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* [Sync] Update to latest code * Add new code files * Add new code files * update code * Try to fix build.sh * Try to fix build.sh * Update code * Update requirements.txt * Update code --------- Co-authored-by: Jiang-Jia-Jun <jiangjiajun@baidu.com>
447 lines
18 KiB
Python
447 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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import numpy as np
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from paddleformers.generation import GenerationConfig
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from fastdeploy import envs
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from fastdeploy.utils import data_processor_logger
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from fastdeploy.input.ernie_tokenizer import ErnieBotTokenizer
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from fastdeploy.input.text_processor import BaseDataProcessor
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_SAMPLING_EPS = 1e-5
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class ErnieProcessor(BaseDataProcessor):
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"""
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初始化模型实例。
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Args:
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model_name_or_path (str): 模型名称或路径。
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Attributes:
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model_name_or_path (str): 存储模型名称或路径。
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decode_status (dict): 存储解码状态信息。
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tokenizer (object): 存储分词器实例。
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eos_token_ids (list): 存储结束符号的token ID列表。
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eos_token_id_len (int): 存储结束符号的token ID列表的长度。
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pad_token_id (int): 存储填充符号的token ID。
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"""
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def __init__(self, model_name_or_path, reasoning_parser_obj=None):
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self.model_name_or_path = model_name_or_path
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data_processor_logger.info(f"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.thinking_parser_dict = dict()
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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} \
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{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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self.eos_token_ids = [self.tokenizer.eos_token_id]
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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.reasoning_parser = None
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if reasoning_parser_obj:
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self.reasoning_parser = reasoning_parser_obj(self.tokenizer)
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def _init_config(self):
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self.use_hf_tokenizer = int(envs.FD_USE_HF_TOKENIZER) == 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, so it will not use "
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f"generation_config field in the model config, details={e}")
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self.generation_config = None
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def process_request(self, request, max_model_len=None, **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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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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request = self._apply_default_parameters(request)
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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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system = request.get("system")
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if request.prompt is None and request.messages is None:
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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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if request.prompt is not None or not request.raw_request:
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prompt = request.prompt if request.prompt is not None else request.messages[0]
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prompt = prompt[0] if isinstance(prompt, list) else prompt
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tokens = self.tokenizer.tokenize(prompt)
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token_ids = self.tokenizer.convert_tokens_to_ids(tokens)
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request.prompt_token_ids = token_ids
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data_processor_logger.info(f"req_id:{request.request_id}, tokens:{tokens}, token_ids: {token_ids}")
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else:
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request.prompt_token_ids = self.messages2ids(request.to_dict())
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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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if request.get("max_tokens") is None:
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request.set("max_tokens",
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max(1, max_model_len - len(request.prompt_token_ids)))
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if request.get("temperature") < _SAMPLING_EPS:
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# zero temperature is equivalent to greedy sampling
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request.set("temperature", 1)
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data_processor_logger.info(f"Processed request {request}")
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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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request = self._apply_default_parameters(request)
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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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system = request.get("system")
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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 request.get('prompt') is None and request.get(
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'messages') is None:
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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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if request.get('prompt'):
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prompt = request.get('prompt')
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prompt = prompt[0] if isinstance(prompt, list) else prompt
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tokens = self.tokenizer.tokenize(prompt)
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token_ids = self.tokenizer.convert_tokens_to_ids(tokens)
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request['prompt_token_ids'] = token_ids
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req_id = request.get("request_id", None)
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data_processor_logger.info(
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f"req_id:{req_id}, tokens:{tokens}, token_ids: {token_ids}"
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)
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else:
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request['prompt_token_ids'] = self.messages2ids(request)
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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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if request.get("max_tokens") is None:
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request["max_tokens"] = max(
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1, max_model_len - len(request['prompt_token_ids']))
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if request.get("temperature") < _SAMPLING_EPS:
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# zero temperature is equivalent to greedy sampling
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request["temperature"] = 1
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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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req_id = response_dict.request_id
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token_ids = response_dict.outputs.token_ids
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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 token_ids[-1] == self.tokenizer.eos_token_id:
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token_ids = token_ids[:-1]
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full_text = self.tokenizer.decode(token_ids)
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if self.reasoning_parser:
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reasoning_content, text = self.reasoning_parser.extract_reasoning_content(
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full_text, response_dict)
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response_dict.outputs.text = text
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response_dict.outputs.reasoning_content = reasoning_content
