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* Update CascadeEvaluator * Update CascadeEvaluator * Update CascadeEvaluator * Update Config * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update
767 lines
30 KiB
Python
767 lines
30 KiB
Python
import json
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import os
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import random
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import re
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import time
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from concurrent.futures import ThreadPoolExecutor
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from threading import Lock
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from typing import Dict, List, Optional, Union
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import httpx
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import jieba
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import requests
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from tqdm import tqdm
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from opencompass.registry import MODELS
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from opencompass.utils.prompt import PromptList
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from .base_api import BaseAPIModel
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PromptType = Union[PromptList, str]
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OPENAI_API_BASE = os.path.join(
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os.environ.get('OPENAI_BASE_URL', 'https://api.openai.com/v1/'),
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'chat/completions',
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)
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OPENAISDK_API_BASE = os.environ.get('OPENAI_BASE_URL',
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'https://api.openai.com/v1/')
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O1_MODEL_LIST = ['o1', 'o3', 'o4']
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@MODELS.register_module()
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class OpenAI(BaseAPIModel):
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"""Model wrapper around OpenAI's models.
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Args:
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path (str): The name of OpenAI's model.
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max_seq_len (int): The maximum allowed sequence length of a model.
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Note that the length of prompt + generated tokens shall not exceed
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this value. Defaults to 2048.
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query_per_second (int): The maximum queries allowed per second
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between two consecutive calls of the API. Defaults to 1.
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retry (int): Number of retires if the API call fails. Defaults to 2.
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key (str or List[str]): OpenAI key(s). In particular, when it
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is set to "ENV", the key will be fetched from the environment
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variable $OPENAI_API_KEY, as how openai defaults to be. If it's a
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list, the keys will be used in round-robin manner. Defaults to
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'ENV'.
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org (str or List[str], optional): OpenAI organization(s). If not
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specified, OpenAI uses the default organization bound to each API
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key. If specified, the orgs will be posted with each request in
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round-robin manner. Defaults to None.
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meta_template (Dict, optional): The model's meta prompt
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template if needed, in case the requirement of injecting or
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wrapping of any meta instructions.
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openai_api_base (str): The base url of OpenAI's API. Defaults to
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'https://api.openai.com/v1/chat/completions'.
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openai_proxy_url (str, optional): An optional proxy url to use when
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connecting to OpenAI's API. When set to 'ENV', the url will be
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fetched from the environment variable $OPENAI_PROXY_URL.
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Defaults to None.
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mode (str, optional): The method of input truncation when input length
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exceeds max_seq_len. 'front','mid' and 'rear' represents the part
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of input to truncate. Defaults to 'none'.
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temperature (float, optional): What sampling temperature to use.
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If not None, will override the temperature in the `generate()`
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call. Defaults to None.
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tokenizer_path (str, optional): The path to the tokenizer. Use path if
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'tokenizer_path' is None, otherwise use the 'tokenizer_path'.
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Defaults to None.
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extra_body (Dict, optional): Add additional JSON properties to
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the request
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think_tag (str, optional): The tag to use for reasoning content.
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Defaults to '</think>'.
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"""
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is_api: bool = True
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def __init__(
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self,
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path: str = 'gpt-3.5-turbo',
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max_seq_len: int = 16384,
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query_per_second: int = 1,
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rpm_verbose: bool = False,
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retry: int = 2,
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key: Union[str, List[str]] = 'ENV',
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org: Optional[Union[str, List[str]]] = None,
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meta_template: Optional[Dict] = None,
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openai_api_base: str = OPENAI_API_BASE,
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openai_proxy_url: Optional[str] = None,
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mode: str = 'none',
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logprobs: Optional[bool] = False,
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top_logprobs: Optional[int] = None,
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temperature: Optional[float] = None,
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tokenizer_path: Optional[str] = None,
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extra_body: Optional[Dict] = None,
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verbose: bool = False,
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think_tag: str = '</think>',
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):
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super().__init__(
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path=path,
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max_seq_len=max_seq_len,
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meta_template=meta_template,
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query_per_second=query_per_second,
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rpm_verbose=rpm_verbose,
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retry=retry,
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verbose=verbose,
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)
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import tiktoken
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self.tiktoken = tiktoken
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self.temperature = temperature
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assert mode in ['none', 'front', 'mid', 'rear']
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self.mode = mode
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self.logprobs = logprobs
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self.top_logprobs = top_logprobs
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self.tokenizer_path = tokenizer_path
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self.hf_tokenizer = None
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self.extra_body = extra_body
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self.think_tag = think_tag
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if isinstance(key, str):
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if key == 'ENV':
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if 'OPENAI_API_KEY' not in os.environ:
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raise ValueError('OpenAI API key is not set.')
