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88297240e7
* [feat] completion api supports passing input token ids in either `prompt` or `prompt_token_ids` * [fix] update comment * [fix] fix type error * [test] add a unittest file for serving api test * [test] try to fix ci error * [chore] rename test function names * [test] try to fix ci error * [test] try to fix ci error * [test] add tests for qwen
558 lines
24 KiB
Python
558 lines
24 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.input.ernie4_5_tokenizer import Ernie4_5Tokenizer
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from fastdeploy.input.text_processor import BaseDataProcessor
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from fastdeploy.utils import data_processor_logger
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_SAMPLING_EPS = 1e-5
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class Ernie4_5Processor(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, tool_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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# Generation config
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try:
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self.generation_config = GenerationConfig.from_pretrained(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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)
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self.generation_config = None
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self.decode_status = dict()
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self.tool_parser_dict = 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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from paddleformers.trl.llm_utils import get_eos_token_id
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self.eos_token_ids = get_eos_token_id(self.tokenizer, 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.reasoning_parser = None
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self.tool_parser_obj = tool_parser_obj
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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 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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data_processor_logger.info(f"Start processing request: {request}")
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request.chat_template = kwargs.get("chat_template")
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request = self._apply_default_parameters(request)
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if request.get("eos_token_ids") is None or len(request.eos_token_ids) == 0:
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request.eos_token_ids = self.eos_token_ids
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# processing stop_sequences
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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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# processing bad_words
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bad_words = request.get("bad_words")
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bad_words_token_ids = request.get("bad_words_token_ids")
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if bad_words:
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bad_words_token_ids = self.update_bad_words(bad_words, bad_words_token_ids)
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request["bad_words_token_ids"] = bad_words_token_ids
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# processing prompt_token_ids
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if request.prompt_token_ids is None or len(request.prompt_token_ids) == 0:
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if request.prompt is not None:
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# prompt = request.prompt if request.prompt is not None else request.messages[0]
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prompt = request.prompt
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assert isinstance(prompt, str) or (
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isinstance(prompt, list) and all([isinstance(t, int) for t in prompt])
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), f"prompt must be a string or a list of integers, but got {type(prompt)}"
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if isinstance(prompt, list): # if prompt is a token id list
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request.prompt_token_ids = prompt
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else:
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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.debug(
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f"request_ids: {request.request_id}, prompt: {prompt}, tokens: {tokens}, token_ids: {token_ids}"
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)
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elif request.messages is not None:
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task = request.to_dict()
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chat_template_kwargs = kwargs.get("chat_template_kwargs")
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if chat_template_kwargs:
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if isinstance(chat_template_kwargs, dict):
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for k, v in chat_template_kwargs.items():
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if k not in task:
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task[k] = v
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else:
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raise ValueError("Invalid input: chat_template_kwargs must be a dict")
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request.prompt_token_ids = self.messages2ids(task)
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else:
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raise ValueError(f"The request should have `prompt_token_ids`, `prompt` or `messages`: {request}.")
