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149 lines
4.7 KiB
Python
149 lines
4.7 KiB
Python
import time
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from concurrent.futures import ThreadPoolExecutor
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from typing import Dict, List, Optional, Union
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import requests
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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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class Nanbeige(BaseAPIModel):
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"""Model wrapper around Nanbeige.
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Documentations:
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Args:
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path (str): Model name, e.g. `nanbeige-plus`
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key (str): Provide API Key
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url (str): Provided URL
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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 2.
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max_seq_len (int): Unused here.
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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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retry (int): Number of retires if the API call fails. Defaults to 2.
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"""
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def __init__(self,
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path: str,
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key: str,
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url: str = None,
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query_per_second: int = 2,
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max_seq_len: int = 2048,
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meta_template: Optional[Dict] = None,
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retry: int = 3):
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super().__init__(path=path,
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max_seq_len=max_seq_len,
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query_per_second=query_per_second,
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meta_template=meta_template,
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retry=retry)
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self.headers = {
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'Authorization': 'Bearer ' + key,
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'Content-Type': 'application/json',
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}
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self.model = path
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self.url = url if url is not None \
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else 'http://stardustlm.zhipin.com/api/gpt/open/chat/send/sync'
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def generate(
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self,
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inputs: List[str or PromptList],
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max_out_len: int = 512,
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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[str or PromptList]): 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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Returns:
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List[str]: A list of generated strings.
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"""
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with ThreadPoolExecutor() as executor:
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results = list(
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executor.map(self._generate, inputs,
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[max_out_len] * len(inputs)))
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self.flush()
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return results
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def _generate(
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self,
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input: str or PromptList,
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max_out_len: int = 512,
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) -> str:
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"""Generate results given an input.
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Args:
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inputs (str or PromptList): 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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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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if isinstance(input, str):
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messages = [{'sender_type': 'USER', 'text': input}]
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else:
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messages = []
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for item in input:
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msg = {'text': item['prompt']}
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if item['role'] == 'HUMAN':
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msg['sender_type'] = 'USER'
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elif item['role'] == 'BOT':
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msg['sender_type'] = 'BOT'
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messages.append(msg)
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data = {
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'model': self.model,
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'messages': messages,
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}
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max_num_retries = 0
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while max_num_retries < self.retry:
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self.acquire()
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raw_response = requests.request('POST',
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url=self.url,
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headers=self.headers,
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json=data)
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self.release()
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if raw_response is None:
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print('Connection error, reconnect.')
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# if connect error, frequent requests will casuse
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# continuous unstable network, therefore wait here
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# to slow down the request
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self.wait()
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continue
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if raw_response.status_code != 200:
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print('请求失败：', raw_response)
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print('失败信息：', raw_response.text)
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max_num_retries += 1
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continue
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response = raw_response.json()
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if response['stardustCode'] == 0:
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return response['reply']
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# exceed concurrency limit
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if response['stardustCode'] == 20035:
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print(response)
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time.sleep(2)
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continue
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print(response)
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max_num_retries += 1
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raise RuntimeError(raw_response.text)
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