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self.keys = os.getenv('OPENAI_API_KEY').split(',')
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else:
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self.keys = [key]
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else:
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self.keys = key
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# record invalid keys and skip them when requesting API
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# - keys have insufficient_quota
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self.invalid_keys = set()
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self.key_ctr = 0
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if isinstance(org, str):
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self.orgs = [org]
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else:
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self.orgs = org
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self.org_ctr = 0
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self.url = openai_api_base
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if openai_proxy_url == 'ENV':
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if 'OPENAI_PROXY_URL' not in os.environ:
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raise ValueError('OPENAI_PROXY_URL is not set.')
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self.proxy_url = os.getenv('OPENAI_PROXY_URL')
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else:
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self.proxy_url = openai_proxy_url
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self.path = path
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def generate(
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self,
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inputs: List[PromptType],
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max_out_len: int = 512,
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temperature: float = 0.7,
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**kwargs,
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) -> List[str]:
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"""Generate results given a list of inputs.
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Args:
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inputs (List[PromptType]): A list of strings or PromptDicts.
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The PromptDict should be organized in OpenCompass'
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API format.
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max_out_len (int): The maximum length of the output.
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temperature (float): What sampling temperature to use,
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between 0 and 2. Higher values like 0.8 will make the output
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more random, while lower values like 0.2 will make it more
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focused and deterministic. Defaults to 0.7.
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Returns:
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List[str]: A list of generated strings.
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"""
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if self.temperature is not None:
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temperature = self.temperature
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with ThreadPoolExecutor() as executor:
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results = list(
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tqdm(
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executor.map(
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self._generate,
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inputs,
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[max_out_len] * len(inputs),
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[temperature] * len(inputs),
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),
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total=len(inputs),
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desc='Inferencing',
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))
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return results
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def _generate(self, input: PromptType, max_out_len: int,
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temperature: float) -> str:
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"""Generate results given a list of inputs.
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Args:
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inputs (PromptType): A string or PromptDict.
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The PromptDict should be organized in OpenCompass'
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API format.
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max_out_len (int): The maximum length of the output.
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temperature (float): What sampling temperature to use,
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between 0 and 2. Higher values like 0.8 will make the output
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more random, while lower values like 0.2 will make it more
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focused and deterministic.
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Returns:
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str: The generated string.
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"""
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assert isinstance(input, (str, PromptList))
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messages, max_out_len = self._preprocess_messages(
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input, max_out_len, self.max_seq_len, self.mode,
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self.get_token_len)
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max_num_retries = 0
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while max_num_retries < self.retry:
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self.wait()
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with Lock():
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if len(self.invalid_keys) == len(self.keys):
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raise RuntimeError('All keys have insufficient quota.')
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# find the next valid key
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while True:
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self.key_ctr += 1
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if self.key_ctr == len(self.keys):
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self.key_ctr = 0
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if self.keys[self.key_ctr] not in self.invalid_keys:
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break
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key = self.keys[self.key_ctr]
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header = {
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'Authorization': f'Bearer {key}',
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'content-type': 'application/json',
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'api-key': key,
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}
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if self.orgs:
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with Lock():
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self.org_ctr += 1
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if self.org_ctr == len(self.orgs):
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self.org_ctr = 0
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header['OpenAI-Organization'] = self.orgs[self.org_ctr]
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try:
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if any(model in self.path for model in O1_MODEL_LIST):
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self.logger.warning(
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f"'max_token' is unsupported for model {self.path}")
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self.logger.warning(
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f'We use max_out_len: {max_out_len} for this query')
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data = dict(
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model=self.path,
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messages=messages,
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max_completion_tokens=max_out_len,
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n=1,
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logprobs=self.logprobs,
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top_logprobs=self.top_logprobs,
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stop=None,
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temperature=temperature,
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)
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else:
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data = dict(
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model=self.path,
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messages=messages,
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max_tokens=max_out_len,
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n=1,
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logprobs=self.logprobs,
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top_logprobs=self.top_logprobs,
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stop=None,
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temperature=temperature,
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)
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if self.extra_body:
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data.update(self.extra_body)
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if isinstance(self.url, list):
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import random
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url = self.url[random.randint(0, len(self.url) - 1)]
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else:
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url = self.url
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if self.proxy_url is None:
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raw_response = requests.post(url,
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headers=header,
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data=json.dumps(data))
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else:
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proxies = {
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'http': self.proxy_url,
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'https': self.proxy_url,
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}
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if self.verbose:
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self.logger.debug(
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f'Start send query to {self.proxy_url}')
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raw_response = requests.post(
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url,
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headers=header,
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data=json.dumps(data),
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proxies=proxies,
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)
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if self.verbose:
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self.logger.debug(
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f'Get response from {self.proxy_url}')
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except requests.ConnectionError:
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self.logger.error('Got connection error, retrying...')
