""" # Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved. # # 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. """ import os from abc import ABC, abstractmethod import numpy as np from paddlenlp.generation import GenerationConfig from paddlenlp.transformers import Llama3Tokenizer, LlamaTokenizer from fastdeploy.utils import data_processor_logger class BaseDataProcessor(ABC): """base class for data processor""" def __init__(self): """ Returns: None """ self.tokenizer = self._load_tokenizer() self.tokenizer.bos_token_id = self.tokenizer._convert_token_to_id( self.tokenizer.bos_token) self.tokenizer.cls_token_id = self.tokenizer._convert_token_to_id( self.tokenizer.cls_token) self.tokenizer.sep_token_id = self.tokenizer._convert_token_to_id( self.tokenizer.sep_token) self.tokenizer.eos_token_id = self.tokenizer._convert_token_to_id( self.tokenizer.eos_token) self.tokenizer.mask_token_id = self.tokenizer._convert_token_to_id( self.tokenizer.mask_token) data_processor_logger.info(( f"tokenizer information: bos_token is {self.tokenizer.bos_token}, {self.tokenizer.bos_token_id}, ", f"cls_token is {self.tokenizer.cls_token}, {self.tokenizer.cls_token_id}, " f"sep_token is {self.tokenizer.sep_token}, {self.tokenizer.sep_token_id}, " f"eos_token is {self.tokenizer.eos_token}, {self.tokenizer.eos_token_id}, " f"mask_token is {self.tokenizer.mask_token}, {self.tokenizer.mask_token_id}" )) @abstractmethod def process_request(self, request, **kwargs): """ Preprocess the request Args: request (Dict): may contain text and messages fields **kwargs: others Returns: bool: Whether preprocessing is successful str: error message """ raise NotImplementedError @abstractmethod def process_response(self, response_dict): """ Preprocess the response Args: response_dict (Dict): response for engine, contain ids fields Returns: Dict: response contain text fields """ raise NotImplementedError def text2ids(self, text, max_model_len=None): """ text to token ids Args: text (str): text Returns: List[int]: token ids list """ raise NotImplementedError def messages2ids(self, messages): """ Convert multi-turn messages into ID sequences. Args: messages (List[List[Dict[str, Any]]]): multi-turn messages. Returns: List[int]: ID sequences """ raise NotImplementedError def ids2tokens(self, token_id, task_id=None): """ token ids to strings Args: token_id (List[int]): token id task_id (str): task id Returns: List[str]: strings """ raise NotImplementedError @abstractmethod def _load_tokenizer(self): """ load tokenizer Returns: tokenizer (AutoTokenizer) """ raise NotImplementedError class DataProcessor(BaseDataProcessor): def __init__(self, model_name_or_path): """ Initializes the DecodeStatus object. Args: model_name_or_path (str): The name or path of the pre-trained model to be loaded. Can also be a path to a directory containing the pre-trained model file. Returns: None. Raises: None. """ self.model_name_or_path = model_name_or_path self._init_config() self.decode_status = dict() self.tokenizer = self._load_tokenizer() data_processor_logger.info( f"tokenizer information: bos_token is {self.tokenizer.bos_token}, {self.tokenizer.bos_token_id}, \ eos_token is {self.tokenizer.eos_token}, {self.tokenizer.eos_token_id} " ) from paddlenlp.trl.llm_utils import get_eos_token_id self.eos_token_ids = get_eos_token_id(self.tokenizer, self.generation_config) self.eos_token_id_len = len(self.eos_token_ids) self.pad_token_id = self.get_pad_id() self.tokenizer.pad_token_id = self.pad_token_id def _init_config(self): """ 初始化配置，包括模型名称、使用Hugging Face Tokenizer等。 Args: 无参数，但是会从环境变量中获取一些配置信息。 Returns: 无返回值，直接修改了类的属性。 Raises: 无异常抛出。 """ self.use_hf_tokenizer = int(os.getenv("USE_HF_TOKENIZER", "0")) == 1 # Generation config try: self.generation_config = GenerationConfig.from_pretrained( self.model_name_or_path) except Exception as e: data_processor_logger.warning( f"Can't find generation config: {e}, so it will not use generation_config field in the model config" ) self.generation_config = None def process_request(self, request, max_model_len=None): """ Preprocess the request Args: request (Dict): may contain text and messages fields Returns: bool: Whether preprocessing is successful str: error message """ if request.get("eos_token_ids") is None or len( request.eos_token_ids) == 