mirror of
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534 lines
18 KiB
Python
534 lines
18 KiB
Python
"""
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# Copyright (c) 2025 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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"""
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import os
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from abc import ABC, abstractmethod
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import numpy as np
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from paddlenlp.generation import GenerationConfig
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from paddlenlp.transformers import Llama3Tokenizer, LlamaTokenizer
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from fastdeploy.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(
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self.tokenizer.bos_token)
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self.tokenizer.cls_token_id = self.tokenizer._convert_token_to_id(
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self.tokenizer.cls_token)
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self.tokenizer.sep_token_id = self.tokenizer._convert_token_to_id(
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self.tokenizer.sep_token)
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self.tokenizer.eos_token_id = self.tokenizer._convert_token_to_id(
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self.tokenizer.eos_token)
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self.tokenizer.mask_token_id = self.tokenizer._convert_token_to_id(
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self.tokenizer.mask_token)
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data_processor_logger.info((
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f"tokenizer information: 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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))
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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, max_model_len=None):
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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_id, 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_id (List[int]): token id
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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, model_name_or_path):
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"""
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Initializes the DecodeStatus object.
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Args:
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model_name_or_path (str): The name or path of the pre-trained model to be loaded.
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Can also be a path to a directory containing the pre-trained model file.
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Returns:
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None.
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Raises:
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None.
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"""
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self.model_name_or_path = model_name_or_path
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self._init_config()
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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(
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f"tokenizer information: bos_token is {self.tokenizer.bos_token}, {self.tokenizer.bos_token_id}, \
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eos_token is {self.tokenizer.eos_token}, {self.tokenizer.eos_token_id} "
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)
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from paddlenlp.trl.llm_utils import get_eos_token_id
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self.eos_token_ids = get_eos_token_id(self.tokenizer,
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self.generation_config)
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self.eos_token_id_len = len(self.eos_token_ids)
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self.pad_token_id = self.get_pad_id()
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self.tokenizer.pad_token_id = self.pad_token_id
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def _init_config(self):
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"""
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初始化配置，包括模型名称、使用Hugging Face Tokenizer等。
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Args:
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无参数，但是会从环境变量中获取一些配置信息。
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Returns:
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无返回值，直接修改了类的属性。
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Raises:
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无异常抛出。
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"""
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self.use_hf_tokenizer = int(os.getenv("USE_HF_TOKENIZER", "0")) == 1
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# Generation config
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try:
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self.generation_config = GenerationConfig.from_pretrained(
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self.model_name_or_path)
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except Exception as e:
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data_processor_logger.warning(
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f"Can't find generation config: {e}, so it will not use generation_config field in the model config"
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)
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self.generation_config = None
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def process_request(self, request, max_model_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 request.get("eos_token_ids") is None or len(
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request.eos_token_ids) == 0:
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request.eos_token_ids = self.eos_token_ids
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stop_sequences = request.get("stop", [])
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if stop_sequences is not None and len(stop_sequences) != 0:
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stop_seqs, stop_seqs_len = self.update_stop_seq(stop_sequences)
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request.set("stop_token_ids", stop_seqs)
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request.set("stop_seqs_len", stop_seqs_len)
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if request.prompt_token_ids is None or len(
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request.prompt_token_ids) == 0:
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if request.prompt is not None:
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request.prompt_token_ids = self.text2ids(
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request.prompt, max_model_len, request.raw_request)
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elif request.messages is not None:
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if self.tokenizer.chat_template is None:
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raise ValueError(
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"This model does not support chat_template.")
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request.prompt_token_ids = self.messages2ids(request.messages)
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else:
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raise ValueError(
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f"The request should have `input_ids`, `text` or `messages`: {request}."
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)
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if max_model_len is not None and len(
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request.prompt_token_ids) > max_model_len:
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request.prompt_token_ids = request.prompt_token_ids[:
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max_model_len -
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1]
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return request
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def process_request_dict(self, request, max_model_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 not request.get('eos_token_ids'):
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request['eos_token_ids'] = self.eos_token_ids
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# 处理stop_sequences
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stop_sequences = request.get('stop', [])
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if stop_sequences:
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stop_seqs, stop_seqs_len = self.update_stop_seq(stop_sequences)
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request['stop_token_ids'] = stop_seqs
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request['stop_seqs_len'] = stop_seqs_len
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# 处理prompt_token_ids
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if not request.get('prompt_token_ids'):
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if 'prompt' in request:
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raw_request = request.get('raw_request', True)
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request['prompt_token_ids'] = self.text2ids(
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request['prompt'], max_model_len, raw_request).tolist()
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elif 'messages' in request:
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if self.tokenizer.chat_template is None:
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raise ValueError(
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"This model does not support chat_template.")