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continue
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try:
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if raw_response.status_code != 200:
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self.logger.error(f'Request failed with status code '
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f'{raw_response.status_code}, response: '
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f'{raw_response.content.decode()}')
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continue
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response = raw_response.json()
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except requests.JSONDecodeError:
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self.logger.error(f'JsonDecode error, got status code '
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f'{raw_response.status_code}, response: '
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f'{raw_response.content.decode()}')
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continue
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self.logger.debug(str(response))
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try:
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if self.logprobs:
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return response['choices']
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else:
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# Extract content and reasoning_content from response
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message = response['choices'][0]['message']
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content = message.get('content', '') or ''
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reasoning_content = message.get('reasoning_content',
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'') or ''
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# Handle reasoning_content similar to OpenAISDK
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if reasoning_content:
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if self.verbose:
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self.logger.info(
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'Extracting reasoning content and tags.'
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'Reasoning Content: %s, \n'
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'Tags: %s, \n'
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'Content: %s', reasoning_content,
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self.think_tag, content)
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if content:
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return reasoning_content + self.think_tag + content
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else:
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return reasoning_content
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else:
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return content.strip()
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except KeyError:
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if 'error' in response:
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if response['error']['code'] == 'rate_limit_exceeded':
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time.sleep(10)
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self.logger.warn('Rate limit exceeded, retrying...')
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continue
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elif response['error']['code'] == 'insufficient_quota':
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self.invalid_keys.add(key)
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self.logger.warn(f'insufficient_quota key: {key}')
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continue
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elif response['error']['code'] == 'invalid_prompt':
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self.logger.warn('Invalid prompt:', str(input))
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return ''
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elif response['error']['type'] == 'invalid_prompt':
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self.logger.warn('Invalid prompt:', str(input))
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return ''
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self.logger.error(
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'Find error message in response: ',
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str(response['error']),
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)
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max_num_retries += 1
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raise RuntimeError('Calling OpenAI failed after retrying for '
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f'{max_num_retries} times. Check the logs for '
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'details.')
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def get_token_len(self, prompt: str) -> int:
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"""Get lengths of the tokenized string. Only English and Chinese
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characters are counted for now. Users are encouraged to override this
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method if more accurate length is needed.
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Args:
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prompt (str): Input string.
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Returns:
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int: Length of the input tokens
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"""
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assert self.tokenizer_path or self.path
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try:
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if self.verbose:
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self.logger.info(f'Used tokenizer_path: {self.tokenizer_path}')
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tokenizer_path = (self.tokenizer_path
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if self.tokenizer_path else self.path)
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try:
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if self.verbose:
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self.logger.info(
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f'Start load tiktoken encoding: {tokenizer_path}')
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enc = self.tiktoken.encoding_for_model(tokenizer_path)
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if self.verbose:
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self.logger.info(
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f'Successfully tiktoken encoding: {tokenizer_path}')
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return len(enc.encode(prompt, disallowed_special=()))
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except Exception as e:
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self.logger.warn(f'{e}, tiktoken encoding cannot load '
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f'{tokenizer_path}')
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from transformers import AutoTokenizer
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if self.hf_tokenizer is None:
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if self.verbose:
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self.logger.info(
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f'Start load hf tokenizer: {tokenizer_path}')
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self.hf_tokenizer = AutoTokenizer.from_pretrained(
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tokenizer_path, trust_remote_code=True)
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self.logger.info(
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f'Successfully load HF Tokenizer from {tokenizer_path}'
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)
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return len(self.hf_tokenizer(prompt).input_ids)
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except Exception:
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self.logger.warn(
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'Can not get tokenizer automatically, '
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'will use default tokenizer gpt-4 for length calculation.')