0: request.eos_token_ids = self.eos_token_ids stop_sequences = request.get("stop", []) if stop_sequences is not None and len(stop_sequences) != 0: stop_seqs, stop_seqs_len = self.update_stop_seq(stop_sequences) request.set("stop_token_ids", stop_seqs) request.set("stop_seqs_len", stop_seqs_len) if request.prompt_token_ids is None or len( request.prompt_token_ids) == 0: if request.prompt is not None: request.prompt_token_ids = self.text2ids( request.prompt, max_model_len, request.raw_request) elif request.messages is not None: if self.tokenizer.chat_template is None: raise ValueError( "This model does not support chat_template.") request.prompt_token_ids = self.messages2ids(request.messages) else: raise ValueError( f"The request should have `input_ids`, `text` or `messages`: {request}." ) if max_model_len is not None and len( request.prompt_token_ids) > max_model_len: request.prompt_token_ids = request.prompt_token_ids[: max_model_len - 1] return request def process_request_dict(self, request, max_model_len=None): """ Preprocess the request Args: request (Dict): may contain text and messages fields Returns: bool: Whether preprocessing is successful str: error message """ if not request.get('eos_token_ids'): request['eos_token_ids'] = self.eos_token_ids # 处理stop_sequences stop_sequences = request.get('stop', []) if stop_sequences: stop_seqs, stop_seqs_len = self.update_stop_seq(stop_sequences) request['stop_token_ids'] = stop_seqs request['stop_seqs_len'] = stop_seqs_len # 处理prompt_token_ids if not request.get('prompt_token_ids'): if 'prompt' in request: raw_request = request.get('raw_request', True) request['prompt_token_ids'] = self.text2ids( request['prompt'], max_model_len, raw_request).tolist() elif 'messages' in request: if self.tokenizer.chat_template is None: raise ValueError( "This model does not support chat_template.") request['prompt_token_ids'] = self.messages2ids( request['messages']).tolist() else: raise ValueError( f"Request must contain 'prompt_token_ids', 'prompt', or 'messages': {request}" ) # 截断超过长度限制的prompt if max_model_len is not None and len( request['prompt_token_ids']) > max_model_len: request['prompt_token_ids'] = request[ 'prompt_token_ids'][:max_model_len - 1] return request def process_response(self, response_dict, **kwargs): """ Preprocess the response Args: response_dict (Dict): response for engine, contain ids fields Returns: Dict: response contain text fields """ is_end = response_dict.finished req_id = response_dict.request_id token_ids = response_dict.outputs.token_ids response_dict.outputs.text = self.ids2tokens(token_ids, req_id) response_dict.usage = { "completion_tokens": response_dict.outputs.index + 1 } if is_end: self.clear_request_status(req_id) data_processor_logger.debug( "Request id: {} has been completed.".format(token_ids)) response_dict.outputs.text = self.ids2tokens(token_ids, req_id) self.clear_request_status(req_id) return response_dict def process_response_dict(self, response_dict, stream=True): """ Preprocess the response Args: response_dict (Dict): response for engine, contain ids fields Returns: Dict: response contain text fields """ is_end = response_dict["finished"] req_id = response_dict["request_id"] token_ids = response_dict["outputs"]["token_ids"] if is_end: data_processor_logger.debug( "Request id: {} has been completed.".format(token_ids)) full_text = self.clear_request_status(req_id) if not stream: response_dict["outputs"]["text"] = full_text else: response_dict["outputs"]["text"] = "" else: response_dict["outputs"]["text"] = self.ids2tokens( token_ids, req_id) return response_dict def text2ids(self, text, max_model_len, raw_request=True): """ text to token ids Args: text (str): text Returns: List[int]: token ids list """ if self.use_hf_tokenizer: tokens = self.tokenizer( text, return_tensors="np", padding=True, truncation=True, ) else: if not raw_request or self.tokenizer.chat_template is None: text = [text] if isinstance(text, str) else text chat_template = False elif self.tokenizer.chat_template is not None: text = [text] if isinstance(text, str) else text text = [ self.tokenizer.apply_chat_template(sentence, tokenize=False) for sentence in text ] chat_template = True tokens = self.tokenizer( text, return_tensors="np", padding=True, truncation=True, max_length=max_model_len, add_special_tokens=chat_template, ) return tokens["input_ids"][0] def messages2ids(self, messages): """ Convert multi-turn messages into ID sequences. Args: messages (List[List[Dict[str, Any]]]): multi-turn messages. Returns: List[int]: ID sequences """ message_result = self.tokenizer.apply_chat_template( messages, return_tensors="pd") return np.array(message_result["input_ids"][0]) def ids2tokens(self, token_id, task_id): """ token ids to strings Args: token_ids (List[int]): token ids task_id (str): task id Returns: List[str]: strings """ if self.use_hf_tokenizer: if task_id not in self.decode_status: # history token ids & history token strings & befer decode str self.decode_status[task_id] = [[], [], ""] previous_token_ids = self.decode_status[task_id][0] decode_str = self.tokenizer.batch_decode( [previous_token_ids + token_id], skip_special_tokens=True, clean_up_tokenization_spaces=False) if isinstance(decode_str, list) and len(decode_str): new_str = decode_str[0].replace(self.decode_status[task_id][2], "", 1) self.decode_status[task_id][1].append(new_str) self.decode_status[task_id][2] = decode_str[0] else: new_str = "" self.decode_status[task_id][0] += token_id return new_str else: if task_id not in self.decode_status: # prefix offset & read offset & history token ids & history token strings self.decode_status[task_id] = [0, 0, [], []] prefix_offset = self.decode_status[task_id][0] read_offset = self.decode_status[task_id][1] previous_token_ids = self.decode_status[task_id][2] decode_str, prefix_offset, read_offset = self.tokenizer.decode_token( previous_token_ids + token_id, prefix_offset, read_offset) self.decode_status[task_id][0] = prefix_offset self.decode_status[task_id][1] = read_offset self.decode_status[task_id][2] += token_id self.decode_status[task_id][3].append(decode_str) return decode_str def _load_tokenizer(self): """ load tokenizer Returns: tokenizer (AutoTokenizer) """ if self.use_hf_tokenizer: from transformers import AutoTokenizer return AutoTokenizer.from_pretrained(self.model_name_or_path, use_fast=False) else: from paddlenlp.transformers import AutoTokenizer return AutoTokenizer.from_pretrained(self.model_name_or_path, padding_side="left", use_fast=True) def clear_request_status(self, task_id): """ clear request status Args: task_id (str): task id Returns: results_all (str): all token strings """ results_all = "" if task_id in self.decode_status: if self.use_hf_tokenizer: results_all = self.decode_status[task_id][2] else: results_all = "".join(self.decode_status[task_id][3]) del self.decode_status[task_id] return results_all def get_pad_id(self): """ get pad_token_id, if not pad_token_id, use eos_token Returns: int: pad_token_id """ if isinstance(self.tokenizer, (LlamaTokenizer, Llama3Tokenizer)) and not self.tokenizer.pad_token_id: return self.tokenizer.eos_token return self.tokenizer.pad_token_id def pad_batch_data(self, insts, pad_id=0, return_seq_len=False, return_array=True, pad_style="right"): """Pad the instances to the max sequence length in batch.""" if len(insts) == 0: padded_insts = np.array([[]], dtype=np.int64) if return_array else [[]] if return_seq_len: seq_len = np.array([], dtype=np.int64) if return_array else [] return padded_insts, seq_len return padded_insts max_len = max(map(len, insts)) if pad_style == "left": padded_insts = [[pad_id] * (max_len - len(inst)) + list(inst) for inst in insts] else: padded_insts = [ list(inst) + [pad_id] * (max_len - len(inst)) for inst in insts ] if return_array: padded_insts = np.array(padded_insts, dtype=np.int64).reshape([-1, max_len]) if return_seq_len: seq_len = [len(inst) for inst in insts] if return_array: seq_len = np.array(seq_len, dtype=np.int64).reshape(-1, 1) return padded_insts, seq_len return padded_insts def update_stop_seq(self, stop_sequences): """ Update stop sequences from request. """ stop_seqs = [] for seq in stop_sequences: if seq != self.tokenizer.eos_token_id: stop_seqs.append( self.tokenizer.convert_tokens_to_ids( self.tokenizer.tokenize(seq))) stop_seqs, stop_seqs_len = self.pad_batch_data(stop_seqs, pad_id=-1, return_seq_len=True, return_array=False) data_processor_logger.debug( f"processed stop_seqs: {stop_seqs}, {stop_seqs_len}") return stop_seqs, stop_seqs_len