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request['prompt_token_ids'] = self.messages2ids(
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request['messages']).tolist()
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else:
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raise ValueError(
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f"Request must contain 'prompt_token_ids', 'prompt', or 'messages': {request}"
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)
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# 截断超过长度限制的prompt
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if max_model_len is not None and len(
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request['prompt_token_ids']) > max_model_len:
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request['prompt_token_ids'] = request[
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'prompt_token_ids'][:max_model_len - 1]
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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.finished
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req_id = response_dict.request_id
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token_ids = response_dict.outputs.token_ids
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response_dict.outputs.text = self.ids2tokens(token_ids, req_id)
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response_dict.usage = {
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"completion_tokens": response_dict.outputs.index + 1
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}
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if is_end:
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self.clear_request_status(req_id)
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data_processor_logger.debug(
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"Request id: {} has been completed.".format(token_ids))
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response_dict.outputs.text = self.ids2tokens(token_ids, req_id)
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self.clear_request_status(req_id)
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return response_dict
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def process_response_dict(self, response_dict, stream=True):
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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["finished"]
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req_id = response_dict["request_id"]
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token_ids = response_dict["outputs"]["token_ids"]
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if is_end:
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data_processor_logger.debug(
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"Request id: {} has been completed.".format(token_ids))
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full_text = self.clear_request_status(req_id)
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if not stream:
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response_dict["outputs"]["text"] = full_text
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else:
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response_dict["outputs"]["text"] = ""
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else:
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response_dict["outputs"]["text"] = self.ids2tokens(
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token_ids, req_id)
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return response_dict
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def text2ids(self, text, max_model_len, raw_request=True):
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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.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 not raw_request or self.tokenizer.chat_template is None:
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text = [text] if isinstance(text, str) else text
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chat_template = False
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elif self.tokenizer.chat_template is not None:
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text = [text] if isinstance(text, str) else text
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text = [
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self.tokenizer.apply_chat_template(sentence,
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tokenize=False)
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for sentence in text
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]
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chat_template = True
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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=max_model_len,
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add_special_tokens=chat_template,
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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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message_result = self.tokenizer.apply_chat_template(
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messages, return_tensors="pd")
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return np.array(message_result["input_ids"][0])
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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.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(
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[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],
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"", 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.use_hf_tokenizer:
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from transformers import AutoTokenizer
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return AutoTokenizer.from_pretrained(self.model_name_or_path,
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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.model_name_or_path,
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padding_side="left",
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use_fast=True)
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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.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_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,
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(LlamaTokenizer,
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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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def pad_batch_data(self,
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insts,
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pad_id=0,
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return_seq_len=False,
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return_array=True,
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pad_style="right"):
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"""Pad the instances to the max sequence length in batch."""
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if len(insts) == 0:
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padded_insts = np.array([[]],
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dtype=np.int64) if return_array else [[]]
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if return_seq_len:
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seq_len = np.array([], dtype=np.int64) if return_array else []
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return padded_insts, seq_len
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return padded_insts
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max_len = max(map(len, insts))
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if pad_style == "left":
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padded_insts = [[pad_id] * (max_len - len(inst)) + list(inst)
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for inst in insts]
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else:
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padded_insts = [
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list(inst) + [pad_id] * (max_len - len(inst)) for inst in insts
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]
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if return_array:
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padded_insts = np.array(padded_insts,
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dtype=np.int64).reshape([-1, max_len])
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if return_seq_len:
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seq_len = [len(inst) for inst in insts]
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if return_array:
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seq_len = np.array(seq_len, dtype=np.int64).reshape(-1, 1)
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return padded_insts, seq_len
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return padded_insts
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def update_stop_seq(self, stop_sequences):
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"""
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Update stop sequences from request.
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"""
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stop_seqs = []
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for seq in stop_sequences:
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if seq != self.tokenizer.eos_token_id:
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stop_seqs.append(
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self.tokenizer.convert_tokens_to_ids(
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self.tokenizer.tokenize(seq)))
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stop_seqs, stop_seqs_len = self.pad_batch_data(stop_seqs,
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pad_id=-1,
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return_seq_len=True,
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return_array=False)
|
||
data_processor_logger.debug(
|
||
f"processed stop_seqs: {stop_seqs}, {stop_seqs_len}")
|
||
return stop_seqs, stop_seqs_len
|