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default_tokenizer = 'gpt-4'
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enc = self.tiktoken.encoding_for_model(default_tokenizer)
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if self.verbose:
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self.logger.info(
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f'Successfully load default tiktoken tokenizer: '
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f' {default_tokenizer}')
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return len(enc.encode(prompt, disallowed_special=()))
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def _bin_trim(self, prompt: str, num_token: int, mode: str) -> str:
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"""Get a suffix of prompt which is no longer than num_token tokens.
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Args:
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prompt (str): Input string.
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num_token (int): The upper bound of token numbers.
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mode (str): The method of input truncation
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('front', 'mid', or 'rear')
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Returns:
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str: The trimmed prompt.
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"""
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token_len = self.get_token_len(prompt)
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if token_len <= num_token:
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return prompt
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pattern = re.compile(r'[\u4e00-\u9fa5]')
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if pattern.search(prompt):
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words = list(jieba.cut(prompt, cut_all=False))
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sep = ''
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else:
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words = prompt.split(' ')
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sep = ' '
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l, r = 1, len(words)
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while l + 2 < r:
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mid = (l + r) // 2
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if mode == 'front':
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cur_prompt = sep.join(words[-mid:])
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elif mode == 'mid':
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cur_prompt = sep.join(words[:mid]) + sep.join(words[-mid:])
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elif mode == 'rear':
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cur_prompt = sep.join(words[:mid])
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if self.get_token_len(cur_prompt) <= num_token:
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l = mid # noqa: E741
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else:
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r = mid
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if mode == 'front':
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prompt = sep.join(words[-l:])
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elif mode == 'mid':
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prompt = sep.join(words[:l]) + sep.join(words[-l:])
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elif mode == 'rear':
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prompt = sep.join(words[:l])
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return prompt
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def _preprocess_messages(
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self,
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input: Union[str, PromptList],
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max_out_len: int,
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max_seq_len: int,
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mode: str,
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get_token_len_func,
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) -> tuple[List[Dict], int]:
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"""Preprocess input into messages format and calculate max output
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length.
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Args:
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input: Input prompt as string or PromptList
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max_out_len: Maximum output length
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max_seq_len: Maximum sequence length
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mode: The method of input truncation
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get_token_len_func: Function to calculate token length
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Returns:
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tuple: (processed messages list, adjusted max_out_len)
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"""
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# Check input length when mode is 'none'
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if mode == 'none':
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input_len = (get_token_len_func(input) if isinstance(
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input, str) else sum(
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get_token_len_func(item['prompt']) for item in input))
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if input_len > max_seq_len:
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raise ValueError(
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f'Input length ({input_len}) exceeds max_seq_len '
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f'({max_seq_len}) and mode is set to "none". Please '
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f'either change the mode or increase the max_seq_len.')
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# Trim input if needed
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def bin_trim_wrapper(text):
|
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trim_length = max_seq_len - 100
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if max_out_len is not None:
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trim_length -= max_out_len
|
|
return self._bin_trim(text, trim_length, mode)
|
|
|
|
if isinstance(input, str) and mode != 'none':
|
|
input = bin_trim_wrapper(input)
|
|
# Convert input to messages format
|
|
if isinstance(input, str):
|
|
messages = [{'role': 'user', 'content': input}]
|
|
input_len = get_token_len_func(input)
|
|
else:
|
|
messages = []
|
|
processed_prompts = []
|
|
for item in input:
|
|
input_content = item['prompt']
|
|
if mode != 'none':
|
|
input_content = bin_trim_wrapper(input_content)
|
|
processed_prompts.append(input_content)
|
|
msg = {'content': input_content}
|
|
if item['role'] == 'HUMAN':
|
|
msg['role'] = 'user'
|
|
elif item['role'] == 'BOT':
|
|
msg['role'] = 'assistant'
|
|
elif item['role'] == 'SYSTEM':
|
|
msg['role'] = 'system'
|
|
messages.append(msg)
|
|
input_len = sum(
|
|
get_token_len_func(prompt) for prompt in processed_prompts)
|
|
|
|
# Adjust max_out_len
|
|
if max_out_len is not None:
|
|
original_max_out_len = max_out_len
|
|
max_out_len = min(max_out_len, max_seq_len - input_len - 100)
|
|
if max_out_len <= 0:
|
|
raise ValueError(
|
|
f'max_out_len ({max_out_len}) is less than or equal to 0. '
|
|
f'This may be due to input length ({input_len}) being too '
|
|
f'close to max_seq_len ({max_seq_len}). Please increase '
|
|
f'max_seq_len or use a truncation mode other than "none".')