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if len(request.prompt_token_ids) == 0:
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raise ValueError("Invalid input: prompt_token_ids must be a non-empty sequence of token IDs")
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# truncate prompts that exceed the length limit
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if max_model_len is not None and len(request.prompt_token_ids) > max_model_len:
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request.prompt_token_ids = request.prompt_token_ids[: max_model_len - 1]
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if request.get("max_tokens") is None:
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request.set("max_tokens", 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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if request.get("top_p") < _SAMPLING_EPS:
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request.set("top_p", _SAMPLING_EPS)
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if self.reasoning_parser and self.reasoning_parser.__class__.__name__ == "ErnieX1ReasoningParser":
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request.enable_thinking = True
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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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data_processor_logger.info(f"Start processing request dict: {request}")
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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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# processing 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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# processing bad_words
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bad_words = request.get("bad_words")
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bad_words_token_ids = request.get("bad_words_token_ids")
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if bad_words:
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bad_words_token_ids = self.update_bad_words(bad_words, bad_words_token_ids)
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request["bad_words_token_ids"] = bad_words_token_ids
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# processing prompt_token_ids
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if not request.get("prompt_token_ids"):
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if request.get("prompt"):
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prompt = request.get("prompt")
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assert isinstance(prompt, str) or (
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isinstance(prompt, list) and all([isinstance(t, int) for t in prompt])
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), f"prompt must be a string or a list of integers, but got {type(prompt)}"
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if isinstance(prompt, list): # if prompt is a token id list
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request["prompt_token_ids"] = prompt
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else:
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request["text_after_process"] = 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(f"req_id:{req_id}, tokens:{tokens}, token_ids: {token_ids}")
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elif request.get("messages"):
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chat_template_kwargs = request.get("chat_template_kwargs")
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if chat_template_kwargs:
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if isinstance(chat_template_kwargs, dict):
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for k, v in chat_template_kwargs.items():
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if k not in request:
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request[k] = v
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else:
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raise ValueError("Invalid input: chat_template_kwargs must be a dict")
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request["prompt_token_ids"] = self.messages2ids(request)
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else:
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raise ValueError(f"Request must contain 'prompt_token_ids', 'prompt', or 'messages': {request}")
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if len(request["prompt_token_ids"]) == 0:
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raise ValueError("Invalid input: prompt_token_ids must be a non-empty sequence of token IDs")
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# truncate prompts that exceed the length limit
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if max_model_len is not None and len(request["prompt_token_ids"]) > max_model_len:
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request["prompt_token_ids"] = request["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(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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if request.get("top_p") < _SAMPLING_EPS:
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request["top_p"] = _SAMPLING_EPS
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if self.reasoning_parser and self.reasoning_parser.__class__.__name__ == "ErnieX1ReasoningParser":
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request["enable_thinking"] = True
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data_processor_logger.info(f"Processed request dict: {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 = {"completion_tokens": response_dict.outputs.index + 1}
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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(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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if self.tool_parser_obj:
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tool_parser = self.tool_parser_obj(self.tokenizer)
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tool_call_info = tool_parser.extract_tool_calls(full_text, response_dict)
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if tool_call_info.tools_called:
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response_dict.outputs.tool_calls = tool_call_info.tool_calls
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response_dict.outputs.text = tool_call_info.content
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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 response_dict.outputs.reasoning_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(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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enable_thinking = kwargs.get("enable_thinking")
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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 and not kwargs.get("include_stop_str_in_output"):
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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 and (
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enable_thinking or self.reasoning_parser.__class__.__name__ == "ErnieX1ReasoningParser"