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else:
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response_dict.outputs.text = full_text
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data_processor_logger.info(f"req_id:{req_id}, token)ids: {token_ids}")
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if response_dict.outputs.text == "" and \
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response_dict.outputs.reasoning_content == "" and \
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response_dict.outputs.tool_call_content == []:
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return None
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return response_dict
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def process_response_dict(self, response_dict, stream, **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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if stream:
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return self.process_response_dict_streaming(
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response_dict, **kwargs)
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else:
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return self.process_response_dict_normal(response_dict, **kwargs)
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def process_response_dict_normal(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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token_ids = response_dict["outputs"]["token_ids"]
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is_end = response_dict["finished"]
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req_id = response_dict["request_id"]
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if is_end and len(token_ids) > 0:
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if token_ids[-1] == self.tokenizer.eos_token_id:
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token_ids = token_ids[:-1]
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delta_text, _, previous_texts = self.ids2tokens(token_ids, req_id)
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if is_end:
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full_text = previous_texts + delta_text
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if self.reasoning_parser:
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reasoning_content, text = self.reasoning_parser.extract_reasoning_content(
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full_text, response_dict)
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response_dict["outputs"]["text"] = text
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response_dict["outputs"][
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"reasoning_content"] = reasoning_content
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else:
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response_dict["outputs"]["text"] = full_text
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data_processor_logger.info(
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f"req_id:{req_id}, decode_status: {self.decode_status[req_id]}"
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)
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del self.decode_status[req_id]
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return response_dict
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def process_response_dict_streaming(self, response_dict, **kwargs):
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"""
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Preprocess the response streaming
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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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enable_thinking = kwargs.get("enable_thinking")
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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 and len(token_ids) > 0:
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if token_ids[-1] == self.tokenizer.eos_token_id:
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token_ids = token_ids[:-1]
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delta_text, previous_token_ids, previous_texts = self.ids2tokens(
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token_ids, req_id)
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if enable_thinking and self.reasoning_parser:
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reasoning_content, text = self.reasoning_parser.extract_reasoning_content_streaming(
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previous_texts, previous_texts + delta_text, delta_text,
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previous_token_ids, previous_token_ids + token_ids, token_ids)
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response_dict["outputs"]["text"] = text
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response_dict["outputs"]["reasoning_content"] = reasoning_content
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else:
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response_dict["outputs"]["text"] = delta_text
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if is_end:
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data_processor_logger.info(
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f"req_id:{req_id}, decode_status: {self.decode_status[req_id]}"
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)
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del self.decode_status[req_id]
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return response_dict
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def messages2ids(self, request_or_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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request_or_messages: Either a request dict containing 'messages' field,
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or a list of message dicts directly
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Returns:
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List of token IDs as strings (converted from token objects)
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"""
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if self.tokenizer.chat_template is None:
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raise ValueError("This model does not support chat_template.")
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spliced_message = self.tokenizer.apply_chat_template(
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request_or_messages,
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tokenize=False,
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split_special_tokens=False,
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add_special_tokens=False)
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req_id = None
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if isinstance(request_or_messages, dict):
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req_id = request_or_messages.get("request_id", None)
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tokens = self.tokenizer.tokenize(spliced_message)
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token_ids = self.tokenizer.convert_tokens_to_ids(tokens)
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data_processor_logger.info(
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f"req_id:{req_id}, tokens:{tokens}, token_ids: {token_ids}")
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return token_ids
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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 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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previous_texts = self.decode_status[task_id][3]
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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] += decode_str
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return decode_str, previous_token_ids, previous_texts
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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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vocab_file_names = [
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"tokenizer.model", "spm.model", "ernie_token_100k.model"
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]
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for i in range(len(vocab_file_names)):
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if os.path.exists(
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os.path.join(self.model_name_or_path,
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vocab_file_names[i])):
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ErnieBotTokenizer.resource_files_names[
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"vocab_file"] = vocab_file_names[i]
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break
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self.tokenizer = ErnieBotTokenizer.from_pretrained(
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self.model_name_or_path)
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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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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)
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data_processor_logger.debug(
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f"processed stop_seqs: {stop_seqs}, {stop_seqs_len}")
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return stop_seqs, stop_seqs_len
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