|
|
if max_out_len < original_max_out_len:
|
|
self.logger.warning(
|
|
f'max_out_len was truncated from {original_max_out_len} '
|
|
f'to {max_out_len} due to input length')
|
|
|
|
return messages, max_out_len
|
|
|
|
|
|
class OpenAISDK(OpenAI):
|
|
|
|
def __init__(self,
|
|
path: str = 'gpt-3.5-turbo',
|
|
max_seq_len: int = 16384,
|
|
query_per_second: int = 1,
|
|
rpm_verbose: bool = False,
|
|
retry: int = 2,
|
|
key: str | List[str] = 'ENV',
|
|
org: str | List[str] | None = None,
|
|
meta_template: Dict | None = None,
|
|
openai_api_base: str | List[str] = OPENAISDK_API_BASE,
|
|
openai_proxy_url: Optional[str] = None,
|
|
mode: str = 'none',
|
|
logprobs: bool | None = False,
|
|
top_logprobs: int | None = None,
|
|
temperature: float | None = None,
|
|
tokenizer_path: str | None = None,
|
|
extra_body: Dict | None = None,
|
|
verbose: bool = False,
|
|
http_client_cfg: dict = {},
|
|
status_code_mappings: dict = {},
|
|
think_tag: str = '</think>'):
|
|
super().__init__(
|
|
path,
|
|
max_seq_len,
|
|
query_per_second,
|
|
rpm_verbose,
|
|
retry,
|
|
key,
|
|
org,
|
|
meta_template,
|
|
openai_api_base,
|
|
openai_proxy_url,
|
|
mode,
|
|
logprobs,
|
|
top_logprobs,
|
|
temperature,
|
|
tokenizer_path,
|
|
extra_body,
|
|
verbose=verbose,
|
|
)
|
|
from openai import OpenAI
|
|
|
|
# support multiple api_base for acceleration
|
|
if isinstance(openai_api_base, List):
|
|
self.openai_api_base = random.choice(openai_api_base)
|
|
else:
|
|
self.openai_api_base = openai_api_base
|
|
|
|
if self.proxy_url or http_client_cfg:
|
|
if self.proxy_url:
|
|
http_client_cfg['proxies'] = {
|
|
'http://': self.proxy_url,
|
|
'https://': self.proxy_url,
|
|
}
|
|
|
|
self.openai_client = OpenAI(
|
|
base_url=self.openai_api_base,
|
|
api_key=key,
|
|
http_client=httpx.Client(
|
|
**http_client_cfg) if http_client_cfg else None,
|
|
)
|
|
|
|
if self.verbose:
|
|
self.logger.info(f'Used openai_client: {self.openai_client}')
|
|
self.status_code_mappings = status_code_mappings
|
|
self.think_tag = think_tag
|
|
|
|
def _generate(
|
|
self,
|
|
input: PromptList | str,
|
|
max_out_len: int,
|
|
temperature: float,
|
|
timeout: int = 3600,
|
|
) -> str:
|
|
"""Generate results given a list of inputs.
|
|
|
|
Args:
|
|
input (PromptType): A string or PromptDict.
|
|
max_out_len (int): The maximum length of the output.
|
|
temperature (float): What sampling temperature to use.
|
|
timeout (int, optional): Timeout in seconds for the API call.
|
|
Defaults to 3600 (60 minutes).
|
|
|
|
Returns:
|
|
str: The generated string.