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):
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reasoning_content, text = self.reasoning_parser.extract_reasoning_content(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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if self.tool_parser_obj:
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tool_parser = self.tool_parser_obj(self.tokenizer)
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tool_call_info = tool_parser.extract_tool_calls(full_text, response_dict)
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if tool_call_info.tools_called:
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response_dict["outputs"]["tool_call"] = tool_call_info.tool_calls
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response_dict["outputs"]["text"] = tool_call_info.content
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response_dict["outputs"]["raw_prediction"] = full_text
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data_processor_logger.info(f"req_id:{req_id}, decode_status: {self.decode_status[req_id]}")
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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 and not kwargs.get("include_stop_str_in_output"):
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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(token_ids, req_id)
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response_dict["outputs"]["raw_prediction"] = delta_text
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if self.reasoning_parser and (
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enable_thinking or self.reasoning_parser.__class__.__name__ == "ErnieX1ReasoningParser"
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):
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reasoning_delta_message = self.reasoning_parser.extract_reasoning_content_streaming(
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previous_texts,
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previous_texts + delta_text,
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delta_text,
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previous_token_ids,
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previous_token_ids + token_ids,
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token_ids,
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)
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response_dict["outputs"]["delta_message"] = reasoning_delta_message
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if self.tool_parser_obj:
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if req_id not in self.tool_parser_dict:
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self.tool_parser_dict[req_id] = self.tool_parser_obj(self.tokenizer)
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tool_parser = self.tool_parser_dict[req_id]
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tool_call_delta_message = tool_parser.extract_tool_calls_streaming(
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previous_texts,
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previous_texts + delta_text,
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delta_text,
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previous_token_ids,
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previous_token_ids + token_ids,
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token_ids,
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response_dict,
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)
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if tool_call_delta_message is None or tool_call_delta_message.tool_calls:
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response_dict["outputs"]["delta_message"] = tool_call_delta_message
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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(f"req_id:{req_id}, decode_status: {self.decode_status[req_id]}")
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del self.decode_status[req_id]
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if req_id in self.tool_parser_dict:
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del self.tool_parser_dict[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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chat_template=request_or_messages.get("chat_template", None),
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)
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request_or_messages["text_after_process"] = spliced_message
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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(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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)
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self.decode_status[task_id][0] = prefix_offset
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self.decode_status[task_id][1] = read_offset
|
|
self.decode_status[task_id][2] += token_id
|
|
self.decode_status[task_id][3] += decode_str
|
|
|
|
return decode_str, previous_token_ids, previous_texts
|
|
|
|
def _load_tokenizer(self):
|
|
"""
|
|
load tokenizer
|
|
|
|
Returns:
|
|
tokenizer (AutoTokenizer)
|
|
"""
|
|
vocab_file_names = [
|
|
"tokenizer.model",
|
|
"spm.model",
|
|
"ernie_token_100k.model",
|
|
]
|
|
for i in range(len(vocab_file_names)):
|
|
if os.path.exists(os.path.join(self.model_name_or_path, vocab_file_names[i])):
|
|
Ernie4_5Tokenizer.resource_files_names["vocab_file"] = vocab_file_names[i]
|
|
break
|
|
self.tokenizer = Ernie4_5Tokenizer.from_pretrained(self.model_name_or_path)
|
|
|
|
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 = []
|
|
if isinstance(stop_sequences, str):
|
|
stop_sequences = [stop_sequences]
|
|
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
|
|
|
|
def process_logprob_response(self, token_ids, **kwargs):
|
|
full_text = self.tokenizer.decode(token_ids, **kwargs)
|
|
return full_text
|
|
|
|
def update_bad_words(self, bad_words, bad_words_token_ids):
|
|
"""Support bad words"""
|
|
|
|
token_ids = bad_words_token_ids
|
|
|
|
if token_ids is None:
|
|
token_ids = []
|
|
for bad_word in bad_words:
|
|
# To prohibit words both at the beginning
|
|
# and in the middle of text
|
|
# (related to add_prefix_space tokenizer parameter)
|
|
for add_prefix_space in [False, True]:
|
|
prefix = " " if add_prefix_space else ""
|
|
prompt = prefix + bad_word.lstrip()
|
|
prompt_token_ids = self.tokenizer.convert_tokens_to_ids(self.tokenizer.tokenize(prompt))
|
|
data_processor_logger.debug(f"processed bad_words: {prompt}, {prompt_token_ids}")
|
|
|
|
if len(prompt_token_ids) != 1:
|
|
if not add_prefix_space:
|
|
data_processor_logger.warning(
|
|
f"Skip bad_words: <{prompt}>."
|
|
f"Bad words should be a single token."
|
|
f"Got tokens: {prompt_token_ids}."
|
|
)
|
|
continue
|
|
|
|
if prompt_token_ids[0] > self.tokenizer.vocab_size:
|
|
if not add_prefix_space:
|
|
data_processor_logger.warning(
|
|
f"Skip bad_words: <{prompt}>."
|
|
f"All token id values should be satisfying:"
|
|
f" 0 <= token_id < {self.tokenizer.vocab_size}."
|
|
f"Got token: {prompt_token_ids}."
|
|
)
|
|
continue
|
|
|
|
if prompt_token_ids not in token_ids:
|
|
token_ids.extend(prompt_token_ids)
|
|
return token_ids
|