task_id] = [0, 0, [], []]
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prefix_offset = self.decode_status[task_id][0]
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read_offset = self.decode_status[task_id][1]
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previous_token_ids = self.decode_status[task_id][2]
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decode_str, prefix_offset, read_offset = self.tokenizer.decode_token(
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previous_token_ids + token_id, prefix_offset, read_offset)
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self.decode_status[task_id][0] = prefix_offset
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self.decode_status[task_id][1] = read_offset
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self.decode_status[task_id][2] += token_id
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self.decode_status[task_id][3].append(decode_str)
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return decode_str
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def _load_tokenizer(self):
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"""
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load tokenizer
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Returns:
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tokenizer (AutoTokenizer)
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"""
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if self.config.use_hf_tokenizer:
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from transformers import AutoTokenizer
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return AutoTokenizer.from_pretrained(self.config.model_dir, use_fast=False)
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else:
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from paddlenlp.transformers import AutoTokenizer
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return AutoTokenizer.from_pretrained(self.config.model_dir)
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def clear_request_status(self, task_id):
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"""
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clear request status
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Args:
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task_id (str): task id
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Returns:
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results_all (str): all token strings
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"""
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results_all = ""
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if task_id in self.decode_status:
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if self.config.use_hf_tokenizer:
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results_all = self.decode_status[task_id][2]
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else:
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results_all = "".join(self.decode_status[task_id][3])
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del self.decode_status[task_id]
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return results_all
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def get_eos_tokens_lens(self):
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"""
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get eos_token_id lens
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Returns:
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int: eos_token_id lens
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"""
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return len(get_eos_token_id(self.tokenizer, self.config.generation_config))
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def get_eos_tokens(self):
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"""
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get all eos_token_id
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Returns:
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List[int]: eos_token_id list
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"""
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return get_eos_token_id(self.tokenizer, self.config.generation_config)
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def get_pad_id(self):
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"""
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get pad_token_id, if not pad_token_id, use eos_token
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Returns:
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int: pad_token_id
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"""
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if isinstance(self.tokenizer, (LlamaTokenizer, Llama3Tokenizer)) and not self.tokenizer.pad_token_id:
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return self.tokenizer.eos_token
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return self.tokenizer.pad_token_id
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