|
|
"""
|
|
from openai import APIStatusError, BadRequestError
|
|
|
|
assert isinstance(input, (str, PromptList))
|
|
|
|
messages, max_out_len = self._preprocess_messages(
|
|
input, max_out_len, self.max_seq_len, self.mode,
|
|
self.get_token_len)
|
|
|
|
num_retries = 0
|
|
while num_retries < self.retry:
|
|
self.wait()
|
|
if any(model in self.path for model in O1_MODEL_LIST):
|
|
self.logger.warning(
|
|
f"'max_token' is unsupported for model {self.path}")
|
|
self.logger.warning(
|
|
f'We use max_out_len: {max_out_len} for this query')
|
|
query_data = dict(
|
|
model=self.path,
|
|
max_completion_tokens=max_out_len,
|
|
n=1,
|
|
messages=messages,
|
|
extra_body=self.extra_body,
|
|
)
|
|
else:
|
|
query_data = dict(
|
|
model=self.path,
|
|
max_tokens=max_out_len,
|
|
n=1,
|
|
temperature=self.temperature,
|
|
messages=messages,
|
|
extra_body=self.extra_body,
|
|
)
|
|
|
|
try:
|
|
if self.verbose:
|
|
self.logger.info('Start calling OpenAI API')
|
|
|
|
responses = self.openai_client.chat.completions.create(
|
|
**query_data, timeout=timeout) # timeout in seconds
|
|
if self.verbose:
|
|
self.logger.info(
|
|
'Successfully get response from OpenAI API '
|
|
'with query: %s', query_data)
|
|
try:
|
|
self.logger.info(responses)
|
|
except Exception:
|
|
pass # noqa F841
|
|
# Check if response is empty or content is empty
|
|
if (not responses.choices or not responses.choices[0].message
|
|
or
|
|
(not responses.choices[0].message.content and not getattr(
|
|
responses.choices[0].message,
|
|
'reasoning_content',
|
|
'',
|
|
))): # noqa: E125
|
|
# There is case that server does not return any content
|
|
if responses.choices[0].finish_reason == 'stop':
|
|
self.logger.info(
|
|
'Server does not return any content '
|
|
'and stop reason is <stop>, '
|
|
'the input query is: %s', query_data)
|
|
return ''
|
|
self.logger.error(
|
|
'Failed to extract content from the responses. '
|
|
'Please check the API response for detail information.'
|
|
'API responses: %s',
|
|
responses,
|
|
)
|
|
num_retries += 1
|
|
continue
|
|
|
|
reasoning_content = (getattr(responses.choices[0].message,
|
|
'reasoning_content', '') or '')
|
|
content = responses.choices[0].message.content or ''
|
|
# Concat Reasoning Content and tags to content
|
|
if reasoning_content:
|
|
if self.verbose:
|
|
self.logger.info(
|
|
'Follow'
|
|
'vllm/reasoning/deepseek_r1_reasoning_parser'
|
|
'to parse the reasoning content and tags'
|
|
'Reasoning Content: %s, \n'
|
|
'Tags: %s, \n'
|
|
'Content: %s',
|
|
reasoning_content,
|
|
self.think_tag,
|
|
content,
|
|
)
|
|
if content:
|
|
return reasoning_content + self.think_tag + content
|
|
else:
|
|
return reasoning_content
|
|
|
|
else:
|
|
return content
|
|
|
|
except (BadRequestError, APIStatusError) as e:
|
|
# Handle BadRequest status
|
|
# You can specify self.status_code_mappings to bypass \
|
|
# API sensitivity blocks
|
|
# For example: status_code_mappings={400: 'Input data \
|
|
# may contain inappropriate content.'}
|
|
status_code = e.status_code
|
|
if (status_code is not None
|
|
and status_code in self.status_code_mappings):
|
|
error_message = self.status_code_mappings[status_code]
|
|
self.logger.error(
|
|
f'error occurs at {self.openai_api_base}')
|
|
self.logger.info(f'Status Code: {status_code}, \n'
|
|
f'Original Error Message: {e}, \n'
|
|
f'Return Message: {error_message} ')
|
|
return error_message
|
|
else:
|
|
self.logger.error(
|
|
f'error occurs at {self.openai_api_base}')
|
|
self.logger.error(e)
|
|
except Exception as e:
|
|
self.logger.error(f'error occurs at {self.openai_api_base}')
|
|
self.logger.error(e)
|
|
num_retries += 1
|
|
raise RuntimeError('Calling OpenAI API failed after retrying for '
|
|
f'{self.retry} times. Check the logs for details.')
|